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Objective: Baicalin has antioxidative, antiviral, and anti-inflammatory properties. However, its ability to alleviate oxidative stress (OS) and DNA damage in liver cells exposed to aflatoxin B1 (AFB1), a highly hepatotoxic compound, remains uncertain. In this study, the protective effects of baicalin on AFB1-induced hepatocyte injury and the mechanisms underlying those effects were investigated. Methods: Stable cell lines expressing CYP3A4 were established using lentiviral vectors to assess oxidative stress levels by conducting assays to determine the content of reactive oxygen species (ROS), malondialdehyde (MDA), and superoxide dismutase (SOD). Additionally, DNA damage was evaluated by 8-hydroxy-2-deoxyguanosine (8-OHdG) and comet assays. Transcriptome sequencing, molecular docking, and in vitro experiments were conducted to determine the mechanisms underlying the effects of baicalin on AFB1-induced hepatocyte injury. In vivo, a rat model of hepatocyte injury induced by AFB1 was used to evaluate the effects of baicalin. Results: In vitro, baicalin significantly attenuated AFB1-induced injury caused due to OS, as determined by a decrease in ROS, MDA, and SOD levels. Baicalin also considerably decreased AFB1-induced DNA damage in hepatocytes. This protective effect of baicalin was found to be closely associated with the TP53-mediated ferroptosis pathway. To elaborate, baicalin physically interacts with P53, leading to the suppression of the expression of GPX4 and SLC7A11, which in turn inhibits ferroptosis. In vivo findings showed that baicalin decreased DNA damage and ferroptosis in AFB1-treated rat liver tissues, as determined by a decrease in the expression of γ-H2AX and an increase in GPX4 and SLC7A11 levels. Overexpression of TP53 weakened the protective effects of baicalin. Conclusions: Baicalin can alleviate AFB1-induced OS and DNA damage in liver cells via the TP53-mediated ferroptosis pathway. In this study, a theoretical foundation was established for the use of baicalin in protecting the liver from the toxic effects of AFB1.

Abstract Salmonella enterica serotype 1,4,[5],12:i:- (Typhimurium monophasic variant) of sequence type (ST) 34 has emerged as the predominant pandemic genotype in recent decades. Despite increasing reports of resistance to antimicrobials in Southeast Asia, Salmonella ST34 population structure and evolution remained understudied in the region. Here we performed detailed genomic investigations on 454 ST34 genomes collected from Vietnam and diverse geographical sources to elucidate the pathogen’s epidemiology, evolution and antimicrobial resistance. We showed that ST34 has been introduced into Vietnam in at least nine occasions since 2000, forming five co-circulating major clones responsible for paediatric diarrhoea and bloodstream infection. Most expansion events were associated with acquisitions of large multidrug resistance plasmids of IncHI2 or IncA/C2. Particularly, the self-conjugative IncA/C2 pST34VN2 (co-transferring bla CTX-M-55, mcr-3.1, and qnrS1) underlies local expansion and intercontinental spread in two separate ST34 clones. At the global scale, Southeast Asia was identified as a potential hub for the emergence and dissemination of multidrug resistant Salmonella ST34, and mutation analysis suggests of selection in antimicrobial responses and key virulence factors.

BackgroundShigella sonnei is a pathogen of growing global importance as a cause of diarrhoeal illness in childhood, particularly in transitional low-middle income countries (LMICs). Here, we sought to determine the incidence of childhood exposure to S. sonnei infection in a contemporary transitional LMIC population, where it represents the dominant Shigella species.MethodsParticipants were enrolled between the age of 12-36 months between June and December 2014. Baseline characteristics were obtained through standardized electronic questionnaires, and serum samples were collected at 6-month intervals over two years of follow-up. IgG antibody against S. sonnei O-antigen (anti-O) was measured using an enzyme-linked immunosorbent assay (ELISA). A four-fold increase in ELISA units (EU) with convalescent IgG titre >10.3 EU was taken as evidence of seroconversion between timepoints.ResultsA total of 3,498 serum samples were collected from 748 participants; 3,170 from the 634 participants that completed follow-up. Measures of anti-O IgG varied significantly by calendar month (p = 0.03). Estimated S. sonnei seroincidence was 21,451 infections per 100,000 population per year (95% CI 19,307-23,834), with peak incidence occurring at 12-18 months of age. Three baseline factors were independently associated with the likelihood of seroconversion; ever having breastfed (aOR 2.54, CI 1.22-5.26), history of prior hospital admission (aOR 0.57, CI 0.34-0.95), and use of a toilet spray-wash in the household (aOR 0.42, CI 0.20-0.89).ConclusionsIncidence of S. sonnei exposure in Ho Chi Minh City is substantial, with significant reduction in the likelihood of exposure as age increases beyond 2 years.

