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Objective:To investigate the diagnostic value of an intelligent assisted grading algorithm for nuclear cataract using anterior segment optical coherence tomography (AS-OCT) images.Methods:A diagnostic test study was conducted.AS-OCT image data were collected from 939 cases of 1 608 eyes of nuclear cataract patients at the Shanghai Tenth People's Hospital of Tongji University from November 2020 to September 2021.The data were obtained from the electronic case system and met the requirements for clinical reading clarity.Among them, there were 398 cases of 664 male eyes and 541 cases of 944 female eyes.The ages of the patients ranged from 18 to 94 years, with a mean age of (65.7±18.6) years.The AS-OCT images were labelled manually from one to six levels according to the Lens Opacities Classification System Ⅲ (LOCS Ⅲ grading system) by three experienced clinicians.This study proposed a global-local cataract grading algorithm based on multi-level ranking, which contains five basic binary classification global local network (GL-Net).Each GL-Net aggregates multi-scale information, including the cataract nucleus region and original image, for nuclear cataract grading.Based on ablation test and model comparison test, the model's performance was evaluated using accuracy, precision, sensitivity, F1 and Kappa, and all results were cross-validated by five-fold.This study adhered to the Declaration of Helsinjki and was approrved by Shanghai Tenth People's Hospital of Tongji University (No.21K216).Results:The model achieved the results with an accuracy of 87.81%, precision of 88.88%, sensitivity of 88.33%, F1 of 88.51%, and Kappa of 85.22% on the cataract dataset.The ablation experiments demonstrated that ResNet18 combining local and global features for multi-level ranking classification improved the accuracy, recall, specificity, F1, and Kappa metrics.Compared with ResNet34, VGG16, Ranking-CNN, MRF-Net models, the performance index of this model were improved.Conclusions:The deep learning-based AS-OCT nuclear cataract image multi-level ranking classification algorithm demonstrates high accuracy in grading cataracts.This algorithm may help ophthalmologists in improving the diagnostic accuracy and efficiency of nuclear cataract.
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Objective:To investigate the mechanism of curcumin in the treatment of diabetic retinopathy (DR) by network pharmacology and molecular docking.Methods:The compounds targets of curcumin were predicted by SEA and SwissTargetPrediction databases, and the DR target genes were obtained by CTD database.The different genes were mapped and matched by Venny database to screen their intersections.The intersecting genes were submitted to GeneMANIA database to construct a protein-protein interaction network.WebGestalt database was used to conduct enrichment analysis and AutoDock Vina was used to perform molecular docking of the core targets.Results:A total of 52 targets of curcumin, 1 599 targets of DR and 48 intersecting targets were detected.The core targets were serine/threonine-protein kinase 1 (AKT1), tumor necrosis factor-α (TNF-α), epidermal growth factor receptor (EGFR), signal transduction and activator of transcription 3 (STAT3) and heat shock protein 90 alpha family class A member 1 (HSP90AA1). Enrichment analysis suggested that these targets were mainly associated with signaling pathways, including the EGFR tyrosine kinase inhibitor resistance signaling pathway, hypoxia-inducible factor-1 (HIF-1) signaling pathway, interleukin (IL)-17 signaling pathway and advanced glycosylation end product-the receptor of advanced glycosylation end product (AGE-RAGE) signaling pathway.Conclusions:Curcumin may play an important role in the treatment of DR by regulating multiple signaling pathways to inhibit the inflammatory response and combat oxidative stress.
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The global spread of SARS-CoV-2 requires an urgent need to find effective therapeutics for the treatment of COVID-19. We developed a data-driven drug repositioning framework, which applies both machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. The retrospective study using the past SARS-CoV and MERS-CoV data demonstrated that our machine learning based method can successfully predict effective drug candidates against a specific coronavirus. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 is able to suppress the CpG-induced IL-6 production in peripheral blood mononuclear cells, suggesting that it may also have anti-inflammatory effect that is highly relevant to the prevention immunopathology induced by SARS-CoV-2 infection. Further pharmacokinetic and toxicokinetic evaluation in rats and monkeys showed a high concentration of CVL218 in lung and observed no apparent signs of toxicity, indicating the appealing potential of this drug for the treatment of the pneumonia caused by SARS-CoV-2 infection. Moreover, molecular docking simulation suggested that CVL218 may bind to the N-terminal domain of nucleocapsid (N) protein of SARS-CoV-2, providing a possible model to explain its antiviral action. We also proposed several possible mechanisms to explain the antiviral activities of PARP1 inhibitors against SARS-CoV-2, based on the data present in this study and previous evidences reported in the literature. In summary, the PARP1 inhibitor CVL218 discovered by our data-driven drug repositioning framework can serve as a potential therapeutic agent for the treatment of COVID-19.
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Objective To assess the diagnostic value of the latent membrane protein 1(LMP-1)gene detection for diagnosing na-sopharyngeal carcinoma(NPC).Methods The related researches on the detection of LMP-1 gene in the nasopharyngeal swab secre-tion for diagnosing NPC by PCR were entirely collected by using the computer retrieval or manual inquiring.Two estimators screened the literature according to the criteria of inclusion and exclusion.The quality evaluation was performed by adopting the QUADAS scale.The heterogeneity test was conducted by using Meta-Disc 1.4 software.According to the heterogeneous character-istics,the corresponding effect model was selected for calculating the pooled sensitivity and specificity and the summary receiver op-erating character(SROC)curve was drawn.Results 139 related articles were retrieved,in which 6 articles were finally included. 394 cases of NPC were definitely diagnosed by the pathological golden standard,802 cases were in the control group.The pooled sensitivity and specificity of the LMP-1 gene for diagnosing NPC were 0.90[95%CI (0.87,0.93)]and 0.98[95%CI (0.96,0.99)] respectively.The area under SROC(AUC)was 0.973 7.Conclusion LMP-1 gene has the higher diagnostic value for NPC and could be used for screening and auxiliary diagnosis of NPC.