Application value of intraoperative electrophysiological monitoring in cerebral eloquent area glioma surgery: a retrospective cohort study

INTRODUCTION

Surgery for gliomas involving eloquent areas is a very challenging microsurgical procedure. Maximizing both the extent of resection (EOR) and preservation of neurological function have always been the focus of attention. Intraoperative neurophysiological monitoring (IONM) is widely used in this kind of surgery. The purpose of this study was to evaluate the efficacy of IONM in eloquent area glioma surgery.

METHODS

Sixty-eight glioma patients who underwent surgical treatment from 2014 to 2019 were included in this retrospective cohort study, which focused on eloquent areas. Clinical indicators and IONM data were analysed preoperatively, two weeks after surgery, and at the final follow-up. Logistic regression, Cox regression, and Kaplan‒Meier analyses were performed, and nomograms were then established for predicting prognosis. The diagnostic value of the IONM indicator was evaluated by the receiver operating characteristic (ROC) curve.

RESULTS

IONM had no effect on the postoperative outcomes, including EOR, intraoperative bleeding volume, duration of surgery, length of hospital stay, and neurological function status. However, at the three-month follow-up, the percentage of patients who had deteriorated function in the monitored group was significantly lower than that in the unmonitored group (23.3% vs. 52.6%; P < 0.05). Logistic regression analysis showed that IONM was a significant factor in long-term neurological function (OR = 0.23, 95% CI (0.07-0.70). In the survival analysis, long-term neurological deterioration indicated worsened overall survival (OS) and progression-free survival (PFS). A prognostic nomogram was established through Cox regression model analysis, which could predict the probability 3-year survival rate. The concordance index was 0.761 (95% CI 0.734-0.788). The sensitivity and specificity of IONM evoked potential (SSEP and TCeMEP) were 0.875 and 0.909, respectively. In the ROC curve analysis, the area under the curve (AUC) for the SSEP and TCeMEP curves was 0.892 (P < 0.05).

CONCLUSIONS

The application of IONM could improve long-term neurological function, which is closely related to prognosis and can be used as an independent prognostic factor. IONM is practical and widely available for predicting postoperative functional deficits in patients with eloquent area glioma.

Effect of bolus materials on dose deposition in deep tissues during electron beam radiotherapy

Several materials are utilized in the production of bolus, which is essential for superficial tumor radiotherapy. This research aimed to compare the variations in dose deposition in deep tissues during electron beam radiotherapy when employing different bolus materials. Specifically, the study developed general superficial tumor models (S-T models) and postoperative breast cancer models (P-B models). Each model comprised a bolus made of water, polylactic acid (PLA), polystyrene, silica-gel or glycerol. Geant4 was employed to simulate the transportation of electron beams within the studied models, enabling the acquisition of dose distributions along the central axis of the field. A comparison was conducted to assess the dose distributions in deep tissues. In regions where the percentage depth dose (PDD) decreases rapidly, the relative doses (RDs) in the S-T models with silica-gel bolus exhibited the highest values. Subsequently, RDs for PLA, glycerol and polystyrene boluses followed in descending order. Notably, the RDs for glycerol and polystyrene boluses were consistently below 1. Within the P-B models, RDs for all four bolus materials are consistently below 1. Among them, the smallest RDs are observed with the glycerol bolus, followed by silica-gel, PLA and polystyrene bolus in ascending order. As PDDs are ~1-3% or smaller, the differences in RDs diminish rapidly until are only around 10%. For the S-T and P-B models, polystyrene and glycerol are the most suitable bolus materials, respectively. The choice of appropriate bolus materials, tailored to the specific treatment scenario, holds significant importance in safeguarding deep tissues during radiotherapy.

Exploring the causal relationship between antihypertensive drugs and glioblastoma by combining drug target Mendelian randomization study, eQTL colocalization, and single-cell RNA sequencing

Recent reports indicate a potential oncogenic role of antihypertensive drugs in common cancers. However, it remains uncertain whether this phenomenon influences the risk of glioblastoma multiforme (GBM). This study aimed to assess the potential causal effects of blood pressure (BP) and antihypertensive drugs on GBM. Genome-wide association study (GWAS) summary statistics for systolic blood pressure (SBP), diastolic blood pressure (DBP), and GBM in Europeans were downloaded. To represent the effects of antihypertensive drugs, we utilized single nucleotide polymorphisms (SNPs) associated with SBP/DBP adjacent to the coding regions of different antihypertensive drugs as instrumental variables to model five antihypertensive drugs, including angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, calcium channel blockers, β-receptor blockers (BBs), and thiazide diuretics. Positive control studies were performed using GWAS data in chronic heart failure. The primary method for causality estimation was the inverse-variance-weighted method. Mendelian randomization analysis showed that BBs with the β1-adrenergic receptor (ADRB1) as a therapeutic target could significantly reduce the risk of GBM by mediating DBP (OR = 0.431, 95% CI: 0.267-0.697, p < .001) and that they could also significantly reduce the risk of GBM by mediating SBP (OR = 0.595, 95% CI: 0.422-0.837, p = .003). Sensitivity analysis and colocalization analysis reinforced the robustness of these findings. Finally, the low expression of the ADRB1 gene in malignant gliomas was found by GBM data from TCGA and single-cell RNA sequencing, which most likely contributed to the poor prognosis of GBM patients. In summary, our study provides preliminary evidence of some causal relationship between ADRB1-targeted BBs and glioblastoma development. However, more studies are needed to validate these findings and further reveal the complex relationship between BP and GBM.

