Background: Sepsis affects over 30 million people worldwide each year, causing approximately 6 million deaths. Challenges in clinical diagnosis and the need for an early diagnosis to prevent mortality due to sepsis have led to dependence on inflammatory biomarkers like Procalcitonin (PCT), C-reactive protein (CRP), and Interleukin-6 (IL-6). Objective: This study was performed to observe the contribution of inflammatory biomarkers in the diagnosis and prognosis of patients with surgical sepsis. Methods: We performed a retrospective observational study in a Clinical Emergency Hospital, which included a number of 125 patients with surgical sepsis admitted between January 2020 and December 2021. The patients were included in the study based on the Sepsis-3 definition. PCT, CRP, IL-6, Sepsis-related Organ Failure Assessment (SOFA) score, Charlson Comorbidity Index (CCI), the time up to surgery, the days of treatment in Intensive Care Unit (ICU) and the total days of hospitalization had been statistically analyzed. Results: The mean age of all patients was 65.14 years. The mean value in all patients for PCT was 20.08 ng/mL, for CRP was 175.42 mg/l, and for IL-6 was 799.6 pg/mL. The strongest correlation between biomarkers was between CRP and IL-6 (r = 0.425; p < 0.0001). Of all biomarkers, the CRP correlated the strongest with patient outcomes (r = 0.544; p < 0.0001). The area under curve (AUC) for the mean values of the inflammatory biomarkers was calculated and the best diagnostic performance was for CRP with 0.816 (95% CI: 0.744– 0.887). Conclusion: CRP and IL-6 were the most efficient in sepsis diagnosis. The association of PCT, CRP and IL-6 has increased the range of certainty in sepsis diagnosis. CRP was the most efficient biomarker in the prognosis of sepsis.

ROS derived from NADPH oxidase, particularly NOX2, are central to antimicrobial defense, coupling direct pathogen killing with redox signaling that shapes inflammation. This narrative review integrates recent advances on NOX2 structure, assembly, and spatiotemporal control in phagocytes, and outlines how ROS interact with NF-κB, MAPK, and Nrf2 networks to coordinate microbicidal activity and immune modulation. We summarize evidence that both ROS deficiency, as in chronic granulomatous disease, and uncontrolled excess, as in sepsis and severe COVID-19, drive clinically significant pathology, emphasizing the need for precise redox balance. Emerging therapeutic strategies include selective NOX2 inhibitors that limit pathological oxidative bursts, redox-modulating peptides that disrupt upstream activation cues, and Nrf2 activators that enhance endogenous antioxidant capacity, with attention to dosing challenges that preserve host defense while mitigating tissue injury. Key gaps remain in biomarker standardization, real-time in vivo ROS monitoring, and translation from animal models to patients, motivating personalized, combination approaches to redox medicine in infectious diseases.

Enterococci cause infections with various localizations, the most common being urinary infections. The purpose of the study was to identify the profile of the antimicrobial resistance of enterococci species (AMRE) isolated from patients hospitalized in three hospitals in Romania. We evaluated AMRE retrospectively (2019–2021) in various biological samples. The microbiological diagnosis was sustained by classical methods of bacteria culture and automatic identification. The sensitivity testing was performed by the Kirby–Bauer method, and the antibiotic minimum inhibitory concentration was tested by the automated Vitek system. We analyzed 86 strains of Enterococcus spp., identifying the following species: 47.7% E. faecalis, 47.7% E. faecium, 3.55% E. gallinarum, and 1% E. hirae. Most of the bacterial strains were isolated from urocultures (38.4%) and hemocultures (32.6%). Overall, the rate of vancomycin resistance was 5.8% for E. faecalis and 15.1%. for E. faecium. The prevalence of multidrug-resistant (MDR) strains was found to be 100% in E. gallinarum, 75.6% in E. faecium, and 21.9% in E. faecalis. The results confirm the high level of AMRE, which creates difficulties with adequate antibiotic prescriptions. The continuous monitoring of AMRE is essential for updating the local diagnostic and treatment protocols for enterococcal infections.

