Efficacy of cardiopulmonary rehabilitation nursing in enhancing pulmonary function, sleep quality, and living ability in COVID-19 patients.

OBJECTIVE: The purpose of this study was to elucidate the impact of cardiopulmonary rehabilitation nursing on the pulmonary function, sleep quality, and living ability of patients afflicted with Coronavirus Disease 2019 (COVID-19). METHODS: A total of 98 patients with COVID-19 treated at The People's Hospital of Guang'an between September 2021 and January 2023 were retrospectively collected as the research subjects. Among them, 48 patients who received standard nursing care from September 2021 to September 2022 were set as the control group, and 50 patients who underwent cardiopulmonary rehabilitation nursing from October 2022 to January 2023 were set as the research group. The pulmonary function indicators [including Forced Expiratory Volume in 1 second (FEV1) and Left Ventricular Ejection Fraction (LVEF)], sleep quality [evaluated using the Pittsburgh Sleep Quality Index (PSQI)], and living ability [assessed by the 36-Item Short Form Survey (SF-36) scale] pre- and post-intervention were compared between the two groups. RESULTS: Pre-intervention, FEV1, LVEF, PSQI scores, inflammatory factor levels [C-reactive protein (CRP), procalcitonin (PCT)], and SF-36 scores showed no significant differences between the two groups (P>0.05). Post-intervention, the research group exhibited notably enhanced FEV1 and LVEF, lower PSQI scores, lower CRP and PCT, and higher SF-36 scores compared with the control group, with statistical significance (P<0.05). Multifactorial logistic regression analysis showed that non-receipt of cardiopulmonary rehabilitation, age ≥60 years, concurrent respiratory failure, coexistent heart failure, and acid-base imbalance were independent risk factors of adverse outcomes in COVID-19 patients (P<0.05). CONCLUSION: Application of cardiopulmonary rehabilitation nursing in COVID-19 patients can significantly improve pulmonary function, sleep quality, and overall quality of life, and relieve the inflammatory state of the patients, thereby enhancing prognosis. This approach has certain value of popularization and application.

Identifying IDH-mutant and 1p/19q noncodeleted astrocytomas from nonenhancing gliomas: Manual recognition followed by artificial intelligence recognition.

Background: The T2-FLAIR mismatch sign (T2FM) has nearly 100% specificity for predicting IDH-mutant and 1p/19q noncodeleted astrocytomas (astrocytomas). However, only 18.2%-56.0% of astrocytomas demonstrate a positive T2FM. Methods must be considered for distinguishing astrocytomas from negative T2FM gliomas. In this study, positive T2FM gliomas were manually distinguished from nonenhancing gliomas, and then a support vector machine (SVM) classification model was used to distinguish astrocytomas from negative T2FM gliomas. Methods: Nonenhancing gliomas (regardless of pathological type or grade) diagnosed between January 2022 and October 2022 (N = 300) and November 2022 and March 2023 (N = 196) will comprise the training and validation sets, respectively. Our method for distinguishing astrocytomas from nonenhancing gliomas was examined and validated using the training set and validation set. Results: The specificity of T2FM for predicting astrocytomas was 100% in both the training and validation sets, while the sensitivity was 42.75% and 67.22%, respectively. Using a classification model of SVM based on radiomics features, among negative T2FM gliomas, the accuracy was above 85% when the prediction score was greater than 0.70 in identifying astrocytomas and above 95% when the prediction score was less than 0.30 in identifying nonastrocytomas. Conclusions: Manual screening of positive T2FM gliomas, followed by the SVM classification model to differentiate astrocytomas from negative T2FM gliomas, may be a more effective method for identifying astrocytomas in nonenhancing gliomas.

Biocompatible Ferrofluid-Based Millirobot for Tumor Photothermal Therapy in Near-Infrared-II Window.

