Prevalence and predictors of self-medication practices among adult household members in Hosanna town, Hadiya zone, central Ethiopia by Sentayehu Admasu Saliya, Awoke Girma Hailu, Sisay Foga Sebro, Misgana Desalegn Menesho

“…Methods A community-based cross-sectional study was conducted from March 1–30, 2024, among 566 randomly selected households in Central Ethiopia. Households were chosen using a simple random sampling technique. …”
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Predicting the risk of gastroparesis in critically ill patients after CME using an interpretable machine learning algorithm – a 10-year multicenter retrospective study by Yuan Liu, Songyun Zhao, Wenyi Du, Wei Shen, Ning Zhou

“…In the present study, four advanced machine learning algorithms—Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and k-nearest neighbor (KNN)—were employed to develop predictive models. …”
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Metabolic Syndrome in People Living with Human Immunodeficiency Virus: An Assessment of the Prevalence and the Agreement between Diagnostic Criteria by Kim Anh Nguyen, Nasheeta Peer, Anniza de Villiers, Barbara Mukasa, Tandi E. Matsha, Edward J. Mills, Andre Pascal Kengne

“…We determined metabolic syndrome (MetS) prevalence and assessed the agreement between different diagnostic criteria in HIV-infected South Africans. Method. A random sample included 748 HIV-infected adult patients (79% women) across 17 HIV healthcare facilities in the Western Cape Province. …”
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Meta-analysis of nifedipine and enalapril combination therapy for hypertensive patients with coronary heart disease: A systematic review and meta-analysis by Kun Wang, Wenchao Ma, Leina Sun, Fangcheng Su

“…Results: A total of 183 articles were initially identified, and after a comprehensive review, 14 clinical randomized controlled trials were chosen for analysis. …”
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Smartphone Delivery of Mobile HIV Risk Reduction Education by Karran A. Phillips, David H. Epstein, Mustapha Mezghanni, Massoud Vahabzadeh, David Reamer, Daniel Agage, Kenzie L. Preston

“…Future studies, with pre-intervention assessments of knowledge and random assignment, are needed to confirm these findings.…”
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Engineering a membrane protein chaperone to ameliorate the proteotoxicity of mutant huntingtin by Jeonghyun Oh, Christy Catherine, Eun Seon Kim, Kwang Wook Min, Hae Chan Jeong, Hyojin Kim, Mijin Kim, Seung Hae Ahn, Nataliia Lukianenko, Min Gu Jo, Hyeon Seok Bak, Sungsu Lim, Yun Kyung Kim, Ho Min Kim, Sung Bae Lee, Hyunju Cho

“…Using yeast toxicity-based screening with a random mutant library, we identify two yeast PEX19 variants and engineer equivalent mutations into human PEX19 (hsPEX19). …”
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Exploring the significance of medical humanities in shaping internship performance: insights from curriculum categories by Chao Ting Chen, Anna Y.Q. Huang, Po-Hsun Hou, Ji-Yang Lin, His-Han Chen, Shiau-Shian Huang, Stephen J. H. Yang

“…Ten-fold cross-validation machine learning models (support vector machines, logistic regression, random forest) were performed to predict the internship grades. …”
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Incidence of suspected human rabies virus exposure and associated risk factors in Ethiopia: systematic review and meta-analysis by Mengistie Kassahun Tariku, Abebe Habtamu Belete, Daniel Tarekegn Worede, Sewnet Wongiel Misikir

“…This meta-analysis was carried out using the program Stata Version 17, the DerSimonian-Laird method, and a random-effects model. The I2 and Cochrane Q test statistics were used to determine the studies’ heterogeneity. …”
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Incorporating stand parameters in nonlinear height-diameter mixed-effects model for uneven-aged Larix gmelinii forests by Muhammad Junaid Ismail, Tika Ram Poudel, Akber Ali, Lingbo Dong

“…We employed generalized nonlinear mixed-effects modeling approach with both fixed and random effects to account for variations at the individual plot level, enhancing the predictive accuracy. …”
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Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma: a SEER database analysis with external validation by Ruixin Wu, Ruixin Wu, Sihao Chen, Sihao Chen, Yi He, Yi He, Ya Li, Song Mu, Aishun Jin, Aishun Jin

“…Independent factors influencing both overall survival (OS) and cancer-specific survival (CSS) were identified using LASSO, Random Forest, and XGBoost regression techniques. …”
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Knowledge, Attitude, and Food Safety Practices among Street Food Vendors at a Metropolitan District in Ghana: A Cross-sectional Study by Abraham Norman Nortey, Hubert Amu, Ebenezer Senu, Alfred Effah

