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ABSTRACT Bacterial vaginosis (BV) is an abnormal gynecological condition caused by the overgrowth of specific bacteria in the vagina. This study aims to develop a novel method for BV detection by integrating surface-enhanced Raman scattering (SERS) with machine learning (ML) algorithms. Vaginal fluid samples were classified as BV positive or BV negative using the BVBlue Test and clinical microscopy, followed by SERS spectral acquisition to construct the data set. Preliminary SERS spectral analysis revealed notable disparities in characteristic peak features. Multiple ML models were constructed and optimized, with the convolutional neural network (CNN) model achieving the highest prediction accuracy at 99%. Gradient-weighted class activation mapping (Grad-CAM) was used to highlight important regions in the images for prediction. Moreover, the CNN model was blindly tested on SERS spectra of vaginal fluid samples collected from 40 participants with unknown BV infection status, achieving a prediction accuracy of 90.75% compared with the results of the BVBlue Test combined with clinical microscopy. This novel technique is simple, cheap, and rapid in accurately diagnosing bacterial vaginosis, potentially complementing current diagnostic methods in clinical laboratories.IMPORTANCEThe accurate and rapid diagnosis of bacterial vaginosis (BV) is crucial due to its high prevalence and association with serious health complications, including increased risk of sexually transmitted infections and adverse pregnancy outcomes. Although widely used, traditional diagnostic methods have significant limitations in subjectivity, complexity, and cost. The development of a novel diagnostic approach that integrates SERS with ML offers a promising solution. The CNN model’s high prediction accuracy, cost-effectiveness, and extraordinary rapidity underscore its significant potential to enhance the diagnosis of BV in clinical settings. This method not only addresses the limitations of current diagnostic tools but also provides a more accessible and reliable option for healthcare providers, ultimately enhancing patient care and health outcomes.

[Display omitted] • Rapid and accurate identification of external water in rainwater pipelines. • A novel use of MSC combined with VMD for absorption spectroscopy analysis. • A new method for small samples identification of UV–Vis absorption spectroscopy. Ultraviolet–visible (UV–Vis) absorption spectroscopy, due to its high sensitivity and capability for real-time online monitoring, is one of the most promising tools for the rapid identification of external water in rainwater pipe networks. However, difficulties in obtaining actual samples lead to insufficient real samples, and the complex composition of wastewater can affect the accurate traceability analysis of external water in rainwater pipe networks. In this study, a new method for identifying external water in rainwater pipe networks with a small number of samples is proposed. In this method, the Generative Adversarial Network (GAN) algorithm was initially used to generate spectral data from the absorption spectra of water samples; subsequently, the multiplicative scatter correction (MSC) algorithm was applied to process the UV–Vis absorption spectra of different types of water samples; following this, the Variational Mode Decomposition (VMD) algorithm was employed to decompose and recombine the spectra after MSC; and finally, the long short-term memory (LSTM) algorithm was used to establish the identification model between the recombined spectra and the water source types, and to determine the optimal number of decomposed spectra K. The research results show that when the number of decomposed spectra K is 5, the identification accuracy for different sources of domestic sewage, surface water, and industrial wastewater is the highest, with an overall accuracy of 98.81%. Additionally, the performance of this method was validated by mixed water samples (combinations of rainwater and domestic sewage, rainwater and surface water, and rainwater and industrial wastewater). The results indicate that the accuracy of the proposed method in identifying the source of external water in rainwater reaches 98.99%, with detection time within 10 s. Therefore, the proposed method can become a potential approach for rapid identification and traceability analysis of external water in rainwater pipe networks. [ABSTRACT FROM AUTHOR]

Dalbergia odorifera T. Chen (Dalbergiae Odoriferae Lignum, DOL), renowned for its therapeutic applications in traditional Chinese medicine and its value in crafting luxury items, faces challenges due to the slow heartwood formation process and subsequent market adulteration. The proliferation of counterfeit products necessitates reliable methods for rapid, on-site authentication. This study aims to establish a rapid, green, and on-site method to identify the authenticity of DOL. A comprehensive analysis was conducted on 10 batches of DOL samples sourced from diverse locations, utilizing a miniature mass spectrometer (mini MS) equipped with a paper capillary spray (PCS) technique. Multivariate statistical approaches were employed to classify the samples and pinpoint chemical markers indicative of authenticity. Subsequent MS-guided separation and thin-layer chromatography (TLC) verified the markers' validity and assessed the greenness profile by Analytical Eco-scale (AES), Green Analytical Procedure Index (GAPI), and Analytical GREEnness (AGREE). A total of 10 batches of DOL samples detected by PCS-mini MS were classified into authentic and counterfeit, by unsupervised cluster analysis. Sativanone (m/z 301.1, VIP=6.0, p=0.000001) and 3′-O-methylviolanone (m/z 331.1, VIP=3.2, p=0.000382) were regarded as the chemical markers for the rapid identification of DOL. The results of the TLC method were consistent with this method, and the new method is greener. The application of mini MS for on-site authentication of DOL via specific chemical markers offers significant advantages, including operational simplicity, high efficiency, greenness, and accuracy. The deployment of this strategy promises to facilitate the effective regulation of DOL, ensuring authenticity and quality. [Display omitted] • The chemical markers of DOL were revealed, providing a basis for identification. • An on-site identification method of DOL based on PCS-mini MS was developed. • Multiple green indicators were used to evaluate the method, to reflect the greenness. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate a new protocol of the bile solubility test performed directly on the blood from positive blood culture bottles to identify Streptococcus pneumoniae rapidly. Seventy-five positive blood cultures (PBC) showing Gram-positive cocci in pairs or chains on Gram stain, including 32 S. pneumoniae isolates and three reference American Type Culture Collection (ATCC) isolates were included to evaluate the performance of a modified bile solubility test (MBST). One milliliter of blood from the PBC bottle was mixed with 0.5 mL of 10% desoxycholate or a saline solution. Both suspensions were analyzed after 10 min of incubation through a Gram stain to detect solubilization. This technique was compared with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry identification, performed on PBC following extraction or on colonies after short or standard incubation, and the optochin susceptibility test on colonies. The capsular serotypes were determined for all S. pneumoniae , and the Belgian National Reference Center confirmed the identification. All 32 clinical isolates and the ATCC isolate of S. pneumoniae were solubilized on the desoxycholate-treated slides, while the other species tested remained visually unchanged on both, the test and control slides. The MBST test demonstrated a 100% sensitivity and specificity with a mean turnaround time (TAT) of just 39 min, making it 14 h and 56 min faster than the optochin susceptibility test. This rapid variant of the bile solubility test appears to be a reliable method to identify S. pneumoniae directly from positive blood culture bottles, with a TAT of 39 min. It is a cost-effective, easy-to-perform, and time-efficient technique. Negative results should be interpreted cautiously, as they may result from mixed infections with S. pneumoniae and other Gram-positive cocci., Competing Interests: The authors declare no conflict of interest.