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Background Implantable Collamer Lens (ICL) surgery has been proven to be a safe, effective, and predictable method for correcting myopia and myopic astigmatism. However, predicting the vault and ideal ICL size remains technically challenging. Despite the growing use of artificial intelligence (AI) in ophthalmology, no AI studies have provided available choices of different instruments and combinations for further vault and size predictions. This study aimed to fill this gap and predict post-operative vault and appropriate ICL size utilizing the comparison of numerous AI algorithms, stacking ensemble learning, and data from various ophthalmic devices and combinations. Results This retrospective and cross-sectional study included 1941 eyes of 1941 patients from Zhongshan Ophthalmic Center. For both vault prediction and ICL size selection, the combination containing Pentacam, Sirius, and UBM demonstrated the best results in test sets [ R 2  = 0.499 (95% CI 0.470–0.528), mean absolute error = 130.655 (95% CI 128.949–132.111), accuracy = 0.895 (95% CI 0.883–0.907), AUC = 0.928 (95% CI 0.916–0.941)]. Sulcus-to-sulcus (STS), a parameter from UBM, ranked among the top five significant contributors to both post-operative vault and optimal ICL size prediction, consistently outperforming white-to-white (WTW). Moreover, dual-device combinations or single-device parameters could also effectively predict vault and ideal ICL size, and excellent ICL selection prediction was achievable using only UBM parameters. Conclusions Strategies based on multiple machine learning algorithms for different ophthalmic devices and combinations are applicable for vault predicting and ICL sizing, potentially improving the safety of the ICL implantation. Moreover, our findings emphasize the crucial role of UBM in the perioperative period of ICL surgery, as it provides key STS measurements that outperformed WTW measurements in predicting post-operative vault and optimal ICL size, highlighting its potential to enhance ICL implantation safety and accuracy.

Exosomes derived from tumor cells play a key role in tumor development. In the present study, we identified the bioactivity of exosomes released from WERI-Rb1 retinoblastoma cells in tumor angiogenesis, as well as the underlying mechanism, through biochemical methods and animal experiments. Our in vitro data showed that exosomes could be engulfed by human vesicle endothelial cells (HUVECs), significantly promote cell viability and induce an inflammatory response in HUVECs by increasing the expression of a series of related genes, such as IL-1, IL-6, IL-8, MCP-1, VCAM1, and ICAM1. Significant increases in migration and tube formation were also observed in the HUVECs incubated with exosomes. Moreover, experiments with a nude mouse xenotransplantation model showed that exosomes injected near tumors could be strongly absorbed by tumor cells. The numbers of endothelial cells and blood vessels were significantly increased in tumor tissues treated with exosomes compared to control tissues. Furthermore, to reveal the mechanism underlying exosome-mediated angiogenesis in retinoblastoma, we analyzed the levels of 12 microRNAs in the exosomes. Specifically, our data showed that miR-92a-3p was enriched in RB exosomes. Accordingly, miR-92a-3p was increased in the HUVECs incubated with these exosomes. After treatment with a miR-92a-3p inhibitor, the promoting effect of exosomes on the migration and tube formation of HUVECs was significantly abrogated. The expression of the angiogenesis-related genes mentioned above was markedly decreased in HUVECs. Similarly, treatment with a microRNA mimic also demonstrated that miR-92a-3p was involved in the angiogenesis of HUVECs. More importantly, bioinformatics analysis predicted that Krüppel-like factor 2 (KLF2), a member of the KLF family of zinc-finger transcription factors, might be an active target of miR-92a-3p. Notably, this prediction was confirmed both in vitro and in vivo. Thus, our work suggests that exosomal miR-92a-3p is involved in tumor angiogenesis and might be a promising therapeutic candidate for retinoblastoma.

Functional data, which provides information about curves, surfaces or anything else varying over a continuum, has become a commonly encountered type of data. The k-nearest neighbor (kNN) method, as a nonparametric method, has become one of the most popular supervised machine learning algorithms used to solve both classification and regression problems. This paper is devoted to the k-nearest neighbor (kNN) estimators of the nonparametric functional regression model when the observed variables take values from negatively associated (NA) sequences. The consistent and complete convergence rate for the proposed kNN estimator is first provided. Then, numerical assessments, including simulation study and real data analysis, are conducted to evaluate the performance of the proposed method and compare it with the standard nonparametric kernel approach.

