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In this paper, an optical multiple image encryption method based on ellipsometry reconstruction is proposed. In our proposal, the cryptosystem is designed as multiple paths, and each path is used t...

Missile is an important weapon system of the army. The spare parts of missile equipment are significant effect on military operations. In order to improve the mission completion rate of missile equipment in wartime, thispaper introduces data sensing method to forecast the demand of valuablespare parts of missile equipment dynamically. Firstly, the information related to valuable spare parts of missile equipment was obtained by data sensing, and the sample size was determined by Bernoulli uniform sampling probability. Secondly, according to the data quality of multisource and multi-modal, the data requirement for dynamic demand prediction of valuable spare parts of missile equipment was obtained. Finally, according to the characteristics of the spare parts, the life of the spare parts was predicted, realizing the dynamic prediction of the demand for valuable spare parts of missile equipment. The results show that the demand of valuable spare parts of missile equipment can be predicted dynamically by using this method, the accuracy is higher than 95%, and the real-time performance is more excellent.

Pancreatic ductal adenocarcinoma has some of the worst prognostic outcomes among various cancer types. Detection of histologic patterns of pancreatic tumors is essential to predict prognosis and decide about the treatment for patients. This histologic classification can have a large degree of variability even among expert pathologists. This study proposes a graph convolutional network-based deep learning model to detect aggressive adenocarcinoma and less aggressive pancreatic tumors from benign cases. Our model uses a convolutional neural network to extract detailed information from every small region in a whole-slide image. Then, we use a graph architecture to aggregate the extracted features from these regions and their positional information to capture the whole-slide level structure and make the final prediction. We evaluated our model on an independent test set and achieved an F1 score of 0.85 for detecting neoplastic cells and ductal adenocarcinoma, significantly outperforming other baseline methods. If validated in prospective studies, this approach has a great potential to assist pathologists in identifying adenocarcinoma and other types of pancreatic tumors in clinical settings.

ObjectivesThe objective of this study is to build and evaluate a natural language processing approach to identify medication mentions in primary care visit conversations between patients and physicians.Materials and MethodsEight clinicians contributed to a data set of 85 clinic visit transcripts, and 10 transcripts were randomly selected from this data set as a development set. Our approach utilizes Apache cTAKES and Unified Medical Language System controlled vocabulary to generate a list of medication candidates in the transcribed text and then performs multiple customized filters to exclude common false positives from this list while including some additional common mentions of the supplements and immunizations.ResultsSixty-five transcripts with 1121 medication mentions were randomly selected as an evaluation set. Our proposed method achieved an F-score of 85.0% for identifying the medication mentions in the test set, significantly outperforming existing medication information extraction systems for medical records with F-scores ranging from 42.9% to 68.9% on the same test set.DiscussionOur medication information extraction approach for primary care visit conversations showed promising results, extracting about 27% more medication mentions from our evaluation set while eliminating many false positives in comparison to existing baseline systems. We made our approach publicly available on the web as an open-source software.ConclusionIntegration of our annotation system with clinical recording applications has the potential to improve patients’ understanding and recall of key information from their clinic visits, and, in turn, to positively impact health outcomes.

Developing efficient photocatalysts has been proved to be of great importance for many emerging applications, including the removal of recalcitrant organic pollutants in wastewaters and transforming solar energy into important chemical feedstocks. One of the major challenges for high performance photocatalysts is that most semiconductor-mediated photocatalysts suffer severe charge recombination which finally hinders the overall photocatalytic efficiency. Herein, a delicately designed epitaxial grown heterostructure composed of zinc oxide (ZnO) nanowire and ultra-small bismuth oxyiodide (BiOI) nanoflakes was synthesized featuring quasi-free lattice mismatch at the ZnO/BiOI interface. With the advances of suitable p–n junction energy band alignment and minimized lattice mismatch, the synthesized ZnO/BiOI heterostructure shows significantly high interfacial charge transfer and separation efficiency. The high performance heterostructured photocatalyst was applied for the photodegradation of Bisphenol-A (BPA) in an artificial organic wastewater. The results showed that the epitaxial-LMF heterostructure is much superior to both ZnO nanowires and BiOI micro-sheets in catalytic efficiency. Analyzing the time-resolved kinetic features of photo-induced charge carriers revealed that it is the high-degree lattice match at the ZnO/BiOI interface that contributes to the significant charge-separation in the LMF heterostructure, leading to the substantial improvement of photocatalytic efficiency. An interesting finding is that a strong Foster-resonance energy transfer (FRET) from ZnO to BiOI in the heterostructure was observed, which could enhance the solar energy utilization. This study provides a general strategy to improve the interfacial charge separation efficiency of heterostructured photocatalysts, thereby greatly promoting the photocatalytic performance.

