nopperl/pmc-image-text · Datasets at Hugging Face

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0.409758	4ba9760644f84c05be5220bcdba1178f	Dough preparation steps. a Mixing batter by hand in Hargeisa (a new heritage zone) inside a plastic container, the most common material for mixing and fermenting flatbread batter. b The flatbread batter halfway through mixing as it becomes increasingly aerated and bubbly in a process that takes 10–15 min. c The flatbread batter after mixing, typically bubbly, smooth, and velvety in texture. Photo credit: Mustafa Said. d Fermented dhanaanis (starter) reserved from a flatbread batter in Hargeisa along with batter residue on the interior surface. This container will be used, unwashed, for subsequent batches to catalyze fermentation, a common method for ensuring adequate fermentation or in colder seasons, depending on the geographic location and corresponding climate. Photo credit: Mustafa Said	PMC9210053	42779_2022_138_Fig4_HTML.jpg
0.441476	25495c2bb9ae4232b3dff22c6608a73c	Commercially produced cajiin in Mogadishu, commonly a mixture of refined wheat and maize moistened with water. a Daily each morning, retail saleswomen provide cajiin producers with unique mixtures of grains and cereals to be processed into cajiin dough, and then retrieve their doughs, like this one, in the afternoon. b In the evenings, saleswomen prepare single-batch balls of cajiin for sale in local markets to customers who use the cajiin to prepare their flatbread batter at night, allow it to ferment overnight, and cook it the following morning for breakfast. Photo credit: Ali Hussein	PMC9210053	42779_2022_138_Fig5_HTML.jpg
0.453327	77a025dd44b946d1a9b4cce0270a4f88	Fireboxes to bake laxoox/canjeero. a Firebox known as girgire in Hargeisa. b Firebox known as burjiko in Mogadishu. Fireboxes hold hot charcoals that heat the cast iron griddles from underneath. Before the advent of fireboxes, laxoox/canjeero was commonly cooked over wood fires on the ground. Today, the flatbread is also frequently cooked over gas stoves, depending on household means, cooking tools, and preferences. Photo credits: Mustafa Said (a) and Abdikarim Omar (b)	PMC9210053	42779_2022_138_Fig6_HTML.jpg
0.475739	99cb528a55374eab940c05177c9b4b99	Shaping and baking laxoox/canjeero. a Preparing the griddle with vegetable oil using a blackened rag, known as a masaxaad, in Mogadishu (innovative zone). Alternately, the first cooked piece of laxoox/canjeero is folded and used to oil the griddle. In lieu of vegetable oil, some households use goat ghee to prevent the flatbread from sticking. Photo credit: Abdikarim Omar. b Pouring the batter on a cast iron griddle atop a gas stove in Hargeisa (new heritage zone). Anecdotes from interviewees indicate that ceramic griddles pre-date cast iron griddles in Somalia and may have been crafted and sold by tradespeople from the Arabian Peninsula. Today, the cast iron griddle is ubiquitous for this purpose in research locations, while online sources show that other griddle types, including non-stick pans, are used in global production. Photo credit: Mustafa Said. c Shaping the batter on the griddle over a firebox (burjiko) in Mogadishu (innovative zone) using a flat-bottomed cup. In a circular motion, the cook pushes batter outwards from the center of the griddle, creating a spiralized effect. The cup never touches the surface of the griddle but leaves behind it a thin trail of batter, while the un-flattened batter puffs up around it. The shape and patterns of the finished flatbread are a signal of quality. Photo credit: Abdikarim Omar	PMC9210053	42779_2022_138_Fig7_HTML.jpg
0.463709	d27bb4573bbe4465a4ca89c3fa4ca50d	Laxoox/canjeero appearance and consumption. a Cooked laxoox/canjeero retains a soft, puffed side that never contacts the griddle surface but rather rises via steam under a well-fitting lid; and a browned side that cooks on the oiled griddle and is crispy when just cooked but quickly softens. The flatbread is pockmarked with holes, or “eyes,” and appears translucent when held up to a light source. Photo credit: Erin Wolgamuth. b A plate of flatbread served for breakfast in Mogadishu (innovative zone), sprinkled with sugar and accompanied by oil and tea. All respondents reported eating this flatbread for breakfast with either sweet (tea, sugar) or savory (meat or vegetable) dishes. Some also consume laxoox/canjeero at other meals and with other dishes. Photo credit: Abdikarim Omar	PMC9210053	42779_2022_138_Fig8_HTML.jpg
0.538912	b8a92a1715474e0897b131334da4af1c	Production styles flowchart, from heritage to new heritage, innovative and global. The latter three groups developed concurrently and were not chronologically subsequent. Heritage production is linked to the historic era of seminomadic Somali pastoralists, while differences among the subsequent three production styles have resulted from the Somali civil conflict and continue today	PMC9210053	42779_2022_138_Fig9_HTML.jpg
0.454537	62f74019ae5f4e37ae007b0143f1571e	Trends in number of active orthopaedic sports medicine podcasts available for listening on Apple, Google, and Spotify.	PMC9210369	gr1.jpg
0.47894	2594d646b41e4b599afae5a92be099f6	Flow of study	PMC9210584	13098_2022_858_Fig1_HTML.jpg
0.465925	341acfee130c4d9891500e5889901726	Flowchart of inclusion of patients.	PMC9211000	ActaO-93-3140-g001.jpg
0.44491	8879c1728ec740fab0c3b0659062bf6c	Directed acyclic graph showing possible relationship between risk factors and recovery trajectory after primary total hip arthroplasty. Confounding variables were selected based on the directed acyclic graph: BMI was adjusted for smoking; ASA was adjusted for age, BMI, and smoking; Smoking was adjusted for age; Approach was adjusted for BMI; Fixation was adjusted for age and approach; Head diameter was adjusted for approach; Bearing type was adjusted for age, BMI, and head diameter; No adjustments were included in the analysis of age and sex.	PMC9211000	ActaO-93-3140-g002.jpg
0.380659	e085a11668d44a80a64dee4735f88de1	Recovery trajectories based on HOOS-PS score across class 1 (n = 2,391), class 2 (n = 664), and class 3 (n = 152). The mean trajectories with their 95% CI per class are shown in bold (black); individual trajectories are in color. Lower HOOS-PS scores indicate better physical functioning. For the purpose of plotting, individuals were assigned to classes based on their most likely class membership; it should be noted that individuals are in fact assigned a probability of class membership in the model.	PMC9211000	ActaO-93-3140-g003.jpg
0.407814	828bda02e6df477da346aa9fc97d7276	Impact of decreases in protective behaviors on COVID-19 infections and hospitalizations under various school reopening scenarios. Horizontally from left to right, effects of reductions (0%, 25%, 50% reduction) in protective behaviors among 18–40-year-old adults for a given level of school reopening (15%, 30%, and 45% reopening), from March 2020 to November 2020. Vertically from top to bottom, effects of increasing school reopening for a given level of protective behaviors among 18–40-year-olds. Yellow plots indicate point prevalence of latent infections and red plots indicate point prevalence of hospitalizations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)	PMC9212859	gr1_lrg.jpg
0.455933	1139eeeabe424b008801afd0c0fe74a6	Impact of increases in adult OOHA on COVID-19 infections and hospitalizations under various school reopening scenarios. Horizontally from left to right, effect of increasing adult OOHA (65%, 70%, 75%, and 80% of prepandemic levels) for a given level of school reopening, from March 2020 to November 2020. Vertically from top to bottom, effect of increasing school reopening for a given level of adult OOHA. Yellow plots indicate point prevalence of latent infections and red plots indicate point prevalence of hospitalizations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)	PMC9212859	gr2_lrg.jpg
0.48744	2ca8f75f41b94a02a73a6871ce834a69	Effective reproductive number (Rt) by age group assuming 45% school reopening beginning September 3, 2020. Rt is defined as the average number of secondary infections resulting from an infected individual in a population where not all individuals are susceptible. A value of 1 represents the threshold needed for an epidemic to be sustained; values less than 1 indicate that the epidemic is dying out and values more than 1 indicate that the epidemic is growing. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)	PMC9212859	gr3_lrg.jpg
0.468155	b2af57a8a4c34fd1bbbec408b69a90b9	Effects of school reopening and adult behavior change on COVID-19 infections and hospitalizations as of November 1, 2020. Scenarios S1–S3 represent 15% school reopening, scenarios S4–S6 represent 30% school reopening, and scenarios S7-S9 represent 45% school reopening. Center vertical lines represent medians; left and right edges represent 25% and 75% quartiles respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)	PMC9212859	gr4_lrg.jpg
0.508127	79b9256952e3468bb88059ea27a3237f	Effects of adult OOHA on COVID-19 infections and hospitalizations under different school reopening scenarios as of November 1, 2020. Scenarios A1–A4 represent 15% school reopening, scenarios A5–A8 represent 30% school reopening, and scenarios A9–A12 represent 45% school reopening. Center vertical lines represent medians; left and right edges represent 25% and 75% quartiles respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)	PMC9212859	gr5_lrg.jpg
0.433374	ec5ac02a4caa4baa95f964b7e3897d61	Comparison of the basic data of the two groups. (a–c) The comparisons of the average age, gender distribution, and blood pressure indexes of patients in the two groups, respectively. ∗Compared with control group, P < 0.05.	PMC9213170	CMMM2022-5863082.001.jpg
0.397318	23a0686ddcd34686987e6d09a59952bd	Comparison of cerebral CT images before and after being processed by intelligent algorithm. (a and c) The original cerebral CT images of patients in the observation group and the images processed by intelligence segmentation algorithm, respectively (the patient was a male aged 71). (b and d) The original cerebral CT images of patients in the control group and the images processed by intelligence segmentation algorithm (the patient was a female aged 74).	PMC9213170	CMMM2022-5863082.002.jpg
0.498146	828e120dc0ee402ba7d2d20dfe15be23	Comparison of evaluation indexes of image processing quality under intelligent segmentation algorithm. (a–c) The comparison charts of Dice value, sensitivity, and specificity, respectively. M1, M2, M3, M4, and M5 represented the 1st, 2nd, 3rd, 4th, and 5th algorithm tests, respectively.	PMC9213170	CMMM2022-5863082.003.jpg
0.404519	353be2025fb04072858cd49e047508db	Comparison of edema/hematoma volume in CT images of patients between two groups. ∗Compared with control group, P < 0.05.	PMC9213170	CMMM2022-5863082.004.jpg
0.500813	7c3a8d00728d40d6ac02013cdc70a783	Comparison of relative edema volume in two CT examinations of two groups. ∗Compared with control group, P < 0.05.	PMC9213170	CMMM2022-5863082.005.jpg
0.420986	b05f304b84ce40c686db2d6acf6faca6	Comparison of signs in two CT images of patients between two groups. ∗Compared with those of the control group, P < 0.05.	PMC9213170	CMMM2022-5863082.006.jpg
0.44013	7fd2302cf806480f8b30c964d4db35c9	Comparison of serological indexes of patients between the two groups. (a) The comparison chart of ANC, ALC, and NLR. (b) The comparison chart of RBC and HGB, (c) The comparison of the cholesterol indexes of patients in the two groups. ∗Compared with the data of the control group, P < 0.05.	PMC9213170	CMMM2022-5863082.007.jpg
0.445113	3c305d74ae09451891ca55a01db0f3e2	Body mass index versus risk of death from COVID-19. In the left panel, estimates are adjusted for age, sex, education and district and in the right panel, the age- and sex-stratified results are adjusted for age, education and district. The areas of the squares are proportional to the amount of statistical information and the lines through them are the 95% confidence intervals. The numbers above each vertical line in the left panel show the death rate ratio for that group and the numbers below each vertical line give the number of COVID−19 deaths in that group. In the right panel, the diamonds are the information-weighted averages of the two results above them. BMI, body mass index; CI, confidence interval; RR, mortality rate ratio	PMC9214141	dyac134f1.jpg
0.441724	44281ec3be7b4fefbe1dff3027855f3d	Network pharmacology analysis of the mechanism of action of ICA in the treatment of colitis. (A) Venn diagram of intersection of ICA targets and colitis targets. (B) ICA target protein interaction network. (C) KEGG pathway analysis of ICA target.	PMC9214228	fphar-13-903762-g001.jpg
0.420451	6c57931e8863417c96c961a04a2420d9	Effects of ICA on colon inflammation in aging rats. (A) Representative figures of HE of colonic epithelial morphology (×200, ×400). (B) The effect of ICA on the expressions of inflammatory cytokines TNF-α and IL-1β in the colon of aging rats was detected by Western blot. a: Adult group, b: Aging group, c: Low dose of ICA-treated aging group (ICA-L group), d: High dose of ICA-treated aging group (ICA-H group). ** p < 0.01 vs. Adult group. # p < 0.05 vs. Aging group. ## p < 0.01 vs. Aging group, n = 4-8.	PMC9214228	fphar-13-903762-g002.jpg
0.447715	d7b8aeabc5554f0dbfdcaf267fdcee9e	Effects of ICA on tight junction proteins of colonic epithelium in aging rats. Expressions of (A,B) Occludin, (C,D) Claudin-1, and (E,F) Claudin5 between colonic epithelial cells in aging rats were detected by IHC and Western blot (×200, ×400). a: Adult group, b: Aging group, c: ICA-L group, d: ICA-H group. ** p < 0.01 vs. Adult group. # p < 0.05 vs. Aging group. ## p < 0.01 vs. Aging group, n = 3-4.	PMC9214228	fphar-13-903762-g003.jpg
0.519151	bec2d1b247924a9b86d4db349857623a	Effects of ICA on colonic oxidative stress in aging rats. The regulation of ICA on antioxidant enzymes (A) SOD, (B)CAT, and (C)GSH-Px in the colon of aging rats were detected by biochemical kits. (D) The expressions of HO-1 and NQO-1 in the colon of aging rats were detected by Western blot. a: Adult group, b: Aging group, c: ICA-L group, d: ICA-H group. * p < 0.05 vs. Adult group. ** p < 0.01 vs. Adult group. # p < 0.05 vs. Aging group. ## p < 0.01 vs. Aging group, n = 5-8.	PMC9214228	fphar-13-903762-g004.jpg
0.427041	70b641c5537348fe8d63706b193c90dd	The increased concentration of TNF-α resulted in the decrease of Occludin in Caco-2 cell monolayers. (A) Schematic diagram of culturing Caco-2 cell monolayers in the Transwell. (B) IAP activity ratio of AP and BP in different days of culture was detected. (C) TEER value of Caco-2 cell monolayers was detected on different days. (D) MTT was used to detect the effect of TNF-α at 10 ng/ml or 20 ng/ml on caco-2 cell viability. (E) Caco-2 cell monolayers were treated with 10 ng/ml or 20 ng/ml TNF-α for 24 or 48 h, and the expression of Occludin was detected by IF, n = 3–10.	PMC9214228	fphar-13-903762-g005.jpg
0.415443	d05f022ca6f1482199d5058364aad537	ICA reversed the decrease in Occludin expression and barrier degradation of Caco-2 cell monolayers caused by TNF-α stimulation. (A) The structure of ICA. (B) Patterns of administration of TNF-α and ICA. (C) The changes of Occludin were detected by IF (800×). In the ICA group, 5 μM ICA or 50 μM ICA was added to the AP side for 30 min, and 20 ng/ml TNF-α was added to the BP side for co-treatment for 24 h. In the DMSO group, 1 μl/ml DMSO was added to the AP side then treatment for 24.5 h. The expression of Occludin protein (D,E) and mRNA (F) was detected by Western blot or qPCR. (G)TEER and (H) 4-KD FITC-dextran detected changes in barrier permeability of monolayers. *** p < 0.001 vs. Control group. # p < 0.05 vs. TNF-α group. ## p < 0.01 vs. TNF-α group. ### p < 0.001 vs. TNF-α group n = 8–27.	PMC9214228	fphar-13-903762-g006.jpg
0.456446	704aa79599ee48bbb4d1a0f7859c9b69	ICA reversed the upregulation of miR-122a in Caco-2 cells induced by TNF-α. Detected the expression levels of (A)miR-122a, (B)miR-200C, (C)miR-429, and (D)miR-144. *** p < 0.001 vs. Control group. ### p < 0.001 vs. TNF-α group, n = 5–12.	PMC9214228	fphar-13-903762-g007.jpg
0.474139	eb3f5bffb1d043349f510ae494e92f0d	In Caco-2 cell monolayers, transfection of miR-122a mimics reversed the down-regulation protection of ICA against TNF-α -induced Occludin and induced improved permeability. (A) miR-122a mimics were designed based on the miR-122a gene. (B) Schematic diagram of transfection of miR-122a mimics in the Petri dish and Transwell Caco-2 cells. Effects of different transfection concentrations on the expression levels of (C) miR-122a and (D) Occludin mRNA in Caco-2 cells in the Petri dish. Changes in (E) miR-122a and (F) Occludin mRNA expression levels in cell monolayers. The miR-122a mimics group and the NC group were treated by transfecting miR-122a mimics or NC RNA for 24 h, and then treat with ICA and TNF-α. Changes in Occludin protein expression in Caco-2 cell monolayers were detected by (G) IF and (H,I) Western blot. Changes in permeability of Caco-2 cell monolayers were detected by (J) TEER and (K) 4-KD FITC-dextran. * p < 0.05 vs. Control group. p < 0.05 vs. Control group. * p < 0.001 vs. Control group. # p < 0.05 vs. TNF-α group. ## p < 0.01 vs. TNF-α group. ### p < 0.001 vs. TNF-α group. & p < 0.05 vs. ICA 5 μM group. &&& p < 0.001 vs. $ p < 0.05 vs. NC group. $$ p < 0.05 vs. NC group. $$$ p < 0.001 vs. NC group. miR-122a mimics 1: Lipofectamine 20001.5 μl, RNA 20 pmol; miR-122a mimics 2: Lipofectamine 2000 1.5 μl, RNA 40 pmol; miR-122a mimics 3: Lipofectamine 2000 2.5 μl, RNA 20 pmol; miR-122a mimics 4: Lipofectamine 2000 2.5 μl, RNA 40 pmol, n = 6–23.	PMC9214228	fphar-13-903762-g008.jpg
0.440987	1e2992ecd3524b32867f75c3a3da690d	Identification of molecular subtypes of UPRRGs by consensus clustering. (A). Clustering heat map at k = 2. (B) PCA plot between the two subtypes. (C) Heat map of the UPR-related gene expression and clinical features in the two subtypes. (D) Survival curves for the two subgroups.	PMC9214238	fgene-13-911346-g001.jpg
0.41881	94fe07ecbeb24313b739a1a5cade995b	Tumor microenvironment in the two subtypes. (A) Stromal score, immune score, ESTIMATE score, and tumor purity based on ESTIMATE algorithm. (B) Six immune cell abundance assessments by the TIMER algorithm. (C) Twenty-nine immune cell abundance assessments by the ssGSEA algorithm. (D) Differences in immune checkpoints between the two subtypes. p < 0.05; p < 0.01; **p < 0.001.	PMC9214238	fgene-13-911346-g002.jpg
0.461085	ff11fe132dfc4b429b9184e8bee27ceb	Differentially expressed genes and functional enrichment analyses. (A) Volcano plot showing the DEGs between the two subgroups. (B) Bubble plot exhibited the functional enrichment of DEGs through GO analysis. (C) GSEA shows the hallmark gene sets in the two subgroups. (D) Heat map depicted the results of the GSVA analysis.	PMC9214238	fgene-13-911346-g003.jpg
0.404226	96f9e9a7df8f48c2aa70708846940b50	Construction of the risk signature based on UPRRGs in the training cohort. (A) Eight optimal UPRRGs filtered by LASSO analysis. (B) Distribution of risk scores and patient status in the two risk groups; (C). Heat map showing the expressions of four candidate genes. (D) Survival curves for the two risk groups. (E) Time-dependent ROC curve of the risk model. (F) Tumor microenvironment analysis in the two risk groups through the ESTIMATE algorithm. p < 0.05; p < 0.01; **p < 0.001.	PMC9214238	fgene-13-911346-g004.jpg
0.417602	ed83edc973684fc687f228000f28c59f	Correlation of the risk signature with clinical features in the training cohort. (A) Differences in risk scores among osteosarcoma patients by age, gender, and metastatic status. (B–C) Survival curves for patients with osteosarcoma regrouped by metastatic status. (D–E) Univariate and multivariate cox regression analyses for integrating risk characteristics and clinical features.	PMC9214238	fgene-13-911346-g005.jpg
0.440948	0b6d8d924671425dbda1b88d18094444	Validation of the constructed risk signature in the verification cohort. (A) Distribution of risk scores and patient status in different risk groups. (B) Heat map displayed the expressions of four candidate genes in the verification cohort. (C) Survival curves of the two risk groups. (D) Time-dependent ROC curve in the verification cohort. (E) Tumor microenvironment analysis by the ESTIMATE algorithm. p < 0.05; p < 0.01; **p < 0.001.	PMC9214238	fgene-13-911346-g006.jpg
0.413429	637f3eb149a14bdda2918f04c1c25360	Construction and evaluation of the nomogram. (A). Nomogram for predicting the prognosis of patients with osteosarcoma. (B) Calibration for 3-and 5-year OS in the training cohort. (C) Calibration for 3-and 5-year OS in the verification cohort. (D) ROC analysis for 3-and 5-year OS in the training cohort. (E) ROC analysis for 3-and 5-year OS in the verification cohort.	PMC9214238	fgene-13-911346-g007.jpg
0.479872	8790223750b54682be290844a54b4581	Candidate gene validation. (A) Expression levels of candidate genes in tumor and normal tissues from osteosarcoma patients. (B) Expression levels of candidate genes in different cell lines. p < 0.05; p < 0.01; **p < 0.001.	PMC9214238	fgene-13-911346-g008.jpg
0.428168	44ddb2341ab34e5586cecdd24acaa967	Heterogeneous federated molecular learning where three institutions focus on different types of moleculesThe server has no access to training data.	PMC9214329	gr1.jpg
0.436588	531535eeff734632b58f70c83d1a0d14	Illustration for the motivation of FLITWe assume two clients as A and B, and the local data on these clients do not share the same distribution as the global one. Local models trained on biased local data will overfit the majority groups of data and underfit others. FLIT measures each sample’s prediction confidence and puts more weight on the uncertain data. As a result, the local data distribution will be better aligned to the global one, and the trained local models will also be more consistent with each other.	PMC9214329	gr2.jpg
0.439665	ee5e01fc380f4e67b01a00f01311ba3d	Performance of baseline and our methods with varying communication roundsAsterisk (∗) denotes that the results are obtained with centralized training. We find our method has a strong advantage with a few communication rounds.	PMC9214329	gr3.jpg
0.460462	bf648548468842b0b5d4d56c73571d14	Performance of baseline and our methods with different number of clientsSee Figure 3 for color legend. The small-scale local training data reduce federated-learning performance for all methods.	PMC9214329	gr4.jpg
0.490355	3349146cb48d40059ca389241a771c0e	Flow diagram of complete data analysis.	PMC9214656	fgene-13-909797-g001.jpg
0.413541	8f4cfff59eb44104be51e05bd5ea7dfd	Identification of oxidative stress-related lncRNAs in LUAD patients. (A) Volcano plot of oxidative stress-associated DEGs in TCGA databases. (B)Heatmap of oxidative stress-associated DEGs in TCGA databases. (C) Sankey relation diagram for differentially expressed oxidative stress genes and oxidative stress-related lncRNAs. (D) Heatmap for the correlations between oxidative stress genes and oxidative stress-related lncRNAs.	PMC9214656	fgene-13-909797-g002.jpg
0.495349	c8305b6d481e4352adebba8ce9dde1d8	Construction and validation of the predictive model in TCGA training set. (A) Univariate Cox regression analysis of OS for part of 182 oxidative stress-related lncRNA prognostic signatures. (B,C) Altogether 25 lncRNAs were selected using LASSO regression. (D) Multivariate Cox regression analysis showed 16 independent prognostic lncRNAs. (E) Distribution of oxidative stress-related lncRNA model-based risk score for the training set. (F) Different patterns of survival status and survival time between high-risk and low-risk groups in the training set. (G) Heatmap to show the expression of 16 lncRNAs between high- and low-risk groups in the training set. (H) Kaplan–Meier curve of high-risk and low-risk patients in the training set.	PMC9214656	fgene-13-909797-g003.jpg
0.428893	07ffb082995e4298ace1ea57e0376f4c	Validation of the prognostic oxidative stress-related lncRNA signature. (A) Risk score, (B) survival status, and (C) heatmap for the testing set. (D) Risk score, (E) survival status, and (F) heatmap for the entire set. (G) Kaplan–Meier curve for the testing set. (H) Kaplan–Meier curve for the entire set.	PMC9214656	fgene-13-909797-g004.jpg
0.425191	11c6079e2d87456096d30728d42bc368	Independent prognostic factors and construction of the nomogram. (A) Univariate analysis of the clinical characteristic and risk score with the OS. (B) Multivariate analysis of the clinical characteristic and risk score with the OS. (C) Nomogram predicts the probability of the 1-, 3-, and 5-year OS. (D) Calibration plot of the nomogram indicates the probability of the 1-, 3-, and 5-year OS.	PMC9214656	fgene-13-909797-g005.jpg
0.434595	c51150546051414f9bc70ebaaffff9ef	Assessment of the predictive risk model and principal component analysis. The 1-, 3-, and 5-year ROC curves of the (A) training set, (B) testing set, and (C) entire set. (D) ROC curves of the clinical characteristics and risk score. (E) Concordance indexes of the risk score and clinical features. (F) PCA between high-risk and low-risk groups based on 16 prognostic lncRNAs in the training set (G) and testing set. (H) PCA between the high-risk and low-risk groups based on entire gene expression profiles, (I) all oxidative stress genes, (J) and risk model based on the representation profiles of the 16 oxidative stress-related lncRNAs in the entire set.	PMC9214656	fgene-13-909797-g006.jpg
