New inducible mast cell-deficient mouse model (Mcpt5/Cma1DTR).

Mast cell-deficient mice are helpful for understanding the roles of mast cells in vivo. To date, a dozen mouse models for mast cell deficiency have been reported. However, mice with a specific depletion of all populations of mast cells have not been reported. We generated knock-in mice, termed Mcpt5/Cma1DTR mice, expressing human diphtheria toxin A (DT) receptor under the endogenous promoter of Mcpt5 (also known as Cma1), which encodes mouse mast cell protease-5. Flow cytometry and histological analysis showed that intraperitoneal injection of DT induced almost complete depletion of mast cells in heterozygote Mcpt5/Cma1DTR/+ mice. The deletion rates of mast cells in peritoneal cavity, mesentery, abdominal skin, ear skin, and glandular stomach were 99.9%, 100%, 98.7%, 97.7%, and 100%, respectively. Passive cutaneous anaphylaxis reaction also revealed mast cell deficiency in ear skin after DT treatment. Other than mast cells, a small percentage of marginal zone B cells in Mcpt5/Cma1DTR/+ mice were killed by DT treatment. In conclusion, the Mcpt5/Cma1DTR/+ mouse model is valuable for achieving conditional depletion of all populations of mast cells without inducing a marked reduction in other cells.

Automated scoring of glomerular injury in TNS2-deficient nephropathy.

Several artificial intelligence (AI) systems have been developed for glomerular pathology analysis in clinical settings. However, the application of AI systems in nonclinical fields remains limited. In this study, we trained a convolutional neural network model, which is an AI algorithm, to classify the severity of Tensin 2 (TNS2)-deficient nephropathy into seven categories. A dataset consisting of 803 glomerular images was generated from kidney sections of TNS2-deficient and wild-type mice. Manual evaluations of the images were conducted to assess their glomerular injury scores. The trained AI achieved approximately 70% accuracy in predicting the glomerular injury score for TNS2-deficient nephropathy. However, the AI achieved approximately 100% accuracy when considering predictions within one score of the true label as correct. The AI's predicted mean score closely matched the true mean score. In conclusion, while the AI model may not replace human judgment entirely, it can serve as a reliable second assessor in scoring glomerular injury, offering potential benefits in enhancing the accuracy and objectivity of such assessments.