Publications

Probability of PRRS virus detection in pooled processing fluid samples

Panorama da bovinocultura no Rio Grande do Sul

Background: The economy of Rio Grande do Sul (RS) relies majorly on agriculture; among the livestock production chains, cattle production has the largest economic, historical, and cultural importance in RS. The cattle industry is the main zootechnical activity in RS. Due to this, there is an actual need for updated characterization of the animal population, considering the population dynamics and the requirements imposed by the Official Veterinary Service (SVO) to meet certain characteristics. This would facilitate appropriate policies and measures to safeguard the health of the cattle in RS, as well as safeguard public health, and consequently avoid the economic impacts of possible health events. Materials, Methods & Results: Based on data from the livestock survey of 2013 from the Department of Animal Health (DDA), the Secretariat of Agriculture, Livestock, and Agribusiness of the State of Rio Grande do Sul (SEAPA-RS), descriptive and spatial analyses of the cattle population were performed using software R and ArcMap TM 10, respectively. It was observed that the state has more than 13 million cattle distributed over approximately 346,000 farms. The majority of the bovine population consists of females over the age of 36 months. The predominant function of these farms is a complete cycle (breeding to fattening). Beef production is the predominant activity, followed by a mix of beef production and dairy production, and then sole dairy production. These characteristics differ depending on the state’s region. Regarding the number of animals per property, 88% of properties are small having up to 50 cattle, and about 1% of properties have more than 500 animals. The general average in the state for the proportion of T:V (calf: cow) is 57 calves per 100 cows, and this is close to the national average. Discussion: About 60.62% of cattle herds consisted of animals aged over 24 months, and most of this group were females over 36 months of age (38.95%). About 50% of properties have up to 10 animals, demonstrating a large proportion of small farms. RS has an imbalance in its production system, with a large number of breeding females for the activity of beef production. The cattle in RS are mostly bred for the production of beef in a full cycle system (with all stages of production on the property), and only 10% of cattle raised in RS are bred solely for milk production. With regard to the proportion of T:V, we concluded that the state’s beef production shows modest productivity and needs to improve production rates to increase financial returns for producers and enable competitiveness in the domestic and international markets. Furthermore, this information correlates with previous studies that have reported that farms in the business of beef production use low technology and low performance animals. Dairy farming, in contrast with beef farming, has been modernizing and developing in recent years by increasing co-operatives and agribusinesses, which has led to greater knowledge through technical assistance to the farms. Extensive farming is dependent on field areas and is historically associated with the natural fields in the campaign region, since dairy farming is dependent on areas where there is a supply of specialized food. Thus, despite the state having a greater concentration of animals in the south-southwest, production indices are similar to other regions, and the type of farming undertaken exerts a great influence on the regional animal population structure.

215 Assessment of the relationship between lactation feeding patterns, litter performance measures, and sow characteristics on sow farrowing efficiency metrics

