PAX6/CXCL14 regulatory axis promotes the repair of corneal injury by enhancing corneal epithelial cell proliferation.

BACKGROUND: Corneal injuries, often leading to severe vision loss or blindness, have traditionally been treated with the belief that limbal stem cells (LSCs) are essential for repair and homeostasis, while central corneal epithelial cells (CCECs) were thought incapable of such repair. However, our research reveals that CCECs can fully heal and maintain the homeostasis of injured corneas in rats, even without LSCs. We discovered that CXCL14, under PAX6's influence, significantly boosts the stemness, proliferation, and migration of CCECs, facilitating corneal wound healing and homeostasis. This finding introduces CXCL14 as a promising new drug target for corneal injury treatment. METHODS: To investigate the PAX6/CXCL14 regulatory axis's role in CCECs wound healing, we cultured human corneal epithelial cell lines with either increased or decreased expression of PAX6 and CXCL14 using adenovirus transfection in vitro. Techniques such as coimmunoprecipitation, chromatin immunoprecipitation, immunofluorescence staining, western blot, real-time PCR, cell colony formation, and cell cycle analysis were employed to validate the axis's function. In vivo, a rat corneal epithelial injury model was developed to further confirm the PAX6/CXCL14 axis's mechanism in repairing corneal damage and maintaining corneal homeostasis, as well as to assess the potential of CXCL14 protein as a therapeutic agent for corneal injuries. RESULTS: Our study reveals that CCECs naturally express high levels of CXCL14, which is significantly upregulated by PAX6 following corneal damage. We identified SDC1 as CXCL14's receptor, whose engagement activates the NF-κB pathway to stimulate corneal repair by enhancing the stemness, proliferative, and migratory capacities of CCECs. Moreover, our research underscores CXCL14's therapeutic promise for corneal injuries, showing that recombinant CXCL14 effectively accelerates corneal healing in rat models. CONCLUSION: CCECs play a critical and independent role in the repair of corneal injuries and the maintenance of corneal homeostasis, distinct from that of LSCs. The PAX6/CXCL14 regulatory axis is pivotal in this process. Additionally, our research demonstrates that the important function of CXCL14 in corneal repair endows it with the potential to be developed into a novel therapeutic agent for treating corneal injuries.

Localized Corneal Biomechanical Alteration Detected In Early Keratoconus Based on Corneal Deformation Using Artificial Intelligence.

PURPOSE: This study aimed to develop a novel method to diagnose early keratoconus by detecting localized corneal biomechanical changes based on dynamic deformation videos using machine learning. DESIGN: Diagnostic research study. METHODS: We included 917 corneal videos from the Tianjin Eye Hospital (Tianjin, China) and Shanxi Eye Hospital (Xi'an, China) from February 6, 2015, to August 25, 2022. Scheimpflug technology was used to obtain dynamic deformation videos under forced puffs of air. Fourteen new pixel-level biomechanical parameters were calculated based on a spline curve equation fitting by 115,200-pixel points from the corneal contour extracted from videos to characterize localized biomechanics. An ensemble learning model was developed, external validation was performed, and the diagnostic performance was compared with that of existing clinical diagnostic indices. The performance of the developed machine learning model was evaluated using precision, recall, F1 score, and the area under the receiver operating characteristic curve. RESULTS: The ensemble learning model successfully diagnosed early keratoconus (area under the curve = 0.9997) with 95.73% precision, 95.61% recall, and 95.50% F1 score in the sample set (n=802). External validation on an independent dataset (n=115) achieved 91.38% precision, 92.11% recall, and 91.18% F1 score. Diagnostic accuracy was significantly better than that of existing clinical diagnostic indices (from 86.28% to 93.36%, all P <0.01). CONCLUSIONS: Localized corneal biomechanical changes detected using dynamic deformation videos combined with machine learning algorithms were useful for diagnosing early keratoconus. Focusing on localized biomechanical changes may guide ophthalmologists, aiding the timely diagnosis of early keratoconus and benefiting the patient's vision.

Screening of sensitive in vivo characteristics for early keratoconus diagnosis: a multicenter study.

