Wound healing is a highly evolved defense mechanism against infection and further injury. It is a complex process involving multiple cell types and biological pathways. Mammalian adult cutaneous wound healing is mediated by a fibroproliferative response leading to scar formation. In contrast, early to mid-gestational fetal cutaneous wound healing is more akin to regeneration and occurs without scar formation. This early observation has led to extensive research seeking to unlock the mechanism underlying fetal scarless regenerative repair. Building upon recent advances in biomaterials and stem cell applications, tissue engineering approaches are working towards a recapitulation of this phenomenon. In this review, we describe the elements that distinguish fetal scarless and adult scarring wound healing, and discuss current trends in tissue engineering aimed at achieving scarless tissue regeneration.
Graphical AbstractHighlights d CREB5 promotes resistance to AR inhibitors and androgen therapies in prostate cancer d CREB5 selectively enhances interaction of AR with target genes critical for survival d CREB5 is amplified or overexpressed in therapy-resistant metastatic prostate cancers d Targeting CREB5 is effective in patient-derived models that are therapy resistant SUMMARY Androgen-receptor (AR) inhibitors, including enzalutamide, are used for treatment of all metastatic castration-resistant prostate cancers (mCRPCs). However, some patients develop resistance or never respond. We find that the transcription factor CREB5 confers enzalutamide resistance in an open reading frame (ORF) expression screen and in tumor xenografts. CREB5 overexpression is essential for an enzalutamide-resistant patient-derived organoid. In AR-expressing prostate cancer cells, CREB5 interactions enhance AR activity at a subset of promoters and enhancers upon enzalutamide treatment, including MYC and genes involved in the cell cycle.In mCRPC, we found recurrent amplification and overexpression of CREB5. Our observations identify CREB5 as one mechanism that drives resistance to AR antagonists in prostate cancers.
Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models have limited use in everyday practice owing to complexity in data processing, as well as model development and deployment1–3. Here we show that unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. Our approach leverages recent advances in natural language processing4,5 to train a large language model for medical language (NYUTron) and subsequently fine-tune it across a wide range of clinical and operational predictive tasks. We evaluated our approach within our health system for five such tasks: 30-day all-cause readmission prediction, in-hospital mortality prediction, comorbidity index prediction, length of stay prediction, and insurance denial prediction. We show that NYUTron has an area under the curve (AUC) of 78.7–94.9%, with an improvement of 5.36–14.7% in the AUC compared with traditional models. We additionally demonstrate the benefits of pretraining with clinical text, the potential for increasing generalizability to different sites through fine-tuning and the full deployment of our system in a prospective, single-arm trial. These results show the potential for using clinical language models in medicine to read alongside physicians and provide guidance at the point of care.
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