Background: Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a serious public health issue. The study aimed to identify the antimicrobial resistance and accessory genes, the clonal relatedness, and the evolutionary dynamics of selected CRKP isolates recovered in an adult and pediatric intensive care unit of a tertiary hospital in Greece. Methods: Twenty-four CRKP isolates recovered during 2018–2022 were included in the study. Next-generation sequencing was performed using the Ion Torrent PGM Platform. The identification of the plasmid content, MLST, and antimicrobial resistance genes, as well as the comparison of multiple genome alignments and the identification of core genome single-nucleotide polymorphism sites, were performed using various bioinformatics software. Results: The isolates belonged to eight sequence types: 11, 15, 30, 35, 39, 307, 323, and 512. A variety of carbapenemases (KPC, VIM, NDM, and OXA-48) and resistance genes were detected. CRKP strains shared visually common genomic regions with the reference strain (NTUH-K2044). ST15, ST323, ST39, and ST11 CRKP isolates presented on average 17, 6, 16, and 866 recombined SNPs, respectively. All isolates belonging to ST15, ST323, and ST39 were classified into distinct phylogenetic branches, while ST11 isolates were assigned to a two-subclade branch. For large CRKP sets, the phylogeny seems to change approximately every seven SNPs. Conclusions: The current study provides insight into the genetic characterization of CRKP isolates in the ICUs of a tertiary hospital. Our results indicate clonal dispersion of ST15, ST323, and ST39 and highly diverged ST11 isolates.

Diabetes mellitus is a chronic metabolic disease that affects more than 10.5% of the world’s adult population. Biochemical and hematological parameters, such as albumin (ALB) and red cell distribution width (RDW), have been shown to be altered in diabetic patients. This study aimed to correlate hematological and biochemical parameters with glycated hemoglobin (HbA1c). A total of 777 adults (372 women and 405 men, aged 19–85 years) were divided into three groups: 218 participants with HbA1c < 5.7% (group A: non-diabetic), 226 with HbA1c ≥ 5.7% and <6.5% (group B: prediabetic) and 333 with HbA1c ≥ 6.5% (group C: diabetic). Biochemical and hematological parameters were compared among the three groups. An analysis of variance was performed to determine the correlations of the parameters among the groups. The ALB and sodium (Na) levels were significantly lower in group C than in groups A (ALB: 3.8 g/dL vs. 4.1 g/dL, p < 0.0001, Na: 138.4 mmol/L vs. 139.3 mmol/L, p < 0.001) and B (ALB: 3.8 g/dL vs. 4.0 g/dL, p < 0.0001, Na: 138.4 mmol/L vs. 139.6 mmol/L, p < 0.0001), whereas the RDW-standard deviation (RDW-SD) and urea were increased in group C as compared to group A (RDW: 45.8 vs. 43.9 fL, p < 0.0001, urea: 55.6 mg/dL vs. 38.5 mg/dL, p < 0.0001). The mean platelet volume (MPV) was increased in group C as compared to group A (9.3 fL vs. 9.1 fL, p < 0.05, respectively). Τhe increase in RDW-SD in group A as compared to B and C demonstrates the impact of hyperglycemia on red blood cells. Albumin and RDW might improve risk assessment for the development of diabetes. These results highlight the potential role of these parameters as an indication for prediabetes that would alert for measurement of HbA1c.

Acinetobacter baumannii has emerged as a major pathogen responsible for healthcare-associated infections, particularly in intensive care units, contributing to significant morbidity and mortality due to its multidrug resistance and ability to persist in clinical environments. This study aimed to investigate the phenotypic and genomic characteristics of all multidrug-resistant A. baumannii isolates collected between January and June 2022 from two tertiary care hospitals in Thessaloniki, Greece. A total of 40 isolates were included. All isolates exhibited resistance to colistin; however, none harbored the mcr-1 to mcr-9 genes, as confirmed by polymerase chain reaction (PCR). PCR-based screening for virulence-associated genes revealed high prevalence rates of basD (100%), pld (95%), csuE (87.5%), and bap (77.5%). In contrast, ompA and pglC were not detected. Twitching motility ranged from 2 to 50 mm, with 25% of the isolates classified as non-motile and 20% as highly motile. Swarming motility was observed in all strains. Additionally, all isolates demonstrated positive α-hemolysis, suggesting a potential virulence mechanism involving tissue damage and iron acquisition. Pulsed-field gel electrophoresis (PFGE) revealed significant genomic diversity among the isolates, indicating a low likelihood of patient-to-patient or clonal transmission within the hospital setting. These findings highlight the complex relationship between antimicrobial resistance and virulence in clinical A. baumannii isolates and emphasize the urgent need for robust infection control strategies and continued microbiological surveillance.

