Search Results (1,589)

C. difficile is a major cause of antibiotic-associated diarrhea and hospital-acquired infections, although increasing community-acquired cases suggest alternative transmission routes. Livestock, particularly pigs, have been proposed as potential reservoirs. This study aimed to investigate the presence of zoonotic ribotypes in piglets from Tenerife (Spain) and to assess their pathogenic potential by detecting toxin genes. A total of 140 samples were analyzed, including 58 fecal samples from slaughtered piglets (4–8 weeks old) and 82 rectal swabs from piglets aged 2–25 days. Samples were cultured, identified by MALDI-TOF MS, and characterized by PCR ribotyping and toxin gene detection. No isolates were obtained from fecal samples collected at slaughter, whereas 14 (17%) rectal swabs were positive. Colonization was strongly age-dependent, with the highest prevalence at 2 days of age (100%), decreasing by day 9 (10.7%), and absent after 21 days (p < 0.05). All isolates were ribotype RT033 with a tcdA⁺/tcdB⁻/cdtA⁺/cdtB⁺ profile. The exclusive detection of RT033, a clade V lineage linked to animal reservoirs and occasional human infections, suggests a potential zoonotic risk, especially for farm workers. These findings reinforce the need for integrated C. difficile surveillance under a One Health framework to monitor emerging ribotypes and their role in community-acquired infections. Full article

Ammonia (NH₃) from intensive agriculture is a primary precursor for secondary fine particulate matter (PM_2.5), necessitating mitigation under the EU National Emission Ceilings (NEC) Directive. This study evaluated a novel feed-based intervention assessed under real-scale commercial conditions in weaning and growing pig units. Indoor NH₃ concentrations were monitored at high frequency (2 h resolution), and treatment effects were analyzed using a Circular Block Bootstrap (CBB) approach to account for diurnal cyclicity and temporal autocorrelation. In the weaning unit, where pits were fully emptied before the trial, the mean indoor NH₃ concentration decreased from 7.51 ppm to 1.37 ppm, representing an 81.7% reduction. In the growing unit, which operated under pre-existing slurry and an overflow system, a significant reduction of 20.9% was observed (from 5.45 ppm to 4.31 ppm). These results demonstrate the intervention’s efficacy in preventing NH₃ release from fresh excreta and suggest that its impact in systems managed under slurry overflow can be further optimized by initially activating pre-existing material. This infrastructure-free solution offers a scalable, economically sustainable pathway to align livestock production with zero-pollution targets while supporting multiple Sustainable Development Goals related to human health, worker welfare, and environmental protection. Full article

This study investigated the epidemiology, antimicrobial resistance, and transmission risks of β-lactamase, cefotaxime-hydrolyzing, Munich (bla_CTX-M)-positive Escherichia coli (CTX-M-EC) in large-scale pig farms in Jiangxi Province (China). In total, 278 samples (manure, wastewater, drinking water, and flies) were collected. CTX-M-EC strains were isolated and analyzed using antimicrobial susceptibility testing, resistance gene profiling, multilocus sequence typing, and genetic environment analysis with gene transfer assessed by transduction experiments. Twenty-seven CTX-M-EC strains (9.71%) were isolated, all exhibiting multi-drug resistance with 100% resistance to cefotaxime, ciprofloxacin, and tetracycline, and >90% resistance to ceftazidime, florfenicol, and trimethoprim-sulfamethoxazole. Four bla_CTX-M subtypes were identified. bla_CTX-M-55 was the predominant subtype (70.37%) and was distributed across diverse sequence types and serotypes. Each strain harbored multiple antibiotic resistance genes, plasmids, and virulence genes. Mobile elements such as ISEcp1 and IS26 were detected surrounding the bla_CTX-M gene, and 96.29% of strains successfully transferred the bla_CTX-M gene via transduction. Clones highly homologous to pig manure strains were detected in flies and sewage, suggesting that this resistance gene can spread between animals, the environment, and vectors. These findings highlight the high transmission risk of bla_CTX-M and underscore the need for rational antibiotic use, waste management, and vector control within a One Health framework. Full article

The present study aimed to assess whether postmortem analysis of aqueous humor in pigs can be used to estimate antemortem serum biochemical values. The experimental design used a control group to establish regression equations linking postmortem aqueous humor to antemortem serum biochemical values. These models enabled reconstruction of the physiological status in decomposed forensic cases associated with heatstroke and hypoxia in pigs that died following a ventilation system failure on a commercial farm, and assessment of physiological distress, cause of death, and potential intentional animal abuse. Concentrations of albumin (ALB), alkaline phosphatase (ALP), alanine aminotransferase (ALT), amylase (AMY), total bilirubin (TBIL), urea nitrogen (UN), creatinine (CRE), calcium (Ca), phosphate (PHOS), sodium (Na), potassium (K), glucose (GLU) and total protein (TP) were measured in aqueous humor and compared with serum samples obtained after slaughter of 30 pigs. Biochemical analyses were performed using a chemistry analyzer with commercial reagent rotors designed. Strong correlations were observed for Na, K and CRE concentrations and for ALT and UN activities between aqueous humor and serum, while TP, ALB, AMY, TBIL and Ca showed weaker associations. Notably, CRE and UN showed strong postmortem correlations with serum values in pigs, consistent with findings in cats and other species, highlighting their reliability as indicators of renal function. Electrolyte concentrations, particularly K and Na, followed consistent and well-recognized patterns described in both human and veterinary forensic studies, with K levels in pigs comparable to those observed in other domestic animals. The results indicate that postmortem aqueous humor analysis of CRE, Na, K, AST, and UN provides a reliable estimation of corresponding serum values in pigs, representing a useful diagnostic and forensic tool in the case of animal welfare. Full article

The major Staphylococcaceae family is recognized as opportunistic pathogens colonizing human and animal skin, mucous membranes, and environments. Musca domestica, the house fly, plays a role in the transmission of AMR bacteria. This study focused on examining the epidemiology and antimicrobial-resistant genes of the family Staphylococcaceae in M. domestica through metagenomic analysis, using samples collected from three animal farms and two residential areas in southern Thailand. Fifty M. domestica were collected from five places surrounding Walailak University, including one chicken farm (CF1), two pig farms (PF2 and PF3), and two residential areas (H1 and H2). All samples were dispatched for analysis using shotgun metagenomic sequencing and analyzed using FastQC, MultiQC, FASTQ, MEGAHIT, QUAST, ABRicate, AMRFinderPlus, ResFinder, ARG-ANNOT, MEGARES, PlasmidFinder, VFDB, Kraken2, Krona and Python. Our findings describe the taxonomic composition of Staphylococcaceae taxa in M. domestica from different environments; the representation of the family Staphylococcaceae in CF1, PF2, PF3, H1, and H2 was recorded at 2%, 0.7%, 0.2%, 0.2%, and 2% of this phylum, respectively. The average populations discovered were Staphylococcus (37.4%), Mammaliicoccus (17.4%), and Macrococcus (10.3%), respectively. Trimethoprim-resistant genes (dfrG and dfrE) were found only in CF1, PF2, and H1. Interestingly, fosfomycin-resistant genes were found only in M. domestica within residential areas. Our findings pertain to the Staphylococcaceae population in M. domestica within residential areas, which exhibited varying multidrug-resistance genes, particularly those resistant to fosfomycin. Full article

Observing pig behavior, such as feed intake, water intake, and resting behavior, is essential for improving the well-being of these animals. However, monitoring such behaviors by traditional methods can be exhausting for both humans and animals, interfering with their development. The research aimed to identify behavioral patterns of pigs in an air-conditioned environment through computer vision. Microcameras were installed in the animals’ stalls to generate videos over an experimental period of 92 days and the temperature and humidity of the air were simultaneously recorded. The physiological variables of the animals were collected to identify whether they were under heat stress. To recognize the drinking, eating, standing and lying behavior of pigs, YOLOv5 was trained and then the model was used to detect the animals. Regions in the images corresponding to the feeders and drinkers were established. To identify feeding behavior and water intake, criteria based on the occupation of the feeding zone by pigs detected in the standing position were established. The results showed that the trained model achieved an average accuracy rate of 97.3% and an average recall of 96.1% in animal detection. The model exhibited 97.5% accuracy and 97.0% recall rates in recognizing the feeding behavior and water consumption of pigs. The proposed method can be used in videos or images and minimizes the need for manual intervention, offering an efficient means of monitoring pig behavior in agricultural environments and contributing to the productivity of pig farming operations. Full article