0.440664	a98778f4c4e14732907298270d286b17	Kaplan–Meier curves of OS difference stratified by LUAD stage (I–II or III–IV), age (≤65 or >65), gender (female or male), and TNM stage (T1–2 or T3–4) between high-risk and low-risk groups in TCGA entire set.	PMC9214656	fgene-13-909797-g007.jpg
0.455578	11af10f6beee46b688dfa2563e2d6a6d	Immune infiltration discrepancy in different risk groups. (A) Heatmap of 22 tumor-infiltrating immune cell types in low- and high-risk groups. (B) Bar chart of the proportions for 22 immune cell types. (C) ssGSEA scores of immune functions in low-risk and high-risk groups. (D) Immune cells in low-risk and high-risk groups. (E–G) TME scores between high- and low-risk groups. p < 0.5, p < 0.01, and **p < 0.001; ns, no sense.	PMC9214656	fgene-13-909797-g008.jpg
0.380328	9663dbd24b654a54b8c0325d615fb7e6	Exploration of tumor mutation burden and visualization of lncRNA networks. (A,B) Waterfall plot of somatic mutation features established with high- and low-risk groups. (C) Tumor mutation burden in the high-risk and low-risk groups. (D) Correlation between risk score and TMB. (E) Kaplan–Meier curve of the OS among the high- and low-TMB groups. (F) Survival analysis among four patient groups stratified by both TMB and risk score. (G) Correlation between the risk score and immune subtype. (H) Connection degree between the oxidative stress-related lncRNAs, oxidative stress-related genes, and risk types.	PMC9214656	fgene-13-909797-g009.jpg
0.455256	e4112b898e754ea3b8e29914274c5f48	Clinical application of the risk signature. (A) Comparison of IC50 of chemotherapeutic drugs among two subgroups. (B) Investigation of anti-tumor drug sensitivity-targeting signature. (C) Expression levels of CTLA4, HAVCR2, PD-1, TIGIT, and PD-L1 in the high- and low-risk groups. (D) Correlation between 16 lncRNAs and chemotherapeutic drugs. (E) TIDE prediction difference in the high-risk and low-risk groups.	PMC9214656	fgene-13-909797-g010.jpg
0.490176	89f68f21fc754c95bc3c077f9dd9b3b4	Functional analysis. (A) Top 10 classes of GO enrichment terms based on DEGs between two groups, including biological process (BP), cellular component (CC), and molecular function (MF). (B) Top 30 pathways of KEGG enrichment terms. (C) Gene set enrichment analysis of the top 10 pathways significantly enriched in the high-risk group. (D) Gene set enrichment analysis of the top 10 pathways enriched considerably in the low-risk group. (E) Cytoscape of lncRNA–mRNA co-expression network. Green nodes represent lncRNAs, while red nodes represent mRNAs.	PMC9214656	fgene-13-909797-g011.jpg
0.41322	e50eec73713c4a7b8f7070fc282e65cc	Schematic (not to scale) whereby particles in contact with the cuticle and within a droplet on a generic leaf surface release material either directly into the cuticle or into the aqueous medium prior to uptake through the leaf surface. The second panel provides a schematic of particles (gray) in the aqueous droplet (blue) on the cuticle surface (cuticle proper in black and sorption compartment in orange) with plant tissue represented by a green continuum. Fickian diffusion and partitioning across the cuticle-solution interface develop two possible pathways for uptake: directly through the particle-cuticle contact or indirectly via the cuticle-solution interface, represented by the blue arrows. The thick black line in the third panel represents the outer boundary of the cuticle proper. Illustrative steady-state concentration profiles developed by Fickian diffusion are provided as color maps in the third panel in the purely illustrative case of 1:1 partitioning.	PMC9214695	as2c00029_0002.jpg
0.393662	fd2d96bd05ba4e308962d4b97b9faa72	Illustration of model boundary conditions and coordinate systems.	PMC9214695	as2c00029_0003.jpg
0.442775	c29db4dccfb844c4b3fa6bb9643d6275	Schematic illustration of the spatial parameters describing a truncated sphere resting on a finite barrier under cylindrical coordinates (r, z). Parameters include the angle from symmetry axis θ, angle to three-phase contact point θc (equivalent to the contact angle), particle radius rp, shortest distance from particle center ρ, distance from particle center to barrier surface zp, and barrier thickness zcut.	PMC9214695	as2c00029_0004.jpg
0.432868	9024a1c1c4624986966431231b978d0e	Dimensionless concentration profiles of aqueous, released material under the thermodynamic limit for (A) a hemisphere on a plane (Zp = 0) and (B) a sphere on a plane (Zp = 1) represented as color maps. The white area is the particle. The spatial dimensions are normalized to the particle radius. Isoconcentration contour lines are included.	PMC9214695	as2c00029_0005.jpg
0.499445	39c475f255ea4eba9e5d27de6ef24c46	(A) Dimensionless steady-state flux profile for −0.8 ≤ Zp ≤ 1. (B) Dimensionless steady-state surface flux integral ∫0θcJ(θ) sin θdθ = JTot/2π for −0.8 ≤ Zp ≤ 1, represented as a blue solid line, for a truncated sphere on a surface under thermodynamic release. The surface flux integral assuming a constant J(θ) = 1 is represented by a red dotted line in (B).	PMC9214695	as2c00029_0006.jpg
0.440033	652e4bb7556a49c992aa9279c9ea8e6f	Results for the surface flux and cuticular concentration for varying Zcut for a particle-cuticle disk interface. The steady-state concentration profile along the symmetry axis is presented for (A) thermodynamic release (Kcut = 106) and (B) kinetic release (Kcut = 10–6). Both demonstrate transitions from linear diffusion. Inlaid diagrams provide clearer illustration for small Zcut. (C) presents the steady-state surface flux profile for varying Zcut for Kcut = 106. (D) presents the surface concentration profile for varying Zcut for Kcut = 10–6.	PMC9214695	as2c00029_0007.jpg
0.459516	61b7882b914e47798fe7217a3eb37623	Logarithm of the dimensionless total steady-state flux JTot/rp2 via the indirect pathway into an infinite cuticle (Zcut ≫ 1) with zero direct flux from the disk contact area, assuming a surface-equilibrated cuticle-solution interface, a hemispherical particle (Zp = 0), and instant repletion of material at the cuticle interface by diffusion through the aqueous solution for varying Kaq. The red dotted line represents the dimensionless steady-state total flux predicted for the direct uptake rate acting alone under the thermodynamic, infinite-thickness regime.	PMC9214695	as2c00029_0008.jpg
0.416324	5093d55dcc0b4651a91c4195669c9479	(A) Interfacial steady-state flux profile J for simultaneous direct and indirect uptake with varying dimensionless aqueous release rate constants, Kaq. (B) Logarithm of the total dimensionless steady-state flux for simultaneous direct and indirect pathways into an infinite cuticle (Zcut ≫ 1) assuming a thermodynamic cuticle-solution interface and instant repletion of material by diffusion through the solution for varying dimensionless particle-solution release rate constants Kaq. The blue line represents the total flux into the cuticle. The red line represents the total flux into the cuticle directly from the particle. The difference between the two corresponds to the flux at the solution-cuticle interface (note that this is negative for Kaq < 100.8).	PMC9214695	as2c00029_0009.jpg
0.457911	73c734d5a2bf4cf9a5ddd2ea66525ee7	Scheme for utilizing atom-wise featurization and topological information on compounds for the identification of pan-kinase family inhibitors (PKFIs) using graph convolutional network (GCN) models. A Schematic of the research framework. B 195,802 test datasets of 60,122 chemicals and 384 kinases were collected from the ChEMBL database and kinase profiling. C Eight families in four kinase groups were targeted in this study. D Each compound is transformed into atom features and a structure graph for GCN architectures to identify PKFIs. E A visualized explanation was made using the grad-CAM method	PMC9214975	12859_2022_4773_Fig1_HTML.jpg
0.459515	c68b8639dba4433fab77298a1595fd63	Explanation of the GCN model’s prediction of lapatinib and other inhibitors in EGFR, JAK, and PIM models. (A) Grad-CAM preferences of lapatinib from the latest graph convolutional layer for both positive and negative classes. Circles are centered at each atom, with green ones for the positive class and orange for the negative class. The larger the circle, the more the atom contributes to the prediction of the model at a specific class. (B) Preferences for different inhibitors within and across families. Within the same family, conserved attention on similar environments is visualized, and family-sensitive pre-moieties can be seen by comparing cross-family inhibitors. (C) Crystallized complexes of the pan-EGFR inhibitor lapatinib (deep blue, PDB ID: 1XKK), pan-JAK inhibitor tofacitinib (light blue, PDB ID: 3EYG), and pan-PIM inhibitor LGH-447 (purple, PDB ID: 5DWR) demonstrated three different modes of kinase inhibition	PMC9214975	12859_2022_4773_Fig2_HTML.jpg
0.415969	ec65103960c6411693b446253c404e5f	Correlation between preferences and the mapping of current checkmol fingerprints. (A) The mapped region of checkmol fingerprint and their odds-ratio ranking (epsilon = 0.5, is added to prevent dividing by zero) of 204 checkmol descriptors within each family of the PKFIs set. Most pre-moieties associated with different modes of kinase inhibition (described previously) are mappable and correlated with the feature distribution in the training sets. However, several pre-moieties are still not precisely defined in the current fingerprint and thus are undistinguishable (i.e., naphthalenyl-nitrogen regions on lapatinib). (B) Overall odds-ratio distribution of checkmol descriptors on EGFR, JAK, and PIM datasets with mapped moieties is indicated. It should be noted that several super peaks are observed with relatively few compounds and thus are not currently discussed	PMC9214975	12859_2022_4773_Fig3_HTML.jpg
0.476443	309b10f987a64ed99452273b9d6ce4b4	Feature encoding of the input compounds. Each compound is encoded by atomic features and a structure graph. Atomic features contain 28 descriptors belonging to six types, including atom types, hybridization, charges, and chemo-properties. For a structure graph, a modified normalized Laplacian matrix (Eq. (4)) was applied as a representation of the compound’s topology information. Padding to 50 atoms with zeros was applied to contain variable numbers of atoms in the input compounds	PMC9214975	12859_2022_4773_Fig4_HTML.jpg
0.453497	ecf6811aa00c44ba81e5de354eaff537	The operation of the graph convolution and GCN model architecture of binary classification is described in this paper. To include the surrounding environments of each atom (local environments), the multiplication of topology graph \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tilde{L}$$\end{document}L~ and the compound features (either the input atomic features AF or the feature map F(l−1) from the last layer) is required, which should be further multiplied by Wl to learn the information provided by the local environment.	PMC9214975	12859_2022_4773_Fig5_HTML.jpg
0.457942	5164f1b158d14b65bc2dbed5d755ac17	Structurally diverse NKT cell ligands α-GalCer (1) and iGb3 (2).	PMC9215340	d2ra02373c-f1.jpg
0.420078	8943a9c5215243a0b6d19ba34c65f908	iGb3 analogues 1,3-β-Gal-LacCer (βG-iGb3, 3), iGb3-C12 (4), C20:2 (5), 6′′′-dh-iGb3-sphinganine (6) and 6′′′-dh-iGb3-sphingosine (7).	PMC9215340	d2ra02373c-f2.jpg
0.420108	0212036101ae46e6b20119d5c384816e	(A) Plots depict the dilution of CFSE amongst thymic (C57BL/6) NKT cells co-cultured for 72 h with Jα18−/− (C57BL/6) splenocytes pulsed overnight with the indicated antigen. Numbers on plots represent the estimated division percentages (FlowJo). (B) Graph depicts the division percentages of thymic NKT cells after 72 h (mean ± SEM). All lipids were used at 1 μg ml−1 except for α-GalCer which was used at 100 ng ml−1n = 5, except iGb3 (C26), where n = 2.	PMC9215340	d2ra02373c-f3.jpg
0.429579	268fb103b3ca47f584c6c89672f27ef0	(A) Cell culture supernatants were collected at 72 h following the co-culture of antigen-pulsed Jα18−/− (C57BL/6) splenocytes with NKT cell-enriched thymocytes (C57BL/6). Graph depicts the cytokine concentrations as gauged by CBA. Pooled means (n = 2 replicates) from 2 independent experiments (mean ± SEM). (B) Graph depicts the ratio of IL-13 to IFN-γ from the indicated conditions. Data taken from 2 independent experiments, each represented by square or circular datapoints that depict the mean of two technical replicates for each experiment	PMC9215340	d2ra02373c-f4.jpg
