Profiling Mannheimia haemolytica infection in dairy calves using near infrared spectroscopy (NIRS) and multivariate analysis (MVA)

Non-destructive diagnosis of Inflammatory Bowel Disease by near-infrared spectroscopy and aquaphotomics

Inflammatory Bowel Disease includes Crohn's Disease and Ulcerative Colitis. Currently, diagnosing involves a series of current diagnostic methods that are invasive, time-consuming, and expensive. Near-infrared spectroscopy and aquaphotomics can detect changes in biofluids and thus have the potential to diagnose disease. This study aimed to investigate the diagnostic ability of near infrared spectroscopy and aquaphotomics for Inflammatory Bowel Disease and its types. This method used blood plasma and saliva samples absorbance spectrum and multivariate analysis with the Principal Component Analysis and, Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Support Vector Machine in the range 1300-1600 nm and 12 water absorbance bands in this range, separately. In the near-infrared range, total accuracy of 100% led to the separation of the healthy group and Inflammatory Bowel Disease and then the separation of the healthy group and patients with Ulcerative Colitis and Crohn's Disease. The aquaphotomics approach was used to investigate the changes in the 12 water absorbance bands and their impact on the accuracy of the diagnostic method. Aquaphotomics also detected 100% of the mentioned samples. We achieved a fast, accurate, non-invasive method based on near-infrared spectroscopy and aquaphotomics to diagnose Inflammatory Bowel Disease and its types using blood plasma or saliva samples. The current study found that monitoring blood plasma or saliva using near-infrared spectra offers an opportunity to thoroughly investigate biofluids and changes in their water spectral patterns caused by complex physiological changes due to Inflammatory Bowel Disease and its types, and to visualize these changes using aquagram.

Impact of an Injectable Trace Mineral Supplement on the Immune Response and Outcome of Mannheimia haemolytica Infection in Feedlot Cattle

The study aimed to assess the impact of injectable trace mineral ("ITM"; Multimin90; Fort Collins, CO) supplementation on bacterial infection in cattle. Angus-crossbred steers (n = 32) were organized into two blocks by initial body weight. Steers were maintained on a ryelage and dry-rolled corn-based growing diet without supplementation of Zn, Cu, Mn, and Se for the duration of the study. The steers were transported 6 h, then randomized into three treatment groups: control received sterile saline ("CON"), ITM administered 1 day after transport (6 days before infection, "ITMPRE"), and ITM administered 2 days post infection (dpi) concurrent with antibiotic treatment ("ITMPOST"). Steers were infected with Mannheimia haemolytica on day 0, and all were treated with tulathromycin at 2 dpi. Plasma levels of Zn, Cu, and Se did not differ among treatments (P ≥ 0.74). Liver Se was higher in ITMPRE at 2 dpi (P < 0.05), and both ITM groups had higher liver Se at 5 dpi (P < 0.05) compared to CON. A time × treatment interaction was detected for liver Cu (P = 0.02). Clinical scores were lower (P < 0.05) in ITMPRE on 1 and 8 dpi and ITMPOST on 8 dpi compared to CON. Thoracic ultrasonography scores were lower in ITMPRE at 2 dpi compared to CON (P < 0.05) and ITMPOST (P < 0.1). No treatment effects (P > 0.10) were observed for bacterial detection from bronchoalveolar lavage (BAL) or nasopharyngeal swabs. At 5 dpi, both ITMPRE and ITMPOST showed higher frequencies of γδ T cells and NK cells in BAL compared to CON (P < 0.05). Before infection, leukocytes from ITMPRE steers produced more IL-6 (P < 0.01) in response to stimulation with the TLR agonist, Pam3CSK4. Use of ITM may be an effective strategy for improving disease resistance in feedlot cattle facing health challenges.

A novel diagnostic approach to Paratuberculosis in dairy cattle using near-infrared spectroscopy and aquaphotomics

Introduction

As a contagious and chronic disease in the livestock industry, Paratuberculosis is a significant threat to dairy herds' genetic and economic resources. Due to intensive breeding and high production of dairy cattle, the incidence and prevalence are higher. Developing non-destructive diagnostic methods for the early detection and identification of healthy animals is paramount for breeding programs. Conventional methods are almost entirely destructive, have low accuracy, lack precision, and are time-consuming. Near-infrared spectroscopy (NIRS) and aquaphotomics can detect changes in biofluids and thus have the potential to diagnose disease. This study aimed to investigate the diagnostic ability of NIRS and aquaphotomics for Paratuberculosis in dairy cattle.