Ferrofluidic robots with excellent deformability and controllability have been intensively studied recently. However, most of these studies are in vitro and the use of ferrofluids for in vivo medicinal applications remains a big challenge. The application of ferrofluidic robots to the body requires the solution of many key problems. In this study, biocompatibility, controllability, and tumor-killing efficacy are considered when creating a ferrofluid-based millirobot for in vivo tumor-targeted therapy. For biocompatibility problems, corn oil is used specifically for the ferrofluid robot. In addition, a control system is built that enables a 3D magnetic drive to be implemented in complex biological media. Using the photothermal conversion property of 1064 nm, the ferrofluid robot can kill tumor cells in vitro; inhibit tumor volume, destroy the tumor interstitium, increase tumor cell apoptosis, and inhibit tumor cell proliferation in vivo. This study provides a reference for ferrofluid-based millirobots to achieve targeted therapies in vivo.

Distinguishing Tumor Cell Infiltration and Vasogenic Edema in the Peritumoral Region of Glioblastoma at the Voxel Level via Conventional MRI Sequences.

RATIONALE AND OBJECTIVES: The peritumoral region of glioblastoma (GBM) is composed of infiltrating tumor cells and vasogenic edema, which are difficult to distinguish manually on MRI. To distinguish tumor cell infiltration and vasogenic edema in GBM peritumoral regions, it is crucial to develop a method that is precise, effective, and widely applicable. MATERIALS AND METHODS: We retrieved the image characteristics of 379,730 voxels (marker of tumor infiltration) from 28 non-enhanced gliomas and 365,262 voxels (marker of edema) from the peritumoral edema region of 14 meningiomas on conventional MRI sequences (T1-weighted image, the contrast-enhancing T1-weighted image, the T2-weighted image, the T2-fluid attenuated inversion recovery image, and the apparent diffusion coefficient map). Using the SVM classifier, a model for predicting tumor cell infiltration and vasogenic edema at the voxel level was developed. The accuracy of the model's predictions was then evaluated using 15 GBM patients who underwent stereotactic biopsies. RESULTS: The area under the curve (AUC), accuracy, sensitivity, and specificity of the prediction model were 0.93, 0.84, 0.83, and 0.85 in the training set, and 0.90, 0.82, 0.83, and 0.83 in the test set (704,992 voxels), respectively. The pathology verification of 28 biopsy points with an accuracy of 0.79. CONCLUSION: At the voxel level, it seems possible to forecast tumor cell infiltration and vasogenic edema in the peritumoral region of GBM based on conventional MRI sequences.

Shared and malignancy-specific functional plasticity of dynamic brain properties for patients with left frontal glioma.

The time-varying brain activity may parallel the disease progression of cerebral glioma. Assessment of brain dynamics would better characterize the pathological profile of glioma and the relevant functional remodeling. This study aims to investigate the dynamic properties of functional networks based on sliding-window approach for patients with left frontal glioma. The generalized functional plasticity due to glioma was characterized by reduced dynamic amplitude of low-frequency fluctuation of somatosensory networks, reduced dynamic functional connectivity between homotopic regions mainly involving dorsal attention network and subcortical nuclei, and enhanced subcortical dynamic functional connectivity. Malignancy-specific functional remodeling featured a chaotic modification of dynamic amplitude of low-frequency fluctuation and dynamic functional connectivity for low-grade gliomas, and attenuated dynamic functional connectivity of the intrahemispheric cortico-subcortical connections and reduced dynamic amplitude of low-frequency fluctuation of the bilateral caudate for high-grade gliomas. Network dynamic activity was clustered into four distinct configuration states. The occurrence and dwell time of the weakly connected state were reduced in patients' brains. Support vector machine model combined with predictive dynamic features achieved an averaged accuracy of 87.9% in distinguishing low- and high-grade gliomas. In conclusion, dynamic network properties are highly predictive of the malignant grade of gliomas, thus could serve as new biomarkers for disease characterization.

Engine combustion modeling method based on hybrid drive.

