Detection and disease diagnosis trends (2017-2022) for Streptococcus suis, Glaesserella parasuis, Mycoplasma hyorhinis, Actinobacillus suis and Mycoplasma hyosynoviae at Iowa State University Veterinary Diagnostic Laboratory.

BACKGROUND: Accurate measurement of disease associated with endemic bacterial agents in pig populations is challenging due to their commensal ecology, the lack of disease-specific antemortem diagnostic tests, and the polymicrobial nature of swine diagnostic cases. The main objective of this retrospective study was to estimate temporal patterns of agent detection and disease diagnosis for five endemic bacteria that can cause systemic disease in porcine tissue specimens submitted to the Iowa State University Veterinary Diagnostic Laboratory (ISU VDL) from 2017 to 2022. The study also explored the diagnostic value of specific tissue specimens for disease diagnosis, estimated the frequency of polymicrobial diagnosis, and evaluated the association between phase of pig production and disease diagnosis. RESULTS: S. suis and G. parasuis bronchopneumonia increased on average 6 and 4.3%, while S. suis endocarditis increased by 23% per year, respectively. M. hyorhinis and A. suis associated serositis increased yearly by 4.2 and 12.8%, respectively. A significant upward trend in M. hyorhinis arthritis cases was also observed. In contrast, M. hyosynoviae arthritis cases decreased by 33% average/year. Investigation into the diagnostic value of tissues showed that lungs were the most frequently submitted sample, However, the use of lung for systemic disease diagnosis requires caution due to the commensal nature of these agents in the respiratory system, compared to systemic sites that diagnosticians typically target. This study also explored associations between phase of production and specific diseases caused by each agent, showcasing the role of S. suis arthritis in suckling pigs, meningitis in early nursery and endocarditis in growing pigs, and the role of G. parasuis, A. suis, M. hyorhinis and M. hyosynoviae disease mainly in post-weaning phases. Finally, this study highlighted the high frequency of co-detection and -disease diagnosis with other infectious etiologies, such as PRRSV and IAV, demonstrating that to minimize the health impact of these endemic bacterial agents it is imperative to establish effective viral control programs. CONCLUSIONS: Results from this retrospective study demonstrated significant increases in disease diagnosis for S. suis, G. parasuis, M. hyorhinis, and A. suis, and a significant decrease in detection and disease diagnosis of M. hyosynoviae. High frequencies of interactions between these endemic agents and with viral pathogens was also demonstrated. Consequently, improved control programs are needed to mitigate the adverse effect of these endemic bacterial agents on swine health and wellbeing. This includes improving diagnostic procedures, developing more effective vaccine products, fine-tuning antimicrobial approaches, and managing viral co-infections.

Further host-genomic characterization of total antibody response to PRRSV vaccination and its relationship with reproductive performance in commercial sows: genome-wide haplotype and zygosity analyses.

BACKGROUND: The possibility of using antibody response (S/P ratio) to PRRSV vaccination measured in crossbred commercial gilts as a genetic indicator for reproductive performance in vaccinated crossbred sows has motivated further studies of the genomic basis of this trait. In this study, we investigated the association of haplotypes and runs of homozygosity (ROH) and heterozygosity (ROHet) with S/P ratio and their impact on reproductive performance. RESULTS: There was no association (P-value ≥ 0.18) of S/P ratio with the percentage of ROH or ROHet, or with the percentage of heterozygosity across the whole genome or in the major histocompatibility complex (MHC) region. However, specific ROH and ROHet regions were significantly associated (P-value ≤ 0.01) with S/P ratio on chromosomes 1, 4, 5, 7, 10, 11, 13, and 17 but not (P-value ≥ 0.10) with reproductive performance. With the haplotype-based genome-wide association study (GWAS), additional genomic regions associated with S/P ratio were identified on chromosomes 4, 7, and 9. These regions harbor immune-related genes, such as SLA-DOB, TAP2, TAPBP, TMIGD3, and ADORA. Four haplotypes at the identified region on chromosome 7 were also associated with multiple reproductive traits. A haplotype significantly associated with S/P ratio that is located in the MHC region may be in stronger linkage disequilibrium (LD) with the quantitative trait loci (QTL) than the previously identified single nucleotide polymorphism (SNP) (H3GA0020505) given the larger estimate of genetic variance explained by the haplotype than by the SNP. CONCLUSIONS: Specific ROH and ROHet regions were significantly associated with S/P ratio. The haplotype-based GWAS identified novel QTL for S/P ratio on chromosomes 4, 7, and 9 and confirmed the presence of at least one QTL in the MHC region. The chromosome 7 region was also associated with reproductive performance. These results narrow the search for causal genes in this region and suggest SLA-DOB and TAP2 as potential candidate genes associated with S/P ratio on chromosome 7. These results provide additional opportunities for marker-assisted selection and genomic selection for S/P ratio as genetic indicator for litter size in commercial pig populations.

