Albumin and other metabolic parameters as potential indicators of purulent vaginal discharge in dairy cows during the transition period.

The aims of this study were to evaluate metabolic profiles obtained at -14, 14, and 28 days in milk (DIM), and to identify potential predictive biomarkers of Holstein dairy cows with purulent vaginal discharge (PVD) at 28 DIM. The body condition score (BCS) and hematocrit (Hct) were evaluated, and a metabolic profile test (MPT) was performed at -14, 14, and 28 DIM using serum samples. Cows at 28 DIM were classified using a vaginoscopy and divided into groups of healthy cows (n=89) and cows with PVD (n=31). Albumin (Alb), total cholesterol (TCho), calcium (Ca) and, magnesium (Mg) levels were lower in cows with PVD than in healthy cows at 14 DIM. At 28 DIM, levels of Alb, TCho, Ca, blood urea nitrogen (BUN), Mg, and Hct were lower in cows with PVD. A multivariate stepwise logistic regression analysis showed that higher non-esterified fatty acids (NEFA; odds ratios; OR=4.47; P<0.01), lower Alb (OR=0.07; P<0.01) and lower TCho (OR=0.99; P=0.08) at 14 DIM, and lower Hct (OR=0.83; P=0.05), lower Alb (OR=0.12; P<0.01), and lower BUN (OR=0.74; P=0.02) at 28 DIM were significantly associated with PVD. In conclusion, serum Alb levels was a potential indicator associated with PVD, reflecting dietary protein deficiency preceding disease. Our findings suggest that MPT should be considered to monitor health status during the postpartum period for early diagnosis of PVD.

An in vitro comparison of antimicrobial efficacy and cytotoxicity between povidone-iodine and chlorhexidine for treating clinical endometritis in dairy cows.

This study aimed to assess the in vitro antimicrobial effects of chlorhexidine (CHX) and povidone-iodine (PI) on clinical isolates of Escherichia coli (E. coli) and Trueperella pyogenes (T. pyogenes) from the vaginal discharge of dairy cows, as well as to compare the cytotoxicity effects of CHX and PI on bovine endometrial epithelial cells (BEnEpC). In Experiment 1, 12 E. coli and 10 T. pyogenes were isolated from the vaginal discharge of cows with a uterine infection. The MIC and MBC against CHX and PI were analyzed in vitro. In Experiment 2, the cytotoxicity effects of CHX and PI on BEnEpC were analyzed using a Viability/Cytotoxicity Kit, wound scratch healing assay, and the expression of pro-inflammatory cytokine genes (IL-6, IL-8, and TNF-α). In Experiment 1, the MIC and MBC values of CHX against E. coli were 0.0002% and 0.0002 to 0.00025%, respectively. The MIC and MBC values of PI were 1.25 to 2.5% and 1.25 to 5%, respectively. For T. pyogenes, the MIC and MBC values of CHX were 0.00002%. The MIC and MBC values of PI were 1.25%. In Experiment 2, the cell viability significantly decreased, and wound closures were significantly inhibited after treatment with ≥ 0.002% CHX and ≥ 0.025% PI. The expression of IL-6, IL-8, and TNF-α significantly increased after treatment with PI. Only IL-6 showed a significant increase after cells were treated with 0.00002% and 0.0002% CHX. The results suggested that both CHX and PI had high antibacterial effects. However, veterinarians and farmers should be aware of their cytotoxicity, which decrease viability of endometrial epithelial cells and inhibit wound healing in vitro.

Prevalence of respiratory bacterial pathogens and associated management factors in dairy calves in Taiwan.

This study aimed to investigate the prevalence at both farm-level and calf-level and to identify the risk factors of respiratory bacterial pathogens in dairy calves in Taiwan. The status of bovine respiratory disease (BRD) was evaluated by using the Wisconsin scoring system from a total of 400 pre-weaned calves from 32 different farms in Taiwan, then the nasopharyngeal swabs were collected. The prevalence of respiratory pathogens was 84.37% at farm-level and 45.50% at calf-level, and Pasteurella multocida (P. multocida) was the most prevalent pathogen. The presence of Mycoplasma bovis (M. bovis), P. multocida, Mannheimia haemolytica (M. haemolytica) and Histophilus somni (H. somni) were all higher in BRD positive calves than BRD negative calves, but only in H. somni was significant (P<0.001). Then nine farm management risk factors were analyzed by using multivariate logistic regression models to determine the risk factors of respiratory bacterial pathogens (farm and calf-level). In the result at farm-level, only unheated colostrum was significantly associated with pathogen positive farms (Odds Ratio (OR)=11.43). At calf-level, the predominant risk factor for each pathogen, M. bovis, P. multocida, M. haemolytica and H. somni, was late first colostrum feeding (OR=272.82), unheated colostrum (OR=3.41), waste milk feeding (OR=6.59) and high pneumonia treatment cost (OR=2.52), respectively. For effective preventive measures, farmer education on milk and colostrum feeding are urgently warranted.

