Objective: Escherichia coli ( E. coli ) is the most common opportunistic clinical micro-organism with high drug resistance. This study aimed to analyse the resistance pattern of E. coli according to patient age and clinical sample type. Design and setting: This retrospective observational study was conducted in a tertiary hospital in southeastern China. Participants: E. coli strains were isolated from blood, urine and sputum of infected inpatients. The patients were divided into four age groups: children (0–14 years old, including neonatal and non-neonatal groups), youths (15–40 years old), middle-aged (41–60 years old) and old (>60 years old). Results: A total of 7165 E. coli strains were collected from all samples. Compared with urine and blood isolates, more sputum isolates were resistant against 12 tested antibiotics. Furthermore, urine isolates were more resistant to levofloxacin than sputum and blood isolates. Although the patients’ age was not associated with resistance rates of E. coli strains isolated from blood, a larger proportion of urine-derived strains from youths were resistant to sulfamethoxazole-trimethoprim and piperacillin-tazobactam than those from old people. The sputum strains from the elderly were more resistant to most of the tested antibiotics compared with sputum strains isolated from children. Conclusions: The resistance profile of E. coli is different among age groups and specimen sources and should be considered during E. coli infection treatment.

Objectives: To establish an individualized nomogram to predict the probability of drug-induced liver injury (DILI) in tuberculosis patients receiving anti-tuberculosis treatment.Methods: The clinical information of patients admitted to a tertiary hospital between January 2010 and December 2022 was retrospectively reviewed from the clinical records. Patients with baseline liver diseases (hepatis B or C infection and fatty liver) or taking liver protective drugs were excluded. The maximum values in liver function test within 180 days after anti-tuberculosis treatment were collected to determine the occurrence of DILI. The candidate variables used for establishing prediction model in this study were the last results within the 30 days before the treatment onset. The final variables were included after univariate and multivariate logistic regression analyses and applied to establish the nomogram model. The discrimination power and prediction accuracy of the prediction model were assessed using the area under the receiver operating characteristic (AUC) curve and a calibration chart. The clinical effectiveness was assessed via decision curve analysis (DCA). The established model was validated in two validation groups.Results: A total of 1979 patients with 25 variables were enrolled in this study, and the incidence of DILI was 4.2% (n = 83). The patients with complete variables were divided into training group (n = 1,121), validation group I (n = 492) and validation group II (n = 264). Five variables were independent factors for DILI and included in the final prediction model presented as nomogram: age (odds ratio [OR] 1.022, p = 0.023), total bilirubin ≥17.1 μmol/L (OR 11.714, p < 0.001), uric acid (OR 0.977, p = 0.047), neutrophil count (OR 2.145, 0.013) and alcohol consumption (OR 3.209, p = 0.002). The AUCs of the prediction model in the training group, validation group I and validation group II were 0.833, 0.668, and 0.753, respectively. The p-values of calibration charts in the three groups were 0.800, 0.996, and 0.853. The DCA curves of the prediction model were above the two extreme curves.Conclusion: The nomogram model in this study could effectively predict the DILI risk among patients under anti-tuberculosis drug treatment.

Background: Urinary tract infections (UTI) is a prevalent condition in those with diabetes, and in severe cases, it may escalate to sepsis. Therefore, it is important to analyze the risk variables associated with sepsis in diabetes individuals with UTI. Methods: This research was a retrospective cross-sectional analysis. From January 2011 to June 2022, a group of individuals with diabetes were identified as having UTI at a tertiary hospital situated in Southeastern China. Patient data, including information on urine culture, was collected retrospectively from a clinical record database. The participants were categorized into the sepsis and non-sepsis groups. The risk variables were derived using both uni-and multiple- variable regression analysis. Results: The research included 1919 patients, of whom 1106 cases (57.63%) had positive urine cultures. In total, 445 blood culture samples were tested, identifying 186 positive cases (41.80%). The prevalence of bacteria in urine and blood samples was highest for Escherichia coli and Klebsiella pneumoniae, respectively. Moreover, 268 individuals (13.97%) exhibited sepsis. The regression analysis indicated a positive correlation between sepsis and albumin (ALB)55.84 mg/L and white blood cell count (WBC) >8.485 X 10⁹/L in diabetic cases with UTIs. By integrating the three aforementioned parameters, the area under the receiver operating characteristic curve was 0.809. Conclusions: The early detection of sepsis in diabetic individuals with UTI may be achieved using a comprehensive analysis of CRP, WBC, and ALB test findings.

