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This study aimed to describe selected epidemiological aspects of horses with acute onset of fever and respiratory signs testing qPCR-positive for S. equi and to determine the effect of vaccination against S. equi on qPCR status. Horses with acute onset of fever and respiratory signs from all regions of the United States were included in a voluntary biosurveillance program from 2008 to 2020 and nasal secretions were tested via qPCR for S. equi and common respiratory viruses. A total of 715/9409 equids (7.6%) tested qPCR-positive for S. equi, with 226 horses showing coinfections with EIV, EHV-1, EHV-4, and ERBV. The median age for the S. equi qPCR-positive horses was 8 ± 4 years and there was significant difference when compared to the median age of the S. equi qPCR-negative horses (6 ± 2 years; p = 0.004). Quarter Horse, Warmblood, and Thoroughbred were the more frequent breed in this horse population, and these breeds were more likely to test qPCR-positive for S. equi compared to other breeds. There was not statistical difference for sex between S. equi qPCR-positive and qPCR-negative horses. Horses used for competition and ranch/farm use were more likely to test qPCR-positive for S. equi (p = 0.006). Horses that tested S. equi qPCR-positive were more likely to display nasal discharge, fever, lethargy, anorexia, and ocular discharge compared to horses that tested S. equi qPCR-negative (p = 0.001). Vaccination against S. equi was associated with a lower frequency of S. equi qPCR-positive status.

This study aimed to describe selected epidemiological aspects of horses with acute onset of fever and respiratory signs testing qPCR-positive for S. equi and to determine the effect of vaccination against S. equi on qPCR status. Horses with acute onset of fever and respiratory signs from all regions of the United States were included in a voluntary biosurveillance program from 2008 to 2020 and nasal secretions were tested via qPCR for S. equi and common respiratory viruses. A total of 715/9409 equids (7.6%) tested qPCR-positive for S. equi, with 226 horses showing coinfections with EIV, EHV-1, EHV-4, and ERBV. The median age for the S. equi qPCR-positive horses was 8 ± 4 years and there was significant difference when compared to the median age of the S. equi qPCR-negative horses (6 ± 2 years; p = 0.004). Quarter Horse, Warmblood, and Thoroughbred were the more frequent breed in this horse population, and these breeds were more likely to test qPCR-positive for S. equi compared to other breeds. There was not statistical difference for sex between S. equi qPCR-positive and qPCR-negative horses. Horses used for competition and ranch/farm use were more likely to test qPCR-positive for S. equi (p = 0.006). Horses that tested S. equi qPCR-positive were more likely to display nasal discharge, fever, lethargy, anorexia, and ocular discharge compared to horses that tested S. equi qPCR-negative (p = 0.001). Vaccination against S. equi was associated with a lower frequency of S. equi qPCR-positive status.

The paper demonstrates a new approach to identify healthy calves ("healthy") and naturally occurring infectious bronchopneumonia ("sick") calves by analysis of the gaseous phase over nasal secretions using 16 piezoelectric sensors in two portable devices. Samples of nasal secretions were obtained from 50 red-motley Holstein calves aged 14--42 days. Calves were subjected to rectal temperature measurements, clinical score according to the Wisconsin respiratory scoring chart, thoracic auscultation, and radiography (Carestream DR, New York, USA). Of the 50 calves, we included samples from 40 (20 "healthy" and 20 "sick") in the training sample. The remaining ten calves (five "healthy" and five "sick") were included in the test sample. It was possible to divide calves into "healthy" and "sick" groups according to the output data of the sensor arrays (maximum sensor signals and calculated parameters Ai/j) using the principal component linear discriminant analysis (PCA--LDA) with an accuracy of 100%. The adequacy of the PCA--LDA model was verified on a test sample. It was found that data of sensors with films of carbon nanotubes, zirconium nitrate, hydroxyapatite, methyl orange, bromocresol green, and Triton X-100 had the most significance for dividing samples into groups. The differences in the composition of the gaseous phase over the samples of nasal secretions for such a classification could be explained by the appearance or change in the concentrations of ketones, alcohols, organic carboxylic acids, aldehydes, amines, including cyclic amines or those with a branched hydrocarbon chain. [ABSTRACT FROM AUTHOR]

The paper demonstrates a new approach to identify healthy calves (“healthy”) and naturally occurring infectious bronchopneumonia (“sick”) calves by analysis of the gaseous phase over nasal secretions using 16 piezoelectric sensors in two portable devices. Samples of nasal secretions were obtained from 50 red-motley Holstein calves aged 14–42 days. Calves were subjected to rectal temperature measurements, clinical score according to the Wisconsin respiratory scoring chart, thoracic auscultation, and radiography (Carestream DR, New York, USA). Of the 50 calves, we included samples from 40 (20 “healthy” and 20 “sick”) in the training sample. The remaining ten calves (five “healthy” and five “sick”) were included in the test sample. It was possible to divide calves into “healthy” and “sick” groups according to the output data of the sensor arrays (maximum sensor signals and calculated parameters Ai/j) using the principal component linear discriminant analysis (PCA–LDA) with an accuracy of 100%. The adequacy of the PCA–LDA model was verified on a test sample. It was found that data of sensors with films of carbon nanotubes, zirconium nitrate, hydroxyapatite, methyl orange, bromocresol green, and Triton X-100 had the most significance for dividing samples into groups. The differences in the composition of the gaseous phase over the samples of nasal secretions for such a classification could be explained by the appearance or change in the concentrations of ketones, alcohols, organic carboxylic acids, aldehydes, amines, including cyclic amines or those with a branched hydrocarbon chain.