Product indiscriminate use of vitamin risks: A review

The influence of multivitamins on neurological and growth disorders: a cross-sectional study

Background

While vitamin deficiencies can pose serious health consequences for the body, excessive intake of vitamins can also lead to health risks. However, there is limited data about the impact of multivitamins on neurological and growth disorders. This study aimed to investigate the relationship between multivitamins and neurological and growth disorders.

Methods

A cross-sectional study was conducted with 16,921 subjects who visited the Children's Hospital of Nanjing Medical University from 2019 to 2021. The subjects were categorized into two groups based on their health status including 9,368 cases (4,484 with neurological disorders and 4,884 with growth disorders) and 7,553 healthy controls. Statistical tests including the T-test, Wilcoxon Rank Sum test, and Chi-Square test were employed to compare the groups, and logistic regression and Weighted Quantile Sum (WQS) regression were used to identify associations.

Results

In the adjusted logistic regression, serum 25 hydroxyvitamin D [25(OH)D], vitamin B2, and vitamin B9 were associated with decreasing risks of neurological disorders, whereas vitamin A, vitamin B1, and vitamin B12 were associated with increasing risks of neurological disorders. Nevertheless, vitamin A and vitamin B2 were associated with increasing risks of growth disorders. In the WQS model, nine multivitamins were positively associated with risks of neurological disorders, and Vitamins D and C were weighted the most. In addition, the inverse association but not statistically significant was observed between multivitamins and growth disorders, particularly growth retardation revealed a negative association, and some individual growth disorders revealed positive associations including obesity and malnutrition.

Conclusion

In general, the study observed that multivitamins may be associated with neurological and growth disorders either positive or negative depending on the type of disorder.

Application of Artificial Intelligence in Drug-Drug Interactions Prediction: A Review

Drug-drug interactions (DDI) are a critical aspect of drug research that can have adverse effects on patients and can lead to serious consequences. Predicting these events accurately can significantly improve clinicians' ability to make better decisions and establish optimal treatment regimens. However, manually detecting these interactions is time-consuming and labor-intensive. Utilizing the advancements in Artificial Intelligence (AI) is essential for achieving accurate forecasts of DDIs. In this review, DDI prediction tasks are classified into three types according to the type of DDI prediction: undirected DDI prediction, DDI events prediction, and Asymmetric DDI prediction. The paper then reviews the progress of AI for each of these three prediction tasks in DDI and provides a summary of the data sets used as well as the representative methods used in these three prediction directions. In this review, we aim to provide a comprehensive overview of drug interaction prediction. The first section introduces commonly used databases and presents an overview of current research advancements and techniques across three domains of DDI. Additionally, we introduce classical machine learning techniques for predicting undirected drug interactions and provide a timeline for the progression of the predicted drug interaction events. At last, we debate the difficulties and prospects of AI approaches at predicting DDI, emphasizing their potential for improving clinical decision-making and patient outcomes.

MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction

Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.

A Comparison of the Sensing Behavior for Pt-Mo/C-, Pt-Zr/C-, Pt-Fe-Ir/C-, and Pt/C-Modified Glassy Carbon Electrodes for the Oxidation of Ascorbic Acid and Dopamine

This study compares the sensing performance for platinum-molybdenum-, platinum-zirconium-, platinum-iron-iridium-, and platinum-modified electrodes in terms of the amperometric detection of ascorbic acid (AA) and dopamine (DA). The Pt, Pt-Mo, Pt-Zr, and Pt-Fe-Ir electrocatalysts are fabricated by chemical reduction on a carbon black support (XC-72) and are further modified on a glassy carbon electrode (GCE) as sensing electrodes. The Pt-Mo/C/GCE exhibits better electrocatalytic activity toward AA and DA than the Pt/C/GCE, Pt-Zr/C/GCE, and Pt-Fe-Ir/C/GCE. The Pt-Mo/C/GCE exhibits a sensitivity of 31.29 µA mM−1 to AA at 0.3 V vs. Ag/AgCl and a sensitivity of 72.24 µA mM−1 to DA at 0.6 V vs. Ag/AgCl and is reproducible and stable. This electrode has a respective limit of detection of 7.69 and 6.14 µM for AA and DA. Sucrose, citric acid, tartaric acid, and uric acid do not interfere with AA and DA detection. The diffusion coefficient and kinetic parameters, such as the catalytic rate constant and the heterogeneous rate constant for AA and DA, are determined using electrochemical approaches.

Herb-Drug Interaction in Inflammatory Diseases: Review of Phytomedicine and Herbal Supplements

Many people worldwide use plant preparations for medicinal purposes. Even in industrialized regions, such as Europe, where conventional therapies are accessible for the majority of patients, there is a growing interest in and usage of phytomedicine. Plant preparations are not only used as alternative treatment, but also combined with conventional drugs. These combinations deserve careful contemplation, as the complex mixtures of bioactive substances in plants show a potential for interactions. Induction of CYP enzymes and pGP by St John's wort may be the most famous example, but there is much more to consider. In this review, we shed light on what is known about the interactions between botanicals and drugs, in order to make practitioners aware of potential drug-related problems. The main focus of the article is the treatment of inflammatory diseases, accompanied by plant preparations used in Europe. Several of the drugs we discuss here, as basal medication in chronic inflammatory diseases (e.g., methotrexate, janus kinase inhibitors), are also used as oral tumor therapeutics.

Role of vitamins in the metabolic syndrome and cardiovascular disease

The prevalence of metabolic syndrome and cardiovascular disease has increased and continues to be the leading cause of mortality worldwide. The etiology of these diseases includes a complex phenotype derived from interactions between genetic, environmental, and nutritional factors. In this regard, it is common to observe vitamin deficiencies in the general population and even more in patients with cardiometabolic diseases due to different factors. Vitamins are essential micronutrients for cellular metabolism and their deficiencies result in diseases. In addition to its role in nutritional functions, increasingly, vitamins are being recognized as modulators of genetics expression and signals transduction, when consumed at pharmacological concentrations. Numerous randomized preclinical and clinical trials have evaluated the use of vitamin supplementation in the prevention and treatment of metabolic syndrome and cardiovascular disease. However, it is controversy regarding its efficacy in the treatment and prevention of these diseases. In this review, we investigated chemical basics, physiological effect and recommended daily intake, problems with deficiency and overdose, preclinical and clinical studies, and mechanisms of action of vitamin supplementation in the treatment and prevention of metabolic syndrome and cardiovascular disease.