To investigate the clinical pharmacokinetics of CA4P, a high-throughput high-performance liquid chromatography-tandem mass spectrometry assay with an identical positive electrospray ionization (ESI) mode was developed for the simultaneous determination of CA4P, its active metabolite CA4, and CA4 glucuronide in human plasma. CA4P and CA4 were easier to protonate in positive ESI mode, whereas CA4G was reported to produce deprotonated ion in negative ESI mode. Because the baseline separation of CA4P and CA4G could not be achieved, using MS positive/negative ion switching is not feasible. In this study, an abundant ammonium adduct ion of CA4G in ESI+ was observed as an ideal precursor ion. The final precursor/product transition pairs chosen for CA4P, CA4, and CA4G were at m/z 397/350, 317/286, and 510/317, respectively. To the best of our knowledge, it is the first report on the simultaneous quantification of CA4P, CA4, and CA4G in biological samples. The proposed method was validated, which showed a wide linear dynamic range, high selectivity and sensitivity, good repeatability, and a short run time. Compared with the literatures, the lower limits of quantification were five- and two-fold more sensitive for CA4G and CA4, respectively. Therefore, this method was successfully applied to the pharmacokinetic study of CA4P in phase I clinical trial.

With the rapid development of modern communication system, more and more attention has been paid to communication interference and anti-interference, which affects the key to smooth communication. In order to ensure the security and stability of communication in modern complex electromagnetic environment, the communication system must have strong anti-interference ability. Wireless communication is the main means of modern communication. Nowadays, the vehicular command and control communication system is mainly composed of ultrashort wave frequency hopping radio. Frequency hopping radio has good anti-interference ability and can carry out accurate and reliable communication in complex electromagnetic environment. In order to deepen the study of FH radio, this paper uses Simulink as an auxiliary means to simulate FH radio, observe the BER of FH radio in single frequency interference, multi frequency interference, tracking interference and channel high signal-to-noise ratio, and analyze the anti-interference performance of FH communication system.

Introduction: Pseudo-Pulseless Electrical Activity (p-PEA) is a form of profound cardiac shock defined as measurable cardiac activity without clinically detectable pulses. p-PEA has a distinct physiology and etiology from VF and true-PEA, and may constitute up to 40% of reported cases of cardiac arrest. Electrical impedance tomography (EIT) uses cutaneous electrodes to generate images based on cross sectional resistance. We utilized EIT to predict the number of interventions required to achieve ROSC from p-PEA. Methods: Female swine (N = 14) under intravenous anesthesia were instrumented with aortic and central venous micromanometer catheters. p-PEA was induced by ventilation with 6% oxygen in 94% nitrogen and was defined as a systolic aortic pressure less than 40 mmHg. Continuous EIT renderings were obtained from circumferential cutaneous thoracic and abdominal electrode arrays. A deep learning model was utilized to detect features within the EIT video clips of the p-PEA disease state to predict the number of treatments required to achieve ROSC. Twelve pigs were randomly selected as training data and 2 pigs as a test set. EIT images were saved as 30 second clips, resulting in 1630 clips generated. To increase generalizability, random epochs ranging from 30 - 100% of the total clip length were generated, resulting in a model capable of detecting this disease state with limited video fragments. Data were labeled based on the number of interventions required to achieve ROSC (100% O 2 , 100% O 2 + CPR, 100% O 2 + CPR + Epi, ROSC not achieved). Results: This approach yielded receiver operator characteristic curves - area under the curve (ROC-AUC, Figure 1) values of 0.75 for micro (weighted) AUC and 0.78 for macro (unweighted) AUC on a 4 class prediction model. Conclusion: EIT combined with machine learning may differentiate the required treatments needed to achieve ROSC in p-PEA.

