Chikusetsu Saponin IVa liposomes modified with a retro-enantio peptide penetrating the blood-brain barrier to suppress pyroptosis in acute ischemic stroke rats.

BACKGROUND: The therapeutic strategies for acute ischemic stroke were faced with substantial constraints, emphasizing the necessity to safeguard neuronal cells during cerebral ischemia to reduce neurological impairments and enhance recovery outcomes. Despite its potential as a neuroprotective agent in stroke treatment, Chikusetsu saponin IVa encounters numerous challenges in clinical application. RESULT: Brain-targeted liposomes modified with THRre peptides showed substantial uptake by bEnd. 3 and PC-12 cells and demonstrated the ability to cross an in vitro blood-brain barrier model, subsequently accumulating in PC-12 cells. In vivo, they could significantly accumulate in rat brain. Treatment with C-IVa-LPs-THRre notably reduced the expression of proteins in the P2RX7/NLRP3/Caspase-1 pathway and inflammatory factors. This was evidenced by decreased cerebral infarct size and improved neurological function in MCAO rats. CONCLUSION: The findings indicate that C-IVa-LPs-THRre could serve as a promising strategy for targeting cerebral ischemia. This approach enhances drug concentration in the brain, mitigates pyroptosis, and improves the neuroinflammatory response associated with stroke.

[Research progress on pathogenic mechanism of sepsis-induced liver injury and treatment with traditional Chinese medicine and its active components].

Sepsis is a systemic inflammatory response syndrome caused by infection, with high morbidity and mortality. Sepsis-induced liver injury(SILI) is one of the manifestations of sepsis-induced multiple organ syndrome. At present, there is no recommended pharmacological intervention for the treatment of SILI. traditional Chinese medicine(TCM), based on the holism and dialectical treatment concept, shows the therapeutic characteristics of multi-target and multi-pathway and can comprehensively prevent and treat SILI by interfering with inflammatory factors, inflammatory signaling pathways, and anti-oxidative stress and inhibiting apoptosis. This article reviewed the experimental studies on the treatment of SILI with TCM to clarify its pathogenic mechanism and therapeutic characteristics, so as to provide more ideas and directions for the development or preparation of new drugs.

INFLUENCE OF HIGH MYOPIA ON CHORIOCAPILLARIS PERFUSION AND CHOROIDAL THICKNESS IN DIABETIC PATIENTS WITHOUT DIABETIC RETINOPATHY.

PURPOSE: To investigate the impact of high myopia on choriocapillaris perfusion and choroidal thickness (CT) in diabetic patients without diabetic retinopathy. METHODS: Healthy subjects and patients with diabetes mellitus were recruited from communities in Guangzhou. They were divided into four groups according to the presence of diabetes and high myopia: healthy control (n = 77), diabetes (n = 77), high myopia (n = 77), and diabetes with high myopia (n = 77). Swept-source optical coherence tomography angiography (SS-OCTA) measured choriocapillaris perfusion and CT. Choriocapillaris perfusion was quantified using the choriocapillaris perfusion index (CPI). RESULTS: A total of 308 subjects (308 eyes) were included in the study. The average CPI was 91.11 ± 0.84, 90.16 ± 1.46, 89.80 ± 1.42, and 89.36 ± 1.19% in the control, diabetes, high myopia, and diabetes with high myopia groups, respectively (P < 0.001); the average CT was 227.55 ± 43.13, 205.70 ± 59.66, 158.38 ± 45.24, and 144.22 ± 45.12 µm, respectively (P < 0.001). After adjusting for age and sex, the average CPI decreased 0.95 ± 0.20% (P < 0.001) in the diabetes group, 1.33 ± 0.20% (P < 0.001) in the high myopia group, and 1.76 ± 0.20% (P < 0.001) in the diabetes with high myopia group relative to the control group; the average CT decreased 23.53 ± 8.12 (P = 0.004), 70.73 ± 9.41 (P < 0.001), and 85.90 ± 8.12 µm (P < 0.001), respectively. Further adjustment for other risk factors yielded a similar result. CONCLUSION: Diabetes and high myopia significantly affect CPI and CT, and the presence of both conditions is more damaging to CPI and CT than diabetes or high myopia alone.

Association between BRAF expression in circulating tumor cells and the clinical feature of patients with multiple myeloma.

