Search Results (21,362)

Digital Twin (DT) technology has emerged as a transformative approach in pipeline engineering, enabling real-time monitoring, predictive analytics, and enhanced decision-making across the asset lifecycle. This review critically examines recent advancements in the application of digital twins for pipeline systems, with a particular focus on condition monitoring, leak detection, corrosion assessment, and predictive maintenance. The study synthesizes findings from a wide range of literature to identify key enabling technologies, including Internet of Things (IoT) sensors, data-driven modeling, computational fluid dynamics (CFD), and machine learning algorithms. Special attention is given to the integration of physics-based and data-driven models for improving the accuracy and reliability of digital twin frameworks. In addition, this paper proposes a unified reference architecture for pipeline digital twins, supported by a mathematical formulation of synchronization and a comparative synthesis of existing approaches. The review highlights how digital twins facilitate early fault detection and operational optimization by continuously synchronizing physical assets with their virtual counterparts. The review also emphasizes the importance of uncertainty-aware and reliability-informed digital twin frameworks for robust decision-making in safety-critical pipeline applications. Applications in subsea, oil and gas, and water distribution pipelines are explored, demonstrating the versatility of DT systems under different environmental and operational conditions. Despite significant progress, challenges remain in data integration, model validation, scalability, and cybersecurity. Furthermore, the lack of standardized architectures and interoperability frameworks limits widespread adoption. This paper concludes by outlining future research directions, including the development of hybrid modeling techniques, edge computing integration, and AI-driven autonomous decision systems. Overall, digital twin technology represents a paradigm shift in pipeline engineering, offering substantial potential to enhance safety, efficiency, and sustainability in complex infrastructure systems. Full article

Background: Patients with malignant diseases receiving chronic opioid therapy are particularly susceptible to drug–drug interactions because of extensive polypharmacy, multimodal anticancer treatment, supportive care, and comorbidities. This study aimed to determine the prevalence of potentially clinically relevant drug–drug interactions and to identify factors associated with their occurrence. Methods: This exploratory observational cross-sectional pilot study included 49 adult oncology patients receiving chronic opioid therapy. Complete medication regimens were screened using the Medscape Drug Interaction Checker and Lexicomp. Lexicomp category D and X interactions were analysed descriptively. Because category D interactions were nearly universal and category X interactions were rare, the presence of at least one Medscape “Serious—Use Alternative” interaction was used pragmatically as the binary outcome for regression modelling. Potential predictors were assessed using univariable binary logistic regression and a parsimonious adjusted model with covariates selected using a clinically informed approach. Because of quasi-complete separation, Firth’s penalized-likelihood logistic regression was used as the primary adjusted analysis. Results: Lexicomp category D interactions were identified in 48 of 49 patients (98.0%), whereas category X interactions were present in 2 patients (4.1%). Medscape serious interactions were detected in 33 patients (67.3%). In the Firth-adjusted model, female sex was associated with lower odds of a Medscape serious interaction (adjusted OR 0.12, 95% CI 0.02–0.52), while cardiovascular disease was associated with higher odds (adjusted OR 5.16, 95% CI 1.27–26.31). Stage IV disease showed a positive but statistically non-significant association, and total medication count was not independently associated with the outcome in sensitivity analysis. Because of the small pilot sample and the resulting wide confidence intervals, these findings should be interpreted as exploratory and hypothesis-generating. Conclusions: Oncology patients receiving chronic opioid therapy had a high burden of potentially clinically relevant drug–drug interactions, predominantly Lexicomp category D interactions, warranting consideration of therapy modification and individualized monitoring rather than absolute avoidance. Regular medication review, use of complementary interaction databases, and individualized clinical assessment may improve pharmacotherapy safety in this vulnerable population. Full article

Background: Antimicrobial resistance represents a growing global health challenge, necessitating the development of effective non-antibiotic antimicrobial approaches. Antimicrobial photodynamic therapy (aPDT) has emerged as a promising localized antimicrobial strategy owing to its broad-spectrum activity, biofilm-targeting capability, and low propensity to induce resistance. However, its clinical translation remains restricted by limited photosensitizer (PS) performance, insufficient light penetration, oxygen dependency, biofilm-associated barriers, and the lack of standardized treatment protocols. Methods: This narrative review summarizes recent strategies developed to enhance the efficacy and translational potential of aPDT, including PS engineering, nanomaterial- and non-nanomaterial-based delivery systems, advanced light-source technologies, hypoxia-modulating approaches, and synergistic therapeutic strategies. In addition, current challenges associated with regulatory approval, manufacturing scalability, treatment standardization, and clinical implementation are discussed. Results: Recent advances have transformed aPDT from a conventional PS–light–oxygen system into a multifunctional antimicrobial platform. Emerging approaches improve bacterial targeting, biofilm penetration, reactive oxygen species generation, oxygen utilization, and therapeutic precision. Nevertheless, many advanced systems remain at the preclinical stage due to complexity, cost, safety concerns, and insufficient clinical validation. Conclusions: aPDT should be considered a targeted therapeutic option for accessible, localized, and biofilm-associated infections rather than a replacement for systemic antimicrobial therapy. Future clinical translation will depend on balancing technological innovation with biosafety, scalability, and protocol standardization. Strategies integrating intelligent PS design, oxygen regulation, and clinically feasible synergistic approaches may provide promising pathways toward the broader application of aPDT in antimicrobial management. Full article

Cardiac myosin-binding protein C (cMyBP-C) is a cardiac-restricted sarcomeric protein; after cardiomyocyte injury, circulating intact cMyBP-C and/or cMyBP-C fragments, collectively referred to here as the cMyC biomarker signal, appear rapidly in blood. In suspected acute coronary syndrome (ACS), its most important potential role is not as another marker of injury but as a decision-enhancing biomarker beyond symptoms, electrocardiography, cardiac troponin T and I concentrations measured with high-sensitivity assays (hs-cTnT and hs-cTnI), time from pain onset, and pre-test probability. This narrative review separates three clinical tasks frequently conflated in the biomarker literature: diagnosis of acute myocardial infarction, emergency-department triage, and prediction of short-term or post-infarction risk. We integrate cMyBP-C sarcomeric architecture, N-terminal regulatory biology, phosphorylation, proteolysis, circulating fragments, assay epitopes, analytical stability, diagnostic algorithms, point-of-care testing, ST-segment elevation myocardial infarction reperfusion biology, and major confounders including renal dysfunction, heart failure, age, sex, and chronic ventricular remodeling. Current evidence supports further evaluation of cMyC as an adjunct in early presenters and accelerated diagnostic pathways. However, diagnostic safety and efficacy have not been consistently reproduced across platforms and populations, and external validation—particularly of rule-out performance—remains insufficient for routine clinical use. Recurrent injury assessment and post-infarction risk phenotyping remain promising but incompletely validated applications. Before guideline adoption, cMyC needs phenotype-specific, multicenter implementation trials demonstrating incremental net benefit, cost-effectiveness, and patient-level safety compared with contemporary hs-cTnT- and hs-cTnI-based clinical decision algorithms. Full article

The global wine industry generates substantial volumes of by-products, leading to significant environmental and economic challenges. This study explores the sustainable valorization of two key by-products from Moldovan red wine production—yeast lees and grape skin powder derived from autochthonous grape varieties Rară Neagră (RN) and Fetească Neagră (FN)—within the circular economy paradigm. Comprehensive physicochemical analyses demonstrated that yeast lees are a rich source of bioactive β-glucans (20.17–21.91%, w/w wet lees), proteins, and triglycerides; β-glucans of this type are reported in the literature to confer immunomodulatory and antioxidant properties, although these bioactivities were not directly evaluated in the present study. Grape skin powders exhibited high dietary fibre content and polyphenolic compounds, with FN showing superior total polyphenol content and antioxidant activity compared to RN. Advanced extraction techniques using green solvents such as glycerol, propylene glycol, and ethanol, including ultrasound-assisted methods, optimized polyphenol recovery while maintaining extract stability. Incorporation of these extracts into innovative oil-in-water cosmetic emulsions revealed notable physicochemical characteristics, with the RN extracts enhancing emulsion firmness via polyphenol–xanthan gum interactions, and the FN extracts providing high antioxidant potential without compromising texture. A preliminary single-subject biophysical assessment suggested good short-term skin compatibility, with hydration improvement and reduced transepidermal water loss in several formulations and no visible pigmentation; these observations require confirmation in a larger volunteer panel with dedicated safety testing. This work provides laboratory-scale evidence of the dual environmental and functional potential of recovering and applying Moldovan winery by-products, supporting their further development—pending pilot-scale and economic validation—as bio-ingredients for the food, cosmetic, and pharmaceutical sectors within a circular bioeconomy framework. Full article

