Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty.

BACKGROUND: In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery. METHODS: The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort. RESULTS: The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116. CONCLUSION: In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk.

Pulmonary embolism following the third thoracic tuberculosis surgery: A case report and literature review.

BACKGROUND: The study aimed to analyze the clinical effects of pulmonary embolism succeeding a third surgery conducted for multiple recurrences in thoracic tuberculosis (TB). CASE REPORT: A 74-year-old female patient developed thoracic tuberculosis and was subsequently treated in our hospital in March 2019, October 2020, and February 2021. The third surgical intervention included anterolateral thoracic lesion resection, internal fixation, posterior spinal tuberculous sinus resection, and debridement with suture. The operative time was 172 min resulting in a substantial intraoperative blood loss (2321 ml). Postoperative re-examination of chest CTPA indicated a strip filling defect and pulmonary embolism in the external branch of the right middle lobe of the lung. After completing the active treatment, the D-dimer quantification, WBC, CRP, and ESR values were 1261 ng/ml, 7.71 × 109 /L, 74.66 mg/L, and 63 mm, respectively. Chest CTPA re-examination after the treatment showed no signs of pulmonary embolism. CONCLUSION: Patients with a long-term history of multiple operations, high BMI, cerebral infarction, diabetes, and older age group were more likely to develop pulmonary embolism after spinal tuberculosis surgery. Thus, the possibility of postoperative pulmonary embolism should be thoroughly analyzed before any subsequent surgical treatment in patients with recurrent spinal tuberculosis.

Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram.

OBJECTIVE: The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. METHODS: The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. RESULTS: The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%-.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01-0.79. CONCLUSION: A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery.

Feasibility of 3.5 mm C2 pedicle screws in children: A computerized tomography analysis.

There have been no studies with large sample sizes on growth of the pedicle of C2 in children. In the present study we measured the pedicle of C2 through computed tomography (CT) imaging in children aged less than 14 years and evaluated the suitability of the 3.5-mm screw for the pedicle in such children. The study was conducted on CT morphometric images of 420 children in our hospital between June 2018 and June 2020. The width (D1), length (D2), height (D3), inclination angle (α), and tail angle (ß) of the C2 pedicle were measured. One-way analysis of variance and Student's t test were used for statistical analyses. The least-square method was used to analyze the curve fitting the trend of anatomical change in the pedicle. The largest degree of goodness of fit determined the best-fitting curve. The size of the pedicle of C2 increased with age. The median ranges of D1, D2, D3, α, and ß were 3.312-5.431 mm, 11.732-23.645 mm, 3.597-8.038 mm, 32.583°-36.640°, and 24.867°-31.567°, respectively. The curves fitting the trends of D1 and D3 were power functions, whereas D2 was fitted by a logarithmic curve. However, no curve fitted α or ß. A 3.5-mm screw can be placed in the pedicle of C2 in children aged more than 1 year. The growth and development trend of this pedicle can provide an anatomical reference for deciding on posterior cervical surgery and for selecting and designing pedicle screws for children.

A Retrospective Study of Factors Associated with Restoration of Thoracic Kyphosis in 43 Patients with Adolescent Idiopathic Scoliosis with Lenke Type 1 Curvature.

BACKGROUND This retrospective study aimed to identify the factors associated with successful surgical correction of thoracic kyphosis (TK) in 43 patients with adolescent idiopathic scoliosis (AIS) with Lenke type 1 curvature, in which the major curve with the largest Cobb angle was mainly in the thoracic region. MATERIAL AND METHODS We collected data from patients with Lenke 1 AIS. The following parameters were measured: Cobb angle, side-bending Cobb angle, cervical lordosis (CL), TK, lumbar lordosis (LL), pelvic incidence (PI), sacral slope (SS), pelvic tilt (PT), the sagittal vertical axis (SVA), the center of a C7 plumb line to the center sacral vertical line (C7-CSVL), correction rate, Ponte osteotomy, flexibility, and screw density. Univariate analysis and multivariate logistic regression analyses were performed. RESULTS Among the 43 cases analyzed, the mean postoperative Cobb angle at the last follow-up, C7-CSVL, SVA, CL, TK, LL, PI, SS, and PT were respectively 21.33±9.47°, 10.41±8.45 mm, 19.68±14.33 mm, 16.19±7.45°, 23.12±7.45°, 50.33±11.37°, 49.70±9.83°, 39.42±8.11°, and 10.16±6.63°. Univariate analysis suggested that preoperative TK, preoperative LL, and Ponte osteotomy were statistically significant (P<0.05), and multivariate analysis suggested that preoperative LL and Ponte osteotomy were statistically significant (P<0.05). CONCLUSIONS The results of this study demonstrated that preoperative TK, preoperative LL, and Ponte osteotomy were related factors for maintaining normal TK. Multivariate analysis suggested that preoperative LL and the use of Ponte osteotomy with full-thickness segmental resection of the spinal posterior column resulted in the successful surgical correction of TK in patients with AIS with Lenke type 1 curvature.

