Seroprevalence of anti-ToxoplasmaIgG among the human population in Indonesia: a systematic review and meta-analysis by Taufik Mulya Perdana, Alfin Harjuno Dwiputro, Stefanie Kusuma, Artha Maressa Theodora Simanjuntak, Frengki Prabowo Saputro Wijayanto

“…We pooled seroprevalence data using the inverse-variance method and a random effects model. Heterogeneity was assessed using I2 statistics and Cochran’s Q test. …”
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Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment by Ojochenemi A. Enejoh, Chinelo H. Okonkwo, Hector Nortey, Olalekan A. Kemiki, Ainembabazi Moses, Ainembabazi Moses, Florence N. Mbaoji, Abdulrazak S. Yusuf, Olaitan I. Awe

“…The dataset was cleaned and molecular descriptors based on Lipinski’s rule of five were selected as input features for the ML model, which was built using the Random Forest algorithm. The optimized ML model was used to screen the COCONUT database and predict potential TGR5 agonists based on their molecular features. 6,656 compounds predicted from the COCONUT database were docked within the active site of TGR5 to calculate their binding energies. …”
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Meta-analysis of the accuracy for RASSF1A methylation in bronchial aspirates for the diagnosis of lung cancer. by Xu-Ping Chen, Shi-Xu He, Meng-You Chen, Fu-Bin Chen, Peng Wu, Ping Shi, Shi-Cai Zhao, Ling-Yan Zhao, Xiao-Min Xiong, Jia Zeng

“…<h4>Methods</h4>Studies published prior to October 30, 2022, were retrieved from the Embase, PubMed, Web of Science, and Wan Fang databases using the keywords "lung cancer", "RASSF1A", "methylation", and "bronchial aspirates". A fixed or random effect model was used to calculate the combined sensitivity, specificity, positive likelihood ratios (LR), negative LR, diagnostic odds ratio (DOR), along with the respective 95% confidence intervals (CIs) and the area under the curve (AUC) with Q index. …”
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Examination of non-conventional dysplasias adjacent to colorectal adenocarcinoma in patients with IBD by Szintia Almási, Zsófia Balajthy, Bence Baráth, Zsófia Krisztina Török, Panna Szaszák, Tamás Lantos, Bence Kővári, Anita Sejben

“…As multiple recent publications reported evidence of a worse prognosis and more common flat morphology compared to conventional dysplasias, their recognition is of great importance, and stricter follow-up with random biopsy samples may be considered.…”
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Establishing of a risk prediction model for metabolic dysfunction-associated steatotic liver disease: a retrospective cohort study by Nan Li, Chenbing Liu, Zhangfan Lu, Wenjian Wu, Feng Zhang, Lihong Qiu, Chao Shen, Di Sheng, Zhong Liu

“…Methods The study included 5107 subjects, divided into training and validation groups in a 7:3 ratio using a random number table method. Collinearity diagnosis and Cox regression were used to identify factors associated with MASLD incidence, and a risk prediction model was created. …”
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Effects of apple cider vinegar on glycemic control and insulin sensitivity in patients with type 2 diabetes: A GRADE-assessed systematic review and dose–response meta-analysis of c... by Donya Arjmandfard, Mehrdad Behzadi, Zahra Sohrabi, Mohsen Mohammadi Sartang

“…All CTs investigating ACV’s effects on glycemic factors were included. We used a random-effects model to calculate WMDs and 95% confidence intervals (CIs). …”
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Combining a Risk Factor Score Designed From Electronic Health Records With a Digital Cytology Image Scoring System to Improve Bladder Cancer Detection: Proof-of-Concept Study by Sandie Cabon, Sarra Brihi, Riadh Fezzani, Morgane Pierre-Jean, Marc Cuggia, Guillaume Bouzillé

“…MethodsThe first step relied on designing a predictive model based on clinical data (ie, risk factors identified in the literature) extracted from the clinical data warehouse of the Rennes Hospital and machine learning algorithms (logistic regression, random forest, and support vector machine). It provides a score corresponding to the risk of developing bladder cancer based on the patient’s clinical profile. …”
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Risk factors associated with human Mpox infection: a systematic review and meta-analysis by Nicola Luigi Bragazzi, Jianhong Wu, Jude Dzevela Kong, James Orbinski, Chigozie Louisa Jane Ugwu, Ali Asgary, Woldegebriel Assefa Woldegerima

“…The Newcastle–Ottawa Scale used to assess the risk of bias for included articles. Fixed- or random-effects meta-analysis was conducted when at least two studies reported ORs or relative risks, with 95% CIs. …”
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Predictive biomarkers for cardiometabolic risk in postmenopausal women: insights into visfatin, adropin, and adiponectin by Anna Maria Cybulska, Daria Schneider-Matyka, Ireneusz Walaszek, Mariusz Panczyk, Dorota Ćwiek, Anna Lubkowska, Elżbieta Grochans, Kamila Rachubińska, Katarzyna Malewicz, Mariusz Chabowski, Mariusz Chabowski

“…Therefore, the aim of this study was to assess the relationship between cardiometabolic parameters and circulating levels of visfatin, adropin, and adiponectin in perimenopausal women with regard to their obesity status.Materials and methodsThis study of 168 perimenopausal women utilized a cross-sectional design with non-random sampling. It involved the use of questionnaires, as well as anthropometric and blood pressure measurements. …”
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The Groundwater Geochemistry and the Human Health Risk Assessment of Drinking Water in an Area with a High Prevalence of Chronic Kidney Disease of Unknown Etiology (CKDu), Sri Lank... by W. S. M. Botheju, J. A. Liyanage, S. D. P. Kannangara

