Systematic Optimization of Universal Real-Time Hypersensitive Fast Detection Method for HBsAg in Serum Based on SERS.

Hepatitis B virus (HBV) is a major cause of liver cirrhosis and hepatocellular carcinoma, with HBV surface antigen (HBsAg) being a crucial marker in the clinical detection of HBV. Due to the significant harm and ease of transmission associated with HBV, HBsAg testing has become an essential part of preoperative assessments, particularly for emergency surgeries where healthcare professionals face exposure risks. Therefore, a timely and accurate detection method for HBsAg is urgently needed. In this study, a surface-enhanced Raman scattering (SERS) sensor with a sandwich structure was developed for HBsAg detection. Leveraging the ultrasensitive and rapid detection capabilities of SERS, this sensor enables quick detection results, significantly reducing waiting times. By systematically optimizing critical factors in the detection process, such as the composition and concentration of the incubation solution as well as the modification conditions and amount of probe particles, the sensitivity of the SERS immune assay system was improved. Ultimately, the sensor achieved a sensitivity of 0.00576 IU/mL within 12 min, surpassing the clinical requirement of 0.05 IU/mL by an order of magnitude. In clinical serum assay validation, the issue of false positives was effectively addressed by adding a blocker. The final sensor demonstrated 100% specificity and sensitivity at the threshold of 0.05 IU/mL. Therefore, this study not only designed an ultrasensitive SERS sensor for detecting HBsAg in actual clinical serum samples but also provided theoretical support for similar systems, filling the knowledge gap in existing literature.

Metabolomic profiling of the aqueous humor in patients with pediatric cataract.

Pediatric cataract, including congenital and developmental cataract, is a kind of pediatric vision-threatening disease with extensive phenotypic heterogeneity and multiple mechanisms. We aimed to investigate the metabolite profile of aqueous humor (AH) in patients with pediatric cataracts, and identify underlying mutual correlations between differential metabolites. Metabolomic profiles of AH were analyzed and compared between pediatric cataract patients (n = 33) and age-related cataract patients without metabolic diseases (n = 29), using global untargeted metabolomics with ultra-high-performance liquid chromatography tandem mass spectrometry. Principal component analysis, partial least squares discriminant analysis and heat map were applied. Enriched pathway analysis was conducted using Kyoto Encyclopedia of Genes and Genomes. Receiver-operating characteristic (ROC) analyses were employed to select potential biomarkers. A total of 318 metabolites were identified, of which 54 differential metabolites (25 upregulated and 29 downregulated) were detected in pediatric cataract group compared with controls (variable importance of projection >1.0, fold change ≥1.5 or ≤ 0.667 and P < 0.05). A significant accumulation of N-Acetyl-Dl-glutamic acid was observed in pediatric cataract group. The differential metabolites were mainly enriched in histidine metabolism (increased L-Histidine and decreased 1-Methylhistamine) and the tryptophan metabolism (increased N-Formylkynurenine and L-Kynurenine). 5-Aminosalicylic acid showed strong positive mutual inter-correlation with L-Tyrosinemethylester and N,N-Diethylethanolamine, both of which were down-regulated in pediatric cataract group. The ROC analysis implied 11 metabolites served as potential biomarkers for pediatric cataract patients (all area under the ROC curve ≥0.900). These results illustrated novel potential metabolites and metabolic pathways in pediatric cataract, which provides new insights into the pathophysiology of pediatric cataract.

DFT Insights into the Variety in the Coordination Modes of the Equatorial Halides in [Au13 Ag12 (PR3 )10 X8 ]+ (X=Cl/Br) Clusters.

The bonding character within metal nanoclusters represents an intriguing topic, shedding light on the inherent driving force for the packing preference in nanomaterials. Herein, density functional theory (DFT) calculations were conducted to investigate the correlation of the series of isomeric [Au13 Ag12 (PR3 )10 X8 ]+ (X=Cl/Br) clusters, which are mainly differentiated by the coordination mode of the equatorial halides (µ2 -, µ3 - and µ4 -) in the rod-like, bi-icosahedral framework. The theoretical simulation corroborates the variety in the configuration of the Au13 Ag12 clusters and elucidates the fast isomerization kinetics among the different configurations. The easy tautomerization and the variety in chloride binding modes correspond to a fluxionality character of the equatorial halides and are verified by the potential energy curve analysis. The structural flexibility of the central Au3 Ag10 block is the main driving force, while the relatively stronger Ag-X bonding interaction (compared to that of Au-X), and a sufficient number of halides are also requisite for the associating Ag-X tautomerizations.

