Bovine respiratory syncytial virus (BRSV) plays a significant role in the etiopathogenesis of the respiratory syndrome in young cattle during their first year of life. Development of rapid and accurate BRSV diagnostic tools would aid in the appropriate control of this important pathogen. The objective of this study was to characterize infections induced by BRSV by means of rapid patient-side immunomigration assays used for diagnosis of human respiratory syncytial virus (hRSV) in humans. Nasal and tracheal swabs were obtained from healthy calves of various beef and dairy breeds - Holstein-Friesian, Simmental, Charolais, Belgian Blue and Limousin, between the ages of 5 and 12 months, from 26 farms. BRSV was identified using two rapid immunomigration assays, TruRSV® and Clearview® RSV, and compared with RT-PCR as a reference technique. BRSV was found in 73.1% of all the herds tested. High agreement with RT-PCR was obtained for TruRSV® (κ = 0.824), while in the case of the Clearview® RSV test, agreement with PCR was moderate (κ = 0.420). The results demonstrate that rapid patient-side immunomigration assays designed to detect hRSV can be used to accurately detect BRSV in field samples collected from cattle.
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http://dx.doi.org/10.1111/tbed.12134 | DOI Listing |
Int J Gen Med
January 2025
Department of Respiratory and Critical Care Medical Department Infectious Diseases Ward, The Traditional Chinese Medicine Hospital Affiliated to Xinjiang Medical University, Urumqi, Xinjiang, People's Republic of China.
Background: This study examines the distribution characteristics of pathogenic bacteria in respiratory infections and their relationship with inflammatory markers to guide clinical drug use.
Methods: We selected 120 patients with lower respiratory tract infection in the electronic medical record system of Xinjiang Provincial People's Hospital from March 2019 to March 2023 for a case-control study. Using Indirect Immunofluorescence Antibody test(IFA), blood routine, C-reactive Protein (CRP), and High-sensitivity C-reactive Protein(hsCRP), we detected nine respiratory pathogens (Respiratory syncytial virus; Influenza A virus; Influenza B virus; Parainfluenza virus; Adenovirus; Mycoplasma pneumoniae; Chlamydia pneumoniae; Legionella pneumophila type 1; Rickettsia Q) in all patients and analyzed their distribution and correlation.
IJID Reg
March 2025
Postgraduate Program in Parasitic Biology, Federal University of Sergipe, Sergipe, Brazil.
Objectives: To investigate the prevalence of nine respiratory viruses and their clinical characteristics in children aged up to 5 years old in the state of Sergipe, Northeast of Brazil in the pre-COVID-19 pandemic period.
Methods: Children with suspected influenza virus infection were included in the study. Clinical samples were screened using real-time quantitative polymerase chain reaction for the diagnosis of adenovirus, parainfluenza (PIV)1, PIV2, PIV3, and human metapneumovirus.
Commun Biol
January 2025
School of Population Medicine and Public Health, Public Health Emergency Management Innovation Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Acute respiratory infections (ARI) with multiple types of viruses are common in infants and children. This study was conducted to assess the difference of oropharyngeal microbiome during acute respiratory viral infection using whole-genome shotgun metagenomic sequencing. The overall taxonomic alpha diversity did not differ by the types of infected virus.
View Article and Find Full Text PDFAcute respiratory infections (ARIs) are a leading cause of death in children under five globally. The seasonal trends and profiles of respiratory viruses vary by region and season. Due to limited information and the population's vulnerability, we conducted the hospital-based surveillance of respiratory viruses in Eastern Uttar Pradesh.
View Article and Find Full Text PDFViruses
December 2024
Beijing Youcare Kechuang Pharmaceutical Technology Co., Ltd., Beijing 100176, China.
Human respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2, a deep-learning model that significantly outperforms its predecessor, AVP-GPT, in designing and screening antiviral peptides.
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