Precise management system for chronic intractable pain patients implanted with spinal cord stimulation based on a remote programming platform: study protocol for a randomized controlled trial (PreMaSy study).

BACKGROUND: Spinal cord stimulation (SCS) is a surgical technique used in patients with chronic intractable pain, and its effectiveness and safety have been validated by multiple studies. However, to maintain an optimal and steady long-term effect is still challenging. Here, we report a new management paradigm integrating smartphone application and remote programming. Chronic pain patients with SCS implants can monitor their pain status on the phone and change stimulation parameters accordingly. The PreMaSy study is a randomized controlled trial to evaluate the clinical effectiveness and safety of this precise management system. METHODS: Patients with chronic intractable pain will be screened for eligibility, and 82 participants are anticipated to be enrolled in this trial. After the electrode implantation, the stimulation effectiveness will be tested. Participants with a reduction of more than 50% in the visual analog scale (VAS) will receive implantation of an implantable pulse generator and randomized (1:1) into the experimental group or control group. All participants will be asked to take online follow-ups and complete assessments using a smartphone application. Daily pain characteristic assessments and monthly quality of life questionnaires are integrated into the App, and participants will be required to complete these assessments. The daily VAS for pain intensity will be monitored and a threshold will be set based on baseline VAS score. The interventional appointment will be scheduled once the threshold is reached. The primary outcome is the health condition and quality of life assessed by the five-level EuroQol five-dimensional questionnaire (EQ-5D-5L). Utility values of EQ-5D-5L will be assessed at baseline and 1, 3, and 6 months post-operative. DISCUSSION: The PreMaSy study aims to evaluate the effectiveness and safety of a novel App-based, patient-centered, self-assessment management system for chronic intractable pain. A randomized controlled trial is designed to test the non-inferiority of this precise management system compared to the monthly online follow-ups. It is also expected to yield valuable experiences regarding precision medicine. TRIAL REGISTRATION: ClinicalTrials.gov NCT05761392. Registered on March 07, 2023.

Prevotella oris-caused meningitis and spinal canal infection: A case report.

BACKGROUND: Prevotella oris-induced meningitis and Prevotella oris-induced meningitis concomitant with spinal canal infection are extremely rare. To the best of our knowledge, only 1 case of Prevotella oris-induced central system infection has been reported. This is the second report on meningitis combined with spinal canal infection due to Prevotella oris. CASE SUMMARY: We report a case of a 9-year-old boy suffering from meningitis and spinal canal infection. The patient presented to the neurosurgery department with lumbosacral pain for 1 mo and headache and vomiting for 1 d. He had been treated with cephalosporin and nonsteroidal anti-inflammatory drugs for fever, otalgia and pharyngalgia in a local hospital 2 mo prior to this admission. During hospitalization, magnetic resonance imaging suggested meningitis and L3-S1 lumbosacral dural sac infection. The cerebrospinal fluid and blood cultures were negative, but the cerebrospinal fluid specimen indicated the presence of Prevotella oris by metagenomic next-generation sequencing. Previous cases of Prevotella oris infection were retrieved from PubMed to characterize the clinicopathological features and identify the prognostic factors and related antimicrobial treatment of infection due to Prevotella oris. CONCLUSION: This report shed light on the characteristics of Prevotella oris infection and highlighted the role of metagenomic next-generation sequencing in pathogen detection.

Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study.

BACKGROUND: Determining the grade and molecular marker status of intramedullary gliomas is important for assessing treatment outcomes and prognosis. Invasive biopsy for pathology usually carries a high risk of tissue damage, especially to the spinal cord, and there are currently no non-invasive strategies to identify the pathological type of intramedullary gliomas. Therefore, this study aimed to develop a non-invasive machine learning model to assist doctors in identifying the intramedullary glioma grade and mutation status of molecular markers. METHODS: A total of 461 patients from two institutions were included, and their sagittal (SAG) and transverse (TRA) T2-weighted magnetic resonance imaging scans and clinical data were acquired preoperatively. We employed a transformer-based deep learning model to automatically segment lesions in the SAG and TRA phases and extract their radiomics features. Different feature representations were fed into the proposed neural networks and compared with those of other mainstream models. RESULTS: The dice similarity coefficients of the Swin transformer in the SAG and TRA phases were 0.8697 and 0.8738, respectively. The results demonstrated that the best performance was obtained in our proposed neural networks based on multimodal fusion (SAG-TRA-clinical) features. In the external validation cohort, the areas under the receiver operating characteristic curve for graded (WHO I-II or WHO III-IV), alpha thalassemia/mental retardation syndrome X-linked (ATRX) status, and tumor protein p53 (P53) status prediction tasks were 0.8431, 0.7622, and 0.7954, respectively. CONCLUSIONS: This study reports a novel machine learning strategy that, for the first time, is based on multimodal features to predict the ATRX and P53 mutation status and grades of intramedullary gliomas. The generalized application of these models could non-invasively provide more tumor-specific pathological information for determining the treatment and prognosis of intramedullary gliomas.

Automatic and Efficient Prediction of Hematoma Expansion in Patients with Hypertensive Intracerebral Hemorrhage Using Deep Learning Based on CT Images.

