Campylobacter jejuni (C. jejuni), a foodborne pathogen, poses notable hazards to human health and has significant economic implications for poultry production. This study aimed to assess C. jejuni contamination levels in chicken carcasses from both backyard and commercial slaughterhouses in Chiang Mai province, Thailand. It also sought to examine the effects of different slaughtering practices on contamination levels and to offer evidence-based recommendations for reducing C. jejuni contamination. Through the sampling of 105 chicken carcasses and subsequent enumeration of C. jejuni, the study captured the impact of various slaughtering practices. Utilizing k-modes clustering on the observational and bacterial count data, the research identified distinct patterns of contamination, revealing higher levels in backyard operations compared to commercial ones. The application of k-modes clustering highlighted the impact of critical slaughtering practices, particularly chilling, on contamination levels. Notably, samples with the lowest bacterial counts were typically from the chilling step, a practice predominantly found in commercial facilities. This observation underpins the recommendation for backyard slaughterhouses to incorporate ice in their post-evisceration soaking process. Mimicking commercial practices, this chilling method aims to inhibit C. jejuni growth by reducing carcass temperature, thereby enhancing food safety. Furthermore, the study suggests backyard operations adopt additional measures observed in commercial settings, like segregating equipment for each slaughtering step and implementing regular cleaning protocols. These strategic interventions are pivotal in reducing contamination risks, advancing microbiological safety in poultry processing, and aligning with global food safety enhancement efforts.

Counting the number of Circulating Tumor Cells (CTCs) for cancer screenings is currently done by cytopathologists with a heavy time and energy cost. AI, especially deep learning, has shown great potential in medical imaging domains. The aim of this paper is to develop a novel hybrid intelligence approach to automatically enumerate CTCs by combining cytopathologist expertise with the efficiency of deep learning convolutional neural networks (CNNs). This hybrid intelligence approach includes three major components: CNN based CTC detection/localization using weak annotations, CNN based CTC segmentation, and a classifier to ultimately determine CTCs. A support vector machine (SVM) was investigated for classification efficiency. The B-scale transform was also introduced to find the maximum sphericality of a given region. The SVM classifier was implemented to use a three-element vector as its input, including the B-scale (size), texture, and area values from the detection and segmentation results. We collected 466 fluoroscopic images for CTC detection/localization, 473 images for CTC segmentation and another 198 images with 323 CTCs as an independent data set for CTC enumeration. Precision and recall for CTC detection are 0.98 and 0.92, which is comparable with the state-of-the-art results that needed much larger and stricter training data sets. The counting error on an independent testing set was 2-3% and 9% (with/without B-scale) and performs much better than previous thresholding approaches with 30% of counting error rates. Recent publications prove facilitation of other types of research in object localization and segmentation are necessary.

This study introduces an optimized integration of flow cytometry and fluorescence in situ hybridization (Flow-FISH) as an approach for the specific enumeration of gram-positive bacteria in probiotic products, overcoming the limitations of conventional methods. The enhanced Flow-FISH technique synergizes the rapid and automated capabilities of flow cytometry with the high specificity of FISH, facilitating the differentiation of viable cells at the species level within probiotic blends. By analyzing lyophilized samples of Lacticaseibacillus rhamnosus, Lactiplantibacillus plantarum, and Bifidobacterium animalis subsp. lactis, and a commercial product, the study highlights the optimized Flow-FISH protocol\'s advantages, including reduced hybridization times to 1.5 h and elimination of centrifugation steps. Comparative evaluations with the widely accepted enumeration methods plate count and Live/Dead (L/D) staining were conducted. The study revealed that Flow-FISH produces higher viable cell counts than plate count, thereby challenging the traditional \"gold standard\" by highlighting its predisposition to underestimate actual viable cell numbers. Against L/D staining, Flow-FISH achieved comparable results, which, despite the different foundational premises of each technique, confirms the accuracy and reliability of our method. In conclusion, the optimized Flow-FISH protocol represents a significant leap forward in probiotic research and quality control. This method provides a rapid, robust, and highly specific alternative for the enumeration of probiotic bacteria, surpassing traditional methodologies. Its ability to enable a more detailed and reliable analysis of probiotic products paves the way for precise quality control and research insights, underscoring its potential to improve the field significantly.

We give exact and asymptotic counting results for the number of galled networks and reticulation-visible networks with few reticulation vertices. Our results are obtained with the component graph method, which was introduced by L. Zhang and his coauthors, and generating function techniques. For galled networks, we in addition use analytic combinatorics. Moreover, in an appendix, we consider maximally reticulated reticulation-visible networks and derive their number, too.

