Development and validation of a simple and practical model for early detection of diabetic macular edema in patients with type 2 diabetes mellitus using easily accessible systemic variables.

OBJECTIVE: Diabetic macular edema (DME) is the leading cause of visual impairment in patients with diabetes mellitus (DM). The goal of early detection has not yet achieved due to a lack of fast and convenient methods. Therefore, we aim to develop and validate a prediction model to identify DME in patients with type 2 diabetes mellitus (T2DM) using easily accessible systemic variables, which can be applied to an ophthalmologist-independent scenario. METHODS: In this four-center, observational study, a total of 1994 T2DM patients who underwent routine diabetic retinopathy screening were enrolled, and their information on ophthalmic and systemic conditions was collected. Forward stepwise multivariable logistic regression was performed to identify risk factors of DME. Machine learning and MLR (multivariable logistic regression) were both used to establish prediction models. The prediction models were trained with 1300 patients and prospectively validated with 104 patients from Guangdong Provincial People's Hospital (GDPH). A total of 175 patients from Zhujiang Hospital (ZJH), 115 patients from the First Affiliated Hospital of Kunming Medical University (FAHKMU), and 100 patients from People's Hospital of JiangMen (PHJM) were used as external validation sets. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, and specificity were used to evaluate the performance in DME prediction. RESULTS: The risk of DME was significantly associated with duration of DM, diastolic blood pressure, hematocrit, glycosylated hemoglobin, and urine albumin-to-creatinine ratio stage. The MLR model using these five risk factors was selected as the final prediction model due to its better performance than the machine learning models using all variables. The AUC, ACC, sensitivity, and specificity were 0.80, 0.69, 0.80, and 0.67 in the internal validation, and 0.82, 0.54, 1.00, and 0.48 in prospective validation, respectively. In external validation, the AUC, ACC, sensitivity and specificity were 0.84, 0.68, 0.90 and 0.60 in ZJH, 0.89, 0.77, 1.00 and 0.72 in FAHKMU, and 0.80, 0.67, 0.75, and 0.65 in PHJM, respectively. CONCLUSION: The MLR model is a simple, rapid, and reliable tool for early detection of DME in individuals with T2DM without the needs of specialized ophthalmologic examinations.

Investigation roles of Adamts1 and Adamts5 in scleral fibroblasts under hypoxia and mice with form-deprived myopia.

Scleral hypoxia is considered a trigger in scleral remodeling-induced myopia. Identifying differentially expressed molecules within the sclera is essential for understanding the mechanism of myopia. We developed a scleral fibroblast hypoxia model and conducted RNA sequencing and bioinformatic analysis. RNA interference technology was then applied to knock down targeted genes with upregulated expression, followed by an analysis of COLLAGEN I protein level. Microarray data analysis showed that the expression of Adamts1 and Adamts5 were upregulated in fibroblasts under hypoxia (t-test, p < 0.05). Western blot analysis confirmed increased protein levels of ADAMTS1 and ADAMTS5, and a concurrent decrease in COLLAGEN I in hypoxic fibroblasts. The knockdown of either Adamts1 or Adamts5 in scleral fibroblasts under hypoxia resulted in an upregulation of COLLAGEN I. Moreover, a form-deprivation myopia (FDM) mouse model was established for validation. The sclera tissue from FDM mice exhibited increased levels of ADAMTS1 and ADAMTS5 protein and a decrease in COLLAGEN I, compared to controls. The study suggests that Adamts1 and Adamts5 may be involved in scleral remodeling induced by hypoxia and the development of myopia.

Prediction models for retinopathy of prematurity occurrence based on artificial neural network.

INTRODUCTION: Early prediction and timely treatment are essential for minimizing the risk of visual loss or blindness of retinopathy of prematurity, emphasizing the importance of ROP screening in clinical routine. OBJECTIVE: To establish predictive models for ROP occurrence based on the risk factors using artificial neural network. METHODS: A cohort of 591 infants was recruited in this retrospective study. The association between ROP and perinatal factors was analyzed by univariate analysis and multivariable logistic regression. We developed predictive models for ROP screening using back propagation neural network, which was further optimized by applying genetic algorithm method. To assess the predictive performance of the models, the areas under the curve, sensitivity, specificity, negative predictive value, positive predictive value and accuracy were used to show the performances of the prediction models. RESULTS: ROP of any stage was found in 193 (32.7%) infants. Twelve risk factors of ROP were selected. Based on these factors, predictive models were built using BP neural network and genetic algorithm-back propagation (GA-BP) neural network. The areas under the curve for prediction models were 0.857, and 0.908 in test, respectively. CONCLUSIONS: We developed predictive models for ROP using artificial neural network. GA-BP neural network exhibited superior predictive ability for ROP when dealing with its non-linear clinical data.

