Chlorogenic acid: a review on its mechanisms of anti-inflammation, disease treatment, and related delivery systems.

Chlorogenic acid is a bioactive compound ubiquitously present in the natural realm, lauded for its salient anti-inflammatory and antioxidant attributes. It executes its anti-inflammatory function by moderating the synthesis and secretion of inflammatory mediators, namely, TNF-α, IL-1ß, IL-6, IL-8, NO, and PGE2. Concurrently, it modulates key signaling pathways and associated factors, including NF-κB, MAPK, Nrf2, and others, bestowing protection upon cells and tissues against afflictions such as cardio-cerebrovascular and diabetes mellitus. Nevertheless, the inherent low bioavailability of chlorogenic acid poses challenges in practical deployments. To surmount this limitation, sophisticated delivery systems, encompassing liposomes, micelles, and nanoparticles, have been devised, accentuating their stability, release mechanisms, and bioactivity. Given its innate anti-inflammatory prowess and safety profile, chlorogenic acid stands as a promising contender for advanced biomedical investigations and translational clinical endeavors.

plotnineSeqSuite: a Python package for visualizing sequence data using ggplot2 style.

BACKGROUND: The visual sequence logo has been a hot area in the development of bioinformatics tools. ggseqlogo written in R language has been the most popular API since it was published. With the popularity of artificial intelligence and deep learning, Python is currently the most popular programming language. The programming language used by bioinformaticians began to shift to Python. Providing APIs in Python that are similar to those in R can reduce the learning cost of relearning a programming language. And compared to ggplot2 in R, drawing framework is not as easy to use in Python. The appearance of plotnine (ggplot2 in Python version) makes it possible to unify the programming methods of bioinformatics visualization tools between R and Python. RESULTS: Here, we introduce plotnineSeqSuite, a new plotnine-based Python package provides a ggseqlogo-like API for programmatic drawing of sequence logos, sequence alignment diagrams and sequence histograms. To be more precise, it supports custom letters, color themes, and fonts. Moreover, the class for drawing layers is based on object-oriented design so that users can easily encapsulate and extend it. CONCLUSIONS: plotnineSeqSuite is the first ggplot2-style package to implement visualization of sequence -related graphs in Python. It enhances the uniformity of programmatic plotting between R and Python. Compared with tools appeared already, the categories supported by plotnineSeqSuite are much more complete. The source code of plotnineSeqSuite can be obtained on GitHub ( https://github.com/caotianze/plotnineseqsuite ) and PyPI ( https://pypi.org/project/plotnineseqsuite ), and the documentation homepage is freely available on GitHub at ( https://caotianze.github.io/plotnineseqsuite/ ).

pandasGWAS: a Python package for easy retrieval of GWAS catalog data.

BACKGROUND: Since the NHGRI-EBI Catalog of human genome-wide association studies was established by NHGRI in 2008, research on it has attracted more and more researchers as the amount of data has grown rapidly. Easy-to-use, open-source, general-purpose programs for accessing the NHGRI-EBI Catalog of human genome-wide association studies are in great demand for current Python data analysis pipeline. RESULTS: In this work we present pandasGWAS, a Python package that provides programmatic access to the NHGRI-EBI Catalog of human genome-wide association studies. Instead of downloading all data locally, pandasGWAS queries data based on input criteria and handles paginated data gracefully. The data is then transformed into multiple associated pandas.DataFrame objects according to its hierarchical relationships, which makes it easy to integrate into current Python-based data analysis toolkits. CONCLUSIONS: pandasGWAS is an open-source Python package that provides the first Python client interface to the GWAS Catalog REST API. Compared with existing tools, the data structure of pandasGWAS is more consistent with the design specification of GWAS Catalog REST API, and provides many easy-to-use mathematical symbol operations.

Characteristics of breast cancer in Central China, literature review and comparison with USA.

INTRODUCTION: This work was to analyze characteristics of breast cancer (BC) in Central China, summarize main characteristics in China and compare with USA. METHODS: BC main characteristics from four hospitals in Central China from 2002 to 2012 were collected and analyzed. All the single and large-scale clinical reports covering at least ten years were selected and summarized to calculate the BC characteristics of China. BC Characteristics in USA were selected based on the database from Surveillance, Epidemiology, and End Results (SEER) Program. RESULTS: Age distribution in Central China was normal with one age peak at 45-49 years, displaying differences from USA and Chinese American with two age peaks. BC characteristics in Central China displayed distinct features from USA and Chinese American, including significant younger onset age, lower proportion of patients with stage I, lymph node negative, small tumor size and ER positive. A total ten long-term and large-scale clinical reports were selected for BC characteristics of Mainland China analysis. A total of 53,571 BC patients were enrolled from 1995 to 2012. The main characteristics of BC in Mainland China were similar as that in Central China, but were significant different from developed regions of China (Hong Kong and Taiwan), USA and Chinese American. CONCLUSIONS: BC characteristics in Central China displayed representative patterns of Mainland China, while showed distinct patterns from Chinese patients in other developed areas and USA.