Mechanistic Insights into Oxidation-Induced Size Conversion of [Au6(dppp)4]2+ to [Au8(dppp)4Cl2]2.

Oxidation-induced conversion of gold nanoclusters is an important strategy for preparing novel atomically precise clusters and elucidating the kinetic correlations of different clusters. Herein, the oxidation-induced growth from [Au6(dppp)4]2+ to [Au8(dppp)4Cl2]2+ (reported by Konishi and co-workers) has been studied by density functional theory calculations. A successive oxidation → Cl- coordination → oxidation → Cl- coordination sequence occurs first to activate the Au6 structure, resulting in the high Au(core)-Au(corner) bond cleavage activity and the subsequent formation of [Au2(dppp)2Cl]+ and [Au4(dppp)2Cl]+ fragments. Then, the dimerization of two Au4 fragments and the rearrangement of the diphosphine coordination occur to generate the thermodynamically stable [Au8(dppp)4Cl2]2+ products. The proposed mechanism agrees with the experimental outcome for the fast reaction rate and the residual of the Au2 components. Specifically, a multivariate linear regression analysis indicates the strong correlation of the oxidation potential of Au6, Au8, Au23, and Au25 clusters with the HOMO energy, the number of Au atoms, and cluster charge state. The main conclusions [e.g., oxidation-induced Au(corner)-Au(core) bond activation, easy 1,2-P transfer steps, etc.] of this study might be widely applicable in improving our understanding of the mechanism of other cluster-conversion reactions.

Extracellular vesicles promote epithelial-to-mesenchymal transition of lens epithelial cells under oxidative stress.

Posterior capsule opacification (PCO), resulting from residual lens epithelial cell (LEC) epithelial-mesenchymal transition (EMT), abnormal proliferation, and migration, is the most common complication of cataract surgery. A recent study determined that extracellular vesicles (EVs) and reactive oxygen species (ROS) regulate the EMT process during cutaneous wound healing and tumour metastasis. However, their underlying mechanism in PCO is unclear. In this study, we examined the secreted EVs from a scratch model in vitro. We found that the production of ROS was increased after mechanical injury, especially at the wound edge, and there was an increased viability of LECs, which can be blocked by diphenyleneiodonium, an NADPH oxidase inhibitor. Cell viability and migration were increased upon treatment with 1 µM H2O2, but significantly reduced when the concentration of H2O2 increased to 100 µM. Transwell assay showed that both post-surgery LECs and LECs treated with 1 µM H2O2 significantly induced the migration of normal LECs by EV secretion. Extraction and quantification of EVs derived from injured and H2O2-treated LECs showed a similar increase in production. Co-incubation of EVs from both injured and H2O2-treated LECs with normal LECs and organ-cultured mouse lenses activated EMT, which was attenuated by a ROS inhibitor. These results suggest that EVs participate in ROS-induced lens EMT, making EVs a potential target for treating PCO.

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.

Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study.

BACKGROUND: Electronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children. METHODS AND FINDINGS: Real-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE ≤ -6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records (≥3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achieved balanced and robust performance, concerns about the compromised quality of real-world clinical data and over-fitting issues should be cautiously considered. CONCLUSIONS: To our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia.

Preoperative profile of inflammatory factors in aqueous humor correlates with postoperative inflammatory response in patients with congenital cataract.

Purpose: To measure the aqueous humor concentrations of inflammatory factors in patients with congenital cataract and to investigate the relationship between the levels and postoperative inflammatory responses. Methods: Aqueous humor samples were prospectively collected from 65 eyes of children with congenital cataracts from January to June 2015. The levels of 41 inflammation-related cytokines, chemokines, and growth factors in aqueous humor were measured using multiplex bead immunoassay. Data on patient demographics and postoperative inflammatory response evaluation of posterior capsule opacification (EPCO) scores were collected for correlation analysis of short- and long-term postoperative inflammatory responses, respectively. Results: Fifteen inflammatory factors were differentially expressed between congenital cataract and age-related cataract. EGF and IL-3 were positively correlated, whereas IL-8 and MCP-1 were negatively correlated with age. TNFα, IL-17A, IL-3, and sCD40L were preferably expressed in specific morphological types of congenital cataract. One month and 3 months postoperatively, PDGF-AA exhibited a positive correlation with the EPCO scores, whereas IL-1RA exhibited a negative correlation. Macrophage-derived chemokine (MDC) showed a positive correlation with the EPCO scores 1 year postoperatively. Conclusions: This study provided a comprehensive preoperative profile of inflammatory factors and their correlations with postoperative inflammatory responses in patients with congenital cataract. These factors may serve as potential biomarkers to predict the postoperative inflammatory response. These findings will also facilitate the development of anti-inflammatory medications in the perioperative period.

