This study aimed at evaluate the effects of different aperture-sized type I collagen/silk fibroin (CSF) scaffolds on the proliferation and differentiation of human dental pulp cells (HDPCs). The CSF scaffolds were designed with 3D mapping software Solidworks. Three different aperture-sized scaffolds (CSF1–CSF3) were prepared by low-temperature deposition 3D printing technology. The morphology was observed by scanning electron microscope (SEM) and optical coherence tomography. The porosity, hydrophilicity and mechanical capacity of the scaffold were detected, respectively. HDPCs (third passage, 1 × 105 cells) were seeded into each scaffold and investigated by SEM, CCK-8, alkaline phosphatase (ALP) activity and HE staining. The CSF scaffolds had porous structures with macropores and micropores. The macropore size of CSF1 to CSF3 was 421 ± 27 μm, 579 ± 36 μm and 707 ± 43 μm, respectively. The porosity was 69.8 ± 2.2%, 80.1 ± 2.8% and 86.5 ± 3.3%, respectively. All these scaffolds enhanced the adhesion and proliferation of HDPCs. The ALP activity in the CSF1 group was higher than that in the CSF3 groups (P < 0.01). HE staining showed HDPCs grew in multilayer within the scaffolds. CSF scaffolds significantly improved the adhesion and ALP activity of HDPCs. CSF scaffolds were promising candidates in dentine-pulp complex regeneration.

Most of currently available commercial photothermal agents suffer from the issues in terms of photothermal instability and low photothermal conversion efficiency (PCE), which indeed impair their practical applications in disease...

Breast cancer is a fatal disease, among which, its sub-type invasive (or infiltrating) ductal carcinomas (IDC) dominate the death cases of it. Detecting the features of such disease in an X-ray image by human eyes can be challenged, especially in cancer's early stage. Thus, this study is aimed at developing a system to assist doctors' diagnoses and help the patient to have a preliminary understanding of their own health conditions. More specifically, an IDC detection system based on the Convolutional Neural Network (CNN) is developed, where the DenseNet121 is applied here. In fact, DenseNet 169 and DenseNet 201 are also tested but their performances are not as good as DenseNet121 in this study. As is expected, the system can automatically judge whether the region in a breast histology image is IDC positive or not. This method achieves a high precision, 0.9725 validation accuracy, 0.97 test accuracy, 0.96 recall, 0.96 F1-score, and 0.965 AUC in the sub-dataset selected from Kaggle's Breast Histopathology images dataset. The time to predict 200 images is about 54 seconds and so the average prediction time for a single image is 2.7 s, which is fast enough for practical use.

Objective: To evaluate the effects of a quantitative measurement and potential diagnosis of dental caries methodology based on fusion modeling of intraoral scanner (IOS) and cone-beam computerized tomography (CBCT).Materials and methods: Sixty extracted human permanent mandibular molar teeth were collected from Tianjin Stomatological Hospital. Two independent caries detection methods were used as index tests: assessment using fusion modeling of IOS and CBCT, visual-tactile examination with periapical radiographic assessment using international caries detection and assessment system (ICDAS) criteria. Histological assessment was used as the reference test. One-way ANOVA test was used for statistical analysis.Results: The teeth samples presented various caries stages among each groups. Fusion modeling of IOS and CBCT gave an accuracy of 96.7% while visual-tactile examination with periapical radiographic assessment reached 83.3% and 81.7%, respectively. Most index tests showed significant correlation with histological assessments. IOS-CBCT fusion group resulted in the highest reliability, and the highest sum of sensitivity (SE) and specificity (SP) (sum SE-SP: 1.60–1.84). Conclusions: The measurement and diagnosis of dental caries methodology based on fusion modeling of IOS and CBCT images was noninvasive and sensitive. The findings reported here shed new light on dental caries detection, classification, diagnosis, treatment and prevention. Clinical relevance: This dental caries measurement and diagnosis method offered a foundation for risk assessment and surveillance, disease prevention and health promotion of dental caries. It could be helpful in supporting the continuing development of caries lesion classification and disease management systems, and facilitating communication between practitioners, patients, researchers and policymakers.