The Medical College of Georgia (often referred to as MCG) is the flagship medical school of the University System of Georgia, the state's only public medical school, and one of the top 10 largest medical schools in the United States. Established in 1828 as the Medical Academy of Georgia, MCG is the oldest and founding school of Augusta University. It is the third-oldest medical school in the Southeast and the 13th oldest in the nation. With 22 departments, it offers both a Doctor of Medicine (MD) as well as MD-PhD, MD-MPH, and MD-MBA degrees. Its national ranking in research is 84, and its ranking in primary care is 68, both out of 185 ranked medical schools.In response to the shortage of physicians, the school has undergone tremendous growth in recent years without lowering admissions requirements. Beginning in 2010, MCG expanded to include multiple regional campuses across the state. In addition to its main clinical campus in Augusta, clinical training is offered at campuses in Albany, Rome, Savannah/Brunswick, and in Athens at the University of Georgia. The Athens campus is the University of Georgia's Health Science Campus where 40 of the school's 230 students obtain full, four-year training as part of a partnership with the University of Georgia. In 2013, the MCG Foundation received $66 million as a gift from Dr. J. Harold Harrison, MD, a notable vascular surgeon and MCG alumnus. This gift allowed for the creation of a number of scholarships, multiple construction projects, and plans for further expansion in the future.
OBJECTIVES:Guselkumab, a monoclonal antibody, selectively targets the p19 subunit of interleukin-23. This randomised, double-blind, placebo-controlled, phase 2 study evaluated guselkumab vs placebo for the treatment of giant cell arteritis (GCA). METHODS:Patients ≥50 years of age with new-onset or relapsing GCA were randomised 2:1 to guselkumab or placebo. Both arms received background glucocorticoid (GC) therapy, with a protocol-defined taper through week 26. The primary endpoint was the proportion of patients achieving GC-free remission at week 28. RESULTS:Thirty-five patients were randomised to receive guselkumab and 18 to receive placebo. All patients were White, 70% were female, and the mean age was 71.5 years; 60% had new-onset and 40% had relapsing GCA. At week 28, 40% (14/35) and 33% (6/18) of patients in the guselkumab and placebo groups, respectively, achieved GC-free remission (P = .64), whereas 31% (11/35) and 39% (7/18) had experienced a GCA flare or discontinued due to worsening GCA. Median time to first GCA flare through week 28 was not estimable (NE) in the guselkumab group (90% CI: 27.7-NE) and 29.7 weeks (90% CI: 20.1-NE) in the placebo group (P = .64). Through week 60, 97% (34/35) and 94% (17/18) of patients in the guselkumab and placebo groups, respectively, had adverse events (AEs); the most common AEs, aside from worsening of GCA (49% and 56%, respectively), were COVID-19 infection (23% and 28%) and headache (17% and 39%). CONCLUSIONS:The study's primary endpoint (GC-free remission) was not met; results do not support the use of guselkumab in the treatment of GCA.
Iron deficiency (ID) is the most common nutritional deficiency in sub-Saharan Africa (SSA). The burden of ID in SSA is further amplified by high rates of infectious diseases, hemoglobinopathies, and health system challenges. Early and accurate diagnosis of ID is necessary to reduce the clinical and public health impact of ID, particularly among vulnerable groups such as children and pregnant women. Identifying ID before the onset of anemia is especially important, as anemia represents a late manifestation of depleted iron stores. Achieving earlier detection in SSA requires improved geographic and financial access to iron status testing, as well as diagnostic approaches that are appropriate for local epidemiological contexts. This review evaluates current strategies for diagnosing ID in SSA and points out key limitations that reduce their effectiveness. We emphasize that the interpretation of commonly used iron biomarkers is frequently confounded by inflammation, malaria, and inherited blood disorders, leading to misclassification and underestimation of the actual disease burden in SSA. These biological challenges are also compounded by systemic barriers, including high testing costs, limited laboratory infrastructure, reliance on distant referral facilities, and gaps in awareness at both healthcare providers and community levels. We further argue that improving ID diagnosis in SSA will require a multifaceted approach that includes the adoption of affordable, context-appropriate diagnostic platforms and the development of regionally derived reference ranges and diagnostic decision frameworks. Such strategies would better reflect the physiological and environmental diversity across SSA and support more accurate identification of both ID with and without anemia. These efforts could provide a practical pathway toward improving early detection, guiding targeted interventions, and reducing the burden of ID across the region.
