Immunomodulatory Function of Pien Tze Huang in T Cell-Mediated Anti-tumor Activity against B16-F10, MC38 and Hep1-6 Tumor Models.

OBJECTIVE: To investigate the anti-tumor effects of Pien Tze Huang (PZH) in mouse models of B16-F10 melanoma, MC38 colorectal cancer, Hep1-6 hepatocellular carcinoma and chemically induced hepatocellular carcinoma model. METHODS: Various tumor models, including B16-F10, MC38 and Hep1-6 tumor hypodermic inoculation models, B16-F10 and Hep1-6 pulmonary metastasis models, Hep1-6 orthotopic implantation model, and chemically induced hepatocellular carcinoma model, were utilized to evaluate the anti-tumor function of PZH. Tumor growth was assessed by measuring tumor size and weight of solid tumors isolated from C57BL/6 mice. For cell proliferation and death of tumor cells in vitro, as well as T cell activation markers, cytokine production and immune checkpoints analysis, single-cell suspensions were prepared from mouse spleen, lymph nodes, and tumors after PZH treatment. RESULTS: PZH demonstrated significant therapeutic efficacy in inhibiting tumor growth (P<0.01). Treatment with PZH resulted in a reduction in tumor size in subcutaneous MC38 colon adenocarcinoma and B16-F10 melanoma models, and decreased pulmonary metastasis of B16-F10 melanoma and Hep1-6 hepatoma (P<0.01). However, in vitro experiments showed that PZH only had slight impact on the cell proliferation and survival of tumor cells (P>0.05). Nevertheless, PZH exhibited a remarkable ability to enhance T cell activation and the production of interferon gamma, tumor necrosis factor alpha, and interleukin 2 in CD4+ T cells in vitro (P<0.01 or P<0.05). Importantly, PZH substantially inhibited T cell exhaustion and boosted cytokine production by tumor-infiltrating CD8+ T cells (P<0.01 or P<0.05). CONCLUSION: This study has confirmed a novel immunomodulatory function of PZH in T cell-mediated anti-tumor immunity, indicating that PZH holds promise as a potential therapeutic agent for cancer treatment.

Enantioselective synthesis of 3,3'-dihydropyrryl-spirooxindoles via an organocatalytic three-component reaction.

An organocatalytic three-component reaction of isatins, malononitrile and isocyanoacetates provided 3,3'-dihydropyrryl-spirooxindoles in excellent yields and enantioselectivities. The products could be readily converted to valuable 3,3'-pyrrolidinyl-spirooxindoles.

Organocatalytic conjugate addition of malononitrile to conformationally restricted dienones.

Organocatalytic conjugate addition of malononitrile to conformationally restricted dienones has been studied. A series of chiral primary and tertiary amine catalysts were screened. A piperidine-based thiourea-tertiary amine was found to be the efficient catalyst. Chiral pyran derivatives were obtained in excellent yields and enantioselectivities via a cascade conjugate addition-intramolecular cyclization pathway. The reaction is remarkably different for the corresponding reaction of conformationally flexible dienones.

Organocatalytic asymmetric conjugate addition and cascade acyl transfer reaction of α-nitroketones.

Organocatalytic asymmetric conjugate addition of α-nitroketones to ß,γ-unsaturated α-keto esters has been developed. A pyrrolidine-based thiourea-tertiary amine was identified as the best catalyst. The reaction was found to proceed via cascade conjugate addition and acyl transfer reaction. A number of α-nitroketones and ß,γ-unsaturated α-keto esters were examined in this transformation. 5-Nitro-2-acyloxypent-2-enoates were obtained in good yields (up to 99%) and enantioselectivities (up to 99% ee). The products could be hydrolyzed to provide 5-nitro-2-oxopentanoates, which are not available from the direct addition of nitromethane to ß,γ-unsaturated α-keto esters.

Simultaneous channel and feature selection of fused EEG features based on Sparse Group Lasso.

Feature extraction and classification of EEG signals are core parts of brain computer interfaces (BCIs). Due to the high dimension of the EEG feature vector, an effective feature selection algorithm has become an integral part of research studies. In this paper, we present a new method based on a wrapped Sparse Group Lasso for channel and feature selection of fused EEG signals. The high-dimensional fused features are firstly obtained, which include the power spectrum, time-domain statistics, AR model, and the wavelet coefficient features extracted from the preprocessed EEG signals. The wrapped channel and feature selection method is then applied, which uses the logistical regression model with Sparse Group Lasso penalized function. The model is fitted on the training data, and parameter estimation is obtained by modified blockwise coordinate descent and coordinate gradient descent method. The best parameters and feature subset are selected by using a 10-fold cross-validation. Finally, the test data is classified using the trained model. Compared with existing channel and feature selection methods, results show that the proposed method is more suitable, more stable, and faster for high-dimensional feature fusion. It can simultaneously achieve channel and feature selection with a lower error rate. The test accuracy on the data used from international BCI Competition IV reached 84.72%.