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Neurosteroids as stress modulators and neurotherapeutics: lessons from the retina

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Neural Regeneration Research 2023年第5期18卷 1004-1008页

作者： Yukitoshi Izumi Makoto Ishikawa Toru Nakazawa Hiroshi Kunikata Kota Sato Douglas F.Covey Charles F.Zorumski department of Psychiatry and Taylor Family Institute for Innovative Psychiatric Research Washington University School of MedicineSt.LouisMOUSA department of ophthalmic imaging and information analytics Tohoku University Graduate School of MedicineSendaiJapan department of Ophthalmology Tohoku University Graduate School of MedicineSendaiJapan department of Retinal Disease Control Tohoku University Graduate School of MedicineSendaiJapan department of Advanced ophthalmic Medicine Tohoku University Graduate School of MedicineSendaiJapan department of Developmental Biology Washington University School of MedicineSt.LouisMOUSA

Neurosteroids are rapidly emerging as important new therapies in neuropsychiatry, with one such agent, brexanolone, already approved for treatment of postpartum depression, and others on the horizon. These steroids have unique properties, including neuroprotective effects that could benefit a wide range of brain illnesses including depression, anxiety, epilepsy, and neurodegeneration. Over the past 25 years, our group has developed ex vivo rodent models to examine factors contributing to several forms of neurodegeneration in the retina. In the course of this work, we have developed a model of acute closed angle glaucoma that involves incubation of ex vivo retinas under hyperbaric conditions and results in neuronal and axonal changes that mimic glaucoma. We have used this model to determine neuroprotective mechanisms that could have therapeutic implications. In particular, we have focused on the role of both endogenous and exogenous neurosteroids in modulating the effects of acute high pressure. Endogenous allopregnanolone, a major stress-activated neurosteroid in the brain and retina, helps to prevent severe pressure-induced retinal excitotoxicity but is unable to protect against degenerative changes in ganglion cells and their axons under hyperbaric conditions. However, exogenous allopregnanolone, at a pharmacological concentration, completely preserves retinal structure and does so by combined effects on gamma-aminobutyric acid type A receptors and stimulation of the cellular process of macroautophagy. Surprisingly, the enantiomer of allopregnanolone, which is inactive at gamma-aminobutyric acid type A receptors, is equally retinoprotective and acts primarily via autophagy. Both enantiomers are also equally effective in preserving retinal structure and function in an in vivo glaucoma model. These studies in the retina have important implications for the ongoing development of allopregnanolone and other neurosteroids as therapeutics for neuropsychiatric illnesses.

关键词： allopregnanolone autophagy enantiomers excitotoxicity gamma-aminobutyric acid type A receptors glaucoma optic nerve oxysterols

An Interactive Visual analytics Framework for Multi-Field Data in a Geo-Spatial Context

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Tsinghua Science and Technology 2013年第2期18卷 111-124页

作者： Zhiyuan Zhang Xiaonan Tong Kevin T. McDonnell Alla Zelenyuk Dan Imre Klaus Mueller the Visual analytics and imaging Lab at Computer Science department Stony Brook University Stony Brook NY 11790 USA an undergraduate student at Stanford University Stanford CA 94305 USA the department of Mathematics Dowling College Oakdale NY 11769 USA Pacific Northwest National Lab Richland WA 99354 USA also with SUNY Korea

Climate research produces a wealth of multivariate data. These data often have a geospatial reference and so it is of interest to show them within their geospatial context. One can consider this configuration as a multifield visualization problem, where the geo-space provides the expanse of the field. However, there is a limit on the amount of multivariate information that can be fit within a certain spatial location, and the use of linked multivariate information displays has previously been devised to bridge this gap. In this paper we focus on the interactions in the geographical display, present an implementation that uses Google Earth, and demonstrate it within a tightly linked parallel coordinates display. Several other visual representations, such as pie and bar charts are integrated into the Google Earth display and can be interactively manipulated. Further, we also demonstrate new brushing and visualization techniques for parallel coordinates, such as fixed-window brushing and correlation-enhanced display. We conceived our system with a team of climate researchers, who already made a few important discoveries using it. This demonstrates our system＇s great potential to enable scientific discoveries, possibly also in other domains where data have a geospatial reference.

