Hongming Ji

Objective: This study aimed to compare the perioperative outcomes of endoscopic-assisted evacuation vs. burr-hole drainage methods in the chronic subdural hematoma (CSDH). Methods: This retrospective cohort study included consecutive surgical cases of CSDH treated at Shanxi Provincial People's Hospital. After eligibility screening, 40 patients who underwent endoscopic-assisted evacuation and 158 who underwent burr-hole drainage were included in the analysis. Postoperative outcomes were systematically evaluated across intraoperative parameters, clinical and laboratory measures, procedural costs, and length of hospital stay. Results: Patients who underwent endoscopic-assisted evacuation had significantly lower residual hematoma rates compared to those who underwent burr-hole drainage (35.00% vs. 54.78%, p = 0.0255). In the multiple regression analysis, the endoscopic group demonstrated improved neurological outcomes compared to the burr-hole group, with an odds ratio of 0.30 (95% CI: 0.12–0.63; p = 0.0021) for achieving a good functional outcome (lower mRS). However, endoscopic-assisted evacuation was associated with longer operative time, averaging 40.12 min longer ( p < 0.0001) and higher hospitalization costs, averaging ¥9,600 more ( p < 0.0001). Occurrence of postoperative complications such as intracranial pneumocephalus and hematoma recurrence were not significantly different between the two groups. Hemoglobin count was lower in the endoscopy group than in the burr-hole group (127.90 ± 14.93 vs. 133.00 ± 14.35 g/L, p = 0.0514) although no anemia-related complications occurred in the endoscopic group. Conclusion: Endoscope-assisted evacuation enables more thorough clearance of hematoma and leads to better recovery for patients, without increasing procedural trauma or postoperative complications, though its higher cost may limit accessibility. These findings may help inform surgical decision-making and resource allocation.

Intravoxel Incoherent Motion (IVIM) offers valuable insights into tumor microstructural heterogeneity through diffusion and perfusion. However, the relatio

Brain and central nervous system (CNS) cancers are a major cause of cancer-related mortality and morbidity among adolescents and young adults (AYAs) aged 15–39, presenting significant global health challenges despite advances in treatment. This study assesses the global burden and future trends of CNS cancers in AYAs using data from the Global Burden of Disease (GBD) 2021 database. Data on incidence, mortality, and disability-adjusted life years (DALYs) from 1990 to 2021 were analyzed for 204 countries and territories. Age-standardized rates for incidence (ASIR), mortality (ASMR), and DALYs (ASDR) were calculated, with temporal trends assessed using Joinpoint regression and future projections estimated using the Bayesian Age-Period-Cohort (BAPC) model. Disparities were evaluated using the Socio-Demographic Index (SDI), a composite measure of income, education, and fertility rates. In 2021, global CNS cancer incidence among AYAs was 57,645 cases, with a prevalence of 271,770. The ASIR was 1.92 per 100,000, the ASMR 0.95 per 100,000, and DALYs totaled 1,744,650. High-SDI regions reported higher ASIR but lower ASMR and ASDR. By 2040, case numbers are projected to rise, while age-standardized rates may stabilize or decline. This study highlights significant global disparities in CNS cancer burden, calling for investments in cancer registries, equitable healthcare access, and tailored prevention and treatment strategies.

The direct effect of macroscopic loads on the microstructure of bone tissue in a vibration environment is not yet known. Therefore, this study aims to investigate the macro- and micro-biomechanical properties of the lumbar spine system under dynamic loading in such an environment. We analyzed the dynamic characteristics of osteon by establishing a macro- and micro-scale model of the lumbar spine, using a submodel-specific boundary displacement method based on St. Venant’s principle. Utilizing the results from the transient dynamic analysis of the entire lumbar spine as boundary conditions, this study simulates the dynamic behavior of osteon in each segment of the spine on a microscopic scale. The macroscopic results of the transient dynamic analysis showed that the rates of change in dynamic displacement amplitude relative to static displacement amplitude for the L1-L5 vertebrae were 212.60%, 242.11%, 314.80%, 1.17%, and 3.75%, respectively. The change in displacement amplitude under dynamic load relative to static load was highest for the L3 vertebra, as observed in the macroscopic model. The stress and strain values in the microscopic osteon of each lumbar spine segment under sinusoidal periodic loading were higher than those in the macroscopic osteon. In the microscopic bone unit, the maximum stress occurred at the cement line during the peak stress moment, while the minimum stress was observed at the innermost bone plate during the moment of minimum stress. Under dynamic loading, the microscopic bone osteon demonstrated a cyclic stress and strain response, with variations observed in different components of the osteon. These findings provide new insights into the biomechanical behavior of the lumbar spine in a vibration environment.

