Vafa Bayat

Background: Depression affects an estimated 332 million people worldwide and is a leading cause of disability, with up to 80% of major depressive episodes preceded by an identifiable adverse life event [17,18]. First-line treatments target symptoms rather than the precipitating event and are resource-intensive: standard CBT averages roughly 12 sessions, and antidepressant discontinuation carries relapse rates near 35% at six months [8]. These limitations create a clear rationale for brief, structured interventions that address the cognitive and somatic sequelae of adverse life events directly. Painhunting therapy is one such intervention, in which each session targets a discrete adverse event through a structured incident-processing procedure. Methods.: We conducted a two-arm, parallel-group, single-site randomised controlled trial comparing Painhunting therapy (Arm A, immediate; n=42) with a waitlist control (Arm B, delayed; n=42) in adults with PHQ-9 ≥ 9 and active psychological distress related to an adverse life event. After the primary endpoint at T2 (approximately two weeks post-randomisation), Arm B crossed over to active treatment, with T3 as the post-crossover endpoint at approximately four weeks. The primary outcome was PHQ-9 at T2 (between-arm contrast); secondary outcomes were ICG, GAD-7, WHO-DAS 2.0 (12-item), and the Global Impression of Change (GIC). Pre-specified analyses included intention-to-treat, per-protocol, and single-exclusion sensitivity populations. Results.: Eighty-four participants were randomised (198 applications, 134 completed screening questionnaire, 119 passed psychometric screening). At T2, mean PHQ-9 was 2.32 (SD 2.59) in Arm A and 16.56 (SD 6.76) in Arm B, yielding an ITT between-arm Cohen d = 2.78 (95% CI 2.19–3.76, p < 0.001). Within-arm paired reductions during each arm’s active-treatment window reproduced this magnitude (Arm A T0→T2 Δ 14.71, Morris d = 2.80; Arm B T2→T3 Δ 14.19, Morris d = 2.77, eligible n=26). Treatment gains were durable at the T4 follow-up (week 8). Aligning each arm to its own end-of-treatment timepoint, the off-treatment drift to week 8 was almost identical between arms: Arm A rose 0.78 points from T2 to T4 (2.19 → 2.97, n=37) and Arm B rose 1.59 points from T3 to T4 (4.74 → 6.33, n=27), the latter falling to 0.77 points once a single documented relapse case (R59) is excluded (4.81 → 5.58, n=26). This small off-treatment rebound then stabilised rather than continuing: Arm A was essentially unchanged from T3 to T4 (Δ +0.05), with concordant maintenance on ICG, GAD-7, and WHO-DAS. At T4, 68% of Arm A and 41% of Arm B remained in remission (PHQ-9 < 5). Secondary measures (ICG, GAD-7, WHO-DAS) moved in the same direction and to comparable magnitude at every timepoint. The waitlist window in Arm B showed essentially no change on any measure (PHQ-9 Δ 0.22, p = 0.81). Sensitivity analyses excluding six sub-threshold T2 cases, the single treated-in-error case (R82), the R59 relapse case, and one late T2 submitter left all conclusions unchanged. Conclusions.: Painhunting therapy produced large and statistically robust reductions in depression, complicated grief, anxiety, and functional disability over a brief course of three to four sessions, with effect sizes substantially exceeding benchmarks reported for established first-line psychotherapies including CBT and EMDR. Critically, these gains persisted at the week-8 follow-up: depression scores in the immediate-treatment arm were essentially unchanged from four weeks to eight weeks post-randomisation, indicating that the benefit reflects durable change rather than a transient post-session dip. Treatment-window concordance between arms, durability of gains at one month off-treatment, and the flat waitlist trajectory together strengthen the evidence for genuine efficacy rather than spontaneous remission. Baseline covariates including therapeutic alliance, treatment expectancy, self-efficacy, age, and sex showed near-zero associations with outcome, reducing the plausibility of allegiance bias or expectancy effects as primary drivers. The differential retention between arms (88% vs 64% at T3) is attributable to the waitlist design and is discussed as a limitation. These findings support proceeding to a confirmatory active-comparator trial against manualized CBT. Trial registration: ClinicalTrials.gov NCT07490691 , prospectively registered.

