This article's primary intent is to define the earliest signs of functional pathology in biological systems while these systems still operate within generally accepted normal parameters. By clarifying early deviations, the goal is to enhance preventative medicine and refine diagnostic algorithms used in population health. The core principle underlying the onset of disease is the investigation of healthy, controlled marginal functional instability in biological systems. This approach seeks to better delineate the boundaries of system homeostasis, which has long been regarded as the undisputed marker of health. The discussion presents an evolutionary perspective on health and disease, considering concepts such as homeostasis, stable non-equilibrium, marginal instability, and homeokinesis. It then advances toward the complexity of precise transitional tuning between health and disease, emphasizing the role of homeodynamic regulation. The authors propose that the cost of homeodynamic adaptation should be considered an integral definition of metabolic expenditure, recovery time, or autonomic activation following a perturbation. Considering neurons are morphologically fractal and functionally graded, homeodynamics may provide insight into biological systems fuzzy-fractal origin of adaptation. Heart Rate Variability (HRV) is presented as an example of a hierarchical, fuzzy-fractal biological model. The optimal informational sensorium is described as an integrated measure of sufficient variability in receptor signal flow, which allows for maximized adaptive flexibility while minimizing the homeodynamic cost of marginal instability. Based on above considerations, authors proposed further research of organized structured variability as system-level property that enables biological network to balance controlled instability while constraining energetic expenditure. The earliest detectable sign of organized variability deterioration is a reduction in scale-dependent complexity with narrowing of functional dynamic range.
Comprehensive xenometabolome characterization is essential for understanding the effects of xenobiotics in biological systems. This study presents a multidimensional analytical workflow integrating orthogonal chromatographic separations, trapped ion mobility spectrometry (TIMS), high-resolution mass spectrometry and biotransformation-informed data processing to address xenometabolome assessment challenges. Zebrafish larvae exposed to 4-Methylbenzotriazole (4-MeBT) were used as a challenging case study. TIMS dimension provided orthogonal experimental evidence for isomer annotation, with inverse reduced mobility (1/K0) supporting conjugation site assignment for the dominant O-S-4MeBT and O-G-4MeBT isomers. The combination of TIMS with the Parallel Accumulation Serial Fragmentation (PASEF) acquisition further reduced spectral complexity, enhanced signal-to-noise ratio, and improved MS/MS coverage (70%), generating high-quality analytical evidence crucial for structural elucidation. To leverage these analytical dimensions, we developed a data processing strategy that leverages in-silico-based suspect screening and biotransformation-informed nontarget screening. In this regard, we introduce two novel frameworks; the "Building Blocks" (BB) concept which interprets unknown bio-TPs as modular assemblies of parent- and pathway-derived substructures, and the "Spectral Characteristics Knowledgebase" (SCKB), which use known biotransformation MS/MS motifs to provide structural insights and facilitate unknown identification. Our results demonstrated the identification of all previously known 4-MeBT bio-TPs with enhanced confidence (O-Sulfate- and O-Glucuronide-4MeBT) and the discovery of 29 new bio-TP features across 12 bio-TP classes, highlighting its efficacy in unraveling complex xenobiotic metabolism. Among these, a putative dimerization product (4-MeBT-263) was reported for the first time in zebrafish. Overall, this workflow has the potential to advance the understanding of bio-TP formation and detoxification processes in xenometabolome studies.