Investigating the relationship between vaginal microbiota and host genetics and their impact on immune response and farrowing traits in commercial gilts.

Our objectives were to evaluate the interaction between host genetics and vaginal microbiota and their relationships with antibody (Ab) response to porcine reproductive and respiratory syndrome virus (PRRSV) vaccination and farrowing performance in commercial gilts. The farrowing performance traits were number born alive, number weaning (NW), total number born, number born dead, stillborn, mummies and preweaning mortality (PWM). The vaginal microbiota was collected on days 4 (D4) and 52 (D52) after vaccination for PRRSV. Blood samples were collected on D52 for Ab measurement. Actinobacteria, Bacterioidetes, Firmicutes, Proteobacteria and Tenericutes were the most abundant Phyla identified in the vaginal microbiota. Heritability ranged from ~0 to 0.60 (Fusobacterium) on D4 and from ~0 to 0.63 (Terrisporobacter) on D52, with 43 operational taxonomic units (OTUs) presenting moderate to high heritability. One major QTL on chromosome 12 was identified for 5 OTUs (Clostridiales, Acinetobacter, Ruminococcaceae, Campylobacter and Anaerococcus), among other 19 QTL. The microbiability for Ab response to PRRSV vaccination was low for both days (<0.07). For farrowing performance, microbiability varied from <0.001 to 0.15 (NW on D4). For NW and PWM, the microbiability was greater than the heritability estimates. Actinobacillus, Streptococcus, Campylobacter, Anaerococcus, Mollicutes, Peptostreptococcus, Treponema and Fusobacterium showed different abundance between low and high Ab responders. Finally, canonical discriminant analyses revealed that vaginal microbiota was able to classify gilts in high and low Ab responders to PRRSV vaccination with a misclassification rate of <0.02. Although the microbiota explained limited variation in Ab response and farrowing performance traits, there is still potential to explore the use of vaginal microbiota to explain variation in traits such as NW and PWM. In addition, these results revealed that there is a partial control of host genetic over vaginal microbiota, suggesting a possibility for genetic selection on the vaginal microbiota.

Assessment of changes in antibiotic use in grow-finish pigs after the introduction of PRRSV in a naïve farrow-to-finish system.

Responsible antibiotic usage (ABU) is crucial for both animal and human health and requires constant improvement of antimicrobial stewardship (AMS). The presence of porcine reproductive and respiratory syndrome virus (PRRSV), a viral pathogen with immunosuppressive effects on swine, can intensify bacterial co-infections, alter antibiotic pharmacokinetics, and potentially lead to increased ABU. This study aimed to measure ABU changes in the grow-finish population associated with PRRSV infection and describe the antibiotic classes employed to manage clinical signs from a farrow-to-finish genetic multiplier system. Three PRRSV statuses (naïve, positive epidemic, and positive endemic) were established to classify the lots based on PRRSV circulation, with a total of 135,063 animals evaluated. The number of pig treatments per animal days at risk (PTDR) was calculated by administration route to quantify ABU across PRRSV status using negative binomial regression and non-parametric tests (P-value < 0.05). Moreover, to improve ABU comparability in the international scenario, the milligrams per population correction unit (mg/PCU) was calculated according to the European Medicines Agency guidelines. In the nursery phase, there was a statistically significant difference between PRRSV statuses for the overall PTDR (injectable and water routes of administration), with an ABU increase of 3.79 and 2.51 times the naïve PTDR for positive epidemic and endemic status, respectively. For the finishing phase, there was a statistically significant difference between PRRSV statuses in the injectable PTDR, with an ABU increase of 2.74 and 2.28 times the naïve PTDR level for positive epidemic and endemic statuses, respectively. In the nursery phase, the mean mg/PCU was 22.27 mg/PCU for naïve, 86.71 for positive epidemic, and 33.37 for positive endemic statuses; in the finishing phase, 81.31, 76.55, and 67.09 mg/PCU, respectively. The most frequently injected antibiotic in the nursery phase was ampicillin, with 49 % of total injections, followed by lincomycin (31 %) and enrofloxacin (20 %), and in the finishing phase, 72 % of injections were lincomycin, followed by enrofloxacin (28 %). The results highlight that the PRRSV outbreak in the source was associated with a grow-finish ABU increase, revealing the importance of preventing PRRSV infection to potentially decrease ABU and improve AMS within swine production systems.

