Bioactive sucralfate-based microneedles promote wound healing through reprogramming macrophages and protecting endogenous growth factors.

Impaired wound healing due to insufficient cell proliferation and angiogenesis is a significant physical and psychological burden to patients worldwide. Therapeutic delivery of exogenous growth factors (GFs) at high doses for wound repair is non-ideal as GFs have poor stability in proteolytic wound environments. Here, we present a two-stage strategy using bioactive sucralfate-based microneedle (SUC-MN) for delivering interleukin-4 (IL-4) to accelerate wound healing. In the first stage, SUC-MN synergistically enhanced the effect of IL-4 through more potent reprogramming of pro-regenerative M2-like macrophages via the JAK-STAT pathway to increase endogenous GF production. In the second stage, sucralfate binds to GFs and sterically disfavors protease degradation to increase bioavailability of GFs. The IL-4/SUC-MN technology accelerated wound healing by 56.6 % and 46.5 % in diabetic mice wounds and porcine wounds compared to their respective untreated controls. Overall, our findings highlight the innovative use of molecular simulations to identify bioactive ingredients and their incorporation into microneedles for promoting wound healing through multiple synergistic mechanisms.

Correction: Predicting small molecules solubility on endpoint devices using deep ensemble neural networks.

[This corrects the article DOI: 10.1039/D3DD00217A.].

Predicting small molecules solubility on endpoint devices using deep ensemble neural networks.

Aqueous solubility is a valuable yet challenging property to predict. Computing solubility using first-principles methods requires accounting for the competing effects of entropy and enthalpy, resulting in long computations for relatively poor accuracy. Data-driven approaches, such as deep learning, offer improved accuracy and computational efficiency but typically lack uncertainty quantification. Additionally, ease of use remains a concern for any computational technique, resulting in the sustained popularity of group-based contribution methods. In this work, we addressed these problems with a deep learning model with predictive uncertainty that runs on a static website (without a server). This approach moves computing needs onto the website visitor without requiring installation, removing the need to pay for and maintain servers. Our model achieves satisfactory results in solubility prediction. Furthermore, we demonstrate how to create molecular property prediction models that balance uncertainty and ease of use. The code is available at https://github.com/ur-whitelab/mol.dev, and the model is useable at https://mol.dev.

14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon.

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.