The article is devoted to topical issues of genetic biomechanics, which studies structural connections between molecular-genetic informatics and inherited physiological complexes. It is known that amino acid sequences of proteins are genetically inherited using code messages in DNA and RNA molecules based on the alphabet of 4 nucleotides. But, as Nobel laureate geneticist T. Steitz emphasizes, all knowledge about these biomolecules encoded in the genome in this biochemical alphabet will not tell us about the inheritance of biomechanical algorithms and functions by genetic automata. Thus, in modern science of biological inheritance, there is no knowledge about a bioinformation system capable of ensuring the inheritance of cooperative phenomena of algorithmic behavior of body parts. These inherited logical forms of algorithmic behavior in biosystems require the search for bioinformation alphabets that could form the basis for the operation of genetic automata and the algorithmic inheritance of biological structures. The article describes the genetic algebraic-operator alphabets, identified as a result of such a search, based on unitary Hadamard matrices, as well as cyclic power groups based on them, which make it possible to model inherited cyclic and biorhythmic structures in connection with the formalisms of quantum logic. The evolutionary paradigm of algebraic-alphabetic Darwinism has been formulated. Related issues of inherited brain mechanisms, artificial intelligence, and the functioning of operators in human-machine systems are discussed.
Microplastic (MP) pollution has emerged as a defining environmental challenge of the 21st century, threatening ecosystems, food security, and human health at an unprecedented scale. Conventional remediation methods-such as filtration, coagulation, and advanced oxidation-remain inefficient, energy-intensive, and incapable of addressing nanoscale fragments or preventing secondary contamination. This review provides a comprehensive and forward-looking synthesis ofnanoparticle-assisted bioremediation, an emerging paradigm that integrates microbial enzymatic degradation with engineered and green-synthesized nanomaterials to achieve sustainable plastic depolymerization. We critically examine how magnetic nanoparticles enable rapid adsorption and recovery, how photocatalytic metal oxides (TiO2, ZnO, Fe2O3, CeO2) and plasmonic hybrids generate reactive oxygen species to oxidatively cleave polymer chains, and how nano-bio hybrid systems stabilize enzymes, enhance substrate accessibility, and accelerate mineralization into CO2, H2O, and biomass. Special emphasis is placed on green-synthesized nanoparticles derived from plants and microbes, which offer enhanced environmental compatibility and catalytic efficiency. The review also evaluates the mechanistic underpinnings, kinetic parameters, and techno-economic feasibility of these systems, identifying critical challenges related to nanoparticle aggregation, enzyme instability, and ecological safety. By uniting insights from nanotechnology, microbiology, and environmental engineering, this work delineates a strategic roadmap toward biodegradable, multifunctional nanomaterials and biofilm-enhanced hybrid reactors capable of large-scale deployment. Nano-bioremediation represents not merely an incremental advance but a transformative step toward a circular, low-carbon bioeconomy, offering a realistic and scalable pathway to mitigate global microplastic pollution and restore environmental integrity.
Active packaging systems have gained prominence as an effective alternative to conventional synthetic materials. Such systems aim to not only preserve product quality and extend shelf life but also to enhance the functional properties of the product. The essential oil (EO) of Zataria multiflora boiss. (Z. multiflora), rich in phenolic compounds, can serve as a valuable functional additive. This study reviews the incorporation of Z. multiflora essential oil (ZEO) into bio-based films/coatings via conventional and advanced methods, including nanoemulsions and Pickering emulsions. The present review aims to assess ZEO effects on the mechanical, barrier, optical, and functional properties of bio-based matrices, such as polysaccharides, proteins, and composites. However, further research is required to optimize ZEO stability, achieve the optimal controlled release of its active substances (e.g., carvacrol), and overcome challenges in industrial scalability and regulatory compliance.
In the context of global energy transition and carbon neutrality goals, converting lignocellulosic biomass into high-value products is essential for a circular bioeconomy. This review systematically explores technological advancements in modern biorefineries, from feedstock deconstruction to precise valorization. Pretreatment strategies-physical, chemical, and integrated pretreatment strategies-effectively overcome lignocellulose recalcitrance, achieving high-purity fractionation of cellulose, hemicellulose, and lignin. Advanced conversion technologies, including electrocatalysis, photocatalysis, biological funneling, and bio-photoelectrochemical hybrid systems, offer superior selectivity, atom economy, and energy efficiency in biomass upgrading. Addressing economic and stability challenges in scale-up, the integration of artificial intelligence and digital twins promises intelligent, carbon-neutral biorefinery systems. This review provides theoretical guidance and technical roadmaps for closed-loop resource utilization and sustainable development.
