RETRACTED ARTICLE: A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications by Hadi Hashemzadeh, Seyedehsamaneh Shojaeilangari, Abdollah Allahverdi, Mario Rothbauer, Peter Ertl, Hossein Naderi-Manesh

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Unlocking The Potential of Hybrid Models for Prognostic Biomarker Discovery in Oral Cancer Survival Analysis: A Retrospective Cohort Study by Leila Nezamabadi Farahani, Anoshirvan Kazemnejad, Mahlagha Afrasiabi, Leili Tapak

“…Concordance index (C-index), mean absolute error (MAE), mean squared error (MSE) and R-squares, were used to evaluate the performance of the models using selected features. Functional enrichment analysis was performed using DAVID database, and external validation utilized three independent datasets (GSE9844, GSE75538, GSE37991, GSE42743).Results: The findings indicated that the PSO-based method outperformed the GA-based method, achieving a smaller MAE (0.061) and MSE (0.005), R-square (0.99) and C-index (0.973), selecting 291 probes from 1069 screened. …”
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Enhancing Agricultural Futures Return Prediction: Insights from Rolling VMD, Economic Factors, and Mixed Ensembles by Yiling Ye, Xiaowen Zhuang, Cai Yi, Dinggao Liu, Zhenpeng Tang

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Molecular Docking: Methodological Approaches of Risk Assessment by A. Kh. Taldaev, I. D. Nikitin, R. P. Terekhov, I. A. Selivanova

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New Approaches to AI Methods for Screening Cardiomegaly on Chest Radiographs by Patrycja S. Matusik, Zbisław Tabor, Iwona Kucybała, Jarosław D. Jarczewski, Tadeusz J. Popiela

“…However, McNemar tests have shown that diagnoses made with TCD, rather than CTR, were more consistent with CMR diagnoses. …”
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Identification of immune-related biomarkers linked to systemic lupus erythematosus and dilated cardiomyopathy through integrated bioinformatics analysis and multiple machine learni... by Gaijie Li, Liwen Lin, Shushu Wang, Kachun Lu, KaMan Szeto, Guiting Zhou, Xianwen Tang, Chuanjin Luo, Chuanjin Luo

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Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets by Kuoyuan Cheng, Laura Martin‐Sancho, Lipika R Pal, Yuan Pu, Laura Riva, Xin Yin, Sanju Sinha, Nishanth Ulhas Nair, Sumit K Chanda, Eytan Ruppin

“…Abstract Tremendous progress has been made to control the COVID‐19 pandemic caused by the SARS‐CoV‐2 virus. …”
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An Emotion-Driven Vocal Biomarker-Based PTSD Screening Tool by Thomas F. Quatieri, Jing Wang, James R. Williamson, Richard DeLaura, Tanya Talkar, Nancy P. Solomon, Stefanie E. Kuchinsky, Megan Eitel, Tracey Brickell, Sara Lippa, Kristin J. Heaton, Douglas S. Brungart, Louis French, Rael Lange, Jeffrey Palmer, Hayley Reynolds

“…<italic>Results:</italic> Speech from low-arousal and positive-valence regions provide the highest discrimination for PTSD. Our model achieved an AUC (area under the curve) of 0.80 in detecting PCL-C ratings, outperforming models with no emotion filtering (AUC &#x003D; 0.68). …”
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Building an Otoscopic screening prototype tool using deep learning by Devon Livingstone, Aron S. Talai, Justin Chau, Nils D. Forkert

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DINAMIC SCREENING OF PRECANCEROUS ESOPHAGUS USING MOLECULAR GENETIC ANALYSIS by V. V. Anishchenko, S. E. Titov, T. L. Poloz, Yu. A. Veryaskina, A. A. Arkhipova, I. V. Bubnov

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Construction of a predictive model for cognitive impairment among older adults in Northwest China by Yu Wang, Ni Wang, Yanjie Zhao, Xiaoyan Wang, Yuqin Nie, Liping Ding

