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0.404594 | 1cc0b6e424e446a9abb397ccfa04fff2 | The consequence of st. 4 node grafting into st. 4 host embryos: the true and the erroneous. (A) Summary of our observations on the result of node grafting. The grafted node developed differently depending on whether the graft was on the anterior or posterior half of the embryo, separated by the horizontal broken line. Only after node grafting at an anterior position did the node-derived AME extend, to which the nearby epiblast converged and developed into the brain portions. (B) The kind of diagram published in many textbooks, which are erroneous. This method of systematic grafting has not been done previously (see Box 2). Node grafting does not elicit secondary posterior embryonic structures of host origin (e.g., somites) (see Figure 4). | PMC9581324 | fcell-10-1019845-g008.jpg |
0.452445 | 9678888bb9c94897965234905d598ae0 | Identification of DEGs in LUAD and their co-expression modules. (A) Genes with notable upregulation or downregulation in LUAD than controls in four LUAD datasets (GSE32863, GSE43458, GSE75037, and TCGA-LUAD). (B, C) Venn diagrams of upregulated or downregulated genes shared by above four datasets. (D) Sample clustering to detect outliers. (E) Calculation of scale independence or mean connectivity under diverse soft‐thresholding power β values. (F) Co-expression modules based on DEGs shared by above four datasets with average linkage clustering. (G) Co-expression module clustering diagram. (H, I) Biological processes and KEGG pathways of module genes. DEGs, differentially expressed genes; LUAD, lung adenocarcinoma; KEGG, Kyoto Encyclopedia of Genes and Genomes. | PMC9582346 | fimmu-13-983570-g001.jpg |
0.471066 | 4ec23411f3ce488eb74486fd949dce78 | Definition of immune cell infiltration-based classification across LUAD. (A) Differences in infiltration levels of immune cell types between LUAD and normal lung tissues in four datasets (GSE32863, GSE43458, GSE75037, and TCGA-LUAD). (B) Forest plot of immune cell types whose infiltration levels significantly correlated to TCGA-LUAD cases’ OS. (C) Definition of two molecular subtypes across TCGA-LUAD on the basis of infiltration levels of seven risky immune cell types. (D) Elbow method for validating the accuracy of this molecular classification across TCGA-LUAD samples. (E) Kaplan–Meier curves of OS between two subtypes in TCGA-LUAD cohort. (F) Validation of this immune cell infiltration-based classification in the GSE72094 cohort. (G) Elbow method for validating the optimal number of clusters in the GSE72094 cohort. (H) Kaplan–Meier curves of OS between two subtypes in the GSE72094 cohort. (I) Heatmap of the associations between co-expression modules and clinical traits (LUAD and normal tissues; C1 and C2 subtypes). (J) Scatter plots of the correlation between module membership in MEblue module and gene significance for C1 subtype. (K) The correlation between module membership in MEturquoise module and gene significance for C2 subtype. LUAD, lung adenocarcinoma; OS, overall survival. | PMC9582346 | fimmu-13-983570-g002.jpg |
0.400311 | 26b172fa117d4fbd84382a6f78fa894c | Signaling pathways, antitumor immunity and drug sensitivity in two immune cell infiltration-based subtypes. (A) KEGG pathways with different enrichment between LUAD and normal lung tissues. (B) KEGG pathways were distinctly enriched between C1 and C2 subtypes. (C) Heatmap of the levels of MHC molecules in two molecular subtypes. (D) Differences in the abundance of immune infiltrate between subtypes. (E) Cluster analysis of various immune cell types across LUAD. (F) Correlation of different immune cells with one another within C1 or C2 subtype. (G) Difference in TMB score between subtypes. (H) Comparison of the mRNA levels of known immune checkpoints in two subtypes. (I) Estimated IC50 values of small molecular compounds in two subtypes. KEGG, Kyoto Encyclopedia of Genes and Genomes; LUAD, lung adenocarcinoma; TMB, tumor mutational burden. | PMC9582346 | fimmu-13-983570-g003.jpg |
0.412463 | a312c4dfd3b1447e862ab06f8b605583 | DNA methylation and genetic mutation of module genes across LUAD. (A) Differentially methylated sites between LUAD and normal lung tissues. (B) Distribution of differentially methylated sites on the chromosomes. (C) DEGs that were potentially influenced by DNA methylation. (D) Landscape of the top 30 mutated module genes across LUAD. LUAD, lung adenocarcinoma; DEGs, differentially expressed genes. | PMC9582346 | fimmu-13-983570-g004.jpg |
0.432275 | 1ff70b7a050546068c27de608dc0314d | Identification of key module genes in LUAD. (A) GSVA of the major pathways enriched by genes in two key modules MEturquoise and MEblue. (B) Module genes are involved in the major pathways. (C) Selection of key module genes via PPI network. (D) Heatmap of the expression and methylation of genes from the PPI network in LUAD and normal lung tissues. (E) Expression of key module genes in LUAD and controls. ****p < 0.0001. LUAD, lung adenocarcinoma; GSVA, gene set variation analysis; PPI, protein–protein interaction. | PMC9582346 | fimmu-13-983570-g005.jpg |
0.431418 | ca9118987fb64fe5b2fde42ba1b30b4e | Definition of a scoring system based on key module genes for LUAD prognosis. (A) Distribution of risk score, survival state, and mRNA levels of key module genes. (B) ROCs for evaluation of the prediction capacity of each key module gene in LUAD prognosis. (C) The key module gene-based nomogram for inferring LUAD cases’ OS time. (D) Agreement in 5-year and 8-year OS outcomes between the actual data and the nomogram estimation. (E, F) Comparison of clinicopathological features and risk score between C1 and C2 subtypes in TCGA-LUAD and GSE72094 datasets. LUAD, lung adenocarcinoma ROCs, receiver operating characteristics; OS, overall survival. | PMC9582346 | fimmu-13-983570-g006.jpg |
0.468285 | 430d77c106e74754906cee5aea0d7166 | EXO1 correlates to antitumor immunity and is essential for growth of LUAD cells. (A–C) Associations of EXO1 mRNA level with (A) immune infiltrates, (B) MHC molecules, and (C) immune checkpoints in LUAD. (D) EXO1 mRNA level in human lung normal epithelial and LUAD cells. (E) EXO1 mRNA level in A549 cells overexpressing EXO1 (EXO1-OE). (F) EXO1 mRNA level in NCI-H1975 cells with EXO1 knockdown (EXO1-siRNA). (G, H) CCK-8 for quantifying optical density (OD) to evaluate proliferation of A549 cells with EXO1-OE and NCI-H1975 cells with EXO1-siRNA. ****p < 0.0001. LUAD, lung adenocarcinoma. | PMC9582346 | fimmu-13-983570-g007.jpg |
0.412305 | e4e6e504399746b08d80da57d3bf447f | EXO1 drives migratory and invasive traits of LUAD cells. (A–D) Representative images of migration assay and number of migratory A549 cells overexpressing EXO1 (EXO1-OE) and NCI-H1975 cells with EXO1 knockdown (EXO1-siRNA). (E–H) Representative images of invasion assay and number of invasive A549 cells with EXO1-OE and NCI-H1975 cells with EXO1-siRNA. Bar, 50 μm. NC, negative control; LUAD, lung adenocarcinoma. *p < 0.05; ***p < 0.001; ****p < 0.0001. | PMC9582346 | fimmu-13-983570-g008.jpg |
