Methods: From March 2020 to March 2023, 100 inpatients with DS were classified into groups A, B, C, and D based on the CARDS classification system. Preoperative radiological data were analyzed to measure the severity of central canal stenosis, facet joint arthropathy, intervertebral disc herniation, and spinal epidural lipomatosis, osteophyte formation, range of motion (ROM), and computed tomography value of the vertebral bodies. The radiological characteristics and clinical contraindications for OLIF were compared among the groups. Results: Of the 100 patients, 51% had clinical contraindications for OLIF, which included 85%, 25%, 62.5%, and 20% of patients in groups A, B, C, and D, respectively. Compared with group B, group A demonstrated greater severity of central canal stenosis, whereas group C showed a higher degree of facet joint arthropathy. More patients in groups A and C had severe central canal stenosis. Regarding the ROM results, group A had segmental stiffness, whereas group D presented relatively unstable slip segments. Conclusions Patients with different DS subtypes have varied radiological characteristics. Groups B and D are suitable candidates for OLIF. Most patients in group A are unsuitable for OLIF because of bony hyperplasia, severe spinal stenosis, and segmental stiffness.

Methods: From March 2020 to March 2023, 100 inpatients with DS were classified into groups A, B, C, and D based on the CARDS classification system. Preoperative radiological data were analyzed to measure the severity of central canal stenosis, facet joint arthropathy, intervertebral disc herniation, and spinal epidural lipomatosis, osteophyte formation, range of motion (ROM), and computed tomography value of the vertebral bodies. The radiological characteristics and clinical contraindications for OLIF were compared among the groups. Results: Of the 100 patients, 51% had clinical contraindications for OLIF, which included 85%, 25%, 62.5%, and 20% of patients in groups A, B, C, and D, respectively. Compared with group B, group A demonstrated greater severity of central canal stenosis, whereas group C showed a higher degree of facet joint arthropathy. More patients in groups A and C had severe central canal stenosis. Regarding the ROM results, group A had segmental stiffness, whereas group D presented relatively unstable slip segments. Conclusions Patients with different DS subtypes have varied radiological characteristics. Groups B and D are suitable candidates for OLIF. Most patients in group A are unsuitable for OLIF because of bony hyperplasia, severe spinal stenosis, and segmental stiffness.

Methods: From March 2020 to March 2023, 100 inpatients with DS were classified into groups A, B, C, and D based on the CARDS classification system. Preoperative radiological data were analyzed to measure the severity of central canal stenosis, facet joint arthropathy, intervertebral disc herniation, and spinal epidural lipomatosis, osteophyte formation, range of motion (ROM), and computed tomography value of the vertebral bodies. The radiological characteristics and clinical contraindications for OLIF were compared among the groups. Results: Of the 100 patients, 51% had clinical contraindications for OLIF, which included 85%, 25%, 62.5%, and 20% of patients in groups A, B, C, and D, respectively. Compared with group B, group A demonstrated greater severity of central canal stenosis, whereas group C showed a higher degree of facet joint arthropathy. More patients in groups A and C had severe central canal stenosis. Regarding the ROM results, group A had segmental stiffness, whereas group D presented relatively unstable slip segments. Conclusions Patients with different DS subtypes have varied radiological characteristics. Groups B and D are suitable candidates for OLIF. Most patients in group A are unsuitable for OLIF because of bony hyperplasia, severe spinal stenosis, and segmental stiffness.

ObjectiveTo observe and compare the effects of different concentrations of cyclophosphamide (CTX) in inducing premature ovarian insufficiency (POI) model in mice and investigate the mechanism of injury. MethodsThirty-two 6~8-week-old female C57BL/6J mice were randomly divided into four groups (n=8 per group) using a weight-based block randomization method. The POI model was established via a single intraperitoneal injection of 75 mg/kg cyclophosphamide (CTX), 120 mg/kg CTX, 120 mg/kg CTX + 12 mg/kg Busulfan, or an equivalent volume of normal saline (control). Ovarian coefficients, serum estradiol (E₂) and follicle-stimulating hormone (FSH) levels were measured. Western blotting was performed to assess changes in ovarian expression levels of NAD-dependent deacetylase sirtuin-5 (SIRT5) and forkhead box O3a (FOXO3a) under different modeling conditions. After determining the optimal CTX concentration for modeling, an additional forty 6~8-week-old femal C57BL/6J mice were randomly divided into five groups (n=8 per group) using a weight-based block randomization method: saline control, 120 mg/kg CTX sampling at 1, 2, 7, or 14 days after modeling. Western blotting was used to evaluate temporal changes of ovarian SIRT5 and FOXO3a protein expression. ResultsCompared with the saline control, all concentrations of CTX (75 mg/kg CTX, 120 mg/kg CTX) and 120 mg/kg CTX + 12 mg/kg Busulfan induced POI injury in mice. The 120 mg/kg CTX group exhibited smaller changes in ovarian coefficients (P<0.001) and E₂ levels (P<0.05), whereas the 120 mg/kg CTX + 12 mg/kg Busulfan group showed rough and reduced luster fur, sluggish response and was in the worst state. Compared with the saline control group, FOXO3a expression was significantly down-regulated (P<0.05), while SIRT5 remained unchanged in the 75 mg/kg CTX group (P>0.05). In contrast, both SIRT5 (P<0.05) and FOXO3a (P<0.05) were significantly down-regulated in the 120 mg/kg CTX group. Further analysis revealed that on day 2 and 7 after 120 mg/kg CTX modeling, the expressions of SIRT5 (P<0.01) and FOXO3a (P<0.001) were significantly down-regulated, with the largest decrease observed on day 7 (SIRT5, P<0.000 1; FOXO3a, P<0.000 1). ConclusionOvarian injury in the POI model induced by 120 mg/kg CTX is milder than that in the POI model induced by 75 mg/kg CTX. Moreover, the expression changes of SIRT5 and FOXO3a are most significant on day 7 after modeling induced by 120 mg/kg CTX, which may be related to the inhibition of the SIRT5-FOXO3a signaling pathway.

