Disease diagnostics using machine learning of B cell and T cell receptor sequences.

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system's own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis, an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences.

Development and validation of the compulsive substance use questionnaire: Attending to substance use automaticity, craving, and continued use despite negative consequences.

Background: Compulsivity characterizes an important subtype of substance use disorders (SUDs), but there has been variability in how compulsive substance use is defined and measured. The current study was designed to (1) develop a new measure of substance use compulsivity, (2) validate this scale in a community sample of adults who drink alcohol, and (3) evaluate the association between this new measure of compulsivity and substance use severity. We hypothesize that compulsivity will be characterized by automaticity, craving and insensitivity to negative consequences, and greater substance use severity will be associated with higher levels of substance use compulsivity.

Relative Preference for In-Person, Telehealth, Digital, and Pharmacologic Mental Health Care After the COVID-19 Pandemic: Cross-Sectional Questionnaire Study.

Background: Most adults and children in the United States fail to receive timely care for mental health symptoms, with even worse rates of care access for individuals who belong to racial and ethnic minority groups. Digital (ie, app-based) care has proven to be an efficacious and empirically supported treatment option with the potential to address low rates of care and reduce care disparities, yet little is known about the relative preference for such treatment. Furthermore, the rapid adoption of telehealth care during the COVID-19 pandemic may have shifted care preferences.

Analyzing the effects of high autistic traits on neural markers of learning and memory: An EEG approach analysis.

Objective: A body of electroencephalographic (EEG) research demonstrates that executive functioning (EF) differences exist in autistic people. Here, we aimed to investigate how and to what extent these EF differences appear in people with high autistic traits in contrast to a low autistic traits comparison.

Methods: The present study used a series of EEG markers (frontal theta power, frontal beta power, the reward positivity ERP component, and the P300 ERP component) to examine potential differences in EF over the course of gambling and oddball tasks.

Retrospective Cohort Study: Scope for Improvement-Barriers to Post-Polypectomy Surveillance in the Integrated Technologies for Improved Polyp Surveillance Cohort.

Background: Adherence to post-polypectomy surveillance is poor despite evidence that it is associated with lower risk of future colorectal cancer.

Methods: We evaluated 6,210 bowel screening participants between 2009-2016 in NHS Greater Glasgow and Clyde to assess potential barriers to post-polypectomy surveillance.

Results: Increasing deprivation (Scottish Index of Multiple Deprivation quintile 1 vs 5; OR 1.