DETECTION OF MORPHOLOGIC PATTERNS OF DIABETIC MACULAR EDEMA USING A DEEP LEARNING APPROACH BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES.

PURPOSE: To develop a deep learning (DL) model to detect morphologic patterns of diabetic macular edema (DME) based on optical coherence tomography (OCT) images. METHODS: In the training set, 12,365 OCT images were extracted from a public data set and an ophthalmic center. A total of 656 OCT images were extracted from another ophthalmic center for external validation. The presence or absence of three OCT patterns of DME, including diffused retinal thickening, cystoid macular edema, and serous retinal detachment, was labeled with 1 or 0, respectively. A DL model was trained to detect three OCT patterns of DME. The occlusion test was applied for the visualization of the DL model. RESULTS: Applying 5-fold cross-validation method in internal validation, the area under the receiver operating characteristic curve for the detection of three OCT patterns (i.e., diffused retinal thickening, cystoid macular edema, and serous retinal detachment) was 0.971, 0.974, and 0.994, respectively, with an accuracy of 93.0%, 95.1%, and 98.8%, respectively, a sensitivity of 93.5%, 94.5%, and 96.7%, respectively, and a specificity of 92.3%, 95.6%, and 99.3%, respectively. In external validation, the area under the receiver operating characteristic curve was 0.970, 0.997, and 0.997, respectively, with an accuracy of 90.2%, 95.4%, and 95.9%, respectively, a sensitivity of 80.1%, 93.4%, and 94.9%, respectively, and a specificity of 97.6%, 97.2%, and 96.5%, respectively. The occlusion test showed that the DL model could successfully identify the pathologic regions most critical for detection. CONCLUSION: Our DL model demonstrated high accuracy and transparency in the detection of OCT patterns of DME. These results emphasized the potential of artificial intelligence in assisting clinical decision-making processes in patients with DME.

Validation of the DIGIROP-birth model in a Chinese cohort.

BACKGROUND: We aimed to validate the predictive performance of the DIGIROP-Birth model for identifying treatment-requiring retinopathy of prematurity (TR-ROP) in Chinese preterm infants to evaluate its generalizability across countries and races. METHODS: We retrospectively reviewed the medical records of preterm infants who were screened for retinopathy of prematurity (ROP) in a single Chinese hospital between June 2015 and August 2020. The predictive performance of the model for TR-ROP was assessed through the construction of a receiver-operating characteristic (ROC) curve and calculating the areas under the ROC curve (AUC), sensitivity, specificity, and positive and negative predictive values. RESULTS: Four hundred and forty-two infants (mean (SD) gestational age = 28.8 (1.3) weeks; mean (SD) birth weight = 1237.0 (236.9) g; 64.7% males) were included in the study. Analyses showed that the DIGIROP-Birth model demonstrated less satisfactory performance than previously reported in identifying infants with TR-ROP, with an area under the receiver-operating characteristic curve of 0.634 (95% confidence interval = 0.564-0.705). With a cutoff value of 0.0084, the DIGIROP-Birth model showed a sensitivity of 48/93 (51.6%), which increased to 89/93 (95.7%) after modification with the addition of postnatal risk factors. In infants with a gestational age < 28 weeks or birth weight < 1000 g, the DIGIROP-Birth model exhibited sensitivities of 36/39 (92.3%) and 20/23 (87.0%), respectively. CONCLUSIONS: Although the predictive performance was less satisfactory in China than in developed countries, modification of the DIGIROP-Birth model with postnatal risk factors shows promise in improving its efficacy for TR-ROP. The model may also be effective in infants with a younger gestational age or with an extremely low birth weight.

Extracellular ATP activates P2X7R-NF-κB (p65) pathway to promote the maturation of bone marrow-derived dendritic cells of mice.

