Antibiotic misuse in Romania has exacerbated the issue of antibiotic resistance, as patients often use antibiotics without proper medical consultation. This study aimed to assess the resistance of prevalent bacteria to different antibiotics. In this observational study conducted over six months, we analyzed 31 pediatric patients aged from 12 days to 13 years using the disk diffusion method. We identified 31 bacterial isolates, including 8 Gram-negative and 8 Gram-positive strains, with the most common being Pseudomonas aeruginosa, Escherichia coli, Streptococcus pneumoniae, methicillin-resistant Staphylococcus aureus, Streptococcus species, and Elizabethkingia meningoseptica. Our findings revealed that the most effective antibiotics were linezolid, ertapenem, and teicoplanin. In contrast, nearly all tested bacteria exhibited resistance to penicillin, followed by oxacillin and ampicillin. Resistance to cephalosporins varied with generation, showing higher resistance to lower-generation cephalosporins. The study highlights significant antibiotic resistance among common bacterial pathogens in Romanian pediatric patients, emphasizing the urgent need for controlled antibiotic use and alternative treatment strategies to combat this growing issue. Effective antibiotics such as linezolid and ertapenem offer potential solutions, whereas reliance on penicillin and lower-generation cephalosporins is increasingly futile.

Oxidative stress biomarkers are elevated in liver cirrhosis, but their clinical utility for severity staging and complication prediction remains uncertain. This retrospective single-centre study enrolled 90 patients with decompensated cirrhosis (Child–Pugh classes B and C) to evaluate serum malondialdehyde (MDA) and 8-isoprostane (8-isoPGF2α) as predictors of Child–Pugh severity, severe ascites, and severe hepatic encephalopathy, and to quantify their incremental value within supervised machine learning models. Four algorithms—logistic regression, Random Forest, Gradient Boosting, and Support Vector Machine—were evaluated using stratified 10-fold cross-validation; logistic regression models with and without oxidative stress biomarkers were compared for the prediction of ascites and encephalopathy. Routine biochemical parameters effectively discriminated Child–Pugh class B from C, with machine learning models achieving AUC-ROC values of 0.921–0.972. Neither MDA nor 8-isoPGF2α differed between Child–Pugh classes or across ascites categories, and both failed to improve ascites prediction (ΔAUC = −0.015). For severe hepatic encephalopathy, the extended model showed modest but consistent improvements in accuracy (+3.4 percentage points), sensitivity (+6.4%), and model fit, suggesting an outcome-specific complementary role consistent with the established involvement of lipid peroxidation in ammonia neurotoxicity. These findings support the use of machine learning for automated cirrhosis severity classification and indicate that oxidative stress biomarkers hold selective relevance for hepatic encephalopathy rather than global disease staging.

Background: Interfacility neonatal transport is essential for regionalized perinatal care, yet its effects on systemic immune responses remain poorly characterized. This study evaluated longitudinal changes in hematologic and inflammatory biomarkers during neonatal transport and investigated the influence of transport modality on these responses. Methods: In this prospective observational study, 161 neonates undergoing air or ground transport to Level III/IV neonatal intensive care units were evaluated at three time points: before transport (T0), immediately after transport (T1), and 12 hours after admission (T2). Physiological stability parameters, complete blood count variables, and inflammatory indices, including neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and mean corpuscular volume-to-lymphocyte ratio (MCVL), were analyzed using repeated-measures and mixed-effects models adjusted for transport duration, sedation, and physiological stability. Results: Physiological stability was maintained throughout transport, with no significant changes in heart rate, oxygen saturation, or body temperature. In contrast, significant longitudinal changes were observed in leukocyte populations and inflammatory indices. Total leukocyte and neutrophil counts decreased over time, whereas lymphocyte counts increased. Mixed-effects analyses identified significant effects of time, transport modality, and Time × Transport interaction for WBC, neutrophils, lymphocytes, NLR, SII, and SIRI. These associations remained significant after adjustment for clinical and physiological confounders. MCVL also demonstrated significant effects of time, transport modality, and their interaction, emerging as a potential novel marker of transport-related immune adaptation. Conclusions: Neonatal transport is associated with significant hematologic and immune adaptations despite preserved physiological stability. Air and ground transport induce distinct patterns of leukocyte redistribution and inflammatory responses. MCVL may represent a promising biomarker of transport-related immune adaptation and warrants further investigation in neonatal transport medicine.

Smoke intoxication is a central event in mass burn incidents, and toxic smoke acts at different levels of the body, blocking breathing and oxygenation. The majority of these patients require early induction of anesthesia to preserve vital functions. We studied the influence of hemoglobin (HMG) and myoglobin (MGB) blockade by hydrochloric acid (HCl) in an interaction model with gaseous anesthetics using molecular docking techniques. In the next part of the study, molecular dynamics (MD) simulations were performed on the top-scoring ligand–receptor complexes to investigate the stability of the ligand–receptor complexes and the interactions between ligands and receptors in more detail. Through docking analysis, we observed that hemoglobin creates more stable complexes with anesthetic gases than myoglobin. Intoxication with gaseous hydrochloric acid produces conformational and binding energy changes of anesthetic gases to the substrate (both the pathway and the binding site), the most significant being recorded in the case of desflurane and sevoflurane, while for halothane and isoflurane, they remain unchanged. According to our theoretical model, the selection of anesthetic agents for patients affected by fire smoke containing hydrochloric acid is critical to ensure optimal anesthetic effects. In this regard, our model suggests that halothane and isoflurane are the most suitable choices for predicting the anesthetic effects in such patients when compared to sevoflurane and desflurane.