0.452462 | 73df319dcbb44656af2a4c0fcd0c130c | EXO1 facilitates PD-L1 and sPD-L1 expression in LUAD cells. (A–E) RT-qPCR and Western blotting of PD-L1 expression in A549 and NCI-H1975 cells with EXO1 overexpression. (F–J) RT-qPCR and Western blotting of PD-L1 expression in A549 and NCI-H1975 cells when EXO1 was knocked out. (K–N) ELISA for sPD-L1 levels in A549 and NCI-H1975 cells with EXO1 overexpression or knockdown. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001. LUAD, lung adenocarcinoma. | PMC9582346 | fimmu-13-983570-g009.jpg |
0.439093 | a47d85295bdb43229230febd495d6766 | OTUs analysis, β-diversity estimation, and α-diversity analysis between the experimental group (VB3) and control group (Control). (A) Venn diagram based on OTU. (B) PCoA for β-diversity estimation. (C–H) Box plot of alpha diversity [(C) observed species number; (D) Shannon's index; (E) Simpson's index; (F) ACE index; (G) Chao1 index; (H) coverage index]. *P < 0.05. | PMC9582987 | fnut-09-959039-g0001.jpg |
0.427037 | 4c5f415b76734c6e8d5deb290eee6611 | Relative abundance of bacteria community in the experimental group (VB3) and control group (Control) at phylum (A) and genus (B) levels. *P < 0.05; **P < 0.01. | PMC9582987 | fnut-09-959039-g0002.jpg |
0.44418 | a9ee859f74794d4ea8c7eaf7a7a4433a | LEfSe analysis between the experimental group (VB3) and control group (Control). Red, control group. Blue, experimental group. (A) Histogram of the results of LEfSe between the experimental and control groups and their respective effect sizes; P-value < 0.05 considered significant. (B) Cladogram showing taxonomic representation of differences between the experimental and control groups. | PMC9582987 | fnut-09-959039-g0003.jpg |
0.521668 | ed4c1d96ed124e7dabecafc7170f8d06 | Principal component analysis (PCA) between the experimental group (VB3) and control group (Control) and partial least squares discriminant analysis (PLS-DA) between the experimental group (VB3) and control group (Control). (A) PCA based on positive ion table. (B) PCA based on negative ion table. (C) PLS-DA based on positive ion table. (D) PLS-DA based on negative ion table. | PMC9582987 | fnut-09-959039-g0004.jpg |
0.408723 | 3c040fb87c8c4813a864137deb699af8 | Differential metabolite cluster dendrogram between the experimental group (VB3) and control group (Control) and variable importance in projection (VIP) of metabolites between the experimental group (VB3) and control group (Control). *P < 0.05; **P < 0.01; ***P < 0.001. | PMC9582987 | fnut-09-959039-g0005.jpg |
0.390975 | f35529a321d4487da32fbc62db51966a | Biochemical categories of the differential metabolites identified between the experimental group (VB3) and control group (Control). (A) Superclass. (B) Class. | PMC9582987 | fnut-09-959039-g0006.jpg |
0.38133 | e9c07533f4ff4bc8a8fd4feacfd176bd | KEGG enrichment analysis between the experimental group (VB3) and control group (Control). | PMC9582987 | fnut-09-959039-g0007.jpg |
0.437311 | e07db630e3f44f2a8571831fe1eec9f4 | Spearman's correlation analysis between gut microbiota (OTU level) and carcass traits (fat rate and lean meat rate). *P < 0.05; **P < 0.01. | PMC9582987 | fnut-09-959039-g0008.jpg |
0.491621 | cbeaef9df22f44eb83f166cb0657826e | Spearman's correlation analysis between hepatic metabolites and gut microbiota (OTU level). *P < 0.05; **P < 0.01; ***P < 0.001. | PMC9582987 | fnut-09-959039-g0009.jpg |
0.426804 | 56bce0fc71d946229096ca81b719452e | CONSORT flow diagram of Timolol 0.5% (w/w) vs placebo during study follow up. NSAIDs: Non-steroidal anti-inflammatory drugs; COVID-19: Coronavirus Disease of 2019; ARD: Acute Radiation Dermatitis | PMC9583052 | 12885_2022_10064_Fig1_HTML.jpg |
0.487217 | 2387bd847a6743ebb5cd883c1ff9f61d | Map of Saudi Arabia showing the five sampling regions of Ae. aegypti, i.e. Jazan, Sahil, Makkah, Jeddah and Madinah with frequency for kdr mutations S989P + V1016G and F1534C observed in this study. ArcGIS website was used to generate the map | PMC9583590 | 13071_2022_5525_Fig1_HTML.jpg |
0.361273 | 46960046e865467f8de8fed3c1327e03 | Genotype frequencies of kdr mutations S989P + V1016G and F1534C in Aedes aegypti from five regions in Saudi Arabia compared with samples from Southeast Asia (Thailand and Myanmar). The bar with one asterisk (*) is the triple homozygous mutant (PP, GG and CC), and the bar with two asterisks (**) is the triple homozygous wild type (SS, VV and FF) | PMC9583590 | 13071_2022_5525_Fig2_HTML.jpg |
0.457721 | dad955c38dbd424cab1e59f2b9902a71 | Kdr genotypes in Aedes aegypti observed from five regions in Saudi Arabia and their possible constituent haplotypes. SS, VV and FF are homozygous wild types for S989P, V1016G and F1534C. PP, GG and GG are homozygous mutants for S989P, V1016G and F1534C. SP, VG and FC are heterozygotes for S989P, V1016G and F1534C. H1 triple wild type (SVF), H2 wild types in 989 + 1016 and mutant at 1534 (SVC), H3 mutant at 989 + 1016 and wild type at 1534 (PGF), H4 triple mutants (PGC) | PMC9583590 | 13071_2022_5525_Fig3_HTML.jpg |
0.442485 | 03d43f6a38014aa9b4d3560b07d18274 | Median-joining haplotype network for domain IIS6 of vgsc for Saudi Arabia, Uganda, Thailand and Myanmar populations of Aedes aegypti. The coloured circle represents the haplotype and the population. Haplotypes are connected according to their similarity, and hatch marks between haplotypes show the number of mutations. H haplotype, LVP Liverpool strain | PMC9583590 | 13071_2022_5525_Fig4_HTML.jpg |
0.446567 | a8da14748b064205bc4e3fa42b73fe74 | Median-joining haplotype network analysis for domain IIIS6 of vgsc for Saudi Arabia, Uganda, Thailand and Myanmar populations of Aedes aegypti. The coloured circle represents the haplotype and population. Haplotypes are connected according to their similarity, and hatch marks between haplotypes show the number of mutations. H haplotype, LVP Liverpool strain | PMC9583590 | 13071_2022_5525_Fig5_HTML.jpg |
0.409226 | 24430a227ff74e8d8ad9aee6576f7ea6 | Features of patients stratified by duration of stress hyperglycaemia during the first week of admission. (A) Severity classification. (B) Kaplan–Meier survival curve. (C) Survival curve after adjusting for age, gender, BMI, Charlson comorbidity index, time to admission, referral status, biliary aetiology and admission TG levels. SHG, stress hyperglycaemia; MAP, mild acute pancreatitis; MSAP, moderately severe acute pancreatitis; SAP, severe acute pancreatitis. | PMC9585288 | fendo-13-998499-g001.jpg |