Lactic acid bacteria (LAB) play an important role in the ripening of cheeses and contribute to the development of the desired profile of aroma and flavor compounds. Therefore, it is very important to monitor the dynamics of bacterial proliferation in order to obtain an accurate and reliable number of their cells at each stage of cheese ripening. This work aimed to identify and conduct a quantitative assessment of the selected species of autochthonous lactic acid bacteria from raw cow\'s milk cheese by the development of primers and probe pairs based on the uniqueness of the genetic determinants with which the target microorganisms can be identified. For that purpose, we applied real-time quantitative PCR (qPCR) protocols to quantify Lactobacillus delbrueckii subsp. bulgaricus, Streptococcus thermophilus, and Lactococcus lactis subsp. cremoris cells in cheese directly after production and over three-month and six-month ripening periods. While L. lactis subsp. cremoris shows good acidification ability and the ability to produce antimicrobial compounds, L. delbrueckii subsp. bulgaricus has good proteolytic ability and produces exo-polysaccharides, and S. thermophilus takes part in the formation of the diacetyl flavor compound by metabolizing citrate to develop aroma, they all play an important role in the cheese ripening. The proposed qPCR protocols are very sensitive and reliable methods for a precise enumeration of L. delbrueckii subsp. bulgaricus, S. thermophilus, and L. lactis subsp. cremoris in cheese samples.

The objective of this study was to determine Salmonella contamination levels, presence and serovar distribution in broiler carcasses before and after chilling, as well as to evaluate the effectiveness of chilling process. A total of 96 pooled neck skin samples (PNSS) of 48 prechill (PreC) and 48 postchill (PosC) carcasses, representing 480 broilers collected in 6 mo\' period were analyzed using ISO 6579-2:2012 Miniaturized Most Probable Number (ISO-mMPN) technique. Species confirmation and serovar identification was performed by Salmonella-specific real-time PCR (Salm-PCR) and conventional serotyping, respectively. Mean Salmonella count was 1.84 log10 MPN/g in PreC, and 1.48 log10 MPN/g in PosC samples, indicating a statistically significant reduction of 0.36 log10 MPN/g (p < 0.05) in the counts by plant\'s air chill system. Salmonella positivity reduced from 97.9% (47/48) in PreC to 85.42% (41/48) in PosC samples, confirmed by Salm-PCR with identified serovars as S. Virchow (89.77 %) followed by S. Schwarzengrund (9.09%) and S. Bredeney (1.14%). Persistence of high load and prevalence of Salmonella with serovar Virchow dominance (other than the ones mandated in current guidelines) in the final product contributes significant and up to date data to relevant literature, and provides unbiased epidemiological reference to legal authorities for future relevant revisions.

Poultry is the primary reservoir of Campylobacter, a leading cause of gastroenteritis in the United States. Currently, the selective plating methodology using selective agars, Campy Cefex and Modified Charcoal Cefoperazone Deoxycholate agar, is preferentially used for the quantification of Campylobacter spp. among poultry products. Due to the specific nature of Campylobacter, this methodology is not sensitive, which can lead to skewed detection and quantification results. Therefore, Campylobacter detection and quantification methods are urgently needed. The objective was to develop a shortened enrichment-based quantification method for Campylobacter (CampyQuant™) in post-chill poultry rinsates using the BAX® System Real-Time PCR assay for Campylobacter. The specificity and sensitivity for the detection of C. jejuni, C. coli, and C. lari in pure culture were determined. The BAX® System Real-Time PCR assay consistently detected and identified each species 100% of the time with an enumeration range of 4.00 to 9.00 Log10 CFU/mL. Enrichment time parameters for low-level concentrations (0.00, 1.00, and 2.00 Log10 CFU/mL) of Campylobacter using the BAX® System Real-Time PCR assay were elucidated. It was determined that an enrichment time of 20 h was needed to detect at least 1.00 Log10 CFU/mL of Campylobacter spp. Using the BAX® System Real-Time PCR assay for Campylobacter. As a result, time of detection, detection limits, and enrichment parameters were used to develop the CampyQuant™ linear standard curve using the detected samples from the BAX® System Real-Time PCR assay to quantify the levels in post-chill poultry rinsates. A linear fit equation was generated for each Campylobacter species using the cycle threshold from the BAX® System Real-Time PCR assay to estimate a pre-enrichment of 1.00 to 4.00 Log10 CFU/mL of rinsates detected. The statistical analyses of each equation yielded an R2 of 0.93, 0.76, and 0.94 with a Log10 RMSE of 0.64, 1.09, and 0.81 from C. jejuni, C. coli, and C. lari, respectively. The study suggests that the BAX® System Real-Time PCR assay for Campylobacter is a more rapid, accurate, and efficient alternative method for Campylobacter enumeration.