Avian influenza viruses (AIVs), including H5N1 and H7N9, from the Orthomyxoviridae family present substantial public health concerns. The predominant circulating clade 2.3.4.4b has demonstrated enhanced capacity for mammalian adaptation, raising concerns about potential reassortment with human seasonal influenza viruses. Unlike H7N9’s limited host range, H5N1 infects birds, various mammals, and humans. Recent concerns include widespread H5N1 infection of U.S. dairy cattle across 18 states, affecting over 1000 herds with 71 human infections (70 H5N1 and 1 H5N5). Key observations include cow-to-cow transmission, viral presence in milk, and transmission to humans, mainly through occupational exposure. Evidence of mammal-to-mammal transmission has been documented in European and Canadian foxes and South American marine mammals. Standard pasteurization effectively inactivates the virus in milk. The continuing mammalian adaptations, particularly mutations like PB2-E627K, PB2-D701N, and PB2-M535I, suggest potential for further evolution in new hosts, emphasizing the need for enhanced surveillance to mitigate pandemic risks.

Avian influenza viruses (AIVs), including H5N1 and H7N9, from the Orthomyxoviridae family present substantial public health concerns. Unlike H7N9’s limited host range, H5N1 infects birds, various mammals, and humans. Recent concerns include widespread H5N1 infection of U.S. dairy cattle across 17 states affecting over 1000 herds with 41 human infections. Key observations include cow-to-cow transmission, viral presence in milk, and transmission to humans, mainly through occupational exposure. Evidence of mam-mal-to-mammal transmission has been documented in European and Canadian foxes and South American marine mammals. Standard pasteurization effectively inactivates the virus in milk. The continuing mammalian adaptations, particularly mutations like PB2-E627K, suggest potential for further evolution in new hosts, emphasizing the need for enhanced surveillance to mitigate pandemic risks.

Objective: We sought to establish the US Department of Veterans Affairs (VA) Veterans Health Administration (VHA) Sequencing for Research Clinical and Epidemiology (SeqFORCE) multilaboratory consortium for SARS-CoV-2 whole-genome sequencing (WGS). Methods: Clinical criteria were established for sending patient and employee samples from 145 VHA medical centers to 10 VHA clinical laboratories using 4 different WGS platforms. A linked pipeline among laboratories for SARS-CoV-2 clade and lineage interpretation, result transmission to electronic health records, and data storage was developed. Results: The SeqFORCE program went live on July 1, 2021. As of December 15, 2024, 51 307 samples have been analyzed by WGS for SARS-CoV-2. The median participant age was 60 years, 76.6% were male, and 13.5% were inpatients; 96.5% were Delta, Omicron, and Recombinant sublineages; and 78.5% represented SARS-CoV-2 postvaccination samples among patients and staff. Conclusions: Establishment of VA SeqFORCE enabled national population analysis for use in epidemiologic response and population health policy as well as expanded SARS-CoV-2 sequencing capacity to meet the demand for clinical and public health sequencing. The program consolidated operations using standardized procedures, test setup, analysis, reporting, and tracking. It also improved oversight and governance of VA contributions to global databases, mitigated system inefficiencies, and prepared VHA for future genomic challenges.

Background: In 2015, the American College of Medical Genetics and Genomics (ACMG), in collaboration with the Association of Molecular Pathologists (AMP), published guidelines for interpreting and classifying germline genomic variants. These guidelines defined five categories: benign, likely benign, uncertain significance, likely pathogenic, and pathogenic, with 28 criteria but no specific implementation algorithms. Results: Here we present Bitscopic Interpreting ACMG Standards 2015 (BIAS-2015 v2.0.0), an open-source software that automates the classification of variants based on 19 ACMG criteria while enabling user-defined weighting and manual adjustments for clinical contexts. BIAS-2015 supports high-throughput classification via command line, along with a web-based GUI, enabling variant review, modification, and interactive curation. Using genomic data from the FDA-recognized ClinGen Evidence Repository (eRepo v2.2.0), we evaluated BIAS-2015s sensitivity, specificity, and F1 values with expert curation. BIAS-2015 demonstrated superior performance to InterVar, achieving a pathogenic sensitivity of 73.99% (vs. 64.31%), benign sensitivity of 80.23% (vs. 53.91%), and an 11x speed improvement, classifying 1,327 variants per second. Conclusion: BIAS-2015 provides an accurate, scalable, and transparent ACMG classification framework. All code and the interactive variant curation platform are available on GitHub.