Accurate and comprehensive reconstruction of in-cylinder combustion process is essential for timely monitoring of engine combustion state. This article developed a method based on the zero-dimensional (0-D) physical model integrated with big data. The traditional 0-D prediction model based on cumulative fuel mass is improved, the factor of in-cylinder temperature is introduced to adjust the heat release rate, which solves the problem of difficulty in calibrating the heat release rate. Then, convolutional neural network-gated recurrent unit (CNN-GRU), as a deep neural network, including a special convolutional layer and a gated recurrent unit (GRU) neural network is designed for the parameters to be calibrated in the model. The 0-D predictive combustion model is constructed by combining the physical model with CNN-GRU, the combustion process is simplified and reconstructed. The fitting results show that the 0-D physical model based on improved cumulative fuel mass approach is an effective method to reflect the heat release law. Under non-calibration conditions, the root mean square error (RMSE) value of peak firing pressure (PFP) based on CNN-GRU prediction model is 0.5862. The prediction model is a promising method to realize online fitting and optimization of combustion process.

Decision system for extent of resection in WHO grade 3 gliomas: a Chinese Glioma Genome Atlas database analysis.

BACKGROUND: Extensive surgical resection has been found to be associated with longer survival in patients with gliomas, but the interactive prognostic value of molecular pathology of the surgical resection is unclear. This study evaluated the impact of molecular pathology and clinical characteristics on the surgical benefit in WHO grade 3 IDH-mutant gliomas. METHODS: Clinical and pathological information of 246 patients with WHO grade 3 IDH-mutant gliomas were collected from the Chinese Glioma Genome Atlas database (2006-2020). The role of the extent of resection on overall survival, stratified by molecular pathology and clinical characteristics, was investigated. We then assessed prognostic factors using a univariate log-rank test and multivariate Cox proportional hazards model in the subgroups. RESULTS: The extent of resection was an independent prognostic factor in the entire cohort, even when adjusted for molecular pathology. Gross total resection was found to be associated with longer survival in all patients and in the astrocytoma group but not in the oligodendroglioma group. Compared with subtotal resections, gross total resections resulted in a longer survival time for astrocytoma patients aged ≤ 45 years. However, there was no survival benefit from total resection in patients with astrocytoma aged > 45 years. CONCLUSIONS: Extensive resection benefits only a proportion of patients with WHO grade 3 IDH-mutant gliomas. Younger patients with astrocytomas had survival benefits from extensive resection. In addition to clinical characteristics (especially age), molecular pathology impacted prognosis in patients with gliomas. Our findings provide guiding information to neurosurgeons while planning surgeries.

Altering patterns of sensorimotor network in patients with different pathological diagnoses and glioma-related epilepsy under the latest glioma classification of the central nervous system.

AIMS: We aimed to clarify the relationship between alterations in functional networks and glioma-related epilepsy (GRE) in patients with different molecular diagnoses. METHODS: We enrolled 160 patients with prefrontal gliomas and different histories of GRE. The patients were grouped based on the latest pathological glioma classification and GRE history. Graph theory analysis was applied to reveal alterations in the sensorimotor networks among various subgroups. Binary logistic regression was used to identify risk factors for preoperative GRE onset. RESULTS: Decreasing shortest path length was found in patients with GRE, regardless of the chromosome 1p/19q status. Nodes located in the premotor and supplementary motor areas showed decreased nodal betweenness centrality and vulnerability in patients with GRE and chromosome 1p/19q intact. Additionally, the node on the primary motor area showed decreased nodal vulnerability but the node on the sensory-related thalamus increased in patients with GRE and chromosome 1p/19q co-deletion. Decreased shortest path length, grade 2, and decreased nodal betweenness centrality of the premotor area were risk factors for GRE. CONCLUSION: Decreased shortest path length was a characteristic alteration in GRE and prefrontal glioma. Alterations in global properties were similar, but nodal properties were different in patients with GRE and different chromosome 1p/19q statuses.

Development of an integrated predictive model for postoperative glioma-related epilepsy using gene-signature and clinical data.

