The emergence of antibiotic-resistant bacteria due to the overuse or inappropriate use of antibiotics has become a significant public health concern. The agri-food chain, which serves as a vital link between the environment, food, and human, contributes to the large-scale dissemination of antibiotic resistance, posing a concern to both food safety and human health. Identification and evaluation of antibiotic resistance of foodborne bacteria is a crucial priority to avoid antibiotic abuse and ensure food safety. However, the conventional approach for detecting antibiotic resistance heavily relies on culture-based methods, which are laborious and time-consuming. Therefore, there is an urgent need to develop accurate and rapid tools for diagnosing antibiotic resistance in foodborne pathogens. This review aims to provide an overview of the mechanisms of antibiotic resistance at both phenotypic and genetic levels, with a focus on identifying potential biomarkers for diagnosing antibiotic resistance in foodborne pathogens. Furthermore, an overview of advances in the strategies based on the potential biomarkers (antibiotic resistance genes, antibiotic resistance-associated mutations, antibiotic resistance phenotypes) for antibiotic resistance analysis of foodborne pathogens is systematically exhibited. This work aims to provide guidance for the advancement of efficient and accurate diagnostic techniques for antibiotic resistance analysis in the food industry.

Antibiotic resistance mechanism and diagnosis of common foodborne pathogens based on genotypic and phenotypic biomarkers.

Rapid identification and antimicrobial susceptibility testing (AST) of bacteria are key interventions to curb the spread and emergence of antimicrobial resistance. The current gold standard identification and AST methods provide comprehensive diagnostic information but often take 3 to 5 days. Here, a compound Raman microscopy (CRM), which integrates Raman spectroscopy and stimulated Raman scattering microscopy in one system, is presented and demonstrated for rapid identification and AST of pathogens in urine. We generated an extensive bacterial Raman spectral dataset and applied deep learning to identify common clinical bacterial pathogens. In addition, we employed stimulated Raman scattering microscopy to quantify bacterial metabolic activity to determine their antimicrobial susceptibility. For proof-of-concept, we demonstrated an integrated assay to diagnose urinary tract infection pathogens, S. aureus and E. coli. Notably, the CRM system has the unique ability to provide Gram-staining classification and AST results within ~3 h directly from urine samples and shows great potential for clinical applications.

Compound Raman microscopy for rapid diagnosis and antimicrobial susceptibility testing of pathogenic bacteria in urine

Intratumoral heterogeneity is a substantial cause of drug resistance development during chemotherapy or other drug treatments for cancer. Therefore, monitoring and measuring cell exposure and response to drugs at the single-cell level are crucial. Previous research suggested that the single-cell growth rate can be used to investigate drug–cell interactions. However, currently established methods for quantifying single-cell growth are limited to isolated or monolayer cells. Here, we introduce a technique that accurately measures both 2D and 3D cell growth rates using label-free ratiometric stimulated Raman scattering (SRS) microscopy. We use deuterated amino acids, leucine, isoleucine, and valine, as tracers and measure the C–D SRS signal from deuterium-labeled proteins and the C–H SRS signal from unlabeled proteins simultaneously to determine the cell growth rate at the single-cell level. The technique offers single-cell level drug sensitivity measurement with a shorter turnaround time (within 12 h) than most traditional assays. The submicrometer resolution of the imaging technique allows us to examine the effects of chemotherapeutic drugs, including kinase inhibitors, mitotic inhibitors, and topoisomerase II inhibitors, on both the cell growth rate and morphology. The capability of quantifying 3D cell growth rates provides insight into a deeper understanding of the cell–drug interaction in the actual tumor environment. 

Measuring Drug Response with Single-Cell Growth Rate Quantification.

Multilevel image thresholding using Expectation Maximization (EM) is an efficient method for image segmentation. However, it has two weaknesses: 1) EM is a greedy algorithm and cannot jump out of local optima. 2) it cannot guarantee the number of required classes while estimating the histogram by Gaussian Mixture Models (GMM). in this paper, to overcome these shortages, a novel thresholding approach by combining EM and Salp Swarm Algorithm (SSA) is developed. SSA suggests potential points to the EM algorithm to fly to a better position. Moreover, a new mechanism is considered to maintain the number of desired clusters. Twenty-four medical test images are selected and examined by standard metrics such as PSNR and FSIM. The proposed method is compared with the traditional EM algorithm, and an average improvement of 5.27% in PSNR values and 2.01% in FSIM values were recorded. Also, the proposed approach is compared with four existing segmentation techniques by using CT scan images that Qatar University has collected. Experimental results depict that the proposed method obtains the first rank in terms of PSNR and the second rank in terms of FSIM. It has been observed that the proposed technique performs better performance in the segmentation result compared to other considered state-of-the-art methods. 

