A flexible and bioactive scaffold for adipose tissue engineering was fabricated and evaluated by dual nozzle three-dimensional printing. A highly elastic poly (L-lactide-co-ε-caprolactone) (PLCL) copolymer, which acted as the main scaffolding, and human adipose tissue derived decellularized extracellular matrix (dECM) hydrogels were used as the printing inks to form the scaffolds. To prepare the three-dimensional (3D) scaffolds, the PLCL co-polymer was printed with a hot melting extruder system while retaining its physical character, similar to adipose tissue, which is beneficial for regeneration. Moreover, to promote adipogenic differentiation and angiogenesis, adipose tissue-derived dECM was used. To optimize the printability of the hydrogel inks, a mixture of collagen type I and dECM hydrogels was used. Furthermore, we examined the adipose tissue formation and angiogenesis of the PLCL/dECM complex scaffold. From in vivo experiments, it was observed that the matured adipose-like tissue structures were abundant, and the number of matured capillaries was remarkably higher in the hydrogel–PLCL group than in the PLCL-only group. Moreover, a higher expression of M2 macrophages, which are known to be involved in the remodeling and regeneration of tissues, was detected in the hydrogel–PLCL group by immunofluorescence analysis. Based on these results, we suggest that our PLCL/dECM fabricated by a dual 3D printing system will be useful for the treatment of large volume fat tissue regeneration.
Purpose: The excision of subungual glomus tumors on the distal phalanx may cause nail deformities. Herein, we report our nail-sparing and sub-nail bed approach for the excision of subungual glomus tumors, which enables subungual glomus tumor excision without removal of the nail plate and further allows access to the tumor mass by dissecting beneath the nail bed and germinal matrix to minimize postoperative pain and nail bed injury. Therefore, the present article describes this operative approach and reports surgical outcomes with respect to patient satisfaction, pain, and the final postoperative nail shape.Methods: Thirty-two cases of clinically diagnosed subungual glomus tumors treated with this approach were retrospectively evaluated. Mean pain scores were measured at 1 week postoperatively and at the last follow-up. Patients were asked for their subjective opinion regarding the final nail shape, and their responses were assessed as “satisfied” or “unsatisfied.” The objective results for the final nail shape were graded as “excellent,” “good,” or “poor” by two orthopedic hand surgeons.Results: The mean postoperative pain score (visual analog scale) at 1 week was 1.8. No patients reported pain at the last follow-up. Subjectively, 96.6% of patients were satisfied with the operation. Objectively, the postoperative nail shape was excellent in 9.3% of cases, good in 87.5%, and poor in 3.1%.Conclusion: This approach provides minimal postoperative pain, high patient satisfaction, and favorable cosmetic outcomes with respect to the nail shape by avoiding removal of the nail plate and incision of the nail bed and germinal matrix.
Stroke is a leading cause of disability among elderly individuals, and gait impairment is a typical characteristic related to the stroke severity experienced by patients. The aim of this study is to propose a novel stroke severity classification method using symmetric gait features with recursive feature elimination with cross-validation (RFECV). An experiment was conducted on data acquired from thirteen chronic stroke patients and eighteen elderly participants. They walked on a treadmill at four different speeds based on their preferred speed. In this study, symmetric gait features representing the ratio between the left-and right-side values were used as inputs along with the general gait features that did not completely contain the patients' gait characteristics. We used four different machine learning (ML) techniques to determine the optimal subset for differentiating between the elderly and stroke groups according to severity based on RFECV. To verify the performance of RFECV and the symmetric gait features, four different feature sets were used: 1) all fortyfive general features, 2) all twenty-one symmetric features, 3) the optimal general feature subset obtained by using RFECV, and 4) the optimal symmetric feature subset obtained by using RFECV. It was confirmed that the classification performance increased when symmetric gait data and the RFECV technique were applied. The best classification result was obtained by RF-RFECV with an RF classifier derived from the symmetric features (accuracy: 96.01%). The findings of this study can help clinicians diagnose the stroke severity experienced by patients based on information obtained using ML technology.
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