Serial perirectal swabs are used to identify colonization of multidrug-resistant bacteria and prevent spread. The purpose of this study was to determine colonization with carbapenem-resistant Enterobacterales (CRE) and vancomycin-resistant Enterococci (VRE). An additional purpose was to establish whether sepsis and epidemic associated with these factors were present in the neonatal intensive care unit (NICU), to which infants with hospital stays exceeding 48 h in an external healthcare center NICU were admitted. Perirectal swab samples were collected in the first 24 h by a trained infection nurse using sterile cotton swabs moistened with 0.9% NaCl from patients admitted to our unit after hospitalization exceeding 48 h in an external center. The primary outcome was positivity in perirectal swab cultures, and the secondary outcomes were whether this caused invasive infection and significant NICU outbreaks. A total of 125 newborns meeting the study criteria referred from external healthcare centers between January 2018 and January 2022 were enrolled. Analysis revealed that CRE constituted 27.2% of perirectal swab positivity and VRE 4.8%, and that one in every 4.4 infants included in the study exhibited perirectal swab positivity. The detection of colonization by these microorganisms, and including them within the scope of surveillance, is an important factor in the prevention of NICU epidemics.

Aim. The SARS-CoV-2 Omicron variant became predominant in Turkey in January 2022, coinciding with a rise in SARS-CoV-2–associated hospitalizations. Material and methods. Patients diagnosed with COVID-19 were collected by the infection control committee through an active surveillance program for COVID-19 throughout the pandemic. The hospitalization rate was included in the model to evaluate the relationship between each wave period and disease severity. The basic demographic data of the patients, and the potential risk factors such as comorbid diseases, and laboratory findings were included in the analysis. Results. The rate of patients who were positive in August (26.8%) was found to be significantly lower than in February (45.9%) (p<.001). The proportion of inpatient patients treatment in August (2.3%) was found to be significantly higher than in February (1.2%) (p <.001). In addition, in the omicron variant patients' wave, the neutrophil/lymphocyte ratio was 5.31; and it was observed to be significantly higher than that of the patients (Median = 3.95) in the delta variant wave (p=.023).Discussion. These results provide clinicians with information about the clinical course of COVID-19 patients and are very important for appropriate clinical decision-making in these patients. It is critical to continue to update COVID-19 management protocols based on the latest research.

Objectives: Infection is a frequent complication of organ transplantation and is associated with significant morbidity and mortality. Methods: Patients who had liver and kidney transplants between 2011 and 2022, who were hospitalized in our hospital, and who were consulted for infectious diseases were retrospectively analyzed from hospital records. Results: Of the patients included in the study, 9 (28%) were female, 23 (72%) were male, and the mean age was 33.7 ± 11.3 years. Patients had congestive heart failure (87.2%, n = 28, hypertension (43.7%, n = 14), and chronic obstructive pulmonary disease (21.8%, n = 7). Twenty (62.5%) kidney transplant recipients and 12 (37.5%) liver transplant recipients were seen within ten years. The most common infections were urinary tract infection in 8 ( 25%) patients and pneumonia in 11 (34.3%). The other infections were gastrointestinal infections such as diarrhoea, bloodstream infections and COVID-19 and Cytomegalovirus. Culture-isolated organisms in 20 (62.5%) of the 32 patients admitted with infections. The microbiological data were notable for some unusual and opportunistic pathogens, including one case of acute cytomegalovirus viremia. Severe sepsis had been seen in six (18.75%) out of 32 patients with documented infections. Conclusions: Infection prevention has become a cornerstone of modern transplantation medicine due to the significant incidence of post-transplant infectious complications resulting from improved immunosuppressive therapies and surgical procedures.

Background: Multimodal large language models (MLLMs) capable of integrating visual and textual information represent a promising advancement for clinical applications requiring image interpretation. Wound care assessment, which demands simultaneous analysis of wound photographs and clinical data, provides an ideal domain to evaluate multimodal vs unimodal artificial intelligence capabilities against human expertise. Objective: This study aims to compare the performance of MLLMs, unimodal ChatGPT 5.0, and human clinical experts on a standardized wound care certification examination. Methods: This cross-sectional comparative study evaluated 3 participant groups on a 25-question wound care certification examination spanning 4 clinical domains (Diagnosis, Treatment, Complication Management, and Wound Subtype Knowledge). Participants included 3 MLLMs (Med-PaLM 2, LLaVA-Med, and BioGPT), 1 unimodal large language model (ChatGPT 5.0), and 4 human clinical experts (general surgeon, wound care nurse, and 2 internal medicine physicians). Statistical analyses included one-way ANOVA with Tukey post hoc tests and domain-specific Kruskal-Wallis comparisons. Results: Human experts achieved the highest accuracy (mean 86%, SD 9.1%), followed by MLLMs (mean 78.7%, SD 12.2%), while ChatGPT 5.0 achieved 64% accuracy, failing the 70% certification threshold. Significant overall group differences were observed ( F _2,5 =8.42, P =.02, η²=0.74). MLLMs significantly outperformed ChatGPT 5.0 (difference=14.7 percentage points, P =.03, Cohen d =1.38), with the multimodal advantage most pronounced in visually dependent domains: Diagnosis (81% vs 43%, P =.008) and Complication Management (72% vs 50%, P =.03). No multimodal advantage was observed for text-based Wound Subtype Knowledge (both 67%). Med-PaLM 2 achieved 92% accuracy, matching that of the wound care nurse, while the general surgeon achieved the highest overall performance (96%). Conclusions: MLLMs demonstrate significant performance advantages over unimodal artificial intelligence in wound care assessment, particularly for visually dependent clinical tasks. While human experts with specialized wound care experience maintain overall superiority, the point estimate of the top-performing MLLM (Med-PaLM 2, 92%) fell within the observed range of human scores; however, the underpowered comparison (power=0.52) and wide CIs preclude definitive conclusions regarding noninferiority or equivalence to human experts. These findings support the potential role of MLLMs as clinical decision-support tools, warranting further adequately powered validation studies.