Apigenin protects from hepatorenal damage caused by lead acetate in rats.

Exposure to lead (Pb) is associated with serious health problems including hepatorenal toxicity. Apigenin is a natural-sourced flavonoid with promising antioxidant and anti-inflammatory effects. In this research, we investigated the potential protective role of apigenin against lead acetate (PbAc)-induced hepatorenal damage. Thus, this experiment studied the exposure of male Wistar Albino rats to apigenin and/or PbAc and their effects in comparison to the control rats. Apigenin administration decreased the levels of Pb and prevented the histopathological deformations in liver and kidney tissues following PbAc exposure. This was confirmed by the normalized levels of liver and kidney function markers. Additionally, apigenin inhibited significantly oxidative reactions through upregulating Nrf2 and HO-1, and activating their downstreamed antioxidants accompanied by a marked depletion of pro-oxidants. Moreover, apigenin decreased the elevated pro-inflammatory cytokines and inhibited cell loss in liver and kidney tissues in response to PbAc intoxication in both tissues. The obtained results demonstrated that apigenin could be used to attenuate the molecular, biochemical, and histological alterations associated with Pb exposure due to its potent antioxidant, anti-inflammatory, and antiapoptotic effects.

Efficacy of laser in re-osseointegration of dental implants-a systematic review.

Despite their high success rates, peri-implantitis can affect the stability and function of dental implants. Various treatment modalities have been investigated for the treatment of peri-implantitis to achieve re-osseointegration. An electronic literature search was performed supplemented by a manual search to identify studies published until January 2022. Articles that evaluated re-osseointegration in peri-implantitis sites in animal models following laser therapy or antimicrobial photodynamic therapy (aPDT) were included. Case reports, case series, systematic reviews, and letters to the editor were excluded. Risk of bias and GRADE assessment were followed to evaluate the quality of the evidence. Six studies out of 26 articles identified on electronic search were included in this review. The studies included animal studies conducted on canine models. Four out of six studies reported a higher degree of re-osseointegration following treatment of implants with laser therapy. The findings suggest that laser decontamination shows potential in enhancing re-osseointegration, particularly with the Er: YAG laser, which effectively decontaminated implant surfaces. However, conflicting outcomes and limitations in the evidence quality warrant caution in drawing definitive conclusions. Based on the limited available evidence, laser therapy may show a higher degree of re-osseointegration of implants than mechanical debridement.

Anonymity Assurance Using Efficient Pseudonym Consumption in Internet of Vehicles.

The Internet of vehicles (IoVs) is an innovative paradigm which ensures a safe journey by communicating with other vehicles. It involves a basic safety message (BSM) that contains sensitive information in a plain text that can be subverted by an adversary. To reduce such attacks, a pool of pseudonyms is allotted which are changed regularly in different zones or contexts. In base schemes, the BSM is sent to neighbors just by considering their speed. However, this parameter is not enough because network topology is very dynamic and vehicles can change their route at any time. This problem increases pseudonym consumption which ultimately increases communication overhead, increases traceability and has high BSM loss. This paper presents an efficient pseudonym consumption protocol (EPCP) which considers the vehicles in the same direction, and similar estimated location. The BSM is shared only to these relevant vehicles. The performance of the purposed scheme in contrast to base schemes is validated via extensive simulations. The results prove that the proposed EPCP technique outperformed compared to its counterparts in terms of pseudonym consumption, BSM loss rate and achieved traceability.

Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment.

In recent years, the rapid progress of Internet of Things (IoT) solutions has offered an immense opportunity for the collection and dissemination of health records in a central data platform. Electrocardiogram (ECG), a fast, easy, and non-invasive method, is generally employed in the evaluation of heart conditions that lead to heart ailments and the identification of heart diseases. The deployment of IoT devices for arrhythmia classification offers many benefits such as remote patient care, continuous monitoring, and early recognition of abnormal heart rhythms. However, it is challenging to diagnose and manually classify arrhythmia as the manual diagnosis of ECG signals is a time-consuming process. Therefore, the current article presents the automated arrhythmia classification using the Farmland Fertility Algorithm with Hybrid Deep Learning (AAC-FFAHDL) approach in the IoT platform. The proposed AAC-FFAHDL system exploits the hyperparameter-tuned DL model for ECG signal analysis, thereby diagnosing arrhythmia. In order to accomplish this, the AAC-FFAHDL technique initially performs data pre-processing to scale the input signals into a uniform format. Further, the AAC-FFAHDL technique uses the HDL approach for detection and classification of arrhythmia. In order to improve the classification and detection performance of the HDL approach, the AAC-FFAHDL technique involves an FFA-based hyperparameter tuning process. The proposed AAC-FFAHDL approach was validated through simulation using the benchmark ECG database. The comparative experimental analysis outcomes confirmed that the AAC-FFAHDL system achieves promising performance compared with other models under different evaluation measures.

