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Lumpy skin disease virus (LSDV) is an extremely infectious, viral, and chronic skin disease that is caused by the Capripox virus. This viral disease is predominantly found in cows. Mosquitoes and ticks are the primary transmitters for the spread of this virus. Recently, LSDV has been rapidly spreading all over the world, especially in several areas of Pakistan, India, and Iran. Thousands of cows have died due to this infectious virus in Pakistan and early detection of LSDV is needed to avoid further loss. The prediction and classification of LSDV are hindered by the lack of publicly available datasets. Despite a few studies using LSDV datasets, such datasets are often small, which may lead to model overfitting. In this regard, we collect the dataset from several online sources, as well as, collecting images from veterinary farms in different areas of Pakistan. Deep learning has been widely used in the medical field for disease detection and classification. Therefore, this study leverages DenseNet deep learning models for LSDV detection and classification. Experiments are performed using VGG-16, ResNet-50, MobileNet-V2, custom-designed convolutional neural network, and Inception-V3. The DenseNet architecture presents a Convolutional Block Attention Module (CBAM) and Spatial Attention (SA) for the prediction and classification of LSD. Results demonstrate that a 99.11% accuracy can be obtained on the augmented dataset while a 94.23% accuracy can be achieved with the original dataset for chicken pox, monkey pox, and LSDV. Comparison with state-of-the-art studies corroborates the superior performance of the proposed model.

The lumpy skin disease virus (LSDV) infects cattle and buffalo and causes lumpy skin disease (LSD). It affects the lymph nodes of the sick animals, causing them to enlarge and appear as lumps (cutaneous nodules) that are 2–5 cm in diameter on their heads, necks, limbs, udders, genitalia, and perinea. A high temperature, a sharp drop in milk supply, discharge from the eyes and nose, salivation, a loss of appetite, depression, damaged hides, and emaciation are further warning signs and symptoms. As per the Food and Agriculture Organization (FAO), the incubation period, or the time between an infection and symptoms, is approximately 28 days. Infected animals can transfer the virus by direct contact with the vectors, direct virus secretion from mouth or nose, shared feeding and watering troughs, and even artificial insemination. The World Organization for Animal Health (WOAH) and the FAO both warn that the spread of illnesses could lead to serious economic losses. This illness reduces cow’s milk production because oral ulcers make the animal weak and lead them to lose their appetite. There are many diagnostics available for LSDV. However, very few tests yield accurate findings. The best methods for preventing and controlling the lumpy skin condition include vaccination and movement restrictions. As a specific cure is not available, the only available treatment for this illness is supportive care for cattle. Recently, India has developed a homologous, live-attenuated vaccine, Lumpi-ProVacInd, which is specifically intended to protect animals against the LSD virus. This study’s primary goal is to accumulate data on symptoms, the most accurate method of diagnosis, treatments, and controls to stop infections from spreading as well as to explore future possibilities for the management of LSDV.

Lumpy skin disease (LSD) is a transboundary viral disease caused by lumpy skin disease virus (LSDV), belonging to the Capripoxvirus genus and Poxviridae family. This study reports on the molecular detection, seroprevalence and biochemical analysis of samples from cattle infected with LSDV in Madhya Pradesh (MP) and Telangana. A total of 189 samples (116 blood, 26 tissue, 47 nasal swabs) were collected from MP during 2022–2023. Molecular detection was performed using conventional PCR targeting the P32 and fusion genes, while seroprevalence was assessed using an indirect ELISA kit on 184 serum samples collected from MP and Telangana between 2022 and 2024. Tissue samples showed a higher positivity rate (69.23%) for the P32 gene, while nasal swabs had a 6.38% positivity rate. The fusion gene was detected in 77.77% of tissue and 66.66% of nasal swab samples. The seroprevalence study revealed that 19.56% of serum samples were positive, with a higher prevalence of 86.11% in MP. Biochemical analysis indicated elevated levels of SGPT, SGOT, BUN, creatinine, albumin, globulin and the A/G ratio in LSDV-infected cattle, though these differences were not statistically significant. The study emphasizes that blood samples are not ideal for LSDV detection and the timing of serum sample collection plays a critical role in seroprevalence studies.

