Correlation between the triglyceride-glucose index and chronic kidney disease among adults with metabolic-associated fatty liver disease: fourteen-year follow-up

Background and aimsAccording to previous studies, triglyceride-glucose (TyG) is related to chronic kidney disease (CKD), but no studies have explored the correlation between TyG and CKD among adults with metabolic dysfunction-associated fatty liver disease (MAFLD). We aimed to explore the associations of the TyG index with CKD among adults with MAFLD.MethodsIn this retrospective observational cohort study, data from 11,860 participants who underwent a minimum of three health assessments between 2008 and 2015 were retrospectively collected. Participants were followed up until the final medical visit or health examination. CKD refers to an eGFR < 60 mL/min per 1·73 m2 or the occurrence of two or more incidents of proteinuria.ResultsWithin a median 10·02-year follow-up period, 2005 (16·9%) participants reported developing CKD. Multivariate Cox regression models indicated a noticeable correlation between the TyG index and CKD incidence (HR per unit increase, 1.19; 95% CI: 1.09–1.29) and between the TyG index and CKD incidence (HR per SD increase, 1.12; 95% CI: 1.06–1.18). The CKD incidence increased by 1.8 times in participants in the highest TyG index quartile relative to patients in the lowest quartile of the TyG index quartile (HR 1·18, 95% CI: 1.01–1.38, P = 0.007). According to subgroup analysis, an elevated TyG index is likely to become more harmful to participants younger than 60 years (P for interaction = 0.035).ConclusionAn elevated TyG index may increase CKD incidence among MAFLD adults, particularly among younger people. Early intervention may help reduce the incidence of CKD

Deep Learning Empowers the Discovery of Self‐Assembling Peptides with Over 10 Trillion Sequences

Abstract Self‐assembling of peptides is essential for a variety of biological and medical applications. However, it is challenging to investigate the self‐assembling properties of peptides within the complete sequence space due to the enormous sequence quantities. Here, it is demonstrated that a transformer‐based deep learning model is effective in predicting the aggregation propensity (AP) of peptide systems, even for decapeptide and mixed‐pentapeptide systems with over 10 trillion sequence quantities. Based on the predicted AP values, not only the aggregation laws for designing self‐assembling peptides are derived, but the transferability relation among the APs of pentapeptides, decapeptides, and mixed pentapeptides is also revealed, leading to discoveries of self‐assembling peptides by concatenating or mixing, as consolidated by experiments. This deep learning approach enables speedy, accurate, and thorough search and design of self‐assembling peptides within the complete sequence space of oligopeptides, advancing peptide science by inspiring new biological and medical applications

Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China

China is one of the countries with the most abundant bamboo forest resources in the world, and Zhejiang province is among the top-3 Chinese provinces with richest bamboo forests. For rational bamboo forests management, it is of great significance to study the spatiotemporal dynamic changes of Aboveground Carbon (AGC) stocks of bamboo forest in Zhejiang. In this study, remote sensing variables, such as spectral, vegetation indices and texture features of bamboo forest in Zhejiang, were extracted from 32 Landsat TM and OLI images got from four different years (2000, 2004, 2008 and 2014). These variables were subsequently selected with stepwise regression method to build an estimation model of AGC of the bamboo forests. The results showed that (1) the accuracy of bamboo forest remote sensing information extracted from the four different years was high with a classification accuracy of >76.26% and an accuracy of users of >91.62%. The classification area of bamboo forest was highly consistent with the area from forest resource inventory, and the area accuracy was over 96.50%; (2) the estimation model performed well in predicting the AGC in Zhejiang for different years. The correlation coefficient for estimated and measured AGC was between 63% and 72% with low root mean square error; (3) the derived AGC of the bamboo forests in Zhejiang province increased gradually from 2000 to 2014, with the AGC density of 6.75 Mg·ha−1, 10.95 Mg·ha−1, 15.25 Mg·ha−1 and 19.07 Mg·ha−1 respectively, and the average annual growth of 0.88 Mg·ha−1. The spatiotemporal evolution of bamboo forest AGC in Zhejiang province had a close relationship with the gradual expansion of bamboo forest in the province and the differentiation of management levels in different regions

