RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Claim: S2 . Figure 1A displays the inhibitory effect of 23 tyrosine kinase inhibitors on tramadol metabolism, with crizotinib, sorafenib, and regorafenib exhibiting the highest inhibitory rates of 97.22%, 96.66%, and 83.65%, respectively.
Evidence type: figure
Caption: Figure 1: Determine the interaction between representative tyrocine kinase inhibitors and tramadol.
(A) The inhibitory effect of 23 types of tyrosine kinase inhibitors on the production of O-desmethyl tramadol in RLM compared with the control group. (B) Evaluate the half-maximal inhibitory concentration (IC50) of crizotinib with various concentrations (0.01, 0.1, 1, 10, 25, 50 and 100 µM) on tramadol metabolism in RLM and HLM. Data are presented as the mean ± SD, n = 3.

  
Context: The Michaelis–Menten curve for tramadol in RLM and HLM, along with the corresponding K m value, is shown in Fig.
Verdict: Supported
Claim: Functional enrichment analysis of the upregulated DEGs revealed that the top five biological processes were skeletal system development, sister chromatid cohesion, ossification, mitotic nuclear division, and extracellular matrix organization ( Figure 3A ).
Evidence type: figure
Caption: Figure 3: GO and KEGG pathway enrichment analyses of upregulated genes.
GO functional classification of DEGs. The x-axis represents the number of DEGs, with individual GO terms plotted on the y-axis. The graph displays only significantly enriched GO terms (P < 0.05). All GO terms were grouped into three categories: (A) BP analysis, (B) CC analysis, and (C) MF analysis. (D) KEGG pathway analysis. The x-axis represents the number of DEGs annotated in a pathway, with individual KEGG terms shown on the y-axis. The graph displays only significantly enriched KEGG terms (P < 0.05).

  
Context: GO annotation and KEGG pathway enrichment analyses were performed to better understand the functional significance of the DEGs.
Verdict: Supported
Claim: The ARGs signature was thus constructed via multi-variate Cox regression analysis, where 3-, 5-, and 7-year of the areas under the curve (AUCs) of the ROC curves were 0.789, 0.799 and 0.837, respectively ( Figure 3D ).
Evidence type: figure
Caption: Figure 3: Identification and construction of ARGs in PCa samples.
(A and B) We conducted the LASSO algorithmto identify the 10 prognostic genes in TCGA training cohort, where the optimal cutoff value was −4 and the minimum account of genes was 18. (C) The bubble plot exhibited the significantly differential expressions of 10 prognostic genes. (D) The receiver operating characteristic curve (ROC) analysis showing the predictive efficiency of 3-, 5- and 7-year in predicting PFS of patients. (E) Kaplan-Meier analysis showing the differential survival outcomes between ARGs-high and ARGs-low samples.

  
Context: To identify the essential ARGs with prognostic significance in PRAD, we selected the top 53 DEGs and performed the LASSO analysis, where 10 hub genes were found ( Figs. 3A and 3B ). Accordingly, the significant differential expression of 10 ARGs in two clusters were shown in dot plot ( Figure 3C ).
Verdict: Supported
Claim: Lastly, we calculated the ARGs scores for each patient in TCGA-PRAD cohort and divided the samples into high-risk and low-risk groups, and Kaplan-Meier analysis revealed that patients in high-risk groups suffered from worse survival outcomes relative to those from the low-risk groups with log-rank test P < 0.001 ( Figure 3E ).
Evidence type: figure
Caption: Figure 3: Identification and construction of ARGs in PCa samples.
(A and B) We conducted the LASSO algorithmto identify the 10 prognostic genes in TCGA training cohort, where the optimal cutoff value was −4 and the minimum account of genes was 18. (C) The bubble plot exhibited the significantly differential expressions of 10 prognostic genes. (D) The receiver operating characteristic curve (ROC) analysis showing the predictive efficiency of 3-, 5- and 7-year in predicting PFS of patients. (E) Kaplan-Meier analysis showing the differential survival outcomes between ARGs-high and ARGs-low samples.

