Analysis and comparison of retinal vascular parameters under different glucose metabolic status based on deep learning

AIM:To develop a deep learning-based model for automatic retinal vascular segmentation,analyzing and comparing parameters under diverse glucose metabolic status(normal,prediabetes,diabetes)and to assess the potential of artificial intelligence(AI)in image segmentation and retinal vascular parameters for predicting prediabetes and diabetes.METHODS:Retinal fundus photos from 200 normal individuals,200 prediabetic patients,and 200 diabetic patients(600 eyes in total)were used.The U-Net network served as the foundational architecture for retinal arteryvein segmentation.An automatic segmentation and evaluation system for retinal vascular parameters was trained,encompassing 26 parameters.RESULTS:Significant differences were found in retinal vascular parameters across normal,prediabetes,and diabetes groups,including artery diameter(P=0.008),fractal dimension(P=0.000),vein curvature(P=0.003),C-zone artery branching vessel count(P=0.049),C-zone vein branching vessel count(P=0.041),artery branching angle(P=0.005),vein branching angle(P=0.001),artery angle asymmetry degree(P=0.003),vessel length density(P=0.000),and vessel area density(P=0.000),totaling 10 parameters.CONCLUSION:The deep learning-based model facilitates retinal vascular parameter identification and quantification,revealing significant differences.These parameters exhibit potential as biomarkers for prediabetes and diabetes.

Anisometropia magnitude and visual deficits in previously untreated anisometropic amblyopia

AIM:To assess the quantitative association between anisometropia magnitude(AM) and the losses of resolution and contrast sensitivity;and to exemplify how the function of fusion and stereopsis vary with AM in previously untreated anisometropic amblyopia. METHODS:A total of 57 patients with previously untreated anisometropic amblyopia without strabismus(range:8-35 years),were measured refractive error,best corrected visual acuity(BCVA),fusion and stereopsis,and 48 patients have completed contrast sensitivity function test.AM was determined by dioptric vector addition model,and the amblyopia depth was determined by the difference of BCVA in logMAR units between the amblyopic and fellow eyes.RESULTS:AM was significantly correlated with both amblyopia depth(Pearson R=0.728,P【0.001) and the inter-ocular difference of the area under the log contrast sensitivity function(AULCSF)(R=0.505,P【0.001).Depth of amblyopia and the inter-ocular difference of AULCSF was also significantly correlated(R=0.761,P【0.001).The more severity of amblyopia,the poorer levels of contrast sensitivity.Most pure anisometropes with AM was less than 3.0D retain fusion and some stereopsis,but when AM were more than 3.0D,especially for the anisometropes whose AM was more than 6.0D,fusion and stereopsis function were seriously impaired.CONCLUSION:In the patients with previously untreated anisometropic amblyopia,higher degree of anisometropia is significantly associated with deeper amblyopia,worse contrast sensitivity,fusion and stereopsis functions.

An evolutionary life history approach to understanding mental health

In recent years,evolutionary life history theory has been used as a heuristic framework to understand mental health.This article reviews the life history theory and its integration with mental disorders and then introduces representative research methods and related empirical studies in the field of evolutionary psychopathology.In the end,this article concludes with future directions for further research examining and developing the evolutionary psychopathological framework.

Learning the Effective Spin Hamiltonian of a Quantum Magnet

To understand the intriguing many-body states and effects in the correlated quantum materials,inference of the microscopic effective Hamiltonian from experiments constitutes an important yet very challenging inverse problem.Here we propose an unbiased and efficient approach learning the effective Hamiltonian through the many-body analysis of the measured thermal data.Our approach combines the strategies including the automatic gradient and Bayesian optimization with the thermodynamics many-body solvers including the exact diagonalization and the tensor renormalization group methods.We showcase the accuracy and powerfulness of the Hamiltonian learning by applying it firstly to the thermal data generated from a given spin model,and then to realistic experimental data measured in the spin-chain compound copper nitrate and triangular-lattice magnet TmMgGaO_(4).The present automatic approach constitutes a unified framework of many-body thermal data analysis in the studies of quantum magnets and strongly correlated materials in general.