Risk factors associated with piglet pre-weaning mortality in a Midwestern U.S. swine production system from 2020 to 2022.

Piglet pre-weaning mortality (PWM) is a significant issue in the U.S. swine industry, causing economic losses and raising sustainability and animal welfare concerns. This study conducted a multivariable analysis to identify factors associated with PWM in a Midwestern U.S. swine production system. Weekly data from 47 sow farms (7207 weaning weeks) were captured from January 2020 to December 2022. Initially, 29 variables regarding farm infrastructure, productivity parameters, health status, and interventions were selected for univariate analysis to assess their association with PWM. The initial multivariable analysis included the variables with P < 0.20 in the univariate analyses. A backward stepwise model selection was conducted by excluding variables with P > 0.05, and the final multivariable model consisted of 19 significant risk factors and 6 interaction terms. The overall average PWM for the study population was 14.02 %. Yearly variations in PWM were observed, with the highest recorded in 2020 (16.61 %) and the lowest in 2021 (15.78 %). Cohorts with a pond water source, lower farrowing rate (71.9 %), higher farrowing parity (5.1), shorter gestation length (116.2 days), and using oxytocin during farrowing had increased PWM. The higher productivity parameters such as mummies rate, stillborn rate, and average total born, the higher the PWM was. Additionally, health status and intervention-related factors were associated with PWM, where higher PWM rates were observed in herds facing porcine reproductive and respiratory syndrome virus (PRRSV) outbreaks, porcine epidemic diarrhea virus (PEDV) positive, the weeks before and during feed medication, and weeks without using Rotavirus vaccine or Rotavirus feedback. Altogether, these results corroborate that PWM is a multifactorial problem, and a better understanding of the risk factors is essential in developing strategies to improve survival rates. Therefore, this study identified the major risk factors associated with PWM for groups of pigs raised under field conditions, and the results underscore the significance of data analysis in comprehending the unique challenges and opportunities inherent to each system.

Development of a pig wean-quality score using machine-learning algorithms to characterize and classify groups with high mortality risk under field conditions.

Mortality during the post-weaning phase is a critical indicator of swine production system performance, influenced by a complex interaction of multiple factors of the epidemiological triad. This study leveraged retrospective data from 1723 groups of pigs marketed within a US swine production system to develop a Wean-Quality Score (WQS) using machine learning techniques. The study evaluated three machine learning models, Random Forest, Support Vector Machine, and Gradient Boosting Machine, to classify groups having high or low 60-day mortality, where high mortality groups represented 25 % of the groups among the study population with the highest mortality values (n=431; 60-day mortality=9.98 %), and the remaining 75 % of the groups were of low mortality (n=1292; 60-day mortality=2.75 %). The best-performing model, Random Forest (RF), outperformed the other ML models in terms of accuracy (0.90), sensitivity (0.84), and specificity (0.92) metrics, and was then selected for further analysis, which consisted of creating the WQS and ranking the most important factors for classifying groups as high or low mortality. The most important factors ranked through the RF model to classify groups with high mortality were pre-weaning mortality, weaning age, average parity of litters in sow farms, and PRRS status. Additionally, stocking conditions such as stocking density and time to fill the barn were important predictors of high mortality. The WQS was developed and correlated (r = 0.74) with the actual 60-day mortality of the groups, offering a valuable tool for assessing post-weaning survivability in swine production systems before weaning. This study highlights the potential of machine learning and comprehensive data utilization to improve the assessment and management of weaned pig quality in commercial swine production, which producers can utilize to identify and intervene in groups, according to the WQS.

Characterizing best practices for tonsil-oral-scrubbing (TOSc) collection for PRRSV RNA detection in sows.