Background Lumpy skin disease (LSD), caused by infection with the lumpy skin disease virus (LSDV), is a highly infectious disease that poses a notable challenge to the cattle industry worldwide. To conduct epidemiological monitoring of LSDV infection in cattle and evaluate the immune efficacy of LSDV vaccines, it is essential to develop a rapid, sensitive, and specific ELISA-based antibody detection method. Results We utilized the LSDV P32 protein, stably expressed in a CHO-K1 suspension cell system, as a coating antigen to develop an indirect ELISA for Capripoxvirus (CaPV) antibody detection. This method specifically recognizes CaPV-positive sera without cross-reactivity with sera positive for bovine viral diarrhea virus, bovine rotavirus, infectious bovine rhinotracheitis virus, and Brucella antibodies. The method demonstrated a maximum serum dilution detection capacity of 1:3200, with intra- and inter-assay variation coefficients below 10%. Comparison with a commercially available kit showed an agreement of 95.7%. Conclusion The indirect ELISA antibody detection method established exhibited excellent specificity, sensitivity, and reproducibility, providing a reliable tool for clinical detection and epidemiological surveys of LSDV. This method offers significant potential for the prevention and control of LSD outbreaks.

Lumpy skin disease (LSD) is a viral disease that affects cattle and buffaloes in Egypt, causing considerable economic losses in the animal sector. This study aimed to investigate the recent outbreak of LSDV in cattle and buffaloes and evaluate the potential role of the hard tick Rhipicephalus annulatus in their transmission through isolation and molecular characterization by multiplex PCR (mPCR) and real-time quantitative PCR (rt-qPCR) assays. A total of 50 skin biopsies (cattle n = 30, buffaloes n = 20), 110 nasal swabs (cattle n = 76, buffaloes n = 44), and 129 blood samples (cattle n = 84, buffaloes n = 45) were collected. In addition, 145 hard ticks of different stages were collected from cattle and buffaloes of different breeds and ages in different governorates in Egypt from November 2021 to June 2022. Multiplex PCR and real-time quantitative PCR (rt-qPCR) assays based on SYBR Green and targets (P32, VP32, G protein, and viral fusion protein) were used. We identified positive results in 17 out of 30 cattle skin biopsies (56.6%), 1 out of 7 buffalo skin scabs (14.3%), and 5 out of 45 buffalo blood samples (11.11%) using mPCR and RT-qPCR methods. We successfully isolated LSDV from hard ticks and cattle infested with ticks and exhibited characteristic signs of LSD on the chorioallantois membrane (CAM) of specific pathogen-free embryonated chicken eggs (SPF-ECE). The isolates were confirmed by multiplex PCR and RT-qPCR. The cyclic threshold (Ct) with correlation-slandered curve values of rt-qPCR ranging from 10.2 to 36.5 showed the amount of LSDV-DNA in different samples. The study's findings demonstrated the widespread circulation of LSDV in both cattle and buffaloes in Egypt and provided strong evidence that hard ticks R. annulatus play a role in the transmission of LSDV in susceptible animals.

Lumpy skin disease virus (LSDV) is a significant threat to cattle, particularly in countries like Thailand, where outbreaks have necessitated the importation of diagnostic kits and vaccines. This study aimed to evaluate several recombinant extracellular enveloped virion (EEV) protein candidates, including F13L, A33R, A34R, and B5R, for their potential use in serological detection assays for LSDV specific antibodies in cattle. Given the challenges associated with LSDV research, such as its classification as a Class III biological agent in Thailand, gene synthesis was employed to produce these proteins. The recombinant proteins were expressed in a prokaryotic system and analyzed using SDS-PAGE and Western blotting. Among the candidates, F13L demonstrated the highest correlation with the results from a commercially available and validated ELISA, yielding 85.7%, and 75% positive for the infected and vaccinated groups, respectively, identifying it a promising candidate for serosurveillance activities during active LSDV outbreaks. Sequence analysis confirmed a 100% match between the F13L designed from the Neethling type strain 2490 and various Thai LSDV strains from the 2021 outbreaks, underscoring its potential as a conserved diagnostic marker. The availability of recombinant F13L and its reactivity with cattle sera from LSDV infected or vaccinated animals, demonstrated in this study, suggests it could also serve as a potential candidate for vaccine development. The study concludes that recombinant F13L shows great promise for the development of LSDV serological assays, though further optimization and validation are necessary to harness its diagnostic potential.