BACKGROUND: This study aimed to develop an integrated model for predicting the occurrence of postoperative seizures in patients with diffuse high-grade gliomas (DHGGs) using clinical and RNA-seq data. METHODS: Patients with DHGGs, who received prophylactic anti-epileptic drugs (AEDs) for three months following surgery, were enrolled into the study. The patients were assigned randomly into training (n = 166) and validation (n = 42) cohorts. Differentially expressed genes (DEGs) were identified based on preoperative glioma-related epilepsy (GRE) history. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis was used to construct a predictive gene-signature for the occurrence of postoperative seizures. The final integrated prediction model was generated using the gene-signature and clinical data. Receiver operating characteristic analysis and calibration curve method were used to evaluate the accuracy of the gene-signature and prediction model using the training and validation cohorts. RESULTS: A seven-gene signature for predicting the occurrence of postoperative seizures was developed using LASSO logistic regression analysis of 623 DEGs. The gene-signature showed satisfactory predictive capacity in the training cohort [area under the curve (AUC) = 0.842] and validation cohort (AUC = 0.751). The final integrated prediction model included age, temporal lobe involvement, preoperative GRE history, and gene-signature-derived risk score. The AUCs of the integrated prediction model were 0.878 and 0.845 for the training and validation cohorts, respectively. CONCLUSION: We developed an integrated prediction model for the occurrence of postoperative seizures in patients with DHGG using clinical and RNA-Seq data. The findings of this study may contribute to the development of personalized management strategies for patients with DHGGs and improve our understanding of the mechanisms underlying GRE in these patients.

Association of homotopic areas in the right hemisphere with language deficits in the short term after tumor resection.

OBJECTIVE: It is important to identify language deficit and recovery in the week following a tumor resection procedure. The homotopic Broca's area and the superior longitudinal fasciculus in the right hemisphere participate in language functional compensation. However, the nodes in these structures, as well as their contributions to language rehabilitation, remain unknown. In this study, the authors investigated the association of homotopic areas in the right hemisphere with language deficit. METHODS: The authors retrospectively reviewed the records of 50 right-handed patients with left hemispheric lower-grade glioma that had been surgically treated between June 2020 and May 2022. The patients were divided into normal and aphasia groups based on their postoperative aphasia quotient (AQ) from the Western Aphasia Battery. Preoperative (within 24 hours before surgery) and postoperative (7 days after tumor resection) diffusion tensor images were used to reveal alterations of structural networks by using graphic theory analysis. The shortest distance between the glioma and the nodes belonging to the language network (SDTN) was quantitatively assessed. Pearson's correlation and causal mediation analyses were used to identify correlations and mediator factors among SDTN, topological properties, and AQs. RESULTS: Postoperative nodal local efficiency of the node dorsal Brodmann area (BA) 44 (A44d; p = 0.0330), nodal clustering coefficient of the nodes A44d (p = 0.0402) and dorsal lateral BA6 (A6dl; p = 0.0097), and nodal degree centrality (p = 0.0058) of the node medial BA7 (A7m) were higher in the normal group than in the aphasia group. SDTN was positively correlated with postoperative AQ (r = 0.457, p = 0.0009) and ΔAQ (r = 0.588, p < 0.0001). The nodal local efficiency of node A44d and the nodal efficiency, nodal betweenness centrality, and degree centrality of node A7m were mediators of SDTN and postoperative AQs. CONCLUSIONS: The decreased ability of nodes A44d, A6dl, and A7m to convey information in the right hemisphere was associated with short-term language deficits after tumor resection. A smaller SDTN induced a worsened postoperative language deficit through a significant decrease in the ability to convey information from these three nodes.

Epilepsy-related white matter network changes in patients with frontal lobe glioma.

