Mihai-Andrei Ruscu

Peripheral venous disease represents one of the most prevalent and most persistently undertreated chronic conditions in adult populations, with venous leg ulceration standing at the severe end of a spectrum that also includes symptomatic chronic venous insufficiency and superficial venous thrombosis. The venous wound microenvironment is shaped by mechanisms that are largely specific to this etiology, including ambulatory venous hypertension, erythrocyte extravasation with cutaneous iron deposition, sustained oxidative stress, proteolytic imbalance and fibroblast senescence. Hydrogels, as three-dimensional hydrophilic polymer networks with high water content, tunable mechanics and versatile payload capacity, offer an appealing platform for addressing several of these mechanisms simultaneously. The preclinical literature is correspondingly abundant. The clinical evidence base, however, remains thin and inconclusive, and the most rigorous synthesis available has been unable to establish whether hydrogel dressings outperform simpler alternatives in venous ulcer healing. This review examines that discrepancy rather than setting it aside. We summarize the physicochemical properties that determine hydrogel performance specifically under compression and in a heavily exudating wound, map hydrogel mechanisms of action onto venous pathobiology, survey therapeutic applications across the venous spectrum, including topical formulations for superficial venous thrombosis, and appraise the preclinical and clinical evidence with attention to why translation has stalled. We conclude by proposing design criteria for venous-specific hydrogel systems and methodological priorities for the trials needed to test them.

Background. Hematological inflammatory indices from the complete blood count have been proposed as inexpensive prognostic markers in sepsis. The systemic immune-inflammation index (SII) and neutrophil-to-lymphocyte ratio (NLR) are the most studied, but the performance of monocyte-containing alternatives (SIRI, AISI) in the elderly, in whom immunosenescence may alter the leukocyte phenotype, remains poorly characterized. Methods. In a single-center retrospective cohort of patients aged ≥65 years admitted to a tertiary ICU with Sepsis-3-defined sepsis (n = 127, 33 deaths), we compared the discrimination of six indices (NLR, PLR, MLR, SII, SIRI, AISI) for 30-day all-cause mortality using AUROC with bootstrap confidence intervals and pairwise DeLong tests. Independent associations were assessed by logistic regression adjusted for APACHE II and age; incremental value over APACHE II was explored using IDI, cNRI, calibration and decision curve analysis, with bootstrap optimism correction. Results. Thirty-day mortality was 26.0%. The monocyte-containing indices (AISI, SIRI, MLR) discriminated better than SII and NLR, and AISI was significantly superior to SII, NLR and PLR on DeLong testing, though not to SIRI, MLR or APACHE II. After adjustment for APACHE II and age, AISI, SIRI and MLR remained independently associated with mortality, whereas SII and PLR did not. Adding AISI to APACHE II improved reclassification and calibration and yielded higher net clinical benefit across clinically relevant thresholds. Conclusions. In this exploratory, single-center analysis, monocyte-containing indices, particularly AISI, were more strongly associated with 30-day mortality in elderly ICU sepsis than SII or NLR. AISI, SIRI and MLR were strongly intercorrelated and near-equivalent, and AISI did not significantly exceed APACHE II in discrimination. These hypothesis-generating findings require prospective external validation before clinical use.

Chronic wounds, diabetic foot ulcers, venous leg ulcers, and pressure injuries affect millions of patients worldwide and cost healthcare systems in the order of $150 billion annually, yet treatment options have changed less than the scale of the problem would suggest. Biofilm formation, documented in up to 78% of chronic wounds, is a central cause: bacteria embedded in extracellular polymeric matrices tolerate antimicrobial concentrations up to 1000-fold higher than planktonic cells and sustain a chronic inflammatory state that actively prevents tissue repair. Hydrogels, crosslinked polymer networks with high water content and tunable physicochemical properties, have been widely studied as platforms for addressing these challenges, though the distance between laboratory results and clinical practice remains considerable. While recent reviews have summarized hydrogel materials or antimicrobial strategies in isolation, this review takes a different approach: we treat infection, biofilm persistence, and impaired regeneration as interconnected processes that must be addressed simultaneously, and we examine biofilm management as a distinct therapeutic target rather than merely a subset of antimicrobial delivery. We analyze hydrogel-based wound care across three integrated domains: design principles (natural, synthetic, and hybrid polymer systems; crosslinking strategies; and stimuli-responsive architectures), antimicrobial delivery (silver, antibiotics, antimicrobial peptides, natural agents, and controlled-release systems), and biofilm management (nanoparticle-mediated disruption, enzymatic EPS degradation, photodynamic approaches, quorum-sensing inhibition, and anti-adhesive surface engineering). For each area, we critically evaluate what the preclinical evidence supports, where it falls short, and what would be needed to bridge the gap to clinical application. Translation remains uneven. Among the many FDA- and EMA-cleared hydrogel dressings currently in clinical use, most are simple moisture-retaining or silver-containing formulations, while the multifunctional systems that dominate the research literature are at earlier stages of development. We discuss the main translational priorities, including more predictive preclinical models, long-term nanomaterial safety, harmonized outcome reporting, manufacturing scalability, and health economic evidence, as areas where further work can meaningfully accelerate clinical adoption.