Background/Objectives: Carbapenem-resistant Klebsiella pneumoniae (CRKP) threatens Intensive Care Units (ICU), particularly in settings where serine (KPC) and metallo-β-lactamases (NDM) co-circulate. The aim of this study was to assess CRKP susceptibility especially to novel β-lactam/β-lactamase inhibitor combinations, characterize the genetic determinants of resistance, and contribute to the understanding of local epidemiology in the ICU of our hospital. Methods: We studied 32 non-duplicate CRKP isolates (30 ICU, 2 wards) collected at Hippokration General Hospital, Thessaloniki (May–Oct 2023). Bacterial identification and Antimicrobial susceptibility testing (AST) were performed by VITEK-2; Minimum inhibitory concentrations (MICs) for ceftazidime/avibactam (CAZ/AVI), meropenem/vaborbactam (MER/VAB), and imipenem/relebactam (IMI/REL) were determined by E-tests. Colistin MICs were performed by broth microdilution. Carbapenemases were screened phenotypically and by immunochromatography and confirmed by multiplex PCR. One bronchial isolate co-harboring bla_NDM and bla_KPC genes underwent WGS. Results: All isolates were carbapenem-resistant and showed extensive resistance to β-lactams and fluoroquinolones. By PCR, 8/32 (25%) carried bla_KPC alone, 8/32 (25.0%) bla_NDM alone, and 16/32 (50%) co-harbored bla_KPC and bla_NDM. KPC-only isolates were generally susceptible in vitro to CAZ/AVI, MER/VAB, and IMI/REL, whereas dual KPC-NDM producers were resistant to all three combinations. Tigecycline showed the highest retained activity; colistin remained active in a minority. WGS of one ST512 (CG258) isolate revealed co-harboring bla_NDM-1 and bla_KPC-3 with additional resistance determinants and plasmid replicons, consistent with high-risk spread. Conclusions: Half of CRKP isolates in this ICU-predominant series co-produced KPC and NDM, severely limiting β-lactam/β-lactamase inhibitor options. These data support routine screening for carbapenemases, strict infection prevention, antimicrobial stewardship, and access to agents active against MBLs.

Diabetes mellitus is a chronic metabolic disease that affects over 10.5 percent of the global adult population. Biochemical and hematological parameters such as albumin (ALB) and red cell dis-tribution width (RDW) have been shown to be altered in diabetic patients. This study aims to cor-relate hematological and biochemical parameters with glycated hemoglobin (HbA1c). 777 adult individuals (372 women and 405 men, aged 19-85 years) were divided into three groups: 218 par-ticipants with HbA1c < 5.7% (group A: non diabetic), 226 with HbA1c ≥ 5.7% and < 6.5% (group B: prediabetic) and 333 with HbA1c ≥ 6.4% (group C: diabetic). Biochemical and hematological parameters were compared among the 3 groups. Analysis of variance was performed to deter-mine the correlations of the parameters among groups. ALB and sodium (Na) levels were signifi-cantly decreased in group C compared to groups A (ALB: 3.8 g/dL vs. 4.1 g/dL, p < 0.0001, Na: 138.4 mmol/L vs. 139.3 mmol/L, p < 0.001), and B (ALB: 3.8 g/dL vs. 4.0 g/dL, p < 0.0001, Na: 138.4 mmol/L vs. 139.6 mmol/L, p < 0.0001), while RDW-standard deviation (RDW-SD) and urea were increased in group C compared to group A ( RDW: 45.8 vs. 43.9 fL, p < 0.0001, Urea: 55.6 mg/dL vs. 38.5 mg/dL, p < 0.0001) . Mean platelet volume (MPV) was increased in group C com-pared to group A (9.3 fL vs. 9.1 fL, p < 0.05, respectively). Τhe increase of RDW-SD from group A towards B and C demonstrates the impact of hyperglycemia on red blood cells. RDW may be re-garded as a potential innovative biomarker for improving risk assessment of developing diabetes. These results highlight the potential role of these parameters as an indication for prediabetes that would alert for measurement of HbA1c.

Early identification of individuals with abnormal glucose metabolism is essential for timely intervention and prevention of diabetes-related complications. Routine laboratory testing generates large amounts of clinical data that may support automated glycemic classification through machine learning approaches. This study aimed to develop and evaluate a machine learning framework for the classification of HbA1c-defined glycemic status using routinely available clinical laboratory features. A retrospective dataset of 1434 individuals with available glycemic measurements was analyzed. Participants were categorized into HbA1c-defined normoglycemic, prediabetic-range, or diabetic-range groups. Three concurrent classification tasks were examined: HbA1c-defined dysglycemia classification, diabetic-range HbA1c classification, and multiclass HbA1c-defined glycemic-status classification. Demographic, biochemical, and hematological variables were used as predictors. Data preprocessing included missing-value handling, feature filtering, and outlier treatment. Several supervised learning algorithms were evaluated, including Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine, and Multinomial Logistic Regression. Model performance was assessed using train–test validation and cross-validation with accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve. For dysglycemia, Gradient Boosting achieved the highest AUC (0.848), while Random Forest achieved the highest accuracy (0.801) and sensitivity (0.908). For diabetic-range HbA1c, Random Forest achieved the highest AUC (0.864), whereas SVM achieved the highest accuracy (0.794). In multiclass classification, Random Forest achieved the highest accuracy (0.610), while Gradient Boosting achieved the highest macro-AUC (0.796) and macro-F1 score (0.603). Pairwise comparisons showed no statistically significant superiority of any classifier after Holm correction. Clinical-baseline and ablation analyses demonstrated that fasting glucose accounted for a substantial proportion of discrimination, with only modest incremental value from additional laboratory variables. These findings support cautious interpretation of routine laboratory-based classification models pending further validation and clinical-utility assessment.