The maturation state of dendritic cell (DC) plays an important role in immune activities. Previously we had found that NF-κB (p65) pathway could promote DC maturation and subsequent immune effects. But the upstream mechanism of this pathway was still unclear. Extracellular adenosine triphosphate (ATP) activating its receptor P2X7R has recently been considered as the fourth signal to activate T lymphocytes. Here we aimed to find out the connection between P2X7R and NF-κB (p65) pathway in DC maturation. Results showed that the expression of P2X7R and the intracellular ATP levels were increased along with the maturation of DC. P2X7R agonist stimulated the morphological changes of DCs into the appearance of mature DCs, and promoted the expression of NF-κB (p65), as well as the release of IFN-γ and IL-12. Whereas, P2X7R inhibitor had the opposite influences. Co-immunoprecipitation assay confirmed the binding of P2X7R and NF-κB (p65). Our study suggested that extracellular ATP could promote DC maturation and release of inflammatory cytokines through the binding of P2X7R and NF-κB (p65). This is the first study to show the P2X7R-NF-κB (p65) pathway in DC. Interference with this pathway may be able to regulate immune responses in areas like infectious diseases, inflammation, transplantation, tumor and autoimmune diseases. In addition, intracellular ATP level could be a new indicator of the maturation state of DC.

Corneal autophagy and ocular surface inflammation: A new perspective in dry eye.

Dry eye disease (DED), a multifactorial ocular surface disorder affecting millions of individuals worldwide, is characterized by inflammation and damage to the ocular surface. It is unclear whether corneal autophagy participates in ocular surface inflammation observed in DED. To test this involvement, dry eye (DE) was induced in female C57BL/6 mice housed in a controlled environment by subcutaneous injection of scopolamine. Expression of the autophagy-related proteins LC3B and ATG5 and activation of autophagy were detected in the corneas of these mice. Treatment with LYN-1604, an activator of autophagy, alleviated the clinical indications in DE mice, including tear production and corneal fluorescence staining. LYN-1604 also reduced the corneal levels of inflammatory response products, including tumor necrosis factor alpha (TNF-α) and matrix metalloproteinases-3 and -9. By contrast, treatment of DE mice with the autophagy inhibitor 3-MA, exacerbated the clinical indications of DE and increased the levels of inflammatory response products. This is the first study to show that autophagy could regulate the level of ocular surface inflammation, suggesting that agents that regulate autophagy could relieve ocular surface inflammation and treat DED.

Effect of the TLR2/MyD88/NF-κB axis on corneal allograft rejection after penetrating keratoplasty.

PURPOSE: To evaluate the effect of the TLR2 (Toll-like receptor 2)/MyD88/NF-κB axis on the allograft rejection after penetrating keratoplasty (PK). METHODS: The PK rat models were randomly divided into four groups: allograft group, dexamethasone group, PDTC group and isograft group. The mean survival time (MST) and rejection index of corneal grafts were observed. The immunohistochemical staining of TGF-α was performed on day 15. The messenger RNA (mRNA) and protein expression of TLR2, MyD88 and NF-κB p65 in corneal grafts were detected by reverse transcription-polymerase chain reaction (RT-PCR) and western blotting. RESULTS: On days 5, 7, 9, 11, 13 and 15, the rejection index in the allograft group was higher than in the other three groups (p < 0.05). The MST in the PDTC group (MST, 23.30 ± 0.42 days, n = 10) and in the dexamethasone group (MST, 24.40 ± 0.50 days, n = 10) were higher than in the allograft group (MST, 14.7 ± 0.70 days, n = 10) (χ(2) = 18.02, p < 0.01; χ(2) = 21.47, p < 0.01). The expression of TNF-α in the PDTC group and in the dexamethasone group decreased compared with the allograft group by immunohistochemistry. On day 15, the mRNA and protein expression of TLR2, MyD88 and NF-κB p65 in the PDTC group and the dexamethasone group were less than in the allograft group (p < 0.05). CONCLUSIONS: Expression of TLR2, MyD88 and NF-κB p65 in rat corneal graft increased significantly and concurred with the allograft rejection, but were effectively inhibited by the treatment with dexamethasone and PDTC after PK. Dexamethasone could improve corneal allograft survival by the TLR2/MyD88/NF-κB axis. PDTC could suppress corneal graft rejection by inhibiting the activity of NF-κB. The TLR2/MyD88/NF-κB axis maybe a potential therapeutic target for corneal allograft rejection.

Case report: Emerging species in post-traumatic endophthalmitis: Acinetobacter johnsonii.