Purpose: To analyze and compare sensitive in vivo characteristics for screening early keratoconus. Methods: This multicenter, case-control study included 712 eyes, after matching for age and biomechanically corrected intraocular pressure, from three clinics in different cities. The keratoconus (n = 288), early keratoconus (n = 91), and normal cornea (n = 333) groups included eyes diagnosed with bilateral keratoconus, fellow eyes with relatively normal topography with unilateral keratoconus, and normal eyes before refractive surgery, respectively. After adjusting for central corneal thickness, differences in vivo characteristics were analyzed among the three groups. The in vivo characteristics were measured by Pentacam and Corvis ST. Fifty-four indices were evaluated to screen for a sensitive index for the detection of early keratoconus. Results: Significant differences were observed in 26 of the 36 corneal biomechanical indeces between the early keratoconus and normal corneas. The area under the receiver operating characteristic curve of tomographic and biomechanical index, Belin/Ambrósio deviation, and Da in differentiating keratoconus from normal cornea was 1.000. Among the top five indeces of the area under the receiver operating characteristic curve for detecting early keratoconus, the corneal biomechanical-related index accounted for 80% (4/5), including A1 dArc length, highest concavity radius, A2 time, and tomographic and biomechanical index, of which the area under the receiver operating characteristic curve of A1 dArc length was 0.901. Conclusion: A1 dArc length and several corneal biomechanical indices are highly sensitive for the detection of early keratoconus, even in the absence of topographic abnormalities. Ophthalmologists should focus on the clinical application of corneal biomechanics and combine corneal tomography for the timely and accurate detection of early keratoconus.

The effects of programmed optical zones on achieved corneal refractive power with myopic astigmatism after small incision lenticule extraction (SMILE): a vector analysis.

PURPOSE: To evaluate the effects of different programmed optical zones (POZs) on achieved corneal refractive power (CRP) with myopic astigmatism after small incision lenticule extraction (SMILE). METHODS: In total, 113 patients (113 eyes) were included in this retrospective study. The eyes were divided into two groups according to POZ: group A (6.5, 6.6, and 6.7 mm, n = 59) and group B (6.8, 6.9, and 7.0 mm, n = 54). Fourier vector analysis was applied to evaluate the error values between the attempted and achieved corneal refractive power (CRP). Alpins vector analysis was used to calculate surgically induced astigmatism (SIA), difference vector (DV), magnitude of error (ME), and astigmatism correction index (ACI). Multivariate regression analysis was performed to assess potential factors associated with the error values. RESULTS: The error values in the group with large POZ were closer to zero, and significantly associated with the POZ at 2 and 4 mm of the cornea (ß = - 0.50, 95% confidence interval [CI] [- 0.80, - 0.20]; ß = - 0.37, 95% CI [- 0.63, - 0.10], P < 0.05, respectively). For the correction of astigmatism, the values of SIA, ME, and ACI were lower in group B than in group A (P < 0.05). The fitting curves between TIA and SIA were y = 0.83x + 0.19 (R2 = 0.84) and y = 1.05x + 0.04 (R2 = 0.90), respectively. CONCLUSIONS: Smaller POZs resulted in higher error values between the achieved- and attempted-CRP in the SMILE procedure, which should be considered when performing surgery.

Identification of ocular refraction based on deep learning algorithm as a novel retinoscopy method.

BACKGROUND: The evaluation of refraction is indispensable in ophthalmic clinics, generally requiring a refractor or retinoscopy under cycloplegia. Retinal fundus photographs (RFPs) supply a wealth of information related to the human eye and might provide a promising approach that is more convenient and objective. Here, we aimed to develop and validate a fusion model-based deep learning system (FMDLS) to identify ocular refraction via RFPs and compare with the cycloplegic refraction. In this population-based comparative study, we retrospectively collected 11,973 RFPs from May 1, 2020 to November 20, 2021. The performance of the regression models for sphere and cylinder was evaluated using mean absolute error (MAE). The accuracy, sensitivity, specificity, area under the receiver operating characteristic curve, and F1-score were used to evaluate the classification model of the cylinder axis. RESULTS: Overall, 7873 RFPs were retained for analysis. For sphere and cylinder, the MAE values between the FMDLS and cycloplegic refraction were 0.50 D and 0.31 D, representing an increase of 29.41% and 26.67%, respectively, when compared with the single models. The correlation coefficients (r) were 0.949 and 0.807, respectively. For axis analysis, the accuracy, specificity, sensitivity, and area under the curve value of the classification model were 0.89, 0.941, 0.882, and 0.814, respectively, and the F1-score was 0.88. CONCLUSIONS: The FMDLS successfully identified the ocular refraction in sphere, cylinder, and axis, and showed good agreement with the cycloplegic refraction. The RFPs can provide not only comprehensive fundus information but also the refractive state of the eye, highlighting their potential clinical value.

