PRELIMINARY PHARMACEUTICAL AND ANALYTICAL EVALUATION OF SHRINGYADI LEHA

Aims: Shringyadi Leha has been prepared and studied for its Physico-chemical parameters. The adopted formula- tion is Shringyadi Leha based on Chakradatta. Shringyadi Leha has significant efficacy in pediatric disorders. Methods and Material: The present study provides details of the preparation of Shringyadi Leha, and methods employed in Physico-chemical parameters. Results: Physico-chemical observations revealed the specific characters of the preparation. The preliminary HPTLC study of the compound revealed the components, eight spots in short UV 254nm, 09 spots in 366nm and 10 spots in long UV 550nm. Conclusions: Evaluation of Shringyadi lehyam can be used as a reference standard for further quality control research for the manufacturing and processing of Shringyadi Leah. Keywords: Shringyadi Leha, Pharmaceutical parameters and HPTLC.

An automatic diagnostic system for pediatric genetic disorders developed by linking genotype and phenotype information

Background: Quantitatively describe the phenotype spectrum of pediatric disorders has remarkable power to assist genetic diagnosis. Here, we developed a matrix which provide this quantitative description of genomic-phenotypic association and constructed an automatic system to assist the diagnose of pediatric genetic disorders. Results: 20,580 patients with genetic diagnostic conclusions from the Children's Hospital of Fudan University during 2015 to 2019 were reviewed. Based on that, a phenotype spectrum matrix -- cGPS (clinical Gene's Preferential Synopsis) -- was designed by Naive Bayes model to quantitatively describe genes' contribution to clinical phenotype categories. Further, for patients who have both genomic and phenotype data, we designed a ConsistencyScore based on cGPS. ConsistencyScore aimed to figure out genes that were more likely to be the genetic causal of the patient's phenotype and to prioritize the causal gene among all candidates. When using the ConsistencyScore in each sample to predict the causal gene for patients, the AUC could reach 0.975 for ROC (95% CI 0.972-0.976 and 0.575 for precision-recall curve (95% CI 0.541-0.604). Further, the performance of ConsistencyScore was evaluated on another cohort with 2,323 patients, which could rank the causal gene of the patient as the first for 75.00% (95% CI 70.95%-79.07%) of the 296 positively genetic diagnosed patients. The causal gene of 97.64% (95% CI 95.95%-99.32%) patients could be ranked within top 10 by ConsistencyScore, which is much higher than existing algorithms (p <0.001). Conclusions: cGPS and ConsistencyScore offer useful tools to prioritize disease-causing genes for pediatric disorders and show great potential in clinical applications.