Background: Periodontitis is a chronic inflammatory disease characterized by a dysbiotic oral microbiome that leads to progressive periodontal tissue destruction. Current treatment primarily relies on scaling and root planing (SRP); however, this approach does not always completely eliminate pathogenic microorganisms or adequately modulate the host inflammatory response. Cold atmospheric plasma (CAP) generates reactive oxygen and nitrogen species (RONS) with potential antimicrobial, antibiofilm, and immunomodulatory properties, making it a promising adjunctive therapeutic modality for the management of periodontitis. Literature Search Strategy: A comprehensive literature search was conducted using PubMed/MEDLINE, Scopus, Web of Science, and Embase to identify relevant studies published between January 2000 and June 2025. The search included in vitro, in vivo, and clinical studies evaluating the antimicrobial, immunomodulatory, and regenerative effects of CAP in periodontitis and related periodontal models. Reference lists of relevant articles were also manually screened to identify additional eligible studies. Objectives: This review aims to provide a comprehensive overview of the antimicrobial and immunomodulatory effects of CAP on periodontitis and to evaluate its potential as an adjunctive treatment modality. Furthermore, the review examines the regenerative potential and underlying mechanisms of CAP, while also addressing its clinical safety and biocompatibility. Key Findings: Preclinical evidence indicates that CAP reduces bacterial viability, disrupts biofilm architecture, and modulates host immune responses through RONS-mediated signaling pathways. In animal models of periodontitis, CAP has been shown to enhance markers of periodontal tissue repair, reduce the expression of pro-inflammatory cytokines, increase LC3-associated phagocytosis in macrophages, and accelerate wound healing. Clinical studies evaluating CAP as an adjunct to SRP have reported improvements in clinical attachment level (CAL) and reductions in the recolonization of periodontal pathogens. Nevertheless, substantial heterogeneity in CAP treatment parameters and a lack of long-term safety and efficacy data limit direct comparisons across studies and highlight the need for further well-designed clinical investigations. Conclusions: CAP appears to be a potentially useful adjunct to SRP in the management of periodontitis. Current preclinical evidence demonstrates significant antimicrobial, immunomodulatory, and regenerative effects; however, clinical evidence remains limited. Further high-quality clinical trials are required to validate these findings, establish standardized treatment protocols, optimize therapeutic parameters, and facilitate the development of regulatory frameworks for clinical implementation. Full article

Urban areas contribute a dominant share of global carbon dioxide (CO₂) emissions, yet city-scale emission estimates remain highly uncertain due to the limited spatial and temporal resolution of existing observation methods. This review paper examined the potential of horizontally scanning Differential Absorption LiDAR (DIAL) systems to address these gaps in urban CO₂ monitoring. We reviewed current top-down and bottom-up approaches, identified their limitations in resolving intra-urban variability, and outlined a conceptual framework for deploying compact, eye-safe CO₂ DIAL systems on elevated urban structures. We reviewed methods that combined horizontal CO₂ scanning with wind measurements to enable high-resolution mapping of CO₂ distributions, assessment of boundary-layer effects, and estimation of urban carbon fluxes using established mass-balance and inversion techniques. Conceptual deployment scenarios, illustrated using Boston as an example, demonstrated how a single elevated LiDAR unit could provide near-city-wide coverage and capture neighborhood-scale emission patterns. We further discussed calibration strategies, safety considerations, and integration with existing sensor networks and column observations. This review concluded horizontal CO₂ LiDAR represents a promising pathway toward high-resolution, observation-based urban carbon monitoring, offering a complementary tool to existing methods and supporting future efforts to verify emission inventories, evaluate mitigation policies, and advance city-scale carbon management. Full article

