IntroductionAging global populations are driving an increased demand for urological care, yet the healthcare system faces a projected decline in urologist availability per capita. This shift positions primary care providers as the essential frontline for initial evaluation and management. This study aimed to assess clinical exposure, knowledge, and self-reported comfort levels regarding common urological conditions among family and internal medicine residents.MethodsA 76-item cross-sectional survey was administered between August 2023 and June 2024 to family medicine and internal medicine residents (n = 74) at 5 residency programs in the greater Los Angeles metropolitan area, spanning community, safety-net, and academic training environments. The survey evaluated four domains: prior training, current clinical procedural and diagnostic experience, comfort levels, and perceived importance of urological topics. Responses were summarized descriptively.ResultsNotable training gaps were identified; 78% of primary care residents did not complete a clinical urology rotation during medical school. Primary care residents reported limited experience and low comfort across most procedural and diagnostic categories. High discomfort was noted in managing penile lesions (70.3%) and chronic prostatitis (63.5%). Conversely, residents reported the greatest experience interpreting urinalysis (94.6% had interpreted more than 20) and the highest comfort managing urinary tract infections (UTIs; 75.7% "very comfortable").ConclusionsIn this single-region, small sample, residents reported substantial gaps in urological training and in comfort managing common non-surgical conditions. Given the evolving specialist workforce, a range of educational strategies-including optional rotations, eConsult platforms, telementoring, and genitourinary fellowships- merit formal evaluation as a means of supporting primary care providers in managing common, non-surgical urologic conditions.

Cytochrome P450 (CYP) enzymes are best known for xenobiotic metabolism, but many human CYPs also catalyze essential reactions in endogenous pathways governing steroidogenesis, bile acid and oxysterol turnover, vitamin D homeostasis, retinoic acid degradation, and tissue-specific lipid metabolism. Pathogenic variation in these enzymes gives rise to a distinctive class of rare Mendelian disorders in which enzyme position within a pathway predicts the dominant biochemical or tissue-restricted readout and, consequently, the clinical phenotype.Here, we synthesize rare CYP-related diseases within a pathway-resolved framework and propose that they are best interpreted not as isolated syndromes, but as tightly coupled metabolic systems linking gene defects, enzymatic block, metabolite signature, and disease expression. Steroidogenic CYP disorders provide the clearest example of biomarker-guided disease architecture, as defects in CYP11A1, CYP21A2, CYP11B1, CYP11B2, CYP17A1, and CYP19A1 generate characteristic steroid profiles with direct endocrine correlates. Vitamin D-related disorders involving CYP2R1, CYP27B1, CYP24A1, and gain-of-function CYP3A4 variants illustrate how altered activation, catabolism, or alternative inactivation produces diagnostically and therapeutically informative metabolite patterns. Bile acid and oxysterol disorders caused by CYP27A1, CYP7B1, and CYP7A1 defects show that the most informative analyte may be cholestanol, oxysterols, or bile acid intermediates, whereas tissue vulnerability shapes neurologic, hepatobiliary, or metabolic presentation. By contrast, organ-specific CYP disorders involving CYP4V2, CYP1B1, CYP4F22, and CYP26B1 are typically phenotype-led, with diagnosis driven by imaging, examination, or tissue pathology rather than circulating biomarkers.Across categories, mechanistic resolution depends on aligning pathway architecture with the appropriate biochemical readout and locus-aware molecular interrogation.

Inherited cardiovascular diseases (ICVDs) are a broad category of hereditary disorders resulting from pathogenic mutations that alter cardiac anatomy, electrical conduction, and metabolic pathways. The main types include hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), cardiac channelopathies, and familial hypercholesterolemia (FH) and are associated with heart failure, malignant arrhythmias, and sudden cardiac death, particularly in young people. Current treatments, such as pharmacological therapies, implantable cardioverter-defibrillators, and heart transplantation, have significantly improved outcomes; however, these treatments are mainly targeted toward symptom control and the prevention of complications without correcting the underlying genetic defects. Recent advancements in genome editing have transformed precision cardiovascular care, with clustered regularly interspaced short palindromic repeats (CRISPR)-based technologies emerging as potential methods for targeting and modifying disease-causing variants. Emerging platforms, such as base editing, prime editing, and epigenome editing, have widened the therapeutic potential of genome engineering by enhancing editing precision and reducing some of the constraints of the traditional CRISPR-Cas9 strategy. In the setting of inherited cardiovascular disorders, promising early results have been obtained in preclinical studies using human induced pluripotent stem cell and animal models, including correction of MYBPC3 and MYH7 mutations in HCM; targeting of TTN-, RBM20-, and LMNA-associated mechanisms in DCM; restoration of ion channel function in inherited arrhythmia syndromes; and modulation of lipid-related genes such as PCSK9, LDLR, and ANGPTL3 in FH. However, major challenges remain, including off-target effects, effective delivery to cardiac tissues, immunological responses, interpretation of genetic variants, and ethical concerns. This review provides a translational, integrated view of the evolution of CRISPR-based genome editing technologies, their application to ICVDs, current preclinical and clinical advances, and the challenges that need to be addressed to achieve safe and effective translation into cardiovascular therapeutics.