Introduction: Pseudo-Pulseless Electrical Activity (p-PEA) is a lifeless form of profound cardiac shock characterized by measurable cardiac mechanical activity without clinically detectable pulses. p-PEA may constitute up to 40% of reported cases of cardiac arrest and its management may be different from ventricular fibrillation or even true-PEA. Currently, diagnosis of p-PEA requires either echocardiography or intravascular catheterization, neither of which are ideal in the prehospital setting. Electrical impedance tomography (EIT) uses skin surface electrodes to generate images based on cross sectional resistance. We investigated the ability of EIT and machine learning (ML) to detect p-PEA. Methods: Female swine (N = 14) under intravenous anesthesia were instrumented with aortic and central venous micromanometer catheters. p-PEA was induced by ventilation with 6% O 2 in 94% N 2 , defined as a systolic aortic pressure less than 40 mmHg. Continuous EIT renderings were obtained from circumferential thoracic and abdominal electrode arrays. A deep learning model was utilized to detect p-PEA using EIT sequences. Twelve pigs were randomly selected as training data and 2 pigs as a test set. EIT images were saved as 30 second clips, resulting in 3033 clips generated. To increase generalizability, random epochs ranging from 30 - 100% of the total clip length were generated, resulting in a model capable of detecting this disease state with limited video fragments. Results: This technique yielded a receiver operator characteristic curve - area under the curve (ROC-AUC) of 0.91 for detection of p-PEA in the testing dataset (Figure 1), with 84% accuracy, 88% sensitivity, and 84% specificity. Conclusions: EIT combined with machine learning may be able to reliably delineate p-PEA in a hypoxic porcine model. This approach may be promising for non-invasive operator-independent p-PEA detection.

Background Technology-based computational strategies that leverage social network site (SNS) data to detect substance use are promising screening tools but rely on the presence of sufficient data to detect risk if it is present. A better understanding of the association between substance use and SNS participation may inform the utility of these technology-based screening tools. Objective This paper aims to examine associations between substance use and Instagram posts and to test whether such associations differ as a function of age, gender, and race/ethnicity. Methods Participants with an Instagram account were recruited primarily via Clickworker (N=3117). With participant permission and Instagram’s approval, participants’ Instagram photo posts were downloaded with an application program interface. Participants’ past-year substance use was measured with an adapted version of the National Institute on Drug Abuse Quick Screen. At-risk drinking was defined as at least one past-year instance having “had more than a few alcoholic drinks a day,” drug use was defined as any use of nonprescription drugs, and prescription drug use was defined as any nonmedical use of prescription medications. We used logistic regression to examine the associations between substance use and any Instagram posts and negative binomial regression to examine the associations between substance use and number of Instagram posts. We examined whether age (18-25, 26-38, 39+ years), gender, and race/ethnicity moderated associations in both logistic and negative binomial models. All differences noted were significant at the .05 level. Results Compared with no at-risk drinking, any at-risk drinking was associated with both a higher likelihood of any Instagram posts and a higher number of posts, except among Hispanic/Latino individuals, in whom at-risk drinking was associated with a similar number of posts. Compared with no drug use, any drug use was associated with a higher likelihood of any posts but was associated with a similar number of posts. Compared with no prescription drug use, any prescription drug use was associated with a similar likelihood of any posts and was associated with a lower number of posts only among those aged 39 years and older. Of note, main effects showed that being female compared with being male and being Hispanic/Latino compared with being White were significantly associated with both a greater likelihood of any posts and a greater number of posts. Conclusions Researchers developing computational substance use risk detection models using Instagram or other SNS data may wish to consider our findings showing that at-risk drinking and drug use were positively associated with Instagram participation, while prescription drug use was negatively associated with Instagram participation for middle- and older-aged adults. As more is learned about SNS behaviors among those who use substances, researchers may be better positioned to successfully design and interpret innovative risk detection approaches.

