0.1% Nano-silver mediates PD-1/PD-L1 pathway and alleviates chronic apical periodontitis in rats.

This study was to investigate whether the programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) and T-helper 17 (Th17)/regulatory T (Treg) balance are associated with chronic apical periodontitis (CAP) relived by 0.1% nano-silver. CAP rat models were established by opening the first molars of the right and left mandible and exposing the pulp cavity to the oral cavity. CAP model was verified by cone-beam computed tomography, X-ray digital radiovisiography, and hematoxylin-eosin (H and E) staining. The rats were randomly divided into the sham, Ca(OH)2, and 0.1% nano-silver groups (n = 12 in each group) 2 weeks after surgery. The pathological changes in the apical area were detected by H and E staining. PD-1, PD-L1, RORγT, IL-17, and Foxp3 in periapical tissues were detected by qRT-PCR and immunohistochemistry. Th17/Treg and PD-1/PD-L1 were analyzed by flow cytometry. After 7, 14, and 21 days of 0.1% nano-silver treatment, inflammatory cells in the apical region were slightly reduced and inflammatory infiltration was relieved compared with the sham group. RORγT, IL-17, PD-1, and PD-L1 decreased and Foxp3 increased after 7, 14, and 21 days of 0.1% nano-silver treatment compared with the sham group (p < 0.05); however, there were no significant differences with Ca(OH)2 group (p > 0.05). Flow cytometry revealed that 0.1% nano-silver solution decreased Th17/Treg and PD-1/PD-L1 ratio. 0.1% Nano-silver significantly reduced the inflammation of CAP in rats. PD-1/PD-L1 was included in Th17/Treg balance restored by 0.1% nano-silver.

INDELpred: Improving the prediction and interpretation of indel pathogenicity within the clinical genome.

Small insertions and deletions (indels) are critical yet challenging genetic variations with significant clinical implications. However, the identification of pathogenic indels from neutral variants in clinical contexts remains an understudied problem. Here, we developed INDELpred, a machine-learning-based predictive model for discerning pathogenic from benign indels. INDELpred was established based on key features, including allele frequency, indel length, function-based features, and gene-based features. A set of comprehensive evaluation analyses demonstrated that INDELpred exhibited superior performance over competing methods in terms of computational efficiency and prediction accuracy. Importantly, INDELpred highlighted the crucial role of function-based features in identifying pathogenic indels, with a clear interpretability of the features in understanding the disease-causing variants. We envisage INDELpred as a desirable tool for the detection of pathogenic indels within large-scale genomic datasets, thereby enhancing the precision of genetic diagnoses in clinical settings.