Evaluation of Cerebrospinal Fluid α-Synuclein Seed Amplification Assay in Progressive Supranuclear Palsy and Corticobasal Syndrome.

Background: Seed amplification assay (SAA) testing has been developed as a biomarker for the diagnosis of α-synuclein-related neurodegenerative disorders.

Objective: The objective of this study was to assess the rate of α-synuclein SAA positivity in progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS) and to analyze clinical and pathological features of SAA-positive and -negative cases.

Methods: A total of 96 cerebrospinal fluid samples from clinically diagnosed PSP (n = 59) and CBS (n = 37) cases were analyzed using α-synuclein SAA.

Investigating the impact of socioeconomic status on amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the gradual death of motor neurons in the brain and spinal cord, leading to fatal paralysis. Socioeconomic status (SES) is a measure of an individual's shared economic and social status, which has been shown to have an association with health outcomes. Understanding the impact of SES on health conditions is crucial, as it can influence and be influenced by health-related variables.

Pharmacometabolomics applied to low-dose interleukin-2 treatment in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a devastating motor neuron disease. The immunosuppressive functions of regulatory T lymphocytes (Tregs) are impaired in ALS, and correlate to disease progression. The phase 2a IMODALS trial reported an increase in Treg number in ALS patients following the administration of low-dose (ld) interleukin-2 (IL-2).

Few-shot Tumor Bud Segmentation Using Generative Model in Colorectal Carcinoma.

Current deep learning methods in histopathology are limited by the small amount of available data and time consumption in labeling the data. Colorectal cancer (CRC) tumor budding quantification performed using H&E-stained slides is crucial for cancer staging and prognosis but is subject to labor-intensive annotation and human bias. Thus, acquiring a large-scale, fully annotated dataset for training a tumor budding (TB) segmentation/detection system is difficult.