Microneedle, as a novel drug delivery system, has attracted widespread attention due to its non-invasiveness, painless and simple administration, controllable drug delivery, and diverse cargo loading capacity. Although microneedles are initially designed to penetrate stratum corneum of skin for transdermal drug delivery, they, recently, have been used to promote wound healing and regeneration of diverse tissues and organs and the results are promising. Despite there are reviews about microneedles, few of them focus on wound healing and tissue regeneration. Here, we review the recent advances of microneedles in this field. We first give an overview of microneedle system in terms of its potential cargos (e.g., small molecules, macromolecules, nucleic acids, nanoparticles, extracellular vesicle, cells), structural designs (e.g., multidrug structures, adhesive structures), material selection, and drug release mechanisms. Then we briefly summarize different microneedle fabrication methods, including their advantages and limitations. We finally summarize the recent progress of microneedle-assisted wound healing and tissue regeneration (e.g., skin, cardiac, bone, tendon, ocular, vascular, oral, hair, spinal cord, and uterine tissues). We expect that our article would serve as a guideline for readers to design their microneedle systems according to different applications, including material selection, drug selection, and structure design, for achieving better healing and regeneration efficacy.

Radiographic evaluation for patients with scoliosis using Cobb method is the current gold standard, but radiography has radiation hazards. Several groups have recently demonstrated the feasibility of using 3D ultrasound for the evaluation of scoliosis. Ultrasound imaging is radiation-free, comparatively more accessible, and inexpensive. However, a reliable and valid 3D ultrasound system ready for clinical scoliosis assessment has not yet been reported. Scolioscan is a newly developed system targeted for scoliosis assessment in clinics by using coronal images of spine generated by a 3D ultrasound volume projection imaging method. The aim of this study is to test the reliability of spine deformity measurement of Scolioscan and its validity compared to the gold standard Cobb angle measurements from radiography in adolescent idiopathic scoliosis (AIS) patients. Prospective study divided into two stages: 1) Investigation of intra- and inter- reliability between two operators for acquiring images using Scolioscan and among three raters for measuring spinal curves from those images; 2) Correlation between the Cobb angle obtained from radiography by a medical doctor and the spine curve angle obtained using Scolioscan (Scolioscan angle). The raters for ultrasound images and the doctors for evaluating radiographic images were mutually blinded. The two stages of tests involved 20 (80 % females, total of 26 angles, age of 16.4 ± 2.7 years, and Cobb angle of 27.6 ± 11.8°) and 49 (69 % female, 73 angles, 15.8 ± 2.7 years and 24.8 ± 9.7°) AIS patients, respectively. Intra-class correlation coefficients (ICC) and Bland-Altman plots and root-mean-square differences (RMS) were employed to determine correlations, which interpreted based on defined criteria. We demonstrated a very good intra-rater and intra-operator reliability for Scolioscan angle measurement with ICC larger than 0.94 and 0.88, respectively. Very good inter-rater and inter-operator reliability was also demonstrated, with both ICC larger than 0.87. For the thoracic deformity measurement, the RMS were 2.5 and 3.3° in the intra- and inter-operator tests, and 1.5 and 3.6° in the intra- and inter-rater tests, respectively. The RMS differences were 3.1, 3.1, 1.6, 3.7° in the intra- and inter-operator and intra- and inter-rater tests, respectively, for the lumbar angle measurement. Moderate to strong correlations (R2 > 0.72) were observed between the Scolioscan angles and Cobb angles for both the thoracic and lumbar regions. It was noted that the Scolioscan angle slightly underestimated the spinal deformity in comparison with Cobb angle, and an overall regression equation y = 1.1797x (R2 = 0.76) could be used to translate the Scolioscan angle (x) to Cobb angle (y) for this group of patients. The RMS difference between Scolioscan angle and Cobb angle was 4.7 and 6.2°, with and without the correlation using the overall regression equation. We showed that Scolioscan is reliable for measuring coronal deformity for patients with AIS and appears promising in screening large numbers of patients, for progress monitoring, and evaluation of treatment outcomes. Due to it being radiation-free and relatively low-cost, Scolioscan has potential to be widely implemented and may contribute to reducing radiation dose during serial monitoring.

The standing radiograph is used as a gold standard to diagnose spinal deformity including scoliosis, a medical condition defined as lateral spine curvature . However, the health concern of X-ray and large inter-observer variation of measurements on X-ray images have significantly restricted its application, particularly for scoliosis screening and close follow-up for adolescent patients. In this study, a radiation-free freehand 3-D ultrasound system was developed for scoliosis assessment using a volume projection imaging method. Based on the obtained coronal view images, two measurement methods were proposed using transverse process and spinous profile as landmarks, respectively. As a reliability study, 36 subjects (age: 30.1 ±14.5; male: 12; female: 24) with different degrees of scoliosis were scanned using the system to test the inter- and intra-observer repeatability. The intra- and inter-observer tests indicated that the new assessment methods were repeatable, with ICC larger than 0.92. Small intra- and inter-observer variations of measuring spine curvature were observed for the two measurement methods (intra-: 1.4 ±1.0 ^° and 1.4 ±1.1 ^° ; inter-: 2.2 ±1.6 ^° and 2.5 ±1.6 ^° ). The results also showed that the spinal curvature obtained by the new method had good linear correlations with X-ray Cobb's method ( , 29 subjects). These results suggested that the ultrasound volume projection imaging method can be a promising approach for the assessment of scoliosis, and further research should be followed up to demonstrate its potential clinical applications for mass screening and curve progression and treatment outcome monitoring of scoliosis patients.

Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ³ 80%, while only half of them attained acceptable specificity ³ 95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.