Multiple myeloma (MM) is the most common hematological malignancy with uncontrolled proliferation of monoclonal plasma cells. Despite treatment improvements, MM remains an incurable disease for most patients. Therefore, promising molecular markers are required for MM treatment decisions. In the present study, we explored the relationship between the BRAF expression in circulating tumor cells (CTCs) and the clinical features of patients with MM. The results showed that CTCs were associated with MM staging, and the expression of BRAF was associated with different CTCs. Moreover, the BRAF gene was correlated with patients' white blood cells, blood albumin levels, and Eastern Cooperative Oncology Group (ECOG) score. BRAF expression positively correlated with total CTCs, hybrid CTCs, and mesenchymal CTCs. Taken together, CTCs tightly correlated with the clinical stages and characteristics of MM. Our findings may provide a promising prognosis biomarker for MM treatment decisions.

Prediction of postoperative complications of pediatric cataract patients using data mining.

BACKGROUND: The common treatment for pediatric cataracts is to replace the cloudy lens with an artificial one. However, patients may suffer complications (severe lens proliferation into the visual axis and abnormal high intraocular pressure; SLPVA and AHIP) within 1 year after surgery and factors causing these complications are unknown. METHODS: Apriori algorithm is employed to find association rules related to complications. We use random forest (RF) and Naïve Bayesian (NB) to predict the complications with datasets preprocessed by SMOTE (synthetic minority oversampling technique). Genetic feature selection is exploited to find real features related to complications. RESULTS: Average classification accuracies in three binary classification problems are over 75%. Second, the relationship between the classification performance and the number of random forest tree is studied. Results show except for gender and age at surgery (AS); other attributes are related to complications. Except for the secondary IOL placement, operation mode, AS and area of cataracts; other attributes are related to SLPVA. Except for the gender, operation mode, and laterality; other attributes are related to the AHIP. Next, the association rules related to the complications are mined out. Then additional 50 data were used to test the performance of RF and NB, both of then obtained the accuracies of over 65% for three classification problems. Finally, we developed a webserver to assist doctors. CONCLUSIONS: The postoperative complications of pediatric cataracts patients can be predicted. Then the factors related to the complications are found. Finally, the association rules that is about the complications can provide reference to doctors.

Loss-of-function mutations in FREM2 disrupt eye morphogenesis.

Cryptophthalmos is a rare congenital disorder characterized by ocular dysplasia with eyelid malformation. Complete cryptophthalmos is characterized by the presence of continuous skin from the forehead over the eyes and onto the cheek, along with complete fusion of the eyelids. In the present study, we characterized the clinical manifestations of three patients with isolated bilateral cryptophthalmos. These patients shared the same c.6499C > T missense mutation in the FRAS1-related extracellular matrix protein 2 (FREM2) gene, while each individual presented an additional nonsense mutation in the same gene (Patient #1, c.2206C > T; Patient #2, c.5309G > A; and Patient #3, c.4063C > T). Then, we used CRISPR/Cas9 to generate mice carrying Frem2R725X/R2156W compound heterozygous mutations, and showed that these mice recapitulated the human isolated cryptophthalmos phenotype. We detected FREM2 expression in the outer plexiform layer of the retina for the first time in the cryptophthalmic eyes, and the levels were comparable to the wild-type mice. Moreover, a set of different expressed genes that may contribute secondarily to the phenotypes were identified by performing RNA sequencing (RNA-seq) of the fetal Frem2 mutant mice. Our findings extend the spectrum of FREM2 mutations, and provide insights into opportunities for the prenatal diagnosis of isolated cryptophthalmos. Furthermore, our work highlights the importance of the FREM2 protein during the development of eyelids and the anterior segment of the eyeballs, establishes a suitable animal model for studying epithelial reopening during eyelid development and serves as a valuable reference for further mechanistic studies of the pathogenesis of isolated cryptophthalmos.

An Interpretable and Expandable Deep Learning Diagnostic System for Multiple Ocular Diseases: Qualitative Study.

