Energy-Efficient Sleep Apnea Detection Using a Hyperdimensional Computing Framework Based on Wearable Bracelet Photoplethysmography.

OBJECTIVE: Sleep apnea syndrome (SAS) is a common sleep disorder, which has been shown to be an important contributor to major neurocognitive and cardiovascular sequelae. Considering current diagnostic strategies are limited with bulky medical devices and high examination expenses, a large number of cases go undiagnosed. To enable large-scale screening for SAS, wearable photoplethysmography (PPG) technologies have been used as an early detection tool. However, existing algorithms are energy-intensive and require large amounts of memory resources, which are believed to be the major drawbacks for further promotion of wearable devices for SAS detection. METHODS: In this paper, an energy-efficient method of SAS detection based on hyperdimensional computing (HDC) is proposed. Inspired by the phenomenon of chunking in cognitive psychology as a memory mechanism for improving working memory efficiency, we proposed a one-dimensional block local binary pattern (1D-BlockLBP) encoding scheme combined with HDC to preserve dominant dynamical and temporal characteristics of pulse rate signals from wearable PPG devices. RESULTS: Our method achieved 70.17% accuracy in sleep apnea segment detection, which is comparable with traditional machine learning methods. Additionally, our method achieves up to 67× lower memory footprint, 68× latency reduction, and 93× energy saving on the ARM Cortex-M4 processor. CONCLUSION: The simplicity of hypervector operations in HDC and the novel 1D-BlockLBP encoding effectively preserve pulse rate signal characteristics with high computational efficiency. SIGNIFICANCE: This work provides a scalable solution for long-term home-based monitoring of sleep apnea, enhancing the feasibility of consistent patient care.

Multiscale Bidirectional Temporal Convolutional Network for Sleep Apnea Detection Based on Wearable Photoplethysmography Bracelet.

Sleep apnea syndrome (SAS), which can lead to a range of Cardiopulmonary diseases, is a common chronic sleep disorder. The unobtrusive detection based on wearable devices is helpful for early diagnosis and treatment of SAS. To this end, this paper presents a method based on a one-dimensional multi-scale bidirectional temporal convolutional neural network (1D-MsBiTCNet) and two model performance optimization techniques, i.e., regularized dropout (RD) and logit adjustment (LA). Among them, 1D-MsBiTCNet has outstanding capabilities in both feature extraction and temporal dependence representation. RD and LA play an effective role in solving the overfitting problem of model training and the class imbalance problem of the dataset, respectively. The proposed model was trained and tested on a photoplethysmography (PPG) dataset (including data from 92 subjects) collected from commercial wearable bracelets. On this dataset, our method achieved accuracy, sensitivity and specificity of 82.76%, 71.58%, 86.74% for per-segment detection, and 97.83%, 88.89%, 100.00% for per-recording severe SAS detection. For the precise quantification of apnea-hypopnea index (AHI), our method achieved a mean absolute error of 5.44 between the predicted AHI and the ground truth AHI. The experimental results show that our proposed method has an outstanding performance and can provide a methodological reference for large-scale SAS automatic detection.

Daily Posture Behavior Patterns Derived From Multitime-Scale Topic Models Using Wearable Triaxial Acceleration for Assessment of Concern About Falling.

Concern about falling is prevalent in older population. This condition would cause a series of adverse physical and psychological consequences for older adults' health. Traditional assessment of concern about falling is relied on self-reported questionnaires and thus is too subjective. Therefore, we proposed a novel multi-time-scale topic modelling approach to quantitatively evaluate concern about falling by analyzing triaxial acceleration signals collected from a wearable pendent sensor. Different posture segments were firstly recognized to extract their corresponding feature subsets. Then, each selected feature related to concern about falling was clustered into discrete levels as feature letters of artificial words in different time scales. As a result, all older participants' signal recordings were converted to a collection of artificial documents, which can be processed by natural language processing methodologies. The topic modelling technique was used to discover daily posture behavior patterns from these documents as discriminants between older adults with different levels of concern about falling. The results indicated that there were significant differences in distributions of posture topics between groups of older adults with different levels of concern about falling. Additionally, the transitions of posture topics over daytime and nighttime revealed temporal regularities of posture behavior patterns of older adult's active and inactive status, which were substantially different for older adults with different levels of concern about falling. Finally, the level of concern about falling was accurately determined with accuracy of 71.2% based on the distributions of posture topics combined with the mobility performance metrics of walking behaviors and demographic information.

