dedup-isc-ft-v107-score
float64 0.3
1
| uid
stringlengths 32
32
| text
stringlengths 1
17.9k
| paper_id
stringlengths 8
11
| original_image_filename
stringlengths 7
69
|
|---|---|---|---|---|
0.44054 | 8f38420f8a1f47bb83799271298b812d | PRISMA Flow Diagram. | PMC9340845 | ijspt_2022_17_5_36814_93627.jpg |
0.455531 | ecdadb48b6a047df8152f93014be34ec | Methodological quality of studies across content areas.Abbreviations: Concussion and Risk Prediction (CRP); Concussion and Testing (CT); Concussion and Dual Task (CDT); Dual Task in Healthy Athletes (DT); Baseline Cognition in Healthy Athletes (BN). | PMC9340845 | ijspt_2022_17_5_36814_93629.jpg |
0.429078 | b01b7a501cab426a9b6e0168e483274c | Distribution of experimental designs across content areas.Abbreviations: Concussion and Risk Prediction (CRP); Concussion and Testing (CT); Concussion and Dual Task (CDT); Dual Task in Health Athletes (DT); Baseline Cognition in Healthy Athletes (BN). | PMC9340845 | ijspt_2022_17_5_36814_93630.jpg |
0.426565 | e62a49fbfd304a85baf7086b9ecc6c9d | Schedule of enrollment, interventions, and assessments | PMC9341151 | 13063_2022_6575_Fig1_HTML.jpg |
0.376566 | 47cbde8eef4943dd812485f96e606baa | The genetic landscape of metastatic NSCLC patients. (A) Comparison of the mutational landscape between primary lesions and their paired lymphatic metastases. The top panel represents the number of somatic mutations in each sample. The middle panel represents the matrix of mutations in a selection of frequently mutated genes. Columns represent samples. The patients’ characteristics are presented in the following. (B) The Pearson correlation analysis of mutations in paired P-LN. P: primary lesions; LN: lymph nodes metastases. | PMC9341247 | fbioe-10-909388-g001.jpg |
0.403795 | d955849fd0f94650a65478dbaa29ede1 | The distribution of TMB of the NSCLC paired samples. (A) The comparison of TMB between primary and lymph nodes metastatic samples. (B) The comparison of TMB in primaries and lymphatic metastases between smoking and never-smoking NSCLC patients. | PMC9341247 | fbioe-10-909388-g002.jpg |
0.385376 | 6ae8d4cbcd18431e9e0c638dbef59e82 | The distribution of mutational signatures of the NSCLC paired samples. (A) Comparison of mutational signatures between primary and lymph nodes metastatic samples. (B) Two distant mutational signatures were identified by the NMF analysis of the matrix of mutational proportion across tumors from primary and metastatic lesions. (C) Comparison of mutational signatures of the private alterations between primary and lymph nodes metastatic samples. | PMC9341247 | fbioe-10-909388-g003.jpg |
0.427823 | 2d6c0ff9cd7c4715b3d8808699e80f73 | Association of 17 candidate metastasis-related driving genes with primary and lymphatic lesions in the Lung_MSK_2017 cohort. | PMC9341247 | fbioe-10-909388-g004.jpg |
0.421482 | b89898e54a90434884473d43c05f252b | The gene cloning, functional analyses and breeding application of emf1. (a) The earlier flowering phenotype of emf1 compared to WT. (b) Cross‐section of WT and emf1 spikelet tomography. Scar bar, 500 μm. (c) Lodicule morphology of WT and emf1 after water absorption. Scar bar, 1 mm. (d) Changes in WT and emf1 lodicule surface area with time after water treatment. (e) The cell and cell wall morphology of lodicule of WT and emf1 at maximum flowering angle observed using transmission electron microscopy. Scar bar, 10 and 2 μm below, respectively. (f‐i) The cellulose (f), hemicellulose (g), pectin (h) and de‐esterified pectin (i) contents in WT and emf1. (j) The gene structure and functional mutation of EMF1. (k) Subcellular localization of EMF1 protein in the cell wall. Scar bar, 20 μm. (l) EMF1 interacts with GLN2 in yeast cells. (m) The FOT of OsGLN2 knockout lines. a, b indicate significant differences at P < 0.01. (n) A hypothesized model showing the molecular mechanism of EMF1 to regulate FOT in rice. (o) The haplotype analysis of EMF1 in 533 diverse cultivated rice. (p) The FOT of japonica varieties with different alleles in the C/T variants in EMF1. **P < 0.01. Significant differences were based on two‐tailed t‐tests. [Colour figure can be viewed at wileyonlinelibrary.com] | PMC9342613 | PBI-20-1441-g001.jpg |
0.463038 | acfb5eba33794824acbee7db335521c1 | Colonoscopy use 5 years to 6 months prior to CRC diagnosis for those with IBD-CRC (censored 6 months prior to CRC diagnosis). | PMC9342763 | pone.0272158.g001.jpg |
0.451107 | 0cf68e472c9c420192c859bfa730303f | Schematic representation of an observed-variable autoregressive path model examining reciprocal interactions between harsh parenting and child conduct or emotional problems, after adjusting for covariates. Lines with single arrowheads represent hypothesised direct effects. Curved lines with two arrowheads represent correlations. Analyses were conducted separately for child conduct and emotional problems | PMC9343272 | 787_2021_1759_Fig1_HTML.jpg |
0.475331 | 5a2fd1d2e26a48748fb6346ae3abfb10 | Correlation matrix of all variables used in the cross-lagged models. Imputed, rather than observed, values are presented. The color bar represents correlation coefficients from − 1 (red) to + 1 (blue). Blue squares represent significant positive correlations. Red squares represent significant negative correlations. Darker color tones represent larger correlation coefficients. White squares represent non-significant correlation coefficients at p < 0.05 | PMC9343272 | 787_2021_1759_Fig2_HTML.jpg |
0.447914 | ccdc583ad6eb47968af15cebd0e742c1 | Medical assistance in dying (MAID) in Belgium (adapted from 6). | PMC9343580 | fpsyt-13-933748-g001.jpg |
0.487402 | 686aa83e95af49999e0b76d23b8fc46e | Medical assistance in dying (MAID) in The Netherlands see text footnote 1. | PMC9343580 | fpsyt-13-933748-g002.jpg |
0.508854 | fea2702903da496bb328047aa4a93a73 | Nature of unbearable suffering (from the official reports of the FCECE)*. *Category of labels changed between 2015 and 2016. | PMC9343580 | fpsyt-13-933748-g003.jpg |
0.444382 | c77274774390496eb21aa20d633e9edd | Decisions of the Federal Commission (from the official reports of the FCECE). *Category of labels changed between 2015 and 2016. | PMC9343580 | fpsyt-13-933748-g004.jpg |
