Discovery of human pancreatic lipase inhibitors from root of Rhodiola crenulata via integrating bioactivity-guided fractionation,chemical profiling and biochemical assay

Although herbal medicines(HMs)are widely used in the prevention and treatment of obesity and obesity-associated disorders,the key constituents exhibiting anti-obesity activity and their molecular mechanisms are poorly understood.Recently,we assessed the inhibitory potentials of several HMs against human pancreatic lipase(hPL,a key therapeutic target for human obesity),among which the root-extract of Rhodiola crenulata(ERC)showed the most potent anti-hPL activity.In this study,we adopted an integrated strategy,involving bioactivity-guided fractionation techniques,chemical profiling,and biochemical assays,to identify the key anti-hPL constituents in ERC.Nine ERC fractions(retention time=12.5e35 min),obtained using reverse-phase liquid chromatography,showed strong anti-hPL activity,while the major constituents in these bioactive fractions were subsequently identified using liquid chromatography-quadrupole time-of-flight mass spectrometry(LC-Q-TOF-MS/MS).Among the identified ERC constituents,1,2,3,4,6-penta-O-galloyl-β-D-glucopyranose(PGG)and catechin gallate(CG)showed the most potent anti-hPL activity,with pIC50 values of 7.59±0.03 and 7.68±0.23,respectively.Further investigations revealed that PGG and CG potently inhibited hPL in a non-competitive manner,with inhibition constant(Ki)values of 0.012 and 0.082 mM,respectively.Collectively,our integrative analyses enabled us to efficiently identify and characterize the key anti-obesity constituents in ERC,as well as to elucidate their anti-hPL mechanisms.These findings provide convincing evidence in support of the anti-obesity and lipid-lowering properties of ERC.

Improved Region Energy Based Image Fusion Rule for Multi-focus Image Fusion

In this paper,we propose a novel improved region energy based image fusion rule.The original images are firstly decomposed by using the lifting scheme of wavelet transform into four sub-bands:LL,LH,HL,HH,by studying principles and characteristics of the wavelet subbands,and we put emphasis on the high frequency subbands.Thus HH,HL,LH sub-bands,which represent three direction of high frequency details,are weighted by different size of three direction Gaussian kernel,then the energy based image fusion rule is applied with a optional size of window,thus the activity level of high frequency subbands are obtained,followed by a local region matching degree in the corresponding direction and resolution,an activity level of low frequency subband is calculated,then perform consistency verification on the selected wavelet coefficients,by doing the inverse wavelet transform the fused image is obtained.The performance of the proposed novel image fusion scheme is conducted and compared with a few existing image fusion algorithm,the experimental results show that the proposed method is an effective multi-focus image fusion algorithm.

HDR syndrome presented with nephrotic syndrome in a Chinese boy: A case report

BACKGROUND HDR syndrome is a rare genetic disease caused by variants in the GATA3 gene and is phenotypically defined by the triad of hypoparathyroidism(H),deafness(D),and renal disease(R).Renal disorders of HDR are mainly developmental ab-normalities,although renal functional abnormalities can also be observed.Ne-phrotic syndrome or nephrotic-level proteinuria is rare in HDR syndrome.Here,we report a Chinese infant with HDR syndrome who presented with early-onset nephrotic syndrome.We suggest that variants in the GATA3 gene might be asso-ciated with nephrotic syndrome.(p.Pro235 Leu),in exon 3 of the GATA3 gene.CONCLUSION We report an infant with HDR syndrome who presented with early-onset nephrotic syndrome in China.We suggest that variants in the GATA3 gene might be associated with infant-onset nephrotic syndrome.

Historical and real-time estimation of snow depth in Eurasia based on multiple passive microwave data

Current snow depth datasets demonstrate large discrepancies in the spatial pattern in Eurasia,and the lagging updates of datasets do not meet the operational requirements of the meteorological service department.This study developed a dynamic retrieval method for daily snow depth over Eurasia based on cross-sensor calibrated microwave brightness temperatures to enhance retrieval accuracy and meet the requirements of operational work.These brightness temperatures were detected by microwave radiometer imager carried on the FengYun 3(FY-3)satellite and the special sensor microwave imager/sounder carried on the USA Defense Meteorological Satellite Program series satellites,which use the fewest sensors to provide the longest data and consequently introduce minimal errors during inter-sensor calibration.Firstly,inter-sensor calibration was conducted amongst brightness temperatures collected by the three sensors.A spatiotemporal dynamic relationship between snow depth and microwave brightness temperature gradient was then established,overcoming the large uncertainties induced by varying snow characteristics.This relationship can be utilised in FY-3 satellite data for operational service to obtain real-time snow depth.The generated daily snow depth dataset from 1988 to 2021 presents similar spatial patterns of snow depth to those observed in situ.Against in situ snow depth,the overall bias and root mean square error are−2.04 and 6.49 cm,respectively,facilitating considerable improvements in accuracy compared with the Advanced Microwave Scanning Radiometer 2 snow depth product,which adopts the static algorithm.Further analysis shows an overall decreasing trend from 1988 to 2021 for annual and monthly mean snow depths,demonstrating a noticeable reduction since around 2000.The reduction in monthly mean snow depth started earlier in shallow snow months than in deep snow months.