Ting Xu

Melt electrowriting (MEW) is an innovative additive manufacturing technology utilized for producing fibrous three-dimensional structures for tissue engineering and regenerative medicine applications. While polycaprolactone (PCL) is the predominant polymer employed in MEW, its limited elasticity and slow degradation rate restrict its effectiveness in tissue regeneration. This study investigates the printability of medical-grade poly(L-lactide-co-caprolactone) (PLCL), which has a faster degradation rate, resulting in MEW PLCL scaffolds characterized by uniform fiber diameter, regular layer stacking, and customizable pore sizes. At a pore size of 500 μ m, the PLCL scaffolds exhibit superior mechanical properties, with a tensile strength of 22.4 MPa and an elastic modulus of 72.6 MPa. Additionally, compared with PCL scaffolds of the same pore size, PLCL scaffolds promote sustained cell proliferation, along with the upregulation of SOX9 and Col II and the downregulation of MMP7. These results indicate enhanced chondrocyte proliferation and increased synthesis of cartilage-specific matrix. When comparing pore sizes of 300 μ m and 500 μ m, PLCL-500 scaffolds support continuous cell proliferation even at lower cell densities. In contrast, PLCL-300 scaffolds promote initial cell adhesion but demonstrate a decline in proliferation after day 3. This constraint downregulates anabolic markers (SOX9, collagen II, Aggrecan) and upregulates matrix-degrading enzyme MMP7, primarily caused by excessive cell packing and compromised nutrient diffusion within scaffolds. Furthermore, the gelatin methacryloyl (GelMA) hydrogel is reinforced by PLCL-500 scaffolds, optimizing their mechanical properties and bioactivity. The composite scaffold exhibits an elongation at break of 1000% and a tensile strength of 0.5 MPa. Importantly, cell viability remains above 90%, with chondrocytes maintaining a spherical phenotype and establishing networks by day 14, accompanied by increased sulfated glycosaminoglycan (sGAG) deposition. This research clarifies the relationships among scaffold dimension, component, mechanical properties, and chondrogenesis, offering a promising strategy for cartilage tissue engineering applications.

Panoramic radiography is an essential auxiliary diagnostic tool for oral diseases. It is a difficult and time-consuming task to conduct extensive panoramic radiography interpretation. These challenges are exacerbated by the creation of electronic medical records and the investigation of oral diseases using collective data. So, we develop a visualization system based on panoramic radiographs. Its function focuses on the intelligent diagnosis and statistical analysis of oral diseases. Firstly, we provide a human-machine collaborative tool for the diagnosis and data extraction of oral diseases in panoramic radiographs. After that, the system generates electronic medical records, including visual charts of oral health status and radiology reports. We further develop statistical correlation analysis to visually evaluate and interactively explore the statistical data from oral health surveys. We conduct intelligent diagnosis, obtain the electronic medical records and do collective analysis based on 521 panoramic radiographs. The available analyses cover disease-prone teeth, disease distribution per tooth position and association of age, sex with oral diseases. The results are reported from a comprehensive case study showing that our system can improve the efficiency in disease detection and data mining. It can also fuel research studies in the field of public oral health and provide robust support for oral healthcare strategies.

Tooth segmentation is a key step for computer aided diagnosis of dental diseases. Numerous machine learning models have been employed for tooth segmentation on dental panoramic radiograph. However, it is a difficult task to achieve accurate tooth segmentation due to complex tooth shapes, diverse tooth categories and incomplete sample set for machine learning. In this paper, we propose ViSTooth, a visualization framework for tooth segmentation on dental panoramic radiograph. First, we employ Mask R-CNN to conduct preliminary tooth segmentation, and a set of domain metrics are proposed to estimate the accuracy of the segmented teeth, including tooth shape, tooth position and tooth angle. Then, we represent the teeth with high-dimensional vectors and visualize their distribution in a low-dimensional space, in which experts can easily observe those teeth with specific metrics. Further, we expand the sample set with the expert-specified teeth and train the tooth segmentation model iteratively. Finally, we conduct case study and expert study to demonstrate the effectiveness and usability of our ViSTooth, in aiding experts to implement accurate tooth segmentation guided by expert knowledge.

