Supplementary file 1_Feasibility of repetitive transcranial magnetic stimulation on non-motor symptoms of spinocerebellar ataxia type 3: a secondary analysis of a randomized clinic... by Hua Wu (2707)

“…Correlation analyses revealed no significant predictors of rTMS response based on age at onset, disease duration, number of expanded CAG repeat lengths, or baseline motor symptom severity scores.…”

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

“…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.</p>Conclusion<p>The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. …”

Table1_Assessing physical fitness adaptations in collegiate male soccer players through training load parameters: a two-arm randomized study on combined small-sided games and runni... by YanXiu Quan (19645810)

“…Although there were positive trends in variables such as RSA and 30-15IFT, these changes were modest and not statistically significant. The results suggest that while the combined SSGs and HIIT approach shows potential, its impact on physical fitness over a 4-week period is limited, with some variables, like CMJ, even showing decreases.…”

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

“…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.</p>Conclusion<p>The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. …”

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

“…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.</p>Conclusion<p>The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. …”

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

“…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.</p>Conclusion<p>The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. …”

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

“…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.</p>Conclusion<p>The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. …”

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

“…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.</p>Conclusion<p>The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. …”

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

“…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.</p>Conclusion<p>The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. …”

Table 1_COVID-19 pandemic increased ESKAPEEc bloodstream infections and amplified carbapenem resistance in Chinese children: a multicenter surveillance study (2016–2023).xls by Xiaoqiang Li (612879)

“…Temporal variations and linear trends were evaluated using chi-square or Fisher’s exact tests.…”

<b>Regional asymmetries in dissolved organic carbon response to nitrogen addition rather than soil organic carbon and microbial biomass carbon</b> by Andong Cai (5237024)

“…The effect size of soil DOC decreased linearly with that of MBC to SOC ratio in humid regions, while it increased linearly in non-humid regions. …”

Table 1_Correlation between lactate/albumin ratio and 28-day mortality in sepsis-associated acute kidney injury patients.docx by Susu He (10361615)

“…The Kaplan–Meier curves showed that participants having elevated LAR exhibited significantly decreased survival rates. Cox proportional hazards models showed that higher LAR was related to higher 28-day death rate in SA-AKI patients (HR: 1.224, 95% CI: 1.160–1.291, p < 0.001). …”

Relationship of different levels of physical activity on the health status of long-term breast cancer survivors: a cross-sectional study by Francisco Álvarez-Salvago (21338477)

“…In the second objective, the regression analysis found "future perspective" (β = 0.314; <i>p</i> < 0.01) and "insomnia" (β = −0.288; <i>p</i> = 0.02) to be significant predictors of higher PA levels (r² = 0.224).…”

Study group background characteristics. by Zicheng Hu (3825313)

“…This ratio was disrupted when blood CD4+ T-cells fell to <50 cells/μL and the CSF WBC count decreased to negligible levels; in this group the CSF:blood HIV-1 RNA ratio decreased nearly 10-fold to an overall ratio of <1:100. …”

PDLB - Balance Sheet by Nguyen Linh (19516642)

“…It built out management capabilities for a much larger bank, and is currently seeing decreasing Q/Q non-interest cost, while assets and interest income are growing nicely.…”

Image 1_Circulating growth differentiation factor-15 concentration and hypertension risk: a dose-response meta-analysis.tif by Zhengqing Yu (12641890)

“…Significant heterogeneity was detected in the pooled analysis and meta-regression analysis suggested that participants’ age and the prevalence of diabetes significantly accounted for the heterogeneity.…”

Image 2_Circulating growth differentiation factor-15 concentration and hypertension risk: a dose-response meta-analysis.tif by Zhengqing Yu (12641890)

“…Significant heterogeneity was detected in the pooled analysis and meta-regression analysis suggested that participants’ age and the prevalence of diabetes significantly accounted for the heterogeneity.…”

Table 4_Circulating growth differentiation factor-15 concentration and hypertension risk: a dose-response meta-analysis.docx by Zhengqing Yu (12641890)

“…Significant heterogeneity was detected in the pooled analysis and meta-regression analysis suggested that participants’ age and the prevalence of diabetes significantly accounted for the heterogeneity.…”

Table 1_Circulating growth differentiation factor-15 concentration and hypertension risk: a dose-response meta-analysis.docx by Zhengqing Yu (12641890)

“…Significant heterogeneity was detected in the pooled analysis and meta-regression analysis suggested that participants’ age and the prevalence of diabetes significantly accounted for the heterogeneity.…”

Table 3_Circulating growth differentiation factor-15 concentration and hypertension risk: a dose-response meta-analysis.docx by Zhengqing Yu (12641890)

“…Significant heterogeneity was detected in the pooled analysis and meta-regression analysis suggested that participants’ age and the prevalence of diabetes significantly accounted for the heterogeneity.…”