Bdellovibrio and like organisms (BALOs) are small predatory bacteria that prey mostly on Gram-negative pathogenic bacteria. There has been a remarkable expansion of BALOs biology in the past ten years owing to advances in genomics, transcriptomics, and imaging technologies. Recent advancement of the predation biology of BALOs, expanded our knowledge on the genetic control, enzyme-mediated, the organization of chromosomes through cryo-electron tomography, comparative genomics and gene editing technologies. Genetic engineering of Bdellovibrio sp. is rapidly developing although it lacks consistency in engineered predatory BALOs systems, and established regulatory standards. The biology, diversity, ecological roles, predatory lifecycle and killing mechanisms of BALOs and structural insights into predator-prey interaction, can facilitate their translation into sustainable and scalable technologies. BALOs have many possible applications in aquaculture, environmental management, agriculture, biomedicine, and food safety emphasizing wide range field (real-world) applications. In this review, we discuss ecology, predation, and the diverse molecular, genetic regulation, mechanisms of prey recognition, invasion, and intracellular growth of BALOs. Additionally, this review fills the gap in the predatory bacteria literature, and provides a comprehensive, integrative synthesis of BALOs research and recent biohybrid application. In contrast to previous works, we holistically describe from its biology to recent advancement in engineering and its implication to create a comprehensive review, which provide a practical framework in making BALOs research more reproducible, scalable, and sustainable by integrating taxonomy, ecology, predatory lifecycle, molecular and structural components, and bio-industrial applications.
Synthetic polymers have been widely investigated due to their diverse triboelectric properties and strong ability to accumulate high surface charge densities. However, the development of bio-derived triboelectric nanogenerators (TENGs) has been constrained by the limited availability of biopolymer pairs with sufficiently contrasting triboelectric behaviors. As a result, the performance of biopolymer-based TENGs generally remains an order of magnitude lower than that of systems constructed from synthetic materials. In this work, we propose an approach to overcome this challenge by tailoring the triboelectric characteristics of UV-crosslinked, vegetable-oil-derived polymers. By employing simple formulation modifications combined with engineered surface microstructures, we realize a marked improvement in triboelectric output-achieving a power density of 65 mW m-2 and a 25-fold increase in voltage generation up to 250 V, relative to materials possessing comparable mechanical and interfacial properties. Furthermore, the resulting polymers demonstrate controlled degradability via alkaline hydrolysis, ensuring an environmentally responsible disposal pathway. This scalable and sustainable materials strategy not only enhances the performance of degradable TENGs but also expands their applicability in eco-conscious energy harvesting, representing a meaningful advancement toward sustainable energy technologies.
The skin is the body's largest organ and is considered as a protective barrier which acts as a highly impermeable region of the human body. But in recent times, it is recognized as a specialized organ that aids in the delivery of a wide range of drug molecules into the skin (intradermal drug delivery) and across the skin into systemic circulation (transdermal drug delivery, TDD). Transdermal administration remains an active research and development area as an alternative route for long- acting drug delivery. It avoids major drawbacks of conventional oral (gastrointestinal side effects, low drug bioavailability, and need for multiple dosing) or parenteral routes (invasiveness, pain, and psychological stress and bio-hazardous waste generated from needles), thereby increasing patient appeal and compliance. The bioavailability of a drug administered transdermally can be improved by several penetration enhancement techniques, which are broadly classified into chemical and physical techniques. Application of the mentioned techniques together with efforts of various scientific and innovative companies had made TDD a multibillion-dollar market and this has led to a growing market with a steady pipeline of new transdermal products receiving regulatory approval. Out of various techniques, thermal therapeutic methods including chemical heating, laser ablation, thermoporation, radiofrequency and photothermal therapy are the top listed emerging techniques. This review article mainly discussed about these thermal ablation techniques with their available commercial products along with advantages and disadvantages. This review also presented anatomy of the skin, penetration pathways across the skin, affecting factors and different generations and mechanisms of TDD. Briefly, this article discussed basics, mechanism, challenges, and future research and development directions of thermal-based TDDS.
