随着微创玻璃体切除术(pars plana vitrectomy，PPV)的广泛开展和手术技术的提高，患者对手术后视觉质量的要求越来越高。白内障是PPV术后最常见并发症，而具有玻璃体切除史的白内障患者屈光变异大，预测难度高。本文综述了生物测量误差、人工晶状体屈光力计算公式选择以及有效晶状体位置预测等影响有玻璃体切除手术史的白内障患者术后屈光误差的主要因素，旨在为降低这一类特殊人群白内障术后屈光误差提供参考。

With the widespread application of minimally invasive vitrectomy and the improvement of surgical techniques, the demands of patients for better postoperative visual quality are increasing. Cataract is the most common complication after vitrectomy, whereas the refractive outcomes of cataract patients with prior vitrectomy are viable and difficult to predict. In this paper, the main factors affecting postoperative refractive error of cataract patients with a history of vitrectomy, such as biometric error, selection of intraocular lens calculation formulas and prediction of effective lens position, were reviewed in order to provide reference for reducing postoperative refractive error of this special group of cataract patients.

目的：在硅油取出联合白内障手术患者中，使用扫频源光学相干断层扫描生物测量仪OA-2000进行生物测量，比较10种人工晶状体(IOL)屈光力计算公式的准确性。方法：回顾性分析2021年3月—7月于中山大学中山眼科中心接受硅油取出联合白内障手术的患者共62例(62眼)，所有患者均使用扫频源光学相干断层扫描生物测量仪OA-2000进行生物学参数测量。计算并比较新公式[Barrett Universal II (BUII)、Emmetropia Verifying Optical(EVO) 2.0、Hill-Radial Basis Function (Hill-RBF) 3.0、Hoffer QST、Kane、Pearl-DGS]及传统公式(Haigis、Hoffer Q、Holladay 1、SRK/T)的预测准确性，主要评价指标为绝对预测误差中位数(MedAE)及平均绝对预测误差(MAE)。按眼轴长度≤23 mm(组1)，>23 mm且≤26 mm(组2)与>26 mm(组3)进行亚组分析。结果：6个新公式、Haigis、SRK/T公式均出现近视漂移(-0.47 ~-0.27 D，P<0.05)，而HofferQ及Holladay 1公式无系统误差(P>0.05)。Kane公式的MedAE(0.55 D)及MAE(0.81 D)最小，但公式间比较差异无统计学意义(P>0.05)。组1中所有公式均出现近视漂移(-1.46~ -1.25 D，P<0.05)，而其他亚组比较差异无统计学意义(-0.32 ~ 0.41 D，P>0.05)。在组1中，Pearl-DGS公式的MedAE(0.97 D)及MAE(1.26 D)最小，且优于Hill-RBF 3.0(P=0.01)及SRK/T公式(P=0.02)；组2中，Kane公式具有最小的MedAE(0.44 D)及MAE(0.66 D)；组3各个公式屈光预测准确性比较差异无统计学意义(P>0.05)。结论：在使用OA-2000进行术前生物测量时，Kane公式在接受硅油取出联合白内障手术患者中的预测准确性较高；而眼轴长度≤23 mm时，Pearl-DGS公式可能更为准确。

