An Organic/Inorganic Synergistic Electrolysis for Overcharge Protection of Electric Vehicle Batteries

Safety is a stumbling block to the applications of the lithium-ion batteries in electric vehicles, especially under the abuse condition of overcharge. Much research on preventing overcharge is being done to ensure the safety of the lithium-ion batteries. However, almost no strategy can balance the safety and the performance of the lithium-ion batteries well for overcharge protection. No data to support the longer term effectiveness of the used strategy were presented in the previous reports. Herein, a new electrolysis reaction, synergistic electrolysis of the organic/inorganic compounds (p-fluorotoluene and Li2CO3) is built for the first time as a controllable gas source to solve the overcharge problem of the prismatic lithium-ion battery cell with a current interrupt device inside. Overall, the balance point between the long-time performance and overcharge protection can be well achieved using the synergistic electrolysis

<i>N</i>‑Methylpropargylamine-Conjugated Hydroxamic Acids as Dual Inhibitors of Monoamine Oxidase A and Histone Deacetylase for Glioma Treatment

Glioma treatment remains a challenge with a low survival rate due to the lack of effective therapeutics. Monoamine oxidase A (MAO A) plays a role in glioma development, and MAO A inhibitors reduce glioma growth. Histone deacetylase (HDAC) inhibition has emerged as a promising therapy for various malignancies including gliomas. We have synthesized and evaluated N-methylpropargylamine-conjugated hydroxamic acids as dual inhibitors of MAO A and HDAC. Compounds display potent MAO A inhibition with IC50 from 0.03 to <0.0001 μM and inhibit HDAC isoforms and cell growth in the micromolar to nanomolar IC50 range. These selective MAO A inhibitors increase histone H3 and α-tubulin acetylation and induce cell death via nonapoptotic mechanisms. Treatment with 15 reduced tumor size, reduced MAO A activity in brain and tumor tissues, and prolonged the survival. This first report on dual inhibitors of MAO A and HDAC establishes the basis of translational research for an improved treatment of glioma

<i>N</i>‑Methylpropargylamine-Conjugated Hydroxamic Acids as Dual Inhibitors of Monoamine Oxidase A and Histone Deacetylase for Glioma Treatment

Glioma treatment remains a challenge with a low survival rate due to the lack of effective therapeutics. Monoamine oxidase A (MAO A) plays a role in glioma development, and MAO A inhibitors reduce glioma growth. Histone deacetylase (HDAC) inhibition has emerged as a promising therapy for various malignancies including gliomas. We have synthesized and evaluated N-methylpropargylamine-conjugated hydroxamic acids as dual inhibitors of MAO A and HDAC. Compounds display potent MAO A inhibition with IC50 from 0.03 to <0.0001 μM and inhibit HDAC isoforms and cell growth in the micromolar to nanomolar IC50 range. These selective MAO A inhibitors increase histone H3 and α-tubulin acetylation and induce cell death via nonapoptotic mechanisms. Treatment with 15 reduced tumor size, reduced MAO A activity in brain and tumor tissues, and prolonged the survival. This first report on dual inhibitors of MAO A and HDAC establishes the basis of translational research for an improved treatment of glioma

Image_2_Transcriptome and single-cell analysis reveal the contribution of immunosuppressive microenvironment for promoting glioblastoma progression.jpeg

Graphical AbstractOverview of the study design. (A) We firstly identified five GBM progression related pathways. By performing functional enrichment analysis on gene sets obtained from three perspectives, such as genes co-survival in TCGA-GBM and CGGA cohort, DEGs of high and low risk groups, and DEGs of IDH1 mutation compared with wild type, we identified five pathways significantly associated with poor prognosis in GBM patients. (B) Secondly, GBM TME-associated functional gene signatures were constructed. Based on the activity profile of these signatures, GBM patients were classified into four distinct subtypes and immunosuppressive subtypes were found. (C) the expression of hub genes from immunosuppressive subtypes were validated in three single-cell RNA-seq datasets, and cell types significantly associated with TME subtypes were identified. The interactions between certain cell types were also elaborated.</p

Table_3_Transcriptome and single-cell analysis reveal the contribution of immunosuppressive microenvironment for promoting glioblastoma progression.xlsx

Graphical AbstractOverview of the study design. (A) We firstly identified five GBM progression related pathways. By performing functional enrichment analysis on gene sets obtained from three perspectives, such as genes co-survival in TCGA-GBM and CGGA cohort, DEGs of high and low risk groups, and DEGs of IDH1 mutation compared with wild type, we identified five pathways significantly associated with poor prognosis in GBM patients. (B) Secondly, GBM TME-associated functional gene signatures were constructed. Based on the activity profile of these signatures, GBM patients were classified into four distinct subtypes and immunosuppressive subtypes were found. (C) the expression of hub genes from immunosuppressive subtypes were validated in three single-cell RNA-seq datasets, and cell types significantly associated with TME subtypes were identified. The interactions between certain cell types were also elaborated.</p