A recent minireview by J. R. A. Williams and J. F. Biddle (Appl Environ Microbiol, 92:e00275-25, 2026, https://doi.org/10.1128/aem.00275-25) substantially reframes our understanding of sedimentary viruses. For decades, viruses in marine sediments have been viewed primarily as agents of mortality, their roles largely confined to the canonical "viral shunt" paradigm developed for pelagic systems. The authors expand this perspective, positioning viruses as active participants in benthic biogeochemistry-contributing to nutrient cycling, modulating microbial diversity, and influencing organic matter processing and carbon sequestration. This conceptual shift highlights sedimentary viruses as an integral and, until now, underappreciated component of global element cycles.
Molecularly imprinted polymers (MIPs) have emerged as powerful synthetic receptors capable of recognizing specific (bio)molecules through tailor-made binding sites. Traditionally based on poly(methyl acrylates) and hydrogen bonding, their extension to π-conjugated systems offers new opportunities for functional integration. In this work, we explore this avenue by combining a plasmonic platform composed of gold nanoparticle dimers fabricated by electron-beam lithography with a PEDOT-based imprinted polymer. The polymer growth is preferentially confined within the nanogaps of the dimers, forming molecularly imprinted cavities for methylene blue (MB). The detection of MB is achieved via both the localized surface plasmon resonance (LSPR) shift and surface-enhanced Raman Scattering (SERS), demonstrating sensitivity down to nanomolar concentrations. This approach illustrates the potential of π-conjugated MIPs for highly sensitive molecular sensing platforms.
Targeted delivery of drugs and hyperthermia in cardiovascular disease demand the accurate delivery of nanoparticles in complex arterial geometries. This paper introduces combined hybrid computational model that concomitantly examines the combined impact of nanoparticle radius and interparticle spacing on the thermal and mass transport characteristics of ternary bio-nanofluid flow under magnetohydrodynamic (MHD) effect. The ternary fluid is composed of blood fluid with suspended nanoparticles such as gold (Au), silver (Ag) silica (SiO2). The mathematical model accounts for geometric properties of nanoparticles such as nanoparticles radius and interparticle spacing for their practical utility for several medical interventions. The numerical analysis is based on hybrid computational strategy, where the solutions are determined through the bvp4c numerical solver and then a novel supervised multi hidden layers Artificial neural network (ANN) is integrated. The proposed model has a high predictive capability with an exceptionally high accuracy with the lowest Mean squared error and ideal regression coefficient MSE=9.6327×10-11, Gradient=9.5681e-08, Mu=1e-09, and R2=1.0. Some of the main findings indicate that less spacing between particles (h=0.1) leads to continuous networks of thermal percolation, which enhance the thermal conductivity by up to 35% to improve the efficiency of hyperthermia, whereas the larger nanoparticles (radius ≥1.5) offer a higher drug-loading capacity, yet the rate of heat transfer decreases by 15-20%. Optimization of the magnetic parameter (M=0.1-0.7) also decreases flow velocity by 28% and extends the nanoparticle residence time at the stenosis by 35% which allows sustained drug delivery, results directly applicable to clinical-strength (1.5-3T) MRI-guided interventions. Radiation parameter (Rd=0.5-2.5) increases temperature of the arteries by 15-20% giving controllable thermal modulation to applications of hyperthermia. The proposed model predicts that optimal nanoparticle preparations (50 nm radius, 20 nm spacing) have to potential to lower the rate of restenosis by 30-40% in relation to traditional drug-eluting stents. The purpose of such an integrated computational-machine learning systems is to give quantitative advice to stent coating design, nanoparticle formulation, and optimization of treatment protocols, and has been directly used in biomedical interventions. The results can be used to offer practical advice to stent manufactures, interventional radiologist and pharmaceutical developers to create evidence-based cardiovascular therapy of the next generation.
