Summary of methodological contributions and future directions. by Andrya J. Durr (4687726)

Human Pluripotent Stem Cell-Derived Hepatocytes Show Higher Transcriptional Correlation with Adult Liver Tissue than with Fetal Liver Tissue by Nidal Ghosheh (8518356)

“…These findings will aid future intervention and improvement of in vitro hepatocyte differentiation protocol in order to generate hepatocytes displaying the complete functionality of mature hepatocytes. …”

<b>External attribution to economic inequality increases algorithm preference</b> by Xuyao Wu (17257993)

“…Future research should attend to potential backfire effects of algorithmic interventions, particularly the risk of undermining individuals’perceptions of fairness in unequal contexts.…”

SUPPORT pilot patient demographics. by Elizabeth Janiak (19251022)

“…</p><p>Conclusions</p><p>Despite the competing demands of the COVID-19 pandemic, the SUPPORT intervention was utilized at higher rates than prior similar interventions and across multiple disease specialties.…”

An overview of the experimental paradigm. by Nienke E. R. van Bueren (11414330)

Table 1_The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients.docx by Zhou Liu (1506679)

“…AUGIB characterized by hemorrhagic shock, hypotension, multiple organ dysfunction (MODS), and even circulatory failure is life-threatening. …”

Image1_Unveiling the cellular landscape: insights from single-cell RNA sequencing in multiple myeloma.tif by Xinhan Li (15206754)

“…</p>Conclusion<p>Our study presents a fresh perspective for future research endeavors and clinical interventions in the field of MM. …”

Data Sheet 1_Closing the loop: establishing an autonomous test-learn cycle to optimize induction of bacterial systems using a robotic platform.pdf by Jan Benedict Spannenkrebs (20595722)

“…As a target product the readily measurable green fluorescent reporter protein (GFP) is produced over multiple, consecutive iterations of testing. An evaluation of chosen (learning) algorithms for single and dual factor optimization was performed. …”

Supplementary file 1_Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.zip by Nina de Lacy (6559520)

“…Early adolescence is an important developmental stage for the increase in prevalence of internalizing disorders and understanding specific factors that predict their onset may be germane to intervention and prevention strategies.</p>Methods<p>We analyzed ~6,000 candidate predictors from multiple knowledge domains (cognitive, psychosocial, neural, biological) contributed by children of late elementary school age (9–10 yrs) and their parents in the ABCD cohort to construct individual-level models predicting the later (11–12 yrs) onset of depression, anxiety and somatic symptom disorder using deep learning with artificial neural networks. …”

Data Sheet 1_An individualized risk prediction tool for ectopic pregnancy within the first 10 weeks of gestation based on machine learning algorithms.docx by Xin Du (208780)

“…</p>Conclusion<p>This study employed the CatBoost algorithm to develop an individualized risk prediction model by integrating multiple features from the initial visit. …”

Published articles per year. by Filip Mess (832869)

“…Most of the studies focused on behavioral outcomes (61/160, 38%), followed by psychological (37/160, 23%) and physiological (31/160, 19%) outcomes of health (multiple answers were possible). In terms of study designs, randomized controlled trials were used in more than a third of all studies (39%), followed by cross-sectional studies (18%), while newer designs (e.g., just-in-time-adaptive-interventions) are currently rarely used. …”

Data for the results. by Filip Mess (832869)

“…Most of the studies focused on behavioral outcomes (61/160, 38%), followed by psychological (37/160, 23%) and physiological (31/160, 19%) outcomes of health (multiple answers were possible). In terms of study designs, randomized controlled trials were used in more than a third of all studies (39%), followed by cross-sectional studies (18%), while newer designs (e.g., just-in-time-adaptive-interventions) are currently rarely used. …”

DataSheet1_Comprehensive Analysis of N6-Methyladenosine Regulators in the Subcluster Classification and Drug Candidates Prediction of Severe Obstructive Sleep Apnea.PDF by Niannian Li (123776)

DataSheet1_Complexity of the online distrust ecosystem and its evolution.PDF by Lucia Illari (17758320)

Data_Sheet_1_Automatic segmentation of white matter hyperintensities in routine clinical brain MRI by 2D VB-Net: A large-scale study.pdf by Wenhao Zhu (4919734)

“…In conclusion, we developed an automatic WMH quantification framework for multiple application scenarios, exhibiting a promising future in clinical practice.…”

Supplementary file 1_Utilizing nutrition-related biomarkers to develop a nutrition-related aging clock for the chinese demographic.docx by Ya-Qing Ma (10010321)

“…The proposed model serves as a reliable tool for predicting biological age and offers a scientific basis for future research on aging mechanisms and personalized interventions.…”

Performance of Artificial Intelligence in Detecting Diabetic Macular Edema from Fundus Photographs and Optical Coherence Tomography Images: A Systematic Review and Meta-analysis by Ching Lam (14590970)

“…OCT-based algorithms of 28 studies yielded pooled AUROC, sensitivity, and specificity of 0.985, 95.9%, and 97.9%. …”

Vaccines, Public Health, and the Politics of Immunity: Safety, Debate, and Global Perspectives by Minehli Arakelians Gheshlagh (21983870)

“…</li><li><b>Case Studies:</b><br>Measles, COVID-19, and emerging viruses are analyzed comparatively, integrating epidemiological data, vaccine rollout strategies, public behavior, and policy interventions. Visual representations—timelines, tables, and network diagrams—are proposed to clarify disease dynamics, intervention strategies, and lessons learned for future preparedness.…”

Table 2_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…The majority of the studies employed K-nearest neighbor or Multiple Imputation by Chained Equations for handling missing values and utilized Recursive Feature Elimination, Least Absolute Shrinkage and Selection Operator, and Boruta's algorithm for feature selection. …”

Table 4_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…The majority of the studies employed K-nearest neighbor or Multiple Imputation by Chained Equations for handling missing values and utilized Recursive Feature Elimination, Least Absolute Shrinkage and Selection Operator, and Boruta's algorithm for feature selection. …”