To evaluate whether the performance of the modified Clinical Pulmonary Infection Score (CPIS) for diagnosis of ventilator-associated pneumonia (VAP) could be improved by incorporating procalcitonin (PCT), lactate and a standardised lung ultrasound score (LUS). Prospective observational study. Internal medicine and respiratory intensive care units (ICUs) of a tertiary care centre in India over 18 months. 97 adult patients receiving invasive mechanical ventilation for 48 hours or more with suspected VAP. The primary outcome was the diagnostic accuracy (area under the receiver operating characteristic curve (AUC)) of the integrated score compared with a reference standard of quantitative bronchoalveolar lavage culture (>104 CFU/mL). Secondary outcomes included ICU length of stay and 14-day mortality. Of the 97 patients, 70 (72.2%) were culture positive. The modified CPIS alone demonstrated limited diagnostic utility (AUC 0.686). In contrast, the novel 4-point Lactate-Lung Ultrasound-Procalcitonin-Clinical Pulmonary Infection Score (LPCPIS) score (integrating CPIS >5, lactate >2.15 mmol/L, PCT >1.3 ng/mL and LUS >14) achieved excellent discriminatory ability (AUC 0.919, 95% CI 0.844 to 0.976). At a cut-off of ≥2, the score yielded 95.7% sensitivity and 77.8% specificity. Crucially, a 4-point LPCPIS bedside score of ≤1 was associated with a highly diminished probability of VAP, yielding a negative predictive value of 100% in this cohort. In comparison, a score ≥3 strongly indicated a high likelihood of VAP, with a positive predictive value of 96.5% and specificity of 92.6%, pending definitive microbiological confirmation. The novel 4-point LPCPIS bedside score demonstrates significant diagnostic superiority over the traditional modified CPIS (AUC 0.919 vs 0.686). By integrating bedside lung ultrasound with biomarkers, this simplified pragmatic tool provides impressive discriminatory power, allowing clinicians in resource-limited settings to reliably assess probability of VAP and guide early antimicrobial decision-making while awaiting definitive microbiological cultures.
Animal pathogenic microorganisms destabilize livestock economies and jeopardize human health through zoonotic transmission and foodborne illness. Traditional culture-based detection methods, while standardized, are time-consuming and labor-intensive, often failing to meet the urgent need for rapid on-site or point-of-care (POC) monitoring required to prevent disease outbreaks and manage animal health effectively. By integrating latest research advances, this study reviews advances in rapid detection technologies for animal pathogens, including the evolution of nucleic acid amplification strategies, with a focused comparison of the analytical sensitivity and field deployability of quantitative polymerase chain reaction (qPCR) and mainstream isothermal amplification techniques (loop-mediated isothermal amplification (LAMP); recombinase polymerase amplification (RPA)). Furthermore, this study reports on the emergence of Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-associated protein (Cas) systems as next-generation diagnostic tools, highlighting their integration with microfluidic Lab-on-a-Chip (LOC) platforms to achieve attomolar sensitivity. We also consider the application of portable nanopore sequencing for real-time pathogen identification from clinical livestock samples and the growing role of Artificial Intelligence (AI) in analyzing complex diagnostic datasets. Advanced molecular methods have achieved significant reductions in time consumption from days to less than one hour while challenges regarding sample preparation from complex clinical matrices such as whole blood, serum, tissue homogenates, and fecal samples remain. The future of animal health surveillance lies in integrated, automated systems that combine the specificity of CRISPR-Cas diagnostics with the connectivity of IoT-enabled biosensors for farm-level early warning. Comparative analysis indicates that isothermal amplification methods (LAMP, RPA) coupled with CRISPR-Cas systems offer the optimal balance of sensitivity, speed, and field deployability for POC veterinary diagnostics, while qPCR/dPCR (dPCR)remain indispensable for quantitative regulatory applications such as disease certification and vaccine efficacy monitoring. We propose a structured technology selection framework to guide researchers and veterinary practitioners in choosing appropriate detection modalities based on specific sensitivity, cost, throughput, and deployment requirements for different livestock species and production systems.
