Elucidating the causal nexus between antibody-mediated immunity and autoimmune diseases: Insights from bidirectional mendelian randomization, gene expression profiling, and drug sensitivity analysis.

OBJECTIVE: This study aimed to elucidate the causal relationships between antibodies and autoimmune diseases using Mendelian randomization (MR). METHODS: Data on 46 antibodies were obtained from genome-wide association studies (GWAS). Autoimmune disease data were sourced from the FinnGen consortium and the IEU OpenGWAS project. Inverse-variance weighted (IVW) analysis was the primary method, supplemented by heterogeneity and sensitivity analyses. We also examined gene expression near significant SNPs and conducted drug sensitivity analyses. RESULTS: Antibodies and autoimmune diseases exhibit diverse interactions. Antibodies produced after Polyomavirus infection tend to increase the risk of several autoimmune diseases, while those following Human herpesvirus 6 infection generally reduce it. The impact of Helicobacter pylori infection varies, with different antibodies affecting autoimmune diseases in distinct ways. Overall, antibodies significantly influence the risk of developing autoimmune diseases, whereas autoimmune diseases have a lesser impact on antibody levels. Gene expression and drug sensitivity analyses identified multiple genes and drugs as potential treatment options for ankylosing spondylitis (AS), with the AIF1 gene being particularly promising. CONCLUSIONS: Bidirectional MR analysis confirms complex causal relationships between various antibodies and autoimmune diseases, revealing intricate patterns of post-infection antibody interactions. Several drugs and genes, notably AIF1, show potential as candidates for AS treatment, offering new avenues for research. Further exploration of the underlying mechanisms is necessary.

Anatomical study of the C6 pedicle and lateral mass in children aged 0-14 years based on CT imaging.

OBJECTIVE: This study aims to investigate the anatomical structure of the C6 pedicle and lateral mass in children aged 0-14 years using CT imaging, providing detailed insights into their growth and development. METHODS: We conducted a comprehensive measurement of C6. Measurements included width, length, and height of the pedicles, as well as the length, width, and thickness of the lateral masses, and several angular metrics. Regression analysis was performed to understand the growth trends, and statistical analyses were carried out to identify differences between age groups, genders, and sides. RESULTS: In children younger than four years, the pedicle width exceeds its height, influencing the diameter of the pedicle screws. By age two to three, the pedicle height and lateral mass thickness reaches 3.0 mm, allowing for the use of 3.0 mm diameter screws. The pedicle transverse angle remains stable. Most parameters showed no significant differences between the left and right sides. Size parameters exhibited significant larger in males than females at ages 0-1, 3-7, and 10-12 years. Regression analysis revealed that the growth trends of size parameters follow cubic or polynomial curves. Most angular metrics follow cubic fitting curves without a clear trend of change with age. CONCLUSION: This study provides a detailed analysis of the anatomical development of the C6 pedicle and lateral masses in children, offering valuable insights for pediatric cervical spine surgeries. The findings highlight the importance of considering age-specific anatomical variations when planning posterior surgical fixation, specifically at C6. It is necessary for us to perform thin-layer CT scans on children and carefully measure various indicators before surgery.

The shared role of neutrophils in ankylosing spondylitis and ulcerative colitis.

This study aimed to analyze single-cell sequencing data to investigate immune cell interactions in ankylosing spondylitis (AS) and ulcerative colitis (UC). Vertebral bone marrow blood was collected from three AS patients for 10X single-cell sequencing. Analysis of single-cell data revealed distinct cell types in AS and UC patients. Cells significantly co-expressing immune cells (P < 0.05) were subjected to communication analysis. Overlapping genes of these co-expressing immune cells were subjected to GO and KEGG analyses. Key genes were identified using STRING and Cytoscape to assess their correlation with immune cell expression. The results showed the significance of neutrophils in both diseases (P < 0.01), with notable interactions identified through communication analysis. XBP1 emerged as a Hub gene for both diseases, with AUC values of 0.760 for AS and 0.933 for UC. Immunohistochemistry verified that the expression of XBP1 was significantly lower in the AS group and significantly greater in the UC group than in the control group (P < 0.01). This finding highlights the critical role of neutrophils in both AS and UC, suggesting the presence of shared immune response elements. The identification of XBP1 as a potential therapeutic target offers promising intervention avenues for both diseases.

