Chemically modified microRNA delivery via DNA tetrahedral frameworks for dental pulp regeneration.

Dental pulp regeneration is a promising strategy for addressing tooth disorders. Incorporating this strategy involves the fundamental challenge of establishing functional vascular networks using dental pulp stem cells (DPSCs) to support tissue regeneration. Current therapeutic approaches lack efficient and stable methods for activating DPSCs. In the study, we used a chemically modified microRNA (miRNA)-loaded tetrahedral-framework nucleic acid nanostructure to promote DPSC-mediated angiogenesis and dental pulp regeneration. Incorporating chemically modified miR-126-3p into tetrahedral DNA nanostructures (miR@TDNs) represents a notable advancement in the stability and efficacy of miRNA delivery into DPSCs. These nanostructures enhanced DPSC proliferation, migration, and upregulated angiogenesis-related genes, enhancing their paracrine signaling effects on endothelial cells. This enhanced effect was substantiated by improvements in endothelial cell tube formation, migration, and gene expression. Moreover, in vivo investigations employing matrigel plug assays and ectopic dental pulp transplantation confirmed the potential of miR@TDNs in promoting angiogenesis and facilitating dental pulp regeneration. Our findings demonstrated the potential of chemically modified miRNA-loaded nucleic acid nanostructures in enhancing DPSC-mediated angiogenesis and supporting dental pulp regeneration. These results highlighted the promising role of chemically modified nucleic acid-based delivery systems as therapeutic agents in regenerative dentistry and tissue engineering.

The Identification of Fritillaria Species Using Hyperspectral Imaging with Enhanced One-Dimensional Convolutional Neural Networks via Attention Mechanism.

Combining deep learning and hyperspectral imaging (HSI) has proven to be an effective approach in the quality control of medicinal and edible plants. Nonetheless, hyperspectral data contains redundant information and highly correlated characteristic bands, which can adversely impact sample identification. To address this issue, we proposed an enhanced one-dimensional convolutional neural network (1DCNN) with an attention mechanism. Given an intermediate feature map, two attention modules are constructed along two separate dimensions, channel and spectral, and then combined to enhance relevant features and to suppress irrelevant ones. Validated by Fritillaria datasets, the results demonstrate that an attention-enhanced 1DCNN model outperforms several machine learning algorithms and shows consistent improvements over a vanilla 1DCNN. Notably under VNIR and SWIR lenses, the model obtained 98.97% and 99.35% for binary classification between Fritillariae Cirrhosae Bulbus (FCB) and other non-FCB species, respectively. Additionally, it still achieved an extraordinary accuracy of 97.64% and 98.39% for eight-category classification among Fritillaria species. This study demonstrated the application of HSI with artificial intelligence can serve as a reliable, efficient, and non-destructive quality control method for authenticating Fritillaria species. Moreover, our findings also illustrated the great potential of the attention mechanism in enhancing the performance of the vanilla 1DCNN method, providing reference for other HSI-related quality controls of plants with medicinal and edible uses.

Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix.

Accurate assessment of isoflavone and starch content in Puerariae Thomsonii Radix (PTR) is crucial for ensuring its quality. However, conventional measurement methods often suffer from time-consuming and labor-intensive procedures. In this study, we propose an innovative and efficient approach that harnesses hyperspectral imaging (HSI) technology and deep learning (DL) to predict the content of isoflavones (puerarin, puerarin apioside, daidzin, daidzein) and starch in PTR. Specifically, we develop a one-dimensional convolutional neural network (1DCNN) model and compare its predictive performance with traditional methods, including partial least squares regression (PLSR), support vector regression (SVR), and CatBoost. To optimize the prediction process, we employ various spectral preprocessing techniques and wavelength selection algorithms. Experimental results unequivocally demonstrate the superior performance of the DL model, achieving exceptional performance with mean coefficient of determination (R2) values surpassing 0.9 for all components. This research underscores the potential of integrating HSI technology with DL methods, thereby establishing the feasibility of HSI as an efficient and non-destructive tool for predicting the content of isoflavones and starch in PTR. Moreover, this methodology holds great promise for enhancing efficiency in quality control within the food industry.

Identification of Turtle-Shell Growth Year Using Hyperspectral Imaging Combined with an Enhanced Spatial-Spectral Attention 3DCNN and a Transformer.

