In vivo confocal laser scanning microscopy (CLSM) is an emerging diagnostic tool allowing fast and easy microscopic tissue examination. For the diagnostics of pathological eyelid margin lesions, the knowledge of the normal eyelid margin is essential.We examined 18 eyelid margins of healthy humans using the in vivo CLSM device and 10 samples of healthy eyelid margins from donor sites with ex vivo CLSM and compared the findings to the corresponding histological sections of donor sites. Cross-section images of different depths and depths of different skin appendages were measured.The depth observed by in vivo CLSM is less than 150 μm into the eyelid. Images of the epidermis and superficial dermis skin, appendages including hair follicle, and sebaceous catheters can be captured associated with histopathology and ex vivo confocal microscopy. In correlation with histopathology, we identified different layers of the eyelid margin, different layers of the epidermis, and skin appendages by ex vivo confocal microscopy.The study offers an overview of the in vivo confocal microscopy human eyelid margin characteristics in comparison to the standard histological examination and confirms that in vivo CLSM could not observe the meibomian gland acini structure.
To observe the expression of MMP-2 and MMP-9 and of the FAK/PI3K/Akt signaling pathway in HSK. Fifty BALB/c mice were infected to establish the model and killed on days 0, 2, 7, 14, and 28. The cornea samples were prepared, respectively. Slit lamp examination, immunofluorescence staining, reverse transcription PCR, and Western blot were used to detect the index. After HSV-1 infection, different degrees of epithelial or stromal damage and corneal opacity were observed. Immunofluorescence staining showed that the expressions of MMP-2 and MMP-9 at different levels of corneal tissue were observed on the 0d, 2d, 7d, 14d, and 28d. Compared with 0d, the relative expression levels of MMP-2 and MMP-9 mRNA at 2d, 7d, 14d, and 28d were significantly increased (all P< 0.05). Compared with 14d, the relative expression of MMP-2 and MMP-9 mRNA decreased on the 2d, 7d, and 28d (all P< 0.05). Western blot showed that the protein expressions of p-FAK, p-PI3K, p-Akt, MMP-2, and MMP-9 at 2d, 14d, and 28d were all significantly higher than 0d (all P< 0.05). At 14 d, the expressions of p-FAK, p-PI3K, p-Akt, and MMP-2 were significantly higher than those at 2d, 7d, and 28d (all P< 0.05). The protein expression of FAK, PI3K, and Akt in corneal of mice in each time period had no significant (all P> 0.05). These data suggest that FAK/PI3K/Akt signaling pathway and MMP-2 and MMP-9 may be involved in the development of HSK.
Cardiac fibrosis is one of the common pathological processes in many cardiovascular diseases characterized by excessive extracellular matrix deposition.SerpinE2 is a kind of protein that inhibits peptidase in extracellular matrix and up-regulated tremendously in mouse model of cardiac fibrosis induced by pressure-overloaded via transverse aortic constriction (TAC) surgery.However, its effect on cardiac fibroblasts (CFs), collagen secretion and the underlying mechanism remains unclear.In this study, DyLight® 488 green fluorescent dye or His-tagged proteins were used to label the exogenous serpinE2 protein.It was showed that extracellular serpinE2 translocated into CFs by low-density lipoprotein receptor-related protein 1 (LRP1) and urokinase plasminogen activator receptor (uPAR) of cell membrane through endocytosis.Knockdown of LRP1 or uPAR reduced the level of serpinE2 in CFs and down-regulated the collagen expression.Inhibition of the endocytosis of serpinE2 could inhibit ERK1/2 and β-catenin signaling pathways and subsequently attenuated collagen secretion.Knockdown of serpinE2 attenuates cardiac fibrosis in TAC mouse.We conclude that serpinE2 could be translocated into cardiac fibroblasts due to endocytosis through directly interact with the membrane protein LRP1 and uPAR, and this process activated the ERK1/2, β-catenin signaling pathways, consequently promoting collagen production.
In order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians' workload.A total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman's membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance.The accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman's membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886.A computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes.
Abstract Background Predicting mortality in the emergency department (ED) is imperative to guide palliative care and end-of-life decisions. However, the clinical usefulness of utilizing the existing screening tools still leaves something to be desired. Methods We advanced the screening tool with the A-qCPR (Age, qSOFA (quick sepsis-related organ failure assessment), cancer, Performance Status Scale, and DNR (Do-Not-Resuscitate) risk score model for predicting one-year mortality in the emergency department of Taipei City Hospital of Taiwan with the potential of hospice need and evaluated its performance compared with the existing screening model. We adopted a large retrospective cohort in conjunction with in-time (the trained and the holdout validation cohort) for the development of the A-qCPR model and out-of-time validation sample for external validation and model robustness to variation with the calendar year. Results A total of 10,474 patients were enrolled in the training cohort and 33,182 patients for external validation. Significant risk scores included age (0.05 per year), qSOFA ≥ 2 (4), Cancer (5), Eastern Cooperative Oncology Group (ECOG) Performance Status score ≥ 2 (2), and DNR status (2). One-year mortality rates were 13.6% for low (score ≦ 3 points), 29.9% for medium (3 < Score ≦ 9 points), and 47.1% for high categories (Score > 9 points). The AUROC curve for the in-time validation sample was 0.76 (0.74–0.78). However, the corresponding figure was slightly shrunk to 0.69 (0.69–0.70) based on out-of-time validation. The accuracy with our newly developed A-qCPR model was better than those existing tools including 0.57 (0.56–0.57) by using SQ (surprise question), 0.54 (0.54–0.54) by using qSOFA, and 0.59 (0.59–0.59) by using ECOG performance status score. Applying the A-qCPR model to emergency departments since 2017 has led to a year-on-year increase in the proportion of patients or their families signing DNR documents, which had not been affected by the COVID-19 pandemic. Conclusions The A-qCPR model is not only effective in predicting one-year mortality but also in identifying hospice needs. Advancing the screening tool that has been widely used for hospice in various scenarios is particularly helpful for facilitating the end-of-life decision-making process in the ED.
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