Genomic alterations of oligodendrogliomas at distant recurrence.

BACKGROUND: Oligodendroglioma is known for its relatively better prognosis and responsiveness to radiotherapy and chemotherapy. However, little is known about the evolution of genetic changes as oligodendroglioma progresses. METHODS: In this study, we evaluated gene evolution invivo during tumor progression based on deep whole-genome sequencing data (ctDNA). We analyzed longitudinal genomic data from six patients during tumor evolution, of which five patients developed distant recurrence. RESULTS: Whole-exome sequencing demonstrated that the rate of shared mutations between the primary and recurrent samples was relatively low. In two cases, even well-known major driver mutations in CIC and FUBP1 that were detected in primary tumors were not detected in the relapse samples. Among these cases, two patients had a conversion from the IDH mutation in the originating state to the IDH1 wild state during the process of gene evolution under chemotherapy treatment, indicating that the cell phenotype and genetic characteristics of oligodendroglioma may change during tumor evolution. Two patients received long-term temozolomide (TMZ) treatment before the operation, and we found that recurrence tumors harbored mutations in the PI3K/AKT and Sonic hedgehog (SHh) signaling pathways. Hypermutation occurred with mutations in MMR genes in one patient, contributing to the rapid progression of the tumor. CONCLUSION: Oligodendroglioma displayed great spatial and temporal heterogeneity during tumor evolution. The PI3K/AKT and SHh signaling pathways may play an important role in promoting treatment resistance and distant relapse during oligodendroglioma evolution. In addition, there was a tendency to increase the degree of tumor malignancy during evolution. Distant recurrence may be a later event duringoligodendroglioma progression. CLINICALTRIALS: gov, Identifier: NCT05512325.

Molecular and clonal evolution in vivo reveal a common pathway of distant relapse gliomas.

The evolutionary trajectories of genomic alterations underlying distant recurrence in glioma remain largely unknown. To elucidate glioma evolution, we analyzed the evolutionary trajectories of matched pairs of primary tumors and relapse tumors or tumor in situ fluid (TISF) based on deep whole-genome sequencing data (ctDNA). We found that MMR gene mutations occurred in the late stage in IDH-mutant glioma during gene evolution, which activates multiple signaling pathways and significantly increases distant recurrence potential. The proneural subtype characterized by PDGFRA amplification was likely prone to hypermutation and distant recurrence following treatment. The classical and mesenchymal subtypes tended to progress locally through subclonal reconstruction, trunk genes transformation, and convergence evolution. EGFR and NOTCH signaling pathways and CDNK2A mutation play an important role in promoting tumor local progression. Glioma subtypes displayed distinct preferred evolutionary patterns. ClinicalTrials.gov, NCT05512325.

MS-Net: Sleep apnea detection in PPG using multi-scale block and shadow module one-dimensional convolutional neural network.

Sleep Apnea (SA) is a respiratory disorder that affects sleep. However, the SA detection method based on polysomnography is complex and not suitable for home use. The detection approach using Photoplethysmography is low cost and convenient, which can be used to widely detect SA. This study proposed a method combining a multi-scale one-dimensional convolutional neural network and a shadow one-dimensional convolutional neural network based on dual-channel input. The time-series feature information of different segments were extracted from multi-scale temporal structure. Moreover, shadow module was adopted to make full use of the redundant information generated after multi-scale convolution operation, which improved the accuracy and ensured the portability of the model. At the same time, we introduced balanced bootstrapping and class weight, which effectively alleviated the problem of unbalanced classes. Our method achieved the result of 82.0% average accuracy, 74.4% average sensitivity and 85.1% average specificity for per-segment SA detection, and reached 93.6% average accuracy for per-recording SA detection after 5-fold cross validation. Experimental results show that this method has good robustness. It can be regarded as an effective aid in SA detection in household use.

Information-Based Similarity of Ordinal Pattern Sequences as a Novel Descriptor in Obstructive Sleep Apnea Screening Based on Wearable Photoplethysmography Bracelets.