The intensive and recurring use of pesticides in agriculture has raised environmental concerns and impacts on ecosystem biodiversity. Glyphosate and 2,4-D are among the most widely used herbicides worldwide for weed control. Continuous and prolonged exposure of the soil to these compounds can compromise natural biogeochemical processes, affecting the structure and functioning of the edaphic microbiota, which plays a crucial role in nutrient cycling and bioremediation processes. In this study, biodegradation of these pesticides was investigated by the Bartha and Pramer spirometry technique, which estimates the heterotrophic microbial activity by the quantification of carbon dioxide released. Experimental data obtained were analyzed via the Ford-Walford model, which enabled the description of the behavior of the microbial degradation of pesticides in soil, estimating the asymptotic limit of stabilization of the CO2 generation. Results indicated that glyphosate shows a more accelerated biodegradation rate, with less environmental persistence and, potentially, less risk of accumulation in the soil, compared to 2,4-D, which showed more environmental persistence. These findings highlight the need for continuous monitoring of pesticide residues in soil and the adoption of sustainable agricultural practices. The integration of microbiological assays and mathematical modeling also provides valuable insights into xenobiotic degradation mechanisms, supporting strategies to mitigate the environmental impacts of persistent agrochemicals.
Acute exacerbations of chronic rhinosinusitis (AECRS) are increasingly recognized as a distinct clinical entity that significantly impacts quality of life. Despite its clinical relevance, data on its prevalence, clinical characteristics, and underlying bacteriology remain limited. This study investigated the clinical features of AECRS and characterized the bacterial profile of middle meatus cultures during exacerbation episodes. Furthermore, characteristics of AECRS were compared across chronic rhinosinusitis (CRS) phenotypes. Retrospective cohort study. Single tertiary care referral center, King Saud University Medical City (KSUMC). Medical records from patients with established AECRS with positive middle meatus cultures from 2015 to 2025 were extracted. Data included demographics, CRS phenotype, comorbidities, prior treatments, microbiological findings, and clinical outcomes. Bacterial etiology and clinical characteristics of AECRS across phenotypes, including chronic rhinosinusitis with nasal polyposis, chronic rhinosinusitis without nasal polyposis, and allergic fungal rhinosinusitis. 104. The study enrolled 104 patients (mean age 39.9 years); 49 (47.1%) had asthma and 21 (20.2%) had aspirin-exacerbated respiratory disease. Endoscopic pus was present in 71 (68.3%), and 77 (74%) had prior sinus surgery. The most common isolates were methicillin-sensitive Staphylococcus aureus 35 (33%), methicillin-resistant Staphylococcus aureus 26 (25%), and Pseudomonas species 24 (23.1%). Oral antibiotics were administered in 75 (72.1%) of AECRS episodes, while oral steroids were used in 27 (26%). Clinical improvement following antibiotic therapy was documented in 64 (85.3%) cases. High resistance was observed for ampicillin, penicillin, and oxacillin. AECRS presents considerable heterogeneity in both clinical presentation and microbiology. Therefore, culture-directed therapy is recommended due to variable antibiotic resistance patterns. Asthma was strongly associated with treatment failure. These findings emphasize the importance of individualized management strategies in AECRS. Retrospective design, and single tertiary care center.