Microglial Ffar4 deficiency promotes cognitive impairment in the context of metabolic syndrome

Metabolic syndrome (MetS) is closely associated with an increased risk of dementia and cognitive impairment, and a complex interaction of genetic and environmental dietary factors may be implicated. Free fatty acid receptor 4 (Ffar4) may bridge the genetic and dietary aspects of MetS development. However, the role of Ffar4 in MetS-related cognitive dysfunction is unclear. In this study, we found that Ffar4 expression is down-regulated in MetS mice and MetS patients with cognitive impairment. Conventional and microglial conditional knockout of Ffar4 exacerbated high-fat diet (HFD)-induced cognitive dysfunction and anxiety, whereas microglial Ffar4 overexpression improved HFD-induced cognitive dysfunction and anxiety. Mechanistically, we found that microglial Ffar4 regulated microglial activation through type I interferon signaling. Microglial depletion and NF-κB inhibition partially reversed cognitive dysfunction and anxiety in microglia-specific Ffar4 knockout MetS mice. Together, these findings uncover a previously unappreciated role of Ffar4 in negatively regulating the NF-κB-IFN-β signaling and provide an attractive therapeutic target for delaying MetS-associated cognitive decline.

Using machine learning to identify patients at high risk of developing low bone density or osteoporosis after gastrectomy: a 10-year multicenter retrospective analysis

INTRODUCTION

Osteoporosis that emerges subsequent to gastrectomy poses a significant threat to the long-term health of patients. The primary objective of this investigation was to formulate a machine learning algorithm capable of identifying substantial preoperative, intraoperative, and postoperative risk factors. This algorithm, in turn, would enable the anticipation of osteoporosis occurrence after gastrectomy.

METHODS

This research encompassed a cohort of 1125 patients diagnosed with gastric cancer, including 108 individuals with low bone density or osteoporosis. A total of 40 distinct variables were collected, comprising patient demographics, pertinent medical history, medication records, preoperative examination attributes, surgical procedure specifics, and intraoperative details. Four distinct machine learning algorithms-extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN)-were employed to establish the predictive model. Evaluation of the models involved receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Shapley additive explanation (SHAP) was employed for visualization and analysis.

RESULTS

Among the four prediction models employed, the XGBoost algorithm demonstrated exceptional performance. The ROC analysis yielded excellent predictive accuracy, showcasing area under the curve (AUC) values of 0.957 and 0.896 for training and validation sets, respectively. The calibration curve further confirmed the robust predictive capacity of the XGBoost model. The DCA demonstrated a notably higher benefit rate for patients undergoing intervention based on the XGBoost model. Moreover, the AUC value of 0.73 for the external validation set indicated favorable extrapolation of the XGBoost prediction model. SHAP analysis outcomes unveiled numerous high-risk factors for osteoporosis development after gastrectomy, including a history of chronic obstructive pulmonary disease (COPD), inflammatory bowel disease (IBD), hypoproteinemia, postoperative neutrophil-to-lymphocyte ratio (NLR) exceeding 3, steroid usage history, advanced age, and absence of calcitonin use.

CONCLUSION

The osteoporosis prediction model derived through the XGBoost machine learning algorithm in this study displays remarkable predictive precision and carries significant clinical applicability.

Interaction, immune infiltration characteristics and prognostic modeling of efferocytosis-related subtypes in glioblastoma

BACKGROUND

Efferocytosis is a biological process in which phagocytes remove apoptotic cells and vesicles from tissues. This process is initiated by the release of inflammatory mediators from apoptotic cells and plays a crucial role in resolving inflammation. The signals associated with efferocytosis have been found to regulate the inflammatory response and the tumor microenvironment (TME), which promotes the immune escape of tumor cells. However, the role of efferocytosis in glioblastoma multiforme (GBM) is not well understood and requires further investigation.

METHODS

In this study, we conducted a comprehensive analysis of 22 efferocytosis-related genes (ERGs) by searching for studies related to efferocytosis. Using bulk RNA-Seq and single-cell sequencing data, we analyzed the expression and mutational characteristics of these ERGs. By using an unsupervised clustering algorithm, we obtained ERG clusters from 549 GBM patients and evaluated the immune infiltration characteristics of each cluster. We then identified differential genes (DEGs) in the two ERG clusters and classified GBM patients into different gene clusters using univariate cox analysis and unsupervised clustering algorithms. Finally, we utilized the Boruta algorithm to screen for prognostic genes and reduce dimensionality, and the PCA algorithm was applied to create a novel efferocytosis-related scoring system.

RESULTS

Differential expression of ERGs in glioma cell lines and normal cells was analyzed by rt-PCR. Cell function experiments, on the other hand, validated TIMD4 as a tumor risk factor in GBM. We found that different ERG clusters and gene clusters have distinct prognostic and immune infiltration profiles. The ERG signature we developed provides insight into the tumor microenvironment of GBM. Patients with lower ERG scores have a better survival rate and a higher likelihood of benefiting from immunotherapy.

CONCLUSIONS

Our novel efferocytosis-related signature has the potential to be used in clinical practice for risk stratification of GBM patients and for selecting individuals who are likely to respond to immunotherapy. This can help clinicians design appropriate targeted therapies before initiating clinical treatment.