Background: Ideas about Artificial intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided are becoming a bigger pool and we need artificial intelligence that will help us with its superhuman powers. Its interaction with medicine is improving more and more, with medicine being a domain that continues to be perfected. Materials and Methods: The most important databases were used to perform this detailed search that addresses artificial intelligence in the medical and surgical fields. Discussion: Machine learning, deep learning, neural networks and computer vision are some of the mechanisms that are becoming a trend in healthcare worldwide. Developed countries such as Japan, France and Germany have already implemented artificial intelligence in their medical systems. The help it gives is in medical diagnosis, patient monitoring, personalized therapy and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques and to choose the best surgical techniques. Conclusions: The goal is to predict complications, reduce diagnostic times, diagnose complex pathologies, guide surgeons intraoperatively and reduce medical errors. We are at the beginning of this, and the potential is enormous, but we must not forget the impediments that may appear and slow down its implementation.

Background/Objectives: Sepsis is a severe medical condition characterized by a dysregulated host response to infection, with potentially fatal outcomes, requiring early diagnosis and rapid intervention. The limitations of traditional sepsis identification methods, as well as the complexity of clinical data generated in intensive care, have driven increased interest in applying artificial intelligence in this field. The aim of this narrative review article is to analyze how artificial intelligence is being used in the diagnosis and prognosis of sepsis, to present the most relevant current models and algorithms, and to discuss the challenges and opportunities related to integrating these technologies into clinical practice. Methods: We conducted a structured literature search for this narrative review, covering studies published between 2016 and 2024 in databases such as PubMed/Medline, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The review covered models based on machine learning (ML), deep neural networks (DNNs), Recurrent Neural Networks (RNNs), and clinical alert systems implemented in hospitals. The clinical data sources used, algorithms applied, system architectures, and performance outcomes are presented. Results: Numerous artificial intelligence models demonstrated superior performance compared to conventional clinical scores (qSOFA, SIRS), achieving AUC values above 0.90 in predicting sepsis and mortality. Systems such as Targeted Real-Time Early Warning System (TREWS) and InSight have been clinically validated and have significantly reduced the time to treatment initiation. However, challenges remain, such as a lack of model transparency, algorithmic bias, difficulties integrating into clinical workflows, and the absence of external validation in multicenter settings. Conclusions: Artificial intelligence has the potential to transform sepsis management through early diagnosis, risk stratification, and personalized treatment. A responsible, multidisciplinary approach is necessary, including rigorous clinical validation, enhanced interpretability, and training of healthcare personnel to effectively integrate these technologies into everyday practice.

Although basal cell carcinoma is a well-known tumor with confirmed clinical and histopathology traits, prognosis factors and treatment options, new facets of this tumor emerge as innovative approach methods develop. Reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) allow a basal cell carcinoma’s in vivo analysis of its depth of invasion, tumor margins prior to surgical approach and the tumor’s response to a non-invasive treatment, evaluating simultaneously the tumor’s vasculature. By RCM and OCT analysis, basal cell carcinoma has registered a groundbreaking discovery regarding a small (but with predictive factor potential) trait – the cleft, developing in between the tumor islands/nodules/chords and the surrounding tumor stroma; it was considered to date as a consequence of the tissue’s histopathology processing. RCM and OCT revealed that the “clefting artifact”, as it is frequently found in the medical literature, is not actually an artifact of laboratory processing, but a tumor trait found in vivo, with apparent mucin deposits. This review aims at merging the methods of evaluating basal cell carcinoma, both non-invasive (dermoscopy, RCM, OCT) and invasive ones (histopathology – with newly proposed classification), with special emphasis on the cleft issue – its assessment with the aforementioned techniques, with potential implications in the patient’s prognosis.