Objective: To compare the accuracy of 10 intraocular lens (IOL) power calculation formulas in patients undergoing combined silicone oil removal and cataract surgery, biometry is performed using the swept-source optical coherence tomography biometer OA-2000. Methods: A retrospective analysis. A total of 62 patients (62 eyes) who underwent combined silicone oil removal and cataract surgery in Zhongshan Ophthalmic Center, Sun Yat-sen University from March to July in 2021 were enrolled. Preoperative biometry was performed by OA-2000 in all patients. New-generation formulas (Barrett Universal II [BUII], Emmetropia Verifying Optical [EVO] 2.0, Hill-Radial Basis Function [Hill-RBF] 3.0, Hoffer QST, Kane and Pearl-DGS) and traditional formulas (Haigis, Hoffer Q, Holladay 1 and SRK/T) were evaluated. The median absolute prediction error (MedAE) and mean absolute prediction error (MAE) were the main parameters used to assess accuracy. Subgroup analyses were performed based on the axial length of 23 mm and 26 mm. Results: Six new-generation formulas, Haigis, and SRK/T showed myopic shift (-0.47 ~ -0.27 D, P<0.05), while no systematic bias was found in Hoffer Q and Holladay 1 displayed (P>0.05). The smallest MedAE (0.55 D) and MAE (0.81 D) were found in Kane formula, but there was no statistically significant difference compared with other formulas (P>0.05). The myopic shift (-1.46 ~ -1.25 D, P<0.05) in eyes shorter than 23 mm were found in all formulas, while there was no significant systematic bias (-0.32 ~ 0.41 D, P>0.05) in other subgroups. In axial length shorter than 23 mm, the Pearl-DGS formula stated the smallest MedAE (0.97 D) and MAE (1.26 D), and was significantly more accurate than Hill-RBF 3.0 (P=0.01) and SRK/T (P=0.02). In eyes with an axial length between 23 mm and 26 mm, the Kane formula had the lowest MedAE (0.44 D) and MAE (0.66 D). No significant difference was found in eyes longer than 26 mm. Conclusion: The Kane formula showed the highest accuracy in patients undergoing combined silicone oil removal and cataract surgery measured by OA-2000, whereas the Pearl-DGS formula could be more accurate in eyes with an axial length shorter than 23 mm.

人工智能(artificial intelligence, AI)在医学领域的广泛应用为探索眼部与全身健康的关系提供了新的机遇。文章回顾了眼科AI在心血管健康、神经系统健康、肾脏健康和衰老过程中的应用。在心血管健康方面，AI能够通过分析眼底图像预测心血管疾病风险因素和未来心血管事件，并提供了简便、有效的风险分层方法。在神经系统健康方面，眼科AI在阿尔茨海默病早期诊断和帕金森病识别方面显示出潜力，尽管对未来事件预测仍具挑战性。针对多发性硬化，眼科AI在诊断和预测残疾进程上展现了良好效果。在肾脏健康中，眼科AI技术通过分析视网膜图像可预测肾功能相关指标、直接检测肾病事件，展示了其在改善肾病筛查方式和减轻医疗负担方面的潜力。在衰老过程中，AI能够利用眼部图像预测生物年龄、视网膜年龄差和晶状体年龄等参数提供了生物衰老指标，为理解衰老与眼部健康的关联提供了新视角。

The widespread application of artificial intelligence (AI) in the medical field has provided new opportunities to explore the relationship between eye and whole body health. This article reviews the application of ophthalmic AI in cardiovascular health, neurological health and aging. In terms of cardiovascular health, AI can predict cardiovascular disease risk factors and future cardiovascular events by analyzing fundus images, and provides a simple and effective risk stratification method. In terms of neurological health, ophthalmic AI shows potential in early diagnosis of Alzheimer's disease and identification of Parkinson's disease, although the prediction of future events remains challenging. For multiple sclerosis, ophthalmic AI has shown good results in diagnosing and predicting the progression of disability. In kidney health, ophthalmic AI technology can predict kidney function-related parameters and detect kidney disease events by analyzing retinal images, demonstrating its potential in improving kidney disease screening methods and reducing medical burdens. In the aging process, AI can use eye images to predict biological age. Parameters such as retinal age gap and LensAge provide biological aging indicators, providing a new perspective for understanding the relationship between aging and eye health.

