Digital ray: enhancing cataractous fundus images using style transfer generative adversarial networks to improve retinopathy detection.

BACKGROUND/AIMS: The aim of this study was to develop and evaluate digital ray, based on preoperative and postoperative image pairs using style transfer generative adversarial networks (GANs), to enhance cataractous fundus images for improved retinopathy detection. METHODS: For eligible cataract patients, preoperative and postoperative colour fundus photographs (CFP) and ultra-wide field (UWF) images were captured. Then, both the original CycleGAN and a modified CycleGAN (C2ycleGAN) framework were adopted for image generation and quantitatively compared using Frechet Inception Distance (FID) and Kernel Inception Distance (KID). Additionally, CFP and UWF images from another cataract cohort were used to test model performances. Different panels of ophthalmologists evaluated the quality, authenticity and diagnostic efficacy of the generated images. RESULTS: A total of 959 CFP and 1009 UWF image pairs were included in model development. FID and KID indicated that images generated by C2ycleGAN presented significantly improved quality. Based on ophthalmologists' average ratings, the percentages of inadequate-quality images decreased from 32% to 18.8% for CFP, and from 18.7% to 14.7% for UWF. Only 24.8% and 13.8% of generated CFP and UWF images could be recognised as synthetic. The accuracy of retinopathy detection significantly increased from 78% to 91% for CFP and from 91% to 93% for UWF. For retinopathy subtype diagnosis, the accuracies also increased from 87%-94% to 91%-100% for CFP and from 87%-95% to 93%-97% for UWF. CONCLUSION: Digital ray could generate realistic postoperative CFP and UWF images with enhanced quality and accuracy for overall detection and subtype diagnosis of retinopathies, especially for CFP.\ TRIAL REGISTRATION NUMBER: This study was registered with ClinicalTrials.gov (NCT05491798).

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.

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.

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.

LensAge index as a deep learning-based biological age for self-monitoring the risks of age-related diseases and mortality.

Age is closely related to human health and disease risks. However, chronologically defined age often disagrees with biological age, primarily due to genetic and environmental variables. Identifying effective indicators for biological age in clinical practice and self-monitoring is important but currently lacking. The human lens accumulates age-related changes that are amenable to rapid and objective assessment. Here, using lens photographs from 20 to 96-year-olds, we develop LensAge to reflect lens aging via deep learning. LensAge is closely correlated with chronological age of relatively healthy individuals (R2 > 0.80, mean absolute errors of 4.25 to 4.82 years). Among the general population, we calculate the LensAge index by contrasting LensAge and chronological age to reflect the aging rate relative to peers. The LensAge index effectively reveals the risks of age-related eye and systemic disease occurrence, as well as all-cause mortality. It outperforms chronological age in reflecting age-related disease risks (p < 0.001). More importantly, our models can conveniently work based on smartphone photographs, suggesting suitability for routine self-examination of aging status. Overall, our study demonstrates that the LensAge index may serve as an ideal quantitative indicator for clinically assessing and self-monitoring biological age in humans.

EE-Explorer: A Multimodal Artificial Intelligence System for Eye Emergency Triage and Primary Diagnosis.

PURPOSE: To develop a multimodal artificial intelligence (AI) system, EE-Explorer, to triage eye emergencies and assist in primary diagnosis using metadata and ocular images. DESIGN: A diagnostic, cross-sectional, validity and reliability study. METHODS: EE-Explorer consists of 2 models. The triage model was developed from metadata (events, symptoms, and medical history) and ocular surface images via smartphones from 2038 patients presenting to Zhongshan Ophthalmic Center (ZOC) to output 3 classifications: urgent, semiurgent, and nonurgent. The primary diagnostic model was developed from the paired metadata and slitlamp images of 2405 patients from ZOC. Both models were externally tested on 103 participants from 4 other hospitals. A pilot test was conducted in Guangzhou to evaluate the hierarchical referral service pattern assisted by EE-Explorer for unspecialized health care facilities. RESULTS: A high overall accuracy, as indicated by an area under the receiver operating characteristic curve (AUC) of 0.982 (95% CI, 0.966-0.998), was obtained using the triage model, which outperformed the triage nurses (P < .001). In the primary diagnostic model, the diagnostic classification accuracy (CA) and Hamming loss (HL) in the internal testing were 0.808 (95% CI 0.776-0.840) and 0.016 (95% CI 0.006-0.026), respectively. In the external testing, model performance was robust for both triage (average AUC, 0.988, 95% CI 0.967-1.000) and primary diagnosis (CA, 0.718, 95% CI 0.644-0.792; and HL, 0.023, 95% CI 0.000-0.048). In the pilot test in the hierarchical referral settings, EE-explorer demonstrated consistently robust performance and broad participant acceptance. CONCLUSION: The EE-Explorer system showed robust performance in both triage and primary diagnosis for ophthalmic emergency patients. EE-Explorer can provide patients with acute ophthalmic symptoms access to remote self-triage and assist in primary diagnosis in unspecialized health care facilities to achieve rapid and effective treatment strategies.