Objective. This work aims to develop a digital twin (DT) framework for fast online adaptive proton therapy planning in prostate stereotactic body radiation therapy (SBRT) with dominant intraprostatic lesion (DIL) boost, achieving clinical-equivalent plan quality with significantly reduced reoptimization time compared to traditional clinical workflows.Approach. The proposed DT framework integrates deep learning-based multi-atlas deformable image registration, daily patient anatomy updates, and knowledge-based plan quality evaluation to enable predictive and adaptive radiotherapy. Leveraging a database of 43 prior prostate SBRT cases, the framework forecasts potential interfractional anatomical variations for a new patient and pre-generates multiple probabilistic treatment plans. Upon acquiring daily cone-beam computed tomography for the new patient, the framework facilitates rapid online plan reoptimization. Plan quality is assessed using the ProKnow scoring system, evaluating dose coverage to the DIL and clinical target volume (CTV), as well as sparing of organs at risk (OARs).Main Results. The DT framework achieved an average reoptimization time of 5.52 ± 2.94 min, producing optimal DT-based plans with a mean plan quality score of 164.01 ± 8.03. These scores matched or exceeded those of the clinical plans, which required substantially longer reoptimization times (17.66 ± 7.93 min) to achieve comparable plan quality (161.23 ± 9.50). DT-based plans provided DIL V100 coverage of 99.27% ± 0.64% and CTV V100 of 99.95% ± 0.07%, with reduced OAR doses, including bladder V20.8 Gy of 10.61 ± 2.64 cc, rectum V23Gy of 0.61 ± 0.32 cc, and urethra D10 of 89.45% ± 1.07%, which were comparable to the clinical quality standard.Significance. The proposed DT framework facilitates rapid, clinically comparable adaptive proton therapy planning, reducing reoptimization time while preserving or enhancing clinical plan quality. By addressing interfractional anatomical variations efficiently, it enhances treatment precision, reduces OAR toxicity, and supports online personalized radiotherapy, offering a transformative approach for prostate SBRT with DIL boost.
Background Heart failure (HF) is linked to disturbances in heart metabolism. Metabolomics shows promise in identifying cardiac-specific metabolic changes across different types of heart disease. However, direct comparison of metabolomic changes in myocardial tissues from humans with end-stage ischemic (ICM) and nonischemic (NICM) heart failure is scarce. Methods Left ventricles were collected from patients with end-stage ICM (n = 14) and NICM (n = 15), along with nonfailing donors (n = 11). Untargeted metabolomics assessed organic acids, amino acids, purines, and pyrimidines. Data were analyzed using partial least squares-discriminant analysis (PLS-DA), heat maps, Kruskal-Wallis with Dunn test, and ANOVA. RT-PCR and Western blotting were used to examine the expression of metabolic genes. Results Myocardial metabolites could distinguish diseased from nonfailing hearts; however, ICM and NICM samples often overlapped, despite ICM patients having higher rates of diabetes and hyperlipidemia. Glycolytic and TCA metabolites showed no differences within the groups. Notably, myocardial UDP-N-Acetyl-glucosamine and O-GlcNAcylation levels were consistently lower, despite increased expression of hexosamine pathway genes in both disease groups. Several amino acids decreased, yet branched-chain amino acids remained stable in ICM and NICM hearts. Both groups showed hyperhomocysteinemia and increased urea cycle intermediates. Glutathione (GSH) and glutathione disulfide (GSSG) levels were depleted regardless of glutathione synthesis gene expression. Adenylated purines and pyrimidines were reduced, with increased purine degradation and a notable upregulation of NC5E, an extracellular nucleotidase, in both disease groups. While ATP and NAD+ levels stayed relatively stable, NADH, FAD+, and NADP+ levels decreased in diseased hearts. Catalase was upregulated in both disease groups despite elevated markers of oxidative stress. Conclusion Human end-stage heart failure is characterized by altered glucose and amino acid metabolism, heightened oxidative stress, increased purine breakdown, and reduced pyrimidine levels, with no differences observed between ischemic and nonischemic cardiomyopathy. These findings enhance our understanding of metabolic alterations in failing human hearts.
Despite cardiovascular disease risk reduction with intensive lifestyle modification, durable healthy behavior change remains elusive. An evidence-based framework for optimizing artificial intelligence (AI) augmented pro-health behavior change interventions features AI, behavioral, and medical expert collaboration in (1) use-case development, (2) real-time risk-benefit oversight, (3) modeling bias mitigation, and (4) personalizing disease management. User safety perquisites include 1) autonomy, (2) data transparency, (3) explainable model trust-building, and (4) risk-reward neuromodulation avoidance.