0.45455 | 45c6c5d52b554402a38d79a049249806 |
(A) Boxplot of 35 pyroptosis-related genes’ relative expression between different types of patients. C: COVID-19 patients; NC: none-COVID-19 patients. (B) The Pearson’s correlation between 35 pyroptosis-related genes in COVID -19 patients, R value represents the Pearson’s correlation coefficient. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. | PMC9343985 | fimmu-13-888661-g001.jpg |
0.420941 | 854b414390844e34a6600cc22ee82fe7 |
(A) Gene set variation analysis (GVSA) analysis shows COVID-19 patients’ leukocytes may have been significantly damaged during viral infection and are undergoing damage repair. C: COVID-19 patients; NC: none-COVID-19 patients. (B) The abundance of leukocytes between the different types of patients. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns, no significance. | PMC9343985 | fimmu-13-888661-g002.jpg |
0.451984 | 18aa5ed8113a4c8d82e99b6e0ecb31cd |
(A) Consensus clustering matrix for k = 2. (B) The heatmap of 35 pyroptosis-related genes between the two PYRclusters. Red represents high expression; blue represents low expression. (C) Boxplot of significant pyroptosis-related genes’ relative expression between two PYRclusters. (D–F) The HFD45, ventilator-free days, D-dimer levels between the two PYRclusters. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns, no significance. | PMC9343985 | fimmu-13-888661-g003.jpg |
0.40384 | 7a531277b4c0440d9d74c03ba1f28731 |
(A) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of 570 DEGs Between two PYRclusters, “up” means these pathways of PYRcluster2 were upregulated when compared to PYRcluster1; “down” means these pathways were downregulated. (B) Leukocytes with significantly different expression levels among PYRclusters. (C) ImmuneScore calculated by “estimate” package between two PYRclusters. (D) Pearson’s correlation between expressions of 35 pyroptosis-related genes and abundance of leukocytes, R value represents the Pearson’s correlation coefficient. Annotated bars above and to the left indicate in which PYRcluster each pyroptosis-related gene or leukocyte is highly expressed. | PMC9343985 | fimmu-13-888661-g004.jpg |
0.478851 | f333a17b3ccf4033a5f0af9da9f4b146 |
(A) Heatmap of the DEGs between the gene clusters, different clinical data was shown in the annotation. (B) Pyrscore between two PYRclusters. (C, D) Pearson’s correlations between pyrscore and ventilator-free days(C), HFD45 (D), R value represents the Pearson’s correlation coefficient; grey area represents the 95% confidence interval for the linear fit. The maximum value of ventilator-free days is 28 since this 28-day time frame was initially chosen because most subjects with ARDS will have died or been extubated by Day 28. (E) Mean-squared error (MSE) of different numbers of variables revealed by the LASSO regression model. The red dots represent the MSE values; the grey lines represent the standard error (SE); the two vertical dotted lines on the left and right, respectively, represent optimal values by minimum criteria and 1-SE criteria. “Lambda” is the tuning parameter. (F) AUC of patients in the training group and test group. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns, no significance. | PMC9343985 | fimmu-13-888661-g005.jpg |
0.453747 | 5cf3165c7091458da61534969466847e |
(A, B, C) Pearson’s correlations between PYRsafescore and HFD45 (A), ventilator-free days (B), APACHE-II (C), R value represents the Pearson’s correlation coefficient; grey area represents the 95% confidence interval for the linear fit. (D) Heatmap of signature genes of PYRsafescore; expression of these genes was highly correlated with HFD45 and PYRsafescore. (E) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of signature genes of PYRsafescore. (F) Transcription factor enrichment of 570 DEGs between PYRclusters using “clusterProfiler” package based on MSigDB gene set: TFT (transcription factor targets) gene set. | PMC9343985 | fimmu-13-888661-g006.jpg |
0.432956 | be99c3b499b740f8b76aa152c8d0997a |
(A) Transcription factors regulatory network of PYRcluster1. “Degree” means the number of edges connected to the node. (B) Pearson’s correlation of differentially expressed pyroptosis-related genes and transcription factors in PYRclusters; R value represents the Pearson’s correlation coefficient. Annotation on the left represents in which PYRcluster each pyroptosis-related gene is significantly highly expressed. (C) Pearson’s correlation between different clinical data and transcription factors; R value represents the Pearson’s correlation coefficient. | PMC9343985 | fimmu-13-888661-g007.jpg |
0.437926 | 3998054b83bd45138ebda811b4177646 | Different patterns of pyroptosis of blood leukocytes in patients with COVID-19. | PMC9343985 | fimmu-13-888661-g008.jpg |
0.448432 | 47d3d2e7f5e04959ae64897e461a6035 | Volcano plots of single site CpG effect estimates for prenatal vitamin use in 1st month of pregnancy and −log10(p-values). Percentages indicate proportion of CpG sites with p-value < 0.01 that have positive or negative effect estimate. Regression models were adjusted for sex, maternal age, gestational age, maternal education, ancestry PCs, laboratory batch, and estimated cell proportions | PMC9344645 | 13072_2022_460_Fig1_HTML.jpg |
0.464605 | 8ccfb093c9444aceb3a7dcda4c1b415c | Pearson correlation of regression coefficients for the adjusted association between DNA methylation levels and prenatal vitamin use during the first month of pregnancy across all CpGs in common between EPIC/450k (n = 413,011 CpGs) | PMC9344645 | 13072_2022_460_Fig2_HTML.jpg |
0.458836 | 33e9799b3ca04d87b6f5e71883b7a5a1 | A Number of CpGs with DNA methylation levels associated (p-value < 0.01) with prenatal vitamin use during the first month of pregnancy unique to and in common with cohorts/tissues. B In upper triangle, correlations between CpG adjusted effect estimates with p-value < 0.01 in cross comparison, and number of such CpGs shown in lower triangle | PMC9344645 | 13072_2022_460_Fig3_HTML.jpg |
