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Alpha-mangostin (α-mangostin) was discovered as a potent natural product against Gram-positive bacteria, whereas the underlying molecular mechanisms are still unclear. This study indicated that α-mangostin (at 4 × MIC) rapidly killed Staphylococcus aureus planktonic cells more effectively (at least 2-log10 CFU/ml) than daptomycin, vancomycin and linezolid at 1 and 3 h in the time-killing test. Interestingly, this study also found that a high concentration of α-mangostin (≥4×MIC) significantly reduced established biofilms of S. aureus. There were 58 single nucleotide polymorphisms (SNPs) in α-mangostin nonsensitive S. aureus isolates by whole-genome sequencing, of which 35 SNPs were located on both sides of the sarT gene and 10 SNPs in the sarT gene. A total of 147 proteins with a different abundance were determined by proteomics analysis, of which 91 proteins increased, whereas 56 proteins decreased. The abundance of regulatory proteins SarX and SarZ increased. In contrast, the abundance of SarT and IcaB was significantly reduced (they belonged to SarA family and ica system, associated with the biofilm formation of S. aureus). The abundance of cell membrane proteins VraF and DltC was augmented, but the abundance of cell membrane protein UgtP remarkably decreased. Propidium iodide and DiBaC4(3) staining assay revealed that the fluorescence intensities of DNA and the cell membrane were elevated in the α-mangostin treated S. aureus isolates. In conclusion, this study reveals that α-mangostin was effective against S. aureus planktonic cells by targeting cell membranes. The anti-biofilm effect of α-mangostin may be through inhibiting the function of SarT and IcaB.

Rapid detection of bloodstream pathogens would greatly facilitate clinicians to make precise antimicrobial treatment in patients with bacteremia. In this study, 114 plasma samples were collected from patients with identified or suspected bacteremia, and pathogens were detected by the conventional blood culture (BC) and cell-free DNA metagenomics next-generation sequencing (cfDNA mNGS). The present study indicated that 76% (38/50) of positive conventional blood culture (BC+ group) patients were positively detected by cfDNA mNGS, and only 4% were mismatched between cfDNA mNGS and conventional bacteria culture. Pathogens in 32.8% of suspected bacteremia patients with negative conventional blood culture (BC– group) were determined accurately by cfDNA mNGS combined with analyzing the patients’ clinical manifestations. Escherichia coli and Klebsiella pneumoniae were the most detected pathogens in identified bacteremia patients by cfDNA mNGS. 76.2% (16/21) of E. coli and 92.3% (12/13) of K. pneumoniae in bacteremia patients were identified by conventional blood cultures that were also detected by cfDNA mNGS. This study demonstrated that genomic coverage of E. coli and K. pneumoniae were more often detected in BC+ group patients and genomic coverage of Acinetobacter johnsonii and Paucibacter sp. KCTC 42545 was more often detected in BC– group patients. In conclusion, cfDNA mNGS could rapidly and precisely provide an alternative detection method for the diagnosis of bacteremia.

Objective To study the metastatic lymph node ratio for grouping of gastric cancer patients and evaluate the prognosis. Methods A total of 392 patients of gastric cancer who underwent radical gastrectomy with stage Ⅱ-Ⅲ according to the American Joint Committee on Cancer (AJCC) 8th in Zhongshan Hospital of Fudan University from January 2004 to July 2018 were retrospectively analyzed. X-tile software was used to group patients based on the metastatic lymph node ratio. The correlation between metastatic lymph node ratio and other clinicopathological factors was made, and the evaluation of prognosis of patients was assessed. The nomogram model of prediction was established. Calibration curves were drawn, and comparison between the curves and data of patients was performed. Internal validation was done by the Bootstrap method. Concordance index (C-index) was calculated to test the accuracy of model. Results X-tile analysis showed that the best cut-off values were 0.20 (20%) and 0.70 (70%) of metastatic lymph node ratio. According to the cut-off values, the patients in this study were divided into 3 subgroups at metastatic lymph node ratio 1(0-20%), metastatic lymph node ratio 2 (21%-69%), and metastatic lymph node ratio 3 (70%-100%) with significant difference in survival statistically(P