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.

Identification of clinical heterogeneity and construction of a novel subtype predictive model in patients with ankylosing spondylitis: An unsupervised machine learning study.

BACKGROUND: Accurate classification of patients with ankylosing spondylitis (AS) is the premise of precision medicine so as to perform different medical interventions for different patient types. AS pathology is closely related to the changes in the immune microenvironment. In this study, we used unsupervised machine learning (UML) to classify patients with AS based on clinical characteristics. We then constructed a novel subtype predictive model for AS based on the clinical classification, after which we investigated the difference in the immune microenvironment to unravel the AS pathogenesis. METHODS: Overall, 196 patients with AS were enrolled. UML was used to cluster AS patients by similar clinical characteristics. Functional ability, disease status, and grading of radiologic features were assessed to verify the accuracy and heterogeneity of UML clustering. Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest algorithm were used to screen and identify predictive factors for the novel subtype of AS. Logistic regression was also performed to construct a predictive model of this novel subtype. Datasets were downloaded from the Gene Expression Omnibus database to assess immune cell infiltration, and the results were validated using data of routine blood tests from 3671 AS patients and 5720 non-AS patients. The differential expression of Fat Mass and Obesity-Associated Protein (FTO), an m6A regulator, between AS patients and healthy control subjects was confirmed using immunohistochemistry. RESULTS: UML clustering identified two clusters. The clinical characteristics of the two clusters were significantly heterogeneous. For the novel subtype of AS identified in UML clustering, a predictive model was built using three predictive factors, namely, C-reactive protein (CRP), absolute value of neutrophils (NEU), and absolute value of monocytes (MONO). The area under the curve of the predictive model was 0.983. Heterogeneity in the neutrophil and monocyte counts in AS was verified through immune cell infiltration analysis. Data from routine blood tests revealed that NEU and MONO were significantly higher in AS patients than in non-AS patients (p < 0.001). FTO expression was negatively correlated with both NEU and MONO. Immunohistochemistry analysis confirmed the downregulated expression of FTO. CONCLUSIONS: UML provides an explicable and remarkable classification of a heterogeneous cohort of AS patients. A novel subtype of AS was identified in UML clustering. CRP, NEU, and MONO were the independent predictive factors for the novel subtype of AS. FTO expression was correlated with immune cell infiltration in AS patients.

Identification of osteosarcoma m6A-related prognostic biomarkers using artificial intelligence: RBM15.

Osteosarcoma has the worst prognosis among malignant bone tumors, and effective biomarkers are lacking. Our study aims to explore m6A-related and immune-related biomarkers. Gene expression profiles of osteosarcoma and healthy controls were downloaded from multiple public databases, and their m6A-based gene expression was utilized for tumor typing using bioinformatics. Subsequently, a prognostic model for osteosarcoma was constructed using the least absolute shrinkage and selection operator and multivariate Cox regression analysis, and its immune cell composition was calculated using the CIBERSORTx algorithm. We also performed drug sensitivity analysis for these two genes. Finally, analysis was validated using immunohistochemistry. We also examined the RBM15 gene by qRT-PCR in an in vitro experiment. We collected routine blood data from 1738 patients diagnosed with osteosarcoma and 24,344 non-osteosarcoma patients and used two independent sample t tests to verify the accuracy of the CIBERSORTx analysis for immune cell differences. The analysis based on m6A gene expression tumor typing was most reliable using the two typing methods. The prognostic model based on the two genes constituting RNA-binding motif protein 15 (RBM15) and YTDC1 had a much lower survival rate for patients in the high-risk group than those in the low-risk group (P < 0.05). CIBERSORTx immune cell component analysis demonstrated that RBM15 showed a negative and positive correlation with T cells gamma delta and activated natural killer cells, respectively. Drug sensitivity analysis showed that these two genes showed varying degrees of correlation with multiple drugs. The results of immunohistochemistry revealed that the expression of these two genes was significantly higher in osteosarcoma than in paraneoplastic tissues. The results of qRT-PCR experiments showed that the expression of RBM15 was significantly higher in both osteosarcomas than in the control cell lines. Absolute lymphocyte value, lymphocyte percentage, hematocrit and erythrocyte count were lower in osteosarcoma than in the control group (P < 0.001). RBM15 and YTHDC1 can serve as potential prognostic biomarkers associated with m6A in osteosarcoma.

