Effects of chemokine receptor CCR7 in the pathophysiology and clinical features of the immuno-inflammatory response in primary pterygium.

OBJECTIVE: Chemokine receptor 7 (CCR7) has been considered a critical biomarker in inflammation and the immune response; however, little is known about CCR7 in pterygia. This study aimed to investigate whether CCR7 participates in the pathogenesis of primary pterygia and how CCR7 affects the progression of pterygia. METHODS: This was an experimental study. Slip-lamp photographs of 85 pterygium patients were used to measure the width, extent, and area of pterygia with computer software. Pterygium blood vessels and general ocular redness were quantitatively analyzed with a specific algorithm. The expression of CCR7 and its ligands C-C motif ligand 19 (CCL19) and C-C motif ligand 21 (CCL21) in control conjunctivae and excised pterygia collected during surgery were analyzed by quantitative real-time polymerase chain reaction (qRT-PCR) and immunofluorescence staining. The phenotype of CCR7-expressing cells was identified by costaining for major histocompatibility complex II (MHC II), CD11b or CD11c. RESULTS: The CCR7 level was significantly increased by 9.6-fold in pterygia compared with control conjunctivae (p = 0.008). The higher the expression of CCR7 was, the more blood vessels appeared in pterygia (r = 0.437, p = 0.002) and the more general ocular redness was (r = 0.51, p < 0.001) in pterygium patients. CCR7 was significantly associated with pterygium extent (r = 0.286, p = 0.048). In addition, we found that CCR7 colocalized with CD11b, CD11c or MHC II in dendritic cells, and immunofluorescence staining showed that CCR7-CCL21 is a potential chemokine axis in pterygium. CONCLUSIONS: This work verified that CCR7 impacts the extent of primary pterygia invading the cornea and inflammation at the ocular surface, which may provide a possibility for a further in-depth understanding of the immunological mechanism in pterygia.

Intelligent cataract surgery supervision and evaluation via deep learning.

PURPOSE: To assess the performance of a deep learning (DL) algorithm for evaluating and supervising cataract extraction using phacoemulsification with intraocular lens (IOL) implantation based on cataract surgery (CS) videos. MATERIALS AND METHODS: DeepSurgery was trained using 186 standard CS videos to recognize 12 CS steps and was validated in two datasets that contained 50 and 21 CS videos, respectively. A supervision test including 50 CS videos was used to assess the DeepSurgery guidance and alert function. In addition, a real-time test containing 54 CSs was used to compare the DeepSurgery grading performance to an expert panel and residents. RESULTS: DeepSurgery achieved stable performance for all 12 recognition steps, including the duration between two pairs of adjacent steps in internal validation with an ACC of 95.06% and external validations with ACCs of 88.77% and 88.34%. DeepSurgery also recognized the chronology of surgical steps and alerted surgeons to order of incorrect steps. Six main steps are automatically and simultaneously quantified during the evaluation process (centesimal system). In a real-time comparative test, the DeepSurgery step recognition performance was robust (ACC of 90.30%). In addition, DeepSurgery and an expert panel achieved comparable performance when assessing the surgical steps (kappa ranged from 0.58 to 0.77). CONCLUSIONS: DeepSurgery represents a potential approach to provide a real-time supervision and an objective surgical evaluation system for routine CS and to improve surgical outcomes.