M3YOLOv5: Feature enhanced YOLOv5 model for mandibular fracture detection.

BACKGROUND: It is very important to detect mandibular fracture region. However, the size of mandibular fracture region is different due to different anatomical positions, different sites and different degrees of force. It is difficult to locate and recognize fracture region accurately. METHODS: To solve these problems, M3YOLOv5 model is proposed in this paper. Three feature enhancement strategies are designed, which improve the ability of model to locate and recognize mandibular fracture region. Firstly, Global-Local Feature Extraction Module (GLFEM) is designed. By effectively combining Convolutional Neural Network (CNN) and Transformer, the problem of insufficient global information extraction ability of CNN is complemented, and the positioning ability of the model to the fracture region is improved. Secondly, in order to improve the interaction ability of context information, Deep-Shallow Feature Interaction Module (DSFIM) is designed. In this module, the spatial information in the shallow feature layer is embedded to the deep feature layer by the spatial attention mechanism, and the semantic information in the deep feature layer is embedded to the shallow feature layer by the channel attention mechanism. The fracture region recognition ability of the model is improved. Finally, Multi-scale Multi receptive-field Feature Mixing Module (MMFMM) is designed. Deep separate convolution chains are used in this modal, which is composed by multiple layers of different scales and different dilation coefficients. This method provides richer receptive field for the model, and the ability to detect fracture region of different scales is improved. RESULTS: The precision rate, mAP value, recall rate and F1 value of M3YOLOv5 model on mandibular fracture CT data set are 97.18%, 96.86%, 94.42% and 95.58% respectively. The experimental results show that there is better performance about M3YOLOv5 model than the mainstream detection models. CONCLUSION: The M3YOLOv5 model can effectively recognize and locate the mandibular fracture region, which is of great significance for doctors' clinical diagnosis.

Teeth U-Net: A segmentation model of dental panoramic X-ray images for context semantics and contrast enhancement.

BACKGROUND AND OBJECTIVE: It is very significant in orthodontics and restorative dentistry that the teeth are segmented from dental panoramic X-ray images. Nevertheless, there are some problems in panoramic X-ray images of teeth, such as blurred interdental boundaries, low contrast between teeth and alveolar bone. METHODS: In this paper, The Teeth U-Net model is proposed in this paper to resolve these problems. This paper makes the following contributions: Firstly, a Squeeze-Excitation Module is utilized in the encoder and the decoder. And proposing a dense skip connection between encoder and decoder to reduce the semantic gap. Secondly, due to the irregular shape of the teeth and the low contrast of the dental panoramic X-ray images. A Multi-scale Aggregation attention Block (MAB) in the bottleneck layer is designed to resolve this problem, which can effectively extract teeth shape features and fuse multi-scale features adaptively. Thirdly, in order to capture dental feature information in a larger field of perception, this paper designs a Dilated Hybrid self-Attentive Block (DHAB) at the bottleneck layer. This module effectively suppresses the task-irrelevant background region information without increasing the network parameters. Finally, the effectiveness of the algorithm is validated using a clinical dental panoramic X-ray image datasets. RESULTS: The results of the three comparison experiments are shown that Accuracy, Precision, Recall, Dice, Volumetric Overlap Error and Relative Volume Difference for dental panoramic X-ray teeth segmentation are 98.53%, 95.62%, 94.51%, 94.28%, 88.92% and 95.97% by the proposed model respectively. CONCLUSION: The proposed modules complement each other in processing every detail of the dental panoramic X-ray images, which can effectively improve the efficiency of preoperative preparation and postoperative evaluation, and promote the application of dental panoramic X-ray in medical image segmentation. There are more accuracy about Teeth U-Net than others model in dental panoramic X-ray teeth segmentation. That is very important to clinical doctors to cure in orthodontics and restorative dentistry.

Preparation, characterization and in vivo pharmacokinetic study of ginsenoside Rb1-PLGA nanoparticles.

This study aimed to construct a Ginsenoside Rb1-PLGA nano drug delivery system, optimize its preparation process, characterize and evaluate the resulting Ginsenoside Rb1-PLGA Nanoparticles (GRb1@PLGA@NPs). GRb1@PLGA@NPs were prepared using the emulsion solvent evaporation method. The optimal preparation process was determined using Plackett-Burman design combined with Box-Behnken experiments. Physical characterization and in vitro release studies were conducted. LC-MS/MS technique was employed to investigate the pharmacokinetic characteristics of GRb1 and GRb1@PLGA@NPs in rat plasma. The optimal preparation process yielded GRb1@PLGA@NPs with a particle size of 120.63 nm, polydispersity index (PDI) of 0.172, zeta potential of - 22.67 mV, encapsulation efficiency of 75%, and drug loading of 11%. In vitro release demonstrated sustained drug release. Compared to GRb1, GRb1@PLGA@NPs exhibited a shortened time to peak concentration by approximately 0.72-fold. The area under the plasma concentration-time curve significantly increased to 4.58-fold of GRb1. GRb1@PLGA@NPs formulated using the optimal process exhibited uniform distribution and stable quality, its relative oral bioavailability was significantly improved compared to free GRb1.

[Detection of matrix metalloproteinase-11 in menstrual blood by enhanced chemiluminescence method].

OBJECTIVE: To explore the forensic application value of detection of matrix metalloproteinase-11 (MMP-11) in menstrual blood by enhanced chemiluminescence method. METHODS: Menstrual blood, vaginal swab, peripheral blood, saliva stain, urine stain and semen stain were collected to detect whether or not there were MMP-11 using enhanced chemiluminescence method. The specificity and reliability of the MMP-11 assay along with its sensitivity were evaluated. RESULTS: The positive detection rate of MMP-11 in menstrual blood was 89.47%, whereas no MMP-11 was found in vaginal swab, peripheral blood, saliva stain, urine stain and semen stain. When 25 microL sample was added, the mass concentration of protein was 1.329 microg/microL, then MMP-11 could be detected. A positive detection rate of 89.58% was observed in MMP-11 positive menstrual blood samples after stored at 4 degrees C for 20 months. CONCLUSION: Enhanced chemiluminescence method is sensitive and specific for detecting MMP-11, and can be applied to distinguish menstrual blood from common stain such as peripheral blood, vaginal fluid.