BACKGROUND Panoramic radiography is an essential auxiliary diagnostic tool for oral diseases. It is a difficult and time-consuming task to generate electronic medical records and explore oral diseases based on collective analysis after conducting extensive panoramic radiography interpretation. OBJECTIVE The objective of our work is to develop a visualization system for the interactive diagnosis and intelligent statistical analysis of oral diseases based on panoramic radiographs. METHODS Firstly, we employ a deep learning model, Mask Region-based Convolutional Neural Network (Mask-RCNN), to conduct tooth segmentation in the panoramic radiographs. Then, we provide a rich set of user interfaces for doctors to revise the tooth segmentations and identify the lesion location for the analysis of oral diseases. After that, we provide a rule-based Natural Language Processing (NLP) method to generate electronic medical records. We further develop statistical correlation analysis to visually evaluate and interactively explore the statistical data from oral health surveys. RESULTS We conduct tooth segmentation and manual diagnosis, obtain their electronic medical records and do collective analysis based on a group of real-world panoramic radiographs. The results are reported from a comprehensive case study showing that our system is capable of improving the efficiency in disease detection and data mining, which also can fuel research studies in the field of public oral health. CONCLUSIONS This paper provides an efficient, automatic, visualizing, and user-friendly system, which helps dentists easily conduct personal oral diagnoses and access collective oral health status. Moreover, extensive population-level oral health surveys and visual analyses provide robust guidance and support for oral healthcare strategies.

Objective: To investigate and explain the beneficial effects of local intra‐articular injection of Salubrinal on temporomandibular joint osteoarthritis (TMJOA) using a rabbit model of anterior disc displacement (ADD).Methods: Rabbits were divided and subjected to surgical ADD. Salubrinal was administered by intra‐articular injection in the TMJ every other day for 2 and 4 weeks after operation. Histological examination and TUNEL staining were then performed. Immunohistochemistry, quantitative real‐time PCR, and Western blot analysis were employed to evaluate the expression of endoplasmic reticulum (ER) stress‐related markers, catabolic factors, extracellular matrix proteins, inflammatory factors, and nuclear factor‐kappa B (NF‐κB) activation.Results: In the ADD groups, we found that Salubrinal partly reversed condylar cartilage deterioration according to the histological analysis. Salubrinal reduced chondrocytes apoptosis while increased matrix components including Collagen II and Aggrecan. Meanwhile, Salubrinal downregulated the catabolic expression of MMP13, ADAMTS5, VEGF, TNF‐α, and IL‐1β. We also observed that Salubrinal inhibited ER stress activation by reducing the expression of GRP78, CHOP, ATF4, and Caspase‐12. Additionally, Salubrinal suppressed the phosphorylation of NF‐κB.Conclusion: These results indicate that Salubrinal alleviates cartilage degradation following ADD, suggesting that intra‐articular injection of Salubrinal is a potential therapeutic approach for preventing TMJOA.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Oral health surveys largely facilitate the prevention and treatment of oral diseases as well as the awareness of population health status. As oral health is always surveyed from a variety of perspectives, it is quite a difficult and complicated task to gain insights from multidimensional oral health surveys. Objective: In this paper, we develop a visualization framework for the visual analytics and deep mining of multidimensional oral health surveys. Methods: First, diseases and groups are embedded into data portraits based on their multidimensional attributes. Based on group classification, correlation patterns are then built for diseases, behaviors, symptoms and cognition to reveal their correlation features. Given the extricated knowledge of diseases, groups, behaviors and their attributes, a knowledge graph is further constructed to reveal semantic information, integrate the graph query function, and describe the features of intrigue to users. Results: A set of meaningful user interactions are integrated, enabling users to intuitively understand the oral health situation and conduct in-depth data exploration and analysis. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system. Conclusions: In this paper, we propose a visualization framework for multidimensional oral health surveys. A series of user-friendly interactions are integrated to propose a visual analysis system that can help users further explore the regulations of oral health conditions. Case studies based on real-world datasets demonstrate the effectiveness of our system in the exploration of oral diseases.