Background: This study compares the differences in city-level cardiovascular disease (CVD) drug-lists and investigates their relationship with CVD mortality rate across 16 cities in Anhui province, China.
Methods: Data on the usage of CVD medicines from 2016 to 2020 in hospitals across various levels in 16 cities and China's 2018 national list of essential medicines (EMs) were collected and mortality, demographic, environmental data related to CVD were analyzed. The negative binomial mixed-effects model was adopted to compare the differences. A generalized estimating equation was applied to evaluate associations between Anhui city-level drug-lists and mortality over the five years.
Results: The drug-lists across cities in Anhui province were short. Analysis revealed that the drug-lists of ten cities were shorter than that of the capital city, Hefei. Healthcare expenditure appeared to impact the length of druglists. After controlling for per capita GDP, population, widowhood rate, and beds per 1000 people, it was found that differences in drug-lists were associated with the CVD mortality rate in Anhui Province.
As the rapid advancements in medical technology and increasing demands for personalized medication, Hospital Intelligent Pharmacy (HIP) integrates artificial intelligence, large-scale health data analytics, the Internet of Things (IoT), and other cutting-edge technologies to optimize end-to-end pharmaceutical supply chain processes, management, and clinical processes. In recent years, regulatory agencies such as the European Medicines Agency (EMA), the Medicines and Healthcare products Regulatory Agency (MHRA), China's National Medical Products Administration (NMPA), and the U.S. Food and Drug Administration (USFDA) have issued policies to promote intelligent pharmacy development. However, HIP still faces challenges including ambiguous definitions, absence of standardized technical protocols, and incomplete evaluation frameworks. To address these issues, international and domestic academic organizations collaboratively developed the International Expert Consensus on Hospital Intelligent Pharmacy. This consensus clarifies HIP's definition, core components, and systematic framework, providing scientific guidance for standardized implementation and clinical application of intelligent pharmacy in hospitals. Utilizing a Delphi method process, expert opinions will be collected, analyzed, and refined. The current consensus defines HIP's scope and principles, outlining a framework with 10 components:intelligent drug supply chain management, drug dispensing, prescription review, pharmacovigilance, medication therapy management, therapeutic drug monitoring, telepharmacy services, pharmacy administration, science popularization, and clinical trials. Future directions focus on 5 key areas:AI-augmented pharmacist competency development, advancing pharmaceutical scientific research, fostering intelligent pharmaceutical publications and journals, addressing ethical and legal challenges, and promoting international harmonization in pharmacy. The consensus offers critical references and exploratory pathways for HIP's global advancement.
Supramolecular chemistry is the name given to the subfields of chemistry that deal with complex frameworks made up of a certain number of molecules. The host and visitor or guest involved in molecular recognition demonstrate molecular complementarity. Significant attention has been generated in the new year for the precise identification and detection of anion species using artificial sensors. Due to the fact that anion play a significant role in the climate or environment. Consideration of the type of non-covalent interaction used to complex the anion guest has allowed for the helpful classification of anion receptors. Calculation and simplicity are necessary for the plane of certain hosts for anion. Anion is important to the environment. Different detecting techniques have been discussed.
Background/objectives: Costus speciosus is a medicinal plant traditionally used in Southeast Asia for its metabolic and anti-inflammatory properties, yet the molecular mechanisms underlying its bioactivity remain underexplored. Among its phytoconstituents, various plant-derived lipophilic compounds have attracted attention due to their structural similarity to endogenous metabolites and their potential role in modulating metabolic pathways relevant to type 2 diabetes mellitus (T2DM). This study aimed to investigate the binding behavior and stability of Costus speciosus-derived metabolites against key molecular targets involved in insulin signaling and oncogenic transformation.
Methods: LC-MS/MS profiling was employed to identify major bioactive metabolites from the rhizome extract. Subsequently, a network pharmacology approach was used to filter relevant targets, followed by molecular docking and 200 ns molecular dynamics simulations to evaluate interaction stability. Binding free energy was computed using the MM-PBSA method to support thermodynamic relevance
Results: A total of 18 compounds were identified via LC-MS/MS, of which 15 were successfully linked to at least one protein target through bioinformatics databases and proceeded to molecular docking analysis. Among these, campestanol showing the highest docking affinity (-10.73 kcal/mol) and the lowest inhibition constant (13.60 nM) toward PIK3CA. Molecular dynamics simulations revealed that the PIK3CA-campestanol complex exhibited comparable or superior stability metrics (RMSD, RMSF, Rg, SASA, RDF, and hydrogen bonding) to the native ligand. MM-PBSA calculations confirmed robust van der Waals and hydrophobic contributions to binding, with total binding energy at -117.144 ±13.887 kJ/mol. These computational findings were further corroborated by prior experimental studies demonstrating campestanol's metabolic regulatory functions.
Conclusions: Campestanol demonstrates stable and favorable binding with PIK3CA, supporting its role as a promising candidate for further in vitro validation in metabolic and oncogenic pathway modulation. This study provides mechanistic insights into Costus speciosus bioactivity and strengthens the rationale for advancing campestanol as a lead compound in PI3K/AKT-targeted therapies for T2DM.
The increasing complexity and competitiveness of the pharmaceutical industry are driving the need for innovative technological solutions. This article explores the use of Artificial Intelligence (AI) in the German pharmaceutical sector, with a focus on addressing key challenges related to regulation, market dynamics, internal structures, and technological capabilities. Through a combination of systematic literature review and qualitative expert interviews, the study identifies major problem areas and derives corresponding AI-based innovation potentials in departments such as research, human resources, and quality management. The findings demonstrate that a strategically guided implementation of AI can lead to substantial process improvements and foster longterm competitive advantages. To facilitate this transformation, the study concludes with actionable recommendations aimed at advancing the integration of AI beyond isolated pilot projects and towards broad, sustainable application within the industry. By combining a systematic literature review with in-depth expert interviews, this study not only provides an overview of the current state of knowledge but, thanks to its qualitative methodology, also offers new insights into the decision-making processes and perceptions of industry experts.