Anterior Umbilication of Lens in a Family with Congenital Cataracts Associated with a Missense Mutation of MIP Gene.

Congenital cataracts (CCs) have significant genotypic and phenotypic heterogeneity. The major intrinsic protein (MIP) gene, one of the causative genes of CCs, plays a vital role in maintaining the homeostasis and transparency of the lens. In this study, we identified a unique phenotype of anterior umbilication of the lens in a four-generation pedigree with CCs. All patients in the observed family had nystagmus, nuclear cataracts, and elongated axial lengths compared with their healthy counterparts except for patient I:2, whose axial length was unavailable, and patientII:4, who had total cataracts. We confirmed, using Sanger sequencing based on whole-exon sequencing (WES) data, that all patients carried a heterozygous variant NM_012064.4:c.97C > T (NP_036196.1:p.R33C) in their MIP gene. To our knowledge, 29 variants of the human MIP gene and the relative phenotypes associated with CCs have been identified. Nevertheless, this is the first report on the anterior umbilication of the lens with nuclear or total opacity caused by the c.97C > T (p.R33C) variant in the MIP gene. These results also provide evidence that the elongated axial length might be associated with this variant. This study further confirms the phenotypic heterogeneity of CCs.

Automated detection of retinal exudates and drusen in ultra-widefield fundus images based on deep learning.

BACKGROUND: Retinal exudates and/or drusen (RED) can be signs of many fundus diseases that can lead to irreversible vision loss. Early detection and treatment of these diseases are critical for improving vision prognosis. However, manual RED screening on a large scale is time-consuming and labour-intensive. Here, we aim to develop and assess a deep learning system for automated detection of RED using ultra-widefield fundus (UWF) images. METHODS: A total of 26,409 UWF images from 14,994 subjects were used to develop and evaluate the deep learning system. The Zhongshan Ophthalmic Center (ZOC) dataset was selected to compare the performance of the system to that of retina specialists in RED detection. The saliency map visualization technique was used to understand which areas in the UWF image had the most influence on our deep learning system when detecting RED. RESULTS: The system for RED detection achieved areas under the receiver operating characteristic curve of 0.994 (95% confidence interval [CI]: 0.991-0.996), 0.972 (95% CI: 0.957-0.984), and 0.988 (95% CI: 0.983-0.992) in three independent datasets. The performance of the system in the ZOC dataset was comparable to that of an experienced retina specialist. Regions of RED were highlighted by saliency maps in UWF images. CONCLUSIONS: Our deep learning system is reliable in the automated detection of RED in UWF images. As a screening tool, our system may promote the early diagnosis and management of RED-related fundus diseases.

Broadening the Mutation Spectrum in GJA8 and CHMP4B: Novel Missense Variants and the Associated Phenotypes in Six Chinese Han Congenital Cataracts Families.