Recent advances in electronics with digital image capture has been facilitated, combining high resolution complementary metal-oxide-semiconductor (CMOS) technology, with adjustable LED and polarised lighting, allowing the capture of incoming light, where photons are converted to electrical charge and this signal is translated into digital data that can be visualised on LED screens. The technological output has been the evolution of handheld digital microscopes utilising digital imaging and analysis software. Various devices are now available commercially which may have an application in routine clinical microbiology. Therefore, it was the aim of this study to compare two devices, namely a 4 K wifi portable digital microscope with a nanozoom Macro Zoom CPL lens, to capture colonial morphologies, with a collection of clinical pathogens. Both devices captured colonial morphologies of five organisms, including Bacillus subtilis, Enterococcus faecalis, Pseudomonas aeruginosa, Salmonella Nottingham and Staphylococcus aureus. Images of colonial morphologies were captured on freshly cultured (24-h) agar plates and were of high resolution and quality. In terms of application into clinical microbiology of these technologies, the capability to capture high quality images from culture plates allows the documentation of unusual colony morphologies, record rare pathogens and allows remote review when disputing scientific interpretations (e.g. a disputed antibiotic susceptibility test result). These digital image capture methods are simple, inexpensive, versatile and widely adaptable and require minimum preparation, handling and processing. These devices now sit between optical glass hand lenses and research-grade microscopes, thereby filling an important and crucial niche in clinical microbiology, which has largely been underdeveloped to date. Renewed employment of such apparatus will encourage the routine photographing of colonial morphologies of clinical isolates for evidence, posterity and future work/studies involving AI networks.
Rapid, accurate, and on-site detection of foodborne pathogens remains a critical challenge in ensuring food safety. Traditional microbiological methods, including culture-based assays, ELISA, and PCR, often require long processing times, specialized equipment, and trained personnel, limiting their practical application in real-time monitoring. Electrochemical biosensors have emerged as promising alternatives due to their portability, fast response, low cost, and compatibility with complex food matrices. Molecularly imprinted polymers (MIPs) offer a robust synthetic recognition layer for these sensors, providing high chemical stability and tenable selectivity while overcoming limitations of biological recognition elements such as antibodies and enzymes. However, conventional MIP fabrication often relies on empirical trial-and-error approaches, which can reduce reproducibility and slow sensor development. This review summarizes recent advances in computationally guided MIP design for electrochemical biosensing of key foodborne pathogens, including Salmonella spp., Listeria monocytogenes, Escherichia coli O157:H7, Staphylococcus aureus, and Pseudomonas aeruginosa. We discuss how molecular docking, density functional theory, and molecular dynamics simulations can predict template-monomer interactions, optimize cavity formation, and guide polymer properties, thereby improving sensor sensitivity, selectivity, stability, and response time. By integrating computational modeling with experimental electrochemical methods, these approaches enable a mechanism-driven development of portable, high-performance biosensors for microbial detection. This convergence of computational and microbiological methods provides a pathway toward next-generation, ready-to-use devices for rapid foodborne pathogen monitoring.
Intravenous fosfomycin has regained importance in recent years due to its effectiveness against difficult-to-treat pathogens. We aimed to determine the minimum inhibitory concentration (MIC) values of fosfomycin in multidrug-resistant (MDR) Klebsiella pneumoniae strains, to classify isolates using epidemiological cutoff values (ECOFF) and to evaluate a cost-effective method for fosfomycin MIC determination. Multidrug resistant K. pneumoniae isolates from blood cultures were included. Identification and susceptibility to antibiotics were determined. The carbapenemase genes were detected. Fosfomycin MICs were determined using the agar dilution method and isolates were classified based on ECOFF values as wild-type (WT) and non-wild-type (non-WT). The distribution of carbapenemase genes according to the fosfomycin MIC values were presented. Additionally, a modified agar dilution test was used to determine fosfomycin MICs more cost-effectively. Media containing various concentrations of fosfomycin were prepared in two-well plates. Five isolates (including E. coli ATCC 25922) selected through random sampling were tested weekly over a period of three weeks, followed by testing of all clinical isolates. The performance of this method was described using categorical agreement (CA), essential agreement (EA), major error (ME) and very major error (VME) rates. Between September 2022 and September 2023, 200 MDR K. pneumoniae isolates were included. Among carbapenemase types, OXA-48 was most frequent, followed by KPC and NDM, with no significant correlation observed between carbapenemase groups and fosfomycin MIC distributions. According to ECOFF values, 75% of isolates were WT and 25% were non-WT. With the modified agar dilution method, fosfomycin MICs did not vary by more than one dilution over three consecutive weeks. EA and CA were 95,5% and 93% with ME and VME rates of 4,5% and 2,5%, respectively. The modified agar dilution method serves as a reliable, cost-efficient alternative for fosfomycin susceptibility testing in routine clinical laboratories.
