Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population.

Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.

The Induction of Combined Hyperthermal Ablation Effect of Irreversible Electroporation with Polydopamine Nanoparticle-Coated Electrodes.

Irreversible electroporation (IRE) is a prominent non-thermal ablation method widely employed in clinical settings for the focal ablation therapy of solid tumors. Utilizing high-voltage, short-duration electric pulses, IRE induces perforation defects in the cell membrane, leading to apoptotic cell death. Despite the promise of irreversible electroporation (IRE) in clinical applications, it faces challenges concerning the coverage of target tissues for ablation, particularly when compared to other thermal ablation therapies such as radiofrequency ablation, microwave ablation, and cryoablation. This study aims to investigate the induced hyperthermal effect of IRE by applying a polydopamine nanoparticle (Dopa NP) coating on the electrode. We hypothesize that the induced hyperthermal effect enhances the therapeutic efficacy of IRE for cancer ablation. First, we observed the hyperthermal effect of IRE using Dopa NP-coated electrodes in hydrogel phantom models and then moved to in vivo models. In particular, in in vivo animal studies, the IRE treatment of rabbit hepatic lobes with Dopa NP-coated electrodes exhibited a two-fold higher increase in temperature (ΔT) compared to non-coated electrodes. Through a comprehensive analysis, we found that IRE treatment with Dopa NP-coated electrodes displayed the typical histological signatures of hyperthermal ablation, including the disruption of the hepatic cord and lobular structure, as well as the infiltration of erythrocytes. These findings unequivocally highlight the combined efficacy of IRE with Dopa NPs for electroporation and the hyperthermal ablation of target cancer tissues.

Automatic classification of 3D positional relationship between mandibular third molar and inferior alveolar canal using a distance-aware network.

The purpose of this study was to automatically classify the three-dimensional (3D) positional relationship between an impacted mandibular third molar (M3) and the inferior alveolar canal (MC) using a distance-aware network in cone-beam CT (CBCT) images. We developed a network consisting of cascaded stages of segmentation and classification for the buccal-lingual relationship between the M3 and the MC. The M3 and the MC were simultaneously segmented using Dense121 U-Net in the segmentation stage, and their buccal-lingual relationship was automatically classified using a 3D distance-aware network with the multichannel inputs of the original CBCT image and the signed distance map (SDM) generated from the segmentation in the classification stage. The Dense121 U-Net achieved the highest average precision of 0.87, 0.96, and 0.94 in the segmentation of the M3, the MC, and both together, respectively. The 3D distance-aware classification network of the Dense121 U-Net with the input of both the CBCT image and the SDM showed the highest performance of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve, each of which had a value of 1.00. The SDM generated from the segmentation mask significantly contributed to increasing the accuracy of the classification network. The proposed distance-aware network demonstrated high accuracy in the automatic classification of the 3D positional relationship between the M3 and the MC by learning anatomical and geometrical information from the CBCT images.

Comparison of 2D, 2.5D, and 3D segmentation networks for maxillary sinuses and lesions in CBCT images.

BACKGROUND: The purpose of this study was to compare the segmentation performances of the 2D, 2.5D, and 3D networks for maxillary sinuses (MSs) and lesions inside the maxillary sinus (MSL) with variations in sizes, shapes, and locations in cone beam CT (CBCT) images under the same constraint of memory capacity. METHODS: The 2D, 2.5D, and 3D networks were compared comprehensively for the segmentation of the MS and MSL in CBCT images under the same constraint of memory capacity. MSLs were obtained by subtracting the prediction of the air region of the maxillary sinus (MSA) from that of the MS. RESULTS: The 2.5D network showed the highest segmentation performances for the MS and MSA compared to the 2D and 3D networks. The performances of the Jaccard coefficient, Dice similarity coefficient, precision, and recall by the 2.5D network of U-net + + reached 0.947, 0.973, 0.974, and 0.971 for the MS, respectively, and 0.787, 0.875, 0.897, and 0.858 for the MSL, respectively. CONCLUSIONS: The 2.5D segmentation network demonstrated superior segmentation performance for various MSLs with an ensemble learning approach of combining the predictions from three orthogonal planes.

Anti-Inflammatory, Antioxidative, and Nitric Oxide-Scavenging Activities of a Quercetin Nanosuspension with Polyethylene Glycol in LPS-Induced RAW 264.7 Macrophages.

