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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.

Mycobacterium Avium subspecies Paratuberculosis (MAP) causes Johne’s disease or Paratuberculosis, a chronic, progressive intestinal disease in ruminants. The incidence and prevalence of Johne’s disease are higher in dairy cattle herds because of intensive breeding and high production. Developing non-destructive diagnostic methods for early detection of this disease by simple sampling is paramount for breeding, economic, and health programs. Conventional methods are almost entirely destructive, have low accuracy, and are time-consuming. Near- infrared spectroscopy (NIRS) and Aquaphotomics can detect changes in biofluids and thus have the potential to diagnose the disease. This study aimed to investigate the diagnostic ability of NIRS and Aquaphotomics for Paratuberculosis in dairy cattle by milk sample. Milk samples from dairy cattle were collected in the NIR range (1300–1600 nm) 60 days before and 100–200 days after calving in two groups, positive and negative, using the three same consecutive ELISA test results of blood plasma and milk, as a reference test. The NIRS and Aquaphotomics methods in quadratic discriminant analysis (QDA) and support vector machine (SVM) models achieved high accuracy in detecting negative and positive groups. In internal validation, SVM and QDA models in 12 water absorbance bands had 100% accuracy. In external validation, milk samples with blood plasma ELISA reference test achieved 100% sensitivity, which is more accurate than milk ELISA as a reference test. The current study found that monitoring milk 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.

Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the main agents that cause severe mastitis disease with clinical signs in dairy cattle. Rapid detection of this disease is so important in order to prevent transmission to other cows and helps to reduce inappropriate use of antibiotics. With the rapid progress in high-throughput technologies, and accumulation of various kinds of '-omics' data in public repositories, there is an opportunity to retrieve, integrate, and reanalyze these resources to improve the diagnosis and treatment of different diseases and to provide mechanistic insights into host resistance in an efficient way. Meta-analysis is a relatively inexpensive option with good potential to increase the statistical power and generalizability of single-study analysis. In the current meta-analysis research, six microarray-based studies that investigate the transcriptome profile of mammary gland tissue after induced mastitis by E. coli infection were used. This meta-analysis not only reinforced the findings in individual studies, but also several novel terms including responses to hypoxia, response to drug, anti-apoptosis and positive regulation of transcription from RNA polymerase II promoter enriched by up-regulated genes. Finally, in order to identify the small sets of genes that are sufficiently informative in E. coli mastitis, the differentially expressed gene introduced by meta-analysis were prioritized by using ten different attribute weighting algorithms. Twelve meta-genes were detected by the majority of attribute weighting algorithms (with weight above 0.7) as most informative genes including CXCL8 (IL8), NFKBIZ, HP, ZC3H12A, PDE4B, CASP4, CXCL2, CCL20, GRO1(CXCL1), CFB, S100A9, and S100A8. Interestingly, the results have been demonstrated that all of these genes are the key genes in the immune response, inflammation or mastitis. The Decision tree models efficiently discovered the best combination of the meta-genes as bio-signature and confirmed that some of the top-ranked genes -ZC3H12A, CXCL2, GRO, CFB- as biomarkers for E. coli mastitis (with the accuracy 83% in average). This research properly indicated that by combination of two novel data mining tools, meta-analysis and machine learning, increased power to detect most informative genes that can help to improve the diagnosis and treatment strategies for E. coli associated with mastitis in cattle.

Non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), together with transcription factors, are critical pre-, co-, and post-transcriptional regulators. In addition to their criteria as ideal biomarkers, they have great potential in disease prognosis, diagnosis, and treatment of complex diseases. Investigation of regulatory mechanisms in the context of bovine mastitis, as most common and economic disease in the dairy industry, to identify elements influencing the expression of candidate genes as key regulators of the mammary immune response is not yet fully understood. Transcriptome profiles (50 RNA-Seq and 50 miRNA-Seq samples) of bovine monocytes induced by Str. uberis were used for co-expression module detection and preservation analysis using the weighted gene co-expression network analysis (WGCNA) approach. Assigned mi-, lnc-, and m-modules used to construct the integrated regulatory networks and miRNA-lncRNA-mRNA regulatory sub-networks. Remarkably, we have identified 18 miRNAs, five lncRNAs, and seven TFs as key regulators of str. uberis -induced mastitis. Most of the genes introduced here, mainly involved in immune response, inflammation, and apoptosis, were new to mastitis. These findings may help to further elucidate the underlying mechanisms of bovine mastitis, and the discovered genes may serve as signatures for early diagnosis and treatment of the disease.

