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Normal and damaged microorganisms are related to food safety. The colony-forming unit (CFU) assay and viability of microorganisms have broad applications in food. Traditionally, the CFU assay has been the benchmark for assessing microbial viability across various fields. However, the normal and damaged microorganisms cannot be distinguished. Here, we introduce an improved technology for foods that uses a visible absorbance microplate reader platform for high-throughput quantitative analysis of microbial lag time, doubling time, and CFU. This platform utilizes a 96-well plate and a microplate reader to accurately determine the viable cell number from a five-microliter sample. It boasts the capability to measure a dynamic range spanning from five to seven orders of magnitude, significantly reducing the time required by over 20-fold in comparison to traditional spread plate methods. Additionally, it demonstrates a remarkable ability to detect a single cell within a well. A mild temperature treatment for cell viability detection was implemented and was able to reflect the real microbial quality. Consequently, the high-throughput method as an improved technology provides essential technical support for microbial detection.

Sequences derived from organisms sharing common evolutionary origins exhibit similarity, while unique sequences, absent in related organisms, act as good diagnostic marker candidates. However, the approach focused on identifying dissimilar regions among closely-related organisms poses challenges as it requires complex multiple sequence alignments, making computation and parsing difficult. To address this, we have developed a biologically inspired universal NAUniSeq algorithm to find the unique sequences for microorganism diagnosis by traveling through the phylogeny of life. Mapping through a phylogenetic tree ensures a low number of cross-contamination and false positives. We have downloaded complete taxonomy data from Taxadb database and sequence data from National Center for Biotechnology Information Reference Sequence Database (NCBI-Refseq) and, with the help of NetworkX, created a phylogenetic tree. Sequences were assigned over the graph nodes, k -mers were created for target and non-target nodes and search was performed over the graph using the depth first search algorithm. In a memory efficient alternative NoSQL approach, we created a collection of Refseq sequences in MongoDB database using tax-id and path of FASTA files. We queried the MongoDB collection for the target and non-target sequences. In both the approaches, we used an alignment free sliding window k -mer–based procedure that quickly compares k -mers of target and non-target sequences and returns unique sequences that are not present in the non-target. We have validated our algorithm with target nodes Mycobacterium tuberculosis , Neisseria gonorrhoeae , and Monkeypox and generated unique sequences. This universal algorithm is a powerful tool for generating diagnostic sequences, enabling the accurate identification of microbial strains with high phylogenetic precision. [ABSTRACT FROM AUTHOR]

Infections caused by microorganisms are a public health problem worldwide. New biodetection systems are essential to diagnose with accuracy resulting in more effective treatment. In this work, we propose a ConA-conjugated graphene quantum dots and polypyrrole film-based biosensor for label-free detection of Candida albicans, Candida glabrata, Candida tropicalis, E. coli, B. subitilis, and S. aureus. We modified polypyrrole and graphene quantum dots (PPY-QDGs) with Concanavalin A (Con A) lectin. ConA is a glucose/mannose-specific lectin. The results showed that ConA lectin has the highest binding affinity for C. tropicalis and S. subtilis. PPY-GQDs-ConA binding profile revealed differential response for Candida spp (C. tropicalis > C. albicans > C. glabrata) and bacterial (B. subtilis > S. aureus > E. coli). The limits of detection (LOD) obtained were 1.42 CFU/mL for C. albicans, and 3.72 CFU/mL for C. glabrata. C. tropicalis yielded a LOD of 0.18 CFU/mL. The respective LODs for the evaluated bacteria were 0.39 CFU/mL for S. aureus, 0.72 CFU/mL for S. subtilis, and 2.63 CFU/mL for E. coli. The differential response obtained for the sensor can be attributed to the heterogeneous distribution of carbohydrates on the microorganism's surfaces. The proposed system based on a flexible substrate is effective for microbiological diagnosis., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

To ensure clean drinking water, viable pathogens in water must be rapidly and efficiently screened. The traditional culture or spread-plate process—the conventional standard for bacterial detection—is laborious, time-consuming, and unsuitable for rapid detection. Therefore, we developed a colorimetric assay for rapid microorganism detection using a metabolism-based approach. The reaction between a viable microorganism and the combination of 2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfophenyl)-2H-tetrazolium sodium salt (WST-8) and 1-methoxy-5-methylphenazinium methyl sulfate (mPMS) results in a color change. In combination with a microplate reader, WST-8-mPMS reactivity was leveraged to develop a colorimetric assay for the rapid detection of various bacteria. The detection limit of the WST-8-mPMS assay for both gram-negative and gram-positive bacteria was evaluated. This WST-8-mPMS assay can be used to perform colorimetrical semi-quantitative detection of various bacterial strains in buffers or culture media within 1 h without incubation before the reaction. The easy-to-use, robust, rapid, and sensitive nature of this novel assay demonstrates its potential for practical and medical use for microorganism detection.