Purpose. This study aims to assess the corneal epithelial remodeling within a 9 mm diameter zone induced by small incision lenticule extraction (SMILE) and evaluate its relationship with the refractive outcomes. Methods. A total of 64 eyes of 64 patients were included. Wide-field optical coherence tomography (OCT) was used to measure the epithelial thickness (ET) across a 9 mm diameter area, preoperatively, and after one day, one week, one month, three months, and six months postoperatively. The ET changes were compared among the different time points and analyzed zones. Results. The ET increases from one week to three months and stabilized from three months to six months. Compared to the preoperative values, the mean ET changes at six months in central (2 mm), paracentral (2–5 mm), mid-peripheral (5–7 mm), and peripheral (7–9 mm) zones were 4.37, 4.36, 1.61, and −1.59 μm, respectively. The correlation between the epithelial thickening and the amount of myopia correction was positive in central (P = 0.001) and paracentral zones (P < 0.001) and negative in peripheral zone (P = 0.006). The intended diameter of the optical zone was negatively related to epithelial hyperplasia in the central (P = 0.020) and paracentral zone (P = 0.006), and the correlation was positive in the mid-peripheral zone (P = 0.001). The epithelial thickening of central zone (P = 0.012) and the difference of mean ET between central and paracentral zone (P = 0.020) were negatively related to the spherical equivalent at six months. Conclusion. An asymmetric lenticule-like pattern of epithelial remodeling occurred in 9 mm diameter cornea at six months after myopic SMILE. The epithelial remodeling may affect the refractive outcomes of SMILE.

Regression for count data is widely performed by models such as Poisson, negative binomial (NB) and zero-inflated regression. A challenge often faced by practitioners is the selection of the right model to take into account dispersion, which typically occurs in count datasets. It is highly desirable to have a unified model that can automatically adapt to the underlying dispersion and that can be easily implemented in practice. In this paper, a discrete Weibull regression model is shown to be able to adapt in a simple way to different types of dispersions relative to Poisson regression: overdispersion, underdispersion and covariate-specific dispersion. Maximum likelihood can be used for efficient parameter estimation. The description of the model, parameter inference and model diagnostics is accompanied by simulated and real data analyses.

The topoisomerase I inhibitor topotecan is an effective chemotherapeutic agent for retinoblastoma; however, treatment resistance remains a major clinical challenge, and its mechanisms remain elusive. Using genome-wide CRISPR-Cas9 knockout screening, we identified ELF2 as a key gene involved in topotecan resistance. Here, we show that surviving retinoblastoma cells exposed to topotecan showed progressively decreased ELF2 expression, accompanied by reduced apoptosis. In a mouse xenograft model, ELF2 disruption diminished the antitumor efficacy of topotecan, with ELF2-knockout cells exhibiting reduced topotecan-induced apoptosis. RNA sequencing further revealed that the MT-CYB pathway, associated with ATP synthesis, contributes to ELF2-mediated resistance. Importantly, clinical analysis demonstrated a correlation between ELF2 expression and tumor volume in retinoblastoma patients treated with topotecan. Together, these findings interrogate the mechanisms underlying topotecan resistance in retinoblastoma and suggest ELF2 as a potential therapeutic target to overcome drug resistance.

Recently, variable selection by penalized likelihood has attracted much research interest. In this paper, we propose adaptive Lasso quantile regression (BALQR) from a Bayesian perspective. The method extends the Bayesian Lasso quantile regression by allowing different penalization parameters for different regression coefficients. Inverse gamma prior distributions are placed on the penalty parameters. We treat the hyperparameters of the inverse gamma prior as unknowns and estimate them along with the other parameters. A Gibbs sampler is developed to simulate the parameters from the posterior distributions. Through simulation studies and analysis of a prostate cancer dataset, we compare the performance of the BALQR method proposed with six existing Bayesian and non-Bayesian methods. The simulation studies and the prostate cancer data analysis indicate that the BALQR method performs well in comparison to the other approaches.

