ParaAntiProt provides paratope prediction using antibody and protein language models.

Efficiently predicting the paratope holds immense potential for enhancing antibody design, treating cancers and other serious diseases, and advancing personalized medicine. Although traditional methods are highly accurate, they are often time-consuming, labor-intensive, and reliant on 3D structures, restricting their broader use. On the other hand, machine learning-based methods, besides relying on structural data, entail descriptor computation, consideration of diverse physicochemical properties, and feature engineering.

Effectiveness of the adolescent-community reinforcement approach for treating Cannabis use disorder in Iranian adolescents: A randomized controlled trial.

The Adolescent Community Reinforcement Approach (A-CRA) is an evidence-based intervention for adolescents with cannabis use disorder (CUD). This study evaluated the effectiveness of A-CRA among Iranian adolescents with CUD. A total of 40 male adolescents aged 15 to 18 (Mean = 15.

Frontal Bone Morphology in Different Age and Gender Groups Using Computed Tomography.

Background:  The shape of the forehead is primarily determined by the frontal bone, which holds significance for health and aesthetics. This study evaluated the morphology of the frontal bone in different age groups and genders using computed tomography (CT).

Methods:  This cross-sectional study was conducted on CT scan results of 220 patients divided into three age groups: 20-29.

Characterizing people who inject drugs with no history of opioid agonist therapy uptake in Iran: Results from a national bio-behavioural surveillance survey in 2020.

Injection drug use is the primary driver of the human immunodeficiency virus HIV epidemic in Iran. We characterized people who inject drugs (PWID) living in Iran who had never received opioid agonist therapy (OAT) and examined barriers to OAT uptake. We recruited 2,684 PWID with a history of drug injection in the previous 12 months using a respondent-driven sampling approach from 11 geographically dispersed cities in Iran.

Using deep learning and pretreatment EEG to predict response to sertraline, bupropion, and placebo.

Objective: Predicting an individual's response to antidepressant medication remains one of the most challenging tasks in the treatment of major depressive disorder (MDD). Our objective was to use the large EMBARC study database to develop an electroencephalography (EEG)-based method to predict response to antidepressant treatment.

Methods: Pre-treatment EEG data were collected from study participants treated with either sertraline (N = 105), placebo (N = 119), or bupropion (N = 35).