CT Anatomical Analysis of C4 Pedicle and Lateral Mass in Children Aged 0-14 in Southern China.

OBJECTIVE: The C4 is the transition point between the upper and lower cervical vertebrae and plays a pivotal role in the middle of the cervical spine. Currently, there are limited reports on large-scale sample studies regarding C4 anatomy in children, and a scarcity of experience exists in pediatric cervical spine surgery. The current study addresses the dearth of anatomical measurements of the C4 vertebral arch and lateral mass in a substantial sample of children. This study aims to measure the imaging anatomy of the C4 vertebral arch and lateral mass in children under 14 years of age across various age groups, investigate the growth and development of these structures. METHODS: We measured 12 indicators, including the size (D1, D2, D3, D4, D5, D6, D7, and D8) and angle (A, C, D, and E) of the C4 vertebral arch and lateral mass, in 513 children who underwent cervical CT examinations at our hospital. We employed the aggregate function for statistical analysis, conducted t-tests for difference statistics, and utilized the least squares method for regression analysis. RESULTS: Overall, as age increased, there was a gradual increase in the size of the vertebral arch and lateral mass. Additionally, the medial inclination angle of the vertebral arch decreased, and the lateral mass flattened gradually. The rate of change decreased gradually with age. The mean value of D1 increased from 2.31 mm to 3.88 mm, of D2 from 16.75 mm to 29.2 mm, of D3 from 2.21 mm to 4.92 mm, and of D4 from 7.34 mm to 11.84 mm. Meanwhile, the mean value of D5 increased from 5.2 mm to 9.71 mm, of D6 from 10.19 mm to 16.16 mm, of D7 from 2.53 mm to 5.67 mm, and of D8 from 6.11 mm to 11.45 mm. Angle A ranged from 49.12° to 54.97°, angle C from 15.28° to 19.83°, angle D from 39.91° to 53.7°, and angle E from 18.63° to 28.08°. CONCLUSION: Prior to cervical spine surgery in children, meticulous CT imaging anatomical measurements is essential. The imaging data serves as a reference for posterior C4 internal fixation, aids in designing posterior cervical screws for pediatric patients, and offer morphological anatomical references for posterior cervical spine surgery and screw design in pediatric patients.

Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods.

BACKGROUND: Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare. METHODS: The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or <8.64 days was categorized as the AHD-negative group (n = 539), and a LOS > 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility. RESULTS: For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. The C-index was 0.788 (95% confidence interval: 0.73214-0.84386). On the decision curve analysis (DCA), the threshold probability of the nomogram ranged from 1 to 99% (training cohort) and 1 to 75% (validation cohort). CONCLUSION: We successfully developed an ML model for predicting AHD in patients undergoing cervical spine surgery, showcasing its potential to support clinicians in AHD identification and enhance perioperative treatment strategies.

Single-cell and spatial transcriptomics reveal metastasis mechanism and microenvironment remodeling of lymph node in osteosarcoma.