Obstructive sleep apnea (OSA) is a common respiratory disorder associated with autonomic nervous system (ANS) dysfunction, resulting in abnormal heart rate variability (HRV). Capable of acquiring heart rate (HR) information with more convenience, wearable photoplethysmography (PPG) bracelets are proven to be a potential surrogate for electrocardiogram (ECG)-based devices. Meanwhile, bracelet-type PPG has been heavily marketed and widely accepted. This study aims to investigate the algorithm that can identify OSA with wearable devices. The information-based similarity of ordinal pattern sequences (OP_IBS), which is a modified version of the information-based similarity (IBS), has been proposed as a novel index to detect OSA based on wearable PPG signals. A total of 92 PPG recordings (29 normal subjects, 39 mild-moderate OSA subjects and 24 severe OSA subjects) were included in this study. OP_IBS along with classical indices were calculated. For severe OSA detection, the accuracy of OP_IBS was 85.9%, much higher than that of the low-frequency power to high-frequency power ratio (70.7%). The combination of OP_IBS, IBS, CV and LF/HF can achieve 91.3% accuracy, 91.0% sensitivity and 91.5% specificity. The performance of OP_IBS is significantly improved compared with our previous study based on the same database with the IBS method. In the Physionet database, OP_IBS also performed exceptionally well with an accuracy of 91.7%. This research shows that the OP_IBS method can access the HR dynamics of OSA subjects and help diagnose OSA in clinical environments.

Fuzzy Approximate Entropy of Extrema Based on Multiple Moving Averages as a Novel Approach in Obstructive Sleep Apnea Screening.

OBJECTIVE: Obstructive sleep apnea (OSA) is a respiratory disease associated with autonomic nervous system dysfunction. As a novel method for analyzing OSA depending on heart rate variability, fuzzy approximate entropy of extrema based on multiple moving averages (Emma-fApEn) can effectively assess the sympathetic tension limits, thereby realizing a good performance in the disease severity screening. METHOD: Sixty 6-h electrocardiogram recordings (20 healthy, 16 mild/moderate OSA and 34 severe OSA) from the PhysioNet database were used in this study. The performances of minima of Emma-fApEn (fApEn-minima), maxima of Emma-fApEn (fApEn-maxima) and classic time-frequency domain indices for each recording were assessed by significance analysis, correlation analysis, parameter optimization and OSA screening. RESULTS: fApEn-minima and fApEn-maxima had significant differences between the severe OSA group and the other two groups, while the mean value (Mean) and the ratio of low-frequency power and high-frequency power (LH) could significantly differentiate OSA recordings from healthy recordings. The correlation coefficient between fApEn-minima and apnea-hypopnea index was the highest (|R| = 0.705). Machine learning methods were used to evaluate the performances of the above four indices. Random forest (RF) achieved the highest accuracy of 96.67% in OSA screening and 91.67% in severe OSA screening, with a good balance in both. CONCLUSION: Emma-fApEn may be used as a simple preliminary detection tool to assess the severity of OSA prior to polysomnography analysis.

Corrigendum: MELK Inhibition Effectively Suppresses Growth of Glioblastoma and Cancer Stem-Like Cells by Blocking AKT and FOXM1 Pathways.

[This corrects the article DOI: 10.3389/fonc.2020.608082.].

An accurate and explainable ensemble learning method for carotid plaque prediction in an asymptomatic population.

BACKGROUND AND OBJECTIVE: The identification of carotid plaque, one of the most crucial tasks in stroke screening, is of great significance in the assessment of subclinical atherosclerosis and preventing the onset of stroke. However, traditional ultrasound examination is not prevalent or cost-effective for asymptomatic people, particularly low-income individuals in rural areas. Thus, it is necessary to develop an accurate and explainable model for early identification of the risk of plaque prevalence that can help in the primary prevention of stroke. METHODS: We developed an ensemble learning method to predict the occurrence of carotid plaques. A dataset comprising 1440 subjects (50% with plaques and 50% without plaques) and ten-fold cross-validation were utilized to evaluate the model performance. Four machine learning methods (extreme gradient boosting (XGBoost), gradient boosting decision tree, random forest, and support vector machine) were evaluated. Subsequently, the interpretability of the XGBoost model, which provided the best performance, was analyzed from three aspects: feature importance, feature effect on prediction model, and feature effect on prediction decision for a specific subject. RESULTS: The XGBoost algorithm provided the best performance (sensitivity: 0.8678, specificity: 0.8592, accuracy: 0.8632, F1 score: 0.8621, area under the curve: 0.8635) in carotid plaque prediction and also had excellent performance under missing data circumstances. Further, interpretability analysis showed that the decisions of the XGBoost model were highly congruent with clinical knowledge. CONCLUSION: The model results are superior to those of state-of-the-art methods. Thus, it is a promising carotid plaque prediction tool that could be used in the primary prevention of stroke.

