Tag: research

Eliana Silva Paladino and Jose Angulo to receive Pijoan and Leman swine fellowships.

The recipients of the Pijoan and Leman fellowships were revealed last week and we are proud to announce that two great candidates were selected.

paladino_elianaDr. Eliana Silva Paladino is receiving the Carlos Pijoan fellowship. Eliana obtained her DVM and MS degrees in Brazil and is currently a PhD student advised by Dr. Julio Alvarez. Eliana’s PhD is focusing on the application of quantitative tools for the management of problems of relevance to the swine industry at both the farm and population (i.e., supra-farm) levels. She is currently focusing on developing systems to quantify the effect of endemic diseases such as swine influenza and the management strategies adopted for their control in affected farms. She is also working on the assessment of trends in submissions received at veterinary diagnostic laboratories over time to evaluate the impact of changes in disease distribution, diagnostic approaches and farm management in the U.S.

jose_angulo Dr. Jose Angulo is receiving the Allen Leman fellowship. Jose obtained his DVM degree in Mexico and is currently a PRRS specialist and managing veterinarian for Zoetis in charge of U.S. customers. Jose will be completing his Masters in Dr. Montse Torremorell’s lab and his work will focus on understanding PRRS infections in growing pigs and how to prevent them.

Science Page: PRRS cumulative incidence by status

This is our Friday rubric: every week a new Science Page from the Bob Morrison’s Swine Health Monitoring Project. The previous editions of the science page are available on our website.

How does PRRS incidence vary based on farm status? This is the question answered in this week’s edition of the Science Page. Three different formulas were used to calculate the incidence in each of the group over type. First, the initial number of farms of each status at the beginning of the year was used as the denominator. Then, the denominator was changed to the total number of farms that entered each status since the beginning of the year. Lastly, weekly incidences calculated for each of the group since the beginning of the year were added. Calculations went back for the last 3 years.

Key points from this week edition:

  • Cumulative incidence is higher in those farms that are under status 2, 2vx and 2fvi.
  • The incidence is lower in farms that had recently an outbreak or those that are completely negative.
  • Different ways of calculating incidence by herd status lead to the same overall conclusion.

Take a look at PRRS incidences in farms of group 2 status, vaccinated or inoculated with live virus over the past years.

PhD seminar: Epidemiological investigation of a non-reportable endemic disease: PRRS in the US

Title: Epidemiological investigation of a non-reportable endemic disease: Porcine reproductive and respiratory syndrome (PRRS) in the US

Presented by:   Pablo Valdes-Donoso

Date:    Friday, June 9, 2017
Time:    3:00 – 4:00 pm
Place:    385-J, AS/VM Building

Abstract: Porcine reproductive and respiratory syndrome (PRRS), caused by a highly mutagenic and resistant RNA-virus, is an endemic disease that has been noted as one of the most important animal production diseases in the US because of its large economic damage on the swine industry. Nonetheless, there is no official control framework for this disease, so producers rely on voluntary regional control programs (RCPs) for its mitigation. Despite efforts to control PRRS, it persists in the environment, affecting a large number of farms every year. Using information shared by a specific RCP (RCP-N212), this dissertation focused on important aspects of PRRS dynamics within an RCP. Specifically, this dissertation encompassed five chapters. An introductory chapter is followed by the second chapter, which quantifies the extent to which RCPs contribute to PRRS control. After that, a prediction of network structure was made to forecast animal movements among farms within the RCP-N212. Then, longitudinal data collected from sow farms were used to measure the impacts of PRRS on production. Finally, a disaggregated disease diffusion model was used to depict PRRS dynamics within the RCP-N212, as well as to evaluate individual and collective strategies adopted by producers. This dissertation provides insight to the evaluation of regional control strategies that may be used as a framework for a formal PRRS control program.

The latest trends in PRRSV diagnostics: less serum samples, more oral fluids, and more 1-7-4 RFLP pattern

Today, Dr. Albert Rovira from the University of Minnesota, Veterinary Diagnostic Laboratory shares with us the trends he has observed in PRRSV diagnostics over the past years. The findings can be found in the slideshow below.

  • The use of tissue samples follows a seasonal pattern and represents clinical cases with a percent of positives of 30%
  • The number of oral fluid samples is increasing. Used for monitoring positive farms and more recently for surveillance in negative farms as well:: 15% of positive samples
  • The number of blood swabs, serum samples, and semen samples, typically used for surveillance in negative farms, is decreasing. Lowest percent of positive samples: 8%
  • RFLP patterns are changing over time. In the past years, 1-7-4 > 1-3-4 or 1-8-4 or 1-4-4
Reminder: what is a RFLP pattern?

RFLP stands for Restriction Fragment Length Polymorphism and is a technique used to detect nucleotide changes in a genetic sequence. The genetic material is put in contact with restriction enzymes which are very specific to a genetic sequence. If the enzyme recognizes the sequence pattern, it will cleave the DNA or RNA fragment. After that, a type is determined based on the number of fragments and its size.

For example with PRRSV, three enzymes are used and the number of fragments each of them produces makes up the numbers of the RFLP pattern. Currently, the RFLP type is not actually performed in the lab. Instead, it is predicted based on the ORF5 RNA sequence and the knowledge of the cutting capabilities of each enzyme.

