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Lumbar disc herniation (LDH) often results in significant pain and disability, and histopathologic evaluation of intervertebral discs offers critical insights into treatment outcomes. This prospective observational study explores histopathologic (HP) changes in intervertebral discs (IVD) and their association with clinical outcomes following surgical treatment for lumbar disc herniation (LDH). A cohort of 141 patients undergoing magnetic resonance imaging (MRI)-confirmed LDH surgery underwent HP evaluation using a semi-quantitative Histopathologic Degeneration Score (HDS). Preoperatively and at a six-month follow-up, comprehensive clinical assessment included the Oswestry Disability Index (ODI) and Visual Analog Scale (VAS), with a minimal clinically important difference (MCID) calculated from ODI and VAS. Results indicated significant associations between higher HDS and adverse clinical outcomes, including persistent pain and greater disability post-surgery. Specifically, HDS ≥ 7 was predictive (OR = 6.25, 95%CI: 2.56-15.23) of disability outcomes measured with MCID-ODI (AUC: 0.692, 95%CI: 0.609-0.767, P < 0.001), and HDS ≥ 8 was predictive (OR = 1.72, 95%CI: 1.04-2.77) of persistent pain measured with MCID-VAS (AUC: 0.628, 95%CI: 0.598-0.737, P = 0.008), highlighting the diagnostic potential of HDS in assessing postoperative recovery. This study underscores the potential of HP evaluation using HDS to provide valuable insights into disease progression and outcomes in LDH patients, complementing conventional radiologic methods. The findings support the application of personalized treatment strategies based on HP findings while acknowledging challenges in interpretation and clinical implementation.
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BACKGROUND: The study aims to investigate the role of hypoxia-inducible factors (HIFs) in the development, progression, and therapeutic potential of glioblastomas. METHODOLOGY: The study, following PRISMA guidelines, systematically examined hypoxia and HIFs in glioblastoma using MEDLINE (PubMed), Web of Science, and Scopus. A total of 104 relevant studies underwent data extraction. RESULTS: Among the 104 studies, global contributions were diverse, with China leading at 23.1%. The most productive year was 2019, accounting for 11.5%. Hypoxia-inducible factor 1 alpha (HIF1α) was frequently studied, followed by hypoxia-inducible factor 2 alpha (HIF2α), osteopontin, and cavolin-1. Commonly associated factors and pathways include glucose transporter 1 (GLUT1) and glucose transporter 3 (GLUT3) receptors, vascular endothelial growth factor (VEGF), phosphoinositide 3-kinase (PI3K)-Akt-mechanistic target of rapamycin (mTOR) pathway, and reactive oxygen species (ROS). HIF expression correlates with various glioblastoma hallmarks, including progression, survival, neovascularization, glucose metabolism, migration, and invasion. CONCLUSION: Overcoming challenges such as treatment resistance and the absence of biomarkers is critical for the effective integration of HIF-related therapies into the treatment of glioblastoma with the aim of optimizing patient outcomes.
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Introduction: Clinicians encounter challenges in promptly diagnosing thoracolumbar injuries (TLIs) and fractures (VFs), motivating the exploration of Artificial Intelligence (AI) and Machine Learning (ML) and Deep Learning (DL) technologies to enhance diagnostic capabilities. Despite varying evidence, the noteworthy transformative potential of AI in healthcare, leveraging insights from daily healthcare data, persists. Research question: This review investigates the utilization of ML and DL in TLIs causing VFs. Materials and methods: Employing Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) methodology, a systematic review was conducted in PubMed and Scopus databases, identifying 793 studies. Seventeen were included in the systematic review, and 11 in the meta-analysis. Variables considered encompassed publication years, geographical location, study design, total participants (14,524), gender distribution, ML or DL methods, specific pathology, diagnostic modality, test analysis variables, validation details, and key study conclusions. Meta-analysis assessed specificity, sensitivity, and conducted hierarchical summary receiver operating characteristic curve (HSROC) analysis. Results: Predominantly conducted in China (29.41%), the studies involved 14,524 participants. In the analysis, 11.76% (N = 2) focused on ML, while 88.24% (N = 15) were dedicated to deep DL. Meta-analysis revealed a sensitivity of 0.91 (95% CI = 0.86-0.95), consistent specificity of 0.90 (95% CI = 0.86-0.93), with a false positive rate of 0.097 (95% CI = 0.068-0.137). Conclusion: The study underscores consistent specificity and sensitivity estimates, affirming the diagnostic test's robustness. However, the broader context of ML applications in TLIs emphasizes the critical need for standardization in methodologies to enhance clinical utility.
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Background and Objectives: To investigate the role of augmented reality (AR) in skull base (SB) neurosurgery. Materials and Methods: Utilizing PRISMA methodology, PubMed and Scopus databases were explored to extract data related to AR integration in SB surgery. Results: The majority of 19 included studies (42.1%) were conducted in the United States, with a focus on the last five years (77.8%). Categorization included phantom skull models (31.2%, n = 6), human cadavers (15.8%, n = 3), or human patients (52.6%, n = 10). Microscopic surgery was the predominant modality in 10 studies (52.6%). Of the 19 studies, surgical modality was specified in 18, with microscopic surgery being predominant (52.6%). Most studies used only CT as the data source (n = 9; 47.4%), and optical tracking was the prevalent tracking modality (n = 9; 47.3%). The Target Registration Error (TRE) spanned from 0.55 to 10.62 mm. Conclusion: Despite variations in Target Registration Error (TRE) values, the studies highlighted successful outcomes and minimal complications. Challenges, such as device practicality and data security, were acknowledged, but the application of low-cost AR devices suggests broader feasibility.