Multiple effects of dose-related GM-CSF on periodontal resorption in deep-frozen grafted teeth: A reverse study.

Autologous tooth grafting is a dental restorative modality based on periodontal ligament healing.Human periodontal ligament stem cells(PDLSCs) are involved in the formation and remodeling of periodontal tissue.Based on previous findings, the proliferation and differentiation of processing cryopreserved periodontal ligament stem cells (PDLSCs) exhibit similarities to those of fresh cells. However, there is evident absorption in the transplanted frozen tooth's roots and bones, with the underlying cause remaining unknown. Granulocyte macrophage colony-stimulating factor(GM-CSF) is named for its produce granulocyte and macrophage precursors from bone marrow precursors, and it also serves as one of the regulatory factors in inflammatory and osteoclast formation. This study aimed to investigate changes in GM-CSF expression in frozen PDLSCs (fhPDLSCs) and evaluate the impact of GM-CSF on PDLSCs with respect to cellular activity and osteogenic ability. The role of GM-CSF in periodontal absorption was further speculated by comparing with IL-1ß. The results revealed a significant increase in GM-CSF levels from fhPDLSCs compared to fresh cells, which exhibited an equivalent inflammatory stimulation effect as 1 ng/ml IL-1ß. Cell viability also increased with increasing concentrations of GM-CSF; however, the GM-CSF from fhPDLSCs was not sufficient to significantly trigger osteoclastic factors. Considering its interaction with IL-1ß and positive feedback mechanism, environments with high doses of GM-CSF derived from fhPDLSCs are more likely to activate osteoclastic responses.Therefore, for frozen tooth replantation, great attention should be paid to anti-inflammation and anti-infection.GM-CSF may serve as a potential therapeutic target for inhibiting periodontal resorption in delayed grafts.

Refined secondary Bjerknes force equation for double bubbles with pulsation, translation, and deformation.

The secondary Bjerknes force (SBF) is the time-averaged interaction between two bubbles driven in a sound field. We derived a refined formula for the interaction force, incorporating the radial vibration and translational and deformational motions of the bubble. The coupling of pulsation, translation, and deformation enhances the interaction between bubbles but also weakens their stability, making it easier for bubbles to merge or break during motion. The effects of the coupling mode on the magnitude and direction of SBFs coupled with pulsation, translation, and deformation were numerically analyzed and studied. Under certain sound-field conditions, the SBF increased with increasing pressure amplitude, initial radius, and initial velocity, while decreased as the distance increased. In addition, the SBF irregularly increased with increasing frequency.

3D Printing Technology with Reverse Guide Design in Autogenous Tooth Transplantation: An in vitro Pictorial Report.

The success of autologous tooth transplantation depends on the activity of the periodontal ligament of the donor tooth. Its activity decreases with a longer exposure time. In order to reduce the exposure time of the donor tooth and quickly prepare the alveolar fossa highly matching with the donor tooth root, the clinical data of cone-beam CT (CBCT) was imported into Mimics 21.0 software in this study to obtain three-dimensional (3D) images of the jaw tissue structure. The images were used to extract the target area and select the target tooth. By analysing the information of the recipient region and the donor region, 3-Matic 13.0 software was used to implant the donor virtual into the target region, and restrictive personalised guidance was designed. Subsequently, the surgical guide template was printed by 3D printing technology, and the alveolar fossa was rapidly prepared in vitro. After the operation, the donor tooth was matched with the complete alveolar cavity using 3-Matic13.0 software. The depth deviation of the prepared alveolar cavity was measured within 2.0 mm, and the width deviation was about 1.0 mm. The maximum width deviation is 2.88 mm due to the tilt of the roots. With a high matching degree to the donor tooth root, it needs further and larger clinical studies. Key Words: CBCT, Tooth autotransplantation, 3D printing technology, Computer-aided imaging, Periodontal ligament.

Quantitative phase imaging of living red blood cells combining digital holographic microscopy and deep learning.

Digital holographic microscopy as a non-contacting, non-invasive, and highly accurate measurement technology, is becoming a valuable method for quantitatively investigating cells and tissues. Reconstruction of phases from a digital hologram is a key step in quantitative phase imaging for biological and biomedical research. This study proposes a two-stage deep convolutional neural network named VY-Net, to realize the effective and robust phase reconstruction of living red blood cells. The VY-Net can obtain the phase information of an object directly from a single-shot off-axis digital hologram. We also propose two new indices to evaluate the reconstructed phases. In experiments, the mean of the structural similarity index of reconstructed phases can reach 0.9309, and the mean of the accuracy of reconstructions of reconstructed phases is as high as 91.54%. An unseen phase map of a living human white blood cell is successfully reconstructed by the trained VY-Net, demonstrating its strong generality.

