Balancing the Binding of Intermediates Enhances Alkaline Hydrogen Oxidation on D-Band Center Modulated Pd Sites.

Exploring efficient alkaline hydrogen oxidation reaction (HOR) electrocatalysts is of great concern for constructing anion exchange membrane fuel cells (AEMFCs). Herein, d-band center modulated PdCo alloys with ultralow Pd content anchored onto the defective carbon support (abbreviated as PdCo/NC hereafter) are proposed as highly efficient HOR catalyst. The as-prepared catalyst exhibits exceptional HOR performance compared to the Pt/C catalyst, achieving thermodynamically spontaneous and kinetically preferential reactions. Specifically, the resultant PdCo/NC demonstrates a marked enhancement in alkaline HOR performance, with the highest mass and specific activities of 1919.6 mA mgPd-1 and 1.9 mA cm-2, 51.1 and 4.2 times higher than those of benchmark of Pt/C, along with an excellent stability in a chronoamperometry test. In the analysis of in situ Raman spectra, it was discovered that tetrahedrally coordinated H-bonded water molecules were formed during the HOR process. This indicates that the promotion of interfacial water molecule formation and enhancement of HOR activities in PdCo/NC are facilitated by defect engineering and the turning of d-band center in PdCo alloy. The essential knowledge obtained in this study could open up a new direction for modifying the electronic structure of cost-effective HOR catalysts through electronic structure engineering.

Analysis and study of the mechanism of narcotic addiction and withdrawal.

Narcotic drugs refer to drugs that have anesthetic effects on the central nervous system, and they easily produce physical dependence and mental dependence and can be addictive due to continuous use, abuse or unreasonable use. In this paper, bioinformatics and data analysis and mining techniques were used to analyze the methylation differences in transcriptional and clinical data of narcotic addiction in public databases, to explore the mechanism of narcotic addiction, and to mine some norepinephrine drugs. This study confirmed the possibility of using norepinephrine as an auxiliary drug for drug addiction rehabilitation. In addition, we also conducted a similar analysis on the addiction of three drugs. The results showed that the differences in the body caused by the ingestion of opiates and cocaine were significantly greater than those caused by the ingestion of methamphetamine.

Structural deep clustering network for stratification of breast cancer patients through integration of somatic mutation profiles.

BACKGROUND AND OBJECTIVE: Breast cancer is among of the most malignant tumor that occurs in women and is one of the leading causes of death from gynecologic malignancy worldwide. The high degree of heterogeneity that characterizes breast cancer makes it challenging to devise effective therapeutic strategies. Accumulating evidence highlights the crucial role of stratifying breast cancer patients into clinically significant subtypes to achieve better prognoses and treatments. The structural deep clustering network is a graph convolutional network-based clustering algorithm that integrates structural information and has achieved state-of-the-art performance in various applications. METHODS: In this study, we employed structural deep clustering network to integrate somatic mutation profiles for stratifying 2526 breast cancer patients from the Memorial Sloan Kettering Cancer Center into two clinically differentiable subtypes. RESULTS: Breast cancer patients in cluster 1 exhibited better prognosis than breast cancer patients in cluster 2, and the difference between them was statistically significant. The immunogenomic landscape further demonstrated that cluster 1 was associated with remarkable infiltration of the tumor infiltrating lymphocytes. The clustering subtype could be used to evaluate the therapeutic benefit of immunotherapy and chemotherapy in breast cancer patients. Furthermore, our approach effectively classified patients from eight different cancer types, demonstrating its generalizability. CONCLUSIONS: Our study represents a step towards a generic methodology for classifying cancer patients using only somatic mutation data and structural deep clustering network approaches. Employing structural deep clustering network to identify breast cancer subtypes is promising and can inform the development of more accurate and personalized therapies.

Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance.

Transcription factors are protein molecules that act as regulators of gene expression. Aberrant protein activity of transcription factors can have a significant impact on tumor progression and metastasis in tumor patients. In this study, 868 immune-related transcription factors were identified from the transcription factor activity profile of 1823 ovarian cancer patients. The prognosis-related transcription factors were identified through univariate Cox analysis and random survival tree analysis, and two distinct clustering subtypes were subsequently derived based on these transcription factors. We assessed the clinical significance and genomics landscape of the two clustering subtypes and found statistically significant differences in prognosis, response to immunotherapy, and chemotherapy among ovarian cancer patients with different subtypes. Multi-scale Embedded Gene Co-expression Network Analysis was used to identify differential gene modules between the two clustering subtypes, which allowed us to conduct further analysis of biological pathways that exhibited significant differences between them. Finally, a ceRNA network was constructed to analyze lncRNA-miRNA-mRNA regulatory pairs with differential expression levels between two clustering subtypes. We expected that our study may provide some useful references for stratifying and treating patients with ovarian cancer.