The thermodimorphic fungi Sporothrix brasiliensis, a zoophilic pathogen frequents associated to domestic cats, is the most pathogenic species in Sporothrix genus. The increasing resistance of fungal strains to conventional antifungal agents, the prolonged nature and the refractory clinical of treatment underscore the need for alternative therapeutic approaches. Among these, photodynamic inactivation (PDI) has emerged as a promising strategy. PDI involves the combination of a photosensitizing molecule (PS), a light source, and molecular oxygen. Upon irradiation, the PS absorbs photons and undergoes a cascade of photophysical and photochemical reactions that lead to the generation of reactive oxygen species (ROS). At sufficiently high concentrations, these ROS induce irreversible cellular damage, ultimately resulting in cell death. In this study, the in vitro photodynamic activity of four porphyrinic photosensitizers against S. brasiliensis isolates was evaluated: two cationic porphyrins - meso-tetrakis(1-methylpyridinium-4-yl) porphyrin (TMPyP) and its zinc-complexed form (ZnTMPyP) - and two anionic porphyrins - meso-tetrakis(4-sulfonatophenyl)porphyrin (TPPS₄) and the its zinc-complexed analogue (ZnTPPS₄). Fungal susceptibility was determined using the microdilution method in accordance with the Clinical and Laboratory Standards Institute (CLSI) guidelines. Both ZnTMPyP and TMPyP exhibited an estimated minimum effective concentration (MEC) of 0.8 μM against S. brasiliensis and Candida parapsilosis (control). Among the anionic PSs, TPPS₄ showed the greatest inhibitory potential, with an estimated MEC of 1.6 μM. Confocal fluorescence microscopy revealed that both cationic and anionic porphyrins were able to internalize within the cellular structures of S. brasiliensis, which may explain the observed inhibitory effects. These findings demonstrate that porphyrin-based PSs, particularly cationic derivatives, effectively inhibit the growth of S. brasiliensis isolates. Collectively, the results support the potential application of porphyrins as alternative therapeutic agents for the treatment of sporotrichosis.
Mycoplasma pneumoniae is a significant pathogen causing respiratory infections globally, with diagnosis complicated by the organism's unique biology and nonspecific clinical presentation. Its distinct biology, including the absence of a cell wall and ambiguous clinical presentation, challenges early and accurate diagnosis, which is critical for guiding optimal antimicrobial therapy and limiting antibiotic resistance. This study summarizes the progression of diagnostic procedures from traditional culture and serology to modern molecular techniques such as real-time PCR, multiplex PCR panels, isothermal amplification methods like SAT, LAMP, and emerging techniques such as NGS and CRISPR. The paper underlines the advantages of molecular approaches over culture and serology in terms of sensitivity, specificity, and turnaround time, focusing on FDA/CE-cleared multiplex PCR systems and quick antigen detection kits with silver amplification as valuable clinical tools. Emerging CRISPR-based diagnostics offer a wonderful opportunity for quick, ultrasensitive, and point-of-care diagnosis. Despite the variety of available assays, issues such as detecting asymptomatic carriage, distinguishing active infection, and ensuring accessibility in low-resource settings persist. The increased global incidence of macrolide-resistant M. pneumoniae strains, as well as the overlap of respiratory symptoms with other diseases, highlight the need for specific, quick, and reliable diagnostic tests. This review advocates for the incorporation of innovative molecular diagnostics into standard clinical workflows to improve early identification, optimize treatment methods, and improve patient outcomes.