Abstract Introduction: The number of piglets weaned per sow per year (PSY) is commonly used as a benchmark to track changes in herd performance or to compare performance between herds [1,2,3]. While PSY is a valuable measure of herd productivity, it does not adequately reflect sow longevity or future reproductive performance [1]. Sow productivity can be influenced by various individual predictors [3,4]. Our objective was to assess individual sow predictors associated with sow efficiency defined as wean-to-estrus interval (days), sows bred within 7 days post-weaning (%), and subsequent farrowing. Methods: Experimental data was sourced from six lactation trials conducted between 2021 and 2022 on a commercial sow farm. The animals were housed in the same lactation rooms, had same genetics, and had no PRRSV or PEDV health challenges. The final dataset had 4,300 observations, including productivity performance, daily lactation feed intake, sow and litter weights, caliper measurements, and subsequent performance. Generalized linear regression models were built using R® program to identify factors with each outcome. A total of 23 predictors were evaluated in each model and trial-id was included as a random effect. Each model was built using a manual stepwise forward selection, interactions and confounders were tested based on biological relevance, and predictors with p-values <0.05 retained in the final model. Pairwise comparison was tested using t-tests with Tukey-Kramer adjustment, considering p-values <0.05 as statistically significant. Results: Predictors associated with WEI included parity (P <0.001), piglets after cross-fostering (P =0.01), ADFI in the first three days of lactation (P = 0.01) and lactation ADFI (P = 0.019) with farrowing season as confounder (P = 0.03). Sows with more than 16 piglets after cross-fostering had 1.3-day increase in WEI (P = 0.01). Lactation ADFI <10lbs in the first 3 days was associated with 1-day increase in WEI (P <0.001). Predictors associated with sows bred within seven days post-weaning included parity (P = 0.05) and interaction with litter birth weight (P = 0.05). For subsequent farrowing, predictors included litter size (P = 0.02), stillborn rate (P = 0.01), ADFI during the first week of lactation (P = 0.01) and body weight change (P = 0.01). Sows with at least one stillborn showed a 7% decrease in subsequent farrowing (P = 0.01), while those with more than 16 piglets after cross-fostering had 12% decrease in farrowing rate (P = 0.02). Conclusion: This study identified relevant predictors of sow efficiency and performance. These findings present opportunities to develop targeted management, nutritional, and reproductive strategies to enhance sow longevity and productivity. References: 1 Koketsu Y, Tani S, Iida R. Factors for improving reproductive performance of sows and herd productivity in commercial breeding herds. Porcine Health Manag. 2017; 3:1. 2. Hyunwoong, Jo., Han-Min, Kim., Beob, Gyun, Kim. (2023). The effect of feed intake during lactation on the wean-to-estrus interval of sows. doi: 10.5187/ait.2021.10.1.45. 3. Koketsu, Y., & Iida, R. (2020). Farm data analysis for lifetime performance components of sows and their predictors in breeding herds. Porcine Health Management, 6, 1-12. 4. Estrada, J., Johnson, D.C., Kyle, K.L., Perez, J., Parr, E., Welch, M.W., Neill, C., Peterson, B.A. and Boler, D.D., 2024. Characterizing sow feed intake during lactation to explain litter and subsequent farrowing performance. Journal of Animal Science, 102, p. skae093.

Nextclade data set for the ORF5-based lineage classification of PRRSV-1

ABSTRACT A Nextclade data set for PRRSV-1 ORF5 based on a global nomenclature for standardized lineage classification was developed. This tool enables rapid sequence analysis, visualization, and comparison with reference strains and vaccines. By providing accessibility, it facilitates broader adoption of PRRSV-1 classification frameworks for research and surveillance.

Next Generation of Voluntary PRRS Virus Regional Control Programs

Porcine reproductive and respiratory syndrome virus (PRRSV) became pandemic in the 1980's and today remains one of the most significant pathogens of the global swine industry. At the herd level, control of PRRSV is complicated by its extreme genetic diversity and its ability to persist in pigs, despite an active immune response. Ultimately, PRRSV control or elimination requires the coordination and active cooperation of producers and veterinarians at the regional level. Early voluntary PRRSV regional control programs focused on routine diagnostic testing and voluntary data-sharing regarding the PRRSV status of participants' herds, but no pre-defined action plans or decision trees were developed to secure project successes (or recover from failures). Given that control of PRRSV is paramount to producer profitability, we propose a coordinated approach for detecting, controlling, and ultimately eliminating wild-type PRRSV from herds participating in regional projects. Fundamental to project success is real-time, multi-platform communication of all data, information, and events that concern the regional project and project participants. New to this approach is the concept of agreed-upon action plans to be implemented by project participants in response to specific events or situations. The simultaneous and coordinated implementation of these strategies allows for early detection of wild-type PRRSV virus introductions and rapid intervention based on agreed-upon response plans. An example is given of a project in progress in the Midwest USA.

