Comprehensive Single-Cell Multi-Omics Study on HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation This paper, titled “Integrated single-cell multiomic analysis of HIV latency reversal reveals novel regulators of viral reactivation,” was jointly completed by Manickam Ashokkumar, Wenwen Mei, and several other researchers from institu...
Automated Clinical Assessment of Abnormal Walking Movements in ABI Patients Through Image Extraction and Classification Systems Academic Background Walking disability is a common physical impairment following Acquired Brain Injury (ABI). ABI typically includes stroke and traumatic brain injury, with a global incidence of approximately 1.5 million c...
Background Introduction Stroke is one of the leading causes of death and disability worldwide, with the total number of stroke patients increasing globally due to population aging and urbanization. Although advances in treatment have reduced mortality rates, the number of survivors requiring rehabilitation has increased significantly. This is parti...
Study on the Role of Machine Learning-Assisted Quantitative Hyperspectral Imaging in Brain Tumor Resection Background Introduction Complete resection of malignant gliomas has always been challenged by the difficulty of distinguishing tumor cells in invasive regions. The background of this study is: In neurosurgery, the application of 5-aminolevulin...
Useful Features for Overall Survival Prediction in Low-Grade Glioma Patients Academic Background Glioma is a type of neoplastic growth in the brain that usually poses a serious threat to the patients’ lives. In most cases, glioma eventually leads to the death of the patient. The analysis of glioma typically involves examining pathological slices of...
Glioma Disease Prediction Based on Optimized Integrated Machine Learning Background and Research Objectives In medical research, gliomas are the most common type of primary brain tumors, encompassing various cancer types with different clinical behaviors and treatment outcomes. Accurate prognosis prediction for glioma patients is crucial for optimi...
Enabling Explainable Artificial Intelligence for Glioma Prognosis: Translational Insights from Transparent Machine Learning Academic Background This study is dedicated to developing a reliable technique to detect whether patients have a specific type of brain tumor—glioma—using various machine learning methods and deep learning methods, combined wi...
Clinical Validation Study on the Application of Cell-Free DNA Fragment Analysis in Enhancing Early Detection of Lung Cancer Background Lung cancer is one of the leading types of cancer threatening the health of both men and women globally. Over 125,000 people die from lung cancer in the United States each year, and globally, the number is close to ...
Asthma Prediction Enhanced by Affinity Graph-Based Classifier: A Machine Learning Approach Using Routine Blood Biomarkers Background Asthma is a chronic respiratory disease that affects approximately 235 million people worldwide. According to the World Health Organization (WHO), the main characteristic of asthma is airway inflammation, leading to s...
Background Depression, particularly Major Depressive Disorder (MDD), is a widespread and debilitating psychological disease often described as the “common cold” of mental health. Many people with MDD experience symptoms such as persistent sadness, hopelessness, cognitive impairment, and loss of motivation for daily activities, severely affecting pe...