Essential oils are widely recognised for their bioactive properties and therapeutic effects. Despite their potential, their practical application is often limited by intrinsic physicochemical constraints, including volatility, chemical instability, hydrophobicity and, most of all, compositional complexity. The development of effective delivery systems is therefore key to stabilise essential oil constituents, improve bioavailability and enable controlled release. Here we provide a comprehensive analysis of formulation strategies for essential oils, focusing on emulsification and encapsulation techniques. Emulsion-based delivery systems, including nanoemulsions and microemulsions, are discussed in relation to their interfacial behaviour, surfactant interactions and preparation methods. In parallel, encapsulation systems, such as vesicular systems, lipid-based carriers, polymeric nanoparticles and inclusion complexes, are evaluated according to their structural characteristics, protective capabilities, encapsulation efficiencies and release profiles. Collectively, these approaches are examined in the context of their influence on the stability, functionality, and overall performance of essential oil formulations. Emphasis is placed on essential oil-specific challenges that arise when preparing a formulation, especially the heterogenous nature of their chemical composition and its impact on physicochemical properties, partitioning, loading capacity, stability and safety. To contextualise these principles, representative examples of essential oil-based formulations (e.g., thyme, clove, rosemary and mint), formulated singular metabolites (e.g., carvacrol, eucalyptol, limonene and eugenol) and essential oil patent applications (>20,000) and products are outlined to illustrate behaviour and performance within the respective delivery system. Overall, the fundamental design considerations that guide the rational selection, preparation and evaluation of formulations for essential oil-based applications are discussed. Considerations for future directions are also outlined. Full article

The objective of this study was to assess the performance of posture recognition systems based on artificial intelligence (AI) using deep learning (DL) and machine learning (ML) approaches for the prevention of work-related musculoskeletal disorders (WMSDs) in manufacturing. The study was conducted as a systematic review and meta-analysis following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Four open-access databases were screened in May 2026 without date restrictions: PubMed/MedLine, Google Scholar, ScienceDirect, and IEEE Xplore. The selected studies had to be original, peer-reviewed studies written in English. The study had to evaluate the performance of an AI posture recognition system (ML or DL) for the prevention of WMSDs in manufacturing using at least one of the following parameters: accuracy, specificity, sensitivity, precision, or F1 score. The risk of bias for each included study was assessed using PROBAST (Prediction Model Study Risk of Bias Assessment Tool). A meta-analysis was conducted to pool the values of the five performance metrics separately. Logit and Freeman-Tukey transformations were applied prior to pooling, and the results were compared after back-transformation. Cochran’s Q test, the I² statistic, and inter-study variability (τ²), computed using the generalized inverse variance method with the restricted maximum likelihood model, were applied to assess heterogeneity. Forest plots including pooled values with 95% confidence intervals were used to present the results. Subgroup analyses and meta-regressions were performed to test the effect of AI methods and ergonomic tools on performance and to explore potential causes of heterogeneity. Finally, publication bias (Egger’s test) and certainty of evidence (GRADE method—Grading of Recommendations Assessment, Development, and Evaluation) were assessed to ensure the generalizability of the results. Ten studies were included: Among the 200 studies identified through database searches and the snowball method, 12 met the inclusion criteria and were selected. Two studies were excluded due to an insufficient number of participants, bringing the final number of studies considered to 10. The logit transformation yielded the best overall fit for the normality of the data distribution for the use of a random-effects model. High posture detection performance was observed, with pooled values ranging from 84.78% (95% CI: 80.23–88.19%, specificity with Freeman-Tukey) to 93.40% (95% CI: 89.57–95.89%, precision with logit). The values obtained with logit were higher than those obtained with Freeman-Tukey, with differences ranging from 2.75% to 4.75% across all performance metrics. Meta-regression showed that DL outperformed ML for all metrics, with differences ranging from 5% to 17%. RULA and REBA achieved better performance than other ergonomic tools. However, high heterogeneity (I² > 90%) and substantial inter-study variability were observed in all analyses, and a very low level of evidence was evidenced for all performance parameters. Consequently, the results should be interpreted with caution, particularly regarding the deployment of the systems in real-world settings. Future work could strengthen training and testing procedures on datasets, as well as external validation. These aspects are essential for effective use in manufacturing environments to ensure operator safety by reducing their exposure to WMSD risks associated with work postures. Full article