Artificial Intelligence-Based Diagnostic Model for Detecting Keratoconus Using Videos of Corneal Force Deformation.

Purpose: To develop a novel method based on biomechanical parameters calculated from raw corneal dynamic deformation videos to quickly and accurately diagnose keratoconus using machine learning. Methods: The keratoconus group was included according to Rabinowitz's criteria, and the normal group included corneal refractive surgery candidates. Independent biomechanical parameters were calculated from dynamic corneal deformation videos. A novel neural network model was trained to diagnose keratoconus. Tenfold cross-validation was performed, and the sample set was divided into a training set for training, a validation set for parameter validation, and a testing set for performance evaluation. External validation was performed to evaluate the model's generalizability. Results: A novel intelligent diagnostic model for keratoconus based on a five-layer feedforward network was constructed by calculating four biomechanical characteristics, including time of the first applanation, deformation amplitude at the highest concavity, central corneal thickness, and radius at the highest concavity. The model was able to diagnose keratoconus with 99.6% accuracy, 99.3% sensitivity, 100% specificity, and 100% precision in the sample set (n = 276), and it achieved an accuracy of 98.7%, sensitivity of 97.4%, specificity of 100%, and precision of 100% in the external validation set (n = 78). Conclusions: In the absence of corneal topographic examination, rapid and accurate diagnosis of keratoconus is possible with the aid of machine learning. Our study provides a new potential approach and sheds light on the diagnosis of keratoconus from a purely corneal biomechanical perspective. Translational Relevance: Our findings could help improve the diagnosis of keratoconus based on corneal biomechanical properties.

Applying Information Gain to Explore Factors Affecting Small-Incision Lenticule Extraction: A Multicenter Retrospective Study.

Purpose: This retrospective study aimed to identify the key factors influencing postoperative refraction after small-incision lenticule extraction (SMILE) using information gain. Methods: This study comprised 2,350 eyes of 1,200 patients who underwent SMILE using a Visumax 500-kHz femtosecond laser (Carl Zeiss Meditec AG) in three ophthalmic centers: Tianjin Eye Hospital (center A), Jinan Mingshui Eye Hospital (center B), and Qingdao Eye Hospital (center C). Anterior segment features, including corneal curvature and central corneal thickness (CCT), were obtained from Pentacam HR (Oculus, Wetzlar, Germany). Information gain was calculated to analyze the importance of features affecting postoperative refraction. Results: Preoperative and postoperative mean spherical equivalent (SE) refraction were -5.00 (-6.13, -3.88) D and 0.00 (-0.25, 0.13) D, respectively. None of the patients lost more than two lines of corrected distance visual acuity. The safety index was 1.32 ± 0.24, 1.03 ± 0.08, and 1.13 ± 0.16 in centers A, B, and C, respectively. The efficacy index was 1.31 ± 0.25, 1.02 ± 0.08, and 1.13 ± 0.17 in centers A, B, and C, respectively. At least 95% of the eyes were within ±1.00 D of the attempted correction. Postoperative refraction was related to preoperative spherical diopter refraction (r = 0.369, p < 0.001), preoperative SE (r = 0.364, p < 0.001), maximum lenticule thickness (r = -0.311, p < 0.001), preoperative uncorrected distance visual acuity (r = 0.164, p < 0.001), residual stromal thickness (r = 0.139, p < 0.001), preoperative mean anterior corneal curvature (r = -0.127, p < 0.001), preoperative flattest anterior corneal curvature (r = -0.122, p < 0.001), nomogram (r = -0.100, p < 0.001) and preoperative CCT (r = -0.058, p = 0.005). Conclusions: SMILE was considered a safe and effective procedure for correcting myopia. Based on information gain, postoperative refraction was influenced by preoperative mean anterior corneal curvature, CCT, refraction, and residual stromal thickness.