Dynamics of three cavitation bubbles with pulsation and symmetric deformation.

A new system of dynamical equations was obtained by using the perturbation and potential flow theory to couple the pulsation and surface deformation of the second-order Legendre polynomials (P2) of three bubbles in a line. The feasibility and effectiveness of the model were verified by simulating the radial oscillations, surface deformation with P2, and shape evolution of three bubbles. The spherical radial pulsation and surface deformation of the three bubbles exhibit periodic behavior. The maximum secondary Bjerknes forces (SBFs) on the three bubbles are found not to depend on the system's resonance frequency. Within a stable region, the SBFs of the three bubbles increase with increasing sound pressure amplitude but decrease with increasing distance between the bubbles. The primary Bjerknes force (PBF) on a bubble is significantly higher than the SBF on it.

Synchronous Phase-Shifting Interference for High Precision Phase Imaging of Objects Using Common Optics.

Quantitative phase imaging and measurement of surface topography and fluid dynamics for objects, especially for moving objects, is critical in various fields. Although effective, existing synchronous phase-shifting methods may introduce additional phase changes in the light field due to differences in optical paths or need specific optics to implement synchronous phase-shifting, such as the beamsplitter with additional anti-reflective coating and a micro-polarizer array. Therefore, we propose a synchronous phase-shifting method based on the Mach-Zehnder interferometer to tackle these issues in existing methods. The proposed method uses common optics to simultaneously acquire four phase-shifted digital holograms with equal optical paths for object and reference waves. Therefore, it can be used to reconstruct the phase distribution of static and dynamic objects with high precision and high resolution. In the experiment, the theoretical resolution of the proposed system was 1.064 µm while the actual resolution could achieve 1.381 µm, which was confirmed by measuring a phase-only resolution chart. Besides, the dynamic phase imaging of a moving standard object was completed to verify the proposed system's effectiveness. The experimental results show that our proposed method is suitable and promising in dynamic phase imaging and measurement of moving objects using phase-shifting digital holography.

Effects of cryopreservation on the biomechanical properties of dentin in cryopreserved teeth: An in-vitro study.

This study focused on the biomechanical properties and microstructural changes in dentin of teeth in different age groups after cryopreserved for different durations. Ninety third molars from three age groups (youth group, middle-age group, and elderly group), were collected and randomly divided into three groups according to freezing time at -196 °C (7 days, 30 days, and 90 days). Control group was shored at ordinary temperature. After rewarming, the compressive strength and elastic modulus of the dentin were measured with an electronic universal tester. Scanning electron microscopy was used to evaluate the microstructure of dentin after cryopreservation. After cryopreservation, the compressive strength of the teeth in each experimental group was not significantly different from control group. With the increase of freezing time and age, dentin's elastic modulus showed a decreasing trend. There were statistically significances between the control group and freezing 90d group, freezing 7d and 90d group, youth and middle-aged group, youth and elderly group (P < 0.05). Both freezing time and age factors were significant for the elastic modulus of dentin(P＜0.05). There was no interaction effect for age and freezing time. In transverse sections of scanning electron microscopy, the dentinal tubule became narrower, partially occluded, and more easily adhered to impurities in the long freezing time and elderly group. In longitudinal sections, with freezing time and age, the inner wall of the dentinal tubules became rough especially in the aged group cryopreserved for 90 days. No significant microcracks exited in any of the longitudinal sections of dentin.

CGUN-2A: Deep Graph Convolutional Network via Contrastive Learning for Large-Scale Zero-Shot Image Classification.

Taxonomy illustrates that natural creatures can be classified with a hierarchy. The connections between species are explicit and objective and can be organized into a knowledge graph (KG). It is a challenging task to mine features of known categories from KG and to reason on unknown categories. Graph Convolutional Network (GCN) has recently been viewed as a potential approach to zero-shot learning. GCN enables knowledge transfer by sharing the statistical strength of nodes in the graph. More layers of graph convolution are stacked in order to aggregate the hierarchical information in the KG. However, the Laplacian over-smoothing problem will be severe as the number of GCN layers deepens, which leads the features between nodes toward a tendency to be similar and degrade the performance of zero-shot image classification tasks. We consider two parts to mitigate the Laplacian over-smoothing problem, namely reducing the invalid node aggregation and improving the discriminability among nodes in the deep graph network. We propose a top-k graph pooling method based on the self-attention mechanism to control specific node aggregation, and we introduce a dual structural symmetric knowledge graph additionally to enhance the representation of nodes in the latent space. Finally, we apply these new concepts to the recently widely used contrastive learning framework and propose a novel Contrastive Graph U-Net with two Attention-based graph pooling (Att-gPool) layers, CGUN-2A, which explicitly alleviates the Laplacian over-smoothing problem. To evaluate the performance of the method on complex real-world scenes, we test it on the large-scale zero-shot image classification dataset. Extensive experiments show the positive effect of allowing nodes to perform specific aggregation, as well as homogeneous graph comparison, in our deep graph network. We show how it significantly boosts zero-shot image classification performance. The Hit@1 accuracy is 17.5% relatively higher than the baseline model on the ImageNet21K dataset.

VDE-Net: a two-stage deep learning method for phase unwrapping.

Phase unwrapping is a critical step to obtaining a continuous phase distribution in optical phase measurements and coherent imaging techniques. Traditional phase-unwrapping methods are generally low performance due to significant noise or undersampling. This paper proposes a deep convolutional neural network (DCNN) with a weighted jump-edge attention mechanism, namely, VDE-Net, to realize effective and robust phase unwrapping. Experimental results revealed that the weighted jump-edge attention mechanism, which is first proposed and simple to calculate, is useful for phase unwrapping. The proposed algorithm outperformed other networks or common attention mechanisms. In addition, an unseen wrapped phase image of a living red blood cell (RBC) was successfully unwrapped by the trained VDE-Net, thereby demonstrating its strong generalization capability.

Micro vision-based measurement of concentricity for both TO base and active area of a APD chip in optical component packaging.

The avalanche photodiode (APD) chip is the core component of the transistor outline (TO). The concentricity between the inner circle (IC) of the APD active area and the outer circle (OC) of the TO base will directly affect a component's key performance indicators, such as external quantum efficiency, receiving sensitivity and responsivity, thereby impacting quality assurance, performance improvement, and stable operation. Nevertheless, as the surge in demand for components increases, the traditional visual inspection relying on manual and microscope has been unable to meet the requirements of mass manufacturing for real-time quality and efficiency. Thus, a Concentricity Microscopic Vision Measurement System (CMVMS) mainly composed of a microscopic vision acquisition unit and an intelligent concentricity measurement unit has been proposed, designed, and implemented. On the basis of analyzing the 3D complex environment of TO components, a coaxial illumination image acquisition scheme that can take into account the characteristics of the OC and IC has been proposed. Additionally, a concentricity image measurement method based on dynamic threshold segmentation has been designed to reduce the interference of complex industrial environment changes on measurement accuracy. The experiment results show that the measurement accuracy of the CMVMS system is over 97%, and with a single measurement time of less than 0.2s, it can better meet the real-time and accuracy requirements. To the best of our knowledge, this is the first report on the realization of real-time concentricity measurement in optical component packaging, and this technology can be extended to other fields of concentricity measurement.

Automatic counting of retinal ganglion cells in the entire mouse retina based on improved YOLOv5.

Glaucoma is characterized by the progressive loss of retinal ganglion cells (RGCs), although the pathogenic mechanism remains largely unknown. To study the mechanism and assess RGC degradation, mouse models are often used to simulate human glaucoma and specific markers are used to label and quantify RGCs. However, manually counting RGCs is time-consuming and prone to distortion due to subjective bias. Furthermore, semi-automated counting methods can produce significant differences due to different parameters, thereby failing objective evaluation. Here, to improve counting accuracy and efficiency, we developed an automated algorithm based on the improved YOLOv5 model, which uses five channels instead of one, with a squeeze-and-excitation block added. The complete number of RGCs in an intact mouse retina was obtained by dividing the retina into small overlapping areas and counting, and then merging the divided areas using a non-maximum suppression algorithm. The automated quantification results showed very strong correlation (mean Pearson correlation coefficient of 0.993) with manual counting. Importantly, the model achieved an average precision of 0.981. Furthermore, the graphics processing unit (GPU) calculation time for each retina was less than 1 min. The developed software has been uploaded online as a free and convenient tool for studies using mouse models of glaucoma, which should help elucidate disease pathogenesis and potential therapeutics.

