Central Involvement in Pure Autonomic Failure: Insights from Neuromelanin-Sensitive Magnetic Resonance Imaging and F-Fluorodopa-Positron Emission Tomography.

Background: Central synucleinopathies, including Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA), involve alpha-synuclein accumulation and dopaminergic cell loss in the substantia nigra (SN) and locus coeruleus (LC). Pure autonomic failure (PAF), a peripheral synucleinopathy, often precedes central synucleinopathies.

Objectives: To assess early brain involvement in PAF using neuromelanin-sensitive magnetic resonance imaging (NM-MRI) and fluorodopa-positron emission tomography (FDOPA-PET), and to determine whether PAF patients with a high likelihood ratio (LR) for conversion to a central synucleinopathy exhibit reduced NM-MRI contrast in the LC and SN compared with controls and low-LR patients.

CONTExT-RA: a cross-sectional study evaluating disease activity, quality of life and the socio-demographic profile of Irish patients with rheumatoid arthritis.

Objectives: CONTExT-RA is a cross-sectional, non-interventional multicentre study which enrolled patients diagnosed with RA and receiving DMARD treatment in a secondary care setting. The study evaluated disease control and associated disease burden amongst this Irish population.

Methods: Patients with RA attending six Irish rheumatology centres were invited to participate.

Examining malaria treatment and prevention spending efficiency in malaria-endemic countries, 2000-2020.

Background: In 2021, an estimated 750,000 people died from malaria. Despite this significant burden, globally, malaria incidence and mortality rates have substantially dropped over the last 30 years. However, growth in spending on malaria and improved outcomes have recently stagnated.

Statistics as a Social Activity: Attitudes toward Amalgamating Evidence.

Amalgamation of evidence in statistics is conducted in several ways. Within a study, multiple observations are combined by averaging, or as factors in a likelihood or prediction algorithm. In multilevel modeling or Bayesian analysis, population or prior information is combined with data using the weighted averaging derived from probability modeling.