This study investigated the use of bi-exponential diffusion-weighted imaging (DWI) combined with structural features to differentiate high-grade glioma (HGG) from solitary brain metastasis (SBM). A total of 57 patients (31 HGG, 26 SBM) who underwent pre-surgical multi-b DWI and structural MRI (T1W, T2W, T1W + C) were included. Volumes of interest (VOI) in the peritumoral edema area (PTEA) and enhanced tumor area (ETA) were selected for analysis. Histogram features of slow diffusion coefficient (D_slow), fast diffusion coefficient (D_fast), and perfusion fraction (frac) were extracted. Results showed that HGG patients had higher skewness of D_fast (P = 0.022) and frac (P = 0.077), higher kurtosis of D_slow (P = 0.019) and frac (P = 0.025), and lower entropy of D_slow (P = 0.005) and frac (P = 0.001) within the ETA. Additionally, HGG exhibited lower mean frac in both ETA (P = 0.007) and PTEA (P = 0.017). Combining skewness of frac in ETA with clear tumor margin enhanced diagnostic performance, achieving an optimal AUC of 0.79. These findings suggest that histogram analysis of diffusion and perfusion characteristics in ETA and structural features can effectively differentiate HGG from SBM.

Objective: This study aims to analyze the global burden of subarachnoid hemorrhage (SAH) among adolescents and young adults (AYAs) aged 15–39 years from 1990 to 2021, highlighting spatial and temporal trends and providing insights for future public health strategies. Methods: Data were collected from the Global Burden of Disease Study 2021 (GBD 2021), which includes comprehensive evaluations of health conditions and associated risk factors across 204 countries and territories. The focus was on SAH incidence, prevalence, mortality, and disability-adjusted life years (DALYs) among AYAs. The data were segmented by age groups (15–19, 20–24, 25–29, 30–34, 35–39 years) and socio-demographic index (SDI) quintiles. Statistical analyses, including Joinpoint regression and decomposition analysis, were employed to assess temporal trends and the impact of population growth, aging, and epidemiological changes. Results: From 1990 to 2021, the global number of SAH incident cases among AYAs increased by 12.6%, from 109,120 cases in 1990 to 122,822 cases in 2021. Prevalent cases rose by 17.1%, from 1,212,170 cases in 1990 to 1,419,127 cases in 2021. Conversely, the number of deaths decreased by approximately 26.6%, from 30,348 cases in 1990 to 22,266 cases in 2021. Similarly, DALYs decreased by 23.7%, from 1,996,041 cases in 1990 to 1,523,328 cases in 2021. Notably, over these thirty years, the age-standardized rates (ASR) of incidence, prevalence, mortality, and DALYs for the AYA population showed an overall decreasing trend, despite fluctuations in specific periods. The age-standardized mortality rate (ASMR) and age-standardized DALYs (ASR for DALYs) decreased continuously with an average annual percentage change (AAPC) of -2.2% (95% CI: -2.36, -2.04) and -2.02% (95% CI: -2.17, -1.88), respectively. The age-standardized incidence rate (ASIR) and age-standardized prevalence rate (ASPR) had an AAPC of -0.8% (95% CI: -0.85, -0.75) and -0.65% (95% CI: -0.66, -0.64), respectively. Particularly, the ASIR showed a continuous decline from 1990 to 2015, followed by a slight increase from 2014 to 2019 (APC: 0.14%, 95% CI: 0.03, 0.25), and accelerated growth from 2019 to 2021 (APC: 1.23%, 95% CI: 0.88, 1.57). The ASPR declined from 1990 to 2019, followed by an increase from 2019 to 2021 (APC: 0.15%, 95% CI: 0.05, 0.25). Regional analysis revealed substantial burdens in the Middle-SDI and Low-Middle-SDI regions, with the Middle-SDI region having the highest incidence, prevalence, mortality, and DALYs. Decomposition analysis indicated that population growth was the primary driver of increased SAH cases, while epidemiological changes contributed significantly to the decline in deaths and DALYs. Conclusion: The findings underscore the need for targeted public health interventions, particularly in low and low-middle-SDI regions, to reduce the burden of SAH among AYAs. Improved healthcare resources, enhanced health education, and preventive strategies are crucial. This study provides valuable data to inform future public health policies and resource allocation, emphasizing the importance of addressing the unique challenges faced by AYAs.

