Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder by Shengjie Xiong, Lixin Liao, Meng Chen, Qing Gan

“…The predictive nomogram and drug predictions offer valuable tools for MDD management.…”
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Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regions by Xuan Yu, Jing Zhou, Yaping Wu, Yan Bai, Nan Meng, Qingxia Wu, Shuting Jin, Huanhuan Liu, Panlong Li, Meiyun Wang

“…The area under the receiver operating characteristic curve (AUC) was employed to assess predictive performance. Results The ROI-based models of intratumoral and peritumoral regions, utilizing deep learning algorithms on multi-sequence MRI, were capable of predicting MGMT promoter methylation status in glioblastoma patients. …”
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Mediastinal staging lymph node probability map in non-small cell lung cancer by J. Bordas-Martinez, J.L. Vercher-Conejero, G. Rodriguez-González, P.C. Notta, C. Martin Cabeza, N. Cubero, R.M. Lopez-Lisbona, M. Diez-Ferrer, C. Tebé, S. Santos, M. Cortes-Romera, A. Rosell

“…This study aims to develop a predictive algorithm that combines EBUS, PET/CT and clinical data to provide a probability of malignancy. …”
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Multiparameter diagnostic model using S100A9, CCL5 and blood biomarkers for nasopharyngeal carcinoma by Lu Long, Ya Tao, Wenze Yu, Qizhuo Hou, Yunlai Liang, Kangkang Huang, Huidan Luo, Bin Yi

“…Variable selection was conducted using least absolute shrinkage and selection operator (LASSO) regression. NPC prediction models were developed using four machine-learning algorithms, and their performance was evaluated with ROC curves. …”
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Artificial Intelligence Control Methodologies for Shape Memory Alloy Actuators: A Systematic Review and Performance Analysis by Stefano Rodinò, Giuseppe Rota, Matteo Chiodo, Antonio Corigliano, Carmine Maletta

“…Feedforward neural networks outperformed recurrent architectures (+3.1%), while Model Predictive Control (MPC) excelled for SMA actuators (+5.8% accuracy) but underperformed for MSMAs (−7.7%). …”
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A Deep Learning Segmentation Model for Detection of Active Proliferative Diabetic Retinopathy by Sebastian Dinesen, Marianne G. Schou, Christoffer V. Hedegaard, Yousif Subhi, Thiusius R. Savarimuthu, Tunde Peto, Jakob K. H. Andersen, Jakob Grauslund

“…Abstract Introduction Existing deep learning (DL) algorithms lack the capability to accurately identify patients in immediate need of treatment for proliferative diabetic retinopathy (PDR). …”
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A Risk Warning Model for Anemia Based on Facial Visible Light Reflectance Spectroscopy: Cross-Sectional Study by Yahan Zhang, Yi Chun, Hongyuan Fu, Wen Jiao, Jizhang Bao, Tao Jiang, Longtao Cui, Xiaojuan Hu, Ji Cui, Xipeng Qiu, Liping Tu, Jiatuo Xu

“…Then, we used 10 different machine learning algorithms to create a predictive model for anemia. …”
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Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors by Yu-Chen Liu, Ye-Hai Liu, Hai-Feng Pan, Wei Wang

“…Methods We developed a three-step analytical framework combining causal inference, predictive modeling, and burden projection to systematically evaluate modifiable factors associated with migraine. …”
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Associations between anthropometric indices and biological age acceleration in American adults: insights from NHANES 2009–2018 data by Xinyun Chen, Xia Chen, Fangyu Shi, Wenhui Yu, Chang Gao, Shenju Gou, Ping Fu

“…Receiver Operating Characteristic (ROC) curve analysis assessed the predictive capabilities of the anthropometric indices. …”
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Machine Learning in Ambient Assisted Living for Enhanced Elderly Healthcare: A Systematic Literature Review by Aabid A. Mir, Ahmad S. Khalid, Shahrulniza Musa, Mohammad Faizal Ahmad Fauzi, Normy Norfiza Abdul Razak, Tong Boon Tang

“…While ML and IoT significantly enhance AAL systems through predictive healthcare and personalized interventions, they also pose substantial privacy risks. …”
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Characterization and stratification of risk factors of stroke in people living with HIV: A theory-informed systematic review by Martins Nweke, Nombeko Mshunqane

“…Diabetes, atrial fibrillation, smoking habits, hypertension, age, and viral load demonstrated a high likelihood of association with stroke in PLWH and should be prioritized when constructing clinical prediction algorithms for HIV-related stroke. Conclusions The most important factors were hypertension and chronic kidney disease, followed by smoking, dyslipidemia, diabetes, HCV, HBV, CD4 count, use of ART, TB, and substance use (cocaine). …”
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Growth dynamics of splenic artery aneurysms: morphology, comorbidities, and vascular anatomical factors by Ahmet Tanyeri, Aygün Katmerlikaya, Rıdvan Akbulut, Mehmet Burak Çildağ

“…Guidelines should be refined and strengthened with patient-specific follow-up and treatment algorithms based on updated clinical data. Clinical trial number Not applicable.…”
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A multimodal approach for enhanced disease management in cauliflower crops: integration of spectral sensors, machine learning models and targeted spraying technology by Rohit ANAND, Roaf Ahmad PARRAY, Indra MANI, Tapan Kumar KHURA, Harilal KUSHWAHA, Brij Bihari SHARMA, Susheel SARKAR, Samarth GODARA, Shideh MOJERLOU, Hasan MIRZAKHANINAFCHI

“…The spectral data sets were analyzed using decision tree and support vector machine (SVM) algorithms to identify the most accurate model for distinguishing diseased and healthy plants. …”
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Machine vision-based automatic fruit quality detection and grading by Amna, Muhammad Waqar AKRAM, Guiqiang LI, Muhammad Zuhaib AKRAM, Muhammad FAHEEM, Muhammad Mubashar OMAR, Muhammad Ghulman HASSAN

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Automated Parkinson’s Disease Diagnosis Using Decomposition Techniques and Deep Learning for Accurate Gait Analysis by S. Jeba Priya, C. Anand Deva Durai, M. S. P. Subathra, S. Thomas George, Andrew Jeyabose

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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…This study aims to identify the best deep learning and machine learning models to identify the unethical behavior patterns of learners using distance education exam data of an educational institution. …”
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Real Time MEMS-Based Joint Friction Identification for Enhanced Dynamic Performance in Robotic Applications by Paolo Righettini, Giovanni Legnani, Filippo Cortinovis, Federico Tabaldi, Jasmine Santinelli

“…The real-time properties of the method also enable future investigations into topics such as MEMS-based diagnostics and predictive maintenance.…”
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Mathematisation of specialised disciplines as the basis for fundamentalising IT training in universities by E. A. Perminov, V. A. Testov

“…Additionally, a significant gap has emerged in higher education regarding the development of basic education curriculum for IT training. …”
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Evaluation of the effects of the body on athletes’ emotions and motivational behaviors from the perspective of big data public health by Qiang Zhang, Diandong Lian, Yiqiao Zhang

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Logic, Dilemma and Realization of Digital Transformation of Classroom Teaching Assessment by LIN Ying, TIAN Yingqian, WANG Jinrong

“…Based on this, the digital transformation of classroom teaching assessment should be realized through: firstly, improving the digital literacy of assessment subjects and adhering to the origin of educating; secondly, strengthening multidimensional data collection and dynamic digital algorithms; thirdly, reshaping the content of embodied assessment to build a diverse and integrated assessment method; fourthly, regulate technology usages and improve data supervision and management systems.…”
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