Turtle shell (Chinemys reecesii) is a prized traditional Chinese dietary therapy, and the growth year of turtle shell has a significant impact on its quality attributes. In this study, a hyperspectral imaging (HSI) technique combined with a proposed deep learning (DL) network algorithm was investigated for the objective determination of the growth year of turtle shells. The acquisition of hyperspectral images was carried out in the near-infrared range (948.72-2512.97 nm) from samples spanning five different growth years. To fully exploit the spatial and spectral information while reducing redundancy in hyperspectral data simultaneously, three modules were developed. First, the spectral-spatial attention (SSA) module was developed to better protect the spectral correlation among spectral bands and capture fine-grained spatial information of hyperspectral images. Second, the 3D convolutional neural network (CNN), more suitable for the extracted 3D feature map, was employed to facilitate the joint spatial-spectral feature representation. Thirdly, to overcome the constraints of convolution kernels as well as better capture long-range correlation between spectral bands, the transformer encoder (TE) module was further designed. These modules were harmoniously orchestrated, driven by the need to effectively leverage both spatial and spectral information within hyperspectral data. They collectively enhance the model's capacity to extract joint spatial and spectral features to discern growth years accurately. Experimental studies demonstrated that the proposed model (named SSA-3DTE) achieved superior classification accuracy, with 98.94% on average for five-category classification, outperforming traditional machine learning methods using only spectral information and representative deep learning methods. Also, ablation experiments confirmed the effectiveness of each module to improve performance. The encouraging results of this study revealed the potentiality of HSI combined with the DL algorithm as an efficient and non-destructive method for the quality control of turtle shells.

Activating Gel Polymer Electrolyte Based Zinc-Ion Conduction with Filler-Integration for Advanced Zinc Batteries.

Quasi solid zinc batteries (QSZBs) based on gel electrolyte have performed as a significant application prospect as advanced high energy density electrochemical storage devices with safety, low cost, eco-friendliness, and flexibility. While, the practical application of QSZBs was enormously restricted by low ionic conductivity and poor strength of pure gel electrolyte. Here, in order to activate the zinc ion conduction in gel electrolyte, the kinds of inorganic fillers constituting the composite electrolyte was investigated. The theoretical study was also revealed by density functional theory to have deep insight into the mechanism. In particular, appropriate filler amount (ZnO#20) can make a noteworthy ion conductivity elevation (1.3 × 10-3 S cm-1) which is much better than the control sample (2.0 × 10-4 S cm-1) at -20 °C. As a result, the symmetric cell with ZnO#20 can achieve a long-term cycling life of over 1500 h. Moreover, the pouch cell coupled with vanadium pentoxide is assembled, and corresponding versatility is also identified with twisting, refrigeration (-20 °C) and cutting.

Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN.

For the past several years, there has been an increasing focus on deep learning methods applied into computational pulse diagnosis. However, one factor restraining its development lies in the small wrist pulse dataset, due to privacy risks or lengthy experiments cost. In this study, for the first time, we address the challenging by presenting a novel one-dimension generative adversarial networks (GAN) for generating wrist pulse signals, which manages to learn a mapping strategy from a random noise space to the original wrist pulse data distribution automatically. Concretely, Wasserstein GAN with gradient penalty (WGAN-GP) is employed to alleviate the mode collapse problem of vanilla GANs, which could be able to further enhance the performance of the generated pulse data. We compared our proposed model performance with several typical GAN models, including vanilla GAN, deep convolutional GAN (DCGAN) and Wasserstein GAN (WGAN). To verify the feasibility of the proposed algorithm, we trained our model with a dataset of real recorded wrist pulse signals. In conducted experiments, qualitative visual inspection and several quantitative metrics, such as maximum mean deviation (MMD), sliced Wasserstein distance (SWD) and percent root mean square difference (PRD), are examined to measure performance comprehensively. Overall, WGAN-GP achieves the best performance and quantitative results show that the above three metrics can be as low as 0.2325, 0.0112 and 5.8748, respectively. The positive results support that generating wrist pulse data from a small ground truth is possible. Consequently, our proposed WGAN-GP model offers a potential innovative solution to address data scarcity challenge for researchers working with computational pulse diagnosis, which are expected to improve the performance of pulse diagnosis algorithms in the future.

Unsupervised Hyperspectral Band Selection via Multimodal Evolutionary Algorithm and Subspace Decomposition.

