Decellularized sciatic nerve matrix as a biodegradable conduit for peripheral nerve regeneration

The use of autologous nerve grafts remains the gold standard for treating nerve defects, but current nerve repair techniques are limited by donor tissue availability and morbidity associated with tissue loss. Recently, the use of conduits in nerve injury repair, made possible by tissue engineering, has shown therapeutic potential. We manufactured a biodegradable, collagen-based nerve conduit containing decellularized sciatic nerve matrix and compared this with a silicone conduit for peripheral nerve regeneration using a rat model. The collagen-based conduit contains nerve growth factor, brain-derived neurotrophic factor, and laminin, as demonstrated by enzyme-linked immunosorbent assay. Scanning electron microscopy images showed that the collagen-based conduit had an outer wall to prevent scar tissue infiltration and a porous inner structure to allow axonal growth. Rats that were implanted with the collagen-based conduit to bridge a sciatic nerve defect experienced significantly improved motor and sensory nerve functions and greatly enhanced nerve regeneration compared with rats in the sham control group and the silicone conduit group. Our results suggest that the biodegradable collagen-based nerve conduit is more effective for peripheral nerve regeneration than the silicone conduit.

Serum prostate-specific antigen as a predictor of prostate volume and lower urinary tract symptoms in acommunity-based cohort： a large-scale Korean screening study

The aim of this study is to assess the ability of serum prostate-specific antigen （PSA） to predict prostate volume （PV） and lower urinary tract symptoms （LUTS） represented by the international prostate symptom score （IPSS）. From January 2001 to December 2011, data were collected from men who first enrolled in the Korean Prostate Health Council Screening Program. Patients with a serum PSA level of 10 ng ml^-1 or age 〈40 years were excluded. Accordingly, a total of 34 857 men were included in our study, and serum PSA, PV and the IPSS were estimated in all patients. Linear and age-adjusted multivariate logistic analyses were used to assess the potential association between PSA and PV or IPSS. The predictive value of PSA for estimating PV and IPSS was assessed based on the receiver operating characteristics-derived area under the curve （AUC）. The mean PV was 29.9 ml, mean PSA level was 1.49 ng ml^-1 and mean IPSS was 15.4. A significant relationship was shown between PSA and PV, and the IPSS and PSA were also significantly correlated after adjusting by age. The AUCs of PSA for predicting PV ~20 ml, 〉25 ml and 〉35 ml were 0.722, 0.728 and 0.779, respectively. The AUCs of PSA for predicting IPSS 〉 7, 〉 13 and 〉 19 were 0. 548, 0.536 and 0. 537, respectively. Serum PSA was a strong predictor of PV in a community-based cohort in a large-scale screening study. Although PSA was also significantly correlated with IPSS, predictive values of PSA for IPSS above the cutoff levels were not excellent. Further investigations are required to elucidate the exact interactions between PSA and LUTS and between PSA and PV in prospective controlled studies. Such studies may suggest how PSA can be used to clinically predict PV and the IPSS.

Caenorhabditis elegans deep lipidome profiling by using integrative mass spectrometry acquisitions reveals significantly altered lipid networks

Lipidomics coverage improvement is essential for functional lipid and pathway construction.A powerful approach to discovering organism lipidome is to combine various data acquisitions,such as full scan mass spectrometry(full MS),data-dependent acquisition(DDA),and data-independent acquisition(DIA).Caenorhabditis elegans(C.elegans)is a useful model for discovering toxic-induced metabolism,highthroughput drug screening,and a variety of human disease pathways.To determine the lipidome of C.elegans and investigate lipid disruption from the molecular level to the system biology level,we used integrative data acquisition.The methyl-tert-butyl ether method was used to extract L4 stage C.elegans after exposure to triclosan(TCS),perfluorooctanoic acid,and nanopolystyrene(nPS).Full MS,DDA,and DIA integrations were performed to comprehensively profile the C.elegans lipidome by Q-Exactive Plus MS.All annotated lipids were then analyzed using lipid ontology and pathway analysis.We annotated up to 940 lipids from 20 lipid classes involved in various functions and pathways.The biological investigations revealed that when C.elegans were exposed to nPS,lipid droplets were disrupted,whereas plasma membrane-functionalized lipids were likely to be changed in the TCS treatment group.The nPS treatment caused a significant disruption in lipid storage.Triacylglycerol,glycerophospholipid,and ether class lipids were those primarily hindered by toxicants.Finally,toxicant exposure frequently involved numerous lipid-related pathways,including the phosphoinositide 3-kinase/protein kinase B pathway.In conclusion,an integrative data acquisition strategy was used to characterize the C.elegans lipidome,providing valuable biological insights into hypothesis generation and validation.

Advances in Liquid Chromatography-Mass Spectrometry-Based Lipidomics: A Look Ahead

Lipidomics is a subfield of metabolic phenotyping that focuses on high-throughput profiling and quantification of lipids.Essential roles of lipidomics in translational and clinical research have emerged,especially over the past decade.Most lipidomic pipelines have been developed using mass spectrometry(MS)-based methods.Because of the complexity of the data,generally,computational demands are much higher in untargeted lipidomic studies.In the current paper,we primarily discussed the recent advances in untargeted liquid chromatography-mass spectrometry-based lipidomics,covering various facets from analytical strategies to functional interpretations.The current practice of tandem MS-based lipid annotation in untargeted lipidomics studies was demonstrated.Notably,we highlighted the essential characteristics of machine learning models,together with a data partitioning strategy,to facilitate appropriate modeling and validation in metabolic phenotyping studies.Critical aspects of data sharing were briefly mentioned.Finally,certain recommendations were suggested toward more standardized and sustainable lipidomics analysis strategies as independent platforms,and as members of the omics family.