OBJECTIVES:A decline in language function is a common complication after glioma surgery, affecting patients' quality of life and survival. This study predicts the postoperative decline in language function and whether it can be recovered based on the preoperative white matter structural network.MATERIALS AND METHODS:Eighty-one right-handed patients with glioma involving the left hemisphere were retrospectively included. Their language function was assessed using the Western Aphasia Battery before and 1 week and 3 months after surgery. Structural connectome combining DTI features was selected to predict postoperative language decline and recovery. Nested cross-validation was used to optimize the models, evaluate the prediction performance of the models, and identify the most predictive features.RESULTS:Five, seven, and seven features were finally selected as the predictive features in each model and used to establish predictive models for postoperative language decline (1 week after surgery), long-term language decline (3 months after surgery), and language recovery, respectively. The overall accuracy of the three models in nested cross-validation and overall area under the receiver operating characteristic curve were 0.840, 0.790, and 0.867, and 0.841, 0.778, and 0.901, respectively.CONCLUSION:We used machine learning algorithms to establish models to predict whether the language function of glioma patients will decline after surgery and whether postoperative language deficit can recover, which may help improve the development of treatment strategies. The difference in features in the non-language decline or the language recovery group may reflect the structural basis for the protection and compensation of language function in gliomas.CLINICAL RELEVANCE STATEMENT:Models can predict the postoperative language decline and whether it can recover in glioma patients, possibly improving the development of treatment strategies. The difference in selected features may reflect the structural basis for the protection and compensation of language function.KEY POINTS:• Structural connectome combining diffusion tensor imaging features predicted glioma patients' language decline after surgery. • Structural connectome combining diffusion tensor imaging features predicted language recovery of glioma patients with postoperative language disorder. • Diffusion tensor imaging and connectome features related to language function changes imply plastic brain regions and connections.

Background: Glioma is the most common intracranial malignancy. Immune-infiltration and tumour stemness are associated with the prognosis of glioma. Although pleckstrin homology containing family A, number 4 (PLEKHA4) is widely expressed in various human cancers, its role in glioma remains unclear. Methods: We examined the features and clinical significance of PLEKHA4 in gliomas by analysing relevant data from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. Gene set enrichment analysis (GSEA) was performed to determine the possible functions and pathways involving PLEKHA4 in glioma. The relationship between PLEKHA4 expression and the degree of oncogenic dedifferentiation was analysed using stemness scores (ss) calculated from epigenetic and transcriptomic features. We also explored the relationship between PLEKHA4 expression and immune cell infiltration in gliomas using the CIBERSORT databases. Furthermore, drug sensitivity analysis was performed using datasets from the GDSC and GTRP databases. In addition, we performed relevant in vitro experimental studies. Results: PLEKHA4 DNA hypomethylation status was associated with its high expression in glioma tissues as well as poor prognoses. Univariate and multivariate Cox analyses indicated that PLEKHA4 expression may be considered as an independent prognostic factor in patients with glioma. GSEA indicated that high PLEKHA4 expression was associated with Janus kinase (JAK)/signal transducer and activator of transcription (STAT), Wingless-Type MMTV Integration Site Family (Wnt), JUN N-terminal kinase (JNK) signalling pathways and involved in apoptotic, cytoskeletal, and cell adhesion biological processes (BPs). In addition, increased PLEKHA4 expression was associated with higher glioma stemness scores than lower PLEKHA4 expression levels. Furthermore, the expression of PLEKHA4 was shown to be associated with glioma infiltration by CD4+ T cells, B cells, neutrophils, macrophages, and dendritic cells. Drug sensitivity analysis also showed that PLEKHA4 expression was negatively correlated with the sensitivity of several small molecule kinase inhibitors. Furthermore, in vitro experiments confirmed that PLEKHA4 knockdown inhibited the proliferation of glioma cells. Conclusions: PLEKHA4 is highly expressed in glioma tissues and correlated with tumour stemness, immune cell infiltration and proliferation, suggesting its potential as a novel prognostic biomarker and therapeutic target in glioma.

PurposeTo establish a predictive model to predict the occurrence of language deficit for patients after surgery of glioma involving language areas (GILAs) under general anesthesia (GA).MethodsPatients with GILAs were retrospectively collected in our center between January 2009 and December 2020. Clinical variables (age, sex, aphasia quotient [AQ], seizures and KPS), tumor-related variables (recurrent tumor or not, volume, language cortices invaded or not, shortest distance to language areas [SDLA], supplementary motor area or premotor area [SMA/PMA] involved or not and WHO grade) and intraoperative multimodal techniques (used or not) were analyzed by univariate and multivariate analysis to identify their association with temporary or permanent language deficits (TLD/PLD). The predictive model was established according to the identified significant variables. Receiver operating characteristic (ROC) curve was used to assess the accuracy of the predictive model.ResultsAmong 530 patients with GILAs, 498 patients and 441 patients were eligible to assess TLD and PLD respectively. The multimodal group had the higher EOR and rate of GTR than conventional group. The incidence of PLD was 13.4% in multimodal group, which was much lower than that (27.6%, P<0.001) in conventional group. Three factors were associated with TLD, including SDLA (OR=0.85, P<0.001), preoperative AQ (OR=1.04, P<0.001) and multimodal techniques used (OR=0.41, P<0.001). Four factors were associated with PLD, including SDLA (OR=0.83, P=0.001), SMA/PMA involved (OR=3.04, P=0.007), preoperative AQ (OR=1.03, P=0.002) and multimodal techniques used (OR=0.35, P<0.001). The optimal shortest distance thresholds in detecting the occurrence of TLD/PLD were 1.5 and 4mm respectively. The optimal AQ thresholds in detecting the occurrence of TLD/PLD were 52 and 61 respectively. The cutoff values of the predictive probability for TLD/PLD were 23.7% and 16.1%. The area under ROC curve of predictive models for TLD and PLD were 0.70 (95%CI: 0.65-0.75) and 0.72 (95%CI: 0.66-0.79) respectively.ConclusionThe use of multimodal techniques can reduce the risk of postoperative TLD/PLD after removing GILAs under general anesthesia. The established predictive model based on clinical variables can predict the probability of occurrence of TLD and PLD, and it had a moderate predictive accuracy.

It is widely acknowledged that writing not only has a significant role to play in English teaching, but also is an effective way to reflect students’ English comprehensive ability. In recent years, more and more scholars have been exploring various new teaching methods of writing. However, currently, there are still some problems existing in English writing teaching, and the teaching performance is not satisfactory. Therefore, it is urgent for teachers to find a new teaching method to improve this situation in the English writing teaching. In recent years, the studies on mind mapping have been conducted in many different areas. However, there are still few studies on mind mapping in English writing teaching. This study uses literature analysis, and comparative analysis to explore how to apply mind mapping to English writing teaching. The purpose of this study is to explore whether applying mind mapping to the English writing teaching is beneficial for improving students’ English writing level, at the same time, to provide a feasible method to make the current situation of the English writing teaching improved and some useful suggestions for further teaching and research.