Changes in Refractive Error Under COVID-19: A 3-Year Follow-up Study.

INTRODUCTION: To investigate changes in refractive error in schoolchildren before and during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: This study included 2792 students, who underwent a 3-year follow-up from 2018 to 2020. All participants underwent yearly noncycloplegic refraction and ocular examinations. Time-related changes in sphere, cylinder, and spherical equivalent (SE) measurements in both genders were analyzed. RESULTS: The myopic sphere (- 0.78 ± 1.83 vs. - 1.03 ± 1.91 D; P = 0.025) and SE (- 1.04 ± 1.90 vs. - 1.32 ± 1.99 D; P = 0.015) progressed significantly from 2018 to 2019. Female participants had a significantly greater change in SE than male participants (P < 0.05), and the low hyperopia, emmetropia, and mild myopia groups significantly deteriorated (P < 0.001) from 2018 to 2019. Significant differences in sphere change (- 0.21 ± 0.97 vs. - 0.36 ± 0.96 D; P < 0.001) and SE change (- 0.23 ± 0.99 vs. - 0.38 ± 0.98 D; P < 0.001) were noted between 2019-2018 and 2020-2019, respectively. The respective changes in cylinder were statistically similar (- 0.03 ± 0.53 vs. - 0.05 ± 0.62 D; P = 0.400). CONCLUSIONS: The refractive status of schoolchildren showed an increasing myopic shift trend before and during the COVID-19 pandemic. The low hyperopia, emmetropia, and mild myopia groups were more sensitive to environmental changes during COVID-19 than before. The myopic shift was greater in female participants than male participants.

Comparative study of small-incision lenticule extraction with and without prophylactic corneal crosslinking: 1-year outcomes.

PURPOSE: To compare myopia and astigmatic correction after small-incision lenticule extraction (SMILE) with or without prophylactic crosslinking (SMILE Xtra). SETTING: Shenyang Aier Eye Hospital, Central South University, China. DESIGN: Retrospective study. METHODS: Patients with comparable manifest sphere and cylinder undergoing SMILE Xtra or SMILE were enrolled. The crosslinking (CXL) energy was 2.7 J/cm2. Only right eyes were selected. Visual and refractive changes were evaluated for 1 year. Astigmatic correction was analyzed using Alpins method. RESULTS: Thirty-six eyes undergoing SMILE Xtra and 40 eyes undergoing SMILE were enrolled. The uncorrected distance visual acuity at 1-day visit was lower after SMILE Xtra than that after SMILE (P = .01). At 12 months, the mean manifest refraction spherical equivalent (MRSE) and manifest cylinder were 0.08 ± 0.32 diopters (D) and -0.29 ± 0.23 D in SMILE Xtra group, whereas -0.25 ± 0.29 D and -0.22 ± 0.19 D in SMILE group (P < .01 and P = .135), respectively. Thirty-four eyes (94%) and 32 eyes (89%) in SMILE Xtra group and 36 eyes (91%) and 39 eyes (98%) in SMILE group exhibited target MRSE and manifest cylinder within ±0.50 D (P = .771 and P = .294), respectively. Compared with SMILE group, spherical correction index (SCI), correction index (CI), and difference vector were higher in SMILE Xtra group since 1-week follow-up (all P < .05). SCI and CI were slightly more than 1.0 after SMILE Xtra even at postoperative 12-month follow-up. CONCLUSIONS: With CXL protocol of 30 mW/cm2 for 90 seconds, SMILE Xtra exhibited comparable astigmatic correction with SMILE up to 1-year follow-up, although slight spherical equivalent and astigmatic overcorrection were evident after SMILE Xtra.

Influence of corneal shape parameters on corneal deformation responses measured with a Scheimpflug camera.

