ABSTRACT
Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.
ABSTRACT
Traditional Chinese medicine (TCM) is the key to unlock treasures of Chinese civilization. TCM and its compound play a beneficial role in medical activities to cure diseases, especially in major public health events such as novel coronavirus epidemics across the globe. The chemical composition in Chinese medicine formula is complex and diverse, but their effective substances resemble "mystery boxes". Revealing their active ingredients and their mechanisms of action has become focal point and difficulty of research for herbalists. Although the existing research methods are numerous and constantly updated iteratively, there is remain a lack of prospective reviews. Hence, this paper provides a comprehensive account of existing new approaches and technologies based on previous studies with an in vitro to in vivo perspective. In addition, the bottlenecks of studies on Chinese medicine formula effective substances are also revealed. Especially, we look ahead to new perspectives, technologies and applications for its future development. This work reviews based on new perspectives to open horizons for the future research. Consequently, herbal compounding pharmaceutical substances study should carry on the essence of TCM while pursuing innovations in the field.
ABSTRACT
The novel coronavirus 2019 is spreading around the world and causing serious concern. However, there is limited information about novel coronavirus that hinders the design of effective drug. Bioactive compounds are rich source of chemo preventive ingredients. In our present research focuses on identifying and recognizing bioactive chemicals in Lantana camara, by evaluating their potential toward new coronaviruses and confirming the findings using molecular docking, ADMET, network analysis and dynamics investigations.. The spike protein receptor binding domain were docked with 25 identified compounds and 2,4-Ditertbutyl-phenol (-6.3kcal/mol) shows highest docking score, its interactions enhances the increase in binding and helps to identify the biological activity. The ADME/toxicity result shows that all the tested compounds can serve as inhibitors of the enzymes CYP1A2 and CYP2D6. In addition, Molecular dynamics simulations studies with reference inhibitors were carried out to test the stability. This study identifies the possible active molecules against the receptor binding domain of spike protein, which can be further exploited for the treatment of novel coronavirus 2019. The results of the toxicity risk for phytocompounds and their active derivatives showed a moderate to good drug score.
ABSTRACT
Due to the pandemic limitations caused by Covid-19, people need to work at home and carry on the meetings virtually. Virtual meeting tools start popularizing and thriving. Those tools allow users to see each other through screen and camera, chat through voice and text, and share content or ideas through screen share. However, screen sharing protein models through virtual meetings is not easy due to the difficulty of viewing protein 3D (Three Dimensional) structures from a 2D (Two Dimensional) screen. Moreover, interactions upon a protein are also limited. ProMVR is a tool the author developed to tackle the issue that protein designers may find limitations working in a traditional 2D or 3D environment and they may find it hard to communicate their ideas with other designers. Since ProMVR is a VR tool, it allows users to “jump into” a virtual environment, take a close look at protein models, and have intuitive interactions. © 2021 Copyright held by the owner/author(s).