Objective To investigate the common molecular features of immunothrombosis, thus enhancing the comprehension of thrombosis triggered by immune and inflammatory responses and offering crucial insights for identifying potential diagnostic and therapeutic targets. Methods Differential gene expression analysis and functional enrichment analysis were conducted on datasets of systemic lupus erythematosus (SLE) and venous thromboembolism (VTE). The intersection of differentially expressed genes in SLE and VTE with those of neutrophil extracellular traps (NET) yielded cross-talk genes (CG) for SLE-NET and VTE-NET interaction. Further analysis included functional enrichment and protein-protein interaction (PPI) network assessments of these CG to identify hub genes. Venn diagrams and receiver operating characteristic (ROC) curve analysis were employed to pinpoint the most effective shared diagnostic CG, which were validated using a graft-versus-host disease (GVHD) dataset. Results Differential expression genes in SLE and VTE were associated with distinct biological processes, whereas SLE-NET-CG and VTE-NET-CG were implicated in pathways related to leukocyte migration, inflammatory response, and immune response. Through PPI network analysis, several hub genes were identified, with matrix metalloproteinase 9 (MMP9) and S100 calcium-binding protein A12 (S100A12) emerging as the best shared diagnostic CG for SLE (AUC: 0.936 and 0.832) and VTE (AUC: 0.719 and 0.759). Notably, MMP9 exhibited good diagnostic performance in the GVHD dataset (AUC: 0.696). Conclusion This study unveils the common molecular features of SLE, VTE, and NET, emphasizing MMP9 and S100A12 as the optimal shared diagnostic CG, thus providing valuable evidence for the diagnosis and therapeutic strategies related to immunothrombosis. Additionally, the expression of MMP9 in GVHD highlights its critical role in the risk of VTE associated with immune system disorders.
Humans ; Computational Biology/methods* ; Lupus Erythematosus, Systemic/immunology* ; Protein Interaction Maps/genetics* ; Venous Thromboembolism/therapy* ; Matrix Metalloproteinase 9/genetics* ; Extracellular Traps/metabolism* ; Gene Regulatory Networks ; Thrombosis/immunology* ; Graft vs Host Disease/genetics* ; Gene Expression Profiling

The technology of three-dimensional(3D)printing emerged in the late 1970s and has since undergone considerable development to find numerous applications in mechanical engineering,industrial design,and biomedicine.In biomedical science,several studies have initially found that 3D printing technology can play an important role in the treatment of diseases in hepatopancreatobiliary surgery.For example,3D printing technology has been applied to create detailed anatomical models of disease organs for preoperative personalized surgical strategies,surgical simulation,intraoperative navigation,medical training,and patient education.Moreover,cancer models have been created using 3D printing technology for the research and selection of chemotherapy drugs.With the aim to clarify the development and application of 3D printing technology in hepatopancreatobiliary surgery,we introduce seven common types of 3D printing technology and review the status of research and application of 3D printing technology in the field of hepatopancreatobiliary surgery.

Objective Photoelectric volumetric tracing(PPG)exhibits high sensitivity and specificity in flap monitoring.Deep learning(DL)is capable of automatically and robustly extracting features from raw data.In this study,we propose combining PPG with 1D convolutional neural networks(1D-CNN)to preliminarily explore the method's ability to distinguish the degree of embolism and to localize the embolic site in skin flap arteries.Methods Data were collected under normal conditions and various embolic scenarios by creating vascular emboli in a dermatome artery model and a rabbit dermatome model.These datasets were then trained,validated,and tested using 1D-CNN.Results As the degree of arterial embolization increased,the PPG amplitude upstream of the embolization site progressively increased,while the downstream amplitude progressively decreased,and the gap between the upstream and downstream amplitudes at the embolization site progressively widened.1D-CNN was evaluated in the skin flap arterial model and rabbit skin flap model,achieving average accuracies of 98.36%and 95.90%,respectively.Conclusion The combined monitoring approach of DL and PPG can effectively identify the degree of embolism and locate the embolic site within the skin flap artery.

