alabnii/sciclaimeval-shared-task huggingface dataset:
Claim: Dorea was positively associated with phenylalanine, butyric acid, and isovaleric acid, while showed a negative correlation with hydracrylic acid, CH 4 , myo-inositol, L-threonine, glycerol, CO 2 , L-cysteine, and D-gluconic acid.
Evidence type: figure
Evidence 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. Lactobacillus was positively correlated with xylitol and showed a negative correlation with 5-aminovaleric acid.
Verdict: Supported
Claim: Phascolarctobacterium was positively associated with 1,3-propanediol, L-proline, cadaverine, and DL-phenylalanine, and was negatively correlated with glycerol, CO 2 , 4-aminobutanoic acid, L-cysteine, and myo-inositol.
Evidence type: figure
Evidence 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. Lactobacillus was positively correlated with xylitol and showed a negative correlation with 5-aminovaleric acid. Dorea was positively associated with phenylalanine, butyric acid, and isovaleric acid, while showed a negative correlation with hydracrylic acid, CH 4 , myo-inositol, L-threonine, glycerol, CO 2 , L-cysteine, and D-gluconic acid. Escherichia - Shigella could be positively associated with H 2 S, L-proline, cadaverine, DL-phenylalanine, benzeneacetic acid, 1,3-propanediol, and were negatively correlated with glycerol, CO 2, 4-aminobutanoic acid, L-cysteine, D-gluconic acid, propionic acid, and L-lysine.
Verdict: Supported
Claim: Lachnospiraceae were positively associated with 1, 3-propanediol and L-proline, and were negatively correlated with hydracrylic acid and CH 4 .
Evidence type: figure
Evidence 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. Lactobacillus was positively correlated with xylitol and showed a negative correlation with 5-aminovaleric acid. Dorea was positively associated with phenylalanine, butyric acid, and isovaleric acid, while showed a negative correlation with hydracrylic acid, CH 4 , myo-inositol, L-threonine, glycerol, CO 2 , L-cysteine, and D-gluconic acid. Escherichia - Shigella could be positively associated with H 2 S, L-proline, cadaverine, DL-phenylalanine, benzeneacetic acid, 1,3-propanediol, and were negatively correlated with glycerol, CO 2, 4-aminobutanoic acid, L-cysteine, D-gluconic acid, propionic acid, and L-lysine. Phascolarctobacterium was positively associated with 1,3-propanediol, L-proline, cadaverine, and DL-phenylalanine, and was negatively correlated with glycerol, CO 2 , 4-aminobutanoic acid, L-cysteine, and myo-inositol.
Verdict: Supported
Claim: Parabacteroides had positive correlations with L-leucine, L-valine, and L-isoleucine, while negatively correlated with glycerol, CO 2 , 4-aminobutanoic acid, L-cysteine, D-gluconic acid, and propionic acid.
Evidence type: figure
Evidence 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. Lactobacillus was positively correlated with xylitol and showed a negative correlation with 5-aminovaleric acid. Dorea was positively associated with phenylalanine, butyric acid, and isovaleric acid, while showed a negative correlation with hydracrylic acid, CH 4 , myo-inositol, L-threonine, glycerol, CO 2 , L-cysteine, and D-gluconic acid. Escherichia - Shigella could be positively associated with H 2 S, L-proline, cadaverine, DL-phenylalanine, benzeneacetic acid, 1,3-propanediol, and were negatively correlated with glycerol, CO 2, 4-aminobutanoic acid, L-cysteine, D-gluconic acid, propionic acid, and L-lysine. Phascolarctobacterium was positively associated with 1,3-propanediol, L-proline, cadaverine, and DL-phenylalanine, and was negatively correlated with glycerol, CO 2 , 4-aminobutanoic acid, L-cysteine, and myo-inositol. Lachnospiraceae were positively associated with 1, 3-propanediol and L-proline, and were negatively correlated with hydracrylic acid and CH 4 .
Verdict: Supported
Claim: Following these were premature birth ( p -value < 0.001), with 156 (82.5%) smokers and 176 (93.1%), miscarriages ( p -value = 0.048), with 149 (78.8%) smokers and 164 (86.8%) non-smokers, neck cancer ( p -value < 0.001) with 144 (76.2%) smokers and 165 (87.3%) non-smokers and lastly gangrene ( p -value < 0.001) with 115 (60.8%) smokers and 139 (73.5%) who answered ‘Yes’ for respective health effects.
Evidence type: figure
Evidence caption: Figure 2: Percentage of known health effects of tobacco use among respondents (n = 378).

  
Context: Figure 2 describes the known health effects of tobacco use among respondents. The two most recognised health effects of smoking by both groups of respondents are lung cancer ( p -value = 0.011) and mouth cancer ( p -value < 0.001), with 170 (89.9%) smokers and 184 (97.4%) non-smokers who answered ‘Yes’ for lung cancer while 152 (80.4%) smokers and 181 (95.8%) non-smokers answered ‘Yes’ for mouth cancer.
Verdict: Supported
Claim: Most non-smokers ( n = 161, 85.2%) never had the urge to smoke upon the sight of pictorial warnings on the cigarette packaging.
Evidence type: figure
Evidence caption: Figure 3: Impact of pictorial warnings on the initiation to smoking by non-smokers (n = 189).

  
Context: Figure 3 shows the impact of pictorial warnings on the initiation of smoking by non-smokers.
Verdict: Supported
Claim: It follows that 12 out of 189 rarely and sometimes (6.3%) had the urge to smoke, respectively.
Evidence type: figure
Evidence caption: Figure 3: Impact of pictorial warnings on the initiation to smoking by non-smokers (n = 189).

  
Context: Figure 3 shows the impact of pictorial warnings on the initiation of smoking by non-smokers. Most non-smokers ( n = 161, 85.2%) never had the urge to smoke upon the sight of pictorial warnings on the cigarette packaging.
Verdict: Supported
Claim: This model demonstrated high efficacy, achieving an AUC of 0.960 (95% CI [0.959–0.961]).
Evidence type: figure
Evidence caption: Figure 2: Performance of the segmentation network.
(A) Receiver operating characteristic (ROC) curve comparing the performance of the segmentation network with three pathologists (senior, medium, and junior) in identifying tumor areas. (B) Confusion matrix for the segmentation network. (C) Original WSIs and annotation maps identified by the segmentation network. Left: original WSI images; right: corresponding annotation maps.

  
Context: A segmentation network was developed to accurately distinguish tumor regions from WSIs.
Verdict: Supported
Claim: Compared with three pathologists of varying experience (junior, medium, and senior), the model’s performance was almost equivalent to that of a medium-level pathologist ( Figure 2A ).
Evidence type: figure
Evidence caption: Figure 2: Performance of the segmentation network.
(A) Receiver operating characteristic (ROC) curve comparing the performance of the segmentation network with three pathologists (senior, medium, and junior) in identifying tumor areas. (B) Confusion matrix for the segmentation network. (C) Original WSIs and annotation maps identified by the segmentation network. Left: original WSI images; right: corresponding annotation maps.

  
Context: A segmentation network was developed to accurately distinguish tumor regions from WSIs. This model demonstrated high efficacy, achieving an AUC of 0.960 (95% CI [0.959–0.961]). The accuracy, sensitivity, and specificity were 0.888 (95% CI [0.887–0.890]), 0.859 (95% CI [0.856–0.861]), and 0.908 (95% CI [0.906–0.909]), respectively.
Verdict: Supported
Claim: Among tumor region samples, the model correctly identified 86% as tumor, while misclassifying 14% as non-tumor.
Evidence type: figure
Evidence caption: Figure 2: Performance of the segmentation network.
(A) Receiver operating characteristic (ROC) curve comparing the performance of the segmentation network with three pathologists (senior, medium, and junior) in identifying tumor areas. (B) Confusion matrix for the segmentation network. (C) Original WSIs and annotation maps identified by the segmentation network. Left: original WSI images; right: corresponding annotation maps.