In 2015, the American College of Medical Genetics and Genomics (ACMG) in collaboration with the Association of Molecular Pathologists (AMP) published guidelines for the interpretation and classification of germline genomic variants. The ACMG terminology guidelines outlined criteria for assigning one of five categories: benign, likely benign, uncertain significance, likely pathogenic and pathogenic. While the paper laid out 28 different classifiers and the justification for them, it did not provide specific algorithms for implementing these classifiers in an automated manner. Here we present the Bitscopic Interpreting ACMG Standards 2015 (BIAS-2015) software as a complete, open-source algorithm which categorizes variants according to the ACMG classification system. BIAS-2015 evaluates 18 of the 28 ACMG criteria to classify variants in an automated and consistent way while recording the rationale for each classifier to enable in-depth review. We used the genomic data from the ClinGen Evidence Repository (eRepo v1.0.29), one of two FDA-recognized human genetic variant databases, to evaluate the performance of the BIAS-2015 algorithm. All code for BIAS-2015 has been made available on GitHub.

Noncoding variants of presumed regulatory function contribute to the heritability of neuropsychiatric disease. A total of 2,221 noncoding variants connected to risk for ten neuropsychiatric disorders, including autism spectrum disorder, attention deficit hyperactivity disorder, bipolar disorder, borderline personality disorder, major depression, generalized anxiety disorder, panic disorder, post-traumatic stress disorder, obsessive-compulsive disorder and schizophrenia, were studied in developing human neural cells. Integrating epigenomic and transcriptomic data with massively parallel reporter assays identified differentially-active single-nucleotide variants (daSNVs) in specific neural cell types. Expression-gene mapping, network analyses and chromatin looping nominated candidate disease-relevant target genes modulated by these daSNVs. Follow-up integration of daSNV gene editing with clinical cohort analyses suggested that magnesium transport dysfunction may increase neuropsychiatric disease risk and indicated that common genetic pathomechanisms may mediate specific symptoms that are shared across multiple neuropsychiatric diseases.

The coronavirus disease 2019 (COVID-19) pandemic has led to a surge in clinical trials evaluating investigational and approved drugs. Retrospective analysis of drugs taken by COVID-19 inpatients provides key information on drugs associated with better or worse outcomes. We conducted a retrospective cohort study of 10 741 patients testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection within 3 days of admission to compare risk of 30-day all-cause mortality in patients receiving ondansetron using multivariate Cox proportional hazard models. All-cause mortality, length of hospital stay, adverse events such as ischemic cerebral infarction, and subsequent positive COVID-19 tests were measured. Administration of ≥8 mg of ondansetron within 48 hours of admission was correlated with an adjusted hazard ratio for 30-day all-cause mortality of 0.55 (95% CI, 0.42–0.70; P < .001) and 0.52 (95% CI, 0.31–0.87; P = .012) for all and intensive care unit–admitted patients, respectively. Decreased lengths of stay (9.2 vs 11.6; P < .001), frequencies of subsequent positive SARS-CoV-2 tests (53.6% vs 75.0%; P = .01), and long-term risks of ischemic cerebral ischemia (3.2% vs 6.1%; P < .001) were also noted. If confirmed by prospective clinical trials, our results suggest that ondansetron, a safe, widely available drug, could be used to decrease morbidity and mortality in at-risk populations.

Background: With the limited availability of testing for the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and concerns surrounding the accuracy of existing methods, other means of identifying patients are urgently needed. Previous studies showing a correlation between certain laboratory tests and diagnosis suggest an alternative method based on an ensemble of tests. Methods: We have trained a machine learning model to analyze the correlation between SARS-CoV-2 test results and 20 routine laboratory tests collected within a 2-day period around the SARS-CoV-2 test date. We used the model to compare SARS-CoV-2 positive and negative patients. Results: In a cohort of 75 991 veteran inpatients and outpatients who tested for SARS-CoV-2 in the months of March through July 2020, 7335 of whom were positive by reverse transcription polymerase chain reaction (RT-PCR) or antigen testing, and who had at least 15 of 20 lab results within the window period, our model predicted the results of the SARS-CoV-2 test with a specificity of 86.8%, a sensitivity of 82.4%, and an overall accuracy of 86.4% (with a 95% confidence interval of [86.0%, 86.9%]). Conclusions: Although molecular-based and antibody tests remain the reference standard method for confirming a SARS-CoV-2 diagnosis, their clinical sensitivity is not well known. The model described herein may provide a complementary method of determining SARS-CoV-2 infection status, based on a fully independent set of indicators, that can help confirm results from other tests as well as identify positive cases missed by molecular testing.