Automatic Experimental Numerosity Generation and Numerical Training for Rodents.

Non-symbolic stimuli representing numerosities are invariably associated with continuous magnitudes, complicating the interpretation of experimental studies on numerosity perception. Although various algorithms for experimental numerosity generation have been proposed, they do not consider the quantifiable distribution of values of continuous magnitudes and the degree of numerosity-magnitudes association. Consequently, they cannot thoroughly exclude the possibility of magnitudes integration or strategy switch between different magnitudes in numerical stimulus perception.

Dual-network carboxymethyl chitosan conductive hydrogels for multifunctional sensors and high-performance triboelectric nanogenerators.

With the development of technology, there is a growing demand for wearable electronics that can fulfill different application scenarios. Hydrogel-based sensors are considered ideal candidates for realizing multifunctional wearable flexible devices. However, there are great challenges in preparing hydrogel-based sensors with both superior mechanical and electrical properties.

Liquid-free ionic conductive elastomers with high mechanical properties and ionic conductivity for multifunctional sensors and triboelectric nanogenerators.

Liquid-free ionic conductive elastomers (ICEs) are ideal materials for constructing flexible electronic devices by avoiding the limitations of liquid components. However, developing all-solid-state ionic conductors with high mechanical strength, high ionic conductivity, excellent healing, and recyclability remains a great challenge. Herein, a series of liquid-free polyurethane-based ICEs with a double dynamic crosslinked structure are reported.

Disparate processing of numerosity and associated continuous magnitudes in rats.

The studies of number sense in different species are severely hampered by the inevitable entanglement of non-numerical attributes inherent in nonsymbolic stimuli representing numerosity, resulting in contrasting theories of numerosity processing. Here, we developed an algorithm and associated analytical methods to generate stimuli that not only minimized the impact of non-numerical magnitudes in numerosity perception but also allowed their quantification. We trained number-naïve rats with these stimuli as sound pulses representing two or three numbers and demonstrated that their numerical discrimination ability mainly relied on numerosity.

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.

Investigations of Water Transport in Shale Reservoir with Dual-Wettability by Using Monte Carlo Method.

How liquids transport in the shale system has been the focus because of fracturing fluid loss. In this study, a single-nanopore model is established for liquid transport in shale while considering the slip effect and effective viscosity of confined fluids. Then, the fractal Monte Carlo (FMC) model is proposed to upscale the single-pore model into shale porous media.

Analysis of the Influencing Factors on the Extraction of Residual Oil through the Gel Foam Flooding of Underground Reservoirs in the Tahe Oilfield.

Fractured-vuggy reservoirs are mainly composed of three types: underground rivers, vugs, and fractured-vuggy structures. Based on the similarity criterion, a 3D model can truly reflect the characteristics of the multi-scale space of a fractured-vuggy reservoir, and it can reflect fluid flow laws in the formation. Water flooding, gas flooding, and gel foam flooding were carried out in the model sequentially.

HMGB1 Mediates Inflammation-Induced DMT1 Increase and Dopaminergic Neurodegeneration in the Early Stage of Parkinsonism.

Both neuroinflammation and iron accumulation play roles in the pathogenesis of Parkinson's disease (PD). However, whether inflammation induces iron dyshomeostasis in dopaminergic neurons at an early stage of PD, at which no quantifiable dopaminergic neuron loss can be observed, is still unknown. As for the inflammation mediators, although several cytokines have been reported to increase in PD, the functions of these cytokines in the SN are double-edged and controversial.

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.

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.

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.

Pharmacological interventions targeting α-synuclein aggregation triggered REM sleep behavior disorder and early development of Parkinson's disease.

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by elevated motor behaviors and dream enactments in REM sleep, often preceding the diagnosis of Parkinson's disease (PD). As RBD could serve as a biomarker for early PD developments, pharmacological interventions targeting α-synuclein aggregation triggered RBD could be applied toward early PD progression. However, robust therapeutic guidelines toward PD-induced RBD are lacking, owing in part to a historical paucity of effective treatments and trials.

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.

Optimal antiplatelet therapy for patients after antiplatelet therapy induced gastrointestinal bleeding: timing.

Adjusting antiplatelet strategies after antiplatelet-associated gastrointestinal bleeding (GIB) is a complex clinical challenge. To assess the risk of outcomes at different times of resumption of antiplatelet therapy in an attempt to find the optimal time to resume therapy. The study analyzed consecutive patients with antiplatelet-associated GIB from Beijing Friendship Hospital Information System between October 2019 and June 2022.

The efficacy of thoracolumbar interfascial plane block for lumbar spinal surgeries: a systematic review and meta-analysis.

Background: The intent of this meta-analysis was to examine the efficacy of thoracolumbar interfascial plane block (TLIP) for pain control after lumbar spinal surgery.

Methods: Randomized controlled trials (RCTs) published on PubMed, CENTRAL, Scopus, Embase, and Web of Science databases up to February 10, 2023, comparing TLIP with no or sham block or wound infiltration for lumbar spinal surgeries were included. Pain scores, total analgesic consumption, and postoperative nausea and vomiting (PONV) were analyzed.

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.

Mean Platelet Volume to Platelet Count Ratio Predicts Left Atrial Stasis in Patients with Non-Valvular Atrial Fibrillation.

Background: The mean platelet volume to platelet count ratio (MPV/PC) has been investigated in the diagnosis, prognosis and risk stratification in several diseases. However, the predictive role of MPV/PC in left atrial stasis (LAS) of non-valvular atrial fibrillation (NVAF) patients remains unknown.

Methods: A total of 217 consecutive NVAF patients undergoing transesophageal echocardiogram (TEE) evaluation were retrospectively enrolled.

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.

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.

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.

Difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning.

Background: Tuberculosis (TB) is a chronic infectious disease. Bone and joint TB is a common type of extrapulmonary TB and often occurs secondary to TB infection. In this study, we aimed to find the difference in the blood examination results of patients with bone and joint TB and patients with TB by using machine learning (ML) and establish a diagnostic model to help clinicians better diagnose the disease and allow patients to receive timely treatment.

MEG3 alleviates ankylosing spondylitis by suppressing osteogenic differentiation of mesenchymal stem cells through regulating microRNA-125a-5p-mediated TNFAIP3.

Osteoblasts are important regulators of bone formation, but their roles in ankylosing spondylitis (AS) remain unclear. This study aims to explore the role of long non-coding RNA (lncRNA) maternally expressed 3 (MEG3) MEG3 in AS. Serum from AS patients as well as AS mesenchymal stem cells (ASMSCs) and healthy donors mesenchymal stem cells (HDMSCs) was collected.