Updates for PRRS outbreak management and applied research

Machine Learning Prediction and Experimental Validation of Antigenic Drift in H3 Influenza A Viruses in Swine

Influenza A viruses (IAV) in swine constitute a major economic burden to an important global agricultural sector, impact food security, and are a public health threat. Despite significant improvement in surveillance for IAV in swine over the past 10 years, sequence data have not been integrated into a systematic vaccine strain selection process for predicting antigenic phenotype and identifying determinants of antigenic drift.

Swine influenza A virus surveillance patterns within the United States, 2023

Tropaeolum majus L. extract rich in anthocyanins reduces oxidative damage in psoriasis-like wounds in mice accelerating healing

PRRSV-2 variant classification: a dynamic nomenclature for enhanced monitoring and surveillance

ABSTRACT Existing genetic classification systems for porcine reproductive and respiratory syndrome virus type 2 (PRRSV-2), such as restriction fragment length polymorphisms and sub-lineages, are unreliable indicators of close genetic relatedness or lack sufficient resolution for epidemiological monitoring routinely conducted by veterinarians. Here, we outline a fine-scale classification system for PRRSV-2 genetic variants in the United States. Based on >25,000 U.S. open reading frame 5 (ORF5) sequences, sub-lineages were divided into genetic variants using a clustering algorithm. Through classifying new sequences every 3 months and systematically identifying new variants across 8 years, we demonstrated that prospective implementation of the variant classification system produced robust, reproducible results across time and can dynamically accommodate new genetic diversity arising from virus evolution. From 2015 to 2023, 118 variants were identified, with ~48 active variants per year, of which 26 were common (detected >50 times). Mean within-variant genetic distance was 2.4% (max: 4.8%). The mean distance to the closest related variant was 4.9%. A routinely updated webtool ( https://stemma.shinyapps.io/PRRSLoom-variants/ ) was developed and is publicly available for end users to assign newly generated sequences to a variant ID. This classification system relies on U.S. sequences from 2015 onward; further efforts are required to extend this system to older or international sequences. Finally, we demonstrate how variant classification can better discriminate between previous and new strains on a farm, determine possible sources of new introductions into a farm/system, and track emerging variants regionally. Adoption of this classification system will enhance PRRSV-2 epidemiological monitoring, research, and communication, and improve industry responses to emerging genetic variants. IMPORTANCE The development and implementation of a fine-scale classification system for PRRSV-2 genetic variants represent a significant advancement for monitoring PRRSV-2 occurrence in the swine industry. Based on systematically applied criteria for variant identification using national-scale sequence data, this system addresses the shortcomings of existing classification methods by offering higher resolution and adaptability to capture emerging variants. This system provides a stable and reproducible method for classifying PRRSV-2 variants, facilitated by a freely available and regularly updated webtool for use by veterinarians and diagnostic labs. Although currently based on U.S. PRRSV-2 ORF5 sequences, this system can be expanded to include sequences from other countries, paving the way for a standardized global classification system. By enabling accurate and improved discrimination of PRRSV-2 genetic variants, this classification system significantly enhances the ability to monitor, research, and respond to PRRSV-2 outbreaks, ultimately supporting better management and control strategies in the swine industry.

328 Assessment of chronically ill or injured feedlot cattle populations at eight United States and nine Canadian feedlots and implications for cattle welfare