Methods

Blood plasma from dairy cattle was collected in the NIR range (1,300 nm to 1,600 nm) 60 days before and 100 days to 200 days after calving in two groups, positive and negative, using the same consecutive enzyme-linked immunosorbent assay test results three times as a reference test.

Results

NIRS and aquaphotomics methods invite 100% accuracy, sensitivity, and specificity to detect Paratuberculosis using data mining by unsupervised method, Principal Component Analysis, and supervised methods: Soft Independent Modeling of Class Analogiest, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Partial Least Square-Discriminant Analysis, and Support Vector Machine models.

Discussion

The current study found that monitoring blood plasma with NIR spectra provides an opportunity to analyze antibody levels indirectly via changes in water spectral patterns caused by complex physiological changes, such as the amount of antibodies related to Paratuberculosis by aquagram.

Strategies for Bovine Respiratory Disease (BRD) Diagnosis and Prognosis: A Comprehensive Overview

Despite significant advances in vaccination strategies and antibiotic therapy, bovine respiratory disease (BRD) continues to be the leading disease affecting the global cattle industry. The etiology of BRD is complex, often involving multiple microbial agents, which lead to intricate interactions between the host immune system and pathogens during various beef production stages. These interactions present environmental, social, and geographical challenges. Accurate diagnosis is essential for effective disease management. Nevertheless, correct identification of BRD cases remains a daunting challenge for animal health technicians in feedlots. In response to current regulations, there is a growing interest in refining clinical diagnoses of BRD to curb the overuse of antimicrobials. This shift marks a pivotal first step toward establishing a structured diagnostic framework for this disease. This review article provides an update on recent developments and future perspectives in clinical diagnostics and prognostic techniques for BRD, assessing their benefits and limitations. The methods discussed include the evaluation of clinical signs and animal behavior, biomarker analysis, molecular diagnostics, ultrasound imaging, and prognostic modeling. While some techniques show promise as standalone diagnostics, it is likely that a multifaceted approach-leveraging a combination of these methods-will yield the most accurate diagnosis of BRD.

Immune Cells in the Spleen of Mice Mediate the Inflammatory Response Induced by Mannheimia haemolytica A2 Serotype

(1) Background: Mannheimia haemolytica (M. haemolytica) is an opportunistic pathogen and is mainly associated with respiratory diseases in cattle, sheep, and goats. (2) Methods: In this study, a mouse infection model was established using a M. haemolytica strain isolated from goats. Histopathological observations were conducted on various organs of the mice, and bacterial load determination and RNA-seq analysis were specifically performed on the spleens of the mice. (3) Results: The findings of this study suggest that chemokines, potentially present in the spleen of mice following a M. haemolytica challenge, may induce the migration of leukocytes to the spleen and suppress the release of pro-inflammatory factors through a negative feedback regulation mechanism. Additionally, an interesting observation was made regarding the potential of hematopoietic stem/progenitor cells congregating in the spleen to differentiate into immune cells, which could potentially collaborate with leukocytes in their efforts to counteract M. haemolytica invasion. (4) Conclusions: This study revealed the immune regulation mechanism induced by M. haemolytica in the mouse spleen, providing valuable insights into host-pathogen interactions and offering a theoretical basis for the prevention, control, and treatment of mannheimiosis.

