Identification of Mycobacterium tuberculosis Resistance to Common Antibiotics: An Overview of Current Methods and Techniques.

Multidrug-resistant tuberculosis (MDR-TB) is an essential cause of tuberculosis treatment failure and death of tuberculosis patients. The rapid and reliable profiling of Mycobacterium tuberculosis (MTB) drug resistance in the early stage is a critical research area for public health. Then, most traditional approaches for detecting MTB are time-consuming and costly, leading to the inappropriate therapeutic schedule resting on the ambiguous information of MTB drug resistance, increasing patient economic burden, morbidity, and mortality. Therefore, novel diagnosis methods are frequently required to meet the emerging challenges of MTB drug resistance distinguish. Considering the difficulty in treating MDR-TB, it is urgently required for the development of rapid and accurate methods in the identification of drug resistance profiles of MTB in clinical diagnosis. This review discussed recent advances in MTB drug resistance detection, focusing on developing emerging approaches and their applications in tangled clinical situations. In particular, a brief overview of antibiotic resistance to MTB was present, referred to as intrinsic bacterial resistance, consisting of cell wall barriers and efflux pumping action and acquired resistance caused by genetic mutations. Then, different drug susceptibility test (DST) methods were described, including phenotype DST, genotype DST and novel DST methods. The phenotype DST includes nitrate reductase assay, RocheTM solid ratio method, and liquid culture method and genotype DST includes fluorescent PCR, GeneXpert, PCR reverse dot hybridization, ddPCR, next-generation sequencing and gene chips. Then, novel DST methods were described, including metabolism testing, cell-free DNA probe, CRISPR assay, and spectral analysis technique. The limitations, challenges, and perspectives of different techniques for drug resistance are also discussed. These methods significantly improve the detection sensitivity and accuracy of multidrug-resistant tuberculosis (MRT) and can effectively curb the incidence of drug-resistant tuberculosis and accelerate the process of tuberculosis eradication.

Rapid Prediction of Multidrug-Resistant Klebsiella pneumoniae through Deep Learning Analysis of SERS Spectra.

Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-to-treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenem-sensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings.

Machine learning analysis of SERS fingerprinting for the rapid determination of Mycobacterium tuberculosis infection and drug resistance.

Over the past decades, conventional methods and molecular assays have been developed for the detection of tuberculosis (TB). However, these techniques suffer limitations in the identification of Mycobacterium tuberculosis (Mtb), such as long turnaround time and low detection sensitivity, etc., not even mentioning the difficulty in discriminating antibiotics-resistant Mtb strains that cause great challenges in TB treatment and prevention. Thus, techniques with easy implementation for rapid diagnosis of Mtb infection are in high demand for routine TB diagnosis. Due to the label-free, low-cost and non-invasive features, surface enhanced Raman spectroscopy (SERS) has been extensively investigated for its potential in bacterial pathogen identification. However, at current stage, few studies have recruited handheld Raman spectrometer to discriminate sputum samples with or without Mtb, separate pulmonary Mtb strains from extra-pulmonary Mtb strains, or profile Mtb strains with different antibiotic resistance characteristics. In this study, we recruited a set of supervised machine learning algorithms to dissect different SERS spectra generated via a handheld Raman spectrometer with a focus on deep learning algorithms, through which sputum samples with or without Mtb strains were successfully differentiated (5-fold cross-validation accuracy = 94.32%). Meanwhile, Mtb strains isolated from pulmonary and extra-pulmonary samples were effectively separated (5-fold cross-validation accuracy = 99.86%). Moreover, Mtb strains with different drug-resistant profiles were also competently distinguished (5-fold cross-validation accuracy = 99.59%). Taken together, we concluded that, with the assistance of deep learning algorithms, handheld Raman spectrometer has a high application potential for rapid point-of-care diagnosis of Mtb infections in future.

Impairment of spermatogenesis and sperm motility by the high-fat diet-induced dysbiosis of gut microbes.

OBJECTIVE: High-fat diet (HFD)-induced metabolic disorders can lead to impaired sperm production. We aim to investigate if HFD-induced gut microbiota dysbiosis can functionally influence spermatogenesis and sperm motility. DESIGN: Faecal microbes derived from the HFD-fed or normal diet (ND)-fed male mice were transplanted to the mice maintained on ND. The gut microbes, sperm count and motility were analysed. Human faecal/semen/blood samples were collected to assess microbiota, sperm quality and endotoxin. RESULTS: Transplantation of the HFD gut microbes into the ND-maintained (HFD-FMT) mice resulted in a significant decrease in spermatogenesis and sperm motility, whereas similar transplantation with the microbes from the ND-fed mice failed to do so. Analysis of the microbiota showed a profound increase in genus Bacteroides and Prevotella, both of which likely contributed to the metabolic endotoxaemia in the HFD-FMT mice. Interestingly, the gut microbes from clinical subjects revealed a strong negative correlation between the abundance of Bacteroides-Prevotella and sperm motility, and a positive correlation between blood endotoxin and Bacteroides abundance. Transplantation with HFD microbes also led to intestinal infiltration of T cells and macrophages as well as a significant increase of pro-inflammatory cytokines in the epididymis, suggesting that epididymal inflammation have likely contributed to the impairment of sperm motility. RNA-sequencing revealed significant reduction in the expression of those genes involved in gamete meiosis and testicular mitochondrial functions in the HFD-FMT mice. CONCLUSION: We revealed an intimate linkage between HFD-induced microbiota dysbiosis and defect in spermatogenesis with elevated endotoxin, dysregulation of testicular gene expression and localised epididymal inflammation as the potential causes. TRIAL REGISTRATION NUMBER: NCT03634644.

Fluorescent carbon dots derived from urine and their application for bio-imaging.

This study aims to obtain water-soluble fluorescent carbon dots (C-dots) from low-value metabolites through a simple, economical, one-step synthetic route. The urine C-dots (UCDs) and hydrothermally treated urine C-dots (HUCDs) were obtained, respectively, using straightforward Sephadex filtration method from human adults and hydrothermal reaction method. The UCDs and HUCDs emit fluorescence upon being excited with ultraviolet light with a quantum yield of 4.8% and 17.8%, respectively. TEM analysis revealed that UCDs and HUCDs had an average size of 2.5 nm and 5.5 nm, respectively. X-ray photoelectron spectroscopy (XPS) analysis showed the UCDs and HUCDs were mainly composed of carbon, oxygen and nitrogen. Fourier-transform infrared (FTIR) spectroscopy demonstrated the presence of functional groups, such as amino, hydroxyl, carboxylate and carbonyl groups onto the C-dots. The UCDs and HUCDs can be directly used for in vivo and in vitro imaging in Hela cells, Caenorhabditis elegans, onion epidermal cells and bean sprouts. The cytotoxicity study revealed that the UCDs and HUCDs were not toxic to normal rat kidney (NKR) cells with good biocompatibility. The results revealed that the C-dots derived from urine have good biocompatibility, strong fluorescence and may have potential to be a safe fluorescent probe for bio-imaging.