Network-Initialized Monte Carlo Based on Generative Neural Networks

We design generative neural networks that generate Monte Carlo configurations with complete absence of autocorrelation from which only short Markov chains are needed before making measurements for physical observables,irrespective of the system locating at the classical critical point,fermionic Mott insulator,Dirac semimetal,or quantum critical point.We further propose a network-initialized Monte Carlo scheme based on such neural networks,which provides independent samplings and can accelerate the Monte Carlo simulations by significantly reducing the thermalization process.We demonstrate the performance of our approach on the two-dimensional Ising and fermion Hubbard models,expect that it can systematically speed up the Monte Carlo simulations especially for the very challenging many-electron problems.

Design and applications of carbon dots-based ratiometric fluorescent probes:A review

Ratiometric fluorescence(FL)probes can eliminate the background interference and provide more accurate detection results than single emission intensity-based nanoprobes.Recently,carbon dots(CDs)-based ratiometric FL probes have received extensive research attention due to their excellent biocompatibility,water solubility,and multi-emission capabilities.In this review,we firstly summarize the construction strategies of CDs-based ratiometric FL probes,including physical mixing,nanohybrid,and dual-emitting CDs strategies.Additionally,we classify the sensing types of CDs-based ratiometric FL probes into five categories according to the difference in spectral variation caused by analytes:“single-response-ON”,“single-response-OFF”,“doubleresponses-ON”,“double-responses-OFF”,and“double-responses-Reverse”types.Finally,a thorough overview of CDs-based ratiometric FL probe applications in ions,molecules,pH,and temperature sensing is provided.We believe this review can show the latest research progress of CDs-based ratiometric FL sensing fields and provide perspectives on future developments for the construction of CDs-based ratiometric FL probes and their potential applications.

Novel mutation c.980_983delATTA compound with c.986C>A mutation of the FRMD7 gene in a Chinese family with X-linked idiopathic congenital nystagmus

Objective:To screen mutations in FERM domain-containing protein 7(FRMD7) gene in two Chinese families with X-linked idiopathic congenital nystagmus(XLICN).Methods:Common ophthalmic data and peripheral blood of two Chinese XLICN families(families A and B) were collected after informed consent.Genomic DNA was prepared from the peripheral blood of members of the two families and from 100 normal controls.Mutations in the FRMD7 gene were determined by directly sequencing polymerase chain reaction(PCR) products.Results:We identified a novel mutation c.980_983delATTA compound with c.986C>A mutation in the 11th exon of FRMD7 in family B,and a previously reported splicing mutation c.782G>C(p.R261G) in family A.The mutations were detected in patients and female carriers,while they were absent in other relatives or in the 100 normal controls.Conclusions:Our results expand the spectrum of FRMD7 mutations in association with XLICN,and further confirm that the mutations of FRMD7 are the underlying molecular mechanism for XLICN.

Carbon dots prepared for fluorescence and chemiluminescence sensing

Carbon dots(CDs) have attracted considerable research interest in recent years due to their unique optical properties, chemical inertness, facile synthesis from a wide range of starting materials, and advantages over classic quantum dots and organic dyes.Various methods have been developed for preparing the CDs, including chemical oxidation, hydro/solvothermal method,electrochemical method, microwave-assisted synthesis, and direct carbonization method. Importantly, the superior electronic properties of CDs including efficient light harvesting and prominent photoinduced electron transfer have aroused considerable attention in fluorescence(FL) and chemiluminescence(CL) sensing field. In this review, we aim to demonstrate the recent progress of CDs in the synthesis, FL and CL sensing applications. This review gives new insights into how to use different synthetic methods to tune the structure of the CDs, with the major focus on FL and CL sensing.