A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network by Owen He, Daoxing Chen, Yimei Li

“…In independent test sets, ReproTox-CMPNN achieved a mean AUC of 0.946, ACC of 0.857 and F1 score of 0.846, surpassing traditional algorithms to establish itself as a new state-of-the-art model in this field. …”
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Integrated analysis of single-cell RNA-seq and spatial transcriptomics to identify the lactylation-related protein TUBB2A as a potential biomarker for glioblastoma in cancer cells... by Yifan Xu, Chonghui Zhang, Jinpeng Wu, Pin Guo, Nan Jiang, Chao Wang, Yugong Feng

“…Notably, the expression levels of these three hub genes and the lactylation level of TUBB2A in GBM tissues were significantly higher compared to those in normal tissues.ConclusionsWe propose and validate a IQR lactylation screening method that provides potential insights for GBM therapy and an effective framework for developing gene screening models applicable to other diseases and pathogenic mechanisms.…”
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Early breast cancer detection via infrared thermography using a CNN enhanced with particle swarm optimization by Riyadh M. Alzahrani, Mohamed Yacin Sikkandar, S. Sabarunisha Begum, Ahmed Farag Salem Babetat, Maryam Alhashim, Abdulrahman Alduraywish, N. B. Prakash, Eddie Y. K. Ng

“…An Enhanced Particle Swarm Optimization (EPSO) algorithm is integrated to automatically fine-tune CNN hyperparameters, thereby minimizing manual effort and enhancing computational efficiency. …”
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Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction by Wesam Ibrahim Hajim, Suhaila Zainudin, Kauthar Mohd Daud, Khattab Alheeti

“…Advanced machine learning (ML) and deep learning (DL) methods have recently been utilized in Drug Response Prediction (DRP), and these models use the details from genomic profiles, such as extensive drug screening data and cell line data, to predict the response of drugs. …”
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Improving synergistic drug combination prediction with signature-based gene expression features in oncology by Mozhgan Mozaffarilegha, Sajjad Gharaghani

“…We compared their performance with that of conventional drug signatures and chemical structure-based descriptors.Results:Our results demonstrate that models incorporating DRS features consistently outperform traditional approaches across all evaluated algorithms. …”
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Machine learning-based ultrasound radiomics for predicting risk of recurrence in breast cancer by Wei Fan, Hao Cui, Xiaoxue Liu, Xudong Zhang, Xinran Fang, Junjia Wang, Zihao Qin, Xiuhua Yang, Jiawei Tian, Lei Zhang

“…The receiver operating characteristic (ROC), calibration, and decision curve analysis (DCA) curves were used to evaluate the model’s clinical applicability and predictive performance.ResultsA total of 12 ultrasound radiomics features were screened, of which wavelet.LHL first order Mean features weighed more and tended to have a high risk of recurrence. …”
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Technical Code Analysis of Geomagnetic Flaw Detection of Suppression Rigging Defect Signal Based on Convolutional Neural Network by Gang Zhao, Changyu Han, Zhongxiang Yu, Zhipan Li, Guoao Yu, Hongmei Zhang, Dadong Zhao, Zhengyi Jiang

“…The single-stage object detection algorithm YOLOv5 (You Only Look Once) based on convolutional neural network model calculation is used, the scale detection layer and positioning loss function of the YOLOv5 algorithm are improved and optimized, and the improved YOLOv5 algorithm is used for experiments. …”
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Helicobacter pylori eradication treatment for gastric carcinoma prevention in asymptomatic or dyspeptic adults: systematic review and Bayesian meta-analysis of randomised controlle... by Teruhiko Terasawa, Isao Miyashiro, Katsuaki Kato, Takaki Yoshikawa, Reo Takaku, Hiroshi Nishida

“…However, concerns about supporting unfocused screening and eradication programme in healthy, asymptomatic populations have arisen. …”
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A secure and efficient user selection scheme in vehicular crowdsensing by Min Zhang, Qing Ye, Zhimin Yuan, Kaihuan Deng

“…The model uses PCA for data dimensionality reduction to eliminate redundant information and then employs LSTM to process time series data, capture long-term dependencies for more accurate user credit prediction, screen high-quality users, and improve perceived data quality. …”
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High-flow nasal cannula therapy versus continuous positive airway pressure for non-invasive respiratory support in paediatric critical care: the FIRST-ABC RCTs by Padmanabhan Ramnarayan, Alvin Richards-Belle, Karen Thomas, Laura Drikite, Zia Sadique, Silvia Moler Zapata, Robert Darnell, Carly Au, Peter J Davis, Izabella Orzechowska, Julie Lester, Kevin Morris, Millie Parke, Mark Peters, Sam Peters, Michelle Saull, Lyvonne Tume, Richard G Feltbower, Richard Grieve, Paul R Mouncey, David Harrison, Kathryn Rowan

