Erratum: Metformin in combination with JS-K inhibits growth of renal cell carcinoma cells via reactive oxygen species activation and inducing DNA breaks: Erratum.

[This corrects the article DOI: 10.7150/jca.36372.

[Mn(PaPy2Q)(NO)]ClO, a Near-Infrared Light activated release of Nitric Oxide drug as a nitric oxide donor for therapy of human prostate cancer cells in vitro and in vivo.

This study was the first to investigate the synthesis of near-infrared light-sensitive NO prodrug [Mn(PaPy2Q)(NO)]ClO, and detection the amount of NO released by the drug in different time and near infrared light (10 mW, 20 mW). It showed that with the increase of light power, the time required for the drug to release NO was shortened, and we selected 20 mW, 10 min as a follow-up study of light power and irradiation time while ensuring the near-infrared light did not affect tumor cells. The cells were irradiated with 20 mW of near-infrared light for 10 min at 6 h after treatment with the drug on PC-3, LNCaP and 22RV1 cells, and NO concentration and cell survival rate were tested at 12 h, 24 h and 48 h.

Metformin in combination with JS-K inhibits growth of renal cell carcinoma cells via reactive oxygen species activation and inducing DNA breaks.

Metformin (MET) is taken as a principal medication for remedying Type 2 diabetes mellitus. Its anti-tumor effect has been reported increasingly, but the precise mechanism of it remains unclear. This study aims to explore the efficacy of MET and MET combined with nitric oxide donor prodrug JS-K on the proliferation, apoptosis, and DNA damage in human renal cell carcinoma (RCC) cells, and investigate the possible molecular mechanism involved.

Pterostilbene Inhibits Human Renal Cell Carcinoma Cells Growth and Induces DNA Damage.

Pterostilbene (PTE) has inhibitory effect on a wide array of tumors. However, the therapeutic potential of PTE in renal cancer cells and the underlying mechanisms have not been evaluated. In this study, the aim is to demonstrate the growth inhibitory and the underlying mechanisms of PTE on human renal cell carcinoma (RCC) cells in vitro.

Algorithms designed for compressed-gene-data transformation among gene banks with different references.

Background: With the reduction of gene sequencing cost and demand for emerging technologies such as precision medical treatment and deep learning in genome, it is an era of gene data outbreaks today. How to store, transmit and analyze these data has become a hotspot in the current research. Now the compression algorithm based on reference is widely used due to its high compression ratio.

Sparse feature learning for multi-class Parkinson's disease classification.

This paper solves the multi-class classification problem for Parkinson's disease (PD) analysis by a sparse discriminative feature selection framework. Specifically, we propose a framework to construct a least square regression model based on the Fisher's linear discriminant analysis (LDA) and locality preserving projection (LPP). This framework utilizes the global and local information to select the most relevant and discriminative features to boost classification performance.

Joint regression and classification via relational regularization for Parkinson's disease diagnosis.

It is known that the symptoms of Parkinson's disease (PD) progress successively, early and accurate diagnosis of the disease is of great importance, which slows the disease deterioration further and alleviates mental and physical suffering. In this paper, we propose a joint regression and classification scheme for PD diagnosis using baseline multi-modal neuroimaging data. Specifically, we devise a new feature selection method via relational learning in a unified multi-task feature selection model.

New proofs of some q-summation and q-transformation formulas.

We obtain an expectation formula and give the probabilistic proofs of some summation and transformation formulas of q-series based on our expectation formula. Although these formulas in themselves are not the probability results, the proofs given are based on probabilistic concepts.