Unsupervised band selection is an essential task to search for representative bands in hyperspectral dimension reduction. Most of existing studies utilize the inherent attribute of hyperspectral image (HSI) and acquire single optimal band subset while ignoring the diversity of subsets. Moreover, the ordered property in HSI is expected to be focused in order to avoid choosing redundant bands. In this paper, we proposed an unsupervised band selection method based on the multimodal evolutionary algorithm and subspace decomposition to alleviate the problems. To explore the diversity of band subsets, the multimodal evolutionary algorithm is first employed in spectral subspace decomposition to seek out multiple global or local solutions. Meanwhile, in view of ordered property, we concentrate more on increasing the difference between neighbor band subspaces. Furthermore, to utilize the obtained multiple diverse band subsets, an integrated utilization strategy is adopted to improve the predicted performance. Experimental results on three popular hyperspectral remote sensing datasets and one collected composition prediction dataset show the effectiveness of the proposed method, and the superiority over state-of-the-art methods on predicted accuracy.

An Intelligent Tongue Diagnosis System via Deep Learning on the Android Platform.

To quickly and accurately identify the pathological features of the tongue, we developed an intelligent tongue diagnosis system that uses deep learning on a mobile terminal. We also propose an efficient and accurate tongue image processing algorithm framework to infer the category of the tongue. First, a software system integrating registration, login, account management, tongue image recognition, and doctor-patient dialogue was developed based on the Android platform. Then, the deep learning models, based on the official benchmark models, were trained by using the tongue image datasets. The tongue diagnosis algorithm framework includes the YOLOv5s6, U-Net, and MobileNetV3 networks, which are employed for tongue recognition, tongue region segmentation, and tongue feature classification (tooth marks, spots, and fissures), respectively. The experimental results demonstrate that the performance of the tongue diagnosis model was satisfying, and the accuracy of the final classification of tooth marks, spots, and fissures was 93.33%, 89.60%, and 97.67%, respectively. The construction of this system has a certain reference value for the objectification and intelligence of tongue diagnosis.

Visual Responses to Moving and Flashed Stimuli of Neurons in Domestic Pigeon (Columba livia domestica) Optic Tectum.

Birds can rapidly and accurately detect moving objects for better survival in complex environments. This visual ability may be attributed to the response properties of neurons in the optic tectum. However, it is unknown how neurons in the optic tectum respond differently to moving objects compared to static ones. To address this question, neuronal activities were recorded from domestic pigeon (Columba livia domestica) optic tectum, responsible for orienting to moving objects, and the responses to moving and flashed stimuli were compared. An encoding model based on the Generalized Linear Model (GLM) framework was established to explain the difference in neuronal responses. The experimental results showed that the first spike latency to moving stimuli was smaller than that to flashed ones and firing rate was higher. The model further implied the faster and stronger response to a moving target result from spatiotemporal integration process, corresponding to the spatially sequential activation of tectal neurons and the accumulation of information in time. This study provides direct electrophysiological evidence about the different tectal neuron responses to moving objects and flashed ones. The findings of this investigation increase our understanding of the motion detection mechanism of tectal neurons.

Refined Multiscale Entropy Analysis of Wrist Pulse for Gender Difference in Traditional Chinese Medicine among Young Individuals.

Pulse signal analysis plays an important role in promoting the objectification of traditional Chinese medicine (TCM). Like electrocardiogram (ECG) signals, wrist pulse signals are mainly caused by cardiac activities and are valuable in analyzing cardiac diseases. A large number of studies have reported ECG signals can distinguish gender characteristics of normal healthy subjects using entropy complexity measures, consistently showing more complexity in females than males. No research up to date, however, has been found on examining gender differences with wrist pulse signals of healthy subjects on entropy complexity measures. This paper is aimed to fill in the research gap, which could, in turn, provide a deeper insight into the pulse signal and might identify potential differences between ECG signals and pulse signals. In particular, several complementary entropy measures with corresponding refined composite multiscale versions are established to perform the analysis for the filtered TCM pulse signals. Experimental results reveal that regardless of entropy measures used, there is no statistically significant gender difference in terms of entropy complexity, indicating that the pulse signal holds less gender characteristics than the ECG signal. In view of these findings, wrist pulse signals could be likely to provide different pieces of information to ECG signals. The present study is the first to quantitatively evaluate gender differences in healthy pulse signals with measures of entropy complexity and more importantly; we expect this study could make contribution to the ongoing pulse intelligent diagnosis and objective analysis, further facilitating the modernization of TCM pulse diagnosis.