A barrier membrane (BM) is essential for guided bone regeneration (GBR) procedures. Absorbable BMs based on collagen have been widely applied clinically due to their excellent biocompatibility. The extracellular matrix (ECM) provides certain advantages that can compensate for the rapid degradation and insufficient mechanical strength of pure collagen membrane due to the porous scaffold structure. Recently, small intestinal submucosa (SIS), one of the most widely used ECM materials, has drawn much attention in bone tissue engineering. In this study, we adopted multilaminate SIS (mSIS) as a BM and evaluated its in vivo and in vitro properties. mSIS exhibited a multilaminate structure with a smooth upper surface and a significantly coarser bottom layer according to microscopic observation. Tensile strength was 13.10 ± 2.56 MPa. In in vivo experiments, we selected a rabbit mandibular defect model and subcutaneous implantation to compare osteogenesis and biodegradation properties with one of the most commonly used commercial collagen membranes. mSIS was retained for up to 3 months and demonstrated longer biodegradation time than commercial collagen membrane. Quantification of bone regeneration revealed significant differences in each group. Micro-computed tomography (micro-CT) revealed that the quantity and maturity of bones in the mSIS group were significantly higher than those in the blank control group (P < 0.05) and were similar to those in a commercial collagen membrane group (P > 0.05) at 4 and 12 weeks after surgery. Hematoxylin and eosin staining revealed large amounts of mature lamellar bone at 12 weeks in mSIS and commercial collagen membrane groups. Therefore, we conclude that mSIS has potential as a future biocompatible BM in GBR procedures.

BACKGROUND Technology-based computational strategies that leverage social network site (SNS) data to detect substance use are promising screening tools but rely on the presence of sufficient data to detect risk if it is present. A better understanding of the association between substance use and SNS participation may inform the utility of these technology-based screening tools. OBJECTIVE This paper aims to examine associations between substance use and Instagram posts and to test whether such associations differ as a function of age, gender, and race/ethnicity. METHODS Participants with an Instagram account were recruited primarily via Clickworker (N=3117). With participant permission and Instagram’s approval, participants’ Instagram photo posts were downloaded with an application program interface. Participants’ past-year substance use was measured with an adapted version of the National Institute on Drug Abuse Quick Screen. At-risk drinking was defined as at least one past-year instance having “had more than a few alcoholic drinks a day,” drug use was defined as any use of nonprescription drugs, and prescription drug use was defined as any nonmedical use of prescription medications. We used logistic regression to examine the associations between substance use and any Instagram posts and negative binomial regression to examine the associations between substance use and number of Instagram posts. We examined whether age (18-25, 26-38, 39+ years), gender, and race/ethnicity moderated associations in both logistic and negative binomial models. All differences noted were significant at the .05 level. RESULTS Compared with no at-risk drinking, any at-risk drinking was associated with both a higher likelihood of any Instagram posts and a higher number of posts, except among Hispanic/Latino individuals, in whom at-risk drinking was associated with a similar number of posts. Compared with no drug use, any drug use was associated with a higher likelihood of any posts but was associated with a similar number of posts. Compared with no prescription drug use, any prescription drug use was associated with a similar likelihood of any posts and was associated with a lower number of posts only among those aged 39 years and older. Of note, main effects showed that being female compared with being male and being Hispanic/Latino compared with being White were significantly associated with both a greater likelihood of any posts and a greater number of posts. CONCLUSIONS Researchers developing computational substance use risk detection models using Instagram or other SNS data may wish to consider our findings showing that at-risk drinking and drug use were positively associated with Instagram participation, while prescription drug use was negatively associated with Instagram participation for middle- and older-aged adults. As more is learned about SNS behaviors among those who use substances, researchers may be better positioned to successfully design and interpret innovative risk detection approaches.