Evaluation of Orthodontic Mini-Implant Placement in the Maxillary Anterior Alveolar Region in 15 Patients by Cone Beam Computed Tomography at a Single Center in South India.

BACKGROUND In this study, we aimed to evaluate orthodontic mini-implant placement in the maxillary anterior alveolar region by cone beam computed tomography (CBCT) in 15 patients at a single center in South India. MATERIAL AND METHODS A total of 15 CBCT scans of orthodontic patients after completion of leveling and aligning stage were included. The thickness of labial alveolar bone, labio-palatal bone, and inter-radicular distance between the maxillary central incisors (U1-U1), maxillary central and lateral incisor (U1-U2), and maxillary lateral incisor and canine (U2-U3) at vertical levels 4 mm, 6 mm, and 8 mm above the interdental cementoenamel junction were measured. Descriptive statistics, ANOVA, and Tukey post hoc tests were done to assess the differences among the groups. An independent t test was done to analyze differences by sex. RESULTS The thickness of cortical bone in the labial region was higher in the U2-U3 site than in the U1-U1 site, at a height of 4 mm. Also, there was a significant difference between 4 mm and 8 mm heights in the U2-U3 region. No significant difference was noted in bone dimensions among men and women and in the labio-palatal bone thickness among the different sites. The inter-radicular distance was the highest between the U2-U3 site, while it was the lowest in the U1-U2 site. CONCLUSIONS The findings from this center showed that when CBCT was used to evaluate orthodontic mini-implant placement in the maxillary anterior alveolar region, the U2-U3 and U1-U1 locations at heights between 6 mm to 8 mm apical to the interdental cementoenamel junction were optimal for placement of the mini-implants.

Joint Power Control and Phase Shift Design for Future PD-NOMA IRS-Assisted Drone Communications under Imperfect SIC Decoding.

Intelligent reflecting surfaces (IRS) and power-domain non-orthogonal multiple access (PD-NOMA) have recently gained significant attention for enhancing the performance of next-generation wireless communications networks. More specifically, IRS can smartly reconfigure the incident signal of the source towards the destination node, extending the wireless coverage and improving the channel capacity without consuming additional energy. On the other side, PD-NOMA can enhance the number of devices in the network without using extra spectrum resources. This paper proposes a new optimization framework for IRS-enhanced NOMA communications where multiple drones transmit data to the ground Internet of Things (IoT) devices under successive interference cancellation errors. In particular, the power budget of each drone, PD-NOMA power allocation of IoT devices, and the phase shift matrix of IRS are simultaneously optimized to enhance the total spectral efficiency of the system. Given the system model and optimization setup, the formulated problem is coupled with three variables, making it very complex and non-convex. Thus, this work first transforms and decouples the problem into subproblems and then obtains the efficient solution in two steps. In the first step, the closed-form solutions for the power budget and PD-NOMA power allocation subproblem at each drone are obtained through Karush-Kuhn-Tucker (KKT) conditions. In the second step, the subproblem of efficient phase shift design for each IRS is solved using successive convex approximation and DC programming. Numerical results demonstrate the performance of the proposed optimization scheme in comparison to the benchmark schemes.

Interdental Papillary Reconstruction by Microtunnelling Technique Using Autologous Biomatrices-A Randomised Controlled Clinical Trial.

Background and objectives: The study aimed to evaluate and compare the amount of papillary gain and black triangle height reduction after intervention with a microtunnelling technique with either Connective tissue graft (CTG) or Platelet-rich fibrin (PRF) as a biomatrix at 6 months using a microsurgical approach. Materials and Methods: Twenty-six patients with interdental papillary loss were included in the study. The patients were selected randomly for the study groups with thirteen patients in each group: a control group where CTG was utilised as a matrix, and a test group where PRF was utilised as a matrix, for interdental papillary reconstruction. A microtunnelling technique was performed for both the study groups under a surgical microscope. The primary parameters assessed were interdental Papillary height (PH) and Black triangle height (BTH) at baseline, with secondary parameters Visual analogue score by dentist (VAS-D) and patient (VAS-P) assessed at 6 months. Results: Both the control and test groups showed a significant reduction in BTH within their respective group at six months (p < 0.05). The gain in papillary height significantly improved only in the CTG group at 6 months. However, significant differences could not be demonstrated for any of the variables such as BTH (p value = 0.582) and PH (p-value = 0.892) between the study groups at 6 months. Conclusions: IDP reconstruction utilising a microtunnelling approach with CTG or PRF was successful without any significant differences between the groups for the parameters assessed at 6 months.

The Effect of Laser Micro Grooved Platform Switched Implants and Abutments on Early Crestal Bone Levels and Peri-Implant Soft Tissues Post 1 Year Loading among Diabetic Patients-A Controlled Clinical Trial.

Background and Objectives: The study aimed to compare the mean crestal bone level (CBL) and peri-implant soft tissue parameters in laser micro-grooved (LMG) platform switched implants and abutments (I&A) post 1 year of functional loading among non-diabetic and type II diabetic individuals. Materials and methods: Patients with an edentulous site having minimum bone height and width of ≥13 mm and ≥6 mm, respectively, were divided into two groups: (i) Non-diabetic-8 (control) and (ii) diabetic-8 (test). LMG Implants were placed and loaded immediately with a provisional prosthesis. Mean crestal bone level (MCBL) was evaluated radiographically at baseline and at 1 year. Peri-implant attachment level (PIAL) and relative position of the gingival margin (R-PGM) were recorded. Implant stability quotient (ISQ) level and implant survival rate (ISR) were evaluated at 1 year. Results: Early MCBL within the groups 1 year postloading was similar both mesially and distally (control-0.00 to 0.16 mm and 0.00 to 0.17 mm, respectively; test-0.00 to 0.21 mm and 0.00 to 0.22 mm, respectively) with statistical significance (p ≤ 0.003, p ≤ 0.001 and p ≤ 0.001, p ≤ 0.001, respectively). However, intergroup comparison showed no significant difference statistically in the MCBL in 1 year post functional loading. The peri-implant soft tissue parameters showed no significant difference between the groups. ISQ level between both groups did not reveal any significant changes (p ≤ 0.92), and ISR was 100%. Conclusions: LMG Implants resulted in minimal and comparable early crestal bone loss and soft tissue changes post 1 year of functional loading in moderately controlled diabetic and non-diabetic individuals, suggesting that this could be a reliable system for use in systemically compromised individuals.

Exploring the impact of word prediction assistive features on smartphone keyboards for blind users.

Assistive technologies have been developed to enhance blind users' typing performance, focusing on speed, accuracy, and effort reduction. One such technology is word prediction software, designed to minimize keystrokes required for text input. This study investigates the impact of word prediction on typing performance among blind users using an on-screen QWERTY keyboard. We conducted a comparative study involving eleven blind participants, evaluating both standard QWERTY input and word prediction-assisted typing. Our findings reveal that while word prediction slightly improves typing speed, it does not enhance typing accuracy and increases both physical and temporal workload compared to the default keyboard. We conclude with recommendations for improving word prediction systems, including more efficient editing methods and the integration of voice pitch variations to aid error recognition.

A deep learning framework for the early detection of multi-retinal diseases.

Retinal images play a pivotal contribution to the diagnosis of various ocular conditions by ophthalmologists. Extensive research was conducted to enable early detection and timely treatment using deep learning algorithms for retinal fundus images. Quick diagnosis and treatment planning can be facilitated by deep learning models' ability to process images rapidly and deliver outcomes instantly. Our research aims to provide a non-invasive method for early detection and timely eye disease treatment using a Convolutional Neural Network (CNN). We used a dataset Retinal Fundus Multi-disease Image Dataset (RFMiD), which contains various categories of fundus images representing different eye diseases, including Media Haze (MH), Optic Disc Cupping (ODC), Diabetic Retinopathy (DR), and healthy images (WNL). Several pre-processing techniques were applied to improve the model's performance, such as data augmentation, cropping, resizing, dataset splitting, converting images to arrays, and one-hot encoding. CNNs have extracted extract pertinent features from the input color fundus images. These extracted features are employed to make predictive diagnostic decisions. In this article three CNN models were used to perform experiments. The model's performance is assessed utilizing statistical metrics such as accuracy, F1 score, recall, and precision. Based on the results, the developed framework demonstrates promising performance with accuracy rates of up to 89.81% for validation and 88.72% for testing using 12-layer CNN after Data Augmentation. The accuracy rate obtained from 20-layer CNN is 90.34% for validation and 89.59% for testing with Augmented data. The accuracy obtained from 20-layer CNN is greater but this model shows overfitting. These accuracy rates suggested that the deep learning model has learned to distinguish between different eye disease categories and healthy images effectively. This study's contribution lies in providing a reliable and efficient diagnostic system for the simultaneous detection of multiple eye diseases through the analysis of color fundus images.