0.515984 | 497a7b7c2acb45ed895b360dc5d639b6 | Scatter plots of adjusted effect estimates between DAN methylation and prenatal vitamin use during the first month of pregnancy. CpGs are included with association P < 0.01 in both cohorts. A Cord blood (nCpGs = 18), B placenta (nCpGs = 101) | PMC9344645 | 13072_2022_460_Fig4_HTML.jpg |
0.448207 | b0ce9afdb3954ebc8ffe1af3caed75e8 | Study population flowchart. | PMC9344948 | 1678-9849-rsbmt-55-e0111-2022-gf1.jpg |
0.485234 | 406ddf56ec4f4b08bfffa8867f295d0a | Values of P/F ratio, alveolar-arterial gradient, ROX index and HACOR score at all the evaluations in survivors and non-survivors (on the left) in subjects with successful and failed NIV (on the right). The values of the HACOR score are reported as median and interquartile range, while all the other parameters are reported as mean ± standard deviation | PMC9345392 | 11739_2022_3058_Fig1_HTML.jpg |
0.400987 | 3907f0f542d44f588edec5f9505af08e | Proportion of survivors and non-survivors (A, B) and subjects with successful and failed NIV (C, D) with HACOR score > 5 and ROX index < 4.88 | PMC9345392 | 11739_2022_3058_Fig2_HTML.jpg |
0.400118 | 4d0c06dd5638447a932290cc43b8e246 | Probability of NIV failure in subjects with good and adverse prognosis | PMC9345392 | 11739_2022_3058_Fig3_HTML.jpg |
0.425899 | 123ea001194249dd970ab56378a775e1 | Map of the 12 states included in our analysis. Counties shaded blue had at least one case of histoplasmosis reported to public health authorities during the 4-year study period. | PMC9345522 | ede-33-654-g001.jpg |
0.416226 | dcd82457844341649d21101180852158 | Histoplasmosis results. A, Map of the estimated posterior probability of presence of Histoplasma capsulatum. B, Standard errors of the estimated posterior probability. C, Estimates and credible intervals of the state-specific intercepts for the probability of detecting a case of histoplasmosis, given H. capsulatum is present. D, County-level estimated detection posterior probability averaged over the 48 months. Note that the low estimate and wide variability for the average rate of histoplasmosis detection in Delaware shown in (C) is likely due to the fact that Delaware only has three counties with just two reported diagnosed cases throughout the study period. | PMC9345522 | ede-33-654-g002.jpg |
0.480422 | 8d9d43a3fbda41dd91e889dcc69b7081 | Flow diagram of the progress through the study phases. | PMC9345644 | cc9-4-e0742-g001.jpg |
0.401112 | 30b26cc13d9f4ba8a8f22f56bbbc88e0 | Box plots comparing the outcome scores between the two groups (ID1 and ID3) over time. T1, T2, T3, and T4 are time points at discharge, 1-mo, 3-mo, and 3.5-mo follow-up. Last panel in each graph shows the improvement in outcomes in ID3 between 3 and 3.5 mo. represents the dispatch of ICU diary (after 1- and 3-mo assessments for groups ID1 and ID3, respectively. EQ5D = European Quality of Life 5 Dimensions, HADS = Hospital Anxiety-Depression Scale, IESr = Impact of Events Revised, QOL = quality of life. | PMC9345644 | cc9-4-e0742-g002.jpg |
0.424949 | a30ffc5c16404b9f88728cf7916a9f6e | Expression of Gal3 and related genes in human patients and mouse models of osteosarcoma(A) mRNA expression of Gal3 (LGALS3), Gal3bp (LGALS3BP), IL-6, and C1GALT1 in tumor versus paired healthy samples from osteosarcoma patients (n = 6). The level of expression was determined using microarray analysis with the robust multiarray analysis (RMA) algorithm. Correlation of IL-6 versus C1GALT1 mRNA expression in tumor samples, ∗∗p < 0.01 by Pearson’s r. (B and C) mRNA (B) and protein (C) expression of Gal3 and Gal3bp in murine osteosarcoma cell lines (K7M2, MOS-J, and POS-1) and the murine melanoma cell line B16OVA determined by qRT-PCR (n = 3) and western blotting. (D) mRNA expression of Gal3, Gal3bp, and IL-6 in tibias and lungs representing healthy versus tumor tissue from orthotopic K7M2 tumor-bearing mice determined by qRT-PCR (n = 3). The data in (B) and (D) were calculated as 2E(−ΔCt) normalized to GAPDH × 10,000 and are presented as the mean ± SD. ∗p < 0.05; ∗∗p < 0.01; ns, not significant. Student’s t test. | PMC9345771 | gr1.jpg |
0.416166 | ad4f4119dc7f46e4a7a6679cbfb54f5f | Characterization of SFV vectors expressing Gal3 inhibitors(A) Diagrams of SFV vectors expressing Gal3 inhibitors: SFV-Gal3-C, SFV-Gal3-N, SFV-C12, and SFV-Gal3-N-C12. Constructs contained an SFV replicase sequence (composed of four nonstructural subunits [nsps]) followed by the viral subgenomic promoter (sgPr), a translation enhancer (b1) linked to the 2A FMDV protease fused in-frame to each Gal3 inhibitor sequences, and an HA tag. (B and C) Gal3 inhibitor expression in BHK-21 cells 24 h after infection with SFV vectors at an MOI of 20, as determined by western blot analysis of cell extracts (CEs) and supernatants (SNs) using an anti-HA antibody (B) and by immunofluorescence staining (C) using anti-nsp2 and anti-HA antibodies. Cell nuclei were stained with DAPI (magnification, 200×; scale bar, 100 μm). (D) Luciferase activity was determined in orthotopic K7M2 tumor-bearing mice at the indicated times after intratumoral injection of 1 × 108 VPs SFV-Luc; signal is measured in photons/s. Data represent the mean ± SD (n = 3). Images of luciferase expression in mice are shown. (E and F) Inhibition of Gal3 binding to activated T cells. CD8+ and CD4+ T cells activated with anti-CD3 and anti-CD28 antibodies and incubated with IL-10 were treated with the indicated recombinant Gal3 inhibitors at 50 μM (CD8+) or 25 μM (CD4+), with an anti-Gal3 antibody (a-Gal3) at 20 μg/mL (CD8+) or 10 μg/mL (CD4+) in the presence of 5 μg/mL recombinant Gal3 (+) for 30 min (CD8+) or 48 h (CD4+). Cells incubated without inhibitors and Gal3 indicated by (−). The binding of Gal3 was determined by flow-cytometric measurement of the mean fluorescence intensity (MFI) of Gal3 on total CD8+ and CD4+ T cells (E) or in CD8+PD1+ and CD4+PD1+ T cells (F). Data are presented as the mean ± SD (n = 3). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001; one-way ANOVA. N, Gal3-N; C, Gal3C; N-C12, Gal3-N-C12. | PMC9345771 | gr2.jpg |
