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BackgroundFactors that may influence the recovery of patients with confirmed SARS-CoV-2 infection hospitalized in the Fangcang shelter were explored, and machine learning models were constructed to predict the duration of recovery during the Omicron BA. 2.2 pandemic.MethodsA retrospective study was conducted at Hongqiao National Exhibition and Convention Center Fangcang shelter (Shanghai, China) from April 9, 2022 to April 25, 2022. The demographics, clinical data, inoculation history, and recovery information of the 13,162 enrolled participants were collected. A multivariable logistic regression model was used to identify independent factors associated with 7-day recovery and 14-day recovery. Machine learning algorithms (DT, SVM, RF, DT/AdaBoost, AdaBoost, SMOTEENN/DT, SMOTEENN/SVM, SMOTEENN/RF, SMOTEENN+DT/AdaBoost, and SMOTEENN/AdaBoost) were used to build models for predicting 7-day and 14-day recovery.ResultsOf the 13,162 patients in the study, the median duration of recovery was 8 days (interquartile range IQR, 6–10 d), 41.31% recovered within 7 days, and 94.83% recovered within 14 days. Univariate analysis showed that the administrative region, age, cough medicine, comorbidities, diabetes, coronary artery disease (CAD), hypertension, number of comorbidities, CT value of the ORF gene, CT value of the N gene, ratio of ORF/IC, and ratio of N/IC were associated with a duration of recovery within 7 days. Age, gender, vaccination dose, cough medicine, comorbidities, diabetes, CAD, hypertension, number of comorbidities, CT value of the ORF gene, CT value of the N gene, ratio of ORF/IC, and ratio of N/IC were related to a duration of recovery within 14 days. In the multivariable analysis, the receipt of two doses of the vaccination vs. unvaccinated (OR = 1.118, 95% CI = 1.003–1.248; p = 0.045), receipt of three doses of the vaccination vs. unvaccinated (OR = 1.114, 95% CI = 1.004–1.236; p = 0.043), diabetes (OR = 0.383, 95% CI = 0.194–0.749; p = 0.005), CAD (OR = 0.107, 95% CI = 0.016–0.421; p = 0.005), hypertension (OR = 0.371, 95% CI = 0.202–0.674; p = 0.001), and ratio of N/IC (OR = 3.686, 95% CI = 2.939–4.629; p < 0.001) were significantly and independently associated with a duration of recovery within 7 days. Gender (OR = 0.736, 95% CI = 0.63–0.861; p < 0.001), age (30–70) (OR = 0.738, 95% CI = 0.594–0.911; p < 0.001), age (>70) (OR = 0.38, 95% CI = 0292–0.494; p < 0.001), receipt of three doses of the vaccination vs. unvaccinated (OR = 1.391, 95% CI = 1.12–1.719; p = 0.0033), cough medicine (OR = 1.509, 95% CI = 1.075–2.19; p = 0.023), and symptoms (OR = 1.619, 95% CI = 1.306–2.028; p < 0.001) were significantly and independently associated with a duration of recovery within 14 days. The SMOTEEN/RF algorithm performed best, with an accuracy of 90.32%, sensitivity of 92.22%, specificity of 88.31%, F1 score of 90.71%, and AUC of 89.75% for the 7-day recovery prediction; and an accuracy of 93.81%, sensitivity of 93.40%, specificity of 93.81%, F1 score of 93.42%, and AUC of 93.53% for the 14-day recovery prediction.ConclusionAge and vaccination dose were factors robustly associated with accelerated recovery both on day 7 and day 14 from the onset of disease during the Omicron BA. 2.2 wave. The results suggest that the SMOTEEN/RF-based model could be used to predict the probability of 7-day and 14-day recovery from the Omicron variant of SARS-CoV-2 infection for COVID-19 prevention and control policy in other regions or countries. This may also help to generate external validation for the model.