Polygenic risk scores (PRS) stratify inherited cardiovascular risk, but their path to clinical implementation remains unclear. We aimed to develop and validate integrated PRS for 8 cardiovascular conditions and outline a framework for their clinical reporting. We analyzed genotype and clinical data from 245,394 All of Us Research Program participants. Publicly available PRS for 8 traits-coronary artery disease, atrial fibrillation, type 2 diabetes, venous thromboembolism (VTE), thoracic aortic aneurysm (TAA), extreme hypertension, severe hypercholesterolemia, and elevated lipoprotein(a)-were combined using PRSmix, an elastic-net approach. Integrated PRS were externally validated in 53,306 Mass General Brigham Biobank participants using logistic regression, adjusting for age, sex, and ancestry. Of 53,306 genotyped Mass General Brigham Biobank participants (55.6% women, mean age 53 ± 17 years), integrated PRS demonstrated robust discrimination and appropriate calibration across 8 cardiovascular traits. Comparing high genetic risk (top 10% of PRS distribution, or top 20% for rarer TAA and VTE) vs average risk (26th-75th percentiles, or 21st-80th percentiles for TAA and VTE) yielded ORs: coronary artery disease (3.7 [95% CI: 3.4-4.1]), type 2 diabetes (3.1 [95% CI: 2.8-3.3]), atrial fibrillation (3.0 [95% CI: 2.7-3.3]), VTE (1.9 [95% CI: 1.6-2.0]), TAA (1.7 [95% CI: 1.5-1.9]), hypertension (2.1 [95% CI: 1.8-2.3]), hypercholesterolemia (4.1 [95% CI: 3.7-4.5]), and lipoprotein(a) (41.0 [95% CI: 27.0-62.2]). Incorporating integrated PRS into clinical models improved risk classification, while prospective analyses confirmed significant associations with incident cardiovascular outcomes. Integrated PRS offer an implementable framework for genetic risk reporting, and are now available as a clinically orderable test. Broader prospective validation studies are needed to further establish clinical utility.
The development of natural preservatives from agricultural by-products is essential for sustainable food systems. This study valorized burdock leaves by extracting a bioactive polysaccharide with yield of 6.422%. A burdock leaf polysaccharide (BPS) was identified as an acidic heteropolysaccharide (molecular weight 1.616 × 105 Da) with a triple-helix structure, mainly composed of galactose and arabinose. It exhibited strong antioxidant activity and notable antibacterial efficacy against Escherichia coli and Staphylococcus aureus (minimum inhibitory concentration (MIC50) = 25 mg/mL). Mechanistic analyses revealed that BPS disrupted membrane integrity, elevated reactive oxygen species (ROS) levels, and induced DNA damage. When applied as a beef coating and stored in a refrigerator at 4 °C, BPS extended shelf-life from 6 to over 12 days by preserving color, limiting weight loss and pH increase, and suppressing bacterial growth and total volatile basic nitrogen (TVB-N) accumulation. These findings highlight BPS as a promising clean-label preservative derived from agricultural waste.