Intelligent model for the detection and classification of encrypted network traffic in cloud infrastructure.

This article explores detecting and categorizing network traffic data using machine-learning (ML) methods, specifically focusing on the Domain Name Server (DNS) protocol. DNS has long been susceptible to various security flaws, frequently exploited over time, making DNS abuse a major concern in cybersecurity. Despite advanced attack, tactics employed by attackers to steal data in real-time, ensuring security and privacy for DNS queries and answers remains challenging. The evolving landscape of internet services has allowed attackers to launch cyber-attacks on computer networks. However, implementing Secure Socket Layer (SSL)-encrypted Hyper Text Transfer Protocol (HTTP) transmission, known as HTTPS, has significantly reduced DNS-based assaults. To further enhance security and mitigate threats like man-in-the-middle attacks, the security community has developed the concept of DNS over HTTPS (DoH). DoH aims to combat the eavesdropping and tampering of DNS data during communication. This study employs a ML-based classification approach on a dataset for traffic analysis. The AdaBoost model effectively classified Malicious and Non-DoH traffic, with accuracies of 75% and 73% for DoH traffic. The support vector classification model with a Radial Basis Function (SVC-RBF) achieved a 76% accuracy in classifying between malicious and non-DoH traffic. The quadratic discriminant analysis (QDA) model achieved 99% accuracy in classifying malicious traffic and 98% in classifying non-DoH traffic.

Harnessing AI and analytics to enhance cybersecurity and privacy for collective intelligence systems.

Collective intelligence systems like Chat Generative Pre-Trained Transformer (ChatGPT) have emerged. They have brought both promise and peril to cybersecurity and privacy protection. This study introduces novel approaches to harness the power of artificial intelligence (AI) and big data analytics to enhance security and privacy in this new era. Contributions could explore topics such as: leveraging natural language processing (NLP) in ChatGPT-like systems to strengthen information security; evaluating privacy-enhancing technologies to maximize data utility while minimizing personal data exposure; modeling human behavior and agency to build secure and ethical human-centric systems; applying machine learning to detect threats and vulnerabilities in a data-driven manner; using analytics to preserve privacy in large datasets while enabling value creation; crafting AI techniques that operate in a trustworthy and explainable manner. This article advances the state-of-the-art at the intersection of cybersecurity, privacy, human factors, ethics, and cutting-edge AI, providing impactful solutions to emerging challenges. Our research presents a revolutionary approach to malware detection that leverages deep learning (DL) based methodologies to automatically learn features from raw data. Our approach involves constructing a grayscale image from a malware file and extracting features to minimize its size. This process affords us the ability to discern patterns that might remain hidden from other techniques, enabling us to utilize convolutional neural networks (CNNs) to learn from these grayscale images and a stacking ensemble to classify malware. The goal is to model a highly complex nonlinear function with parameters that can be optimized to achieve superior performance. To test our approach, we ran it on over 6,414 malware variants and 2,050 benign files from the MalImg collection, resulting in an impressive 99.86 percent validation accuracy for malware detection. Furthermore, we conducted a classification experiment on 15 malware families and 13 tests with varying parameters to compare our model to other comparable research. Our model outperformed most of the similar research with detection accuracy ranging from 47.07% to 99.81% and a significant increase in detection performance. Our results demonstrate the efficacy of our approach, which unlocks the hidden patterns that underlie complex systems, advancing the frontiers of computational security.

FibroVit-Vision transformer-based framework for detection and classification of pulmonary fibrosis from chest CT images.