Lumpy skin disease virus (LSDV) causes an infectious disease in cattle. Due to its direct relationship with the survival of arthropod vectors, geospatial and climatic features play a vital role in the epidemiology of the disease. The objective of this study was to assess the ability of some machine learning algorithms to forecast the occurrence of LSDV infection based on meteorological and geological attributes. Initially, ExtraTreesClassifier algorithm was used to select the important predictive features in forecasting the disease occurrence in unseen (test) data among meteorological, animal population density, dominant land cover, and elevation attributes. Some machine learning techniques revealed high accuracy in predicting the LSDV occurrence in test data (up to 97%). In terms of area under curve (AUC) and F1 performance metric scores, the artificial neural network (ANN) algorithm outperformed other machine learning methods in predicting the occurrence of LSDV infection in unseen data with the corresponding values of 0.97 and 0.94, respectively. Using this algorithm, the model consisted of all predictive features and the one which only included meteorological attributes as important features showed similar predictive performance. According to the findings of this research, ANN can be used to forecast the occurrence of LSDV infection with high precision using geospatial and meteorological parameters. Applying the forecasting power of these methods could be a great help in conducting screening and awareness programs, as well as taking preventive measures like vaccination in areas where the occurrence of LSDV infection is a high risk.

Lumpy skin disease (LSD) is a critical problem for cattle populations, affecting both individual cows and the entire herd. Given cattle’s critical role in meeting human needs, effective management of this disease is essential to prevent significant losses. The study proposes a deep learning approach using the MobileNetV2 model and the RMSprop optimizer to address this challenge. Tests on a dataset of healthy and lumpy cattle images show an impressive accuracy of 95%, outperforming existing benchmarks by 4–10%. These results underline the potential of the proposed methodology to revolutionize the diagnosis and management of skin diseases in cattle farming. Researchers and graduate students are the audience for our paper.

Objective The resurgence of lumpy skin disease virus isolates of different genotypic natures abolishes the accuracy of assays that target either vaccine or field strain genome. The aim of the present study was to develop a universal real-time PCR assay using TaqMan chemistry to cover field, vaccine, and recombinant strains of lumpy skin disease virus isolates. Results The PCR assay was designed based on a LSDV044 target region that offers a unique identification locus to facilitate the sensitive and specific detection of all isolates known to date. The efficiency of amplification, determined over five orders of magnitude, was 93%, with the standard deviation remaining in the range of 0.11–0.23. Evaluation of the assay repeatability on three different days revealed that the inter-run variability ranged from 0.83 to 1.22 over five repetitions across three runs. This new screening assay is proposed as a fast, efficient, and sensitive tool that can be employed in the basic or applied surveillance studies regardless of the genotype. Moreover, the assay can be used for the routine laboratory testing of animal samples during eradication programs for lumpy skin disease.

The current epidemic in Asia, driven by LSDV recombinants, poses difficulties to existing DIVA PCR tests, as these do not differentiate between homologous vaccine strains and the recombinant strains. We, therefore, developed and validated a new duplex real-time PCR capable of differentiating Neethling-based vaccine strains from classical and recombinant wild-type strains that are currently circulating in Asia. The DIVA potential of this new assay, seen in the in silico evaluation, was confirmed on samples from LSDV infected and vaccinated animals and on isolates of LSDV recombinants (n = 12), vaccine (n = 5), and classic wild-type strains (n = 6). No cross-reactivity or a-specificity with other capripox viruses was observed under field conditions in non-capripox viral stocks and negative animals. The high analytical sensitivity is translated into a high diagnostic specificity as more than 70 samples were all correctly detected with Ct values very similar to those of a published first-line pan capripox real-time PCR. Finally, the low inter- and intra-run variability observed shows that the new DIVA PCR is very robust which facilitates its implementation in the lab. All validation parameters that are mentioned above indicate the potential of the newly developed test as a promising diagnostic tool which could help to control the current LSDV epidemic in Asia.