关键词： geospatial visualization visual analytics information visualization multivariate visualization parallel coordinates coordinated displays linking and brushing

Image Fusion Using Wavelet Transformation and XGboost Algorithm

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Computers, Materials & Continua 2024年第4期79卷 801-817页

作者： Shahid Naseem Tariq Mahmood Amjad Rehman Khan Umer Farooq Samra Nawazish Faten S.Alamri Tanzila Saba department of information Sciences Division of S&TUniversity of EducationLahore54770Pakistan Artificial Intelligence and Data analytics(AIDA)Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of information Sciences University of EducationVehari CampusVehari61100Pakistan department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan department of Mathematical Sciences College of SciencePrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Recently,there have been several uses for digital image *** fusion has become a prominent application in the domain of imaging *** create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same *** fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual *** fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on *** research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet *** fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient *** precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain *** tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is *** use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter *** simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original *** study enhances the performance of the eXtreme Gradient Boosting(XGBoos

关键词： Image fusion max-min average CWT XGBoost DCT inclusive innovations spatial and frequency domain

Applications of Deep Learning to MRI Images:A Survey

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Big Data Mining and analytics 2018年第1期1卷 1-18页

作者： Jin Liu Yi Pan Min Li Ziyue Chen Lu Tang Chengqian Lu Jianxin Wang the School of information Science and Engineering Central South University the department of Computer Science Georgia State University

Deep learning provides exciting solutions in many fields, such as image analysis, natural language processing, and expert system, and is seen as a key method for various future applications. On account of its non-invasive and good soft tissue contrast, in recent years, Magnetic Resonance imaging(MRI) has been attracting increasing attention. With the development of deep learning, many innovative deep learning methods have been proposed to improve MRI image processing and analysis performance. The purpose of this article is to provide a comprehensive overview of deep learning-based MRI image processing and analysis. First, a brief introduction of deep learning and imaging modalities of MRI images is given. Then, common deep learning architectures are introduced. Next, deep learning applications of MRI images, such as image detection, image registration, image segmentation, and image classification are discussed. Subsequently, the advantages and weaknesses of several common tools are discussed, and several deep learning tools in the applications of MRI images are ***, an objective assessment of deep learning in MRI applications is presented, and future developments and trends with regard to deep learning for MRI images are addressed.

关键词： magnetic resonance imaging deep learning image detection image registration image segmentation image classification

An ophthalmic Evaluation of Central Serous Chorioretinopathy

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Computer Systems Science & Engineering 2023年第1期44卷 613-628页

作者： L.K.Shoba P.Mohan Kumar department of information Technology Jeppiaar Engineering CollegeChennai600119India department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Nowadays in the medicalfield,imaging techniques such as Optical Coherence Tomography(OCT)are mainly used to identify retinal *** this paper,the Central Serous Chorio Retinopathy(CSCR)image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application *** approach,which was focused on image quality,improves medical image *** enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter(BADWUMF).The classifier used here is tofigure out whether the OCT image is a CSCR case or not.150 images are checked for this research work(75 abnormal from Optical Coherence Tomography Image Retinal Database,in-house clinical database,and 75 normal images).This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be *** total precision is 90 percent obtained from the Two Class Support Vector Machine(TCSVM)classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale(SNNWPB)classifier using the MATLAB 2019a program.

