Publications

Abstract 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.

O objetivo deste trabalho foi avaliar e classificar as principais causas de morte de leitoes com ate seis dias de idade, em umsistema intensivo de suinos confinados (SISCO), localizado na regiao medio-norte do estado de Mato Grosso durante o anode 2006. Observaram-se 48.416 leitoes provenientes de 2.054 matrizes. Verificou-se taxa de mortalidade de 7,43% do totalde nascidos vivos. As causas mais frequentes de mortalidade, em relacao ao total de nascidos vivos, foram esmagamento(24,14%), debilitacao (8,01%), sindrome diarreica (6,0%), defeitos geneticos (2,41%) e outras causas sem diagnosticoconfirmado durante a necropsia (15,20%). A taxa de natimortalidade em relacao ao total de nascidos foi de 5,59%. Comrelacao a sazonalidade das mortes, observou-se maior indice de mortalidade por sindrome diarreica durante a primavera,que coincide com os meses mais quentes do ano, nao observando tendencia para morte por esmagamento em nenhumaepoca do ano.

RESUMO: Os neoplasmas cutâneos em cães apresentam elevada e relevante prevalência em todo o Brasil. Sendo assim, objetivou-se determinar a frequência e algumas características epidemiológicas dos neoplasmas cutâneos em cães diagnosticados no Laboratório de Patologia da Universidade Federal de Mato Grosso (LPV-UFMT), Cuiabá, entre os anos de 2007 a 2014. Foram revisados os protocolos de necropsias e biopsias do LPV-UFMT, de cães com neoplasmas cutâneos. Dados referentes à idade, sexo, raça dos cães afetados, localização anatômica, tamanho, diagnóstico histomorfológico e comportamento biológico foram coletados e analisados através de uma análise estatística descritiva. Adicionalmente o teste χ2, foi utilizado para associações entre comportamento biológico e tamanho da massa. Dos 3566 exames realizados, 656 (18,4%) foram diagnosticados como tumores cutâneos sendo cães adultos e idosos das raças Pit Bull, Boxer e Poodle os mais acometidos. Dentre os 11 padrões morfológicos mais diagnosticados o mastocitoma, carcinoma de células escamosas (CCE) e os tumores de origem vascular foram os mais frequentemente relatados. Em relação ao sítio anatômico, a cabeça foi a mais acometida. A maioria dos tumores benignos apresentaram menos de 1cm de diâmetro e os malignos de 3 a 5cm. O aumento de neoplasmas em adultos e idosos pode estar relacionado ao somatório de danos causados por agentes carcinogênicos e o comprometimento imunológico.

Few practical methods are available to monitor the PRRSV status of the sows. Common sampling methods for sows like serum sampling, and tonsil scraping involve restraining individual sows and are labor-intensive, time-consuming, relatively invasive, and therefore, have limited use in large-scale production settings. Thus, a practical and rapid method of sampling large numbers of sows is needed. This study aimed to develop a new sampling method, named tonsil-oral scraping (TOSc) and compare TOSc to serum and tonsil scraping in terms of PRRSV qPCR detection rate and Ct values in thirty matched sows, thirty days after PRRSV outbreak. TOSc recovered a mixture of oral fluids and tonsil exudates from the sow oral cavity within seconds without restraining the animals. Results showed that, numerically, the TOSc samples had higher PRRSV qPCR detection rate(100%) compared to serum(16.8%) and tonsil scraping(73.3%). Moreover, TOSc samples had lower average Ct values(29.7) than tonsil scraping(30.7) and serum(35.2). There was no significant difference in the detection rate between TOSc and tonsil scraping (Tukey test, p=0.9925), while there was a significant difference between serum and tonsil scraping (Tukey test, p<0.001). In terms of Ct values, there was no statistically significant difference between TOSc and tonsil scrapings (Dunn Test, p>0.05), while there was a significant difference between tonsil scraping with serum (Dunn Test, p<0.01), and TOSc with serum (Dunn Test, p<0.01). Our results suggest great potential of the TOSc as a novel, practical, and rapid tool for PRRSV RNA detection in sows to be able to assess sow herd status.

) 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.

