Cerebrospinal Fluid Leakage Combined with Blood Biomarkers Predicts Poor Wound Healing After Posterior Lumbar Spinal Fusion: A Machine Learning Analysis.

Objective: The objective of this study aimed to investigate the risk factors for poor wound healing (PWH) after posterior lumbar spinal fusion. Currently, there is limited research on the application of machine learning in analyzing PWH after spinal surgery. Thus, our primary aim is to using machine learning identify these risk factors and construct a clinical risk prediction model.

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.

Spinal Infections? mNGS Combined with Microculture and Pathology for Answers.

Introduction: This study evaluates the efficacy of metagenomic next-generation sequencing (mNGS) in diagnosing spinal infections and developing therapeutic regimens that combine mNGS, microbiological cultures, and pathological investigations.

Methods: Data were collected from 108 patients with suspected spinal infections between January 2022 and December 2023. Lesion tissues were obtained via C-arm assisted puncture or open surgery for mNGS, conventional microbiological culture, and pathological analysis.

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.

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).

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.

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.

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.

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.

MOF-on-MOF-Derived Hollow Co O /In O Nanostructure for Efficient Photocatalytic CO Reduction.

The photocatalytic transformation of carbon dioxide (CO ) into carbon-based fuels or chemicals using sustainable solar energy is considered an ideal strategy for simultaneously alleviating the energy shortage and environmental crises. However, owing to the low energy utilization of sunlight and inferior catalytic activity, the conversion efficiency of CO photoreduction is far from satisfactory. In this study, a MOF-derived hollow bimetallic oxide nanomaterial is prepared for the efficient photoreduction of CO .

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.

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.

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.

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.

A Predictive Clinical-Radiomics Nomogram for Differentiating Tuberculous Spondylitis from Pyogenic Spondylitis Using CT and Clinical Risk Factors.

Objective: The study aimed to develop and validate a nomogram model with clinical risk factors and radiomic features for differentiating tuberculous spondylitis (TS) from pyogenic spondylitis (PS).

Methods: A total of 254 patients with TS (n = 141) or PS (n = 113) were randomly divided into training (n = 180) and validation (n = 74) groups. In addition, 43 patients (TS = 22 and PS = 21) were collected to construct a test cohort.

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

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. 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.

A Novel Disease (Water Bubble Disease) of the Giant Freshwater Prawn Caused by : Antibiotic Treatment and Effects on the Antioxidant Enzyme Activity and Immune Responses.

The giant freshwater prawn, , is an important and economical aquaculture species widely farmed in tropical and subtropical areas of the world. A new disease, "water bubble disease (WBD)", has emerged and resulted in a large loss of cultured in China. A water bubble with a diameter of about 7 mm under the carapace represents the main clinical sign of diseased prawns.

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.

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis.

Introduction: Ankylosing spondylitis (AS) is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS mainly affects the axial bone, sacroiliac joint, hip joint, spinal facet, and adjacent ligaments. We used machine learning (ML) methods to construct diagnostic models based on blood routine examination, liver function test, and kidney function test of patients with AS.

Predicting Surgical Site Infection Risk after Spinal Tuberculosis Surgery: Development and Validation of a Nomogram.

The purpose of this study was to predict the surgical site infection risk after spinal tuberculosis surgery based on a nomogram. We collected the clinical data of patients who underwent spinal tuberculosis surgery in our hospital and included all the data in the least absolute shrinkage and selection operator (LASSO) regression analysis. Next, the selected parameters were analyzed using logistic regression.

Mechanism of COVID-19-Related Proteins in Spinal Tuberculosis: Immune Dysregulation.

Purpose: The purpose of this article was to investigate the mechanism of immune dysregulation of COVID-19-related proteins in spinal tuberculosis (STB).

Methods: Clinical data were collected to construct a nomogram model. C-index, calibration curve, ROC curve, and DCA curve were used to assess the predictive ability and accuracy of the model.

Monocyte-to-Lymphocyte Ratio Was an Independent Factor of the Severity of Spinal Tuberculosis.

Purpose: The purpose was to explore the relationship between monocyte-to-lymphocyte ratio (MLR) and the severity of spinal tuberculosis.

Methods: A total of 1,000 clinical cases were collected, including 496 cases of spinal tuberculosis and 504 cases of nonspinal tuberculosis. Laboratory blood results were collected, including C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), white blood cells (WBC), hemoglobin (HGB), platelets (PLT), neutrophil count, percentage of neutrophils, lymphocyte count, percentage of lymphocytes, monocyte count, percentage of monocytes, MLR, platelets -to- monocyte ratio (PMR), platelets -to- lymphocyte ratio (PLR), neutrophil -to- lymphocyte ratio (NLR), and platelets -to- neutrophil ratio (PNR).

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.