A follow up study of cycle threshold values of SARS-CoV-2 in Hunan Province, China

Since the epidemic of the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), many governments have used reverse transcription polymerase chain reaction (RT-PCR) to detect the virus. However, there are fewer measures of CT values information based on RT-PCR results, and the relationship between CT values and factors from consecutive tests is not clear enough. So in this study, we analyzed the connection between CT values and the factors based on cohort data from Delta variant of SARS-CoV-2 in Hunan Province. Previous studies have showed that the mean age of the cases was 33.34 years (±18.72 years), with a female predominance (55.03%, n = 71), and the greatest proportion of clinical symptoms were of the common type (60.47%, n = 78). There were statistical differences between the N and ORF1ab genes in the CT values for the cases. Based on the analysis of the association between CT values and the factors, the lowest CT values were obtained for the unvaccinated, older and clinically symptomatic group at 3–10 days, the maximum peak of viral load occurred. Therefore, it is recommended to use patient information to focus on older, clinically symptomatic, unvaccinated patients and to intervene promptly upon admission

Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study

Abstract Objectives To develop a deep learning (DL) model for differentiating between osteolytic osteosarcoma (OS) and giant cell tumor (GCT) on radiographs. Methods Patients with osteolytic OS and GCT proven by postoperative pathology were retrospectively recruited from four centers (center A, training and internal testing; centers B, C, and D, external testing). Sixteen radiologists with different experiences in musculoskeletal imaging diagnosis were divided into three groups and participated with or without the DL model’s assistance. DL model was generated using EfficientNet-B6 architecture, and the clinical model was trained using clinical variables. The performance of various models was compared using McNemar’s test. Results Three hundred thirty-three patients were included (mean age, 27 years ± 12 [SD]; 186 men). Compared to the clinical model, the DL model achieved a higher area under the curve (AUC) in both the internal (0.97 vs. 0.77, p = 0.008) and external test set (0.97 vs. 0.64, p < 0.001). In the total test set (including the internal and external test sets), the DL model achieved higher accuracy than the junior expert committee (93.1% vs. 72.4%; p < 0.001) and was comparable to the intermediate and senior expert committee (93.1% vs. 88.8%, p = 0.25; 87.1%, p = 0.35). With DL model assistance, the accuracy of the junior expert committee was improved from 72.4% to 91.4% (p = 0.051). Conclusion The DL model accurately distinguished osteolytic OS and GCT with better performance than the junior radiologists, whose own diagnostic performances were significantly improved with the aid of the model, indicating the potential for the differential diagnosis of the two bone tumors on radiographs. Critical relevance statement The deep learning model can accurately distinguish osteolytic osteosarcoma and giant cell tumor on radiographs, which may help radiologists improve the diagnostic accuracy of two types of tumors. Key points • The DL model shows robust performance in distinguishing osteolytic osteosarcoma and giant cell tumor. • The diagnosis performance of the DL model is better than junior radiologists’. • The DL model shows potential for differentiating osteolytic osteosarcoma and giant cell tumor. Graphical Abstrac

Schisandrin A regulates the Nrf2 signaling pathway and inhibits NLRP3 inflammasome activation to interfere with pyroptosis in a mouse model of COPD

Abstract Chronic obstructive pulmonary disease (COPD) is a serious chronic lung disease. Schisandrin A (SchA) is one of the most important active ingredients in Schisandra chinensis and has been used to treat various lung diseases in several countries. Here, we studied the pharmacological effect of SchA on airway inflammation induced by cigarette smoke (CS) and explored the therapeutic mechanism of SchA in COPD model mice. Our results showed that SchA treatment significantly improved the lung function of CS-induced COPD model mice and reduced the recruitment of leukocytes and hypersecretion of interleukin-6 (IL-6), interleukin-1β (IL-1β) and tumor necrosis factor α (TNF-α) in bronchoalveolar lavage fluid (BALF). H&E staining showed that SchA treatment could effectively reduce emphysema, immune cell infiltration and airway wall destruction. In addition, we found that SchA treatment can stimulate the expression of heme oxygenase-1 (HO-1) through the nuclear factor-erythroid 2-related factor (Nrf2) pathway, significantly reduce oxidative stress, increase catalase (CAT) and superoxide dismutase (SOD) levels, and suppress the level of malondialdehyde (MDA) in COPD model mice. Moreover, SchA treatment suppressed the generation of the NLRP3/ASC/Caspase1 inflammasome complex to inhibit the inflammatory response caused by IL-1β and IL-18 and pyroptosis caused by GSDMD. In conclusion, our study shows that SchA treatment can inhibit the production of ROS and the activation of the NLRP3 inflammasome by upregulating Nrf-2, thereby producing anti-inflammatory effects and reducing lung injury in COPD model mice. More importantly, SchA exhibited similar anti-inflammatory effects to dexamethasone in COPD model mice, and we did not observe substantial side effects of SchA treatment. The high safety of SchA makes it a potential candidate drug for the treatment of COPD