Background: Neonatal sepsis, a severe infection in newborns, remains one of the leading causes of morbidity and mortality among preterm infants. This study aimed to investigate the distribution of pathogens responsible for early-onset sepsis (EOS) and late-onset sepsis (LOS), the annual variability of pathogens responsible for each type of infection, and potential trends in their profiles in preterm infants from a tertiary care neonatal intensive care unit over a ten-year period. Methods: We analyzed 177 episodes of confirmed bloodstream infection between 1 January 2014 and 31 December 2023. An episode of confirmed bloodstream infection was defined by the growth of a single potential pathogen in the blood of an infant who met four criteria: showing clinical symptoms of infection, having abnormal hematologic parameters, receiving appropriate antibiotics for less than 5 days, and being born before 37 weeks of gestation. Pathogens were isolated from aseptically collected blood samples, processed within 2 h, incubated using the BACTEC system, and identified by Gram stains and selective media cultures. Pathogen identification was performed using standardized biochemical tests or MALDI-TOF MS. Sepsis was classified as EOS if it occurred within the first 72 h of life and as LOS if it occurred after 72 h. Results: Among the confirmed bloodstream infections, EOS accounted for 31 cases, while LOS accounted for 146 cases. Escherichia coli was identified as the primary pathogen responsible for early-onset sepsis (EOS), while Coagulase-negative Staphylococcus (CoNS) was most commonly associated with late-onset sepsis (LOS). The differences in the prevalence of these bacteria between EOS and LOS were statistically significant. However, no significant differences were found in the distribution of pathogens across different years, nor were there significant trends in their frequency over the study period. Our results demonstrate significant stability in the distribution of pathogens causing sepsis over the ten-year observation period, even during the COVID-19 pandemic. Conclusions: Understanding the temporal distribution of pathogens in neonatal sepsis can help prevent the overuse of antibiotics and support the implementation of screening programs, empiric therapy, and strategies to prevent healthcare-associated infections.

Although vertigo is a recognized complication of SARS-CoV-2 infection, its correlation with altered brain function remains unexplored. In this study, we utilized resting-state fMRI to investigate the cerebellum in eight patients experiencing persistent central vertigo following COVID-19, comparing them to 16 healthy controls. We conducted a whole-brain voxel-wise analysis, followed by a cerebellar map-based analysis, revealing significant between-group differences in Amplitude of Low-Frequency Fluctuations (ALFF), Regional Homogeneity (ReHo), and functional connectivity (FC), all localized to the cerebellum. We observed that the vertigo group, among other changes, showed reduced synchronization of neural activity in the flocculonodular lobe, a cerebellar region crucial for coordinating balance and eye movements. Next, we applied a machine learning algorithm to determine whether cerebellar changes related to SARS-CoV-2 exhibited distinct patterns, enabling effective classification of study participants as either vertigo-affected or healthy. The algorithm demonstrated outstanding discriminatory power with an average Area Under the Curve score of 0.901 (95% CI: 0.894 - 0.908). Consequently, we employed the SHapley Additive exPlanations approach to explain which factors were mostly responsible for our algorithm’s performance. Interestingly, we found that, rather than changes to the flocculonodular lobe, heightened neural activity in cerebellar regions associated with motor imagery, self-projection, and spatial simulation was of gratest impotance for our model. Nevertheless, incorporating data from additional cerebellar regions, analyzed using various other methods such as fractional ALFF, ReHo, and FC, further enhanced its capability, highlighting the complexity and multifaceted nauture of SARS-CoV-2-related changes.

Aims: Metabolic‐bariatric surgery ( MBS ) remains a cornerstone of obesity treatment, yet 15%–30% of patients fail to achieve its intended benefits. Existing clinical and biochemical markers offer limited value in identifying who will respond favourably to this intervention. We hypothesize that the long‐term success of MBS is influenced by individual differences in preoperative brain function. Materials and methods: We collected presurgical resting‐state fMRI data from 45 patients undergoing MBS , with the aim of identifying neural patterns associated with achieving at least 50% excess weight loss 12 months post‐surgery. The data were analysed using both conventional methods and a high‐powered machine learning approach. For the latter, we trained five predictive models on functional connectivity, regional brain activity, and clinical variables. We then applied SHapley Additive exPlanations ( SHAP ) to the best‐performing model to interpret its internal logic, thereby revealing the neural features most strongly linked to treatment success. Results: Conventional methods proved inadequate for this study. A multilayer perceptron model, trained exclusively on functional connectivity data, achieved a noteworthy AUC of 0.85. Its SHAP analysis revealed key neural circuits in the postcentral gyrus, dorsolateral prefrontal cortex, and angular gyrus—regions associated with interoception, executive control, and social‐cognitive processes such as theory of mind. Conclusion: Explainable AI ‐powered fMRI analysis uncovered subtle neural patterns that conventional methods failed to detect. These findings suggest that a patient's “neural readiness” for MBS may extend beyond self‐regulatory circuits. It may also depend on their capacity to perceive and interpret internal bodily signals and to process emotional information—both personal and social.