In order to solve the problem that it is difficult to configure reasonably for the equipment maintenance material in each maintenance level, this paper proposes a method based on simulation modeling for the equipment maintenance material configuration considering with the complex relationship of various elements and the randomness of various maintenance activities. A basic simulation system framework is proposed to realize the more accurate demand for maintenance equipment which can be difficult for the traditional mathematical modeling and description. Finally the proposed model can lay the foundation for the optimal allocation of maintenance support resources in the future.

With its rich varieties and unique features, decorative glass has a great impact on the creation of indoor light environments. It may influence multiple aspects such as the color and amount of light, the relationship between light and shadow, and the dimension of the whole space. In this paper, decorative glass is studied in reference to its innovative applications in the world, thereby providing certain inspiration for staff in the design industry.

Wear is the main cause of failure of gear transmission system. In order to solve the problem that it is difficult to extract fault features from complex environmental noise for condition recognition, this paper proposes a method based on EEMD and INPP for gearbox wear condition recognition. Firstly, EEMD method is used to decompose the original vibration signal of gearbox, and then the decomposition results are sorted by kurtosis criterion, and the components with large kurtosis index are selected for time-frequency domain analysis to get the time-frequency domain high dimensional feature set; then the improved Neighborhood Preserving Project (INPP) algorithm is used to reduce the dimension of high-dimensional features, and then the reduced dimension features are obtained for state recognition. Finally, the algorithm is verified by the vibration response data of gearbox and compare with several algorithms, and the results show that the proposed algorithm has stable dimensionality reduction effect, good classification effect, and shows the effectiveness of the method.

Gearbox is an important structure of rotating machinery, and the accurate fault diagnosis of gearboxes is of great significance for ensuring efficient and safe operation of rotating machinery. Aiming at the problem that there is little common compound fault data of gearboxes, and there is a lack of an effective diagnosis method, a gearbox fault simulation experiment platform is set up, and a diagnosis method for the compound fault of gearboxes based on multi-feature and BP-AdaBoost is proposed. Firstly, the vibration signals of six typical states of gearbox are obtained, and the original signals are decomposed by empirical mode decomposition and reconstruct the new signal to achieve the purpose of noise reduction. Then, perform the time domain analysis and wavelet packet analysis on the reconstructed signal, extract three time domain feature parameters with higher sensitivity, and combine them with eight frequency band energy feature parameters obtained by wavelet packet decomposition to form the gearbox state feature vector. Finally, AdaBoost algorithm and BP neural network are used to build the BP-AdaBoost strong classifier model, and feature vectors are input into the model for training and verification. The results show that the proposed method can effectively identify the gearbox failure modes, and has higher accuracy than the traditional fault diagnosis methods, and has certain reference significance and engineering application value.

Aiming at the characteristics of magnetron of airport surveillance radar, such as short operation time, complexity and nonlinear vibration signal, a fault reasoning method based on T-S fuzzy fault tree model was proposed to realize fault reasoning analysis of magnetron of radar receiving and receiving system. A T-S fuzzy fault tree fault inference model is constructed by using the field expert’s opinion expressed by fuzzy number language set. The experimental results show that compared with the traditional manual fault detection method, this method can make more comprehensive and efficient inference analysis for the magnetron fault events. This method provides a new idea for the application of magnetron fault reasoning analysis in engineering.

