Objective To analyze the magnetic resonance images (MRI) of patients with spinal epidural lipomatosis (SEL) in high-altitude areas and to determine the optimal cut-off value for diagnosis with epidural fat thickness. Methods This retrospective study included patients who underwent lumbosacral MRI examination for lumbosacral pain in Ping’an District Hospital of Traditional Chinese Medicine, Haidong City, China from January 1, 2021 to December 31, 2022. The epidural fat thickness in vertebral segments T12/L1 to L5/S1 was compared between the SEL group and the non-SEL group. The diagnostic efficacy with different cut-off values at each vertebral segment was evaluated. Between-group comparisons were performed using the t-test, Mann-Whitney U test, chi-square test, or modified chi-square test. The area under the receiver operating characteristic (AUC) was used to evaluate the diagnostic efficiency. The DeLong test was used to compare AUC between the two groups. Results A total of 370 patients were included (60 in the SEL group and 310 in the non-SEL group). There were no significant differences in age, sex, height, body weight, and body mass index between the two groups (all P > 0.05). At different vertebral segments, the epidural fat thickness was significantly higher in the SEL group than in the non-SEL group (all P < 0.05). The cut-off values for SEL diagnosis with epidural fat thickness in segments T12/L1 to L5/S1 were 2.23, 4.25, 4.85, 5.57, 7.21, and 8 mm, respectively. The AUC of MRI SEL diagnosis with epidural fat thickness in segment L5/S1 was the highest (0.945, 95% confidence interval [CI]: 0.916-0.966, P < 0.001). SEL diagnosis with epidural fat thickness > 8 mm in segment L5/S1 was the most accurate, with an AUC of 0.931 (95% CI: 0.901-0.955, P < 0.001), a sensitivity of 95.0%, and a specificity of 91.3%; this AUC was significantly higher than those of diagnosis with other cut-off values (all P < 0.05). Conclusion SEL patients have significantly increased epidural fat in the spinal canal. Epidural fat thickness > 8 mm in segment L5/S1 can be used for diagnosis of SEL with improved efficiency and accuracy.

Peripheral pulmonary lesions(PPL),including peripheral pulmonary nodules,are common lung problems.As the increase of patients with lung nodules,the demand for tissue sampling also increases.Safe and accurate biopsy techniques are very important for patients to identify benign and malignant lesions.Electronic bronchoscopy is one of the biopsy techniques for the diagnosis of PPL in recent decades.Various guiding techniques,such as radial probe endobronchial ultrasound and virtual navigation bronchoscope,have been proved to improve the performance of conventional bronchoscopy.This paper aims to provide an review of the available data on advanced bronchoscopic techniques and explore their application in diagnosing PPL.

Objective To investigate the independent risk factors for postoperative deep vein thrombosis in lung cancer patients and to construct a risk prediction model.Methods Clinical data of 354 inpatients who underwent thoracoscopic surgery for lung cancer in Department of Thoracic Surgery of First Affiliated Hospital of Army Medical University between May 2019 and May 2023 were retrospectively collected and analyzed.LASSO regression was used to screen potential factors,followed by multivariate logistic regression to identify risk factors,and then a nomogram prediction model was constructed.Calibration curves,receiver operating characteristic(ROC)curves,and decision curves were drawn to evaluate the model's calibration,discrimination,sensitivity,specificity,and clinical utility.The net reclassification improvement(NRI)and integrated discrimination improvement(IDI)indices were employed to compare the predictive performance of the constructed model with the Caprini score for outcome events.Results LASSO regression identified 17 potential influencing factors.Multivariate regression analysis showed that D-dimer,central venous catheter(CVC)placement,and lower extremity varicose veins were independent risk factors for postoperative DVT in lung cancer patients(P＜0.05).Calibration curve analysis showed the model had good agreement between the predicted and observed values.ROC curve analysis indicated that the sensitivity and specificity of the model was 0.812 and 0.963,respectively,with an area under the curve(AUC)value of 0.912(95％CI:0.840～0.983).In comparison,the Caprini model had a sensitivity and specificity of 0.625 and 0.860,respectively,with an AUC value of 0.752(95％CI:0.657～0.846).The NRI and IDI for the model group compared to the Caprini model were 0.709 and 0.431,respectively.Decision curve analysis showed that the net benefit of applying the model from this study was higher than that of the Caprini model.Conclusion D-dimer,CVC,and varicose veins of lower extremities are independent risk factors for DVT after thoracoscopic surgery in patients with lung cancer.Our constructed nomogram model can effectively predict the risk of DVT after thoracoscopic surgery in patients with lung cancer.

Pediatric idiopathic nephrotic syndrome (INS) is characterized by massive albuminuria, hypoproteinemia, edema and hyperlipidemia, with a long course and high probability of relapse and prolongation. Long-term complications caused by long-term usage of hormones and immunosuppressants in children with INS seriously affect their physical and mental health and quality of life. Most children with steroid-sensitive nephrotic syndrome can be cured before adulthood, while some of them relapse in adulthood. Long-term prognosis of children with steroid-resistant nephrotic syndrome is poor. There have been few studies in China followed the long-term outcomes and its related factors of children with INS over 10 years. The paper reviewed the literatures on the long-term outcomes of children with INS, including renal survival, growth, mental health, learning and work, marriage and fertility, disease recurrence and long-term related complications, to explore the factors related to the poor long-term outcomes of children with INS and to assist in clinical decision-making and follow-up management.