BACKGROUND: Although artificial intelligence performs promisingly in medicine, few automatic disease diagnosis platforms can clearly explain why a specific medical decision is made. OBJECTIVE: We aimed to devise and develop an interpretable and expandable diagnosis framework for automatically diagnosing multiple ocular diseases and providing treatment recommendations for the particular illness of a specific patient. METHODS: As the diagnosis of ocular diseases highly depends on observing medical images, we chose ophthalmic images as research material. All medical images were labeled to 4 types of diseases or normal (total 5 classes); each image was decomposed into different parts according to the anatomical knowledge and then annotated. This process yields the positions and primary information on different anatomical parts and foci observed in medical images, thereby bridging the gap between medical image and diagnostic process. Next, we applied images and the information produced during the annotation process to implement an interpretable and expandable automatic diagnostic framework with deep learning. RESULTS: This diagnosis framework comprises 4 stages. The first stage identifies the type of disease (identification accuracy, 93%). The second stage localizes the anatomical parts and foci of the eye (localization accuracy: images under natural light without fluorescein sodium eye drops, 82%; images under cobalt blue light or natural light with fluorescein sodium eye drops, 90%). The third stage carefully classifies the specific condition of each anatomical part or focus with the result from the second stage (average accuracy for multiple classification problems, 79%-98%). The last stage provides treatment advice according to medical experience and artificial intelligence, which is merely involved with pterygium (accuracy, >95%). Based on this, we developed a telemedical system that can show detailed reasons for a particular diagnosis to doctors and patients to help doctors with medical decision making. This system can carefully analyze medical images and provide treatment advices according to the analysis results and consultation between a doctor and a patient. CONCLUSIONS: The interpretable and expandable medical artificial intelligence platform was successfully built; this system can identify the disease, distinguish different anatomical parts and foci, discern the diagnostic information relevant to the diagnosis of diseases, and provide treatment suggestions. During this process, the whole diagnostic flow becomes clear and understandable to both doctors and their patients. Moreover, other diseases can be seamlessly integrated into this system without any influence on existing modules or diseases. Furthermore, this framework can assist in the clinical training of junior doctors. Owing to the rare high-grade medical resource, it is impossible that everyone receives high-quality professional diagnosis and treatment service. This framework can not only be applied in hospitals with insufficient medical resources to decrease the pressure on experienced doctors but also deployed in remote areas to help doctors diagnose common ocular diseases.

Dynamic response to initial stage blindness in visual system development.

Sensitive periods and experience-dependent plasticity have become core issues in visual system development. Converging evidence indicates that visual experience is an indispensable factor in establishing mature visual system circuitry during sensitive periods and the visual system exhibits substantial plasticity while facing deprivation. The mechanisms that underlie the environmental regulation of visual system development and plasticity are of great interest but need further exploration. Here, we investigated a unique sample of human infants who experienced initial stage blindness (beginning at birth and lasting for 2-8 months) before the removal of bilateral cataracts. Retinal thickness (RT), axial length (AL), refractive status, visual grating acuity and genetic integrity were recorded during the preoperative period or at surgery and then during follow-up. The results showed that the development of the retina is malleable and associated with external environmental influences. Our work supported that the retina might play critical roles in the development of the experience-dependent visual system and its malleability might partly contribute to the sensitive period plasticity.

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.

BACKGROUND: Ocular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial. METHODS: In this paper, we develop an effective cost-sensitive deep residual convolutional neural network (CS-ResCNN) classifier to diagnose ophthalmic diseases using retro-illumination images. First, the regions of interest (crystalline lens) are automatically identified via twice-applied Canny detection and Hough transformation. Then, the localized zones are fed into the CS-ResCNN to extract high-level features for subsequent use in automatic diagnosis. Second, the impacts of cost factors on the CS-ResCNN are further analyzed using a grid-search procedure to verify that our proposed system is robust and efficient. RESULTS: Qualitative analyses and quantitative experimental results demonstrate that our proposed method outperforms other conventional approaches and offers exceptional mean accuracy (92.24%), specificity (93.19%), sensitivity (89.66%) and AUC (97.11%) results. Moreover, the sensitivity of the CS-ResCNN is enhanced by over 13.6% compared to the native CNN method. CONCLUSION: Our study provides a practical strategy for addressing imbalanced ophthalmological datasets and has the potential to be applied to other medical images. The developed and deployed CS-ResCNN could serve as computer-aided diagnosis software for ophthalmologists in clinical application.

Proteomics analysis and proteogenomic characterization of different physiopathological human lenses.