Background: The increased prevalence of Klebsiella pneumoniae infections and resistance rates are a current cause for concern. However, data for resistance rates in K. pneumoniae strains from primary hospitals and the resistance distribution among the different isolate sample sources are scarce. Methods: All the K. pneumoniae strains were isolated from patients who visited a primary health care center located in Central Zhejiang Province from January 2011 to December 2017. The specimens included blood, sputum, cervical secretions and urine. The species were identified by the Vitek 2 Compact Bacterial Identification and Monitoring System or VITEK-MS and the extended spectrum β-lactamase (ESBL) and drug resistance profiles were identified using the AST-GN13 Gram negative susceptibility card (VITEK-2). The genotype of strains from urine sources was analyzed by detecting TEM and SHV genes. Finally, the drug resistance rates among the isolates from different sample sources were analyzed using the Chi square test with SPSS software. Results: A total of 5319 K. pneumoniae strains were isolated in this study. Among the 20 antimicrobial drugs studied, the resistance rates of K. pneumoniae strains varied from 1.4% (ertapenem) to 23.1% (nitrofurantoin). The antibiotic resistance rates varied significantly among the isolate samples sources for all, with the highest rates for all antibiotics except for nitrofurantoin found in urine samples. In addition, the ESBL-positive rate in urine samples was 27.1%, significantly higher than that of cervical secretions (20.2%), blood (16.5%) and sputum (15.2%). Compared to the ESBL-negative strains, higher resistance rates were detected in the ESBL-positive strains. The most common genotype of isolates from urine was SHV (28%, 23/82), following by TEM (14.6%, 12/82). Conclusion: The highest resistance rates of K. pneumoniae strains to most antibiotics found in urine samples are partly due to the ESBLs, indicating that a special attention should be paid in the treatment of urinary tract infection.

Difficult-to-treat resistant Pseudomonas aeruginosa (DTR-PA) is an MDR subset resistant to all first-line antipseudomonal agents. It is particularly concerning in respiratory infections like bronchiectasis, leading to poor outcomes, limited treatment options, and higher healthcare costs. This study aimed to investigate the antimicrobial resistance profiles and sequence types (STs) of Pseudomonas aeruginosa isolates, providing insight into their resistance patterns and the factors contributing to resistance. In 2021, 38 multidrug-resistant P. aeruginosa isolates were collected from bronchiectasis patients from a single medical center in Ningbo, China. The isolates were obtained from various clinical samples, including sputum, secretion, urine, and blood. Minimum inhibitory concentration testing revealed that 97.4% of the isolates were sensitive to amikacin and tobramycin, with none showing resistance to polymyxin B. Resistance rates to imipenem and meropenem were 84.2% and 57.9%, respectively, with 44.7% of isolates classified as DTR-PA. Multilocus sequence typing identified ST277 (18.4%), ST1076 (13.2%), and ST3012 (13.2%) as the predominant DTR-PA sequence types. The presence of bla_PAO, aph(3')-IIb, and catB7, in all isolates and bla_OXA-50 (16 isolates) and crpP genes (24 isolates) in coexisitance in 11 of 16 isolates, suggested a strong association with the DTR phenotype. Phylogenetic analysis grouped DTR-PA isolates into distinct evolutionary lineages (II and III), underscoring their genetic relatedness and potential for clonal spread. Our findings suggest that co-harboring bla_OXA-50 and crpP contributes to the development of DTR-PA, highlighting the need for continuous monitoring of these resistance determinants. While the study provides important insights into antimicrobial resistance in DTR-PA, further research is needed to explore resistance development across different infection sites and clinical settings.