0.401154 | 6d832459dc084334baa9045bca493bf8 | Evaluation of the antitumor effect of SFV vectors expressing Gal3 inhibitors in osteosarcoma(A) Treatment schedule for orthotopic osteosarcoma mouse models. Tumor cells were injected intratibially on day 0. The tumors were treated with 1 × 108 VPs SFV on day 7, and tumor size and survival were monitored. (B) K7M2 tumor growth in mice treated with the indicated vectors (n = 10) or PBS (n = 9). A representative experiment is shown of two experiments with similar results. Data are shown as the mean ± SD. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001; extra sum-of-squares F test. (C) Individual tumor growth of the mice presented in (B). Discontinuous red line indicates time when control mice developed tumors >400 mm2. (D) Kaplan-Meier survival plot of the mice described in (A). The graph corresponds to pooled data from two experiments using SFV-Gal3-C (n = 10), SFV-Gal3-N (n = 19), SFV-Gal3-N-C12 (n = 19), SFV-C12 (n = 9), SFV-Luc (n = 10), and PBS (n = 17). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001; log-rank test. (E) Kaplan-Meier survival curves of cured K7M2 tumor-bearing mice rechallenged with K7M2 cells (n = 5). p > 0.05 (not significant); log-rank test. (F) Tumor growth evaluation of MOS-J tumor-bearing mice treated as described in (A) with the indicated vectors (n = 9–10). Data are shown as the mean ± SD. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001; extra sum-of-squares F test. (G) Kaplan-Meier survival plot of the MOS-J tumor-bearing mice described in (A). ∗p < 0.05, log-rank test. (H) Kaplan-Meier survival plot of cured MOS-J tumor-bearing mice rechallenged with MOS-J cells (n = 3). ∗p < 0.05, log-rank test. | PMC9345771 | gr3.jpg |
0.436246 | ca56425015b34d2f9eaeb551cd0f3789 | Assessment of the antimetastatic effect of SFV vectors in an orthotopic K7M2 osteosarcoma mouse model(A) Summary table for survival, presence of metastases, and presence of bone tumors from two pooled experiments evaluating K7M2 tumor-bearing mice treated with the indicated SFV vectors or PBS. (B) Analysis of lung metastases. K7M2 tumor-bearing mice were analyzed on day 15 after treatment with PBS, SFV-Luc, or SFV-Gal3-N-C12, and healthy mice without tumors were used as controls. MicroCT analysis (upper images) and H&E staining (lower images) of lung tissue samples from one representative mouse in each group (magnification, 20×; scale bar, 4 mm). Quantification of the volume of the healthy lung parenchyma in all mice in the different treatment groups. Data are presented as the mean ± SD (n = 3, each group). p > 0.05 (not significant); one-way ANOVA. (C and D) Analysis of gene expression by RNA-seq. Mice bearing K7M2 tumors were treated with SFV-Gal3-N-C12 (NC12, n = 4), SFV-Luc (LUC, n = 3), or PBS (n = 5) as described in Figure 3A. On day 14 the mice were sacrificed, and total RNA was extracted from the tumors for sequencing. (C) Upon gene set enrichment analysis, an enriched gene set of prometastatic genes involved in osteosarcoma pathology was downregulated in the SFV-Gal3-N-C12 (NC12) group compared with the SFV-Luc (LUC) group at nominal p < 0.01 and false discovery rate (FDR) < 0.05. Normalized enrichment score (NES), −1.82; ∗∗padjusted < 0.01. (D) A heatmap and hierarchical clustering representing the differential expression of the most significant prometastatic genes between the treatment groups. | PMC9345771 | gr4.jpg |
0.400127 | 80bc7717dbde416fa5af47f80566cde5 | Analysis of immune cell populations in primary K7M2 tumors after treatment with SFV vectors by immunohistochemistry and RNA-seq(A) Immunohistochemistry (IHC) analysis of CD3+ T cells in primary osteosarcoma tumors from mice sacrificed at 14–17 days after intratumoral treatment with the indicated vectors or PBS. Representative IHC images are shown. Quantification of CD3+ T cells presented as the percentage of cells stained positive for CD3 in IHC images (magnification, 400×; scale bar, 200 μm). CD3+ T cells were counted in five different fields in each sample, and the mean was used to perform statistical analysis. Data are shown as the mean ± SD (n = 3). p > 0.05 (not significant); one-way ANOVA. (B) Relative abundances (in percentages) of 29 different immune cell populations determined by analysis of RNA-seq data for primary tumors from K7M2 tumor-bearing mice obtained as described in Figure 4C with the online tool ImmuCellAI-mouse. The abundance of each population was normalized by considering 1 to be the total (100%) population abundance. (C) Normalized abundances of natural killer (NK) cells, type 1 dendritic cells (cDC1s), plasmacytoid dendritic cells (pDCs), M1 macrophages, and M2 macrophages. Data are shown as the mean ± SD (n = 3–5). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001; one-way ANOVA. (D) Heatmap representing the differential expression and hierarchical clustering of the most significant immunomodulatory genes between treatment groups. NC12, SFV-Gal3-N-C12; LUC, SFV-Luc. | PMC9345771 | gr5.jpg |
0.470206 | 2a1d7489371740b0bfeb60c81e2652b3 | Characterization of the tumor microenvironment of K7M2 tumors after treatment with SFV vectors(A and B) Flow-cytometric analyses of different immune cell populations in primary K7M2 tumors (tibias) (A) and lung metastases (B) on day 3 after treatment with PBS, SFV-Gal3-N-C12 (N-C12), or SFV-Luc (Luc). Data are shown as the number of cells/mg tissue and as the mean ± SD (n = 5). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001; one-way ANOVA. (C) Ratios of CD4+/CD8+ T cells, CD8+ T cells/M2 macrophages, and CD8+/CD4+Foxp3+ T cells in the tumor samples analyzed in (A). Data are shown as the mean ± SD (n = 5). ∗p < 0.05; one-way ANOVA. (D) Gp70 tetramer (Tet+) staining (%) of the CD8+ T cell population (%) and surface expression of Gal3 in the CD8+Tet+ T cell population (MFI) in K7M2 tumors on day 14 after treatment with the indicated vectors. Data are shown as the mean ± SD (n = 4). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001; one-way ANOVA. (E) IFN-γ production in TILs isolated from K7M2 primary tumors on day 14 after treatment with the indicated SFV vectors. IFN-γ levels were measured by ELISA, and IFN-γ spot numbers and IFN-γ mean spot sizes were measured by ELISPOT. Data are shown as the mean ± SD (n = 3). ∗p < 0.05, ∗∗p < 0.01; one-way ANOVA. +, splenocytes plus mitogen; −, only splenocytes; K7M2, only K7M2 cells. | PMC9345771 | gr6.jpg |
