Clinical, Histologic and Histomorphometric Evaluation of Bone Strip Allograft with Resorbable Membrane in Horizontal Alveolar Ridge Augmentation: A Preliminary Study

Objective: Alveolar ridge preservation in patients with inadequate bone volume is one treatment option for successful implant placement and can be done by using bone graft materials. On the other hand, Ceno Bone has been recently produced by Hamanand Saz Baft Kish Co. as a bone bioimplant of allograft origin. This study aimed to assess the clinical, histologic and histomorphometric results of Bone Strip Allograft (CenoBone) for horizontal alveolar ridge augmentation.Methods: In this semi-experimental clinical trial, 7 areas requiring horizontal ridge augmentation  and subsequent implant placement in the maxilla were selected using non-randomized consecutive sampling. Surgeries were mostly performed via the buccal cortical plate of the edentulous ridge. The buccal bone was decorticated, Ceno Bone was fixed by titanium screws, covered with Ceno Membrane (resorbable) and sutured. Buccolingual width of the ridge was measured in stage-one surgery and six months later in stage-two surgery for implant placement. A core biopsy was also taken to assess the trabecular thickness, percentage of new bone formation, percentage of remnant particles, degree of inflammation, foreign body reaction, vitality, bone-biomaterial contact and number of blood vessels by microscopic, histologic and histomorphometric analyses of the slides. The clinical ridge width values in the first- and second-stage surgeries were analyzed using  Wilcoxon Signed Rank test.Results: The mean clinical ridge width at 2mm distance from the ridge crest was 2.49 (0.72) mm in the first-stage and 4.79 (0.75) mm in the second-stage surgery. The mean clinical ridge width at  5mm distance from the ridge crest was 3.6 (0.57) mm in the first-stage and 6.3 (1.13) mm in the second-stage surgery. At both sites, application of Ceno Bone significantly increased the clinical ridge width in the second-stage surgery (both ps<0.05). Also, inflammation in most specimens (85.7%) was grade I and no case of foreign body reaction was seen. Bone was vital in all patients. The  mean  trabecular  thickness was  87.96  (38.74)μ.  The percentage  of new  bone  formation was58.43 (26.42%) and the percentage of remnant particles was 4.07% (2.44%).Conclusion: The results of this preliminary study revealed that application of CenoBone stimulates osteogenesis and significantly increases the clinical ridge width at 2 and 5mm distances from the ridge crest for implant placement

A study on drug delivery tracing with radiolabeled mesoporous hydroxyapatite nanoparticles conjugated with 2DG/DOX for breast tumor cells

Background: Mesoporous nanoparticles have a great potential in targeted therapy approaches due to their ideal properties for encapsulation of various drugs, proteins and also biologically active molecules. Material and methods: We used mesoporous hydroxyapatite (HA) nanoparticles as a drug carrier and developed radiolabeled mesoporous HA containing of 2-deoxy-D-glucose (2DG) and Doxorubicin (DOX) with technetium-99m (99mTc) for imaging in in vitro and in vivo studies. Results: 2DG and DOX in presence of mesoporous HA nanoparticles more reduced the fraction of viable cells in the MDA-MB-231, MCF-7 human and MC4-L2 Balb/c mice breast cancer cells. The radiochemical purity of the nano-2DG-DOX complex with 99mTc was calculated to 96.8%. The results of cellular uptake showed a 44.77% increase in uptake of the [99mTc]-nano-2DG-DOX compared to the complex without nanoparticles (p < 0.001). Conclusion: Radioisotopic imaging demonstrated a high biochemical stability for [99mTc]-nano-2DG-DOX complex. The results demonstrated that [99mTc]-nano-2DG-DOX, may be used as an attractive candidate in cancer imaging and treatment managing.BACKGROUND: Mesoporous nanoparticles have a great potential in targeted therapy approaches due to their ideal properties for encapsulation of various drugs, proteins and also biologically active molecules. MATERIAL AND METHODS: We used mesoporous hydroxyapatite (HA) nanoparticles as a drug carrier and developed ra­diolabeled mesoporous HA containing of 2-deoxy-D-glucose (2DG) and Doxorubicin (DOX) with technetium-99m (99mTc) for imaging in in vitro and in vivo studies. RESULTS: 2DG and DOX in presence of mesoporous HA nanoparticles more reduced the fraction of viable cells in the MDA-MB-231, MCF-7 human and MC4-L2 Balb/c mice breast cancer cells. The radiochemical purity of the nano-2DG-DOX complex with 99mTc was calculated to 96.8%. The results of cellular uptake showed a 44.77% increase in uptake of the [99mTc]- nano-2DG-DOX compared to the complex without nanoparticles (p &lt; 0.001). CONCLUSIONS: Radioisotopic imaging demonstrated a high biochemical stability for [99mTc]-nano-2DG-DOX complex. The results demonstrated that [99mTc]-nano-2DG-DOX, may be used as an attractive candidate in cancer imaging and treatment managing.