Pulmonary Fibrosis (PF) is an immedicable respiratory condition distinguished by permanent fibrotic alterations in the pulmonary tissue for which there is no cure. Hence, it is crucial to diagnose PF swiftly and precisely. The existing research on deep learning-based pulmonary fibrosis detection methods has limitations, including dataset sample sizes and a lack of standardization in data preprocessing and evaluation metrics. This study presents a comparative analysis of four vision transformers regarding their efficacy in accurately detecting and classifying patients with Pulmonary Fibrosis and their ability to localize abnormalities within Images obtained from Computerized Tomography (CT) scans. The dataset consisted of 13,486 samples selected out of 24647 from the Pulmonary Fibrosis dataset, which included both PF-positive CT and normal images that underwent preprocessing. The preprocessed images were divided into three sets: the training set, which accounted for 80% of the total pictures; the validation set, which comprised 10%; and the test set, which also consisted of 10%. The vision transformer models, including ViT, MobileViT2, ViTMSN, and BEiT were subjected to training and validation procedures, during which hyperparameters like the learning rate and batch size were fine-tuned. The overall performance of the optimized architectures has been assessed using various performance metrics to showcase the consistent performance of the fine-tuned model. Regarding performance, ViT has shown superior performance in validation and testing accuracy and loss minimization, specifically for CT images when trained at a single epoch with a tuned learning rate of 0.0001. The results were as follows: validation accuracy of 99.85%, testing accuracy of 100%, training loss of 0.0075, and validation loss of 0.0047. The experimental evaluation of the independently collected data gives empirical evidence that the optimized Vision Transformer (ViT) architecture exhibited superior performance compared to all other optimized architectures. It achieved a flawless score of 1.0 in various standard performance metrics, including Sensitivity, Specificity, Accuracy, F1-score, Precision, Recall, Mathew Correlation Coefficient (MCC), Precision-Recall Area under the Curve (AUC PR), Receiver Operating Characteristic and Area Under the Curve (ROC-AUC). Therefore, the optimized Vision Transformer (ViT) functions as a reliable diagnostic tool for the automated categorization of individuals with pulmonary fibrosis (PF) using chest computed tomography (CT) scans.

Bio-Inspired Artificial Intelligence with Natural Language Processing Based on Deceptive Content Detection in Social Networking.

In recent research, fake news detection in social networking using Machine Learning (ML) and Deep Learning (DL) models has gained immense attention. The current research article presents the Bio-inspired Artificial Intelligence with Natural Language Processing Deceptive Content Detection (BAINLP-DCD) technique for social networking. The goal of the proposed BAINLP-DCD technique is to detect the presence of deceptive or fake content on social media. In order to accomplish this, the BAINLP-DCD algorithm applies data preprocessing to transform the input dataset into a meaningful format. For deceptive content detection, the BAINLP-DCD technique uses a Multi-Head Self-attention Bi-directional Long Short-Term Memory (MHS-BiLSTM) model. Finally, the African Vulture Optimization Algorithm (AVOA) is applied for the selection of optimum hyperparameters of the MHS-BiLSTM model. The proposed BAINLP-DCD algorithm was validated through simulation using two benchmark fake news datasets. The experimental outcomes portrayed the enhanced performance of the BAINLP-DCD technique, with maximum accuracy values of 92.19% and 92.56% on the BuzzFeed and PolitiFact datasets, respectively.

Enhanced Pelican Optimization Algorithm with Deep Learning-Driven Mitotic Nuclei Classification on Breast Histopathology Images.

Breast cancer (BC) is a prevalent disease worldwide, and accurate diagnoses are vital for successful treatment. Histopathological (HI) inspection, particularly the detection of mitotic nuclei, has played a pivotal function in the prognosis and diagnosis of BC. It includes the detection and classification of mitotic nuclei within breast tissue samples. Conventionally, the detection of mitotic nuclei has been a subjective task and is time-consuming for pathologists to perform manually. Automatic classification using computer algorithms, especially deep learning (DL) algorithms, has been developed as a beneficial alternative. DL and CNNs particularly have shown outstanding performance in different image classification tasks, including mitotic nuclei classification. CNNs can learn intricate hierarchical features from HI images, making them suitable for detecting subtle patterns related to the mitotic nuclei. In this article, we present an Enhanced Pelican Optimization Algorithm with a Deep Learning-Driven Mitotic Nuclei Classification (EPOADL-MNC) technique on Breast HI. This developed EPOADL-MNC system examines the histopathology images for the classification of mitotic and non-mitotic cells. In this presented EPOADL-MNC technique, the ShuffleNet model can be employed for the feature extraction method. In the hyperparameter tuning procedure, the EPOADL-MNC algorithm makes use of the EPOA system to alter the hyperparameters of the ShuffleNet model. Finally, we used an adaptive neuro-fuzzy inference system (ANFIS) for the classification and detection of mitotic cell nuclei on histopathology images. A series of simulations took place to validate the improved detection performance of the EPOADL-MNC technique. The comprehensive outcomes highlighted the better outcomes of the EPOADL-MNC algorithm compared to existing DL techniques with a maximum accuracy of 97.83%.