  
Context: A segmentation network was developed to accurately distinguish tumor regions from WSIs. This model demonstrated high efficacy, achieving an AUC of 0.960 (95% CI [0.959–0.961]). The accuracy, sensitivity, and specificity were 0.888 (95% CI [0.887–0.890]), 0.859 (95% CI [0.856–0.861]), and 0.908 (95% CI [0.906–0.909]), respectively. Compared with three pathologists of varying experience (junior, medium, and senior), the model’s performance was almost equivalent to that of a medium-level pathologist ( Figure 2A ). The confusion matrix shows specific performance metrics of the segmentation model.
Verdict: Supported
Claim: Among non-tumor region samples, 91% were correctly identified as non-tumor, with 9% misclassified as tumor ( Figure 2B ).
Evidence type: figure
Evidence caption: Figure 2: Performance of the segmentation network.
(A) Receiver operating characteristic (ROC) curve comparing the performance of the segmentation network with three pathologists (senior, medium, and junior) in identifying tumor areas. (B) Confusion matrix for the segmentation network. (C) Original WSIs and annotation maps identified by the segmentation network. Left: original WSI images; right: corresponding annotation maps.

  
Context: A segmentation network was developed to accurately distinguish tumor regions from WSIs. This model demonstrated high efficacy, achieving an AUC of 0.960 (95% CI [0.959–0.961]). The accuracy, sensitivity, and specificity were 0.888 (95% CI [0.887–0.890]), 0.859 (95% CI [0.856–0.861]), and 0.908 (95% CI [0.906–0.909]), respectively. Compared with three pathologists of varying experience (junior, medium, and senior), the model’s performance was almost equivalent to that of a medium-level pathologist ( Figure 2A ). The confusion matrix shows specific performance metrics of the segmentation model. Among tumor region samples, the model correctly identified 86% as tumor, while misclassifying 14% as non-tumor.
Verdict: Supported
Claim: Interestingly, a strong bacterial growth inhibition was observed at a concentration of 2.75 µg/mL during all the incubation times ( Figure 1A ).
Evidence type: figure
Evidence caption: Figure 1: Effect of cystatin C on P. gingivalis growth and macrophage viability.
(A) 5 × 106 CFU/mL of P. gingivalis were cultured in the presence of cystatin C at different concentrations, for 1, 12, 24, 48, and 96 h. Cells without stimulation were used as a control group. when compared to the control group. The significance represents a p < 0.05 (*). (B) 1 × 105 cells were co-incubatedat different concentrations of cystatin C for 24 h. No significant differences were found when comparing different concentrations of cystatin C with control group p& γ τ 0.05 (ns). Data represent the mean ± SD of five independent experiments (n = 5).

  
Context: To analyze the extracellular antimicrobial activity of cystatin C against P. gingivalis , bacteria were incubated with different concentrations of the peptide (1.25, 1.5, 1.75, 2, 2.5, 2.75 µg/mL) and increasing time periods (1, 12, 24, 48, and 96 h). Cystatin C exhibited a dose-and time-dependent antimicrobial activity after 12 h of incubation, as shown in Figure 1A . Additionally, maximum microbicidal activity was exhibited at concentrations between 2.25 and 2.75 µg/mL after 12 h. Concentrations of 2.25 µg/mL, 2.5 µg/mL, and 2.75 µg/mL inhibited bacterial growth at 60%, 80%, and 95%, respectively, after 24 h of incubation and compared to the control group ( p < 0.05). Furthermore, bacterial inhibition with these three concentrations was also observed after 48 h of incubation.
Verdict: Supported
Claim: The nucleotide sequence presented one specific change in nucleotide 280, first position of the codon (GTG) (ATG), resulting in a valine (V) to methionine (M) change in position 94 ( Figure 4 ).
Evidence type: figure
Evidence caption: Figure 4: Alignment of OXA-143 amino acid sequences.
The polymorphic sites at the amino acid sequence level. The identity of the sequences was compared to the OXA-143 variant (GenBank protein number access WP_063861042.1 or its nucleic number access NG_049441.1). Numbers in the heading row indicate the base pair position of the polymorphic sites. Dots (.) show the identity with the Aba OXA-143. Acronyms: Aba, Acinetobacter baumannii; Api, A. pittii.

