Correlation between diabetic retinopathy and diabetic nephropathy: a two-sample Mendelian randomization study

Rationale & objectiveA causal relationship concerning diabetic retinopathy (DR) and diabetic nephropathy (DN) has been studied in many epidemiological observational studies. We conducted a two-sample mendelian randomization study from the perspective of genetics to assess these associations.Methods20 independent single nucleotide polymorphisms (SNPs) associated with diabetic retinopathy were selected from the FinnGen consortium. Summary-level data for diabetic nephropathy were obtained from the publicly available genome-wide association studies (GWAS) database, FinnGen and CKDGen consortium. Inverse variance weighted (IVW) was selected as the primary analysis. MR-Egger, weighted median (WM), simple mode and weighted mode were used as complementary methods to examine causality. Additionally, sensitivity analyses including Cochran’s Q test, MR-Egger, MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO), and leave-one-out analyses were conducted to guarantee the accuracy and robustness of our MR analysis.ResultsOur current study demonstrated positive associations of genetically predicted diabetic retinopathy with diabetic nephropathy (OR=1.32; P=3.72E-11), type 1 diabetes with renal complications (OR=1.96; P= 7.11E-11), and type 2 diabetes with renal complications (OR=1.26, P=3.58E-04). Further subtype analysis and multivariate mendelian randomization (MVMR) also reached the same conclusion. A significant casualty with DN was demonstrated both in non-proliferative DR (OR=1.07, P=0.000396) and proliferative DR (OR=1.67, P=3.699068E-14). All the findings were robust across several sensitivity analyses.ConclusionConsistent with previous clinical studies, our findings revealed a positive correlation between DR and DN, providing genetic evidence for the non-invasive nature of DR in predicting DN

Classification of temporal lobe epilepsy based on neuropsychological tests and exploration of its underlying neurobiology

ObjectiveTo assist improving long-term postoperative seizure-free rate, we aimed to use machine learning algorithms based on neuropsychological data to differentiate temporal lobe epilepsy (TLE) from extratemporal lobe epilepsy (extraTLE), as well as explore the relationship between magnetic resonance imaging (MRI) and neuropsychological tests.MethodsTwenty-three patients with TLE and 23 patients with extraTLE underwent neuropsychological tests and MRI scans before surgery. The least absolute shrinkage and selection operator were firstly employed for feature selection, and a machine learning approach with neuropsychological tests was employed to classify TLE using leave-one-out cross-validation. A generalized linear model was used to analyze the relationship between brain alterations and neuropsychological tests.ResultsWe found that logistic regression with the selected neuropsychological tests generated classification accuracies of 87.0%, with an area under the receiver operating characteristic curve (AUC) of 0.89. Three neuropsychological tests were acquired as significant neuropsychological signatures for the diagnosis of TLE. We also found that the Right-Left Orientation Test difference was related to the superior temporal and the banks of the superior temporal sulcus (bankssts). The Conditional Association Learning Test (CALT) was associated with the cortical thickness difference in the lateral orbitofrontal area between the two groups, and the Component Verbal Fluency Test was associated with the cortical thickness difference in the lateral occipital cortex between the two groups.ConclusionThese results showed that machine learning-based classification with the selected neuropsychological data can successfully classify TLE with high accuracy compared to previous studies, which could provide kind of warning sign for surgery candidate of TLE patients. In addition, understanding the mechanism of cognitive behavior by neuroimaging information could assist doctors in the presurgical evaluation of TLE

Towards an integrated approach for land spatial ecological restoration zoning based on ecosystem health assessment

Mitigating ecosystem degradation has been a worldwide strategy, and China has been implementing land spatial ecological restoration for an all-around ecological preservation in recent years. A comprehensive diagnosis of the ecosystem health status and an effective division of spatial zoning are essential to formulating and implementing ecological restoration strategies at the regional scale. Here, the ecosystem health index (EHI) was computed for the years 2010 and 2020 using the vigor-organization-resilience model. Then, a three-step statistic-based, spatial continuity-based, and practice-based (SSP) zoning framework was developed to classify land spatial ecological restoration zones with the consideration of ecosystem health status, spatial relation, and local practices. We applied the integrated zoning approach using the urban agglomeration in the middle reaches of Yangtze River (UAMRYR) in China as the study area. The results showed that: (1) the EHI had a slight decreasing trend from 2010 to 2020, with a spatial distribution pattern of healthy, unhealthy, and to healthy from the center to the periphery in the UAMRYR. (2) Eight land spatial ecological restoration zones were designated and adjusted through the SSP zoning framwork to be space-full and practical. Zone VIII accounted for the largest proportion (41.12%), followed by the Zone Ⅰ (21.57%). (3) Finally, corresponding land spatial ecological restoration strategies were proposed for each zone. This study contributes to land spatial ecological restoration zoning and differentiated restoration strategies in the UAMRYR, shedding light on restoration regulation and Sustainable Development Goals achievement in China and global regions with complicated environmental problems

Ultra-narrow band perfect absorbance induced by magnetic lattice resonances in dielectric dimer metamaterials

Nanostructured dielectric metamaterials have received extensive attention in the field of nanophotonics owing to their low radiative losses and coexisting electric and magnetic lattice resonance features. Unfortunately, suffering from the poor electromagnetic field localization and weak magnetic response in the typical dielectric metamaterials, it remains challenging to simultaneously realize ultra-narrow band perfect absorbance and intensified electromagnetic field resonances. Herein, we theoretically demonstrate a kind of dielectric metamaterials formed by dielectric cylindrical dimer array that supports magnetic lattice resonances. Benefiting from the collective diffraction coupling among the powerful magnetic dipole resonance in the dielectric dimer array, the proposed dielectric metamaterials synchronously manifest ultra-narrow spectral characteristics with bandwidth less than 8 nm, perfect absorbance amplitude as high as 99.7% and strong electric/magnetic field enhancement factor. The effects of the structure parameters on the optical properties of the proposed nanostructure are investigated based on numerical simulations. The linewidth of absorbance spectrum can be narrowed down to approximately 3 nm with optimal design. These excellent optical features supported by the dielectric dimer metamaterials can be explored as a high-efficiency refractive index sensor with sensitivity of 824 nm/RIU and figure of merit as high as 242 RIU−1. This work paves an exciting way for narrow band perfect absorbance and localized field enhancement, exhibiting tremendous enormous potential in biochemical sensing, surface enhanced spectroscopy, and nonlinear nanophotonics