Meharry Medical College

The BioDIGS project is a nationwide initiative involving students, researchers and educators across more than 40 research and teaching institutions. Participants lead sample collection, computational analysis and results interpretation to understand the relationships between the soil microbiome, environment and health.

Neural NLP models are often miscalibrated, assigning high confidence to incorrect predictions, which undermines selective prediction and high-stakes deployment. Post-hoc calibration methods adjust output probabilities but leave internal computation unchanged, while ensemble and Bayesian approaches improve uncertainty at substantial training or storage cost. We propose UAT-LITE, an inference-time framework that makes self-attention uncertainty-aware using approximate Bayesian inference via Monte Carlo dropout in pretrained transformer classifiers. Token-level epistemic uncertainty is estimated from stochastic forward passes and used to modulate self-attention during contextualization, without modifying pretrained weights or training objectives. We additionally introduce a layerwise variance decomposition to diagnose how predictive uncertainty accumulates across transformer depth. Across the SQuAD 2.0 answerability, MNLI, and SST-2, UAT-LITE reduces Expected Calibration Error by approximately 20

Agentic AI systems powered by Large Language Models (LLMs) are transforming clinical workflows, yet their autonomous handling of Protected Health Information (PHI) creates critical HIPAA compliance vulnerabilities that existing frameworks fail to address. This paper introduces a HIPAA-compliant Agentic AI framework enforcing regulatory compliance through three core mechanisms: Attribute-Based Access Control for dynamic PHI governance, a hybrid regex and BERT-based sanitization pipeline delivering defense-in-depth redaction across pre and post-inference stages, and immutable audit trails for compliance verification. We evaluate end-to-end system effectiveness on MIMIC-IV discharge summaries across 107,800 runs, measuring policy-consistent PHI exposure, residual leakage, and clinical utility under multiple authorization settings and ablations. The results show that layered governance substantially reduces PHI exposure while preserving utility for authorized roles, and remains resilient under prompt-injection stress tests.

Background/Objectives: Radiomics-based machine learning models have demonstrated high accuracy in differentiating benign from malignant orbital masses, with early studies suggesting performance comparable to expert radiologists. However, translation into clinical practice remains limited due to dataset constraints, including retrospective study designs, single-center cohorts, and underrepresentation of diverse patient populations. This review aims to evaluate the current evidence supporting radiomics in orbital disease while critically examining barriers to generalizability and equity across ophthalmology, otolaryngology, and plastic surgery. Methods: A narrative literature review was conducted to assess radiomics applications in orbital oncology and reconstruction. Studies evaluating diagnostic accuracy, margin assessment, postoperative surveillance, and surgical planning across ophthalmology, head and neck surgery, and reconstructive surgery were analyzed, with particular attention paid to dataset composition, validation strategies, and imaging standardization. Results: Radiomics models demonstrated high diagnostic performance in differentiating orbital tumors, optimizing surgical planning, and aiding postoperative monitoring. However, most studies relied on small, homogeneous datasets lacking racial, ethnic, and pediatric representation. External validation was uncommon, and imaging heterogeneity limited reproducibility. These deficiencies restrict the clinical translation of radiomics and risk exacerbating healthcare disparities, particularly among underrepresented populations. Conclusions: Radiomics holds promise as a precision medicine tool for orbital diagnosis, surgical navigation, and postoperative care. Nevertheless, its clinical adoption is constrained by dataset bias, lack of standardization, and limited prospective validation. Future progress requires multi-institutional, demographically diverse datasets and standardized imaging protocols to ensure equitable and generalizable implementation across specialties.

Introduction: Mpox is an Orthopoxvirus infection with evolving clinical presentations, including genital-dominant disease that can resemble common sexually transmitted infections (STIs). Secondary bacterial superinfection is uncommon but may alter lesion morphology and delay diagnosis. Case Presentation: A 64-year-old unhoused heterosexual male presented with a two-week history of painful, malodorous groin ulcers after unprotected intercourse. Lesions began as groin-confined vesicles and later spread to the face and peri-auricular area. Because of ulceration, purulence, and sexual exposure, initial evaluation focused on more common sexually transmitted infections, all of which tested negative. Dermatology was consulted when the eruption showed umbilicated vesiculopustules at uniform developmental stages. Punch biopsies demonstrated neutrophilic pustules consistent with Orthopoxvirus infection, and PCR confirmed mpox. During hospitalization, several lesions became purulent with foul odor; culture grew methicillin-sensitive Staphylococcus aureus. Linezolid therapy produced rapid improvement. Discussion: This case demonstrates genital-dominant mpox in a heterosexual male, a less commonly reported demographic, and highlights how lesion morphology may mimic bacterial or viral STIs. Bacterial superinfection can obscure classic diagnostic features, contribute to malodor and purulence, and delay recognition. PCR remains essential for confirmation, particularly when presentation overlaps with other genital ulcer etiologies. Conclusion: Clinicians should include mpox in the differential diagnosis of genital ulcer disease regardless of sexual orientation and maintain vigilance for bacterial superinfection in non-healing or purulent lesions. Early PCR testing and culture-directed therapy are essential for accurate diagnosis and optimal management.