Endothelial inflammation and atherosclerosis remain leading drivers of cardiovascular disease, yet the post-transcriptional regulators orchestrating these events are not yet completely understood. In this review, we analyse recent preclinical and clinical studies to dissect microRNA (miRNA)-mediated control of vascular endothelial biology. We describe how miR-181b-5p and miR-223 modulate NLRP3 inflammasome activation and pyroptosis, how miR-615-5p, miR-138, and miR-133a coordinate endothelial nitric oxide synthase (eNOS) activity and nitric oxide bioavailability, and how miR-33a/b, miR-150, and miR-342-3p influence lipid efflux and foam-cell formation in atherogenesis. We also discuss miRNA signatures that correlate with endothelial dysfunction in human cohorts. By integrating mechanistic pathways with emerging biomarker data, this study underscores the relevance of miRNAs as both diagnostic and potential targets in vascular diseases.

Medical residency is a demanding period of specialty training, marked by intense clinical exposure, heavy workload, and continuous knowledge development. Identifying how residents prefer to learn may help residency programs refine their educational strategies and better align teaching approaches with the needs of trainees. This study assessed learning-style preferences among medical residents using the Felder–Soloman Index of Learning Styles. A total of 152 residents from clinical and surgical specialties in a Brazil-ian medical residency program were included. In the overall sample, the predominant preferences were sensing (90.79%), visual (73.68%), active (62.50%), and sequential (70.39%). Comparisons between clinical and surgical specialties showed a significant dif-ference only in the understanding domain, with residents from clinical specialties being more frequently classified as sequential than those from surgical specialties (80.0% vs. 63.2%). The limited variation across groups suggests that most residents share a broadly similar learning profile, although some cognitive-processing preferences may differ ac-cording to specialty-related demands. These findings support the use of diversified educa-tional strategies in medical residency, combining visual resources, active learning meth-ods, and structured practical activities adapted to clinical and surgical training contexts.

Medical residency is a demanding stage of specialty training characterized by extensive clinical exposure, heavy workloads, and ongoing knowledge acquisition. Characterizing residents’ learning-style preferences may help residency programs refine their educational strategies and better address residents’ needs. This study assessed learning-style preferences using the Felder–Soloman Index of Learning Styles in 152 residents from clinical and surgical specialties in a Brazilian medical residency program. In the overall sample, the predominant preferences were sensing (90.79%), visual (73.68%), active (62.50%), and sequential (70.39%). Clinical and surgical residents differed significantly only in the understanding domain: sequential preference was more frequent among clinical than surgical residents (80.0% [95% CI: 68.7–87.9] vs. 63.2% [95% CI: 52.7–72.6]). The limited variation between groups suggests that most residents shared a broadly similar learning profile, although some cognitive-processing preferences may differ according to specialty-related demands. These findings may inform diversified educational strategies that combine visual resources, practical activities, and structured progression across clinical and surgical training contexts.

Background: Additive manufacturing has emerged as a promising technology for the fabrication of patient-specific metallic implants with complex architectures for craniofacial reconstruction. Among metallic biomaterials, cobalt–chromium–molybdenum (Co–28Cr–6Mo) alloys exhibit excellent mechanical strength and corrosion resistance. Plasma anodization has been proposed as a surface engineering strategy to enhance the biological performance of metallic implants; how-ever, its ability to improve osseointegration and bone regeneration remains uncertain. This study investigated the biological response of plasma-anodized additively manufactured Co–28Cr–6Mo implants in a rat critical-size calvarial defect model. Methods: Forty-eight male Wistar rats were randomly assigned to eight experimental groups (n = 6 per group) receiving untreated Co–28Cr–6Mo implants, plasma-anodized Co–28Cr–6Mo implants, titanium-coated plas-ma-anodized Co–28Cr–6Mo implants, or empty defects (control). Animals were euthanized after 6 or 12 weeks. Biological performance was assessed by macroscopic and radiographic examina-tions, histological analyses (Stevens Blue and Von Kossa staining), and quantitative histomor-phometric evaluation of newly formed bone area. Results: All implant surfaces demonstrated excellent in vivo biocompatibility, with no evidence of chronic inflammation, implant rejection, corrosion, or postoperative complications throughout the experimental period. Histological analyses consistently revealed limited bone formation confined to the margins of the defects and the persistence of fibrous connective tissue between the implant surface and the host bone. Quan-titative histomorphometry showed a slight increase in newly formed bone after 12 weeks com-pared with 6 weeks; however, no statistically significant differences were observed among the Control, Co–28Cr–6Mo, Co–28Cr–6Mo PIII, and Co–28Cr–6Mo/Ti PIII groups (p > 0.05). Conclu-sions: Plasma anodization preserved the excellent biocompatibility of additively manufactured Co–28Cr–6Mo implants but did not significantly improve osseointegration or bone regeneration in a rat critical-size calvarial defect model. These findings indicate that plasma anodization alone is insufficient to enhance the regenerative performance of Co–Cr–Mo implants and suggest that additional bioactive surface functionalization strategies may be necessary to promote bone re-pair in critical-size craniofacial defects.

Background: Artificial intelligence (AI) is transforming medical education and clinical practice, requiring digital, critical, and ethical competencies. This study aimed to assess medical students’ attitudes toward AI, including perceived benefits, risks, and use in medical training. Methods: A mixed-methods, descriptive study used an electronic questionnaire comprising sociodemographic data and 13 items adapted from the authorized Artificial Intelligence Attitude Scale. Responses were recorded on a five-point Likert scale, with reverse-coding of negative items. Total and subscale scores for benefits, risks/threats, and use were calculated. Participants were medical students from the 1st to 6th years at the School of Medicine of Jundiaí (FMJ). Results: Among 436 students, attitudes toward AI were generally favorable. No significant gender difference was observed (p = 0.291), whereas scores differed significantly across training years (p = 0.025), with higher scores among advanced students. Despite recognizing AI’s benefits, students reported concerns regarding reliability, clinical autonomy, ethical implications, and gaps in practical knowledge. Conclusions: AI is positively perceived by medical students, particularly those in advanced years. These findings support structured AI integration into medical curricula to promote critical, ethical, and responsible use in medical education and future clinical practice.