The effectiveness and uses of chitosan nanoparticles (CNPs) in drug delivery systems are examined in this work. Important results include the improved drug encapsulating efficiency: CNPs showed up to 90% encapsulation for different therapeutic agents. Furthermore, the research shows that CNPs provide extended-release patterns, greatly enhancing medication bioavailability especially for hydrophobic compounds. One interesting outcome was the greater drug stability in acidic surroundings, which are common in the stomach, where CNPs turn into a gel and later inflate in the intestine where the drug is released. Moreover, CNPs showed a 2–3-fold improvement in the absorption of encapsulated pharmaceuticals relative to traditional formulations, therefore indicating their capacity to overcome the problems of low oral bioavailability. These nanoparticles’ pH-sensitive character produced a 50–70% increase in drug release at certain pH values, hence maximizing therapeutic results. Significantly less systemic toxicity was seen in the in vivo tests, and at therapeutic dosages there were no noted side effects. Histological study confirmed the biocompatibility and non-toxicity of CNPs, therefore attesting their fit for long-term usage. These results highlight the great potential of CNPs in providing effective, focused, continuous drug release, hence improving therapeutic effectiveness and patient compliance.

Background and Objectives: Healthcare-associated infections (HAIs) are on the rise worldwide because the range of etiologic agents involved is very diverse and their antimicrobial resistance poses a threat to population health in the third millennium. Materials and Methods: We conducted a retrospective, longitudinal, descriptive clinical–epidemiological study in a hospital with a neurosurgical profile in northeastern Romania (“Prof. Dr. N. Oblu” Clinical Emergency Hospital in Iasi), during 2020–2024. The study is centered on the involvement of Acinetobacter spp. in the occurrence and evolution of HAIs. Results: The highest incidence of Acinetobacter spp. HAIs was recorded in the intensive care unit (ICU)—82.78% compared to neurosurgical wards (15.38%), with predominance in males (69.23%) and rural residence patients (55.67%). Most HAIs were represented by ventilator-associated pneumonia (42.13%) and lower respiratory tract infections (23.08%). Strains with high virulence and pathogenicity (CR-MDR, ESBL-MDR) were found, with the highest proportion of CR-MDR strains (88.27%). Conclusions: Our study provides useful data for surveillance of the antimicrobial resistance of pathogens involved in HAIs at the hospital level and for guiding antibiotic therapy in hospital-acquired infections.

Background/Objectives: One common musculoskeletal disorder seriously compromising quality of life and burdening healthcare systems is lumbar disk herniation (LDH). LDH affects quality of life, healthcare costs, and occupational productivity, and it is expected to affect 40% of the population, mostly between 30 and 50 years of age. Methods: Over three years, this research assessed treatment results and the effect of comorbidities in a sample of 3074 individuals. Results: Surgical treatments—especially microdiscectomy—have a high success rate; over 90% of patients said their pain and functioning six months after surgery had improved significantly. Comparatively, conservative treatment approaches—physical therapy and epidural steroid injections—showed about 60% success in 70% of patients, indicating a clear need for early surgical assessment since 25% of originally conservatively managed patients needed surgery within one year. Significantly affecting treatment success are demographic variables; patients with preoperative Oswestry Disability Index (ODI) scores above 50 saw a 40-point improvement post-surgery compared to a 20-point gain for those following conservative therapy. High comorbidity rates—including obesity (mean of 148.33) and cardiovascular illnesses (mean of 530.33)—that are associated with extended recovery durations and complications were also seen in this research. Conclusions: Our results support a customized treatment plan, stressing the need of integrating thorough rehabilitation plans with prompt surgical interventions to maximize patient outcomes. This study emphasizes the need for a patient-centered treatment paradigm in controlling LDH, thereby trying to improve recovery and lower the healthcare load.

Background and Objectives: Musculoskeletal disorders (MSDs) are a leading cause of absenteeism among healthcare workers (HCWs), impacting healthcare delivery. Pediatric HCWs face specific physical demands such as lifting and awkward postures. While absenteeism rose during the COVID-19 pandemic, its effects on pediatric MSD-related leave remain unclear. This study examined MSD-related absenteeism trends among pediatric HCWs in a Romanian hospital across the pre-pandemic (2017–2019), pandemic (2020–2021), and post-pandemic (2022–2023) periods. Materials and Methods: We conducted a retrospective observational study using records from the hospital’s occupational health database. We included all HCWs who took MSD-related leave during 2017–2023. Diagnoses included arthropathies, dorsopathies, other osteoarticular/connective tissue disorders, and acute trauma or fractures. We used chi-square tests, ANOVA, and regression models to identify trends and predictors. Results: A total of 3388 cases were analyzed. Post-pandemic absenteeism increased significantly (40.1%), especially among women (86.8%), nurses (46.7%), and workers aged ≥46 (62.7%). A seasonal shift was observed, with spring peaks (March 9.7% and May 9.9%) replacing the pre-pandemic autumn peaks (October 11.9% and November 12.8%). The regression models identified age, occupation, and diagnosis type as significant predictors of leave duration. Conclusions: MSD-related absenteeism rose post-pandemic and showed altered seasonal patterns. Occupational and demographic predictors identified through a multivariate analysis highlight the need for anticipatory, evidence-based strategies to support pediatric HCWs, enhance workforce resilience, and sustain healthcare performance.

