Background and Objectives: Coronavirus Disease 2019 (COVID-19) may cause extensive multi-organ pathology, particularly in the lungs, heart, kidneys, and liver. While hypercoagulability—often signaled by elevated D-dimer—has been thoroughly investigated, the concurrent pathological findings across organs and their interrelation with distinct D-dimer levels remain incompletely characterized. This study aimed to evaluate the pathological changes observed in autopsied or deceased COVID-19 patients, focusing on the prevalence of organ-specific lesions, and to perform subgroup analyses based on three D-dimer categories. Methods: We conducted a retrospective review of 69 COVID-19 patients from a Romanian-language dataset, translating all clinical and pathological descriptions into English. Pathological findings (pulmonary microthrombi, bronchopneumonia, myocardial fibrosis, hepatic steatosis, and renal tubular necrosis) were cataloged. Patients were grouped into three categories by admission D-dimer: <500 ng/mL, 500–2000 ng/mL, and ≥2000 ng/mL. Laboratory parameters (C-reactive protein, fibrinogen, and erythrocyte sedimentation rate) and clinical outcomes (intensive care unit [ICU] admission, mechanical ventilation, and mortality) were also recorded. Intergroup comparisons were performed with chi-square tests for categorical data and one-way ANOVA or the Kruskal–Wallis test for continuous data. Results: Marked organ pathology was significantly more frequent in the highest D-dimer group (≥2000 ng/mL). Pulmonary microthrombi and bronchopneumonia increased stepwise across ascending D-dimer strata (p < 0.05). Myocardial and renal lesions similarly showed higher prevalence in patients with elevated D-dimer. Correlation analysis revealed that severe lung and heart pathologies were strongly associated with high inflammatory markers and a greater risk of ICU admission and mortality. Conclusions: Our findings underscore that COVID-19-related organ damage is magnified in patients with significantly elevated D-dimer. By integrating pathology reports with clinical and laboratory data, we highlight the prognostic role of hypercoagulability and systemic inflammation in the pathogenesis of multi-organ complications. Stratifying patients by D-dimer may inform more tailored management strategies, particularly in those at highest risk of severe pathology and adverse clinical outcomes.

Background and Objectives: This systematic review investigates the relationship between preoperative psychological disorders and postoperative satisfaction among patients undergoing aesthetic surgery. While aesthetic procedures can enhance self-image, growing evidence indicates that underlying mental health conditions, particularly BDD, depression, and anxiety, may compromise surgical outcomes. Materials and Methods: A comprehensive literature search was performed in PubMed, the Cochrane Library, and Google Scholar between January 2010 and December 2024. Eligible observational studies assessed preoperative psychological conditions—primarily body dysmorphic disorder, depression, and anxiety—using validated instruments, such as the Body Dysmorphic Disorder Questionnaire (BDDQ), Body Dysmorphic Disorder Examination (BDDE), Hospital Anxiety and Depression Scale (HADS), Beck Depression Inventory (BDI), and structured clinical interviews, and reported postoperative patient-reported satisfaction following aesthetic surgery. Study quality was evaluated using an adapted QUIPS framework. Results: Across the 13 included studies, six reported a negative association between moderate-to-severe preoperative psychological symptoms and postoperative satisfaction, five found no significant association, and two described positive or conditional associations. Methodological heterogeneity in psychological assessment tools and satisfaction measures was a major source of divergence across studies. Despite these differences, the evidence underscores the need for standardized, validated psychological evaluation protocols in aesthetic surgery. Incorporating mental health screening into routine surgical planning can enhance ethical practice, reduce dissatisfaction, and improve long-term patient outcomes. Conclusions: These findings advocate for a multidisciplinary approach that includes psychological assessment as an essential component of patient care in aesthetic medicine.