0.464044 | f86646fe6ccd44488d7378c80b5d2faf | Analysis of exhaustion markers expressed by tumor-infiltrating lymphocytes in K7M2 tumors after treatment with SFV vectors(A) Expression of PD1, LAG3, or TIM3 (MFI) in CD8+ T cells from K7M2 tumors on day 3 and day 7 after treatment with PBS, SFV-Luc (LUC), or SFV-Gal3-N-C12 (N-C12). Data are shown as the mean ± SD (n = 5, each group). ∗p < 0.05, ∗∗p < 0.01; one-way ANOVA. (B) Pie charts showing the percentage of CD8+ T cells coexpressing the activation/exhaustion markers PD1, LAG3, and TIM3. (C) Same analysis as in (A) performed with tumor-infiltrating CD4+ T cells. Data are shown as the mean ± SD (n = 5, each group). p > 0.05 (not significant); one-way ANOVA. (D) Pie charts showing the percentage of CD4+ T cells coexpressing the activation/exhaustion markers PD1, LAG3, and TIM3. | PMC9345771 | gr7.jpg |
0.492505 | f6707d725e6642c1a2b99eb174335650 | Modulation of immune cell populations involved in pulmonary osteosarcoma metastasesFlow-cytometric analyses of CD4+ or CD8+ T cells expressing PD1, Foxp3, CD25, and/or Gal3 in the primary tumors (tibias) (A) and pulmonary metastases (B) of mice bearing K7M2 tumors on day 14 after treatment with PBS, SFV-Gal3-N-C12 (N-C12), or SFV-Luc (Luc). Data are shown as the mean percentage ± SD of the total CD4+ or CD8+ T cells (n = 5). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001; one-way ANOVA. | PMC9345771 | gr8.jpg |
0.419761 | c33c53322bc140b19c3a7aa18a27b896 |
The imbalance of regulatory T cells and effector T cells promotes the progression of chronic liver diseases and hepatocellular carcinoma. Chronic liver diseases such as alcoholic liver disease and non-alcoholic fatty liver disease induced by factors such as alcohol abuse and high-fat diet, respectively, can induce liver fibrosis, cirrhosis, and even hepatocellular carcinoma. The imbalance of regulatory T cells with T helper 17 cells or CD8 T cells is involved in the pathogenesis of liver inflammation, fibrosis, and cancer progression. ALD: Alcoholic liver disease; HCC: Hepatocellular carcinoma; NAFLD: Non-alcoholic fatty liver disease; Treg: Regulatory T cells; Th: T helper. | PMC9346458 | WJG-28-3346-g001.jpg |
0.480259 | 3e8313583ab345f2a8589cf2d849c7d8 |
The alteration of intrahepatic immunity predicts the prognosis of hepatocellular carcinoma patients. Usually, an increase of regulatory T cells, T helper (Th) 2 cells, and Th17 cells, as well as M2 macrophages is positively associated with hepatocellular carcinoma (HCC) progression in patients, whereas an abundance of CD8 T cells, Th1 T cells, and M1 macrophages is associated with HCC therapy and good prognosis for HCC patients. HCC: Hepatocellular carcinoma; Treg: Regulatory T cells; Th: T helper. | PMC9346458 | WJG-28-3346-g002.jpg |
0.415387 | 15f7fc68e9e14888b2ddc88581fa4193 |
Factors mediated the imbalance of regulatory T cells/effector T cells. Factor such as Hepatitis B virus, gut microbiota, and non-alcoholic fatty liver disease, as well as hepatocellular carcinoma tumor cells, can modulate several important molecules produced in the liver. Alteration of these molecules has been associated with the change of frequency and/or function of regulatory T cells in chronic liver disease, resulting in an imbalance of regulatory T cells/effector T cells. HCC: Hepatocellular carcinoma; HBV: Hepatitis B virus; NAFLD: Non-alcoholic fatty liver disease; Teff: Effector T cells; Treg: Regulatory T cells; GDF: Growth differentiation factor; HIF: Hypoxia-inducible transcription factors; Gal: Galectin; miR: micro ribonucleic acid; TLR: Toll-like receptor; YAP: Yes-associated protein; TGF-β: Transforming growth factor-beta. | PMC9346458 | WJG-28-3346-g003.jpg |
0.482131 | d17f565196dc4b7ea489d42b31f2aeb1 | PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram for identifying articles eligible for inclusion. FHIR: Fast Healthcare Interoperability Resources. | PMC9346559 | medinform_v10i7e35724_fig1.jpg |
0.42248 | 1a04f3301f6c4787bcf020e28f81bd9a | Number of publications per year (all: all FHIR publications identified in the databases with the search terms “FHIR” OR “Fast Healthcare Interoperability Resources”; included: studies included in this review). FHIR: Fast Healthcare Interoperability Resources. | PMC9346559 | medinform_v10i7e35724_fig2.jpg |
0.458627 | 345f57fa97f643b5a6a2eb2712b1497b | Network of coauthorships. Each point represents an author. Point size and color indicate the number of publications of this author (between 1 and 6). Lines indicate that authors have coauthored at least one paper together. Line thickness represents the number of coauthorships. | PMC9346559 | medinform_v10i7e35724_fig3.jpg |
0.434755 | 3e857499398e486f90f253a1677d4965 | Number of studies according to research domain. | PMC9346559 | medinform_v10i7e35724_fig4.jpg |
0.440211 | 1716aeb128d04d2584127a384e7330f9 | Project conceptual framework. EIS: early intervention services; RE-AIM: Reach, Effectiveness, Adoption, Implementation, and Maintenance framework; RLHS: rapid-learning health system. | PMC9346564 | resprot_v11i7e37346_fig1.jpg |
0.50166 | 42d96cce8fdb471da054344bcc9d0992 | Rapid-learning health system for early intervention for psychosis. | PMC9346564 | resprot_v11i7e37346_fig2.jpg |
0.462463 | 49eff84de82e4418b989d652bf41726f | Involvement of stakeholders in our rapid-learning health system for early intervention for psychosis. | PMC9346564 | resprot_v11i7e37346_fig3.jpg |
0.398476 | 2606d8da29a94da6b7f73a71a3bbf588 | Concern about COVID‐19 (a) and melanoma (b) in the whole cohort and in subgroups that did or did not postpone or miss appointments. The total number of patients in each subgroup was set to 100 %. Reasons for changed appointments (c). The total number of patients with postponed or missed appointments (n = 38) was set to 100 %.