Pig weighing is an essential procedure for monitoring growth and animal health; however, conventional methods are often labor-intensive, costly, and potentially stressful. In this context, this study proposes a non-invasive approach for estimating the body weight of pigs during the growing stage based on computer vision and the YOLOv11 algorithm, enabling automatic segmentation and individual identification in multi-animal environments. The study used RGB images of 10 group-housed pigs captured throughout the growing phase, in which automatic dorsal segmentation was combined with individual identification through numerical markings. From the generated binary masks, the segmented dorsal area was extracted and used as a predictor variable in Linear Regression and a Multilayer Perceptron (MLP) Artificial Neural Network. The YOLOv11 model showed consistent performance in the segmentation task, achieving test-set metrics of Precision = 0.849, Recall = 0.886, mAP@0.50 = 0.936, and mAP@0.50–0.95 = 0.819, demonstrating good generalization capability in scenarios with intense animal interaction. In the weight prediction stage, Linear Regression and the MLP achieved high coefficients of determination (R² = 0.96 and 0.95, respectively) with low errors (RMSE = 1.52 kg and 1.63 kg; MAE = 1.20 kg and 1.25 kg), indicating a strong correlation between segmented dorsal area and actual body weight. Class-wise analysis revealed superior performance for classes 7 and 9, with R² values up to 0.98 and RMSE below 1.1 kg, whereas class 8 showed greater error dispersion, associated with higher morphological variability and a smaller number of available samples. These results demonstrate that the direct use of morphometric information extracted from segmented masks in 2D images constitutes a robust, accurate, and low-cost approach for automatic pig body-weight estimation. Moreover, this study is among the few addressing this task specifically during the growing stage, highlighting its potential for future deployment in embedded systems and intelligent monitoring platforms for precision pig farming, although further evaluation of computational efficiency and real-time performance is still required. Full article

Continuous monitoring of pig behavior is essential for timely health management and welfare assessment in commercial production systems. Although vision-based methods have been widely studied, their practical application in commercial barns is often limited by variable lighting, frequent occlusion, and high stocking density. Acoustic sensing offers a non-contact alternative that is independent of lighting conditions; however, reliable behavior classification from pig vocalizations remains challenging in commercial environments because of background noise and temporal variability in sound patterns. In this study, an attention-guided acoustic framework, termed ATF-Conformer, was developed for pig vocalization classification under farm conditions. A five-class vocalization dataset was collected from finishing Landrace pigs and multiparous sows on a commercial farm, including cough, scream, estrus, feeding, and normal behavior sounds. The proposed framework combined spectrogram denoising with interactive attention to enhance behavior-related acoustic information, while a time-frequency-decoupled Conformer encoder was introduced to improve feature representation under noisy conditions. Final classification was performed using mask-based temporal pooling with an additive angular margin Softmax objective. In five-fold grouped cross-validation, ATF-Conformer achieved an accuracy of 97.34% ± 0.42 and outperformed several existing acoustic models across multiple evaluation metrics. A similar accuracy of 97.38% was obtained on an independent test set, indicating stable performance across datasets. These results suggest that the proposed method can support continuous, non-invasive pig vocalization-based behavior monitoring and may assist farm owners or workers in pen-level screening of frequent cough or abnormal vocal events, thereby supporting targeted on-site inspection in precision livestock farming. Full article