Metagenomic next-generation sequencing (mNGS) has expanded the scope of clinical diagnostics by enabling culture-independent detection of microorganisms in patient samples. However, mNGS clinical utility remains constrained by substantial computational demands, reference database biases, and the persistent challenge of distinguishing true pathogens from host background, commensal flora and environmental contamination. Traditional alignment and k-mer-based bioinformatics pipelines frequently struggle to balance speed, sensitivity, and the ability to detect highly divergent or novel organisms. This review critically synthesizes the current landscape of Artificial Intelligence (AI) and Machine Learning (ML) applications across the mNGS diagnostic pipeline, examining deep learning architectures-including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Transformers-as integrated into raw read processing, host sequence depletion, primary taxonomic classification, and ancillary detection of antimicrobial resistance (AMR) and virulence factors. While several AI methodologies report high classification accuracy in benchmarking studies, we note that most performance claims derive from simulated datasets or controlled mock communities rather than prospective clinical validation. Significant gaps persist, including limited AI integration in front-end signal optimization, inadequate automated clinical reporting, absence of standardized benchmarking metrics, and unresolved questions regarding data leakage, reproducibility, and generalizability. Successful clinical translation will require addressing the interpretability limitations of current explainable AI approaches, navigating complex and evolving regulatory landscapes for Software as a Medical Device (SaMD), and bridging the gap between computational feasibility and demonstrated patient-outcome benefit. The development of genomic foundation models and multi-modal clinical integration holds promise for advancing mNGS toward real-time, actionable diagnostics, though substantial evidence gaps remain between current proof-of-concept demonstrations and validated clinical deployment.
Although animal-based studies provide information with the highest physiological and metabolic relevance, in vitro methods are essential for assessing diets and feedstuffs during initial screening. These methods have accelerated our understanding of digestion and fermentation in the rumen ecosystem, contributing to the optimization of animal production and the mitigation of greenhouse gas emissions. This paper reviews the characteristics, strengths, weaknesses, and standardization procedures for the current in vitro rumen fermentation methods. Overall, these methods reduce animal-to-animal variation and are faster than in vivo trials; however, their main limitations are the closed system's characteristics and inability to cover host-microbe-environment interactions. Thus, selecting the appropriate method requires careful evaluation of the investigator's aims. The in vitro batch culture (IVBC) and the ANKOM RF Gas Production System (AGPS) are valuable methods for assessing gas production and digestibility; both also measure environmental impacts, such as methane production. The rumen simulation Technique (RUSITEC) is closest to rumen conditions and could have a greater impact on long-term rumen microbiota research. On the other hand, the ANKOM DaisyII incubator is an efficient apparatus for forage digestibility determination in livestock species. Finally, we highlight guidelines to standardize and increase the robustness and reproducibility of these methods.
Rapid detection techniques play a critical role in the field of food hazard factor analysis, as food safety constitutes a fundamental cornerstone of public health. However, the application of Surface-Enhanced Raman Scattering (SERS)-lateral flow immunoassay (LFIA) as a rapid detection method in this area still faces significant challenges. Consequently, traditional quantification methods that rely on average Raman signal intensity often prove inadequate for accurate measurement under such conditions. In response to these limitations, we developed a digital SERS-LFIA approach, which employs digital analytical techniques to process Raman spectral data for the ultrasensitive and quantitative analysis of sibutramine (SIB), a central appetite suppressant formerly used in obesity treatment that is now frequently illicitly added to foods such as weight-loss teas. Specifically, the method utilized core-shell Ag-coated Au nanoparticles functionalized with Raman reporter molecules as SERS tags. SERS-LFIA strips were prepared via antigen-antibody recognition for SIB detection. Rapid Raman mapping of the T-line region generated spatially resolved signal matrices, which were converted to binary values 0/1 by applying an intensity threshold at the reporter's characteristic peak. Quantification was accomplished by correlating the positive signal frequency with SIB concentration, effectively mitigating the interference caused by signal heterogeneity and background signals on quantification. Compared with conventional Raman average intensity analysis, the digital SERS-LFIA achieved limits of detection of 0.28 ng/mL, demonstrating excellent quantitative detection capability. In summary, by incorporating digital SERS technology, this study extends the application of LFIA to enable ultrasensitive and efficient quantitative detection analytes in complex food samples.
