The study evaluated the evolution of the incidence of infections with Klebsiella in the County Clinical Emergency Hospital of Craiova (SCJUC), Romania. Also, we monitored antibiotic resistance over more than two years and detected changes in resistance to various antimicrobial agents. Our study included 2062 patients (823 women and 1239 men) hospitalised in SCJUC during the period 1st of September 2017 to 30 June 2019. In 458 patients (22.21%) from the 2062 total patients, the collected samples (1116) were positive and from those, we isolated 251 strains of Klebsiella spp. We conducted a longitudinal analysis of the prevalence of Klebsiella spp. over calendar months, which showed a prevalence in surgical wards that ranged between 5.25% and 19.49% in June 2018, while in medical wards the variation was much wider, between 5.15% and 17.36% in April 2018. Klebsiella spp. strains showed significant resistance to Amoxicillin/Clavulanate, Aztreonam and Cephalosporins such as Ceftriaxone, Ceftazidime and Cefepime. We examined the possible link with the consumption of antibiotics in the same month by performing a multiple linear regression analysis. The evolution of antibiotic resistance in Klebsiella was correlated with the variation of resistance in other bacteria, which suggests common resistance mechanisms in the hospital environment. By performing the regression for dependency between antibiotic resistance and antibiotic consumption, we observed some correlations between antibiotic consumption and the development of antibiotic resistance after 1, 2 and even 3 months (e.g., resistance to meropenem was influenced by the consumption in the hospital ward of imipenem 1 month and two months before, but only 1 month before by the consumption of meropenem). The clustering of strains showed filiation between multiresistant Klebsiella spp. strains isolated from specific patients from the ICU. The evolution of prevalence and antibiotic resistance in Klebsiella correlated with the resistance in other bacteria, which suggest common resistance mechanisms in the hospital environment, and also with the consumption of antibiotics.

(1) Background: The new SARS-COV-2 pandemic overwhelmed intensive care units, clinicians, and radiologists, so the development of methods to forecast the diagnosis’ severity became a necessity and a helpful tool. (2) Methods: In this paper, we proposed an artificial intelligence-based multimodal approach to forecast the future diagnosis’ severity of patients with laboratory-confirmed cases of SARS-CoV-2 infection. At hospital admission, we collected 46 clinical and biological variables with chest X-ray scans from 475 COVID-19 positively tested patients. An ensemble of machine learning algorithms (AI-Score) was developed to predict the future severity score as mild, moderate, and severe for COVID-19-infected patients. Additionally, a deep learning module (CXR-Score) was developed to automatically classify the chest X-ray images and integrate them into AI-Score. (3) Results: The AI-Score predicted the COVID-19 diagnosis’ severity on the testing/control dataset (95 patients) with an average accuracy of 98.59%, average specificity of 98.97%, and average sensitivity of 97.93%. The CXR-Score module graded the severity of chest X-ray images with an average accuracy of 99.08% on the testing/control dataset (95 chest X-ray images). (4) Conclusions: Our study demonstrated that the deep learning methods based on the integration of clinical and biological data with chest X-ray images accurately predicted the COVID-19 severity score of positive-tested patients.

Background: Myocardial infarction (MI) in young adults, once a rarity, is increasingly recognized as a distinct clinical entity. Unlike traditional MI patients, younger individuals often present without established risk factors or advanced atherosclerosis, prompting a reevaluation of pathophysiologic paradigms and risk assessment strategies. Objective: This review synthesizes current evidence on the epidemiology, pathophysiology, and diagnostic challenges of MI in adults under 55 years, with emphasis on risk factor profiles. We distinguish between traditional cardiovascular risk factors—smoking, dyslipidemia, hypertension, diabetes, obesity, and family history—and emerging contributors, including elevated lipoprotein(a), recreational drug use (cocaine, cannabis, amphetamines), autoimmune and inflammatory conditions, psychosocial stress, sleep disorders, genetic predisposition, and non-atherosclerotic mechanisms such as myocardical infarction with non-obstructive coronary arteries, spontaneous coronary artery dissection, SCAD and Takotsubo syndrome. Methods: A narrative literature review was conducted, focusing on studies from the last five years addressing MI in young adults, including data from large registries, cohort studies, and recent experimental findings.

