Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.3
2.4
Journals
Veterinary Sciences
Special Issues
Prevention, Diagnosis, and Management of Bovine Respiratory Diseases—Volume II
share announcement

Prevention, Diagnosis, and Management of Bovine Respiratory Diseases—Volume II

A special issue of Veterinary Sciences (ISSN 2306-7381). This special issue belongs to the section "Veterinary Microbiology, Parasitology and Immunology".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 3694

Special Issue Editor

Prof. Dr. Brad J. White

E-Mail Website
Guest Editor
Beef Cattle Institute, College of Veterinary Medicine, Kansas State University, Manhattan, KS 66506, USA
Interests: bovine respiratory disease; beef cattle; disease diagnosis; predictive models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bovine respiratory disease (BRD) is a frequent and economically important disease in the cattle industry. The overall impact varies by production system and animal demographics. Despite advances in understanding of epidemiology, prevention methods, and therapeutic interventions, BRD remains a major disease in the cattle industry. The purpose of this Special Issue on “Prevention, Diagnosis, and Management of Bovine Respiratory Disease” is to align knowledge from multiple areas of research on this syndrome with the goal of generating a resource for current and future researchers. The scope of this Special Issue covers all aspects of BRD in cattle, ranging from the stages of production (pre-weaned and post-weaned cattle) to the type of operation (beef, dairy, intensively and extensively managed). Contributions can include a variety of aspects of BRD management, including epidemiology, diagnosis, economics, prevention, vaccinations, and therapy. This Special Issue will build on the existing literature and create a collection of articles not only summarizing current knowledge, but also providing a baseline for the next steps in BRD research.

Prof. Dr. Brad J. White
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Veterinary Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bovine respiratory disease
  • Mannheimia haemolytica
  • Pasteurella multocida
  • Histophilus somni
  • Mycoplasma bovis
  • bovine viral diarrhea
  • infectious bovine rhinotracheitis
  • parainfluenza 3
  • epidemiology
  • vaccinations
  • prevention and control
  • diagnosis
  • mortality
  • economics

Related Special Issue

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

11 pages, 5629 KiB  
Article
Image Classification and Automated Machine Learning to Classify Lung Pathologies in Deceased Feedlot Cattle
by Eduarda M. Bortoluzzi, Paige H. Schmidt, Rachel E. Brown, Makenna Jensen, Madeline R. Mancke, Robert L. Larson, Phillip A. Lancaster and Brad J. White
Vet. Sci. 2023, 10(2), 113; https://doi.org/10.3390/vetsci10020113 - 03 Feb 2023
Cited by 4 | Viewed by 2055
Abstract
Bovine respiratory disease (BRD) and acute interstitial pneumonia (AIP) are the main reported respiratory syndromes (RSs) causing significant morbidity and mortality in feedlot cattle. Recently, bronchopneumonia with an interstitial pattern (BIP) was described as a concerning emerging feedlot lung disease. Necropsies are imperative [...] Read more.
Bovine respiratory disease (BRD) and acute interstitial pneumonia (AIP) are the main reported respiratory syndromes (RSs) causing significant morbidity and mortality in feedlot cattle. Recently, bronchopneumonia with an interstitial pattern (BIP) was described as a concerning emerging feedlot lung disease. Necropsies are imperative to assist lung disease diagnosis and pinpoint feedlot management sectors that require improvement. However, necropsies can be logistically challenging due to location and veterinarians’ time constraints. Technology advances allow image collection for veterinarians’ asynchronous evaluation, thereby reducing challenges. This study’s goal was to develop image classification models using machine learning to determine RS diagnostic accuracy in right lateral necropsied feedlot cattle lungs. Unaltered and cropped lung images were labeled using gross and histopathology diagnoses generating four datasets: unaltered lung images labeled with gross diagnoses, unaltered lung images labeled with histopathological diagnoses, cropped images labeled with gross diagnoses, and cropped images labeled with histopathological diagnoses. Datasets were exported to create image classification models, and a best trial was selected for each model based on accuracy. Gross diagnoses accuracies ranged from 39 to 41% for unaltered and cropped images. Labeling images with histopathology diagnoses did not improve average accuracies; 34–38% for unaltered and cropped images. Moderately high sensitivities were attained for BIP (60–100%) and BRD (20–69%) compared to AIP (0–23%). The models developed still require fine-tuning; however, they are the first step towards assisting veterinarians’ lung diseases diagnostics in field necropsies. Full article
(This article belongs to the Special Issue Prevention, Diagnosis, and Management of Bovine Respiratory Diseases—Volume II)
►▼ Show Figures

Figure 1

13 pages, 803 KiB  
Article
An Evaluation of Temporal Distributions of High, Low, and Zero Cohort Morbidity of Cumulative First Treatment Bovine Respiratory Disease and Their Associations with Demographic, Health, and Performance Outcomes in US Feedlot Cattle
by Blaine Johnson, Brad White, Phillip Lancaster and Robert Larson
Vet. Sci. 2023, 10(2), 89; https://doi.org/10.3390/vetsci10020089 - 24 Jan 2023
Cited by 1 | Viewed by 1341
Abstract
Timing and magnitude of bovine respiratory disease (BRD) can impact intervention and overall economics of cattle on feed. Furthermore, there is a need to better describe when cattle are being treated for BRD. The first objective was to perform a cluster analysis on [...] Read more.
Timing and magnitude of bovine respiratory disease (BRD) can impact intervention and overall economics of cattle on feed. Furthermore, there is a need to better describe when cattle are being treated for BRD. The first objective was to perform a cluster analysis on the temporal distributions of cumulative first treatment BRD from HIGH (≥15% of cattle received treated for BRD) and LOW cohorts (>0 and <15% of cattle received treated for BRD) to assess cohort-level timing (days on feed) of BRD first treatments. The second objective was to determine associations among cluster groups (temporal patterns) and demographic risk factors, health outcomes, and performance. Cluster analysis determined that optimal number of clustering groups for the HIGH morbidity cohort was six clusters and LOW morbidity cohort was seven clusters. Cohorts with zero BRD treatment records were added for statistical comparisons. Total death loss, BRD morbidity, average daily gain (ADG), railing rate, days to 50% BRD, cattle received, shrink, arrival weight, and sex were associated with temporal groups (p < 0.05). These data could be used as a tool for earlier identification and potential interventions for cohorts based on the BRD temporal pattern. Full article
(This article belongs to the Special Issue Prevention, Diagnosis, and Management of Bovine Respiratory Diseases—Volume II)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop