Prediction of mild cognitive impairment using blood multi-omics data by Daniel Frank Zhang, Daniel Frank Zhang, Cigdem Sevim Bayrak, Qi Zeng, Qi Zeng, Qi Zeng, Minghui Wang, Bin Zhang, Bin Zhang, Bin Zhang, Bin Zhang

“…Mild cognitive impairment (MCI) represents an initial phase of memory or other cognitive function decline and is viewed as an intermediary stage between normal aging and Alzheimer’s disease (AD), the most prevalent type of dementia. …”
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Personalized whole-brain activity patterns predict human corticospinal tract activation in real-time by Uttara U. Khatri, Kristen Pulliam, Muskan Manesiya, Melanie Vieyra Cortez, José del R. Millán, Sara J. Hussain

“…These findings represent a pivotal step towards using personalized brain state-dependent TMS interventions to promote poststroke CST function.…”
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Rice Disease Detection: TLI-YOLO Innovative Approach for Enhanced Detection and Mobile Compatibility by Zhuqi Li, Wangyu Wu, Bingcai Wei, Hao Li, Jingbo Zhan, Songtao Deng, Jian Wang

“…First, transfer learning is used to import the pretrained model weights into the TLI-YOLO model, which significantly reduces the dataset requirements and accelerates model convergence. …”
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Multi-Algorithm-Integrated Tertiary Lymphoid Structure Gene Signature for Immune Landscape Characterization and Prognosis in Colorectal Cancer Patients by Xianqiang Liu, Dingchang Li, Yue Zhang, Hao Liu, Peng Chen, Yingjie Zhao, Guanchao Sun, Wen Zhao, Guanglong Dong

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Data-driven diabetes mellitus prediction and management: a comparative evaluation of decision tree classifier and artificial neural network models along with statistical analysis by Idris Zubairu Sadiq, Babangida Sanusi Katsayal, Bashiru Ibrahim, Maryam Ibrahim, Hassan Aliyu Hassan, Umar Muhammad Ghali, Abdullahi Garba Usman, Abubakar Usman, Sani Isah Abba

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DTC-m6Am: A Framework for Recognizing N6,2′-O-dimethyladenosine Sites in Unbalanced Classification Patterns Based on DenseNet and Attention Mechanisms by Hui Huang, Fenglin Zhou, Jianhua Jia, Huachun Zhang

“…For the data imbalance problem, we use the focal loss function to balance the learning effect of positive and negative samples and improve the performance of the model on imbalanced data. …”
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Automated ejection fraction and risk stratification in cardiomyopathy patients with diverse LV geometry using 2D echocardiography by Ziwei Zhu, Ke Fan, Shuyuan Zhang, Tingting Hu, Jingyi Li, Ze Zhao, Ye Jin, Shuyang Zhang

“…Abstract Cardiomyopathy often alters left ventricular geometry (LVG), impairing cardiac function. We developed a deep learning (DL) model to estimate left ventricular ejection fraction (LVEF) from echocardiographic images while accounting for LVG variability and assessed prognostic factors across LVG subtypes. …”
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Predicting 30-Day Venous Thromboembolism Following Total Joint Arthroplasty: Adjusting for Trends in Annual Length of Stay by Johnathan R. Lex, MBChB, MASc, Robert Koucheki, MD, MEng, Aazad Abbas, MD, Jesse I. Wolfstadt, MD, MSc, FRCSC, FAAOS, Alexander S. McLawhorn, MD, MBA, Bheeshma Ravi, MD, PhD, FRCSC

“…The logistic regression model trained on all data with a balanced loss scoring function performed the best (area under the curve = 0.600). …”
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Explainable Boosting Machines Identify Key Metabolomic Biomarkers in Rheumatoid Arthritis by Fatma Hilal Yagin, Cemil Colak, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…The discovered metabolites offer significant insights into RA pathophysiology and may function as diagnostic biomarkers or therapeutic targets. …”
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A Multi-Head Attention-Based Lightweight Generative Adversarial Network for Thyroid Ultrasound Video Super-Resolution by Yuchen Ning, Xianshuang Meng, Lingxiao Zhou, Jun Liu, Shijie Qiu, Jingfang Wu, Xi Wei, Jun Ying, Siwei Zhu, Yantian Zhang

“…This innovative method encompasses a lightweight optical flow extraction network for swift analysis, a recurrent fusion reconstruction module based on multi-head mechanism, and a hybrid loss function designed to expedite network convergence. …”
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Automatic brain quantification in children with unilateral cerebral palsy by Jaime Simarro, Jaime Simarro, Jaime Simarro, Thibo Billiet, Thanh Vân Phan, Simon Van Eyndhoven, Monica Crotti, Monica Crotti, Monica Crotti, Lize Kleeren, Lize Kleeren, Lize Kleeren, Lisa Mailleux, Lisa Mailleux, Nofar Ben Itzhak, Nofar Ben Itzhak, Diana M. Sima, Els Ortibus, Els Ortibus, Els Ortibus, Ahmed M. Radwan, Ahmed M. Radwan