MMP9 and STAT1 are biomarkers of the change in immune infiltration after anti-tuberculosis therapy, and the immune status can identify patients with spinal tuberculosis.

BACKGROUND: Due to a lack of studies on immune-related pathogenesis and a clinical diagnostic model, the diagnosis of Spinal Tuberculosis (STB) remains uncertain. Our study aimed to investigate the possible pathogenesis of STB and to develop a clinical diagnostic model for STB based on immune cell infiltration. METHODS: Label-free quantification protein analysis of five pairs of specimens was used to determine the protein expression of the intervertebral disc in STB and non-STB. GO enrichment analysis, and KEGG pathway analysis were used to investigate the pathogenesis of STB. The Hub proteins were then eliminated. Four datasets were downloaded from the GEO database to analyze immune cell infiltration, and the results were validated using blood routine test data from 8535TB and 7337 non-TB patients. Following that, clinical data from 164 STB and 162 non-STB patients were collected. The Random-Forest algorithm was used to screen out clinical predictors of STB and build a diagnostic model. The differential expression of MMP9 and STAT1 in STB and controls was confirmed using immunohistochemistry. RESULTS: MMP9 and STAT1 were STB Hub proteins that were linked to disc destruction in STB. MMP9 and STAT1 were found to be associated with Monocytes, Neutrophils, and Lymphocytes in immune cell infiltration studies. Data from 15,872 blood routine tests revealed that the Monocytes ratio and Neutrophils ratio was significantly higher in TB patients than in non-TB patients (p < 0.001), while the Lymphocytes ratio was significantly lower in TB patients than in non-TB patients (p < 0.001). MMP9 and STAT1 expression were downregulated following the anti-TB therapy. For STB, a clinical diagnostic model was built using six clinical predictors: MR, NR, LR, ESR, BMI, and PLT. The model was evaluated using a ROC curve, which yielded an AUC of 0.816. CONCLUSIONS: MMP9 and STAT1, immune-related hub proteins, were correlated with immune cell infiltration in STB patients. MR, NR, LR ESR, BMI, and PLT were clinical predictors of STB. Thus, the immune cell Infiltration-related clinical diagnostic model can predict STB effectively.

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.

Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts.

Introduction: The diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS. Methods: In this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients. Results: The ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care. Discussion: In this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.

Can the Risk of Postoperative Cerebrospinal Fluid Leakage Be Predicted for Patients Undergoing Cervical Spine Surgery? Development and Evaluation of a New Predictive Nomogram.

OBJECTIVE: Previous studies have retrospectively analyzed the likely causes of cerebrospinal fluid leakage (CSFL) during cervical spine surgery and the management of CSFL after its occurrence. In the present study, we aimed to develop and validate a nomogram for the risk of CSFL in Chinese patients who had undergone cervical decompression and internal fixation (CDIF) surgery. METHODS: We performed a retrospective analysis of patients who had undergone CDIF surgery. Of the 1286 included patients, 54 were in the CSFL group and 1232 were in the normal group. The patients were randomly divided into training and validation tests. The risk assessment for CSFL included 21 characteristics. The feature selection for the CSFL model was optimized using the least absolute shrinkage and selection operator regression model in the training test. Multivariate logistic regression analysis was performed to construct the model according to the selected characteristics. The clinical usefulness of the predictive model was assessed using the C-index, calibration curve, and decision curve analysis with identification and calibration. RESULTS: The risk prediction nomogram included the diagnosis, revision surgery, ossification of the posterior longitudinal ligament, cervical instability, and a history of malignancy in the training test. The model demonstrated high predictive power, with a C-index of 0.914 (95% confidence interval, 0.876-0.951) and an area under the curve of 0.914. The results of the decision curve analysis demonstrated the clinical usefulness of the CSFL risk nomogram when the probability threshold for CSFL was 1%-62%. CONCLUSIONS: Our proposed nomogram for CSFL risk includes the diagnosis, revision surgery, ossification of the posterior longitudinal ligament, cervical instability, and a history of malignancy. The nomogram can be used to evaluate the risk of CSFL for patients undergoing CDIF surgery.