Purpose: To broaden the mutation and phenotype spectrum of the GJA8 and CHMP4B genes and to reveal genotype-phenotype correlations in a cohort of Chinese patients with congenital cataracts (CCs). Methods: Six Chinese Han families with CCs inherited in an autosomal dominant (AD) pattern were recruited for this study. All patients underwent full ocular examinations. Genomic DNA was extracted from the leukocytes of peripheral blood collected from all available patients and their unaffected family members. Whole-exome sequencing (WES) was performed on all probands and at least one of their parents. Candidate variants were further confirmed by Sanger sequencing. Bioinformatic analysis with several computational predictive programs was performed to assess the impacts of the candidate variants on the structure and function of the proteins. Results: Four heterozygous candidate variants in three different genes (CRYBB2, GJA8, and CHMP4B) were identified in affected individuals from the six families, including two novel missense variants (GJA8: c.64G > C/p. G22R, and CHMP4B: c.587C > G/p. S196C), one missense mutation (CRYBB2: c.562C > T/p. R188C), and one small deletion (GJA8: c.426_440delGCTGGAGGGGACCCT/p.143_147delLEGTL). The three missense mutations were predicted as deleterious in all four computational prediction programs. In the homologous model, the GJA8: p.143_147delLEGTL mutation showed a sequence deletion of five amino acids at the cytoplasmic loop of the Cx50 protein, close to the third transmembrane domain. Patients carrying mutations in the same gene showed similar cataract phenotypes at a young age, including total cataracts, Y-sutural with fetal nuclear cataracts, and subcapsular cataracts. Conclusion: This study further expands the mutation spectrum and genotype-phenotype correlation of CRYBB2, GJA8, and CHMP4B underlying CCs. This study sheds light on the importance of comparing congenital cataract phenotypes in patients at the same age stage. It offers clues for the pathogenesis of CCs and allows for an early prenatal diagnosis for families carrying these genetic variants.

Elongated axial length and myopia-related fundus changes associated with the Arg130Cys mutation in the LIM2 gene in four Chinese families with congenital cataracts.

BACKGROUND: Congenital cataract (CC) is a congenital abnormality characterized by lens opacity present at birth and is associated with highly heterogeneous clinical manifestations. Lens-specific integral membrane protein (LIM2) gene expression is localized to tight junctional domains of different lens fiber membranes. To date, only four mutations in LIM2 have been reported to be associated with congenital or presenile cataracts. Due to the rarity of variants detected in the gene, there is limited progress in understanding the correlation between the genotype and phenotype of patients with mutations in LIM2. METHODS: A total of four Chinese families with CCs were recruited for this study, including three families inheriting in an autosomal dominant (AD) pattern and one sporadic case. Genomic DNA was extracted from the leukocytes of peripheral blood collected from all available patients. Whole-exome sequencing (WES) was performed on all probands and at least one of their parents. Bioinformatics analysis was performed to evaluate the pathogenicity of the candidate variants. Exon 4 of LIM2 was amplified by polymerase chain reaction and directly sequenced. All patients underwent full ocular examinations. This was an observational study to explore the genotype-phenotype relationships in the four families with a common candidate variant. RESULTS: Various ocular phenotypes were detected in these families, mainly including CCs, elongated axial length, and myopia-related fundus changes. The LIM2 gene mutation, p.Arg130Cys, was detected in all patients. This was further confirmed by Sanger sequencing. The proportion of probands with this mutation in our CCs database was 3.1% (4/130), which indicated that this mutation appears to be a frequent cause of cataracts in the Han Chinese population. This variation has been reported by other investigators before and was correlated with isolated cataracts. CONCLUSIONS: This is the first study that reports various ocular phenotypes associated with the p.Arg130Cys mutation in the LIM2 gene, which indicated the phenotypic heterogeneity of this gene. LIM2 might not only function as an integral membrane protein in lens fiber cells but also be associated with the axial development of the eyeball. Functional studies of the LIM2 gene are important and should receive more attention.

Deep learning for automated glaucomatous optic neuropathy detection from ultra-widefield fundus images.

BACKGROUND/AIMS: To develop a deep learning system for automated glaucomatous optic neuropathy (GON) detection using ultra-widefield fundus (UWF) images. METHODS: We trained, validated and externally evaluated a deep learning system for GON detection based on 22 972 UWF images from 10 590 subjects that were collected at 4 different institutions in China and Japan. The InceptionResNetV2 neural network architecture was used to develop the system. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity were used to assess the performance of detecting GON by the system. The data set from the Zhongshan Ophthalmic Center (ZOC) was selected to compare the performance of the system to that of ophthalmologists who mainly conducted UWF image analysis in clinics. RESULTS: The system for GON detection achieved AUCs of 0.983-0.999 with sensitivities of 97.5-98.2% and specificities of 94.3-98.4% in four independent data sets. The most common reasons for false-negative results were confounding optic disc characteristics caused by high myopia or pathological myopia (n=39 (53%)). The leading cause for false-positive results was having other fundus lesions (n=401 (96%)). The performance of the system in the ZOC data set was comparable to that of an experienced ophthalmologist (p>0.05). CONCLUSION: Our deep learning system can accurately detect GON from UWF images in an automated fashion. It may be used as a screening tool to improve the accessibility of screening and promote the early diagnosis and management of glaucoma.