Ferroptosis is an iron-dependent form of regulated cell death characterized by excessive lipid peroxidation. Molecules like GPX4, ACSL4, and SLC7A11 form the core regulatory network. GPX4 inhibits lipid peroxide accumulation, ACSL4 promotes ferroptosis through lipid metabolism remodeling, and SLC7A11 confers resistance to ferroptosis by maintaining redox homeostasis. Ferroptosis has a dual role in cancer: inducing it eliminates cancer cells, while its evasion enhances drug resistance and metastasis. Targeting ferroptosis represents an emerging investigational direction for anticancer therapeutic development, with single-target agents, combination regimens, and nanocarrier-based delivery systems exhibiting preliminary tumor-suppressive signals across diverse preclinical model systems. Despite extensive research, existing reviews lack systematic integration of ferroptosis-tumor microenvironment (TME) crosstalk, comparative analysis of cancer type-specific ferroptosis sensitivity, and critical evaluation of recent clinical progress. This review addresses these gaps by synthesizing molecular mechanisms, cancer-specific roles, TME interactions, and therapeutic applications, along with a critical assessment of clinical translation barriers, providing a framework for ferroptosis-targeted cancer therapy.
Rare bi-allelic variation is a major contributor to human disease risk, yet its effects are difficult to study at scale in population cohorts owing to the limited number of individuals with putatively deleterious bi-allelic genotypes and the challenges of accurately phasing low-frequency variants. Here, we present recessive, gene-based analyses of rare and low-frequency variants in up to 948,690 exome- or whole-genome-sequenced individuals across six biobanks with linked electronic health records. Through statistical phasing, we inferred putatively damaging compound-heterozygous genotypes, increasing the number of bi-allelic damaging genotypes by 19%. Restricting to predicted loss-of-function (pLoF) variants, we identified 5,563 genes harboring bi-allelic genotypes, a 19.8% increase in putative knockouts. We then considered all low-frequency variants (minor allele frequency [MAF] <5%) and performed gene-based recessive association testing using putatively damaging bi-allelic genotypes, identifying 58 significant associations (false discovery rate [FDR] ≤1% or prec≤7.5 × 10-7) after meta-analysis and Cauchy combination of nonsynonymous annotations. Comparing recessive and additive models, we found 17 instances where recessive effects were more pronounced, including several previously unreported associations, such as HBB with heart failure (prec = 2.6 × 10-14; padd = 0.98), LECT2 with height (prec = 3.7 × 10-14; padd = 4.1 × 10-10), and ENSG00000267561 with height (prec = 2.9 × 10-9; padd = 0.37). This study demonstrates the potential of federated approaches to study the effects of rare bi-allelic variation.
Metabolic engineering for high-value compounds such as acarbose, a diabetes drug, requires systematic understanding of metabolic regulation. Here, we applied a multi-dimensional systems biology approach in Actinoplanes sp. SE50/110, a non-model acarbose-producing bacterium. We reconstructed an improved genome-scale metabolic model (iASE1267) with expanded metabolic coverage and a MEMOTE score of 80%, enabling more accurate phenotype predictions. Using a dual-objective OptRAM strain design strategy, we identified two sets of static engineering targets, including AcbR overexpression with adenylosuccinate lyase repression, and overexpression of dTDP-glucose 4,6-dehydratase with repression of 4-(cytidine 5'-diphospho)-2-methyl-D-erythritol kinase. Time-course metabolic modeling further revealed dynamic metabolic valves-ASPO1, PC, and PYK-governing flux redistribution. Integrating these targets, we reconstructed a core transcription-metabolism network and identified two pleiotropic negative transcription factors (TFs). Experimental validation of these TFs and metabolic genes increased acarbose titers by 18%-23%. This work establishes a framework integrating static/dynamic metabolic modeling with transcriptional networks for engineering non-model microbes.
