MXenes, a burgeoning class of two-dimensional transition metal carbides and nitrides, have emerged as promising candidates for biomedical applications owing to their exceptional physicochemical properties and versatile surface chemistry. This review comprehensively examines the biocompatibility and immunomodulatory behavior of MXenes, with a particular emphasis on their potential in drug delivery systems. We elucidate the critical aspects of MXene–protein interactions, including protein corona formation, cellular uptake pathways, and the influence of surface functionalization on biological interfaces. Special attention is given to the immunological profile of MXenes, exploring their immunogenic potential and immunomodulatory capabilities within therapeutic contexts. Furthermore, we assess the viability of MXenes as nanocarriers for drugs and bioactive compounds, analyzing a wide array of functionalization strategies and stimuli-responsive release mechanisms aimed at enhancing therapeutic efficacy. Despite their immense potential, challenges such as long-term stability, cytotoxicity, and clinical translatability persist. We conclude by outlining these limitations and proposing strategic avenues for future research. This review serves as a vital resource for researchers at the intersection of materials science and biomedicine, particularly those advancing next-generation, two-dimensional nanomaterial-based drug delivery platforms.

RNA therapeutics have come of age as clinically validated modalities including mRNA, siRNA, antisense oligonucleotides (ASOs), and in vivo genome editing, with lipid nanoparticles (LNPs) as the main non-viral delivery system. This review defines programmable LNPs as systems whose composition and interfacial chemistry are tuned to control organ tropism, cell specificity, intracellular trafficking, and immune interactions. We summarize design rules across four core components (ionizable lipid, phospholipid, cholesterol, PEG-lipid) and highlight levers like apparent pK_a optimization (∼6–7 for hepatic delivery), biodegradable linkers, PEG-anchor-dependent shedding, ligands (e.g., GalNAc), and selective organ-targeting (SORT) lipids that redirect biodistribution beyond the liver. We survey advances in data-guided formulation, including DNA-barcoded in vivo libraries, machine learning, and physics-based prediction, plus scalable manufacturing (microfluidics, confined impinging-jet mixing, tangential-flow filtration) and Quality-by-Design with process-analytical technologies. A comprehensive characterization toolkit (size/ζ-potential, cryo-EM/SAXS, RNA encapsulation and integrity, apparent pK_a, in vivo barcoding) maps to critical quality attributes. Applications span vaccines, protein replacement, siRNA/ASO delivery, and CRISPR platforms, with clinical examples like patisiran, COVID-19 and RSV mRNA vaccines, in-human transthyretin (TTR) editing, and individualized melanoma vaccination. We analyze translational constraints like endosomal escape, reactogenicity and anti-PEG immunity, complement activation, and lot-to-lot control, plus success factors: corona-aware design, dose-efficient potency at low lipid burden, redosing strategies, and fit-for-purpose biomarkers. Together, programmable LNPs offer a generalizable path to extrahepatic, cell-aware RNA medicine when coupled to rigorous analytics and platform manufacturing.

Multifunctional nano-contrast agent carriers are redefining molecular imaging by combining high-fidelity visualization with targeted delivery, controlled release, and, increasingly, therapeutic action. This review encompasses the development of nano-contrast platforms from conventional dendrimer, liposome, chitosan, and silica systems to modular nano-contrast platforms for multimodal, multi-parametric, and activatable imaging in clinically relevant environments. We dissect engineering strategies that govern surface chemistry, ligand organization, stimulus responsiveness, and microenvironmental sensing, and relate them to theranostic performance, immune system engagement, and quantitative image readouts. Biodistribution, pharmacokinetics, and safety are discussed from both classical and model-informed perspectives, with design principles that favor predictable behavior, manufacturability, and regulatory acceptance. Current clinical translation, regulatory pathway evolution, and market dynamics are critically reviewed to elucidate that a few nano-contrast agents have reached patients despite a widespread experimental landscape. Finally, we discuss emerging trends, including biomimetic and ultrasmall carriers, metal–organic and hybrid frameworks, AI-assisted design, digital twins, and precision medicine workflows, which are likely to shape the next-generation nano-contrast theranostics. By systematically relating material selection and carrier architecture to imaging function and translational limitations, this review suggests concrete research priorities for taking nano-contrast agents from sophisticated prototypes to robust, patient-tailored tools.

Reliable inference of census population size ( N ) from mark–recapture data is essential in ecology, conservation, and epidemiology, yet standard estimators often rely on asymptotic approximations that can misrepresent uncertainty. We introduce the Exact Hypergeometric Posterior (EHP), obtained by normalizing the hypergeometric likelihood for the classic two-sample design, which yields an exact posterior probability mass function over integer N . Closed-form normalization enables rapid evaluation of posterior summaries and exact highest-posterior-density (HPD) credible intervals. In this study, we show how heavy right tails naturally arise in sparse-recapture regimes and provide a principled truncation using an upper bound K , such as a plausible carrying capacity or sampling-frame limit, to regularize inference when needed. For repeated sampling, independent events can be combined by posterior multiplication and renormalization, yielding substantial gains in precision without large-sample approximations. Additionally, we extend the likelihood to account for loss of marked individuals and differential catchability by introducing an availability (retention) term $$\phi (t)$$ ϕ ( t ) and a relative catchability parameter $$\omega $$ ω via Fisher’s noncentral hypergeometric model. Simulations and empirical examples confirm close agreement between analytic posteriors and Monte Carlo reference posteriors using our method. Together, these results provide a transparent, computationally tractable framework for finite-sample population-size inference in closed and partially open systems.