Understanding Parkinson's: The microbiome and machine learning approach.

Objective: Given that Parkinson's disease is a progressive disorder, with symptoms that worsen over time, our goal is to enhance the diagnosis of Parkinson's disease by utilizing machine learning techniques and microbiome analysis. The primary objective is to identify specific microbiome signatures that can reproducibly differentiate patients with Parkinson's disease from healthy controls.

Methods: We used four Parkinson-related datasets from the NCBI repository, focusing on stool samples.

Meningitis caused by in a patient with liver cirrhosis: A case report and review of the literature.

Unlabelled: Candida meningitis is almost always caused by , but other species, such as , can cause it on rare occasions. is increasingly linked to immunocompromised hosts but also affects immunocompetent hosts. To the best of our knowledge, we present the ninth (9th) case of meningitis, the first from Saudi Arabia.

Explainability of CNN-based Alzheimer's disease detection from online handwriting.

With over 55 million people globally affected by dementia and nearly 10 million new cases reported annually, Alzheimer's disease is a prevalent and challenging neurodegenerative disorder. Despite significant advancements in machine learning techniques for Alzheimer's disease detection, the widespread adoption of deep learning models raises concerns about their explainability. The lack of explainability in deep learning models for online handwriting analysis is a critical gap in the literature in the context of Alzheimer's disease detection.

Air embolism complicating endoscopic retrograde cholangiopancreatography.

Venous air embolism is a rare but potentially life threatening complication of endoscopic retrograde cholangiopancreatography. Diagnosis is difficult because of the lack of specific signs or symptoms. A 62-year-old man underwent endoscopic retrograde cholangiopancreatography for choledocholithiasis.

Two polygenic mouse models of major depressive disorders identify TMEM161B as a potential biomarker of disease in humans.

Treatments are only partially effective in major depressive disorders (MDD) but no biomarker exists to predict symptom improvement in patients. Animal models are essential tools in the development of antidepressant medications, but while recent genetic studies have demonstrated the polygenic contribution to MDD, current models are limited to either mimic the effect of a single gene or environmental factor. We developed in the past a model of depressive-like behaviors in mice (H/Rouen), using selective breeding based on behavioral reaction after an acute mild stress in the tail suspension test.