In Vitro Metabolism of Six C-Glycosidic Flavonoids from Passiflora incarnata L.

Several medical plants, such as Passiflora incarnata L., contain C-glycosylated flavonoids, which may contribute to their efficacy. Information regarding the bioavailability and metabolism of these compounds is essential, but not sufficiently available. Therefore, the metabolism of the C-glycosylated flavones orientin, isoorientin, schaftoside, isoschaftoside, vitexin, and isovitexin was investigated using the Caco-2 cell line as an in vitro intestinal and epithelial metabolism model. Isovitexin, orientin, and isoorientin showed broad ranges of phase I and II metabolites containing hydroxylated, methoxylated, and sulfated compounds, whereas schaftoside, isoschaftoside, and vitexin underwent poor metabolism. All metabolites were identified via UHPLC-MS or UHPLC-MS/MS using compound libraries containing all conceivable metabolites. Some structures were confirmed via UHPLC-MS experiments with reference compounds after a cleavage reaction using glucuronidase and sulfatase. Of particular interest is the observed cleavage of the C–C bonds between sugar and aglycone residues in isovitexin, orientin, and isoorientin, resulting in unexpected glucuronidated or sulfated luteolin and apigenin derivatives. These findings indicate that C-glycosidic flavones can be highly metabolized in the intestine. In particular, flavonoids with ortho-dihydroxy groups showed sulfated metabolites. The identified glucuronidated or sulfated aglycones demonstrate that enzymes expressed by Caco-2 cells are able to potentially cleave C–C bonds in vitro.

Heuristic Potential of Antoni Kępiński’s Information Metabolism Model and Data Smog

In this paper I present the explanatory potential of Antoni Kępiński’s model of information metabolism in the question of data smog (data glut, information overload). Kępiński’s model is not well known and the bibliography concerning the model of information metabolism is still rather poor. In the article I present the model as a good heuristics for explaining information exchange between the system and the environment. The particular aim of my deliberations is to use the model of information metabolism to discuss the pathological, although common phenomenon of data smog. This allows for a holistic view of the problem and allows for new statements on data smog, ethical consequences and counteracting the negative consequences of information overload.

Longevity Analysis by Comparing the Overall Metabolism and Life Routine Regularity for Two Periods via GH-Method: Math-Physical Medicine

The author describes one of his hypothetical theories on the relationship between life longevity and overall metabolism, the macrosystem view, specifically the stress and daily life routine regularity, two micro-categories. He has spent ~25,000 h over 7.5 years (2010–2019) to conduct research on metabolism, endocrinology, and chronic diseases, specifically diabetes. These big data analytics is based on ~600,000 data over 2.5 years. His developed metabolism model has shed some light about the impact on his life longevity due to his overall metabolic changes, especially his stress level and life routine regularity. Having a strong lifestyle management leads into a good metabolic state, which then converts into a strong immunity to fight against three major disease categories, chronic diseases and complications (50% of death), cancers (29% of death), and infectious diseases (11% of death), with the remaining 10% of non-diseases related to death cases. This is a logical way to achieve longevity which is the core of geriatrics.

Comparison of effective health ages between the sophisticated model for researchers and simplified model for patients using GH-Method: math-physical medicine (No. 323)

This is the fourth article the author has written regarding the subject of effective health age (“Health Age”) related to the medical branch of geriatrics. Originally, he used his metabolism indexes data which were collected and processed via a sophisticated software for researchers. Later, he developed a simplified APP on the iPhone for other patients. This specific article discusses the differences of health input data and output results based on metabolism indexes and estimated health ages between these two different software versions. A comparison study between the difference of estimated health ages by using two different computer software versions was completed. The finding indicates that the complex metabolism model of his chronic software version would gain an extra 1.4% of accuracy on estimating his health age when compared to the simplified APP version. The author is not a fortune teller who uses a crystal ball to predict his or other people’s future life expectancy. Rather, he is a scientist who applies solid and sophisticated scientific techniques, such as math-physical medicine with biomedical evidence, to develop a simple arithmetical formula which can serve as a useful tool for the general population to maintain their health and achieve their desired longevity.

Comparison of effective health ages between the sophisticated model for researchers and simplified model for patients using GH-Method: math-physical medicine (No. 323)

This is the fourth article the author has written regarding the subject of effective health age (“Health Age”) related to the medical branch of geriatrics. Originally, he used his metabolism indexes data which were collected and processed via a sophisticated software for researchers. Later, he developed a simplified APP on the iPhone for other patients. This specific article discusses the differences of health input data and output results based on metabolism indexes and estimated health ages between these two different software versions. A comparison study between the difference of estimated health ages by using two different computer software versions was completed. The finding indicates that the complex metabolism model of his chronic software version would gain an extra 1.4% of accuracy on estimating his health age when compared to the simplified APP version. The author is not a fortune teller who uses a crystal ball to predict his or other people’s future life expectancy. Rather, he is a scientist who applies solid and sophisticated scientific techniques, such as math-physical medicine with biomedical evidence, to develop a simple arithmetical formula which can serve as a useful tool for the general population to maintain their health and achieve their desired longevity.