PURPOSE: Epilepsy is a common symptom in patients with frontal lobe glioma. Tumor-related epilepsy was recently considered a type of network disease. Glioma can severely influence the integrity of the white matter network. The association between white matter network changes and presurgical epilepsy remains unclear in glioma patients. This study aims to identify alterations to the subcortical brain networks caused by glioma and glioma-related epilepsy. METHODS: Sixty-one patients with frontal lobe gliomas were enrolled and stratified into the epileptic and non-epileptic groups. Additionally, 14 healthy participants were enrolled after matching for age, sex, and education level. All participants underwent diffusion tensor imaging. Graph theoretical analysis was applied to reveal topological changes in their white matter networks. Regions affected by tumors were excluded from the analysis. RESULTS: Global efficiency was significantly decreased (p = 0.008), while the shortest path length increased (p = 0.02) in the left and right non-epileptic groups compared to the controls. A total of five edges exhibited decreased fiber count in the non-epileptic group (p < 0.05, false discovery rate-corrected). The topological properties and connectional edges showed no significant differences when comparing the epileptic groups and the controls. Additionally, the degree centrality of several nodes connected to the alternated edges was also diminished. CONCLUSIONS: Compared to the controls, the epilepsy groups showed raletively intact WM networks, while the non-epileptsy groups had damaged network with lower efficiency and longer path length. These findings indicated that the occurrence of glioma related epilepsy have association with white matter network intergrity.

Decreasing distance from tumor to the language network causes language deficit.

Preoperative language deficits are associated with alterations in the language networks of patients with gliomas. This study investigated how gliomas affect language performance by altering the language network. Ninety patients with lower-grade gliomas were included, and their preoperative language performance was evaluated using the Western Aphasia Battery. We also calculated the topological properties based on resting state functional magnetic resonance imaging. All patients were classified according to aphasia quotient (AQ) into the aphasia (AQ < 93.8), mild anomia (AQ > 93.8 and naming section <9.8), and normal groups (AQ > 93.8). The shortest distance from the tumor to the language network (SDTN) was evaluated to identify the effect on language performance induced by the tumor. One-way analysis of variance and post hoc analysis with Sidak correction were used to analyze the differences in topological properties among the three groups. Causal mediation analysis was used to identify indirectly affected mediators. Compared with the mild anomia group, longer shortest path length (p = .0016), lower vulnerability (p = .0331), and weaker nodal efficiencies of three nodes (right caudal Brodmann area [BA] 45, right caudal BA 22, and left BA 41/42, all p < .05) were observed in the aphasia group. The SDTN mediated nodal degree centrality and nodal vulnerability (left rostroventral BA 39), which negatively affected the AQs. Conventional language eloquent and mirrored areas participated in the language network alterations induced by gliomas. The SDTN was a mediator that affected the preoperative language status in patients with gliomas.

Contralesional Cortical and Network Features Associated with Preoperative Language Deficit in Glioma Patients.

Lower-grade Gliomas anchored in eloquent areas cause varying degrees of language impairment. Except for a tumor's features, contralesional compensation may explain these differences. Therefore, studying changes in the contralateral hemisphere can provide insights into the underlying mechanisms of language function compensation in patients with gliomas. This study included 60 patients with eloquent-area or near-eloquent-area gliomas. The participants were grouped according to the degree of language defect. T1 and diffusion tensor imaging were obtained. The contralesional cortical volume and the subcortical network were compared between groups. Patients with unimpaired language function showed elevated cortical volume in the midline areas of the frontal and temporal lobes. In subcortical networks, the group also had the highest global efficiency and shortest global path length. Ten nodes had intergroup differences in nodal efficiency, among which four nodes were in the motor area and four nodes were in the language area. Linear correlation was observed between the efficiency of the two nodes and the patient's language function score. Functional compensation in the contralesional hemisphere may alleviate language deficits in patients with gliomas. Structural compensation mainly occurs in the contralesional midline area in the frontal and temporal lobes, and manifests as an increase in cortical volume and subcortical network efficiency.

Micro-Nanocarriers Based Drug Delivery Technology for Blood-Brain Barrier Crossing and Brain Tumor Targeting Therapy.