Abstract Cattle health management in the feedlot sector continues to evolve and improve cattle welfare by promoting an understanding of illness and injury to minimize their effects. Many papers have focused on treating individual ill or injured cattle in cattle feeding facilities. However, care, treatment, and outcomes for chronically ill or injured cattle (“chronic cattle”) have received little attention in the literature. The purpose of this descriptive study was to present demographics, diagnoses, and outcomes of chronic cattle in 17 feedlots in the U.S. and Canada and discuss potential risk factors and welfare implications. Individual chronic cattle information and treatment records from 17 feedlots (U.S.: 8; Canada: 9) over 7 yr (2014 – 2020) were retrospectively retrieved from a large feedlot consulting and data-management service (Feedlot Health Management Services, a division of TELUS Agriculture Solutions Inc.). Chronic cattle were defined as those that had spent time in a feedlot’s designated chronic pen. Descriptive results are presented as means ± standard deviations. U.S. feedlots averaged 31,280 ± 23,231 cattle/yr, had an average chronicity rate of 2.24 ± 2.42 % (median 1.53%), and contributed 52,709 chronic cattle to the dataset. Canadian feedlots averaged 21,913 ± 10,497 cattle/yr, had an average chronicity rate of 1.41 ± 0.82% (median 1.10%), and contributed 15,940 chronic cattle to the dataset. U.S. chronic cattle were sourced across all seasons primarily from auctions and ranches, had an average arrival weight of 163 ± 59 kg, and an average days on feed (DOF) of 362 ± 172 d. Canadian chronic cattle were primarily auction-sourced in the fall, weighed 272 ± 95 kg at arrival, and averaged 246 ± 141 DOF. Chronic cattle were treated at least once for respiratory issues (U.S.: 46.3%; Canada: 26.9%), bullers (U.S.: 34.5%; Canada: 5.8%), musculoskeletal issues (U.S.: 2.2%; Canada: 1.4%), lameness (U.S.: 1.7%; Canada: 28.1%), metabolic issues (U.S.: 0.5%; Canada: 4.5%), or other diagnoses (U.S.: 2.2%; Canada: 6.3%), with many cattle treated for multiple issues (U.S.: 12.5%; Canada: 27.0%). Outcomes for chronic cattle were shipped (U.S.: 73.1%; Canada: 52.5%), railed (U.S.: 8.1%; Canada: 20.7%), euthanized (U.S.: 0.7%; Canada: 9.9%) died unassisted (U.S.: 18.1%; Canada: 16.2%), or transferred (U.S.: < 0.1%; Canada: 0.8%). These results are some of the first to describe chronic cattle populations and present interesting trends for further analysis. For example, differences in buller and lameness rates could reflect actual differences but may be artifices of the dataset and feedlot definitions. Additionally, high marketed cattle rates (shipped and railed) indicate positive outcomes for most cattle, but variable mortality rates merit further scrutiny.

Integrating Disease Diagnostic Data with Pre-Weaning Performance and Post-Weaning Productivity to Identify Risk Factors Associated with Pig Mortality in a U.S. Swine Production System

Aggregated diagnostic data collected over time from a swine production system is an important data source to investigate swine productivity and health, especially when combined with other records concerning the pre-weaning and post-weaning phases of production. The combination of multiple data streams collected over the lifetime of the pigs is the essence of the whole-herd epidemiological analysis investigation. This approach is even more important when investigating risk factors associated with swine wean-to-finish (W2F) mortality because of its multicausal and dynamic nature of mortality. The objective of this study was to use a retrospective dataset ("master table") containing information on 1,742 groups of pigs marketed over time to identify the major risk factors associated with W2F mortality. The master table was built by combining historical breed-to-market performance and health data with disease diagnostic records (Dx Codes) from marketed groups of growing pigs ("closeouts"). After building the master table, univariate analyses were conducted to screen for risk factors to be included in the initial multivariable model. After a stepwise backward model selection approach, 5 variables and 2 interactions remained in the final model. Notably, the only disease diagnosis variable significantly associated with W2F mortality was porcine reproductive and respiratory syndrome virus (PRRSV). Closeouts with reported clinical signs suggestive of Salmonella spp. and/or Escherichia coli infection were also significantly associated with higher W2F mortality. Source sow farm factors that remained significantly associated with W2F mortality were the sow farm PRRS status at the time of weaning, average weaning age, and the average pre-weaning mortality. Two interactions were significantly associated with wean-to-finish pig mortality: 1) sow farm PRRS status and a laboratory diagnosis of PRRSV and 2) average weaning age and a laboratory diagnosis of PRRS. Closeouts originating from PRRS epidemic or PRRS negative sow farms, when diagnosed with PRRS in the growing phase, had the highest W2F mortality rates. Likewise, PRRS diagnosis in the growing phase was an important factor in mortality, regardless of the average weaning age of the closeouts. Overall, this study demonstrated the utility of a whole-herd approach when analyzing diagnostic information along with other breeding-to-market productivity and health factors, to measure the major risk factors associated with W2F mortality in specified time frames and pig populations.

Additional file 6: of ISU FLUture: a veterinary diagnostic laboratory web-based platform to monitor the temporal genetic patterns of Influenza A virus in swine

Nexus format phylogenetic tree. Hemagglutinin H1 Subtype Reference Sequences. Reference sequences used for determining the clade an H1 subtype hemagglutinin sequence falls into. (TXT 21 kb)

Active Participatory Regional Surveillance for Notifiable Swine Pathogens

We evaluated an active participatory design for the regional surveillance of notifiable swine pathogens based on testing 10 samples collected by farm personnel in each participating farm. To evaluate the performance of the design, public domain software was used to simulate the introduction and spread of a pathogen among 17,521 farms in a geographic region of 1,615,246 km

The genomic landscape of swine influenza A viruses in Southeast Asia

Abstract Swine are a primary source for the emergence of pandemic influenza A viruses. The intensification of swine production, along with global trade, has amplified the transmission and zoonotic risk of swine influenza virus (swIAV). Effective surveillance is essential to uncover emerging virus strains, however gaps remain in our understanding of the swIAV genomic landscape in Southeast Asia. By collecting more than 4,000 nasal swabs and 4,000 sera from pigs in Cambodia, we unmasked the co-circulation of multiple lineages of genetically diverse swIAV of pandemic concern. Genomic analyses revealed a novel European avian-like H1N2 swine reassortant variant with North American triple reassortant internal genes, that emerged approximately seven years before its first detection in pigs in 2021. Using phylogeographic reconstruction, we identified south central China as the dominant source of swine viruses disseminated to other regions in China and Southeast Asia. We also identified nine distinct swIAV lineages in Cambodia, which diverged from their closest ancestors between two to 15 years ago, indicating significant undetected diversity in the region, including reverse zoonoses of human H1N1/2009 pandemic and H3N2 viruses. A similar period of cryptic circulation of swIAVs occurred in the decades before the H1N1/2009 pandemic. The hidden diversity of swIAV observed here further emphasizes the complex underlying evolutionary processes present in this region, reinforcing the importance of genomic surveillance at the human-swine interface for early warning of disease emergence to avoid future pandemics.

Development of Benchmark Curves to Early Detect Health-Related Productivity Deviations Using Production Indicators in Swine Nursery and Finishing Lots.

The nursery and finishing phases are critical for profitability and sustainability in swine production, but effective methods for early disease detection in these stages remain underdeveloped. This study used routinely collected production indicators from nursery (42-56 days postfarrowing) and finishing lots (115-120 days postfarrowing) to create production benchmark curves for anomaly detection. These curves were developed for farms without diagnosed health challenges and compared to those with diagnosed health issues, based on tissue submissions and diagnostic codes (Dx codes). Statistical methods such as resampling techniques, Bayesian statistics, and standard deviation (SD) thresholds were employed to build the benchmark curves. The main objective was to test and compare the benchmarks using detection, early detection rate (EDR), time-to-detect (TTD), and false positive rate (FPR). Results showed that bootstrapping (BOOT), jackknife (JK), and Markov chain Monte Carlo (MCMC) methods provided the highest EDRs, although they were prone to false positives. For nursery lots, it was observed that using cumulative average with one SD for feed disappearance (EDR 49.2% and FPR 9.8%) and estimated weight (EDR 47.2% and FPR 8.8%) showed the best balance between EDR and FPR, and using MCMC for mortality showed the best balance between EDR and FPR (EDR 38.8% and FPR 13.8%). For finishing lots, using cumulative average with one SD showed a more balanced performance with FPR below 14.0% and EDR of 21.3% for feed disappearance, 67.4% weight, and 59.7% for mortality. These findings demonstrate the potential of using production indicators for early health challenge detection in swine operations.