关键词： OCT CSCR macula segmentation boosted anisotropic diffusion with unsharp maskingfilter two class support vector machine classifier and shallow neural network with powell-beale classifier

Comparing imaging capabilities of spectral domain and swept source optical coherence tomography angiography in healthy subjects and central serous retinopathy

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Eye and Vision 2018年第1期5卷 182-190页

作者： Fupeng Wang Qinqin Zhang Anthony J.Deegan Jun Chang Ruikang K.Wang department of Bioengineering University of WashingtonSeattleWashington 98105USA School of information Science and Engineering Shandong UniversityJinan 250100China

Background:There are two forms of system implementation of optical coherence tomography angiography(OCTA)in ophthalmic imaging,i.e.,spectral domain(SD-)and swept source OCTA(SS-OCTA).The purpose of this paper is to compare the SD-OCTA and SS-OCTA for elucidating structural and vascular features associated with central serous retinopathy(CSR),and to evaluate the effects of CSR on SD-and SS-OCTA’s imaging ***:Normal subjects and CSR patients were imaged by SD-and SS-OCTA using 3×3 mm and 6×6 mm scan *** signal strengths at the superficial retina,deep retina,Sattler’s layer and Haller’s layer were used to compare the ability of SD-and SS-OCTA to image structural *** addition,the ability to acquire angiograms were discussed by evaluating retinal vessel *** serous volume(CSV)was measured and it was correlated with difference in signal strengths(ΔS)between two OCTA ***:Seven normal eyes and seven diseased eyes were *** showed no significant differences between SD-and SS-OCT in detecting structural features of the retinal layer according to the paired ***,when imaging the Sattler’s layer for normal eyes,a significant difference is found between SD-and SS-OCT(pimaging the retinal layers however,when imaging beyond retinal layers,SS-OCTA appears advantageous in detecting returning *** CSR cases,the CSV may have an impact on sub-C

关键词： Optical coherence tomography Spectral domain OCTA Swept source OCTA Central serous retinopathy Signal strength Central serous volume

Distribution and associated factors of intraocular pressure in the older population:Tehran Geriatric Eye Study

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International Journal of Ophthalmology(English edition) 2023年第3期16卷 418-426页

作者： Hassan Hashemi Samira Heydarian Mohammadreza Aghamirsalim Abbasali Yekta Alireza Hashemi Masoumeh Sajadi Mehdi Khabazkhoob Noor Research Center for ophthalmic Epidemiology Noor Eye HospitalTehran 19839-63113Iran department of Rehabilitation Sciences School of Allied Medical SciencesMazandaran University of Medical SciencesSari ***Iran Translational Ophthalmology Research Center Tehran University of Medical SciencesTehran ***Iran department of Optometry School of Paramedical SciencesMashhad University of Medical SciencesMashhad 3453545Iran Noor Ophthalmology Research Center Noor Eye HospitalTehran ***Iran department of Basic Sciences School of Nursing and MidwiferyShahid Beheshti University of Medical SciencesTehran ***Iran

AIM:To determine the distribution and associated factors of intraocular pressure(IOP) in an Iranian elderly population 60 years of age and ***:The present report is part of the Tehran Geriatric Eye study(TGES),a population-based cross-sectional study that was conducted on the residents of Tehran 60 years of age and *** sampling was performed using multistage stratified random cluster sampling methods from 22 districts of Tehran,*** and history information,blood samples,and blood pressure were collected from all *** examinations included measurement of uncorrected and best-corrected visual acuity,objective and subjective refraction,and slit-lamp *** IOP was measured using Goldmann applanation tonometry(GAT).Corneal imaging and ocular biometry were performed using Pentacam ***:The data of 3892 eyes of 2124 individuals were analyzed for this *** mean age of the study participants was 66.49±5.31y(range:60 to 95y).The mean IOP was 15.2 mm Hg(95%CI:15.1 to 15.4),15.3 mm Hg(95%CI:15.1 to 15.5) and 15.1 mm Hg(95%CI:15.0 to 15.3) in all participants,males,and females,*** the study participants,1.3% had an IOP of ≥20 mm *** mean IOP increased from 15.1 mm Hg in the age group 60-64y to 16.3 mm Hg in the age group ≥*** to the final multiple GEE model,the IOP was statistically significantly higher in men than in *** the studied age groups,except for the 75-79-year-old age group,had significantly higher IOP compared to the 60-64-year-old age *** IOP was significantly higher in underweight compared to other body mass index ***,the IOP had a statistically significant direct relationship with the mean corneal power(mean CP),central corneal thickness(CCT),and systolic blood ***:The present study presents the distribution of IOP in an Iranian elderly population.A higher IOP(within the range 14 to 17 mm Hg) is significantly associated with older age

