Data Sheet 2_Identification of novel gut microbiota-related biomarkers in cerebral hemorrhagic stroke.zip by Fengli Ye (22123540)

“…Functional enrichment, gene set enrichment analysis (GSEA), and protein–protein interaction (PPI) analyses were performed. …”

Table 1_Exploring the alleviating effects of Bifidobacterium metabolite lactic acid on non-alcoholic steatohepatitis through the gut-liver axis.docx by Hongmei Zhao (250090)

“…</p>Methods<p>A NAFLD mouse model was established using a HFD, and different intervention groups were set up to study the protective effects of Bifidobacterium and its metabolite lactic acid. …”

Data Sheet 1_GBD: incidence rates and prevalence of anxiety disorders, depression and schizophrenia in countries with different SDI levels, 1990–2021.pdf by Jueqi Wang (21376073)

“…</p>Results<p>In countries with different SDI levels, the age-standardized average annual percentage change (AAPC) in the incidence of anxiety were all shown to be increasing, and there were large gender differences between the different SDI levels, with a maximum of 0.97 (0.76–1.18) for females in countries with a high SDI level, Age-standardized more rates per 100,000 people in high SDI countries, from 658.87 in 1990 to 841.56 in 2021, and the largest gender differences in countries with a low to moderate SDI level, with AAPCs for males and females of 0.04 (0.04–0.05), 0.86 (0.63–1.09); for depression, only the countries with medium-high SDI levels were statistically significant compared to the countries with medium-low SDI levels, with AAPCs of 0.05 (0.04–0.07), 0.04 (0.04–0.05); for schizophrenia in addition to the AAPCs of the countries with medium-high SDI levels showed an increase of 0.16 (0.13–0.18); the rest decreased.…”

Image 1_Machine learning identifies neutrophil extracellular traps-related biomarkers for acute ischemic stroke diagnosis.tif by Haipeng Zhang (3413288)

“…NDEGs were significantly enriched in immune regulation and leukocyte apoptosis (GO) and NETs formation pathway (KEGG). …”

Table 1_Machine learning identifies neutrophil extracellular traps-related biomarkers for acute ischemic stroke diagnosis.docx by Haipeng Zhang (3413288)

“…NDEGs were significantly enriched in immune regulation and leukocyte apoptosis (GO) and NETs formation pathway (KEGG). …”

Supplementary Material for: Leveraging NLP for Psychiatric Phenotyping from Spanish EHR: Enabling the Investigation of Transdiagnostic Symptom Profiles at Scale by figshare admin karger (2628495)

“…The COMBINED algorithm improved overall recall, without significantly decreasing precision (F1 of 0.78 and 0.77 on HOMO and CSJDM, respectively). …”

Data Sheet 1_Inflammation-related biomarkers and berberine therapy in post-stroke depression: evidence from bioinformatics, machine learning, and experimental validation.docx by Wei Liu (20030)

“…Objective<p>Post-stroke depression (PSD), a common neuropsychiatric complication, significantly hinders stroke recovery and quality of life. …”

<b>Canagliflozin-induced adaptive metabolism in bone</b> by Sher Bahadur Poudel (20686192)

“…Beyond this, these drugs induce various metabolic changes, including weight loss and impaired bone integrity. There is a significant gap in understanding SGLT2i-induced skeletal changes, as SGLT2 is not expressed in osteoblasts or osteocytes, which use glucose to remodel the bone matrix. …”

Supplementary file 1_Single-cell and bulk transcriptomic analyses reveal PANoptosis-associated immune dysregulation of fibroblasts in periodontitis.zip by Erli Wu (17785482)

“…</p>Results<p>scRNA-seq analysis revealed a decreased proportion of HGFs alongside enrichment of multiple PANoptosis-related pathways in PD samples. …”

Image 1_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

Image 8_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

Image 6_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

Image 2_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

Image 7_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

Image 5_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

Image 4_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

Image 3_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

DataSheet1_Identification of common signature genes and pathways underlying the pathogenesis association between nonalcoholic fatty liver disease and heart failure.docx by Gerui Li (15648920)

“…Experimental validation indicated unbalanced macrophage polarization in HF and NAFLD mouse models, and the expression of CD163 and CCR1 were significantly down-regulated.</p>Conclusion<p>This study identified M2 polarization impairment characterized by decreased expression of CD163 and CCR1 as a common pathogenic pathway in NAFLD and HF. …”

Data Sheet 1_What regulates decomposition in agroecosystems? Insights from reading the tea leaves.pdf by Marshall D. McDaniel (4341325)

“…<p>Litter decomposition is a critical Earth process, recycling nutrients and setting a portion of plant tissue on a path toward soil organic matter. …”

Presentation 1_An integrated transcriptome and physiological analysis of nitrogen use efficiency in rice (Oryza sativa L. ssp. indica) under drought stress.pdf by Yu Wang (12152)

“…Compared to the normal treatment, drought stress led to a significant reduction in NUE across growth stages, with decreases ranging from 2.18% to 31.67%. …”