We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

HospiMedica

Download Mobile App
Recent News AI Critical Care Surgical Techniques Patient Care Health IT Point of Care Business Focus

Combination Therapy Promotes Rapid Wound Healing

By HospiMedica International staff writers
Posted on 12 Nov 2018
A new study shows that incorporating a gene-suppressing drug into a surfactant polymer gel dressing helps wounds heal more rapidly and with higher fidelity.

Researchers at Albert Einstein College of Medicine (New York, NY, USA), Boston Children’s Hospital (BCH; MA, USA), and Stanford University (CA, USA) found that an enzyme called fidgetin-like 2 (FL2) severs microtubules in skin cells, which help the cells as they migrate towards wounds in order to heal them. They theorized that by reducing FL2 levels, the reparatory skin cells might be able reach their destination faster. They therefore developed small interfering RNA molecules (siRNAs) that specifically inhibit the gene that codes for FL2.

Image: Inhibiting cleaving FL2 enzymes help wounds repair better and faster (Photo courtesy of Albert Einstein College of Medicine).
Image: Inhibiting cleaving FL2 enzymes help wounds repair better and faster (Photo courtesy of Albert Einstein College of Medicine).

They siRNAs were combined with PluroGel, an over-the-counter antimicrobial protective gel that keeps wounds moist when applied to bandages and other wound dressings. The siRNAs were also integrated into collagen microparticles, which readily release their siRNA load after coming in contact with the skin. The FL2-siRNA/PluroGel combination was then applied to mice with either skin excisions or burns. Two control groups were used; mice treated with PluroGel alone, and mice treated with PluroGel/siRNA that did not target the FL2 gene.

By 14-days post injury, the wounds of mice in both control groups were more than one-third larger than those of mice treated with the FL2-siRNA/PluroGel combination. In addition, the burn wounds of all the mice treated with the FL2-siRNA/PluroGel combination had closed completely by day 14; by comparison, 25% and 30% of treated wounds in the PluroGel and PluroGel/nontarget siRNA control groups remained unhealed at that time, respectively. The study was published on October 25, 2018, in Advances in Wound Care.

“Not only did wound healing occur more rapidly and completely, but actual regeneration occurred, with hair follicles and the skin's supportive collagen network restored in wounded skin, clinically important improvements that are unprecedented in wound care,” said senior author professor of physiology and biophysics David Sharp, PhD, of Albert Einstein College of Medicine. “We foresee this therapy having broad application for all sorts of wounds, from playground cuts to battlefield injuries to chronic wounds.”

“These results show that FL2-siRNA plus PluroGel is a highly promising wound treatment," said co-lead author Adam Kramer, MSc, a PhD candidate at Albert Einstein College of Medicine. “By lowering FL2 levels in skin cells, the FL2-siRNA helps cells reach wound sites much faster than they ordinarily would, essential for minimizing scarring and preventing wounds from becoming chronic. And by hydrating wounds and inhibiting microbes, PluroGel offers important additional wound-healing benefits.”

Related Links:
Albert Einstein College of Medicine
Boston Children’s Hospital
Stanford University


Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
Gold Member
SARS‑CoV‑2/Flu A/Flu B/RSV Sample-To-Answer Test
SARS‑CoV‑2/Flu A/Flu B/RSV Cartridge (CE-IVD)
Silver Member
Wireless Mobile ECG Recorder
NR-1207-3/NR-1207-E
New
Enterprise Imaging & Reporting Solution
Syngo Carbon

Latest Critical Care News

Wearable Multiplex Biosensors Could Revolutionize COPD Management

New Low-Energy Defibrillation Method Controls Cardiac Arrhythmias

New Machine Learning Models Help Predict Heart Disease Risk in Women