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Integra Artificial Skin

✓ Approved

Integra LifeSciences · therapeutic agent

What is Integra Artificial Skin?

Integra Artificial Skin is a therapeutic agent developed by Integra LifeSciences. It is approved for therapeutic indications via others.

Drug Profile

CompanyIntegra LifeSciences
RouteOthers
StatusApproved
Sources: ClinicalTrials.gov, Integra LifeSciences

Therapeutic Indications

Integra Artificial Skin is developed for 3 unique indications across 3 therapeutic areas.

Therapeutic AreaConditionPhase
General disorders and administration site conditionsImpaired healing✓ Approved
Injury, poisoning and procedural complicationsThermal burn✓ Approved
Surgical and medical proceduresAdjuvant therapy✓ Approved

Related Research Articles

PubMedFrontiers in psychology2026-10-22

AI-contextualized self-efficacy and exercise engagement among vocational college students in China: a cross-sectional serial mediation analysis of exercise self-efficacy and exercise outcome expectations.

Hou Guoliang G, Gan Lintong L

It remains unclear whether artificial intelligence-contextualized self-efficacy, reflecting general efficacy beliefs about coping with difficulties and achieving goals with artificial intelligence support, is associated with exercise engagement. This study examined this association and specific and serial statistical indirect effects involving exercise self-efficacy and exercise outcome expectations. A cross-sectional questionnaire survey was conducted among 2,279 students recruited by convenience sampling from one vocational college in China. Participants completed self-report measures of artificial intelligence-contextualized self-efficacy, exercise self-efficacy, exercise outcome expectations, and exercise engagement. Confirmatory factor analysis examined the measurement model. PROCESS Models 4 (preliminary) and 6 (primary) evaluated indirect effects using 5,000 bootstrap resamples and percentile 95% confidence intervals. Supplementary latent-variable structural equation models examined the theory-specified mediator ordering and its reversal. All regression and structural models adjusted for gender, age, and grade. In the main PROCESS analyses, artificial intelligence-contextualized self-efficacy was positively associated with exercise engagement in the total-association model (β = 0.427, p < 0.001). After including exercise self-efficacy and exercise outcome expectations, the direct association remained significant (β = 0.246, p < 0.001). The specific indirect effects through exercise self-efficacy (B = 0.047, 95% confidence interval [0.0352, 0.0598]) and exercise outcome expectations (B = 0.048, 95% confidence interval [0.0378, 0.0586]) were significant. The serial indirect effect through exercise self-efficacy and exercise outcome expectations in the theory-specified order was also significant (B = 0.016, 95% confidence interval [0.0116, 0.0198]). Supplementary models yielded identical fit for the two mediator orderings. In this convenience sample, artificial intelligence-contextualized self-efficacy was associated with self-reported exercise engagement, with statistical indirect effects involving exercise self-efficacy and exercise outcome expectations. These minimally adjusted cross-sectional findings do not establish temporal ordering, causal mechanisms, or effects of actual artificial intelligence use.

PMID 42903132
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PubMedBJPsych bulletin2026-10-22

The intoxication of immediacy: psychiatrist's reflections on algorithmic psychiatry.

Sünkel-Laing Benjamin B

The rapid integration of artificial intelligence into psychiatric practice poses a profound yet under-examined threat to core clinical faculties. Drawing on 'cognitive debt', this article explores the 'atrophy' of the psychiatric encounter. By collapsing temporality and saturating uncertainty with instant information, artificial intelligence disrupts the capacity to tolerate ambivalence and dwell in the unknown. Referencing Jaspers' 'incomprehensible void' and Laing's 'ontological insecurity', the author argues that the current 'intoxication of immediacy' may recast psychiatrists as executors of algorithms. At this fleeting vantage point, psychoanalysis offers one frame within which erosion of the clinical self may be recognised and resisted.

PMID 42902733
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PubMedBioconjugate chemistry2026-10-22

Artificial Intelligence-Driven Multiscale Design of Protein-Based Materials.

Zhou Xinzhi X, Zhao Chen C, Zhang Qiang Q, Yao Yuan Y

Protein filaments, including silk, collagen, and amyloids, are natural building blocks whose hierarchical self-assembly produces adaptive mechanics and diverse functions. Conventional protein engineering has had limited success in resolving sequence-structure-function relationships across length scales. Recent advances in artificial intelligence (AI) now provide a framework for physics-informed multiscale design, from molecular motifs to functional macroscopic architectures. Machine-learning models can encode biophysical principles and guide the generation of protein-based materials. When combined with high-throughput experimentation, generative AI can integrate sequence design, structure prediction, and functional validation into closed-loop workflows. These workflows can accelerate the development of programmable biomaterials. This review summarizes AI-driven advances in protein-based materials and emerging links with autonomous robotics.

PMID 42902938
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PubMedJournal of biomedical materials research. Part B, Applied biomaterials2026-10-22

Beyond Antibacterial Activity: Engineering Multifunctional Biomaterials for Multidrug-Resistant Infections.