BACKGROUND: The aim of the present study was to identify the proteomic differences among human lenses in different physiopathological states and to screen for susceptibility genes/proteins via proteogenomic characterization. METHODS: The total proteomes identified across the regenerative lens with secondary cataract (RLSC), congenital cataract (CC) and age-related cataract (ARC) groups were compared to those of normal lenses using isobaric tagging for relative and absolute protein quantification (iTRAQ). The up-regulated proteins between the groups were subjected to biological analysis. Whole exome sequencing (WES) was performed to detect genetic variations. RESULTS: The most complete human lens proteome to date, which consisted of 1251 proteins, including 55.2% previously unreported proteins, was identified across the experimental groups. Bioinformatics functional annotation revealed the common involvement of cellular metabolic processes, immune responses and protein folding disturbances among the groups. RLSC-over-expressed proteins were characteristically enriched in the intracellular immunological signal transduction pathways. The CC groups featured biological processes relating to gene expression and vascular endothelial growth factor (VEGF) signaling transduction, whereas the molecular functions corresponding to external stress were specific to the ARC groups. Combined with WES, the proteogenomic characterization narrowed the list to 16 candidate causal molecules. CONCLUSIONS: These findings revealed common final pathways with diverse upstream regulation of cataractogenesis in different physiopathological states. This proteogenomic characterization shows translational potential for detecting susceptibility genes/proteins in precision medicine.

Construction and implications of structural equation modeling network for pediatric cataract: a data mining research of rare diseases.

BACKGROUND: The majority of rare diseases are complex diseases caused by a combination of multiple morbigenous factors. However, uncovering the complex etiology and pathogenesis of rare diseases is difficult due to limited clinical resources and conventional statistical methods. This study aims to investigate the interrelationship and the effectiveness of potential factors of pediatric cataract, for the exploration of data mining strategy in the scenarios of rare diseases. METHODS: We established a pilot rare disease specialized care center to systematically record all information and the entire treatment process of pediatric cataract patients. These clinical records contain the medical history, multiple structural indices, and comprehensive functional metrics. A two-layer structural equation model network was applied, and eight potential factors were filtered and included in the final modeling. RESULTS: Four risk factors (area, density, location, and abnormal pregnancy experience) and four beneficial factors (axis length, uncorrected visual acuity, intraocular pressure, and age at diagnosis) were identified. Quantifiable results suggested that abnormal pregnancy history may be the principle risk factor among medical history for pediatric cataracts. Moreover, axis length, density, uncorrected visual acuity and age at diagnosis served as the dominant factors and should be emphasized in regular clinical practice. CONCLUSIONS: This study proposes a generalized evidence-based pattern for rare and complex disease data mining, provides new insights and clinical implications on pediatric cataract, and promotes rare-disease research and prevention to benefit patients.

UWAFA-GAN: Ultra-Wide-Angle Fluorescein Angiography Transformation via Multi-scale Generation and Registration Enhancement.

Fundus photography, in combination with the ultra-wide-angle fundus (UWF) techniques, becomes an indispensable diagnostic tool in clinical settings by offering a more comprehensive view of the retina. Nonetheless, UWF fluorescein angiography (UWF-FA) necessitates the administration of a fluorescent dye via injection into the patient's hand or elbow unlike UWF scanning laser ophthalmoscopy (UWF-SLO). To mitigate potential adverse effects associated with injections, researchers have proposed the development of cross-modality medical image generation algorithms capable of converting UWF-SLO images into their UWF-FA counterparts. Current image generation techniques applied to fundus photography encounter difficulties in producing high-resolution retinal images, particularly in capturing minute vascular lesions. To address these issues, we introduce a novel conditional generative adversarial network (UWAFA-GAN) to synthesize UWF-FA from UWF-SLO. This approach employs multi-scale generators and an attention transmit module to efficiently extract both global structures and local lesions. Additionally, to counteract the image blurriness issue that arises from training with misaligned data, a registration module is integrated within this framework. Our method performs non-trivially on inception scores and details generation. Clinical user studies further indicate that the UWF-FA images generated by UWAFA-GAN are clinically comparable to authentic images in terms of diagnostic reliability. Empirical evaluations on our proprietary UWF image datasets elucidate that UWAFA-GAN outperforms extant methodologies. The code is accessible at https://github.com/Tinysqua/UWAFA-GAN.

Morphologic and Functional Assessment of Photoreceptors in Laser-Induced Retinopathy Using Adaptive Optics Scanning Laser Ophthalmoscopy and Microperimetry.