  
Context: Sequencing of the bla OXA-143 PCR product obtained from A. pitti clinical isolates identified gene encoding OXA-499. Our bla OXA-143 sequence ( MF594724 ) shared 99% nucleotide and amino acid identity with bla OXA-499 of A. pittii (GenBank accession numbers NG_049775.1 and ALM96709.1 ).
Verdict: Supported
Claim: The LightGBM model demonstrated the best performance and was selected to construct the radiomics signature, with the area under curve (AUC) values of 0.896 (95% CI [0.825–0.967]) and 0.824 (95% CI [0.658–0.989]) in the training and testing datasets, respectively ( Figure 3A ).
Evidence type: figure
Evidence caption: Figure 3: Development of radiomics model.
(A) The ROC curves of the radiomics model; (B) the confusion matrix of predicting lymph node metastasis.

  
Verdict: Supported
Claim: In the deep learning models, the performance of M3 was significantly higher than that of other datasets, with an AUC value of 0.826 in the training cohort and 0.674 in the test cohort ( Figure 4 ).
Evidence type: figure
Evidence caption: Figure 4: The ROC curves of M3.

  
Verdict: Supported
Claim: As shown in Figure 8 , the integrated model achieved an AUC of 0.838 compared with 0.654, 0.768, and 0.893 for clinicians D1, D2, and D3, respectively, suggesting differences between the model and the expert evaluation.
Evidence type: figure
Evidence caption: Figure 8: The ROC curves of the integrated model and diagnostic results of each radiologist in the test cohort.
The integrated model reached a high AUCs (AUC: 0.838), doctor1 acquired 0.654 of AUC, the AUC of doctor2 was 0.768 and doctor3 demonstrated the highest with 0.893.

  
Context: The performance of the integrated model was compared with that of clinical experts. Three clinicians with varying levels of experience in oral diseases, i.e ., less than 5 years (D1), less than 10 years but longer than D1 (D2), and more than 15 years (D3), were asked to interpret the imaging and clinical data. The decision of the three clinicians was compared with that of the integrated model by plotting ROC curves.
Verdict: Supported
Claim: In clinical samples, except Veillonella , Leptotrichia and Streptococcus , other bacteria occupied the most of bacterial community.
Evidence type: figure
Evidence caption: Figure 3: Bacterial community structure analysis.
(A) The distributions of the predominant bacteria at genus level. (B) The relative abundance of differential predominant bacteria among aerobic culture, anaerobic culture and clinical groups analyzed at genus level by ANOVA.

  
Context: To display the composition of dominant bacteria, the stacked column plot showed the percentage of each high-abundance bacteria.
Verdict: Supported
Claim: The results indicated that there were more bacterial categories in clinical samples than cultures by media.
Evidence type: figure
Evidence caption: Figure 3: Bacterial community structure analysis.
(A) The distributions of the predominant bacteria at genus level. (B) The relative abundance of differential predominant bacteria among aerobic culture, anaerobic culture and clinical groups analyzed at genus level by ANOVA.

  
Context: To display the composition of dominant bacteria, the stacked column plot showed the percentage of each high-abundance bacteria. In clinical samples, except Veillonella , Leptotrichia and Streptococcus , other bacteria occupied the most of bacterial community. No matter under anaerobic and normoxia conditions, the percentages of other bacteria, Veillonella and Leptotrichia were clearly decreased. Meanwhile, there were observed increases in Desemzia , Actietobacter , Lactobacillus , Limosilactobacillus , Ligilactobacillus , Vagococcus and Streptococcus ( Figure 3A ).
Verdict: Supported
Claim: The results showed that there were decreases in Gram-positive bacteria, facultatively anaerobic bacteria and stress tolerance ability, but increases in Gram-negative bacteria, anaerobic respiration, biofilms forming and potential pathogenicity in clinical samples compared with cultured samples under aerobic and anaerobic conditions ( Figure 4 ).
Evidence type: figure
Evidence caption: Figure 4: BugBase analysis among different groups.
The three lines from the bottom up are the lower quartile, the mean and the upper quartile in BugBase analysis.

