Analysis of Prognostic Factors and Establishment of a Nomogram to Predict Risk for COVID-19 Convalescent Patients Based on Metabolomic and Lipidomic (preprint)

Ethnopharmacological relevance: Qimai Feiluoping Mixture (QM) is a traditional Chinese herbal formulation that has demonstrated efficacy in improving both clinical symptoms and radiological indications of pulmonary fibrosis in patients convalescing from Coronavirus Disease 2019 (COVID-19).Aim of the study: To analyze factors associated with the prognosis of COVID-19 patients. It seeks to develop and validate a nomogram based on metabolomic and lipidomic for predicting improvements in lung imaging in COVID-19 patients. Additionally, the study evaluates the clinical application value of this nomogram.Methods and materials: A retrospective analysis was conducted on the clinical data of COVID-19 recovery patients from January 2020 to April 2022. Non-targeted metabolomic and lipidomic plasma analysis of the patients were performed using LC-MS and normal phase (NP)-HPLC coupled with mass spectrometry. Patients were divided into training and validation sets in a 7:3 ratio based on their omics data. Multivariate logistic regression analysis was conducted to identify independent risk factors associated with the recovery of lung imaging. Based on these factors, a nomogram prediction model was developed. The efficacy of the model was evaluated using receiver operating characteristic (ROC) curves and calibration curves. In addition, decision curve analysis (DCA) was performed to assess the performance of the predictive model in clinical applications.Results The use of QM was found to be associated with the recovery of lung imaging in COVID-19 patients (P < 0.05). Among the 75 metabolites detected in the metabolomic test and 32 lipids identified in the lipidomic test, Pro Ser Ser Val, PC36:1(18:0_18:1), and BMP36:3(18:2_18:1) were utilized for constructing the predictive model. The model demonstrated good discriminative ability, with an Area Under the Curve (AUC) of 0.821 (95% CI: 0.718–0.924) in the training set and 0.808 (95% CI: 0.627–0.989) in the validation set. The calibration curves indicated good agreement between predicted probabilities and actual probabilities in both the training and validation sets. Finally, the DCA curve suggested that the model has good clinical utility.Conclusion The utilization of QM may beneficially influence the recovery of lung imaging in patients with COVID-19. A straightforward nomogram, developed based on metabolomic and lipidomic, could be a valuable tool for clinicians to predict the likelihood of lung imaging recovery in COVID-19 patients.

Electrochemical Aptasensing of SARS-CoV-2 Based on Triangular Prism DNA Nanostructures and Dumbbell Hybridization Chain Reaction.

Development of convenient, accurate, and sensitive methods for rapid screening of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection is highly desired. In this study, we have developed a facile electrochemical aptasensor for the detection of the SARS-CoV-2 S1 protein amplified by dumbbell hybridization chain reaction (DHCR). A triangular prism DNA (TPDNA) nanostructure is first assembled and modified at the electrode interface. Due to the multiple thiol anchors, the immobilization is quite stable. The TPDNA nanostructure also provides an excellent scaffold for better molecular recognition efficiency on the top single-strand region (DHP0). The aptamer sequence toward the SARS-CoV-2 S1 protein is previously localized by partial hybridization with DHP0. In the presence of the target protein, the aptamer sequence is displaced and DHP0 is exposed. After further introduction of the fuel stands of DHCR, compressed DNA linear assembly occurs, and the product can be stacked on the TPDNA nanostructure for the enrichment of electrochemical species. This electrochemical method successfully detects the target protein in clinical samples, which provides a simple, robust, and accurate platform with great potential utility.

Mining intrinsic information of convalescent patients after suffering coronavirus disease 2019 in Wuhan.

OBJECTIVE: To summarize the potential characteristics of convalescent patients with coronavirus disease 2019 (COVID-19) in China based on emerging clinical tongue data and guide the treatment and recovery of COVID-19 patients from the perspective of Traditional Chinese Medicine tongue diagnosis. METHODS: In this study, we developed and validated radiomics-based and lab-based methods as a novel approach to provide individualized pretreatment evaluation by analyzing different features to mine the orderliness behind tongue data of convalescent patients. In addition, this study analyzed the tongue features of convalescent patients from clinical tongue qualitative values, including thick and thin, fur, peeling, fat and lean, tooth marks and cracked, and greasy and putrid fur. RESULTS: We included 2164 tongue images in total (34% from day 0, 35.4% from day 14 and 30.6% from day 28) from convalescent patients. The significance results are shown as follows. Firstly, as the recovery time prolongs, the L average values of tongue and coat decrease from 60.21 to 57.18 and from 60.06 to 57.03 respectively. Secondly, the decrease of abnormality rate of tongue coat, included greasy tongue fur, putrid fur, teeth-mark, thick-thin fur, are of significant statistical difference ( < 0.05). Thirdly, the average value of gray-level co-occurrence matrices increases from 0.173 to 0.194, the average value of entropy increases from 0.606 to 0.665, the average value of inverse difference normalized decrease from 0.981 to 0.979, and the average value of dissimilarity decrease from 0.1576 to 0.1828. The details of other radiomics features are describe in results section. CONCLUSIONS: Our experiment shows that patients in different recovery periods have a relationship with quantitative values of tongue images, including L color space of the tongue and coat radiomics features analysis. This relationship can help clinical doctors master the recovery and health of patients as soon as possible and improve their understanding of the potential mechanisms underlying the dynamic changes and mechanisms underlying COVID-19.

DNA Hairpins and Dumbbell-Wheel Transitions Amplified Walking Nanomachine for Ultrasensitive Nucleic Acid Detection.

Nucleic acids, including circulating tumor DNA (ctDNA), microRNA, and virus DNA/RNA, have been widely applied as potential disease biomarkers for early clinical diagnosis. In this study, we present a concept of DNA nanostructures transitions for the construction of DNA bipedal walking nanomachine, which integrates dual signal amplification for direct nucleic acid assay. DNA hairpins transition is developed to facilitate the generation of multiple target sequences; meanwhile, the subsequent DNA dumbbell-wheel transition is controlled to achieve the bipedal walker, which cleaves multiple tracks around electrode surface. Through combination of strand displacement reaction and digestion cycles, DNA monolayer at the electrode interface could be engineered and target-induced signal variation is realized. In addition, pH-assisted detachable intermolecular DNA triplex design is utilized for the regeneration of electrochemical biosensor. The high consistency between this work and standard quantitative polymerase chain reaction is validated. Moreover, the feasibilities of this biosensor to detect ctDNA and SARS-CoV-2 RNA in clinical samples are demonstrated with satisfactory accuracy and reliability. Therefore, the proposed approach has great potential applications for nucleic acid based clinical diagnostics.