Disease management and epidemiological investigations of porcine reproductive and respiratory syndrome virus-type 2 (PRRSV-2) often rely on grouping together highly related sequences. In the USA, the last five years have seen a major shift within the swine industry when classifying PRRSV-2, beginning to move away from RFLP (restriction fragment length polymorphisms)-typing and adopting the use of phylogenetic lineage-based classification. However, lineages and sub-lineages are large and genetically diverse, making them insufficient for identifying new and emerging variants. Thus, within the lineage system, a dynamic fine-scale classification scheme is needed to provide better resolution on the relatedness of PRRSV-2 viruses to inform disease management and monitoring efforts and facilitate research and communication surrounding circulating PRRSV viruses. Here, we compare fine-scale systems for classifying PRRSV-2 variants (i.e., genetic clusters of closely related ORF5 sequences at finer scales than sub-lineage) using a database of 28,730 sequences from 2010 to 2021, representing &amp;gt;55% of the U.S. pig population. In total, we compared 140 approaches that differed in their tree-building method, criteria, and thresholds for defining variants within phylogenetic trees. Three approaches resulted in variant classifications that were reproducible and robust even when the input data or input phylogenies were changed. For these approaches, the average genetic distance among sequences belonging to the same variant was 2.1–2.5%, and the genetic divergence between variants was 2.5–2.7%. Machine learning classification algorithms were trained to assign new sequences to an existing variant with &amp;gt;95% accuracy, which shows that newly generated sequences can be assigned to a variant without repeating the phylogenetic and clustering analyses. Finally, we identified 73 sequence-clusters (dated &amp;lt;1 year apart with close phylogenetic relatedness) associated with circulation events on single farms. The percent of farm sequence-clusters with an ID change was 6.5–8.7% for our approaches. In contrast, ~43% of farm sequence-clusters had variation in their RFLP-type, further demonstrating how our proposed fine-scale classification system addresses shortcomings of RFLP-typing. Through identifying robust and reproducible classification approaches for PRRSV-2, this work lays the foundation for a fine-scale system that would more reliably group related field viruses and provide better resolution for decision-making surrounding disease management.

This study evaluated the use of endemic enteric coronaviruses polymerase chain reaction (PCR)-negative testing results as an alternative approach to detect the emergence of animal health threats with similar clinical diseases presentation. This retrospective study, conducted in the United States, used PCR-negative testing results from porcine samples tested at six veterinary diagnostic laboratories. As a proof of concept, the database was first searched for transmissible gastroenteritis virus (TGEV) negative submissions between January 1 st , 2010, through April 29 th , 2013, when the first porcine epidemic diarrhea virus (PEDV) case was diagnosed. Secondly, TGEV- and PEDV-negative submissions were used to detect the porcine delta coronavirus (PDCoV) emergence in 2014. Lastly, encountered best detection algorithms were implemented to prospectively monitor the 2023 enteric coronavirus-negative submissions. Time series (weekly TGEV-negative counts) and Seasonal Autoregressive-Integrated Moving-Average (SARIMA) were used to control for outliers, trends, and seasonality. The SARIMA’s fitted and residuals were then subjected to anomaly detection algorithms (EARS, EWMA, CUSUM, Farrington) to identify alarms, defined as weeks of higher TGEV-negativity than what was predicted by models preceding the PEDV emergence. The best-performing detection algorithms had the lowest false alarms (number of alarms detected during the baseline) and highest time to detect (number of weeks between the first alarm and PEDV emergence). The best-performing detection algorithms were CUSUM, EWMA, and Farrington flexible using SARIMA fitted values, having a lower false alarm rate and identified alarms 4 to 17 weeks before PEDV and PDCoV emergences. No alarms were identified in the 2023 enteric negative testing results. The negative-based monitoring system functioned in the case of PEDV propagating epidemic and in the presence of a concurrent propagating epidemic with the PDCoV emergence. It demonstrated its applicability as an additional tool for diagnostic data monitoring of emergent pathogens having similar clinical disease as the monitored endemic pathogens.

The performance of 5 forecasting models was investigated for predicting nursery mortality using the master table built for 3,242 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&amp;amp;gt;%) or low (5&amp;amp;lt;%) 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 conditions variables collected post-placement in nursery sites.