Quercetin (Qu) is a dietary antioxidant and a member of flavonoids in the plant polyphenol family. Qu has a high ability to scavenge reactive oxygen species (ROS) and reactive nitrogen species (RNS) molecules; hence, exhibiting beneficial effects in preventing obesity, diabetes, cancer, cardiovascular diseases, and inflammation. However, quercetin has low bioavailability due to poor water solubility, low absorption, and rapid excretion from the body. To address these issues, the usage of Qu nanosuspensions can improve physical stability, solubility, and pharmacokinetics. Therefore, we developed a Qu and polyethylene glycol nanosuspension (Qu-PEG NS) and confirmed its interaction by Fourier transform infrared analysis. Qu-PEG NS did not show cytotoxicity to HaCaT and RAW 264.7 cells. Furthermore, Qu-PEG NS effectively reduced the nitrogen oxide (NO) production in lipopolysaccharide (LPS)-induced inflammatory RAW 264.7 cells. Additionally, Qu-PEG NS effectively lowered the levels of COX-2, NF-κB p65, and IL-1ß in the LPS-induced inflammatory RAW 264.7 cells. Specifically, Qu-PEG NS exhibited anti-inflammatory properties by scavenging the ROS and RNS and mediated the inhibition of NF-κB signaling pathways. In addition, Qu-PEG NS had a high antioxidant effect and antibacterial activity against Escherichia coli and Bacillus cereus. Therefore, the developed novel nanosuspension showed comparable antioxidant, anti-inflammatory, and antibacterial functions and may also improve solubility and physical stability compared to raw quercetin.

Fabrication of Eudragit polymeric nanoparticles using ultrasonic nebulization method for enhanced oral absorption of megestrol acetate.

Megestrol acetate (MGA) is used as a progestagen to treat advanced cancers in the breast or uterus and anorexia-cachexia syndrome in cancer patients. Due to its low solubility (BCS class II), MGA bioavailability needs to be enhanced for efficacy and safety. We developed MGA-encapsulated Eudragit® L100 (EUD) nanoparticles (MGA-EUD (1:1) and MGA-EUD (2:1)) using an ultrasonic nebulization method. MGA-EUD (1:1) and MGA-EUD (2:1) consisted of MGA and EUD at the mass ratios of 1:1 and 2:1. Their physicochemical properties, i.e. particle size, loading efficiency, morphology, and crystallinity were determined. Dissolution tests were performed using USP method II. For pharmacokinetics, they were orally administered at 50 mg/kg to mice. Microcrystalline MGA suspension (MGA-MC, Megace®, BMS) was used as control. MGA-EUD (1:1) and MGA-EUD (2:1) had a smooth and spherical shape of 0.70 and 1.05 µm in diameter with loading efficiencies of 93 and 95% showing amorphous states of MGA. They significantly enhanced the dissolution potential of MGA. Oral bioavailability of MGA-EUD (1:1) and MGA-EUD (2:1) increased 2.0- and 1.7-fold compared to that of MGA-MC. It suggests that ultrasonic nebulization method for the fabrication of polymeric nanoparticles is a promising approach to improve the bioavailability of poorly soluble drugs.

Communications Between the Trigeminal Nerve and the Facial Nerve in the Face: A Systematic Review.

The aim of the article is to elucidate the communications between the trigeminal nerve and facial nerve in the face. In a PubMed search, 328 studies were found using the terms 'trigeminal nerve, facial nerve, and communication.' The abstracts were read and 39 full-text articles were reviewed. Among them, 11 articles were analyzed. In the studies using dissection, the maxillary branch (V2) had the highest frequency (95.0% ±â€Š8.0%) of communication with the facial nerve, followed by the mandibular branch (V3) (76.7% ±â€Š38.5%). The ophthalmic branch (V1) had the lowest frequency of communication (33.8% ±â€Š19.5%). In a Sihler stain, all of the maxillary branches and mandibular branches had communications with the facial nerve and 85.7% (12/14 hemifaces) of the ophthalmic branches had communications. The frequency of communications between the trigeminal nerve and facial nerve were significantly higher (P = 0.00, t-test) in the studies using a Sihler stain (94.7% ±â€Š1.1%) than the studies using dissection (76.9 ±â€Š35.8). The reason for the significantly higher frequency of trigeminal-facial communication in the studies using a Sihler stain is because of the limitation of the Sihler stain itself. This technique cannot differentiate the motor nerves from sensory nerves at the periphery, and a crossover can be misinterpreted as communication near to nerve terminal.

