A universal method to fabricate high-valence transition metal-based HER electrocatalysts and direct Raman spectroscopic evidence for interfacial water regulation.

Valence modulation of transition metal oxides represents a highly effective approach in designing high-performance catalysts, particularly for pivotal applications such as the hydrogen evolution reaction (HER) in solar/electric water splitting and the hydrogen economy. Recently, there has been a growing interest in high-valence transition metal-based electrocatalysts (HVTMs) due to their demonstrated superiority in HER performance, attributed to the fundamental dynamics of charge transfer and the evolution of intermediates. Nevertheless, the synthesis of HVTMs encounters considerable thermodynamic barriers, which presents challenges in their preparation. Moreover, the underlying mechanism responsible for the enhancement in HVTMs still needs to be discovered. Hence, the universal synthesis strategies of the HVTMs are discussed, and direct Raman spectroscopic evidence for intermediates regulation is revealed to guide the further design of the HVTM electrocatalysts. This work offers new insights for facile designing of HVTMs electrocatalysts for energy conversion and storage through adjusting the reaction pathway.

Tunable d-band center of a NiFeMo alloy with enlarged lattice strain enhancing the intrinsic catalytic activity for overall water-splitting.

Developing efficient bifunctional electrocatalysts for the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) under alkaline conditions is prospective for reducing energy consumption during water electrolysis. In this work, we successfully synthesized nanocluster structure composites composed of NiFeMo alloys with controllable lattice strain by the electrodeposition method at room temperature. The unique structure of NiFeMo/SSM (stainless steel mesh) facilitates the exposure of abundant active sites and promotes mass transfer and gas exportation. The NiFeMo/SSM electrode exhibits a low overpotential of 86 mV at 10 mA cm-2 for the HER and 318 mV at 50 mA cm-2 for the OER, and the assembled device reveals a low voltage of 1.764 V at 50 mA cm-2. Moreover, both the experimental results and theoretical calculations reveal that the dual doping of Mo and Fe can induce the tunable lattice strain of nickel, which in turn changes the d-band center and electronic interaction of the catalytically active site, and finally enhances the HER and OER catalytic activity. This work may provide more options for the design and preparation of bifunctional catalysts based on non-noble metals.

Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning.

Molecular subtypes of breast cancer are important references to personalized clinical treatment. For cost and labor savings, only one of the patient's paraffin blocks is usually selected for subsequent immunohistochemistry (IHC) to obtain molecular subtypes. Inevitable block sampling error is risky due to the tumor heterogeneity and could result in a delay in treatment. Molecular subtype prediction from conventional H&E pathological whole slide images (WSI) using the AI method is useful and critical to assist pathologists to pre-screen proper paraffin block for IHC. It is a challenging task since only WSI-level labels of molecular subtypes from IHC can be obtained without detailed local region information. Gigapixel WSIs are divided into a huge amount of patches to be computationally feasible for deep learning, while with coarse slide-level labels, patch-based methods may suffer from abundant noise patches, such as folds, overstained regions, or non-tumor tissues. A weakly supervised learning framework based on discriminative patch selection and multi-instance learning was proposed for breast cancer molecular subtype prediction from H&E WSIs. Firstly, co-teaching strategy using two networks was adopted to learn molecular subtype representations and filter out some noise patches. Then, a balanced sampling strategy was used to handle the imbalance in subtypes in the dataset. In addition, a noise patch filtering algorithm that used local outlier factor based on cluster centers was proposed to further select discriminative patches. Finally, a loss function integrating local patch with global slide constraint information was used to fine-tune MIL framework on obtained discriminative patches and further improve the prediction performance of molecular subtyping. The experimental results confirmed the effectiveness of the proposed AI method and our models outperformed even senior pathologists, which has the potential to assist pathologists to pre-screen paraffin blocks for IHC in clinic.

Purified Vitexin Compound 1 Serves as a Promising Antineoplastic Agent in Ovarian Cancer.

Ovarian cancer is a common gynecologic aggressive neoplasm. The mortality of ovarian cancer is top among gynecologic malignancies due to the insidious onset, atypical early symptoms, and chemoresistance. Therefore, it is urgent to seek another promising treatment for ovarian cancer. Purified vitexin compound 1 (VB1) is a kind of neolignan from the seed of traditional Chinese herb vitex negundo that possessed diverse pharmacological effects. VB1 can exhibit anti-neoplastic activities against various cancers. However, the role of VB1 in ovarian cancer treatment has not been elaborated, and the mechanism is unknown. The aim of this study was to investigate the therapeutic effects of VB1 in ovarian cancer cells both in vitro and in vivo, along with the molecular mechanism of action. In vitro, VB-1 can effectively suppress the proliferation, induce apoptosis, and block cell cycle at G2/M phase with a concentration dependent manner in ovarian cancer cells. Western blot assay showed that VB1 induce apoptosis via upregulating expression of cleaved-caspase3 and block cell cycle at G2/M phase through upregulating expression of P21. Meanwhile, VB1 can effectively inhibit tumor growth in xenograft mouse model. Our research indicated that VB1 can significantly exert its anti-neoplastic effects and may represent a new class of agents in ovarian cancer therapy.

SOX10 - A Novel Marker for the Differential Diagnosis of Breast Metaplastic Squamous Cell Carcinoma.

INTRODUCTION: Differential diagnosis of metaplastic squamous cell carcinoma of breast (MSCCB) is difficult. In particular, in terms of metastatic MSCCB, because of the low speciality of traditional markers such as mammaglobin, gross cystic disease fluid protein-15 (GCDFP-15) and GATA binding protein 3 (GATA3), the most common problem is differentiating the spread of MSCCB to the lung from a primary lung squamous cell carcinoma. It is urgently required to explore a novel marker to aid in differential diagnosis. AIM: The aim of this study is to explore a novel marker to aid in the differential diagnosis of MSCCB from other squamous cell carcinomas (SCC) in other organs. METHODS: We tested the expression of SOX10 in 375 human SCC specimens with immunohistochemistry (IHC). RESULTS: In a series of 20 MSCCB, 9 (45%) were positive for SOX10. All of them were triple-negative MSCCB. Conversely, SOX10 was totally negative in another 205 SCC originating from lung, skin, cervix, oral mucosa, and esophagus. In a series of 150 triple-negative breast cancer and their metastatic foci, SOX10 labeling in the primary tumor and metastasis was 78% and 79.3%, respectively, and the agreement rate was 97.3% (P>0.05). CONCLUSION: Our findings demonstrate that SOX10 was recommended for differentiating MSCCB from non-mammary metastasis to the breast, as well as for distinguishing primary SCC from metastatic MSCCB, and SOX10 may be valuable in the pathological diagnosis of breast-derived metaplastic squamous cell carcinoma.