Predicting model of mild and severe types of COVID-19 patients using Thymus CT radiomics model: A preliminary study.

OBJECTIVE: To predict COVID-19 severity by building a prediction model based on the clinical manifestations and radiomic features of the thymus in COVID-19 patients. METHOD: We retrospectively analyzed the clinical and radiological data from 217 confirmed cases of COVID-19 admitted to Xiangyang NO.1 People's Hospital and Jiangsu Hospital of Chinese Medicine from December 2019 to April 2022 (including 118 mild cases and 99 severe cases). The data were split into the training and test sets at a 7:3 ratio. The cases in the training set were compared in terms of clinical data and radiomic parameters of the lasso regression model. Several models for severity prediction were established based on the clinical and radiomic features of the COVID-19 patients. The DeLong test and decision curve analysis (DCA) were used to compare the performances of several models. Finally, the prediction results were verified on the test set. RESULT: For the training set, the univariate analysis showed that BMI, diarrhea, thymic steatosis, anorexia, headache, findings on the chest CT scan, platelets, LDH, AST and radiomic features of the thymus were significantly different between the two groups of patients (P < 0.05). The combination model based on the clinical and radiomic features of COVID-19 patients had the highest predictive value for COVID-19 severity [AUC: 0.967 (OR 0.0115, 95%CI: 0.925-0.989)] vs. the clinical feature-based model [AUC: 0.772 (OR 0.0387, 95%CI: 0.697-0.836), P < 0.05], laboratory-based model [AUC: 0.687 (OR 0.0423, 95%CI: 0.608-0.760), P < 0.05] and model based on CT radiomics [AUC: 0.895 (OR 0.0261, 95%CI: 0.835-0.938), P < 0.05]. DCA also confirmed the high clinical net benefits of the combination model. The nomogram drawn based on the combination model could help differentiate between the mild and severe cases of COVID-19 at an early stage. The predictions from different models were verified on the test set. CONCLUSION: Severe cases of COVID-19 had a higher level of thymic involution. The thymic differentiation in radiomic features was related to disease progression. The combination model based on the radiomic features of the thymus could better promote early clinical intervention of COVID-19 and increase the cure rate.

Identification and analyses of the proteins interacting with the nucleocapsid protein of the Porcine Deltacoronavirus

Preliminary screening and identification of the host proteins interacting with the nucleocapsid(N)protein of the porcine deltacoronavirus(DCoV). Co-immunoprecipitation and liquid chromatography- tandem mass spectrometry were used to screen out the host proteins interacting with the N protein of the porcine DCoV. Bioinformatics analysis was carried out, and then co-immunoprecipitation was used for identification. Sodium dodecyl sulfate - polyacrylamide gel electrophoresis of the immunoprecipitation products revealed different protein bands around 40 kDa and 100 kDa. Sixty-eight host proteins interacting with the N protein of the porcine DCoV were screened by mass spectrometry. Two candidate interacting proteins(ANXA2 and TUBB2 B)were selected by analyses using the Gene Ontology, Clusters of Orthologous Groups, and Kyoto Encyclopedia of Genes and Genomes databases. After co-immunoprecipitation verification, the N protein of the porcine DCoV was found to interact with TUBB2 B. Our study provides a new direction for further exploration of the role of the N protein of the porcine DCoV in infection.

Fibrous aggregates: Amplifying aggregation-induced emission to boost health protection.

Environmental monitoring and personal protection are critical for preventing and for protecting human health during all infectious disease outbreaks (including COVID-19). Fluorescent probes combining sensing, imaging and therapy functions, could not only afford direct visualizing existence of biotargets and monitoring their dynamic information, but also provide therapeutic functions for killing various bacteria or viruses. Luminogens with aggregation-induced emission (AIE) could be well suited for above requirements because of their typical photophysical properties and therapeutic functions. Integration of these molecules with fibers or textiles is of great interest for developing flexible devices and wearable systems. In this review, we mainly focus on how fibers and AIEgens to be combined for health protection based on the latest advances in biosensing and bioprotection. We first discuss the construction of fibrous sensors for visualization of biomolecules. Next recent advances in therapeutic fabrics for individual protection are introduced. Finally, the current challenges and future opportunities for "AIE + Fiber" in sensing and therapeutic applications are presented.