The optimized gate recurrent unit based on improved evolutionary algorithm to predict stock market returns

In order to accelerate the learning ability of neural network structure parameters and improve the prediction accuracy of deep learning algorithms, an evolutionary algorithm, based on a prior Gaussian mutation (PGM) operator, is proposed to optimize the structure parameters of a gated recurrent unit (GRU) neural network. In this algorithm, the sensitivity learning process of GRU model parameters into the Gaussian mutation operator, used the variance of the GRU model parameter training results as the Gaussian mutation variance to generate the optimal individual candidate set. Then, the optimal GRU neural network structure is constructed using the evolutionary algorithm of the prior Gaussian mutation operator. Moreover, the PGM-EA-GRU algorithm is applied to the prediction of stock market returns. Experiments show that the prediction model effectively overcomes the GRU neural network, quickly falling into a local optimum and slowly converging. Compared to the RF, SVR, RNN, LSTM, GRU, and EA-GRU benchmark models, the model significantly improves the searchability and prediction accuracy of the optimal network structure parameters. It also validates the effectiveness and the progressive nature of the PGM-EA-GRU model proposed in this paper with stock market return prediction

Bottlenecks and opportunities in immunotherapy for glioma: a narrative review

Glioma is the most aggressive brain tumor having invasive ability and a highly heterogeneous phenotype. Many patients with glioma respond poorly to traditional surgery or temozolomide-based chemotherapy. Over the past few decades, developments in immunotherapeutic strategies have provided newer insights into the treatment of gliomas. Immunotherapy is based on the principle of normalization or recovery of T cell-mediated anti-tumor immunoreaction. Different innovative strategies have been used; these include enhancement of immunogenicity by administration of tumor antigens or dendritic cell vaccines, replenishment of cytotoxic T cells by adoptive T cell transfer, repair of exhausted T cells by immune checkpoint inhibitors, and the use of other immune activators such as oncolytic viruses. However, many immunotherapy-based clinical trials did not meet the expected therapeutic endpoints in patients with glioma. Gliomas use unique strategies to generate an immune-suppressive microenvironment; these include limiting immunogenicity and repressing T cell infiltration or activation. This may be addressed by the incorporation of immunotherapy with standard therapy or by use of certain innovative approaches such as tumor-treating fields. In this review, we summarize the updated immunotherapies in glioma and discuss current limitations and future prospects

Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy

Abstract Objectives This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson’s disease (PD) and multiple system atrophy (MSA). Methods A total of 201 participants, including 57 patients with PD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process. Results The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively. Conclusions Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences

A new mechanism of trastuzumab resistance in gastric cancer: MACC1 promotes the Warburg effect via activation of the PI3K/AKT signaling pathway

Abstract Background Trastuzumab, a humanized antibody targeting HER2, exhibits remarkable therapeutic efficacy against HER2-positive gastric cancer. However, recurrent therapeutic resistance presents revolutionary claims. Warburg effect and AKT signaling pathway was involved in the resistance to trastuzumab. Our previous studies have demonstrated that overexpression of metastasis associated with the colon cancer 1 (MACC1) predicted poor prognosis of GC and promoted tumor cells proliferation and invasion. In this study, we found that MACC1 was significantly upregulated in trastuzumab-resistant cell lines. Besides, downregulation of MACC1 reversed this resistance. Methods The effect of trastuzumab and glycolysis inhibitor combination on cell viability, apoptosis, and cell metabolism was investigated in vitro using established trastuzumab-resistant GC cell lines. We assessed the impact of trastuzumab combined with oxamate on tumor growth and metabolism in an established xenograft model of HER2-positive GC cell lines. Results Here, we found that MACC1 was significantly upregulated in trastuzumab-resistant cell lines. Besides, downregulation of MACC1 in trastuzumab-resistant cells reversed this resistance. Overexpression of MACC1-induced trastuzumab resistance, enhanced the Warburg effect, and activated the PI3K/AKT signaling pathway, while downregulation of MACC1 presented the opposite effects. Moreover, when the PI3K/AKT signaling pathway was inhibited, the effects of MACC1 on resistance and glycolysis were diminished. Our findings indicated that MACC1 promoted the Warburg effect mainly through the PI3K/AKT signaling pathway, which further enhanced GC cells trastuzumab resistance. Conclusions Our results indicate that co-targeting of HER2 and the Warburg effect reversed trastuzumab resistance in vitro and in vivo, suggesting that the combination might overcome trastuzumab resistance in MACC1-overexpressed, HER2-positive GC patients

Aberrant temporal-spatial complexity of intrinsic fluctuations in major depression

Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCN were associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal-spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD

Additional file 1: of A new mechanism of trastuzumab resistance in gastric cancer: MACC1 promotes the Warburg effect via activation of the PI3K/AKT signaling pathway

Figures S1 to S6. Figure S1: The expression of proteins in GC cells and the sequences of ectopic MACC1 and shRNA. Figure S2: The combination of trastuzumab and glycolysis inhibitors synergisticly inhibit glycolysis in HER2 positive GC cells. Figure S3: The combination of trastuzumab and glycolysis inhibitors synergisticly inhibit glycolysis in HER2 positive GC cells. Figure S4: MACC1 enhanced the Warburg effect in vivo. Figure S5: Combination of trastuzumab and oxamate effectively inhibited the Warburg effect in vivo. Figure S6: The apoptosis of indicated cells after treated with Ttzm. (ZIP 38363 kb

Additional file 2: of A new mechanism of trastuzumab resistance in gastric cancer: MACC1 promotes the Warburg effect via activation of the PI3K/AKT signaling pathway

Tables S1 to S4. Table S1: IC50 of different GC cell lines. Table S2: IC50 and RI (resistance index) of MKN45 cells after treatment with trastuzumab at different inducing concentrations. Table S3: Fa and CI for trastuzumab and glucolysis inhibitor combinations on inhibition of cell viability. Table S4: Fa and CI for trastuzumab and glucolysis inhibitor combinations on inhibition of glucose uptake. (DOCX 45 kb