Spring viremia of carp virus (SVCV) poses a major threat to aquaculture industry, triggering severe economic losses and substantial damage to fish health. Effective antiviral therapeutic approaches are currently insufficient in aquaculture, highlighting an urgent demand for viable intervention strategies. This study assessed the antiviral and immunomodulatory potential of Houttuynia cordata against SVCV in common carp. Combined cellular and in vivo fish experiments demonstrated that Houttuynia cordata markedly suppresses SVCV replication, reduces viral titer, and enhances disease resistance of carp. The extract appears to suppress RARα-A, upregulate IFN-1/CXCL11, and recruit T lymphocytes via CXCL11, thereby enhancing innate and adaptive immunity against SVCV. Experimental results indicate that Houttuynia cordata alleviates hemorrhagic symptoms induced by SVCV infection. Dietary supplementation with this herb realizes dual functions of viral suppression and immune regulation for carp aquaculture. The findings provide a practical prevention and control strategy against SVCV infection and offer novel insights for research on green antiviral agents. Furthermore, this study verifies the potential value of Houttuynia cordata in improving biological safety and facilitating sustainable development of aquaculture, laying a solid foundation for the development of eco-friendly antiviral preparations. Full article

Background/Objectives: Nonselective beta blockers (NSBBs) are widely used in cirrhosis for portal hypertension, but their safety in older adults with decompensated cirrhosis remains uncertain. Methods: We conducted a retrospective multicenter cohort study using the TriNetX Research Network. Adults aged 30–90 years with decompensated cirrhosis were stratified into three groups: 30–60 years, 61–90 years, and a prespecified subgroup of 81–90 years. NSBB exposure (propranolol, nadolol, or carvedilol) within 30 days of decompensation was assessed, and 1:1 propensity score matching was performed within each age group. The primary outcome was 12-month all-cause mortality. Secondary outcomes included acute kidney injury (AKI), electrolyte abnormalities, and acute care utilization. Results: After matching, 40,074 pairs (30–60 years), 96,084 pairs (61–90 years), and 19,024 pairs (81–90 years) were analyzed. NSBB use was associated with lower 12-month mortality in ages 30–60 (4.0% vs. 4.6%; RR 0.86, 95% confidence interval [CI] 0.81–0.92). 61–90-year cohort, mortality was similar between groups (7.4% vs. 7.4%; RR 0.99, 95% CI 0.96–1.02). Patients aged 81–90 years, NSBB use was associated with higher mortality (10.9% vs. 10.0%; RR 1.08, 95% CI 1.02–1.16). Across age strata, NSBB was associated with increased AKI, hyperkalemia, and acute care visits. Conclusions: NSBB therapy was associated with reduced mortality in younger adults (30–60 years) but not in older patients (61–90 years) and was associated with increased mortality among those aged 81–90 years. These findings support individualized NSBB use and suggest that patient age should be considered when weighing potential benefits and risks. Full article

Background: Hereditary angioedema (HAE) is a rare, potentially life-threatening disease in which delayed recognition and inappropriate medication use may result in preventable morbidity and mortality. We aimed to assess physicians’ knowledge regarding HAE-related triggers, clinical features, and management strategies across multiple medical specialties. Methods: This single-center, cross-sectional survey was conducted among 350 physicians at a tertiary training and research hospital. A structured electronic questionnaire assessed knowledge of HAE pathophysiology, diagnosis, medication-related triggers, and management. A predefined composite knowledge score (0–14) was calculated. Results: Although self-reported familiarity with HAE was high, overall disease-specific knowledge was limited. Awareness of critical medication-related triggers—including angiotensin-converting enzyme inhibitors, dipeptidyl peptidase-4 inhibitors, and estrogen-containing therapies—was low across all specialties, with no significant between-group differences. Substantial knowledge gaps were identified in the recognition of clinical features, diagnostic evaluation, and acute management. In multivariable analysis, prior clinical exposure to HAE patients was the only independent predictor of higher knowledge scores (B = 1.097, p = 0.001), whereas specialty group, gender, and years of professional experience were not independently associated with knowledge scores. However, the regression model explained only a small proportion of the variance in knowledge scores. Conclusions: Significant gaps in clinically relevant HAE knowledge persist among physicians from multiple medical specialties. Prior clinical exposure was associated with higher knowledge scores, whereas specialty group, gender, and years of professional experience were not independently associated with physician knowledge. Targeted, practice-oriented educational interventions focusing on medication-related triggers and acute management may help bridge these knowledge gaps, complement experiential learning, and ultimately enhance patient safety. Full article