Therefore, the RFLP pattern gives us a way to cluster PRRSV strains in groups but very little indication about how similar they are to each other.

Science page: How farm structure and demography impact disease detection

This is our Friday rubric: every week a new Science Page from the Bob Morrison’s Swine Health Monitoring Project. The previous editions of the science page are available on our website.

This week’s edition reports the latest research on modeling the spread of swine vesicular diseases based on farm structure and number of sites. The model was then used to establish an expected time to detection. Two virus strains (high versus low virulence) were evaluated with the model to assess how the strain would influence the time to detection in a farm.

Key points from this week edition:

  • The models showed that the virus persisted longer in farms with a farrowing unit.
  • It is more difficult to diagnose FMD when the strains cause low mortality or no mortality.

Click on the link to see the details about disease spread models.

 

Survival of porcine coronaviruses in feed ingredients and impact of feed additives

A lot of research has been done at the University of Minnesota regarding the survival of porcine coronaviruses in the feed and how to impact their survival. We are presenting today two papers published this spring looking at this important topic. First, Trudeau et al. showed that the feed ingredient which lead to the longest porcine coronaviruses’ survivability was soybean meal. Then, Cottingim et al. showed that some feed additives could inactivate PDCoV.

Importance of porcine coronaviruses and their relationship to swine feed

Porcine coronaviruses of importance in the swine industry nowadays are Porcine Epidemic Diarrhea virus (PEDV), Transmissible Gastroentiritis virus (TGEV), and Porcine Delta Coronavirus (PDCoV). All cause enteric issues in swine and some can lead to up to 100% mortality in nursing piglets. The role of feed ingredients in spreading PEDV and causing outbreaks in Northern America in 2013 has been questioned since then.

Survival of PEDV, TGEV, and PDCoV in complete feed and feed ingredients

The first research project evaluated the persistence of PEDV, TGEV, and PDCoV in porcine feed and feed ingredients. To do so, complete feed and major feed ingredients samples (spray dried porcine plasma, meat meal, meat and bone meal, blood meal, corn,
soybean meal, and corn dried distillers grains with solubles) where inoculated with PEDV, TGEV, or PDCoV and kept for up to 56 days. Aliquots were tested 11 times between the inoculation day and the end of the trial. Time necessary to reduce the viral concentration by 1 log was recorded.

Soybean meal took the longest time to attain the reduction in concentration for all of the coronaviruses, reaching 7.5 days for PEDV, and 42 days for both PDCoV and TGEV. This study also demonstrated that there was a modest positive correlation between moisture content and persistence of TGEV and PDCoV. On the other end, there was a moderate negative correlation between ether extract content and TGEV survival, not observed with the other two viruses.

Click on the banner below to access the full article in open access.

Trudeau coronavirus feed swine survival PED

Feed additives and PDCoV survival

In this second project, the survival of PDCoV was evaluated after being put in contact with nursery feed samples containing one of six different commercial feed acids (UltraAcid P, Activate DA, KEMGEST, Acid Booster, Luprosil, and Amasil), salt, or sugar. Acids were added following the recommended concentrations in the first part of the experiment and then, were double-dosed. Feed samples were inoculated with PDCoV and kept for up to 35 days. Like in the previous article, days to achieve a reduction of virus concentration by 1 log were recorded.

At recommended values, there was no difference between viral load reduction in feed samples with or without additives. When acids were added to the feed at a double concentration, the time period to attain the reduction in viral load was decreased to 0.28 days or less for all acids except for Amasil which increased it to 4.95 days (control: 0.35 days). The difference between acidifiers may be explained by the active ingredients used in the products. Furthermore, the addition of salt decreased PDCoV survival whereas sugar increased it.

Click on the banner below to access the full article in open access.

Cottingim feed additives survival PDCoV coronavirus swine

 

Science page: Comparing EWMAs

This is our Friday rubric: every week a new Science Page from the Bob Morrison’s Swine Health Monitoring Project. The previous editions of the science page are available on our website.

But first, we would like to congratulate our 4th-year student Hunter Baldry for receiving the ZinPro scholarship in recognition of her accomplishments as a food-animal student at the University of Minnesota. Keep up the good work, Hunter!

This week, the Science Page answers one of your questions: Is the trend of PRRSV outbreaks recorded for the original 13 participants still related to the PRRSV outbreaks evolution monitored for all the MSHMP participants?

Key points from this week edition:

  • The EWMA of the original 13 participating systems is still a good representation of the overall EWMA.
  • Questions from participants are always welcome!

Reminder: What is the EWMA?

The Exponential Weighted Moving Average (EMWA) is a statistical method that averages data over time, continually decreasing the weight of data as it moves further back in time.  An EWMA chart is particularly good at monitoring processes that drift over time and is used to detect small shifts in a trend.

In our project, EWMA is used to follow the evolution of the % of farms at risk that broke with PRRSV every week. EWMA incorporates all the weekly percentages recorded since the beginning of the project and gives less and less weight to the results as they are more removed in time. Therefore, the % of farms at risk that broke with PRRSV last week will have much more influence on the EMWA than the % of farms at risk that broke with PRRSV during the same week last year.

Take a look at the original 13 and overall EMWAs.