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.

Deep learning from "passive feeding" to "selective eating" of real-world data.

Artificial intelligence (AI) based on deep learning has shown excellent diagnostic performance in detecting various diseases with good-quality clinical images. Recently, AI diagnostic systems developed from ultra-widefield fundus (UWF) images have become popular standard-of-care tools in screening for ocular fundus diseases. However, in real-world settings, these systems must base their diagnoses on images with uncontrolled quality ("passive feeding"), leading to uncertainty about their performance. Here, using 40,562 UWF images, we develop a deep learning-based image filtering system (DLIFS) for detecting and filtering out poor-quality images in an automated fashion such that only good-quality images are transferred to the subsequent AI diagnostic system ("selective eating"). In three independent datasets from different clinical institutions, the DLIFS performed well with sensitivities of 96.9%, 95.6% and 96.6%, and specificities of 96.6%, 97.9% and 98.8%, respectively. Furthermore, we show that the application of our DLIFS significantly improves the performance of established AI diagnostic systems in real-world settings. Our work demonstrates that "selective eating" of real-world data is necessary and needs to be considered in the development of image-based AI systems.

Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing.

A challenge of chronic diseases that remains to be solved is how to liberate patients and medical resources from the burdens of long-term monitoring and periodic visits. Precise management based on artificial intelligence (AI) holds great promise; however, a clinical application that fully integrates prediction and telehealth computing has not been achieved, and further efforts are required to validate its real-world benefits. Taking congenital cataract as a representative, we used Bayesian and deep-learning algorithms to create CC-Guardian, an AI agent that incorporates individualized prediction and scheduling, and intelligent telehealth follow-up computing. Our agent exhibits high sensitivity and specificity in both internal and multi-resource validation. We integrate our agent with a web-based smartphone app and prototype a prediction-telehealth cloud platform to support our intelligent follow-up system. We then conduct a retrospective self-controlled test validating that our system not only accurately detects and addresses complications at earlier stages, but also reduces the socioeconomic burdens compared to conventional methods. This study represents a pioneering step in applying AI to achieve real medical benefits and demonstrates a novel strategy for the effective management of chronic diseases.

An artificial intelligent platform for live cell identification and the detection of cross-contamination.

BACKGROUND: About 30% of cell lines have been cellular cross-contaminated and misidentification, which can result in invalidated experimental results and unusable therapeutic products. Cell morphology under the microscope was observed routinely, and further DNA sequencing analysis was performed periodically to verify cell line identity, but the sequencing analysis was costly, time-consuming, and labor intensive. The purpose of this study was to construct a novel artificial intelligence (AI) technology for "cell face" recognition, in which can predict DNA-level identification labels only using cell images. METHODS: Seven commonly used cell lines were cultured and co-cultured in pairs (totally 8 categories) to simulated the situation of pure and cross-contaminated cells. The microscopy images were obtained and labeled of cell types by the result of short tandem repeat profiling. About 2 million patch images were used for model training and testing. AlexNet was used to demonstrate the effectiveness of convolutional neural network (CNN) in cell classification. To further improve the feasibility of detecting cross-contamination, the bilinear network for fine-grained identification was constructed. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the cell semantic segmentation was conducted by DilatedNet. RESULTS: The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. CONCLUSIONS: The deep CNN model proposed in this study has the ability to recognize small differences in cell morphology, and achieved high classification accuracy.

The Metabolic Reprogramming of Frem2 Mutant Mice Embryos in Cryptophthalmos Development.