PURPOSE: To assess the cone photoreceptors' morphology and associated retinal sensitivity in laser-induced retinopathy (LIR) using adaptive optics scanning laser ophthalmoscopy (AO-SLO) and microperimetry (MP). DESIGN: Cohort study. METHODS: This study included 13 patients (15 eyes) with LIR and 38 age-matched healthy volunteers (38 eyes). Participants underwent comprehensive evaluations including AO-SLO, MP, and spectral-domain OCT. Lesion morphology, cone density, dispersion, and regularity in AO-SLO were assessed and correlated with visual function. RESULTS: In AO-SLO images, LIR lesions were predominantly characterized by hyporeflective regions, suggesting potential cone loss at the fovea, accompanied by the presence of sizable clumps of hyperreflective material within these lesions. The average size of lesions in affected eyes was 97,128±107,478 µm², ranging from 6705 to 673,348 µm². Compared with the healthy contralateral eye and control group, LIR demonstrated significantly reduced cone density, increased cone dispersion, and notably decreased cone regularity in all 4 quadrants at 3° eccentricity (all P values < .05). Lesion morphology in AO-SLO correlated with ellipsoid zone defects observed in OCT, showing a positive correlation in size (r = 0.84, P < .001) but not with retinal sensitivities (P = .09). Similarly, cone density at 3° eccentricity did not correlate with retinal sensitivities (P = .13). CONCLUSIONS AND RELEVANCE: The study provides crucial insights into the morphologic and functional impacts of LIR on cone photoreceptors, revealing significant morphologic changes in cones that do not consistently align with functional outcomes. This research highlights the need for continued exploration into the relationship between retinal structure and function in LIR, and the importance of heightened public awareness and preventive strategies to mitigate the risk of LIR.

Association of renal function with diabetic retinopathy and macular oedema among Chinese patients with type 2 diabetes mellitus.

OBJECTIVES: To investigate the associations of renal function with diabetic retinopathy (DR) and diabetic macular oedema (DMO) in diabetic patients. METHODS: A total of 1877 diabetic participants aged 30 to 80 years were consecutively recruited between October 2017 and April 2019. The presence of DR, vision-threatening DR (VTDR) and DMO were graded using seven-field fundus photographs. Renal function was defined as normal, mildly impaired or indicative of chronic kidney disease (CKD) based on different estimated glomerular filtration rates (GFR). RESULTS: In the multivariable logistic regression model, decreased GFR was associated with the presence of any DR only in the presence of microalbuminuria (OR = 2.40 for mildly impaired and 3.37 for CKD, all P < 0.05), while an impaired GFR was an independent risk factor for VTDR regardless of microalbuminuria status (all P < 0.05). The risks of any DR (OR = 1.74 for quartile 2 and 3.09 for quartile 4) and VTDR (OR = 3.27 for quartile 2 and 6.41 for quartile 4) increased gradually along with microalbuminuria quartile (all P < 0.05). CKD (OR = 3.07, P = 0.012) and high microalbuminuria (OR = 3.22 for quartile 3 and 5.25 for quartile 4, all P < 0.05) were independent DMO risk factors. CONCLUSIONS AND RELEVANCE: There is a strong association between GFR and VTDR, whereas the association between GFR and any DR was significant only under the premise of microalbuminuria. High microalbuminuria and CKD were significantly associated with DMO.

Deep learning system for distinguishing optic neuritis from non-arteritic anterior ischemic optic neuropathy at acute phase based on fundus photographs.

Purpose: To develop a deep learning system to differentiate demyelinating optic neuritis (ON) and non-arteritic anterior ischemic optic neuropathy (NAION) with overlapping clinical profiles at the acute phase. Methods: We developed a deep learning system (ONION) to distinguish ON from NAION at the acute phase. Color fundus photographs (CFPs) from 871 eyes of 547 patients were included, including 396 ON from 232 patients and 475 NAION from 315 patients. Efficientnet-B0 was used to train the model, and the performance was measured by calculating the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Also, Cohen's kappa coefficients were obtained to compare the system's performance to that of different ophthalmologists. Results: In the validation data set, the ONION system distinguished between acute ON and NAION achieved the following mean performance: time-consuming (23 s), AUC 0.903 (95% CI 0.827-0.947), sensitivity 0.796 (95% CI 0.704-0.864), and specificity 0.865 (95% CI 0.783-0.920). Testing data set: time-consuming (17 s), AUC 0.902 (95% CI 0.832-0.944), sensitivity 0.814 (95% CI 0.732-0.875), and specificity 0.841 (95% CI 0.762-0.897). The performance (κ = 0.805) was comparable to that of a retinal expert (κ = 0.749) and was better than the other four ophthalmologists (κ = 0.309-0.609). Conclusion: The ONION system performed satisfactorily distinguishing ON from NAION at the acute phase. It might greatly benefit the challenging differentiation between ON and NAION.