We report optically-generated focused ultrasound for non-invasive brain stimulation with ultrahigh precision of 83 µm, which is two orders of magnitude smaller than that of conventional transcranial focused ultrasound.

Magnetic Resonance Fingerprinting (MRF) can be accelerated with simultaneous multislice (SMS) imaging for joint T1 and T2 quantification. However, the high inter‐slice and in‐plane acceleration in SMS‐MRF causes severe aliasing artifacts, limiting the multiband (MB) factors to typically 2 or 3. Deep learning has demonstrated superior performance compared to the conventional dictionary matching approach for single‐slice MRF, but its effectiveness in SMS‐MRF remains unexplored. In this paper, we introduced a new deep learning approach with decoupled spatiotemporal feature learning for SMS‐MRF to achieve high MB factors for accurate and volumetric T1 and T2 quantification in neuroimaging. The proposed method leverages information from both spatial and temporal domains to mitigate the significant aliasing in SMS‐MRF. Neural networks, trained using either acquired SMS‐MRF data or simulated data generated from single‐slice MRF acquisitions, were evaluated. The performance was further compared with both dictionary matching and a deep learning approach based on residual channel attention U‐Net. Experimental results demonstrated that the proposed method, trained with acquired SMS‐MRF data, achieves the best performance in brain T1 and T2 quantification, outperforming dictionary matching and residual channel attention U‐Net. With a MB factor of 4, rapid T1 and T2 mapping was achieved with 1.5 s per slice for quantitative brain imaging. [ABSTRACT FROM AUTHOR]

Urinary tract infections (UTIs) are the most common outpatient infections. Obtaining the concentration of live pathogens in the sample is crucial for the treatment. Still, the enumeration depends on urine culture and plate counting, which requires days of turn-around time (TAT). Single-cell Raman spectra combined with deuterium isotope probing (Raman-DIP) has been proven to identify the metabolic-active bacteria with high accuracy but is not able to reveal the number of live pathogens due to bacteria replication during the Raman-DIP process. In this study, we established a new approach of using sodium acetate to inhibit the replication of the pathogen and applying Raman-DIP to identify the active single cells. By combining microscopic image stitching and recognition, we could further improve the efficiency of the new method. Validation of the new method on nine artificial urine samples indicated that the exact number of live pathogens obtained with Raman-DIP is consistent with plate-counting while shortening the TAT from 18 h to within 3 h, and the potential of applying Raman-DIP for pathogen enumeration in clinics is promising.

Abstract Background Hepatocellular carcinoma (HCC) was frequently considered as a kind of malignant tumor with a poor prognosis. Cyclin-dependent kinases (CDK) 4 was considered to be cell-cycle-related CDK gene. In this study, we explored the clinical significance of CDK4 in HCC patients. Methods Data of HCC patients were obtained from The Cancer Genome Atlas database (TCGA) and the Gene Expression Omnibus (GEO) database. Kaplan–Meier analysis and Cox regression model were performed to calculate median survival time (MST) and the hazard ration (HR), respectively. The joint-effect analysis and prognostic risk score model were constructed to demonstrate significance of prognosis-related genes. The differential expression of prognostic genes was further validated using reverse transcription-quantitative PCR (RT-qPCR) of 58 pairs of HCC samples. Results CDK1 and CDK4 were considered prognostic genes in TCGA and GSE14520 cohort. The result of joint-effect model indicated patients in CDK1 and CDK4 low expression groups had a better prognosis in TCGA (adjusted HR = 0.491; adjusted P = 0.003) and GSE14520 cohort (adjusted HR = 0.431; adjusted P = 0.002). Regarding Kaplan–Meier analysis, high expression of CDK1 and CDK4 was related to poor prognosis in both the TCGA (P