Meta-analysis of accuracy of intraocular lens power calculation formulas in short eyes.

IMPORTANCE: Intraocular lens (IOL) power selection is a critical factor affecting visual outcome after IOL implantation in short eyes. Many formulas have been developed to achieve a precise prediction of the IOL power. However, controversy regarding the accuracy remains. BACKGROUND: To investigate the accuracy of different IOL power calculation formulas in short eyes. DESIGN: Meta-analysis. PARTICIPANTS: Patients with the axial length of eyes less than 22 mm from previously reported studies. METHODS: A comprehensive search in Pubmed, EMBASE, Cochrane Data Base of Systematic Reviews and the Cochrane Central Register of Controlled Trials was conducted by October 2016. We assessed the methodological quality using a modified QUADAS-2 tool and performed analysis on weighted mean differences of mean absolute errors (MAE) among different formulas. MAIN OUTCOMES MEASURES: The between-group difference of MAE was evaluated with weighted mean difference and 95% confidence intervals. RESULTS: Ten observational studies, involving 1161 eyes, were enrolled to compare six formulas: Haigis, Holladay 2, Hoffer Q, Holladay 1, SRK/T and SRK II. Among them, the Holladay 2 introduced the smallest overall MAE (0.496D) without statistical significance. The difference of MAE is statistically significant between Haigis and Hoffer Q (mean difference = -0.07D, P = 0.003), Haigis and SRK/T (mean difference = -0.07D, P = 0.009), Haigis and SRK II (mean difference = -0.41D, P = 0.01). For publication bias and small-study effect, neither funnel plot nor egger's test detected statistical finding. CONCLUSION AND RELEVANCE: The overall evidence from the studies confirmed the superiority of Haigis over Hoffer Q, SRK/T and SRK II in prediction IOL power in short eyes.

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.

Height, weight and body mass index of children with congenital cataracts before surgical treatment.

BACKGROUND: To investigate the distribution of the height, weight and body mass index (BMI) of children with congenital cataracts (CC) before surgical treatment. METHODS: This prospective study included children with CC ≤14 years of age presenting at the Zhongshan Ophthalmic Center from Jan. 2013 to Aug. 2016. The height, weight, and BMI measurements of all participating children were obtained and compared with the World Health Organization Child Growth Reference (WHO Reference), matched by age and gender. The presence of a family history of CC or complicated systemic diseases as well as parental education levels and family income were also recorded. RESULTS: In total, 595 children with CC were included. The mean age was 52.75 ± 33.99 months, and 34.29% (204/595) of them were unilateral cases. Among all of the children, 6.72% (40/595) of cases were complicated by systemic diseases. More than 1/5 (21.01%, 125/595) of the children had a family history of CC and exhibited bilateral involvement. Less than 1/4 (23.2) of the mothers were highly educated, and more than half of the families had a family income below the city average. Height, weight, and BMI measurements of most children with CC were within the normal ranges (±95% CI of the WHO Reference). Compared to the WHO Reference, both girls and boys aged 2-5 years revealed shorter heights, and the girls aged 5-14 years exhibited a shorter height, lower body weight and lower BMI. The heights of the children with CC and systemic diseases were also shorter than the WHO Reference. The children with CC who had a family history of disease had shorter heights and lower BMIs than children with CC but no family history, and the measurements of both groups were lower than the WHO Reference values. CONCLUSIONS: The height, weight and BMI of most of the children with CC in this study were within the normal ranges of the WHO Reference. However, the children with CC and concomitant systemic diseases and those with a family history of CC had shorter heights and lower BMIs. This information aids in our understanding of the physical development of children with CC.

Correction: Mechanistic insights into Ag+ induced size-growth from [Au6(DPPP)4]2+ to [Au7(DPPP)4]2+ clusters.

[This corrects the article DOI: 10.1039/D2NA00301E.].

Comparing the impact of three-dimensional digital visualization technology versus traditional microscopy on microsurgeons in microsurgery: a prospective self-controlled study.