Tetramethylpyrazine (TMP) is one of the active ingredients extracted from the Chinese herb Chuanxiong, which has been used to treat cerebrovascular and cardiovascular diseases, pulmonary diseases and cancer. However, the molecular mechanisms underlying the actions of TMP have not been fully elucidated. In a previous study we showed that TMP-mediated glioma suppression and neural protection involves the inhibition of CXCR4 expression. The SDF-1/CXCR4 axis plays a fundamental role in many physiological and pathological processes. In this study, we further investigated whether the regulation of the SDF-1/CXCR4 pathway is also involved in the TMP-mediated inhibition of neovascularization or fibrosis and improvement of microcirculation. Using a scratch-wound assay, we demonstrated that TMP significantly suppressed the migration and tubule formation of the human umbilical vein endothelial cell line ECV304 in vitro. The expression of CXCR4 in ECV304 cells is notably down-regulated after TMP treatment. In addition, TMP significantly suppresses corneal neovascularization in a rat model of corneal alkali burn injury. The expression of CXCR4 on days 1, 3 and 7 post-injury was determined through RT-PCR analysis. Consistent with our hypotheses, the expression of CXCR4 in the rat cornea is significantly increased with alkali burn and dramatically down-regulated with TMP treatment. Moreover, TMP treatment significantly attenuates bleomycin-induced rat pulmonary fibrosis, while immunofluorescence shows a notably decreased amount of CXCR4-positive cells in the TMP-treated group. Furthermore, TMP significantly down-regulates the expression of CXCR4 in platelets, lymphocytes and red blood cells. Whole-blood viscosity and platelet aggregation in rats are significantly decreased by TMP treatment. These results show that TMP exerts potent effects in inhibiting neovascularization, fibrosis and thrombosis under pathological conditions; thus, the underlying mechanism of TMP might partially contribute to the down-regulation of CXCR4.

Folklore has it that a very simple supervised classification rule, based on the typically false assumption that the predictor variables are independent, can be highly effective, and often more effective than sophisticated rules. We examine the evidence for this, both empirical, as observed in real data applications, and theoretical, summarising explanations for why this simple rule might be effective. /// La tradition veut qu'une règle très simple assumant l'independance des variables prédictives, une hypothèse fausse dans la plupart des cas, peut être très efficace, souvent même plus efficace qu'une méthode plus sophistiquée en ce qui concerne l'attribution de classes a un groupe d'objects. A ce sujet, nous examinons les preuves empiriques, observées sur des données réelles, et les preuves théoriques, c'est-a-dire les raisons pour lesquelles cette simple règle pourrait faciliter le processus de tri.

The aim of this study was to evaluate the difference between Caucasian and Chinese healthy subjects with regards to Corvis ST dynamic corneal response parameters (DCRs). Two thousand eight hundred and eighty-nine healthy Caucasian and Chinese subjects were included in this multicenter retrospective study. Subsequently, Chinese eyes were matched to Caucasians by age, intraocular pressure (IOP), and Corneal Thickness (CCT) using a case-control matching algorithm. The DCRs assessed were Deformation Amplitude (DA) Applanation 1 velocity (A1v), integrated radius (1/R), deformation amplitude ratio (DAratio), stiffness parameter at applanation 1 (SPA1), ARTh (Ambrósio's Relational Thickness to the horizontal profile), and the novel Stress Strain Index (SSI). After age-, CCT-, and IOP- matching, 503 Chinese were assigned to 452 Caucasians participants. Statistical analysis showed a statistical significant difference between Chinese and Caucasian Healthy subjects in the values of SPA1 ( = 0.008), Arth ( = 0.008), and SSI ( < 0.001). Conversely, DA, A1v, DAratio, and 1/R were not significantly different between the two ethnical groups ( > 0.05). We found significant differences in the values of the DCRs provided by the Corvis ST between Chinese and Caucasian healthy subjects.