Deep Neural Network-based Video Processing to Obtain Upper-Extremity Motor Performance Toward Assessment of Cognitive and Motor Function.

Dementia is an increasing global health challenge. Motoric Cognitive Risk Syndrome (MCR) is a predementia stage that can be used to predict future occurrence of dementia. Traditionally, gait speed and subjective memory complaints are used to identify old adults with MCR. Our previous studies indicated that dual-task upper-extremity motor performance (DTUEMP) quantified by a single wrist-worn sensor was correlated with both motor and cognitive function. Therefore, the DTUEMP had a potential to be used in the diagnosis of MCR. Instead of using inertial sensors to capture kinematic data of upper-extremity movements, here we proposed a deep neural network-based video processing model to obtain DTUEMP metrics from a 20-second repetitive elbow flexion-extension test under dual-task condition. In details, we used a deep residual neural network to obtain joint coordinate set of the elbow and wrist in each frame, and then used optical flow method to correct the joint coordinates generated by the neural network. The coordinate sets of all frames in a video recording are used to generate angle sequence which represents rotation angle of the line between the wrist and elbow. Then, the DTUEMP metrics (the mean and SD of flexion and extension phase) are derived from angle sequence. Multi-task learning (MTL) is used to assess cognitive and motor function represented by MMSE and TUG scores based on DTUEMP metrics, with single-task learning (STL) linear model as a benchmark. The results showed a good agreement (r ≥ 0.80 and ICC ≥ 0.58) between the derived DTUEMP metrics from our proposed model and the ones from clinically validated sensor processing model. We also found that there were correlations with statistical significance (p < 0.05) between some of video-derived DTUEMP metrics (i.e. the mean of flexion time and extension time) and clinical cognitive scale (Mini-Mental State Examination, MMSE). Additionally, some of video-derived DTUEMP metrics (i.e. the mean and standard deviation of flexion time and extension time) was also associated with the scores of timed-up and go (TUG) which is a gold standard to measure functional mobility. Mean absolute percentage error (MAPE) of MTL surpassed that of STL (For MMSE, MTL: 18.63%, STL: 23.18%. For TUG, MTL: 17.88%, STL: 22.53%). The experiments with different light conditions and shot angles verified the robustness of our proposed video processing model to extract DTUEMP metrics in potentially various home environments (r ≥ 0.58 and ICC ≥ 0.71). This study shows possibility of replacing sensor processing model with video processing model for analyzing the DTUEMP and a promising future to adjuvant diagnosis of MCR via a mobile platform.

Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network.

Obstructive sleep apnea (OSA), which has high morbidity and complications, is diagnosed via polysomnography (PSG). However, this method is expensive, time-consuming, and causes discomfort to the patient. Single-lead electrocardiogram (ECG) is a potential alternative to PSG for OSA diagnosis. Recent studies have successfully applied deep learning methods to OSA detection using ECG and obtained great success. However, most of these methods only focus on heart rate variability (HRV), ignoring the importance of ECG-derived respiration (EDR). In addition, they used relatively simple networks, and cannot extract more complex features. In this study, we proposed a one-dimensional squeeze-and-excitation (SE) residual group network to thoroughly extract the complementary information between HRV and EDR. We used the released and withheld sets in the Apnea-ECG dataset to develop and test the proposed method, respectively. In the withheld set, the method has an accuracy of 90.3%, a sensitivity of 87.6%, and a specificity of 91.9% for per-segment detection, indicating an improvement over existing methods for the same dataset. The proposed method can be integrated with wearable devices to realize inexpensive, convenient, and highly efficient OSA detectors.

Similarity Changes Analysis for Heart Rate Fluctuation Regularity as a New Screening Method for Congestive Heart Failure.