BACKGROUND: Osteosarcoma (OS) is the most common primary malignant bone tumor and is highly prone to metastasis. OS can metastasize to the lymph node (LN) through the lymphatics, and the metastasis of tumor cells reestablishes the immune landscape of the LN, which is conducive to the growth of tumor cells. However, the mechanism of LN metastasis of osteosarcoma and remodeling of the metastatic lymph node (MLN) microenvironment is not clear. METHODS: Single-cell RNA sequencing of 18 samples from paracancerous, primary tumor, and lymph nodes was performed. Then, new signaling axes closely related to metastasis were identified using bioinformatics, in vitro experiments, and immunohistochemistry. The mechanism of remodeling of the LN microenvironment in tumor cells was investigated by integrating single-cell and spatial transcriptomics. RESULTS: From 18 single-cell sequencing samples, we obtained 117,964 cells. The pseudotime analysis revealed that osteoblast(OB) cells may follow a differentiation path from paracancerous tissue (PC) → primary tumor (PT) → MLN or from PC → PT, during the process of LN metastasis. Next, in combination of bioinformatics, in vitro and in vivo experiments, and immunohistochemistry, we determined that ETS2/IBSP, a new signal axis, might promote LN metastasis. Finally, single-cell and spatial dissection uncovered that OS cells could reshape the microenvironment of LN by interacting with various cell components, such as myeloid, cancer-associated fibroblasts (CAFs), and NK/T cells. CONCLUSIONS: Collectively, our research revealed a new molecular mechanism of LN metastasis and clarified how OS cells influenced the LN microenvironment, which might provide new insight for blocking LN metastasis.

Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study.

Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients.

Integrating artificial intelligence in osteosarcoma prognosis: the prognostic significance of SERPINE2 and CPT1B biomarkers.

The principal aim of this investigation is to identify pivotal biomarkers linked to the prognosis of osteosarcoma (OS) through the application of artificial intelligence (AI), with an ultimate goal to enhance prognostic prediction. Expression profiles from 88 OS cases and 396 normal samples were procured from accessible public databases. Prognostic models were established using univariate COX regression analysis and an array of AI methodologies including the XGB method, RF method, GLM method, SVM method, and LASSO regression analysis. Multivariate COX regression analysis was also employed. Immune cell variations in OS were examined using the CIBERSORT software, and a differential analysis was conducted. Routine blood data from 20,679 normal samples and 437 OS cases were analyzed to validate lymphocyte disparity. Histological assessments of the study's postulates were performed through immunohistochemistry and hematoxylin and eosin (HE) staining. AI facilitated the identification of differentially expressed genes, which were utilized to construct a prognostic model. This model discerned that the survival rate in the high-risk category was significantly inferior compared to the low-risk cohort (p < 0.05). SERPINE2 was found to be positively associated with memory B cells, while CPT1B correlated positively with CD8 T cells. Immunohistochemical assessments indicated that SERPINE2 was more prominently expressed in OS tissues relative to adjacent non-tumorous tissues. Conversely, CPT1B expression was elevated in the adjacent non-tumorous tissues compared to OS tissues. Lymphocyte counts from routine blood evaluations exhibited marked differences between normal and OS groups (p < 0.001). The study highlights SERPINE2 and CPT1B as crucial biomarkers for OS prognosis and suggests that dysregulation of lymphocytes plays a significant role in OS pathogenesis. Both SERPINE2 and CPT1B have potential utility as prognostic biomarkers for OS.

Age and flexors as risk factors for cervical radiculopathy: A new machine learning method.

This study aimed to investigate the risk factors for cervical radiculopathy (CR) along with identifying the relationships between age, cervical flexors, and CR. This was a retrospective cohort study, including 60 patients with CR enrolled between December 2018 and June 2020. In this study, we measured C2 to C7 Cobb angle, disc degeneration, endplate degeneration, and morphology of paraspinal muscles and evaluated the value of predictive methods using receiver operating characteristic curves. Next, we established a diagnostic model for CR using Fisher discriminant model and compared different models by calculating the kappa value. Age and cervical flexor factors were used to construct clinical predictive models, which were further evaluated by C-index, receiver operating characteristic curve, calibration curve, and decision curve analysis. Multivariate analysis showed that age and cervical flexors were potential risk factors for CR, while the diagnostic model indicated that both exerted the best diagnostic effect. The obtained diagnostic equation was as follows: y1 = 0.33 × 1 + 10.302 × 2-24.139; y2 = 0.259 × 1 + 13.605 × 2-32.579. Both the C-index and AUC in the training set reached 0.939. Moreover, the C-index and AUC values in the external validation set reached 0.961. We developed 2 models for predicting CR and also confirmed their validity. Age and cervical flexors were considered potential risk factors for CR. Our noninvasive inspection method could provide clinicians with a more potential diagnostic value to detect CR accurately.

Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms.

This study focused on the development and validation of a diagnostic model to differentiate between spinal tuberculosis (STB) and pyogenic spondylitis (PS). We analyzed a total of 387 confirmed cases, out of which 241 were diagnosed with STB and 146 were diagnosed with PS. These cases were randomly divided into a training group (n = 271) and a validation group (n = 116). Within the training group, four machine learning (ML) algorithms (least absolute shrinkage and selection operator [LASSO], logistic regression analysis, random forest, and support vector machine recursive feature elimination [SVM-RFE]) were employed to identify distinctive variables. These specific variables were then utilized to construct a diagnostic model. The model's performance was subsequently assessed using the receiver operating characteristic (ROC) curves and the calibration curves. Finally, internal validation of the model was undertaken in the validation group. Our findings indicate that PS patients had an average platelet-to-neutrophil ratio (PNR) of 277.86, which was significantly higher than the STB patients' average of 69.88. The average age of PS patients was 54.71 years, older than the 48 years recorded for STB patients. Notably, the neutrophil-to-lymphocyte ratio (NLR) was higher in PS patients at 6.15, compared to the 3.46 NLR in STB patients. Additionally, the platelet volume distribution width (PDW) in PS patients was 0.2, compared to 0.15 in STB patients. Conversely, the mean platelet volume (MPV) was lower in PS patients at an average of 4.41, whereas STB patients averaged 8.31. Hemoglobin (HGB) levels were lower in PS patients at an average of 113.31 compared to STB patients' average of 121.64. Furthermore, the average red blood cell (RBC) count was 4.26 in PS patients, which was less than the 4.58 average observed in STB patients. After evaluation, seven key factors were identified using the four ML algorithms, forming the basis of our diagnostic model. The training and validation groups yielded area under the curve (AUC) values of 0.841 and 0.83, respectively. The calibration curves demonstrated a high alignment between the nomogram-predicted values and the actual measurements. The decision curve indicated optimal model performance with a threshold set between 2% and 88%. In conclusion, our model offers healthcare practitioners a reliable tool to efficiently and precisely differentiate between STB and PS, thereby facilitating swift and accurate diagnoses.

Predictive Factors for Seizures after Revascularization in Patients with Moyamoya Disease.

BACKGROUND: Moyamoya disease (MMD) is a rare and complex cerebrovascular disorder that is diagnosed through imaging studies, such as computed tomography or magnetic resonance imagin, which show progressive narrowing of the terminal portion of the internal carotid arteries and the development of compensatory capillary collaterals. The objective of our study was to identify and clarify the predictive factors for seizures in patients with MMD. METHODS: From January 2019 to March 2023, a total of 102 patients with MMD were enrolled in this study. Ten patients with seizures after surgery as the main presentation were included. Patients with epilepsy were compared to those without epilepsy in terms of their clinical characteristics. Multivariable analysis was applied to determine factors linked with postoperative seizures. RESULTS: Ten patients developed seizures after revascularization for MMD. Logistic regression analysis revealed that early seizure (odds ratio [OR], 0.068; 95% CI, 0.014-0.342; P = 0.001), cortical involvement (OR, 9.593; 95% CI, 2.256-40.783; P = 0.002), and postoperative hyperperfusion (OR, 7.417; 95% CI, 1.077-51.093; P = 0.042) were significantly associated with seizures. In a multivariate analysis, it was found that early seizures were significantly associated with a higher likelihood of experiencing seizures (OR, 5.88; 95% CI, 1.01-33.96; P = 0.048), while patients who had seizures were more likely to have cortical involvement (OR, 8.90; 95% CI, 1.55-50.96; P = 0.014) or postoperative hyperperfusion (OR, 12.44; 95% CI, 1.21-127.74; P = 0.034). CONCLUSIONS: Epilepsy in patients with MMD link with several clinical factors. In patients with MMD who undergo bypass surgery, early seizures, cortical involvement, and postoperative hyperperfusion are significant independent predictive factors for the development of epilepsy.

