Disease-responsive drug delivery: the next generation of smart delivery devices

With the advent of highly potent and cytotoxic drugs, it is increasingly critical that they be targeted and released only in cells of diseased tissues, while sparing physiologically normal neighbors. Simple ligand-based targeting of drug carriers, although promising, cannot always provide the required specificity to achieve this since often normal cells also express significant levels of the targeted receptors. Therefore, stimuli-responsive delivery systems are being explored to allow drug release from nano- and microcarriers and implantable devices, primarily in the presence of physiological or disease-specific pathophysiological signals. Designing smart biomaterials that respond to temperature or pH changes, protein and ligand binding, disease-specific degradation, e.g. enzymatic cleavage, has become an integral part of this approach. These strategies are used in combination with nano- and microparticle systems to improve delivery efficiency through several routes of administration, and with injectable or implantable systems for long term controlled release. This review focuses on recent developments in stimuli-responsive systems, their physicochemical properties, release profiles, efficacy, safety and biocompatibility, as well as future perspectives.     

Cephalometric changes after long-termearly treatment with face mask and maxillary intraoral appliance therapy

This article reports on a retrospective study of 25 children (mean age, 4 years 2 months) exhibiting Class III malocclusions and anterior cross-bites who were treated with a face mask and a maxillary intraoral appliance. Cephalometric radiographs were taken for all treated patients at three intervals: before treatment (TO), after treatment (T1), and at posttreatment follow-up (T2). A control group consisted of 10 untreated Class III children (mean age, 3 years 11 months). Cephalometric radiographs were taken periodically for observation in this group. Paired t tests and independent t tests were performed to determine the significance of skeletal and dental changes related to treatment. Early therapy produced significant skeletal and dentoalveolar changes. The maxilla moved further forward in the treated group. Mandibular growth was similar in both treated and untreated groups. There was an improvement in the maxillomandibular relationship in the treated group. This was because of the proclination of the maxillary incisors and the retroclination of the mandibular incisors. Self-correction of the original anterior cross-bite in the untreated group occurred. Long-term follow-up revealed a decrease in overjet mainly caused by the proclination of the mandibular incisors. However, positive overjet was maintained throughout the study period. Despite some relapse, the treated group showed a net positive improvement in occlusion.     

An Automatic Breast Tumor Detection and Classification including Automatic Tumor Volume Estimation Using Deep Learning Technique

Objective: This study aims to develop automatic breast tumor detection and classification including automatic tumor volume estimation using deep learning techniques based on computerized analysis of breast ultrasound images. When the skill levels of the radiologists and image quality are important to detect and diagnose the tumor using handheld ultrasound, the ability of this approach tends to assist the radiologist’s decision for breast cancer diagnosis. Material and Methods: Breast ultrasound images were provided by the Department of Radiology of Thammasat University and Queen Sirikit Center of Breast Cancer of Thailand. The dataset consists of 655 images including 445 benign and 210 malignant. Several data augmentation methods including blur, flip vertical, flip horizontal, and noise have been applied to increase the training and testing dataset. The tumor detection, localization, and classification were performed by drawing the appropriate bounding box around it using YOLO7 architecture based on deep learning techniques. Then, the automatic tumor volume estimation was performed using a simple pixel per metric technique. Result: The model demonstrated excellent tumor detection performance with a confidence score of 0.95. In addition, the model yielded satisfactory predictions on the test sets, with a lesion classification accuracy of 95.07%, a sensitivity of 94.97%, a specificity of 95.24%, a PPV of 97.42%, and an NPV of 90.91%. Conclusion: An automatic breast tumor detection and classification including automatic tumor volume estimation using deep learning technique yielded satisfactory predictions in distinguishing benign from malignant breast lesions. In addition, automatic tumor volume estimation was performed. Our approach could be integrated into the conventional breast ultrasound machine to assist the radiologist’s decision for breast cancer diagnosis.