Displaced abomasum (DA) is a digestive disorder that causes severe economic losses through the reduction in milk yield and early culling of cows. The predictive potential of DA-susceptible cases is of great importance to reduce economic losses. This study aimed for early prediction of DA. However, identifying cows at risk of DA can be difficult because DA is a complex trait and its incidence is low. For this purpose, in this study, the ability of five machine learning algorithms, namely Logistic Regression (LR), Naïve Bayes (NB), Decision Tree, Random Forest (RF) and Gradient Boosting Machines (GBM), to predict cases of DA was investigated. For these predictions, 20 herd–cow-specific features and sire genetic information from 7 Holstein dairy herds that calved between 2010 and 2020 were available. Model performance metrics indicated that GBM and RF algorithms outperformed the others in predicting DA with F2 measures of 0.32. The true positive rate in the RF was the highest compared to other methods at 0.75, followed by GBM at 0.70. Given the highly imbalanced data, this study showed the potential in forecasting cases susceptible to DA. This prediction tool can aid dairy farmers in making preventative management decisions by identifying cows susceptible to DA.

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. 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. 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. 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.

Paratuberculosis is a granulomatous intestinal infection that affects ruminant animals worldwide. The disease is often detected when most animals are already infected due to the long incubation period and the high transmissibility of the infectious agent. The lack of a comprehensive method to diagnose Paratuberculosis is a global challenge. Therefore, a non-destructive, fast, and cost-effective diagnostic method for early detection of Paratuberculosis is crucial. Near-infrared spectroscopy (NIRS) and Aquaphotomics have the potential to diagnose the disease by detecting changes in biological fluids. This study aimed to investigate the diagnostic ability of NIRS and Aquaphotomics for Paratuberculosis in dairy cattle by monitoring and data mining of saliva. The diagnostic models were developed according to saliva spectra of dairy cattle in the NIR range and 12 water absorbance bands from 100 to 200 days after calving in two groups: positive and negative, based on the same results of seven ELISA tests of blood plasma, as a reference test. Both NIRS and Aquaphotomics methods had high diagnostic accuracy. Using QDA and SVM models, 99% total accuracy, 98% sensitivity, and 100% specificity were achieved in internal validation. The total accuracy in external validation was 90%. This study presents two novel approaches to diagnosing Paratuberculosis in dairy cattle using saliva. The study found that changes in water absorbance spectral patterns of saliva caused by complex physiological changes, such as the amount of antibody related to Paratuberculosis in dairy cattle as biomarkers, are crucial in detecting Paratuberculosis using NIRS and Aquaphotomics.

Non-alcoholic fatty liver disease (NAFLD) is associated with an increased risk of cardiovascular events. HDL exerts various protective functions on the cardiovascular system including anti-inflammatory activity by suppressing adhesion molecules expression in inflammation-induced endothelial cells. This study was designed to search if the anti-inflammatory capacity of apolipoprotein B-depleted plasma (apoB-depleted plasma) is altered in NAFLD patients. A total of 83 subjects including 42 NAFLD and 41 control subjects were included in this cross-sectional study. Anti-inflammatory function of HDL was determined as the ability of apoB-depleted plasma to inhibit tumor necrosis factor-α (TNF-α)-induced expression of adhesion molecules in human umbilical vein endothelial cells (HUVECs). Incubation of inflammation-stimulated HUVECs with the NAFLD patients' apo-B depleted plasma led to higher levels of expression of adhesion molecules compared to the control subjects' plasma samples, reflecting an impaired anti-inflammatory capacity of apoB-depleted plasma in the NAFLD patients. Impaired anti-inflammatory capacity of apoB-depleted plasma was correlated with fatty liver and obesity indices. After adjustment with obesity indices, the association of anti-inflammatory capacity of apoB-depleted plasma with NAFLD remained significant. Impaired anti-inflammatory activity of apoB-depleted plasma was independently associated with NAFLD.

Background Non-alcoholic fatty liver disease (NAFLD) patients are at a substantial risk for developing cardiovascular disease (CVD). High-density lipoprotein (HDL) is well known to have protective effects against the development of atherosclerotic CVD. One of the major antiatherogenic effects of HDL is its anti-oxidative function. Objectives This study investigated the association of anti-oxidative capacity of HDL with subclinical atherosclerosis in NAFLD and non-NAFLD subjects. Methods A total of 143 subjects including 51 NAFLD and 92 control subjects were included in this case–control study. HDL oxidative index (HOI) was determined spectrophotometrically using a cell-free method in the presence of a fluorescent substrate dichlorofluorescein diacetate (DCFDA). Paraoxonase 1 (PON1) activity, superoxide dismutase (SOD) activity, and malondialdehyde (MDA) plasma levels were assessed in both groups. Results The NAFLD patients with impaired HDL anti-oxidative function (HOI  ≥ 1) had higher MDA levels, aspartate amino transferase (AST), liver stiffness (LS), and carotid intima-media thickness (cIMT) values compared to the controls. HDL oxidative index (HOI) was positively correlated with MDA levels and cIMT and negatively correlated with SOD activity. Conclusions Higher circulating levels of MDA were associated with the impaired anti-oxidative function of HDL in NAFLD. The impaired anti-oxidative capacity of HDL might be related to NAFLD severity and subclinical atherosclerosis in NAFLD patients.