Image_5_Transcriptome and single-cell analysis reveal the contribution of immunosuppressive microenvironment for promoting glioblastoma progression.jpeg

Graphical AbstractOverview of the study design. (A) We firstly identified five GBM progression related pathways. By performing functional enrichment analysis on gene sets obtained from three perspectives, such as genes co-survival in TCGA-GBM and CGGA cohort, DEGs of high and low risk groups, and DEGs of IDH1 mutation compared with wild type, we identified five pathways significantly associated with poor prognosis in GBM patients. (B) Secondly, GBM TME-associated functional gene signatures were constructed. Based on the activity profile of these signatures, GBM patients were classified into four distinct subtypes and immunosuppressive subtypes were found. (C) the expression of hub genes from immunosuppressive subtypes were validated in three single-cell RNA-seq datasets, and cell types significantly associated with TME subtypes were identified. The interactions between certain cell types were also elaborated.</p

Table_2_Transcriptome and single-cell analysis reveal the contribution of immunosuppressive microenvironment for promoting glioblastoma progression.xlsx

Graphical AbstractOverview of the study design. (A) We firstly identified five GBM progression related pathways. By performing functional enrichment analysis on gene sets obtained from three perspectives, such as genes co-survival in TCGA-GBM and CGGA cohort, DEGs of high and low risk groups, and DEGs of IDH1 mutation compared with wild type, we identified five pathways significantly associated with poor prognosis in GBM patients. (B) Secondly, GBM TME-associated functional gene signatures were constructed. Based on the activity profile of these signatures, GBM patients were classified into four distinct subtypes and immunosuppressive subtypes were found. (C) the expression of hub genes from immunosuppressive subtypes were validated in three single-cell RNA-seq datasets, and cell types significantly associated with TME subtypes were identified. The interactions between certain cell types were also elaborated.</p

Image_4_Transcriptome and single-cell analysis reveal the contribution of immunosuppressive microenvironment for promoting glioblastoma progression.jpeg

Graphical AbstractOverview of the study design. (A) We firstly identified five GBM progression related pathways. By performing functional enrichment analysis on gene sets obtained from three perspectives, such as genes co-survival in TCGA-GBM and CGGA cohort, DEGs of high and low risk groups, and DEGs of IDH1 mutation compared with wild type, we identified five pathways significantly associated with poor prognosis in GBM patients. (B) Secondly, GBM TME-associated functional gene signatures were constructed. Based on the activity profile of these signatures, GBM patients were classified into four distinct subtypes and immunosuppressive subtypes were found. (C) the expression of hub genes from immunosuppressive subtypes were validated in three single-cell RNA-seq datasets, and cell types significantly associated with TME subtypes were identified. The interactions between certain cell types were also elaborated.</p

Image_3_Transcriptome and single-cell analysis reveal the contribution of immunosuppressive microenvironment for promoting glioblastoma progression.jpeg

Graphical AbstractOverview of the study design. (A) We firstly identified five GBM progression related pathways. By performing functional enrichment analysis on gene sets obtained from three perspectives, such as genes co-survival in TCGA-GBM and CGGA cohort, DEGs of high and low risk groups, and DEGs of IDH1 mutation compared with wild type, we identified five pathways significantly associated with poor prognosis in GBM patients. (B) Secondly, GBM TME-associated functional gene signatures were constructed. Based on the activity profile of these signatures, GBM patients were classified into four distinct subtypes and immunosuppressive subtypes were found. (C) the expression of hub genes from immunosuppressive subtypes were validated in three single-cell RNA-seq datasets, and cell types significantly associated with TME subtypes were identified. The interactions between certain cell types were also elaborated.</p

Table_1_Transcriptome and single-cell analysis reveal the contribution of immunosuppressive microenvironment for promoting glioblastoma progression.docx

Graphical AbstractOverview of the study design. (A) We firstly identified five GBM progression related pathways. By performing functional enrichment analysis on gene sets obtained from three perspectives, such as genes co-survival in TCGA-GBM and CGGA cohort, DEGs of high and low risk groups, and DEGs of IDH1 mutation compared with wild type, we identified five pathways significantly associated with poor prognosis in GBM patients. (B) Secondly, GBM TME-associated functional gene signatures were constructed. Based on the activity profile of these signatures, GBM patients were classified into four distinct subtypes and immunosuppressive subtypes were found. (C) the expression of hub genes from immunosuppressive subtypes were validated in three single-cell RNA-seq datasets, and cell types significantly associated with TME subtypes were identified. The interactions between certain cell types were also elaborated.</p