Prescription Precision: A Comprehensive Review of Intelligent Prescription Systems.

Intelligent Prescription Systems (IPS) represent a promising frontier in healthcare, offering the potential to optimize medication selection, dosing, and monitoring tailored to individual patient needs. This comprehensive review explores the current landscape of IPS, encompassing various technological approaches, applications, benefits, and challenges. IPS leverages advanced computational algorithms, machine learning techniques, and big data analytics to analyze patient-specific factors, such as medical history, genetic makeup, biomarkers, and lifestyle variables. By integrating this information with evidence-based guidelines, clinical decision support systems, and real-time patient data, IPS generates personalized treatment recommendations that enhance therapeutic outcomes while minimizing adverse effects and drug interactions. Key components of IPS include predictive modeling, Drug-Drug Interaction detection, adverse event prediction, dose optimization, and medication adherence monitoring. These systems offer clinicians invaluable decision-support tools to navigate the complexities of medication management, particularly in the context of polypharmacy and chronic disease management. While IPS holds immense promise for improving patient care and reducing healthcare costs, several challenges must be addressed. These include data privacy and security concerns, interoperability issues, integration with existing electronic health record systems, and clinician adoption barriers. Additionally, the regulatory landscape surrounding IPS requires clarification to ensure compliance with evolving healthcare regulations. Despite these challenges, the rapid advancements in artificial intelligence, data analytics, and digital health technologies are driving the continued evolution and adoption of IPS. As precision medicine gains momentum, IPS is poised to play a central role in revolutionizing medication management, ultimately leading to more effective, personalized, and patient-centric healthcare delivery.

The Use of Lipid-Based Nanocarriers to Improve Ovarian Cancer Treatment: An Overview of Recent Developments.

Ovarian cancer poses a formidable health challenge for women globally, necessitating innovative therapeutic approaches. This review provides a succinct summary of the current research status on lipid-based nanocarriers in the context of ovarian cancer treatment. Lipid-based nanocarriers, including liposomes, solid lipid nanoparticles (SLNs), and nanostructured lipid carriers (NLCs), offer a promising solution for delivering anticancer drugs with enhanced therapeutic effectiveness and reduced adverse effects. Their versatility in transporting both hydrophobic and hydrophilic medications makes them well-suited for a diverse range of anticancer drugs. Active targeting techniques like ligand-conjugation and surface modifications have been used to reduce off-target effects and achieve tumour-specific medication delivery. The study explores formulation techniques and adjustments meant to enhance drug stability and encapsulation in these nanocarriers. Encouraging results from clinical trials and preclinical investigations underscore the promise of lipid-based nanocarriers in ovarian cancer treatment, providing optimism for improved patient outcomes. Notwithstanding these advancements, challenges related to clearance, long-term stability, and scalable manufacturing persist. Successfully translating lipidbased nanocarriers into clinical practice requires addressing these hurdles. To sum up, lipidbased nanocarriers are a viable strategy to improve the effectiveness of therapy for ovarian cancer. With their more focused medication administration and lower systemic toxicity, they may completely change the way ovarian cancer is treated and increase patient survival rates. Lipidbased nanocarriers need to be further researched and developed to become a therapeutically viable treatment for ovarian cancer.

New drug discovery of cardiac anti-arrhythmic drugs: insights in animal models.

Cardiac rhythm regulated by micro-macroscopic structures of heart. Pacemaker abnormalities or disruptions in electrical conduction, lead to arrhythmic disorders may be benign, typical, threatening, ultimately fatal, occurs in clinical practice, patients on digitalis, anaesthesia or acute myocardial infarction. Both traditional and genetic animal models are: In-vitro: Isolated ventricular Myocytes, Guinea pig papillary muscles, Patch-Clamp Experiments, Porcine Atrial Myocytes, Guinea pig ventricular myocytes, Guinea pig papillary muscle: action potential and refractory period, Langendorff technique, Arrhythmia by acetylcholine or potassium. Acquired arrhythmia disorders: Transverse Aortic Constriction, Myocardial Ischemia, Complete Heart Block and AV Node Ablation, Chronic Tachypacing, Inflammation, Metabolic and Drug-Induced Arrhythmia. In-Vivo: Chemically induced arrhythmia: Aconitine antagonism, Digoxin-induced arrhythmia, Strophanthin/ouabain-induced arrhythmia, Adrenaline-induced arrhythmia, and Calcium-induced arrhythmia. Electrically induced arrhythmia: Ventricular fibrillation electrical threshold, Arrhythmia through programmed electrical stimulation, sudden coronary death in dogs, Exercise ventricular fibrillation. Genetic Arrhythmia: Channelopathies, Calcium Release Deficiency Syndrome, Long QT Syndrome, Short QT Syndrome, Brugada Syndrome. Genetic with Structural Heart Disease: Arrhythmogenic Right Ventricular Cardiomyopathy/Dysplasia, Dilated Cardiomyopathy, Hypertrophic Cardiomyopathy, Atrial Fibrillation, Sick Sinus Syndrome, Atrioventricular Block, Preexcitation Syndrome. Arrhythmia in Pluripotent Stem Cell Cardiomyocytes. Conclusion: Both traditional and genetic, experimental models of cardiac arrhythmias' characteristics and significance help in development of new antiarrhythmic drugs.