Bioaerosols are small airborne particles composed of microbiological fragments, including bacteria, viruses, fungi, pollens, and/or by-products of cells, which may be viable or non-viable wherever applicable. Exposure to these agents can cause a variety of health issues, such as allergic and infectious diseases, neurological disorders, and cancer. Therefore, detecting and identifying bioaerosols is crucial, and bioaerosol sampling is a key step in any bioaerosol investigation. This review provides an overview of the current bioaerosol sampling methods, both passive and active, as well as their applications and limitations for rapid on-site monitoring. The challenges and trends for detecting airborne microorganisms using molecular and immunological methods are also discussed, along with a summary and outlook for the development of prompt monitoring technologies.

Wastewater has detrimental effects on the natural environment. The activated sludge method, a widely adopted approach for wastewater treatment, has proven highly effective. Within this process, microorganisms play a pivotal role, necessitating continuous monitoring of their quantity and diversity. Conventional methods, such as microscopic observation, are time-consuming. With the widespread integration of computer vision technologies into object detection, deep learning-based object detection algorithms, notably the You Only Look Once (YOLO) model, have garnered substantial interest for their speed and precision in detection tasks. In this research, we applied the YOLO model to detect microorganisms in microscopic images of activated sludge. Furthermore, addressing the irregular shapes of microorganisms, we developed an improved YOLO model by incorporating deformable convolutional networks and an attention mechanism to enhance its detection capabilities. We conducted training and testing using a custom dataset comprising five distinct objects. The performance evaluations used in this study utilized metrics such as the mean average precision at intersections over a union threshold of 0.5 (mAP@0.5), with the improved YOLO model achieving a mAP@0.5 value of 93.7%, signifying a 4.3% improvement over the YOLOv5 model. Comparative analysis of the improved YOLO model and other object detection algorithms on the same dataset revealed a higher accuracy for the improved YOLO model. These results demonstrate the superior performance of the improved YOLO model in the task of detecting microorganisms in activated sludge, providing an effective auxiliary method for wastewater treatment monitoring.

The persistent threat posed by infectious pathogens remains a formidable challenge for humanity. Rapidly spreading infectious diseases caused by airborne microorganisms have far-reaching global consequences, imposing substantial costs on society. While various detection technologies have emerged, including biochemical, immunological, and molecular approaches, these methods still exhibit significant limitations such as time-intensive procedures, instability, and the need for specialized operators. This study presents an innovative solution that harnesses the potential of surface acoustic wave (SAW) sensors for the detection of airborne microorganisms. The research involves the establishment of a sensor model within the framework of COMSOL Multiphysics, utilizing a predefined piezoelectric multi-physics interface and employing a 2D modeling approach. Chitosan, selected as the sensing film for the model, interfaces with lithium niobate (LiNbO3), the chosen piezoelectric material responsible for detecting airborne pathogens. The analysis of microbe presence centers on solid displacement and electric potential frequencies, operating within the 850–900 MHz range. Notably, the first and second resonant frequencies are identified at 856 and 859 MHz, respectively. To enhance understanding, this study proposes a novel mathematical model grounded in Stokes’ Law and mass balance equations. This model serves to analyze microbe concentration, offering a fresh perspective on quantifying the presence of airborne pathogens. Through these endeavors, this research contributes to advancing the field of airborne microorganism detection, offering a promising avenue for addressing the challenges posed by infectious diseases.

Infectious diseases seriously threaten human health. The early detection of the causative organism and antibiotic resistance genes of infection remains a major clinical challenge. Next-generation sequencing has been widely used in diagnosing infectious diseases. Cheaper and faster targeted next-generation sequencing technology is progressively being used in clinical practice. However, the awareness among clinicians about the clinical utility of this technology is limited. The article presents the theoretical background, advantages and disadvantages, clinical applications of targeted next-generation sequencing, and its contrasts with metagenomics next-generation sequencing. It provides a reference for clinicians to select laboratory methods for identifying pathogens in clinical practice.