A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning.

Vaginitis is a gynecological disease affecting the health of millions of women all over the world. The traditional diagnosis of vaginitis is based on manual microscopy, which is time-consuming and tedious. The deep learning method offers a fast and reliable solution for an automatic early diagnosis of vaginitis. However, deep neural networks require massive well-annotated data. Manual annotation of microscopic images is highly cost extensive because it not only is a time-consuming process but also needs highly trained people (doctors, pathologists, or technicians). Most existing active learning approaches are not applicable in microscopic images due to the nature of complex backgrounds and numerous formed elements. To address the problem of high cost of labeling microscopic images, we present a data-efficient framework for the identification of vaginitis based on transfer learning and active learning strategies. The proposed informative sample selection strategy selected the minimal training subset, and then the pretrained convolutional neural network (CNN) was fine-tuned on the selected subset. The experiment results show that the proposed pipeline can save 37.5% annotation cost while maintaining competitive performance. The proposed promising novel framework can significantly save the annotation cost and has the potential of extending widely to other microscopic imaging applications, such as blood microscopic image analysis.

End-to-End Deep Learning-Based Cells Detection in Microscopic Leucorrhea Images.

Vaginitis is a prevalent gynecologic disease that threatens millions of women's health. Although microscopic examination of vaginal discharge is an effective method to identify vaginal infections, manual analysis of microscopic leucorrhea images is extremely time-consuming and labor-intensive. To automate the detection and identification of visible components in microscopic leucorrhea images for early-stage diagnosis of vaginitis, we propose a novel end-to-end deep learning-based cells detection framework using attention-based detection with transformers (DETR) architecture. The transfer learning was applied to speed up the network convergence while maintaining the lowest annotation cost. To address the issue of detection performance degradation caused by class imbalance, the weighted sampler with on-the-fly data augmentation module was integrated into the detection pipeline. Additionally, the multi-head attention mechanism and the bipartite matching loss system of the DETR model perform well in identifying partially overlapping cells in real-time. With our proposed method, the pipeline achieved a mean average precision (mAP) of 86.00% and the average precision (AP) of epithelium, leukocyte, pyocyte, mildew, and erythrocyte was 96.76, 83.50, 74.20, 89.66, and 88.80%, respectively. The average test time for a microscopic leucorrhea image is approximately 72.3 ms. Currently, this cell detection method represents state-of-the-art performance.

Morphological components detection for super-depth-of-field bio-micrograph based on deep learning.

Accompanied with the clinical routine examination demand increase sharply, the efficiency and accuracy are the first priority. However, automatic classification and localization of cells in microscopic images in super depth of Field (SDoF) system remains great challenges. In this paper, we advance an object detection algorithm for cells in the SDoF micrograph based on Retinanet model. Compared with the current mainstream algorithm, the mean average precision (mAP) index is significantly improved. In the experiment of leucorrhea samples and fecal samples, mAP indexes are 83.1% and 88.1%, respectively, with an average increase of 10%. The object detection model proposed in this paper can be applied to feces and leucorrhea detection equipment, and significantly improve the detection efficiency and accuracy.

SDoF-Net: Super Depth of Field Network for Cell Detection in Leucorrhea Micrograph.

Accompanied with the rapid increase of the demand for routine examination of leucorrhea, efficiency and accuracy become the primary task. However, in super depth of field (SDoF) system, the problem of automatic detection and localization of cells in leucorrhea micro-images is still a big challenge. The changing of the relative position between the cell center and focus plane of microscope lead to variable cell morphological structure in the two-dimensional image, which is an important reason for the low accuracy of current deep learning target detection algorithms. In this paper, an object detection method based on Retinanet in state of super depth of field is proposed, which can achieve high precision detecting of leucorrhea components by the SDoF feature aggregation module. Compared with the current mainstream algorithms, the mean average accuracy (mAP) index has been improved significantly, the mAP index is 82.7% for SDoF module and 83.0% for SDoF+ module, with an average increase of more than 10%. These improved features can significantly improve the efficiency and accuracy of the algorithm. The algorithm proposed in this paper can be integrated into the leucorrhea automatic detection system.

Automatic Semicircular Canal Segmentation of CT Volumes Using Improved 3D U-Net with Attention Mechanism.

