Publications by authors named "Xubin Quan"
Background: Patients with severe LDH, particularly those complicated by spinal stenosis or vertebral instability, frequently require PLIF to alleviate nerve compression and reconstruct spinal biomechanical stability. Aiming to optimize individualized surgical planning, it is necessary to establish accurate predictive models derived from multidimensional clinical data.
Methods: In this retrospective, multi-center study, the data utilized in this study were sourced from the Degenerative Spine Diseases in China (DSDC2024, NCT05867732).
Hance is a traditional medicinal and edible plant, but research on its polysaccharides has been limited. In this study, a purified polysaccharide, named AOP-1-1, was successfully extracted. Its structure was meticulously analysed through techniques, such as NMR and HPLC.
View Article and Find Full Text PDFTo overcome the limitations of SERS in food safety monitoring, particularly significant interference from citrate ions, this study introduces an intelligent SERS-based platform for food safety monitoring. The platform utilizes sodium borohydride to activate silver nanoparticles, and calcium ions can facilitate the nanoparticles aggregation to promote self-assembly and the form of "hotspots", but will also amplify citrate ions signal. Iodine ions was introduced to eliminate the interference of citrate signals and background fluorescence interference.
View Article and Find Full Text PDFArticle Synopsis
- Lumbar disc herniation (LDH) is a prevalent source of lower back pain and sciatica, with posterior lumbar interbody fusion (PLIF) being a standard treatment method, prompting a study on predicting blood transfusion needs during surgery.
- This study involved 6,241 patients across 22 medical centers in China and utilized various machine learning techniques to create an optimal predictive model for intraoperative blood transfusion using robust evaluation methods.
- The best-performing model, a simulated annealing support vector machine recursive + stacking model, achieved an area under the curve of 0.884, leading to the creation of a publicly accessible web calculator to aid clinicians in decision-making and improve patient management.
Article Synopsis
- - The research focuses on developing a clinical model to predict which patients undergoing posterior lumbar interbody fusion (PLIF) for lumbar spinal stenosis are likely to experience prolonged surgical times, which can lead to complications and affect recovery.
- - A total of 3,233 patients from 22 hospitals in China from 2015 to 2022 were included in the study, and their data was analyzed using machine-learning techniques to identify key factors associated with longer surgery durations.
- - The study utilized a training cohort and four test groups, applying various algorithms and performance evaluations to create a predictive model, ultimately aiming to enhance patient safety and surgical outcomes.
Purpose: The aim of this study was to develop and validate a machine learning (ML) model for predicting the risk of new osteoporotic vertebral compression fracture (OVCF) in patients who underwent percutaneous vertebroplasty (PVP) and to create a user-friendly web-based calculator for clinical use.
Methods: A retrospective analysis of patients undergoing percutaneous vertebroplasty: A retrospective analysis of patients treated with PVP between June 2016 and June 2018 at Liuzhou People's Hospital was performed. The independent variables of the model were screened using Boruta and modelled using 9 algorithms.
Article Synopsis
- The research focused on creating a machine learning model to predict the risk of extended hospital stays before surgery, aiming to enhance patient management.
- Patients who had posterior spinal deformity surgery in China between 2015 and 2022 were included, and their preoperative data was analyzed using various machine learning techniques to identify risk factors.
- The K Nearest Neighbors algorithm was the most effective in predicting prolonged hospitalization, with key factors including preoperative hemoglobin, height, BMI, age, and white blood cells, leading to the development of a web-based calculator for practical use.
Pesticide residue poses a significant global public health concern, necessitating improved detection methods. Here, a novel platform was introduced based on surface-enhanced Raman spectroscopy (SERS) to detect ten distinct types of pesticides. Notably, the sensitivity of this approach is exemplified by detecting trace amounts of 50 pM (10 ppt) thiabendazole.
View Article and Find Full Text PDFLabel-Free Detection of DNA via Surface-Enhanced Raman Spectroscopy Using Au@Ag Nanoparticles.
Nanomaterials (Basel)
September 2022
DNA is a building block of life; surface-enhanced Raman spectroscopy (SERS) has been broadly applied in the detection of biomolecules but there are challenges in obtaining high-quality DNA SERS signals under non-destructive conditions. Here, we developed a novel label-free approach for DNA detection based on SERS, in which the Au@AgNPs core-shell structure was selected as the enhancement substrate, which not only solved the problem of the weak enhancement effect of gold nanoparticles but also overcame the disadvantage of the inhomogeneous shapes of silver nanoparticles, thereby improving the sensitivity and reproducibility of the SERS signals of DNA molecules. The method obtained SERS signals for four DNA bases (A, C, G, and T) without destroying the structure, then further detected and qualified different specific structures of DNA molecules.
View Article and Find Full Text PDFBackground: Lung metastasis greatly affects medical therapeutic strategies in osteosarcoma. This study aimed to develop and validate a clinical prediction model to predict the risk of lung metastasis among osteosarcoma patients based on machine learning (ML) algorithms.
Methods: We retrospectively collected osteosarcoma patients from the Surveillance Epidemiology and End Results (SEER) database and from four hospitals in China.