Purpose Hybrid simulation, which is a general technique for obtaining the seismic response of an entire structure, is an improvement of the traditional seismic test technique. In order to improve the analysis accuracy of the numerical substructure in hybrid simulation, the purpose of this paper is to propose an innovative hybrid simulation technique. The technique combines the multi-scale finite element (MFE) analysis method and hybrid simulation method with the objective of achieving the balance between the accuracy and efficiency for the numerical substructure simulation. Design/methodology/approach To achieve this goal, a hybrid simulation system is established based on the MTS servo control system to develop a hybrid analysis model using an MFE model. Moreover, in order to verify the efficiency of the technique, the hybrid simulation of a three-storey benchmark structure is conducted. In this simulation, a ductile column—represented by a half-scale scale specimen—is selected as the experimental element, meanwhile the rest of the frame is modelled as microscopic and macroscopic elements in the Abaqus software simultaneously. Finally, to demonstrate the stability and accuracy of the proposed technique, the seismic response of the target structure obtained via hybrid simulation using the MFE model is compared with that of the numerical simulation. Findings First, the use of the hybrid simulation with the MFE model yields results similar to those obtained by the fine finite element (FE) model using solid elements without adding excessive computing burden, thus advancing the application of the hybrid simulation in large complex structures. Moreover, the proposed hybrid simulation is found to be more versatile in structural seismic analysis than other techniques. Second, the hybrid simulation system developed in this paper can perform hybrid simulation with the MFE model as well as handle the integration and coupling of the experimental elements with the numerical substructure, which consists of the macro- and micro-level elements. Third, conducting the hybrid simulation by applying earthquake motion to simulate seismic structural behaviour is feasible by using Abaqus to model the numerical substructure and harmonise the boundary connections between three different scale elements. Research limitations/implications In terms of the implementation of the hybrid simulation with the MFE model, this work is helpful to advance the hybrid simulation method in the structural experiment field. Nevertheless, there is still a need to refine and enhance the current technique, especially when the hybrid simulation is used in real complex engineering structures, having numerous micro-level elements. A large number of these elements may render the relevant hybrid simulations unattainable because the time consumed in the numeral calculations can become excessive, making the testing of the loading system almost difficult to run smoothly. Practical implications The MFE model is implemented in hybrid simulation, enabling to overcome the problems related to the testing accuracy caused by the numerical substructure simplifications using only macro-level elements. Originality/value This paper is the first to recognise the advantage of the MFE analysis method in hybrid simulation and propose an innovative hybrid simulation technique, combining the MFE analysis method with hybrid simulation method to strike a delicate balance between the accuracy and efficiency of the numerical substructure simulation in hybrid simulation. With the help of the coordinated analysis of FEs at different scales, not only the accuracy and reliability of the overall seismic analysis of the structure is improved, but the computational cost can be restrained to ensure the efficiency of hybrid simulation.

In this study, a duplex-structured SnO2-Sb-CNT composite anode (Ti/SnO2-Sb/SnO2-Sb-CNT) was fabricated using pulse electrodeposition technique. SnO2-Sb-CNT composite composed of 4–5 nm SnO2-Sb particles was synthesized via co-precipitation prior to the electrode preparation and characterized with BET and TEM. Characterization of Ti/SnO2-Sb/SnO2-Sb-CNT by FESEM, EDS, XRD, XPS, LSV, CV and EIS revealed that the novel duplex structured electrode had larger electroactive surface area and lower charge transfer resistance than conventional Ti/SnO2-Sb prepared by thermochemical decomposition, due to its more compact and rough surface. It also improved the electrochemical stability of Ti/SnO2-Sb/SnO2-Sb-CNT with a service lifetime up to 86 h. The oxygen evolution potential (OEP) of Ti/SnO2-Sb/SnO2-Sb-CNT was determined to be 2.2 V (vs Ag/AgCl). Bulk electrolysis of bisphenol A (BPA) was conducted to evaluate the electrocatalytic performance of the electrodes at various current densities (10–40 mA cm−2) and initial pH (3–11) of solution by determination of BPA degradation, total organic carbon (TOC) removal and mineralization current efficiency (MCE). Ti/SnO2-Sb/SnO2-Sb-CNT intended to adsorb more BPA molecules, which may contribute to its high current efficiency for electrochemical oxidation of BPA. The presence of humic acid (HA) could decrease the performance efficiency by inhibiting the adsorption of BPA by Ti/SnO2-Sb/SnO2-Sb-CNT. A mechanism is proposed to illustrate the enhanced degradation of BPA by Ti/SnO2-Sb/SnO2-Sb-CNT by synergistic adsorption and electrochemical oxidation.