Probiotics are the largest non-herbal/traditional dietary supplements category worldwide. To be effective, a probiotic strain must be delivered viable at an adequate dose proven to deliver a health benefit. The objective of this article is to provide an overview of the various technologies available for probiotic enumeration, including a general description of each technology, their advantages and limitations, and their potential for the future of the probiotics industry. The current \"gold standard\" for analytical quantification of probiotics in the probiotic industry is the Plate Count method (PC). PC measures the bacterial cell\'s ability to proliferate into detectable colonies, thus PC relies on cultivability as a measure of viability. Although viability has widely been measured by cultivability, there has been agreement that the definition of viability is not limited to cultivability. For example, bacterial cells may exist in a state known as viable but not culturable (VBNC) where the cells lose cultivability but can maintain some of the characteristics of viable cells as well as probiotic properties. This led to questioning the association between viability and cultivability and the accuracy of PC in enumerating all the viable cells in probiotic products. PC has always been an estimate of the number of viable cells and not a true cell count. Additionally, newer probiotic categories such as Next Generation Probiotics (NGPs) are difficult to culture in routine laboratories as NGPs are often strict anaerobes with extreme sensitivity to atmospheric oxygen. Thus, accurate quantification using culture-based techniques will be complicated. Another emerging category of biotics is postbiotics, which are inanimate microorganisms, also often referred to as tyndallized or heat-killed bacteria. Obviously, culture dependent methods are not suitable for these products, and alternative methods are needed for their quantification. Different methodologies provide a more complete picture of a heterogeneous bacterial population versus PC focusing exclusively on the eventual multiplication of the cells. Alternative culture-independent techniques including real-time PCR, digital PCR and flow cytometry are discussed. These methods can measure viability beyond cultivability (i.e., by measuring cellular enzymatic activity, membrane integrity or membrane potential), and depending on how they are designed they can achieve strain-specific enumeration.

This study aimed to compare microscopic counting, culture, and quantitative or real-time PCR (qPCR) to quantify sulfate-reducing bacteria in environmental and engineered sludge samples. Four sets of primers that amplified the dsrA and apsA gene encoding the two key enzymes of the sulfate-reduction pathway were initially tested. qPCR standard curves were constructed using genomic DNA from an SRB suspension and dilutions of an enriched sulfate-reducing sludge. According to specificity and reproducibility, the DSR1F/RH3-dsr-R primer set ensured a good quantification based on dsrA gene amplification; however, it exhibited inconsistencies at low and high levels of SRB concentrations in environmental and sulfate-reducing sludge samples. Ultimately, we conducted a qPCR method normalized to dsrA gene copies, using a synthetic double-stranded DNA fragment as a calibrator. This method fulfilled all validation criteria and proved to be specific, accurate, and precise. The enumeration of metabolically active SRB populations through culture methods differed from dsrA gene copies but showed a plausible positive correlation. Conversely, microscopic counting had limitations due to distinguishing densely clustered organisms, impacting precision. Hence, this study proves that a qPCR-based method optimized with dsrA gene copies as a calibrator is a sensitive molecular tool for the absolute enumeration of SRB populations in engineered and environmental sludge samples.

Introduction. Cryptosporidium presents one of the main waterborne public health threats due to its resistance to chlorine disinfection and ability to cause large-scale outbreaks. The standard method used in the UK water industry for detection and enumeration of Cryptosporidium is based on fluorescence microscopy and is laborious and expensive. Molecular methods such as quantitative polymerase chain reaction (qPCR) can be more amenable to streamlining through automation, improving workflows and standardizing procedures.Hypothesis. The null hypothesis was that there was no difference in the detection or enumeration between the standard method and a qPCR.Aim. We aimed to develop and evaluate a qPCR for the detection and enumeration of Cryptosporidium in drinking water, and to compare the assay with the standard method used in the UK.Methodology. We first developed and evaluated a qPCR method by incorporating an internal amplification control and calibration curve into a real-time PCR currently used for Cryptosporidium genotyping. Then we compared the qPCR assay with the standard method of immunofluorescent microscopy for the detection and enumeration of 10 and 100 Cryptosporidium oocysts in 10 l of artificially contaminated drinking water.Results. The results demonstrated that detection of Cryptosporidium by this qPCR was reliable at low numbers of oocysts; however, enumeration was less reliable and more variable than immunofluorescence microscopy.Conclusions. Despite these results, qPCR offers practical advantages over microscopy. There is potential for the use of PCR-based methods for Cryptosporidium analysis if parts of the upstream sample preparation are revised, and alternative technologies for enumeration (such as digital PCR) are also explored to improve analytical sensitivity.