Primary autoimmune haemolytic anaemia is associated with increased risk of ischaemic stroke: A binational cohort study from Denmark and France.

Primary autoimmune haemolytic anaemia (AIHA) causes the destruction of red blood cells and a subsequent pro-thrombotic state, potentially increasing the risk of ischaemic stroke. We investigated the risk of ischaemic stroke in patients with AIHA in a binational study. We used prospectively collected data from nationwide registers in Denmark and France to identify cohorts of patients with primary AIHA and age- and sex-matched general population comparators.

Drug-induced immune hemolytic anemia: detection of new signals and risk assessment in a nationwide cohort study.

More than 130 drugs have been suspected to induce immune hemolytic anemia. Comparative studies measuring the risk of drug-induced immune hemolytic anemia (DIIHA) are lacking. We aimed (1) to detect new signals of DIIHA, excluding vaccines, and (2) to assess the association between all suspected drugs and the occurrence of immune hemolytic anemia in a nationwide comparative study.

[Rhombencephalosynapsis : a rare cerebellar malformation not to miss].

Rhombencephalosynapsis (RS) is a rare cerebellar malformation characterized by the association of partial or total vermis agenesis and fusion of the cerebellar hemispheres. This malformation is often associated with supratentorial cerebral abnormalities and more rarely with extra-cranial abnormalities. The severity of the clinical manifestations and the prognosis of RS depend on the findings of the posterior fossa and the associated anomalies.

Popular and Scientific Discourse on Autism: Representational Cross-Cultural Analysis of Epistemic Communities to Inform Policy and Practice.

Background: Social media provide a window onto the circulation of ideas in everyday folk psychiatry, revealing the themes and issues discussed both by the public and by various scientific communities.

Objective: This study explores the trends in health information about autism spectrum disorder within popular and scientific communities through the systematic semantic exploration of big data gathered from Twitter and PubMed.

Methods: First, we performed a natural language processing by text-mining analysis and with unsupervised (machine learning) topic modeling on a sample of the last 10,000 tweets in English posted with the term #autism (January 2021).