aPercentages do not sum up to 100 % because 5 patients provided more than one answer.
bOther sickness than a SARS‐CoV‐2 infection.
cOther reasons were stated by 7 patients and specified as free text by 4 of them. The first patient postponed his appointment because his wife was sick, the second had another surgery planned, the third did not find the visit necessary and the fourth was afraid of an insufficient standard of hygiene.
dOut of 11 appointment changes due to medical provider‐related reasons, 5 occurred in the Vivantes Skin Cancer Center and 6 in external dermatological practices.
eReasons for medical‐provider‐related appointment changes could not be recapitulated in 6 cases, among these 3 at the Vivantes Skin Cancer Center and 3 in external dermatological practices. | PMC9348098 | DDG-20-962-g001.jpg |
0.471406 | 86de07bacf9d47e19dffe64805bba91a | Recruitment process flowchart of the Mela‐COVID Follow‐up study. | PMC9348098 | DDG-20-962-g002.jpg |
0.470084 | 0cc79cf2adb5462caee1c778ad592501 | Number of new SARS‐CoV‐2 infections per day between 01 Mar 2020 and 01 May 2021 in Germany. Source: Robert Koch‐Institute, available at: https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Daten/Fallzahlen_Kum_Tab.html?fbclid=IwAR0ddnAvxHA‐nN5ElOfQfEDUjFiH7rmeDeS1tYTlsvQ6B04FTScs08S5dpA (accessed 28 Jun 2021). Bars: Data collection periods of the Mela‐COVID and the Mela‐COVID Follow‐up study. | PMC9348098 | DDG-20-962-g003.jpg |
0.417067 | 0cb31d54e81a461c8a3724b0f0e2cbfc | Concern about COVID‐19 (a, b) and melanoma (c, d) in five categories (a, c) and on a 0–100 scale (b, d) in the subcohort that participated both in the Mela‐COVID and in the Mela‐COVID Follow‐up study. Participants were significantly more concerned about COVID‐19 after one year of pandemic (Mela‐COVID Follow‐Up) than after its first wave (Mela‐COVID; p < 0.001 both when comparing concern in 5 categories and on a 0–100 scale). Concern about melanoma did not differ significantly at both times. *** p = 0.001. | PMC9348098 | DDG-20-962-g004.jpg |
0.456959 | d1ed1d492b2a4c0ea681c0c931c47a87 | Diagnostic plots of the final model for morbidly obese individuals (grey dots) and non-obese individuals (black dots). a Observed versus individual predicted ciprofloxacin concentrations. b Observed versus population predicted ciprofloxacin concentrations. c CWRES versus time after dose. d Conditional weighted residuals versus population predicted concentration. The grey line represents the line of identity and the dashed lines represent the 1.96, − 1.96 interval indicating the range within which 95% of the observations are expected to fall. CWRES conditional weighted residuals | PMC9349153 | 40262_2022_1130_Fig1_HTML.jpg |
0.47881 | 30cfbbe818da414d81de94497aa2cd26 | Concentration-time curves in plasma (top panels) and soft tissue (lower panels) after a two or three times daily IV infusion of 400 mg for a typical non-obese or obese individual based on the empirical extended model derived from data by Hollenstein et al. [8]. IV intravenous, dd daily doses | PMC9349153 | 40262_2022_1130_Fig2_HTML.jpg |
0.426589 | 9da8f975d56a4c9d82bdb9d9a66e5134 | Reflexive PrEP Decision-Making | PMC9350448 | 10.1177_10497323221092701-fig1.jpg |
0.430173 | 0fae0f13acaf458abc79c35e216c6b5f |
Flow diagram of study selection process.