Acinetobacter johnsonii is an uncommon cause of endophthalmitis. This case report describes a 40-year-old male admitted with pain, redness, and vision loss in his right eye after an open globe injury by a steel fragment. Clinical assessment confirmed post-traumatic endophthalmitis with an intraocular foreign body. The patient underwent a vitreous biopsy, lensectomy, vitrectomy, and intravitreal antibiotics, followed by laser photocoagulation and foreign body extraction via the pars plana. Acinetobacter johnsonii was isolated from the vitreous culture. A combination of vancomycin, levofloxacin and ceftazidime was administered, leading to reduced infection and inflammation. Postoperatively at one month, the patients' best-corrected visual acuity had improved to 20/63. The anterior segment exhibited no inflammation, the vitreous cavity was clear, and the retina with hemorrhage and laser treatment remained stable. The one-year follow-up confirmed the continued stability of the ocular condition. Acinetobacter johnsonii, a rare cause of endophthalmitis often linked to trauma or surgery, should be recognized as a possible pathogen in post-traumatic endophthalmitis cases, meriting clinical consideration.

Development and validation of a deep learning system to classify aetiology and predict anatomical outcomes of macular hole.

AIMS: To develop a deep learning (DL) model for automatic classification of macular hole (MH) aetiology (idiopathic or secondary), and a multimodal deep fusion network (MDFN) model for reliable prediction of MH status (closed or open) at 1 month after vitrectomy and internal limiting membrane peeling (VILMP). METHODS: In this multicentre retrospective cohort study, a total of 330 MH eyes with 1082 optical coherence tomography (OCT) images and 3300 clinical data enrolled from four ophthalmic centres were used to train, validate and externally test the DL and MDFN models. 266 eyes from three centres were randomly split by eye-level into a training set (80%) and a validation set (20%). In the external testing dataset, 64 eyes were included from the remaining centre. All eyes underwent macular OCT scanning at baseline and 1 month after VILMP. The area under the receiver operated characteristic curve (AUC), accuracy, specificity and sensitivity were used to evaluate the performance of the models. RESULTS: In the external testing set, the AUC, accuracy, specificity and sensitivity of the MH aetiology classification model were 0.965, 0.950, 0.870 and 0.938, respectively; the AUC, accuracy, specificity and sensitivity of the postoperative MH status prediction model were 0.904, 0.825, 0.977 and 0.766, respectively; the AUC, accuracy, specificity and sensitivity of the postoperative idiopathic MH status prediction model were 0.947, 0.875, 0.815 and 0.979, respectively. CONCLUSION: Our DL-based models can accurately classify the MH aetiology and predict the MH status after VILMP. These models would help ophthalmologists in diagnosis and surgical planning of MH.

Development and Validation of a Deep Learning Model to Predict the Occurrence and Severity of Retinopathy of Prematurity.

Importance: Retinopathy of prematurity (ROP) is the leading cause of childhood blindness worldwide. Prediction of ROP before onset holds great promise for reducing the risk of blindness. Objective: To develop and validate a deep learning (DL) system to predict the occurrence and severity of ROP before 45 weeks' postmenstrual age. Design, Setting, and Participants: This retrospective prognostic study included 7033 retinal photographs of 725 infants in the training set and 763 retinal photographs of 90 infants in the external validation set, along with 46 characteristics for each infant. All images of both eyes from the same infant taken at the first screening were labeled according to the final diagnosis made between the first screening and 45 weeks' postmenstrual age. The DL system was developed using retinal photographs from the first ROP screening and clinical characteristics before or at the first screening in infants born between June 3, 2017, and August 28, 2019. Exposures: Two models were specifically designed for predictions of the occurrence (occurrence network [OC-Net]) and severity (severity network [SE-Net]) of ROP. Five-fold cross-validation was applied for internal validation. Main Outcomes and Measures: Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity to evaluate the performance in ROP prediction. Results: This study included 815 infants (450 [55.2%] boys) with mean birth weight of 1.91 kg (95% CI, 1.87-1.95 kg) and mean gestational age of 33.1 weeks (95% CI, 32.9-33.3 weeks). In internal validation, mean AUC, accuracy, sensitivity, and specificity were 0.90 (95% CI, 0.88-0.92), 52.8% (95% CI, 49.2%-56.4%), 100% (95% CI, 97.4%-100%), and 37.8% (95% CI, 33.7%-42.1%), respectively, for OC-Net to predict ROP occurrence and 0.87 (95% CI, 0.82-0.91), 68.0% (95% CI, 61.2%-74.8%), 100% (95% CI, 93.2%-100%), and 46.6% (95% CI, 37.3%-56.0%), respectively, for SE-Net to predict severe ROP. In external validation, the AUC, accuracy, sensitivity, and specificity were 0.94, 33.3%, 100%, and 7.5%, respectively, for OC-Net, and 0.88, 56.0%, 100%, and 35.3%, respectively, for SE-Net. Conclusions and Relevance: In this study, the DL system achieved promising accuracy in ROP prediction. This DL system is potentially useful in identifying infants with high risk of developing ROP.