“…Model performance was evaluated on the basis of the area under the curve, sensitivity, specificity, accuracy, F1 score, precision, and recall.ResultsA total of 12,332 older adults were recruited and screened with the Mini-Mental State Examination Scale. …”
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ALGEBRAIC MODELS OF STRIP LINES IN A MULTILAYER DIELECTRIC MEDIUM by A. N. Kovalenko, A. N. Zhukov

“…The use of the Chebyshev basis and the improvement of the series convergence made it possible to develop an effective algorithm for calculating the basic electrodynamic parameters of the strip lines - the propagation constants and the wave impedances of the natural waves. …”
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Screening biomarkers related to cholesterol metabolism in osteoarthritis based on transcriptomics by ChenDeng Lao, Wei Wei, JianWen Cheng, ShiJie Liao, XiaoLin Luo, Qian Huang, HengZhen Huang, JinMin Zhao

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Research Progress in the Screening of Antimicrobial Substances Based on Machine Learning by HOU Jiangxia, JIANG Jinhui, WANG Chenxin, WANG Lan, SHI Liu, WU Wenjin, GUO Xiaojia, CHEN Sheng, CHEN Lang, CAO Feng, SUN Li, ZHOU Zhi

“…As a branch of artificial intelligence, machine learning algorithms have demonstrated exceptional capabilities in processing large-scale data, feature extraction, and model optimization, leading to their increasing application in the screening of antimicrobial substances. …”
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Screening for nasopharyngeal carcinoma in high-incidence regions——Next steps by Allan Hildesheim

“…Future efforts should focus on implementing screening programs in high-incidence populations, assessing and refining screening algorithms, and exploring new, potentially more cost-effective screening methods. …”
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Artificial Intelligence in Cardiovascular Diagnosis: Innovations and Impact on Disease Screenings by Amber Ahmad, Sahil Ahmad, Rida Ahmad, Jahnavi Bodi, Abdulla Mohamed, Ahmad Wasim

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Explainable Artificial Intelligence Driven Segmentation for Cervical Cancer Screening by Niruthikka Sritharan, Nishaanthini Gnanavel, Prathushan Inparaj, Dulani Meedeniya, Pratheepan Yogarajah

“…This represents a pioneering application of explainability techniques in the context of cervical cancer screening. Among the classification models explored, including fine-tuned variants of VGGNet and XceptionNet, VGG16-Adapted128 achieved the highest performance, marked by an accuracy of 0.94, precision of 0.94, recall of 0.94, and an F1 score of 0.94. …”
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A study on predicting the risk of coronary artery disease in OSAHS patients based on a four-variable screening tool potential predictive model and its correlation with the severity... by Yanli Yao, Yu Li, Yulan Chen, Xuan Qiu, Gulimire Aimaiti, Ayiguzaili Maimaitimin

“…ObjectiveThis study aims to evaluate the potential association between the four-variable screening tool (the 4 V) potential predictive model in predicting coronary artery disease (CAD) risk in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS) and its correlation with the severity of coronary atherosclerosis, as measured by the Gensini scoring system.Methods1197 OSAHS patients with suspected CAD who were hospitalized in the First Affiliated Hospital of Xinjiang Medical University between March 2020 and February 2024 were selected. …”
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All-Cause Mortality Risk in Elderly Patients with Femoral Neck and Intertrochanteric Fractures: A Predictive Model Based on Machine Learning by Min A, Liu Y, Fu M, Hou Z, Wang Z

“…Cox proportional hazards regression is used to explore the association between fractures type and mortality. Boruta algorithm was used to screen the risk factors related to death. …”
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Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy by Haofang Zhang, Changbao Xu, Chenge Hu, Yunlai Xue, Daoke Yao, Yifan Hu, Ankang Wu, Miao Dai, Hang Ye

“…Our study aimed to construct a machine learning algorithm predictive model to predict the risk of fungal infection following F-URL. …”
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