Using historical narratives and a qualitative research approach, this paper analyses the social, cultural and cognitive causes of differentiation and stratification among Chinese migrants in the Republic of Ireland. It discusses individuals’ diversified capabilities, attitudes and actual levels of integration, as well as their disparate patterns of self-identification. In the research area of Chinese migrants in Ireland, in-group diversity and its implications is still a novel research topic. With this introductory work, the authors seek to draw more attention to this particular group, especially to the need for studies of encounters between subgroups and longitudinal investigations. The paper points out that subgroups of Chinese migrants in Ireland are divided according to social classification and self-categorisation, which have distinct significances for subgroup members’ integration and identity.

Effective filtering and noise reduction, feature extraction and fault diagnosis, and prognostics technology are important to Prognostics and Health Management (PHM) of equipment. Therefore, a multifeature fusion fault diagnosis method based on the combination of quadratic filtering and QPSO-KELM algorithm is proposed. In the quadratic filtering, stable filtering can reduce the impact of noise and fast-kurtogram can filtrate fault frequency bands with rich fault information. Then, the time-domain, frequency-domain, and time-frequency parameters of the secondary filter signal are extracted. MSSST was used to analyze the filtered signal, and the time-frequency image was obtained. The time-frequency parameter was extracted from the time-frequency image by 2DPCA, and all the extracted parameters are taken as the fusion fault feature of the gearbox. Finally, the fault feature parameters are taken as the training sample and testing sample of QPSO-KELM for training and testing to achieve the purpose of fault diagnosis. The experimental results show that the proposed method can effectively filter the noise, complete the fault mode identification of gearbox, and improve the fault diagnosis accuracy better than other methods.

There is great demand for an improved barrier membrane with osteogenic potential for guided bone regeneration (GBR). Natural acellular porcine pericardium (APP) is increasingly used in regenerative medicine as a kind of common extracellular matrix materials. This study aimed to investigate its potential application in GBR, especially its osteoconductive and osteoinductive properties. Bio-Gide (BG), a commercial collagen membrane, was set as the control group. APP samples were characterized by physicochemical analyses and their biological effects on human bone mesenchymal stem cells (hBMSCs) and human gingival fibroblasts (hGFs) were also examined. Additionally, the osteogenic potential of APP was tested on a bilateral critical-sized calvarial defect model. We discovered that the smooth surface of APP tended to recruit more hBMSCs. Moreover, promoted proliferation and osteogenic differentiation of hBMSCs was detected on this side of APP, with increased alkaline phosphatase activity and upregulated expression of bone-specific genes. Besides, the rough side of APP showed good biocompatibility and barrier function with hGFs. Histologic observation and analysis of calvarial defect healing over 4 weeks revealed enhanced bone regeneration under APP compared with BG and the control group. The results of this study indicate that APP is a potential osteoconductive and osteoinductive biomaterial for GBR.

Objective. The aim of the study is to evaluate the effects of multilaminated small intestinal submucosa (mSIS) combined with bone substitute material to repair peri-implant defects during guided bone regeneration procedures. Methods. Twelve implants were placed in bilateral lower premolars of three beagle dogs, and a peri-implant buccal bone defect (3 mm width and 4 mm height) was created at each implant site. A total of 12 sites were filled with a particulate bone substitute material and then randomly divided into three treatment groups: covered by mSIS membrane (mSIS group), covered by collagen membrane (BG group), and no treatment (control group), each group of four sites. After 12 weeks of healing, all of the animals were euthanized and dissected blocks were obtained for micro-computed tomography (micro-CT) and histological analyses. Results. Micro-CT results revealed similar horizontal width of augmented tissue and new bone formation between mSIS and BG groups (P<0.05). Histological analyses revealed that the differences in horizontal widths of newly formed bone and bone-to-implant contact between mSIS and BG groups were not significant (P>0.05). All of these parameters were significantly different from those in the control group (P<0.05). Conclusions. These findings confirmed that mSIS combined with the bone substitute material enhanced bone regeneration in peri-implant defects, in a manner similar to that of a collagen membrane.