Risk Factors in the Prediction of Leg Numbness after Spinal Endoscopic Surgery: Evaluation and Development of a Nomogram.

Purpose: This study aims at constructing a clinical predictive model that predicted the risk factors for leg numbness after spinal endoscopic surgery. Methods: We collected the clinical data of patients, including general information, imaging parameters, and clinical score, from our hospital's electronic database. Based on the postoperative leg numbness visual analog scale (LN-VAS), the clinical data were divided into the leg numbness group (≥25) and the improvement group (<25). All parameters were included in the least absolute shrinkage and selection operator (LASSO) regression analysis, while the parameters with the area under the curve (AUC) greater than 0.7 were selected to construct nomograms. Furthermore, the accuracy and validity of the model were evaluated using the C-index, decision curve analysis (DCA), calibration curve, and receiver operating characteristic curve (ROC). Results: A total of 73 patients' clinical data were included in the training set, where 51 patients were assigned to the improvement group and 22 to the leg numbness group. The nomogram was constructed using four selected parameters, including symptom duration, lumbar spinal stenosis (LSS), pelvic incidence (PI), and preoperative low back pain visual analog scale (LBP-VAS). The nomogram predictions were found to range between 0.01 and 0.99. The values of the C-index, AUC, and internally validated C-index were 0.96, 0.96, and 0.94, respectively. Our result showed that the clinical net benefit of the nomogram ranged between 0.01 and 0.99. Conclusion: Our clinical prediction model demonstrated high predictive ability and clinical validity. Moreover, we found that symptom duration, LSS, PI, and preoperative LBP-VAS were the predictive risk factors for leg numbness after spinal endoscopic surgery.

MRI T2WI High Signal Is a Risk Factor for Perioperative Complications in Patients with Cervical Spondylosis with Spinal Cord Compression: A Propensity Matching Score Analysis.

Objective: The purpose of this study was to compare the perioperative complications and clinical efficacy of patients with cervical spondylosis with spinal cord compression (CSWSCC) with or without MRI T2WIHS (T2-weighted image high signal) by means of propensity matching score grouping. Methods: We analyzed a single-center data of 913 surgical patients with CSWSCC by propensity matching score in this study, of which 326 patients had preoperative cervical MRI T2WIHS. The patient's general condition and perioperative indicators were collected. The MRI T2WIHS and normal groups were paired 1 : 1 to eliminate selection bias by propensity matching score. Finally, a total of 312 pairs were matched successfully. The results of perioperative complications and other outcome variables were compared between the two groups by Cox function analysis. Results: The postoperative blood loss, operation time, blood transfusion volume, systemic complications, local complications, volume of drainage, abnormal use of antibiotic, length of hospital stay, and JOA (Japanese Orthopaedic Association) improvement rate were analyzed. As the only complication with significant statistical difference, the incidence of IRI (ischemia-reperfusion injury) in patients with MRI T2WIHS was significantly higher. The length of hospital stay was more significantly increased in patients with MRI T2WIHS; on the contrary, the JOA improvement rate decreased significantly. Conclusion: This study confirmed that there was no significant difference in the incidence of perioperative complications in CSWSCC patients with or without MRI T2WIHS, except for the IRI. Moreover, the JOA improvement rate of patients without MRI T2WIHS was significantly better, with the length of hospital stay reduced.

Machine learning-based clustering in cervical spondylotic myelopathy patients to identify heterogeneous clinical characteristics.

Background: Anterior cervical decompression and fusion can effectively treat cervical spondylotic myelopathy (CSM). Accurately classifying patients with CSM who have undergone anterior cervical decompression and fusion is the premise of precision medicine. In this study, we used machine learning algorithms to classify patients and compare the postoperative efficacy of each classification. Methods: A total of 616 patients with cervical spondylotic myelopathy who underwent anterior cervical decompression and fusion were enrolled. Unsupervised machine learning algorithms (UMLAs) were used to cluster subjects according to similar clinical characteristics. Then, the results of clustering were visualized. The surgical outcomes were used to verify the accuracy of machine learning clustering. Results: We identified two clusters in these patients who had significantly different baseline clinical characteristics, preoperative complications, the severity of neurological symptoms, and the range of decompression required for surgery. UMLA divided the CSM patients into two clusters according to the severity of their illness. The repose to surgical treatment between the clusters was significantly different. Conclusions: Our results showed that UMLA could be used to rationally classify a heterogeneous cohort of CSM patients effectively, and thus, it might be used as the basis for a data-driven platform for identifying the cluster of patients who can respond to a particular treatment method.

