Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling.

Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19.

Artificial intelligence and glaucoma:opportunities and challenges / 中华实验眼科杂志

Advances in artificial intelligence (AI) have led to innovation and revolution in many different research fields.AI-assisted diagnostic reading of medical images has achieved substantial progress,whose accuracy has been close to human experts.Glaucoma is the leading reason for irreversible blindness in the world.Early diagnosis and treatment of glaucoma would remarkably improve prognosis.However,early diagnosis of glaucoma is difficult because it depends on comprehensive assessment of intraocular pressure,visual field,changes of retinal nerve fiber layer,etc.Thus,development of AI which could assist diagnosis of glaucoma is faced with great challenges and difficulties.In the future,invention of AI-assisted diagnostic platform of glaucoma based on accumulation of labeled clinical data and training computers to read multimodule test results will create huge social economic benefit and revolutionize diagnosis of glaucoma.