ABSTRACT
BackgroundThe 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. MethodsBased 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. FindingsThe multicenter study included 2436 pictures corresponding to 657 subjects (155 COVID-19 infection, 23{middle dot}6%) in development dataset (train and validation) and 2138 pictures corresponding to 478 subjects (64 COVID-19 infections, 13{middle dot}4%) in test dataset. The image-level performance of COVID-19 prescreening model in the China-Spain multicenter study achieved an AUC of 0{middle dot}913 (95% CI, 0{middle dot}898-0{middle dot}927), with a sensitivity of 0{middle dot}695 (95% CI, 0{middle dot}643-0{middle dot}748), a specificity of 0{middle dot}904 (95% CI, 0{middle dot}891-0{middle dot}919), an accuracy of 0{middle dot}875(0{middle dot}861-0{middle dot}889), and a F1 of 0{middle dot}611(0{middle dot}568-0{middle dot}655). InterpretationThe 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. FundingThis project is supported by Aimomics (Shanghai) Intelligent
ABSTRACT
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.
ABSTRACT
The Coronavirus disease 2019 (COVID-19) has affected several million people. With the outbreak of the epidemic, many researchers are devoting themselves to the COVID-19 screening system. The standard practices for rapid risk screening of COVID-19 are the CT imaging or RT-PCR (real-time polymerase chain reaction). However, these methods demand professional efforts of the acquisition of CT images and saliva samples, a certain amount of waiting time, and most importantly prohibitive examination fee in some countries. Recently, some literatures have shown that the COVID-19 patients usually accompanied by ocular manifestations consistent with the conjunctivitis, including conjunctival hyperemia, chemosis, epiphora, or increased secretions. After more than four months study, we found that the confirmed cases of COVID-19 present the consistent ocular pathological symbols; and we propose a new screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19 with very high accuracy. We believe a system implementing such an algorithm should assist the triage management or the clinical diagnosis. To further evaluate our algorithm and approved by the Ethics Committee of Shanghai public health clinic center of Fudan University, we conduct a study of analyzing the eye-region images of 303 patients (104 COVID-19, 131 pulmonary, and 68 ocular patients), as well as 136 healthy people. Remarkably, our results of COVID-19 patients in testing set consistently present similar ocular pathological symbols; and very high testing results have been achieved in terms of sensitivity and specificity. We hope this study can be inspiring and helpful for encouraging more researches in this topic.
ABSTRACT
Objective: To establish the HPLC fingerprints of compound Yinchen granules. Methods: The column was Agilent SB-C18(250 mm×4. 6 mm, 5 μm) and the mobile phase was acetonitrile (A)-0. 2% phosphoric acid solution (B) with gradient elution at a flow rate of 1. 0 ml·min-1. The column temperature was 25℃. The detection wavelength switching technology was used in 180-mi-nute elution time. Results: The HPLC fingerprints of compound Yinchen granules were established. Twenty-two common peaks were confirmed, of which five peaks were identified and 18 peaks were assigned to each crude drug. The overall similarity of the fingerprints of 10 batches of samples was 0. 9 or more when compared with the control map. Conclusion: The fingerprints of compound Yinchen granules can provide reference for the overall quality control of compound Yinchen granules.
ABSTRACT
Objective To establish the standard for quality control ofQingyan Granule. Methods The chief components of the preparation, Sophora Tonkinensis radix et rhizoma, Adenophorae radix, Lonicera japonica caulis, and Ophiopogonis radix were identified by TLC qualitatively. The contents of licorice glycosides and glycyrrhizic acid were determined by HPLC. The separation was performed on Thermo Syncronis C18 column (4.6 mm×250 mm, 5μm) with mobile phase consisted of acetonitrile with 0.05% phosphoric acid solution (A)-0.05% phosphoric acid solution (B), and gradient elution (0-8 min, 19%A;8-35 min, 19%→50%A). Detection wavelength was 237 nm, and flow rate was 1 mL/min.Results The spots in TLC were clear. There were spots with same color on the corresponding location of reference substance and reference herbal, negative control without interference. The linear range for licorice glycosides was 0.05-0.5μg (r=0.999 9). The average recovery was 99.97%, RSD=1.74% (n=9). The linear range for glycyrrhizic acid was 0.1-2μg (r=0.999 9). The average recovery was 99.74%, RSD=1.28% (n=9). Conclusion The method is simple, accurate, with high reproducibility, which can be used for quality control ofQingyan Granule.
