Table1_Predicting gene expression from histone modifications with self-attention based neural networks and transfer learning.XLSX

It is well known that histone modifications play an important part in various chromatin-dependent processes such as DNA replication, repair, and transcription. Using computational models to predict gene expression based on histone modifications has been intensively studied. However, the accuracy of the proposed models still has room for improvement, especially in cross-cell lines gene expression prediction. In the work, we proposed a new model TransferChrome to predict gene expression from histone modifications based on deep learning. The model uses a densely connected convolutional network to capture the features of histone modifications data and uses self-attention layers to aggregate global features of the data. For cross-cell lines gene expression prediction, TransferChrome adopts transfer learning to improve prediction accuracy. We trained and tested our model on 56 different cell lines from the REMC database. The experimental results show that our model achieved an average Area Under the Curve (AUC) score of 84.79%. Compared to three state-of-the-art models, TransferChrome improves the prediction performance on most cell lines. The experiments of cross-cell lines gene expression prediction show that TransferChrome performs best and is an efficient model for predicting cross-cell lines gene expression.</p

Bletilla striata Polysaccharide-/Chitosan-Based Self-Healing Hydrogel with Enhanced Photothermal Effect for Rapid Healing of Diabetic Infected Wounds via the Regulation of Microenvironment

The management of diabetic ulcers poses a significant challenge worldwide, and persistent hyperglycemia makes patients susceptible to bacterial infections. Unfortunately, the overuse of antibiotics may lead to drug resistance and prolonged infections, contributing to chronic inflammation and hindering the healing process. To address these issues, a photothermal therapy technique was incorporated in the preparation of wound dressings. This innovative solution involved the formulation of a self-healing and injectable hydrogel matrix based on the Schiff base structure formed between the oxidized Bletilla striata polysaccharide (BSP) and hydroxypropyltrimethylammonium chloride chitosan. Furthermore, the introduction of CuO nanoparticles encapsulated in polydopamine imparted excellent photothermal properties to the hydrogel, which promoted the release of berberine (BER) loaded on the nanoparticles and boosted the antibacterial performance. In addition to providing a reliable physical protection to the wound, the developed hydrogel, which integrated the herbal components of BSP and BER, effectively accelerated wound closure via microenvironment regulation, including alleviated inflammatory reaction, stimulated re-epithelialization, and reduced oxidative stress based on the promising results from cell and animal experiments. These impressive outcomes highlighted their clinical potential in safeguarding the wound against bacterial intrusion and managing diabetic ulcers

Additional file 1 of SKP alleviates the ferroptosis in diabetic kidney disease through suppression of HIF-1α/HO-1 pathway based on network pharmacology analysis and experimental validation

Additional file 1: Table S1. Specific information on compounds in The Herb-ingredient-targets gene network