Background: Lumbar disc herniation (LDH) recurrence remains a pressing clinical challenge, with limited predictive tools available to support early identification and personalized intervention. Predicting recurrence after lumbar disc herniation (LDH) remains clinically important but algorithmically difficult due to extreme class imbalance and low signal-to-noise ratio. Objective: This study proposes a hybrid machine learning framework that integrates supervised classifiers, unsupervised anomaly detection, and decision threshold tuning to predict LDH recurrence using routine clinical data. Methods: A dataset of 977 patients from a Romanian neurosurgical center was used. We trained a deep neural network, random forest, and an autoencoder (trained only on non-recurrence cases) to model baseline and anomalous patterns. Their outputs were stacked into a meta-classifier and optimized via sensitivity-focused threshold tuning. Evaluation was performed via stratified cross-validation and external holdout testing. Results: Baseline models achieved high accuracy but failed to recall recurrence cases (0% sensitivity). The proposed ensemble reached 100% recall internally with a threshold of 0.05. Key predictors included hospital stay duration, L4–L5 herniation, obesity, and hypertension. However, external holdout performance dropped to 0% recall, revealing poor generalization. Conclusions: The ensemble approach enhances detection of rare recurrence cases under internal validation but exhibits poor external performance, emphasizing the challenge of rare-event modeling in clinical datasets. Future work should prioritize external validation, longitudinal modeling, and interpretability to ensure clinical adoption.

Background: Pan-drug-resistant (PDR) Acinetobacterinfections are an escalating ICU threat, demanding both patient-level triage and facility-wide forecasting. Objective: The aim of this study was to build a dual-scale AI framework that (i) predicts PDR status at infection onset and (ii) forecasts hospital-level PDR burden through 2027. Methods: We retrospectively analyzed 270 Acinetobacter infection episodes (2020–2024) with 65 predictors spanning demographics, timelines, infection type, resistance-class flags, and a 25-drug antibiogram. TabTransformer and XGBoost were trained on 2020–2023 episodes (n = 210), evaluated by stratified 5-fold CV, and externally tested on 2024 episodes (n = 60). Metrics included AUROC, AUPRC, accuracy, and recall at 90% specificity; AUROC was optimism-corrected via 0.632 + bootstrap and DeLong-tested for drift. SHAP values quantified feature impact. Weekly PDR incidence was forecast with an attention–LSTM model retrained monthly (200 weekly origins, 4-week horizon) and benchmarked against seasonal-naïve, Prophet, and SARIMA models (MAPE and RMSE). Quarterly projections (TFT-lite) extended forecasts to 2027. Results: The CV AUROC was 0.924 (optimism-corrected 0.874); an ensemble of TabTransformer + XGBoost reached 0.958. The 2024 AUROC fell to 0.586 (p < 0.001), coinciding with a PDR prevalence drop (75→38%) and three covariates with PSIs > 1.0. Isotonic recalibration improved the Brier score from 0.326 to 0.207 and yielded a net benefit equivalent to 26 unnecessary isolation-days averted per 100 ICU admissions at a 0.20 threshold. SHAP highlighted Ampicillin/Sulbactam resistance, unknown acquisition mode, and device-related infection as dominant drivers. The attention–LSTM achieved a median weekly MAE of 0.10 (IQR: 0.028–0.985) vs. 1.00 for the seasonal-naïve rule, outperforming it on 48.5% of weeks and surpassing Prophet and SARIMA (MAPE = 6.2%, RMSE = 0.032). TFT-lite projected a ≥ 25% PDR tipping point in 2025 Q1 with a sustained rise in 2027. Conclusions: The proposed framework delivers explainable patient-level PDR risk scores and competitive 4-week and multi-year incidence forecasts despite temporal drift, supporting antimicrobial stewardship and ICU capacity planning. Shrinkage and bootstrap correction were applied to address the small sample size (EPV = 2.1), which poses an overfitting risk. Continuous recalibration and multi-center validation remain priorities.