Magnetic sensors present a transformative solution for non-invasive biomedical monitoring by overcoming critical limitations associated with conventional sensing technologies, such as optical sensors, whose performance degrades due to sensor-skin coupling effects. We systematically examine the sensor-skin coupling effect, emphasizing its impact on diagnostic accuracy. Our analysis reveals the complex challenges associated with sensor-skin interfaces, including biomechanical, pigmentary, and textural variations that affect sensor performance. We introduce a novel methodology that combines advanced biomaterial development, adaptive calibration techniques, and sophisticated signal processing algorithms. Our findings highlight that skin-specific characteristics contribute to measurement uncertainties in existing sensing technologies, indicating an urgent need for adaptive interface solutions. We propose a conceptual model for developing biocompatible sensor interfaces that maintain measurement integrity across varied physiological conditions, drawing insights from materials science, bioengineering, and clinical research. This review provides a comprehensive examination of sensor-skin interactions and outlines a roadmap for next-generation health monitoring technologies, along with strategic recommendations for enhancing the reliability of non-invasive diagnostics through innovative biomaterial solutions.

Background: Hypoxia plays a key role in cancer progression, mainly by stabilizing and activating hypoxia-inducible factor-1 (HIF-1). For HIF-1 to function under low oxygen conditions, it must interact with the transcriptional coactivator p300, a critical step for promoting cancer cell survival and adaptation in hypoxic environments. Methods: Consequently, we used drug design and molecular simulation techniques to screen phytochemical databases, including traditional Chinese and African medicine sources, for compounds that could disrupt the p300/HIF-1 interaction. Results: In this study, we identified potential compounds with high docking scores such as EA-176920 (−8.719), EA-46881231 (−8.642), SA-31161 (−9.580), SA-5280863 (−8.179), NE-5280362 (−10.287), NE-72276 (−9.017), NA-11210533 (−10.366), NA-11336960 (−7.818), TCM-5281792 (−12.648), and TCM-6441280 (−9.470 kcal/mol) as lead compounds. Furthermore, the compound with the highest docking score from each database (EA-176920, SA-31161, NE-5280362, NA-11210533, and TCM-5281792) was subjected to further analysis. The stable binding affinity of these compounds with p300 was confirmed by Post-simulation binding free energy (−22.0020 kcal/mol, −25.4499 kcal/mol, −32.4530 kcal/mol, −33.9918 kcal/mol, and −57.7755 kcal/mol, respectively) and KD analysis. Moreover, the selected compounds followed the Lipinski rules with favorable ADMET properties like efficient intestinal absorption, high water solubility, and no toxicity. Conclusions: Our findings highlight the potential of natural compounds to target key protein–protein interactions in cancer and lay the groundwork for future in vitro and in vivo studies to explore their therapeutic potential. Specifically, disrupting the p300/HIF-1 interaction could interfere with hypoxia-driven pathways that promote tumor growth, angiogenesis, and metastasis, offering a promising strategy to suppress cancer progression at the molecular level.

Background: SARS‐CoV‐2 is a positive‐sense single‐stranded RNA virus that has a propensity for infecting epithelial cells and the respiratory system. The two important proteins, structural and nonstructural proteins, make the architecture of this virus. Aim: This research aimed at studying significant mutations in spike protein of SARS‐CoV‐2 variants of concern (VoCs) and finding shared mutations among omicron and other four variants (alpha, beta, gamma, and delta). The purpose of this study was to draw structural comparisons between wild type and mutant proteins, followed by identifying potent inhibitors (ligand) that could be used against SARS‐CoV‐2 spike protein and its latest omicron VoC. Methodology: In this research, we had studied 16 major mutations as well as shared mutations (6) present in spike region of SARS‐CoV‐2. Subsequently, we determined the structure of the wild‐type SARS‐CoV‐2 protein from the Protein Data Bank (PDB) with the ID 7R4I. Furthermore, the structure of the mutant protein of SARS‐CoV‐2 omicron variant was modeled in SWISS‐MODEL. The ligand dataset for spike protein of SARS‐CoV‐2 was also collected from literature and different databases. Both wild type and mutant proteins were docked with ligand database in Molecular Operating Environment (MOE). The docking analysis was performed, and two best ligand molecules, AZ_2 and AZ_13, were finalized based on their energy values, interactions, and docking scores to be used against our wild and mutant proteins. Results: AZ_2 demonstrated a docking score of −6.1753 in MOE, with energy values of −4.3889 and −6.1753. It formed key hydrogen bond interactions. AZ_13 showed a docking score of −5.9, with energy values of −9.3 and −5.9, forming hydrogen donor and acceptor interactions with Asp950 (3.06 Å), Ile312 (3.13 Å), and Glu309 (3.27 Å). These interactions suggest strong binding affinity and potential efficacy. Thus, present research work emphasized on identification of significant mutations and finding a potent target‐based drug against SARS‐CoV‐2 and its omicron variant. Outcomes: Based on this computational analysis performed, it is suggested that proposed compound can be used as remedy against SARS‐CoV‐2 and its omicron variant.