Exploring the causal relationship between inflammatory cytokines and inflammatory arthritis: A Mendelian randomization study.

OBJECTIVES: Previous studies have reported an association between inflammatory cytokines and inflammatory arthritis, including Ankylosing spondylitis (AS), rheumatoid arthritis (RA), and psoriatic arthritis (PsA). This study aims to explore the causal relationship between inflammatory cytokines and AS, RA, and PsA using Mendelian randomization (MR). METHODS: We conducted a bidirectional two-sample MR analysis using genetic summary data from a publicly available genome-wide association study (GWAS) that included 41 genetic variations of inflammatory cytokines, as well as genetic variant data for AS, RA, and PsA from the FinnGen consortium. The main analysis method used was Inverse variance weighted (IVW) to investigate the causal relationship between exposure and outcome. Additionally, other methods such as MR Egger, weighted median (WM), simple mode, and weighted mode were employed to strengthen the final results. Sensitivity analysis was also performed to ensure the reliability of the findings. RESULTS: The results showed that macrophage colony-stimulating factor (MCSF) was associated with an increased risk of AS (OR = 1.163, 95 % CI = 1.016-1.33, p = 0.028). Conversely, high levels of TRAIL and beta nerve growth factor (ß-NGF) were associated with a decreased risk of AS (OR = 0.892, 95 % CI = 0.81-0.982, p = 0.002; OR = 0.829, 95 % CI = 0.696-0.988, p = 0.036). Four inflammatory cytokines were found to be associated with an increased risk of PsA: vascular endothelial growth factor (VEGF) (OR = 1.161, 95 % CI = 1.057-1.275, p = 0.002); Interleukin 12p70 (IL12p70) (OR = 1.189, 95 % CI = 1.049-1.346, p = 0.007); IL10 (OR = 1.216, 95 % CI = 1.024-1.444, p = 0.026); IL13 (OR = 1.159, 95 % CI = 1.05-1.28, p = 0.004). Interleukin 1 receptor antagonist (IL-1rα) was associated with an increased risk of seropositive RA (OR = 1.181, 95 % CI = 1.044-1.336, p = 0.008). Similarly, genetic susceptibility to inflammatory arthritis was found to be causally associated with multiple inflammatory cytokines. Lastly, the sensitivity analysis supported the robustness of these findings. CONCLUSIONS: This study provides additional insights into the relationship between inflammatory cytokines and inflammatory arthritis, and may offer new clues for the etiology, diagnosis, and treatment of inflammatory arthritis.

COL3A1 is a potential diagnostic biomarker for synovial chondromatosis and affects the cell cycle and migration of chondrocytes.

BACKGROUND: Synovial chondromatosis (SC) primarily affects the major joints and is characterized by the formation of benign cartilaginous nodules. In the present study, we evaluated the differences in the histology and gene expression of SC and normal cartilages and further elucidated the function of hub genes in SC. METHODS: Histological staining and biochemical analysis were performed to measure collagen and glycosaminoglycan (GAG) contents in SC and normal cartilage samples. Then, microarray analysis was performed using knee joint samples (three normal and three SC samples) to identify the differentially expressed genes (DEGs). Subsequently, bioinformatics analysis was performed to identify the hub genes and explore the mechanisms underlying SC. The intersection of the top 10 upregulated DEGs, top 10 downregulated DEGs, and hub genes was validated in SC tissues. Lastly, in vitro experiments and our clinical cohort were used to determine the potential biological functions and diagnostic value, respectively, of the most significant gene. RESULTS: The GAG and collagen contents were comparable to or higher in SC tissues than in normal tissues. Microarray analysis revealed 143 upregulated and 107 downregulated DEGs in SC. Furthermore, functional enrichment analysis revealed an association between immunity and metabolism-related pathways and SC development. Among 20 hub genes, two intersection genes, namely, collagen type III alpha 1 chain (COL3A1) and HSPA8, were notably expressed in SC tissues, with COL3A1 exhibiting a more significant difference in mRNA expression. Furthermore, COL3A1 can promote chondrocyte migration and cell cycle progression. Additionally, clinical data revealed COL3A1 can be a diagnostic marker for primary SC (AUC = 0.82) and be a positive correlation with neutrophil-to-lymphocyte ratio. CONCLUSIONS: These results suggest that SC tissues contained the abundant GAG and collagen. COL3A1 can affect the function of chondrocytes and be a diagnostic marker of primary SC patients. These findings provide a novel approach and a fundamental contribution for diagnosis and treatment in SC.

