The emergence of SARS-CoV-2 variants with mutations in the receptor-binding domain (RBD) poses a significant challenge to antibody-mediated immunity and ongoing vaccine effectiveness. In this work, we present RBD-Escape, a transparent and experimentally grounded computational framework for estimating how combinations of RBD mutations influence recognition by polyclonal neutralizing antibodies. The framework integrates deep mutational scanning (DMS) measurements from 33 monoclonal antibodies characterized across multiple independent studies and aggregates these data into a unified model of antibody escape. Unlike black-box predictive approaches, RBD-Escape directly leverages experimentally measured escape profiles, providing an interpretable representation of mutation-driven immune evasion. The proposed framework estimates the impact of arbitrary mutation combinations by modeling both additive and redundant antibody escape effects. Validation against independent neutralization datasets demonstrates strong agreement between predicted binding scores and experimentally measured neutralization outcomes, indicating that aggregated DMS data can effectively capture major patterns of antibody escape. Application of the framework to the Omicron variant revealed extensive predicted reductions in antibody recognition relative to earlier SARS-CoV-2 variants, consistent with its observed immune-evasive properties. In addition to quantitative escape prediction, RBD-Escape provides interactive visualization capabilities that facilitate exploration of mutation-specific and variant-level escape patterns. Although the current implementation focuses exclusively on mutations within the RBD and does not account for contributions from other spike protein regions, it offers a rapid, interpretable, and experiment-driven approach for evaluating emerging variants. The framework has potential applications in genomic surveillance, vaccine evaluation, and the assessment of mutation-driven antigenic change, but predictions should be interpreted as hypothesis-generating rather than definitive (Lucas et al., 2021).
Subarachnoid hemorrhage (SAH) is a serious cerebrovascular disease with high mortality, and the mean age at morbidity is younger than in other types of stroke. Early brain injury (EBI) plays a key role in the poor prognoses of SAH. In EBI, multiple forms of cell death have been identified and well studied; however, the role of ferroptosis has not been elucidated. Hence, in this study, we developed an in vivo (SAH rat model) and in vitro model (SH-SY5Y oxyhemoglobin injury model) to understand the role of ferroptosis in EBI, then explored the protective mechanism of ferrostatin-1 (Fer-1). Firstly, we found that neurological scores, blood–brain barrier permeability, brain edema deteriorated after SAH in the in vivo model, cell viability was decreased after SAH in both cortex and SH-SY5Y cells. Further, iron content in cortex was increased after SAH, while transferrin receptor 1 and ferroportin (Fpn) were increased in oxyhemoglobin-treated in vitro model. Additionally, glutathione content and glutathione peroxidase 4 activity were reduced in SAH rats, and lipid peroxides were increased in the oxyhemoglobin-treated cells. Finally, administration of Fer-1 upregulated Fpn and decreased the iron content, then improved the lipid peroxidation and EBI. However, Fer-1 had no effect on the apoptosis. Our study indicated that the ferroptosis was involved in EBI of SAH, and the inhibitor Fer-1 provided neuroprotection against EBI by alleviating ferroptosis, the potential protective mechanism might be via suppressing lipid peroxidation.
Mood disorders are commonly reported by student-athletes but are often untreated, compromising academic and athletic performances. Sociodemographic, institutional, and individual factors modify mood disorder symptom risk and treatment-seeking/avoidance, but studies of combined effects in student-athletes are rare. The present non-experimental, epidemiological study uses multivariable logistic regression and relative risk to evaluate these effects on mood disorder symptoms, diagnoses, and treatment in a sample of n = 51,882 student-athletes responding to the U.S. National College Health Assessment. Data spanned 2011-2019, collected at different points during those years. Over 75% of student-athlete respondents reported mood disorder symptoms, 27.4% reported depression-related functional barriers, and similar to 6% reported intentional self-harm/considering/attempting suicide. Of those experiencing functional barriers, just 21.1% reported treatment. Gender, sexuality, race/ethnicity, international status, year in school, school type, and treatment history significantly affected symptom risk. Diagnosis/treatment was significantly influenced by gender, sexuality, race/ethnicity, treatment history, and academic impacts. Among those reporting diagnoses, 80.5% received treatment, with gender, sexuality, race/ethnicity, year in school, treatment history, and academics predictive of untreated diagnoses. Sociodemographic, institutional, and individual factors significantly affected mood disorder symptoms and treatment-seeking propensity. This impact potentially aids athletic trainers and other providers in early screening for at-risk individuals, encouraging treatment, and improving student-athlete mental health.
The widespread implementation of telebehavioral health (TBH) services, accelerated by the COVID-19 pandemic, created an urgent need for standardized competencies in counselor education. Many practitioners report minimal formal training, leading to significant variability in competence and a concerning gap between their confidence and actual preparedness. This article issues a call to action for counseling programs to adopt the interprofessional TBH competency framework from the Coalition for Technology in Behavioral Science (CTiBS). This structured, evidence-based roadmap outlines knowledge and skills for competent, ethical TBH practice across seven domains and three progressive levels: novice, proficient, and authority. Adopting this framework will enhance counselor development, align training with the Council for Accreditation of Counseling and Related Educational Programs (CACREP) standards and reimbursement policies, and ensure practitioners are prepared for modern service delivery. This approach fosters ethical, culturally responsive care and prepares counselors to meet future demands in an era of rapidly evolving technology.
OBJECTIVE:To assess psychiatry faculty knowledge, use, and perceptions of artificial intelligence (AI) in undergraduate medical education (UME) and graduate medical education (GME), and examine awareness of institutional AI policies. METHODS:A national survey was distributed to psychiatry clerkship directors and residency program directors at 105 allopathic and osteopathic medical schools and 219 psychiatry residency programs across the USA from November 2024 to February 2025. The survey assessed AI use patterns, attitudes, perceptions, and awareness of institutional policies. Data were analyzed using descriptive statistics and Fisher's exact test for group comparisons. RESULTS:Responses were obtained from 31 UME faculty (29.5% response rate) and 46 GME faculty (21% response rate). All UME faculty and 74% of GME faculty believed students use AI. However, 36% of UME and 57% of GME faculty were unaware of institutional student AI policies. Knowledge of faculty AI policies was even more limited (38.7% UME, 63% GME uncertain). Only 35.5% of UME and 27.9% of GME faculty reported current AI use, primarily for research support, literature review, and content development. Overall, 50% of UME and 54.1% of GME respondents perceived AI in medical education as beneficial. CONCLUSIONS:Despite widespread belief that students use AI, psychiatry faculty demonstrate limited awareness of institutional policies and minimal integration of AI literacy in curricula. These findings highlight the urgent need for faculty development programs, clear institutional policies, and structured AI curricula to bridge the gap between rapid AI adoption by learners and educational infrastructure.