Nuclear translocation of ß-catenin induced by E-cadherin endocytosis causes recurrent erosion of diabetic cornea.

Recurrent epithelial erosion and refractory corneal ulcer are the clinical features of diabetic keratopathy (DK), which eventually lead to corneal scar and visual disturbance. In this study, we sought to determine the abnormalities of cell junction in diabetic corneal epithelial cells and the effect of high glucose on the ß-catenin/E-cadherin complex. Corneal histology showed that corneal epithelial cells of high glucose mice were loosely arranged, and the immunohistochemistry showed that the expression of E-cadherin decreased, the levels of ß-catenin increased in nuclear. High glucose-induced degradation and endocytosis of E-cadherin of corneal epithelial cells reduce the formation of ß-catenin/E-cadherin complex and promote the nuclear translocation of ß-catenin. Moreover, high glucose also activated the transcription and expression of matrix metallopeptidase and snail, which interfered with the adhesion of corneal epithelial cells to the basement membrane. These findings reveal that DK is associated with the dissociation of cell junctions. The maintenance of the stability of the ß-catenin/E-cadherin complex may be a potential therapeutic target of refractory corneal ulcers in patients with diabetes.

Machine learning-based prediction of anatomical outcome after idiopathic macular hole surgery.

BACKGROUND: To develop a machine learning (ML) model for the prediction of the idiopathic macular hole (IMH) status at 1 month after vitrectomy and internal limiting membrane peeling (VILMP) surgery. METHODS: A total of 288 IMH eyes from four ophthalmic centers were enrolled. All eyes underwent optical coherence tomography (OCT) examinations upon admission and one month after VILMP. First, 1,792 preoperative macular OCT parameters and 768 clinical variables of 256 eyes from two ophthalmic centers were used to train and internally validate ML models. Second, 224 preoperative macular OCT parameters and 96 clinical variables of 32 eyes from the other two centers were utilized for external validation. To fulfill the purpose of predicting postoperative IMH status (i.e., closed or open), five ML algorithms were trained and internally validated by the ten-fold cross-validation method, while the best-performing algorithm was further tested by an external validation set. RESULTS: In the internal validation, the mean area under the receiver operating characteristic curves (AUCs) of the five ML algorithms were 0.882-0.951. The AUC, accuracy, sensitivity, and specificity of the best-performing algorithm (i.e., random forest, RF) were 0.951, 0.892, 0.973, and 0.904, respectively. In the external validation, the AUC of RF was 0.940, with an accuracy of 0.875, a specificity of 0.875, and a sensitivity of 0.958. CONCLUSIONS: Based on the preoperative OCT parameters and clinical variables, our ML model achieved remarkable accuracy in predicting IMH status after VILMP. Therefore, ML models may help optimize surgical planning for IMH patients in the future.

Efficacy of the WINROP algorithm for retinopathy of prematurity screening in Southern China.

AIM: To evaluate the predicting efficacy of severe retinopathy of prematurity (ROP) by the WINROP algorithm (http://winrop.com) in Southern China. METHODS: All preterm infants with the gestational age (GA) less than 32wk were included. Their ROP screening results and serial postnatal body weight were analysed retrospectively. Weekly body weight was entered into and measured by the WINROP system. The outcomes were analysed, and the sensitivity, specificity, positive predictive value and negative predictive value (NPV) were calculated. RESULTS: Totally 432 infants with a median GA of 30.0 (24.0-31.9)wk, and a median birth weight (BW) of 1360 (540-2700) g were included. Among these 432 infants, 50 were diagnosed as type 1 ROP but only 28 were identified by the WINROP algorithm. The sensitivity was 56% (28/50) and the NPV was 92% (252/274). However, for infants with BW <1000 g or GA <28wk, the sensitivity was 93.8% (15/16) and 93.3% (14/15), respectively. Meanwhile, with several postnatal complications added as additional risk factors, the sensitivity was increased to 96% (48/50). CONCLUSION: The sensitivity of the WINROP algorithm from the Southern Chinese cohort is not as high as that reported in developed countries. This algorithm is effective for detecting severe ROP from extremely small or preterm infants. Modification of the algorithm with additional risk factors could improve the predictive value for infants with a GA>28wk in China.