  
Context: To assess the predicted function of the bacterial community in clinical samples and the cultured bacteria samples, microbial phenotype prediction was evaluated by BugBase followed by the Mann– Whitney U test.
Verdict: Supported
Claim: Alpha diversity indices of ACE and Chao1 showed no significant difference between NEMM and SHI under the normoxia condition.
Evidence type: figure
Evidence caption: Figure 5: General bacterial community structure analysis between NEMM and SHI media during aerobic (normoxia) and anaerobic culture.
(A) Alpha diversity was measured with ACE index and Chao1 index. P < 0.05 was considered statistically significant. (B) NMDS was measured with Unweighted UniFrac. Each sample is represented by a dot. (C) Venn diagram displays the number of common and unique characteristics among the groups.

  
Context: To compare NEMM with SHI, which are suitable for cultivating specific bacterial types, we further analyzed 16S rRNA gene high-throughput sequencing data.
Verdict: Supported
Claim: Venn diagram showed that the OTU numbers of NEMM cultured bacteria under anaerobic and normoxia conditions were higher than that in SHI cultured bacteria under the same conditions ( Figure 5C ).
Evidence type: figure
Evidence caption: Figure 5: General bacterial community structure analysis between NEMM and SHI media during aerobic (normoxia) and anaerobic culture.
(A) Alpha diversity was measured with ACE index and Chao1 index. P < 0.05 was considered statistically significant. (B) NMDS was measured with Unweighted UniFrac. Each sample is represented by a dot. (C) Venn diagram displays the number of common and unique characteristics among the groups.

  
Context: To compare NEMM with SHI, which are suitable for cultivating specific bacterial types, we further analyzed 16S rRNA gene high-throughput sequencing data. Alpha diversity indices of ACE and Chao1 showed no significant difference between NEMM and SHI under the normoxia condition. However, under anaerobic culture conditions, NEMM had greater alpha diversity indices of ACE and Chao1 than SHI ( Figure 5A ). NMDS analysis showed that regardless of whether anaerobic or normoxia, cultured bacteria samples had similar features ( Figure 5B ).
Verdict: Supported
Claim: Of the 19 most predominant bacterial genera found in NEMM and SHI media, Ligilactobacillus , Limosilactobacillus , Lactobacillus , and Desemzia showed the highest abundance levels.
Evidence type: figure
Evidence caption: Figure 6: The relative abundance of differential predominant bacteria between NEMM and SHI analyzed at genus level by ANOVA.

  
Context: Differential predominant bacteria between NEMM and SHI were analyzed in the relative abundance of bacteria at the genus level by ANOVA.
Verdict: Supported
Claim: The results showed that the microbiota developed in NEMM were predicted stronger anaerobic ability, whether under anaerobic conditions or normoxic conditions.
Evidence type: figure
Evidence caption: Figure 7: BugBase analysis among different groups.
Note: X-axis, group name; Y-axis, Relative abundance in percentage. The three lines from bottom to top are lines indicating lower quartile, average and upper quartile.

  
Context: Bugbase was used to predict the function of bacterial community in NEMM and SHI under anaerobic and normoxia culture conditions.
Verdict: Supported
Claim: Under normoxic culture conditions, there were no significant differences in Gram-positive bacteria among the four groups (aNEMM, aSHI, nNEMM and nSHI).
Evidence type: figure
Evidence caption: Figure 7: BugBase analysis among different groups.
Note: X-axis, group name; Y-axis, Relative abundance in percentage. The three lines from bottom to top are lines indicating lower quartile, average and upper quartile.

  
Context: Bugbase was used to predict the function of bacterial community in NEMM and SHI under anaerobic and normoxia culture conditions. The results showed that the microbiota developed in NEMM were predicted stronger anaerobic ability, whether under anaerobic conditions or normoxic conditions.
Verdict: Supported