In intensive farming, contactless individual pig identification is crucial for precision feeding and health monitoring. However, real-world barn conditions—such as fluctuating illumination, severe occlusions, non-rigid poses, and high inter-individual similarity—pose significant challenges. Existing models struggle to balance high accuracy with lightweight deployment. To address this, we propose YOLO-ESO, an optimized detection framework based on YOLOv10n. YOLO-ESO introduces three core innovations: (1) integrating the C2f_ODConv module into the backbone to strengthen feature learning under complex poses via dynamic convolution; (2) redesigning the neck with a Semantics and Detail Infusion (SDI) module to improve multi-scale fusion while suppressing background noise; and (3) embedding an Efficient Multi-Scale Attention (EMA) mechanism before the detection head to capture fine-grained identity cues like texture and contours. Evaluated on a real-world pig dataset, YOLO-ESO achieves an mAP@0.5 of 96.6%, an mAP@0.5:0.95 of 71.1%, and an F1 of 92.0%. YOLO-ESO surpasses state-of-the-art detectors including YOLOv8, YOLOv11, and RT-DETR, while introducing only 8.7 GFLOPs and 3.48 million parameters. Overall, the proposed YOLO-ESO provides an accurate and lightweight solution for robust individual pig identification in complex farming environments, showing strong potential for practical deployment in precision livestock farming. Full article

Methicillin-resistant Staphylococcus aureus (MRSA) is a major antimicrobial-resistant pathogen affecting both human and animal health. Although historically associated with healthcare settings, MRSA is now established in livestock production and throughout the production chain. Its detection in animals, food products, and processing environments reflects the complex ecology of antimicrobial resistance (AMR) in modern food systems. This narrative review synthesizes current evidence on the molecular basis of methicillin resistance and multidrug resistance determinants, as well as the epidemiology of MRSA in food-associated settings. Particular emphasis is placed on its occurrence in animal-derived foods and key reservoirs within farms, slaughterhouses, and processing environments. Livestock-associated populations are dominated by clonal complex CC398. In contrast, CC9 is prevalent in pig production systems in Asia, while CC5-related lineages occur at the human and animal interface. MRSA has been detected in retail meat and animal-derived foods at low but measurable prevalence, indicating contamination during slaughter and processing. Virulence determinants include staphylococcal enterotoxins linked to food poisoning and Panton–Valentine leukocidin associated with severe infections. Biofilm formation and adhesins further support persistence and colonization. Epidemiological and molecular evidence indicates that livestock, processing environments, and food-contact surfaces act as interconnected reservoirs sustaining MRSA circulation. Human exposure occurs primarily through occupational contact and environmental pathways, whereas foodborne transmission appears less common. Effective control requires integrated surveillance, responsible antimicrobial use in livestock production, and strict hygiene practices throughout the production chain within a One Health framework. Full article

Porcine reproductive and respiratory syndrome virus (PRRSV)-positive pigs can be exposed to high concentration of gases, such as ammonia and hydrogen sulfide when manure and urine stored in the pits beneath them is agitated and pumped. Such acute exposure can lead to adverse health effects such as respiratory system irritation. This study aimed to explore whether PRRS-positive growing pigs experience changes in viremia detection after manure pit agitation and pumping has been performed. To address this objective, two PRRS-positive growing pig farms were conveniently selected and visited twice during the week before and after manure agitation and pumping. Blood samples were collected to assess detection of viremia, using reverse transcription-polymerase chain reaction (RT-PCR). A logistic regression model was used to evaluate serum detection of PRRSV before and after the manure management event, accounting for pig age. Although PRRSV was detected in the serum of some pigs, under the conditions of the study, there were no statistically significant changes that would indicate that viremia detections change after the pigs had been exposed to barn manure pit agitation and pumping. Full article

The continued global spread of WOAH-listed pathogens via trade, transport, and travel calls for the implementation of biosecurity measures to protect the health of our national livestock industries, plus ongoing surveillance to verify that such measures are operative. Despite this urgency, surveillance must be practical and affordable. Herein, we evaluated the performance and cost of participatory surveillance, a nontraditional surveillance design, using the U.S. swine industry as an example. In this context, “participatory” meant that herd veterinarians and/or producers collected and submitted samples from the herd to accredited laboratories for testing. To create an infected population (Phase 1), we simulated the introduction and spread of an unspecified notifiable pathogen within the 48 contiguous U.S states (66,637 swine farms, within 8,080,470 km²) using the USDA Animal Disease Spread Model software (v3.5.10.0). In Phase 2, we calculated the probability of detecting ≥1 infected farm as a function of producer participation, farm-level sensitivity, farm-level prevalence, and sampling frequency. The participatory design was effective: ≥90% probability of detecting the notifiable pathogen at 0.05% farm prevalence (33 positive farms among 66,637 farms) when farm-level sensitivity was ≥20% and producer participation was ≥40%. Depending on the specimen collected, the shipment method, and the test selected, costs ranged from $0.03 to $0.07 USD (€0.02 to €0.06) per pig in inventory. Thus, a surveillance design based on collecting and testing specimens from a few targeted pigs on each of many farms would be both affordable and effective at a national level. Full article