A multi-center study of prediction of macular hole status after vitrectomy and internal limiting membrane peeling by a deep learning model.

BACKGROUND: To develop a deep learning (DL) model for prediction of idiopathic macular hole (MH) status after vitrectomy and internal limiting membrane peeling (VILMP) based on optical coherence tomography (OCT) images from four ophthalmic centers. METHODS: Eyes followed up at 1 month after VILMP for full-thickness MH were included. In the internal training set, 920 preoperative macular OCT images (as the input) and post-operative status of MH (closed or open, as the output) of 256 eyes from two ophthalmic centers were used to train the DL model using VGG16 algorithm. In the external validation set, 72 preoperative macular OCT images of 36 MH eyes treated by VILMP from another two ophthalmic centers were used to validate the prediction accuracy of the DL model. RESULTS: In internal training, the mean of overall accuracy for prediction of MH status after VILMP was 84.6% with a mean area under the receiver operating characteristic (ROC) curve (AUC) of 91.04% (sensitivity 85.37% and specificity 81.99%). In external validation, the overall accuracy of predicting MH status after VILMP was 84.7% with an AUC of 89.32% (sensitivity 83.33% and specificity 87.50%). The heatmaps showed that the area critical for prediction was at the central macula, mainly at the MH and its adjacent retina. CONCLUSIONS: The DL model trained by preoperative macular OCT images can be used to predict postoperative MH status after VILMP. The prediction accuracy of our DL model has been validated by multiple ophthalmic centers.

Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning.

BACKGROUND: This study aimed to predict the treatment outcomes in patients with diabetic macular edema (DME) after 3 monthly anti-vascular endothelial growth factor (VEGF) injections using machine learning (ML) based on pretreatment optical coherence tomography (OCT) images and clinical variables. METHODS: An ensemble ML system consisting of four deep learning (DL) models and five classical machine learning (CML) models was developed to predict the posttreatment central foveal thickness (CFT) and the best-corrected visual acuity (BCVA). A total of 363 OCT images and 7,587 clinical data records from 363 eyes were included in the training set (304 eyes) and external validation set (59 eyes). The DL models were trained using the OCT images, and the CML models were trained using the OCT images features and clinical variables. The predictive posttreatment CFT and BCVA values were compared with true outcomes obtained from the medical records. RESULTS: For CFT prediction, the mean absolute error (MAE), root mean square error (RMSE), and R2 of the best-performing model in the training set was 66.59, 93.73, and 0.71, respectively, with an area under receiver operating characteristic curve (AUC) of 0.90 for distinguishing the eyes with good anatomical response. The MAE, RMSE, and R2 was 68.08, 97.63, and 0.74, respectively, with an AUC of 0.94 in the external validation set. For BCVA prediction, the MAE, RMSE, and R2 of the best-performing model in the training set was 0.19, 0.29, and 0.60, respectively, with an AUC of 0.80 for distinguishing eyes with a good functional response. The external validation achieved a MAE, RMSE, and R2 of 0.13, 0.20, and 0.68, respectively, with an AUC of 0.81. CONCLUSIONS: Our ensemble ML system accurately predicted posttreatment CFT and BCVA after anti-VEGF injections in DME patients, and can be used to prospectively assess the efficacy of anti-VEGF therapy in DME patients.

Self-assembling hydrogel loaded with 5-FU PLGA microspheres as a novel vitreous substitute for proliferative vitreoretinopathy.

The vitreous substitute for proliferative vitreoretinopathy (PVR) surgery remains an unmet clinical need in ophthalmology. In our study, we developed an in situ formed hydrogel by crosslinking polyvinyl alcohol (PVA) and chitosan as a potential vitreous substitute. 5-fluorouracil (5-FU) Poly (lactic-co-glycolic acid) (PLGA) microspheres were developed and loaded onto the PVA/chitosan hydrogels to treat PVR. In vitro, PVA/chitosan hydrogels at four concentrations were subjected to morphological, physical, rheological analyses, and cytotoxicity was evaluated together with the characterization of 5-FU PLGA microspheres. In vivo, pharmacologically induce PVR rabbits were performed a vitrectomy. In the PVA group, 3% PVA/chitosan hydrogel was injected into the vitreous cavity. In the PVA/MS group, 3% PVA/chitosan hydrogel and 5-FU PLGA microspheres were injected. In the Control group, phosphate-buffered saline was injected. Therapeutic efficacy was evaluated with postoperative examinations and histological analyses. This study demonstrated that the 3% PVA/chitosan hydrogel showed properties similar to those of the human vitreous and could be a novel in situ crosslinked vitreous substitute for PVR. Loading 5-FU PLGA microspheres onto this hydrogel may represent an effective strategy to improve the prognosis of PVR.

