Protective Mechanism of Electroacupuncture Combined with Enriched Rehabilitative Training on Hippocampal Neurons in Rats with Cerebral Ischemia-Reperfusion Injury by Li GONG, Wei TANG, Zhen QIU, Mengxing LI

“…Objective:To observe the effects of electroacupuncture combined with enriched rehabilitative training on endoplasmic reticulum stress (ERS) -associated proteins in rats with cerebral ischemia-reperfusion injury, and to investigate the protective mechanism of that in relation to the hippocampal neurons.Methods:A total of 108 enrolled Sprague-Dawley (SD) rats were divided into normal group, sham surgery group, model group, electroacupuncture group (EA group), enriched rehabilitative training group (RT group), and electroacupuncture combined with enriched rehabilitative training group (EA+RT group) according to the random number table method. Then cerebral ischemia-reperfusion models were established by modified thread thrombus method. …”
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Electronic cigarette use in relation to changes in smoking status and respiratory symptoms by Linnéa Hedman<sup>+<sup>, Gustaf Lyytinen<sup>+<sup>, Helena Backman, Magnus Lundbäck, Caroline Stridsman, Anne Lindberg, Hannu Kankaanranta, Lina Rönnebjerg, Eva Rönmark, Linda Ekerljung

“…Methods A prospective, population-based study of random samples of the population (age 16–69 years) was performed within The Obstructive Lung Disease in Northern Sweden (OLIN) study and West Sweden Asthma Study (WSAS). …”
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Clinical Study on Lumbar Disc Herniation Treated by Sling and Tuina Exercise Technique by Yi DING, Chenchen GUO, LI Li

“…The eligible patients were divided into tuina treatment group, sling exercise therapy group, and sling and tuina exercise technique group by random number table method, with 28 cases in each group. …”
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Efficacy of intermittent fasting on improving liver function in individuals with metabolic disorders: a systematic review and meta-analysis by Mousa Khalafi, Sara K Rosenkranz, Faeghe Ghasemi, Shokoufeh Kheradmand, Aref Habibi Maleki, Mallikarjuna Korivi, Jung-Piao Tsao

“…Further randomized clinical trials are needed to elucidate the effects of IF on liver function in adults with metabolic disorders.…”
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Treatment outcomes and its associated factors among neonates admitted with sepsis in Hiwot Fana Comprehensive Specialized University Hospital, Harar, Ethiopia by Betelhem Gezahegn, Ahmed Abdella, Fentahun Meseret, Ahmed Mohammed, Mulualem Keneni, Tesfaye Asfaw, Diribsa Tizazu, Assefa Desalew

“…Neonates were selected using systematic random sampling. Relevant data were extracted from medical records using a checklist. …”
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Predicting egg production rate and egg weight of broiler breeders based on machine learning and Shapley additive explanations by Hengyi Ji, Yidan Xu, Ganghui Teng

“…We systematically compared the performances of the following seven ML models in predicting egg production rate and egg weight: random forest (RF), multilayer perceptron (MLP), support vector regression (SVR), least squares support vector machine (LSSVM), k-nearest neighbors (kNN), XGBoost, and LightGBM. …”
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NIFTy: near-infrared fluorescence (NIRF) imaging to prevent postsurgical hypoparathyroidism (PoSH) after thyroid surgery—a phase II/III pragmatic, multicentre randomised controlled... by Deborah D Stocken, Maureen Twiddy, Hisham Mehanna, Jonathan Wadsley, Neil Corrigan, Katie Gordon, Julie Croft, Neil Sharma, Anna Perry, Mark Strachan, Gemma Ainsworth, Elizabeth Glenister, Sabapathy Prakash P Balasubramanian

“…Analysis in both phases will be using multilevel logistic regression incorporating random effects with respect to surgeon and adjusting for minimisation factors. …”
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Associations between age, red cell distribution width and 180-day and 1-year mortality in giant cell arteritis patients: mediation analyses and machine learning in a cohort study by Si Chen, Rui Nie, Xiaoran Shen, Yan Wang, Haixia Luan, Xiaoli Zeng, Yanhua Chen, Hui Yuan