Vaginal drug delivery in women's health remains underutilized and insufficiently studied, largely due to the complexity and dynamic nature of the vaginal microenvironment. Variations in vaginal pH, hormonal levels, and microbiota composition introduce significant biological variability, complicating formulation design and contributing to inconsistent therapeutic outcomes and poor patient adherence. Conventional vaginal formulations often fail to account for these individual differences, highlighting the need for more adaptive and predictive approaches. Emerging advances in artificial intelligence (AI) and machine learning (ML) offer promising strategies to address these challenges by enabling multi-parameter, data-driven formulation development that explicitly considers biological variability. Despite their transformative potential, the application of AI/ML in vaginal drug delivery remains limited. This review addresses this gap by examining the emerging role of AI/ML in the design, optimization, and development of next-generation vaginal formulations, including mucoadhesive, mucus-penetrating, bio-responsive, and controlled-release systems. Key factors influencing formulation performance, such as vaginal microbiota composition, are discussed, and ethical, regulatory, and technical challenges relevant to integrating AI/ML into vaginal drug delivery are also considered. Although research is in its early stages, existing studies indicate that AI/ML integration can enhance predictability, efficiency, and reproducibility in vaginal formulation development. Finally, we highlight the potential of developing personalized medicines through the integration of AI/ML and digital health monitoring devices to create patient-centered vaginal drug delivery systems, thereby advancing therapeutic outcomes and transforming women's health.
Injectable hydrogels constitute a highly versatile and promising class of biomaterials for therapeutic use in both acute and chronic wounds. Engineered to mimic the structural and functional attributes of the native extracellular matrix (ECM), these hydrogels form a biomimetic, hydrated three-dimensional network that facilitates critical wound healing processes, such as cellular infiltration, angiogenesis, and extracellular matrix remodeling. Composed of a broad spectrum of biocompatible polymers, including naturally derived polysaccharides, such as alginate, hyaluronic acid, and chitosan, along with various synthetic polymers such as polyethylene glycol and polyvinyl alcohol injectable hydrogels can be precisely tailored in terms of viscoelastic properties, degradation kinetics, and bio-functionalization to meet specific clinical requirements. Their minimally invasive administration through a syringe or catheter, combined with in situ gelation triggered by physiological stimuli, such as pH, temperature, or ionic strength, allows conformal adaptation to complex wound geometries while minimizing surgical trauma. Furthermore, these hydrogels serve as adaptable scaffolds for the spatial and temporal controlled delivery of therapeutic agents, including growth factors, antimicrobial compounds, stem cells, and extracellular vesicles, enabling dynamic modulation of the wound microenvironment. Such functionalities facilitate regulated inflammation, oxidative stress mitigation, and tissue regeneration. Despite their substantial potential, challenges persist regarding mechanical stability under physiological load, immunomodulatory capacity, and regulatory pathways for clinical translation. Recent advancements-such as the integration of nanostructured components, stimuli-responsive crosslinking mechanisms, and bio-orthogonal chemistries-have expanded the functional capabilities of injectable hydrogels and improved their therapeutic efficacy. This review offers a comprehensive analysis of the present status and future directions of injectable hydrogel systems for wound healing, emphasizing innovative material strategies, delivery mechanisms, and translational hurdles. These insights highlight the critical role of injectable hydrogels in advancing the development of next-generation, precision-guided wound management technologies.
Microneedle biosensors enable dynamic monitoring of interstitial fluid biomarkers, but remain constrained by sensing interface susceptibility to motion artifacts and the prohibitive energy consumption of wireless cloud-based processing. Here, we report a bio-inspired, self-anchoring microinterventional in-sensor computing system. By leveraging a starfish-inspired suction cup-microneedle self-anchoring mechanism, the system effectively counteracts microneedle extrusion, attenuating signal fluctuations by 38-fold and enhancing signal intensity by up to 5.49-fold compared to conventional planar devices. Crucially, the high-fidelity data acquisition reduces the computational burden, enabling the deployment of a lightweight algorithm (43 KB) on a coin-sized embedded circuit, achieving 98.68% diagnostic accuracy and a 120-h battery life via local closed-loop feedback. Validation in a porcine model confirmed the system's capability to capture continuous biochemical dynamics. This co-design of a robust biomimetic interface and lightweight deep learning paves the way for next-generation wearables capable of performing high-fidelity, on-chip metabolic risk stratification in dynamic daily settings.