Time-Distributed Vision Transformer Stacked With Transformer for Heart Failure Detection Based on Echocardiography Video by Mgs M. Luthfi Ramadhan, Adyatma W. A. Nugraha Yudha, Muhammad Febrian Rachmadi, Kevin Moses Hanky Jr Tandayu, Lies Dina Liastuti, Wisnu Jatmiko

“…This study proposed a novel deep learning model consisting of a time-distributed vision transformer stacked with a transformer. …”
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Predicting cardiotoxicity in drug development: A deep learning approach by Kaifeng Liu, Huizi Cui, Xiangyu Yu, Wannan Li, Weiwei Han

“…This study not only improved the predictive accuracy of cardiotoxicity models but also promoted a more reliable and scientifically interpretable method for drug safety assessment. …”
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Pulmonary arterial hypertension in systemic lupus erythematosus: identification of risk factors and haemodynamics characteristics in a multicentre retrospective cohort by Carlo Alberto Scirè, Alessandra Bortoluzzi, Micaela Fredi, Matthias Schneider, Veronica Codullo, Carlomaurizio Montecucco, Alain Meyer, Ilaria Cavazzana, Lorenzo Cavagna, Franco Franceschini, Andreas Schwarting, Konstantinos Triantafyllias, Gamal Chehab, Christina Düsing, Jutta Richter, Ettore Silvagni, Oliver Sander, Giovanni Zanframundo, Claudia Bracaglia, Matthieu Canuet, Stefano Ghio, Rebecca Fischer, Emiliano Marasco, Sezgin Sahin, Lisa Keller, Marianne Riou, Stefanie Keymel

“…The variables we identified could be used to implement a screening algorithm to identify patients with SLE at risk of developing PAH.…”
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A Cell Component-Related Prognostic Signature for Head and Neck Squamous Cell Carcinoma Based on the Tumor Microenvironment by Siyu Li, Yajun Gu, Junguo Wang, Dengbin Ma, Xiaoyun Qian, Xia Gao

“…In this study, we aimed to develop a cell component-related prognostic model based on TME. We screened cell component enrichments from samples in The Cancer Genome Atlas (TCGA) HNSCC cohort using the xCell algorithm. …”
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Acetic Acid Production from Aspergillus terreus Isolated from Some Agricultural Soils Collected from Selected Locations within the North Gondar Zone, Amhara Region, Ethiopia by Kidist Alemayehu, Tamene Milkessa Jiru, Nega Berhane

“…A sequence similarity of 98.5% to A. terreus isolate LL2 (KIA) was obtained by comparing the Aspergillus isolate to a reference sequence in the GenBank using the BLAST algorithm. It can be concluded from this study that A. terreus isolated from agricultural soil in the north Gondar zone of Ethiopia could produce more acetic acid using barely straw as a substrate.…”
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Risk factors for osteoporosis in men aged 40 years or older: the results of the program «Osteoscreening-Russia» by O. A. Nikitinskaya, N. V. Toroptsova, E. L. Nasonov

“…The survey was conducted using a unified questionnaire. Screening also involved a densitometric study of distal forearm bone mineral density using a peripheral X-ray osteodensitometer (Osteometer Meditech DTX-200). …”
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A computer-driven ventilator liberation protocol in pediatric patients: a single-center pilot randomized controlled trial by Song Chen, Changxue Xiao, Xue Lu, Min Liao, Chengjun Liu, Feng Xu, Jing Li

“…The test group underwent ventilator liberation driven by a computerized algorithm combining protocolized screening, air leak testing, and spontaneous breathing testing. …”
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Cross-validation of the safe supplement screener (S3) predicting consistent third-party-tested nutritional supplement use in NCAA Division I athletes by Kinta D. Schott, Avaani Bhalla, Emma Armstrong, Ryan G. N. Seltzer, Floris C. Wardenaar

“…IntroductionThis cross-sectional study aimed to cross-validate an earlier developed algorithm-based screener and explore additional potential predictors for whether athletes will use third-party-tested (TPT) supplements.MethodsTo justify the initial model behind the supplement safety screener (S3) algorithm which predicts whether athletes will use TPT supplements, a cross-validation was performed using this independent dataset based on responses of a large group of collegiate NCAA DI athletes. …”
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Machine learning for prediction of Helicobacter pylori infection based on basic health examination data in adults: a retrospective study by Qiaoli Wang, Tao Liang, Yuexi Li, Peng Zhou, Xiaoqin Liu

“…After feature screening, 10 factors were selected for the prediction model. …”
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Analysis of risk factors of acute respiratory failure after radical resection of esophageal cancer by two methods by LEI Xiuwen, ZHU Xiaolei, TIAN Long

“…The combination of the two methods is conducive to the joint screening of risk factors for ARF after radical resection for esophageal cancer, and the three rules are more valuable in guiding clinical intervention." …”
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Exploring timely and safe discharge from ICU: a comparative study of machine learning predictions and clinical practices by Chao Ping Wu, Rachel Benish Shirley, Alex Milinovich, Kaiyin Liu, Eduardo Mireles-Cabodevila, Hassan Khouli, Abhijit Duggal, Anirban Bhattacharyya