/pdf/an-efficient-image-segmentation-method-based-on-expectation-3a5louke.pdf

An efficient image segmentation method based on expectation maximization and Salp swarm algorithm

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTInnovative Approaches for Drug Discovery: Quantifying Drug Distribution and Response with Raman ImagingErin L. DunningtonErin L. DunningtonDepartment of Chemistry, University of Washington, Seattle, Washington 98195, United StatesMore by Erin L. DunningtonView Biographyhttps://orcid.org/0009-0005-7063-8346, Brian S. WongBrian S. WongDepartment of Chemistry, University of Washington, Seattle, Washington 98195, United StatesMore by Brian S. WongView Biographyhttps://orcid.org/0000-0001-6405-468X, and Dan Fu*Dan FuDepartment of Chemistry, University of Washington, Seattle, Washington 98195, United States*[email protected]More by Dan FuView Biographyhttps://orcid.org/0000-0001-9243-8306Cite this: Anal. Chem. 2024, 96, 20, 7926–7944Publication Date (Web):April 16, 2024Publication History Received16 March 2024Accepted8 April 2024Revised8 April 2024Published online16 April 2024Published inissue 21 May 2024https://pubs.acs.org/doi/10.1021/acs.analchem.4c01413https://doi.org/10.1021/acs.analchem.4c01413review-articleACS PublicationsCopyright © 2024 American Chemical SocietyRequest reuse permissionsArticle Views732Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Anatomy,Biological imaging,Imaging,Lipids,Pharmaceuticals Get e-Alerts 

Innovative Approaches for Drug Discovery: Quantifying Drug Distribution and Response with Raman Imaging.

Rapid antimicrobial susceptibility testing by stimulated Raman scattering metabolic imaging and morphological deformation of bacteria.

mmWave radar-based gait recognition is a novel user identification method that captures human gait biometrics from mmWave radar return signals. This technology offers privacy protection and is resilient to weather and lighting conditions. However, its generalization performance is yet unknown and limits its practical deployment. To address this problem, in this paper, a non-synthetic dataset is collected and analyzed to reveal the presence of spatial and temporal domain shifts in mmWave gait biometric data, which significantly impacts identification accuracy. To mitigate this issue, a novel self-aligned domain adaptation method called GaitSADA is proposed. GaitSADA improves system generalization performance by using a two-stage semi-supervised model training approach. The first stage employs semi-supervised contrastive learning to learn a compact gait representation from both source and target domain data, aligning source-target domain distributions implicitly. The second stage uses semi-supervised consistency training with centroid alignment to further close source-target domain gap by pseudo-labelling the target-domain samples, clustering together the samples belonging to the same class but from different domains, and pushing the class centroid close to the weight vector of each class. Experiments show that GaitSADA outperforms representative domain adaptation methods with an improvement ranging from 15.41\% to 26.32\% on average accuracy in low data regimes. Code and dataset will be available at https://exitudio.github.io/GaitSADA

GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition

Rapid antimicrobial susceptibility testing (AST) is urgently needed to slow down the emergence of antibiotic-resistant bacteria and treat infections with correct antibiotics. Stimulated Raman scattering (SRS) microscopy is a technique that enables rapid chemical-bond imaging with sub-cellular resolution. It can obtain the AST results with a single bacterium resolution. Although the SRS imaging assay is relatively fast, taking less than 2 h, the calculation of single-cell metabolism inactivation concentration ( S C-MIC) is performed manually and takes a long time. The bottleneck tasks that hinder the S C-MIC throughput include bacterial segmentation and intensity thresholding. To address these issues, we devised a hybrid algorithm to segment single bacteria from SRS images with automatic thresholding. Our proposed method comprises a U-Net convolutional neural network (CNN), DropBlock, and secondary segmentation post-processing. Our results show that S C-MIC calculation can be accomplished within 1 min and more accurate segmentation results using deep learning-based bacterial segmentation method, which is essential for its clinical applications. 

Automatic quantitative analysis of metabolism inactivation concentration in single bacterium using stimulated Raman scattering microscopy with deep learning image segmentation

Rapid antifungal susceptibility testing (AFST) is urgently needed in clinics to treat invasive fungal infections with the appropriate antifungal drugs and to slow the emergence of antifungal resistance. However, current AFST methods are time-consuming (24-48 h) due to the slow growth of fungal cells and the methods not being able to work directly for clinical samples. Here, we demonstrate rapid AFST by measuring the metabolism in single fungal cells using stimulated Raman scattering imaging and deuterium probing. Distinct metabolic responses were observed in Candida albicans to different classes of antifungal drugs: while the metabolism was inhibited by amphotericin B, it was stimulated by azoles (fluconazole and voriconazole) and micafungin. Accordingly, we propose metabolism change as a biomarker for rapid AFST. The results were obtained in 4 h with 100% categorical agreement with the gold standard broth microdilution test. In addition, a protocol was developed for direct AFST from positive blood cultures. This method overcomes the limitation of slow growth in conventional methods and has the potential for the rapid diagnosis of candidemia and other clinical fungal infections.

Rapid Antifungal Susceptibility Testing Based on Single-Cell Metabolism Analysis Using Stimulated Raman Scattering Imaging.

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Rapid antimicrobial susceptibility testing by stimulated Raman scattering metabolic imaging and morphological deformation of bacteria.

GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition

Automatic quantitative analysis of metabolism inactivation concentration in single bacterium using stimulated Raman scattering microscopy with deep learning image segmentation

Rapid Antifungal Susceptibility Testing Based on Single-Cell Metabolism Analysis Using Stimulated Raman Scattering Imaging.