Estimating the sensory-associated metabolites profiling of matcha based on PDO attributes as elucidated by NIRS and MS approaches

Matcha has been globally valued by consumers for its distinctive fragrance and flavor since ancient times. Currently, the protected designation of origin (PDO) certified matcha, characterized by unique sensory attributes, has garnered renewed interest from consumers and the industry. Given the challenges associated with assessing sensory perceptions, the origin of PDO-certified matcha samples from Guizhou was determined using NIRS and LC-MS platforms. Notably, the accuracy of our established attribute models, based on informative wavelengths selected by the CARS-PLS method, exceeds 0.9 for five sensory attributes, particularly the particle homogeneity attribute (with a validation correlation coefficient of 0.9668). Moreover, an LC-MS method was utilized to analyze non-target matcha metabolites to identify the primary flavor compounds associated with each flavor attribute and to pinpoint the key constituents responsible for variations in grade and flavor intensity. Additionally, high three-way intercorrelations between descriptive sensory attributes, metabolites, and the selected informative wavelengths were observed through network analysis, with correlation coefficients calculated to quantify these relationships. In this research, the integration of matcha chemical composition and sensory panel data was utilized to develop predictive models for assessing the flavor profile of matcha based on its chemical properties.

NMR-based metabolomics of plasma from dairy calves infected with two primary causal agents of bovine respiratory disease (BRD)

Each year, bovine respiratory disease (BRD) results in significant economic loss in the cattle sector, and novel metabolic profiling for early diagnosis represents a promising tool for developing effective measures for disease management. Here, ¹H-nuclear magnetic resonance (¹H-NMR) spectra were used to characterize metabolites from blood plasma collected from male dairy calves (n = 10) intentionally infected with two of the main BRD causal agents, bovine respiratory syncytial virus (BRSV) and Mannheimia haemolytica (MH), to generate a well-defined metabolomic profile under controlled conditions. In response to infection, 46 metabolites (BRSV = 32, MH = 33) changed in concentration compared to the uninfected state. Fuel substrates and products exhibited a particularly strong effect, reflecting imbalances that occur during the immune response. Furthermore, ¹H-NMR spectra from samples from the uninfected and infected stages were discriminated with an accuracy, sensitivity, and specificity ≥ 95% using chemometrics to model the changes associated with disease, suggesting that metabolic profiles can be used for further development, understanding, and validation of novel diagnostic tools.

Adaptive Spectral Model for abnormality detection based on physiological status monitoring of dairy cows

This research study developed milk spectral data-driven approach, called Adaptive Spectral Model for Abnormality Detection - ASMAD, for detection of physiological abnormalities of individual dairy cows. The algorithm is based on the logic that milk spectra of each individual cow is highly animal-specific, which means it could be used as a respective individual marker for identification. When the algorithm fails to recognize the milk spectra as coming from a certain animal, instead of being treated as a mistake, this outcome is accepted as a deviation of the respective individual marker, and a potential indicator of abnormal physiological state. For the purpose of ASMAD development, near infrared spectra of milk of seven dairy cows have been collected daily during 1-year period. The abnormality detection model is built using supervised recognition method Soft Independent Modeling of Class Analogies - SIMCA, and optimized with respect to spectral pre-processing, choice of the wavelength region and size of the time-window when developing the adaptive model. The sensitivity and specificity of ASMAD were dependent on the animal, and in the ranges 40.00-64.29% and 87.23-98.86%, respectively. Considering significant level of day-to-day spectral variation and multitude of physiological and environmental factors influence on milk constituents and spectra, these results represent a significant potential for creating a health-status monitoring and detection of abnormal physiological states in dairy animals. The adaptive modeling based on the time series of spectral data collected from the individual organism utilized in this work for monitoring physiological status and abnormality detection in dairy cows, has a good potential to be used for similar purposes in other animals and humans.

Portable near-infrared spectroscopy: A rapid and accurate blood test for diagnosis of Haemonchus contortus infection and for targeted selective treatment of sheep