BACKGROUND: A Tonsil-Oral-Scrubbing (TOSc) method was developed to sample the sow's oropharyngeal and tonsillar area without snaring and has shown comparable porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection rates with tonsil scraping in infected sows. This study investigated the effect of specific TOSc collection factors on the PRRSV RT-rtPCR results (detection rates and Ct values). Those factors include whether the sow was snared or not snared at TOSc collection ("snared" vs. "not snared"); whether the sow was laying down or standing at collection ("laying down" vs. "standing"); and type of collectors used for TOSc collection ("TOSc prototype" vs. "Spiral-headed AI catheter (SHAC)"). Volume of fluid was compared between "snared" and "not snared" groups, and collection time was compared between "laying down" and "standing" groups as well. RESULTS: The effect for each factor was assessed in three independent studies following the same design: TOSc was collected twice from each studied sow, once with the baseline level for a factor ("not snared", or "standing", or "TOSc prototype"), and another time followed by the other level of the paired factor ("snared", "laying down", or "SHAC", correspondingly). Results showed that "not snared" TOSc had numerically higher PRRSV RNA detection rate (60.7% vs. 52.5%, p = 0.11), significantly lower median Ct values (31.9 vs. 32.3, p < 0.01), and significantly higher volume of fluid than "snared" samples (1.8 mL vs. 1.2 mL, p < 0.01); "laying down" TOSc samples did not differ statistically (60.7% vs. 60.7%) in the PRRSV RNA detection rate, obtained numerically lower median Ct values (30.9 vs. 31.3, p = 0.19), but took 40% less collection time compared to "standing" TOSc samples; samples collected using the "TOSc prototype" had numerically higher PRRSV RNA detection rate (91.7% vs. 88.3%, p = 0.27) and significantly lower median Ct values (32.8 vs. 34.5, p < 0.01) than that from "SHAC". CONCLUSIONS: Under the conditions of this study best practices for TOSc collection aiming higher detection rate of PRRSV RNA while minimizing time for collection were suggested to be sampling TOSc without snaring, when sows are laying down, and using a prototype TOSc collector.

Evaluating oral swab samples for PRRSV surveillance in weaning-age pigs under field conditions.

Introduction: The use of serum and family oral fluids for porcine reproductive and respiratory syndrome virus (PRRSV) surveillance in weaning-age pigs has been previously characterized. Characterizing more sample types similarly offers veterinarians and producers additional validated sample options for PRRSV surveillance in this subpopulation of pigs. Oral swab sampling is relatively easy and convenient; however, there is sparse information on how it compares to the reference sample type for PRRSV surveillance under field conditions. Therefore, this study's objective was to compare the PRRSV reverse-transcription real-time polymerase chain reaction (RT-rtPCR) test outcomes of oral swabs (OS) and sera samples obtained from weaning-age pig litters. Method: At an eligible breeding herd, six hundred twenty-three weaning-age piglets from 51 litters were each sampled for serum and OS and tested for PRRSV RNA by RT-rtPCR. Results and Discussion: PRRSV RT-rtPCR positivity rate was higher in serum samples (24 of 51 litters, 83 of 623 pigs, with a mean cycle threshold (Ct) value of RT-rtPCR-positive samples per litter ranging from 18.9 to 32.0) compared to OS samples (15 of 51 litters, 33 of 623 pigs, with a mean Ct of RT-rtPCR positive samples per litter ranging from 28.2 to 36.9); this highlights the importance of interpreting negative RT-rtPCR results from OS samples with caution. Every litter with a positive PRRSV RT-rtPCR OS had at least one viremic piglet, highlighting the authenticity of positive PRRSV RT-rtPCR tests using OS; in other words, there was no evidence of environmental PRRSV RNA being detected in OS. Cohen's kappa analysis (Ck = 0.638) indicated a substantial agreement between both sample types for identifying the true PRRSV status of weaning-age pigs.

In-silico characterization of the relationship between the Porcine reproductive and respiratory syndrome virus prevalence at the piglet and litter levels in a farrowing room.

BACKGROUND: Family oral fluids (FOF) sampling has been described as a sampling technique where a rope is exposed to sows and respective suckling litters and thereafter wrung to obtain fluids. PCR-based testing of FOF reveals presence of PRRS virus RNA only at the litter level, as opposed to conventional individual-animal-based sampling methods that demonstrate PRRSV RNA at the piglet level. The relationship between the PRRSV prevalence at the individual piglet level and at the litter level in a farrowing room has not been previously characterized. Using Monte Carlo simulations and data from a previous study, the relationship between the proportion of PRRSV-positive (viremic) pigs in the farrowing room, the proportion of litters in the farrowing room with at least one viremic pig, and the likely proportion of litters to be positive by a FOF RT-rtPCR test in a farrowing room was characterized, taking into account the spatial distribution (homogeneity) of viremic pigs within farrowing rooms. RESULTS: There was a linear relationship between piglet-level- and litter-level prevalence, where the latter was always larger than the former. When the piglet-level prevalence was 1%, 5%, 10%, 20%, and 50%, the true-litter level prevalence was 5.36%, 8.93%, 14.29%, 23.21%, and 53.57%, respectively. The corresponding apparent-litter prevalence by FOF was 2.06%, 6.48%, 11.25%, 21.60%, and 51.56%, respectively. CONCLUSION: This study provides matching prevalence estimates to help guide sample size calculations. It also provides a framework to estimate the likely proportion of viremic pigs, given the PRRSV RT-rtPCR positivity rate of FOF samples submitted from a farrowing room.

Field Implementation of Forecasting Models for Predicting Nursery Mortality in a Midwestern US Swine Production System.