Immune cell infiltration-related clinical diagnostic model for Ankylosing Spondylitis.

Background: The pathogenesis and diagnosis of Ankylosing Spondylitis (AS) has remained uncertain due to several reasons, including the lack of studies on the local and systemic immune response in AS. To construct a clinical diagnostic model, this study identified the micro RNA-messenger RNA (miRNA-mRNA) interaction network and immune cell infiltration-related hub genes associated with AS. Materials and Methods: Total RNA was extracted and purified from the interspinous ligament tissue samples of three patients with AS and three patients without AS; miRNA and mRNA microarrays were constructed using the extracted RNA. Bioinformatic tools were used to construct an miRNA-mRNA network, identify hub genes, and analyze immune infiltration associated with AS. Next, we collected the blood samples and clinical characteristics of 359 patients (197 with AS and 162 without AS). On the basis of the clinical characteristics and results of the routine blood tests, we selected immune-related cells whose numbers were significantly different in patients with AS and patients without AS. Univariate and multivariate logistic regression analysis was performed to construct a nomogram. Immunohistochemistry staining analysis was utilized to verify the differentially expression of LYN in AS and controls. Results: A total of 225 differentially expressed miRNAs (DE miRNAs) and 406 differentially expressed mRNAs (DE mRNAs) were identified from the microarray. We selected 15 DE miRNAs and 38 DE mRNAs to construct a miRNA-mRNA network. The expression of LYN, an immune-related gene, correlated with the counts of monocytes, neutrophils, and dendritic cells. Based on the independent predictive factors of sex, age, and counts of monocytes, neutrophils, and white blood cells, a nomogram was established. Receiver operating characteristic (ROC) analysis was performed to evaluate the nomogram, with a C-index of 0.835 and AUC of 0.855. Conclusion: LYN, an immune-related hub gene, correlated with immune cell infiltration in patients with AS. In addition, the counts of monocytes and neutrophils were the independent diagnostic factors for AS. If verified in future studies, a diagnostic model based on these findings may be used to predict AS effectively.

Novel prognostic biomarkers, METTL14 and YTHDF2, associated with RNA methylation in Ewing's sarcoma.

Ewing's sarcoma has a poor prognosis and high metastasis rate; thus, it is critical to explore prognostic biomarkers of m6A-related genes. Two datasets were downloaded from the Gene Expression Omnibus database, m6A-related genes were extracted, and prognostic models were constructed using the least absolute shrinkage and selection operator and multivariate COX regression analyses. Immune cell composition and drug sensitivity analyses were performed, and our analysis was validated using laboratory methods of immunohistochemical specific staining and qRT-PCR. Ewing's sarcoma prognostic model demonstrated that the survival rate of cases in the high-risk group was much lower than that of the low-risk group. Naïve B cells, macrophages M0, macrophages M1, and resting mast cells are closely associated with Ewing's sarcoma. METTL14 and YTHDF2 are strongly associated with multiple drug sensitivity. Immunohistochemical specific staining revealed higher expression of both METTL14 and YTHDF2 in Ewing's sarcoma than in the paraneoplastic tissues. The results of qRT-PCR showed that METTL14 expression was significantly higher in both ES cell lines than in the control cell line. The prognostic model constructed using m6A-related genes METTL14 and TYHDF2, can be a potential prognostic biomarker for Ewing's sarcoma, with the survival rate of cases in the high-risk group being much lower than that of the low-risk group.

Comparison of Clinical Data Between Patients With Complications and Without Complications After Spinal Tuberculosis Surgery: A Propensity Score Matching Analysis.

