The increasing incidence of epilepsy has led to the need for automatic systems that can provide accurate diagnoses in order to improve the life quality of people suffering from this neurological disorder. This paper proposes a method to automatically classify epilepsy types using EEG recordings from two databases. This approach uses the spectral power density of intrinsic mode functions (IMFs) that are obtained through the empirical mode decomposition (EMD) of EEG signals. The spectral power density of IMFs has been applied as features for the classification of focal and non-focal, as well as of focal and generalized EEG signals. The data are then classified using K-nearest Neighbor (KNN) and Naïve Bayes (NB) classifiers. The focal and non-focal data were classified with high accuracy, with KNN and NB classifiers achieving a maximum classification rate of 99.90% and 99.80%, respectively. Focal and generalized epilepsy data were classified with high rates of accuracy during wakefulness and sleep stages, with KNN achieving a maximum rate of 99.49% and NB achieving 99.20%. This method shows significant improvements in the classification of EEG signals in epilepsy compared to previous studies. It could potentially aid clinical decisions for epilepsy patients.

Background: Glycated hemoglobin (HbA1c) is widely used for the diagnosis and monitoring of diabetes mellitus; however, its interpretation is largely based on fixed diagnostic thresholds. This study moves beyond describing a glycemic continuum by translating the non-linear HbA1c–microvascular relationship into an individualized risk estimation framework. Methods: In this cross-sectional observational study, adult subjects from a real-world clinical cohort were analyzed using HbA1c as a continuous variable. Associations between HbA1c and metabolic parameters were assessed using correlation analysis. Linear regression was applied to evaluate the relationship between HbA1c and cumulative diabetes-related complication burden. Non-linear associations between HbA1c and the risk of presenting at least one complication were explored using restricted cubic spline logistic regression models. Additional risk estimation analyses focused on the HbA1c gray zone (5.5–6.4%). Results: HbA1c showed a strong continuous association with fasting plasma glucose (ρ = 0.73, p < 0.001) and was positively associated with cumulative complication burden (β = 0.016 per 1% increase in HbA1c, p = 0.009). Non-linear modeling revealed a progressive increase in complication risk beginning below the diagnostic threshold for diabetes, with an inflection of the risk curve within the HbA1c gray zone. Individuals within this interval exhibited a higher prevalence and increased odds of presenting at least one complication compared with lower HbA1c values, although some estimates did not reach statistical significance. Conclusions: HbA1c acts as a continuous and non-linear marker of metabolic stress, with potentially biologically meaningful increases in complication risk emerging below traditional diagnostic thresholds. We demonstrate a non-linear acceleration of microvascular risk within the 5.5–6.4% interval, rather than a simple linear gradient. These findings support the concept of a glycemic risk continuum and highlight the clinical relevance of the HbA1c sub-diagnostic interval for early risk stratification and preventive strategies.

Background: Antimicrobial resistance is one of the world’s most serious health issues. Antibiotic resistance, excessive drug expense, and an increased risk of adverse reactions are all common outcomes of incorrect antibiotic prescribing. The goal of this study was to evaluate the prevalence of antibiotic prescriptions for inpatients to find areas for improvement. Methods: A retrospective study at Emergency Clinical County Hospital of Oradea, Romania was performed for five years between 2017 and 2021. Data was collected using medical records of the patients and reports from the pharmacy. Antibiotic consumption was expressed as DDD/100 BD according to the World Health Organization (WHO) by antibiotics, classes, and AWaRe classification. Results: The prevalence of antibiotic prescription was 53.8% during five years evaluated with a significant increase in 2021. A total of 13,677.42 DDD/100 BD antibiotics were prescribed, especially for surgical and medical prophylaxes. The most prescribed antibiotics were ceftriaxone, followed by metronidazole, and cefuroxime but there were some differences between years and wards. The most frequent antibiotic classes prescribed were cephalosporins (43.73%). The use of Watch Group antibiotics was high in all wards (59.69%). Conclusions: The prevalence of antibiotic use was high with cephalosporins being the most prescribed antibiotics. As a result, interventions are required.