Background/Objectives: Psoriatic disease is a systemic inflammatory condition associated with increased cardiometabolic risk, but the impact of contemporary systemic therapies and narrowband ultraviolet B (NB-UVB) phototherapy on vascular and systemic inflammatory markers remains incompletely characterized. We aimed to systematically synthesize prospective evidence on treatment-associated changes in vascular inflammation and systemic inflammatory biomarkers in adults with moderate-to-severe psoriatic disease. Specifically, we evaluated changes assessed by 18F-FDG PET/CT imaging and circulating biomarkers following biologic therapies or NB-UVB phototherapy. Methods: We systematically searched MEDLINE, Embase, Web of Science, Scopus, and CENTRAL from inception to 31 January 2026 for prospective interventional and observational studies in adults with psoriasis or psoriatic arthritis treated with biologic agents targeting TNF-α, IL-12/23, IL-17, or IL-23, or with NB-UVB phototherapy. Eligible studies were required to report serial assessments of vascular inflammation by 18F-FDG PET/CT (typically aortic target-to-background ratio) and/or systemic inflammatory markers (high-sensitivity C-reactive protein, interleukin-6, TNF-α, GlycA, or hematologic indices such as the neutrophil-to-lymphocyte ratio) over at least 8 weeks of follow-up. We imposed no language restrictions and included only full-text, peer-reviewed prospective studies. Risk of bias was evaluated using RoB 2 for randomized trials and ROBINS-I for nonrandomized studies. Random-effects meta-analyses were prespecified for outcomes reported by at least two clinically comparable studies; however, because of substantial heterogeneity in reporting and methodology, effect estimates were summarized using a structured narrative synthesis. Results: Thirteen prospective studies (n ≈ 900 adults, published 2015–2025) met inclusion criteria, including four studies with serial 18F-FDG PET/CT imaging and one additional PET/CT study providing baseline observational data on vascular inflammation, as well as eight biomarker-focused prospective cohorts. Across randomized mechanistic trials and observational studies, biologic therapies reduced aortic target-to-background ratio by approximately 6–12% over 12–24 weeks (e.g., mean change from 2.42 to 2.18 with TNF-α inhibition and from 2.51 to 2.20 with IL-17 blockade), and no study reported worsening of PET-derived vascular indices under effective systemic treatment. Biologic and other systemic therapies produced concordant reductions in hs-CRP (typically by 30–50%), IL-6, TNF-α, GlycA, and blood-count-derived indices including neutrophil-to-lymphocyte ratio, with biomarker improvements frequently paralleling reductions in cutaneous disease activity and cardiometabolic risk markers. Two NB-UVB cohorts demonstrated significant hs-CRP reductions of roughly 20–30% and modulation of vitamin D-related inflammatory proteins, suggesting systemic anti-inflammatory effects, although these changes appeared less pronounced than with biologic therapy and were not accompanied by vascular imaging. Conclusions: Contemporary systemic psoriasis therapies, particularly biologic agents targeting the IL-23/Th17 axis and TNF-α, are associated with consistent reductions in aortic vascular inflammation and broad improvements in systemic inflammatory biomarkers, whereas NB-UVB phototherapy confers more modest but measurable systemic anti-inflammatory effects, although the current evidence does not allow differentiation between individual biologic classes in terms of magnitude of effect. Although reductions in vascular and systemic inflammatory markers were observed across therapies targeting TNF-α, IL-12/23, IL-17, and IL-23, the small number of mechanistic imaging studies and absence of head-to-head comparisons do not allow robust differentiation between biologic classes or support a uniform class effect. The convergence of imaging and biomarker data reinforces psoriasis as a clinically relevant model of inflammation-driven atherosclerosis and supports the concept that effective control of psoriatic inflammation may contribute to cardiovascular risk modification, highlighting the need for integrated cardiovascular risk assessment in routine care. However, the imaging evidence base remains limited to four small mechanistic PET/CT studies with relatively short follow-up, which constrains the strength and generalizability of conclusions regarding vascular inflammation. Larger, adequately powered, event-driven prospective trials with standardized imaging and biomarker endpoints are needed to determine whether these vascular and systemic anti-inflammatory effects translate into reduced cardiovascular events in psoriatic disease; because of methodological and reporting heterogeneity across the 13 included studies, these conclusions are based on a structured narrative synthesis rather than a formal quantitative meta-analysis. PROSPERO registration number: CRD420261296646.

Venous thromboembolism (VTE), which includes deep vein thrombosis and pulmonary embolism, is a significant and preventable cause of morbidity and mortality worldwide. Despite the existence of clinical prediction models, biomarker-based risk assessments, and imaging techniques, gaps remain in accurately identifying and managing high-risk patients. In recent years, artificial intelligence has emerged as a transformative tool in healthcare, offering promising applications for enhancing VTE prevention strategies. This narrative review synthesizes current evidence on the use of artificial intelligence (AI) technologies including machine learning (ML), deep learning (DL), and natural language processing (NLP). We explore how supervised ML algorithms, such as random forests, support vector machines, and gradient boosting, improve predictive performance compared to traditional models by capturing complex, nonlinear relationships within electronic health record data. We also examine the role of DL models, particularly convolutional neural networks, in interpreting imaging data, achieving diagnostic accuracies comparable to expert radiologists. Additionally, the review highlights NLP applications in extracting risk-relevant information from unstructured clinical notes and the emerging integration of wearable device data and time-series analysis for dynamic risk assessment. We argue that the successful integration of AI into routine VTE prevention workflows requires rigorous prospective validation, cross-institutional collaboration, and thoughtful implementation into clinical decision support systems.

Background: Artificial intelligence (AI) has emerged as a transformative technology across dermatological practice, from automated lesion classification to whole-slide pathology analysis. Despite rapid growth in primary studies, a comprehensive synthesis of diagnostic performance, application breadth, and real-world implementation remains lacking. Methods: We conducted a PRISMA systematic review and meta-analysis of studies published from January 2000 to March 2025. We searched the PubMed, Cochrane, and ScienceDirect databases for studies reporting AI diagnostic performance in dermatology. Results: Of 30 included studies (28 valid after exclusion of two retracted publications), 60% focused on melanoma and related lesions. AI diagnostic performance improved markedly over five identified temporal eras (2003–2025), with a pooled AUROC of 0.92 (95% CI 0.87–0.96), Reitsma sensitivity of 0.88 (0.82–0.93), and Reitsma specificity of 0.85 (0.75–0.91). AI matched or surpassed specialist dermatologists in 71% of direct comparisons. Three randomized controlled trials (RCTs) were identified, with heterogeneous findings across different clinical applications: AI assistance significantly improved non-expert diagnostic accuracy in one trial (53.9% vs. 43.8%; p = 0.019), significantly reduced acne severity via personalized treatment recommendations in a second, and showed non-inferior diagnostic performance, but was not cost-effective in the third. The sole cost-effectiveness analysis found AI-assisted surveillance not cost-effective over a 2-year horizon. Conclusions: AI achieves dermatologist-level diagnostic accuracy in controlled settings; however, real-world evidence, algorithmic equity across skin phototypes, and health economic viability remain critical unresolved challenges. Prospective validation, mandatory demographic subgroup reporting, and cost-effectiveness modeling are essential prerequisites for safe and equitable clinical implementation.