bioRxiv Science⌕ Search

Biology subjects

Adamek, J.

Publications and source records attributed to Adamek, J..

2 recordsLinked to original sources

Altered Cortical Activation Associated with Mirror Overflow Driven by Non-Dominant Hand Movement in Attention-Deficit/Hyperactivity Disorder

Mirror overflow is involuntary movement that accompanies unilateral voluntary movement on the opposite side of the body, and is commonly seen in Attention-Deficit/Hyperactivity Disorder (ADHD). Children with ADHD show asymmetry in mirror overflow between dominant and non-dominant hand, yet there are competing mechanistic accounts of why this occurs. Using EEG during a sequential, unimanual finger-tapping task, we found that children with ADHD exhibited significantly more mirror overflow than typically developing (TD) controls, especially during the tapping of the non-dominant hand. Furthermore, source-level EEG oscillation analysis revealed that children with ADHD showed decreased alpha (8-12 Hz) event-related desynchronization (ERD) compared with controls in both hemispheres, but only during tapping of the non-dominant hand. Moreover, only the ERD ipsilateral to the mirror overflow during non-dominant hand movement correlated with both magnitude of overflow movements and higher ADHD symptom severity (Conners ADHD Hyperactivity/Impulsiveness scale) in children with ADHD. TD controls did not show these relationships. Our findings suggest that EEG differences in finger-tapping in ADHD are related primarily to voluntary movement in the non-dominant hand. Our results are also consistent with the Ipsilateral Corticospinal Tract (CST) Hypothesis, which posits that the atypical persistence of mirror overflow in ADHD may originate in the sensorimotor areas ipsilateral to mirror overflow and be transmitted via non-decussating CST fibers.

neuroscience↗

The first AI-based mobile application for antibiotic resistance testing

Antimicrobial resistance is a major global health threat and its development is promoted by antibiotic misuse. While disk diffusion antibiotic susceptibility testing (AST, also called antibiogram) is broadly used to test for antibiotic resistance in bacterial infections, it faces strong criticism because of inter-operator variability and the complexity of interpretative reading. Automatic reading systems address these issues, but are not always adapted or available to resource-limited settings. We present the first artificial intelligence (AI)-based, offline smartphone application for antibiogram analysis. The application captures images with the phones camera, and the user is guided throughout the analysis on the same device by a user-friendly graphical interface. An embedded expert system validates the coherence of the antibiogram data and provides interpreted results. The fully automatic measurement procedure of our applications reading system achieves an overall agreement of 90% on susceptibility categorization against a hospital-standard automatic system and 98% against manual measurement (gold standard), with reduced inter-operator variability. The applications performance showed that the automatic reading of antibiotic resistance testing is entirely feasible on a smartphone. Moreover our application is suited for resource-limited settings, and therefore has the potential to significantly increase patients access to AST worldwide.

microbiology↗