Pluronic F127 hydrogel-loaded extracellular vesicles from adipose-derived mesenchymal stem cells promote tracheal cartilage regeneration via SCNN1B delivery.

Extracellular vesicles (EVs) derived from adipose-derived mesenchymal stem cells (AMSC-EVs) have been highlighted as a cell-free therapy due to their regenerative capability to enhance tissue and organ regeneration. Herein, we aimed to examine the mechanism of PF127-hydrogel@AMSC-EVs in promoting tracheal cartilage defect repair. Based on bioinformatics methods, SCNN1B was identified as a key gene for the osteogenic differentiation of AMSCs induced by AMSC-EVs. EVs were isolated from rat AMSCs and then loaded onto thermo-sensitive PF-127 hydrogel to develop PF127-hydrogel@AMSC-EVs. It was established that PF127-hydrogel@AMSC-EVs could effectively deliver SCNN1B into AMSCs, where SCNN1B promoted AMSC osteogenic differentiation. The promotive effect was evidenced by enhanced ALP activity, extracellular matrix mineralization, and expression of s-glycosaminoglycan, RUNX2, OCN, collagen II, PERK, and ATF4. Furthermore, the in vivo experiments revealed that PF127-hydrogel@AMSC-SCNN1B-EVs stimulated tracheal cartilage regeneration in rats through PERK/ATF4 signaling axis activation. Therefore, PF127-hydrogel@AMSC-SCNN1B-EVs may be a novel cell-free biomaterial to facilitate tracheal cartilage regeneration and cartilage injury repair.

A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis.

Despite numerous clinical trials and pre-clinical developments, the diagnosis of cracked tooth, especially in the early stages, remains a challenge. Cracked tooth syndrome is often accompanied by dramatic painful responses from occlusion and temperature stimulation, which has become one of the leading causes for tooth loss in adults. Current clinical diagnostical approaches for cracked tooth have been widely investigated based on X-rays, optical light, ultrasound wave, etc. Advances in artificial intelligence (AI) development have unlocked the possibility of detecting the crack in a more intellectual and automotive way. This may lead to the possibility of further enhancement of the diagnostic accuracy for cracked tooth disease. In this review, various medical imaging technologies for diagnosing cracked tooth are overviewed. In particular, the imaging modality, effect and the advantages of each diagnostic technique are discussed. What's more, AI-based crack detection and classification methods, especially the convolutional neural network (CNN)-based algorithms, including image classification (AlexNet), object detection (YOLO, Faster-RCNN), semantic segmentation (U-Net, Segnet) are comprehensively reviewed. Finally, the future perspectives and challenges in the diagnosis of the cracked tooth are lighted.

A method of crack detection based on digital image correlation for simulated cracked tooth.

BACKGROUND: Early clinical cracked tooth can be a perplexing disorder to diagnose and manage. One of the key problems for the diagnosis of the cracked tooth is the detection of the location of the surface crack. METHODS: This paper proposes an image-based method for the detection of the micro-crack in the simulated cracked tooth. A homemade three-axis motion platform mounted with a telecentric lens was built as an image acquisition system to observe the surface of the simulated cracked tooth, which was under compression with a magnitude of the masticatory force. By using digital image correlation (DIC), the deformation map for the crown surface of the cracked tooth was calculated. Through image analysis, the micro-crack was quantitatively visualized and characterized. RESULTS: The skeleton of the crack path was successfully extracted from the image of the principal strain field, which was further verified by the image from micro-CT. Based on crack kinematics, the crack opening displacement was quantitatively calculated to be 2-10 µm under the normal mastication stress, which was in good agreement with the value reported in the literature. CONCLUSIONS: The crack on the surface of the simulated cracked tooth could be detected based on the proposed DIC-based method. The proposed method may provide a new solution for the rapid clinical diagnosis of cracked teeth and the calculated crack information would be helpful for the subsequent clinical treatment of cracked teeth.

Application of smart hydrogel materials in cartilage injury repair: A systematic review and meta-analysis.

OBJECTIVE: Cartilage injury is a common clinical condition, and treatment approaches have evolved over time from traditional conservative and surgical methods to regenerative repair. In this context, hydrogels, as widely used biomaterials in the field of cartilage repair, have garnered significant attention. Particularly, responsive hydrogels (also known as "smart hydrogels") have shown immense potential due to their ability to respond to various physicochemical properties and environmental changes. This paper aims to review the latest research developments of hydrogels in cartilage repair, utilizing a more systematic and comprehensive meta-analysis approach to evaluate the research status and application value of responsive hydrogels. The goal is to determine whether these materials demonstrate favorable therapeutic effects for subsequent clinical applications, thereby offering improved treatment methods for patients with cartilage injuries. METHOD: This study employed a systematic literature search method to summarize the research progress of responsive hydrogels by retrieving literature on the subject and review studies. The search terms included "hydrogel" and "cartilage," covering data from database inception up to October 2023. The quality of the literature was independently evaluated using Review Manager v5.4 software. Quantifiable data was statistically analyzed using the R language. RESULTS: A total of 7 articles were retrieved for further meta-analysis. In the quality assessment, the studies demonstrated reliability and accuracy. The results of the meta-analysis indicated that responsive hydrogels exhibit unique advantages and effective therapeutic outcomes in the field of cartilage repair. Subgroup analysis revealed potential influences of factors such as different types of hydrogels and animal models on treatment effects. CONCLUSION: Responsive hydrogels show significant therapeutic effects and substantial application potential in the field of cartilage repair. This study provides strong scientific evidence for their further clinical applications and research, with the hope of promoting advancements in the treatment of cartilage injuries.