Haemonchus contortus is the most prevalent and important gastrointestinal nematode (GIN) in small ruminants. Since it reduces the packed cell volume (PCV), causing anemia, early diagnosis can be used for targeted selective treatment (TST) of sheep, reducing antiparasitic drug use and anthelmintic resistance. This study aimed to predict PCV values through near-infrared spectroscopy (NIRS) and to develop a classification and diagnosis model of H. contortus infection using PCV values, eggs per gram of feces (EPG) counts and mean daily weight gain (DWG). A total of 1728 spectra were collected from blood samples of 216 lambs with a portable NIR spectroscope. In parallel, other parameters indicative of infection were measured: PCV by hematocrit, FAffa MAlan CHArt (FAMACHA) scores, EPG and DWG. To evaluate the relationship between NIRS spectra and the evaluated parameters, principal component analysis (PCA) was used for an exploratory analysis, regression by the partial least squares method (PLS) for the prediction of PCV values via NIRS, and PCA linear discriminant analysis (PCA-LDA) as a classification model for diagnosis. The absorption peaks in the NIRS region associated with the excitation of overtones of nitrogen-hydrogen (N-H) functional groups of proteins had a strong impact on the principal components (PCs), indicating that blood proteins, especially hemoglobin, can be estimated by the NIRS technique. The model for predicting PCV by PLS presented a standard error of prediction of 2.53, root-mean-square error of 2.48, and coefficient of determination of 0.84, indicating good correlation between the PCV values predicted by the model and the PCV obtained by hematocrit. The PCA-LDA model presented 93.33% sensitivity and 82.18% accuracy, both higher than those of the FAMACHA method, as was expected for resilient Morada Nova lambs. The multivariate models associated with the NIRS technique reported here can be used in the future as a quick and versatile tool for H. contortus infection diagnosis and TST application in lambs.

Aquaphotomics monitoring of strawberry fruit during cold storage - A comparison of two cooling systems

The objective of this study was to use aquaphotomics and near-infrared (NIR) spectroscopy to follow the changes in strawberries during cold storage in the refrigerator with an electric field generator (supercooling fridge, SCF) and without it (control fridge, CF). The NIR spectra of strawberries stored in these refrigerators were collected over the course of 15 days using a portable mini spectrometer and their weight was measured daily. The spectral data in the region of the first overtone of water (1,300-1,600 nm) were analyzed using aquaphotomics multivariate analysis. The results showed a decrease in weight loss of strawberries, but the loss of weight was significantly lower in SCF, compared to the CF. The reduction of weight loss due to exposure to an electric field was comparable to the use of coatings. The aquaphotomics analysis showed that the NIR spectra adequately captured changes in the fruit over the storage period, and that it is possible to predict how long the fruit spent in storage, regardless of the storage type. During aquaphotomics analysis, 19 water absorbance bands were found to be consistently repeating and to have importance for the description of changes in strawberries during cold storage. These bands defined the water spectral pattern (WASP), multidimensional biomarker that was used for the description of the state and dynamics of water in strawberries during time spent in storage. Comparison of WASPs of strawberries in CF and SCF showed that exposure to an electric field leads to a delay in ripening by around 3 days. This was evidenced by the increased amount of structural, strongly bound water and vapor-like trapped water in the strawberries stored in SCF. This particular state of water in strawberries stored in SCF was related to the hardening of the strawberry skin and prevention of moisture loss, in agreement with the results of significantly decreased weight loss.

Bovine Respiratory Syncytial Virus (BRSV) Infection Detected in Exhaled Breath Condensate of Dairy Calves by Near-Infrared Aquaphotomics

Bovine respiratory syncytial virus (BRSV) is a major contributor to respiratory disease in cattle worldwide. Traditionally, BRSV infection is detected based on non-specific clinical signs, followed by reverse transcriptase-polymerase chain reaction (RT-PCR), the results of which can take days to obtain. Near-infrared aquaphotomics evaluation based on biochemical information from biofluids has the potential to support the rapid identification of BRSV infection in the field. This study evaluated NIR spectra (n = 240) of exhaled breath condensate (EBC) from dairy calves (n = 5) undergoing a controlled infection with BRSV. Changes in the organization of the aqueous phase of EBC during the baseline (pre-infection) and infected (post-infection and clinically abnormal) stages were found in the WAMACS (water matrix coordinates) C1, C5, C9, and C11, likely associated with volatile and non-volatile compounds in EBC. The discrimination of these chemical profiles by PCA-LDA models differentiated samples collected during the baseline and infected stages with an accuracy, sensitivity, and specificity >93% in both the calibration and validation. Thus, biochemical changes occurring during BRSV infection can be detected and evaluated with NIR-aquaphotomics in EBC. These findings form the foundation for developing an innovative, non-invasive, and in-field diagnostic tool to identify BRSV infection in cattle.