PURPOSE: The rate of complications among patients undergoing surgery has increased due to infection with SARS-CoV-2 and other variants of concern. However, Omicron has shown decreased pathogenicity, raising questions about the risk of postoperative complications among patients who are infected with this variant. This study aimed to investigate complications and related factors among patients with recent Omicron infection prior to undergoing orthopedic surgery. METHODS: A historical control study was conducted. Data were collected from all patients who underwent surgery during 2 distinct periods: (1) between Dec 12, 2022 and Jan 31, 2023 (COVID-19 positive group), (2) between Dec 12, 2021 and Jan 31, 2022 (COVID-19 negative control group). The patients were at least 18 years old. Patients who received conservative treatment after admission or had high-risk diseases or special circumstances (use of anticoagulants before surgery) were excluded from the study. The study outcomes were the total complication rate and related factors. Binary logistic regression analysis was used to identify related factors, and odds ratio (OR) and 95% confidence interval (CI) were calculated to assess the impact of COVID-19 infection on complications. RESULTS: In the analysis, a total of 847 patients who underwent surgery were included, with 275 of these patients testing positive for COVID-19 and 572 testing negative. The COVID-19-positive group had a significantly higher rate of total complications (11.27%) than the control group (4.90%, p < 0.001). After adjusting for relevant factors, the OR was 3.08 (95% CI: 1.45-6.53). Patients who were diagnosed with COVID-19 at 3-4 weeks (OR = 0.20 (95% CI: 0.06-0.59), p = 0.005), 5-6 weeks (OR = 0.16 (95% CI: 0.04-0.59), p = 0.010), or ≥7 weeks (OR = 0.26 (95% CI: 0.06-1.02), p = 0.069) prior to surgery had a lower risk of complications than those who were diagnosed at 0-2 weeks prior to surgery. Seven factors (age, indications for surgery, time of operation, time of COVID-19 diagnosis prior to surgery, C-reactive protein levels, alanine transaminase levels, and aspartate aminotransferase levels) were found to be associated with complications; thus, these factors were used to create a nomogram. CONCLUSION Omicron continues to be a significant factor in the incidence of postoperative complications among patients undergoing orthopedic surgery. By identifying the factors associated with these complications, we can determine the optimal surgical timing, provide more accurate prognostic information, and offer appropriate consultation for orthopedic surgery patients who have been infected with Omicron.
Humans ; COVID-19/complications* ; Male ; Female ; Middle Aged ; Postoperative Complications/epidemiology* ; SARS-CoV-2 ; Orthopedic Procedures/adverse effects* ; Aged ; Nomograms ; Adult ; Retrospective Studies ; Risk Factors

OBJECTIVE: To investigate the genetic causal relationship of leukocyte telomere length (LTL) with prostatitis, orchitis and epididymitis by two-sample Mendelian randomization (MR). METHODS: Using LTL as the exposure factor and prostatitis, orchitis and epididymitis as outcome factors, we mined the Database of Genome-Wide Association Studies (GWAS). Then, we analyzed the causal relationship of LTL with prostatitis, orchitis and epididymitis by Mendelian randomization using inverse variance weighting (IVW) as the main method and weighted median and MR-Egger regression as auxiliary methods, determined the horizontal multiplicity by MR-Egger intercept test, and conducted sensitivity analysis using the leaving-one-out method. RESULTS: A total of 121 related single nucleotide polymorphisms (SNPs) were identified in this study. IVW showed LTL to be a risk factor for prostatitis (OR = 1.383, 95% CI: 1.044－1.832, P = 0.024), and for orchitis and epididymitis as well (OR = 1.770, 95% CI: 1.275－2.456, P = 0.000 6). CONCLUSION Genetic evidence from Mendelian randomized analysis indicates that shortening of LTL reduces the risk of prostatitis, orchitis and epididymitis.
Humans ; Male ; Mendelian Randomization Analysis ; Epididymitis/genetics* ; Prostatitis/genetics* ; Polymorphism, Single Nucleotide ; Leukocytes ; Orchitis/genetics* ; Genome-Wide Association Study ; Telomere ; Risk Factors