Background: Differentiation of glioma and solitary brain metastasis (SBM), which requires biopsy or multi-disciplinary diagnosis, remains sophisticated clinically. Histogram analysis of MR diffusion or molecular imaging hasn’t been fully investigated for the differentiation and may have the potential to improve it. Methods: A total of 65 patients with newly diagnosed glioma or metastases were enrolled. All patients underwent DWI, IVIM, and APTW, as well as the T1W, T2W, T2FLAIR, and contrast-enhanced T1W imaging. The histogram features of apparent diffusion coefficient (ADC) from DWI, slow diffusion coefficient (Dslow), perfusion fraction (frac), fast diffusion coefficient (Dfast) from IVIM, and MTRasym@3.5ppm from APTWI were extracted from the tumor parenchyma and compared between glioma and SBM. Parameters with significant differences were analyzed with the logistics regression and receiver operator curves to explore the optimal model and compare the differentiation performance. Results: Higher ADC_kurtosis (P = 0.022), frac_kurtosis (Pskewness (P10 (P = 0.045), frac₁₀ (P90 (P = 0.001), frac_mean (Pentropy (Pkurtosis (OR = 0.431, 95%CI 0.256–0.723, P = 0.002) was independent factor for SBM differentiation. The model combining (MTRasym@3.5ppm)₁₀, frac₁₀, and frac_kurtosis showed an AUC of 0.857 (sensitivity: 0.857, specificity: 0.750), while the model combined with frac₁₀ and frac_kurtosis had an AUC of 0.824 (sensitivity: 0.952, specificity: 0.591). There was no statistically significant difference between AUCs from the two models. (Z = -1.14, P = 0.25). Conclusions: The frac₁₀ and frac_kurtosis in enhanced tumor region could be used to differentiate glioma and SBM and (MTRasym@3.5ppm)₁₀ helps improving the differentiation specificity.

The incidence of pneumonia in ICU patients with TBI is very high, seriously affecting the prognosis. This study aims to construct a predictive model for pneumonia in ICU patients with TBI and provide help for the prevention of TBI-related pneumonia. Clinical data of ICU patients with TBI were collected from the Medical Information Mart for Intensive Care (MIMIC)-IV database and hospital data. Variables were screened by lasso and multivariate logistic regression to construct a predictive nomogram model, verified in internal validation cohort and external validation cohort by receiver operator characteristic (ROC) curve, calibration curve and decision curve analysis (DCA). A total of 1850 ICU patients with TBI were enrolled in the study from the MIMIC-IV database, including 1298 in the training cohort and 552 in internal validation cohort. The external validation cohort included 240 ICU patients with TBI from hospital data. Nine variables were selected from the training cohort by lasso regression and multivariate logistic regression, and a pneumonia prediction nomogram was constructed. This nomogram has a high discrimination in training, internal validation and external validation cohorts (AUC = 0.857, 0.877, 0.836). The calibration curve and DCA showed that this nomogram had a high calibration and better clinical decision-making efficiency. The nomogram showed excellent discrimination and clinical utility to predict pneumonia, and could identify pneumonia high-risk patients early, thus providing personalised treatment strategies for ICU patients with TBI.

Background: To investigate the value of diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and amide proton transfer-weighted (APTW) imaging in differentiating the primary glioma from solitary brain metastases (SBM) with the histogram and texture features. Methods: A total of 65 patients with newly diagnosed glioma or metastases were enrolled. All patients underwent DWI, IVIM, and APTW imaging, as well as the T1W, T2W, T2FLAIR, and enhanced T1W imaging. The first-order texture and histogram features of apparent diffusion coefficient (ADC) from DWI, diffusion coefficient (D), perfusion fraction (f), pseudo-diffusion coefficient (D*) from IVIM, and MTRasym (3.5 ppm) from APTW were extracted from the tumor parenchyma and compared between glioma and SBM. Parameters with significant differences were analyzed with the logistics regression and receiver operator curves to explore the optimal model and compare the differentiation performance. Results: Higher ADC_kurtosis (p=0.02), f₉₀ (p＜0.01), and f_kurtosis (p=0.01) were found for glioma, while higher MTRasym (3.5ppm)₁₀ (p= 0.05), f_entropy (p＜0.01), and f_mean (p＜0.01) were observed for SBM. f_kurtosis (OR =0.66, 95%CI 0.48-0.92, p=0.02), and f_mean (OR=1.44, 95%CI 1.16-1.18, pkurtosis, and f_mean showed an AUC of 0.83, while the model combined with f_kurtosis and f_mean with improved sensitivity had an AUC of 0.81. Conclusion: The f_mean and f_kurtosis in enhanced tumor region could be used to differentiate glioma and SBM.