Genetic predisposition to inflammation:a new risk factor of Alzheimer's disease

Inflammation has been shown to play an important role in the progression of Alzheimer＇s disease （AD）. Recent epidemical study indicates that the incidence of AD in some populations is substantially influenced by the gene polymorphisms of the inflammation mediators. Meanwhile, an ensured risk factor, the ApoE ε4 allele is also reported to directly promote inflammation. Accordingly, it appears that an individual genetic background has partly determined his predisposition for AD by the extent of the inflammation response to the chronic stimulus by β-amyloid peptide （Aβ） deposits and other antigen stressor in the elderly. Hence we present a hypothesis that the inflammation genotypes may contribute to AD susceptibility. This may provide a new orientation both for future identification of individuals at risk and for personalized medication.

An Analysis and Prediction of Health Insurance Costs Using Machine Learning-Based Regressor Techniques

One of the most significant annual expenses that a person has is their health insurance coverage. Health insurance accounts for one-third of GDP, and everyone needs medical treatment to varying degrees. Changes in medicine, pharmaceutical trends, and political factors are only a few of the many factors that cause annual fluctuations in healthcare costs. This paper describes how a system may analyse a person’s medical history to display their insurance plans and make predictions about their health insurance premiums. The performance of four ML models—XGBoost, Lasso, KNN, and Ridge—is evaluated using R2-score and RMSE. The analysis of medical health insurance cost prediction using Lasso regression, Ridge regression, and K-Nearest Neighbours (KNN), and XGBoost (XGB) highlights notable differences in performance. KNN has the lowest R2-score of 55.21 and an RMSE of 4431.1, indicating limited predictive ability. Ridge Regression improves on this by an R2-score of 78.38 but has a higher RMSE of 4652.06. Lasso Regression slightly edges out Ridge with an R2-score of 79.78, yet it suffers from an advanced RMSE of 5671.6. In contrast, XGBoost excels with the highest R2-score of 86.81 and the lowermost RMSE of 4450.4, demonstrating superior predictive accuracy and making it the most effective model for this task. The best method for accurately predicting health insurance premiums was XGBoost Regression. The findings beneficial for policymakers, insurers, and healthcare providers as they can use this information to allocate resources more efficiently and enhance cost-effectiveness in the healthcare industry.

Herbal compounds as promising therapeutic agents in precision medicine strategies for cancer:A systematic review

Background:The field of personalized medicine has gained increasing attention in cancer care,with the aim of tailoring treatment strategies to individual patients for improved outcomes.Herbal medicine,with its long-standing historical use and extensive bioactive compounds,offers a rich source of potential treatments for various diseases,including cancer.Objective:To provide an overview of the current knowledge and evidence associated with incorporating herbal compounds into precision medicine strategies for cancer diseases.Additionally,to explore the general characteristics of the studies included in the analysis,focusing on their key features and trends.Search strategy:A comprehensive literature search was conducted from multiple online databases,including Pub Med,Scopus,Web of Science,and CINAHL-EBSCO.The search strategy was designed to identify studies related to personalized cancer medicine and herbal interventions.Inclusion criteria:Publications pertaining to cancer research conducted through in vitro,in vivo,and clinical studies,employing natural products were included in this review.Data extraction and analysis:Two review authors independently applied inclusion and inclusion criteria,data extraction,and assessments of methodological quality.The quality assessment and biases of the studies were evaluated based on modified Jadad scales.A detailed quantitative summary of the included studies is presented,providing a comprehensive description of their key features and findings.Results:A total of 121 studies were included in this review for analysis.Some of them were considered as comprehensive experimental investigations both in vitro and in vivo.The majority(n=85)of the studies included in this review were conducted in vitro,with 44 of them specifically investigating the effects of herbal medicine on animal models.Additionally,7 articles with a combined sample size of 31,271 patients,examined the impact of herbal medicine in clinical settings.Conclusion:Personalized medication can optimize the use of herbal medicine in cancer treatment by considering individual patient factors such as genetics,medical history,and other treatments.Additionally,active phytochemicals found in herbs have shown potential for inhibiting cancer cell growth and inducing apoptosis,making them a promising area of research in preclinical and clinical investigations.

Regulation of drug metabolism and toxicity by multiple factors of genetics,epigenetics,lncRNAs,gut microbiota,and diseases:a meeting report of the 21st International Symposium on Microsomes and Drug Oxidations(MDO)

Variations in drug metabolism may alter drug efficacy and cause toxicity;better understanding of the mechanisms and risks shall help to practice precision medicine.At the 21 st International Symposium on Microsomes and Drug Oxidations held in Davis,California,USA,in October 2-6,2016,a number of speakers reported some new findings and ongoing studies on the regulation mechanisms behind variable drug metabolism and toxicity,and discussed potential implications to personalized medications.A considerably insightful overview was provided on genetic and epigenetic regulation of gene expression involved in drug absorption,distribution,metabolism,and excretion(ADME) and drug response.Altered drug metabolism and disposition as well as molecular mechanisms among diseased and special populations were presented.In addition,the roles of gut microbiota in drug metabolism and toxicology as well as long non-coding RNAs in liver functions and diseases were discussed.These findings may offer new insights into improved understanding of ADME regulatory mechanisms and advance drug metabolism research.

Intelligent nanomaterials for cancer therapy:recent progresses and future possibilities

Intelligent nanomedicine is currently one of the most active frontiers in cancer therapy development.Empowered by the recent progresses of nanobiotechnology,a new generation of multifunctional nanotherapeutics and imaging platforms has remarkably improved our capability to cope with the highly heterogeneous and complicated na-ture of cancer.With rationally designed multifunctionality and programmable assembly of functional subunits,the in vivo behaviors of intelligent nanosystems have become increasingly tunable,making them more efficient in performing sophisticated actions in physiological and path-ological microenvironments.In recent years,intelligent nanomaterial-based theranostic platforms have showed great potential in tumor-targeted delivery,biological barrier circumvention,multi-responsive tumor sensing and drug release,as well as convergence with precise medication approaches such as personalized tumor vaccines.On the other hand,the increasing system complexity of anti-cancer nanomedicines also pose significant challenges in charac-terization,monitoring and clinical use,requesting a more comprehensive and dynamic understanding of nano-bio interactions.This review aims to briefly summarize the recent progresses achieved by intelligent nanomaterials in tumor-targeted drug delivery,tumor immunotherapy and temporospatially specific tumor imaging,as well as impor-tant advances of our knowledge on their interaction with biological systems.In the perspective of clinical translation,we have further discussed the major possibilities provided by disease-oriented development of anti-cancer nano-materials,highlighting the critical importance clinically-oriented system design.