Exclusive expression of KANK4 promotes myofibroblast mobility in keloid tissues.

Keloids are characterized by abnormal wound healing with excessive accumulation of extracellular matrix. Myofibroblasts are the primary contributor to extracellular matrix secretion, playing an essential role in the wound healing process. However, the differences between myofibroblasts involved in keloid formation and normal wound healing remain unclear. To identify the specific characteristics of keloid myofibroblasts, we initially assessed the expression levels of well-established myofibroblast markers, α-smooth muscle actin (α-SMA) and transgelin (TAGLN), in scar and keloid tissues (n = 63 and 51, respectively). Although myofibroblasts were present in significant quantities in keloids and immature scars, they were absent in mature scars. Next, we conducted RNA sequencing using myofibroblast-rich areas from keloids and immature scars to investigate the difference in RNA expression profiles among myofibroblasts. Among significantly upregulated 112 genes, KN motif and ankyrin repeat domains 4 (KANK4) was identified as a specifically upregulated gene in keloids. Immunohistochemical analysis showed that KANK4 protein was expressed in myofibroblasts in keloid tissues; however, it was not expressed in any myofibroblasts in immature scar tissues. Overexpression of KANK4 enhanced cell mobility in keloid myofibroblasts. Our results suggest that the KANK4-mediated increase in myofibroblast mobility contributes to keloid pathogenesis.

Artificial PGA/Collagen-based Bilayer Conduit in Short Gap Interposition Setting Provides Comparable Regenerative Potential to Direct Suture.

The aim of this study was to evaluate whether the Nerbridge, an artificial polyglycolic acid conduit with collagen matrix, is comparable to direct nerve suture in a rat sciatic nerve injury model in a short-gap interposition (SGI) setting. Methods: Sixty-six female Lewis rats were randomly divided into the sham group (n = 13); no reconstruction (no-recon) group (n = 13; rat model with 10 mm sciatic nerve defect); direct group (n = 20; rat sciatic nerve injury directly connected by 10-0 Nylon); and SGI group (n = 20; sciatic nerve injury repaired using 5-mm Nerbridge). Motor function and histological recovery were evaluated. The sciatic nerve and gastrocnemius muscle were harvested for quantification of the degree of nerve regeneration and muscle atrophy. Results: The SGI and direct groups achieved equal recovery in both functional and histological outcomes. At weeks 3 and 8 postsurgery, there was a significant improvement in the sciatic functional index of the SGI group when compared with that of the no-recon group (P < 0.05). Furthermore, the direct and SGI groups had less muscle atrophy at 4 and 8 weeks postsurgery compared with the no-recon group (P < 0.05). The axon density and diameter at the distal site in the SGI group were significantly higher than that in the no-recon group and comparable to that in the direct and sham groups. Conclusion: An artificial nerve conduit has equal potential as direct suture in motor nerve reconstruction when used in the SGI setting.

Development of a Novel Scar Screening System with Machine Learning.

BACKGROUND: Hypertrophic scars and keloids tend to cause serious functional and cosmetic impediments to patients. As these scars are not life threatening, many patients do not seek proper treatment. Thus, educating physicians and patients regarding these scars is important. The authors aimed to develop an algorithm for a scar screening system and compare the accuracy of the system with that of physicians. This algorithm was designed to involve health care providers and patients. METHODS: Digital images were obtained from Google Images (Google LLC, Mountain View, Calif.), open access repositories, and patients in the authors' hospital. After preprocessing, 3768 images were uploaded to the Google Cloud AutoML Vision platform and labeled with one of the four diagnoses: immature scars, mature scars, hypertrophic scars, and keloid. A consensus label for each image was compared with the label provided by physicians. RESULTS: For all diagnoses, the average precision (positive predictive value) of the algorithm was 80.7 percent, the average recall (sensitivity) was 71 percent, and the area under the curve was 0.846. The algorithm afforded 77 correct diagnoses with an accuracy of 77 percent. Conversely, the average physician accuracy was 68.7 percent. The Cohen kappa coefficient of the algorithm was 0.69, while that of the physicians was 0.59. CONCLUSIONS: The authors developed a computer vision algorithm that can diagnose four scar types using automated machine learning. Future iterations of this algorithm, with more comprehensive accuracy, can be embedded in telehealth and digital imaging platforms used by patients and primary doctors. The scar screening system with machine learning may be a valuable support tool for physicians and patients. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, II.

Peripheral Nerve Regeneration Using Different Germ Layer-Derived Adult Stem Cells in the Past Decade.

Peripheral nerve injuries (PNIs) are some of the most common types of traumatic lesions affecting the nervous system. Although the peripheral nervous system has a higher regenerative ability than the central nervous system, delayed treatment is associated with disturbances in both distal sensory and functional abilities. Over the past decades, adult stem cell-based therapies for peripheral nerve injuries have drawn attention from researchers. This is because various stem cells can promote regeneration after peripheral nerve injuries by differentiating into neural-line cells, secreting various neurotrophic factors, and regulating the activity of in situ Schwann cells (SCs). This article reviewed research from the past 10 years on the role of stem cells in the repair of PNIs. We concluded that adult stem cell-based therapies promote the regeneration of PNI in various ways.