comparative-analysis-automatic-fecal-analyzer-versus-direct-wet-smear

Comparative Analysis of Automatic Fecal Analyzer versus Direct Wet Smear Microscopy for Detecting Parasitic Infections in Stool Samples

With socio-economic development, the prevalence of intestinal parasitic diseases has significantly decreased year by year. However, parasitic infections ...

Southern Medical University

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;  = 21), carbapenem-resistant K. pneumoniae, (CRKP;  = 50), and carbapenem-sensitive K. pneumoniae (CSKP;  = 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.  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.

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

Infection with Neisseria gonorrhoeae in adults usually leads to vaginitis and acute urethritis, and infection through the birth canal in newborns can lead to acute neonatal conjunctivitis. In view of certain factors such as a high missed detection rate of N.gonorrhoeae from staining microscopy method, the time-consuming nature and limited sensitivity of bacterial culture method, complicated and inability of absolute quantification from the ordinary PCR method.

Establishment of droplet digital PCR for the detection of Neisseria gonorrhoeae.

Human infection with  is mainly manifested as non-gonococcal urethritis, where it can lead to cervicitis, premature rupture of membranes and abortion in women, as well as infertility in males, which becomes a major problem in clinical diagnosis and treatment. At present, real-time fluorescence quantitative PCR and culture are the two main methods for detecting UU. The real-time fluorescence quantitative PCR method is cumbersome and cannot accomplish absolute quantification on nucleic acids, while the cultivation method has limitations such as low sensitivity and being time-consuming. The aim of this study is to establish a more rapid and accurate droplet digital PCR(ddPCR) method for the detection of UU.

Article	Year
Rapid Prediction of Multidrug-Resistant Klebsiella pneumoniae through Deep Learning Analysis of SERS Spectra. Microbiology spectrum\| PubMed ID: 36877048	2023
Establishment of droplet digital PCR for the detection of Neisseria gonorrhoeae. Diagnostic microbiology and infectious disease\| PubMed ID: 38896891	2024
Development of a droplet digital PCR method for the detection of . Practical laboratory medicine\| PubMed ID: 39839815	2025

Yun-Hu Zhao

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