Cured meats are traditional products consumed worldwide. The presence of indigenous microbiota during production can expose consumers to health risks. This study evaluated the potential of Hanseniaspora opuntiae, Meyerozyma caribbica, and Penicillium nalgiovense in inhibiting the growth of mycotoxin-producing fungi (Aspergillus westerdijkiae, Penicillium verrucosum, and Penicillium citrinum) isolated from a Brazilian dry-cured loin. Radial inhibition assays were conducted on Potato Dextrose Agar (PDA) and Socol-based Agar (SBA), which simulated the initial maturation conditions of the meat product. The results showed that P. nalgiovense exhibited the highest efficacy in inhibiting the mycotoxin-producing fungi at 15 °C and 25 °C. At 15 °C, the growth of P. nalgiovense was favored over the toxigenic fungi. Ochratoxin A (OTA) was quantified using high-performance liquid chromatography with fluorescence detection (HPLC-FLD). The production of OTA varied significantly among the studied fungi, with concentrations ranging from 10.42 μg/kg to 1054,87 μg/kg in PDA and from 5,18 μg/kg to 142,22 μg/kg in SBA, indicating that the composition of the medium influenced mycotoxin production. The findings highlight the potential of P. nalgiovense as biocontrol, especially at 15 °C. Future research should address the effects of P. nalgiovense application and reduced maturation temperatures on the sensory attributes of Brazilian dry-cured loin.
Reproducibility in Candida albicans biofilm research is often hindered by variations in culture media and strain-dependent responses. This study evaluates the effect of oxygen-scavenging agents on biofilm induction to identify a simple and standardized liquid medium suitable for rapid and consistent biofilm formation. Modified Sabouraud Dextrose Broth (SDB) was supplemented with ferrous sulfate (FeSO₄), sodium sulfite (Na₂SO₃), ascorbic acid, and tested across four clinical isolates of C. albicans. FeSO₄-supplemented SDB promoted significant biofilm formation within 24 h and the highest biomass at 48 h (OD595 = 0.90-0.95), significantly greater than Na₂SO₃ (OD595 = 0.60-0.70) and ascorbic acid (OD595 = 0.30-0.45) (****p < 0.0001). The untreated control showed negligible biofilm formation. These findings establish FeSO₄-enriched SDB as a rapid, reproducible, and easily adaptable liquid medium for biofilm induction in C. albicans, providing a valuable tool for antifungal screening and biofilm-associated pathogenesis studies.
Outer Membrane Vesicles (OMVs) are nanostructures naturally produced by Gram-negative bacteria, playing a relevant role in processes such as horizontal gene transfer, quorum sensing modulation, antibacterial and antibiofilm activity, and presenting potential applications in nanotechnology, including drug delivery systems. Considering the diversity of methods employed for their isolation and purification, this study aimed to compare the morphological characteristics, overall composition, concentration, and potential cytotoxic effects of OMVs isolated by ultracentrifugation (OMVs-UC) and by a commercial exosome isolation kit (OMVs-Kit). To the best of our knowledge, this is the first study to provide a systematic comparison between ultracentrifugation and a commercial precipitation-based kit for OMV isolation in Burkholderia thailandensis, integrating multiple analytical approaches to evaluate how the isolation method affects vesicle characteristics. The results indicated that the kit offers greater operational simplicity, enabling the recovery of OMVs with morphological patterns and composition similar to those obtained by ultracentrifugation. The concentrations obtained were 7.08 × 108 particles/mL for OMVs-UC and 2.46 × 108 particles/mL for OMVs-Kit, with mean diameters of 249 nm and 145.8 nm, respectively, according to Nanoparticle Tracking Analysis (NTA). Despite minor variations attributed to the distinct isolation and purification processes, the composition of OMVs was predominantly similar between methods. Furthermore, OMVs obtained by both approaches did not exhibit cytotoxic effects in VERO CCL-81 cells, reinforcing their potential for biotechnological applications. Overall, the commercial kit represents a viable alternative to ultracentrifugation, allowing faster and simplified OMV isolation while maintaining comparable vesicle characteristics.