Background/Objectives: Pharmacovigilance workflows rely heavily on unstructured text across diverse sources. Here, we systematically reviewed how large language models (LLMs) are being explored as support tools for adverse drug reaction (ADR) detection, extraction, triage, and documentation, highlighting their potential for precision medicine and big data-enabled safety monitoring. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, we systematically searched PubMed, Scopus, and Web of Science for studies published between January 2022 and March 2026. Ultimately, 83 empirical studies satisfied the inclusion criteria. A narrative synthesis was conducted to address methodological heterogeneity across these studies. Results: LLM applications were concentrated in constrained information-extraction and classification tasks, including signal evaluation, clinical-note extraction, social media surveillance, and literature screening. Quantitative performance varied substantially by system design: error-correction prompting yielded an F1-score of 0.921 for ADR named entity recognition, whereas retrieval-augmented generation improved data-retrieval accuracy from 8.3% to 78.3%. Most studies were retrospective, benchmark-based, or proof-of-concept evaluations. Across 581 paired pre-consensus domain judgements, observed inter-rater agreement was 90.4% and Cohen’s κ was 0.837 (95% CI 0.772–0.895). Hallucination, low specificity, prompt sensitivity, narrow datasets, and weak external validation remained common limitations. Conclusions: Current evidence supports supervised, task-specific applications of LLMs for extraction, triage, retrieval, and documentation rather than autonomous pharmacovigilance decision-making. Prospective evaluation, external validation, transparent reporting, and accountable human oversight are required before high-stakes clinical or regulatory deployment. Full article

Submarine sand waves are widespread on shallow continental shelves. Their complex morphology and potential mobility create challenges for engineering surveys, navigation safety, and seabed stability assessment. Multibeam surveys provide accurate bathymetry but are costly and spatially limited, whereas satellite-based methods offer broader coverage but remain challenging in complex sand wave fields. Here, we propose a spatial-sequential 2DCNN–LSTM model for retrieving submarine sand wave bathymetry from Sentinel-2 surface reflectance imagery. The model represents each target point as a sequence of local multispectral image patches, allowing convolutional layers to extract two-dimensional textural features and LSTM layers to learn profile-scale rhythmic continuity associated with sand wave morphology. The model was trained using multibeam bathymetry and applied to a large extrapolation area of approximately 4000 km² on the Taiwan Banks. Evaluation on the large extrapolated area against in situ bathymetric data achieved a root mean square error (RMSE) of 3.78 m, a mean absolute error (MAE) of 2.99 m, and a mean relative error (MRE) of 9.1%. The results demonstrate that sand wave-induced optical textures can provide useful information for broad-scale bathymetric reconstruction, although model performance remains dependent on image texture visibility controlled by hydrodynamic, illumination, and atmospheric conditions. This framework offers a cost-effective approach for satellite-based monitoring of large submarine sand wave fields, providing a new perspective for engineering applications. Full article

Wound healing is a multifaceted biological process comprising the phases of hemostasis, inflammation, proliferation, and remodeling, all of which require supportive microenvironment for optimal tissue regeneration. Biopolymer-based hydrogels, derived from materials such as cellulose and its derivatives, chitosan, alginate, and hyaluronic acid, have emerged as promising wound dressing materials due to their excellent biocompatibility, biodegradability, moisture-retention capacity, and potential to mimic the native extracellular matrix. The structural characteristics, wound healing functions, and underlying mechanisms of these biopolymers are critically examined and summarized in tabular form. The review further highlights the incorporation of natural and synthetic therapeutic agents, growth factors, stem-cell-derived products, and peptides into biopolymer matrices to enhance therapeutic efficacy. The examined research findings indicate significant increases in fluid intake, moisture retention, antibacterial activity, angiogenesis, collagen deposition, tissue regeneration, and wound healing rates. Translational difficulties, regulatory issues, clinical research, and new patent activity pertaining to advanced wound healing biomaterials are also covered in the review. Despite tremendous improvements, issues still exist in bulk manufacturing, long-term safety, reproducibility, mechanical stability, and clinical validation. Future innovations are anticipated to concentrate on smart, multipurpose, and customized hydrogel systems that can integrate drug delivery, biosensing, and regenerative capabilities while reacting dynamically to wound microenvironments. Overall, biopolymer-based hydrogels are a flexible, rapidly developing platform with significant promise to improve next-generation skin tissue engineering and change the treatment of both acute and chronic wounds. Full article