The treatment of diabetic ulcer (DU) remains a major clinical challenge due to the complex wound-healing milieu that features chronic wounds, impaired angiogenesis, persistent pain, bacterial infection, and exacerbated inflammation. A strategy that effectively targets all these issues has proven elusive. Herein, we use a smart black phosphorus (BP)-based gel with the characteristics of rapid formation and near-infrared light (NIR) responsiveness to address these problems. The in situ sprayed BP-based gel could act as 1) a temporary, biomimetic “skin” to temporarily shield the tissue from the external environment and accelerate chronic wound healing by promoting the proliferation of endothelial cells, vascularization, and angiogenesis and 2) a drug “reservoir” to store therapeutic BP and pain-relieving lidocaine hydrochloride (Lid). Within several minutes of NIR laser irradiation, the BP-based gel generates local heat to accelerate microcirculatory blood flow, mediate the release of loaded Lid for “on-demand” pain relief, eliminate bacteria, and reduce inflammation. Therefore, our study not only introduces a concept of in situ sprayed, NIR-responsive pain relief gel targeting the challenging wound-healing milieu in diabetes but also provides a proof-of-concept application of BP-based materials in DU treatment.

This review systematically summarizes the research status, challenges, prospects, and potential bench-to-bedside translation of minimally invasive nanomedicines.

Abstract Objective To mine Twitter and quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions across studies, and create a symptom lexicon for future research. Materials and Methods We retrieved tweets using COVID-19-related keywords, and performed semiautomatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard concept IDs in the Unified Medical Language System, and compared the distributions to those reported in early studies from clinical settings. Results We identified 203 positive-tested users who reported 1002 symptoms using 668 unique expressions. The most frequently-reported symptoms were fever/pyrexia (66.1%), cough (57.9%), body ache/pain (42.7%), fatigue (42.1%), headache (37.4%), and dyspnea (36.3%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (28.7%) and ageusia (28.1%), were frequently reported on Twitter, but not in clinical studies. Conclusion The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings.