Foot-and-mouth disease-associated myocarditis is age dependent in suckling calves.

Myocarditis is considered a fatal form of foot-and-mouth disease (FMD) in suckling calves. In the present study, a total of 17 calves under 4 months of age and suspected clinically for FMD were examined for clinical lesions, respiratory rate, heart rate, and heart rhythm. Lesion samples, saliva, nasal swabs, and whole blood were collected from suspected calves and subjected to Sandwich ELISA and reverse transcription multiplex polymerase chain reaction (RT-mPCR) for detection and serotyping of FMD virus (FMDV). The samples were found to be positive for FMDV serotype "O". Myocarditis was suspected in 6 calves based on tachypnoea, tachycardia, and gallop rhythm. Serum aspartate aminotransferase (AST), creatinine kinase myocardial band (CK-MB) and lactate dehydrogenase (LDH), and cardiac troponins (cTnI) were measured. Mean serum AST, cTn-I and LDH were significantly higher (P < 0.001) in < 2 months old FMD-infected calves showing clinical signs suggestive of myocarditis (264.833 ± 4.16; 11.650 ± 0.34 and 1213.33 ± 29.06) than those without myocarditis (< 2 months old: 110.00 ± 0.00, 0.06 ± 0.00, 1050.00 ± 0.00; > 2 months < 4 months: 83.00 ± 3.00, 0.05 ± 0.02, 1159.00 ± 27.63) and healthy control groups (< 2 months old: 67.50 ± 3.10, 0.047 ± 0.01, 1120.00 ± 31.62; > 2 months < 4 months: 72.83 ± 2.09, 0.47 ± 0.00, 1160.00 ± 18.44). However, mean serum CK-MB did not differ significantly amongst the groups. Four calves under 2 months old died and a necropsy revealed the presence of a pathognomic gross lesion of the myocardial form of FMD known as "tigroid heart". Histopathology confirmed myocarditis. This study also reports the relevance of clinical and histopathological findings and biochemical markers in diagnosing FMD-related myocarditis in suckling calves.

Application of machine learning models for risk estimation and risk prediction of classical swine fever in Assam, India.

The present study is aimed to develop an early warning system of Classical swine fever (CSF) disease by applying machine learning models and to study the climate-disease relationship with respect to the spatial occurrence and outbreaks of the disease in the north-eastern state of Assam, India. The disease incidence data from the year 2005 to 2021 was used. The linear discriminant analysis (LDA) revealed that significant environmental and remote sensing risk factors like air temperature, enhanced vegetation index, land surface temperature, potential evaporation rate and wind speed were significantly contributing to CSF incidences in Assam. Furthermore, the climate-based disease modelling was applied to relevant ecological and environmental risk factors determined using LDA and risk maps were generated. The western and eastern regions of the state were predicted to be at high risk of CSF with presence of significant hotspots. For the districts that are significantly clustered, the Basic reproduction number (R0) was calculated after the predicted results were superimposed onto the risk maps. The R0 value ranged from 1.04 to 2.07, implying that the eastern and western regions of Assam are more susceptible to CSF. Machine learning models were implemented using R statistical software version 3.1.3. The random forest, classification tree analysis and gradient boosting machine were found to be the best-fitted models for the study group. The models' performance was measured using the Receiving Operating Characteristic (ROC) curve, Cohen's Kappa, True Skill Statistics, Area Under ROC Curve, ACCURACY, ERROR RATE, F1 SCORE, and Logistic Loss. As a part of the suggested study, these models will help us to understand the disease transmission dynamics, risk factors and spatio-temporal pattern of spread and evaluate the efficacy of control measures to battle the economic losses caused by CSF outbreaks. Supplementary Information: The online version contains supplementary material available at 10.1007/s13337-023-00847-6.

Contaminant retention characteristics of fly ash-bentonite mixes.

It is important to determine the contaminant retention characteristics of materials when assessing their suitability for use as liners in landfill sites. Sand-bentonite mixtures are commonly used as liners in the construction of landfill sites for industrial and hazardous wastes. Sand is considered to be a passive material with a negligible chemical retention capacity; fly ash, however, offers the additional advantage of adsorbing the heavy metals present in landfill leachates. There have been few studies of the contaminant retention characteristics of fly ash-bentonite mixes. The study reported here determined the contaminant retention characteristics of different fly ashes, bentonite and selected fly ash-bentonite mixes for Pb2+ using 24 h batch tests. The tests were conducted by varying the initial concentrations of metal ions under uncontrolled pH conditions. The efficiency of the removal of Pb2+ by the different types of fly ash and fly ash-bentonite mixes was studied. The influence of multiple sources of fly ash on the retention characteristics of fly ash-bentonite mixes was investigated.