Network pharmacology combined with molecular docking and experimental validation to explore the potential mechanism of Cinnamomi ramulus against ankylosing spondylitis.

BACKGROUND: Cinnamomi ramulus (C. ramulus) is frequently employed in the treatment of ankylosing spondylitis (AS). However, the primary constituents, drug targets, and mechanisms of action remain unidentified. METHODS: In this study, various public databases and online tools were employed to gather information on the compounds of C. ramulus, drug targets, and disease targets associated with ankylosing spondylitis. The intersection of drug targets and disease targets was then determined to identify the common targets, which were subsequently used to construct a protein-protein interaction (PPI) network using the STRING database. Network analysis and the analysis of hub genes and major compounds were conducted using Cytoscape software. Furthermore, the Metascape platform was utilized for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Molecular docking studies and immunohistochemical experiments were performed to validate the core targets. RESULTS: The network analysis identified 2-Methoxycinnamaldehyde, cinnamaldehyde, and 2-Hydroxycinnamaldehyde as the major effective compounds present in C. ramulus. The PPI network analysis revealed PTGS2, MMP9, and TLR4 as the most highly correlated targets. GO and KEGG analyses indicated that C. ramulus exerts its therapeutic effects in ankylosing spondylitis through various biological processes, including the response to hormones and peptides, oxidative stress response, and inflammatory response. The main signaling pathways involved were IL-17, TNF, NF-kappa B, and Toll-like receptor pathways. Molecular docking analysis confirmed the strong affinity between the key compounds and the core targets. Additionally, immunohistochemical analysis demonstrated an up-regulation of PTGS2, MMP9, and TLR4 levels in ankylosing spondylitis. CONCLUSIONS: This study provides insights into the effective compounds, core targets, and potential mechanisms of action of C. ramulus in the treatment of ankylosing spondylitis. These findings establish a solid groundwork for future fundamental research in this field.

The causal relationship between autoimmune diseases and osteoporosis: a study based on Mendelian randomization.

Objective: The relationship between different autoimmune diseases and bone mineral density (BMD) and fractures has been reported in epidemiological studies. This study aimed to explore the causal relationship between autoimmune diseases and BMD, falls, and fractures using Mendelian randomization (MR). Methods: The instrumental variables were selected from the aggregated statistical data of these diseases from the largest genome-wide association study in Europe. Specifically, 12 common autoimmune diseases were selected as exposure. Outcome variables included BMD, falls, and fractures. Multiple analysis methods were utilized to comprehensively evaluate the causal relationship between autoimmune diseases and BMD, falls, and fractures. Additionally, sensitivity analyses, including Cochran's Q test, MR-Egger intercept test, and one analysis, were conducted to verify the result's reliability. Results: Strong evidence was provided in the results of the negatively association of ulcerative colitis (UC) with forearm BMD. UC also had a negatively association with the total body BMD, while inflammatory bowel disease (IBD) depicted a negatively association with the total body BMD at the age of 45-60 years. Horizontal pleiotropy or heterogeneity was not detected through sensitivity analysis, indicating that the causal estimation was reliable. Conclusion: This study shows a negative causal relationship between UC and forearm and total body BMD, and between IBD and total body BMD at the age of 45-60 years. These results should be considered in future research and when public health measures and osteoporosis prevention strategies are formulated.

Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression.

