Precision Oncology

The growing demand for sustainable, high-performance materials has accelerated research into natural polymer–nanoclay composites as alternatives to petroleum-based plastics and conventional biomaterials. Natural polymers such as cellulose, chitosan, and alginate offer biodegradability, biocompatibility, and chemical functionality, but their standalone use is often limited by inadequate mechanical strength, thermal stability, and barrier performance. Incorporation of naturally occurring nanoclays, including montmorillonite and halloysite nanotubes, overcomes these limitations by providing structural reinforcement, enhanced barrier properties, and tunable bioactive delivery capabilities. This review critically examines recent advances in the design, interfacial chemistry, and multifunctional performance of natural polymer–nanoclay composites, with particular emphasis on stimuli-responsive behavior and bioactive loading strategies. Mechanisms governing pH-, temperature-, moisture-, ionic-, and light-responsive responses are discussed in relation to controlled release and adaptive functionality. A comparative perspective highlights how shared material principles are tailored to meet the distinct performance and regulatory requirements of food packaging and biomedical applications, including shelf-life extension, antimicrobial activity, wound healing, and drug delivery. Fabrication approaches, scale-up challenges, and safety and regulatory considerations relevant to both sectors are also addressed. Despite substantial progress, challenges remain in achieving scalable manufacturing, ensuring long-term safety, and minimizing environmental persistence of nanoclay components. Overall, natural polymer–nanoclay composites represent a promising class of multifunctional, sustainable materials capable of bridging food safety and biomedical innovation through rational material design.

Critical Congenital Heart Disease (CHD) is one of the most important causes of neonatal morbidity and mortality and early prenatal diagnosis is essential for effective treatment and better clinical outcomes. In this study, ECHOFOCUS-CHD, an AI-inspired autonomous framework for the diagnosis of Critical Congenital Heart Disease from ultrasound images of fetuses is proposed. A quantitative experimental research design was used and 200 prenatal ultrasound scans acquired from hospitals and fetal echocardiography databases are used. A Convolutional Neural Network (CNN) was created to classify fetal cardiac conditions into normal, mild, moderate and critical CHD categories. The models are enhanced by using image preprocessing methods like noise reduction, normalization, contrast enhancement, and data augmentation. An overall accuracy of 94.5%, sensitivity of 92.8%, specificity of 95.6% with an AUC value of 0.96% was obtained, which shows an excellent diagnostic capability in the proposed system. The reliability of the framework was also verified using confusion matrix and ROC curve analyses. The system was also able to automatically classify the heart defects of foals using images from their prenatal scans, aiding clinical decisions during pregnancy and early treatment planning. The results show that AI-powered prenatal ultrasound analysis has the potential to greatly improve the effectiveness of early detection of CHD and prenatal screening.

Obesity, dyslipidemia, and type 2 diabetes are examples of metabolic disorders that pose serious worldwide health risks. These conditions are typified by oxidative stress, persistent low-grade inflammation, and disturbed lipid and glucose metabolism. The study of complementary and alternative methods has been prompted by the fact that, despite their effectiveness, conventional pharmaceutical treatments are frequently constrained by side effects, high prices, and incomplete efficacy. With an emphasis on holistic and multi-targeted therapies using single herbs and polyherbal combinations, traditional Indian medical systems, especially Ayurveda, offer a centuries-old storehouse of botanical knowledge. Numerous preclinical investigations in animal models show that these plant-based treatments can improve overall metabolic homeostasis by regulating important molecular pathways like PPARs, AMPK, and GLUT4, suppressing pro-inflammatory cytokines, enhancing antioxidant defenses, and modulating lipid and glucose metabolism. Synergistic effects are sometimes seen in polyherbal formulations, which provide better benefits across several physiological pathways than single-plant therapies. Although these results demonstrate the therapeutic value of Ayurvedic treatments and their conformity to contemporary scientific concepts, issues with standardization, mechanistic clarification, and comparative effectiveness with mainstream medications still exist. A promising framework for the creation of safe, efficient, and evidence-based phytotherapeutics to control the rising worldwide burden of metabolic illnesses is provided by combining traditional Indian medical knowledge with modern research.

Background: Liver, breast, kidney, and brain cancers remain major contributors to global cancer morbidity and mortality. Conventional therapies are limited by systemic toxicity, drug resistance, and tumor heterogeneity. Smart nanoplatforms offer targeted delivery, controlled release, and theranostic capabilities to address these challenges. Objective: This systematic review evaluates the development and clinical translation of smart nanoplatforms between 2019 and 2024, focusing on their design, omics integration, therapy response, and clinical outcomes in liver, breast, kidney, and brain cancers. Methods: Studies published between 2019 and 2024 were systematically analyzed, encompassing preclinical research, clinical trials, and multi-omics-guided nanoparticle strategies. Nanoplatforms were categorized into lipid-based, polymeric, inorganic, and hybrid/bioinspired systems. The review highlights therapy response, biomarker monitoring, and adaptive approaches informed by omics data. Results: Lipid-based and polymeric nanoparticles demonstrated enhanced tumor targeting and reduced systemic toxicity. Inorganic and hybrid/bioinspired platforms enabled imaging-guided therapy and immune evasion. Integration of genomics, transcriptomics, proteomics, and metabolomics with AI-driven analytics facilitated personalized therapy and adaptive treatment strategies. Clinical trials reported improved patient tolerability, quality of life, and preliminary survival benefits, though translational barriers—including tumor heterogeneity, blood–brain barrier penetration, manufacturing, and regulatory hurdles—remain significant. Conclusions: Smart nanoplatforms represent a transformative approach to precision oncology. The combination of targeted delivery, multi-omics guidance, and AI-driven therapy optimization has the potential to enhance treatment efficacy and patient-specific outcomes. Future research should focus on scalable manufacturing, regulatory standardization, and integration of innovative trial designs to accelerate clinical adoption.