Multidrug-resistant (MDR) bacteria of the utmost importance are extended-spectrum β-lactamase (ESBL) and carbapenemase-producing Enterobacterales (CRE), carbapenem-resistant Acinetobacter baumannii (CRAB), carbapenem-resistant Pseudomonas aeruginosa (CRPA), methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus spp. (VRE). In this study, an evaluation of MDR bacteria in surgical intensive care units in a tertiary referral hospital was conducted. The study aimed to characterize β-lactamases and other resistance traits of Gram-negative bacteria isolated in surgical intensive care units (ICUs). Disk diffusion and the broth dilution method were used for antibiotic susceptibility testing, whereas ESBL screening was performed through a double disk synergy test and an inhibitor-based test with clavulanic acid. A total of 119 MDR bacterial isolates were analysed. ESBL production was observed in half of the Proteus mirabilis, 90% of the Klebsiella pneumoniae and all of the Enterobacter cloacae and Escherichia coli isolates. OXA-48 carbapenemase, carried by the L plasmid, was detected in 34 K. pneumoniae and one E. coli and Enterobacter cloacae complex isolates, whereas NDM occurred sporadically and was identified in three K. pneumoniae isolates. OXA-48 positive isolates coharboured ESBLs belonging to the CTX-M family in all but one isolate. OXA-23 carbapenemase was confirmed in all A. baumannii isolates. The findings of this study provide valuable insight of resistance determinants of Enterobacterales and A. baumannii which will enhance surveillance and intervention strategies that are necessary to curb the ever-growing carbapenem resistance rates.

For breast cancer patients, surgery remains the cornerstone in treatment. Perioperative and postoperative period is associated with impaired immune function that can have profound implications for cancer patients in terms of tumor recurrence and me...

Pulmonary surfactant, consisting primarily of phospholipids and four surfactant-specific proteins, is among the first structures that is exposed to inhalation anesthetics. Consequently, changes of pulmonary surfactant due to this exposure could cause respiratory complications after long anesthetic procedures. Fourier transform infrared (FTIR) spectroscopy was used to explore the effects of two inhalation anesthetics, sevoflurane and isoflurane, on a commercially available pulmonary surfactant. The research was primarily focused on the effect of anesthetics on the lipid component of the surfactant. Four different concentrations of anesthetics were added, and the doses were higher from the low clinical doses typically used. Recorded spectra were analyzed using principal component analysis, and the Student’s t-test was performed to confirm the results. The exposure to both anesthetics induced similar changes, consistent with the increase of the anesthetic concentration. The most pronounced effect was on the hydrophilic head group of phospholipids, which is in agreement with the disruption of the hydrogen bond, caused by the anesthetics. A change in the band intensities of CH2 stretching vibrations, indicative of a disordering effect of anesthetics on the hydrophobic tails of phospholipids, was also observed. Changes induced by isoflurane appear to be more pronounced than those induced by sevoflurane. Furthermore, our results suggest that FTIR spectroscopy is a promising tool in studying anesthetic effects on pulmonary surfactant.

Liver transplantation represents the only curative method for end-stage liver disorders and certain liver malignancies. Over the last three decades, advancements in immunosuppression, surgical technique, and intensive care measures have resulted in improved patient and graft survival outcomes, but a deficit of donor organs is constantly the major issue that limits our ability to reduce the mortality on the liver transplant waiting list. To address this, marginal grafts and those from donors after cardiac death are increasingly employed, but these strategies necessitated novel methods to improve the preservation and quality of liver grafts and the most promising one is liver machine perfusion (LMP). LMP evolved significantly in the past 10 years, and consequently, it is gradually establishing itself as a standard protocol at many transplant centers. However, many unresolved questions remain concerning the indications, types, and protocols associated with LMP. Therefore, continuous research is necessary to determine optimal guidelines and recommendations for its clinical application. This review aims to analyze the development of liver machine perfusion, including its different modalities underlying mechanisms of operation, and provide an overview of its historical evolution, current status, and future prospects.

Background: Postoperative kidney dysfunction is a common and serious complication in surgical patients. Kidney function is typically assessed using the estimated glomerular filtration rate (eGFR), most often calculated with the CKD-EPI equation based on serum creatinine. While several machine learning models have been developed to predict acute kidney injury, few have focused on predicting postoperative eGFR category. This study aimed to develop a machine learning model capable of classifying surgical patients into eGFR categories G1, G2, or G3+ in the early postoperative period, based on preoperative and intraoperative data. Methods: We developed the two-layer “eGFR-AI” architecture. In the first layer, two XGBoost models compute the probability of eGFR being above 89 and 59 mL/min/1.73 m², respectively, and their outputs are passed to a second-layer logistic regression model that produces the final classification. The dataset included 200 patients admitted postoperatively to the intensive care unit of a tertiary academic hospital during the first half of 2024. Input features comprised age, sex, body mass index, type and duration of surgery, ASA status, presence of sepsis or shock at admission, and history of arterial hypertension, diabetes mellitus, or chronic kidney disease. Model performance was evaluated using accuracy, F1 score, and area under the ROC curve (ROC-AUC) on a held-out testing set. Feature importance analysis and statistical testing of associations with acute kidney injury were also performed. Results: On a held-out test set, the final model achieved an accuracy of 0.75, a weighted F1 score of 0.75, and a weighted ROC-AUC of 0.92 (balanced accuracy 0.76; Matthews correlation coefficient 0.65; Cohen’s κ 0.62). The first-layer models reached ROC-AUC values of 0.85 (eGFR > 89) and 0.96 (eGFR > 59). In a head-to-head comparison on the same partition, the stacked model performed comparably to standard baseline classifiers (multinomial logistic regression, random forest, support-vector machine, single multiclass XGBoost, CatBoost, LightGBM) without demonstrating superiority. Chronic kidney disease and presence of sepsis or shock at admission emerged as the strongest predictors. In an exploratory analysis (n = 8 events), all patients diagnosed with acute kidney injury fell into the G3+ category. Conclusions: In this single-center pilot study, “eGFR-AI” shows that early postoperative kidney function category can be predicted from a small set of routinely available preoperative and intraoperative variables, with performance comparable to standard classifiers and the added benefit of calibrated, interpretable category-level probabilities. Given the limited unicentric cohort and reduced category granularity, these findings should be regarded as preliminary and hypothesis-generating: they support the feasibility of the approach and motivate external, multicenter validation before any clinical application.

Postoperative cognitive dysfunction (POCD) is a newly developed cognitive function deficit after surgery. The aim of the study was to determine the incidence of POCD after anesthesia and possible risk factors. A prospective study was conducted on 9...