Congestive heart failure (CHF) is a chronic cardiovascular condition associated with dysfunction of the autonomic nervous system (ANS). Heart rate variability (HRV) has been widely used to assess ANS. This paper proposes a new HRV analysis method, which uses information-based similarity (IBS) transformation and fuzzy approximate entropy (fApEn) algorithm to obtain the fApEn_IBS index, which is used to observe the complexity of autonomic fluctuations in CHF within 24 h. We used 98 ECG records (54 health records and 44 CHF records) from the PhysioNet database. The fApEn_IBS index was statistically significant between the control and CHF groups (p < 0.001). Compared with the classical indices low-to-high frequency power ratio (LF/HF) and IBS, the fApEn_IBS index further utilizes the changes in the rhythm of heart rate (HR) fluctuations between RR intervals to fully extract relevant information between adjacent time intervals and significantly improves the performance of CHF screening. The CHF classification accuracy of fApEn_IBS was 84.69%, higher than LF/HF (77.55%) and IBS (83.67%). Moreover, the combination of IBS, fApEn_IBS, and LF/HF reached the highest CHF screening accuracy (98.98%) with the random forest (RF) classifier, indicating that the IBS and LF/HF had good complementarity. Therefore, fApEn_IBS effusively reflects the complexity of autonomic nerves in CHF and is a valuable CHF assessment tool.

Sleep apnea screening based on Photoplethysmography data from wearable bracelets using an information-based similarity approach.

BACKGROUND AND OBJECTIVE: Sleep apnea (SA) is a common sleep disorder in daily life and is also an aggravating factor for various diseases. Having the potential to replace traditional but complicated diagnostic equipment, portable medical devices are receiving increasing attention, and thus, the demand for supporting algorithms is growing. This study aims to identify SA with wearable devices. METHODS: Static information-based similarity (sIBS) and dynamic information-based similarity (dIBS) were proposed to analyze short-term fluctuations in heart rate (HR) with wearable devices. This study included overnight photoplethysmography (PPG) signals from 92 subjects obtained from wearable bracelets. RESULTS: The results showed that sIBS achieved the highest correlation coefficient with the apnea-hypopnea index (R=-0.653, p=0). dIBS showed a good balance in sensitivity and specificity (75.0% and 72.1%, respectively). Combining sIBS and dIBS with other classical time-frequency domain indices could simultaneously achieve good accuracy and balance (84.7% accuracy, 76.7% sensitivity and 89.6% specificity). CONCLUSIONS: This research showed that both classic time-frequency domain indices and IBS indices changed significantly only in the severe SA group. This novel method could serve as an effective way to assess SA and provide new insight into its pathophysiology.

Chrysophanol Induced Glioma Cells Apoptosis via Activation of Mitochondrial Apoptosis Pathway.

Glioma is a common intracranial tumor originated from neuroglia cell. Chrysophanol is an anthraquinone derivative proved to exert anticancer effects in various cancers. This paper investigated the effect and mechanism of chrysophanol in glioma. Glioma cell lines U251 and SHG-44 were adopted in the experiments. The cells were treated with chrysophanol at different concentrations (0, 10, 20 50, 100 and 200 µM) for 48 h in the study, and then processed with MitoTempo. Mitochondria and cytosol were isolated to investigate the role of mitochondria during chrysophanol functioning on glioma cells. Cell viability was detected through 3-(4,5-Dimethyl-2-Thiazolyl)-2,5-Diphenyl Tetrazolium Bromide (MTT) assay, and cell apoptosis, cell cycle as well as relative reactive oxygen species (ROS) were assessed by flow cytometry. Expressions of Cytosol Cyt C, cleaved caspase-3, cleaved caspase-9, Cyclin D1 and Cyclin E were evaluated by western blot. In U251 and SHG-44 cells, with chrysophanol concentration rising, cell viability, expressions of Cyclin D1 and Cyclin E were decreased while cell apoptosis, levels of cleaved caspase-3, cleaved caspase-9 and Cytosol Cyt C as well as ROS accumulation were increased with cell cycle arrested in G1 phase. Besides, chrysophanol promoted ROS accumulation, cell apoptosis and transfer of Cyt C from mitochondria to cytosol in cells while MitoTempo partly reversed the effect of chrysophanol. Chrysophanol promoted cell apoptosis via activating mitochondrial apoptosis pathway in glioma.

The novel approach of temporal dependency complexity analysis of heart rate variability in obstructive sleep apnea.

