Percutaneous spinal cord stimulation cylindrical lead placement for managing refractory neuropathic pain: A case series with an endoscopic-assisted approach.

Background: The paddle lead (PL) and cylindrical lead (CL) remain the main implant categories in spinal cord stimulation (SCS) for treating neuropathic pain. Surgeons often complain about the greater trauma associated with PL implantation, while percutaneous endoscopic technique offers a promising approach for minimizing the trauma associated to PL implantation. However, there remains a dearth of real-world case study on endoscopy-assisted CL implantation.

PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery.

Prediction of protein-protein binding (PPB) affinity plays an important role in large-molecular drug discovery. Deep learning (DL) has been adopted to predict the changes of PPB binding affinities upon mutations, but there was a scarcity of studies predicting the PPB affinity itself. The major reason is the paucity of open-source dataset with PPB affinity data.

Global Trends in Orthopedic Biofilm Research: A Bibliometric Analysis of 1994-2022.

Objective: Bibliometric analysis is commonly used to visualize the knowledge foundation, trends, and patterns in a specific scientific field by performing a quantitative evaluation of the relevant literature. The purpose of this study was to perform a bibliometric analysis of recent studies in the field of orthopedic biofilm research and identify its current trends and hotspots.

Methods: Research studies were retrieved from the Web of Science Core Collection and Scopus databases and analyzed in bibliometrix with R package (4.

Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms.

Background: The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are key to the accurate differential diagnosis of LSS. The aim of this study is to develop an automatic dura-contouring tool for radiographic quantification on computed tomography myelogram (CTM) for patients with LSS.

A Suture Technique for Ruptured Annulus Fibrosus Following Decompression Under Percutaneous Transforaminal Endoscopic Discectomy.

The suture technique for a ruptured annulus fibrosus (AF) under full-endoscopy remains challenging. Direct suturing of a ruptured annular tear after full decompression has been shown to decrease the recurrence rate of lumbar disc herniation during endoscopic surgery. Traditional suture operations under endoscopy involve only simple suturing of the ruptured AF.

Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography.

Background: The accurate preoperative identification of decompression levels is crucial for the success of surgery in patients with multi-level lumbar spinal stenosis (LSS). The objective of this study was to develop machine learning (ML) classifiers that can predict decompression levels using computed tomography myelography (CTM) data from LSS patients.

Methods: A total of 1095 lumbar levels from 219 patients were included in this study.

Mapping the hotspots and future trends of electrical stimulation for peripheral nerve injury: A bibliometric analysis from 2002 to 2023.

Peripheral nerve injuries often result in severe personal and social burden, and even with surgical treatment, patients continue to have poor clinical outcomes. Over the past two decades, electrical stimulation has been shown to promote axonal regeneration and alleviate refractory neuropathic pain. The aim of this study was to analyse this field using a bibliometric approach.

Global Scientific Trends in Virtual Reality for Pain Treatment From 2000 to 2022: Bibliometric Analysis.

Background: Virtual reality (VR) is a computer simulation technique that has been increasingly applied in pain management over the past 2 decades.

Objective: In this study, we used bibliometrics to explore the literature on VR and pain control, with the aim of identifying research progress and predicting future research hot spots.

Methods: We extracted literature on VR and pain control published between 2000 and 2022 from the Web of Science Core Collections and conducted bibliometric analyses.

Prevalence and risk factors of bone metastasis and the development of bone metastatic prognostic classification system: a pan-cancer population study.

Background: The prevalence of bone metastasis (BM) varies among primary cancer patients, and it has a significant impact on prognosis. However, there is a lack of research in this area. This study aims to explore the clinical characteristics, prevalence, and risk factors, and to establish a prognostic classification system for pan-cancer patients with BM.

Early Prognostication of Critical Patients With Spinal Cord Injury: A Machine Learning Study With 1485 Cases.

Study Design: A retrospective case-series.

Objective: The study aims to use machine learning to predict the discharge destination of spinal cord injury (SCI) patients in the intensive care unit.

Summary Of Background Data: Prognostication following SCI is vital, especially for critical patients who need intensive care.

Novel Nomograms and Web-Based Tools Predicting Overall Survival and Cancer-specific Survival of Solitary Plasmacytoma of the Spine.

Study Design: Retrospective analysis.

Objective: This study aimed to establish nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) in patients with solitary plasmacytoma of the spine (SPS).

Summary Of Background Data: SPS is a rare type of malignant spinal tumor.

Research topics and hotspot trends of lumbar spondylolisthesis: A text-mining study with machine learning.

Objectives: The study aimed to conduct a bibliometric analysis of publications concerning lumbar spondylolisthesis, as well as summarize its research topics and hotspot trends with machine-learning based text mining.