      精准的屈光规划——人工晶状体屈光力计算，是屈光性白内障手术的重要前提。人工晶状体计算公式使用患者术前的眼部生物学参数如眼轴长度、角膜曲率、前房深度、晶状体厚度等指标来预测患者在白内障术后的屈光状态。人工晶状体屈光力计算公式自1967年出现以来，不断发展革新，准确性得到了极大的提升。封面展示了几种基于不同原理而构建的计算公式：回归公式(以SRK公式为代表)、基于模型眼的会聚公式(以Holladay 1等为代表)、射线追踪公式(以Olsen为代表)与人工智能公式(以RBF为代表)。目前对于普通白内障患者，现有公式能够获得良好的预测准确性。然而，对于临床上很多特殊的患者(如眼球解剖参数异常、既往有其他眼病或眼部手术史)，如何准确预测术后的屈光状态仍存在较大的挑战。路途漫漫，学者们为提升人工晶状体屈光力计算准确性的努力却从未止步，力求为患者提供最佳的白内障手术效果。

      精准的屈光规划——人工晶状体屈光力计算，是屈光性白内障手术的重要前提。人工晶状体计算公式使用患者术前的眼部生物学参数如眼轴长度、角膜曲率、前房深度、晶状体厚度等指标来预测患者在白内障术后的屈光状态。人工晶状体屈光力计算公式自1967年出现以来，不断发展革新，准确性得到了极大的提升。封面展示了几种基于不同原理而构建的计算公式：回归公式(以SRK公式为代表)、基于模型眼的会聚公式(以Holladay 1等为代表)、射线追踪公式(以Olsen为代表)与人工智能公式(以RBF为代表)。目前对于普通白内障患者，现有公式能够获得良好的预测准确性。然而，对于临床上很多特殊的患者(如眼球解剖参数异常、既往有其他眼病或眼部手术史)，如何准确预测术后的屈光状态仍存在较大的挑战。路途漫漫，学者们为提升人工晶状体屈光力计算准确性的努力却从未止步，力求为患者提供最佳的白内障手术效果。

目的：比较单一EYESi虚拟手术模拟器(Dry-lab)、Wet-lab以及两种方式联合教学在超声乳化白内障吸除显微手术培训中的效果及差异，以期探索更科学高效的教学方式。方法：选取中山大学中山眼科中心接受住院医师规范化培训的1年级住院医师18名，随机分为Dry-lab组、Wet-lab组和联合组，每组各6人，分别接受8次有效Dry-lab训练、8次Wet-lab训练、4次有效Dry-lab训练联合4次Wet-lab训练。培训前后问卷调查评估三种教学方式的模拟效果与学员满意度，并在猪眼模型上考核超声乳化白内障吸除手术的三个主要步骤，以评估学员的培训效果。结果：Dry-lab与Wet-lab训练均能有效帮助学员学习使用显微器械，操作手感较好。但在立体感(P=0.007)、与人眼操作相比近似度(P<0.001)以及对掌握技术的帮助度(P=0.003)上，Wet-lab优于Dry-lab训练；而在缩短培训用时(P<0.001)上，Dry-lab培训更具优势。联合培训模式培训效果优于单一Wet-lab训练(P=0.014)和模拟器培训(P=0.012)，整体满意度高于Wet-lab训练(P=0.042)和Dry-lab培训(P=0.042)。结论：Dry-lab与Wet-lab训练在超声乳化白内障吸除显微手术培训中各有优势，而两者相结合的教学模式更为高效，培训效果更佳，整体满意度高。

Objective: To compare the effects and distinctions among three methods of phacoemulsification training: EYESi simulator (Dry-lab), Wet-lab, and a combined approach, in order to find out more scientific and efficient teaching method. Methods: 18 first-year residents undergoing residency training at Zhongshan Ophthalmic Center, Sun Yatsen University were randomly assigned to three groups: Dry-lab, Wet-lab, and Combined. Each group, consisting of 6 individuals, underwent a specific training regimen—8 sessions of effective simulator training for the Dry-lab group, 8 Wet-lab sessions for the Wet-lab group, and a combination of 4 effective simulator sessions with 4 Wet-lab sessions for the Combined group. The questionnaires were administered to assess simulation effects and student satisfaction before and after each training sessions. Additionally, the training effects were recorded in the three main steps of phacoemulsification cataract extraction surgery on a pig eye model. Results: Both the EYESi simulator and Wet-lab were proved to be effective in facilitating the learning of microscopic instrument use with commendable operating experience. However, Wet-lab is superior to the virtual simulator in terms of stereoscopic sensation (P=0.007), similarity to the human eye (P<0.001), and assistance in mastering techniques(P=0.003). Simulator training is found to be more advantageous in shortening the overall training time (P<0.001). The training effect of the joint training mode is better than that of single Wet-lab training (P=0.014) and simulator training (P=0.012), and the overall satisfaction is higher than that of Wet-lab training (P=0.042) and simulator training (P=0.042). Conclusion: The EYESi virtual surgery simulator and Wet-lab training have their advantages respectively in training for phacoemulsification cataract extraction microsurgery, and the combined teaching mode is more efficient, with better training effects and overall satisfaction.

