Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach by Huali Jiang, Weijie Chen, Benfa Chen, Tao Feng, Heng Li, Dan Li, Shanhua Wang, Weijie Li

“…Machine learning algorithms (Support Vector Machine (SVM), Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO)) were applied to identify hub genes. …”
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Leveraging Artificial Intelligence and Data Science for Integration of Social Determinants of Health in Emergency Medicine: Scoping Review by Ethan E Abbott, Donald Apakama, Lynne D Richardson, Lili Chan, Girish N Nadkarni

“…With a significant focus on the ED and notable NLP model performance, there is an imperative to standardize SDOH data collection, refine algorithms for diverse patient groups, and champion interdisciplinary synergies. …”
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MSGEGA: Multiscale Gaussian Enhancement and Global-Aware Network for Infrared Small Target Detection by Yuyang Xi, Liuwei Zhang, Ying Jiang, Feng Qian, Fanjiao Tan, Qingyu Hou

“…Specifically, the proposed method demonstrates significant advantages on the screened dataset, achieving an AUC of 0.992. At a detection rate of 0.871, it maintains a false alarm rate of 0.9<italic>e</italic>-5, outperforming all comparison algorithms. …”
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Estimation of the water content of needles under stress by Erannis jacobsoni Djak. via Sentinel-2 satellite remote sensing by Jiaze Guo, Xiaojun Huang, Xiaojun Huang, Xiaojun Huang, Debao Zhou, Junsheng Zhang, Gang Bao, Gang Bao, Siqin Tong, Siqin Tong, Yuhai Bao, Yuhai Bao, Dashzebeg Ganbat, Dorjsuren Altanchimeg, Davaadorj Enkhnasan, Mungunkhuyag Ariunaa

“…Multiple vegetation indices are screened via recursive feature elimination cross validation (RFECV), and then support vector regression (SVR) and back-propagation neural network (BP) models are used to predict the leaf weight content fresh (LWCF) and leaf weight content dry (LWCD) of needles over a large area. …”
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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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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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U-shaped relationship between frailty and non-HDL-cholesterol in the elderly: a cross-sectional study by Yu Pan, Yan Yuan, Juan Yang, Zhu Qing Feng, Xue Yin Tang, Yi Jiang, Gui Ming Hu, Jiang Chuan Dong

“…The variables underwent screening through Least Absolute Shrinkage and Selection Operator (LASSO) regression, univariate logistic regression, and Light Gradient Boosting Machine (LightGBM), with models developed through multivariate logistic regression and the LightGBM algorithm. …”
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Rapid Resilience Assessment and Weak Link Analysis of Power Systems Considering Uncertainties of Typhoon by Wenqing Ma, Xiaofu Xiong, Jian Wang

“…Second, for the resilience assessment process, the impact increment method is used to reduce the dimensionality of multiple fault state analysis in the power system, and resilience indexes are calculated by screening the contingency set based on depth-first traversal through a backtracking algorithm. …”
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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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Artificial Intelligence Powered Automated and Early Diagnosis of Acute Lymphoblastic Leukemia Cancer in Histopathological Images: A Robust SqueezeNet-Enhanced Machine Learning Fram... by Vineet Mehan

“…The growing prevalence of acute lymphoblastic leukemia cancer worldwide underlines the critical need for early and more precise detection to counter this deadly disease. …”
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Comparison of sample preparation methods for higher heating values in various sugarcane varieties using near-infrared spectroscopy by Kantisa Phoomwarin, Khwantri Saengprachatanarug, Jetsada Posom, Seree Wongpichet, Kittipong Laloon, Arthit Phuphaphud

“…Spectral data were pre-processed using seven techniques to minimize noise, and four variable selection algorithms–Variable Importance in Projection, Successive Projection Algorithm, Genetic Algorithm, and correlation-based selection via Partial Least Squares Regression–were employed to improve modelling accuracy.In parallel, four machine learning models–AdaBoost Regressor, Gradient Boosting, K-Nearest Neighbors, and Random Forest–were applied to the same dataset for Higher heating value prediction. …”
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High prevalence of resistance to macrolides and fluoroquinolones in Mycoplasma genitalium isolated from patients in two Russian megalopolises – Moscow and St. Petersburg in 2021– 2... by Edelstein I.A., Gushchin A.E., Gladin D.P., Romanov A.V., Negasheva E.S., Frigo N.V., Kozlov R.S., Potekaev N.N., Kozlova N.S., Borukhovich D.G.

“…The most common combination of mutations was A2059G (23S rRNA) + S80I (parC), which made up to 33% (18/54) in St. Petersburg and 25.7% (25/97) in Moscow. …”
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A novel nomogram for survival prediction in renal cell carcinoma patients with brain metastases: an analysis of the SEER database by Fei Wang, Xihao Wang, Zhigang Feng, Jun Li, Hailiang Xu, Hengming Lu, Lianqu Wang, Zhihui Li

“…Potential risk factors were initially screened applying the eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning algorithms. …”
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A beginner’s approach to deep learning applied to VS and MD techniques by Stijn D’Hondt, José Oramas, Hans De Winter

“…There are many ways in which DL can be applied to these molecular modelling techniques to achieve more accurate results in a more efficient manner or expedite the data analysis of the acquired results. …”
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DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure by Wenwu Tang, Wenwu Tang, Zhixin Wang, Xinzhu Yuan, Liping Chen, Haiyang Guo, Zhirui Qi, Ying Zhang, Xisheng Xie

“…In addition, we further explored potential mechanism and function of hub genes in HF of patients with MHD through GSEA, immune cell infiltration analysis, drug analysis and establishment of molecular regulatory network.ResultsTotally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. …”
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Exploration of biomarkers for predicting the prognosis of patients with diffuse large B-cell lymphoma by machine-learning analysis by Shifen Wang, Hong Tao, Xingyun Zhao, Siwen Wu, Chunwei Yang, Yuanfei Shi, Zhenshu Xu, Dawei Cui

“…Moreover, four hub genes (CXCL9, CCL18, C1QA and CTSC) were significantly screened from the three datasets using RF algorithms. …”
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Rapid Detection of Antibiotic Mycelial Dregs Adulteration in Single-Cell Protein Feed by HS-GC-IMS and Chemometrics by Yuchao Feng, Yang Li, Wenxin Zheng, Decheng Suo, Ping Gong, Xiaolu Liu, Xia Fan

“…In addition, the feasibility of quantitative analysis of the AMDs content in adulterated SCPF based on partial least squares regression (PLSR) algorithm. In total, 88 volatile organic compounds (VOCs) were detected. …”
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Research and application of image recognition-based identification for flesh browning of loquat fruits by CHEN Yujia, DENG Chaojun, ZHANG Tingting, WANG Xiuping, CHEN Xiuping, ZHAO Jianing, MA Cuilan, JIANG Jimou

“…The Euclidean distance algorithm indicated that the percentage of browning area in the white flesh type was significantly higher than that in the red flesh type. …”
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