Predictive microbiology has become an important tool for shelf life assessment, process optimization, and microbial risk management in meat systems. Despite extensive academic development, the adoption of predictive tools by the meat industry remains limited, mainly due to constraints related to usability, data requirements, and the need for specialized expertise. This scoping review examines user-friendly predictive microbiology software tools applicable to meat and meat products, with emphasis on their relevance for durability studies and challenge tests under realistic processing and storage conditions. A systematic scoping literature survey was performed using combinations of the descriptors "predictive microbiology software", "shelf life prediction", "meat", and "meat products", resulting in the identification of 76 predictive platforms, of which 20 were considered accessible and operationally feasible for industrial environments. Rather than applying numerical rankings, these tools were critically assessed using a structured qualitative framework considering modeling architecture (primary, secondary, and tertiary models), required input variables, ability to handle dynamic temperature profiles, type of microbial endpoint (pathogen-focused versus spoilage-related), usability for non-specialist users, and practical applicability for shelf life validation. Most available tools effectively describe microbial behavior under controlled conditions and support product formulation, cold-chain evaluation, and process design. However, only a limited subset integrates spoilage-related endpoints, consumer rejection thresholds, or product-specific resilience, which are essential for realistic shelf life determination. In addition, most models remain pathogen-centered, with limited consideration of spoilage microbiota, microbial consortia, and fluctuating storage conditions. Practical perspectives are discussed to facilitate the integration of predictive microbiology tools into routine shelf life validation and decision-making processes within the meat industry.
Food contaminated with Salmonella remains a major cause of foodborne illness worldwide, posing a significant public health concern. Rapid detection of viable Salmonella in food is essential for effective food safety management. Viability PCR (vPCR) techniques using photo-reactive dyes such as PMA (Propidium monoazide) or PMAxx have emerged as promising approaches to suppress DNA amplification from membrane-compromised (dead) cells. However, most previous PMA-qPCR studies have mainly evaluated artificially contaminated meat samples, while their performance in naturally contaminated food matrices with complex microbiota remains limited. This study optimized a PMAxx real-time PCR assay for the detection of viable and viable but non-culturable (VBNC) Salmonella and evaluated its application in retail meat samples. The PMAxx real-time PCR technique effectively eliminated signals from 108 CFU/mL of dead cells in culture media samples. The limit of detection (LOD) was 10 CFU/g of viable cells in spiked chicken samples following 18 h of pre-enrichment in Buffered Peptone Water (BPW). Under 0.85% NaCl at 4 °C, Salmonella entered the VBNC state after 32 weeks, while PMAxx real-time PCR still detected signals. The method was evaluated using 33 chicken and pork samples collected from supermarkets and traditional markets. A total of 23 samples tested positive, including 13 samples detected after only 6 h of pre-enrichment. The results show 100% agreement with the traditional culture method performed in accordance with ISO 6579-1:2017. These findings indicate that the PMAxx real-time PCR assay can be applied for the rapid screening of viable Salmonella contamination in fresh food samples.