| PMC9350608 | WJR-14-238-g001.jpg |
0.431698 | 2b674f1f0c5c4715a1b7a26a90e78682 | Progress of mean weight in both groups | PMC9350655 | JIAPS-27-204-g001.jpg |
0.466298 | 571ee9f8a3804c6385888af4b1c99647 | Schematic representations of inorganic (left) and organic (right) linkage isomers. | PMC9350666 | d2sc02979k-f1.jpg |
0.44944 | 90310c069d554e97a1e519dab825f65a | Core-level N (1s) XPS spectra for (a) NO2-PDI and (b) ONO-PDI in the powder state. | PMC9350666 | d2sc02979k-f2.jpg |
0.452308 | d0cbb4146dd94113b39ce5eda2bac4c6 | FTIR spectra of NO2-PDI (top) and ONO-PDI (bottom) in KBr disks. | PMC9350666 | d2sc02979k-f3.jpg |
0.417401 | e71279e54bf240ec8f34d43d909dff58 | Time-dependent laser-irradiated UV-vis absorption spectra of NO2-PDI in (a) acetonitrile and (c) toluene. Time-dependent laser irradiated emission spectra (exc. at 532 nm) of NO2-PDI in (b) acetonitrile and (d) toluene. | PMC9350666 | d2sc02979k-f4.jpg |
0.450517 | 8c9f40e7925345a1b57864e41f5f0aa3 | (top) fsTA spectra (λex = 532 nm) of NO2-PDI in (a) TOL and (b) ACN showing the excited-state dynamics after photoexcitation at 532 nm. (middle) Evolution associated spectra reconstructed from global analysis of the time vs. wavelength based three-dimensional fsTA spectrograph. (bottom) Relative population profiles of the excited states fitted using kinetic models. (c) (top) nsTA spectra (λex = 532 nm) of NO2-PDI in TOL. (middle) Evolution associated spectra reconstructed from global analysis of the time vs. wavelength based three-dimensional nsTA spectrograph. (bottom) Relative population profile of the excited-state (B) fitted using kinetic models. | PMC9350666 | d2sc02979k-f5.jpg |
0.43931 | e7afa4a18aaa4a73932061ce7ca0ab60 | (a) A pictorial illustration presenting a plausible kinetic mechanism explaining the excited-state dynamics and associated photochemistry of NO2-PDI in the polar aprotic solvent acetonitrile and non-polar solvent toluene. Here, the S0 state represents the ground state of NO2-PDI and ONO-PDI, the S1FC state represents the Franck–Condon state of NO2-PDI, and the S1CR(CT) state is a long-lived transient species observed in nsTA measurements, representing the conformationally relaxed singlet excited-state of NO2-PDI having charge-transfer character. (b) Optimized geometries showing the transition from the S1FC state first to the S1CR(CT) state and finally to the six-membered TS through the nitro-aromatic torsion relaxation pathway computed at the CAM-B3LYP/6-311++G(d,p) level (the –R group was replaced by a –H atom to reduce the computational cost and increase clarity). | PMC9350666 | d2sc02979k-f6.jpg |
0.519361 | 39eba94dac6b459087c41533f3b5030e | (A) The structure of tyrosol; (B) the structure of salidroside. | PMC9351785 | jfda-23-03-359f1.jpg |
0.475353 | 1e6e3e51eabb4d068b7ca029f68d82a4 | Original and Vietnamese versions of the Group Rule image.Reprinted from the RAP-A and Happy House Participant Workbooks under a CC BY license, with permission from Astrid Wurfl, original copyright 2021. | PMC9352022 | pone.0271959.g001.jpg |
0.504021 | a7a86d4d1acd4539b4fa80c74a0fd830 | Original and Vietnamese versions of the Relaxation Brick image.Reprinted from the RAP-A and Happy House Participant Workbooks under a CC BY license, with permission from Astrid Wurfl, original copyright 2021. | PMC9352022 | pone.0271959.g002.jpg |
0.481411 | 038a70d4baaa4a34af302ee2422f34a3 | The original and Vietnamese actresses in the Saskia video.Reprinted from the RAP-A and Happy House videos under a CC BY license, with permission from Astrid Wurfl, original copyright 2021. | PMC9352022 | pone.0271959.g003.jpg |
0.476504 | f59b9218889e44fd9179b1046e462f9b | Molecular networking analysis of the CH2Cl2-soluble fraction of C. orchioides. (A) Spectrum match of the node of molecular networking with GNPS
library. (B) Structures of top ranked NAP candidates using GNPS and
SUPNAT library. (C) Automatic classification and visualization of
each cluster by the MolNetEnhancer. The chemical class of the largest
(the cluster filled with red color) clusters were revealed as triterpenoids.
The singleton node was excluded in this figure. | PMC9352156 | ao2c03243_0001.jpg |
0.536998 | 5ce503602384436ea42b9e3bab7c08d2 | Key HMBC, COSY, and NOESY correlations of compound 1. | PMC9352156 | ao2c03243_0002.jpg |
0.574304 | 81976d76f6774fa68e5ed5cdfa41adf8 | X-ray ORTEP plot for the molecular structure of compound 1. | PMC9352156 | ao2c03243_0003.jpg |
0.493949 | 5967bb2610de492ca1f930f4c1116fda | Key HMBC, COSY, and NOESY correlations of compound 2. | PMC9352156 | ao2c03243_0004.jpg |
0.546428 | 66c1b4a8c1504cdcbafea45c31b0b89f | X-ray ORTEP plot for the molecular structure of compound 2. | PMC9352156 | ao2c03243_0005.jpg |
0.480699 | 721503557246452489f7e73cfbbff30c | Key HMBC, COSY, and NOESY correlations of compound 3. | PMC9352156 | ao2c03243_0006.jpg |
0.619415 | ab23d29a9f7c41f080318410f908a22f | X-ray ORTEP plot for the molecular structure of compound 3. | PMC9352156 | ao2c03243_0007.jpg |
0.445473 | 4ecad514eb3c4dff95cf73ca13e7705b | 3D model simulation after MM2 minimization (minimum
RMS gradient
= 0.01) for the comparison of relative configuration of compound 4. (A) Comparison of 3D computational models of (20S*,22R*)-4 and (20S*,22S*)-4 and 3J values upon dihedral angle. (B) Comparison of
(20S*,22R*,24S*)-4 and
(20S*,22R*,24R*)-4 and calculated interproton distances. | PMC9352156 | ao2c03243_0008.jpg |
0.530284 | 5b07a36952f64f9e902a174b032747b2 | 3D computational model for the compound 5 and key
NOESY correlation. | PMC9352156 | ao2c03243_0009.jpg |