The performance of five forecasting models was investigated for predicting nursery mortality using the master table built for 3242 groups of pigs (~13 million animals) and 42 variables, which concerned the pre-weaning phase of production and conditions at placement in growing sites. After training and testing each model's performance through cross-validation, the model with the best overall prediction results was the Support Vector Machine model in terms of Root Mean Squared Error (RMSE = 0.406), Mean Absolute Error (MAE = 0.284), and Coefficient of Determination (R2 = 0.731). Subsequently, the forecasting performance of the SVM model was tested on a new dataset containing 72 new groups, simulating ongoing and near real-time forecasting analysis. Despite a decrease in R2 values on the new dataset (R2 = 0.554), the model demonstrated high accuracy (77.78%) for predicting groups with high (>5%) or low (<5%) nursery mortality. This study demonstrated the capability of forecasting models to predict the nursery mortality of commercial groups of pigs using pre-weaning information and stocking condition variables collected post-placement in nursery sites.

Veterinarian perceptions and practices in prevention and control of influenza virus in the Midwest United States swine farms.

Influenza A virus (IAV) is an endemic respiratory pathogen affecting swine worldwide and is a public health concern as a zoonotic pathogen. Veterinarians may respond to IAV infection in swine with varied approaches depending on their perception of its economic impact on human and animal health. This study considered three primary veterinary practice categories: swine exclusive veterinary practitioner, large animal practitioner, which corresponds to veterinarians that work predominantly with food animals including but not exclusively porcine, and mixed animal practitioner, which corresponds to veterinarians working with companion and food animals. This survey aimed to assess U.S. veterinarian perceptions, biosecurity practices, and control methods for IAV in swine. In this study, 54.5% (188/345) of the veterinarians that were targeted responded to all portions of the survey. The study results presented different perceptions regarding IAV among veterinarians in different types of veterinary practices and the current IAV mitigation practices implemented in swine farms based on strategic decisions. Collectively, this study also revealed the veterinarians' perceptions that IAV as a health problem in swine is increasing, IAV has a moderate economic impact, and there is a high level of concern regarding IAV circulating in swine. These findings highlight the need for IAV surveillance data, improved vaccine strategies, as well as important opportunities regarding methods of control and biosecurity. Additionally, results of this survey suggest biosecurity practices associated with the veterinarian's swine operations and prevention of zoonotic diseases can be strengthened through annual IAV vaccination of humans and support of sick leave policies for farm workers.

Risk factors associated with sow mortality in breeding herds under one production system in the Midwestern United States.

Sow mortality has significantly increased throughout the world over the past several years, and it is a growing concern to the global swine industry. Sow mortality increases economic losses, including higher replacement rates, affects employees' morale, and raises concerns about animal well-being and sustainability. This study aimed to assess herd-level risk factors associated with sow mortality in a large swine production system in the Midwestern United States. This retrospective observational study used available production, health, nutritional, and management information between July 2019 and December 2021. A Poisson mixed regression model was used to identify the risk factors and to build a multivariate model using the weekly mortality rate per 1000 sows as the outcome. Different models were used to identify the risk factors according to this study's main reasons for sow mortality (total death, sudden death, lameness, and prolapse). The main reported causes of sow mortality were sudden death (31.22 %), lameness (28.78 %), prolapse (28.02 %), and other causes (11.99 %). The median (25th-75th percentile) distribution of the crude sow mortality rate/1000 sows was 3.37 (2.19 - 4.16). Breeding herds classified as epidemic for porcine reproductive and respiratory syndrome virus (PRRSV) were associated with higher total death, sudden death, and lameness death. Open pen gestation was associated with a higher total death and lameness compared with stalls. Pulses of feed medication was associated with lower sow mortality rate for all outcomes. Farms not performing bump feeding were associated with higher sow mortality due to lameness and prolapses, while Senecavirus A (SVA)-positive herds were associated with a higher mortality rate for total deaths and deaths due to lameness. Disease interactions (herds Mycoplasma hyopneumoniae positive and epidemic for PRRSV; SVA positive herds and epidemic for PRRSV) were associated with higher mortality rates compared to farms with single disease status. This study identified and measured the major risk factors associated with total sow mortality rate, sudden deaths, lameness deaths, and prolapse deaths in breeding herds under field conditions.

Utilizing productivity and health breeding-to-market information along with disease diagnostic data to identify pig mortality risk factors in a U.S. swine production system.