Objective To investigate the recognition efficiency of AI model based on deep learning for cardiac conduction system(CCS).Methods HE staining sections of cardiac muscle and CCS of 17 cases of non-sudden death were selected,and the gold standard was unanimous recognition by 2 forensic pathologists with more than 20 years of CCS diagnosis experience.Inception V3 algorithm was used to establish AI model and complete CCS identification training and testing.Confusion matrix,accuracy,precision,recall,F1 score,ROC curve and AUC value were used to evaluate the effectiveness of AI model,and accuracy,sensitivity and specificity were used to evaluate the efficiency of manual independent and AI-assisted manual recognition for CCS.Results The accuracy of AI model was 87.3%,the precision was 91.9%,the recall was 81.9%,the F1 score was 86.6%,and the AUC value was 95.3%.The accuracy of AI model was higher than that of senior forensic pathologists.There was no statistical significance in the accuracy of AI-assisted senior forensic pathologists in identifying CCS compared with manual independent detection(P>0.05),while the accuracy of AI-assisted intermediate and junior forensic pathologists in identifying CCS was increased by 8%and 14.33%,respectively,with statistical significance(P<0.05).The accuracy rate of AI-assisted junior forensic pathologists to identify CCS was higher than that of intermediate forensic pathologists in self-diagnosis.Conclusion The AI model could be used for the automatic recognition of CCS,and could improve the diagnostic efficiency of CCS and narrow the gap between the forensic pathologists with low experience and that with high experience.

Objectives:To explore the value of heparin-binding protein (HBP) in the diagnosis and prognosis of patients with respiratory viral infections.Methods:The patients who were admitted to Emergency Department of Nanjing Hospital Affiliated to Nanjing Medical University from November 2018 to November 2020 were selected as the viral infection group, and the non-infected patients admitted in the same period as the non-viral infection group. Data of all patients’ general clinical information, peripheral white blood cell count (WBC), neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), procalcitonin (PCT), and HBP in 24 h were collected. The differences in various indicators were compared between the two groups of patients, the receiver operating characteristic (ROC) curves were drawn, and the diagnostic value of each indicator for patients with respiratory virus infection were evaluated. The prognostic indicators such as sequential organ failure assessment (SOFA) score, the acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score within 24 h were recorded, and duration of ICU stay, antiviral treatment, ventilator and vasoactive agents to total length of hospital stay of patients in the viral infection group were calculated. The Spearman correlation analysis of HBP and the above indicators was performed to determine the prognostic value of HBP in patients with respiratory virus infections.Results:A total of 106 patients were included in the viral infection group, and 107 in the non-viral infection group. There were no significant differences in sex, age, and body mass index (BMI) between the two groups of patients (P>0.05). Compared with the non-viral infection group, the serum CRP and HBP of the viral infection group were significantly higher (P<0.05), while the WBC and NLR levels were significantly lower (P<0.05). There was no statistical difference in PCT between the two groups (P>0.05). HBP had the best diagnosis efficiency for respiratory viral infections, the areas under the ROC was 0.895, the optimal cut-off point was 13.625 μg/L, the sensitivity was 92.50% and the specificity was 76.60%. Correlation analysis showed that serum HBP levels within 24 h in the viral infection group were positively correlated with SOFA score and APACHEⅡ score in 24 h after admission (r = 0.756, P<0.05; r = 0.747, P<0.05). In the viral infection group, duration of ICU stay, antiviral treatment, and ventilator and vasoactive agents to total length of hospital stay were also positively correlated with serum HBP level (r = 0.873, 0.748, 0.830, and 0.794, P<0.05).Conclusions:HBP can be used as a favorable diagnostic indicator for patients with respiratory virus infections and has a good evaluation value for the prognosis.

Objective:We aimed to explore a colorectal cancer risk prediction model through machine learning algorithm based on the big data in laboratory medicine.Methods:According to the labeling of colonoscopy combined with pathology or referring to the ICD-10 code, the colonoscopy patients in Shanghai Changhai Hospital from 2013.1.1 to 2019.6.30 and the outpatients and inpatients from 2010.1.1 to 2019.6.30 were divided into colorectal cancer groups and non-colorectal cancer group. Four machine learning algorithms, Extreme gradient boosting(Xgboost),Artificial Neural Network(ANN),Support Vector Machine(SVM),Random Forest(RF), are used to mine all routine laboratory test item data of the enrolled patients, select model features and establish a classification model for colorectal cancer. And the effectiveness of the model was prospectively verified in patients in the whole hospital of Changhai Hospital from 2019.7.1 to 2020.8.31.Result:A colorectal cancer risk prediction model (CRC-Lab7) including 7 characteristics of fecal occult blood, carcinoembryonic antigen, red blood cell distribution width, lymphocyte count, albumin/globulin, high-density lipoprotein cholesterol and hepatitis B virus core antibody was constructed by the XgBoost algorithm. The AUC of the model in the validation set and prospective validation set were 0.799 and 0.816, respectively, which was significantly higher than that of fecal occult blood (AUC was 0.68 and 0.706, respectively). It also has high diagnostic accuracy for colorectal cancer with negative fecal occult blood or under 50 years old.Conclusion:In this study, a colorectal cancer risk prediction model was established by mining routine laboratory big data. The model′s performance is better than fecal occult blood, and it has high diagnostic accuracy for colorectal cancer in patients with negative fecal occult blood and younger than 50 years old.