PURPOSE: To investigate the effect of corneal shape parameters on corneal deformation responses measured with a Scheimpflug camera. METHODS: A total of 241 eyes of 241 participants were enrolled in this study. The anterior and posterior corneal curvature radii (CCR), anterior and posterior corneal Q-values, and corneal diameters of the participants were measured using the Pentacam HR. A total of 17 corneal deformation parameters including time, velocity, deflection amplitude, length, and area during ingoing applanation, highest concavity, and outgoing applanation were recorded by corneal visualization using Scheimpflug technology (Corvis ST). The effect of corneal shape parameters on corneal deformation responses was evaluated using multivariate regression models. RESULTS: Multivariate regression analyses showed that six, five, four, and three corneal deformation parameters were significantly correlated with anterior CCR, posterior CCR, anterior Q-value, and posterior Q-value, respectively. Steeper anterior corneal curvature was associated with faster velocity during ingoing applanation and greater deformation during outgoing applanation. Steeper posterior corneal curvature was correlated with faster velocity during outgoing applanation and greater deformation during ingoing applanation. Eyes that had steeper corneal curvatures were associated with less stiff corneas. More negative anterior Q-value corresponded with faster velocity and greater deformation during ingoing applanation. Eyes that had more prolate posterior corneal surfaces showed more resistance to corneal deformation at the highest concavity. However, corneal diameter was not selected in any corneal deformation parameters models. CONCLUSION: Corneal deformation response is significantly influenced by anterior and posterior corneal curvature and corneal asphericity, but not corneal diameter.

Applying Machine Learning Techniques in Nomogram Prediction and Analysis for SMILE Treatment.

PURPOSE: To analyze the outcome of machine learning technique for prediction of small incision lenticule extraction (SMILE) nomogram. DESIGN: Prospective, comparative clinical study. METHODS: A comparative study was conducted on the outcomes of SMILE surgery between surgeon group (nomogram set by surgeon) and machine learning group (nomogram predicted by machine learning model). The machine learning model was trained by 865 ideal cases (spherical equivalent [SE] within ±0.5 diopter [D] 3 months postoperatively) from an experienced surgeon. The visual outcomes of both groups were compared for safety, efficacy, predictability, and SE correction. RESULTS: There was no statistically significant difference between the baseline data in both groups. The efficacy index in the machine learning group (1.48 ± 1.08) was significantly higher than in the surgeon group (1.3 ± 0.27) (t = -2.17, P < .05). Eighty-three percent of eyes in the surgeon group and 93% of eyes in the machine learning group were within ±0.50 D, while 98% of eyes in the surgeon group and 96% of eyes in the machine learning group were within ±1.00 D. The error of SE correction was -0.09 ± 0.024 and -0.23 ± 0.021 for machine learning and surgeon groups, respectively. CONCLUSIONS: The machine learning technique performed as well as surgeon in safety, but significantly better than surgeon in efficacy. As for predictability, the machine learning technique was comparable to surgeon, although less predictable for high myopia and astigmatism.

Postoperative Corneal Complications in Small Incision Lenticule Extraction: Long-Term Study.

PURPOSE: To investigate the incidence and clinical results of corneal complications after small incision lenticule extraction (SMILE). METHODS: A retrospective cohort study including 3,223 patients (6,373 eyes) who were treated for myopia or myopic astigmatism was conducted. Postoperative corneal complications were recorded. Postoperative follow-up visits were scheduled on days 1 and 7 and months 1, 3, 6, and 12. RESULTS: Of the 6,373 cases, 432 eyes (6.8%) developed at least one corneal complication postoperatively. These included punctate epithelial erosions (3.26%), diffuse lamellar keratitis (DLK) (2.17%), corneal infiltrates (0.39%), interface debris/secretion (0.30%), interface haze (0.17%), interface foreign body (0.24%), corneal striae (0.14%), corneal edema (0.09%), and epithelial ingrowth (0.02%). Of cases with corneal complications, 308 (71.3%) had an uncorrected distance visual acuity (UDVA) of better than 20/25 and 49 (11.3%) eyes lost two or more lines of corrected distance visual acuity (CDVA) on the first day after surgery. By 3 months, only 2 eyes (0.9%) had lost two or more lines of CDVA. At 6 months, 1 eye (1.0%) did not achieve a UDVA of 20/25 as a result of stage 3 DLK, but achieved 20/20 by 1 year. The postoperative spherical equivalent in cases without complications was lower than that in cases with complications at 1 day and 1 and 3 months (P = .001, .011, and .001, respectively), but there was no statistical difference at 6 and 12 months. CONCLUSIONS: In this large cohort study, a variety of corneal complications were noted after SMILE. Although some of these complications may temporarily affect visual recovery, most resolve with appropriate treatment. [J Refract Surg. 2019;35(3):146-152.].