From their discovery, antibiotics have significantly improved clinical treatments of infections, thus leading to diminishing morbidity and mortality in critical care patients, as well as surgical, transplant and other types of medical procedures. In contemporary medicine, a significant debate regarding the development of multi-drug resistance involves all types of pathogens, especially in acute care hospitals due to suboptimal or inappropriate therapy. The possibility of nanotechnology using nanoparticles as matrices to encapsulate a lot of active molecules should increase drug efficacy, limit adverse effects and be an alternative helping to combat antibiotic resistance. The major aim of this study was to obtain and to analyze physico-chemical features of chitosan used as a drug-delivery system in order to stop the antibiotic resistance of different pathogens. It is well known that World Health Organization stated that multidrug resistance is one of the most important health threats worldwide. In last few years, nano-medicine emerged as an improved therapy to combat antibiotic-resistant infections agents. This work relies on enhancement of the antimicrobial efficiency of ceftriaxone against gram(+) and gram(−) bacteria by antibiotic encapsulation into chitosan nanoparticles. Physicochemical features of ceftriaxone-loaded polymer nanoparticles were investigated by particle size distribution and zeta potential, Fourier-transform infrared spectroscopy (FTIR), Thermal Gravimetric Analysis (TG/TGA), Scanning Electron Microscopy (SEM) characteristics techniques. The obtained results revealed an average particle size of 250 nm and a zeta potential value of 38.5 mV. The release profile indicates an incipient drug deliverance of almost 15%, after 2 h of approximately 83%, followed by a slowed drug release up to 24 h. Characteristics peaks of chitosan were confirmed by FTIR spectra indicating a similar structure in the case of ceftriaxone-loaded chitosan nanoparticles. A good encapsulation of the antibiotic into chitosan nanoparticles was also provided by thermo-gravimetric analysis. Morphological characteristics shown by SEM micrographs exhibit spherical nanoparticles of 30–250 nm in size with agglomerated architectures. Chitosan, a natural polymer which is used to load different drugs, provides sustained and prolonged release of antibiotics at a specific target by possessing antimicrobial activity against gram(+) and gram(−) bacteria. In this research, ceftriaxone-loaded chitosan nanoparticles were investigated as a carrier in antibiotic delivery.

Recently, nanotechnology research studies have been proven that use of various nanoparticles as drug delivery systems to target and to annihilate pathogenic microorganisms may be a good solution for prevention and treatment of severe infection. In the last few years, antimicrobial drug encapsulation into nano-sized systems has materialized as a promising alternative that increased drug efficacy and minimized adverse effects. Physicochemical properties of erythromycin-loaded polymer nanoparticles were assessed using particle size distribution, HPLC, FTIR, TG/DTA, and SEM characterization techniques. The as-prepared samples exhibited an average particle size of 340 and 270 nm, respectively, with erythromycin content of 99.7% in both samples. From the release profile of erythromycin from PLA/PLGA, a prolonged drug release can be observed from both Ery-PLA and Ery-PLGA nanostructures. Morphology images exhibited spherical, rigid, and ring-shaped nanoparticles. Thermal analytical study in the case of Ery-PLA and Ery-PLGA samples showed that pure drug has an endothermic peak at around 150 °C assigned to a melting point. The antibiotic melting peak disappeared for both antibiotic-loaded PLA and PLGA nanoparticles thermographs, denoting the presence of erythromycin. This indicates that the antibiotic is uniformly dispensed throughout the host polymer matrix at nanometer scale. FTIR spectra of Ery-PLA and Ery-PLGA nano-architectures with almost similar peaks indicated no alteration in chemical structure of drug-loaded polymer nanoparticles.

Nowadays, infection diseases are one of the most significant threats to humans all around the world. An encouraging strategy for solving this issue and fighting resistant microorganisms is to develop drug carriers for a prolonged release of the antibiotic to the target site. The purpose of this work was to obtain metronidazole-encapsulated chitosan nanoparticles using an ion gelation route and to evaluate their properties. Due to the advantages of the ionic gelation method, the synthesized polymeric nanoparticles can be applied in various fields, especially pharmaceutical and medical. Loading capacity and encapsulation efficiency varFied depending on the amount of antibiotic in each formulation. Physicochemical characterization using scanning electron microscopy revealed a narrow particle size distribution where 90% of chitosan particles were 163.7 nm in size and chitosan-loaded metronidazole nanoparticles were 201.3 nm in size, with a zeta potential value of 36.5 mV. IR spectra revealed characteristic peaks of the drug and polymer nanoparticles. Cell viability assessment revealed that samples have no significant impact on tested cells. Release analysis showed that metronidazole was released from the chitosan matrix for 24 h in a prolonged course, implying that antibiotic-encapsulated polymer nanostructures are a promising drug delivery system to prevent or to treat various diseases. It is desirable to obtain new formulations based on drugs encapsulated in nanoparticles through different preparation methods, with reduced cytotoxic potential, in order to improve the therapeutic effect through sustained and prolonged release mechanisms of the drug correlated with the reduction of adverse effects.

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.

Background and Objectives: Healthcare-associated infections (HAIs) caused by Pseudomonas aeruginosa represent a challenge for healthcare systems worldwide, with significant incidence, high mortality, and increasing antibiotic resistance. This paper aims to describe and analyze the epidemiology of HAI cases with Pseudomonas aeruginosa in a tertiary neurosurgical hospital in the northeastern region of Romania during 2020–2024. Materials and Methods: Study inclusion criteria were patients of any age (as the hospital also admits pediatric patients from the region) and a diagnosis of P. aeruginosa HAI, with a signed informed consent form by the adult patient or relatives for pediatric patients. Results: During 2020–2024, 163 cases of P. aeruginosa HAIs were identified. The annual incidence decreased from 4.41‰ in 2020 to 2.37‰ in 2024 (p = 0.0038). Most cases occurred in the intensive care unit (ICU) (119/163; 73.0%), followed by neurosurgery wards (37/163; 22.7%). Overall, 110 cases (67.5%) occurred in men and 53 (32.5%) in women. The analysis also showed a predominance of late-onset cases and the presence of antimicrobial-resistant phenotypes among the identified P. aeruginosa isolates. Conclusions: This study provides an integrated epidemiological characterization of P. aeruginosa HAIs in a regional neurosurgical hospital, highlighting the predominance of ICU-associated cases, late-onset infections, and antimicrobial-resistant phenotypes. These findings support the need for continued epidemiological surveillance and antimicrobial resistance monitoring in high-risk neurosurgical settings.