Communications Between the Facial Nerve and the Vestibulocochlear Nerve, the Glossopharyngeal Nerve, and the Cervical Plexus.

The aim of this review is to elucidate the communications between the facial nerves or facial nerve and neighboring nerves: the vestibulocochlear nerve, the glossopharyngeal nerve, and the cervical plexus.In a PubMed search, 832 articles were searched using the terms "facial nerve and communication." Sixty-two abstracts were read and 16 full-text articles were reviewed. Among them, 8 articles were analyzed.The frequency of communication between the facial nerve and the vestibulocochlear nerve was the highest (82.3%) and the frequency of communication between the facial nerve and the glossopharyngeal nerve was the lowest (20%). The frequency of communication between the facial nerve and the cervical plexus was 65.2 ± 43.5%. The frequency of communication between the cervical branch and the marginal mandibular branch of the facial nerve was 24.7 ± 1.7%.Surgeons should be aware of the nerve communications, which are important during clinical examinations and surgical procedures of the facial nerves such as those communications involved in facial reconstructive surgery, neck dissection, and various nerve transfer procedures.

Paclitaxel-eluting nanofiber-covered self-expanding nonvascular stent for palliative chemotherapy of gastrointestinal cancer and its related stenosis.

Self-expanding non-vascular metal stents (SEMS) is now a choice of treatment for tumor-induced obstructive symptoms of gastrointestinal tract. But in-growing tumor causes re-stenosis. Here, we studied a paclitaxel-eluting nanofiber-covered stent for palliative chemotherapy of gastrointestinal cancer and its related stenosis. In vivo and in vitro feasibility of nanofiber-covered nonvascular stent was evaluated in this study. Nanofiber-covered stent released paclitaxel (PTX) in controlled manner for 30 days. PTX-NFM significantly inhibited the growth of CT-26 colon cancer in comparison with PTX injection. PTX maintained higher tumor concentrations over 1.0 µg/ml for more than 14 days without systemic exposure. TUNEL and H&E staining proved locally concentrated PTX induced the higher apoptosis than PTX injection. In this way, PTX-eluting nanofiber-covered stent possibly inhibits in-growth of cancer and extends patency of stent. Clinical feasibility of PTX-eluting nanofiber nonvascular stent for cholangiocarcinoma and gastrointestinal cancers will be investigated in further studies.

Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network.

OBJECTIVES: This study aimed to develop a robust and accurate deep learning network for detecting the posterior superior alveolar artery (PSAA) in dental cone-beam CT (CBCT) images, focusing on the precise localization of the centre pixel as a critical centreline pixel. METHODS: PSAA locations were manually labelled on dental CBCT data from 150 subjects. The left maxillary sinus images were horizontally flipped. In total, 300 datasets were created. Six different deep learning networks were trained, including 3D U-Net, deeply supervised 3D U-Net (3D U-Net DS), multi-scale deeply supervised 3D U-Net (3D U-Net MSDS), 3D Attention U-Net, 3D V-Net, and 3D Dense U-Net. The performance evaluation involved predicting the centre pixel of the PSAA. This was assessed using mean absolute error (MAE), mean radial error (MRE), and successful detection rate (SDR). RESULTS: The 3D U-Net MSDS achieved the best prediction performance among the tested networks, with an MAE measurement of 0.696 ± 1.552 mm and MRE of 1.101 ± 2.270 mm. In comparison, the 3D U-Net showed the lowest performance. The 3D U-Net MSDS demonstrated a SDR of 95% within a 2 mm MAE. This was a significantly higher result than other networks that achieved a detection rate of over 80%. CONCLUSIONS: This study presents a robust deep learning network for accurate PSAA detection in dental CBCT images, emphasizing precise centre pixel localization. The method achieves high accuracy in locating small vessels, such as the PSAA, and has the potential to enhance detection accuracy and efficiency, thus impacting oral and maxillofacial surgery planning and decision-making.

CACSNet for automatic robust classification and segmentation of carotid artery calcification on panoramic radiographs using a cascaded deep learning network.