关键词： intraocular pressure glaucoma elderly ocular biometry

Harnessing AI-human synergy for deep learning research analysis in ophthalmology with large language models assisting humans

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Eye Science 2024年第1期1卷 7-25页

作者：罗明杰张玮星张哲铭庞健宇林桢哲赵兰琴林铎儒林浩添 State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510060China Center for Precision Medicine and department of Genetics and Biomedical Informatics Zhongshan School of MedicineSun Yat-sen UniversityGuangzhou 510060China Hainan Eye Hospital and Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityHaikou 570311China

Background:Research innovations inocular disease screening,diagnosis,and management have been boosted by deep learning(DL)in the last *** assess historical research trends and current advances,we conducted an artificial intelligence(AI)-human hybrid analysis of publications on DL in ***:All DL-related articles in ophthalmology,which were published between 2012 and 2022 from Web of Science,were included.500 high-impact articles annotated with key research information were used to fine-tune a large language models(LLM)for reviewing medical literature and extracting *** verifying the LLM's accuracy in extracting diseases and imaging modalities,we analyzed trend of DL in ophthalmology with 2535 ***:Researchers using LLM for literature analysis were 70%(P=0.0001)faster than those who did not,while achieving comparable accuracy(97%versus 98%,P=0.7681).The field of DL in ophthalmology has grown 116%annually,paralleling trends of the broader DL *** publications focused mainly on diabetic retinopathy(P=0.0003),glaucoma(P=0.0011),and age-related macular diseases(P=0.0001)using retinal fundus photographs(FP,P=0.0015)and optical coherence tomography(OCT,P=0.0001).DL studies utilizing multimodal images have been growing,with FP and OCT combined being the most *** the 500 high-impact articles,laboratory studies constituted the majority at 65.3%.Notably,a discernible decline in model accuracy was observed when categorizing by study design,notwithstanding its statistical ***,43 publicly available ocular image datasets were ***:This study has characterized the landscape of publications on DL in ophthalmology,by identifying the trends and breakthroughs among research topics and the fast-growing *** study provides an efficient framework for combined AI-human analysis to comprehensively assess the current status and future trends in the field.

关键词： large language model AI-human collaboration research trends ophthalmology model performance

In situ transmission electron microscopy and artificial intelligence enabled data analytics for energy materials

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Journal of Energy Chemistry 2022年第5期31卷 454-493页

作者： Hongkui Zheng Xiner Lu Kai He department of Materials Science and Engineering Clemson UniversityClemson 29634USA

Energy materials are vital to energy conversion and storage devices that make renewable resources viable for electrification technologies. In situ transmission electron microscopy(TEM) is a powerful approach to characterize the dynamic evolution of material structure, morphology, and chemistry at the atomic scale in real time and in operando. In this review, recent advancements of in situ TEM techniques for studying energy materials, including catalysts, batteries, photovoltaics, and thermoelectrics, are systematically discussed and summarized. The topics include a broad range of material transformations that are in situ stimulated by heating, biasing, lighting, electron-beam illuminating, and cryocooling under vacuum, liquid, or gas environments within TEM, as well as the mechanistic understanding of the associated solid-solid, solid-liquid, and solid-gas reactions elucidated by in situ TEM examination and operando measurements. Special focus is also put on the emerging progress of artificial intelligence enabled microscopy data analytics, including machine learning enhanced tools for retrieving useful information from massive TEM imaging, diffraction, and spectroscopy datasets, highlighting its merits and potential for automated in situ TEM experimentation and analysis. Finally, the pressing challenges and future perspectives on in situ TEM study for energy-related materials are discussed.

关键词： In situ TEM Cryo-TEM Energy materials Batteries Catalysts Photovoltaic Thermoelectric Artificial intelligence Machine learning