The Coronavirus disease 2019 (COVID-19) has affected several million people since 2019. Despite various vaccines of COVID-19 protect million people in many countries, the worldwide situations of more the asymptomatic and mutated strain discovered are urging the more sensitive COVID-19 testing in this turnaround time. Unfortunately, it is still nontrivial to develop a new fast COVID-19 screening method with the easier access and lower cost, due to the technical and cost limitations of the current testing methods in the medical resource-poor districts. On the other hand, there are more and more ocular manifestations that have been reported in the COVID-19 patients as growing clinical evidence[1]. This inspired this project. We have conducted the joint clinical research since January 2021 at the ShiJiaZhuang City, Hebei province, China, which approved by the ethics committee of The fifth hospital of ShiJiaZhuang of Hebei Medical University. We undertake several blind tests of COVID-19 patients by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Meantime as an important part of the ongoing globally COVID-19 eye test program by AIMOMICS since February 2020, we propose a new fast screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras. This could reliably make a rapid risk screening of COVID-19 with the sustainable stable high performance in different countries and races. For this clinical trial in ShiJiaZhuang, we compare and analyze 1194 eye-region images of 115 patients, including 66 COVID-19 positive patients, 44 rehabilitation patients (nucleic acid changed from positive to negative), 5 liver patients, as well as 117 healthy people. Remarkably, we consistently achieved very high testing results (> 0.94) in terms of both sensitivity and specificity in our blind test of COVID-19 patients. This confirms the viability of the COVID-19 fast screening by the eye-region manifestations. Particularly and impressively, the results have the similar conclusion as the other clinical trials of the globally COVID-19 eye test program[1]. Hopefully, this series of ongoing globally COVID-19 eye test study, and potential rapid solution of fully self-performed COVID risk screening method, can be inspiring and helpful to more researchers in the world soon. Our model for COVID-19 rapid prescreening have the merits of the lower cost, fully self-performed, non-invasive, importantly real-time, and thus enables the continuous health surveillance. We further implement it as the open accessible APIs, and provide public service to the world. Our pilot experiments show that our model is ready to be usable to all kinds of surveillance scenarios, such as infrared temperature measurement device at airports and stations, or directly pushing to the target people groups smartphones as a packaged application.
BackgroundThis study aimed to investigate and identify the characteristics of peripheral natural killer (NK) cells of hepatocellular carcinoma (HCC) patients who infected with hepatitis B virus. MethodsFlow cytometry was utilized to identify the frequency and receptors of NK cells, in the meantime, to analyze the killing ability and cytotoxicity of NK cells of patients of which embraced 36 cases of HBVassociated HCC, 34 individuals who suffered in HBV-associated cirrhosis (LC) and 30 non-liver dysfunction healthy individuals as control (HC). ResultsCell counts for NK and CD56 dim NK were reduced in patients with HCC, however, there was no statistically significant. The count of CD56 bright NK cells in the HCC subpopulation was remarkably higher than the subset of the HC (P < 0.05). The counts of activated receptors of NKG2D/p30 were elevated in patients with HCC, as well as the NKp44 and the NKp46. There was no statistically meaningful difference of inhibitory receptor expressions such as CD158a/b on peripheral NK cells of patients with HCC and LC and those with HC (P > 0.05). Following an IL-12 stimulation, the production of INF-γ/-α of patients with HCC was less than those produced by HC (P < 0.05). However, killing activity and cytotoxicity, as the primary responsibilities of the natural killer cells, were upregulated in HCC individuals. HBV associated individuals were found lower counts and capability to produce cytokines of natural killer cells. Nevertheless, the killing capacity and cytotoxicity of NK cells were stronger than those of the HC group. This may be associated with the increased activating receptors expression of NK cells. ConclusionsThis study demonstrated that the activating receptors expression and the cytotoxicity of NK cells in the blood were independent predictors of development in HCC patients, and the recovery of NKG2D + CD56 dim NK cells could increase the prognostic value.
Background The worldwide surge in coronavirus cases has led to the COVID-19 testing demand surge. Rapid, accurate, and cost-effective COVID-19 screening tests working at a population level are in imperative demand globally. Methods Based on the eye symptoms of COVID-19, we developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras. The convolutional neural networks (CNNs)-based model was trained on these eye images to complete binary classification task of identifying the COVID-19 cases. The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1. The application programming interface was open access. Findings The multicenter study included 2436 pictures corresponding to 657 subjects (155 COVID-19 infection, 23.6%) in development dataset (train and validation) and 2138 pictures corresponding to 478 subjects (64 COVID-19 infections, 13.4%) in test dataset. The image-level performance of COVID-19 prescreening model in the China-Spain multicenter study achieved an AUC of 0.913 (95% CI, 0.898-0.927), with a sensitivity of 0.695 (95% CI, 0.643-0.748), a specificity of 0.904 (95% CI, 0.891 -0.919), an accuracy of 0.875(0.861-0.889), and a F1 of 0.611(0.568-0.655). Interpretation The CNN-based model for COVID-19 rapid prescreening has reliable specificity and sensitivity. This system provides a low-cost, fully self-performed, non-invasive, real-time feedback solution for continuous surveillance and large-scale rapid prescreening for COVID-19. Funding This project is supported by Aimomics (Shanghai) Intelligent
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