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Abstract Objective To develop a nomogram model for the prediction of the risk of prolonged length of hospital stay (LOS) in spinal fusion patients. Methods A retrospective cohort study was carried out on 6272 patients who had undergone spinal fusion surgery. Least absolute shrinkage and selection operator (LASSO) regression was performed on the training sets to screen variables, and the importance of independent variables was ranked via random forest. In addition, various independent variables were used in the construction of models 1 and 2. A receiver operating characteristic curve was used to evaluate the models’ predictive performance. We employed Delong tests to compare the area under the curve (AUC) of the different models. Assessment of the models’ capability to improve classification efficiency was achieved using continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The Hosmer–Lemeshow method and calibration curve was utilised to assess the calibration degree, and decision curve to evaluate its clinical practicality. A bootstrap technique that involved 10 cross-validations and was performed 10,000 times was used to conduct internal and external validation. The were outcomes of the model exhibited in a nomogram graphics. The developed nomogram was validated both internally and externally. Results Model 1 was identified as the optimal model. The risk factors for prolonged LOS comprised blood transfusion, operation type, use of tranexamic acid (TXA), diabetes, electrolyte disturbance, body mass index (BMI), surgical procedure performed, the number of preoperative diagnoses and operative time. The diagnostic performance of the nomogram model was satisfactory, with AUC values of 0.784 and 0.795 for the internal and external validation sets, respectively. Model discrimination was favourable in both the internal (C-statistic, 0.811) and external (C-statistic, 0.814) validation sets. Calibration curve and Hosmer-Lemeshow test showed acceptable agreement between predicted and actual results. The decision curve shows that the model provides net clinical benefit within a certain decision threshold range. Conclusions This study developed and validated a nomogram to identify the risk of prolonged LOS in spinal fusion patients, which may help clinicians to identify high-risk groups at an early stage. Predictors identified included blood transfusion, operation type, use of TXA, diabetes, electrolyte disturbance, BMI, surgical procedure performed, number of preoperative diagnoses and operative time.

Abstract Background Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers. Methods Asymptomatic carriers were from Yangzhou Third People’s Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48 h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score. Results A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers. Conclusions Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model.

Abstract Objective To investigate the clinical manifestations and imaging characteristics of pulmonary cryptococcosis, and discuss its guidance in diagnosing. Methods The clinical data of patients diagnosed with cryptococcosis in our hospital from January 2014 to May 2020 were collected and retrospectively analyzed. Patients were divided into the immunocompromised group and the immunocompetent group. The symptomatic features, laboratory examination, imaging manifestations, and curative effect were analyzed. Results The most common symptoms of patients were cough and sputum production, followed by fever. The immunocompetent group has a significantly higher accident rate of cough and fever than the immunocompromised group, while the immunocompromised group has a significantly higher accident rate of headache and dizziness (P

Abstract Background High prevalence of obstructive sleep apnea (OSA) in the pulmonary hypertension (PH) population suggests that chronic intermittent hypoxia (CIH) is an important pathogenic factor of PH. However, the exact mechanism of CIH induced PH is not clear. One of the molecules that plays a key role in regulating pulmonary artery function under hypoxic conditions is superoxide dismutase 2 (SOD2). Methods Our study utilized heterozygous SOD2 −/+ mice firstly in CIH model to explore the exact role of SOD2 in CIH causing PH. Expression of SOD2 was analyzed in CIH model. Echocardiography and pulmonary hypertension were measured in wild type (WT) and SOD2−/+ mice under normal air or CIH condition. Hematoxylin–Eosin (H&E) staining and masson staining were carried out to evaluate pulmonary vascular muscularization and remodeling. Micro-PET scanning of in vivo 99mTc-labelled- MAG3-anti-CD11b was applied to assess CD11b in quantification and localization. Level of nod-like receptor pyrin domain containing 3 (NLRP3) was analyzed by real time PCR and immunohistochemistry (IHC). Results Results showed that SOD2 was down-regulated in OSA/CIH model. Deficiency of SOD2 aggravated CIH induced pulmonary hypertension and pulmonary vascular hypertrophy. CD11b+ cells, especially monocytic myeloid cell line-Ly6C+Ly6G− cells, were increased in the lung, bone marrow and the blood under CIH condition, and down-regulated SOD2 activated NLRP3 in CD11b+ cells. SOD2-deficient-CD11b+ myeloid cells promoted the apoptosis resistance and over-proliferation of human pulmonary artery smooth muscle cells (PASMCs) via up-regulating NLRP3. Conclusion CIH induced down-regulating of SOD2 increased pulmonary hypertension and vascular muscularization. It could be one of the mechanism of CIH leading to PH.