A Nomogram Model for Predicting the Polyphenol Content of Pu-Erh Tea.

To investigate different contents of pu-erh tea polyphenol affected by abiotic stress, this research determined the contents of tea polyphenol in teas produced by Yuecheng, a Xishuangbanna-based tea producer in Yunnan Province. The study drew a preliminary conclusion that eight factors, namely, altitude, nickel, available cadmium, organic matter, N, P, K, and alkaline hydrolysis nitrogen, had a considerable influence on tea polyphenol content with a combined analysis of specific altitudes and soil composition. The nomogram model constructed with three variables, altitude, organic matter, and P, screened by LASSO regression showed that the AUC of the training group and the validation group were respectively 0.839 and 0.750, and calibration curves were consistent. A visualized prediction system for the content of pu-erh tea polyphenol based on the nomogram model was developed and its accuracy rate, supported by measured data, reached 80.95%. This research explored the change of tea polyphenol content under abiotic stress, laying a solid foundation for further predictions for and studies on the quality of pu-erh tea and providing some theoretical scientific basis.

Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective.

Background: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10-12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63-0.83. Conclusion: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.

Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective

Background Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83. Conclusion Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.

Severe Acute Respiratory Syndrome Coronavirus 2 ORF8 Protein Inhibits Type I Interferon Production by Targeting HSP90B1 Signaling.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global pandemic that has currently infected over 430 million individuals worldwide. With the variant strains of SARS-CoV-2 emerging, a region of high mutation rates in ORF8 was identified during the early pandemic, which resulted in a mutation from leucine (L) to serine (S) at amino acid 84. A typical feature of ORF8 is the immune evasion by suppressing interferon response; however, the mechanisms by which the two variants of ORF8 antagonize the type I interferon (IFN-I) pathway have not yet been clearly investigated. Here, we reported that SARS-CoV-2 ORF8L and ORF8S with no difference inhibit the production of IFN-ß, MDA5, RIG-I, ISG15, ISG56, IRF3, and other IFN-related genes induced by poly(I:C). In addition, both ORF8L and ORF8S proteins were found to suppress the nuclear translocation of IRF3. Mechanistically, the SARS-CoV-2 ORF8 protein interacts with HSP90B1, which was later investigated to induce the production of IFN-ß and IRF3. Taken together, these results indicate that SARS-CoV-2 ORF8 antagonizes the RIG-I/MDA-5 signaling pathway by targeting HSP90B1, which subsequently exhibits an inhibitory effect on the production of IFN-I. These functions appeared not to be influenced by the genotypes of ORF8L and ORF8S. Our study provides an explanation for the antiviral immune suppression of SARS-CoV-2 and suggests implications for the pathogenic mechanism and treatment of COVID-19.

[Analysis of variation and evolution of SARS-CoV-2 genome].

OBJECTIVE: To analyze the evolution and variation of SARS-CoV-2 during the epidemic starting at the end of 2019. METHODS: We downloaded the full-length genome sequence of SARS-CoV-2 from the databases of GISAID and NCBI. Using the software for bioinformatics including MEGA-X, BEAST, and TempEst, we constructed the genomic evolution tree, inferred the time evolution signal of the virus, calculated the tMRCA time of the virus and analyzed the selection pressure of the virus during evolution. RESULTS: The phylogenetic tree showed that SARS-CoV-2 belonged to the Sarbecovirus subgenus of ß Coronavirus genus together with bat coronavirus BetaCoV/bat/Yunnan/RaTG13/2013, bat-SL-CoVZC45, bat-SL-CoVZXC21 and SARS-CoV. The genomic sequences of SARS-CoV-2 isolated from the ongoing epidemic showed a weak time evolution signal with an average tMRCA time of 73 days (95% CI: 38.9-119.3 days). No positive time evolution signal was found between SARS-CoV-2 and BetaCoV/bat/Yunnan/RaTG13/2013, but the former virus had a strong positive temporal evolution relationship with bat-SL-CoVZC45 and SARS-CoV. The major cause for mutations of SARS-CoV-2 was the pressure of purification selection during the epidemic. CONCLUSIONS: SARS-CoV-2 may have emerged as early as November, 2019, originating most likely from bat-associated coronavirus. This finding may provide evidence for tracing the sources and evolution of the virus.