The greatest obstacle to using drugs to treat brain tumors is the blood-brain barrier (BBB), making it difficult for conventional drug molecules to enter the brain. Therefore, how to safely and effectively penetrate the BBB to achieve targeted drug delivery to brain tumors has been a challenging research problem. With the intensive research in micro- and nanotechnology in recent years, nano drug-targeted delivery technologies have shown great potential to overcome this challenge, such as inorganic nanocarriers, organic polymer-carriers, liposomes, and biobased carriers, which can be designed in different sizes, shapes, and surface functional groups to enhance their ability to penetrate the BBB and targeted drug delivery for brain tumors. In this review, the composition and overcoming patterns of the BBB are detailed, and then the hot research topics of drug delivery carriers for brain tumors in recent years are summarized, and their mechanisms of action on the BBB and the factors affecting drug delivery are described in detail, and the effectiveness of targeted therapy for brain tumors is evaluated. Finally, the challenges and dilemmas in developing brain tumor drug delivery systems are discussed, which will be promising in the future for targeted drug delivery to brain tumors based on micro-nanocarriers technology.

Characteristics of Microstructural Changes Associated with Glioma Related Epilepsy: A Diffusion Tensor Imaging (DTI) Study.

(1) Background: Glioma is the most common primary tumor in the central nervous system, and glioma-related epilepsy (GRE) is one of its common symptoms. The abnormalities of white matter fiber tracts are involved in attributing changes in patients with epilepsy (Rudà, R, 2012).This study aimed to assess frontal lobe gliomas' effects on the cerebral white matter fiber tracts. (2) Methods: Thirty patients with frontal lobe glioma were enrolled and divided into two groups (Ep and nEep). Among them, five patients were excluded due to apparent insular or temporal involvement. A set of 14 age and gender-matched healthy controls were also included. All the enrolled subjects underwent preoperative conventional magnetic resonance images (MRI) and diffusion tensor imaging (DTI). Furthermore, we used tract-based spatial statistics to analyze the characteristics of the white matter fiber tracts. (3) Results: The two patient groups showed similar patterns of mean diffusivity (MD) elevations in most regions; however, in the ipsilateral inferior fronto-occipital fasciculus (IFOF), superior longitudinal fasciculus (SLF), and superior corona radiata, the significant voxels of the EP group were more apparent than in the nEP group. No significant fractional anisotropy (FA) elevations or MD degenerations were found in the current study. (4) Conclusions: Gliomas grow and invade along white matter fiber tracts. This study assessed the effects of GRE on the white matter fiber bundle skeleton by TBSS, and we found that the changes in the white matter skeleton of the frontal lobe tumor-related epilepsy were mainly concentrated in the IFOF, SLF, and superior corona radiata. This reveals that GRE significantly affects the white matter fiber microstructure of the tumor.

Development and Validation of a Deep Learning Model for Brain Tumor Diagnosis and Classification Using Magnetic Resonance Imaging.

