An enduring challenge in personalized medicine is to select right drug for individual patients. Testing drugs on patients in large clinical trials is one way to assess their efficacy and toxicity, but it is impractical to test hundreds of drugs currently under development. Therefore the preclinical prediction model is highly expected as it enables prediction of drug response to hundreds of cell lines in parallel. Recently, two large-scale pharmacogenomic studies screened multiple anticancer drugs on over 1000 cell lines in an effort to elucidate the response mechanism of anticancer drugs. To this aim, we here used gene expression features and drug sensitivity data in Cancer Cell Line Encyclopedia (CCLE) to build a predictor based on Support Vector Machine (SVM) and a recursive feature selection tool. Robustness of our model was validated by cross-validation and an independent dataset, the Cancer Genome Project (CGP). Our model achieved good cross validation performance for most drugs in the Cancer Cell Line Encyclopedia (≥80 % accuracy for 10 drugs, ≥ 75 % accuracy for 19 drugs). Independent tests on eleven common drugs between CCLE and CGP achieved satisfactory performance for three of them, i.e., AZD6244, Erlotinib and PD-0325901, using expression levels of only twelve, six and seven genes, respectively. These results suggest that drug response could be effectively predicted from genomic features. Our model could be applied to predict drug response for some certain drugs and potentially play a complementary role in personalized medicine.

Lonicera japonica is a typical Chinese herbal medicine. We previously reported a method to isolate polysaccharides from Lonicera japonica (LJP). In this study, we first performed a qualitative analysis of LJP using the Fourier Transform Infrared Spectrometer (FT-IR) and explored the monosaccharide composition of LJP using the pre-column derivatization high performance liquid chromatography (HPLC) method. We then investigated the immunomodulatory function of LJP in cyclophosphamide (CTX)-induced immunosuppressed mouse models. The results showed that LJP had the characteristic absorption of typical polysaccharides consisting of 6 types of monosaccharides. In addition, LJP can increase significantly the organ index, splenic lymphocyte proliferation, macrophage phagocytosis, and natural killer (NK) cell activity in CTX-treated mice. LJP could also restore the levels of serum cytokines interleukin (IL-2), tumor necrosis factor (TNF-α) and Interferon-γ (IFN-γ) in the CTX-treated mice. Finally, the results on measuring the T-lymphocytes subsets of spleen also confirmed LJP-induced immunomodulatory activity in immunosuppressed mice from another perspective. Therefore, LJP could be used as a potential immunomodulatory agent.

Identification of human age-associated gene co-expressions in functional modules using liquid association Identification of human age-associated gene co-expressions in functional modules using liquid association

Light field imaging technology captures a wider view of the scene light information and has become a mainstream method to broaden the scope of camera views. Unlike traditional images, light field images have a more complex structure. This complexity arises from the need to ensure the consistency of the internal structure of the light field image and address the occlusion between adjacent images during the reconstruction process.^{1 – 3} Therefore, conducting extensive research on the structural properties of light field images, advancing technologies for occlusion removal, and developing efficient reconstruction methods are essential for the widespread adoption of light field imaging. These efforts will ensure that light field imaging can be effectively applied in various fields, such as virtual reality, augmented reality, and computational photography.

The light field is an important way to record the spatial information of the target scene. The purpose of this paper is to obtain depth information through the processing of light field information and provide a basis for intelligent medical treatment. In this paper, we first design an attention module to extract the features of light field images and connect all the features as a feature map to generate an attention image. Then, the attention map is integrated with the convolution layer in the neural network in the form of weights to enhance the weight of the subaperture viewpoint, which is more meaningful for depth estimation. Finally, the obtained initial depth results were optimized. The experimental results show that the MSE, PSNR, and SSIM of the depth map obtained by this method are increased by about 13%, 10 dB, and 4%, respectively, in some scenarios with good performance.