Latch Applicator for Efficient Delivery of Dissolving Microneedles Based on Rapid Release of Elastic Strain Energy by Thumb Force (Adv. Funct. Mater. 11/2023)

Dissolving microneedle (DMN) is an attractive alternative to parenteral and enteral drug administration owing to its painless self-administration and safety due to non-generation of medical waste. For reproducible and efficient DMN administration, various DMN application methods, such as weights, springs, and electromagnetic devices, have been studied. However, these applicators have complex structures that are complicated to use and high production costs. In this study, a latch applicator that consists of only simple plastic parts and operates via thumb force without any external complex device is developed. Protrusion-shaped latches and impact distances are designed to accumulate thumb force energy through elastic deformation and to control impact velocity. The optimized latch applicator with a pressing force of 25 N and an impact velocity of 5.9 m s⁻¹ fully inserts the drug-loaded tip of the two-layered DMN into the skin. In an ovalbumin immunization test, DMN with the latch applicator shows a significantly higher IgG antibody production rate than that of intramuscular injection. The latch applicator, which provides effective DMN insertion and a competitive price compared with conventional syringes, has great potential to improve delivery of drugs, including vaccines.     

Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep learning (DL) algorithms, to distinguish malignant skin lesions from benign lesions in dermoscopic images. This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoencoder (OSSAE) based feature extractor with backpropagation neural network (BPNN), named the OSSAE-BPNN technique. The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region. In addition, the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis. Moreover, the parameter tuning of the SSAE model is carried out by the use of sea gull optimization (SGO) algorithm. To showcase the enhanced outcomes of the OSSAE-BPNN model, a comprehensive experimental analysis is performed on the benchmark dataset. The experimental findings demonstrated that the OSSAE-BPNN approach outperformed other current strategies in terms of several assessment metrics.     

基于MultiResUNet-SMIS的皮肤黑色素瘤图像分割

针对现有的皮肤黑色素瘤病灶分割精度不高的问题,结合现有卷积神经网络方法提出皮肤黑色素瘤图像分割方法 MultiResUNet-SMIS.首先,依据皮肤黑色素瘤成像特点,引入不同空洞率的空洞卷积替换普通卷积,在参数量相同的前提下扩大感受野,使网络模型能够适用于多尺度病灶分割任务;其次加入空间和通道注意力机制以重新分配特征权重,扩大感兴趣特征影响,抑制无关特征;最后融合Focal loss与Dice loss提出一种新的loss函数FD loss用于计算回归损失,解决前景背景像素不均衡问题,进一步提高网络模型的分割精度.实验结果表明,MultiResUNet-SMIS在ISIC-2018数据集上的Dice指数、IoU指数以及Acc准确率分别达到了89.47%、82.67%、96.13%,与原MultiResUNet以及UNet、UNet++、DeepLab V3+等主流方法相比, MultiResUNet-SMIS在皮肤黑色素瘤图像分割中具有更好的效果.     

Topical delivery of paclitaxel for treatment of skin cancer

Context: Skin cancer represents the most growing types of cancer in human and ultraviolet radiation can be cited as one of the prime factor for its occurrence. Current therapy of skin cancer suffers from numerous side effects; for effective therapy, topical application of formulation of paclitaxel (PTX) can be considered as a novel approach.

Objective: The present study is an attempt to prepare formulation of solid lipid nanoparticles (SLN) of PTX for the effective treatment of various form of skin carcinoma.

Methods: The SLN were prepared by high-speed homogenization and ultrasonication method. The prepared SLN were characterized. The optimized PTX SLN were loaded in carbopol gel. The prepared gels were evaluated for its gelling properties and finally studied for in vivo anti-cancer efficacy and histopathological study.

Results: The particle size distribution was found to be in the range of 78.82–587.8?nm. The product yield (%) was found between 60% and 66% and showed a highest entrapment efficiency of 68.3%. The in vitro release of the drug from SLN dispersion was found to be biphasic with the initial burst effect, followed by slow release. SLN-loaded gel were subjected to permeability study and the results show steady-state flux (J_ss), permeability coefficient (K_p), and enhancement ratio were significantly increased in SLN-loaded gel formulation as compared with PTX-loaded gel. The histopathological study clearly reveals the efficacy of the SLN-F3 3G in the treatment of skin cancer.

Conclusion: The experimental formulations show controlled release of PTX and thus expected to show reduce dose-related side effects.     

Skin detection for single images using dynamic skin color modeling

Up-to-date skin detection techniques use adaptive skin color modeling to overcome the varying skin color problem. Most methods for tracking skin regions in videos utilize the correlation between contiguous frames. This paper proposes a new approach for detecting skin in a single image. This approach uses a local skin model to shift a globally trained skin model to adapt the final skin model to the current image. Experimental results show that the proposed method can achieve better accuracy. Two improvements for speeding up the processing are also discussed.     

实时视频中的快速人脸检测方法

提出一种实时视频中的快速人脸检测方法。采用DirectShow获取和回放视频帧,进行前处理。对所得彩色图像进行肤色分割,通过肤色的统计特性得到人脸候选区域,将该区域设置为感兴趣区域。运用基于局部扫描的AdaBoost算法对每个感兴趣区域进行检测。实验结果证明了该方法的有效性。     

嵌入豁口磁芯的13.56MHzRFID读写器天线

本文针对天线线圈产生的磁场强度分布不均匀的现象,提出一种增强天线局部磁场强度的方案.首先,本文从理论出发解释了高频磁场的趋肤效应;其次,基于高频磁场的趋肤效应,提出往线圈中央添加开了豁口的金属磁芯,用以增强磁芯边缘和豁口位置的磁场强度;最后,通过三维电磁仿真软件AnsoftHFSS和MATLAB软件研究不同张角的豁口磁芯对环形天线的磁场强度的影响,旨在找出最优豁口的设计方案,对实际设计的13.56MHz RFID读写器天线具有一定的参考价值.