Importance: Deep learning may be able to use patient magnetic resonance imaging (MRI) data to aid in brain tumor classification and diagnosis. Objective: To develop and clinically validate a deep learning system for automated identification and classification of 18 types of brain tumors from patient MRI data. Design, Setting, and Participants: This diagnostic study was conducted using MRI data collected between 2000 and 2019 from 37 871 patients. A deep learning system for segmentation and classification of 18 types of intracranial tumors based on T1- and T2-weighted images and T2 contrast MRI sequences was developed and tested. The diagnostic accuracy of the system was tested using 1 internal and 3 external independent data sets. The clinical value of the system was assessed by comparing the tumor diagnostic accuracy of neuroradiologists with vs without assistance of the proposed system using a separate internal test data set. Data were analyzed from March 2019 through February 2020. Main Outcomes and Measures: Changes in neuroradiologist clinical diagnostic accuracy in brain MRI scans with vs without the deep learning system were evaluated. Results: A deep learning system was trained among 37 871 patients (mean [SD] age, 41.6 [11.4] years; 18 519 women [48.9%]). It achieved a mean area under the receiver operating characteristic curve of 0.92 (95% CI, 0.84-0.99) on 1339 patients from 4 centers' data sets in diagnosis and classification of 18 types of tumors. Higher outcomes were found compared with neuroradiologists for accuracy and sensitivity and similar outcomes for specificity (for 300 patients in the Tiantan Hospital test data set: accuracy, 73.3% [95% CI, 67.7%-77.7%] vs 60.9% [95% CI, 46.8%-75.1%]; sensitivity, 88.9% [95% CI, 85.3%-92.4%] vs 53.4% [95% CI, 41.8%-64.9%]; and specificity, 96.3% [95% CI, 94.2%-98.4%] vs 97.9%; [95% CI, 97.3%-98.5%]). With the assistance of the deep learning system, the mean accuracy of neuroradiologists among 1166 patients increased by 12.0 percentage points, from 63.5% (95% CI, 60.7%-66.2%) without assistance to 75.5% (95% CI, 73.0%-77.9%) with assistance. Conclusions and Relevance: These findings suggest that deep learning system-based automated diagnosis may be associated with improved classification and diagnosis of intracranial tumors from MRI data among neuroradiologists.

Identifying biomarkers associated with tumor growth rate: a longitudinal preoperative magnetic resonance imaging follow-up of low-grade gliomas.

Background: Although the influence of molecular biomarkers on the biological behavior of tumor cells has been investigated, their quantitative influence on the velocity of tumor growth remains unclear. This study aimed to identify the molecular biomarkers associated with tumor growth rates in World Health Organization (WHO) grade II gliomas, or low-grade gliomas (LGGs). Methods: Preoperative magnetic resonance imaging (MRI) data of patients with LGGs were retrospectively reviewed. Patients with at least 2 preoperative MRIs taken more than 90 days apart were enrolled. Patients with isocitrate dehydrogenase (IDH) wild-type tumors or with no recorded IDH status were excluded. A linear mixed-effects model was used to assess the velocity of tumor diameter expansion. The effect of biomarker expression on tumor growth rate was assessed using a multivariate linear mixed-effects regression model. Results: Data from 56 patients were used in our study. The overall velocity of diameter expansion (VDE) for LGGs was 2.1 mm/year. Higher expression level of mutant p53 were significantly associated with a higher tumor growth rate (+1.9 mm/year, P<0.01), while higher expression level of alpha-thalassemia/mental retardation syndrome X-linked protein (ATRX) were significantly associated with a lower tumor growth rate (-1.3 mm/year, P<0.01). Tumors with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation were found to grow significantly more slowly than those with no methylation (-3.1 mm/year, P<0.01). The telomerase reverse transcriptase (TERT) promoter type and expressions levels of Ki-67 and epidermal growth factor receptor (EGFR) showed no significant independent impact on tumor growth rates. Conclusions: The status of biomarkers is significantly associated with the tumor growth rate in LGGs.

Image-Based Differentiation of Intracranial Metastasis From Glioblastoma Using Automated Machine Learning.

Purpose: The majority of solitary brain metastases appear similar to glioblastomas (GBMs) on magnetic resonance imaging (MRI). This study aimed to develop and validate an MRI-based model to differentiate intracranial metastases from GBMs using automated machine learning. Materials and Methods: Radiomics features from 354 patients with brain metastases and 354 with GBMs were used to build prediction algorithms based on T2-weighted images, contrast-enhanced (CE) T1-weighted images, or both. The data of these subjects were subjected to a nested 10-fold split in the training and testing groups to build the best algorithms using the tree-based pipeline optimization tool (TPOT). The algorithms were independently validated using data from 124 institutional patients with solitary brain metastases and 103 patients with GBMs from the cancer genome atlas. Results: Three groups of models were developed. The average areas under the receiver operating characteristic curve (AUCs) were 0.856 for CE T1-weighted images, 0.976 for T2-weighted images, and 0.988 for a combination in the testing groups, and the AUCs of the groups of models in the independent validation were 0.687, 0.831, and 0.867, respectively. A total of 149 radiomics features were considered as the most valuable features for the differential diagnosis of GBMs and metastases. Conclusion: The models established by TPOT can distinguish glioblastoma from solitary brain metastases well, and its non-invasiveness, convenience, and robustness make it potentially useful for clinical applications.

