Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies

Purpose@#Fetal well-being is usually assessed via fetal heart rate (FHR) monitoring during the antepartum period. However, the interpretation of FHR is a complex and subjective process with low reliability. This study developed a machine learning model that can classify fetal cardiotocography results as normal or abnormal. @*Materials and Methods@#In total, 17492 fetal cardiotocography results were obtained from Ajou University Hospital and 100 fetal cardiotocography results from Czech Technical University and University Hospital in Brno. Board-certified physicians then reviewed the fetal cardiotocography results and labeled 1456 of them as gold-standard; these results were used to train and validate the model. The remaining results were used to validate the clinical effectiveness of the model with the actual outcome. @*Results@#In a test dataset, our model achieved an area under the receiver operating characteristic curve (AUROC) of 0.89 and area under the precision-recall curve (AUPRC) of 0.73 in an internal validation dataset. An average AUROC of 0.73 and average AUPRC of 0.40 were achieved in the external validation dataset. Fetus abnormality score, as calculated from the continuous fetal cardiotocography results, was significantly associated with actual clinical outcomes [intrauterine growth restriction: odds ratio, 3.626 (p=0.031); Apgar score 1 min: odds ratio, 9.523 (p<0.001), Apgar score 5 min: odds ratio, 11.49 (p=0.001), and fetal distress: odds ratio, 23.09 (p<0.001)]. @*Conclusion@#The machine learning model developed in this study showed precision in classifying FHR signals. This suggests that the model can be applied to medical devices as a screening tool for monitoring fetal status.

Electrolyte and acid-base imbalance in native calves with enteropathogenic diarrhea

Abstract: Diarrhea is the most common cause of death in calves, and remains a major health challenge. Although there are many studies on the related pathogens, the understanding of the clinicopathological changes is limited. This study aimed to identify the pathogens and observe the clinicopathological changes in electrolytes and acute phase proteins (APPs) associated with diarrhea.Blood samples and fecal samples were collected from 141 calves for the determination of APPs, electrolyte and acid-base status and identification of enteropathogens, respectively. Single or co-infections with enteropathogens, including virus (bovine viral diarrhea virus, coronavirus, and rotavirus), Eimeria, Cryptosporidium, and Escherichia coliK99 were detected in both non-diarrheic and diarrheic calves. Levels of APPs such as serum amyloid A, haptoglobin and fibrinogen were comparable between diarrheic and nondiarrheic calves. Hypoglycemia, high blood urea, electrolytes and acid-base imbalance (hyponatremia, hypochloremia, and decreased bicarbonate), and strong ion difference (SID) acidosis showed a significant association in diarrheic calves (p < 0.01). Particularly, significant hyponatremia, bicarbonate loss, SID acidosis, hypoglycemia, and elevated blood urea nitrogen were found in rotavirusinfected calves. Monitoring the clinicopathological parameters of APPs and electrolyte levels could be vital in the clinical management of diarrheic calves.

Diagnosis of Lymphoid Malignancy by PCR for Analysis of Antigen Receptor Rearrangement after Blood Transfusion in a Dog with Acute Lymphocytic Leukemia

Acute lymphocytic leukemia (ALL) is uncommon lymphoid malignancy in dogs, and its diagnosis is challenging. A 14-year-old spayed female mixed breed dog was transferred to a veterinary medical teaching hospital for an immediate blood transfusion. The dog showed lethargy, pale mucous membranes, and a weak femoral pulse. Complete blood count revealed non-regenerative anemia and severe leukopenia with thrombocytopenia. ALL was tentatively diagnosed based on the predominance of immature lymphoblasts on blood film examination. For confirmation of lymphoid malignancy, PCR for antigen receptor rearrangement (PARR) on a peripheral blood sample and flow cytometry analysis were performed after blood transfusion. Flow cytometry analysis revealed that lymphocyte subsets were of normal composition, but PARR detected a T-cell malignancy. The dog was diagnosed with ALL and survived 1 wk after diagnosis. In conclusion, after blood transfusion, flow cytometry was not a reliable diagnostic method for an ALL dog, whereas PARR could detect lymphoid malignancy. Our results suggest that PARR should be the first-line diagnostic tool to detect canine lymphoid malignancy after a blood transfusion.