Deep learning for smartphone-aided detection system of Helicobacter Pylori in gastric biopsy by Guanmeng Gao, Zihan Wei, Fei Pei, Yajie Du, Beiying Liu

“…All stained slides were scanned for analysis by the Faster-R-CNN with ResNet 50 or VGG16, then the model performance was evaluated. Furthermore, the real-time microscopic field, smartphone and AI algorithm were connected through 5G networks and the AI results were sent back to the smartphone for confirmation by the pathologists. …”
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Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors by Ran Zhang, Guo Chen, Shasha Gao, Lu Chen, Yongchao Cheng, Xiuquan Gu, Yue Wang

“…The collected data was subsequently utilized to develop a correlation model linking the multi-physical parameters to gas sensitive performance using intelligent algorithms. …”
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Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network by Kristian Stojšić, Dina Miletić Rigo, Slaven Jurković

“…The trained model performed similarly to state-of-the-art lumbar spine segmentation models, with an average DSC value of 0.914 ± 0.007 for the vertebrae and 0.902 for the spinal canal. …”
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Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy by Hua Chen, Kehui Mei, Yuan Zhou, Nan Wang, Guangxing Cai

“…Then, the features of the dataset are initially screened using the mutual information method, and further secondary feature selection is performed using the recursive feature elimination method based on the XGBoost algorithm. …”
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The use of heart rate variability, oxygen saturation, and anthropometric data with machine learning to predict the presence and severity of obstructive sleep apnea by Rafael Rodrigues dos Santos, Matheo Bellini Marumo, Alan Luiz Eckeli, Helio Cesar Salgado, Luiz Eduardo Virgílio Silva, Renato Tinós, Rubens Fazan

“…IntroductionObstructive sleep apnea (OSA) is a prevalent sleep disorder with a high rate of undiagnosed patients, primarily due to the complexity of its diagnosis made by polysomnography (PSG). Considering the severe comorbidities associated with OSA, especially in the cardiovascular system, the development of early screening tools for this disease is imperative. …”
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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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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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MYOPIA PREVALENCE AMONG STUDENTS DURING COVID-19 PANDEMIC. A SYSTEMATIC REVIEW AND META-ANALYSIS by Natasha Hana Savitri, Adinda Sandya Poernomo, Muhammad Bagus Fidiandra1, Eka Candra Setyawan1, Arinda Putri Auna Vanadia1, Bulqis Inas Sakinah1, Lilik Djuari

“…Data retrieval used the PICO method and journal adjustments were selected using the PRISMA algorithm. Data analysis was performed using a random-effects model. …”
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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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The CD163 + tissue-infiltrating macrophages regulate ferroptosis in thyroid-associated ophthalmopathy orbital fibroblasts via the TGF-β/Smad2/3 signaling pathway by Xuemei Li, Siyi Wang, Hanwen Cao, Simin Xu, Chao Xiong, Jinhai Yu, Yunxiu Chen, Zhangjun Ren, Min Li, Ying Hu, Puying Gan, Qihua Xu, Yaohua Wang, Hongfei Liao

“…Finally, potential clinical drugs targeting CD163 + macrophages with high ferroptosis activity in TAO were predicted using the Random Walk with Restart (RWR) algorithm combined with the DGIdb database. Results We first utilized TAO-related datasets from the GEO database, combined with the FerrDb ferroptosis database, to identify changes in iron metabolism genes during TAO progression through differential expression analysis, screening 7 key ferroptosis-related proteins. …”
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Significance of Immune-Related Genes in the Diagnosis and Classification of Intervertebral Disc Degeneration by Bo Wu, Xinzhou Huang, Mu Zhang, Wei Chu

“…Then, we utilized a random forest (RF) model to screen six candidate IRGs to predict the risk of IDD. …”
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High-performance computing for static security assessment of large power systems by Venkateswara Rao Kagita, Sanjaya Kumar Panda, Ram Krishan, P. Deepak Reddy, Jabba Aswanth

“…We perform extensive experiments to evaluate the efficacy of the proposed algorithm. As a result, we establish that the proposed parallel algorithm with high-performance computing (HPC) computing is much faster than the traditional algorithms. …”
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High‐resolution mapping of cancer cell networks using co‐functional interactions by Evan A Boyle, Jonathan K Pritchard, William J Greenleaf

“…This work establishes new algorithms for probing cancer cell networks and motivates the acquisition of further CRISPR screen data across diverse genotypes and cell types to further resolve complex cellular processes.…”
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A Framework for Segmentation and Classification of Cervical Cells Under Long-tailed Distribution by YANG Xiao na, LI Chao wei, SHAO Hui li, HE Yong jun

“…This framework first performs cell nucleus segmentation, uses U-Net as the base model for layer reduction, adds AG module, and uses ACBlock module instead of traditional standard convolutional blocks; then uses ResNeSt for coarse classification of segmented data, fuses manual features extracted based on physicians ′ experience and machine features extracted by ResNeSt network for fine classification , and uses active learning iteratively to expand the cervical cell categories and fuse the ACBlock module in the BBN model to process the long-tail data; finally, the diagnostic indexes of abnormal cells are refined and abnormal cells are screened according to the TBS diagnostic criteria and the physician ′s diagnostic experience. …”
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YOLOv5-DTW: Gesture recognition based on YOLOv5 and dynamic time warping for digital media design by Lu Zhao, Jing Yu

“…Dynamic time warping (DTW) algorithm is used to fuse different surface EMG signals, calculate the similarity between samples and models, and realize gesture recognition. …”
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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

“…Methods This retrospective study uses data from patients in the medical ICU from 2015-to-2019 to develop ML models. The models were based on dynamic ICU-readily available features such as hourly vital signs, laboratory results, and interventions and were developed using various ML algorithms. …”
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Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data by Tongtong Jin, Ayitijiang· Halili

“…This study systematically developed machine learning (ML) models to predict depression risk in disabled elderly individuals using longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), providing a potentially generalizable tool for early screening.MethodsThis study utilized longitudinal data from the CHARLS 2011–2015 cohort. …”
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Numerical analysis method of stress wave transmission attenuation of coal and rock structural plane by Wenlong SHEN, Renren ZHU, Ziqiang CHEN, Guocang SHI

“…The simulation and machine learning of stress wave transmission in the experimental process of Split Hopkinson Pressure Bar (SHPB) were carried out by combining the Barton-Bandis nodal ontology model, UDEC discrete element simulation and Gray Wolf Algorithm optimized BP neural network technology. …”
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A Two-Stage Penalized Logistic Regression Approach to Case-Control Genome-Wide Association Studies by Jingyuan Zhao, Zehua Chen

“…This approach consists of a screening stage and a selection stage. In the screening stage, main-effect and interaction-effect features are screened by using L1-penalized logistic like-lihoods. …”
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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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