Rapid and accurate detection of water-borne bacteria is critical for safeguarding public health and preventing the spread of infections. Conventional bacterial identification techniques are often time-consuming, resource-intensive, and require specialized personnel, limiting their suitability for automated water-quality monitoring. This study presents an optical detection and classification framework that uses some pre-trained architectures to automatically classify optically acquired images of water-borne bacterial pathogens. The system distinguishes among four classes: Escherichia coli (E. coli), Fecal streptococci (Strept), co-occurrence of both bacteria (Both), and safe water (None). Experimental evaluation demonstrates strong classification performance, with ResNet-50, ResNet-152, and EfficientNet-B7 achieving accuracies of 94.15%, 94.45%, and 95.54%, respectively. Except DenseNet-201, which yields the worst results (accuracy of 77.49%), the corresponding precision, recall, and F1-scores of the other pre-trained models exceed 94%. Furthermore, an analysis of the ROC (Receiver Operating Characteristic) curve reveals that the Area Under the Curve (AUC) exceeds 98% for each bacterial class, thereby demonstrating strong discriminative performance. The results highlight the potential of transfer learning-based convolutional neural networks for accurate, rapid, and cost-effective bacterial detection, emphasizing their promise for scalable and automated water-quality monitoring systems. This approach represents a step toward improved monitoring tools aligned with sustainable water, sanitation, and hygiene (WASH) objectives.
Bacillus cereus GW-01, an efficient degrader of β-cypermethrin (β-CY), has a high safety profile and probiotic potential for regulating intestinal flora and fermented foods, which is difficult to genetically engineer for modification due to its restrictive modification system. This study successfully developed a CRISPR/enCas12f-based genome editing system, first selecting the plcR gene for proof-of-concept validation with 100% knockout efficiency. Subsequently, this system was utilized to delete the virulence gene nheABC in GW-01, yielding a safer probiotic strain. Compared with the wild-type strain GW-01, the probiotic-related indicators of the ΔnheABC mutant, including cell surface hydrophobicity, auto-aggregation ability and biofilm formation ability, were 80%, 90% and 2.9 (OD₅₉₅), respectively. There were no significant differences in these indicators between the mutant and the wild type. Meanwhile, the ΔnheABC mutant still maintained a high β-cypermethrin degradation efficiency of 80% at the concentration of 30 μg/mL. This work facilitates functional genomic research and genetic modification of Bacillus cereus GW-01. The established CRISPR/enCas12f system enables targeted gene deletion to explore gene functions and phenotypic mechanisms, and paves the way for its development into safe probiotics and excellent microbial chassis.
Vaginitis frequently poses serious threats to women's health. If left untreated, it can increase the risk of infection by other pathogens such as HPV, leading to cervical dysplasia. This study developed a rapid detection method based on multiplex recombinase polymerase amplification (mRPA) combined with lateral flow strips (LFS) for the simultaneous identification of five common vaginitis pathogens: Neisseria gonorrhoeae, Gardnerella vaginalis, Candida albicans, Ureaplasma urealyticum, and Trichomonas vaginalis. The novelty of this technology lies in the fact that, the 5' end of the RPA primer was modified by a nucleic acid tagged (NAT) sequence to complementarily pair with the LFS capture probe. The entire testing process can be completed within 50-55 min and provides visual results. Furthermore, the lowest detection limits of the platform for Neisseria gonorrhoeae, Gardnerella vaginalis, Candida albicans, Ureaplasma urealyticum, and Trichomonas vaginalis respectively were 1.5 × 10 CFU/mL, 1.5 × 102 CFU/mL, 1.5 × 10 CFU/mL, 1.35 × 102 copies/μL, and 1.02 × 102 copies/μL, and there was no nucleic acid cross-reactivity with other pathogens. Clinical validation using 122 samples showed a sensitivity of 97.1%, specificity of 90%, and accuracy of 95.9% compared to conventional culture and microscopy. This mRPA-LFS platform is rapid, specific, and sensitive, showing promise as an effective point-of-care testing (POCT) tool.