Background: Schistosomiasis japonica represents a significant public health concern in South Asia. There is an urgent need to optimize existing schistosomiasis diagnostic techniques. This study aims to develop models for the different stages of liver fibrosis caused by Schistosoma infection utilizing ultrasound radiomics and machine learning techniques. Methods: From 2018 to 2022, we retrospectively collected data on 1,531 patients and 5,671 B-mode ultrasound images from the Second People's Hospital of Duchang City, Jiangxi Province, China. The datasets were screened based on inclusion and exclusion criteria suitable for radiomics models. Liver fibrosis due to Schistosoma infection (LFSI) was categorized into four stages: grade 0, grade 1, grade 2, and grade 3. The data were divided into six binary classification problems, such as group 1 (grade 0 vs. grade 1) and group 2 (grade 0 vs. grade 2). Key radiomic features were extracted using Pyradiomics, the Mann-Whitney U test, and the Least Absolute Shrinkage and Selection Operator (LASSO). Machine learning models were constructed using Support Vector Machine (SVM), and the contribution of different features in the model was described by applying Shapley Additive Explanations (SHAP). Results: This study ultimately included 1,388 patients and their corresponding images. A total of 851 radiomics features were extracted for each binary classification problems. Following feature selection, 18 to 76 features were retained from each groups. The area under the receiver operating characteristic curve (AUC) for the validation cohorts was 0.834 (95% CI: 0.779–0.885) for the LFSI grade 0 vs. LFSI grade 1, 0.771 (95% CI: 0.713–0.835) for LFSI grade 1 vs. LFSI grade 2, and 0.830 (95% CI: 0.762–0.885) for LFSI grade 2 vs. LFSI grade 3. Conclusion: Machine learning models based on ultrasound radiomics are feasible for classifying different stages of liver fibrosis caused by Schistosoma infection. Author summary: Schistosomiasis is a devastating disease caused by parasitic worms, leading to stunting, reduced learning ability in children, and impaired work capacity in adults. Currently, there is no ideal staging system to assess schistosomiasis-related liver conditions. Advances in machine learning can help us understand and evaluate liver ultrasound images in entirely new dimensions. Regions with high infection rates are predominantly underdeveloped and characterized by a scarcity of medical resources, where B-mode ultrasound equipment serves as one of the primary diagnostic tools. This study aims to develop an intelligent recognition model based on ultrasound radiomics and machine learning to provide a basis for the ultrasound diagnosis of schistosomiasis. [ABSTRACT FROM AUTHOR]

Bacterial infection‐induced inflammatory response could cause irreversible death of pulp tissue in the absence of timely and effective therapy. Given that, the narrow structure of root canal limits the therapeutic effects of passive diffusion‐drugs, considerable attention has been drawn to the development of nanomotors, which have high tissue penetration abilities but generally face the problem of insufficient fuel concentration. To address this drawback, dual‐fuel propelled nanomotors (DPNMs) by encapsulating L‐arginine (L‐Arg), calcium peroxide (CaO2) in metal‐organic framework is developed. Under pathological environment, L‐Arg could release nitric oxide (NO) by reacting with reactive oxygen species (ROS) to provide the driving force for movement. Remarkably, the depleted ROS could be supplemented through the reaction between CaO2 with acids abundant in the inflammatory microenvironment. Owing to high diffusivity, NO achieves further tissue penetration based on the first‐stage propulsion of nanomotors, thereby removing deep‐seated bacterial infection. Results indicate that the nanomotors effectively eliminate bacterial infection based on antibacterial activity of NO, thereby blocking inflammatory response and oxidative damage, forming reparative dentine layer to avoid further exposure and infection. Thus, this work provides a propagable strategy to overcome fuel shortage and facilitates the therapy of deep lesions. [ABSTRACT FROM AUTHOR]