Interpretation of Electrocardiogram Heartbeat by CNN and GRU.

The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the medical field. In this paper, a classification method is proposed to classify ECG signals. Firstly, wavelet transform is used to denoise the original data, and data enhancement technology is used to overcome the problem of an unbalanced dataset. Secondly, an integrated convolutional neural network (CNN) and gated recurrent unit (GRU) classifier is proposed. The proposed network consists of a convolution layer, followed by 6 local feature extraction modules (LFEM), a GRU, and a Dense layer and a Softmax layer. Finally, the processed data were input into the CNN-GRU network into five categories: nonectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. The MIT-BIH arrhythmia database was used to evaluate the approach, and the average sensitivity, accuracy, and F1-score of the network for 5 types of ECG were 99.33%, 99.61%, and 99.42%. The evaluation criteria of the proposed method are superior to other state-of-the-art methods, and this model can be applied to wearable devices to achieve high-precision monitoring of ECG.

Tissue Fluid Triggered Enzyme Polymerization for Ultrafast Gelation and Cartilage Repair.

The in situ gelation of injectable precursors is desirable in the field of tissue regeneration, especially in the context of irregular defect filling. The current driving forces for fast gelation include the phase-transition of thermally sensitive copolymers, click chemical reactions with tissue components, and metal coordination effect. However, the rapid formation of tough hydrogels remains a challenge. Inspired by aerobic metabolism, we herein propose a tissue-fluid-triggered cascade enzymatic polymerization process catalyzed by glucose oxidase and ferrous glycinate for the ultrafast gelation of acryloylated chondroitin sulfates and acrylamides. The highly efficient production of carbon radicals and macromolecules contribute to rapid polymerization for soft tissue augmentation in bone defects. The copolymer hydrogel demonstrated the regeneration-promoting capacity of cartilage. As the first example of using artificial enzyme complexes for in situ polymerization, this work offers a biomimetic approach to the design of strength-adjustable hydrogels for bio-implanting and bio-printing applications.

Resolvin E1 accelerates pulp repair by regulating inflammation and stimulating dentin regeneration in dental pulp stem cells.

BACKGROUND: Unresolved inflammation and tissue destruction are considered to underlie the failure of dental pulp repair. As key mediators of the injury response, dental pulp stem cells (DPSCs) play a critical role in pulp tissue repair and regeneration. Resolvin E1 (RvE1), a major dietary omega-3 polyunsaturated fatty-acid metabolite, is effective in resolving inflammation and activating wound healing. However, whether RvE1 facilitates injured pulp-tissue repair and regeneration through timely resolution of inflammation and rapid mobilization of DPSCs is unknown. Therefore, we established a pulp injury model and investigated the effects of RvE1 on DPSC-mediated inflammation resolution and injured pulp repair. METHODS: A pulp injury model was established using 8-week-old Sprague-Dawley rats. Animals were sacrificed on days 1, 3, 7, 14, 21, and 28 after pulp capping with a collagen sponge immersed in PBS with RvE1 or PBS. Hematoxylin-eosin and Masson's trichrome staining, immunohistochemistry, and immunohistofluorescence were used to evaluate the prohealing properties of RvE1. hDPSCs were incubated with lipopolysaccharide (LPS) to induce an inflammatory response, and the expression of inflammatory factors after RvE1 application was measured. Effects of RvE1 on hDPSC proliferation, chemotaxis, and odontogenic differentiation were evaluated by CCK-8 assay, transwell assay, alkaline phosphatase (ALP) staining, alizarin red staining, and quantitative PCR, and possible signaling pathways were explored using western blotting. RESULTS: In vivo, RvE1 reduced the necrosis rate of damaged pulp and preserved more vital pulps, and promoted injured pulp repair and reparative dentin formation. Further, it enhanced dentin matrix protein 1 and dentin sialoprotein expression and accelerated pulp inflammation resolution by suppressing TNF-α and IL-1ß expression. RvE1 enhanced the recruitment of CD146+ and CD105+ DPSCs to the damaged molar pulp mesenchyme. Isolated primary cells exhibited the mesenchymal stem cell immunophenotype and differentiation. RvE1 promoted hDPSC proliferation and chemotaxis. RvE1 significantly attenuated pro-inflammatory cytokine (TNF-α, IL-1ß, and IL-6) release and enhanced ALP activity, nodule mineralization, and especially, expression of the odontogenesis-related genes DMP1, DSPP, and BSP in LPS-stimulated DPSCs. RvE1 regulated AKT, ERK, and rS6 phosphorylation in LPS-stimulated DPSCs. CONCLUSIONS: RvE1 promotes pulp inflammation resolution and dentin regeneration and positively influences the proliferation, chemotaxis, and differentiation of LPS-stimulated hDPSCs. This response is, at least partially, dependent on AKT, ERK, and rS6-associated signaling in the inflammatory microenvironment. RvE1 has promising application potential in regenerative endodontics.