Methods: The data were extracted from the Web of Science Core Collection (WoSCC) database and then analyzed in Rstudio1.3.

Web-Based Nomograms for Overall Survival and Cancer-Specific Survival of Bladder Cancer Patients with Bone Metastasis: A Retrospective Cohort Study from SEER Database.

Background: Our study aimed to explore the prognostic factors of bladder cancer with bone metastasis (BCBM) and develop prediction models to predict the overall survival (OS) and cancer-specific survival (CSS) of BCBM patients.

Methods: A total of 1438 patients with BCBM were obtained from the SEER database. Patients from 2010 to 2016 were randomly divided into training and validation datasets (7:3), while patients from 2017 were divided for external testing.

Research trends and hotspots on connectomes from 2005 to 2021: A bibliometric and latent Dirichlet allocation application study.

Background: This study aimed to conduct a bibliometric analysis of publications on connectomes and illustrate its trends and hotspots using a machine-learning-based text mining algorithm.

Methods: Documents were retrieved from the Web of Science Core Collection (WoSCC) and Scopus databases and analyzed in Rstudio 1.3.

Machine Learning Predict Survivals of Spinal and Pelvic Ewing's Sarcoma with the SEER Database.

Study Design: Retrospective Cohort Study.

Objectives: This study aimed to develop survival prediction models for spinal Ewing's sarcoma (EWS) based on machine learning (ML).

Methods: We extracted the SEER registry's clinical data of EWS diagnosed between 1975 and 2016.

Accuracy and reliability analysis of a machine learning based segmentation tool for intertrochanteric femoral fracture CT.

Introduction: Three-dimensional (3D) reconstruction of fracture fragments on hip Computed tomography (CT) may benefit the injury detail evaluation and preoperative planning of the intertrochanteric femoral fracture (IFF). Manually segmentation of bony structures was tedious and time-consuming. The purpose of this study was to propose an artificial intelligence (AI) segmentation tool to achieve semantic segmentation and precise reconstruction of fracture fragments of IFF on hip CTs.

Development and Validation of a Personalized Prognostic Prediction Model for Patients With Spinal Cord Astrocytoma.

Background: The study aimed to investigate the prognostic factors of spinal cord astrocytoma (SCA) and establish a nomogram prognostic model for the management of patients with SCA.

Methods: Patients diagnosed with SCA between 1975 and 2016 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and testing datasets (7:3). The primary outcomes of this study were overall survival (OS) and cancer-specific survival (CSS).

Surgically Relevant Morphological Parameters of the L5-S1 Interlaminar Window: A Statistical Analysis Based on 3D Reconstruction of CT Data.

Background:  The interlaminar window is the most important anatomical corridor during the posterior approach for lumbar and lumbosacral pathologies. Three-dimensional (3D) reconstruction of the L5-S1 interlaminar window including accurate measurements may be beneficial for the surgeon. The aim of this study was to measure relevant surgical parameters of the L5-S1 interlaminar window based on 3D reconstruction of lumbar computed tomography (CT).

Machine Learning-based Prediction of Prolonged Intensive Care Unit Stay for Critical Patients with Spinal Cord Injury.

Study Design: A retrospective cohort study.

Objective: The objective of the study was to develop machine-learning (ML) classifiers for predicting prolonged intensive care unit (ICU)-stay and prolonged hospital-stay for critical patients with spinal cord injury (SCI).

Summary Of Background Data: Critical patients with SCI in ICU need more attention.

Aucubin Attenuates Liver Ischemia-Reperfusion Injury by Inhibiting the HMGB1/TLR-4/NF-κB Signaling Pathway, Oxidative Stress, and Apoptosis.

Liver ischemia-reperfusion injury (IRI) is a common clinical event with high morbidity in patients undergoing complex liver surgery or having abdominal trauma. Inflammatory and oxidative stress responses are the main contributing factors in liver IRI. The iridoid glucoside aucubin (AU) has good anti-inflammatory and antioxidative effects; however, there are no relevant reports on the protective effect of glucosides on hepatic IRI.

Deep learning-based lumbosacral reconstruction for difficulty prediction of percutaneous endoscopic transforaminal discectomy at L5/S1 level: A retrospective cohort study.

Background: Deep learning has been validated as a promising technique for automatic segmentation and rapid three-dimensional (3D) reconstruction of lumbosacral structures on CT. Simulated foraminoplasty of percutaneous endoscopic transforaminal discectomy (PETD) through the Kambin triangle may benefit viability assessment of PETD at L5/S1 level.

Material And Methods: Medical records and radiographic data of patients with L5/S1 lumbar disc herniation (LDH) who received a single-level PETD from March 2013 to February 2018 were retrospectively collected and analyzed.