“…In this descriptive cross-sectional study, 406 street food vendors were recruited based on a simple random sampling technique from the Sekondi-Takoradi Metropolis, Ghana, using a structured questionnaire. …”
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US state-level containment policies not associated with food insecurity changes during the early COVID-19 pandemic: a multilevel analysis by Samantha M Sundermeir, Erin Tigue, Francesco Acciai, Emma Moynihan, Meredith T Niles, Roni Neff

“…Design: To investigate these relationships, we developed a framework linking COVID-19-related containment policies with different domains of food security and then used multilevel random effects models to examine associations between state-level containment policies and household food security. …”
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Cxcr1 gene snp variability that affects mastitis resistance in holstein cows in türkiye by Kozet AVANUS, Alper YILMAZ, Halil GÜNEŞ, Ahmet ALTINEL, Bülent EKİZ, Hülya YALÇINTAN, Dilara KEÇİCİ, Nurşen DOĞAN

“…Several SNP loci in the CXCR1 gene, including c.606G>A, c.678G>A, c.1104G>A, c.1119+6C, c.1119+7A, and c.1119+10, significantly deviated from Hardy-Weinberg equilibrium (HWE) (P<0.0001), indicating violations of HWE assumptions such as random mating and absence of selection. The deviations at c.606G>A, c.678G>A, and c.1104G>A suggest strong selection pressures, likely due to artificial selection in Holstein cattle. …”
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Anemia among Women Attending Antenatal Care at the University of Gondar Comprehensive Specialized Referral Hospital, Northwest Ethiopia, 2017 by Wubet Worku Takele, Amare Tariku, Fasil Wagnew Shiferaw, Amare Demsie, Wondale Getinet Alemu, Degefaye Zelalem Anlay

“…A facility-based cross-sectional quantitative study was conducted among 362 participants from June 03-July 08, 2017, at the University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia. The systematic random sampling technique was employed. Structured interviewer administered questionnaire was used. …”
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Effect of Low-Load Blood Flow Restriction Training After Anterior Cruciate Ligament Reconstruction: A Systematic Review by Baris B. Koc, Alexander Truyens, Marion J.L.F. Heymans, Edwin J.P. Jansen, Martijn G.M. Schotanus

“… # Results A total of six randomized controlled trials were included. Random sequence generation and allocation concealment was defined as high risk in two of the six studies. …”
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Identification and Characterization of the Diverse Stress-Responsive R2R3-RMYB Transcription Factor from Hibiscus sabdariffa L. by Bahaeldeen Babikar Mohamed, Beenish Aftab, Muhammad Bilal Sarwar, Bushra Rashid, Zarnab Ahmad, Sameera Hassan, Tayyab Husnain

“…Differential display reverse transcriptase PCR and random amplification of cDNA ends (RACE) was used to explore the osmotic stress-responsive transcripts. …”
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Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases by Yuji Miyamoto, Takeshi Nakaura, Mayuko Ohuchi, Katsuhiro Ogawa, Rikako Kato, Yuto Maeda, Kojiro Eto, Masaaki Iwatsuki, Yoshifumi Baba, Toshinori Hirai, Hideo Baba

“…Treatment response was classified as responder (complete or partial response) or non-responder (stable or progressive disease), based on the best overall response according to RECIST criteria, version 1.1. Employing Random Forest and Boruta algorithms, we identified significant features for responder-non-responder differentiation. …”
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Detection of Alzheimer Disease in Neuroimages Using Vision Transformers: Systematic Review and Meta-Analysis by Vivens Mubonanyikuzo, Hongjie Yan, Temitope Emmanuel Komolafe, Liang Zhou, Tao Wu, Nizhuan Wang

“…Pooled diagnostic accuracy estimates, including sensitivity, specificity, likelihood ratios, and diagnostic odds ratios, were derived using random-effects models. Subgroup analyses comparing the diagnostic performance of different ViT network architectures were performed. …”
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Developing a machine learning model for predicting varicocelectomy outcomes: a pilot study by Coşkun Kaya, Mehmet Erhan Aydın, Özer Çelik, Aykut Aykaç, Mustafa Sungur

“…The Extra Trees Classifier, Light Gradient Boosting Machine Classifier, eXtreme Gradient Boosting Classifier, Logistic Regression, and Random Forest Classifier techniques were used as ML algorithms.41 males were included in the study. 31 (75.6%) and 10 (24.4%) patients were classified as Group 1 and 2, respectively. …”
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Prevalence of childhood overweight and obesity in Malaysia: a systematic review and meta-analysis by Ker Yang Chua, Ker Yung Chua, Karuthan Chinna, Chooi Ling Lim, Maheeka Seneviwickrama

“…A meta-analysis of prevalence and 95% confidence interval (CI) using a random-effects model and heterogeneity (I2) was calculated. …”
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