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.

Paragonimus and its hosts in China: An update.

Paragonimiasis is an important foodborne parasitic disease. Over 50 species of Paragonimus have been reported worldwide, and China has the widest distribution and largest number of species. The detection of Paragonimus metacercariae from second intermediate hosts has been reported in 22 provinces and municipalities. The most frequently reported species are P. westermani, P. skrjabini, P. heterotremus and Euparagonimus cenocopiosus. In this review, we collected and reviewed relevant reports on the detection of Paragonimus metacercariae in second intermediate hosts from 1937 to 2020 from all areas of China. We provide an updated and current summary of Paragonimus species and their hosts in China. Data on the geographical range, species distribution, and second intermediate host species of Paragonimus were extracted. ArcGIS10.2 software was used to generate distribution maps of Paragonimus for four time periods: 1937-1990, 1991-2005, 2006-2020 and 1937-2020. We analyzed the geographic and spatiotemporal dynamics of Paragonimus prevalence in natural foci and provided a basis for further research and paragonimiasis prevention strategies in China.

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.

The complete mitochondrial genome of the freshwater crab Chinapotamon maolanense (Decapoda: Brachyura: Potamoidea).

The complete mitochondrial genome of Chinapotamon maolanense was obtained for the first time. The complete mitochondrial genome of C. maolanense is 17,130 bp in length, including 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, and 1 control region. In addition, the mitogenome has 18 noncoding regions ranging from 1 to 1553 bp in length.

The complete mitochondrial genome of the freshwater crab Neilupotamon xinganense (Decapoda: Brachyura: Potamoidea).

In this study, we first obtained the complete mitochondrial genome of Neilupotamon xinganense (Decapoda: Brachyura: Potamoidea). The genome is 16,965 bp in length and typically consists of 37 genes (13 protein-coding genes, 22 tRNAs genes, two rRNAs genes, and one putative control region). In addition, the mitogenome has 20 non-coding regions ranging from 1 to 683 bp in length. This study provides DNA data for further researches on population genetics and phylogenetics.

The effects of imidacloprid combined with endosulfan on IgE-mediated mouse bone marrow-derived mast cell degranulation and anaphylaxis.

Low levels of endosulfan are known to stimulate mast cells to release allergic mediators, while imidacloprid can inhibit IgE-mediated mast cell degranulation. However, little information about the effects of both pesticides together on mast cell degranulation is available. To measure the effects, IgE-activated mouse bone marrow-derived mast cells (BMMCs) were treated with imidacloprid and endosulfan, individually, and simultaneously at equi-molar concentrations in tenfold steps ranging from 10-4 to 10-11 M, followed by measuring several allergy-related parameters expressed in BMMCs: the mediator production and influx of Ca2+, the phosphorylation content of NF-κB in the FcεRI signaling pathway. Then, the effects of the mixtures on IgE-induced passive systemic anaphylaxis (PSA) of BALB/c was detectded. This study clearly showed that the application of equi-molar mixtures of both pesticides with 10-4-10-5 M significantly inhibited the IgE-mediated mouse bone marrow-derived mast cells degranulation in vitro and 10-4 M of them decreased IgE-mediated PSA in vivo, as the application of imidacloprid at the same concentration alone did. Morever endosulfan alone had no remarkable stimulatory effects on any of the factors measured. In conclusion, simultaneous application of equi-molar concentrations of both pesticides generally showed highly similar responses compared to the responses to imidacloprid alone, suggesting that the effects of the mixture could be solely attributed to the effects of imidacloprid.

[Epidemiological survey on the infection of Paragonimus westermani in Jiangxi Province].

OBJECTIVE: To investigate Paragonimus westermani infection in the intermediate hosts and animal reservoivs in Jiangxi Province. METHODS: Two forest farms in Jingan and Wanzai Counties and one town in Yushan County of Jiangxi Province were selected as pilots for epidemiological and retrospective survey. The intermediate hosts (snails, crabs) and reservoir hosts (cat, dog, civet cat, wildcat, etc.) were collected and examined. Data on the changes of ecological environment and people's behaviors were also collected. RESULTS: The average infection rate in Semisulcospira libertina and Sinopotamon spp. was 0.21% and 54.3% respectively, and that of reservoir hosts was 5.6%. Compared with those in 20 years ago, the infection rate in Sinopotamon spp. decreased considerably. CONCLUSION: The three areas are still endemic for P. westermani with lower prevalence than before possibly due to the change of ecological environment.