The study objective is to compare the rate of detection of various microorganisms in the prostatic fluid and ejaculate using real-time polymerase chain reaction in patients with category IV chronic prostatitis.Materials and methods. Between December of 2016 and July 2019, a prospective study including 81 patients with category IV chronic prostatitis per the National Institutes of Health Prostatitis Syndrome Classification (1999) was performed. The patients referred to the clinic of the Ivanovo State Medical Academy for preconception preparation, infertility or erectile disfunction. At the examination, all patients lacked symptoms characteristic of category II or III chronic prostatitis. Transrectal ultrasound of the prostate, microscopic examination of the prostatic fluid and (or) ejaculate, quantitative examination of urogenital tract microbiota using real-time polymerase chain reaction were performed.Results. Comparison of microbiota of the prostatic fluid and ejaculate showed significant differences in the total amount of bacterial mass: in the prostatic fluid mean titer was 3.7 ± 1.6, in the ejaculate it was 2.6 ± 1.8 (p

Introduction and Objective. The article presents the methodology of preparing a cosmetic sample for analysi, and the creation of a dataset for teaching artificial intelligence to recognize specific species of microorganisms in cosmetic samples in terms of compliance with the ISO standard document, to develop of the Microorganism Detection System (SDM). Materials and method. Methodology of preparation a cosmetic sample for testing covers the steps from taking a cosmetic sample to obtaining separated living microorganisms through staining to photos, which in the final stage are used for analysis of the purity of cosmetics by SDM, as well as for learning and testing of the deep convolutional neural network (CNN) for detecting and classifying cells of specific species of bacteria, fungi and yeast in cosmetics, according to the document of standard PN-EN ISO 17516-2014:11. Results. A new techique was devised for preparing a cosmetic sample for the development of Microorganism Detection System (SDM) software, and artificial intelligence learning to recognize specific microbial species. Based on metod demonstrated, the Intelligent algorithms of SDM proved to be effective in counting and recognizing specific microorganisms (average accuracy for Candida albicans -- 97%, Escherichia coli -- 76%, Pseudomonas aeruginosa -- 70%, Staphylococcus aureus -- 85%), which are the most important species for the assessment of the purity of cosmetics. In addition, the reproducibility of the developed method was verified, and the results obtained were comparable to the breeding methods currently used, based on specific standards. Conclusions. The experiments confirmed the high sensitivity and specificity of the SDM method, its repeatability and, above all, the comparability of the results with clasic methods of European standards. [ABSTRACT FROM AUTHOR]

The Microorganism Detection System (SDM) is a new solution using artificial intelligence, unique on the international scale, to correctly identify and count microorganisms, with particular emphasis on specificlisted microorganisms (Document of Standard PN-EN ISO 17516-2014:11) -- Candida albicans, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus. SDM enables the use of algorithms for microscopic image interpretation in the microbiological assessment of the cosmetics in accordance with the standard, providing an answer to whether the tested product complies with the standard. Apart from the software part of SDM, an integral part of the system is an innovative methodology for preparing a cosmetic sample for testing. The experiments confirm the high sensitivity and specificity of the SDM method, its repeatability and, above all, the comparability of the results with the methods of European standards. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Squeeze-and-excitation-based Mask Region Convolutional Neural Network (SEM-RCNN) for Environmental Microorganisms (EM) detection tasks. Mask RCNN, one of the most applied object detection models, uses ResNet for feature extraction. However, ResNet cannot combine the features of different image channels. To further optimize the feature extraction ability of the network, SEM-RCNN is proposed to combine the different features extracted by SENet and ResNet. The addition of SENet can allocate weight information when extracting features and increase the proportion of useful information. SEM-RCNN achieves a mean average precision (mAP) of 0.511 on EMDS-6. We further apply SEM-RCNN for blood-cell detection tasks on an open source database (more than 17,000 microscopic images of blood cells) to verify the robustness and transferability of the proposed model. By comparing with other detectors based on deep learning, we demonstrate the superiority of SEM-RCNN in EM detection tasks. All experimental results show that the proposed SEM-RCNN exhibits excellent performances in EM detection.