BACKGROUND: HLA-B27 positivity is normal in patients undergoing rheumatic diseases. The diagnosis of many diseases requires an HLA-B27 examination. METHODS: This study screened totally 1503 patients who underwent HLA-B27 examination, liver/kidney function tests, and complete blood routine examination in First Affiliated Hospital of Guangxi Medical University. The training cohort included 509 cases with HLA-B27 positivity whereas 611 with HLA-B27 negativity. In addition, validation cohort included 147 cases with HLA-B27 positivity whereas 236 with HLA-B27 negativity. In this study, 3 ML approaches, namely, LASSO, support vector machine (SVM) recursive feature elimination and random forest, were adopted for screening feature variables. Subsequently, to acquire the prediction model, the intersection was selected. Finally, differences among 148 cases with HLA-B27 positivity and negativity suffering from ankylosing spondylitis (AS) were investigated. RESULTS: Six factors, namely red blood cell count, human major compatibility complex, mean platelet volume, albumin/globulin ratio (ALB/GLB), prealbumin, and bicarbonate radical, were chosen with the aim of constructing the diagnostic nomogram using ML methods. For training queue, nomogram curve exhibited the value of area under the curve (AUC) of 0.8254496, and C-value of the model was 0.825. Moreover, nomogram C-value of the validation queue was 0.853, and the AUC value was 0.852675. Furthermore, a significant decrease in the ALB/GLB was noted among cases with HLA-B27 positivity and AS cases. CONCLUSION: To conclude, the proposed ML model can effectively predict HLA-B27 and help doctors in the diagnosis of various immune diseases.

Epidemiological characteristics of ankylosing spondylitis in Guangxi Province of China from 2014 to 2021.

Introduction: To explore the epidemiological characteristics of ankylosing spondylitis (AS) in Guangxi Province of China through a large sample survey of more than 50 million aboriginal aboriginal population. Material and methods: A systematic search was conducted using the International Classification of Diseases 10 (ICD-10) codes M45.x00(AS), M45.x03+(AS with iridocyclitis), and M40.101(AS with kyphosis) to search the database in the National Health Statistics Network Direct Reporting System (NHSNDRS). 14004 patients were eventually included in the study. The parameters analyzed included the number of patients, gender, marriage, blood type, occupation, age at diagnosis, and location of household registration data each year, and statistical analysis was performed. Results: AS incidence rates increased from 1.30 (95% CI: 1.20-1.40) per 100,000 person-years in 2014 to 5.71 (95% CI: 5.50-5.92) in 2020 in Guangxi Province, and decreased slightly in 2021. Males have a higher incidence than females; the ratio was 5.61 : 1. The mean age of diagnosis in male patients was 45.4 (95% CI: 45.1-45.7) years, in females 47.6 (95% CI: 46.8-48.4) years. The most frequent blood type was O, and the most frequent occupation was farmer. The AS incidence rate was disparate in different cities. Liuzhou city had the highest eight-year average AS incidence rates from 2014 to 2021, and Chongzuo city had the lowest (p < 0.05). There was no significant difference in the incidence between different ethnic groups (p > 0.05). Conclusions: The AS person-years incidence rate was increasing in Guangxi province of China from 2014 to 2020, which had obvious gender and regional differences, showing the characteristics of local area aggregation.

Predictors of osteoporotic fracture in postmenopausal women: a meta-analysis.