0.49855 | 4b0ec625a1ec43c5918e1d8179af0505 | Trend analysis for clinical outcomes stratified by duration of stress hyperglycaemia. POF, persistent organ failure; MODS, Multiple Organ Dysfunction Syndrome; ANC, acute necrotic collection; LOHS, length of hospital stays. | PMC9585288 | fendo-13-998499-g002.jpg |
0.454845 | d98e1c60752c42dfa8db904bfb502f80 | Assessment of secondary metabolites of T. viride against pathogenic wilt causing fungus, F. oxysporum f. sp. ciceris by volatile and nonvolatile methods. (Tv1- ITCC 6889, Tv2- ITCC 7204, Tv3- ITCC 7764, Tv4- ITCC 7847, Tv5- ITCC 8276, Control-without Trichoderma strain). (For each method, treatment bars having at least one letter common are not statistically significant using Duncan’s Multiple Range Test at p<0.05, n=3, error bars represent standard deviations). | PMC9585344 | fpls-13-990392-g001.jpg |
0.418644 | a606ab9888a0496595be16e1d7a9f057 |
In-vivo assessment of percent seed germination and wilting of chickpea by TvT formulation of T. viride against pathogenic funus, F. oxysporum f. sp. Ciceris. (T1- Absolute control; T2- Carbendazim 50% WP; T3- Talc formulation; T4- TvT formulation at recommended dose; T5- TvT formulation at double dose; T6- TvT formulation at ½ of recommended dose). (For each parameter, treatment bars having at least one letter common are not statistically significant using Duncan’s Multiple Range Test at p<0.05, n=3, error bars represent standard deviations). | PMC9585344 | fpls-13-990392-g002.jpg |
0.430355 | 91770d862d484a50a70ec3515234fc41 |
In-vivo assessment of wilt incidence in chickpea caused by wilt causing fungi, F. oxysporum f. sp. ciceris post application of TvT formulation of T. viride. (A- Absolute control; B- Carbendazim 50% WP; C- Talc formulation; D- TvT formulation at recommended dose; E- TvT formulation at double dose; F- TvT formulation at ½ of recommended dose). | PMC9585344 | fpls-13-990392-g003.jpg |
0.411959 | c74f8fb92ae547fabfd96151d98b80a4 | Effect of application of optimized TvT and TvP formulations against F. oxysporum f. sp. ciceris on percent seed germination and wilting of chickpea under field condition. (T1- Absolute control; T2- Carbendazim 50% WP; T3- Talc formulation; T4- TvT formulation at recommended dose; T5- TvT formulation at double dose; T6- TvT formulation at ½ of recommended dose; T7- TvP formulation at recommended dose; T8- TvP formulation at double dose; T9- TvP formulation at ½ of recommended dose. | PMC9585344 | fpls-13-990392-g004.jpg |
0.419591 | f81fb2c256be465b83d8932563d5e681 | Field assessment of wilt incidence in chickpea caused by F. oxysporum f. sp. ciceris post application of TvT and TvP formulations of T. viride. (A- Absolute control; B- Carbendazim 50% WP; C- Talc formulation; D- TvT formulation at recommended dose; E- TvT formulation at double dose; F- TvT formulation at ½ of recommended dose; G- TvP formulation at recommended dose; H- TvP formulation at double dose; I- TvP formulation at ½ of recommended dose). | PMC9585344 | fpls-13-990392-g005.jpg |
0.49353 | 484be671150d41898da01140ae1f95b4 | Understanding the hazard posed by bioengineering requires the characterization of hazardous building blocks. Bioengineering enables the creation of novel or modified organisms through oligonucleotide synthesis and assembly of building blocks within a biological vehicle. These biological functional building blocks are inspired from sequences extracted and sequenced from natural organisms. Such organisms can be composed of hazardous and non-hazardous functional elements (denoted by the red and blue blocks, respectively). The hazardous functions within organisms may have arisen from environmental selective pressures such as virus mutation to enable, for example, a jump from a vector to a human host, or a transfer of transposable elements among species. | PMC9585941 | fbioe-10-979497-g001.jpg |
0.548919 | 791db7866d5f4017a07883d517180272 | Functional biological hazards are differentiated from and include several functions beyond virulence factors. While some hazardous functions overlap with virulence factors, we define several hazardous functions outside the traditional definition of virulence factors. Many virulence factors that may be contained in avirulent organisms, such as siderophores and transcription factors, are not considered hazardous functions as they do not directly and uniquely perform hazardous functions. Hazardous functions are further described in the text and are color coded according coarse functional metadata groups as follows: Red—functions that do direct damage to cells such as toxins; Orange—functions involved in active host subversion or those involved in nonproteinacous toxin and drug pathways; Yellow—other virulence factors uniquely involved in pathogenicity (e.g., invasion), non-virulence factors that may contribute to detrimental host response (e.g., bioregulators and antibiotic resistance proteins), and prions; White—virulence factors that may also participate in non-hazardous microorganism functions; Gray—virulence factors that do not have a direct hazardous function. Note that the figure is non-exhaustive. | PMC9585941 | fbioe-10-979497-g002.jpg |
0.508088 | d3b32e872b1844daa898942e62312ffb | Pathogenic species are enriched in hazardous functional categories. Shown are the hazard fingerprints for Neisseria (A), Burkholderia (B), Clostridium (C), Streptococcus (D), Bacillus (E), Pseudomonas (F), E. coli
(G), and Mycobacterium (H). The fingerprints are shown as rows in a heat plot with the values in each column representing the normalized fraction of CDSs within each functional category (as defined in Table 1). Only the relevant categories from Table 1 are included (i.e., those that provided alignments). The pathogenic subgroups within each organism group are defined in Table 3 and separated by the blue lines on the heat plot. Abbreviations: DMG_NOTOX, damage without toxin activity GO term, DMG_TOX, damage with toxin activity GO term; ACT, active host subversion; ADH, adherence; INH, inhibits host cell death; MOT, motility; PAS, passive host subversion; INV, invasion; APOP, host cell apoptosis; AR, antibiotic resistance; TOT, TOTAL (sum of all other categories). | PMC9585941 | fbioe-10-979497-g003.jpg |
0.424473 | 5b6e9392f3a54741b8aec7bf2c315953 | Hazardous functions separate pathogens from non-pathogens. Shown are the dendrograms for Neisseria (A), Burkholderia (B), Clostridium (C), Streptococcus (D), Bacillus (E), Pseudomonas (F), E. coli
(G), and Mycobacterium (H), with pathogenic species colored in red and non-pathogens colored in green. An additional plot for E. coli, stratified by the groups shown in Figure 3 is shown in Supplementary Figure S1. | PMC9585941 | fbioe-10-979497-g004.jpg |
0.413858 | 6efd819e2db54d30aef7ee6cae0f342f | Pathogen and Nonpathogen Fingerprint Scores Reveal Stratification Among Functional Hazard Categories. The plot shows a difference in average scores between the pathogens and nonpathogens (y-axis) as a function of the average nonpathogen score (x-axis) for the functional categories from the heat plots in Figure 3. The error bars represent the standard error. The abbreviations are the same as Figure 3. | PMC9585941 | fbioe-10-979497-g005.jpg |
0.377307 | e87fb3fefa144d49af14b5afd422ce36 | Nuclear LDs in hepatocytes isolated from L-CKO mice. A: Representative widefield fluorescence photomicrographs of hepatocytes isolated from control (Lap1fl/fl) and L-CKO mice stained with BODIPY (green) and DAPI (blue). Scale bars: 25 μm. B: Zoomed-in views of the areas within the orange rectangles in Panel A with nuclei outlined in yellow (based on DAPI labeling). Colored arrowheads indicate number of LDs per nucleus: blue 0–1 LDs, red 2–5, orange 6-10, and yellow >10. Scale bars: 25 μm. C: Stacked column graphs with different colors representing the percentages of hepatocyte nuclei containing the indicated numbers of nuclear LDs. We analyzed a total of 604 (control) and 488 (L-CKO) nuclei of hepatocytes cultured on three different coverslips (n = 3 per genotype). The numbers at the top of the graphs indicate the mean percentages of hepatocyte nuclei with two or more nuclear LDs. These values and those within graphs are means ± SEM. ∗∗∗∗P < 0.0001 for percentage of hepatocyte nuclei with two or more nuclear LDs by two-tailed Student’s t test. D: Hepatocytes were stained with DAPI and Feret diameter (left panel) and area (right panel) of nuclei measured using Image J. Each dot represents a nucleus. 300 (control) and 279 (L-CKO) nuclei were plotted; ∗∗∗P < 0.001 by two-tailed Student’s t test. E: Transmission electron micrographs of liver sections showing nuclei from one control and two L-CKO mice. Scale bars: 2 μm. LD, lipid droplet; L-CKO mice, Alb-Cre;Lap1fl/fl mice. | PMC9587410 | gr1.jpg |
0.451782 | 117beb20154e49508b9bfee11e65e0c1 | Effects of OA on LDs in hepatocytes from L-CKO mice. A: Representative confocal fluorescence photomicrographs of hepatocytes from control (Lap1fl/fl) and L-CKO mice stained with BODIPY (green) and DAPI (blue). Panels to the right show zoomed-in views of the areas marked with white squares in the middle panels. Scale bars: 40 μm (left and middle panels) and 10 μm (right panels). B: Percentages of hepatocytes from control and L-CKO mice, cultured without (-OA) or with (+OA) OA in the media, with nuclear LDs. Columns show mean percentage of nuclei with LDs, black symbols indicate data from separate experiments (> 170 nuclei counted for each experiment and condition), and error bars show SEM; ns = not significant, ∗∗∗P < 0.001 by one-way ANOVA with Tukey’s multiple comparison test. C: A confocal single slice image from the center of a nucleus (left panel) captures the majority of nuclear LDs seen in a maximum intensity projection image of the same nucleus (right panel). Nuclear LDs not seen in the single slice photos but captured in the maximum intensity projection are indicated by arrowheads. Scale bar: 10 μm. D: XY surface view of the nucleus shown in C and XZ and YZ orthogonal projections. Scale bar: 10 μm. E: A zoomed-in view of the nuclear LD labeled with an arrow in the YZ projection in D, showing its location on the nuclear surface. Scale bar: 1 μm. Micrographs in panels C–E show hepatocytes from L-CKO mice labeled with BODIPY (green) and DAPI (blue). LD, lipid droplet; L-CKO mice, Alb-Cre;Lap1fl/fl mice; OA, oleic acid. | PMC9587410 | gr2.jpg |
0.407377 | adb1032a7a094e42b8c64c27d474e4c0 | CCT⍺ and ADRP localization in hepatocytes from L-CKO mice. A: Representative confocal photomicrographs of hepatocytes from control (Lap1fl/fl) and L-CKO mice, cultured with or without OA in the medium. The left panels from each genotype show labeling by anti-CCT⍺ Abs, and the right panels show an overlay of labeling with anti-CCT⍺ Abs (red), BODIPY (green), and DAPI (blue). Arrows indicate a nucleus lacking nucleoplasmic CCTα. Scale bars: 10 μm. B: Percentages of nuclei from control and L-CKO hepatocytes, cultured without (-OA) or with (+OA) OA in the media, with diffuse nucleoplasmic CCTα labeling detected by fluorescence microscopy. Columns show mean percentages of nuclei with diffuse nucleoplasmic CCTα localization, black circles indicate data from separate experiments (40–348 nuclei counted for each experiment and condition), and error bars show SEM. ns = not significant, ∗P < 0.05, ∗∗P < 0.01 by one-way ANOVA with Tukey’s multiple comparison test. C: representative 3D image reconstruction from confocal micrographs of a hepatocyte from an L-CKO mouse, cultured with OA in the media and labeled with anti-CCTα Abs (red), BODIPY (green), and DAPI (blue). Scale bar: 10 μm. D: Representative confocal photomicrographs of hepatocytes from an L-CKO mouse; the left panel shows labeling with anti-ADRP Abs, whereas the middle panel shows an overlay of labeling with anti-ADRP Abs (red), BODIPY (green), and DAPI (blue). The right panel shows a zoomed-in view of the area marked with a white square in the middle panel. Arrowhead indicates a nuclear LD lacking ADRP; arrow indicates a cytoplasmic LD with ADRP. Scale bars: 10 μm (left and middle panels) and 1 μm (right panel). ADRP, adipose differentiation-related protein; CCT⍺, CTP:phosphocholine cytidylyltransferase ⍺; OA, oleic acid; LD, lipid droplet; L-CKO mice, Alb-Cre;Lap1fl/fl mice. | PMC9587410 | gr3.jpg |
0.38862 | 408af784469d45c3bec653051defb88a | A: Representative confocal photomicrographs in left panels show hepatocytes from control (Lap1fl/fl) and L-CKO mice labeled with anti-lamin A/C Abs and color confocal photomicrographs in right panels show an overlay of anti-lamin A/C Abs (red) and LipiDye (green) labeling. Note intranuclear lamin A/C and lipids in L-CKO hepatocytes. Scale bars: 10 μm. B: Percentages of nuclei with nuclear envelope invaginations in hepatocytes from control and L-CKO mice. Columns show mean percentage of nuclei with nuclear invaginations, black circles indicate data from separate experiments (75–411 nuclei counted for each experiment and genotype), and error bars show SEM. ∗P < 0.05 by Student’s t test. C: Representative orthogonal confocal sections showing XY, XZ, and YZ planes of a partial view of a L-CKO mouse hepatocyte nucleus labeled with anti-Sun2 Abs (red) and LipiDye (green). Arrow indicates a LD inside a nuclear envelope invagination. Scale bar: 5 μm. D: Representative 3D reconstruction of a hepatocyte nucleus from a L-CKO mouse, based on confocal microscopy data from labeling with anti-Sun2 Abs (red) and LipiDye (green). Arrow indicates a LD inside a nuclear envelope invagination. Scale bar: 10 μm. LD, lipid droplet; L-CKO mice, Alb-Cre;Lap1fl/fl mice. | PMC9587410 | gr4.jpg |
0.375573 | fc82f60d71f7405c9040c97402375774 | Nuclear envelope invaginations in hepatocytes from L-CKO mice are type 1 nucleoplasmic reticula. A: Representative confocal fluorescence photomicrograph of a hepatocyte from a L-CKO mouse labeled with anti-lamin A/C Abs (red), Ab MAb414 that recognizes several nuclear pore complex proteins (green) and DAPI (blue); overlap of red and green appears yellow. Pore complexes are at the periphery and excluded from the interior of the nucleus. Scale bar: 10 μm. B: 3D reconstruction of the nucleus shown in A labeled with anti-lamin A/C Abs (red), Ab MAb414 (green), and DAPI (blue). Top panel shows the lamin A/C signal (red), middle panel the pore complex signal (green), and bottom panel those signals plus DAPI (blue) together, with overlap of red and green appearing yellow. Lamin A/C is located in the nuclear interior and the periphery while pore complexes are only at the periphery and excluded from the interior of the nucleus. Scale bar: 10 μm. C: Representative confocal fluorescence photomicrograph of a hepatocyte from a L-CKO mouse labeled with Abs against TRAPα, a marker of the ER/outer nuclear membrane (red), BODIPY (green), and DAPI (blue). TRAPα is essentially excluded from the nucleus. Scale bar: 10 μm. D: Representative 3D reconstruction of a hepatocyte from a L-CKO mouse labeled with Abs against TRAPα, (red), BODIPY (green), and DAPI (blue). Scale bar: 10 μm. TRAPα, translocon-associated protein α; L-CKO mice, Alb-Cre;Lap1fl/fl mice. | PMC9587410 | gr5.jpg |
0.441623 | 948790fd6f1f43cbb115a96597e01e6f | Hepatocytes isolated from male mice with acute depletion of lamin A/C or LAP1. A: Immunoblots of hepatocyte protein lysates from male Lmnafl/fl or Lap1fl/fl mice 4 weeks after injection with AAV-LacZ or AAV-Cre, probed with Abs against lamin A/C, LAP1, and β-actin. Migrations of lamin A and lamin C and LAP1A/B (these two isoforms migrate together), LAP1C and β-actin are indicated at the right. Each lane contains protein lysates from three different wells of hepatocytes isolated from one mouse for each condition. B: Representative widefield immunofluorescence photomicrographs of hepatocytes stained with anti-lamin B1 Abs (green) in left panels and overlay with DAPI (blue) in right panels. Arrows indicate nuclei with absence of lamin B1 from parts of the nuclear envelope. Scale bar: 25 μm. C: Representative widefield fluorescence photomicrographs of hepatocytes stained with BODIPY with nuclei outlined in yellow (based on DAPI labeling). Arrows indicate nuclei with nuclear LDs. Scale bar: 25 μm. D: Stacked column graphs with different colors representing the percentages of hepatocyte nuclei containing the indicated numbers of nuclear LDs. We analyzed a total of 743 (AAV-LacZ injected Lmnafl/fl mouse), 522 (AAV-Cre injected Lmnafl/fl mouse), and 566 (AAV-Cre injected Lap1fl/fl mouse) nuclei of hepatocytes cultured on three different coverslips (n=3 per group). The numbers at the top of the graphs indicate the mean percentages of hepatocyte nuclei with two or more nuclear LDs. These values and those within graphs are means ± SEM. ∗∗∗P < 0.001 for percentage of hepatocyte nuclei with two or more nuclear LDs by one-way ANOVA followed by Tukey’s multiple comparison test. LAP1, lamina-associated polypeptide 1; L-CKO mice, Alb-Cre;Lap1fl/fl mice; LD, lipid droplet; AAV, adeno-associated virus; AAV-Cre, pAAV-TBG.PI.Cre.rBG; AAV-LacZ, pAAV.TBG.PI.LacZ.bGH. | PMC9587410 | gr6.jpg |
0.423149 | b4117debc16649169a2c2d71831fbb2a | Analysis of nuclear LDs in hepatocytes isolated from high-fat diet–fed and fasted L-CKO mice. A: Percent change of initial body mass of mice while on a high-fat diet. Control (Lap1fl/fl) and L-CKO mice at 8 weeks of age were fed a high-fat diet for 8 weeks, and body mass was measured weekly; n = 5–7 mice per group. Each circle and square indicate mean values of body mass. Error bars indicate SEM. B: Liver to body mass ratios of control and L-CKO mice after 8 weeks on a high-fat diet; n = 4–7 mice per group. Each circle and square indicate value from an individual mouse, rectangular bars show means, and error bars indicate SEM; ∗∗P < 0.01 by two-tailed Student’s t test. C: Representative widefield fluorescence photomicrographs of hepatocytes from L-CKO mice stained with BODIPY (green) and DAPI (blue). Hepatocytes were isolated from chow-fed (left panel) or high-fat diet–fed (right panel) L-CKO mice. Arrows indicate nuclei containing LDs. Scale bar: 25 μm. D: stacked column graph with different colors representing the percentages of hepatocyte nuclei containing the indicated numbers of nuclear LDs. We analyzed a total of 112 (chow diet) and 179 (high-fat diet) nuclei of hepatocytes cultured on three different coverslips (n=3 per group). The numbers at the top of the graphs indicate the mean percentages of hepatocyte nuclei with two or more nuclear LDs. These values and those within graphs are means ± SEM. ∗∗∗P < 0.001 for percentage of hepatocyte nuclei with two or more nuclear LDs by two-tailed Student’s t test. E: Representative widefield fluorescence photomicrographs of hepatocytes from L-CKO mice stained with BODIPY (green) and DAPI (blue). Hepatocytes were isolated from mice fed a normal chow diet (left panel) or mice fasted for 24 h (right panel). Arrows indicate nuclei containing LDs. Scale bar: 25 μm. F: Stacked column graph with different colors representing the percentage of nuclei containing the indicated numbers of nuclear LDs in hepatocytes isolated from L-CKO mice fed normally or after 24 h of fasting. We analyzed a total of 479 (fed) and 599 (fasted) nuclei of hepatocytes cultured on three different coverslips (n=3 per group). The numbers on the top of the graphs indicate the mean percentages of hepatocyte nuclei with two or more nuclear LDs. Data are shown as means ± SEM. ∗∗∗P < 0.001 for percentage of hepatocyte nuclei with two or more nuclear LDs by two-tailed Student’s t test. L-CKO mice, Alb-Cre;Lap1fl/fl mice; LDs, lipid droplets | PMC9587410 | gr7.jpg |
0.472859 | b35e9305e0a84569ac69093223b09b6e | Effect of MTP depletion on nuclear LDs in hepatocytes from control and L-CKO mice. A: Immunoblots of hepatocyte protein lysates from control and L-CKO mice after 6 weeks of scrambled control or MTP ASO treatment probed with Abs against MTP, LAP1, and β-actin. Migrations of MTP and LAP1A/B (these two isoforms migrate together) and LAP1C and β-actin are indicated at the right. Each lane contains protein lysates from two different wells of hepatocytes isolated from one mouse for each condition. B: Representative widefield fluorescence photomicrographs of hepatocytes from control or L-CKO mice stained with BODIPY (green) and DAPI (blue). Hepatocytes were isolated from indicated mice after administration of control ASO (upper panels) or MTP ASO (lower panels). Arrows indicate nuclei containing LDs. Scale bars: 25 μm. C: Stacked column graph with different colors representing the percentage of nuclei containing the indicated numbers of nuclear LDs. We analyzed a total of 774 (control mice given control ASO), 368 (L-CKO mice given control ASO), 511 (control mice given MTP ASO), and 462 (L-CKO mice given MTP ASO) nuclei from hepatocytes cultured on three different cover slips (n = 3 per group). The numbers at the top of the graphs indicate the mean percentages of hepatocyte nuclei with two or more nuclear LDs. These values and those within graphs are means ± SEM. ns = not significant, ∗∗∗P < 0.001 by two-tailed Student’s t test. L-CKO mice, Alb-Cre;Lap1fl/fl mice; LDs, lipid droplets; MTP, microsomal triglyceride transfer protein; ASO, antisense oligonucleotide; LAP1, lamina-associated polypeptide 1. | PMC9587410 | gr8.jpg |
0.485796 | 38548d8229cd4d1c9bcf4a0531c79937 | Drum cutting load. | PMC9588080 | 41598_2022_22738_Fig10_HTML.jpg |
0.461483 | 22689c3ce9004f2dbcd67c69799918f1 | Body displacement. | PMC9588080 | 41598_2022_22738_Fig11_HTML.jpg |
0.422204 | aeb1f86533d24830a21996d67f1518bf | Force distribution of the floor. | PMC9588080 | 41598_2022_22738_Fig12_HTML.jpg |
0.411476 | 40b8f652bd2942a3bf6630cd91d7bafa | Deformation velocity distribution of the floor. | PMC9588080 | 41598_2022_22738_Fig13_HTML.jpg |
0.482603 | 7f94e0257c77461bb9b91004055733ee | Schematic diagram of the digging anchor machine tracks. | PMC9588080 | 41598_2022_22738_Fig14_HTML.jpg |
0.423658 | 58ee64bfdb934f0292a3f78250f29b5f | Load bearing wheel force. | PMC9588080 | 41598_2022_22738_Fig15_HTML.jpg |
0.500914 | 041f4e68a76c41d38f1cf16c8cb12c38 | Digging anchor machine ground pressure. | PMC9588080 | 41598_2022_22738_Fig16_HTML.jpg |
0.479351 | 464bf35e189d4f2f87c924faac4ecd17 | Schematic diagram of the soil trough test. | PMC9588080 | 41598_2022_22738_Fig17_HTML.jpg |
0.566302 | 8ba82bce69094926b742de0536e63a83 | Force versus displacement curve of the No. 12 track shoe. | PMC9588080 | 41598_2022_22738_Fig18_HTML.jpg |
0.372801 | 263553de5c6b4a10aa0348dc8a99ca55 | Shear floor velocity and force vector field. | PMC9588080 | 41598_2022_22738_Fig19_HTML.jpg |
0.42739 | bd2d7fe04d3648c7aef4d3969631a16d | Digging anchor machine model. | PMC9588080 | 41598_2022_22738_Fig1_HTML.jpg |
0.399883 | 1fd0af52bce7423793726a732a6d557e | Different grouser heights. | PMC9588080 | 41598_2022_22738_Fig20_HTML.jpg |
0.418427 | d7fb69461cad4598a9e76228d09f15ee | Different grouser spacing. | PMC9588080 | 41598_2022_22738_Fig21_HTML.jpg |
0.442239 | 2b877b9217c7421e909866662e0cea1e | Shear test, track shoe wear cloud. | PMC9588080 | 41598_2022_22738_Fig22_HTML.jpg |
0.381401 | b33a787e76dc4f1fa69db559e10e4ec0 | Wear amount with different grouser heights. | PMC9588080 | 41598_2022_22738_Fig23_HTML.jpg |
0.38025 | d4ace885d40044488b52ac05d670ad1c | Wear depth with different grouser spacing. | PMC9588080 | 41598_2022_22738_Fig24_HTML.jpg |
0.497475 | fcf5f4992cd04e3e899cf5362609e1fe | Particle adhesive model. | PMC9588080 | 41598_2022_22738_Fig2_HTML.jpg |
0.471155 | 05e1804ebfe34ca190cf1f6863ce5087 | Flow chart of the interparticle contact model calculation. | PMC9588080 | 41598_2022_22738_Fig3_HTML.jpg |
0.430727 | d6fde8ba6b644d8f8e533cace9affa87 | Uniaxial compression test model and results. | PMC9588080 | 41598_2022_22738_Fig4_HTML.jpg |
0.426577 | 14e5330f3be84b28980f7485fee970a9 | Cutting operation flow. | PMC9588080 | 41598_2022_22738_Fig5_HTML.jpg |
0.48415 | 916df510a6554954a6814e81fea34b44 | Cutting dynamics model. | PMC9588080 | 41598_2022_22738_Fig6_HTML.jpg |
0.502213 | 2d95e743013d42f8ae54d82d0a808a22 | Roadway model. | PMC9588080 | 41598_2022_22738_Fig7_HTML.jpg |
0.407856 | 26f1fd26313a4d7f831a57afb61d8e50 | Cutting coupling model. | PMC9588080 | 41598_2022_22738_Fig8_HTML.jpg |
0.508959 | 2dad405ebc1b4783aaab0bdbfaf0afe4 | Drum centroid trajectory. | PMC9588080 | 41598_2022_22738_Fig9_HTML.jpg |
0.390304 | 8c92eba63bc943449c42dc722b25f688 | Hospital rankings for top-quartile hospitals (designated 1–51) based on observed HOB rates compared with the simple- and complex-model–derived SIR ranking.a Gray bars represent rank of the top quartile of hospitals based on observed unadjusted HOB rate per 100 admissions. Blue diamonds represent the simple model SIR-based rank. Orange circles represent the complex model SIR-based rank. aFor example, hospital 10 (of 51) is in the top 95th percentile based on observed (unadjusted) HOB; it drops in rank with simple model SIR adjustment to the 56th–60th percentile and further decreases to the 41st–45th percentile in the complex model SIR-adjusted model. Note that among the 51 hospitals, some also increased in rank after the complex-model SIR adjustment (ie, hospitals 13, 28, 34, 36, 40, 43, 47). Full movements of rankings in all 4 quartiles are summarized in Supplementary Table S3 (online). | PMC9588439 | S0899823X22002112_fig1.jpg |
0.451585 | e568c8ac403840fe8883bf362fccdeff | The overall flow chart of the experiment. | PMC9589260 | fgene-13-967363-g001.jpg |
0.429656 | 0e33afffaa6e4423b8079082bbbc19c8 | Differential expression levels of mRNA and lncRNA in HF. (A,C). Volcano plot of 727 DEmRNAs and 162 DElncRNAs. Up-regulated genes are indicated in red, and down-regulated genes are indicated in blue. Genes with no significant change are marked as grey dots. (B,D). Expression heat map of Top 100 DEmRNA and Top 100 DElncRNA. Red means genes are up-regulated, blue means genes are down-regulated. | PMC9589260 | fgene-13-967363-g002.jpg |
0.466439 | 0f168d7a709e4f849de9327ef89ecc3d | Line graph of the change in Pearson correlation coefficient between the original and reconstructed matrices under different parameter combinations. (A–C) represent the variation of the Pearson correlation coefficient between
Xi
and
UHi0
under different parameter combinations, respectively. (D) represents the mean of these three data under different parameter combinations. The circled point in each subplot represents the maximum value of the Pearson correlation coefficient for the group. (E) represents the changing trend of the algorithm’s objective function with the increase of the number of iterations in the training process under the optimal parameters. | PMC9589260 | fgene-13-967363-g003.jpg |
0.526621 | 6e59ec2941b14012b97db7207cde2943 | Three kinds of members Venn diagrams of the top 3 modules. The intersection of miRNAs (A), mRNAs (B) and lncRNAs (C) in module 1, module 12 and module 30. | PMC9589260 | fgene-13-967363-g004.jpg |
0.4121 | a9d13459ffe2450c98324350c704dcfe | Enrichment analysis results of module 12 (A). The results of DO enrichment analysis. (B) The results of KEGG enrichment analysis. The size of circles represented the number of genes enriched. | PMC9589260 | fgene-13-967363-g005.jpg |
0.408715 | cc9808ef52064dd381ca589b08ae2297 | The results of PPI network. (A). The PPI network were analyzed using NetworkAnalyzer plugin. (B). The hub genes with the top 10 scores. The size and color of the nodes represent the importance of genes in the interaction network. The larger the node or the darker the color, the more important the corresponding gene is in the network. The connection between the nodes represents the interaction between the two genes, and the wider the connection line, the stronger the interaction between the two genes. | PMC9589260 | fgene-13-967363-g006.jpg |
