Intensive efforts have been made to develop models of hRSV infection or disease using various animals. However, the limitations such as semi-permissiveness and short duration of infection have impeded their applications in both the pathogenesis of hRSV and therapeutics development. Here, we present a mouse model based on a Rag2 gene knockout using CRISPR/Cas9 technology. Rag2^−/− mice sustained high viral loads upon intranasal inoculation with hRSV. The average peak titer rapidly reached 1 × 10^9.8 copies/g and 1c10⁶ TCID₅₀ in nasal cavity, as well as 1 × 10⁸ copies/g and 1 × 10⁵ TCID₅₀ in the lungs up to 5 weeks. Mild interstitial pneumonia, severe bronchopneumonia, elevated cytokines and NK cells were seen in Rag2^−/− mice. A humanized monoclonal antibody showed strong antiviral activity in this animal model, implying that Rag2^−/− mice that support long-term stable infection are a useful tool for studying the transmission and pathogenesis of human RSV, as well as evaluating therapeutics.

Objective: In non-clinical safety evaluation of drugs, pathological result is one of the gold standards for determining toxic effects. However, pathological diagnosis might be challenging and affected by pathologist expertise. In carcinogenicity studies, drug-induced squamous cell carcinoma (SCC) of the mouse stomach represents a diagnostic challenge for toxicopathologists. This study aims to establish a detection model for mouse gastric squamous cell carcinoma (GSCC) using deep learning algorithms, to improve the accuracy and consistency of pathological diagnoses.Methods: A total of 93 cases of drug-induced mouse GSCC and 56 cases of normal mouse stomach tissue from carcinogenicity studies were collected. After scanning into digital slides, semi-automated data annotation was performed. All images underwent preprocessing, including tissue extraction, artifact removal, and exclusion of normal epithelial regions. The images were then randomly divided into training, validation, and test sets in an 8:1:1 ratio. Five different convolutional neural networks (CNNs)-FCN, LR-ASPP, DeepLabv3+, U-Net, and DenseNet were applied to identify GSCC and non-GSCC regions. Tumor prediction images (algorithm results shown as overlays) derived from the slide images were compared, and the performance of the constructed models was evaluated using Precision, Recall, and F1-score.Results: The Precision, Recall, and F1-scores of DenseNet, U-Net, and DeepLabv3 + algorithms were all above 90%. Specifically, the DenseNet model achieved an overall Precision of 0.9044, Recall of 0.9291, and F1-score of 0.9157 in the test set. Compared to the other algorithms, DenseNet exhibited the highest F1-score and Recall, demonstrating superior generalization ability.Conclusion: The DenseNet algorithm model developed in this study shown promising application potential for assisting in the diagnosis of mouse GSCC. As artificial intelligence (AI) technology continues to advance in non-clinical safety evaluation of drugs, CNN-based toxicological pathology detection models will become essential tools to assist pathologists in precise diagnosis and consistency evaluation.

MicroRNAs (miRNAs) are promising biomarkers for different pathological models because of their stable and detectable characters in biofluids. Here, we collected urine samples from 5 beagle dogs on the 3th, 6th, and 12th day in an acute kidney injury (AKI) caused by gentamycin. miRNA levels were measured with high-throughput sequencing and the results were then differentially investigated. Gene Ontology (GO) and KEGG pathway analysis were performed to analyze potential target genes corresponding to the differentially expressed miRNAs (DE-miRNAs). Relationships between hub genes and DE-miRNAs were analyzed with STRING and Cytoscape. We identified 234 DE-miRNAs 3, 6, and 12 days after gentamycin treatment (p < 0.05). Top 10 up- and down-regulated candidate target genes of DE-miRNAs were predicted by overlapping TargetScan and miRanda results). GO and KEGG analyses for DE-miRNAs demonstrated that the DE-miRNAs target genes are mainly involved in kidney injury-related pathways, such as the insulin signaling pathway, oxytocin signaling pathway, and hedgehog signaling pathway. The network of miRNA-hub genes suggests that miR-452, miR-106a, and 106b participate in regulating the largest number of hub genes. We evaluated the miRNA signature via a canine model built by gentamycin-caused acute kidney injury. Our results represent a valuable resource for evaluating miRNAs as biomarkers of renal toxicity.