“…Background Despite the increasing use of non-invasive respiratory support in paediatric intensive care units, there are no large randomised controlled trials comparing two commonly used non-invasive respiratory support modes, continuous positive airway pressure and high-flow nasal cannula therapy. …”
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An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processes by Qian Yi, Xin Wu, Junkang Zhuo, Congbo Li, Chuanjiang Li, Huajun Cao

“…Using life cycle assessment (LCA) theory, carbon emission sources are accurately analyzed and quantified, and a full life cycle carbon emission model is established. The key process parameters affecting part performance and carbon emission were screened through mechanism analysis, and the incremental data were fused by the Elasticity Weight Consolidation (EWC) algorithm to establish an EWC-BPNN heat treatment carbon emission prediction model. …”
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Structural strength optimization design of ultra-high-pressure and ultra-wear-resistant pneumatic ball valve opened and closed at large explosion instantaneously using finite eleme... by Xianmei Liu, Mingcun Zhang

“…By building an ultra-high pressure burst test bench, this paper combines strain gauges and high-speed cameras to verify the accuracy of the model and corrects the simulation boundary conditions based on the Kalman filter algorithm. …”
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Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic... by Peng Lyu, Na Xie, Xu-peng Shao, Shuai Xing, Xiao-yue Wang, Li-yun Duan, Xue Zhao, Jia-min Lu, Rong-fei Liu, Duo Zhang, Wei Lu, Kai-liang Fan

“…Abstract This study aims to predict and diagnose pediatric septic shock through the screening of immune infiltration-related biomarkers. …”
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Poor Nutritional Status Increases Risk of Postoperative Adverse Events after Surgical Management of Traumatic Ankle Fracture by Sanket Mehta MD, Nicholas Danford MD

“…Patients with poor nutritional status were older (56 vs. 51 years), more likely to present in shock (7.5% vs. 2.3%) and due to motor vehicle collision mechanism (all P< 0.001). …”
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Maternal factors associated with early-onset neonatal sepsis among caesarean-delivered babies at Mbarara Regional Referral Hospital, Uganda: a case-control study by James M. Maisaba, Richard Migisha, Asiphas Owaraganise, Leevan Tibaijuka, David Collins Agaba, Joy Muhumuza, Joseph Ngonzi, Stella Kyoyagala, Musa Kayondo

“…We enrolled mother-baby pairs for both groups, obtaining maternal data via structured questionnaires The diagnosis of EONS was made using the WHO Young Infant Integrated Management of Childhood Illnesses algorithm. …”
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Multi-MicroRNA Analysis Can Improve the Diagnostic Performance of Mammography in Determining Breast Cancer Risk by Ji-Eun Song, Ji Young Jang, Kyung Nam Kang, Ji Soo Jung, Chul Woo Kim, Ah Sol Kim

“…Breast cancer risk scores for each Breast Imaging-Reporting and Data System (BI-RADS) category in multi-microRNA analysis were analyzed to examine the correlation between breast cancer risk scores and mammography categories. We generated two models using two classification algorithms, SVM and GLM, with a combination of four miRNA biomarkers showing high performance and sensitivities of 84.5% and 82.1%, a specificity of 85%, and areas under the curve (AUCs) of 0.967 and 0.965, respectively, which showed consistent performance across all stages of breast cancer and patient ages. …”
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Nanomaterial isolated extracellular vesicles enable high precision identification of tumor biomarkers for pancreatic cancer liquid biopsy by Zachary F. Greenberg, Samantha Ali, Andrew Brock, Jinmai Jiang, Thomas D. Schmittgen, Song Han, Steven J. Hughes, Kiley S. Graim, Mei He

“…Through modelling the ATP6V0B cycling threshold, we reported 3 models with AUCs between 0.86 and 0.88, showcasing an enabling and clinically translatable liquid biopsy approach for early detection of pancreatic cancer using circulating EVs. …”
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Identification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis by Hang Chen, Biao Wu, Biao Wu, Kunyu Guan, Liang Chen, Kangjie Chai, Maoji Ying, Dazhi Li, Weicheng Zhao

“…Through further differential analysis and screening using machine learning algorithms, APLNR, PCDH12, PODXL, SLC40A1, TM4SF18, and TNFRSF25 were identified as key diagnostic genes for atherosclerosis. …”
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Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome by Yaxuan He, Zekai Chen, Zhaohui Tang, Yuexiang Qin, Fang Wang

“…Traditional invasive diagnostic methods are costly, inconvenient, and unsuitable for large-scale screening. Developing a non-invasive, accurate prediction model is clinically significant for early MetS detection and prevention. …”
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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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