Purpose: This study used a propensity score matching (PSM) analysis to explore the risk factors of post-operative complications and compared the differences in clinical data between them following spinal tuberculosis surgery. Methods: The clinical data of patients with spinal tuberculosis were collected in our hospital from June 2012 to June 2021, including general information, laboratory results, surgical information, and hospitalization costs. The data were divided into two groups: complication and without complication groups. The baseline data of the two groups were obtained using the PSM analysis. Univariate and multivariate logistic analyses were used to analyze the differences between the two groups. Results: A total of 292 patients were included in the PSM analysis: 146 patients with complications and 146 patients without complications. The operation time, incision length, hospital stay, and albumin quantity in the complications group were 162 ± 74.1, 11.2 ± 4.76, 14.7 ± 9.34, and 1.71 ± 2.82, respectively, and those in the without complication group were 138 ± 60.5, 10.2 ± 3.56, 11.7 ± 7.44, and 0.740 ± 2.44, respectively. The laboratory costs, examination costs, guardianship costs, oxygen costs, and total costs in the complications group were higher than those in the without complication group. A significant difference was observed in the albumin quantity by logistic regression analysis (P < 0.05). Conclusion: Several costs in the complication group were higher than in the without complication group. The albumin quantity may be an independent factor to predict post-operative complications of spinal tuberculosis by logistic regression analysis.

Multiple Spinal Tuberculosis with Severe Kyphosis: A Case Report.

Background: The purpose of this study was to analyze the clinical efficacy of a patient with multiple tuberculosis of the spine combined with severe kyphosis. Case Summary: A 56-year-old male patient presented with low back pain with numbness and fatigue in both lower extremities for 5 months. Chest and back showed intermittent acid pain. The patient had not a history of constitutional symptoms. Preoperative X-ray and CT examination revealed multiple vertebral segmental bone destruction, multiple abscess calcification, and severe kyphosis. Preoperative MRI examination showed that the tuberculous abscess broke through the spinal canal and compressed the spinal cord and nerve roots. The patient underwent posterior lumbar abscess debridement, expanded decompression of the spinal canal, and nerve lysis in our hospital. The operation time was 70 min, and the intraoperative blood loss was 200 ml. The postoperative drainage volume was 250 ml. The patient was hospitalized for a total of 13 days, and the patient's vital signs were stable before and after surgery. The patient was satisfied with the treatment. Conclusion: For the patient with multiple spinal tuberculosis complicated with severe kyphosis and multiple calcified abscesses in this study, we considered performing abscess debridement to relieve the symptoms of back pain and achieved good clinical efficacy.

Upregulated of ANXA3, SORL1, and Neutrophils May Be Key Factors in the Progressionof Ankylosing Spondylitis.

Introduction: The specific pathogenesis of ankylosing spondylitis (AS) remains unclear, and our study aimed to investigate the possible pathogenesis of AS. Materials and Methods: Two datasets were downloaded from the GEO database to perform differentially expressed gene analysis, GO enrichment analysis, KEGG pathway analysis, DO enrichment analysis, GSEA analysis of differentially expressed genes, and construction of diagnostic genes using SVM and WGCNA along with Hypoxia-related genes. Also, drug sensitivity analysis was performed on diagnostic genes. To identify the differentially expressed immune genes in the AS and control groups, we analyzed the composition of immune cells between them. Then, we examined differentially expressed genes in three AS interspinous ligament specimens and three Degenerative lumbar spine specimens using high-throughput sequencing while the immune cells were examined using the neutrophil count data from routine blood tests of 1770 HLA-B27-positive samples and 7939 HLA-B27-negative samples. To assess the relationship between ANXA3 and SORL1 and disease activity, we took the neutrophil counts of the first 50 patients with above-average BASDAI scores and the last 50 patients with below-average BASDAI scores for statistical analysis. We used immunohistochemistry to verify the expression of ANXA3 and SORL1 in AS and in controls. Results: ANXA3 and SORL1 were identified as new diagnostic genes for AS. These two genes showed a significant differential expression between AS and controls, along with showing a significant positive correlation with the neutrophil count. The results of high-throughput sequencing verified that these two gene deletions were indeed differentially expressed in AS versus controls. Data from a total of 9707 routine blood tests showed that the neutrophil count was significantly higher in AS patients than in controls (p < 0.001). Patients with AS with a high BASDAI score had a much higher neutrophil count than those with a low score, and the difference was statistically significant (p < 0.001). The results of immunohistochemistry showed that the expression of ANXA3 and SORL1 in AS was significantly higher than that in the control group. Conclusion: Upregulated of ANXA3, SORL1, and neutrophils may be a key factor in the progression of Ankylosing spondylitis.