Bayer Ilker S IS

Antimicrobial resistance is increasingly limiting the effectiveness of conventional antibiotic therapy, particularly in chronic wounds, biofilm-associated infections, and implant-related infections. Antimicrobial biomaterials have emerged as promising alternatives; however, much of the field remains organized around material classes or pathogen killing alone, with comparatively limited consideration of the complex biological environment in which infection develops and persists. This review proposes a function-based framework in which antimicrobial biomaterials are designed as integrated therapeutic systems rather than as passive antimicrobial carriers. Six complementary engineering functions are identified: infection targeting, overcoming bacterial defenses, multimodal pathogen eradication, host immune modulation, tissue regeneration, and adaptive or stimuli-responsive behavior. For each function, key design principles, representative strategies, and translational challenges are critically examined. Particular attention is given to the extent to which these functions have been experimentally integrated, including a systematic assessment of demonstrated versus largely conceptual pairwise combinations. Because the relative importance of each function varies by clinical application, the review also considers how these priorities shift across tissue and device context, including skin and soft tissue, bone, and short-term devices such as indwelling catheters. Rather than emphasizing efficacy alone, the review also examines translational limitations and failure points, including inadequate activity against clinically relevant organisms, limited clinical validation of established antimicrobial technologies, and regulatory failure of promising antimicrobial candidates. These examples highlight the persistent gap between laboratory performance and clinical utility. The review further considers the emerging tension between broad-spectrum antimicrobial strategies and approaches designed to preserve the host microbiome through species-selective targeting. Special emphasis is placed on halogen-based antimicrobial systems, catalytic nanozymes, and the emerging application of artificial intelligence to biomaterial design. Finally, a prioritized roadmap is proposed for advancing multifunctional antimicrobial biomaterials from mechanistic proof-of-concept toward clinically relevant therapeutic systems, while identifying key assumptions and knowledge gaps that remain to be validated.

PMID 42902928
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PubMedJournal of patient safety2026-10-22

Artificial Intelligence in Pharmacy Services: A Comparative Bibliometric Analysis of United States and Global Research Trends.

Hao Chenye C, Fu Suqin S

Artificial intelligence (AI) technologies offer transformative potential to address pharmacy workforce challenges and enhance patient safety. This study aimed to characterize the global landscape of AI research in pharmacy services and identify gaps between research activity and patient safety priorities. A bibliometric analysis was conducted on AI-pharmacy services publications indexed in Web of Science (2015-2025). Performance analysis and science mapping techniques were applied using Bibliometrix software to compare United States and non-U.S. research output, thematic evolution, and collaboration patterns. A total of 4426 documents met inclusion criteria, comprising 1619 (36.58%) United States and 2807 (63.42%) non-U.S. publications. Both corpora exhibited exponential growth. The United States contributed 29.30% of global output with higher international coauthorship rates (35.08% versus 19.91%). Thematic mapping identified "prediction" and "risk" as motor themes in U.S. research. However, co-word analysis revealed a significant translational gap: critical patient safety themes-medication error prevention and workflow optimization-were conspicuously underrepresented. Neither "medication error" nor "patient safety" appeared among high-frequency keywords despite substantial clinical relevance. Current AI-pharmacy research prioritizes algorithmic development over patient safety implementation. Strategic realignment toward medication error prevention and safety-focused applications is warranted to realize AI's potential in enhancing pharmaceutical care quality.

PMID 42902889
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PubMedJournal of cutaneous medicine and surgery2026-10-22

Optimising Local Anaesthesia Techniques in Wide-Awake Skin and Soft Tissue Surgery: A Systematic Review and Meta-Analysis.

Maes Margaux M, Colpaert Steven D M SDM

This study aimed to provide an easy-to-implement guide with the most important techniques for administering local anaesthesia in wide-awake skin and soft tissue surgery, ensuring minimum pain and maximum efficiency. Until December 2024, several databases (MEDLINE, Embase and The Cochrane Library) were used to identify studies with following eligible interventions: needle and injection techniques, additives, communication methods, additional analgesic measures and environmental factors. Studies missing relevant outcomes, peer review, full-text availability or not written in English were excluded. Data extraction and risk of bias assessment (RoB 2 tool) were conducted independently. Meta-analysis was performed in JASP (v0.19.3) using a random-effects model, with mean differences (MDs) as effect measure. Heterogeneity was assessed (τ2, Q and I2), and meta-regression explored intervention type as moderator. Out of 48 included studies (3664 patients), 16 were eligible for meta-analysis. All interventions consistently showed pain reduction (MD -1.50, P = .010), and heterogeneity was high (I2 = 97.3). Meta-regression did not reveal significant differences between techniques. Techniques like warming, slower injection speeds and cryopreparation showed favourable results, whereas vibration, verbal framing and waiting after epinephrine administration offer promising, low-risk strategies. As wide-awake skin and soft tissue surgery continues to expand in use and scope, this review highlights practical, patient-centred strategies to optimise local anaesthetic injections, ensuring they are no longer perceived as 'the most painful part of the procedure'.

PMID 42902633
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