The California University of Science and Medicine (CUSM) School of Medicine is a newly accredited medical school located in Colton, California/San Bernardino, San Bernardino County, United States. CUSM received preliminary LCME accreditation in February 2018, and enrolled students for classes conferring an M.D. degree beginning July 2018. The new CUSM campus, completed in 2020, has two lecture halls, nine college rooms, 15 clinical skills rooms and three labs. More than 2,400 students applied to CUSM's inaugural class, with 64 students enrolling in the class of 2022. In 2019, more than 5,300 students applied to CUSM, with 98 students enrolling in the class of 2023. In 2020, more than 5,300 students applied to CUSM, with 130 students enrolling in the class of 2024. The average GPA of accepted students is 3.6, with an average MCAT score of 513.The California University of Science and Medicine currently is affiliated with Arrowhead Regional Medical Center as the primary teaching hospital. The majority of the students in the class of 2022 (80%), class of 2023 (80%), and class of 2024 (98%) comes from California..
To characterize national patterns of ocular trauma related to pickleball, dodgeball, and kickball in the United States, with emphasis on incidence, demographics, injury mechanisms, and diagnoses. Data from the U.S. Consumer Product Safety Commission’s National Electronic Injury Surveillance System (2014–2023) were reviewed. Injury cases were identified using body part codes for the eye, face, and head combined with product code 3235 (“Other Ball Sports”). Trauma narratives were subsequently manually reviewed to confirm sport involvement alongside ocular and/or orbital injury. National injury estimates were calculated using the NEISS-provided sample weights. Annual incidence per million people was calculated using data from the U.S. Census Bureau and American Community Survey. Statistical analyses included descriptive statistics and inferential testing using chi-square tests, ANOVA, and linear regression. A total of 120 confirmed cases were identified, corresponding to an estimated 7974 ocular injuries (95
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.
This cross-sectional survey of a California medical school found that caffeine consumption increases across medical training, with third-year students consuming more caffeine, particularly from coffee, energy drinks, and over-the-counter stimulants, than first- and second-year students, and higher intake being associated with elevated modified CAGE scores, suggesting stress-related stimulant use.
Acute myeloid leukemia (AML) remains a biologically heterogeneous disease with historically limited targeted therapies and poor outcomes. The development of menin inhibitors represents a promising shift, particularly for patients harboring KMT2A rearrangements (KMT2Ar) and NPM1 mutations (NPM1m). This manuscript reviews the molecular rationale of menin inhibition for aberrant homeobox/myeloid ectopic insertion site 1 (HOX/MEIS1)-driven gene expression and leukemogenesis, clinical trial outcomes, and safety data for menin inhibitors, with a focus on recently FDA-approved revumenib and several other agents in development, ziftomenib (KO-539), bleximenib (JNJ-75276617), and icovamenib (BMF-219). We also focused our discussion on future directions to include resistance mechanisms, biomarker identification and monitoring strategies, and combination therapies. Menin inhibition is now being clinically integrated into relapsed/refractory and frontline treatment settings.
This study presents an advanced artificial intelligence (AI) model designed to accurately classify and detect kidney stones using medical imaging. Leveraging cloud-based computational resources, the model was trained to differentiate between stone-containing and normal kidneys while simultaneously identifying the precise localization of stones within images. The dataset consisted of 6,720 radiologic images representing clinically relevant stone cases and normal renal anatomy, with an 80-10-10 split for training, validation, and testing to ensure reliable assessment. Notably, the model achieved exceptional diagnostic performance, reflected by an average precision of 1.00 and both precision and recall reaching 99.9%. A perfect confusion matrix, demonstrating 100% correct classification of both stone and non-stone images, further underscores the robustness of the model. Model development required no physical hardware investment due to the use of cloud infrastructure, ensuring a cost-efficient and environmentally sustainable workflow. While results demonstrate strong clinical potential for automated nephrolithiasis detection, further evaluation on larger, multi-institutional datasets and across varied imaging modalities is recommended to strengthen generalizability. This work highlights the growing role of AI-enhanced diagnostic tools in urologic imaging, offering the promise of faster interpretation, improved workflow efficiency, and earlier identification of kidney stone disease.
