Along with the increasing knowledge of long noncoding RNA, the interaction between the long noncoding RNA (lncRNA) and tumor immune infiltration is increasingly valued. However, there is a lack of understanding of correlation between regulation of specific lncRNAs and tumor-infiltrating macrophages within melanoma. In this research, a macrophage associated lncRNA signature was identified by multiple machine learning algorithms and the robust and effectiveness of signature also validated in other independent datasets. The signature contained six specific lncRNAs (PART1, LINC00968, LINC00954, LINC00944, LINC00518 and C20orf197) was constructed, which could diagnose melanoma and predict the prognosis of patients. Moreover, our signature achieves higher accuracy than the previous well-established markers and regarded as an independent prognostic indicator. The pathway enrichment revealed that these lncRNAs were closely correlated with many immune processes. In addition, the signature was associated with different immune microenvironment and applied to predict response of immune checkpoint inhibitor therapy (low risk of patients well respond to anti-PD-1 therapy and high risk is insensitive to anti-CTLA-4 therapy). Therefore, our finding supplies a more accuracy and effective lncRNA signature for tumor-infiltrating macrophages targeting treatment approaches and affords a new clinical application for predicting the response of immunotherapies in melanomas.

The SMILE lenticule is a complete corneal stroma that is removed from SMILE surgery. Since the increasing number of SMILE surgeries, a large number of SMILE lenticules have been produced, so the reuse and preservation of the stromal lens has become a research hotspot. Due to the rapid development of the preservation and clinical reuse of SMILE lenticules, there have been many related studies in recent years, so we updated it on this basis. We searched PubMed, Web of Science, Embase, Elsevier Science, CNKI, WANFANG Data and other databases for all articles published on the preservation and clinical reuse of SMILE lenticules, screened useful articles, selected relevant articles published in the last five years as the main body for summary, and then reached a conclusion. The existing preservation methods of SMILE lenticule include Moist chamber storage at low temperature, cryopreservation technique dehydrating agent and corneal storage medium, which have their own advantages and disadvantages. Presently, smile lenticules can be used for the treatment of corneal ulcers and perforations, corneal tissue defects, hyperopia, presbyopia and keratectasia, which have been proven to be relatively effective and safe. More research on smile lenticule reuse needs to be carried out to confirm its long-term efficacy.

Introduction The primary objective of this study was to develop an end-to-end model that can accurately identify corneal endothelial cells and diagnose keratoconus based on corneal endothelial images acquired from a non-contact specular microscope. Methods This was a retrospective case–control study performed at the Refractive Surgery Center of West China Hospital. A total of 403 keratoconus eyes (221 patients) and 370 myopic eyes (185 normal controls) were consecutively recruited from January 2021 to September 2022. Specular microscopy was used to image and measure the morphometric parameters of the corneal endothelial cells. A Fully Convolutional Network model with a ResNet50 (FCN_ResNet50) was established to perform the endothelial segmentation. The images were then classified using an ensemble machine learning system consisting of four pre-trained deep learning networks: DenseNet121, ResNet50, Inception_v3, and MobileNet_v2. The performance of the models was evaluated based on different metrics, such as accuracy, intersection over union (IoU), and mean IoU. Results We established a fully end-to-end deep-learning model for the segmentation of endothelial and diagnosis of keratoconus. For endothelial segmentation, the accuracy of the FCN_ResNet50 model achieved near 90% with mean IoU converging to about 80%. The ensemble machine learning system can achieve over 92% accuracy, and > 98% area under curve (AUC) values to diagnose keratoconus with endothelial cell images. In addition, we constructed a diagnostic model based on deep-learning features and developed an associated nomogram which manifested an excellent performance for diagnosis and monitoring the progression of keratoconus. Conclusions Our research developed an end-to-end model to automatically identify and assess corneal endothelial morphological changes in keratoconus eyes. Moreover, we also constructed a novel nomogram, which can provide valuable information for the diagnosis, monitoring, and management of the disease.

Background: Corneal crosslinking is widely applied to enhance corneal biomechanical properties and delay the progression of keratoconus. The surgical procedure and application of ultraviolet A irradiation (UVA) during corneal crosslinking have been recognized to induce the reactivation of simplex herpes virus (HSV) but are rarely reported and poorly analyzed. Case Presentation: We report the first case series of herpetic keratitis in 4 keratoconus patients undertaking corneal crosslinking, who were all clinically diagnosed at routine follow-up visits 3 days to 1 month after the surgery. Different from the typical new onset of secondary herpetic keratitis that mainly presents with epithelial lesions and severe eye pain, these patients all presented with stromal infiltrates and were generally asymptomatic except for vision blurring in 2 patients. All patients responded well to antiviral therapy, topical steroids, and epithelial nourishment medication, leaving corneal macula or nebula at the last follow-up visit. Conclusion: Close follow-up is essential and the most effective way to diagnose herpetic keratitis after corneal crosslinking due to the lack of subjective symptoms. The prophylactic use of antiviral therapy on asymptomatic patients is controversial and should be evaluated based on long-term prognosis.

Purpose: To evaluate the postoperative visual outcomes, that is, corneal higher-order aberrations (HOAs) and visual quality, of patients with an angle kappa greater than 0.30 mm who underwent angle kappa adjustment during small-incision lenticule extraction (SMILE) 2 years after surgery compared to eyes with an angle kappa less than 0.30 mm. Methods: This was a retrospective study and included 12 patients from October 2019 to December 2019 who underwent the SMILE procedure for correction of myopia and myopic astigmatism and had one eye with a large kappa angle and another eye with a small kappa angle. Twenty-four months after surgery, an optical quality analysis system (OQAS II; Visiometrics, Terrassa, Spain) was used to measure the modulation transfer function cutoff frequency (MTF_cutoff), Strehl2D ratio, and objective scatter index (OSI). HOAs were measured with a Tracey iTrace Visual Function Analyzer (Tracey version 6.1.0; Tracey Technologies, Houston, TX, USA). Assessment of subjective visual quality was achieved using the quality of vision (QOV) questionnaire. Results: At 24 months postoperatively, the mean spherical equivalent (SE) refraction was − 0.32 ± 0.40 and − 0.31 ± 0.35 in the S-kappa group (kappa <0.3 mm) and the L-kappa group (kappa ≥0.3 mm), respectively (P > 0.05). The mean OSI was 0.73 ± 0.32 and 0.81 ± 0.47, respectively (P > 0.05). There was no significant difference in MTF_cutoff and Strehl2D ratio between the two groups (P > 0.05). Total HOA, coma, spherical, trefoil, and secondary astigmatism were not significantly different (P > 0.05) between the two groups. Conclusion: Adjustment of angle kappa during SMILE helps reduce the decentration, results in less HOAs, and promotes visual quality. It provides a reliable method to optimize the treatment concentration in SMILE.