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SARS‐CoV‐2 viruses are highly transmissible and immune evasive. It is critical to develop broad‐spectrum prophylactic and therapeutic antibodies for potential future pandemics. Here, we used the phage display method to discover nanobodies (Nbs) for neutralizing SARS‐CoV‐2 viruses especially Omicron strains. The leading nanobody (Nb), namely, Nb4, with excellent physicochemical properties, can neutralize Delta and Omicron subtypes, including BA.1, BA.1.1 (BA.1 + R346K), BA.2, BA.5, BQ.1, and XBB.1. The crystal structure of Nb4 in complex with the receptor‐binding domain (RBD) of BA.1 Spike protein reveals that Nb4 interacts with an epitope on the RBD overlapping with the receptor‐binding motif, and thus competes with angiotensin‐converting enzyme 2 (ACE2) binding. Nb4 is expected to be effective for neutralizing most recent Omicron variants, since the epitopes are evolutionarily conserved among them. Indeed, trivalent Nb4 interacts with the XBB1.5 Spike protein with low nM affinity and competes for ACE2 binding. Prophylactic and therapeutic experiments in mice indicated that Nb4 could reduce the Omicron virus loads in the lung. In particular, in prophylactic experiments, intranasal administration of multivalent Nb4 completely protected mice from Omicron infection. Taken together, these results demonstrated that Nb4 could serve as a potent and broad‐spectrum prophylactic and therapeutic Nb for COVID‐19. [ABSTRACT FROM AUTHOR]

Based on the patient's clinical characteristics and laboratory indicators, different machine‐learning methods were used to develop models for predicting the negative conversion time of nonsevere coronavirus disease 2019 (COVID‐19) patients. A retrospective analysis was performed on 376 nonsevere COVID‐19 patients admitted to Wuxi Fifth People's Hospital from May 2, 2022, to May 14, 2022. The patients were divided into training set (n = 309) and test set (n = 67). The clinical features and laboratory parameters of the patients were collected. In the training set, the least absolute shrinkage and selection operator (LASSO) was used to select predictive features and train six machine learning models: multiple linear regression (MLR), K‐Nearest Neighbors Regression (KNNR), random forest regression (RFR), support vector machine regression (SVR), XGBoost regression (XGBR), and multilayer perceptron regression (MLPR). Seven best predictive features selected by LASSO included: age, gender, vaccination status, IgG, lymphocyte ratio, monocyte ratio, and lymphocyte count. The predictive performance of the models in the test set was MLPR > SVR > MLR > KNNR > XGBR > RFR, and MLPR had the strongest generalization performance, which is significantly better than SVR and MLR. In the MLPR model, vaccination status, IgG, lymphocyte count, and lymphocyte ratio were protective factors for negative conversion time; male gender, age, and monocyte ratio were risk factors. The top three features with the highest weights were vaccination status, gender, and IgG. Machine learning methods (especially MLPR) can effectively predict the negative conversion time of non‐severe COVID‐19 patients. It can help to rationally allocate limited medical resources and prevent disease transmission, especially during the Omicron pandemic. [ABSTRACT FROM AUTHOR]