BACKGROUND: Cryptophthalmos is characterized by congenital ocular dysplasia with eyelid malformation. The pathogenicity of mutations in genes encoding components of the FRAS1/FREM protein complex is well established, but the underlying pathomechanisms of this disease are still unclear. In the previous study, we generated mice carrying Frem2 R725X/R2156W compound heterozygous mutations using CRISPR/Cas9 and showed that these mice recapitulated the human cryptophthalmos phenotype. METHODS: In this study, we tracked changes in the metabolic profile of embryos and expression of metabolism-related genes in Frem2 mutant mice on E13.5 compared with wild-type mice. RNA sequencing (RNA-seq) was utilized to decipher the differentiated expression of genes associated with metabolism. Untargeted metabolomics and targeted metabolomics analyses were performed to detect and verify the shifts in the composition of the embryonic metabolome. RESULTS: Differentially expressed genes participating in amino acid metabolism and energy metabolism were observed by RNA-seq. Transcriptomic analysis suggests that 821 (39.89%) up-regulated genes and 320 (32.99%) down-regulated genes were involved in the metabolic process in the enriched GO terms. A total of 92 significantly different metabolites were identified including creatine, guanosine 5'-monophosphate, cytosine, cytidine 5'-monophosphate, adenine, and L-serine. Interestingly, major shifts related to ATP binding cassette transporters (ABC transporters) and the biosynthesis of amino acids in the composition of the embryonic metabolome were observed by KEGG metabolic analysis, indicating that these pathways could also be involved in the pathogenesis of cryptophthalmos. CONCLUSION: We demonstrate that Frem2 mutant fetal mice have increased susceptibility to the disruption of eye morphogenesis in association with distinct transcriptomic and metabolomic signatures. Our findings suggest that the metabolomic signature established before birth may play a role in mediating cryptophthalmos in Frem2 mutant mice, which may have important implications for the pathogenesis of cryptophthalmos.

Novel mutation in OCRL leading to a severe form of Lowe syndrome.

AIM: To investigate the phenotype and genotype of a family with X-linked recessive Lowe syndrome. METHODS: All the members in the Chinese pedigree underwent comprehensive ophthalmologic and systemic examinations. Genomic DNA was isolated from peripheral blood of the pedigree members and 100 unrelated healthy Chinese subjects. Direct sequencing was performed to screen the exons and intron boundaries of OCRL. RESULTS: The ophthalmological and systemic examinations suggested that the affected individual had Lowe syndrome. The phenotype in the pedigree is severe and consistent among all the affected individuals except for an individual who additionally suffered from congenital heart disease and laryngeal cartilage dysplasia. Directional Sanger sequencing identified a complex mutation c.(2368_2368delG; c.2370A>C) in the Rho-GTPase activating protein domain. This complex mutation causes termination of protein synthesis at amino acid 824 and result in a new peptide with 823 amino acids (p.Ala790ProfsX34). This mutation was not detected in 100 unrelated healthy Chinese subjects. CONCLUSION: Our findings expand the phenotypic and genotypic spectrum of Lowe syndrome.

Development and effects of tacrolimus-loaded nanoparticles on the inhibition of corneal allograft rejection.

Tacrolimus has been widely applied to prevent organ rejection after transplantation. However, the conventional pharmaceutical formulation of tacrolimus limits its applications in ocular therapy due to its hydrophobicity and low corneal penetrability. We optimized tacrolimus-loaded methoxy poly (ethylene glycol-block-poly (d, l)-lactic-co-glycolic acid) nanoparticles (TAC-NPs) by simple and effective nanotechnology as a drug delivery system for corneal graft rejection to overcome these drawbacks. The prepared TAC-NPs were 82.9 ± 1.3 nm in size, and the drug loading and encapsulation efficiency were 8.01 ± 0.23% and 80.10 ± 2.33%. Furthermore, New Zealand rabbits were used to analyze the single-dose pharmacokinetics of the TAC-NPs using high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS). In rats with allogenic penetrating keratoplasty, the administration of TAC-NPs dispersion drops improved the TAC concentrations in the aqueous humor and cornea, consistent with a significantly higher effective inhibition of IL-2, IL-17, and VEGF expression compared with conventional 0.1% tacrolimus drops. Meanwhile, we also compared two different topical administration methods (including eye drop and subconjunctival injection) of TAC-NPs to maximize the sustained release characteristic of NPs. In summary, the small-sized TAC-NPs enhanced transcorneal permeation and absorption of TAC and more effectively inhibited corneal allograft rejection, which indicated that biodegradable polymeric nanomaterials-based drug delivery system had great potential for improving the clinical therapy efficacy of hydrophobic drugs.

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.