An Inter- and Intra-Rater Agreement Assessment of Two Novel Classification Systems for Coronal Imbalance in Adult Scoliosis and Spine Deformity.

Objective: Coronal malalignment is a common feature of adult spinal deformity, and accurate classification is essential for diagnosis and treatment planning. However, variations in interpretation among clinicians can impact classification consistency. By assessing the reliability and applicability of these systems across different medical experts, this study seeks to establish a standardized approach to enhance clinical outcomes.

Molecular architecture of the altered cortical complexity in autism.

Autism spectrum disorder (ASD) is characterized by difficulties in social interaction, communication challenges, and repetitive behaviors. Despite extensive research, the molecular mechanisms underlying these neurodevelopmental abnormalities remain elusive. We integrated microscale brain gene expression data with macroscale MRI data from 1829 participants, including individuals with ASD and typically developing controls, from the autism brain imaging data exchange I and II.

[Discussion on the genetic mechanisms and efficacy of growth hormone therapy in children with idiopathic short stature].

Idiopathic short stature (ISS) is a term that encompasses a group of short stature disorders with unknown etiology. The genetic factors associated with ISS are complex, and the known genetic mechanisms include alterations in hormones, hormone receptors, or related pathways, defects in fundamental cellular processes (such as intracellular signaling pathways and transcriptional regulation), issues with extracellular matrix or paracrine signaling, as well as genetic variations in the genes encoding these proteins. Recombinant human growth hormone (rhGH) therapy is currently an effective clinical method for improving height in children with ISS.

Investigating the causal links between inflammatory cytokines and scoliosis through bidirectional Mendelian randomization analysis.

Background: Scoliosis, characterized by a lateral curvature of the spine, affects millions globally. The role of inflammatory cytokines in the pathogenesis of scoliosis is increasingly acknowledged, yet their causal relationships remain poorly defined.

Aims: This study aims to explore the genetic-level causal relationships between inflammatory cytokines and scoliosis utilizing bidirectional Mendelian randomization (MR) analysis.

Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI).

Background: Tuberculosis spondylitis (TS), commonly known as Pott's disease, is a severe type of skeletal tuberculosis that typically requires surgical treatment. However, this treatment option has led to an increase in healthcare costs due to prolonged hospital stays (PLOS). Therefore, identifying risk factors associated with extended PLOS is necessary.

A dynamic graph structural framework for implicit sentiment identification based on complementary semantic and structural information.

Implicit sentiment identification has become the classic challenge in text mining due to its lack of sentiment words. Recently, graph neural network (GNN) has made great progress in natural language processing (NLP) because of its powerful feature capture ability, but there are still two problems with the current method. On the one hand, the graph structure constructed for implicit sentiment text is relatively single, without comprehensively considering the information of the text, and it is more difficult to understand the semantics.

A comprehensive study of the novel 4D hyperchaotic system with self-exited multistability and application in the voice encryption.

This paper describes a novel 4-D hyperchaotic system with a high level of complexity. It can produce chaotic, hyperchaotic, periodic, and quasi-periodic behaviors by adjusting its parameters. The study showed that the new system experienced the famous dynamical property of multistability.

Photocatalytic degradation of methylene blue by TiO/NdO composite thin films.

To improve the photocatalytic property of TiO thin films, the composite thin films of TiO/NdO structure were fabricated by electron-beam physical vapor deposition method, and the photocatalytic property of the fabricated films was experimentally studied in the present work. The XRD and Raman analyses show that the TiO/NdO films are mainly hexagonal crystalline phase of NdO. The XPS analysis for the chemical state changes of Ti, O and Nd of the basic elements in the films is confirming the electron flow in the internal electric field which generated in the TiO/NdO films.

Emerging trends on the anatomy teaching reforms in the last 10 years: Based on VOSviewer and CiteSpace.

The study of organism structural composition, known as anatomy, is essential in comprehending the intricate arrangements of life and plays a crucial role in medical education and practice. It bridges foundational and clinical disciplines, shaping medical education, and practice. With evolving technology, medical education faces new challenges necessitating pedagogical innovations.

Posterior fixation, anterior debridement and bone grafting in the treatment of thoracic and lumbar tuberculosis in children younger than 3 years of age.

Purpose: This study aimed to investigate the clinical outcomes of posterior fixation, combined with one- or two-stage anterior debridement and bone grafting in treating children younger than 3 years of age with thoracic and lumbar tuberculosis.

Methods: This was a retrospective study involving 16 young children with thoracic or lumbar tuberculosis. Surgical data were recorded.

MRI-based interpretable radiomics nomogram for discrimination between Brucella spondylitis and Pyogenic spondylitis.