OBJECTIVE: To investigate the effects of Danmu Extract Syrup (DMS) on lipopolysaccharide (LPS)-induced acute lung injury (ALI) in mice and explore the mechanism. METHODS: Seventy-two male Balb/C mice were randomly divided into 6 groups according to a random number table (n=12), including control (normal saline), LPS (5 mg/kg), LPS+DMS 2.5 mL/kg, LPS+DMS 5 mL/kg, LPS+DMS 10 mL/kg, and LPS+Dexamethasone (DXM, 5 mg/kg) groups. After pretreatment with DMS and DXM, the ALI mice model was induced by LPS, and the bronchoalveolar lavage fluid (BALF) were collected to determine protein concentration, cell counts and inflammatory cytokines. The lung tissues of mice were stained with hematoxylin-eosin, and the wet/dry weight ratio (W/D) of lung tissue was calculated. The levels of tumor necrosis factor-α (TNF-α), interleukin (IL)-6 and IL-1 β in BALF of mice were detected by enzyme linked immunosorbent assay. The expression levels of Claudin-5, vascular endothelial (VE)-cadherin, vascular endothelial growth factor (VEGF), phospho-protein kinase B (p-Akt) and Akt were detected by Western blot analysis. RESULTS: DMS pre-treatment significantly ameliorated lung histopathological changes. Compared with the LPS group, the W/D ratio and protein contents in BALF were obviously reduced after DMS pretreatment (P<0.05 or P<0.01). The number of cells in BALF and myeloperoxidase (MPO) activity decreased significantly after DMS pretreatment (P<0.05 or P<0.01). DMS pre-treatment decreased the levels of TNF-α, IL-6 and IL-1 β (P<0.01). Meanwhile, DMS activated the phosphoinositide 3-kinase/protein kinase B (PI3K/Akt) pathway and reversed the expressions of Claudin-5, VE-cadherin and VEGF (P<0.01). CONCLUSIONS DMS attenuated LPS-induced ALI in mice through repairing endothelial barrier. It might be a potential therapeutic drug for LPS-induced lung injury.
Mice ; Male ; Animals ; Proto-Oncogene Proteins c-akt/metabolism* ; Lipopolysaccharides ; Phosphatidylinositol 3-Kinases/metabolism* ; Interleukin-1beta/metabolism* ; Vascular Endothelial Growth Factor A/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Claudin-5/metabolism* ; Acute Lung Injury/chemically induced* ; Lung/pathology* ; Interleukin-6/metabolism* ; Drugs, Chinese Herbal

Child ; Humans ; Mpox (monkeypox)/prevention & control* ; Disease Outbreaks

Objective To establish an early prediction model for the diagnosis of severe acute pancreatitis based on the improved machine learning models,and to analyze its clinical value.Methods A case-control study was conducted on 352 patients with acute pancreatitis admitted to the Gastroenterology and Hepatobiliary Surgery Departments of the Army Medical Center of PLA and Emergency and Critical Care Medicine Department of No.945 Hospital of Joint Logistics Support Force of PLA from January 2014 to August 2023.According to the severity of the disease,the patients were divided into the severe group(n=88)and the non-severe group(n=264).The RUSBoost model and improved Archimead optimization algorithm was used to analyze 39 routine laboratory biochemical indicators within 48 h after admission to construct an early diagnosis and prediction model for severe acute pancreatitis.The task of feature screening and hyperparameter optimization was completed simultaneously.The ReliefF algorithm feature importance rank and multivariate logistic analysis were used to analyze the value of the selected features.Results In the training set,the area under curve(AUC)of the improved machine learning model was 0.922.In the testing set,the AUC of the improved machine learning model reached 0.888.The 4 key features of predicting severe acute pancreatitis based on the improved Archimedes optimization algorithm were C-reactive protein,blood chlorine,blood magnesium and fibrinogen level,which were consistent with the results of ReliefF algorithm feature importance ranking and multivariate logistic analysis.Conclusion The application of improved machine learning model analyzing the laboratory examination results can help to early predict the occurrence of severe acute pancreatitis.

Aim To explore the improvement effect of Bazi Bushen capsule on thymic degeneration in SAMP6 mice and the possible mechanism.Methods Twenty 12 week old male SAMP6 mice were randomly divided into the model group(SAMP6)and the Bazi Busheng capsule treatment group(SAMP6+BZBS).Ten SAMR1 mice were assigned to a homologous control group(SAMR1).The SAMP6+BZBS group was oral-ly administered Bazi Bushen capsule suspension(2.8 g·kg-1)daily,while the other two groups were orally administered an equal amount of distilled water.After nine weeks of administration,the morphology of the thymus in each group was observed and the thymus in-dex was calculated;HE staining was used to observe the structural changes of thymus tissue;SA-β-gal stai-ning was used to detect thymic aging;flow cytometry was used to detect the proportion of thymic CD3+T cells in each group;Western blot was used to detect the levels of p16,Bax,Bcl-2,and cleaved caspase-3 proteins in thymus;immunofluorescence was applied to detect the proportion of cortical thymic epithelial cells in each group;ELISA was employed to detect IL-7 lev-els in thymus.Results Compared with the SAMP6 group,the thymic index of the SAMP6+BZBS group significantly increased(P＜0.05);the disordered thy-mic structure was significantly improved;the positive proportion of SA-β-gal staining significantly decreased(P＜0.01);the proportion of CD3+T cells apparently increased(P＜0.05);the level of p16 protein signifi-cantly decreased(P＜0.05);the level of Bcl-2 pro-tein significantly increased(P＜0.05),while the lev-el of cleaved caspase-3 protein markedly decreased(P＜0.05);the proportion of cortical thymic epithelial cells evidently increased;the level of IL-7 significantly increased(P＜0.01).Conclusions Bazi Bushen capsule can delay thymic degeneration,inhibit cell ap-optosis in thymus and promote thymic cell development in SAMP6 mice,which may be related to increasing the proportion of cortical thymic epithelial cells and promoting IL-7 secretion.