随着功能性人工晶状体的推广应用，白内障患者的屈光预测准确性日益受到重视。尽管人工晶状体屈光力计算公式在近年来不断发展革新，但解剖参数异常或既往有其他眼病、眼部手术史的白内障患者屈光预测仍存在挑战。根据生物学参数特点与病史选择适合的人工晶状体屈光力计算公式是准确进行白内障手术屈光预测的重要保障。

With the widespread application of functional intraocular lense (IOL), the accuracy of refractive prediction in cataract patients is increasingly important. Although IOL power calculation formulas have been innovated continuously in recent years, there are still challenges in predicting refractive powerin cataract patients with abnormal anatomical parameters, ocular comorbidities, or a history of ocular surgery. Based on the the characteristics of biological parameters and medical history to selcet appropriate IOL power formula, it is an important guarantee for accurate refractive prediction in cataract patients.

在晶状体纤维细胞分化的终末阶段，细胞核、线粒体、内质网及高尔基体等膜性细胞器会发生程序性的降解，这对晶状体透明性的维持至关重要。然而，晶状体细胞器降解过程的机制尚不明确。研究晶状体细胞器的降解过程可为阐明白内障的发病机制提供理论依据，也有望为晶状体再生提供新的干预靶点。本文就晶状体细胞器降解过程及其机制进行综述。

During terminal differentiation of lens fiber cells, nuclei and other organelles experience programmed elimination.This process is essential for the maintenance of lens transparency. However, the mechanisms underlying lens organelle degradation remain unclear. Identification of the mechanisms can provide a theoretical basis for elucidating the pathogenesis of cataract and is expected to reveal new intervention targets for lens regeneration. In this review, we discuss potential mechanisms and the process of lens organelle degradation.

眼睛是人体最重要的感觉器官之一，主要由角膜和晶状体构成的屈光系统和视网膜构成的视觉神经系统2个部分构成。眼睛各组织的发育和功能异常都可影响视功能，甚至致盲。现有的致盲眼病的治疗方式均存在各自瓶颈问题，新的诊治方法亟待开发。近年来，得益于干细胞和组织工程学的发展，结合现有眼各组织的发育理论知识，研究者们利用多种来源的干细胞在体外成功诱导出具有组织特异结构和功能的眼类器官。眼类器官研究为利用干细胞研究眼组织发育和眼病发病机制、药物筛选以及替代治疗创造了新机遇，将干细胞治疗眼病的转化研究推向了一个更高平台。本文将对现有眼类器官的技术发展及应用进行综述。

Being one of the most important sensory organs, the eye is composed of the cornea, the lens, which are responsible for refraction, and the retina, which is the neural sensory part of the eye. Various kinds of developmental abnormalities and functional defects could lead to visual dysfunctions, and even blindness. Current treatments for blindness-causing eye diseases all have their own limitations, awaiting new efficient diagnostic and treating methods. Thanks to the development in stem cell biology and bioengineering, taking advantage of the rich knowledge accumulated on the mechanisms governing eye development, researchers have successfully generated various ocular organoids using multiple sources of stem cells in vitro, which resemble their counterparts in vivo on both the structural level and functional level. Ocular organoids provide valuable material and models for studying eye development, pathology, drug screening, and cell replacement therapy, pushing translational studies of ocular stem cell to a new era. Here, the paper reviews the development and application of ocular organoid technologies.