Abstract Background The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. Methods A total of 2169 adult COVID-19 patients were enrolled from Wuhan, China, from February 10th to April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups, as well as between survivors and non-survivors. In addition, we developed a decision tree model to predict death outcome in severe patients. Results Of the 2169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed as severe illness, and 75 patients died. An older median age and a higher proportion of male patients were found in severe group or non-survivors compared to their counterparts. Significant differences in clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A decision tree, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in training and test datasets. The accuracy of this model were 0.98 in both datasets. Conclusion We performed a comprehensive analysis of COVID-19 patients from the outbreak in Wuhan, China, and proposed a simple and clinically operable decision tree to help clinicians rapidly identify COVID-19 patients at high risk of death, to whom priority treatment and intensive care should be given.

Background/Aims: Non-small cell lung carcinoma (NSCLC) metastasis to bone leads to skeletal-related events and a poor quality of life. Unravelling the mechanism of metastasis is crucial for improving survival. Previous work has implicated the role of CXCR4 in bone metastasis; however, the underlying mechanisms are unknown. Methods: The human bone metastasis tissue samples were obtained from lung cancer patients during surgery with consents. The patients were followed up and the overall survival curve was analysed. The expression of CXCR4, VCAM1, and ADAM17 was measured with real-time PCR, western blot and immunochemistry staining in human tissue or NSCLC cell lines. The effects of CXCR4, soluble VCAM1 and ADAM17 on NSCLC proliferation, migration and invasion were measured with CCK-8, monolayer scratch assay and transwell chamber, respectively. The amount of soluble VCAM1 in the conditioned medium was detected with ELISA. Tartrate-resistant acid phosphatasethe (TRAP) staining was performed to stain the multinucleated cells regarded as osteoclasts. Results: In this study, CXCR4 was found to be highly expressed in bone destruction area of metastatic NSCLC samples and related to poor survival in NSCLC patients with bone metastasis. CXCR4 potentiated NSCLC with enhanced proliferation and invasion abilities, while CXCR4 knockdown significantly suppressed the growth and invasion. Furthermore, CXCR4 promoted lung cancer-induced osteoclast differentiation with increased osteoclast formation. We also found that soluble VCAM1 (sVCAM1) secreted in NSCLC contributed to the osteoclastogenesis induced by CXCR4. The overexpression of CXCR4 increased sVCAM1, and the sVCAM1 secreted from CXCR4-overexpressing NSCLC cells recruited and arrested additional osteoclast progenitors to promote osteoclastogenesis. ADAM17 was confirmed to act as a downstream mediator of CXCR4. The chemical inhibition of ADAM17 with TAPI-2 decreased sVCAM1 secretion and the number of TRAP+ osteoclasts. Conclusion: Taken together, these results indicated that CXCR4 potentiated NSCLC and promoted osteoclastogenesis through sVCAM1, which was cleaved by ADAM17. These data support the pivotal role of the cross talk between CXCR4 and ADAM17-VCAM1 in NSCLC-induced bone metastasis.

Reappraisal is an adaptive emotion regulation strategy while the role of self-perspective in reappraisal process of depressed patients is largely unknown in terms of goals (valence/arousal) and tactics (detachment/immersion). In this study, 12 depressed individuals and 15 controls were scanned with MRI during which they either attend naturally to emotional stimuli, or adopt detachment/immersion strategy. Behaviorally, no group differences in self-reported emotion regulation effectiveness were found. In addition, we observed that (1) patients were less able to downregulate amygdala activation with recruitment ofmore dorsal lateral prefrontal cortex (dlPFC) when adopting detachment strategy regardless of valence, and this preserved ability to regulate emotion was inversely associated with severity of symptoms; (2) patients had deficits in upregulating amygdala activation when adopting immersion strategy, with less inferior frontal gyrus (IFG) activation and strengthening coupling of dlPFC and ventral medial prefrontal cortex (vmPFC) with amygdala; (3) comparison between groups yielded that patients showed stronger vmPFC activation under either self-detached or self-immersed condition. In conclusion, impaired modulatory effects of amygdala in depressed patients are compensated with strengthening cognitive control resources, with dissociable effects for different self-perspectives in reappraisal. These results may help clarify the role of self-perspective underlying reappraisal in major depression. [ABSTRACT FROM AUTHOR]