Marek\'s disease (MD), caused by the Marek\'s disease virus (MDV), is a common infectious tumor disease in chickens and was the first neoplastic disease preventable by vaccination. However, the vaccine cannot completely prevent virulent MDV infections, allowing both the vaccine and virulent MDV to coexist in the same chicken for extended periods. This study aims to investigate the changes in viral load of the very virulent strain Md5 and the rHVT-IBD vaccine in different chicken tissues using a real-time PCR assay. The results showed that the rHVT-IBD vaccine significantly reduced the viral load of MDV-Md5 in different organs, while the load of rHVT-IBD was significantly increased when co-infected with Md5. Additionally, co-infection with Md5 and rHVT-IBD in chickens not only changed the original viral load of both viruses but also affected the positive rate of Md5 at 14 days post-vaccination. The positive rate decreased from 100% to 14.29% (feather tips), 0% (skin), 33.33% (liver), 16.67% (spleen), 28.57% (thymus), 33.33% (bursa), and 66.67% (PBL), respectively. This study enhances our understanding of the interactions between HVT vector vaccines and very virulent MDV in chickens and provides valuable insights for the future development of MD vaccines.

Current strategies to understand the molecular basis of Marek\'s disease virus (MDV) virulence primarily consist of cataloging divergent nucleotides between strains with different phenotypes. However, most comparative genomic studies of MDV rely on previously published consensus genomes despite the confirmed existence of MDV strains as mixed viral populations. To assess the reliability of interstrain genomic comparisons relying on published consensus genomes of MDV, we obtained two additional consensus genomes of vaccine strain CVI988 (Rispens) and two additional consensus genomes of the very virulent strain Md5 by sequencing viral stocks and cultured field isolates. In conjunction with the published genomes of CVI988 and Md5, this allowed us to perform three-way comparisons between multiple consensus genomes of the same strain. We found that consensus genomes of CVI988 can vary in as many as 236 positions involving 13 open reading frames (ORFs). By contrast, we found that Md5 genomes varied only in 11 positions involving a single ORF. Notably, we were able to identify 3 single-nucleotide polymorphisms (SNPs) in the unique long region and 16 SNPs in the unique short (US) region of CVI988GenBank.BAC that were not present in either CVI988Pirbright.lab or CVI988USDA.PA.field. Recombination analyses of field strains previously described as natural recombinants of CVI988 yielded no evidence of crossover events in the US region when either CVI988Pirbright.lab or CVI988USDA.PA.field were used to represent CVI988 instead of CVI988GenBank.BAC. We were also able to confirm that both CVI988 and Md5 populations were mixed, exhibiting a total of 29 and 27 high-confidence minor variant positions, respectively. However, we did not find any evidence of minor variants in the positions corresponding to the 19 SNPs in the unique regions of CVI988GenBank.BAC. Taken together, our findings suggest that continued reliance on the same published consensus genome of CVI988 may have led to an overestimation of genomic divergence between CVI988 and virulent strains and that multiple consensus genomes per strain may be necessary to ensure the accuracy of interstrain genomic comparisons.

Continuous release of image databases with fully or partially identical inner categories dramatically deteriorates the production of autonomous Computer-Aided Diagnostics (CAD) systems for true comprehensive medical diagnostics. The first challenge is the frequent massive bulk release of medical image databases, which often suffer from two common drawbacks: image duplication and corruption. The many subsequent releases of the same data with the same classes or categories come with no clear evidence of success in the concatenation of those identical classes among image databases. This issue stands as a stumbling block in the path of hypothesis-based experiments for the production of a single learning model that can successfully classify all of them correctly. Removing redundant data, enhancing performance, and optimizing energy resources are among the most challenging aspects. In this article, we propose a global data aggregation scale model that incorporates six image databases selected from specific global resources. The proposed valid learner is based on training all the unique patterns within any given data release, thereby creating a unique dataset hypothetically. The Hash MD5 algorithm (MD5) generates a unique hash value for each image, making it suitable for duplication removal. The T-Distributed Stochastic Neighbor Embedding (t-SNE), with a tunable perplexity parameter, can represent data dimensions. Both the Hash MD5 and t-SNE algorithms are applied recursively, producing a balanced and uniform database containing equal samples per category: normal, pneumonia, and Coronavirus Disease of 2019 (COVID-19). We evaluated the performance of all proposed data and the new automated version using the Inception V3 pre-trained model with various evaluation metrics. The performance outcome of the proposed scale model showed more respectable results than traditional data aggregation, achieving a high accuracy of 98.48%, along with high precision, recall, and F1-score. The results have been proved through a statistical t-test, yielding t-values and p-values. It\'s important to emphasize that all t-values are undeniably significant, and the p-values provide irrefutable evidence against the null hypothesis. Furthermore, it\'s noteworthy that the Final dataset outperformed all other datasets across all metric values when diagnosing various lung infections with the same factors.