Risk of acute angle-closure and changes in intraocular pressure after pupillary dilation in patients with diabetes.

BACKGROUND: To evaluate the risk of AAC and intraocular pressure (IOP) changes in diabetic patients after pupil dilation. METHODS: This cross-sectional study enrolled 2,287 diabetic patients among community residents in Guangzhou, China. All participants underwent routine pupil dilation unless they had a history of glaucoma. IOP was measured using a non-contact tonometer before and one hour after pupil dilation with tropicamide 0.5% and phenylephrine 0.5% eye drop. The proportion of AAC and changes in IOP after pupil dilation were evaluated. RESULTS: Only one of the 2,287 participants (0.04%) with diabetes developed post-dilation AAC. The mean pre and post-dilation IOP in the right was 16.1 ± 2.7 and 16.5 ± 2.8 mmHg (P < 0.001); mean pre and post-dilation IOP in the left was 16.5 ± 2.7 and 16.8 ± 2.8 mmHg (P < 0.001). Sixty-one participants (2.7%) showed an increase in IOP ≥ 5 mmHg and 25 participants (1.1%) showed a post-dilation IOP > 25 mmHg, including 11 participants (0.5%) who had both an increase in IOP ≥ 5 mmHg and post-dilation IOP > 25 mmHg. Lower pre-dilation IOP (OR = 0.827; 95% CI, 0.742-0.922; P = 0.001) and shallower anterior chamber depth (ACD) (OR = 0.226; 95% CI, 0.088-0.585; P = 0.002) were significant risk factors for an increase in IOP ≥ 5 mmHg in multivariate logistic regression analysis. CONCLUSIONS: The risk of developing AAC after pupil dilation in diabetic patients was very low. Lower pre-dilation IOP and shallower ACD are risk factors for increased post-dilation IOP.

Influence of Serum Uric Acid on Macular Choroidal Thickness and Ganglion Cell Inner Plexiform Layer Thickness.

Purpose: The purpose of this study was to determine the influence of serum uric acid (UA) on macular choroidal and ganglion cell inner plexiform layer (GC-IPL) thickness. Methods: This cross-sectional study enrolled adult individuals in communities in Guangzhou, China. All participants underwent a comprehensive ophthalmologic examination. They were divided into four groups according to UA quartiles. The choroidal and GC-IPL thickness was measured by swept-source optical coherence tomography (SS-OCT). Results: A total of 719 subjects (1389 eyes) were included in the study. The average UA was 348.50 ± 86.16 mmol/L. The average choroidal and GC-IPL thickness decreased with UA quartiles (P < 0.001). Multivariate linear regression analyses showed that UA was negatively associated with average choroidal (ß = -0.073, 95% confidence interval [CI] = -0.117 to -0.028, P = 0.001) and GC-IPL thickness (ß = -0.006, 95% CI = -0.009 to -0.002, P = 0.001). After adjusting for confounding factors, the average choroidal thickness was decreased in quartile 4 as compared with quartile 1 by -14.737 µm (95% CI = -24.460 to -5.015, P = 0.003). The average GC-IPL thickness was decreased in quartile 4 versus quartile 1 by -1.028 (95% CI = -1.873 to -0.290, P = 0.007). Conclusions: Higher UA levels were independently associated with macular choroid and GC-IPL thinning. These contribute to a better understanding of ocular pathological mechanisms. Translational Relevance: The associated UA with choroidal and GC-IPL thickness helps to understand the ocular pathological and retinal neurodegenerative mechanism.

Six-Year Change in Cataract Surgical Coverage and Postoperative Visual Outcomes in Rural Southern China: The Yangxi Eye Study.