The vestibular system is the sensory apparatus that helps the body maintain its postural equilibrium, and semicircular canal is an important organ of the vestibular system. The semicircular canals are three membranous tubes, each forming approximately two-thirds of a circle with a diameter of approximately 6.5 mm, and segmenting them accurately is of great benefit for auxiliary diagnosis, surgery, and treatment of vestibular disease. However, the semicircular canal has small volume, which accounts for less than 1% of the overall computed tomography image. Doctors have to annotate the image in a slice-by-slice manner, which is time-consuming and labor-intensive. To solve this problem, we propose a novel 3D convolutional neural network based on 3D U-Net to automatically segment the semicircular canal. We added the spatial attention mechanism of 3D spatial squeeze and excitation modules, as well as channel attention mechanism of 3D global attention upsample modules to improve the network performance. Our network achieved an average dice coefficient of 92.5% on the test dataset, which shows competitive performance in semicircular canals segmentation task.

Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN.

Fecal samples can easily be collected and are representative of a person's current health state; therefore, the demand for routine fecal examination has increased sharply. However, manual operation may pollute the samples, and low efficiency limits the general examination speed; therefore, automatic analysis is needed. Nevertheless, recognition exhaustion time and accuracy remain major challenges in automatic testing. Here, we introduce a fast and efficient cell-detection algorithm based on the Faster-R-CNN technique: the Resnet-152 convolutional neural network architecture. Additionally, a region proposal network and a network combined with principal component analysis are proposed for cell location and recognition in microscopic images. Our algorithm achieved a mean average precision of 84% and a 723 ms detection time per sample for 40,560 fecal images. Thus, this approach may provide a solid theoretical basis for real-time detection in routine clinical examinations while accelerating the process to satisfy increasing demand.

Detection and compensation of dispersion mismatch for frequency-domain optical coherence tomography based on A-scan's spectrogram.

Balanced dispersion between reference and sample arms is critical in frequency-domain optical coherence tomography (FD-OCT) to perform imaging with the optimal axial resolution, and the spectroscopic analysis of each voxel in FD-OCT can provide the metric of the spectrogram. Here we revisited dispersion mismatch in the spectrogram view using the spectroscopic analysis of voxels in FD-OCT and uncovered that the dispersion mismatch disturbs the A-scan's spectrogram and reshapes the depth-resolved spectra in the spectrogram. Based on this spectroscopic effect of dispersion mismatch on A-scan's spectrogram, we proposed a numerical method to detect dispersion mismatch and perform dispersion compensation for FD-OCT. The proposed method can visually and quantitatively detect and compensate for dispersion mismatch in FD-OCT, with visualization, high sensitivity, and independence from sample structures. Experimental results of tape and mouse eye suggest that this technique can be an effective method for the detection and compensation of dispersion mismatch in FD-OCT.

Automatic classification of cells in microscopic fecal images using convolutional neural networks.

The analysis of fecal-type components for clinical diagnosis is important. The main examination involves the counting of red blood cells (RBCs), white blood cells (WBCs), and molds under the microscopic. With the development of machine vision, some vision-based detection schemes have been proposed. However, these methods have a single target for detection, with low detection efficiency and low accuracy. We proposed an algorithm to identify the visible image of fecal composition based on intelligent deep learning. The algorithm mainly includes region proposal and candidate recognition. In the process of segmentation, we proposed a morphology extraction algorithm in a complex background. As for the candidate recognition, we proposed a new convolutional neural network (CNN) architecture based on Inception-v3 and principal component analysis (PCA). This method achieves high-average Precision of 90.7%, which is better than the other mainstream CNN models. Finally, the images within the rectangle marks were obtained. The total time for detection of an image was roughly 1200 ms. The algorithm proposed in the present paper can be integrated into an automatic fecal detection system.

Trichomonas Detection in Leucorrhea Based on VIBE Method.

Trichomonas examination is one of the important items in the leucorrhea routine detection. And it cannot be recognized by still images because of the unstable morphology and unfixed focal location caused by motion characteristic. We proposed an improved VIBE algorithm. 6 videos (totally 1414 frames) are collected for testing. In order to compare the effects of the algorithms, we segment each frame artificially as ground truth. Experiments show that percentage of correct classification (PCC) achieves 88%. The proposed improved method can effectively suppress the false detection caused by the formed components such as epithelial cells in the leucorrhea microscopic image and the missed detection caused by the background model update during the movement. At the same time, improvements can effectively suppress smear and ghost areas. The algorithm proposed in this paper can be integrated into the leucorrhea automatic detection system.