Automatic Segmentation of Retinal Fluid and Photoreceptor Layer from Optical Coherence Tomography Images of Diabetic Macular Edema Patients Using Deep Learning and Associations with Visual Acuity.

Diabetic macular edema (DME) is a highly common cause of vision loss in patients with diabetes. Optical coherence tomography (OCT) is crucial in classifying DME and tracking the results of DME treatment. The presence of intraretinal cystoid fluid (IRC) and subretinal fluid (SRF) and the disruption of the ellipsoid zone (EZ), which is part of the photoreceptor layer, are three crucial factors affecting the best corrected visual acuity (BCVA). However, the manual segmentation of retinal fluid and the EZ from retinal OCT images is laborious and time-consuming. Current methods focus only on the segmentation of retinal features, lacking a correlation with visual acuity. Therefore, we proposed a modified U-net, a deep learning algorithm, to segment these features from OCT images of patients with DME. We also correlated these features with visual acuity. The IRC, SRF, and EZ of the OCT retinal images were manually labeled and checked by doctors. We trained the modified U-net model on these labeled images. Our model achieved Sørensen-Dice coefficients of 0.80 and 0.89 for IRC and SRF, respectively. The area under the receiver operating characteristic curve (ROC) for EZ disruption was 0.88. Linear regression indicated that EZ disruption was the factor most strongly correlated with BCVA. This finding agrees with that of previous studies on OCT images. Thus, we demonstrate that our segmentation network can be feasibly applied to OCT image segmentation and assist physicians in assessing the severity of the disease.

Automated coronary artery calcium scoring using nested U-Net and focal loss.

Coronary artery calcium (CAC) is a great risk predictor of the atherosclerotic cardiovascular disease and CAC scores can be used to stratify the risk of heart disease. Current clinical analysis of CAC is performed using onsite semiautomated software. This semiautomated CAC analysis requires experienced radiologists and radiologic technologists and is both demanding and time-consuming. The purpose of this study is to develop a fully automated CAC detection model that can quantify CAC scores. A total of 1,811 cases of cardiac examinations involving contrast-free multidetector computed tomography were retrospectively collected. We divided the database into the Training Data Set, Validation Data Set, Testing Data Set 1, and Testing Data Set 2. The Training, Validation, and Testing Data Set 1 contained cases with clinically detected CAC; Testing Data Set 2 contained those without detected calcium. The intraclass correlation coefficients between the overall standard and model-predicted scores were 1.00 for both the Training Data Set and Testing Data Set 1. In Testing Data Set 2, the model was able to detect clinically undetected cases of mild calcium. The results suggested that the proposed model's automated detection of CAC was highly consistent with clinical semiautomated CAC analysis. The proposed model demonstrated potential for clinical applications that can improve the quality of CAC risk stratification.

Cryptosporidium parvum infection and management-based risk factors of dairy calves in Taiwan.

Cryptosporidiosis is one of the major causes of diarrhea in calves. Cryptosporidium parvum is considered the most important calf diarrhea pathogen in the Cryptosporidium species. Not only could infected calves spread C. parvum, but infected adult cattle could also shed oocysts. The objectives of this study were (1) to investigate the prevalence of C. parvum in dairy herds in Taiwan, including calves, the dams in delivery enclosure, the floor, and the drinking water; (2) to clarify the relationship of diarrhea, management, and C. parvum infection. Twenty dairy herds in Taiwan were selected by random sampling, including 226 calves and 198 dams, and other environmental samples were collected. A questionnaire was filled out by the farm owners to collect information regarding the management of calves and the delivery enclosure. Hierarchical logistic regression was used to analyze the risk factors for C. parvum infection. The prevalence of C. parvum infection in calves was 26.5% (60/226), while in dams, it was 19.7% (39/198). The C. parvum infection in calves increased with environmental contamination of C. parvum and clinical signs of diarrhea, while it decreased with a yard provided in the delivery enclosure. In conclusion, the management of the delivery enclosure appears to be more important for preventing cryptosporidiosis in calves in Taiwan.