Stroke is one of the major causes of death worldwide, and is closely associated with atherosclerosis of the carotid artery. Panoramic radiographs (PRs) are routinely used in dental practice, and can be used to visualize carotid artery calcification (CAC). The purpose of this study was to automatically and robustly classify and segment CACs with large variations in size, shape, and location, and those overlapping with anatomical structures based on deep learning analysis of PRs. We developed a cascaded deep learning network (CACSNet) consisting of classification and segmentation networks for CACs on PRs. This network was trained on ground truth data accurately determined with reference to CT images using the Tversky loss function with optimized weights by balancing between precision and recall. CACSNet with EfficientNet-B4 achieved an AUC of 0.996, accuracy of 0.985, sensitivity of 0.980, and specificity of 0.988 in classification for normal or abnormal PRs. Segmentation performances for CAC lesions were 0.595 for the Jaccard index, 0.722 for the Dice similarity coefficient, 0.749 for precision, and 0.756 for recall. Our network demonstrated superior classification performance to previous methods based on PRs, and had comparable segmentation performance to studies based on other imaging modalities. Therefore, CACSNet can be used for robust classification and segmentation of CAC lesions that are morphologically variable and overlap with surrounding structures over the entire posterior inferior region of the mandibular angle on PRs.

Deep learning-based automatic segmentation of the mandibular canal on panoramic radiographs: A multi-device study.

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion: This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

Automatic detection and classification of nasopalatine duct cyst and periapical cyst on panoramic radiographs using deep convolutional neural networks.

OBJECTIVE: The aim of this study was to evaluate a deep convolutional neural network (DCNN) method for the detection and classification of nasopalatine duct cysts (NPDC) and periapical cysts (PAC) on panoramic radiographs. STUDY DESIGN: A total of 1,209 panoramic radiographs with 606 NPDC and 603 PAC were labeled with a bounding box and divided into training, validation, and test sets with an 8:1:1 ratio. The networks used were EfficientDet-D3, Faster R-CNN, YOLO v5, RetinaNet, and SSD. Mean average precision (mAP) was used to assess performance. Sixty images with no lesion in the anterior maxilla were added to the previous test set and were tested on 2 dentists with no training in radiology (GP) and on EfficientDet-D3. The performances were comparatively examined. RESULTS: The mAP for each DCNN was EfficientDet-D3 93.8%, Faster R-CNN 90.8%, YOLO v5 89.5%, RetinaNet 79.4%, and SSD 60.9%. The classification performance of EfficientDet-D3 was higher than that of the GPs' with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 94.4%, 94.4%, 97.2%, 94.6%, and 97.2%, respectively. CONCLUSIONS: The proposed method achieved high performance for the detection and classification of NPDC and PAC compared with the GPs and presented promising prospects for clinical application.

Surface roughness and gloss alteration of polished resin composites with various filler types after simulated toothbrush abrasion.

Background/purpose: Few studies have focused on the influence of simulated toothbrush abrasion on the surface qualities of novel nanofilled and nanohybrid composites. The aim of the study was to evaluate the surface roughness and gloss values of resin-based composite (RBC) materials with various filler types before and after simulated toothbrush abrasion. Materials and methods: One nanofilled (Filtek Z350 XT [FT3]), two nanohybrids (Harmonize [HM] and Clearfil Majesty [CM]) and one microhybrid (Filtek Z250 [FT2]) were evaluated. Twelve specimens of each material were made and polished with silicon carbide sandpapers. Initial surface roughness and gloss values were measured as negative controls. Then, all specimens were subjected to simulated toothbrush abrasion on a custom-made apparatus. After 2000, 4000 and 8000 cycles, the surface roughness and gloss values of all specimens were tested. One additional specimen from each group was selected for scanning electron microscope (SEM) analysis. Results: For FT3, Ra and GU values did not significantly change until after 8000 cycles during the process of toothbrushing (P > 0.05). For HM, CM and FT2, the Ra and GU values significantly decreased after 4000 and 8000 cycles of toothbrush abrasion (P < 0.05). After 8000 cycles of toothbrush abrasion, FT3 presented the lowest surface roughness and highest gloss values of all materials (P < 0.05). SEM images showed that surface textures and irregularities corresponded to the results of surface roughness and gloss. Conclusion: Surface roughness and gloss after simulated toothbrush abrasion were material dependent. Nanofilled resin composite presented the lowest Ra values and highest GU values.

Ultrasonic interfacial crosslinking of TiO2-based nanocomposite hydrogels through thiol-norbornene reactions for sonodynamic antibacterial treatment.

Nanocomposite (NC) hydrogels used for sonodynamic therapy (SDT) face challenges such as lacking interfacial interactions between the polymers and nanomaterials as well as presenting uneven dispersion of nanomaterials in the hydrogel network, reducing their mechanical properties and treatment efficiency. Here, we demonstrate a promising approach of co-engineering nanomaterials and interfacial crosslinking to expand the materials construction and biomedical applications of NC hydrogels in SDT. In this work, mesoporous silica-coated titanium dioxide nanoparticles with thiolated surface functionalization (TiO2@MS-SH) are utilized as crosslinkers to react with norbornene-functionalized dextran (Nor-Dex) through ultrasound-triggered thiol-norbornene reactions, forming TiO2@MS-SH/Nor-Dex NC hydrogels. The TiO2@MS-SH nanoparticles act not only as multivalent crosslinkers to improve the mechanical properties of hydrogels under ultrasound irradiation but also as reactive oxygen species (ROS) generators to allow the use of TiO2@MS-SH/Nor-Dex NC hydrogels in SDT applications. Particularly, the TiO2@MS-SH/Nor-Dex NC hydrogels present tailorable microstructures, properties, and sonodynamic killing of bacteria through the modulation of the ultrasound frequency. Taken together, a versatile TiO2-based NC hydrogel platform prepared under ultrasonic interfacial crosslinking reactions is developed for advancing the applications in SDT.

Structure-preserving quality improvement of cone beam CT images using contrastive learning.

Cone-beam CT (CBCT) is widely used in dental clinics but exhibits limitations in assessing soft tissue pathology because of its lack of contrast resolution and low Hounsfield Units (HU) quantification accuracy. We aimed to increase the image quality and HU accuracy of CBCTs while preserving anatomical structures. We generated CT-like images from CBCT images using a patchwise contrastive learning-based GAN model. Our model was trained on unpaired CT and CBCT datasets with the novel combination of losses and the feature extractor pretrained on our training dataset. We evaluated the quality of the images generated by our model in terms of Fréchet inception distance (FID), peak signal-to-noise ratio (PSNR), mean absolute error (MAE), and root mean square error (RMSE). Additionally, the structure preservation performance was assessed by the structure score. As a result, the generated CT-like images by our model were significantly superior to those generated by various baseline models in terms of FID, PSNR, MAE, RMSE, and structure score. Therefore, we demonstrated that our model provided the complementary benefits of preserving the anatomical structures of the input CBCT images and improving the image quality to be similar to those of CT images.

SinusC-Net for automatic classification of surgical plans for maxillary sinus augmentation using a 3D distance-guided network.

The objective of this study was to automatically classify surgical plans for maxillary sinus floor augmentation in implant placement at the maxillary posterior edentulous region using a 3D distance-guided network on CBCT images. We applied a modified ABC classification method consisting of five surgical approaches for the deep learning model. The proposed deep learning model (SinusC-Net) consisted of two stages of detection and classification according to the modified classification method. In detection, five landmarks on CBCT images were automatically detected using a volumetric regression network; in classification, the CBCT images were automatically classified as to the five surgical approaches using a 3D distance-guided network. The mean MRE for landmark detection was 0.87 mm, and SDR for 2 mm or lower, 95.47%. The mean accuracy, sensitivity, specificity, and AUC for classification by the SinusC-Net were 0.97, 0.92, 0.98, and 0.95, respectively. The deep learning model using 3D distance-guidance demonstrated accurate detection of 3D anatomical landmarks, and automatic and accurate classification of surgical approaches for sinus floor augmentation in implant placement at the maxillary posterior edentulous region.

V2-Net: An Attention-guided Volumetric Regression Network for Tooth Landmark Localization on CT Images with Metal Artifacts.