Doenças de bovinos em Mato Grosso diagnosticadas no Laboratório de Patologia Veterinária da UFMT (2005-2014)

RESUMO: Foi realizado um levantamento nos arquivos do Laboratório de Patologia Veterinária (LPV) da Universidade Federal de Mato Grosso (UFMT) das doenças de bovinos registradas entre os anos 2005 a 2014. Foram revisados 1124 casos. Destes, 27,6% foram amostras obtidas de necropsias realizadas por técnicos do LPV-UFMT e 72,3% foram amostras encaminhadas ao LPV-UFMT por veterinários de campo. Em 49,38% dos casos (555/1124) o diagnóstico da doença foi feito através da análise morfológica de lesões e/ou através de exames complementares. Raiva foi a principal causa de morte de bovinos neste estudo (7,82%). As doenças inflamatórias e parasitárias foram as mais prevalentes sendo diagnosticadas em 27,49% dos casos, seguida das doenças tóxicas e toxiinfecções com 9,78%. As demais categorias foram distribuídas em ordem decrescente em: neoplasmas e lesões tumoriformes (4%), doenças degenerativas (3,02%), distúrbios causados por agentes físicos (2,84%), distúrbios metabólicos e nutricionais (1,42%) e outras categorias (0,71%).

Can hierarchical modeling improve our understanding of bovine abortion due to Neospora caninum infection?

The purpose of this study was to use mixed logistic regression to evaluate the association between Neospora caninum serostatus and abortion, accounting for the effects of farms. The main objective was to explore the interpretation of this model and discuss the contribution of this analysis to our understanding of the disease's epidemiology. A mixed-effects logistic model using farms as a random effect and the serostatus for N. caninum, age of cattle and farm location as fixed effects was performed. The data from 1256 cows over 15 months of age from 60 farms were used, and the abortion information was obtained from farm records. A significant association between N. caninum serostatus and abortion was found (p<0.0001). Seropositive cows had 6.63 times greater odds of having a history of previous abortion (95% CI: 4.35-13.37). There was remarkable variability across farms in the probability of a cow having a history of an abortion. Including the effects of the farms in the regression, it was possible to estimate an intraclass correlation coefficient (ICC) of 16%. That means that 16% of the variation in abortion occurrence that was not explained by the fixed effects was due to farms. In practical terms, this variation means that while there are farms with several seropositive cows and no/few abortion cases, the opposite is also true, with a high number of abortions in farms with low/medium seroprevalence. The remaining variability (84%) was not explained by the effect of N. caninum, age, region, and the effect of farms, i.e., it is due to unknown factors that are causing abortions. The results of this study reinforce the importance of N. caninum as a cause of abortions and demonstrate the significant heterogeneity in the probability of a cow having a history of an abortion that cannot be explained completely by N. caninum serostatus, age or location. Including the hierarchical structure of the population along with correct interpretation of the model estimates helps us understand the effect of the farms, i.e., the probability of a cow abortion conditional to the farms, which also contributes to our understanding of the epidemiology of abortions caused by neosporosis. The use of hierarchical models and reporting the ICC should be encouraged.

Additional file 4: of ISU FLUture: a veterinary diagnostic laboratory web-based platform to monitor the temporal genetic patterns of Influenza A virus in swine

Nexus format phylogenetic tree. Hemagglutinin H3 Subtype Reference Sequences. Reference sequences used for determining the clade an H3 subtype hemagglutinin sequence falls into. (TXT 29 kb)