Semantic segmentation for tooth cracks using improved DeepLabv3+ model.

Objective: Accurate and prompt detection of cracked teeth plays a critical role for human oral health. The aim of this paper is to evaluate the performance of a tooth crack segmentation model (namely, FDB-DeepLabv3+) on optical microscopic images. Method: The FDB-DeepLabv3+ model proposed here improves feature learning by replacing the backbone with ResNet50. Feature pyramid network (FPN) is introduced to fuse muti-level features. Densely linked atrous spatial pyramid pooling (Dense ASPP) is applied to achieve denser pixel sampling and wider receptive field. Bottleneck attention module (BAM) is embedded to enhance local feature extraction. Results: Through testing on a self-made hidden cracked tooth dataset, the proposed method outperforms four classical networks (FCN, U-Net, SegNet, DeepLabv3+) on segmentation results in terms of mean pixel accuracy (MPA) and mean intersection over union (MIoU). The network achieves an increase of 11.41% in MPA and 12.14% in MIoU compared to DeepLabv3+. Ablation experiments shows that all the modifications are beneficial. Conclusion: An improved network is designed for segmenting tooth surface cracks with good overall performance and robustness, which may hold significant potential in computer-aided diagnosis of cracked teeth.

Ingestion of microplastics by silver carp (Hypophthalmichthys molitrix) larvae: Quantification of ingestion and assessment of microbiota dysbiosis.

The adverse effects of microplastics ingested by aquatic organisms have been reported previously. However, most studies are primarily qualitative; therefore, it is challenging to determine the direct interactions between microplastics and organisms. In this study, for the first time, the microplastic intake behavior of silver carp (Hypophthalmichthys molitrix) larvae, a popular fish in China, as well as intestine accumulation and excretion of the microplastics were quantitatively investigated. The results showed that the intake of microplastics by silver carp larvae was negatively correlated with the particle size of microplastics but positively correlated with the exposure concentration. After intaking microplastics of different sizes, small-sized microplastics (≤ 150 µm) were rapidly excreted from the intestine of silver carp, whereas some large-sized microplastics (≥ 300 µm) remained in the intestine for a long time. The presence of food significantly increased the intake of large-sized microplastics, while small-sized microplastics intake was unaffected by the food. More importantly, the ingested microplastics caused specific changes in the diversity of intestinal microflora, potentially leading to abnormal immune and metabolic functions. The results of this study provide a new understanding on the potential impacts of microplastics on aquatic organisms.

Automatic Detection of Cracks in Cracked Tooth Based on Binary Classification Convolutional Neural Networks.

Cracked tooth syndrome is a commonly encountered disease in dentistry, which is often accompanied by dramatic painful responses from occlusion and temperature stimulation. Current clinical diagnostic trials include traditional methods (such as occlusion test, probing, cold stimulation, etc.) and X-rays based medical imaging (periapical radiography (PR), cone-beam computed tomography (CBCT), etc.). However, these methods are strongly dependent on the experience of the clinicians, and some inconspicuous cracks are also extremely easy to be overlooked by visual observation, which will definitely affect the subsequent treatments. Inspired by the achievements of applying deep convolutional neural networks (CNNs) in crack detection in engineering, this article proposes an image-based crack detection method using a deep CNN classifier in combination with a sliding window algorithm. A CNN model is designed by modifying the size of the input layer and adding a fully connected layer with 2 units based on the ResNet50, and then, the proposed CNN is trained and validated with a self-prepared cracked tooth dataset including 20,000 images. By comparing validation accuracy under seven different learning rates, 10-5 is chosen as the best learning rate for the following testing process. The trained CNN is tested on 100 images with 1920 × 1080-pixel resolutions, which achieves an average accuracy of 90.39%. The results show that the proposed method can effectively detect cracks in images under various conditions (stained, overexplosion, images affected by other diseases). The proposed method in this article provides doctors with a more intelligent diagnostic solution, and it is not only suitable for optical photographs but also for automated diagnosis of other medical imaging images.

Transesophageal Echocardiography Examination in Ailuropoda melanoleuca.

OBJECTIVE: Cardiac function analysis for Ailuropoda melanoleuca, also called giant panda, with transthoracic echocardiography has several limitations, such as limited probe position area and poor echo window. We want to determine the feasibility of transesophageal echocardiography (TEE) for cardiac examination in Ailuropoda melanoleuca. METHODS: Eight-year-old male Ailuropoda melanoleuca was anesthetized by intramuscular ketamine and followed by inhalation isofl urane with spontaneous respiration. TEE probe was inserted through mouth. Comprehensive examinations were done by two-dimensional (2D), Doppler method, 3D zoom and 3D full volume mode for their cardiac evaluation. RESULTS: The structure of heart was similar to human. We found mild mitral regurgitation. Right ventricle and pulmonary artery were free of any organic lesions. We did comprehensive multiplane examination by American Society of Echocardiography/Society of Cardiovascular Anesthesia guideline, but transgastric (TG) and deep TG view were not approachable. 3D reconstruction offered high quality image and quantitative calculations. Complete examinations took less than 30 minutes. There was no significant hemodynamic change during probe insertion, and no oropharyngeal trauma certifi ed by dentist. CONCLUSIONS: TEE is a reasonable semi-invasive tool for cardiac function evaluation. Though some anatomical limitations prohibited comprehensive 2D views, improvements such as 3D images help to give detailed cardiac examination.