Aggregated diagnostic data collected over time from swine production systems is an important data source to investigate swine productivity and health, especially when combined with 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 investigation. This approach is particularly valuable for investigating the multifaceted and ever-changing factors contributing to wean-to-finish (W2F) swine 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. 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 diagnosis variable significantly associated with W2F mortality was porcine reproductive and respiratory syndrome virus (PRRSV). Closeouts with clinical signs suggestive of Salmonella spp. or Escherichia coli infection were also associated with higher W2F mortality. Source sow farm factors that remained significantly associated with W2F mortality were the sow farm PRRS status, average weaning age, and the average pre-weaning mortality. After testing for the possible interactions in the final model, 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 breeding-to-market productivity and health information, to measure the major risk factors associated with W2F mortality in specified time frames and pig populations.

Emergent Molecular Techniques Applied to the Detection of Porcine Viruses.

Molecular diagnostic tests have evolved very rapidly in the field of human health, especially with the arrival of the recent pandemic caused by the SARS-CoV-2 virus. However, the animal sector is constantly neglected, even though accurate detection by molecular tools could represent economic advantages by preventing the spread of viruses. In this regard, the swine industry is of great interest. The main viruses that affect the swine industry are described in this review, including African swine fever virus (ASFV), porcine reproductive and respiratory syndrome virus (PRRSV), porcine epidemic diarrhea virus (PEDV), and porcine circovirus (PCV), which have been effectively detected by different molecular tools in recent times. Here, we describe the rationale of molecular techniques such as multiplex PCR, isothermal methods (LAMP, NASBA, RPA, and PSR) and novel methods such as CRISPR-Cas and microfluidics platforms. Successful molecular diagnostic developments are presented by highlighting their most important findings. Finally, we describe the barriers that hinder the large-scale development of affordable, accessible, rapid, and easy-to-use molecular diagnostic tests. The evolution of diagnostic techniques is critical to prevent the spread of viruses and the development of viral reservoirs in the swine industry that impact the possible development of future pandemics and the world economy.

A cross-sectional assessment of PRRSV nucleic acid detection by RT-qPCR in serum, ear-vein blood swabs, nasal swabs, and oral swabs from weaning-age pigs under field conditions.

Introduction: The porcine reproductive and respiratory syndrome virus (PRRSV) continues to challenge swine production in the US and most parts of the world. Effective PRRSV surveillance in swine herds can be challenging, especially because the virus can persist and sustain a very low prevalence. Although weaning-age pigs are a strategic subpopulation in the surveillance of PRRSV in breeding herds, very few sample types have been validated and characterized for surveillance of this subpopulation. The objectives of this study, therefore, were to compare PRRSV RNA detection rates in serum, oral swabs (OS), nasal swabs (NS), ear-vein blood swabs (ES), and family oral fluids (FOF) obtained from weaning-age pigs and to assess the effect of litter-level pooling on the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) detection of PRRSV RNA. Methods: Three eligible PRRSV-positive herds in the Midwestern USA were selected for this study. 666 pigs across 55 litters were sampled for serum, NS, ES, OS, and FOF. RT-qPCR tests were done on these samples individually and on the litter-level pools of the swabs. Litter-level pools of each swab sample type were made by combining equal volumes of each swab taken from the pigs within a litter. Results: Ninety-six piglets distributed across 22 litters were positive by PRRSV RT-qPCR on serum, 80 piglets distributed across 15 litters were positive on ES, 80 piglets distributed across 17 litters were positive on OS, and 72 piglets distributed across 14 litters were positive on NS. Cohen's kappa analyses showed near-perfect agreement between all paired ES, OS, NS, and serum comparisons (). The serum RT-qPCR cycle threshold values (Ct) strongly predicted PRRSV detection in swab samples. There was a ≥ 95% probability of PRRSV detection in ES-, OS-, and NS pools when the proportion of positive swab samples was ≥ 23%, ≥ 27%, and ≥ 26%, respectively. Discussion: ES, NS, and OS can be used as surveillance samples for detecting PRRSV RNA by RT-qPCR in weaning-age pigs. The minimum number of piglets to be sampled by serum, ES, OS, and NS to be 95% confident of detecting ≥ 1 infected piglet when PRRSV prevalence is ≥ 10% is 30, 36, 36, and 40, respectively.

Scoping review on the epidemiology, diagnostics and clinical significance of porcine astroviruses.