The traditional drug discovery and development (DDD) pipeline has long been characterized by inefficiency and high costs. The rapid evolution of artificial intelligence (AI), especially AI-based toolkits for protein structure and interaction prediction, exemplified by AlphaFold 3 (AF3), promises to reshape the landscape of drug innovation. AF3 advances structural biology by predicting protein structures and their interactions with a range of biomolecules, including nucleic acids, small-molecule ligands, and ions, potentially changing the conventional DDD paradigm, although prediction accuracy varies substantially across these categories. This perspective examines the impact of AF3 on drug target identification and validation, hit identification, lead optimization, and personalized clinical decision-making, while addressing the technical challenges, ethical considerations, and governance implications. Solutions to overcome current limitations are proposed to advance the development of novel therapeutics.

This article describes an emotion-annotated corpus of Turkish-language posts published on X (formerly Twitter) that discuss 'sessiz istila' ('silent invasion'), a contested phrase used in Turkish public debate to frame the presence of refugees, particularly Syrian refugees, as a threat. We retrieved posts using the academictwitteR interface to the Twitter Academic Research API with the keyword 'sessiz istila', restricted to publicly available Turkish-language posts published between 1 June 2021 and 31 December 2022, yielding 47,024 posts. We normalized every post with a rule set implemented in R that replaces retweet markers, URLs, user mentions, and hashtags with Turkish placeholder tokens, converts text to lowercase, and substitutes each emoji with a Turkish description drawn from a purpose-built emoji lexicon of 3664 entries released alongside the corpus. Emotion labels were assigned automatically by an Emotion Recognition Model obtained by fine-tuning BERTurk (bert-base-turkish-cased) on a class-balanced subset of the TREMO dataset containing 18,018 validated entries, 3003 for each of the six Ekman categories (happiness, fear, sadness, disgust, surprise and anger). We retained predictions with softmax confidence below 0.6 but flagged them as 'ambiguous' rather than discarding them so that users can apply their own confidence policy. The released corpus therefore contains 40,880 confidently labeled posts and 6144 ambiguous posts, each with its creation timestamp, normalized text, date, year, and emotion label. The dataset supports secondary research on the circulation of anti-immigrant framing and on the reactions it provokes, on event-driven emotional dynamics in social media, and on benchmarking and error analysis of emotion classifiers for Turkish, an agglutinative and comparatively low-resource language. The balanced TREMO training file and the Turkish emoji lexicon are reusable across corpora.

Screw fixation is the standard surgical treatment for slipped capital femoral epiphysis. Screw-related adverse events, including implant breakage, joint penetration, fixation failure, growth-mediated construct change, removal failure, and postremoval fracture, are inconsistently reported throughout the pediatric orthopaedic literature. In this current concept review, we propose a practical, life-cycle classification encompassing implant insertion, in vivo fixation, growth and remodeling, implant removal, and postremoval follow-up. Reported screw-related failures cluster into four practical evidence domains: placement-related risk, construct-longevity failure, mechanical implant failure, and removal/postremoval complications. These domains are organized into seven chronological life-cycle categories. A four-domain synthesis mapped to a seven-stage life-cycle classification provides a practical framework and shared vocabulary for preoperative counseling, implant selection, postoperative surveillance, removal planning, and standardized reporting.

Background Hospital pharmacists collect rich admission data, i.e., medication history, brought-in medicines, prescription and laboratory records, and vital signs, yet the medication-related problems these data contain are surfaced by ad hoc, non-auditable judgment. Existing drug-related problem classifications code a problem after it has been recognized; they do not specify which admission data element to examine or which decision rule to apply. The prespecified aims of this study were to develop a taxonomy of pharmacist-identifiable medication-related risk signals with explicit operational definitions and data sources, and to apply it, as a feasibility application in a very small cohort, to routine admission data from patients who developed in-hospital ischemic stroke. A four-stage pharmacist-led assessment framework was derived post hoc as a secondary output. Methodology In this single-center retrospective medication safety audit (Strengthening the Reporting of Observational Studies in Epidemiology), 12 signal categories, each with an operational definition and data source, were fixed before record review and mapped to existing classifications. The taxonomy was applied to 17 consecutive patients with in-hospital ischemic stroke (August 2020-September 2023). Two pharmacists reviewed the records jointly and classified each case by consensus, counting only signals documented before the index date. Analysis was descriptive; category-level counts with explicit denominators were the primary quantities. Results All 17 cases could be classified against all 12 categories from admission data. Category-level counts were: poor adherence or medication management, 10 of 17; residual medication discrepancy, 5 of 17; treatment self-discontinuation, 3 of 17; insulin instruction mismatch, 2 of 17; suboptimal blood pressure control, 9 of 13 treated hypertensive patients; and suboptimal glycemic control, 5 of 8 patients with diabetes. As a secondary summary, 14 of 17 patients had at least one signal. Inter-rater reliability, content validity, and pharmacist workload were not measured. Conclusions In this 17-patient feasibility application, a predefined, operationally defined taxonomy could be applied to data pharmacists already collect. It yields a potentially feasible structured and auditable framework using routinely available admission data, not a validated or demonstrably low-burden tool. Because no comparator was studied, the counts are neither specific to nor predictive of stroke; independent prospective evaluation of reliability, validity, and workload is required.