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.

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.

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.

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.

Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning.

OBJECTIVE: The identification of spinal tuberculosis subphenotypes is an integral component of precision medicine. However, we lack proper study models to identify subphenotypes in patients with spinal tuberculosis. Here we identified possible subphenotypes of spinal tuberculosis and compared their clinical results. METHODS: A total of 422 patients with spinal tuberculosis who received surgical treatment were enrolled. Clustering analysis was performed using the K-means clustering algorithm and the routinely available clinical data collected from patients within 24 h after admission. Finally, the differences in clinical characteristics, surgical efficacy, and postoperative complications among the subphenotypes were compared. RESULTS: Two subphenotypes of spinal tuberculosis were identified. Laboratory examination results revealed that the levels of more than one inflammatory index in cluster 2 were higher than those in cluster 1. In terms of disease severity, Cluster 2 showed a higher Oswestry Disability Index (ODI), a higher visual analysis scale (VAS) score, and a lower Japanese Orthopedic Association (JOA) score. In addition, in terms of postoperative outcomes, cluster 2 patients were more prone to complications, especially wound infections, and had a longer hospital stay. CONCLUSION: K-means clustering analysis based on conventional available clinical data can rapidly identify two subtypes of spinal tuberculosis with different clinical results. We believe this finding will help clinicians to rapidly and easily identify the subtypes of spinal tuberculosis at the bedside and become the cornerstone of individualized treatment strategies.

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.

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.

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.

Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning.

OBJECTIVE: This article aims at exploring the role of hypoxia-related genes and immune cells in spinal tuberculosis and tuberculosis involving other organs. METHODS: In this study, label-free quantitative proteomics analysis was performed on the intervertebral discs (fibrous cartilaginous tissues) obtained from five spinal tuberculosis (TB) patients. Key proteins associated with hypoxia were identified using molecular complex detection (MCODE), weighted gene co-expression network analysis(WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature Elimination (SVM-REF) methods, and their diagnostic and predictive values were assessed. Immune cell correlation analysis was then performed using the Single Sample Gene Set Enrichment Analysis (ssGSEA) method. In addition, a pharmaco-transcriptomic analysis was also performed to identify targets for treatment. RESULTS: The three genes, namely proteasome 20 S subunit beta 9 (PSMB9), signal transducer and activator of transcription 1 (STAT1), and transporter 1 (TAP1), were identified in the present study. The expression of these genes was found to be particularly high in patients with spinal TB and other extrapulmonary TB, as well as in TB and multidrug-resistant TB (p-value < 0.05). They revealed high diagnostic and predictive values and were closely related to the expression of multiple immune cells (p-value < 0.05). It was inferred that the expression of PSMB9, STAT 1, and TAP1 could be regulated by different medicinal chemicals. CONCLUSION: PSMB9, STAT1, and TAP1, might play a key role in the pathogenesis of TB, including spinal TB, and the protein product of the genes can be served as diagnostic markers and potential therapeutic target for TB.

To infer the probability of cervical ossification of the posterior longitudinal ligament and explore its impact on cervical surgery.

The ossification of the posterior longitudinal ligament (OPLL) in the cervical spine is commonly observed in degenerative changes of the cervical spine. Early detection of cervical OPLL and prevention of postoperative complications are of utmost importance. We gathered data from 775 patients who underwent cervical spine surgery at the First Affiliated Hospital of Guangxi Medical University, collecting a total of 84 variables. Among these patients, 144 had cervical OPLL, while 631 did not. They were randomly divided into a training cohort and a validation cohort. Multiple machine learning (ML) methods were employed to screen the variables and ultimately develop a diagnostic model. Subsequently, we compared the postoperative outcomes of patients with positive and negative cervical OPLL. Initially, we compared the advantages and disadvantages of various ML methods. Seven variables, namely Age, Gender, OPLL, AST, UA, BMI, and CHD, exhibited significant differences and were used to construct a diagnostic nomogram model. The area under the curve (AUC) values of this model in the training and validation groups were 0.76 and 0.728, respectively. Our findings revealed that 69.2% of patients who underwent cervical OPLL surgery eventually required elective anterior surgery, in contrast to 86.8% of patients who did not have cervical OPLL. Patients with cervical OPLL had significantly longer operation times and higher postoperative drainage volumes compared to those without cervical OPLL. Interestingly, preoperative cervical OPLL patients demonstrated significant increases in mean UA, age, and BMI. Furthermore, 27.1% of patients with cervical anterior longitudinal ligament ossification (OALL) also exhibited cervical OPLL, whereas this occurrence was only observed in 6.9% of patients without cervical OALL. We developed a diagnostic model for cervical OPLL using the ML method. Our findings indicate that patients with cervical OPLL are more likely to undergo posterior cervical surgery, and they exhibit elevated UA levels, higher BMI, and increased age. The prevalence of cervical anterior longitudinal ligament ossification was also significantly higher among patients with cervical OPLL.

Long non-coding RNA and circular RNA and coding RNA profiling of plasma exosomes of osteosarcoma by RNA seq.

Osteosarcoma (OS) is a primary bone tumor with high malignancy and the mechanism of hematogenous metastasis in OS is still not clear. The plasma exosomes derived from osteosarcoma play a key role in the process of tumor metastasis. Here, we established RNA-seq dataset for lncRNAs, circRNAs and mRNAs in plasma exosomes from 10 OS patients and 5 healthy donors. A total of 329.52 Gb of clean data was obtained. Besides, 1754 lincRNAs, 7096 known and 1935 new circRNA was identified. Finally, gene expression profiles and differentially expressed genes (DEGs) were analyzed among these 15 samples. There were 331 DEGs of mRNA, 132 of lincRNA and 489 of circRNA was obtained, respectively. This data set provides a significant resource for relevant researchers to excavate potential dysregulated lncRNAs, circRNAs and mRNAs of plasma exosomes in OS versus normal conditions.

Interaction gene set between osteoclasts and regulatory CD4+ T cells can accurately predict the prognosis of patients with osteosarcoma.

Osteoclasts (OCs) and regulatory CD4+ T cells (CD4+ Tregs) are important components in the tumor microenvironment (TME) of osteosarcoma. In this study, we collected six osteosarcoma samples from our previous study (GSE162454). We also integrated a public database (GSE152048), which included single cell sequencing data of 11 osteosarcoma patients. We obtained 138,192 cells and then successfully identified OCs and CD4+ Tregs. Based on the interaction gene set between OCs and CD4+ Tregs, patients from GSE21257 were distinguished into two clusters by consensus clustering analysis. Both the tumor immune microenvironment and survival prognosis between the two clusters were significantly different. Subsequently, five model genes were identified by protein-protein interaction network based on differentially upregulated genes of cluster 2. Quantitative RT-PCR was used to detect their expression in human osteoblast and osteosarcoma cells. A prognostic model was successfully established using these five genes. Kaplan-Meier survival analysis found that patients in the high-risk group had worse survival (p = 0.029). Therefore, our study first found that cell-cell communication between OCs and CD4+ Tregs significantly alters TME and is connected to poor prognosis of OS. The model we constructed can accurately predict prognosis for osteosarcoma patients.