Biocompatibility

Liquid biopsy using circulating tumor DNA (ctDNA) is gaining momentum as a powerful non-invasive tool for multi-cancer early detection (MCED) and precision medicine. The current paper highlights the role of artificial intelligence (AI), encompassing machine learning and deep learning techniques, in refining the analytical process of ctDNA to facilitate early cancer detection, diagnosis, prediction of the tissue of origin, and individualized disease management. Clinical trials in humans for various cancers, such as lung, colorectal, pancreatic, breast, and ovarian cancer, illustrate the ability of AI-enhanced ctDNA technology to detect even minute molecular changes in terms of mutations, epigenetic patterns, fragmentation features, and chromosome anomalies with higher sensitivity and specificity. In addition, the review highlights the biological relevance of ctDNA, the clinical utility of AI-based MCED systems, and the benefits of non-invasive testing, continuous surveillance, and detection of multiple cancers through a single blood sample. However, significant drawbacks, including low ctDNA concentration in early-stage tumors, false positives and negatives, non-standardization, ethical issues, and expensive technology, are substantial impediments to clinical adoption. Nonetheless, AI-powered ctDNA diagnostics hold immense promise for revolutionizing cancer screening in the future.

Analytical method validation (AMV) in the veterinary field forms an important aspect of quality control in pharmaceutical companies in which drugs are used to treat various species of animals to guarantees safety, efficacy and regulatory actions are adhered to. This review points to the significance of the species-based approaches in pharmacokinetics of drug absorption, distribution, metabolism, and residue detection with a focus on the issues of physiological diversity, complex biological samples and ethical requirement. Modern methods such as HPLC, UPLC, GC, LC-MS/MS, UV-Vis, and FTIR are characterized by sensitivity, specificity, and reproducibility and newer techniques such as capillary electrophoresis and microfluidic-based technology, high-resolution mass spectrometry and bioinformatics-directed validation incorporates enhanced sensitivity, specificity, reproducibility, reduced sample volumes, and increased throughput. Future methodological advances in multi-dimensional platforms, green analytical chemistry, chemometrics, and machine learning offer new ways of addressing matrix effects and resource limitations and regulatory limitations. Taken together, these strategies can contribute to effective veterinary drug monitoring, food safety, and sustainable and species-specific AMV practices and direct future research at achieving harmonized and technologically more advanced validation processes.

Poor water solubility is still a big problem in medication development since it typically makes oral formulations less bioavailable and less effective at treating diseases. The goal of this project was to create and test solid lipid nanoparticles (SLNs) as a new way to deliver medications that don't dissolve well. Four SLN formulations were made and tested using a hot homogenization followed by ultrasonication method. The tests looked at the size of the particles, the polydispersity index (PDI), the zeta potential, the entrapment efficiency, the drug loading, and the in vitro drug release. The results showed that higher concentrations of surfactants and lipids made the particles smaller, trapped more drugs, and released them over a longer period of time. Formulation F4 had the best performance, with a particle size of 130 nm, an entrapment efficiency of 88%, and a drug release rate of 85% at 24 hours. Statistical analysis showed that there were big differences across the formulations (p