ABSTRACT
Alleviating excessive inflammation while accelerating chronic wound healing to prevent wound infection has remained challenging, especially during the coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 when patients experienced difficulties with receive appropriate healthcare. We addressed this issue by developing handheld electrospun aloe-nanofiber membranes (ANFMs) with convenient, environmentally friendly properties and a therapeutic capacity for wound closure. Our results showed that ANFMs fabricated with high molecular weight polyvinyl alcohol (PVA) to form fibers during electrospinning had uniform fibrous architecture and a porous structure. Given the value of aloe gel in accelerating wound healing, liquid extracts from ANFMs significantly downregulated the expression of the pro-inflammatory genes, interleukin-6 (IL-6) and inducible nitric oxide synthase (iNOS), and markedly suppress the generation of reactive oxygen species (ROS) induced by lipopolysaccharide in RAW264.7 macrophages. These results indicated the excellent antioxidant and anti-inflammatory effects of ANFMs. After implantation into a mouse diabetic wound model for 12 days in situ, ANFMs notably expedited chronic wound healing via promoting angiogenesis and enhancing cell viability. Our ANFMs generated by handheld electrospinning in situ healed chronic wounds offer a convenient and promising alternative for patients to heal their own wounds under variable conditions.
ABSTRACT
Recent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables. Consecutive patients admitted to Hong Kong's public hospitals between 1 January and 22 August 2020 and diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8 September 2020. An external independent cohort from Wuhan was used for model validation. COVID-19 testing was performed in 237,493 patients and 4442 patients (median age 44.8 years old, 95% confidence interval (CI): [28.9, 60.8]); 50% males) were tested positive. Of these, 209 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, diabetes mellitus, hypertension, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, stroke, dementia, liver diseases, gastrointestinal bleeding, cancer, increases in neutrophil count, potassium, urea, creatinine, aspartate transaminase, alanine transaminase, bilirubin, D-dimer, high sensitive troponin-I, lactate dehydrogenase, activated partial thromboplastin time, prothrombin time, and C-reactive protein, as well as decreases in lymphocyte count, platelet, hematocrit, albumin, sodium, low-density lipoprotein, high-density lipoprotein, cholesterol, glucose, and base excess. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. The derived score system was evaluated with out-of-sample five-cross-validation (AUC: 0.86, 95% CI: 0.82-0.91) and external validation (N = 202, AUC: 0.89, 95% CI: 0.85-0.93). A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results.