  
Context: To identify the essential ARGs with prognostic significance in PRAD, we selected the top 53 DEGs and performed the LASSO analysis, where 10 hub genes were found ( Figs. 3A and 3B ). Accordingly, the significant differential expression of 10 ARGs in two clusters were shown in dot plot ( Figure 3C ). The ARGs signature was thus constructed via multi-variate Cox regression analysis, where 3-, 5-, and 7-year of the areas under the curve (AUCs) of the ROC curves were 0.789, 0.799 and 0.837, respectively ( Figure 3D ).
Verdict: Supported
Claim: Expectedly, high ARGs scores correlated positively with clinical T stages ( P = 3.414e−15), positive rates of lymph nodes ( P = 3.815e−08), as well as advanced Gleason scores ( P = 3.352e−23) ( Figs. 4A – 4C ).
Evidence type: figure
Caption: Figure 4: Correlation analysis between ARGs with clinical factors and prognostic significance of ARGs in PRAD.
(A–C) Kruskal-Wallis test showing that increasing ARGs-score correlated with higher T stages (P = 3.414e−15), higher positive rate of lymph nodes (P = 3.815e−08) and Gleason scores (P = 3.352e−23). (D) Univariate- and multi-variate Cox regression analysis exhibited the robustness of ARGs in PRAD samples. (E) The receiver operating characteristic curve (ROC) analysis showing the predictive efficiency of 3-, 5- and 7-year in predicting PFS of PSA levels. (F) Kaplan-Meier analysis showing the significance of PSA in PRAD samples. (G) DCA analysis was further used to evaluate the performance of ARGs model and PSA levels in predicting clinical outcomes of PRAD patients.

  
Context: Since we have already established the ARGs model, we intended to figure out the underlying relationships between ARGs and other clinical variables.
Verdict: Supported
Claim: Kaplan-Meier analysis implicated that patients in the cluster two group had worse PFS outcomes compared with those in the cluster 1 group, in which the log-rank test P value was 0.013 ( Figure 5K ).
Evidence type: figure
Caption: Figure 5: External validation of ARGs signature and clustering analysis across PRAD samples.
 (A) The ROC analysis of ARGs exhibiting its predictive efficiency of 3-, 5- and 7-year in predicting PFS. (B) Kaplan-Meier curves showing the differential survival outcomes in AGRs-high and ARGs-low samples in GSE116918. (C and D) The ROC analysis and Kaplan-Meier curves of ARGs in GSE70769. (E and F) The ROC analysis and Kaplan-Meier curves of ARGs in MSKCC-PRAD cohort. (G) Color‐coded heatmap corresponding to the consensus matrix for k = 2 obtained by consensus clustering. The color gradients from 0 to 1 represent the degree of consensus, with white corresponding to 0 and dark blue corresponding to 1. (H) Consensus among clusters for each category number k. (I) Delta area curve of consensus clustering indicating the relative change in area under the cumulative distribution function (CDF) curve for each category number k compared to k − 1. (J) PCA analysis indicated the identified two groups based on ARGs signature. (K) Kaplan-Meier analysis indicated the differential survival outcomes of two groups. (L) Gene set enrichment analysis (GSEA) exhibiting the enriched crosstalk between Cluster1 and Cluster2.

  
Context: Utilizing the unsupervised clustering algorithm, we conducted consensus clustering analysis to distinguish PRAD patients in the training cohort into subgroups based on the expression of ARGs. The K = 2 was identified with the optimal clustering stability ( Figs. 5G – 5I ). We further conducted the PCA analysis and could classify the patients into two distinct groups with individual features (cluster 1 & cluster 2) in Figure 5J .
Verdict: Supported
Claim: Positive correlations between AR and ORC1 were confirmed in the TCGA-PRAD samples ( Figure 7D ).
Evidence type: figure
Caption: Figure 7: AR activates ORC1 to sustain tumor progression and enzalutamide resistance in PRAD. (A) The anchorage-independent growth of C4-2B and 22RV-1 cells in soft agar (scale bars = 200 µm, left). Quantification of the soft agar colony formation assay results (right). (B) Sphere formation assays revealing the self-renewal ability of ctrl and ORC1-overexpressing cells (scale bars = 200 µm, left). (C) Western-blotting assay indicating the regulations between AR and ORC1. (D) The scatter diagram indicating the positive correlations between AR and ORC1. (E) ChIP assays were utilized to determine the level of AR binding (left) and the enrichment of H3K27ac (right) at the promoter of ORC1 in AR deficiency or control PCa cells. (F) Cell proliferation determined in indicated cells with enzalumitide treatment. C4-2B-Parental and C4-2B-ENZR cells were transiently transfected as indicated. (G) Mice bearing C4-2B-ENZR xenografts were treated with contro DMSO and enzalutamide (10 mg/kg p.o.) for 4 weeks (n = 5/group). (H) The tumor volumes in indicated groups were detected the curves were generated. (I) The tumor weight in indicated groups were recorded and compared. ***p < 0.001.
Context: To elucidate the function of ORC1 in PRAD, we established stable ORC1-overexpressing PRAD cells (22RV-1 and C4-2B). The up-regulation of ORC1 significantly enhanced PRAD cell soft agar colony formation efficiency ( Figure 7A ). Besides, the self-renewal potentiality of PRAD cells was also increased when cells were transfected with ORC1, indicating that ORC1 could also maintain stemness features of PRAD ( Figure 7B ). Next, we intended to figure out the underlying regulations between AR and ORC1. Firstly, we found AR overexpression could notably elevate ORC1 expression levels, whereas AR knockdown decreased its levels, along with another known AR downstream target of PSA ( Figure 7C ).
Verdict: Supported
Claim: Survival analysis revealed that prognosis differed significantly between the two DERMGs-related subtypes, and cluster 1 had a notable survival advantage ( Figure 2G ).
Evidence type: figure
Caption: Figure 2: Identification of DERMGs-related subtypes in STAD.
(A) Forest plot of univariate Cox of four DERMGs associated with overall survival (OS) in STAD patients. (B) Relative change in area under CDF area for k = 2 to 6. (C) The cumulative distribution functions. (CDF) for k = 2 to 6. (D) Tracking plot for k = 2 to 6. (E) Consensus score matrix of all samples when k = 2. (F) t-SNE dimension reduction analysis verifies the reliability of the clustering results. (G) Kaplan-Meier survival curve based on two clusters. (H) The expression heat map of four DERMGs associated with OS of STAD patients in two clusters and different clinicopathological subgroups.

  
Context: The above 14 DERMGs were then subjected into the univariate Cox analysis and four genes associated with overall survival (OS) of STAD patient ( p < 0.05) were identified, namely RBM15, DNMT1, WDR4 and ALYREF ( Figure 2A ). To probe the related subtypes in STAD, 345 STAD samples were clustered based on the expression of four RMGs using consensus cluster analysis. From the result, two subtypes were identified, designated as cluster 1 and cluster 2 ( Figs. 2B – 2E ). The cluster 1 contained 164 cases and cluster 2 contained 181 cases. The t-SNE result revealed that cluster 1 and cluster 2 could be clearly separated, further demonstrating the reliability of the clustering result ( Figure 2F ).
Verdict: Supported
Claim: Subsequently, a univariate analysis of these 186 genes yielded seven prognosis-related genes, including ETS2, SERPINE1, FOS, SHISA5, IL1A, TP53AIP1, and IGFBP7 ( Figure 4A ).
Evidence type: figure
Caption: Figure 4: Analysis of cellular senescence-related prognostic genes.
(A) Forest plot of prognosis-related genes; (B) waterfall plot of SNVs in the seven prognosis-related genes; (C) Percentage plot of CNVs in the seven prognosis-related genes; (D) The expression of the seven prognosis-related genes expressed in tumor and normal tissues; (E) CDF curve of TCGA cohort; (F) CDF-delta area curves for TCGA cohort. The Delta area curve for consensus clustering indicates the relative change in the area under the CDF curve for each category number, k, relative to k-1. The horizontal axis represents the category number, k, and the vertical axis represents the relative change in area under the CDF curve; (G) Heat map of sample clustering at consensus k = 3; (H) KM curve of the relationship between the prognoses of the three subtypes in TCGA; (I) Heat map of the expression of seven prognosis-related genes among three subtypes in TCGA dataset.

  
Context: The above pathways comprised 253 genes, of which 186 were present in TCGA dataset.
Verdict: Supported
Claim: Among CNVs, IGFBP7 had the highest frequency of acquired CNVs (CNV_gain), whereas SHISA5 had the highest frequency of deletion mutations (CNV_loss) ( Figure 4C ).
Evidence type: figure
Caption: Figure 4: Analysis of cellular senescence-related prognostic genes.
(A) Forest plot of prognosis-related genes; (B) waterfall plot of SNVs in the seven prognosis-related genes; (C) Percentage plot of CNVs in the seven prognosis-related genes; (D) The expression of the seven prognosis-related genes expressed in tumor and normal tissues; (E) CDF curve of TCGA cohort; (F) CDF-delta area curves for TCGA cohort. The Delta area curve for consensus clustering indicates the relative change in the area under the CDF curve for each category number, k, relative to k-1. The horizontal axis represents the category number, k, and the vertical axis represents the relative change in area under the CDF curve; (G) Heat map of sample clustering at consensus k = 3; (H) KM curve of the relationship between the prognoses of the three subtypes in TCGA; (I) Heat map of the expression of seven prognosis-related genes among three subtypes in TCGA dataset.

  
Context: The above pathways comprised 253 genes, of which 186 were present in TCGA dataset. Subsequently, a univariate analysis of these 186 genes yielded seven prognosis-related genes, including ETS2, SERPINE1, FOS, SHISA5, IL1A, TP53AIP1, and IGFBP7 ( Figure 4A ). Mutations in these seven genes in TNBC were further examined. Among SNVs, ETS2 showed the highest mutation frequency (primarily mutation type: missense) ( Figure 4B ).
Verdict: Supported
Claim: Similarly, T cells CD4, also displayed distinct difference among three groups in TIMER and EPIC analysis ( Figs. 7C – 7D ).
Evidence type: figure
Caption: Figure 7: Immune characterization of cellular senescence-related subtypes in TCGA dataset.
(A) Comparison of ESTIMATE-predicted immune scores among the three subtypes (kruskal.test); (B) Comparison of MCPcounter-predicted cell scores among the three subtypes(kruskal.test); (C) Comparison of TIMER-predicted cell scores among the three subtypes (kruskal.test); (D) Comparison of EPIC-predicted cell scores among the three subtypes (kruskal.test); (E) Differences in immune checkpoint gene expression among the three subtypes (kruskal.test); (F) TIDE analysis for differential scores among the three subtypes (wilcox.test) (p > 0.05 denotes no significance; ****p < 0.0001, ***p < 0.001, **p < 0.01, and *p < 0.05).

  
Context: The relationship between cellular senescence-related subtypes and immunity was analyzed to investigate the immune characteristics of these subtypes. The immune scores of patients in TCGA dataset were calculated by “ESTIMATE”. Significant differences were observed among the three subtypes, with a high degree of immune infiltration in clusters 1 and 3 (kruskal.test) ( Figure 7A ). Subsequently, the scores for the relevant cells were calculated. Significant differences were noted for all cells except CD8 T cells and cytotoxic lymphocytes (kruskal.test) ( Figure 7B ).
Verdict: Supported
Claim: Risk scores also correlated positively with “ImmuneScore”, “StromalScore”, and “ESTIMATEScore” ( Figure 10G ).
Evidence type: figure
Caption: Figure 10: Immunological characteristics of RiskScore subgroups and sensitivity analysis in TCGA-TNBC cohort.
(A) Differences in MCPcounter-predicted cell scores between risk groups; (B) Differences in immune and stromal scores between risk groups; (C) comparison of TIMER-predicted cell scores between risk groups; (D) comparison of EPIC-predicted cell scores between risk groups; (E) Differentially expressed immune checkpoints between risk groups; (F) Correlation analysis of cell scores with risk scores; (G) Correlation analysis of ImmuneScore, StromalScore, and ESTIMATEScore with risk scores; (H) The box plot of estimated IC50 for drugs (wilcox.test; p > 0.05 denotes no significance, *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001).

  
Context: A significant positive correlation between the Riskscore and T cells, cytotoxic lymphocytes, and monocytic lineage ( Figure 10F ) was observed.
Verdict: Supported
Claim: Conversely, low-frequency adaptation resulted in a 4% increase in numerosity (linear regression slope of 1.03, compared to the baseline slope of 0.99), the line shifted towards the upper side compared to the baseline (no adaptation), indicating an overestimation of perceived numerosity ( Figure 2B ).
Evidence type: figure
Caption: Figure 2: Results.
(A) Adaptation index under grouping and no-grouping conditions; (B) The perceived numerosity, averaged across trials and subjects, varies as a function of physical numerosity in the three adaptation conditions. The analysis includes best-fitting linear regressions (R2 >0.98 in all conditions). The regression slopes are as follows: no adaptation (dotted line) = 0.99; high-frequency adaptation (square) = 0.90; low-frequency adaptation (triangle) = 1.03 (p < 0.001 in both conditions).

  
Context: The adaptation effect is measured by adaptation index. We calculated the adaptation index separately for the grouping and no-grouping conditions. The results are presented in Figure 2A , which illustrates the adaptation index under both grouping and no-grouping conditions. Notably, the grouping condition exhibits a more pronounced adaptation effect compared to the no-grouping condition ( t = 3.233, p = 0.23, Cohen’s d = 1.572). Figure 2B illustrates the mean perceived numerosity (averaged across all subjects) plotted against the physical number of pulses. The mean estimates without adaptation, represented by the gray dotted line, closely aligned with the veridical values. A zero-anchored linear regression model ( R 2 = 0.99) effectively described the data, exhibiting a best-fitting slope of 0.99. The introduction of high-frequency adaptation consistently led to a 9% reduction in apparent numerosity across all tested numerosities (linear regression slope of 0.90), as can be seen from Figure 2B , the line shifted towards the upper side compared to the baseline (no adaptation), indicating an overestimation of perceived numerosity.
Verdict: Supported
Claim: The sensory precision under the grouping condition was not significantly higher than that under the non-grouping condition ( Figure 3A ), the reason for this result is that the grouping effect is affected by the motor adaptation, so that no grouping advantage is found.
Evidence type: figure
Caption: Figure 3: (A) CVs for different adaption level by group condition. (B) RTs for different adaption level by group condition.
“G” for grouping condition, and “NG” for no-grouping condition. ***p < 0.001; **p < 0.01; *p < 0.05.

  
Context: In Experiment 1, our findings were consistent with previous studies ( Anobile et al., 2016 ; Anobile et al., 2020a ; Anobile et al., 2020b ), demonstrating that the adaptation to high frequency hand tapping was underestimated in the subsequent numerosity estimation task, while the adaptation to low frequency hand tapping was overestimated ( Figure 3A ). These results indicate that the adaptation effect persists even when subjects adopt the “groupitizing” strategy. Previous research has established that visually grouped arrays tend to result in faster and more accurate numerosity perception compared to ungrouped arrays, a phenomenon referred to as “groupitizing” ( Anobile et al., 2020a ; Anobile et al., 2020b ; Ciccione & Dehaene, 2020 ). However, contrary to these findings, our results did not reveal a significant grouping effect on perception precision.
Verdict: Supported
Claim: Figure 1B shows that the four groups have a notable clustering of compositions of gut microbiota, with PC1 (28.61%) and PC2 (15.15%) exhibiting a total variance of 43.76%.
Evidence type: figure
Caption: Figure 1: Effects of SNP upon the diversity of fecal microflora from humans.
(A) ACE, Chao, Shannon, and Simpson indices; Comparison of bacterial compositions across all groups based on (B) PCoA and (C) PLS-DA at the OTU level. Data are presented as means ± SDs (n = 8).

  
Context: β diversity, also termed between-habitat diversity, is generally used for diversity comparison across ecosystems. Besides, the research also evaluated principal coordinate analysis (PCoA), which reveals diversity in the compositions of gut microbiota.
Verdict: Supported
Claim: Figure 8 shows that the T2DM group has lower contents of total SCFAs, acetic acid, propionic acid, butyric acid, isobutyric acid, valeric acid, and isovaleric acid, when compared with the HC group ( P < 0.05).
Evidence type: figure
Caption: Figure 8: Total SCFAs (acetic acid, butyric acid, propionic acid, isobutyric acid, isovaleric acid, and valeric acid) after 24-h in-vitro fermentation with T2DM human fecal microflora.
Data were expressed as means ± SDs (n = 8).

  
Verdict: Supported
Claim: Faecalibacterium was positively associated with pentanoic acid and 4-aminobutanoic acid, while were negatively correlated with hydracrylic acid.
Evidence type: figure
Caption: Figure 10: Correlation analysis of characteristic metabolites and bacteria.
Corr represents correlation. Red and blue separately denote positive and negative correlations. The darker the color, the stronger the correlation. *P < 0.05, **P < 0.01, and ***P < 0.001.

  
Context: To further explore correlations of metabolites in human fecal matter and intestinal flora after SNP treatment, thermography was used to analyze contents of metabolites showing significant differences with 30 most abundant genera. Figure 10 shows the results. Bifidobacterium was positively associated with glycerol, CO 2 , 4-aminobutanoic acid, L-cysteine, and D-gluconic acid, and could be negatively correlated with DL-phenylalanine.
Verdict: Supported
Claim: Lactobacillus was positively correlated with xylitol and showed a negative correlation with 5-aminovaleric acid.
Evidence type: figure
Caption: Figure 10: Correlation analysis of characteristic metabolites and bacteria.
Corr represents correlation. Red and blue separately denote positive and negative correlations. The darker the color, the stronger the correlation. *P < 0.05, **P < 0.01, and ***P < 0.001.

  
Context: To further explore correlations of metabolites in human fecal matter and intestinal flora after SNP treatment, thermography was used to analyze contents of metabolites showing significant differences with 30 most abundant genera. Figure 10 shows the results. Bifidobacterium was positively associated with glycerol, CO 2 , 4-aminobutanoic acid, L-cysteine, and D-gluconic acid, and could be negatively correlated with DL-phenylalanine. Faecalibacterium was positively associated with pentanoic acid and 4-aminobutanoic acid, while were negatively correlated with hydracrylic acid.
Verdict: Supported