“…Results of the inverse distance weighted (IDW) interpolation tool indicated the nephrotoxic heavy metals contents including Cd, Pb, As, and Cr in CKDu hotspot were changed in the ranges of 9.78–187.25 μg/L, 0.08–0.66 μg/L, 20.76–103.30 μg/L, and 0.03–0.34 μg/L. The random distribution patterns were shown by the result in Moran’s index values. …”
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Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings by Mosbeh R. Kaloop, Abidhan Bardhan, Pijush Samui, Jong Wan Hu, Mohamed Elsharawy

“…Two deep learning methods viz deep belief network (DBN) and deep neural network (DNN), and five machine learning methods namely feedforward neural network, extreme learning machine, weighted extreme learning machine, random forest, and gradient boosting machine were evaluated, and compared in predicting the design wind pressures on tall buildings. …”
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First Measurements of Ambient PM2.5 in Kinshasa, Democratic Republic of Congo and Brazzaville, Republic of Congo Using Field-calibrated Low-cost Sensors by Celeste McFarlane, Paulson Kasereka Isevulambire, Raymond Sinsi Lumbuenamo, Arnold Murphy Elouma Ndinga, Ranil Dhammapala, Xiaomeng Jin, V. Faye McNeill, Carl Malings, R. Subramanian, Daniel M. Westervelt

“…Employing two calibration models, namely, multiple linear regression and random forests, decreased the MAE to 3.4 µg m−3 and increased R2 to 0.96. …”
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Radiomic prediction for durable response to high‐dose methotrexate‐based chemotherapy in primary central nervous system lymphoma by Haoyi Li, Mingming Xiong, Ming Li, Caixia Sun, Dao Zheng, Leilei Yuan, Qian Chen, Song Lin, Zhenyu Liu, Xiaohui Ren

“…The radiomic‐clinical integrated models were developed using the random forest method. Model performance was externally validated to verify its clinical utility. …”
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Evaluation of Machine Learning Models for Stress Symptom Classification of Cucumber Seedlings Grown in a Controlled Environment by Kyu-Ho Lee, Samsuzzaman, Md Nasim Reza, Sumaiya Islam, Shahriar Ahmed, Yeon Jin Cho, Dong Hee Noh, Sun-Ok Chung

“…Four ML classifiers: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF), were trained to detect stress symptoms based on selected features, highlighting that stress symptoms were detectable after day 4. …”
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Reproducibility and repeatability of 18F-(2S, 4R)-4-fluoroglutamine PET imaging in preclinical oncology models. by Gregory D Ayers, Allison S Cohen, Seong-Woo Bae, Xiaoxia Wen, Alyssa Pollard, Shilpa Sharma, Trey Claus, Adria Payne, Ling Geng, Ping Zhao, Mohammed Noor Tantawy, Seth T Gammon, H Charles Manning

“…<h4>Results</h4>In a two-way random effects Gauge R&R model, variance among mice and their measurement variance were 0.5717 and 0.024. …”
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Effect of Co-Administration of Midazolam and Dexmedetomidine on Haemodynamics and Stress Response in Elderly Patients with Non-Small Cell Lung Cancer by Yanjun Zhao, Dongjiao An, Liang Bi

“…Patients were randomized 1:1 to receive either dexmedetomidine (control group) or dexmedetomidine plus midazolam (study group) for anesthesia during lobectomy via the random number table method, with 77 patients in each group. …”
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The relationship between vision status and its influencing factors among primary and secondary school students: The masking effect of physical activity level and the moderating eff... by Jiyan Xu, Yanjun Li, Siyuan Ma, Shimeng Dai, Weiwei Xu, Mengjiao Sang, Kaijie Feng

“…Methods A survey was conducted among 1,670 primary and secondary school students in the Ningxia Hui Autonomous Region of China using a snapshot method and random sampling techniques from September to October 2023. …”
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Triple burden of malnutrition among Vietnamese 0·5–11-year-old children in 2020–2021: results of SEANUTS II Vietnam by Nga Thuy Tran, Van Khanh Tran, Duong Thanh Tran, Tu Tran Ngoc Nguyen, Son Duy Nguyen, Ha Thu Nguyen, Tu Song Nguyen, Tung Van Thanh Le, Phuong Thi Lan Nguyen, Hanh Thi Dang, Hoa Anh Le, Gerard Wong, Ilse Khouw

“…Setting: A multistage cluster systematic random sampling method was implemented in different regions in Vietnam: North Mountainous, Central Highlands, Red River Delta, North Central and Coastal Area, Southeast and Mekong River Delta. …”
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Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients by Liping Xu, Fang Cao, Lian Wang, Weihua Liu, Meizhu Gao, Li Zhang, Fuyuan Hong, Miao Lin

“…Introduction The study presented here aimed to establish a predictive model for heart failure (HF) and all-cause mortality in peritoneal dialysis (PD) patients with machine learning (ML) algorithm.Methods We retrospectively included 1006 patients who initiated PD from 2010 to 2016. XGBoost, random forest (RF), and AdaBoost were used to train models for assessing risk for 1-year and 5-year HF hospitalization and mortality. …”
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Vitamin D Deficiency as a Risk Factor for Diabetic Retinopathy: A Systematic Review and Meta-Analysis by Claudia Elena Petrea, Laura Andreea Ghenciu, Roxana Iacob, Emil Robert Stoicescu, Dorel Săndesc

“…The pooled analysis revealed a significant association between lower vitamin D levels and increased odds of DR, with a combined odds ratio (OR) of 1.15 (95% CI: 1.10–1.20) under the fixed-effects model and 1.17 (95% CI: 1.08–1.27) under the random-effects model. Mean serum vitamin D levels were lower in individuals with DR (18.11 ± 5.35 ng/mL) compared to those without DR (19.71 ± 7.44 ng/mL), with a progressive decline observed across DR severity stages. …”
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