Deciphering distinct pathogenic mechanisms of ankylosing spondylitis and systemic sclerosis via shared biomarkers ZSWIM6 and CCL3L3: Insights from an integrative bioinformatics approach.

Ankylosing Spondylitis (AS) and Systemic Sclerosis (SSc) are both autoimmune diseases, albeit with distinct anatomical targets. AS primarily affects the spine and sacroiliac joints, triggering inflammation and eventual fusion of the vertebrae. SSc predominantly impacts the skin and connective tissues, leading to skin fibrosis, thickening, and potential damage to vital organs such as the lungs, heart, and kidneys.

Exploring T Cell and NK Cell Involvement in Ankylosing Spondylitis Through Single-Cell Sequencing.

To uncover the complex immune mechanisms driving inflammation in ankylosing spondylitis and lay the groundwork for identifying new therapeutic targets and innovative approaches, we conducted 10× single-cell sequencing on bone marrow cell samples collected from the vertebrae of three AS patients and three non-AS patients. Using single-cell sequencing data, we analysed the expression of differentially expressed genes (DEGs) by comparing AS patients with non-AS patients. Key genes among the related DEGs were identified through protein-protein interaction networks and hub gene screening and further validated using immunohistochemistry.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

Background: () 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 , drug targets, and disease targets associated with ankylosing spondylitis.

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.

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).

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 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.

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.