0.529688	19d85a9e1b0c46fa8f04a4cb2b78d4bb	Schematic diagram of the human body model. (A) Positioning ACJ and TFJ in this position. (B) Positioning LHBT and PTT in this position.	PMC9217061	fgene-13-894716-g001.jpg
0.404525	26a5ea2ad27c4c1f84c2550153479916	Needle placement based on target structures relative to ultrasound images. (A) Correct localization map of the ACJ. (B) Correct localization map of the TFJ. (C) Map of mislocalization of the long head of the biceps tendon. (D) Map of the mislocalization of the posterior tibialis muscle.	PMC9217061	fgene-13-894716-g002.jpg
0.428461	8f581dab31564b6eb386dd8d7bd4e1c2	Flowchart of the included patients.	PMC9218196	fped-10-921880-g0001.jpg
0.480358	a5639904b1be47c8a03a8cb77a1f2c06	Regulatory interactions of SERCA (gray) with PLB (blue) and DWORF (red). Structures: PLB5, PDB:2KYV (59); PLB1, PDB:1FJP, (60); PLB-SERCA, DWORF–SERCA (18, 61); DWORF-7MPA, (25). DWORF, dwarf ORF; PDB, Protein Data Bank; PLB, phospholamban; SERCA, sarco/endoplasmic reticulum Ca2+-ATPase.	PMC9218510	gr1.jpg
0.405391	f6e2dcb3cb0544c0ae5d700de51ff15c	Dynamics of PLB and DWORF binding to SERCA during elevations in intracellular Ca2+.A, a simplified Post-Albers scheme of the SERCA enzymatic cycle, highlighting states that predominate at low (blue) and high (red) intracellular [Ca2+]. B, FRET-based binding curves displaying a shifted dissociation constant (KD) of PLB–SERCA binding between the ATP-bound (blue) and TG-bound (black) states of SERCA. C and D, apparent KDs of PLB or DWORF binding to different SERCA enzymatic states of the catalytic cycle as in panel (A) with lines representing mean ± SEM (n = 6). Ligands used to stabilize each state are shown in parentheses. Differences in micropeptide KDs between SERCA states were analyzed by one-way ANOVA with Tukey’s post hoc (∗p < 0.05). E, apparent KDs of PLB and DWORF for SERCA in ATP-containing solutions with low (blue) and high (red) concentrations of intracellular Ca2+ with lines representing mean ± SEM (n = 5). Differences in KD evaluated by Student’s t test. F, confocal microscopy quantification of intracellular Ca2+ measured by X-rhod-1 fluorescence (gray raw data, with black smoothed trendline) with simultaneous measurement of changes in PLB–SERCA FRET (YFP/Cer ratio) (gray raw data, with blue smoothed trendline). G, quantification of ER luminal Ca2+ measured by R-CEPIA1er fluorescence with simultaneous measurement of PLB–SERCA FRET (YFP/Cer ratio). H, quantification of intracellular Ca2+ measured by X-rhod-1 fluorescence with simultaneous measurement of DWORF–SERCA FRET (YFP/Cer ratio) I, quantification of ER luminal Ca2+ measured by R-CEPIA1er fluorescence with simultaneous measurement of DWORF–SERCA FRET (YFP/Cer ratio). DWORF, dwarf ORF; ER, endoplasmic reticulum; PLB, phospholamban; SERCA, sarco/endoplasmic reticulum Ca2+-ATPase.	PMC9218510	gr2.jpg
0.448116	5c5dc54845284d7c803175e29708377f	PLB reassociation with SERCA after Ca2+transients are delayed by slow dissociation from the PLB pentamer.A, schematic diagram of shifts in PLB and DWORF binding equilibria during Ca2+ release. B, representative single exponential decay fit of the kinetics of DWORF–SERCA binding during Ca2+ release. C, kinetics of PLB–SERCA unbinding during Ca2+ release. D, kinetics of PLB–PLB binding during Ca2+ release. E, the latency of FRET ratio changes compared to Ca2+ release with lines representing mean ± SEM. Differences determined by one-way ANOVA with Dunn’s post hoc test (∗p < 0.05, see Table S4 for complete statistical analysis). F, schematic diagram of shifts PLB and DWORF binding equilibria during Ca2+ uptake. G, representative single exponential decay fit of the kinetics of DWORF–SERCA unbinding during Ca2+ uptake. H, kinetics of PLB–SERCA rebinding during Ca2+ uptake. I, kinetics of PLB–PLB binding during Ca2+ release. J, the latency of FRET ratio changes compared to Ca2+ release with lines representing mean ± SEM. Differences determined by one-way ANOVA with Dunn’s post hoc test (∗p < 0.05, see Table S7 for complete statistical analysis). DWORF, dwarf ORF; PLB, phospholamban; SERCA, sarco/endoplasmic reticulum Ca2+-ATPase.	PMC9218510	gr3.jpg
0.401675	3630130bc0294ac88a9f08ef4e4371db	A computational model simulated the dynamics of PLB and DWORF interactions with SERCA.A, simplified diagram of modeled regulatory interactions. B, a fit of the model to a representative FRET change measured by confocal microscopy. C, simulation of changes in the populations of regulatory species during a Ca2+ elevation, where relative amounts of SERCA and PLB are equal. Black circles represent the results of the model of the normal, nonfailing heart. Red triangles represent adjustment of the model to simulate heart failure. D, simulation of the effect of cardiac pacing on PLB–SERCA at three heart rates (beats per min, BPM), where the ratio of SERCA:PLB was 1:3. E, increasing pacing frequency modestly decreased PLB–SERCA binding. F, increasing pacing frequency increased PLB oligomerization. G, PLB–SERCA oscillation amplitude decreased with faster pacing. H, simulation of the effect of increasing DWORF expression relative to SERCA on PLB–SERCA binding. I, increasing DWORF resulted in a decrease in the equilibrium level of PLB–SERCA. J, increasing DWORF increased PLB oligomerization. K, increasing DWORF relative to SERCA resulted in larger oscillations in PLB–SERCA binding. For H–K, the ratio of SERCA:PLB was 1:3 and pacing rate was 180 BPM. DWORF, dwarf ORF; PLB, phospholamban; SERCA, sarco/endoplasmic reticulum Ca2+-ATPase.	PMC9218510	gr4.jpg
0.401988	fcf38f08430f469088e06d87d61a6e0f	PROMIZING stepwise algorithm and study flowchart. CPAP: continuous positive airway pressure, SNR: screened and non-randomized, PROMIZING: Proportional assist ventilation for minimizing the duration of mechanical ventilation study, PSV: pressure support ventilation, SBT: spontaneous breathing trials	PMC9219177	13054_2022_4063_Fig1_HTML.jpg
0.504341	2e6e2b49b5ab401c91fa35c4b326839a	Ventilator settings and respiratory parameters at baseline (pre-randomization) according groups. Data presented as mean ± standard deviation. Pairwise comparisons between groups by Tukey Honest Significant Difference Test where p = 0.05 was taken as a threshold for these post-hoc comparisons: * Difference (p < 0.05) between Not ready for weaning group vs. SNR group. † Difference (p < 0.05) between ZERO CPAP tolerance failure group vs. SNR group. § Difference (p < 0.05) between SBT failure group vs. SNR group. SNR: screened and non-randomized, FiO2: fraction of inspired oxygen, CPAP: continuous positive airway pressure, PEEP: positive end-expiratory pressure, SBT: spontaneous breathing trial	PMC9219177	13054_2022_4063_Fig2_HTML.jpg
0.401733	418752598ce24a8b80154090938b611b	Computed Tomography–Myelogram image from a 6-year-old Labrador with T12–T13 acute non-compressive nucleus pulposus extrusion. (A) Vacuum phenomenon and light attenuation of the ventral contrast line diameter (sagittal view); (B) T12–T13 hyperintensity sign and bilateral light attenuation of the diameter of the contrast lines (dorsal view); (C) normal T12–T13 disk space with residual mineralized disk material ventrally (transverse view).	PMC9219513	animals-12-01557-g001.jpg
0.405987	edf8a674a1f147fc9299a4ca7b1b4ce5	Electrical stimulation protocols on a dog in a postural standing position. (A) Co-contraction protocol with the stimulation of both the quadriceps femoris group and the hamstring muscles group. (B) Segmental technique of the sciatic nerve by means of functional electrical stimulation.	PMC9219513	animals-12-01557-g002.jpg
0.417309	5b2d3a0d732e44cf80b9fd709e617635	Laser therapy class IV program applied on the spinal cord injury.	PMC9219513	animals-12-01557-g003.jpg
0.492971	108352e3b9224cc8b31125d35b8914e9	Laser therapy class IV program applied to the coxofemoral joint using the four-point technique. (A) Proximo cranial region; (B) proximo caudal region; (C) disto caudal region; (D) disto cranial region.	PMC9219513	animals-12-01557-g004.jpg
0.498589	1461efad7c8f4dde953f88c1b6164dc4	Locomotor exercise in two dogs. (A) Land treadmill; (B) underwater treadmill.	PMC9219513	animals-12-01557-g005.jpg
0.435493	00597652f1b9436eab163ab3f37e8947	Kinesiotherapy exercises. (A) Obstacle rail; (B) gait stimulation on alternating floors; knuckling position.	PMC9219513	animals-12-01557-g006.jpg
0.425964	d2713d747f6740b8baee21180742857b	Kinesiotherapy exercises. (A) Postural standing on a balance board; (B) bicycle movements on a central stimulation pad.	PMC9219513	animals-12-01557-g007.jpg
0.437066	83c457272f214e94a25dcae7fac90a28	Supportive treatment. (A) Passive range of motion exercises for the knee joint; (B) deep kneading massage technique for direct muscular treatment.	PMC9219513	animals-12-01557-g008.jpg
0.615286	034c15e4e4e54d989fcda6ba76dc470d	Flow cohort diagram describing the outcome measures of both the control and study group throughout the rehabilitation period and after clinical discharge. OFS: Open Field Score; SSS: Spinal Shock Scale; CG: control group; SG: study group; T0 (day 0); T1 (day 1); T2 (day 2); T3 (day 3); T4 (day 5); T5 (day 7); T6 (day 14); T7 (day 21); T8 (day 30); T9 (day 60); F1 (7 days); F2 (15 days); F3 (one month); F4 (three months); F5 (six months); F6 (one year); F7 (two years); F8 (three years); F9 (four years).	PMC9219513	animals-12-01557-g009.jpg
0.391954	f40fa235ce07431088844508e0238626	Evolution of the spinal shock scale-estimated marginal means in the study group throughout the intensive neurorehabilitation process. Y-axis: Spinal shock scale score; T0 (day 0); T1 (day 1); T2 (day 2); T3 (day 3); T4 (day 5); T5 (day 7); T6 (day 14); T7 (day 21); T8 (day 30); T9 (day 60).	PMC9219513	animals-12-01557-g010.jpg
0.416116	467106cba0d342e5a63f0e6b8dc43f40	Evolution of the Open Field Score (OFS)-estimated marginal means in both the study and control groups throughout intensive neurorehabilitation. Y-axis: OFS; T0 (day 0); T1 (day 1); T2 (day 2); T3 (day 3); T4 (day 5); T5 (day 7); T6 (day 14); T7 (day 21); T8 (day 30); T9 (day 60).	PMC9219513	animals-12-01557-g011.jpg
0.466098	9195f892587649eaa5a015c65b3c949d	Evolution of the OFS-estimated marginal means in both the study and control groups during the follow-up periods. Y-axis: OFS (Open Field Score); F1 (7 days); F2 (15 days), F3 (one month), F4 (three months), F5 (six months), F6 (one year), F7 (two years), F8 (three years); F9 (four years).	PMC9219513	animals-12-01557-g012.jpg
0.501761	a03d7e6a12a64f88968496f9956df0c9	Experimental set-up for the effect on planktonic bacteria and biofilms; DBD: dielectric barrier discharge.	PMC9219831	antibiotics-11-00752-g001.jpg
0.463131	03e583afd5774f4bbf32bbf6b0e0b317	Colony forming units (CFU) counts (A), mass (B), and metabolic activity (C) of multi-species biofilms on dentin specimens and subsequent exposing of 30 s, 60 s, and 120 s to cold plasma. ** p < 0.01 vs. control (post-hoc Bonferroni).	PMC9219831	antibiotics-11-00752-g002.jpg
0.430944	1a4a289294d24bcdad43f451e5081d93	Colony forming units (CFU) counts (A), mass (B) and metabolic activity (C) of a multi-species biofilm on titanium specimens and subsequent exposing 30 s, 60 s and 120 s to cold plasma. ** p < 0.01 vs. control (post-hoc Bonferroni).	PMC9219831	antibiotics-11-00752-g003.jpg
0.446647	1b425c16427d47d391728c8ab6502ec3	Colony forming units (CFU) counts (A) after 4 h and 24 h, mass (B), and metabolic activity (C) after 24 h of a multi-species biofilm formation on dentin specimens immediately after the application of 120 s of cold plasma.	PMC9219831	antibiotics-11-00752-g004.jpg
0.497995	11d5a999f219478985afdd63984c85e5	Colony forming units (CFU) counts (A) after 4 h and 24 h, mass (B), and metabolic activity (C) after 24 h of a multi-species biofilm formation on titanium specimens immediately after the application of 120 s of cold plasma.	PMC9219831	antibiotics-11-00752-g005.jpg
0.513875	d75fd03151834448bdd6616e013769f3	Gingival fibroblast counts per mm2 on dentin (A) and titanium (B) specimens after pretreatment of 120 s of cold plasma.	PMC9219831	antibiotics-11-00752-g006.jpg
0.405222	562a42a3bb6d4b87bb2c0f28b5c39b59	(a) Roche total spike and (b) Snibe neutralizing antibody responses after the first, second, and third dose of inactivated virus or mRNA vaccine. Abbreviations: D1D10: 10 days post-dose 1, D2D20: 20 days post-dose 2, D3D20: 20 days post-dose 3.	PMC9220327	antibodies-11-00038-g001.jpg