For virtual surgical planning in orthognathic surgery, marking tooth landmarks on CT images is an important procedure. However, the manual localization procedure of tooth landmarks is time-consuming, labor-intensive, and requires expert knowledge. Also, direct and automatic tooth landmark localization on CT images is difficult because of the lower resolution and metal artifacts of dental images. The purpose of this study was to propose an attention-guided volumetric regression network (V2-Net) for accurate tooth landmark localization on CT images with metal artifacts and lower resolution. V2-Net has an attention-guided network architecture using a coarse-to-fine-attention mechanism that guided the 3D probability distribution of tooth landmark locations within anatomical structures from the coarse V-Net to the fine V-Net for more focus on tooth landmarks. In addition, we combined attention-guided learning and a 3D attention module with optimal Pseudo Huber loss to improve the localization accuracy. Our results show that the proposed method achieves state-of-the-art accuracy of 0.85 ± 0.40 mm in terms of mean radial error, outperforming previous studies. In ablation studies, we observed that the proposed attention-guided learning and a 3D attention module improved the accuracy of tooth landmark localization in CT images of lower resolution and metal artifacts. Furthermore, our method achieved 97.92% in terms of the success detection rate within the clinically accepted accuracy range of 2.0 mm.

Annexin II binds to capsid protein VP1 of enterovirus 71 and enhances viral infectivity.

Enterovirus type 71 (EV71) causes hand, foot, and mouth disease (HFMD), which is mostly self-limited but may be complicated with a severe to fatal neurological syndrome in some children. Understanding the molecular basis of virus-host interactions might help clarify the largely unknown neuropathogenic mechanisms of EV71. In this study, we showed that human annexin II (Anx2) protein could bind to the EV71 virion via the capsid protein VP1. Either pretreatment of EV71 with soluble recombinant Anx2 or pretreatment of host cells with an anti-Anx2 antibody could result in reduced viral attachment to the cell surface and a reduction of the subsequent virus yield in vitro. HepG2 cells, which do not express Anx2, remained permissive to EV71 infection, though the virus yield was lower than that for a cognate lineage expressing Anx2. Stable transfection of plasmids expressing Anx2 protein into HepG2 cells (HepG2-Anx2 cells) could enhance EV71 infectivity, with an increased virus yield, especially at a low infective dose, and the enhanced infectivity could be reversed by pretreating HepG2-Anx2 cells with an anti-Anx2 antibody. The Anx2-interacting domain was mapped by yeast two-hybrid analysis to VP1 amino acids 40 to 100, a region different from the known receptor binding domain on the surface of the picornavirus virion. Our data suggest that binding of EV71 to Anx2 on the cell surface can enhance viral entry and infectivity, especially at a low infective dose.

A novel dental infiltration resin based on isosorbide-derived dimethacrylate with high biocompatibility, hydrolysis resistance, and antibacterial effect.

Objectives: The available infiltration resin has raised biosafety and treatment stability concerns because of the cytotoxicity of the main component, TEGDMA, and its susceptibility to hydrolysis in the oral environment. This study aimed to develop a TEGDMA-free infiltration resin to overcome these drawbacks. Methods: Using the synthetic bioderived monomer bis(methacrylate) isosorbide (IBM) and the zwitterionic compound 2-methacryloyloxyethyl phosphorylcholine (MPC), a novel infiltrant IBMA was developed and preferentially selected. We investigated the performance of the IBMA resin regarding cytotoxicity, antibiofilm adhesion, and hydrolysis resistance and further verified its ability to restore the demineralized enamel and stability of the infiltrated area under artificial aging conditions. Results: Compared with the commercial TEGDMA-based infiltration resin ICON, IBMA not only demonstrated similar enamel morphologic and esthetic restorative effects in chalky lesions but also exhibited favorable cell viability, durable Streptococcus mutans UA159 biofilm-repellent performance, and higher enamel microhardness (204.0 ± 5.12 HV) of the infiltrated enamel. Specifically, because of the high crosslink density [(47.77 ± 5.76) ×103 mol/mm3] and low water sorption [12.79 ± 2.56 µg/mm3] of the polymer network, the IBMA resin was more resistant to hydrolysis than ICON, which prevents the disruption of the infiltrant's micropore-blocking effect after aging. Enamel lesions treated with IBMA demonstrated good color stability after the tea-staining challenge, which was significantly better than that in the ICON group. Conclusion: Based on these findings, the IBMA resin exhibits favorable cell viability, hydrolysis resistance, and biofilm-repellent properties, which alleviates the defects of traditional TEGDMA systems. Therefore, it is a better alternative for microinvasive treatment involving early caries and enamel whitish discoloration.