Type X collagen knockdown inactivate ITGB1/PI3K/AKT to suppress chronic unpredictable mild stress-stimulated triple-negative breast cancer progression.

Triple-negative breast cancer (TNBC) is the most malignant subtype of breast cancer, has a poor prognosis and limited access to efficient targeted treatments. Chronic unpredictable mild stress (CUMS) is highly risk factor for TNBC occurrence and development. Type X collagen (COL10A1), a crucial protein component of the extracellular matrix, ranks second among all aberrantly expressed genes in TNBC, and it is significantly up-regulated under CUMS. Nevertheless, the impact of CUMS and COL10A1 on TNBC, along with the underlying mechanisms are still unclear. In this research, we studied the effect of CUMS-induced norepinephrine (NE) elevation on TNBC, and uncovered that it notably enhanced TNBC cell proliferation, migration, and invasion in vitro, and also fostering tumor growth and lung metastasis in vivo. Additionally, our investigation found that COL10A1 directly interacted with integrin subunit beta 1 (ITGB1), then activates the downstream PI3K/AKT signaling pathway, thereby promoting TNBC growth and metastasis, while it was reversed by knocking down of COL10A1 or ITGB1. Our study demonstrated that the TNBC could respond to CUMS, and advocate for COL10A1 as a pivotal therapeutic target in TNBC treatment.

Discovery of 5-aminopyrido[2,3-d]pyrimidin-7(8H)-one derivatives as new hematopoietic progenitor kinase 1 (HPK1) inhibitors.

Hematopoietic progenitor kinase 1 (HPK1) is a negative regulator of T-cell receptor signaling. While HPK1 is considered as a promising target for cancer immunotherapy, no small-molecule HPK1 inhibitors have been approved for cancer treatment. Herein, we report the discovery of a series of new HPK1 inhibitors with a 5-aminopyrido[2,3-d]pyrimidin-7(8H)-one scaffold. The most potent compound 9f inhibited HPK1 kinase activity with an IC50 of 0.32 nM in the time-resolved fluorescence resonance energy transfer (TR-FRET) assays, while displayed reasonable selectivity in a panel of 416 kinases. Cellular engagement of HPK1 by compound 9f was confirmed through the nano-bioluminescence resonance energy transfer (Nano-BRET) experiments. Compound 9f effectively reduced the phosphorylation of the downstream protein SLP-76 in primary peripheral blood mononuclear cells (PBMCs) and human T lymphocytic leukemia Jurkat cells. Compound 9f also enhanced the IL-2 and IFN-γ secretion in PBMCs. Furthermore, the binding mode of compound 9f with HPK1 was confirmed by the resolved cocrystal structure. Taken together, this study provides HPK1 inhibitors with a novel scaffold and clear binding mode for further development of HPK1-targeted therapeutic agents.

Can using a pre-trained deep learning model as the feature extractor in the bag-of-deep-visual-words model always improve image classification accuracy?

This article investigates whether higher classification accuracy can always be achieved by utilizing a pre-trained deep learning model as the feature extractor in the Bag-of-Deep-Visual-Words (BoDVW) classification model, as opposed to directly using the new classification layer of the pre-trained model for classification. Considering the multiple factors related to the feature extractor -such as model architecture, fine-tuning strategy, number of training samples, feature extraction method, and feature encoding method-we investigate these factors through experiments and then provide detailed answers to the question. In our experiments, we use five feature encoding methods: hard-voting, soft-voting, locally constrained linear coding, super vector coding, and fisher vector (FV). We also employ two popular feature extraction methods: one (denoted as Ext-DFs(CP)) uses a convolutional or non-global pooling layer, and another (denoted as Ext-DFs(FC)) uses a fully-connected or global pooling layer. Three pre-trained models-VGGNet-16, ResNext-50(32×4d), and Swin-B-are utilized as feature extractors. Experimental results on six datasets (15-Scenes, TF-Flowers, MIT Indoor-67, COVID-19 CXR, NWPU-RESISC45, and Caltech-101) reveal that compared to using the pre-trained model with only the new classification layer re-trained for classification, employing it as the feature extractor in the BoDVW model improves the accuracy in 35 out of 36 experiments when using FV. With Ext-DFs(CP), the accuracy increases by 0.13% to 8.43% (averaged at 3.11%), and with Ext-DFs(FC), it increases by 1.06% to 14.63% (averaged at 5.66%). Furthermore, when all layers of the pre-trained model are fine-tuned and used as the feature extractor, the results vary depending on the methods used. If FV and Ext-DFs(FC) are used, the accuracy increases by 0.21% to 5.65% (averaged at 1.58%) in 14 out of 18 experiments. Our results suggest that while using a pre-trained deep learning model as the feature extractor does not always improve classification accuracy, it holds great potential as an accuracy improvement technique.

Protective effect of alpha-mangostin against oxidative stress induced-retinal cell death.

It is known that oxidative stress plays a pivotal role in age-related macular degeneration (AMD) pathogenesis. Alpha-mangostin is the main xanthone purified from mangosteen known as anti-oxidative properties. The aim of the study was to test the protective effect of alpha-mangostin against oxidative stress both in retina of light-damaged mice model and in hydrogen peroxide (H2O2)-stressed RPE cells. We observed that alpha-mangostin significantly inhibited light-induced degeneration of photoreceptors and 200 µM H2O2-induced apoptosis of RPE cells. 200 µM H2O2-induced generation of reactive oxygen species (ROS) and light-induced generation of malondialdehyde (MDA) were suppressed by alpha-mangostin. Alpha-mangostin stimulation resulted in an increase of superoxide dismutase (SOD) activity, glutathione peroxidase (GPX) activity and glutathione (GSH) content both in vivo and vitro. Furthermore, the mechanism of retinal protection against oxidative stress by alpha-mangostin involves accumulation and the nuclear translocation of the NF-E2-related factor (Nrf2) along with up-regulation the expression of heme oxygenas-1 (HO-1). Meanwhile, alpha-mangostin can activate the expression of PKC-δ and down-regulate the expression of mitogen-activated protein kinases (MAPKs), including ERK1/2, JNK, P38. The results suggest that alpha-mangostin could be a new approach to suspend the onset and development of AMD.