0.436725 | 751cc1f3a5ca41af8e0be0ee20be2366 | Annotation
of compounds 1–6 on
the triterpenoid clusters. | PMC9352156 | ao2c03243_0010.jpg |
0.589983 | ed90f47733774b7a8b3a3fbc8f14f467 | 1H NMR spectra of (a) chitosan and (b) CST
in CF3COOH/D2O. | PMC9352254 | ao2c02776_0002.jpg |
0.516135 | 528347ef290d4b7f82cbeada0aca6aa9 | UV–vis spectra of CST, chitosan, thymol, and chitosan mixed
with 0.05% w/v thymol in 0.1 M HCl solution. | PMC9352254 | ao2c02776_0003.jpg |
0.481141 | 9c5aa2b6023c4c07a4d1f59f1434d11e | Visual observation and absorption spectra of CST coated on gold
nanoparticles with various concentrations of CST (a) 0.006%w/v, (b)
0.008%w/v, (c) 0.010%w/v, and (d) 0.020%w/v and CST as the control
on the synthesis step. | PMC9352254 | ao2c02776_0004.jpg |
0.514413 | 5e579e9471184e61bfb880f5489d017a | XRD pattern of (a) chitosan, (b) CST,
(c) CST coated on gold nanoparticles,
and (d) thymol. | PMC9352254 | ao2c02776_0005.jpg |
0.432084 | 125ce0be6934452ead4f9fb96f4f58fb | TEM images and size distribution of CST coated on gold
nanoparticles
at a CST concentration of (a) 0.006% w/v, (b) 0.008% w/v, (c) 0.01%
w/v, and (d) 0.02% w/v. | PMC9352254 | ao2c02776_0006.jpg |
0.471403 | 5abbbadb2f37461d9c4263e2becbd2c2 | Effect
of (a) pH, (b) ionic strength, and (c) time on the stability
of CST coated on gold nanoparticles. | PMC9352254 | ao2c02776_0007.jpg |
0.523933 | dbfbb432f8934e87a7bdf7f16e8f87ed | Bacterial inhibition photographs of chitosan,
CST, and CST coated
on gold nanoparticles and control against using the agar well diffusion
method (a) S. mutans and (b) S. sobrinus. | PMC9352254 | ao2c02776_0008.jpg |
0.456483 | 354b4d26d2e0401fb1f9cf4bf01ac845 | Schematic Illustration of the Synthesis
of (a) CST and (b) CST Coated
on the Gold Nanoparticle Surface | PMC9352254 | ao2c02776_0009.jpg |
0.5175 | 5fcad26a3b7d4e61ad2c11e10f4e7ec2 | Plasma lipidome data visualization of Tuberculosis patients (N = 35) and Control (N = 37) group. (a) Principal components analysis 3D score plot of the two group in the positive ion mode. (b) Heatmap of all lipidome features between two group in the positive ion mode. (c) Principal components analysis 3D score plot of the two group in the negative ion mode. (d) Heatmap of all lipidome features between the two group in the negative ion mode. C control group, T Tuberculosis group. | PMC9352691 | 41598_2022_17521_Fig1_HTML.jpg |
0.440946 | c8d0c8b701ab4b25b5d5ec3c61d75f34 | Partial least squares-discriminant analysis (PLS-DA) score plots of Tuberculosis patients and controls plasma lipidome. (a) PLS-DA 3D score plot of the two group in the positive ion mode. (b) PLS-DA 3D score plot of the two group in the negative ion mode. C control group, T Tuberculosis group. | PMC9352691 | 41598_2022_17521_Fig2_HTML.jpg |
0.399863 | 7b2b4bd3617f4290bd8dc68d2f953033 | Lipid biomarkers multivariate and correlation analysis. (a) Random Forest predictive model of the lipid biomarkers. (b) Linear Support Vector Machine predictive model of the lipid biomarkers. (c) Correlation of the lipid biomarkers in Tuberculosis group (d) Correlation of the lipid biomarkers Control group. Var variable, AUC area under the curve, CI confidence interval, CAR acylcarnitine, Cer ceramide, Hex2Cer hexosylceramide, LPC lysophosphatidylcholines, LPC (O-) Ether-linked lysophosphatidylcholines, PC phosphatidylcholine, PC (O-) Ether-linked phosphatidylcholine, LPE lysophosphatidylethanolamines, LPE (O-) Ether-linked lysophosphatidylethanolamines, PE phosphatidylethanolamine, PE (O-) Ether-linked phosphatidylethanolamine, PI phosphatidylinositol, NAE N-acetyl ethanolamine, DG diacylglycerol, TG triacylglycerol, FA free fatty acid. | PMC9352691 | 41598_2022_17521_Fig3_HTML.jpg |
0.430753 | 20fb4e4482d349c680f3baeb394a34fe | Lipid ontology enrichment and lipid-gene association network analysis. (a) Lipid ontology (LION) PCA-heatmap of Tuberculosis and Control group. (b) Bubble plot of lipid-gene association pathways. C control group, T Tuberculosis group, PC phosphatidylcholine, TG triacylglycerol, LION Lipid ontology. | PMC9352691 | 41598_2022_17521_Fig4_HTML.jpg |
0.413732 | c3f312198df04029885b69d0de23c616 | Tuberculosis (TB) and non-TB classification in three cohort by lipid-genes biomarkers using Random Forest predictive model. (a) Model performance (AUC = 0.919) of TB versus Control classification in GSE107991 dataset. (b) Model performance (AUC = 0.884) of TB versus latent tuberculosis infection (LTBI) classification in GSE107991 dataset. (c) Model performance (AUC = 0.829) of TB versus non-TB classification in E-MTAB-8290 dataset. (d) Model performance (AUC = 0.958) of TB versus Control classification in GSE101705 dataset. Var variable, AUC area under the curve, CI confidence interval, TB Tuberculosis, LTBI Latent tuberculosis infection. | PMC9352691 | 41598_2022_17521_Fig5_HTML.jpg |
0.396831 | 0da09ef3bc5d48ffa8226c10ff5df75e | Top: Point of interest (POI) distribution (input). Bottom: Incidence distribution (Output). | PMC9353319 | gr10_lrg.jpg |
0.454052 | 474e17c448c349c3ae5c08f6c19e8e3f | Top: Result comparison of the A_training set in different periods. Bottom: the distribution of the training and the testing dataset. | PMC9353319 | gr11_lrg.jpg |
0.467763 | 8e35447e1b874d47aad2e9680440025d | Generator loss (LOSS_G) and Discriminator loss (LOSS_D) during training A and training B. | PMC9353319 | gr12_lrg.jpg |
0.430576 | e622907dfc6d4a5ba7fbe7288c631655 | Training and testing image pairs of training A and training B. | PMC9353319 | gr13_lrg.jpg |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.