Background and Objectives: Antibiotics are the most frequently prescribed drugs in hospitals and their prescription is increased during pregnancy and labor. There are limited data about this issue, and the safe use of antibiotics in pregnancy and antibiotic resistance remains a concern. The aim of the study is to evaluate the use of antibiotics among pregnant women attending hospital for five years. Materials and Methods: Antibiotic consumption and treatment information of patients were retrospectively collected from a hospital software program and expressed as defined daily dose (DDD) according to the World Health Organization (WHO) methodology for inpatients between 2017 and 2021. We evaluated antibiotic prescription by name, classes, and Food and Drug Administration (FDA) categories. Results: Antibiotic consumption shows a decreasing trend between 2017 and 2019, but an increasing one between 2020 and 2021. Ceftriaxone was the most prescribed antibiotic in each year, followed by cefixime, amoxicillin, metronidazole, cefuroxime, ampicillin, and ciprofloxacin. We noticed that first- and fourth-generation cephalosporins were not prescribed to these patients. A very small percentage of women in this study received antibiotics such as aminoglycosides; fluoroquinolones were generally contraindicated in the perinatal period. A large percentage of prescriptions were antibiotics classified as category B by the FDA. The most common infections that occurred in the perinatal period were those of the kidney and urinary tract in a higher number than in other studies. Conclusions: Our study shows that many classes of antibiotics used in perinatal women belong to category B antibiotics, the most prescribed being cephalosporins. Because of insufficient safety evidence and the potential for teratogenic effects on the fetus, restricted use among the category C and D antibiotic classes was seen and anticipated. Improving maternal health requires the involvement of healthcare experts in risk assessment and evaluation of existing data for appropriate antibiotic selection, dose, duration of medication, and monitoring.

Background/Aim: Biomaterials are essential in modern medicine, both for patients and research. Their ability to acquire and maintain functional vascularization is currently debated. The aim of this study was to evaluate the vascularization induced by two collagen-based scaffolds (with 2D and 3D structures) and one non-collagen scaffold implanted on the chick embryo chorioallantoic membrane (CAM). Materials and Methods: Classical stereomicroscopic image vascular assessment was enhanced with the IKOSA software by using two applications: the CAM assay and the Network Formation Assay, evaluating the vessel branching potential, vascular area, as well as tube length and thickness. Results: Both collagen-based scaffolds induced non-inflammatory angiogenesis, but the non-collagen scaffold induced a massive inflammation followed by inflammatory-related angiogenesis. Vessels branching points/Region of Interest (Px^2) and Vessel branching points/Vessel total area (Px^2), increased exponentially until day 5 of the experiment certifying a sustained and continuous angiogenic process induced by 3D collagen scaffolds. Conclusion: Collagen-based scaffolds may be more suitable for neovascularization compared to non-collagen scaffolds. The present study demonstrates the potential of the CAM model in combination with AI-based software for the evaluation of vascularization in biomaterials. This approach could help to reduce and replace animal experimentation in the pre-screening of biomaterials.

This paper aimed to evaluate the effects of the COVID-19 pandemic on prescription rates and antibiotic resistance in a university hospital. A retrospective study was conducted on the medical records of patients admitted to the Bihor Emergency Clinical County Hospital in Romania in 2019 (pre-pandemic) and 2021 (during the pandemic period). We evaluated the antibiotic consumption index (ACI) and susceptibility rates. The overall percentage of antibiotic prescribing increased in 2021, while the total number of patients decreased. Genito-urinary, digestive, respiratory infections, heart diseases and wounds were the most common conditions for antibiotic prescriptions, but the number of them decreased in 2021. There was a decrease in the proportion of antibiotics from the Watch and Reserve class and an increase in the proportion of antibiotics from the Access class. Antibiotic use has been reduced despite an increase in the number of patients, with a high consumption in the Watch group in the ICU wards. By contrast, surgical wards had the highest rate of antibiotic prescriptions, but a decrease in the number of patients. The patients who were administered antibiotics were hospitalized for diagnoses other than COVID-19. Almost all prescribed antibiotics displayed decreasing sensitivity rates. The number of isolated ESKAPE pathogens, except for Staphylococcus aureus methicillin-resistant strains, were increased. Strategies to control antibiotic prescriptions and the spread of resistant pathogens should be improved.