Evaluation of four common electronic mosquito repellers on Aedes albopictus and Culex pipiens.

Introduction: Mosquitoes represent a way of spreading infectious diseases, as vectors of pathogens. Many types of ultrasonic devices have recently been promoted as effective and suitable alternatives to the use of biocides known as toxic to humans and environment.

Materials And Methods: Four ultrasonic mosquito repellers have been analysed and tested on females of two species, Culex pipiens and Aedes albopictus, in laboratory conditions.

New finding on a migratory bird, the fowl tick Argas (Persicargas) persicus (Oken, 1818), in Italy.

Migratory birds reach Europe from sub-Saharan Africa, and some avian species may harbor and transport infected ectoparasites, mainly ticks, native to the territories of departure. In 2022, a project focused on identifying the introduction of pathogens in Italy from Africa via migratory birds represented an important opportunity to investigate this particular route of tick dispersal. Among ticks collected from migratory birds on the island of Ventotene, Latium Region, we found one larva of a soft tick on a common whitethroat (Curruca communis) that was morphologically and molecularly identified to the species level as Argas (Persicargas) persicus (Oken 1818).

Croplands proximity is associated with amyotrophic lateral sclerosis incidence and age at onset.

Background: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease resulting from an intricate interplay between genetics and environmental factors. Many studies have explored living in rural areas as a possible risk factor for ALS, without focusing simultaneously on incidence, age at onset and phenotypic features.

Objective: To evaluate the effect of croplands residential proximity on ALS incidence and phenotype, focusing on age of onset, site of onset and progression rate.

Genetics-driven risk predictions leveraging the Mendelian randomization framework.

Accurate predictive models of future disease onset are crucial for effective preventive healthcare, yet longitudinal data sets linking early risk factors to subsequent health outcomes are limited. To overcome this challenge, we introduce a novel framework, redictive sk modeling using ndelian andomization (PRiMeR), which utilizes genetic effects as supervisory signals to learn disease risk predictors without relying on longitudinal data. To do so, PRiMeR leverages risk factors and genetic data from a healthy cohort, along with results from genome-wide association studies of diseases of interest.