0.409758

4ba9760644f84c05be5220bcdba1178f

Dough preparation steps. a Mixing batter by hand in Hargeisa (a new heritage zone) inside a plastic container, the most common material for mixing and fermenting flatbread batter. b The flatbread batter halfway through mixing as it becomes increasingly aerated and bubbly in a process that takes 10–15 min. c The flatbread batter after mixing, typically bubbly, smooth, and velvety in texture. Photo credit: Mustafa Said. d Fermented dhanaanis (starter) reserved from a flatbread batter in Hargeisa along with batter residue on the interior surface. This container will be used, unwashed, for subsequent batches to catalyze fermentation, a common method for ensuring adequate fermentation or in colder seasons, depending on the geographic location and corresponding climate. Photo credit: Mustafa Said

PMC9210053

42779_2022_138_Fig4_HTML.jpg

0.441476

25495c2bb9ae4232b3dff22c6608a73c

Commercially produced cajiin in Mogadishu, commonly a mixture of refined wheat and maize moistened with water. a Daily each morning, retail saleswomen provide cajiin producers with unique mixtures of grains and cereals to be processed into cajiin dough, and then retrieve their doughs, like this one, in the afternoon. b In the evenings, saleswomen prepare single-batch balls of cajiin for sale in local markets to customers who use the cajiin to prepare their flatbread batter at night, allow it to ferment overnight, and cook it the following morning for breakfast. Photo credit: Ali Hussein

PMC9210053

42779_2022_138_Fig5_HTML.jpg

0.453327

77a025dd44b946d1a9b4cce0270a4f88

Fireboxes to bake laxoox/canjeero. a Firebox known as girgire in Hargeisa. b Firebox known as burjiko in Mogadishu. Fireboxes hold hot charcoals that heat the cast iron griddles from underneath. Before the advent of fireboxes, laxoox/canjeero was commonly cooked over wood fires on the ground. Today, the flatbread is also frequently cooked over gas stoves, depending on household means, cooking tools, and preferences. Photo credits: Mustafa Said (a) and Abdikarim Omar (b)

PMC9210053

42779_2022_138_Fig6_HTML.jpg

0.475739

99cb528a55374eab940c05177c9b4b99

Shaping and baking laxoox/canjeero. a Preparing the griddle with vegetable oil using a blackened rag, known as a masaxaad, in Mogadishu (innovative zone). Alternately, the first cooked piece of laxoox/canjeero is folded and used to oil the griddle. In lieu of vegetable oil, some households use goat ghee to prevent the flatbread from sticking. Photo credit: Abdikarim Omar. b Pouring the batter on a cast iron griddle atop a gas stove in Hargeisa (new heritage zone). Anecdotes from interviewees indicate that ceramic griddles pre-date cast iron griddles in Somalia and may have been crafted and sold by tradespeople from the Arabian Peninsula. Today, the cast iron griddle is ubiquitous for this purpose in research locations, while online sources show that other griddle types, including non-stick pans, are used in global production. Photo credit: Mustafa Said. c Shaping the batter on the griddle over a firebox (burjiko) in Mogadishu (innovative zone) using a flat-bottomed cup. In a circular motion, the cook pushes batter outwards from the center of the griddle, creating a spiralized effect. The cup never touches the surface of the griddle but leaves behind it a thin trail of batter, while the un-flattened batter puffs up around it. The shape and patterns of the finished flatbread are a signal of quality. Photo credit: Abdikarim Omar

PMC9210053

42779_2022_138_Fig7_HTML.jpg

0.463709

d27bb4573bbe4465a4ca89c3fa4ca50d

Laxoox/canjeero appearance and consumption. a Cooked laxoox/canjeero retains a soft, puffed side that never contacts the griddle surface but rather rises via steam under a well-fitting lid; and a browned side that cooks on the oiled griddle and is crispy when just cooked but quickly softens. The flatbread is pockmarked with holes, or “eyes,” and appears translucent when held up to a light source. Photo credit: Erin Wolgamuth. b A plate of flatbread served for breakfast in Mogadishu (innovative zone), sprinkled with sugar and accompanied by oil and tea. All respondents reported eating this flatbread for breakfast with either sweet (tea, sugar) or savory (meat or vegetable) dishes. Some also consume laxoox/canjeero at other meals and with other dishes. Photo credit: Abdikarim Omar

PMC9210053

42779_2022_138_Fig8_HTML.jpg

0.538912

b8a92a1715474e0897b131334da4af1c

Production styles flowchart, from heritage to new heritage, innovative and global. The latter three groups developed concurrently and were not chronologically subsequent. Heritage production is linked to the historic era of seminomadic Somali pastoralists, while differences among the subsequent three production styles have resulted from the Somali civil conflict and continue today

PMC9210053

42779_2022_138_Fig9_HTML.jpg

0.454537

62f74019ae5f4e37ae007b0143f1571e

Trends in number of active orthopaedic sports medicine podcasts available for listening on Apple, Google, and Spotify.

PMC9210369

gr1.jpg

0.47894

2594d646b41e4b599afae5a92be099f6

Flow of study

PMC9210584

13098_2022_858_Fig1_HTML.jpg

0.465925

341acfee130c4d9891500e5889901726

Flowchart of inclusion of patients.

PMC9211000

ActaO-93-3140-g001.jpg

0.44491

8879c1728ec740fab0c3b0659062bf6c

Directed acyclic graph showing possible relationship between risk factors and recovery trajectory after primary total hip arthroplasty. Confounding variables were selected based on the directed acyclic graph: BMI was adjusted for smoking; ASA was adjusted for age, BMI, and smoking; Smoking was adjusted for age; Approach was adjusted for BMI; Fixation was adjusted for age and approach; Head diameter was adjusted for approach; Bearing type was adjusted for age, BMI, and head diameter; No adjustments were included in the analysis of age and sex.

PMC9211000

ActaO-93-3140-g002.jpg

0.380659

e085a11668d44a80a64dee4735f88de1

Recovery trajectories based on HOOS-PS score across class 1 (n = 2,391), class 2 (n = 664), and class 3 (n = 152). The mean trajectories with their 95% CI per class are shown in bold (black); individual trajectories are in color. Lower HOOS-PS scores indicate better physical functioning. For the purpose of plotting, individuals were assigned to classes based on their most likely class membership; it should be noted that individuals are in fact assigned a probability of class membership in the model.

PMC9211000

ActaO-93-3140-g003.jpg

0.407814

828bda02e6df477da346aa9fc97d7276

Impact of decreases in protective behaviors on COVID-19 infections and hospitalizations under various school reopening scenarios. Horizontally from left to right, effects of reductions (0%, 25%, 50% reduction) in protective behaviors among 18–40-year-old adults for a given level of school reopening (15%, 30%, and 45% reopening), from March 2020 to November 2020. Vertically from top to bottom, effects of increasing school reopening for a given level of protective behaviors among 18–40-year-olds. Yellow plots indicate point prevalence of latent infections and red plots indicate point prevalence of hospitalizations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

PMC9212859

gr1_lrg.jpg

0.455933

1139eeeabe424b008801afd0c0fe74a6

Impact of increases in adult OOHA on COVID-19 infections and hospitalizations under various school reopening scenarios. Horizontally from left to right, effect of increasing adult OOHA (65%, 70%, 75%, and 80% of prepandemic levels) for a given level of school reopening, from March 2020 to November 2020. Vertically from top to bottom, effect of increasing school reopening for a given level of adult OOHA. Yellow plots indicate point prevalence of latent infections and red plots indicate point prevalence of hospitalizations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

PMC9212859

gr2_lrg.jpg

0.48744

2ca8f75f41b94a02a73a6871ce834a69

Effective reproductive number (Rt) by age group assuming 45% school reopening beginning September 3, 2020. Rt is defined as the average number of secondary infections resulting from an infected individual in a population where not all individuals are susceptible. A value of 1 represents the threshold needed for an epidemic to be sustained; values less than 1 indicate that the epidemic is dying out and values more than 1 indicate that the epidemic is growing. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

PMC9212859

gr3_lrg.jpg

0.468155

b2af57a8a4c34fd1bbbec408b69a90b9

Effects of school reopening and adult behavior change on COVID-19 infections and hospitalizations as of November 1, 2020. Scenarios S1–S3 represent 15% school reopening, scenarios S4–S6 represent 30% school reopening, and scenarios S7-S9 represent 45% school reopening. Center vertical lines represent medians; left and right edges represent 25% and 75% quartiles respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

PMC9212859

gr4_lrg.jpg

0.508127

79b9256952e3468bb88059ea27a3237f

Effects of adult OOHA on COVID-19 infections and hospitalizations under different school reopening scenarios as of November 1, 2020. Scenarios A1–A4 represent 15% school reopening, scenarios A5–A8 represent 30% school reopening, and scenarios A9–A12 represent 45% school reopening. Center vertical lines represent medians; left and right edges represent 25% and 75% quartiles respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

PMC9212859

gr5_lrg.jpg

0.433374

ec5ac02a4caa4baa95f964b7e3897d61

Comparison of the basic data of the two groups. (a–c) The comparisons of the average age, gender distribution, and blood pressure indexes of patients in the two groups, respectively. ∗Compared with control group, P < 0.05.

PMC9213170

CMMM2022-5863082.001.jpg

0.397318

23a0686ddcd34686987e6d09a59952bd

Comparison of cerebral CT images before and after being processed by intelligent algorithm. (a and c) The original cerebral CT images of patients in the observation group and the images processed by intelligence segmentation algorithm, respectively (the patient was a male aged 71). (b and d) The original cerebral CT images of patients in the control group and the images processed by intelligence segmentation algorithm (the patient was a female aged 74).

PMC9213170

CMMM2022-5863082.002.jpg

0.498146

828e120dc0ee402ba7d2d20dfe15be23

Comparison of evaluation indexes of image processing quality under intelligent segmentation algorithm. (a–c) The comparison charts of Dice value, sensitivity, and specificity, respectively. M1, M2, M3, M4, and M5 represented the 1st, 2nd, 3rd, 4th, and 5th algorithm tests, respectively.

PMC9213170

CMMM2022-5863082.003.jpg

0.404519

353be2025fb04072858cd49e047508db

Comparison of edema/hematoma volume in CT images of patients between two groups. ∗Compared with control group, P < 0.05.

PMC9213170

CMMM2022-5863082.004.jpg

0.500813

7c3a8d00728d40d6ac02013cdc70a783

Comparison of relative edema volume in two CT examinations of two groups. ∗Compared with control group, P < 0.05.

PMC9213170

CMMM2022-5863082.005.jpg

0.420986

b05f304b84ce40c686db2d6acf6faca6

Comparison of signs in two CT images of patients between two groups. ∗Compared with those of the control group, P < 0.05.

PMC9213170

CMMM2022-5863082.006.jpg

0.44013

7fd2302cf806480f8b30c964d4db35c9

Comparison of serological indexes of patients between the two groups. (a) The comparison chart of ANC, ALC, and NLR. (b) The comparison chart of RBC and HGB, (c) The comparison of the cholesterol indexes of patients in the two groups. ∗Compared with the data of the control group, P < 0.05.

PMC9213170

CMMM2022-5863082.007.jpg

0.445113

3c305d74ae09451891ca55a01db0f3e2

Body mass index versus risk of death from COVID-19. In the left panel, estimates are adjusted for age, sex, education and district and in the right panel, the age- and sex-stratified results are adjusted for age, education and district. The areas of the squares are proportional to the amount of statistical information and the lines through them are the 95% confidence intervals. The numbers above each vertical line in the left panel show the death rate ratio for that group and the numbers below each vertical line give the number of COVID−19 deaths in that group. In the right panel, the diamonds are the information-weighted averages of the two results above them. BMI, body mass index; CI, confidence interval; RR, mortality rate ratio

PMC9214141

dyac134f1.jpg

0.441724

44281ec3be7b4fefbe1dff3027855f3d

Network pharmacology analysis of the mechanism of action of ICA in the treatment of colitis. (A) Venn diagram of intersection of ICA targets and colitis targets. (B) ICA target protein interaction network. (C) KEGG pathway analysis of ICA target.

PMC9214228

fphar-13-903762-g001.jpg

0.420451

6c57931e8863417c96c961a04a2420d9

Effects of ICA on colon inflammation in aging rats. (A) Representative figures of HE of colonic epithelial morphology (×200, ×400). (B) The effect of ICA on the expressions of inflammatory cytokines TNF-α and IL-1β in the colon of aging rats was detected by Western blot. a: Adult group, b: Aging group, c: Low dose of ICA-treated aging group (ICA-L group), d: High dose of ICA-treated aging group (ICA-H group). ** p < 0.01 vs. Adult group. # p < 0.05 vs. Aging group. ## p < 0.01 vs. Aging group, n = 4-8.

PMC9214228

fphar-13-903762-g002.jpg

0.447715

d7b8aeabc5554f0dbfdcaf267fdcee9e

Effects of ICA on tight junction proteins of colonic epithelium in aging rats. Expressions of (A,B) Occludin, (C,D) Claudin-1, and (E,F) Claudin5 between colonic epithelial cells in aging rats were detected by IHC and Western blot (×200, ×400). a: Adult group, b: Aging group, c: ICA-L group, d: ICA-H group. ** p < 0.01 vs. Adult group. # p < 0.05 vs. Aging group. ## p < 0.01 vs. Aging group, n = 3-4.

