Instytut Podstawowych Problemów Techniki

Streszczenie:

Adhesive and stretchable nanofibrous hydrogels have attracted extensive attraction in wound dressings, especially for joint wound treatment. However, adhesive hydrogels tend to display poor stretchable behavior. It is still a significant challenge to integrate excellent adhesiveness and stretchability in a nanofibrous hydrogel. Herein, a highly adhesive, stretchable, and breathable nanofibrous hydrogel was developed via an in situ hybrid cross-linking strategy of electrospun nanofibers comprising dopamine (DA) and gelatin methacryloyl (GelMA). Benefiting from the balance of cohesion and adhesion based on photocross-linking of methacryloyl (MA) groups in GelMA and the chemical/physical reaction between GelMA and DA, the nanofibrous hydrogels exhibited tunable adhesive and mechanical properties through varying MA substitution degrees of GelMA. The optimized GelMA60-DA exhibited 2.0 times larger tensile strength (2.4 MPa) with an elongation of about 200%, 2.3 times greater adhesive strength (9.1 kPa) on porcine skin, and 3.1 times higher water vapor transmission rate (10.9 kg m–2 d–1) compared with gelatin nanofibrous hydrogels. In parallel, the GelMA60-DA nanofibrous hydrogels could facilitate cell growth and accelerate wound healing. This work presented a type of breathable nanofibrous hydrogels with excellent adhesive and stretchable capacities, showing great promise as wound dressings.

Słowa kluczowe:

nanofibrous hydrogels, hybrid cross-linking, adhesivity, stretchability, breathable capability

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Streszczenie:

In recent years, the main quest of science has been the pioneering of the groundbreaking biomedical strategies needed for achieving a personalized medicine. Ribonucleic acids (RNAs) are outstanding bioactive macromolecules identified as pivotal actors in regulating a wide range of biochemical pathways. The ability to intimately control the cell fate and tissue activities makes RNA-based drugs the most fascinating family of bioactive agents. However, achieving a widespread application of RNA therapeutics in humans is still a challenging feat, due to both the instability of naked RNA and the presence of biological barriers aimed at hindering the entrance of RNA into cells. Recently, material scientists’ enormous efforts have led to the development of various classes of nanostructured carriers customized to overcome these limitations. This work systematically reviews the current advances in developing the next generation of drugs based on nanotechnology-assisted RNA delivery. The features of the most used RNA molecules are presented, together with the development strategies and properties of nanostructured vehicles. Also provided is an in-depth overview of various therapeutic applications of the presented systems, including coronavirus disease vaccines and the newest trends in the field. Lastly, emerging challenges and future perspectives for nanotechnology-mediated RNA therapies are discussed.

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Streszczenie:

Biomedical image datasets are often scarce, expensive to annotate, and vary in
quality due to differences in imaging hardware and techniques. Generative models, particularly diffusion models, have recently demonstrated strong potential to
synthesize realistic medical images, offering a promising strategy for data augmentation. Yet, their application in clinical contexts requires careful validation, as trust,
interpretability, and reliability are essential when medical decisions are at stake.
This work introduces a human-in-the-loop framework for assessing the reliability
and risks of diffusion models in generating breast ultrasound cancer images. Using a Denoising Diffusion Probabilistic Model (D-DDPM), we jointly generate
ultrasound images and corresponding tumor masks from two benchmark datasets
(BUS-BRA and UDIAT). The evaluation pipeline integrates quantitative image
quality metrics (FID, IS, KID), radiologist interpretation, inter-rater agreement
(Cohen’s/Fleiss’ Kappa, Krippendorff’s Alpha), and alignment with large language
model (LLM) outputs. Results show that while D-DDPM can produce images
that are visually similar to real data and sometimes yield higher agreement among
experts than original images, inter-rater reliability remains weak, particularly for
malignant tumors. Radiologists consistently outperform LLMs in classification,
though majority voting across experts improves diagnostic accuracy. These findings highlight both the promise and risks of diffusion models in medical imaging,
including that synthetic ultrasound data can supplement limited datasets; however,
robust expert validation remains indispensable to ensure clinical trustworthiness
and safe integration.

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