Rapid detection of microorganisms is a major challenge in the medical and industrial sectors. In a pharmaceutical laboratory, contamination of medical products may lead to severe health risks for patients, such as sepsis. In the specific case of advanced therapy medicinal products, contamination must be detected as early as possible to avoid late production stop and unnecessary costs. Unfortunately, the conventional methods used to detect microorganisms are based on time-consuming and labor-intensive approaches. Therefore, it is important to find new tools to detect microorganisms in a shorter time frame. This review sums up the current methods and represents the evolution in techniques for microorganism detection. First, there is a focus on promising ligands, such as aptamers and antimicrobial peptides, cheaper to produce and with a broader spectrum of detection. Then, we describe methods achieving low limits of detection, thanks to Raman spectroscopy or precise handling of samples through microfluids devices. The last part is dedicated to techniques in real-time, such as surface plasmon resonance, preventing the risk of contamination. Detection of pathogens in complex biological fluids remains a scientific challenge, and this review points toward important areas for future research.

Rapid positive blood culture reporting allows early and appropriate treatment of severe infections to improve patient prognosis. This study evaluated performance of the VersaTREK system with gas pressure detection and tornado stirring method and the conventional BacT/ALERT 3D system. Time to positivity (TTP) of simulated blood cultures without whole blood using 17 ATCC strains was faster with VersaTREK than BacT/ALERT 3D, averaging 6.3 h in aerobic bottles and 12.7 hours in anaerobic bottles. In simulated blood cultures with whole blood using 53 clinical isolates, on average, VersaTREK was faster in aerobic bottles by 6.5 h but slower in anaerobic bottles by 3.8 h. Fifty of 53 simulated blood cultures with whole blood (94%) showed fastest TTP with VersaTREK. TTP of VersaTREK for anaerobic bacteria Bacteroides fragilis and Clostridium perfringens , Helicobacter cinaedi , and Candida glabrata was fast, and viable bacteria numbers in bottles using the Miles and Misra method increased quickly.

Infectious diseases are major threats to human health and lead to a serious public health burden. The emergence of new pathogens and the mutation of known pathogens challenge our ability to diagnose and control infectious diseases. Nanopore sequencing technology exhibited versatile applications in pathogenic microorganism detection due to its flexible data throughput. This review article introduced the applications of nanopore sequencing in clinical microbiology and infectious diseases management, including the monitoring of emerging infectious diseases outbreak, identification of pathogen drug resistance, and disease-related microbial communities characterization.

Normal and damaged microorganisms are related to food safety. The colony-forming unit (CFU) assay and viability of microorganisms have broad applications in food. Traditionally, the CFU assay has been the benchmark for assessing microbial viability across various fields. However, the normal and damaged microorganisms cannot be distinguished. Here, we introduce an improved technology for foods that uses a visible absorbance microplate reader platform for high-throughput quantitative analysis of microbial lag time, doubling time, and CFU. This platform utilizes a 96-well plate and a microplate reader to accurately determine the viable cell number from a five-microliter sample. It boasts the capability to measure a dynamic range spanning from five to seven orders of magnitude, significantly reducing the time required by over 20-fold in comparison to traditional spread plate methods. Additionally, it demonstrates a remarkable ability to detect a single cell within a well. A mild temperature treatment for cell viability detection was implemented and was able to reflect the real microbial quality. Consequently, the high-throughput method as an improved technology provides essential technical support for microbial detection. [ABSTRACT FROM AUTHOR]

The advent of molecular-based diagnostics has radically improved our ability to analyze the prevalence and activities of microorganisms in environmental systems. In this research. a new molecular tool (gene probe spectroscopy or GPS) was tested which may further enhance the capabilities of existing molecular techniques for carrying-out environmental analysis. With GPS, a microorganism (or a group or related microorganisms) is characterized by its hybridization to a set of non-specific oligonucleotide probes in a dot-blot format. The hybridization intensities for each probe/microorganism combination is determined experimentally and stored in a matrix database (A). Samples containing unknown mixtures of microorganisms are then hybridized with the same set of probes to obtain a set of composite hybridization intensities (b). By employing linear inverse theory, the concentration (c) of each microorganism present in the unknown sample may be computed by matrix inversion techniques: C = A -lb. With an award from the Water Resources Center, we investigated this approach using a model system consisting of four different bacteriophage (T2, M13, lambda, and 1'7) and six non-specific oligonucleotide probes each of which cross-reacts with two or more phage. In a series of tests, phage concentrations were correctly predicted by the method. illustrating its efficacy for identifying and enumerating microorganisms in fluid samples. Its application to "real" environments will require the use of DNA chip technology in which oligonucleotide probes are arrayed on glass slides. Future work will be directed toward the merging of the techniques developed in this project, namely GPS, with the DNA chip format,