Exploring a novel class tryptophan hydroxylase 1 inhibitor derived from Sambucus williamsii Hance for the osteoporosis treatment

Objective:Gut-derived serotonin strongly inhibits bone formation by inhibiting osteoblast proliferation.Our previous study demonstrated that the lignan-rich fraction prepared from Sambucus willimasii Hance,a folk herbal medicine used to treat bone fractures and joint diseases in China,exerted bone-protective effects,and its actions were modulated by suppressing the synthesis of gut-derived serotonin via the inhibition of intestinal tryptophan hydroxylase 1(TPH-1).However,there is no direct evidence for the action of lignans on TPH-1.This study aimed to verify the direct action of lignans on the TPH-1 and its influence on serotonin synthesis and bone properties.Methods:Molecular docking and surface plasmon resonance were performed to determine the affinities of lignans to TPH-1.The cell viability and the protein activity and expression of TPH-1 were measured in RBL2H3 cells.The serum serotonin level and bone mineral density upon lignan treatment in ovariectomized mice were determined.Result:The lignans showed high binding scores and binding affinities to TPH-1,inhibited the activity and protein expression of TPH-1,suppressed the serum serotonin levels in ovariectomized mice as well as promoted bone mineral density.Conclusion:This is the first study to report that lignans are novel TPH-1 inhibitors and that these lignans could be potential agents for the management of serotonin-related diseases,including osteoporosis.

Nocturnal hypertension and riser pattern are associated with heart failure rehospitalization in patients with heart failure with preserved ejection fraction

BACKGROUND Nocturnal hypertension is reported as a risk factor for cardiovascular disease.This study aimed to explore the potential association between nocturnal hypertension and heart failure(HF)rehospitalization in patients with HF with preserved ejection fraction(HFpEF).METHODS A total of 538 patients with HFpEF from May 2018 to December 2021 were consequently recruited in this study and followed up until they were readmitted for HF or the end of this study.Cox regression analysis was used to reveal the potential association between nighttime blood pressure(BP)levels,nocturnal hypertension and nocturnal BP patterns and HF rehospitalization.Kaplan-Meier curve was used to assess the cumulative event-free survival rate between groups.RESULTS There were 537 patients with HFpEF were included in the final analysis.The mean age of the study population was 77.14±8.68 years,and 41.2% of patients were men.After a median follow-up duration of 10.93(4.19–21.13)months,176 patients(32.7%)with HFpEF were readmitted for HF.Cox regression analysis had revealed that nighttime systolic BP level[hazards ratio(HR)=1.018,95%CI:1.008–1.028,P=0.001],nighttime diastolic BP level(HR=1.024,95%CI:1.007–1.042,P=0.007),nocturnal hypertension(HR=1.688,95%CI:1.229–2.317,P=0.001)were associated with HF rehospitalization.Kaplan-Meier analysis had demonstrated that patients with nocturnal hypertension had significantly lower event-free survival rate(log-rank P<0.001).Furthermore,patients with a riser pattern had a higher risk of HF rehospitalization(HR=1.828,95%CI:1.055–3.166,P=0.031)and lower eventfree survival rate(log-rank P=0.003)than those with a dipper pattern.These findings were also confirmed in patients with HFpEF and hyperuricemia.CONCLUSIONS Nighttime BP levels,nocturnal hypertension and a riser pattern are independently associated with HF rehospitalization in patients with HFpEF,and prominently in patients with HFpEF and hyperuricemia.Well controlled nighttime BP levels should be emphasized and considered in patients with HFpEF.

Probabilistic outlier detection for sparse multivariate geotechnical site investigation data using Bayesian learning

Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances(i.e.,outliers)that do not conform with the expected pattern of regular data instances.With sparse multivariate data obtained from geotechnical site investigation,it is impossible to identify outliers with certainty due to the distortion of statistics of geotechnical parameters caused by outliers and their associated statistical uncertainty resulted from data sparsity.This paper develops a probabilistic outlier detection method for sparse multivariate data obtained from geotechnical site investigation.The proposed approach quantifies the outlying probability of each data instance based on Mahalanobis distance and determines outliers as those data instances with outlying probabilities greater than 0.5.It tackles the distortion issue of statistics estimated from the dataset with outliers by a re-sampling technique and accounts,rationally,for the statistical uncertainty by Bayesian machine learning.Moreover,the proposed approach also suggests an exclusive method to determine outlying components of each outlier.The proposed approach is illustrated and verified using simulated and real-life dataset.It showed that the proposed approach properly identifies outliers among sparse multivariate data and their corresponding outlying components in a probabilistic manner.It can significantly reduce the masking effect(i.e.,missing some actual outliers due to the distortion of statistics by the outliers and statistical uncertainty).It also found that outliers among sparse multivariate data instances affect significantly the construction of multivariate distribution of geotechnical parameters for uncertainty quantification.This emphasizes the necessity of data cleaning process(e.g.,outlier detection)for uncertainty quantification based on geoscience data.