PMC9214228

fphar-13-903762-g003.jpg

0.519151

bec2d1b247924a9b86d4db349857623a

Effects of ICA on colonic oxidative stress in aging rats. The regulation of ICA on antioxidant enzymes (A) SOD, (B)CAT, and (C)GSH-Px in the colon of aging rats were detected by biochemical kits. (D) The expressions of HO-1 and NQO-1 in the colon of aging rats were detected by Western blot. a: Adult group, b: Aging group, c: ICA-L group, d: ICA-H group. * p < 0.05 vs. Adult group. ** p < 0.01 vs. Adult group. # p < 0.05 vs. Aging group. ## p < 0.01 vs. Aging group, n = 5-8.

PMC9214228

fphar-13-903762-g004.jpg

0.427041

70b641c5537348fe8d63706b193c90dd

The increased concentration of TNF-α resulted in the decrease of Occludin in Caco-2 cell monolayers. (A) Schematic diagram of culturing Caco-2 cell monolayers in the Transwell. (B) IAP activity ratio of AP and BP in different days of culture was detected. (C) TEER value of Caco-2 cell monolayers was detected on different days. (D) MTT was used to detect the effect of TNF-α at 10 ng/ml or 20 ng/ml on caco-2 cell viability. (E) Caco-2 cell monolayers were treated with 10 ng/ml or 20 ng/ml TNF-α for 24 or 48 h, and the expression of Occludin was detected by IF, n = 3–10.

PMC9214228

fphar-13-903762-g005.jpg

0.415443

d05f022ca6f1482199d5058364aad537

ICA reversed the decrease in Occludin expression and barrier degradation of Caco-2 cell monolayers caused by TNF-α stimulation. (A) The structure of ICA. (B) Patterns of administration of TNF-α and ICA. (C) The changes of Occludin were detected by IF (800×). In the ICA group, 5 μM ICA or 50 μM ICA was added to the AP side for 30 min, and 20 ng/ml TNF-α was added to the BP side for co-treatment for 24 h. In the DMSO group, 1 μl/ml DMSO was added to the AP side then treatment for 24.5 h. The expression of Occludin protein (D,E) and mRNA (F) was detected by Western blot or qPCR. (G)TEER and (H) 4-KD FITC-dextran detected changes in barrier permeability of monolayers. *** p < 0.001 vs. Control group. # p < 0.05 vs. TNF-α group. ## p < 0.01 vs. TNF-α group. ### p < 0.001 vs. TNF-α group n = 8–27.

PMC9214228

fphar-13-903762-g006.jpg

0.456446

704aa79599ee48bbb4d1a0f7859c9b69

ICA reversed the upregulation of miR-122a in Caco-2 cells induced by TNF-α. Detected the expression levels of (A)miR-122a, (B)miR-200C, (C)miR-429, and (D)miR-144. *** p < 0.001 vs. Control group. ### p < 0.001 vs. TNF-α group, n = 5–12.

PMC9214228

fphar-13-903762-g007.jpg

0.474139

eb3f5bffb1d043349f510ae494e92f0d

In Caco-2 cell monolayers, transfection of miR-122a mimics reversed the down-regulation protection of ICA against TNF-α -induced Occludin and induced improved permeability. (A) miR-122a mimics were designed based on the miR-122a gene. (B) Schematic diagram of transfection of miR-122a mimics in the Petri dish and Transwell Caco-2 cells. Effects of different transfection concentrations on the expression levels of (C) miR-122a and (D) Occludin mRNA in Caco-2 cells in the Petri dish. Changes in (E) miR-122a and (F) Occludin mRNA expression levels in cell monolayers. The miR-122a mimics group and the NC group were treated by transfecting miR-122a mimics or NC RNA for 24 h, and then treat with ICA and TNF-α. Changes in Occludin protein expression in Caco-2 cell monolayers were detected by (G) IF and (H,I) Western blot. Changes in permeability of Caco-2 cell monolayers were detected by (J) TEER and (K) 4-KD FITC-dextran. * p < 0.05 vs. Control group. ** p < 0.05 vs. Control group. *** p < 0.001 vs. Control group. # p < 0.05 vs. TNF-α group. ## p < 0.01 vs. TNF-α group. ### p < 0.001 vs. TNF-α group. & p < 0.05 vs. ICA 5 μM group. &&& p < 0.001 vs. $ p < 0.05 vs. NC group. $$ p < 0.05 vs. NC group. $$$ p < 0.001 vs. NC group. miR-122a mimics 1: Lipofectamine 20001.5 μl, RNA 20 pmol; miR-122a mimics 2: Lipofectamine 2000 1.5 μl, RNA 40 pmol; miR-122a mimics 3: Lipofectamine 2000 2.5 μl, RNA 20 pmol; miR-122a mimics 4: Lipofectamine 2000 2.5 μl, RNA 40 pmol, n = 6–23.

PMC9214228

fphar-13-903762-g008.jpg

0.440987

1e2992ecd3524b32867f75c3a3da690d

Identification of molecular subtypes of UPRRGs by consensus clustering. (A). Clustering heat map at k = 2. (B) PCA plot between the two subtypes. (C) Heat map of the UPR-related gene expression and clinical features in the two subtypes. (D) Survival curves for the two subgroups.

PMC9214238

fgene-13-911346-g001.jpg

0.41881

94fe07ecbeb24313b739a1a5cade995b

Tumor microenvironment in the two subtypes. (A) Stromal score, immune score, ESTIMATE score, and tumor purity based on ESTIMATE algorithm. (B) Six immune cell abundance assessments by the TIMER algorithm. (C) Twenty-nine immune cell abundance assessments by the ssGSEA algorithm. (D) Differences in immune checkpoints between the two subtypes. *p < 0.05; **p < 0.01; ***p < 0.001.

PMC9214238

fgene-13-911346-g002.jpg

0.461085

ff11fe132dfc4b429b9184e8bee27ceb

Differentially expressed genes and functional enrichment analyses. (A) Volcano plot showing the DEGs between the two subgroups. (B) Bubble plot exhibited the functional enrichment of DEGs through GO analysis. (C) GSEA shows the hallmark gene sets in the two subgroups. (D) Heat map depicted the results of the GSVA analysis.

PMC9214238

fgene-13-911346-g003.jpg

0.404226

96f9e9a7df8f48c2aa70708846940b50

Construction of the risk signature based on UPRRGs in the training cohort. (A) Eight optimal UPRRGs filtered by LASSO analysis. (B) Distribution of risk scores and patient status in the two risk groups; (C). Heat map showing the expressions of four candidate genes. (D) Survival curves for the two risk groups. (E) Time-dependent ROC curve of the risk model. (F) Tumor microenvironment analysis in the two risk groups through the ESTIMATE algorithm. *p < 0.05; **p < 0.01; ***p < 0.001.

PMC9214238

fgene-13-911346-g004.jpg

0.417602

ed83edc973684fc687f228000f28c59f

Correlation of the risk signature with clinical features in the training cohort. (A) Differences in risk scores among osteosarcoma patients by age, gender, and metastatic status. (B–C) Survival curves for patients with osteosarcoma regrouped by metastatic status. (D–E) Univariate and multivariate cox regression analyses for integrating risk characteristics and clinical features.

PMC9214238

fgene-13-911346-g005.jpg

0.440948

0b6d8d924671425dbda1b88d18094444

Validation of the constructed risk signature in the verification cohort. (A) Distribution of risk scores and patient status in different risk groups. (B) Heat map displayed the expressions of four candidate genes in the verification cohort. (C) Survival curves of the two risk groups. (D) Time-dependent ROC curve in the verification cohort. (E) Tumor microenvironment analysis by the ESTIMATE algorithm. *p < 0.05; **p < 0.01; ***p < 0.001.

PMC9214238

fgene-13-911346-g006.jpg

0.413429

637f3eb149a14bdda2918f04c1c25360

Construction and evaluation of the nomogram. (A). Nomogram for predicting the prognosis of patients with osteosarcoma. (B) Calibration for 3-and 5-year OS in the training cohort. (C) Calibration for 3-and 5-year OS in the verification cohort. (D) ROC analysis for 3-and 5-year OS in the training cohort. (E) ROC analysis for 3-and 5-year OS in the verification cohort.

PMC9214238

fgene-13-911346-g007.jpg

0.479872

8790223750b54682be290844a54b4581

Candidate gene validation. (A) Expression levels of candidate genes in tumor and normal tissues from osteosarcoma patients. (B) Expression levels of candidate genes in different cell lines. *p < 0.05; **p < 0.01; ***p < 0.001.

PMC9214238

fgene-13-911346-g008.jpg

0.428168

44ddb2341ab34e5586cecdd24acaa967

Heterogeneous federated molecular learning where three institutions focus on different types of moleculesThe server has no access to training data.

PMC9214329

gr1.jpg

0.436588

531535eeff734632b58f70c83d1a0d14

Illustration for the motivation of FLITWe assume two clients as A and B, and the local data on these clients do not share the same distribution as the global one. Local models trained on biased local data will overfit the majority groups of data and underfit others. FLIT measures each sample’s prediction confidence and puts more weight on the uncertain data. As a result, the local data distribution will be better aligned to the global one, and the trained local models will also be more consistent with each other.

PMC9214329

gr2.jpg

0.439665

ee5e01fc380f4e67b01a00f01311ba3d

Performance of baseline and our methods with varying communication roundsAsterisk (∗) denotes that the results are obtained with centralized training. We find our method has a strong advantage with a few communication rounds.

PMC9214329

gr3.jpg

0.460462

bf648548468842b0b5d4d56c73571d14

Performance of baseline and our methods with different number of clientsSee Figure 3 for color legend. The small-scale local training data reduce federated-learning performance for all methods.

PMC9214329

gr4.jpg

0.490355

3349146cb48d40059ca389241a771c0e

Flow diagram of complete data analysis.

PMC9214656

fgene-13-909797-g001.jpg

0.413541

8f4cfff59eb44104be51e05bd5ea7dfd

Identification of oxidative stress-related lncRNAs in LUAD patients. (A) Volcano plot of oxidative stress-associated DEGs in TCGA databases. (B)Heatmap of oxidative stress-associated DEGs in TCGA databases. (C) Sankey relation diagram for differentially expressed oxidative stress genes and oxidative stress-related lncRNAs. (D) Heatmap for the correlations between oxidative stress genes and oxidative stress-related lncRNAs.

PMC9214656

fgene-13-909797-g002.jpg

0.495349

c8305b6d481e4352adebba8ce9dde1d8

Construction and validation of the predictive model in TCGA training set. (A) Univariate Cox regression analysis of OS for part of 182 oxidative stress-related lncRNA prognostic signatures. (B,C) Altogether 25 lncRNAs were selected using LASSO regression. (D) Multivariate Cox regression analysis showed 16 independent prognostic lncRNAs. (E) Distribution of oxidative stress-related lncRNA model-based risk score for the training set. (F) Different patterns of survival status and survival time between high-risk and low-risk groups in the training set. (G) Heatmap to show the expression of 16 lncRNAs between high- and low-risk groups in the training set. (H) Kaplan–Meier curve of high-risk and low-risk patients in the training set.

PMC9214656

fgene-13-909797-g003.jpg

0.428893

07ffb082995e4298ace1ea57e0376f4c

Validation of the prognostic oxidative stress-related lncRNA signature. (A) Risk score, (B) survival status, and (C) heatmap for the testing set. (D) Risk score, (E) survival status, and (F) heatmap for the entire set. (G) Kaplan–Meier curve for the testing set. (H) Kaplan–Meier curve for the entire set.

PMC9214656

fgene-13-909797-g004.jpg

0.425191

11c6079e2d87456096d30728d42bc368

Independent prognostic factors and construction of the nomogram. (A) Univariate analysis of the clinical characteristic and risk score with the OS. (B) Multivariate analysis of the clinical characteristic and risk score with the OS. (C) Nomogram predicts the probability of the 1-, 3-, and 5-year OS. (D) Calibration plot of the nomogram indicates the probability of the 1-, 3-, and 5-year OS.

PMC9214656

fgene-13-909797-g005.jpg

0.434595

c51150546051414f9bc70ebaaffff9ef

Assessment of the predictive risk model and principal component analysis. The 1-, 3-, and 5-year ROC curves of the (A) training set, (B) testing set, and (C) entire set. (D) ROC curves of the clinical characteristics and risk score. (E) Concordance indexes of the risk score and clinical features. (F) PCA between high-risk and low-risk groups based on 16 prognostic lncRNAs in the training set (G) and testing set. (H) PCA between the high-risk and low-risk groups based on entire gene expression profiles, (I) all oxidative stress genes, (J) and risk model based on the representation profiles of the 16 oxidative stress-related lncRNAs in the entire set.

PMC9214656

fgene-13-909797-g006.jpg

0.440664

a98778f4c4e14732907298270d286b17

Kaplan–Meier curves of OS difference stratified by LUAD stage (I–II or III–IV), age (≤65 or >65), gender (female or male), and TNM stage (T1–2 or T3–4) between high-risk and low-risk groups in TCGA entire set.

PMC9214656

fgene-13-909797-g007.jpg

0.455578

11af10f6beee46b688dfa2563e2d6a6d

Immune infiltration discrepancy in different risk groups. (A) Heatmap of 22 tumor-infiltrating immune cell types in low- and high-risk groups. (B) Bar chart of the proportions for 22 immune cell types. (C) ssGSEA scores of immune functions in low-risk and high-risk groups. (D) Immune cells in low-risk and high-risk groups. (E–G) TME scores between high- and low-risk groups. *p < 0.5, **p < 0.01, and ***p < 0.001; ns, no sense.

PMC9214656

fgene-13-909797-g008.jpg

0.380328

9663dbd24b654a54b8c0325d615fb7e6

Exploration of tumor mutation burden and visualization of lncRNA networks. (A,B) Waterfall plot of somatic mutation features established with high- and low-risk groups. (C) Tumor mutation burden in the high-risk and low-risk groups. (D) Correlation between risk score and TMB. (E) Kaplan–Meier curve of the OS among the high- and low-TMB groups. (F) Survival analysis among four patient groups stratified by both TMB and risk score. (G) Correlation between the risk score and immune subtype. (H) Connection degree between the oxidative stress-related lncRNAs, oxidative stress-related genes, and risk types.

PMC9214656

fgene-13-909797-g009.jpg

0.455256

e4112b898e754ea3b8e29914274c5f48

Clinical application of the risk signature. (A) Comparison of IC50 of chemotherapeutic drugs among two subgroups. (B) Investigation of anti-tumor drug sensitivity-targeting signature. (C) Expression levels of CTLA4, HAVCR2, PD-1, TIGIT, and PD-L1 in the high- and low-risk groups. (D) Correlation between 16 lncRNAs and chemotherapeutic drugs. (E) TIDE prediction difference in the high-risk and low-risk groups.

PMC9214656

fgene-13-909797-g010.jpg

0.490176

89f68f21fc754c95bc3c077f9dd9b3b4

Functional analysis. (A) Top 10 classes of GO enrichment terms based on DEGs between two groups, including biological process (BP), cellular component (CC), and molecular function (MF). (B) Top 30 pathways of KEGG enrichment terms. (C) Gene set enrichment analysis of the top 10 pathways significantly enriched in the high-risk group. (D) Gene set enrichment analysis of the top 10 pathways enriched considerably in the low-risk group. (E) Cytoscape of lncRNA–mRNA co-expression network. Green nodes represent lncRNAs, while red nodes represent mRNAs.

PMC9214656

fgene-13-909797-g011.jpg

0.41322

e50eec73713c4a7b8f7070fc282e65cc

Schematic (not to scale) whereby particles in contact with the cuticle and within a droplet on a generic leaf surface release material either directly into the cuticle or into the aqueous medium prior to uptake through the leaf surface. The second panel provides a schematic of particles (gray) in the aqueous droplet (blue) on the cuticle surface (cuticle proper in black and sorption compartment in orange) with plant tissue represented by a green continuum. Fickian diffusion and partitioning across the cuticle-solution interface develop two possible pathways for uptake: directly through the particle-cuticle contact or indirectly via the cuticle-solution interface, represented by the blue arrows. The thick black line in the third panel represents the outer boundary of the cuticle proper. Illustrative steady-state concentration profiles developed by Fickian diffusion are provided as color maps in the third panel in the purely illustrative case of 1:1 partitioning.

PMC9214695

as2c00029_0002.jpg

0.393662

fd2d96bd05ba4e308962d4b97b9faa72

Illustration of model boundary conditions and coordinate systems.

PMC9214695

as2c00029_0003.jpg

0.442775

c29db4dccfb844c4b3fa6bb9643d6275

Schematic illustration of the spatial parameters describing a truncated sphere resting on a finite barrier under cylindrical coordinates (r, z). Parameters include the angle from symmetry axis θ, angle to three-phase contact point θc (equivalent to the contact angle), particle radius rp, shortest distance from particle center ρ, distance from particle center to barrier surface zp, and barrier thickness zcut.

PMC9214695

as2c00029_0004.jpg

0.432868

9024a1c1c4624986966431231b978d0e

Dimensionless concentration profiles of aqueous, released material under the thermodynamic limit for (A) a hemisphere on a plane (Zp = 0) and (B) a sphere on a plane (Zp = 1) represented as color maps. The white area is the particle. The spatial dimensions are normalized to the particle radius. Isoconcentration contour lines are included.

PMC9214695

as2c00029_0005.jpg

0.499445

39c475f255ea4eba9e5d27de6ef24c46

(A) Dimensionless steady-state flux profile for −0.8 ≤ Zp ≤ 1. (B) Dimensionless steady-state surface flux integral ∫0θcJ(θ) sin θdθ = JTot/2π for −0.8 ≤ Zp ≤ 1, represented as a blue solid line, for a truncated sphere on a surface under thermodynamic release. The surface flux integral assuming a constant J(θ) = 1 is represented by a red dotted line in (B).

PMC9214695

as2c00029_0006.jpg

0.440033

652e4bb7556a49c992aa9279c9ea8e6f

Results for the surface flux and cuticular concentration for varying Zcut for a particle-cuticle disk interface. The steady-state concentration profile along the symmetry axis is presented for (A) thermodynamic release (Kcut = 106) and (B) kinetic release (Kcut = 10–6). Both demonstrate transitions from linear diffusion. Inlaid diagrams provide clearer illustration for small Zcut. (C) presents the steady-state surface flux profile for varying Zcut for Kcut = 106. (D) presents the surface concentration profile for varying Zcut for Kcut = 10–6.

PMC9214695

as2c00029_0007.jpg

0.459516

61b7882b914e47798fe7217a3eb37623

Logarithm of the dimensionless total steady-state flux JTot/rp2 via the indirect pathway into an infinite cuticle (Zcut ≫ 1) with zero direct flux from the disk contact area, assuming a surface-equilibrated cuticle-solution interface, a hemispherical particle (Zp = 0), and instant repletion of material at the cuticle interface by diffusion through the aqueous solution for varying Kaq. The red dotted line represents the dimensionless steady-state total flux predicted for the direct uptake rate acting alone under the thermodynamic, infinite-thickness regime.

PMC9214695

as2c00029_0008.jpg

0.416324

5093d55dcc0b4651a91c4195669c9479

(A) Interfacial steady-state flux profile J for simultaneous direct and indirect uptake with varying dimensionless aqueous release rate constants, Kaq. (B) Logarithm of the total dimensionless steady-state flux for simultaneous direct and indirect pathways into an infinite cuticle (Zcut ≫ 1) assuming a thermodynamic cuticle-solution interface and instant repletion of material by diffusion through the solution for varying dimensionless particle-solution release rate constants Kaq. The blue line represents the total flux into the cuticle. The red line represents the total flux into the cuticle directly from the particle. The difference between the two corresponds to the flux at the solution-cuticle interface (note that this is negative for Kaq < 100.8).

PMC9214695

as2c00029_0009.jpg

0.457911

73c734d5a2bf4cf9a5ddd2ea66525ee7

Scheme for utilizing atom-wise featurization and topological information on compounds for the identification of pan-kinase family inhibitors (PKFIs) using graph convolutional network (GCN) models. A Schematic of the research framework. B 195,802 test datasets of 60,122 chemicals and 384 kinases were collected from the ChEMBL database and kinase profiling. C Eight families in four kinase groups were targeted in this study. D Each compound is transformed into atom features and a structure graph for GCN architectures to identify PKFIs. E A visualized explanation was made using the grad-CAM method

PMC9214975

12859_2022_4773_Fig1_HTML.jpg

0.459515

c68b8639dba4433fab77298a1595fd63

Explanation of the GCN model’s prediction of lapatinib and other inhibitors in EGFR, JAK, and PIM models. (A) Grad-CAM preferences of lapatinib from the latest graph convolutional layer for both positive and negative classes. Circles are centered at each atom, with green ones for the positive class and orange for the negative class. The larger the circle, the more the atom contributes to the prediction of the model at a specific class. (B) Preferences for different inhibitors within and across families. Within the same family, conserved attention on similar environments is visualized, and family-sensitive pre-moieties can be seen by comparing cross-family inhibitors. (C) Crystallized complexes of the pan-EGFR inhibitor lapatinib (deep blue, PDB ID: 1XKK), pan-JAK inhibitor tofacitinib (light blue, PDB ID: 3EYG), and pan-PIM inhibitor LGH-447 (purple, PDB ID: 5DWR) demonstrated three different modes of kinase inhibition

PMC9214975

12859_2022_4773_Fig2_HTML.jpg

0.415969

ec65103960c6411693b446253c404e5f

Correlation between preferences and the mapping of current checkmol fingerprints. (A) The mapped region of checkmol fingerprint and their odds-ratio ranking (epsilon = 0.5, is added to prevent dividing by zero) of 204 checkmol descriptors within each family of the PKFIs set. Most pre-moieties associated with different modes of kinase inhibition (described previously) are mappable and correlated with the feature distribution in the training sets. However, several pre-moieties are still not precisely defined in the current fingerprint and thus are undistinguishable (i.e., naphthalenyl-nitrogen regions on lapatinib). (B) Overall odds-ratio distribution of checkmol descriptors on EGFR, JAK, and PIM datasets with mapped moieties is indicated. It should be noted that several super peaks are observed with relatively few compounds and thus are not currently discussed

PMC9214975

12859_2022_4773_Fig3_HTML.jpg

0.476443

309b10f987a64ed99452273b9d6ce4b4

Feature encoding of the input compounds. Each compound is encoded by atomic features and a structure graph. Atomic features contain 28 descriptors belonging to six types, including atom types, hybridization, charges, and chemo-properties. For a structure graph, a modified normalized Laplacian matrix (Eq. (4)) was applied as a representation of the compound’s topology information. Padding to 50 atoms with zeros was applied to contain variable numbers of atoms in the input compounds

PMC9214975

12859_2022_4773_Fig4_HTML.jpg

0.453497

ecf6811aa00c44ba81e5de354eaff537

The operation of the graph convolution and GCN model architecture of binary classification is described in this paper. To include the surrounding environments of each atom (local environments), the multiplication of topology graph \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tilde{L}$$\end{document}L~ and the compound features (either the input atomic features AF or the feature map F(l−1) from the last layer) is required, which should be further multiplied by Wl to learn the information provided by the local environment.

PMC9214975

12859_2022_4773_Fig5_HTML.jpg

0.457942

5164f1b158d14b65bc2dbed5d755ac17

Structurally diverse NKT cell ligands α-GalCer (1) and iGb3 (2).

PMC9215340

d2ra02373c-f1.jpg

0.420078

8943a9c5215243a0b6d19ba34c65f908

iGb3 analogues 1,3-β-Gal-LacCer (βG-iGb3, 3), iGb3-C12 (4), C20:2 (5), 6′′′-dh-iGb3-sphinganine (6) and 6′′′-dh-iGb3-sphingosine (7).

PMC9215340

d2ra02373c-f2.jpg

0.420108

0212036101ae46e6b20119d5c384816e

(A) Plots depict the dilution of CFSE amongst thymic (C57BL/6) NKT cells co-cultured for 72 h with Jα18−/− (C57BL/6) splenocytes pulsed overnight with the indicated antigen. Numbers on plots represent the estimated division percentages (FlowJo). (B) Graph depicts the division percentages of thymic NKT cells after 72 h (mean ± SEM). All lipids were used at 1 μg ml−1 except for α-GalCer which was used at 100 ng ml−1n = 5, except iGb3 (C26), where n = 2.

PMC9215340

d2ra02373c-f3.jpg

0.429579

268fb103b3ca47f584c6c89672f27ef0

(A) Cell culture supernatants were collected at 72 h following the co-culture of antigen-pulsed Jα18−/− (C57BL/6) splenocytes with NKT cell-enriched thymocytes (C57BL/6). Graph depicts the cytokine concentrations as gauged by CBA. Pooled means (n = 2 replicates) from 2 independent experiments (mean ± SEM). (B) Graph depicts the ratio of IL-13 to IFN-γ from the indicated conditions. Data taken from 2 independent experiments, each represented by square or circular datapoints that depict the mean of two technical replicates for each experiment

PMC9215340

d2ra02373c-f4.jpg

0.529688

19d85a9e1b0c46fa8f04a4cb2b78d4bb

Schematic diagram of the human body model. (A) Positioning ACJ and TFJ in this position. (B) Positioning LHBT and PTT in this position.

PMC9217061

fgene-13-894716-g001.jpg

0.404525

26a5ea2ad27c4c1f84c2550153479916

Needle placement based on target structures relative to ultrasound images. (A) Correct localization map of the ACJ. (B) Correct localization map of the TFJ. (C) Map of mislocalization of the long head of the biceps tendon. (D) Map of the mislocalization of the posterior tibialis muscle.

PMC9217061

fgene-13-894716-g002.jpg

0.428461

8f581dab31564b6eb386dd8d7bd4e1c2

Flowchart of the included patients.