RGD- and VEGF-Mimetic Peptide Epitope-Functionalized Self-Assembling Peptide Hydrogels Promote Dentin-Pulp Complex Regeneration.

INTRODUCTION: Cell-based tissue engineering is a promising method for dentin-pulp complex (DPC) regeneration. The challenges associated with DPC regeneration include the generation of a suitable microenvironment that facilitates the complete odontogenic differentiation of dental pulp stem cells (DPSCs) and the rapid induction of angiogenesis. Thus, the survival and subsequent differentiation of DPSCs are limited. Extracellular matrix (ECM)-like biomimetic hydrogels composed of self-assembling peptides (SAPs) were developed to provide an appropriate microenvironment for DPSCs. For functional DPC regeneration, the most important considerations are to provide an environment that promotes the adequate attachment of DPSCs and rapid vascularization of the regenerating pulp. Morphogenic signals in the form of growth factors (GFs) have been incorporated into SAPs to promote productive DPSC behaviors. However, the use of GFs has several drawbacks. We envision using a scaffold with SAPs coupled with long-term factors to increase DPSC attachment and vascularization as a method to address this challenge. METHODS: In this study, we developed synthetic material for an SAP-based scaffold with RGD- and vascular endothelial growth factor (VEGF)-mimetic peptide epitopes with the dual functions of dentin and pulp regeneration. DPSCs and human umbilical vein endothelial cells (HUVECs) were used to evaluate the biological effects of SAP-based scaffolds. Furthermore, the pulpotomized molar rat model was employed to test the reparative and regenerative effects of SAP-based scaffolds. RESULTS: This scaffold simultaneously presented RGD- and VEGF-mimetic peptide epitopes and provided a 3D microenvironment for DPSCs. DPSCs grown on this composite scaffold exhibited significantly improved survival and angiogenic and odontogenic differentiation in the multifunctionalized group in vitro. Histological and functional evaluations of a partially pulpotomized rat model revealed that the multifunctionalized scaffold was superior to other options with respect to stimulating pulp recovery and dentin regeneration in vivo. CONCLUSION: Based on our data obtained with the functionalized SAP scaffold, a 3D microenvironment that supports stem cell adhesion and angiogenesis was generated that has great potential for dental pulp tissue engineering and regeneration.

Resolvin E1 Ameliorates Pulpitis by Suppressing Dental Pulp Fibroblast Activation in a Chemerin Receptor 23-dependent Manner.

INTRODUCTION: Timely resolution of pulp inflammation is a prerequisite for the healing of inflamed dental pulp. Stromal cells, particularly fibroblasts, play a critical role in the inflammation resolution process. Resolvin E1 (RvE1) is a lipid-derived endogenous proresolution molecule that mediates this resolution process. In the present study, we investigated the effects of RvE1 on dental fibroblasts during the pathogenesis of pulpitis. METHODS: The pulp tissues in maxillary incisors of male Sprague-Dawley rats (N = 50) were exposed to the oral environment for 0, 9, 24, and 48 hours, after which they were treated with RvE1 or its vehicle. The inflammatory changes after 24 hours were assessed using hematoxylin-eosin staining, immunohistochemistry, enzyme-linked immunosorbent assay, and quantitative polymerase chain reaction. Chemerin receptor 23 (ChemR23) expression in rat pulp tissues and human dental fibroblasts was detected by immunofluorescence, Western blot analysis, and quantitative polymerase chain reaction. Finally, small interfering RNA-based knockdown studies were performed to evaluate the effects of RvE1 inhibition on proinflammatory genes and nuclear factor kappa B signaling of human dental pulp fibroblasts. RESULTS: Early treatment (within 24 hours after pulp exposure) with RvE1 promoted a decline in the number of inflammatory cells and gene expression of proinflammatory cytokines. Moreover, it reduced ChemR23 expression in the fibroblastlike cells of inflamed pulp tissues. In vitro, ChemR23 was widely expressed in human dental fibroblasts. RvE1 significantly suppressed cytokine production by fibroblasts, with down-regulation of the nuclear translocation of nuclear factor kappa B p65 in these cells. Knockdown of ChemR23 almost abolished the anti-inflammatory effect of RvE1. CONCLUSIONS: RvE1 can suppress the activation of dental pulp fibroblasts in a ChemR23-dependent manner and inhibit inflammation in the relevant early stages of pulpitis.