Interpretation of low altitude aerial images of non-urban environment

In this research project a set of computer vision algorithms for interpretation of the non-urban environment from low altitude aerial images is presented. Considering the size and spread of natural resources in non-urban areas, automating the task of gathering information about various land-covers is of particular importance. The utilization of videos captured by small aerial vehicles has many advantages over traditional high altitude aerial photography or satellite imaging for small scale environmental monitoring and agricultural applications. In this thesis the proposed Modular Interpretation Algorithm (MIA) shifts between the Coarse Tuning Algorithm (CTA), which is computationally efficient and the Fine Tuning Algorithm (FTA), which is capable of finding the target land-cover in complex situations

Land cover boundary extraction in rural aerial videos

In this paper a new approach to finding and tracking various land cover boundaries such as rivers, agricultural fields, channels and roads for use in visual navigation system of an unmanned aerial vehicle is presented. We use a combination of statistical estimation and optimization techniques for extraction of dominant boundaries in noisy aerial images. A set of perceptual grouping restrictions is used to connect the acquired piecewise boundaries and to find the heading direction of the main boundary. The results are further refined by applying a set of texture and colour cues and eliminating any false hypothesis. Our results show our method outperforms single feature object tracking algorithms in this application

Real time aerial natural image interpretation for autonomous ranger drone navigation

In this paper a method for real time interpretation of environmental features for use in visual navigation system of an Unmanned Aerial Vehicle (UAV) is presented. The proposed approach uses manually sampled colour values to classify different land covers. Colour information of a set of manually selected windows is compared to select the best attributes needed for discrimination between different land covers in various (natural) lighting conditions. Each frame is then partially scanned and desired environmental features are extracted and classified. The results show that the proposed technique meets the minimum speed and accuracy requirement of aforementioned application

Cyber-Physical Customer Management for Internet of Robotic Things-Enabled Banking

In-person banking is still an important part of financial services around the world. Hybrid bank branches with service robots can improve efficiency and reduce operating costs. An efficient autonomous Know-Your-Customer (KYC) is required for hybrid banking. In this paper, an automated deep learning-based framework for interbank KYC in robot-based cyber-physical banking is proposed. A deep biometric architecture was used to model the customer&#x2019;s KYC and anonymize the collected visual data to ensure the customer&#x2019;s privacy. The symmetric-asymmetric encryption-decryption module in addition to the blockchain network was used for secure and decentralized transmission and validation of the biometric information. A high-capacity fragile watermarking algorithm based on the integer-to-integer discrete wavelet transform in combination with the Z6 and A6 lattice vector quantization for the secure transmission and storage of in-person banking documents is also proposed. The proposed framework was simulated and validated using a Pepper humanoid robot for the automated biometric-based collection of handwritten bank checks from customers adhering to COVID-19 pandemic safety guidelines. The biometric information of bank customers such as fingerprint and name is embedded as a watermark in the related bank documents using the proposed framework. The results show that the proposed security protection framework can embed more biometric data in bank documents in comparison with similar algorithms. Furthermore, the quality of the secured bank documents is 20&#x0025; higher in comparison with other proposed algorithms. Also, the hierarchal visual information communication and storage module that anonymizes the identity of people in videos collected by robots can satisfy the privacy requirements of the banks. Overall, the proposed framework can provide a rapid, efficient, and cost-effective inter-bank solution for future in-person banking while adhering to the security requirements and banking regulations

Secure Medical Image Communication Using Fragile Data Hiding Based on Discrete Wavelet Transform and A&#x2085; Lattice Vector Quantization

Secure communication of medical images is essential to telemedicine. Message Authentication Codes (MAC) can be embedded inside medical images to protect their integrity. Fragile watermarking algorithms are suitable options since they can be used to detect any tampering attempt. In this paper, a novel fragile data-hiding algorithm based on Integer-to-Integer Discrete Wavelet Transforms (IIDWT) and $A_{5}$ Lattice Vector Quantization (LVQ) is proposed. In the proposed data-hiding algorithm, a combination of the medical image Metadata and a MAC is embedded into the medical image. The Metadata includes information about the patient such as name, family, birthday, the place where it is created such as the name of the hospital, and the physician&#x2019;s name. To preserve the privacy of the patients and the physician/hospital, the Metadata is then replaced with fake information. The receiver can extract the Metadata and the MAC. If the extracted MAC is the same as the expected MAC, the integrity of the medical image is guaranteed. Otherwise, a tampering attempt is detected. The proposed algorithm can embed 50&#x0025; more data than similar algorithms in medical images while keeping the Peak Signal to Noise Ratio (PSNR) in acceptable ranges. Furthermore, the proposed algorithm is applied to a dataset of medical images and high PSNR values above 53.88 dB are experienced

Modular interpretation of low altitude aerial images of non-urban environment

In this paper we present a modular algorithm for interpretation of low altitude aerial images of non-urban environment. Non-urban land-covers, e.g., rivers, grass, unlike urban land-covers, have naturally unstructured boundaries and are usually containing diverse combination of colour and texture. The proposed method consists of a coarse and computationally efficient module, and a fine interpretation module. The coarse module is able to produce approximate estimations of land-covers using a single colour-base feature and contextual information. In cases when the coarse module fails, the fine module is able to accurately classify the desired land-cover. The fine module uses a combination of boundary, colour, texture and context features for accurate interpretation of the land-covers. The modular method inherits the high accuracy from the fine module and low computational expense from the coarse interpretation module. Experimental results show that the proposed algorithm can detect the target land-covers in low altitude aerial images of non-urban environment with acceptable accuracy and low computational requirements