Osteoporosis affects more than 200 million women worldwide, with postmenopausal women being particularly susceptible to this condition and its severe sequelae disproportionately, such as osteoporotic fractures. To date, the current focus has been more on symptomatic treatment, rather than preventive measures. To address this, we performed a meta-analysis aiming to identify potential predictors of osteoporotic fractures in postmenopausal women, with the ultimate goal of identifying high-risk patients and exploring potential therapeutic approaches. We searched Embase, MEDLINE and Cochrane with search terms (postmenopausal AND fracture) AND ("risk factor" OR "predictive factor") in May 2022 for cohort and case-control studies on the predictors of osteoporotic fracture in postmenopausal women. Ten studies with 1,287,021 postmenopausal women were found eligible for analyses, in which the sample size ranged from 311 to 1,272,115. The surveyed date spanned from 1993 to 2021. Our results suggested that age, BMI, senior high school and above, parity ≥ 3, history of hypertension, history of diabetes mellitus, history of alcohol intake, age at menarche ≥ 15, age at menopause < 40, age at menopause > 50, estrogen use and vitamin D supplements were significantly associated with osteoporotic fracture in postmenopausal women. Our findings facilitate the early prediction of osteoporotic fracture in postmenopausal women and may contribute to potential therapeutic approaches. By focusing on preventive strategies and identifying high-risk individuals, we can work toward reducing the burden of osteoporosis-related fractures in this vulnerable population.

Association of survivin positive circulating tumor cell levels with immune escape and prognosis of osteosarcoma.

PURPOSE: Function of survivin protein (encoded by BIRC5) in circulating tumor cells (CTCs) of osteosarcoma (OS) has not been investigated. The goal of this study is to determine whether the expression of survivin protein of CTCs is associated with circulating immune cell infiltration and disease prognosis of OS. METHODS: Blood samples of 20 patients with OS were collected. CanPatrol™ CTC enrichment technology combined with in situ hybridization (ISH) was applied to enrich and test CTCs and survivin protein. Bioinformation analysis combined with data of routine blood test was used to verify the association between survivin and immune cell infiltration in circulatory system. To screen independent prognostic factors, Kaplan-Meier survival curve, univariate and multivariable Cox regression analyses were performed. RESULTS: Bioinformatics analysis showed that BIRC5 was strongly negatively related to lymphocyte, including T cell, NK cell and B cell, which released that BIRC5 played a key role in immune escape via reducing immune cell infiltration in circulatory system. Meanwhile, the number of survivin+ CTCs was significantly negatively connection with lymphocyte count (R = -0.56, p = 0.011), which was consistent with bioinformatics analysis. Kaplan-Meier curve showed that the overall survival rate in high survivin+ CTCs group was significantly lower than low group (88.9% vs 36.4%, p = 0.04). Multivariable Cox regression analyses showed that survivin+ CTCs were an independent prognostic factor (p = 0.019). CONCLUSION: These findings suggested that survivin protein played a key role in immune escape of CTCs and the presence of survivin+ CTCs might be a promising prognostic factor in OS patients.

Using Machine Learning to Predict Surgical Site Infection After Lumbar Spine Surgery.

Objective: The objective of this study was to utilize machine learning techniques to analyze perioperative factors and identify blood glucose levels that can predict the occurrence of surgical site infection following posterior lumbar spinal surgery. Methods: A total of 4019 patients receiving lumbar internal fixation surgery from an institute were enrolled between June 2012 and February 2021. First, the filtered data were randomized into the test and verification groups. Second, in the test group, specific variables were screened using logistic regression analysis, Lasso regression analysis, support vector machine, and random forest. Specific variables obtained using the four methods were intersected, and a dynamic model was constructed. ROC and calibration curves were constructed to assess model performance. Finally, internal model performance was verified in the verification group using ROC and calibration curves. Results: The data from 4019 patients were collected. In total, 1327 eligible cases were selected. By combining logistic regression analysis with three machine learning algorithms, this study identified four predictors associated with SSI, namely Modic changes, sebum thickness, hemoglobin, and glucose. Using this information, a prediction model was developed and visually represented. Then, we constructed ROC and calibration curves using the test group; the area under the ROC curve was 0.988. Further, calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index of our model was 0.986 (95% CI 0.981-0.994). Finally, we used the validation group to validate the model internally; the AUC was 0.987. Calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index was 0.982 (95% CI 0.974-0.999). Conclusion: Logistic regression analysis and machine learning were employed to select four risk factors: Modic changes, sebum thickness, hemoglobin, and glucose. Then, a dynamic prediction model was constructed to help clinicians simplify the monitoring and prevention of SSI.