0.535644 | eeb00acc052c424cb4f9d3055b81a484 | Comparison of reconstruction capabilities of the four algorithms. (A–C) are histograms of the Pearson correlation coefficients of the four algorithms between
Xi
and
UHi0
under the same experimental conditions (D). The histogram of the mean comparison of the Pearson correlation coefficients in the first three graphs. | PMC9589260 | fgene-13-967363-g007.jpg |
0.413473 | 53205c6ac55c4b46bfc1f8f350069d75 | The histogram of the Top 50 genes and their corresponding weights in module 12. | PMC9589260 | fgene-13-967363-g008.jpg |
0.441684 | 20de51c6fb6341a4930a40e952a13c80 | Top 1 to Top 50 features to classify whether subjects are sick or not and shows the trend of increasing AUC with the features used. The classifiers used by (A–D). RF, SVM, LR, and DNN, respectively. | PMC9589260 | fgene-13-967363-g009.jpg |
0.438666 | da8b2041f7c24accaa615ba19950e819 | ROC curves for classifying subjects using Top features (A–D). The classifiers used are RF, SVM, LR, and DNN, respectively. | PMC9589260 | fgene-13-967363-g010.jpg |
0.406132 | af9cc1793ca34c278cd32b88aad42afb | ROC curves for classifying subjects on external validation set using Top 13 features. The classifiers used by (A–D) are RF, SVM, LR, and DNN, respectively. | PMC9589260 | fgene-13-967363-g011.jpg |
0.47915 | 7e185659a6b349aeb07e1d0d574df9ec | Violin plots for AUC using four classifiers to classify the features of the four algorithms in their respective significant modules. (A–D). The comparison results obtained using RF, SVM, LR, and DNN. | PMC9589260 | fgene-13-967363-g012.jpg |
0.471699 | 570f9793a0784540ba590e56d8e65c06 | GDP growth (%) | PMC9589781 | 181_2022_2311_Fig1_HTML.jpg |
0.409163 | 934b182d4cdc402683d90c0947b57625 | CPI inflation (%) | PMC9589781 | 181_2022_2311_Fig2_HTML.jpg |
0.452227 | 36e6da5219934eea970fd261392b02f7 | Consensus and uncertainty measures for growth forecasts | PMC9589781 | 181_2022_2311_Fig3_HTML.jpg |
0.427273 | 541282f16c344b5ebc6aacf752207034 | Consensus and uncertainty measures for inflation forecasts | PMC9589781 | 181_2022_2311_Fig4_HTML.jpg |
0.459475 | 1e3007f3e0fc4383850902a1a3a838f9 | EPU and uncertainty measures for growth forecasts | PMC9589781 | 181_2022_2311_Fig5_HTML.jpg |
0.48896 | 8554a5367bb042ae8dba0fc4c3432596 | Geometrical calculation of the Small-In, Large-Out (SILO) effect (distance to the perceived stimuli). Similar triangles were defined to determine the theoretical distance in convergence (Small-In) and divergence (Large-Out). IPD = Interpupillary distance; D = Distance from the center of rotation of the eye to the vectogram; d = distance measured in convergence; d’ = distance measured in divergence; x = half the separation distance between the two circular targets (figure not to scale). | PMC9590030 | vision-06-00063-g001.jpg |
0.44842 | f221e29bbfee4a04bf74cb39f99c5c18 | Bland–Altman plot of the differences between theoretical (s or s’) and measured (m or m’) distances to the target stimuli for each vergence demand (BO: Base out; BI: Base in). For visualization purposes, axes are not drawn at the same scale. Individual data points are shown as full bullets. | PMC9590030 | vision-06-00063-g002.jpg |
0.456966 | 03254c07a9664fed88b1afbc5d66f022 | Correlation of accommodative-convergence over accommodation (AC/A) ratio and stereo-localization accuracy (es) for each vergence demand (BO: Base out; BI: Base in). For visualization purposes, axes are not drawn at the same scale. Individual data points are shown as empty bullets. | PMC9590030 | vision-06-00063-g003.jpg |
0.400741 | 677c1530fbee4c67b57b4d398c063bbd | Molecular and morphological assessment of the crypt-base columnar and regenerative stem cell marker expression spectrum(A) Dual color in situ hybridization (ISH) and multiplex immunohistochemistry (IHC) to show expression pattern of representative CBC and RSC markers alongside homeostatic immune and stromal cell distribution in normal mouse and human intestine. Scale bar, 100 μm.(B) Distribution of human multicompartmental scRNA expression of LGR5 (CBC marker) and ANXA1 (RSC marker) in normal compartments (orange bars) and cancer cell compartments (purple bars). Mean expression and 95% confidence intervals are shown.(C) Uniform Manifold Approximation and Projection (UMAP) plot of single epithelial cells from human normal and colorectal cancer samples showing cell populations enriched for CBC (green cells), RSC (red cells), and mixed CBC and RSC gene expression (yellow cells). Cells with no enriched stem cell signature expression are shown in gray.(D) Stem cell-marker-expressing cell count and organoid forming efficiency from plated single cells following FACS segregation of KPN mouse primary tumors, measured at day 7 post seeding (mean ± SD shown). | PMC9592560 | gr1.jpg |
0.436581 | 89d4bf53a7f444289daa4ca45490bee1 | Application of stem cell index to mouse and human neoplasia(A–C) Using stem cell index to map human colorectal precursor lesions (A), colorectal cancer consensus molecular subtypes (CMS) (B), and colorectal cancer intrinsic subtypes (CRIS) (C) across a molecularly defined CBC to RSC expression spectrum (using S-CORT datasets).(D and E) Dual-color ISH for LGR5 (CBC marker, green) and ANXA1 (RSC marker, red) expression in representative human precursor lesions (D) and representative human colorectal cancers (E) segregated by consensus molecular subtype.(F and G) Using stem cell index to map mouse autochthonous tumors (F) and matched derived organoids (G) across a molecularly defined CBC to RSC expression spectrum.(H) Dual-color ISH for Lgr5 (CBC marker, green) and Anxa1 (RSC marker, red) expression in representative genotype tumors across the CBC to RSC spectrum. Statistical analysis, ANOVA, p values as stated. All animals crossed with Vil-CreERT2. Scale bars, 100 μm. Driver alleles initialization: A is Apcfl/+, ApcMin is ApcMin, B is BrafV600E, K is KrasG12D, P is p53fl/fl, T is Tgfβr1fl/fl, N is Rosa26N1icd/+. | PMC9592560 | gr2.jpg |