Efficacy of intravitreal conbercept injection in the treatment of retinopathy of prematurity.

BACKGROUND: To evaluate the safety and efficacy of intravitreal conbercept (IVC) injection in the treatment of retinopathy of prematurity (ROP). METHODS: Patients with ROP who underwent IVC injection in Zhujiang Hospital from June 2015 to July 2016 were studied retrospectively. The primary outcome was defined as the regression of plus disease. The secondary outcomes were defined as the presence of recurrence, number of injections and the final regression of disease. RESULTS: A total of 48 eyes of 24 patients with ROP were included. Among them, 9 eyes of 5 patients had zone I ROP, 35 eyes of 18 patients had zone II ROP and 4 eyes of 2 patients had aggressive posterior ROP. The mean gestational age was 28.5±1.6 weeks, the mean birth weight was 1209.6±228.6 g, the mean postmenstrual age of first injection was 34.2±1.9 weeks and the mean follow-up period was 31.0±4.7 weeks. Forty of 48 eyes (83.3%) received IVC only once, and the regression of plus disease occurred at an average of 3.5±1.5 weeks after the first injection of conbercept. For eight recurrent eyes (16.7%), four eyes received a second IVC and the remaining four eyes received laser photocoagulation, and the regression of plus disease occurred in 3 weeks. No lens opacity, vitreous haemorrhage, entophthalmia or retinal detachment was observed during follow-up. CONCLUSION: IVC injection is an effective treatment for ROP.

Optimal timing of vitrectomy for severe mechanical ocular trauma: A retrospective observational study.

Mechanical ocular trauma could lead to disastrous visual outcomes. There has been a controversy regarding the timing of vitrectomy for such cases. This study aimed to find out the optimal timing of vitrectomy for severe mechanical ocular trauma. Patients with severe mechanical ocular trauma who had undergone vitrectomy were enrolled and followed up for at least 6 months. Clinical data were collected including ocular trauma score (OTS), the timing of vitrectomy upon injury, visual acuity, vitrectomy results, post-operation complications and etc. All cases were classified according to the timing of vitrectomy upon injury into 3 groups: group A 1-7 days, group B 8-14 days, group C more than 14 days. A total of 62 cases were enrolled, including 20 eyes in group A, 25 eyes in group B, and 17 eyes in group C. No significant differences were shown of the gender, age or OTS among the 3 groups. Both functional success rate and visual outcome were optimal in group B, then in group A, and worst in group C. These results suggested that the best timing of vitrectomy for severe mechanical ocular trauma is 8-14 days upon injury; second best is 1-7 days; worst is after 14 days.

Levels of Inflammatory Cytokines IL-1ß, IL-6, IL-8, IL-17A, and TNF-α in Aqueous Humour of Patients with Diabetic Retinopathy.

Diabetic retinopathy is the leading cause of blindness in working age individuals in developed countries. However, the role of inflammation in the pathogenesis of DR is not completely understood. This is an observational clinical research enrolling 80 type II diabetic patients who had undergone cataract surgeries either with DR or without DR. All cases were further categorized by the proliferative stages of retinal neovascularization and by the lengths of diabetic history. The levels of inflammatory cytokines including IL-1ß, IL-6, IL-8, IL-17, and TNF-α in aqueous humour were tested. Results in this study indicated that these cytokine levels were increased in DR patients and might have a synergistic effect on the pathogenesis of this disease. They were also elevated along with the progression of neovascularization, reflecting the severity of DR. The results also suggested that for diabetic patients, the higher these levels are, the sooner retinal complications might appear. In conclusion, the levels of inflammatory cytokines IL-1ß, IL-6, IL-8, IL-17A, and TNF-α in aqueous humour may be associated with the pathogenesis, severity, and prognosis of DR.