“…It also highlights ML models can serve as a promising screening tool to enhance ICU discharge, presenting a pathway toward more efficient and reliable critical care decision-making.…”
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Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images by Mingzhi Zhang, Tsz Kin Ng, Yi Zheng, Guihua Zhang, Jian-Wei Lin, Ji Wang, Jie Ji, Peiwen Xie, Yongqun Xiong, Hanfu Wu, Cui Liu, Huishan Zhu, Jinqu Huang, Leixian Lin

“…The deep learning (DL) performance was compared with the diabetic retinopathy experts.Setting Data were collected from Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Chaozhou People’s Hospital and The Second Affiliated Hospital of Shantou University Medical College from January 2010 to December 2023.Participants 7790 volumes of 7146 eyes from 4254 patients were annotated, of which 6281 images were used as the development set and 1509 images were used as the external validation set, split based on the centres.Main outcomes Accuracy, F1-score, sensitivity, specificity, area under receiver operating characteristic curve (AUROC) and Cohen’s kappa were calculated to evaluate the performance of the DL algorithm.Results In classifying DME with non-DME, our model achieved an AUROCs of 0.990 (95% CI 0.983 to 0.996) and 0.916 (95% CI 0.902 to 0.930) for hold-out testing dataset and external validation dataset, respectively. …”
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Exploring the role of repetitive negative thinking in the transdiagnostic context of depression and anxiety in children by Kuiliang Li, Lei Ren, Xiao Li, Chang Liu, Xuejiao Tan, Ming Ji, Xi Luo

“…Network analysis revealed that RNT’s core features exhibited the highest bridge betweenness and bridge expected influence, indicating a critical mediating role in the co-occurrence of symptoms. The random forest model showed optimal predictive performance (AUC = 0.90, recall = 0.95), supporting its applicability for early screening. …”
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Cloud-based optimized deep learning framework for automated glaucoma detection using stationary wavelet transform and improved grey-wolf-optimization with ELM approach by Debendra Muduli, Syed Irfan Yaqoob, Santosh Kumar Sharma, Anuradha S. Kanade, Mohammad Shameem, Harendra S. Jangwan, P.M. Ashok Kumar, Abu Taha Zamani

“…The model's performance was evaluated in a cloud-based environment using two datasets: ORIGA and G1020. …”
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3D Film Animation Image Acquisition and Feature Processing Based on the Latest Virtual Reconstruction Technology by Siwei Wu, Shan Xiao, Yihua Di, Cheng Di

“…Finally, the target 3D face is reconstructed using the feature points of the target face for model matching. The experimental results show that the algorithm reconstructs faces with high realism and accuracy, and the algorithm can reconstruct expression faces.…”
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Psychometric properties of the German version of the Traumatic Grief Inventory-Self Report Plus (TGI-SR+) by Julia Treml, Viktoria Schmidt, Elmar Braehler, Matthias Morfeld, Anette Kersting

“…Despite the same name, both versions of PGD differ in symptom count, content, and diagnostic algorithm. A single instrument to screen for both PGD diagnoses is critical for bereavement research and care. …”
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Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study by Ting Peng, Rujia Miao, Hao Xiong, Yanhui Lin, Duzhen Fan, Jiayi Ren, Jiangang Wang, Yuan Li, Jianwen Chen

“…In the test group, all AUC were also greater than 0.80. The LightGBM model showed the best IR prediction performance with an accuracy of 0.7542, sensitivity of 0.6639, specificity of 0.7642, F1 ConclusionBy leveraging low-cost laboratory indicators and questionnaire data, the LightGBM model effectively predicts IR status in nondiabetic individuals, aiding in large-scale IR screening and diabetes prevention, and it may potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.…”
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Evaluating the role of insulin resistance in chronic intestinal health issues: NHANES study findings by Dongyao Zhao, Meihua Zhao, Bing Gao, He Lu

“…Key variables were selected via the Boruta algorithm and incorporated into weighted multivariate logistic regression models. …”
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LABORATORY OF CLINICAL IMMUNOLOGY N.V. SKLIFOSOVSKY RESEARCH INSTITUTE FOR EMERGENCY MEDICINE (HISTORY AND PRESENT) by M. A. Godkov, G. V. Bulava

“…During 45 years of work of immunological service formed the algorithm of the adequate immunological screening was formed, number of innovative methods of diagnosis was developed, the ideology of post-test counseling of patients by immunologists was created, mathematical methods of storage, modeling and processing of research results was introduced. …”
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Efficient evidence selection for systematic reviews in traditional Chinese medicine by Yizhen Li, Zhe Huang, Zhongzhi Luan, Shujing Xu, Yunan Zhang, Lin Wu, Darong Wu, Dongran Han, Yixing Liu

“…Methods We integrated an established deep learning model (Evi-BERT combined rule-based method) with Boolean logic algorithms and an expanded retrieval strategy to automatically and accurately select potential evidence with minimal human intervention. …”
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