Tandem occlusion of the anterior circulation is a specific acute large vessel occlusion, which is defined in most of the literature as a combination of intracranial large vessel occlusion and high degree of stenosis or occlusion of the ipsilateral extracranial internal carotid artery.Tandem occlusion is presented in approximately 15% patients with acute ischemic stroke in the anterior circulation who receive endovascular treatment. In patients with tandem occlusion, endovascular treatment is more effective than drug therapy; however, controversy still exists regarding the optimal management strategy of endovascular treatment. This article reviews the recent advance in endovascular treatment of tandem occlusion in the anterior circulation in recent years, to improve the overall care standard for acute ischemic stroke with tandem occlusion of the anterior circulation.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Objective To explore the efficiency of artificial intelligence and radiomics-assisted X-ray in diagnosis of lumbar osteoporotic vertebral compression fractures(OVCF).Methods The clinical data of 455 patients diagnosed as lumbar OVCF by MRI in our hospital were selected.The patients were divided into the training group(n=364)and the validation group(n=91),X-ray films were extracted,the image delineation,feature extraction and data analysis were carried out,and the artificial intelligence radiomics deep learning was applied to establish a diagnostic model for OVCF.After verifying the effectiveness of the model by receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration curve,and decision curve analysis(DCA),the efficiencies of manual reading,model reading,and model-assisted manual reading of X-ray in the early diagnosis of OVCF were compared.Results The ROC curve,AUC and calibration curve proved that the model had good discrimination and calibration,and excellent diagnostic performance.DCA demonstrated that the model had a higher clinical net benefit.The diagnostic efficiency of the manual reading group:the accuracy rate was 0.89,the recall rate was 0.62.The diagnostic efficiency of the model reading group:the accuracy rate was 0.93,the recall rate was 0.86,the model diagnosis showed good predictive performance,which was significantly better than the manual reading group.The diagnostic efficiency of the model-assisted manual reading group:the accuracy rate was 0.92,the recall rate was 0.72,and the recall rate of the model-assisted manual reading group was higher than that of the manual reading group,but lower than that of the model reading group,indicating the superiority of the model diagnosis.Conclusion The diagnostic model established based on artificial intelligence and radiomics in this study has reached an ideal level of efficacy,with better diagnostic efficacy compared with manual reading,and can be used to assist X-ray in the early diagnosis of OVCF.

Objective To develop a clinical prediction model for predicting risk factors for prolonged hospital stay after anterior cervical discectomy and fusion(ACDF).Methods The clinical data of 914 patients underwent ACDF treatment for cervical spondylotic myelopathy(CSM)were retrospectively analyzed.According to the screening criteria,800 eligible patients were eventually included,and the patients were divided into the development cohort(n=560)and the validation cohort(n=240).LASSO regression was used to screen variables,and multivariate Logistic regression analysis was used to establish a prediction model.The prediction model was evaluated from three aspects:differentiation,calibration and clinical effectiveness.The performance of the model was evaluated by area under the curve(AUC)and Hosmer-Lemeshow test.Decision curve analysis(DCA)was used to evaluate the clinical effectiveness of the model.Results In this study,the five factors that were significantly associated with prolonged hospital stay were male,abnormal BMI,mild-to-moderate anemia,stage of surgery(morning,afternoon,evening),and alcohol consumption history.The AUC of the development cohort was 0.778(95%CI:0.740 to 0.816),with a cutoff value of 0.337,and that of the validation cohort was 0.748(95%CI:0.687 to 0.809),with a cutoff value of 0.169,indicating that the prediction model had good differentiation.At the same time,the Hosmer-Lemeshow test showed that the model had a good calibration degree,and the DCA proved that it was effective in clinical application.Conclusion The prediction model established in this study has excellent comprehensive performance,which can better predict the risk of prolonged hospital stay,and can guide clinical intervention as soon as possible,so as to minimize the postoperative hospital stay and reduce the cost of hospitalization.