Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.

Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT.

Background Despite its high sensitivity in diagnosing coronavirus disease 2019 (COVID-19) in a screening population, the chest CT appearance of COVID-19 pneumonia is thought to be nonspecific. Purpose To assess the performance of radiologists in the United States and China in differentiating COVID-19 from viral pneumonia at chest CT. Materials and Methods In this study, 219 patients with positive COVID-19, as determined with reverse-transcription polymerase chain reaction (RT-PCR) and abnormal chest CT findings, were retrospectively identified from seven Chinese hospitals in Hunan Province, China, from January 6 to February 20, 2020. Two hundred five patients with positive respiratory pathogen panel results for viral pneumonia and CT findings consistent with or highly suspicious for pneumonia, according to original radiologic interpretation within 7 days of each other, were identified from Rhode Island Hospital in Providence, RI. Three radiologists from China reviewed all chest CT scans (n = 424) blinded to RT-PCR findings to differentiate COVID-19 from viral pneumonia. A sample of 58 age-matched patients was randomly selected and evaluated by four radiologists from the United States in a similar fashion. Different CT features were recorded and compared between the two groups. Results For all chest CT scans (n = 424), the accuracy of the three radiologists from China in differentiating COVID-19 from non-COVID-19 viral pneumonia was 83% (350 of 424), 80% (338 of 424), and 60% (255 of 424). In the randomly selected sample (n = 58), the sensitivities of three radiologists from China and four radiologists from the United States were 80%, 67%, 97%, 93%, 83%, 73%, and 70%, respectively. The corresponding specificities of the same readers were 100%, 93%, 7%, 100%, 93%, 93%, and 100%, respectively. Compared with non-COVID-19 pneumonia, COVID-19 pneumonia was more likely to have a peripheral distribution (80% vs 57%, P < .001), ground-glass opacity (91% vs 68%, P < .001), fine reticular opacity (56% vs 22%, P < .001), and vascular thickening (59% vs 22%, P < .001), but it was less likely to have a central and peripheral distribution (14% vs 35%, P < .001), pleural effusion (4% vs 39%, P < .001), or lymphadenopathy (3% vs 10%, P = .002). Conclusion Radiologists in China and in the United States distinguished coronavirus disease 2019 from viral pneumonia at chest CT with moderate to high accuracy. © RSNA, 2020 Online supplemental material is available for this article. A translation of this abstract in Farsi is available in the supplement. ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.

Multiple on-line HPLC coupled with biochemical detection methods to evaluate bioactive compounds in Danshen injection.

On-line high performance liquid chromatography (HPLC) coupled with three biochemical detection (BCD) methods was applied to evaluate bioactive components in Danshen injection. On-line HPLC-photo-diode array-fluorescence detection based on the fluorogenic substrate 7-acetoxy-1-methyl quinolinium iodide, was built to search acetylcholinesterase (AChE) inhibitors in Danshen injection. On-line HPLC coupled with the scavenging assay of 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2'-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) free radicals was developed to screen antioxidants. The three active profiles were obviously different. Radical scavenging profiles revealed seven strong peaks in the chromatographic fingerprint possessing obvious free radical inhibition effects, while some minor peaks exhibited stronger AChE inhibition activities. The main radical scavengers and AChE inhibitors were identified by HPLC-MS. Several unknown ingredients showing strong AChE inhibition activities needed further identification except protocatechuic aldehydrate, salvianolic acid H or I and lithospermic acid. The on-line multiple on-line HPLC-BCD methods will provide powerful tools in the field of pharmacognosy for fast-track identification of interesting and/or novel bioactive compounds.

[Expression of hCTLA4-Ig mediated by adeno-associated virus in porcine islets and their biological activity].

OBJECTIVE: To evaluate the expression of hCTLA4-Ig and their biological function in newborn porcine islets (NPIs) transfected with AAV-hCTLA4-Ig. METHODS: Cultured NPIs were transfected with AAV-hCTLA4-Ig. The expression of CTLA4-Ig in these NPIs was assayed by RT-PCR and immunocytochemistry. The levels of IL-2, IFN-gamma, and TNF-alpha in the culture medium were assayed by ELISA after these cells the co-cultured with human. The response of glucose-stimulated insulin secretion was observed in the transgene group and the control group. RESULTS: The expressions of CTLA4-Ig mRNA and protein were detected in the transgene group. The levels of cytokines were obviously lower in the transgene group than those in the control group (P<0.01). There was no significant difference in the response of glucose-stimulated insulin release between the transgene group and the control group (P>0.05). CONCLUSION: AAV mediated hCTLA4-Ig expression in NPIs could inhibit T lymphocyte to produce cytokines, while the endocrine functions of the NPIs were not significantly affected.

[Human peripheral blood CD4+ T-lymphocytes reconstituting severe combined immunodeficient mouse-human cell immune system and its immunological function].

OBJECTIVE: To observe whether severe combined immunodeficiency disease (SCID) mice can reconstitute human cell immune system by adoptive transferring of human peripheral blood CD4+ T-lymphocytes to the peritoneal cavity in SCID mice, and to determine the characteristics and function of SCID mice immune system after the reconstitution. METHODS: SCID mice were injected mature human CD4+ T-lymphocytes to the peritoneal cavity, accompanied with the stimulation of rIL-2 after the injection. Six weeks after the injection, mice were killed in batch, the form and dimension of liver and spleen were observed. The DNA of human lymphocytes was detected in SCID mouse peripheral blood by PCR amplification. The lymphocytes phenotype of SCID mouse immune organs were assayed with immunohistochemistry. The concentration of human cytokines in SCID mouse blood serum was assayed with ELISA after transplanting xenografts. RESULTS: Intraperitoneal injection of SCID mice with mature human peripheral blood CD4+ T lymphocytes could graft human cell immune system to SCID mice. Human CD4+ T lymphocytes were found in the liver and spleen, and the immunological function of lymphocytes was normal. The HLA-II constant region segment of human lymphocytes was found in hu-CD4+ T-SCID mouse peripheral blood by PCR amplification. Human IL-2, TNF-alpha, and INF-gamma were found in the serum of hu-CD4+ T-SCID mice. CONCLUSION: Intraperitoneal injection of SCID mice with mature human peripheral blood CD4+ T lymphocytes can result in a human cell immune system. The method is simple, quick and has abundant donors.