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).

Effect of Intensified Transcranial Direct-current Stimulation Targeting Bilateral Dorsolateral Prefrontal Cortex on Craving Reduction in Patients with Opioid (Heroin) Use Disorder.

Objective: : This study aimed to analyze the effect of the intensified transcranial direct-current stimulation (tDCS) targeting bilateral dorsolateral prefrontal cortex (DLPFC) on craving reduction in patients with opioid use disorder.

Methods: : This quasi-experimental study was conducted on 30 individuals who participated voluntarily at Baharan Camp of Shahid Mahalati. The participants had already completed the detoxification phase and stayed at the camp to resolve their craving and gain occupational skills to reintegrate into the community.

Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers.

Background: Mood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing treatment.

Blended smartphone intervention for patients in opioid maintenance treatment in Iran: protocol for a randomized controlled trial.

Background: The pattern of substance use in Iran is characterized by a high prevalence of opioid use and opioid use disorder (OUD). Although opioid maintenance therapy (OMT) has been introduced in Iran, approximately 50% of people with opioid use disorder remain unreached. Moreover, psychosocial treatment of OUD and common mental health symptoms during OMT is limited.

Prevalence of alcohol consumption among clients of opioid agonist treatment (OAT) centers in Golestan province, Iran.

Introduction: Opioid agonist treatments (OATs) with methadone and buprenorphine are known to be effective treatments for people with opioid use disorder (OUD). However, concomitant use of other substances such as alcohol can negatively affect OAT outcomes. This study aimed to determine the prevalence of alcohol use among clients of OAT centers in the Golestan province in the northern part of Iran.

Identification of the socio-cultural barriers of drug addiction treatment in Iran.

Introduction: Socio-cultural norms can either be encouraging or a barrier to addiction treatment. More, rigorous research is needed on nonindigenous models in addiction treatment, to better understand the role of socio-cultural differences.

Methods: The present qualitative study is part of the project, "The Inclusive Assessment of the Barriers of Drug Addiction Treatment Services in Iran," which was conducted in Tehran from 2018 to 2021.

Post-Exposure Prophylaxis for COVID-19: A Systematic Review.

Introduction: SARS-CoV-2 cause pneumonia can spread across the lung and lead to acute respiratory distress syndrome (ARDS) in severe cases. Post-exposure prophylaxis has shown great potential to prevent the transmission of some viral infections; however, such results for COVID-19 are still inconclusive.

Methods: Therefore, the aim of this study was to systematically review the resources that utilized postexposure prophylaxis (PEP) for COVID-19 and the possible clinical benefits of such drugs.

Cryoablation for the Palliation of Painful Bone Metastasis: A Systematic Review.

Background: Cryoablation is a minimally invasive procedure to treat painful bone metastases in patients with cancer. We designed a systematic review to understand the safety and effects of cryoablation on the pain and quality of life (QoL) of cancer patients.

Method: We searched PubMed, ISI, Cochrane library, and Scopus databases using the keywords "Cryoablation," "Pain," and "Bone metastasis.

Discriminating between bipolar and major depressive disorder using a machine learning approach and resting-state EEG data.

Objective: Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a crucial clinical challenge as effective treatment is quite different for each condition. In this study electroencephalography (EEG) was explored as an objective biomarker for distinguishing MDD from BD using an efficient machine learning algorithm (MLA) trained by a relatively large and balanced dataset.

Methods: A 3 step MLA was applied: (1) a multi-step preprocessing method was used to improve the quality of the EEG signal, (2) symbolic transfer entropy (STE), an effective connectivity measure, was applied to the resultant EEG and (3) the MLA used the extracted STE features to distinguish MDD (N = 71) from BD (N = 71) subjects.

Management of gabapentin misuse in a patient with previous history of opioid use disorder: Case report.

We present a 37-year-old male who misuses 12 g of gabapentin per day associated with dependence and withdrawal. He had a previous history of opioid use disorder (OUD) which has been in remission. An outpatient gradual dose reduction regimen was tried and failed and the patient decided to discontinue gabapentin abruptly.

Population Size Estimation of People Who Use Illicit Drugs and Alcohol in Iran (2015-2016).

Background: Estimating the number of people using illicit drugs and alcohol is necessary for informing health policy and programming. However, it is often challenging to reliably estimate the size of these marginalized populations through direct methods. In this study, we estimated the population size of these groups using the indirect Network Scale-Up (NSU) method in Iran from 2015 to 2016.

A Robust eLORETA Technique for Localization of Brain Sources in the Presence of Forward Model Uncertainties.

Objective: In this paper, we present a robust version of the well-known exact low-resolution electromagnetic tomography (eLORETA) technique, named ReLORETA, to localize brain sources in the presence of different forward model uncertainties.

Methods: We first assume that the true lead field matrix is a transformation of the existing lead field matrix distorted by uncertainties and propose an iterative approach to estimate this transformation accurately. Major sources of the forward model uncertainties, including differences in geometry, conductivity, and source space resolution between the real and simulated head models, and misaligned electrode positions, are then simulated to test the proposed method.

A Machine Learning Algorithm to Discriminating Between Bipolar and Major Depressive Disorders Based on Resting EEG Data.

Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a crucial clinical challenge due to the lack of known biomarkers. Conventional methods of diagnosis rest exclusively on symptomatic presentation, and personal and family history. As a result, BD-depressed episode (BD-DE) is often misdiagnosed as MDD, and inappropriate therapy is given.

Barriers and Facilitators to Substance Use Disorder Treatment: An Overview of Systematic Reviews.

Objective: This investigation explored the barriers and facilitators to substance use disorder (SUD) treatment in the integrated paradigm.

Methods: A search technique for barriers and facilitators of SUD treatment was applied to the PubMed and Web of Science databases to identify relevant systematic reviews. The eligibility criteria included systematic review (SR) or SR plus meta-analysis (MA) articles published before the end of 2021, human research, and the English language.

Effects of the COVID-19 pandemic on lifestyle among Iranian population: A multicenter cross-sectional study.

Background: Quarantine, an unpleasant experience, was implemented in many countries to limit the spread of Coronavirus disease 2019 (COVID-19), which it could associated whit lifestyle changes. The present study aimed to determine the changes in Iranian's lifestyle during COVID-19 pandemic.

Materials And Methods: In the present cross-sectional study, 2710 Iranian people completed an online researcher-made questionnaire asking lifestyle regarding COVID-19, which includes five sections about physical activity, stress and anxiety, nutrition habit, sleep disorders, and interpersonal relationship in addition to demographic data from January to February 2021, using the multistage cluster sampling method.

Effects of Transcranial Direct Current Stimulation on Attentional Bias to Methamphetamine Cues and Its Association With EEG-Derived Functional Brain Network Topology.

Background: Although transcranial direct current stimulation (tDCS) has shown to potentially mitigate drug craving and attentional bias to drug-related stimuli, individual differences in such modulatory effects of tDCS are less understood. In this study, we aimed to investigate a source of the inter-subject variability in the tDCS effects that can be useful for tDCS-based treatments of individuals with methamphetamine (MA) use disorder (IMUD).

Methods: Forty-two IMUD (all male) were randomly assigned to receive a single-session of either sham or real bilateral tDCS (anodal right/cathodal left) over the dorsolateral prefrontal cortex.

Correction to "Engineering Water and Solute Dynamics and Maximal Use of CNT Surface Area for Efficient Water Desalination".

[This corrects the article DOI: 10.1021/acsomega.9b00188.