This study evaluated the effects of including spray-dried porcine plasma (SDP) in nursery diets and enzymatically hydrolyzed plasma (EHP) in drinking water on piglet growth performance and post-weaning diarrhea (PWD). Four treatments were tested: CONTROL (soy protein concentrate, SPC), P1SDP (5% SDP in phase 1), P1 + P2SDP (5% SDP and 2% SDP in phases 1 and 2), and EHP (0.88% in water during phases 1 and 2). No significant differences among treatments were observed during phase 1. During phase 2 (14–28 days) pigs fed SDP or pigs provided EHP in water had higher average daily gain (ADG; p = 0.001) and feed conversion (GFR; p = 0.013) versus the other groups. Pigs fed SDP in the first two phases had an average d-42 body weight that was 1.54 kg heavier than controls. Post-weaning diarrhea was not observed at any time during the study. These results support the use of SDP and EHP as effective nutritional strategies to enhance the growth and resilience of pigs during the post-weaning period. Both ingredients contribute to sustainable pig production by improving efficiency and promoting circular economy practices through the valorization of animal by-products. Full article

African swine fever (ASF), caused by African swine fever virus (ASFV), is a highly destructive transboundary disease in domestic pigs. The circulating virus in this study belonged to ASFV genotype II, commonly associated with high virulence. In endemic regions such as Thailand, limited vaccine availability and shortages of naïve breeding stock necessitate reliance on early detection, surveillance, and the retention of convalescent sows, thereby raising concerns regarding viral persistence and reproductive performance. This study evaluated the long-term reproductive performance of convalescent sows compared with naïve cohorts under co-habitation conditions, while assessing the efficacy of passive surveillance and strict biosecurity in preventing viral transmission from both internal and external sources. Convalescent sows showed reproductive performance comparable to naïve cohorts across two parities. Long-term co-habitation with naïve sentinel pigs was not associated with detectable viral transmission, although low-level viral persistence or intermittent shedding cannot be excluded. From a disease control perspective, the transition from delayed detection to enhanced passive surveillance facilitated early clinical recognition and targeted removal (“tooth extraction”) of infected animals, effectively limiting intra-herd transmission without full depopulation. Importantly, irrespective of the uncertain carrier status, strict biosecurity and rapid response protocols appeared effective in mitigating both external introduction and within-farm transmission of ASFV. These findings suggest that, under appropriate management and biosecurity conditions, convalescent sows may be reintegrated into production systems with caution. Full article

Complex farming environments, breed variation, and the high cost of manual annotation remain major obstacles to robust pig detection, while cross-breed detection under few-shot conditions has been insufficiently explored in previous studies. To address this gap, we propose a few-shot pig detection framework that combines an improved YOLOv7 detector with CycleGAN-based pseudo-sample generation. The detector was enhanced through anchor optimization, Efficient Channel Attention (ECA), and Log-Sum-Exp (LSE) pooling to improve localization and feature discrimination in dense pigsty scenes. In addition, an optimized CycleGAN with perceptual loss was used to generate synthetic Duroc-like pig images to enrich the limited target-domain training set. The framework was evaluated using a two-dataset design: a White Pig Base Dataset was used to establish the source-domain detector and validate the architectural improvements, whereas a Duroc Pig Few-Shot Dataset was used to assess cross-breed adaptation under a 10-shot setting. The experimental results show that the proposed method achieved 98.16% mAP on the White pig dataset and 85.52% mAP on the Duroc Few-Shot Dataset. On the Duroc Few-Shot Dataset, the final framework outperformed Faster R-CNN, CenterNet, and YOLOv8, and also surpassed DCGAN- and SRGAN-based augmentation strategies. These results indicate that the proposed method provides an effective and practical solution for cross-breed few-shot pig detection, with potential value for intelligent livestock monitoring under annotation-limited conditions. Full article