Objective:To investigate the relationship between different obesity metabolic phenotypes and the incidence of new carotid artery plaque.Methods:The present study is a retrospective cohort study, collecting individuals from the Health Management Center of the Second Affiliated Hospital of Dalian Medical University who had two or more cervical vascular color ultrasound examinations and met the inclusion criteria from 2014 to 2022, and collected their baseline clinical data. According to whether the subjects were obese and had metabolic syndrome, they were divided into metabolically healthy non-obese group, metabolically unhealthy non-obese group, metabolically healthy obese group, and metabolically unhealthy obese group. The first physical examination time of the subjects was taken as the starting point of follow-up, and cervical vascular color ultrasound was performed during the follow-up physical examination, with the outcome event being carotid artery plaque. Kaplan-Meier survival curve analysis was used to analyze the cumulative incidence of carotid artery plaques in the four groups and log-rank test was performed, and a multifactorial Cox proportional hazards model was used to analyze the relationship between different obesity metabolic phenotypes and the risk of carotid artery plaque incidence.Results:A total of 4 890 subjects were enrolled, aged (45.4±9.6) years, and 2 754 (56.3%) males. The follow-up time was 1.14(0.93, 2.20) years. Compared with the other 3 obesity metabolic phenotypes, the incidence of carotid plaques in the metabolically unhealthy obesity group was the highest (15.4% (286/1 861)). Kaplan-Meier survival curve analysis showed that the cumulative incidence of carotid plaques in metabolically unhealthy obese subjects was about 2.962 times that of metabolically healthy non-obese subjects (log-rank P<0.001). Multivariate Cox regression results showed that the risk of carotid plaque in metabolically unhealthy obese subjects was 1.650 times that of metabolically healthy non-obese subjects (95% CI: 1.203-2.264, P=0.002). Conclusion:Metabolically unhealthy obesity phenotype is an independent risk factor for carotid plaque.

Objective:To study the correlation between different body weight metabolic phenotypes and their changes and new-onset hyperuricemia in physical examination population.Methods:This study was a retrospective cohort study. A total of 31 956 people who underwent routine physical examination and met the inclusion and exclusion criteria at the Health Management Center of the Second Affiliated Hospital of Dalian Medical University from January 1, 2014 to August 31, 2022 were selected as the study subjects to establish a dynamic physical examination cohort. The end point of follow-up was new-onset hyperuricemia or the end of follow-up period. Cox regression stepwise fitting model was used to analyze the risk of different body weight metabolic phenotypes and hyperuricemia, and stratified analysis was performed for gender. According to body weight metabolic phenotype, the subjects were divided into normal metabolism and normal weight(NMNW) group, normal metabolism and obesity (NMO) group, abnormal metabolism and normal weight (AMNW) group and abnormal metabolism and obesity (AMO) group. The risk of hyperuricemia was calculated according to the changes of body weight metabolic phenotype during the follow-up period. In the sensitivity analysis, the robustness of the results was verified by changing the diagnostic criteria for hyperuricemia, removing patients with hyperuricemia at the first year of follow-up, and removing subjects aged ≥65 years.Results:Compared with the NMNW group, the risk of hyperuricemia in the NMO group, AMNW group and AMO group increased by 78.9%, 61.3%, 115.4%, respectively ( χ2=272.88, 128.15, 496.12, all P<0.001). Patients who were initially classified as NMNW at baseline, if transitioned to NMO or AMO by the follow-up endpoint, their risk of hyperuricemia increased by 122.5% ( χ2=8.01, P<0.05) and 137.4% ( χ2=15.99, P<0.001), respectively. When the baseline AMNW group changed to AMO, the risk of hyperuricemia was increased by 119.2% ( χ2=6.63, P<0.05). For patients with AMO as baseline, if they turned into NMNW and AMNW at the end of follow-up, their risk of hyperuricemia would decrease by 58.3% ( χ2=43.67, P<0.001) and 27.2% ( χ2=16.07, P<0.001). Patients with a baseline of NMO who transitioned to NMNW and AMNW at the follow-up endpoint had their risk of developing hyperuricemia decreased by 36.7% ( χ2=25.35, P<0.001) and 30.9% ( χ2=9.70, P<0.05), respectively. Conclusions:The transition from metabolic health and non-overweight obesity to metabolic abnormalities and overweight obesity is associated with an increased risk of hyperuricemia, and improvements in metabolic health or weight are associated with a decreased risk of hyperuricemia.

Objective:To explore the relationship between weight change and the development of hyperuricemia (HUA) in adults receiving health checkups.Methods:A retrospective cohort study. A total of 37 722 subjects who underwent two or more health checkups at the Health Management Center of the Second Affiliated Hospital of Dalian Medical University from January 2014 to December 2022 were included, and the general information and laboratory findings at the time of the initial health checkups and follow-up were collected. Weight change was defined as the ratio of difference between the weight at the last follow-up and the baseline weight to baseline weight. The subjects were grouped with weight change: significant weight loss group (weight change ≤-5.0%), mild weight loss group (-5.0%