0.405879 | 80d20c8ed7a24bf99b7b9074fc2b7123 | Driver genes and pathways associated with variable stem cell molecular phenotype(A) Human genotype-stem cell phenotype correlation based on stem cell index distribution in TCGA tumors with different putative driver gene single-nucleotide variant (SNV) mutation genotypes, contrasted to normal tissue from same dataset (driver gene initials: A is APC, K is KRAS, P is p53, B is BRAF).(B) Comparison of mutation type and prevalence disrupting the Wnt pathway, MAPK and PIK3CA pathways, and the TGFβ superfamily in TCGA tumors subdivided into CBC- and RSC-predominant deciles.(C) Segregation of mouse and human lesions by CBC (x axis) and RSC (y axis) signature expression. Predominant (above median) expression signature in each tumor is defined by color (CBC in green and RSC in red), and the 10% most polarized CBC- or RSC-expressing tumors were segregated into CBC- and RSC-enriched deciles for comparison.(D) Gene set enrichment analysis of hallmark and select pathways in bulk transcriptome from human tumors (TCGA) and murine lesions (Glasgow dataset) segregated into CBC- and RSC-predominant deciles. Pathways shown have PFDR ≤ 0.25 apart from YAP in the mouse lesions and Fibroblast TGFβ response in the human tumors.(E) Correlation of key pathway expression signatures with CBC or RSC gene expression across a range of mouse models. Different genotypes are identified by different colors as determined by the key. Driver alleles initialization: A is Apcfl/+, ApcMin is ApcMin, B is BrafV600E, K is KrasG12D, P is p53fl/fl, T is Tgfβr1fl/fl, N is Rosa26N1icd/+, Alk4 is Alk4fl/fl. | PMC9592560 | gr3.jpg |
0.389549 | bbad2efeaf6b439ba43f61666ba9045a | Microenvironmental landscaping and crosstalk influences epithelial stem cell phenotype(A) Representative multiplex IHC images of immune, stromal, and matrix landscapes in mouse tumors selected from across the stem cell phenotypic axis. Scale bars, 100 μm.(B) Variable proportion of different cell/matrix components in tumors from each genotype quantified from multiplex IHC images (n = 3 mice per genotype).(C) Impact of media supplementation of IFN-γ (1 μL/mL) and TGFβ1 (0.5 μL/mL) on stem cell phenotype of wild-type and AKPT and KPN mouse tumor organoids. t test, p values as stated.(D) Stem cell index applied to single-cell transcriptome data derived from organoids grown in Matrigel or collagen matrix (from Ramadan et al., 2021). | PMC9592560 | gr4.jpg |
0.450508 | 52a0ba6eb7a04e338f55ddb731dcd8b2 | Adaptive shift of stem cell phenotype under selective pressure(A) Shift in stem cell-marker expression detected by qRT-PCR following exposure of wild-type organoids to increasing concentrations of media IFN-γ. Statistical analysis, t test, p values as stated.(B) Skewed expression of stem cell markers, detected by FACS for Ly6a and GFP, following exposure of Lgr5-GFP labeled murine organoids to 5 μL/mL of media IFN-γ.(C) Schematic showing timing of recombination, Diphtheria Toxin (DT) activation, and tissue harvesting of Lgr5DTR;ApcMin mice.(D) Using stem cell index to map polyp tissue from ApcMin and Lgr5DTR;ApcMin to show dynamic change in stem cell molecular phenotype measured by stem cell index, before and after DTR activation and CBC cell ablation.(E) Gene set enrichment analysis showing enrichment of Ifn-γ signaling and Yap signaling between day 0 (unrecombined) and day 1 (after DTR stem cell ablation).(F) Dual-color ISH for Lgr5 (CBC marker, green) and Anxa1 (RSC marker, red) to show marker expression change in ApcMin and Lgr5DTR;ApcMin polyps before and after CBC ablation. Scale bars, 100 μm. | PMC9592560 | gr5.jpg |
0.473051 | 4565c05308d74aaaaaec20552e8735d5 | Human translational implications(A) Forest plots of progression-free survival (PFS) Hazard Ratios (HRs) (TCGA-COADREAD) and disease-free survival (DFS) HRs (Jorissen et al., 2009; Marisa et al., 2013) for quintiles of tumor stem cell index. Data are presented as HR with error bars indicating the 95% confidence interval (CI). p values from a Cox proportional hazards regression are shown.(B) FOxTROT (track A) trial schedule showing specimen acquisition before (specimen 1) and after 6 weeks (specimen 2) of 5-FU and oxaliplatin chemotherapy.(C) Ladder plots showing GSVA (RSC-CBC signature) of human tumor samples before and after chemotherapy, with patients grouped as “static” or “plastic” depending on magnitude of signature change following therapy.(D) No change in cell proliferation score in groups of tumors segregated by post treatment shift in stem cell index.(E) Proportion of patients with a documented response to chemotherapy in the FOxTROT trial when grouped by static or plastic stem cell response to treatment. Statistical analysis, Fisher’s exact test, p value as stated.(F) Low within-subject variation of the stem cell index from random non-adjacent biopsies from the BOSS trial. | PMC9592560 | gr6.jpg |
0.430618 | cb91d11aebea49e3ba1a3eb8ea1def33 | Fitness landscape modelTumor stem cell phenotype can be represented as a fitness landscape model, where Lgr5+ve CBC and Lgr5−ve RSC represent distinct but interlinked fitness peaks situated along a phenotypic axis. (A) Epithelial cells “climb” these fitness peaks (arrows) through the combination of acquired epithelial mutations and the influence of microenvironmental signaling, placing them at distinct points within the fitness landscape. Bulk transcriptome data can be used to calculate the stem cell index, which reflects stem cell population admixture and can be used as a measure of individual tumor position within this phenotypic axis (boxes). (B) Application of a selective pressure to a fitness peak (e.g Lgr5+ CBC ablation) alters the morphogenic signaling landscape and shifts the stem cell equilibrium toward an alternative phenotype at day 1. Rapid regeneration of the lost CBC population restores the stem cell equilibrium after 5 days. These dynamic shifts can be measured by change in the stem cell index (boxes). Key: green dots, CBC cells; red dots, RSC cells; yellow dots, both marker-expressing cells; gray dots, no stem cell-marker-expressing cells. | PMC9592560 | gr7.jpg |
0.507063 | 0b459ba2dac14134a228fcd09942a4ff | Schematic diagram of production and delivery of exosomal drug: mesenchymal stem cells are obtained from the patient’s own body to produce exosomes, and then endogenous or exogenous drug is loaded, and finally injected into the patient. | PMC9592696 | fphar-13-961127-g001.jpg |
0.538133 | e7865188ccf54d7e9c76f54b560326b3 | Schematic diagram of exogenous drug loading principle: electroporation method: under the action of external electric field, the phospholipid bilayer produces repairable pores, and the drug enters the exosomes through the holes. Chemical transfection: under the action of commercial transfection agent, exogenous drugs enter the exosomes. | PMC9592696 | fphar-13-961127-g002.jpg |
0.486216 | f80ce214e5154911a9893606dc5bf17e | Schematic summary of improved endogenous drug loading method. Protein drugs can be fused with special binding proteins and anchored on membrane proteins to improve drug loading. Special gene sequences can be added to RNA drugs, and the loading efficiency of drugs can be improved by interacting with corresponding RNA binding proteins. Or change the hydrophobicity of RNA drugs and increase the amount of RNA entering exosomes. | PMC9592696 | fphar-13-961127-g003.jpg |
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