Tempol (4-hydroxy-2,2,6,6-Tetramethylpiperidine-1-oxyl, TPL), a nitroxide compound, inhibits proliferation and increases the vulnerability of cancer cells to apoptosis induced by cytotoxic agents. However, the molecular mechanism of TPL inhibiting cancer cell proliferation has not been fully understood. In this study, we evaluated the metabolic effect of TPL on cancer cells and explored its cancer therapeutic potential. Extracellular flow assays showed that TPL inhibited cellular basal and maximal oxygen consumption rates of mitochondrial. 13C metabolic flux analysis showed that TPL treatment had minimal effect on glycolysis. However, we found that TPL inhibits glutamine metabolism by interfering with the oxidative tricarboxylic acid cycle (TCA) process and reductive glutamine process. We found that the inhibitory effect of TPL on metabolism occurs mainly on the step from citrate to α-ketoglutarate or vice versa. We also found that activity of isocitrate dehydrogenase IDH1 and IDH2, the key enzymes in TCA, were inhibited by TPL treatment. In xenograft mouse model, TPL treatment reduced tumor growth by inhibiting cellular proliferation of xenograft tumors. Thus, we provided a mechanism of TPL inhibiting cancer cell proliferation by interfering with glutamine utilization that is important for survival and proliferation of cancer cells. The study may help the development of a therapeutic strategy of TPL combined with other anticancer medicines.

Recently, more and more multi-layer perceptron (MLP) -like models have been proposed. Among them, CycleMLP is good at dense feature prediction tasks, which is potentially useful for hyperspectral image (HSI) classification. However, the receptive field of CycleMLP tends to be cross-shaped, which will lead to insufficient spatial information extraction. Additionally, most of the HSI classification methods only use information from single HSI data. Lack of diversity in the features of a single modality limits classification performance. To address these issues, a novel spatial-spectral involution MLP network (SSIN) is proposed for HSI classification. SSIN contains two paths for extracting different kinds of information, namely the image path and the coordinate path. In the image path, we combine the MLP structure with the involution operation and propose involution MLP (InvoMLP). It obtains the spatial kernel weights corresponding to each pixel individually, thus improving the spatial interaction capability. At the same time, InvoMLP has the same receptive field range as conventional convolution, i.e., a rectangular receptive field. In the coordinate path, we build a lightweight module for extracting information. Unlike the information of images, the coordinates are intuitive information about the location distribution. Considering that the coordinate information contains the global spatial distribution of HSI, fusing it with the image information could improve long-distance dependencies of feature maps. Experimental results on four HSI datasets illustrate that SSIN can outperform some state-of-the-art methods.

In the study of heat transfer in tree-like branching network, neither the heat convection caused by fluid flow in the tree-like branching network nor the asymmetric structure of the tree-like branching network can be ignored. In this work, we assume the porous media is embedded with a tree-like branching network that are characterized by damaged pipes. We investigated the effects of surface roughness on heat conduction and heat convection in the porous media embedded with the damaged tree-like branching network based on the fractal features of tree-like branching networks and the basic theory of thermodynamics. The proposed model for thermal conductivity can be expressed as a function of micro-structural parameters of the composite, such as the relative roughness, the ratio of thermal conductivity of the wall to that of the fluid in the micro-channel, the diameter ratio, the length ratio, the branching level, the number of damaged channels, the total number of branching levels, and the main tube porosity of the porous media. The effects of the micro-structural parameters of the model on its effective thermal conductivity have been analyzed in detail. It is believed that the joint expression of heat conduction and heat convection could enrich and develop the physical study of heat transport in porous media.

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