PURPOSE: The purpose of this study was to investigate a 6-year change in cataract surgical coverage (CSC), effective cataract surgical coverage (eCSC), and visual outcomes in an elderly population in rural southern China. DESIGN: This is a prospective population-based study with a 6-year follow-up. METHODS: The study included rural residents aged 50 years and above in southern China with comprehensive eye examinations at baseline and follow-up in 2014 and 2020, respectively. RESULTS: Five thousand six hundred thirty-eight participants underwent baseline examinations (mean age 66.1±10.2 y, 50.8% women); and 3141 (64.9%) of 4841 eligible survivors attended the 6-year follow-up. Cataract surgical coverage was 41.7% and 40.6% at baseline and follow-up, respectively, while eCSC were 32.6% and 26.6%. In multivariate models, the 6-year likelihood of cataract surgery decreased with older age [odds ratio (OR)=0.97 per year, 95% confidence interval (CI): 0.94, 0.99, P =0.012] and worse baseline presenting uncorrected visual acuity (PVA) in the worse-seeing eye (OR=0.35 per unit logarithm of the minimum angle of resolution (logMAR), 95% CI: 0.25, 0.48, P <0.001), and increased with prior cataract surgical history at baseline (OR=3.88, 95% CI: 1.91, 7.09, P <0.001). The likelihood of receiving effective cataract surgery decreased with worse baseline PVA in the worse eye (OR=0.49 per unit logMAR, 95% CI: 0.24, 0.97, P =0.042) and better-seeing eye (OR=0.68 per unit logMAR, 95% CI: 0.48, 0.95, P =0.026). Posterior capsular opacification was the main reason for PVA <6/18, reporting it in logMAR (0.5) in operated eyes (38.4% at baseline; 28.1% at follow-up). CONCLUSIONS: World Health Organization has established a global target of increasing eCSC by 30% before 2030, but no increase was found in rural southern China between 2014 and 2020, let alone reaching the World Health Organization target of 56.3%. Strategies to improve surgery incidence should focus on older persons and those with worse preoperative PVA.

The interior environment design for entrepreneurship education under the virtual reality and artificial intelligence-based learning environment.

Nowadays, with the rapid growth of artificial intelligence (AI), entrepreneurship education has attracted more and more attention from society. To this end, it is necessary to gradually transform the traditional teaching mode into a new type of teaching that is more innovative, practical, and inclusive and in line with entrepreneurship education. The focus of the teaching mode change is on the optimization of the teaching environment. For this purpose, a method derived from distributed virtual reality (DVR) technology is specially designed. It refers to the fact that multiple users can join together through a computer network and participate in a virtual space at the same time to experience the virtual experience together. Based on this, the distributed 3D interior design is innovatively proposed. The innovation is mainly reflected in the application of VR technology, which is different from traditional software design. According to the functions and needs of the entrepreneurship teaching environment, first, the distributed feature information is collected, and second, the corresponding color image model is constructed by the fusion method, and edge contour detection and corresponding feature data extraction are carried out for the distributed image. Using a Red, Green, and Blue (RGB) color decomposition method, the pixel feature decomposition of spatially distributed image color is performed. And the feature reorganization of the 3D point cloud is combined to optimize the color space and color features of the combined design. On this basis, the distributed 3D interior design system is designed with VR and visual simulation technology. Finally, the Three-Dimensional Studio Max (3ds MAX) is used to establish 3D modeling, and the modeling software Multigen Creator is adopted to carry out the hierarchical structural design. The test results manifest that the Normalized Root Mean Square Error (RMSE) and information saturation of the distributed 3D interior design are reduced by 0.2 compared with the traditional design, the time overhead is shortened to one-sixth of the original, and the effect is more in line with the design requirements. It is hoped that this design method can provide new ideas and new perspectives for the optimization of the entrepreneurship teaching environment.

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy.

Background: Artificial intelligence (AI), which has been used to diagnose diabetic retinopathy (DR), may impact future medical and ophthalmic practices. Therefore, this study explored AI's general applications and research frontiers in the detection and gradation of DR. Methods: Citation data were obtained from the Web of Science Core Collection database (WoSCC) to assess the application of AI in diagnosing DR in the literature published from January 1, 2012, to June 30, 2022. These data were processed by CiteSpace 6.1.R3 software. Results: Overall, 858 publications from 77 countries and regions were examined, with the United States considered the leading country in this domain. The largest cluster labeled "automated detection" was employed in the generating stage from 2007 to 2014. The burst keywords from 2020 to 2022 were artificial intelligence and transfer learning. Conclusion: Initial research focused on the study of intelligent algorithms used to localize or recognize lesions on fundus images to assist in diagnosing DR. Presently, the focus of research has changed from upgrading the accuracy and efficiency of DR lesion detection and classification to research on DR diagnostic systems. However, further studies on DR and computer engineering are required.