Constitutive Activation of ß-Catenin in Differentiated Osteoclasts Induces Bone Loss in Mice.

BACKGROUND/AIMS: Activation of the Wnt/ß-catenin signalling pathway has been widely investigated in bone biology and shown to promote bone formation. However, its specific effects on osteoclast differentiation have not been fully elucidated. Our study aimed to identify the role of ß-catenin in osteoclastogenesis and bone homeostasis. METHODS: In the present study, exon 3 in the ß-catenin gene (Ctnnb1) allele encoding phosphorylation target serine/threonine residues was flanked by floxP sequences. We generated mice exhibiting conditional ß-catenin activation (Ctsk-Cre;Ctnnb1flox(exon3)/+, designated CA-ß-catenin) by crossing Ctnnb1flox(exon3)/flox(exon3) mice with osteoclast-specific Ctsk-Cre mice. Bone growth and bone mass were analysed by micro-computed tomography (micro-CT) and histomorphometry. To further examine osteoclast activity, osteoclasts were induced from bone marrow monocytes (BMMs) isolated from CA-ß-catenin and Control mice in vitro. Osteoclast differentiation was detected by tartrate-resistant acid phosphatase (TRAP) staining, immunofluorescence staining and reverse transcription-quantitative PCR (RT-qPCR) analysis. RESULTS: Growth retardation and low bone mass were observed in CA-ß-catenin mice. Compared to controls, CA-ß-catenin mice had significantly reduced trabecular bone numbers under growth plates as well as thinner cortical bones. Moreover, increased TRAP-positive osteoclasts were observed on the surfaces of trabecular bones and cortical bones in the CA-ß-catenin mice; consistent results were observed in vitro. In the CA-ß-catenin group, excessive numbers of osteoclasts were induced from BMMs, accompanied by the increased expression of osteoclast-associated marker genes. CONCLUSION: These results indicated that the constitutive activation of ß-catenin in osteoclasts promotes osteoclast formation, resulting in bone loss.

Conversion of orbital angular momentum of light in chiral fiber gratings.

We examine mode couplings in chiral fiber grating (CFG) with N-fold rotation symmetry in the cross section and show how the angular momentum matching condition in couplings determines the generation and conversion of orbital angular momentum (OAM) beams. Then we discuss interactions of OAM and spin angular momentum in single- and double-helix long-period CFGs excited by the fundamental core modes. Subsequently, taking right-handed elliptic-core long-period CFGs as example, we demonstrate a dual-OAM converter generating OAM beams with charge +2 and charge +4 at dual wavelengths, both with a conversion efficiency greater than 97%, as well as a broadband converter based on adiabatic coupling, with a bandwidth about 10 nm for a conversion efficiency greater than 95%.

[Comparative effects of the maltitol chewing gums on reducing plaque].

OBJECTIVE: To estimate the clinical effect of the maltitol chewing gums in plaque control. METHODS: Thirty 13-15 years old susceptible adolescent were divided into three groups randomly, group A (maltitol chewing gums), group B (xylitol chewing gums) and group C (gum base chewing gums). Subjects chewed gums 5 times each day, 10 min each time. At baseline and at 4-week, subjects were evaluated for supragingival plaque. SPSS 17.0 software package was used for statistical analysis. RESULTS: Four weeks later, plaque index of the three groups continuously step down. Significant difference was observed between baseline and 4-week (P = 0.000, 0.000, 0.006). Four weeks later, there was statistically significant difference in clearance rate of plaque among the three groups (P = 0.015). There was still no statistically significant difference between group A and group B (P = 0.687), but they were both different from C group(P = 0.019, 0.007). CONCLUSION: Maltitol chewing gum can lead to similar effect on reduction of plaque as xylitol chewing gum.