Background: Pyogenic spondylitis (PS) and Brucella spondylitis (BS) are commonly seen spinal infectious diseases. Both types can lead to vertebral destruction, kyphosis, and long-term neurological deficits if not promptly diagnosed and treated. Therefore, accurately diagnosis is crucial for personalized therapy.

The Potential of a CT-Based Machine Learning Radiomics Analysis to Differentiate Brucella and Pyogenic Spondylitis.

Background: Pyogenic spondylitis (PS) and Brucella spondylitis (BS) are common spinal infections with similar manifestations, making their differentiation challenging. This study aimed to explore the potential of CT-based radiomics features combined with machine learning algorithms to differentiate PS from BS.

Methods: This retrospective study involved the collection of clinical and radiological information from 138 patients diagnosed with either PS or BS in our hospital between January 2017 and December 2022, based on histopathology examination and/or germ isolations.

Improvement of Pulmonary Function and Reconstructed 3-Dimensional Lung Volume After Halo-Pelvic Traction Combined With Posterior Correction for Severe Rigid Spinal Scoliosis: A Multicenter Study.

Background And Objectives: Severe rigid spinal scoliosis (SRSS) leads to severe restrictive ventilation dysfunction. Currently, the reports about the influence of preoperative halo-pelvic traction (HPT) combined with correction surgery on pulmonary function in patients with SRSS were relatively few. This study aims to investigate (1) the influence of preoperative HPT on lung volume and pulmonary function, (2) the further influence of the following correction surgery on lung volume and pulmonary function, and (3) the relationship among deformity correction, pulmonary function test outcomes, and computed tomography-based lung volume.

Electrochemical analysis of asymmetric supercapacitors based on BiCoO@g-CN nanocomposites.

Supercapacitors are gaining popularity these days because of their good cycle stability, superior specific capacitance, high power density, and energy density. Herein, we report the synthesis of bismuth cobalt oxide (BiCoO) combined with graphitic carbon nitride (g-CN) by the hydrothermal method. The BiCoO@g-CN nanocomposite was well characterized using XRD, FE-SEM, FT-IR, and DRS-UV techniques.

Development and validation of a novel nomogram to predict the risk of the prolonged postoperative length of stay for lumbar spinal stenosis patients.

Background: Lumber spinal stenosis (LSS) is the increasingly reason for spine surgery for elder patients since China is facing the fastest-growing aging population. The aim of this research was to create a model to predict the probabilities of requiring a prolonged postoperative length of stay (PLOS) for lumbar spinal stenosis patients, minimizing the healthcare burden.

Methods: A total of 540 LSS patients were enrolled in this project.

Knowledge-Fusion-Based Iterative Graph Structure Learning Framework for Implicit Sentiment Identification.

Implicit sentiment identification is a significant classical task in text analysis. Graph neural networks (GNNs) have recently been successful in implicit sentiment identification, but the current approaches still suffer from two problems. On the one hand, there is a lack of structural information carried by the single-view graph structure of implicit sentiment texts to accurately capture obscure sentiment expressions.

Incorporation of graphene oxide/titanium dioxide with different polymer materials and its effects on methylene blue dye rejection and antifouling ability.

Exposure of synthetic dye, such as methylene blue (MB), in water bodies led to a serious threat to living things because they are toxic and non-degradable. Amongst the introduced dye removal methods, membrane separation process can be considered a powerful technique for treating dye contamination. However, this method commonly suffered from drawbacks, such as short membrane lifetime, low permeability and selectivity.

Corrigendum: Population genetics analysis of Tolai hares () in Xinjiang, China using genome-wide SNPs from SLAF-seq and mitochondrial markers.

[This corrects the article DOI: 10.3389/fgene.2022.

Some novel mathematical analysis on the fractional-order 2019-nCoV dynamical model.

Since December 2019, the whole world has been facing the big challenge of Covid-19 or 2019-nCoV. Some nations have controlled or are controlling the spread of this virus strongly, but some countries are in big trouble because of their poor control strategies. Nowadays, mathematical models are very effective tools to simulate outbreaks of this virus.

Development and validation of a diagnostic model for differentiating tuberculous spondylitis from brucellar spondylitis using machine learning: A retrospective cohort study.

Background: Tuberculous spondylitis (TS) and brucellar spondylitis (BS) are commonly observed in spinal infectious diseases, which are initially caused by bacteremia. BS is easily misdiagnosed as TS, especially in underdeveloped regions of northwestern China with less sensitive medical equipment. Nevertheless, a rapid and reliable diagnostic tool remains to be developed and a clinical diagnostic model to differentiate TS and BS using machine learning algorithms is of great significance.