Consanguineous familial study revealed biallelic FIGLA mutation associated with premature ovarian insufficiency

Abstract Background To dissect the genetic alteration in two sisters with premature ovarian insufficiency (POI) from a consanguineous family. Methods Whole-exome sequencing technology was used in the POI proband, bioinformatics analysis was carried out to identify the potential genetic cause in this pedigree. Sanger sequencing analyses were performed to validate the segregation of the variant within the pedigree. In silico analysis was also used to predict the effect and pathogenicity of the variant. Results Whole-exome sequencing analysis identified novel and rare homozygous mutation associated with POI, namely mutation in FIGLA (c.2 T > C, start codon shift). This homozygous mutation was also harbored by the proband’s sister with POI and was segregated within the consanguineous pedigree. The mutation in the start codon of the FIGLA gene alters the open reading frame, leading to a FIGLA knock-out like phenotype. Conclusions Biallelic mutations in FIGLA may be the cause of POI. This study will aid researchers and clinicians in genetic counseling of POI and provides new insights into understanding the mode of genetic inheritance of FIGLA mutations in POI pathology

Assimilating Multiresolution Leaf Area Index of Moso Bamboo Forest from MODIS Time Series Data Based on a Hierarchical Bayesian Network Algorithm

The highly accurate multiresolution leaf area index (LAI) is an important parameter for carbon cycle simulation for bamboo forests at different scales. However, current LAI products have discontinuous resolution with 1 km mostly, that makes it difficult to accurately quantify the spatiotemporal evolution of carbon cycle at different resolutions. Thus, this study used MODIS LAI product (MOD15A2) and MODIS reflectance data (MOD09Q1) of Moso bamboo forest (MBF) from 2015, and it adopted a hierarchical Bayesian network (HBN) algorithm coupled with a dynamic LAI model and the PROSAIL model to obtain high-precision LAI data at multiresolution (i.e., 1000, 500, and 250 m). The results showed the LAIs assimilated using the HBN at the three resolutions corresponded with the actual growth trend of the MBF and correlated significantly with the observed LAI with a determination coefficient (R2) value of > 0.80. The highest-precision assimilated LAI was obtained at 1000-m resolution with R2 values of 0.91. The LAI assimilated using the HBN algorithm achieved better accuracy than the MODIS LAI with increases in the R2 value of 2.7 times and decreases in the root mean square error of 87.8%. Therefore, the HBN algorithm applied in this study can effectively obtain highly accurate multiresolution LAI time series data for bamboo forest

New Gene Variants Associated with the Risk of Chronic HBV Infection

Some patients with chronic hepatitis B virus (HBV) infection failed to clear HBV, even persistently continue to produce antibodies to HBV. Here we performed a two stage genome wide association study in a cohort of Chinese patients designed to discover single nucleotide variants that associate with HBV infection and clearance of HBV. The first stage involved genome wide exome sequencing of 101 cases (HBsAg plus anti-HBs positive) compared with 102 control patients (anti-HBs positive, HBsAg negative). Over 80% of individual sequences displayed 20 × sequence coverage. Adapters, uncertain bases \u3e 10% or low-quality base calls (\u3e 50%) were filtered and compared to the human reference genome hg19. In the second stage, 579 chronic HBV infected cases and 439 HBV clearance controls were sequenced with selected genes from the first stage. Although there were no significant associated gene variants in the first stage, two significant gene associations were discovered when the two stages were assessed in a combined analysis. One association showed rs506121-“T” allele [within the dedicator of cytokinesis 8 (DOCK8) gene] was higher in chronic HBV infection group than that in clearance group (P = 0.002, OR = 0.77, 95% CI [0.65, 0.91]). The second association involved rs2071676—A allele within the Carbonic anhydrase (CA9) gene that was significantly elevated in chronic HBV infection group compared to the clearance group (P = 0.0003, OR = 1.35, 95% CI [1.15, 1.58]). Upon replication these gene associations would suggest the influence of DOCK8 and CA9 as potential risk genetic factors in the persistence of HBV infection