BACKGROUND: Emerging three-dimensional digital visualization technology (DVT) provides more advantages than traditional microscopy in microsurgery; however, its impact on microsurgeons' visual and nervous systems and delicate microsurgery is still unclear, which hinders the wider implementation of DVT in digital visualization for microsurgery. METHODS AND MATERIAL: Forty-two microsurgeons from the Zhongshan Ophthalmic Center were enrolled in this prospective self-controlled study. Each microsurgeon consecutively performed 30 min conjunctival sutures using a three-dimensional digital display and a microscope, respectively. Visual function, autonomic nerve activity, and subjective symptoms were evaluated before and immediately after the operation. Visual functions, including accommodative lag, accommodative amplitude, near point of convergence and contrast sensitivity function (CSF), were measured by an expert optometrist. Heart rate variability was recorded by a wearable device for monitoring autonomic nervous activity. Subjective symptoms were evaluated by questionnaires. Microsurgical performance was assessed by the video-based Objective Structured Assessment of Technical Skill (OSATS) tool. RESULTS: Accommodative lag decreased from 0.63 (0.18) diopters (D) to 0.55 (0.16) D ( P =0.014), area under the log contrast sensitivity function increased from 1.49 (0.15) to 1.52 (0.14) ( P =0.037), and heart rate variability decreased from 36.00 (13.54) milliseconds (ms) to 32.26 (12.35) ms ( P =0.004) after using the DVT, but the changes showed no differences compared to traditional microscopy ( P >0.05). No statistical significance was observed for global OSATS scores between the two rounds of operations [mean difference, 0.05 (95% CI: -1.17 to 1.08) points; P =0.95]. Subjective symptoms were quite mild after using both techniques. CONCLUSIONS: The impact of DVT-based procedures on microsurgeons includes enhanced accommodation and sympathetic activity, but the changes and surgical performance are not significantly different from those of microscopy-based microsurgery. Our findings indicate that short-term use of DVT is reliable for microsurgery and the long-term effect of using DVT deserve more consideration.

Early Diagnosis of Syndromic Congenital Cataracts in a Large Cohort of Congenital Cataracts.

PURPOSE: To explore the factors related to the diagnosis yield of syndromic congenital cataracts and describe the phenotype-genotype correlation in congenital cataract patients. DESIGN: Prospective cohort study. METHODS: Setting: the participants from underwent clinical examinations between 2021 and 2022. Facial and anterior eye segment photographs, pre- and postoperative ocular parameters, and medical and family histories were recorded. Bioinformatics analysis was performed using whole-exome sequencing data. Statistical and correlation analyses were performed using the basic characteristics, deep phenotype, and genotype data. PARTICIPANTS: 115 patients with unrelated congenital cataract. INTERVENTIONS: performing clinical examinations, whole-exome sequencing, and bioinformatics analysis for all participants. MAIN OUTCOMES AND MEASURES: factors related to the genetic diagnosis yield of syndromic congenital cataracts. RESULTS: Bilaterally asymmetrical cataracts were identified to be associated with syndromic congenital cataracts. The overall genetic diagnostic yield in the cohort was 72.2%. In total, 34.8% of the probands were early diagnosed with various syndromes with the help of genetic information. A phenotype-genotype correlation was detected for some genes and deep phenotypes. CONCLUSIONS: We highlight the importance of screening syndromic diseases in the patients with asymmetrical congenital cataracts. Application of whole-exome sequencing helps provide early diagnosis and treatment for the patients with syndromic congenital cataracts. This study also achieved a high genetic diagnostic yield, expanded the genotypic spectrum, and found phenotype-genotype correlations. A comprehensive analysis of cataract symmetricity, family history, and deep phenotypes makes the genotype prediction of some congenital cataract patients possible.

Cost-effectiveness and cost-utility of a digital technology-driven hierarchical healthcare screening pattern in China.

Utilization of digital technologies for cataract screening in primary care is a potential solution for addressing the dilemma between the growing aging population and unequally distributed resources. Here, we propose a digital technology-driven hierarchical screening (DH screening) pattern implemented in China to promote the equity and accessibility of healthcare. It consists of home-based mobile artificial intelligence (AI) screening, community-based AI diagnosis, and referral to hospitals. We utilize decision-analytic Markov models to evaluate the cost-effectiveness and cost-utility of different cataract screening strategies (no screening, telescreening, AI screening and DH screening). A simulated cohort of 100,000 individuals from age 50 is built through a total of 30 1-year Markov cycles. The primary outcomes are incremental cost-effectiveness ratio and incremental cost-utility ratio. The results show that DH screening dominates no screening, telescreening and AI screening in urban and rural China. Annual DH screening emerges as the most economically effective strategy with 341 (338 to 344) and 1326 (1312 to 1340) years of blindness avoided compared with telescreening, and 37 (35 to 39) and 140 (131 to 148) years compared with AI screening in urban and rural settings, respectively. The findings remain robust across all sensitivity analyses conducted. Here, we report that DH screening is cost-effective in urban and rural China, and the annual screening proves to be the most cost-effective option, providing an economic rationale for policymakers promoting public eye health in low- and middle-income countries.