“…The results of the machine learning analysis indicated that the model built using the random forest algorithm performed the best, with an area under the curve of 0.879. …”
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Identification of Potential lncRNAs and miRNAs as Diagnostic Biomarkers for Papillary Thyroid Carcinoma Based on Machine Learning by Fei Yang, Jie Zhang, Baokun Li, Zhijun Zhao, Yan Liu, Zhen Zhao, Shanghua Jing, Guiying Wang

“…Optimal diagnostic lncRNA and miRNA biomarkers were identified via random forest. The regulatory network between optimal diagnostic lncRNA and mRNAs and optimal diagnostic miRNA and mRNAs was identified, followed by the construction of ceRNA network of lncRNA-mRNA-miRNA. …”
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Active and Passive Smoking as Catalysts for Cognitive Impairment in Rural Indonesia: A Population-based Study by Faizal Muhammad, Afifah Syifaul Ummah, Farida Aisyah, Rivan Danuaji, Diah Kurnia Mirawati, Subandi Subandi, Baarid Luqman Hamidi, Ervina Arta Jayanti Hutabarat, Reviono Reviono, Yulie Erida Nur Rahmawati, Isa Ridwan

“…The participants were selected using simple random sampling from the health center visitors aged 30–60 years. …”
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Design of Intelligent Feature Selection Technique for Phishing Detection by Sharvari Sagar Patil, Narendra M. Shekokar, Sridhar Chandramohan Iyer

“…Based on the evaluation, our proposed methodology of dynamic feature selection gives the best accuracy of 99.07 % with the random forest classifier model. Our work contributes to advancing phishing detection methodology by developing a dynamic feature selection technique. …”
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Emotion recognition deficits in children and adolescents with autism spectrum disorder: a comprehensive meta-analysis of accuracy and response time by Maryam Masoomi, Mahdieh Saeidi, Rommy Cedeno, Rommy Cedeno, Zahra Shahrivar, Mehdi Tehrani-Doost, Zerimar Ramirez, Divya Aishwarya Gandi, Sasidhar Gunturu, Sasidhar Gunturu

“…Data on emotion recognition accuracy and response times were synthesized using standardized mean differences in random-effects models. Heterogeneity was assessed using the I2 statistic, and sensitivity analyses were performed to ensure robustness.ResultsIndividuals with ASD exhibited significantly lower overall emotion recognition accuracy compared to TD individuals (SMD = −1.29, 95% CI: −2.20 to −0.39, p &lt; 0.01) and NDDs (SMD = −0.89, 95% CI: −1.23 to −0.55, p = 0.02). …”
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Utilization of partograph and associated factors among obstetric caregivers in Ethiopia: a systematic review and meta-analysis by Mulat Ayele, Eyob Shitie Lake, Gizachew Yilak, Getinet Kumie, Biruk Beletew Abate, Alemu Birara Zemariam, Befkad Derese Tilahun

“…Therefore, this systematic review and meta-analysis aimed to estimate the pooled prevalence of partograph utilization and its associated factors in Ethiopia.MethodComprehensive literature searches were conducted in PubMed, Google Scholar, and HINARI from 1 September 2013 to 23 October 2023. A random-effects model was used to estimate pooled prevalence and adjusted odds ratio. …”
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Yorkshire Enhanced Stop Smoking (YESS) study: a protocol for a randomised controlled trial to evaluate the effect of adding a personalised smoking cessation intervention to a lung... by Richard D Neal, David Baldwin, Sarah Lewis, John Britton, Rachael L Murray, Qi Wu, Kate Brain, Samantha L Quaife, Matthew EJ Callister, Philip A J Crosbie, Harriet D Quinn-Scoggins, Grace M McCutchan, Alex Ashurst, Suzanne Rogerson, Rebecca Thorley

“…Eligible smokers will be randomised (1:1 in permuted blocks of random size up to size 6) to receive either an enhanced, personalised SC support package, including CT scan images, or continued standard best practice. …”
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ABO-Incompatible Adult Living Donor Liver Transplantation in the Era of Rituximab: A Systematic Review and Meta-Analysis by Dipesh Kumar Yadav, Yong Fei Hua, Xueli Bai, Jianying Lou, Risheng Que, Shunling Gao, Yun Zhang, Ji Wang, Qinfen Xie, Muhammad Ibrahim Alhadi Edoo, Vikram Kumar Chutturghoon, Tingbo Liang

“…Meta-analyses were conducted based on the evaluation of heterogeneity using a fixed-effect model and a random-effect model to assess the short- and long-term outcomes following ABOi ALDLT with rituximab prophylaxis. …”
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Hierarchizing multi-scale environmental effects on agricultural pest population dynamics: a case study on the annual onset of Bactrocera dorsalis population growth in Senegalese or... by Caumette, Cécile, Diatta, Paterne, Piry, Sylvain, Chapuis, Marie-Pierre, Faye, Emile, Sigrist, Fabio, Martin, Olivier, Papaïx, Julien, Brévault, Thierry, Berthier, Karine

“…We then developed a flexible analysis pipeline centred on a recent machine learning algorithm, which allows the combination of gradient boosting and grouped random effects models or Gaussian processes, to hierarchize the effects of multi-scale environmental variables on the onset of annual BD population growth in orchards. …”
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Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study by Kun Chen, Chunxiao Sui, Ziyang Wang, Zifan Liu, Lisha Qi, Xiaofeng Li

“…The habitat radiomic model based on the segmented habitat 4 involving decision tree (DT) screening and random forest (RF) classifier was identified as the optimal model with an AUCmean of 0.806. …”
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Isoacid supplementation influences feed sorting, chewing behaviors, and enteric methane emissions differentially in mid-lactation dairy cows depending on dietary forage level by M.R.A. Redoy, S. Ahmed, M. Bulnes, D.H. Kleinschmit, M.E. Uddin

“…Sixty-four mid-lactation Holstein cows were used in a 10-wk long randomized complete block design trial. Parity, DIM, and prior milk yield (MY) for multiparous cows or genetic merit for primiparous cows were used as blocking factors. …”
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Modeling the determinants of attrition in a two-stage epilepsy prevalence survey in Nairobi using machine learning by Daniel M. Mwanga, Isaac C. Kipchirchir, George O. Muhua, Charles R. Newton, Damazo T. Kadengye, Abankwah Junior, Albert Akpalu, Arjune Sen, Bruno Mmbando, Charles R. Newton, Cynthia Sottie, Dan Bhwana, Daniel Mtai Mwanga, Damazo T. Kadengye, Daniel Nana Yaw, David McDaid, Dorcas Muli, Emmanuel Darkwa, Frederick Murunga Wekesah, Gershim Asiki, Gergana Manolova, Guillaume Pages, Helen Cross, Henrika Kimambo, Isolide S. Massawe, Josemir W. Sander, Mary Bitta, Mercy Atieno, Neerja Chowdhary, Patrick Adjei, Peter O. Otieno, Ryan Wagner, Richard Walker, Sabina Asiamah, Samuel Iddi, Simone Grassi, Sloan Mahone, Sonia Vallentin, Stella Waruingi, Symon Kariuki, Tarun Dua, Thomas Kwasa, Timothy Denison, Tony Godi, Vivian Mushi, William Matuja

“…Hyperparameters were tuned using 10-fold cross-validation, and model performance evaluated using metrics like Area under the curve (AUC), accuracy, Brier score and F1 score over 500 bootstrap samples of the test data. Results: Random forest (AUC = 0.98, accuracy = 0.95, Brier score = 0.06, and F1 = 0.94), extreme gradient boost (XGB) (AUC = 0.96, accuracy = 0.91, Brier score = 0.08, F1 = 0.90) and support vector machine (SVM) (AUC = 0.93, accuracy = 0.93, Brier score = 0.07, F1 = 0.92) were the best performing models (base learners). …”
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Biological subphenotypes in patients hospitalized with suspected infection in Thailand: a secondary analysis of a prospective observational studyResearch in context by Prapassorn Poolchanuan, Taylor D. Coston, Viriya Hantrakun, Parinya Chamnan, Gumphol Wongsuvan, Pavan K. Bhatraju, Narisara Chantratita, Direk Limmathurotsakul, T. Eoin West, Shelton W. Wright

“…Methods: In a cohort of prospectively enrolled patients hospitalized with suspected infection in northeastern Thailand, we measured 15 circulating biomarkers from a random selection of 585 subjects and developed latent profile models to identify subphenotypes. …”
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