Porcine astroviruses (PoAstVs) have been reported globally and are divided into at least five distinct lineages (PoAstV1-PoAsV5). The primary objective of this review was to summarize the scientific literature about the frequency of detection, associated clinical presentations and type of samples and diagnostic tools used for the detection of porcine astroviruses. The secondary objective was to summarize the body of knowledge about the causal role in disease of PoAstVs using the Bradford Hill framework. A search was conducted using Centre for Biosciences and Agriculture International (CABI), MEDLINE, American Association of Swine Veterinarians (AASV) Swine Information Library (SIL) abstracts, swine conferences including American College of Veterinary Pathologists (ACVP) and American Association of Veterinary Laboratory Diagnosticians (AAVLD). From 168 studies identified by the search, 29 studies were eligible. Results indicated that 69% (20/29) of the literature on PoAstVs have been published between 2011 and 2018. Of 29 papers, 52% were detection studies (15 of 29) and 48% (14 of 29) were case-control studies. Seventy-two per cent (21 of 29) reported differential diagnosis and 10% (3 of 29) reported histologic lesions, out of which 67% (2 of 3) associated the detection of PoAstV3 with development of polioencephalomyelitis. PCR-based assays were the most common diagnostic tools.

Implementing a user-friendly format to analyze PRRSV next-generation sequencing results and associating breeding herd production performance with number of PRRSV strains and recombination events.

The open reading frames (ORF)5 represents approximately 4% of the porcine reproductive and respiratory syndrome virus (PRRSV)-2 genome (whole-PRRSV) and is often determined by the Sanger technique, which rarely detects >1 PRRSV strain if present in the sample. Next-generation sequencing (NGS) may provide a more appropriate method of detecting multiple PRRSV strains in one sample. This work assessed the effect of PRRSV genetic variability and recombination events, using NGS, on the time-to-low prevalence (TTLP) and total losses in breeding herds (n 20) that detected a PRRSV outbreak and adopted measures to eliminate PRRSV. Serum, lung or live virus inoculation material collected within 3-weeks of outbreak, and subsequently, processing fluids (PFs) were tested for PRRSV by RT-qPCR and NGS. Recovered whole-PRRSV or partial sequences were used to characterize within and between herd PRRSV genetic variability. Whole-PRRSV was recovered in five out of six (83.3%) lung, 16 out of 22 (72.73%) serum and in five out of 95 (5.26%) PF. Whole-PRRSV recovered from serum or lung were used as farm referent strains in 16 out of 20 (80%) farms. In four farms, only partial genome sequences were recovered and used as farm referent strains. At least two wild-type PRRSV strains (wt-PRRSV) were circulating simultaneously in 18 out of 20 (90%) and at least one vaccine-like strain co-circulating in eight out of 20 (40%) farms. PRRSV recombination events were detected in 12 farms (59%), been 10 out of 12 between wt-PRRSV and two out of 12 between wt-PRRSV and vaccine-like strains. Farms having ≥3 strains had a 12-week increase TTLP versus herds ≤2 strains detected. Farms with ≤2 strains (n 10) had 1837 and farms with no recombination events detected (n 8) had 1827 fewer piglet losses per 1000 sows versus farms with ≥3 PRRSV strains (n 8) or detected recombination (n 10), respectively. NGS outcomes and novel visualization methods provided more thorough insight into PRRSV dynamics, genetic variability, detection of multiple strains co-circulating in breeding herds and helped establish practical guidelines for using PRRSV NGS outputs.

Comparison of virus detection, productivity, and economic performance between lots of growing pigs vaccinated with two doses or one dose of PRRS MLV vaccine, under field conditions.

Porcine reproductive and respiratory syndrome virus (PRRSV) causes a significant economic impact on swine production. It has been demonstrated that PRRS modified-live virus (MLV) vaccination of pigs, with one full dose, significantly reduces clinical consequences of wild-type PRRSV infection compared to non-vaccinates. However, there is limited information about the effect that two doses of PRRSV MLV vaccine have on the performance of growing pigs, compared to vaccination with a single dose. This study was conducted with the objectives to compare (a) the wild-type PRRSV detection in oral fluids over time, (b) key closeout productivity indicators, and (c) economic performance between lots of growing pigs vaccinated with two doses of Ingelvac PRRS® MLV vaccine and lots vaccinated with a single dose of the same vaccine. This randomized field trial included 15 lots of growing pigs from PRRSV positive-unstable sow farms and 66 lots from PRRSV positive-stable sow farms, according to the American association of swine veterinarians' terminology. All pig lots received the first vaccination either around processing or weaning age. Lots allocated in the two doses group received the second vaccination three to four weeks after the first vaccination. The pig lots were monitored for PRRSV detection over time. Six oral fluids samples were collected in three weeks intervals and were tested for wild-type PRRSV-2 RNA by RT-qPCR and open reading frame 5 (ORF)- 5 sequencing. Regression models were used to compare wild-type PRRSV detection dynamics on oral fluids samples and to compare key closeout performance indicators between one dose group and two doses group. Additionally, a benefit-cost ratio analysis compared economic performance between one dose group and two doses group. The proportion of wild-type PRRSV detection on oral fluids samples and the log counts of viral RNA per ml of oral fluids from the two doses group was lower than the one dose group on lots originated from PRRSV positive-stable sow farms, with a risk ratio of 1.24 and a rate ratio of 1.17, respectively. The two doses group had a significantly lower mortality rate than the one dose group, with a rate ratio of 1.21. The effect size increased on lots originated from PRRSV positive-unstable sow farms, and on lots with higher frequency and diversity of wild-type PRRSV detection during the growth phase. No differences in growth performance were detected between two doses group and one dose group. The second MLV vaccination dose had a benefit-cost ratio of 1.83. For lots originated from PRRSV positive-unstable farms, the benefit-cost ratio was 4.45, and for lots originated from PRRSV positive-stable farms, the benefit-cost ratio was 0.45. Under study conditions, vaccinating growing pig lots with two doses of PRRS MLV vaccine was a useful strategy to immunize growing pigs against PRRSV, lowering the wild-type PRRSV detection, lowering mortality rate, and increasing profitability, compared to lots of growing pigs that received a single dose of the same vaccine.

Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus.

Effective biosecurity practices in swine production are key in preventing the introduction and dissemination of infectious pathogens. Ideally, on-farm biosecurity practices should be chosen by their impact on bio-containment and bio-exclusion; however, quantitative supporting evidence is often unavailable. Therefore, the development of methodologies capable of quantifying and ranking biosecurity practices according to their efficacy in reducing disease risk has the potential to facilitate better-informed choices of biosecurity practices. Using survey data on biosecurity practices, farm demographics, and previous outbreaks from 139 herds, a set of machine learning algorithms were trained to classify farms by porcine reproductive and respiratory syndrome virus status, depending on their biosecurity practices and farm demographics, to produce a predicted outbreak risk. A novel interpretable machine learning toolkit, MrIML-biosecurity, was developed to benchmark farms and production systems by predicted risk and quantify the impact of biosecurity practices on disease risk at individual farms. By quantifying the variable impact on predicted risk, 50% of 42 variables were associated with fomite spread while 31% were associated with local transmission. Results from machine learning interpretations identified similar results, finding substantial contribution to predicted outbreak risk from biosecurity practices relating to the turnover and number of employees, the surrounding density of swine premises and pigs, the sharing of haul trailers, distance from the public road and farm production type. In addition, the development of individualized biosecurity assessments provides the opportunity to better guide biosecurity implementation on a case-by-case basis. Finally, the flexibility of the MrIML-biosecurity toolkit gives it the potential to be applied to wider areas of biosecurity benchmarking, to address biosecurity weaknesses in other livestock systems and industry-relevant diseases.

Effect of pooling family oral fluids on the probability of PRRSV RNA detection by RT-rtPCR.

Family oral fluids (FOFs) are an aggregate sample type shown to be a cost-efficient and convenient option for determining the porcine reproductive and respiratory syndrome virus (PRRSV) status of weaning age pigs. This study investigates the effect of pooling PRRSV-positive FOF samples with PRRSV-negative FOF samples at different levels (1/3, 1/5, 1/10, 1/20) on the probability of PRRSV RNA detection by reverse-transcription real-time polymerase chain reaction (RT-rtPCR). Mathematical models were built to assess how much the probability of RT-rtPCR PRRSV detection changed with increasing proportion of PRRSV-positive samples present within pools and how partially sampling a farrowing room influenced the probability of RT-rtPCR detection of PRRSV RNA in pooled samples at different prevalence scenarios. A general example of a guideline for FOF-based sampling under different prevalence scenarios to detect PRRSV RNA by RT-rtPCR with at least 95 % certainty is presented. At the sample level, the probability of detecting PRRSV RNA by RT-rtPCR decreased from 100 % to 87 %, 68 %, and 26 % when diluting up to 1/20 for PRRSV positive FOF having an initial Cycle threshold (Ct) below 34, between 34 and 36, or above 36, respectively. When PRRSV prevalence is near-zero (1 or 2 litters positive out of 56), the most cost-efficient farrowing room sampling strategy to detect PRRSV RNA with at least 95 % certainty was pooling FOF samples up to 1/10; at higher prevalence (≥ 3 of 56 litters positive), the most cost-efficient strategy was submitting samples in pools of 20. Subsampling a farrowing room for FOF pools was also demonstrated to be a valuable cost-saving strategy. Overall, based on the conditions of this study, pooling FOFs up to 1/20 is a valid option in situations of cost constraint and regardless of pooling level chosen, capturing as many litters as possible improves the probability of PRRSV detection.