Increasing nodal vulnerability and nodal efficiency implied recovery time prolonging in patients with supplementary motor area syndrome.

Supplementary motor area (SMA) syndrome is a surgery-related complication that commonly occurs after removing SMA glioma, and needs weeks to recover. However, susceptible factors of patients suffering from SMA syndrome remain unknown. Graphic theory was applied to reveal topological properties of sensorimotor network (SMN) by processing resting-state functional magnetic resonance images in 66 patients with SMA gliomas. Patients were classified into SMA and non-SMA groups based on whether they suffered from SMA syndrome. We collected recovery time and used causal mediation analysis to find association between topological properties and recovery time. Compared with the non-SMA group, higher vulnerability (left: p = .0018; right: p = .0033) and lower fault tolerance (left: p = .0022; right: p = .0248) of the whole SMN were found in the SMA group. Moreover, higher nodal properties of lesional-hemispheric cingulate cortex (nodal efficiency: left, p = .0389; right, p = .0169; nodal vulnerability: left, p = .0185; right, p = .0085) and upper limb region of primary motor cortex (PMC; nodal efficiency: left, p = .0132; right, p = .0001; nodal vulnerability: left, p = .0091; right, p = .0209) were found in the SMA group. Nodal efficiency and nodal vulnerability of cingulate cortex and upper limb region of PMC were important predictors for SMA syndrome occurring and recovery time prolonging. Neurosurgeons should carefully deal with upper limb region of PMC and cingulate cortex, and protect them if these two region were unnecessary to damage during SMA glioma resection.

Expression changes in ion channel and immunity genes are associated with glioma-related epilepsy in patients with diffuse gliomas.

BACKGROUND: Glioma-related epilepsy (GRE) is a common symptom in patients with diffuse gliomas. However, the underlying mechanisms of GRE remain unclear. The current study aimed to investigate the underlying epileptogenic mechanisms of GRE through RNA sequencing analysis. METHODS: Demographic, RNA sequencing, and follow-up data of 643 patients were reviewed. Patients were divided into test and validation groups (223 and 420 patients, respectively) by different time periods for RNA sequencing. The differentially expressed genes (DEGs) associated with preoperative GRE were identified using R software. Functional enrichment analysis was subsequently performed, and tissue-infiltrating immune cells were also estimated. Weighted correlation network analysis (WGCNA) was conducted to further identify key modules exhibiting the highest correlation with preoperative GRE. Overlapping genes between the DEG set and key gene set identified by WGCNA were selected and verified in the validation cohort. The protein-protein interaction (PPI) network analysis was then constructed to identify hub genes for preoperative GRE. RESULTS: A total of 219 DEGs were identified, among which 112 were upregulated and 107 downregulated in patients with GRE. Functional enrichment analysis revealed that upregulated DEGs were related to ion channel activity, while downregulated genes were related to immunity. Forty-two genes were further selected from overlapping DEGs and the key gene set. Among these genes, 31 genes showed significant differences in the validation cohort. Finally, the PPI network analysis identified six genes, including SCN3B, KCNIP2, KCNJ11, VEGFA, MMP9, and ANXA2, as hub genes for GRE. CONCLUSION: The current study revealed that ion channel activity and immunity dysfunction in diffuse glioma patients contributed to the occurrence of GRE, and SCN3B might be a shared therapeutic target for both diffuse gliomas and GRE. These findings could improve the understanding of the mechanisms of GRE and promote individualized medications for glioma management.