“…In this study, we developed and validated a deep learning-based pipeline to automatically quantify lesion-free brain volumes. …”
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Regression-based leaf nitrogen concentration estimation of young Cephalotaxus hainanensis in small and imbalanced samples by Mengmeng Shi, Tian Wang, Ling Lin, Qingxue Li, Zhulin Chen, Xingjing Chen, Peng Wang, Xuefeng Wang

“…This study used a non-destructive method that combines computer vision and machine learning to estimate the LNC in Cephalotaxus hainanensis, an endangered tree species. …”
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Framework for extracting multi-objective operation rules for cascade reservoirs based on causal features and physical mechanisms by Donglin Gu, Baowei Yan, Jianbo Chang, Yixuan Zou, Dongxu Yang, Mingbo Sun, Xiaoyu Diao

“…Study region: The cascade reservoirs in the middle and lower Yalong River, China Study Focus: Coordinated optimal operation of cascade reservoirs can improve hydropower benefits. Deep learning models can extract reservoir operation rules by mapping known conditions to outflow discharge decisions. …”
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Testing an inverse modeling approach with gradient boosting regression for stroke volume estimation using patient thermodilution data by Vasiliki (Vicky) Bikia, Dionysios Adamopoulos, Dionysios Adamopoulos, Dionysios Adamopoulos, Marco Roffi, Marco Roffi, Georgios Rovas, Stéphane Noble, Stéphane Noble, François Mach, François Mach, Nikolaos Stergiopulos

“…Stroke volume (SV) is a major indicator of cardiovascular function, providing essential information about heart performance and blood flow adequacy. …”
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Identification of mesoscale eddies based on improved YOLOv8 model: a case study in the South China Sea by Jianhao Gao, Jianhao Gao, Jianhao Gao, Feng Zhou, Feng Zhou, Feng Zhou, Di Tian, Di Tian, Muping Zhou, Muping Zhou, Hailong Guo

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Automatic MRI Lymph Node Annotation From CT Labels by Souraja Kundu, Yuji Iwahori, M. K. Bhuyan, Manish Bhatt, Boonserm Kijsirikul, Aili Wang, Akira Ouchi, Yasuhiro Shimizu

“…This study focuses on creating automatic lymph node annotation in MRI images using available CT annotations via deep-learning models. Training such models typically requires partial MRI labels for semi-supervision. …”
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Noninvasive imaging biomarker reveals invisible microscopic variation in acute ischaemic stroke (≤ 24 h): a multicentre retrospective study by Kui Sun, Rongchao Shi, Xinxin Yu, Ying Wang, Wei Zhang, Xiaoxia Yang, Mei Zhang, Jian Wang, Shu Jiang, Haiou Li, Bing Kang, Tong Li, Shuying Zhao, Yu Ai, Jianfeng Qiu, Haiyan Wang, Ximing Wang

“…Abstract To develop and validate non-contrast computed tomography (NCCT)-based radiomics method combines machine learning (ML) to investigate invisible microscopic acute ischaemic stroke (AIS) lesions. …”
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Critical role of Oas1g and STAT1 pathways in neuroinflammation: insights for Alzheimer’s disease therapeutics by Zhixin Xie, Linxi Li, Weizhong Hou, Zhongxi Fan, Lifan Zeng, Limin He, Yunxiang Ji, Jingbai Zhang, Fangran Wang, Zhou Xing, Yezhong Wang, Yongyi Ye

“…Interferon responses and Interferon-stimulated genes (ISGs) significantly function in neuroinflammatory and neurodegenerative diseases involving AD. …”
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Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network by Stefano A. Bini, MD, Nicholas Gillian, PhD, Thomas A. Peterson, PhD, Richard B. Souza, PhD, PT, Brooke Schultz, MS, ACE-CPT, Wojciech Mormul, MS, Marek K. Cichoń, MS, Agnieszka Barbara Szczotka, MS, Ivan Poupyrev, PhD

“…Conclusions: The findings highlight the potential of leveraging raw data from wearable inertial sensors and machine learning algorithms to reproduce gait lab-quality kinematic data outside the laboratory settings for the study of knee function following joint injury, surgery, or the progression of joint disease.…”
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Self-supervised light fluence correction network for photoacoustic tomography based on diffusion equation by Zhaoyong Liang, Zongxin Mo, Shuangyang Zhang, Long Chen, Danni Wang, Chaobin Hu, Li Qi

“…Our self-supervised QPAT network model, which we termed SQPA-Net, introduces light fluence estimation based on diffusion equation to the loss function, and thus guides the model to learn an implicit representation of photoacoustic light transport within tissue. …”
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