PMC9218196

fped-10-921880-g0001.jpg

0.480358

a5639904b1be47c8a03a8cb77a1f2c06

Regulatory interactions of SERCA (gray) with PLB (blue) and DWORF (red). Structures: PLB5, PDB:2KYV (59); PLB1, PDB:1FJP, (60); PLB-SERCA, DWORF–SERCA (18, 61); DWORF-7MPA, (25). DWORF, dwarf ORF; PDB, Protein Data Bank; PLB, phospholamban; SERCA, sarco/endoplasmic reticulum Ca2+-ATPase.

PMC9218510

gr1.jpg

0.405391

f6e2dcb3cb0544c0ae5d700de51ff15c

Dynamics of PLB and DWORF binding to SERCA during elevations in intracellular Ca2+.A, a simplified Post-Albers scheme of the SERCA enzymatic cycle, highlighting states that predominate at low (blue) and high (red) intracellular [Ca2+]. B, FRET-based binding curves displaying a shifted dissociation constant (KD) of PLB–SERCA binding between the ATP-bound (blue) and TG-bound (black) states of SERCA. C and D, apparent KDs of PLB or DWORF binding to different SERCA enzymatic states of the catalytic cycle as in panel (A) with lines representing mean ± SEM (n = 6). Ligands used to stabilize each state are shown in parentheses. Differences in micropeptide KDs between SERCA states were analyzed by one-way ANOVA with Tukey’s post hoc (∗p < 0.05). E, apparent KDs of PLB and DWORF for SERCA in ATP-containing solutions with low (blue) and high (red) concentrations of intracellular Ca2+ with lines representing mean ± SEM (n = 5). Differences in KD evaluated by Student’s t test. F, confocal microscopy quantification of intracellular Ca2+ measured by X-rhod-1 fluorescence (gray raw data, with black smoothed trendline) with simultaneous measurement of changes in PLB–SERCA FRET (YFP/Cer ratio) (gray raw data, with blue smoothed trendline). G, quantification of ER luminal Ca2+ measured by R-CEPIA1er fluorescence with simultaneous measurement of PLB–SERCA FRET (YFP/Cer ratio). H, quantification of intracellular Ca2+ measured by X-rhod-1 fluorescence with simultaneous measurement of DWORF–SERCA FRET (YFP/Cer ratio) I, quantification of ER luminal Ca2+ measured by R-CEPIA1er fluorescence with simultaneous measurement of DWORF–SERCA FRET (YFP/Cer ratio). DWORF, dwarf ORF; ER, endoplasmic reticulum; PLB, phospholamban; SERCA, sarco/endoplasmic reticulum Ca2+-ATPase.

PMC9218510

gr2.jpg

0.448116

5c5dc54845284d7c803175e29708377f

PLB reassociation with SERCA after Ca2+transients are delayed by slow dissociation from the PLB pentamer.A, schematic diagram of shifts in PLB and DWORF binding equilibria during Ca2+ release. B, representative single exponential decay fit of the kinetics of DWORF–SERCA binding during Ca2+ release. C, kinetics of PLB–SERCA unbinding during Ca2+ release. D, kinetics of PLB–PLB binding during Ca2+ release. E, the latency of FRET ratio changes compared to Ca2+ release with lines representing mean ± SEM. Differences determined by one-way ANOVA with Dunn’s post hoc test (∗p < 0.05, see Table S4 for complete statistical analysis). F, schematic diagram of shifts PLB and DWORF binding equilibria during Ca2+ uptake. G, representative single exponential decay fit of the kinetics of DWORF–SERCA unbinding during Ca2+ uptake. H, kinetics of PLB–SERCA rebinding during Ca2+ uptake. I, kinetics of PLB–PLB binding during Ca2+ release. J, the latency of FRET ratio changes compared to Ca2+ release with lines representing mean ± SEM. Differences determined by one-way ANOVA with Dunn’s post hoc test (∗p < 0.05, see Table S7 for complete statistical analysis). DWORF, dwarf ORF; PLB, phospholamban; SERCA, sarco/endoplasmic reticulum Ca2+-ATPase.

PMC9218510

gr3.jpg

0.401675

3630130bc0294ac88a9f08ef4e4371db

A computational model simulated the dynamics of PLB and DWORF interactions with SERCA.A, simplified diagram of modeled regulatory interactions. B, a fit of the model to a representative FRET change measured by confocal microscopy. C, simulation of changes in the populations of regulatory species during a Ca2+ elevation, where relative amounts of SERCA and PLB are equal. Black circles represent the results of the model of the normal, nonfailing heart. Red triangles represent adjustment of the model to simulate heart failure. D, simulation of the effect of cardiac pacing on PLB–SERCA at three heart rates (beats per min, BPM), where the ratio of SERCA:PLB was 1:3. E, increasing pacing frequency modestly decreased PLB–SERCA binding. F, increasing pacing frequency increased PLB oligomerization. G, PLB–SERCA oscillation amplitude decreased with faster pacing. H, simulation of the effect of increasing DWORF expression relative to SERCA on PLB–SERCA binding. I, increasing DWORF resulted in a decrease in the equilibrium level of PLB–SERCA. J, increasing DWORF increased PLB oligomerization. K, increasing DWORF relative to SERCA resulted in larger oscillations in PLB–SERCA binding. For H–K, the ratio of SERCA:PLB was 1:3 and pacing rate was 180 BPM. DWORF, dwarf ORF; PLB, phospholamban; SERCA, sarco/endoplasmic reticulum Ca2+-ATPase.

PMC9218510

gr4.jpg

0.401988

fcf38f08430f469088e06d87d61a6e0f

PROMIZING stepwise algorithm and study flowchart. CPAP: continuous positive airway pressure, SNR: screened and non-randomized, PROMIZING: Proportional assist ventilation for minimizing the duration of mechanical ventilation study, PSV: pressure support ventilation, SBT: spontaneous breathing trials

PMC9219177

13054_2022_4063_Fig1_HTML.jpg

0.504341

2e6e2b49b5ab401c91fa35c4b326839a

Ventilator settings and respiratory parameters at baseline (pre-randomization) according groups. Data presented as mean ± standard deviation. Pairwise comparisons between groups by Tukey Honest Significant Difference Test where p = 0.05 was taken as a threshold for these post-hoc comparisons: * Difference (p < 0.05) between Not ready for weaning group vs. SNR group. † Difference (p < 0.05) between ZERO CPAP tolerance failure group vs. SNR group. § Difference (p < 0.05) between SBT failure group vs. SNR group. SNR: screened and non-randomized, FiO2: fraction of inspired oxygen, CPAP: continuous positive airway pressure, PEEP: positive end-expiratory pressure, SBT: spontaneous breathing trial

PMC9219177

13054_2022_4063_Fig2_HTML.jpg

0.401733

418752598ce24a8b80154090938b611b

Computed Tomography–Myelogram image from a 6-year-old Labrador with T12–T13 acute non-compressive nucleus pulposus extrusion. (A) Vacuum phenomenon and light attenuation of the ventral contrast line diameter (sagittal view); (B) T12–T13 hyperintensity sign and bilateral light attenuation of the diameter of the contrast lines (dorsal view); (C) normal T12–T13 disk space with residual mineralized disk material ventrally (transverse view).

PMC9219513

animals-12-01557-g001.jpg

0.405987

edf8a674a1f147fc9299a4ca7b1b4ce5

Electrical stimulation protocols on a dog in a postural standing position. (A) Co-contraction protocol with the stimulation of both the quadriceps femoris group and the hamstring muscles group. (B) Segmental technique of the sciatic nerve by means of functional electrical stimulation.

PMC9219513

animals-12-01557-g002.jpg

0.417309

5b2d3a0d732e44cf80b9fd709e617635

Laser therapy class IV program applied on the spinal cord injury.

PMC9219513

animals-12-01557-g003.jpg

0.492971

108352e3b9224cc8b31125d35b8914e9

Laser therapy class IV program applied to the coxofemoral joint using the four-point technique. (A) Proximo cranial region; (B) proximo caudal region; (C) disto caudal region; (D) disto cranial region.

PMC9219513

animals-12-01557-g004.jpg

0.498589

1461efad7c8f4dde953f88c1b6164dc4

Locomotor exercise in two dogs. (A) Land treadmill; (B) underwater treadmill.

PMC9219513

animals-12-01557-g005.jpg

0.435493

00597652f1b9436eab163ab3f37e8947

Kinesiotherapy exercises. (A) Obstacle rail; (B) gait stimulation on alternating floors; knuckling position.

PMC9219513

animals-12-01557-g006.jpg

0.425964

d2713d747f6740b8baee21180742857b

Kinesiotherapy exercises. (A) Postural standing on a balance board; (B) bicycle movements on a central stimulation pad.

PMC9219513

animals-12-01557-g007.jpg

0.437066

83c457272f214e94a25dcae7fac90a28

Supportive treatment. (A) Passive range of motion exercises for the knee joint; (B) deep kneading massage technique for direct muscular treatment.

PMC9219513

animals-12-01557-g008.jpg

0.615286

034c15e4e4e54d989fcda6ba76dc470d

Flow cohort diagram describing the outcome measures of both the control and study group throughout the rehabilitation period and after clinical discharge. OFS: Open Field Score; SSS: Spinal Shock Scale; CG: control group; SG: study group; T0 (day 0); T1 (day 1); T2 (day 2); T3 (day 3); T4 (day 5); T5 (day 7); T6 (day 14); T7 (day 21); T8 (day 30); T9 (day 60); F1 (7 days); F2 (15 days); F3 (one month); F4 (three months); F5 (six months); F6 (one year); F7 (two years); F8 (three years); F9 (four years).

PMC9219513

animals-12-01557-g009.jpg

0.391954

f40fa235ce07431088844508e0238626

Evolution of the spinal shock scale-estimated marginal means in the study group throughout the intensive neurorehabilitation process. Y-axis: Spinal shock scale score; T0 (day 0); T1 (day 1); T2 (day 2); T3 (day 3); T4 (day 5); T5 (day 7); T6 (day 14); T7 (day 21); T8 (day 30); T9 (day 60).

PMC9219513

animals-12-01557-g010.jpg

0.416116

467106cba0d342e5a63f0e6b8dc43f40

Evolution of the Open Field Score (OFS)-estimated marginal means in both the study and control groups throughout intensive neurorehabilitation. Y-axis: OFS; T0 (day 0); T1 (day 1); T2 (day 2); T3 (day 3); T4 (day 5); T5 (day 7); T6 (day 14); T7 (day 21); T8 (day 30); T9 (day 60).

PMC9219513

animals-12-01557-g011.jpg

0.466098

9195f892587649eaa5a015c65b3c949d

Evolution of the OFS-estimated marginal means in both the study and control groups during the follow-up periods. Y-axis: OFS (Open Field Score); F1 (7 days); F2 (15 days), F3 (one month), F4 (three months), F5 (six months), F6 (one year), F7 (two years), F8 (three years); F9 (four years).

PMC9219513

animals-12-01557-g012.jpg

0.501761

a03d7e6a12a64f88968496f9956df0c9

Experimental set-up for the effect on planktonic bacteria and biofilms; DBD: dielectric barrier discharge.

PMC9219831

antibiotics-11-00752-g001.jpg

0.463131

03e583afd5774f4bbf32bbf6b0e0b317

Colony forming units (CFU) counts (A), mass (B), and metabolic activity (C) of multi-species biofilms on dentin specimens and subsequent exposing of 30 s, 60 s, and 120 s to cold plasma. ** p < 0.01 vs. control (post-hoc Bonferroni).

PMC9219831

antibiotics-11-00752-g002.jpg

0.430944

1a4a289294d24bcdad43f451e5081d93

Colony forming units (CFU) counts (A), mass (B) and metabolic activity (C) of a multi-species biofilm on titanium specimens and subsequent exposing 30 s, 60 s and 120 s to cold plasma. ** p < 0.01 vs. control (post-hoc Bonferroni).

PMC9219831

antibiotics-11-00752-g003.jpg

0.446647

1b425c16427d47d391728c8ab6502ec3

Colony forming units (CFU) counts (A) after 4 h and 24 h, mass (B), and metabolic activity (C) after 24 h of a multi-species biofilm formation on dentin specimens immediately after the application of 120 s of cold plasma.

PMC9219831

antibiotics-11-00752-g004.jpg

0.497995

11d5a999f219478985afdd63984c85e5

Colony forming units (CFU) counts (A) after 4 h and 24 h, mass (B), and metabolic activity (C) after 24 h of a multi-species biofilm formation on titanium specimens immediately after the application of 120 s of cold plasma.

PMC9219831

antibiotics-11-00752-g005.jpg

0.513875

d75fd03151834448bdd6616e013769f3

Gingival fibroblast counts per mm2 on dentin (A) and titanium (B) specimens after pretreatment of 120 s of cold plasma.

PMC9219831

antibiotics-11-00752-g006.jpg

0.405222

562a42a3bb6d4b87bb2c0f28b5c39b59

(a) Roche total spike and (b) Snibe neutralizing antibody responses after the first, second, and third dose of inactivated virus or mRNA vaccine. Abbreviations: D1D10: 10 days post-dose 1, D2D20: 20 days post-dose 2, D3D20: 20 days post-dose 3.

PMC9220327

antibodies-11-00038-g001.jpg