Measles remains one of the most contagious infectious diseases globally and continues to pose substantial public health risks despite decades of effective vaccination. This narrative review examines both classical and contemporary computational approaches used for measles monitoring, prediction, and control, with particular attention given to the emerging role of artificial intelligence (AI). We synthesized findings from 46 studies; 31 focused directly on measles and 15 on methodologically relevant studies from related infectious diseases (COVID-19, influenza, malaria), selected through searches of PubMed, Scopus, Web of Science, IEEE Xplore, and preprint servers, conducted between June and December 2025. Traditional compartmental models (SIR, SEIR, MSEIR), statistical tools (ARIMA, SARIMA), and seroepidemiological analysis provide transparent, well-characterized frameworks for estimating transmission dynamics and simulating intervention scenarios. Spatial modeling, network analysis, and Monte Carlo simulations have added geographic granularity to outbreak characterization. More recently, AI and machine learning (ML) methods, including supervised algorithms (Random Forest, XGBoost, SVM), deep learning architectures (CNN, LSTM), and hybrid mechanistic ML models, have shown improved predictive performance by integrating multiple data sources: epidemiological records, demographic profiles, mobility patterns, and behavioral indicators. AI-based approaches appear most valuable for high-dimensional risk prediction and image-based diagnostic tasks, while classical models retain clear advantages for policy-oriented scenario analysis. However, no AI-based or hybrid model identified in this review has been adopted into routine national measles surveillance or used for vaccination policy decisions at scale. Important challenges remain: data quality varies across settings, model generalizability cannot be assumed, and computational infrastructure disparities limit deployment in high-burden regions. Explainable AI, federated learning, workforce training for model interpretation, and integration of vaccination registries with mobility and genomic surveillance data represent concrete future directions for strengthening computational support for measles elimination.

Background/Objectives: Alcohol misuse among medical students is commonly attributed to academic stress, yet the specific role of coping mechanisms in this relationship has received limited attention. We investigated whether substance use coping, rather than stress exposure itself, drives alcohol use risk in Romanian medical students, and whether distinct coping-based subgroups can be identified through cluster analysis. Methods: We conducted a cross-sectional survey among 244 medical students (mean age 21.95 ± 3.27 years; 67.2% female) at the University of Medicine and Pharmacy of Craiova, Romania. Alcohol use was measured with the AUDIT and coping strategies with the Brief COPE. Analyses included Mann–Whitney U tests, Spearman correlations, multiple linear and binary logistic regression, and k-means clustering. Results: At-risk drinking (AUDIT ≥ 8) was identified in 19.7% of participants. The tendency to use substances to cope with stress (substance use coping) was the strongest predictor of AUDIT scores in both linear regression (B = 2.090, p < 0.001, R² = 0.513) and logistic regression (OR = 2.026, p < 0.001). Male sex independently predicted at-risk status (OR = 2.572, p = 0.025), while planning was protective in both models (B = −0.657, p = 0.005; OR = 0.691, p = 0.029). Humor also emerged as a significant risk factor (OR = 1.638, p = 0.005). K-means analysis (k = 5) revealed five coping profiles with significantly different AUDIT distributions (Kruskal–Wallis H = 47.26, p < 0.001). The Substance-Oriented cluster (13.1% of students) had a mean AUDIT of 12.66, compared with 3.00–4.13 in other clusters. Conclusions: In a subgroup of medical students, alcohol use appears integrated into the coping repertoire rather than merely being a consequence of stress. The identified coping profiles should be interpreted as prototypical configurations with overlapping boundaries rather than discrete categorical types, given the low silhouette coefficient (0.094) of the cluster solution. The strong predictive effect of substance use coping should be interpreted with the caveat that the Brief COPE Substance Use subscale and the AUDIT share content related to alcohol use behavior, which may inflate the observed association. These findings point to the need for coping-specific interventions. Planning skills training and a more nuanced understanding of humor’s role in drinking contexts may offer avenues for prevention. However, the logistic model’s sensitivity of 50.0% indicates that coping-based identification alone would miss approximately half of at-risk students, underscoring the need for further refinement before clinical application.

Background: University students are increasingly vulnerable to both depressive symptoms and hazardous alcohol use, particularly in the aftermath of the COVID-19 pandemic. Disruptions in circadian rhythms, hormonal dysregulation, and changing social dynamics may heighten susceptibility to maladaptive coping behaviors such as alcohol consumption. While this relationship has been widely studied in Western populations, limited data exist for Eastern European contexts. This study investigated the association between alcohol consumption and depressive symptoms among Romanian university students and explored potential gender differences in this post-pandemic cohort. Methods: A cross-sectional study was conducted among 103 Romanian university students at the University of Medicine and Pharmacy of Craiova, Romania. Participants anonymously completed a combined survey integrating the Alcohol Use Disorders Identification Test (AUDIT) and the Depression subscale of the Depression, Anxiety and Stress Scale (DASS-21). Statistical analyses included Pearson correlation, linear regression, and subgroup comparisons to evaluate associations between alcohol use and depression severity. Results: The mean AUDIT score was 5.4 ± 5.8, while the mean DASS-21 Depression score was 13.8 ± 9.5. A strong positive correlation was observed between AUDIT and depression scores (r = 0.72, 95% CI [0.62, 0.80], p < 1 × 10⁻¹⁷). Linear regression revealed that AUDIT scores significantly predicted depression severity (R² = 0.496, p < 0.001), with each one-point increase in AUDIT score associated with a 1.31-point rise in depression score. Male students reported significantly higher alcohol use than females (p = 0.005), while depression scores did not differ significantly by gender (p = 0.110). The alcohol–depression association was similarly strong across genders. Conclusions: Hazardous alcohol use was highly prevalent and strongly associated with increased depressive symptoms among university students. These findings highlight the need for integrated mental health and substance use screening programs in university settings to support early identification and intervention.