Medical History Predicts Phenome-Wide Disease Onset and Enables the Rapid Response to Emerging Health Threats

Using Medical Records to Predict Common Disease Incidence and Support Rapid Response to Emerging Health Threats Research Background and Motivation The COVID-19 pandemic exposed systemic issues and a lack of data-driven guidance globally, significantly affecting the identification of high-risk populations and pandemic preparedness. Assessing individ...

Diffusion Model Optimization with Deep Learning

Diffusion Model Optimization with Deep Learning

Dimond: A Study on Optimizing Diffusion Models through Deep Learning Academic Background In brain science and clinical applications, Diffusion Magnetic Resonance Imaging (dMRI) is an essential tool for non-invasively mapping the microstructure and neural connectivity of brain tissue. However, accurately estimating parameters of the diffusion signal...

Integrative Molecular and Spatial Analysis Reveals Evolutionary Dynamics and Tumor-Immune Interplay of In Situ and Invasive Acral Melanoma

Integrative Molecular and Spatial Analysis Reveals Evolutionary Dynamics and Tumor-Immune Interplay of In Situ and Invasive Acral Melanoma

Integrated Molecular and Spatial Analysis Reveals Evolutionary Dynamics and Tumor-Immune Interactions in In Situ and Invasive Acral Melanoma Academic Background of the Paper Melanoma is a type of skin cancer, and acral melanoma (AM) is a kind that occurs on non-exposed areas such as the palms, soles, and under the nails and is particularly common a...

Fixel-Based Analysis Reveals Tau-Related White Matter Changes in Early Stages of Alzheimer’s Disease

Report: Revealing White Matter Changes Associated with Tau in Early Alzheimer’s Disease through Fixel-based Analysis Research Background Alzheimer’s Disease (AD) is generally thought to primarily affect the grey matter (GM), but more and more evidence shows that the white matter (WM) also experiences abnormalities. Current research largely relies o...

Development and Standardization of an Osteoradionecrosis Classification System in Head and Neck Cancer: Implementation of a Risk-Based Model

Development and Standardization of an Osteoradionecrosis Classification System in Head and Neck Cancer: Implementation Based on Risk Model In recent years, the side effects brought by the treatment of head and neck cancer (HNC), especially radiotherapy, have become a focal point of academic attention. Osteoradionecrosis (ORN) is one of the most sev...

Clinical and Genomic-Based Decision Support System to Define the Optimal Timing of Allogeneic Hematopoietic Stem-Cell Transplantation in Patients with Myelodysplastic Syndromes

Clinical and Genomic-Based Decision Support System to Define the Optimal Timing of Allogeneic Hematopoietic Stem-Cell Transplantation in Patients with Myelodysplastic Syndromes

Background Myelodysplastic Syndromes (MDS) are a group of heterogeneous diseases originating from bone marrow hematopoietic stem cells, characterized by reduced blood cell production. Although certain progress in treatment has been made in recent years, allogeneic hematopoietic stem-cell transplantation (HSCT) remains the only potentially curative ...

Intimate Care Products and Incidence of Hormone-Related Cancers: A Quantitative Bias Analysis

Incidence of Hormone-Related Cancers and Intimate Care Products Introduction In recent years, there has been an increasing concern about the safety of intimate care products that may contain potential endocrine disrupting chemicals, such as phthalates, parabens, and bisphenols. These chemicals are believed to alter endogenous hormone levels, influe...

Feasibility of Electronic Patient-Reported Outcomes in Older Cancer Patients

Multicenter Prospective Study: Feasibility of Electronic Patient-Reported Outcomes (ePROs) in Elderly Cancer Patients Research Background In recent years, telemedicine has developed rapidly, especially during the COVID-19 pandemic, and is considered a solution to the problem of medical personnel shortages. Electronic patient-reported outcomes (ePRO...

Self-Supervised Deep Learning-Based Denoising for Diffusion Tensor MRI

Self-Supervised Deep Learning-Based Denoising for Diffusion Tensor MRI

Background Introduction Diffusion Tensor Magnetic Resonance Imaging (DTI) is a widely used neuroimaging technique for imaging the microstructure of brain tissues and white matter tracts. However, noise in Diffusion-Weighted Images (DWI) can reduce the accuracy of microstructural parameters derived from DTI data and also necessitate longer acquisiti...

DeepDTI: High-Fidelity Six-Direction Diffusion Tensor Imaging Using Deep Learning

DeepDTI: High-Fidelity Six-Direction Diffusion Tensor Imaging Using Deep Learning

DeepDTI: High-Fidelity Six-Direction Diffusion Tensor Imaging Using Deep Learning Research Background and Motivation Diffusion Tensor Imaging (DTI) boasts unparalleled advantages in mapping the microstructure and structural connectivity of live human brain tissue. However, traditional DTI techniques require extensive angular sampling, leading to pr...