Antibiotic resistance is spreading at a faster rate than new antibiotic agents applied for clinical remedies. It is an urgent need to discover potential compounds to combat multidrug-resistant (MDR) bacteria. Marine fungi offer a promising avenue for mining antibiotic-like molecules with chemical diversity. To discover structurally novel and antibiotic metabolites, we screened the in-house marine fungus genome library and found a fungus Stephanonectria keithii LZD-10-1 containing a non-ribosomal peptide synthetase (NRPS) cluster with 18 modules to synthesize a new subfamily of peptaibols with effective eradication against MDR pathogens. Targeting isolation of the cultured fungus afforded six new peptaibols, which exhibit the ability to kill MDR bacteria by targeting bacterial membrane phospholipids, especially phosphatidylglycerol (PG), leading to the dysfunction of bacterial membranes. Furthermore, their efficacies against methicillin-resistant Staphylococcus aureus (MRSA) in both Galleria mellonella and mouse wound infection models were observed. This study underscores the significance of employing genome-guided approaches to identify untapped marine fungi as potential sources for novel antibiotic candidates with unique scaffolds.

Objective To investigate the predictive factors for acute kidney injury (AKI) in patients with acute liver failure (ALF), and to establish a new predictive model. Methods Clinical data were collected from 253 patients who were diagnosed with ALF in The First Affiliated Hospital of Zhengzhou University from January 2015 to October 2021, and according to the presence or absence of AKI, these patients were divided into non-AKI group with 170 patients and AKI group with 83 patients. Related clinical data and laboratory markers were collected. Non-normally distributed continuous data were expressed as M ( P 25 - P 75 ), and the Mann-Whitney U test was used for comparison between two groups; categorical data were expressed as cases (%), and the chi-square test was used for comparison between two groups. The binary logistic regression analysis was used to investigate the risk factors for AKI in ALF patients, and the receiver operating characteristic (ROC) curve was used to evaluate the performance of the indices obtained in predicting AKI in ALF patients. Results Compared with the non-AKI group, the AKI group had a significantly higher proportion of patients with hypertension, diabetes, hepatic encephalopathy, ascites, and pulmonary infection, significantly higher levels of white blood cell count (WBC), international normalized ratio (INR), C-reactive protein, procalcitonin (PCT), neutrophil-to-lymphocyte ratio, and Model for End-Stage Liver Disease (MELD) score, and significantly lower levels of platelet count, lymphocyte-to-monocyte ratio, and PNI (all P < 0.05). The multivariate logistic regression analysis showed that WBC (odds ratio [ OR ]=1.267, 95% confidence interval [ CI ]: 1.124-1.428, P < 0.001), INR ( OR =1.663, 95% CI : 1.205-2.293, P =0.002), PCT ( OR =1.416, 95% CI : 1.137-1.764, P =0.002), and MELD score ( OR =1.098, 95% CI : 1.029-1.172, P =0.005) were risk factors for the development of AKI in patients with ALF. The ROC curve analysis showed that the combination of WBC+INR+PCT+MELD had the largest area under the ROC curve (AUC) of 0.908 in predicting AKI in ALF patients, while WBC, INR, PCT, and MELD alone had an AUC of 0.776, 0.771, 0.746, and 0.780, respectively, in predicting AKI. Conclusion WBC, INR, PCT, and MELD score are independent influencing factors for AKI in patients with ALF, and the predictive model established based on these four indices has a relatively high predictive value.

Objective:To analyze the clinical and histopathological features of patients with chronic hepatitis B (CHB) combined with metabolic-associated fatty liver disease (MAFLD).Methods:Clinical data of 529 cases who had liver biopsies at the First Affiliated Hospital of Zhengzhou University between January 2015 and October 2021 were collected. Among them were 290 cases with CHB, 155 cases with CHB combined with MAFLD, and 84 cases with MAFLD. Three groups of patients clinical data, including general information, biochemical indicators, FibroScan indicators, viral load, and histopathology, were analyzed. A binary logistic regression analysis was used to explore the factors influencing MAFLD in patients with CHB.Results:(1) Age, male status, proportion of hypertension and diabetes, body mass index, fasting blood glucose, γ-glutamyl transpeptidase, low-density lipoprotein, cholesterol, triglycerides, uric acid, creatinine, and the controlled attenuation parameter for hepatic steatosis were higher in CHB combined with MAFLD than in CHB patient groups. In contrast, the high-density lipoprotein, HBeAg positivity rate, viral load level, and liver fibrosis grade (S stage) were lower in CHB patients, and the differences were statistically significant ( P < 0.05). (2) Alanine aminotransferase, aspartate aminotransferase, γ-glutamyl transpeptidase, triglycerides, uric acid, creatinine, and the controlled attenuation parameter for hepatic steatosis in CHB combined with the MAFLD were lower than those in MAFLD patient groups, while high-density lipoprotein was higher than that of MAFLD patients, and the difference was statistically significant ( P < 0.05). There was no statistically significant difference in the grade of liver inflammation and fibrosis (GS stage) between the two groups ( P > 0.05). Binary multivariate logistic regression analysis showed that overweight/obesity, triglycerides, low-density lipoprotein, the controlled attenuation parameter for hepatic steatosis, and HBeAg positivity were independent influencing factors for MAFLD in CHB patients. Conclusion:Patients with CHB combined with metabolic disorders are prone to developing MAFLD, and there is a certain correlation between HBV viral factors, the degree of liver fibrosis, and the fatty degeneration of hepatocytes.