Patients with hypertensive intracerebral hemorrhage (ICH) have a high hematoma expansion (HE) incidence. Noninvasive prediction HE helps doctors take effective measures to prevent accidents. This study retrospectively analyzed 253 cases of hypertensive intraparenchymal hematoma. Baseline non-contrast-enhanced CT scans (NECTs) were collected at admission and compared with subsequent CTs to determine the presence of HE. An end-to-end deep learning method based on CT was proposed to automatically segment the hematoma region, region of interest (ROI) feature extraction, and HE prediction. A variety of algorithms were employed for comparison. U-Net with attention performs best in the task of segmenting hematomas, with the mean Intersection overUnion (mIoU) of 0.9025. ResNet-34 achieves the most robust generalization capability in HE prediction, with an area under the receiver operating characteristic curve (AUC) of 0.9267, an accuracy of 0.8827, and an F1 score of 0.8644. The proposed method is superior to other mainstream models, which will facilitate accurate, efficient, and automated HE prediction.

White matter regeneration induced by aligned fibrin nanofiber hydrogel contributes to motor functional recovery in canine T12 spinal cord injury.

A hierarchically aligned fibrin hydrogel (AFG) that possesses soft stiffness and aligned nanofiber structure has been successfully proven to facilitate neuroregeneration in vitro and in vivo. However, its potential in promoting nerve regeneration in large animal models that is critical for clinical translation has not been sufficiently specified. Here, the effects of AFG on directing neuroregeneration in canine hemisected T12 spinal cord injuries were explored. Histologically obvious white matter regeneration consisting of a large area of consecutive, compact and aligned nerve fibers is induced by AFG, leading to a significant motor functional restoration. The canines with AFG implantation start to stand well with their defective legs from 3 to 4 weeks postoperatively and even effortlessly climb the steps from 7 to 8 weeks. Moreover, high-resolution multi-shot diffusion tensor imaging illustrates the spatiotemporal dynamics of nerve regeneration rapidly crossing the lesion within 4 weeks in the AFG group. Our findings indicate that AFG could be a potential therapeutic vehicle for spinal cord injury by inducing rapid white matter regeneration and restoring locomotion, pointing out its promising prospect in clinic practice.

Identification of differentially expressed genes and fusion genes associated with malignant progression of spinal cord gliomas by transcriptome analysis.

Glioma, the most common histological subtype of primary spinal cord tumors, is considered as a rare central nervous system neoplasm. In this study, 9 glioma samples (4 of grade II and 5 of grade IV with H3K27M positive) were analyzed to examine the molecular mechanisms underlying the malignant progression of gliomas, transcriptome sequencing. Differentially expressed genes (DEGs) in grade IV vs. grade II were analyzed by using the Limma package in R. Enrichment analysis was performed for the individual DEGs through VennPlex software and the Database for Annotation. Gene mutations and fusions were analyzed using the Genome Analysis Toolkit and STAR-Fusion. A total of 416 DEGs were identified in grade IV vs. grade II. Functional analysis of the DEGs showed that GALR1 and GRM5 of neuroactive ligand-receptor interactions signaling pathways may be relaed to malignant progression of gliomas. Further systematic transcriptional profiling identified 11 in-frame/frameshift gene fusions in the tumors. Notably, one novel gene fusions, GATSL2-GTF2I was detected in all of the grade II samples. In summary, the molecular alterations observed in glioma progression may improve the characterization of different human spinal cord glioma grades. The transcriptome analysis of intramedullary spinal cord glioma will provide a new candidate gene list for further mechanism research.

MiR-302c-3p suppresses invasion and proliferation of glioma cells via down-regulating metadherin (MTDH) expression.

Glioma is the most common malignant brain tumors with poor prognosis. The molecular events involved in the development and progression of glioma remain unclear. In this study, the expression levels of miR-302c-3p were examined in glioma tissues by qRT-PCR. The in vitro and in vivo functional effects of miR-302c-3p were examined further. Luciferase reporter assays were conducted to confirm the targeting associations. Results showed that the expression level of miR-302c-3p in glioma tissues was significantly lower than those in normal brain tissues (P < 0.001). The decreased expression of mi-302c-3p in glioma was positively associated with WHO grade (P < 0.001). Up-regulation of MTDH was also detected in glioma tumors compared with normal brain tissues (P = 0.0027) and is inversely correlated with miR-302c-3p expression (P = 0.003, R(2) = 0.4065). MTDH mRNA is a direct target of miR-302c-3p, whose ectopic expression decreases MTDH expression through binding to its 3'-untranslated region. Overexpression of miR-302c-3p results in a dramatic inhibition of glioma cells proliferation and invasion in vitro and in vivo. These data suggest that miR-302c-3p play a pivotal role in the progression of glioma by targeting MTDH and is a potential inhibitor in glioma treatment.

[Clinical classification and surgical management of cervicothoracic intraspinal lipomas].

OBJECTIVE: To explore the clinical classification and surgical management of cervicothoracic intraspinal lipomas. METHODS: A total of 22 patients with cervicothoracic intraspinal lipomas were analyzed retrospectively with regards to clinical manifestations, radiographic features, intraoperative findings, surgical techniques and follow-ups. RESULTS: Total (n = 4), subtotal (n = 7) and partial (n = 11) resection was performed. Long-term neurological outcomes were evaluated by modified McCormick classification scheme. Their symptoms improved (n = 15), unchanged (n = 3) and deteriorated (n = 4). And cervicothoracic intraspinal lipomas could be classified into extradural, transitional, chaotic and secondary intramedullary groups. CONCLUSION: Different groups of cervicothoracic intraspinal lipomas vary in the degree of resection and surgical efficacy. Total resection may be performed on most extradural lipomas. The surgical objective of transitional lipomas is decompression. Chaotic and secondary intramedullary lipomas should target effective resection to avoid neurological function injury. Intraoperative use of laser facilitates tumor resection. Intraoperative electrophysiological monitoring protects spinal cord.