Due to complex interplay between host and viral factors, pathogenesis of chronic hepatitis C (CHC) is considered a challenging issue. Infection with hepatitis C virus (HCV) is not confined only to liver but can induce disturbances in many other organs and systems. Our primary aim for this study was to evaluate biological response rates and sustained virological response (SVR) in patients diagnosed with CHC, treated with Interferon-alpha (IFN-α), Pegylated (PEG)-IFN-α2a or -α2b plus Ribavirin. The second aim of the study was the identification of predictive factors for a favorable response to antiviral therapy in patients diagnosed with CHC. We enrolled in this study 210 patients diagnosed with CHC who have accomplished all inclusion and exclusion criteria, treated with PEG-IFN plus Ribavirin. Patients' recovery progress has been evaluated by determining: age, gender; biochemical tests: alanine aminotransferase (ALT), aspartate aminotransferase (AST); serological assays - detect anti-HCV antibody and molecular assays - detect, quantify and/or characterize hepatitis C viral load (ribonucleic acid) (HCV-RNA); liver histopathological (HP) examination. According to their response to treatment, they were classified into responders (n=145) and non-responders (n=65). Liver biopsies were histopathologically evaluated for necroinflammatory grade and fibrosis stage according to the modified Ishak and Metavir scoring systems for chronic hepatitis. Demographic, laboratory, and HP results were introduced in statistical analysis. These parameters were included in area under curve (AUC) analysis in order to estimate their degree of influence on getting early virological response (EVR) and SVR. Our study demonstrates that factors connected to treatment failure in CHC are linked to older age, high hepatitis C viral load, and impaired glucose tolerance at beginning of treatment [high fasting glucose and insulin, high homeostatic model assessment of insulin resistance (HOMA-IR) index] and also to liver histology features (high fibrosis score, liver steatosis, iron infiltration, and more or less high necroinflammatory activity). Analyzing results of our study shows that HOMA-IR index, serum insulin levels, baseline HCV-RNA, baseline mean blood glucose and HP score like Ishak fibrosis score, steatosis score and liver iron score may have a predictive value for obtaining an EVR in patients diagnosed with CHC.

Background/Objectives: Diabetes mellitus (DM) is a prevalent disease with an increased risk of complications. Identifying risk factors for mortality in these patients is crucial, as early recognition can facilitate prompt therapeutic intervention. Machine learning (ML) models have proved to be valuable tools in different scenarios of healthcare decision making. We aimed to develop and test an ML model to predict all-cause mortality in a large cohort of subjects with DM. Methods: We included 1969 consecutive patients with DM type 1 (T1DM, n = 255) and type 2 (T2DM, n = 1714). eXtreme Gradient Boosting (XGBoost) was used for the prediction of all-cause mortality in this cohort and the Shapley additive explanation (SHAP) was used to assess the importance of each feature of the classifier. The missing values were imputed using the Missforest methodology. Results: The all-cause mortality rate was 21% during 5.5 ± 1.1 years of follow-up. The ML model achieved 90% sensitivity and 87% specificity with an AUC of 0.88 and an accuracy of 88% for predicting all-cause mortality. The SHAP analysis identified a lower glomerular filtration rate (eGFR), duration of insulin therapy, and a lower level of hemoglobin as the first three factors that contribute to the higher mortality rate. Conclusions: ML models can become valuable tools in clinical practice due to their unique ability to simultaneously assess the cumulative influence of multiple parameters and discover high-order interactions. The application of such models in clinical practice could improve the early identification of subjects at risk for complications and mortality and prompt early therapeutical interventions.

Background and Objectives: Hepatitis B virus infection remains a major public health concern. The interaction between hepatitis B virus (HBV) hepatitis B virus and the host inflammatory response is an important contributing factor driving liver damage and diseases outcomes. The management of chronic hepatitis B virus infection is an area of massive unmet clinical need worldwide. Our primary aim for this study was to evaluate biological response rates and sustained virological response in patients with chronic hepatitis B treated with Peg-IFN α-2a/b. The second aim of the study was the identification of metabolic changes and insulin resistance. Materials and Methods: We enrolled in this study 166 patients who fulfilled all inclusion and exclusion criteria. These treatment-naive patients with chronic HBV were treated with Pegylated Interferon α-2a/b. HBV infection was defined by the presence of HBV serological markers (HBsAg, anti-HBsAb, anti-HBcAb, HBeAg, anti HBeAb) by Enzyme-Linked Immuno Sorbent Assay (ELISA) and serum HBV-DNA levels were estimated by a commercially available quantitative polymerase chain reaction (PCR) assay. Results: Patients’ recovery progress has been evaluated by determining the following: age, gender; biochemical tests; alanine aminotransferase, aspartate aminotransferase; serological assays for HBV serological markers (HBsAg, anti-HBsAc/Ab, anti-HBcAc/Ab, HBeAg, anti HBeAc/Ab); molecular tests to detect viral particles, testing for HBV DNA (PCR) to confirm the diagnosis and quantify the number of viral copies in the blood (viremia); liver ultrasound—performed through epigastric and intercostal approach (transversal and longitudinal sections). Conclusions: Our results indicated that only HOMA index values, that of fasting insulin, together with baseline HBV DNA, alanine aminotransferase values, mean blood glucose at the beginning of treatment may be predictive of the early viral response in chronic hepatitis B.