Obstructive sleep apnea (OSA) is a serious sleep disorder, which leads to changes in autonomic nerve function and increases the risk of cardiovascular disease. Heart rate variability (HRV) has been widely used as a non-invasive method for assessing the autonomic nervous system (ANS). We proposed the two-dimensional sample entropy of the coarse-grained Gramian angular summation field image (CgSampEn2D) index. It is a new index for HRV analysis based on the temporal dependency complexity. In this study, we used 60 electrocardiogram (ECG) records from the Apnea-ECG database of PhysioNet (20 healthy records and 40 OSA records). These records were divided into 5-min segments. Compared with the classical indices low-to-high frequency power ratio (LF/HF) and sample entropy (SampEn), CgSampEn2D utilizes the correlation information between different time intervals in the RR sequences and preserves the temporal dependency of the RR sequences, which improves the OSA detection performance significantly. The OSA screening accuracy of CgSampEn2D (93.3%) is higher than that of LF/HF (80.0%) and SampEn (73.3%). Additionally, CgSampEn2D has a significant association with the apnea-hypopnea index (AHI) (R = -0.740, p = 0). CgSampEn2D reflects the complexity of the OSA autonomic nerve more comprehensively and provides a novel idea for the screening of OSA disease.

Csnk1a1 inhibition modulates the inflammatory secretome and enhances response to radiotherapy in glioma.

Glioblastoma multiforme (GBM), a fatal brain tumour with no available targeted therapies, has a poor prognosis. At present, radiotherapy is one of the main methods to treat glioma, but it leads to an obvious increase in inflammatory factors in the tumour microenvironment, especially IL-6 and CXCL1, which plays a role in tumour to resistance radiotherapy and tumorigenesis. Casein kinase 1 alpha 1 (CK1α) (encoded on chromosome 5q by Csnk1a1) is considered an attractive target for Tp53 wild-type acute myeloid leukaemia (AML) treatment. In this study, we evaluated the anti-tumour effect of Csnk1a1 suppression in GBM cells in vitro and in vivo. We found that down-regulation of Csnk1a1 or inhibition by D4476, a Csnk1a1 inhibitor, reduced GBM cell proliferation efficiently in both Tp53 wild-type and Tp53-mutant GBM cells. On the contrary, overexpression of Csnk1a1 promoted cell proliferation and colony formation. Csnk1a1 inhibition improved the sensitivity to radiotherapy. Furthermore, down-regulation of Csnk1a1 reduced the production and secretion of pro-inflammatory factors. In the preclinical GBM model, treatment with D4476 significantly inhibited the increase in pro-inflammatory factors caused by radiotherapy and improved radiotherapy sensitivity, thus inhibiting tumour growth and prolonging animal survival time. These results suggest targeting Csnk1a1 exert an anti-tumour role as an inhibitor of inflammatory factors, providing a new strategy for the treatment of glioma.

GRP78 determines glioblastoma sensitivity to UBA1 inhibition-induced UPR signaling and cell death.

Glioblastoma multiforme (GBM) is an extremely aggressive brain tumor for which new therapeutic approaches are urgently required. Unfolded protein response (UPR) plays an important role in the progression of GBM and is a promising target for developing novel therapeutic interventions. We identified ubiquitin-activating enzyme 1 (UBA1) inhibitor TAK-243 that can strongly induce UPR in GBM cells. In this study, we evaluated the functional activity and mechanism of TAK-243 in preclinical models of GBM. TAK-243 significantly inhibited the survival, proliferation, and colony formation of GBM cell lines and primary GBM cells. It also revealed a significant anti-tumor effect on a GBM PDX animal model and prolonged the survival time of tumor-bearing mice. Notably, TAK-243 more effectively inhibited the survival and self-renewal ability of glioblastoma stem cells (GSCs) than GBM cells. Importantly, we found that the expression level of GRP78 is a key factor in determining the sensitivity of differentiated GBM cells or GSCs to TAK-243. Mechanistically, UBA1 inhibition disrupts global protein ubiquitination in GBM cells, thereby inducing ER stress and UPR. UPR activates the PERK/ATF4 and IRE1α/XBP signaling axes. These findings indicate that UBA1 inhibition could be an attractive strategy that may be potentially used in the treatment of patients with GBM, and GRP78 can be used as a molecular marker for personalized treatment by targeting UBA1.