Effect of Amnioguard and Biomesh GTR Membranes with Novabone Putty in the Treatment of Periodontal Osseous Defects-A Communication.

(1) Background: Guided tissue regeneration was an effective surgical procedure in the management of intrabony defects and has undergone a number of changes in terms of materials and techniques. The aim of this study is to compare AmnioGuard and BioMesh in combination with NovaBone putty in intrabony defects. (2) Methodology: Ten patients who needed regenerative periodontal therapy were randomly allocated into two groups based on the inclusion criteria. These patients were subjected to phase I therapy followed by which Group A patients were treated with AmnioGuard + NovaBone putty whereas Group B with BioMesh + NovaBone putty. The clinical indices were obtained at baseline, 3 months and 6 months post-operatively while radiographic parameters were obtained at 6 months post-op. (3) Results & Conclusion: At six months after surgery, Group B (33% bone gain) showed a statistically significant change from Group A (16% bone gain) in both the clinical and radiographic measures (p < 0.05).

Evaluation of efficacy of non-resorbable membranes compared to resorbable membranes in patients undergoing guided bone regeneration.

Background: Replacement of missing teeth in patients with prolonged edentulism poses a challenge for clinicians. An extended period of edentulism results in severe atrophy of alveolar ridges rendering them unsatisfactory for rehabilitation using an implant-supported prosthesis. To overcome this difficulty, Guided Bone Regeneration (GBR) was introduced and constructed upon the principles of Guided Tissue Regeneration (GTR) procedures. Evidence suggests that GBR has proven to be a predictable treatment modality for treating vertical and horizontal ridge deficiencies. Objective: The present systematic review aimed to evaluate the efficacy of non-resorbable (N-RES) membranes compared to resorbable (RES) membranes in patients undergoing GBR. Methods: An electronic search of three databases, including PubMed, Web of Science, and Scopus, was conducted for articles published until March 2022. A supplementary manual search of references from these articles was performed to include any articles that may have been overlooked in the electronic search. Articles that evaluated the efficacy of RES membranes and N-RES membranes in GBR were included. Case reports, case series, commentaries, letters to the editor, narrative or systematic reviews were excluded. Articles in languages other than English were also excluded. The articles were assessed against risk of bias 2 tool for Randomized Control Trials (RCTs) and ROBINS-I tool for Non-Randomized Clinical Trials (N-RCTs). The Grading of Recommendations Assessment, Development and Evaluation (GRADE) assessment was followed based on the Cochrane Handbook for quality assessment. A summary of findings table was used to present the results. Results: One hundred and fifty one articles were identified in an electronic search. Eight articles met the inclusion criteria and were included in the present systematic review. The studies were conducted on partially or completely edentulous patients with alveolar ridge deficiencies undergoing vertical or horizontal bone for subsequent implant placement. The majority of the studies reported similar results for bone gain in both RES and N-RES membrane groups. Conclusion: The available evidence suggests that RES and N-RES membranes are equally effective in GBR. However, the evidence must be interpreted with caution due to its 'low quality' GRADE assessment. Clinical implications: Further research focusing on human clinical trials with well-matched subjects with homogeneity in the type and method of GBR and method of assessment of new bone formation will derive conclusive results on the efficacy of RES and N-RES membranes in achieving new bone formation.

CBCT Evaluation of Sticky Bone in Horizontal Ridge Augmentation with and without Collagen Membrane-A Randomized Parallel Arm Clinical Trial.

Guided bone regeneration (GBR) is a reliable technique used to treat ridge deficiencies prior or during implant placement. Injectable-platelet rich fibrin (i-PRF) laced with a bone substitute (sticky bone) has heralded the way for advancing the outcomes of bone regeneration. This study evaluated the efficacy of sticky bone in horizontal ridge augmentation with and without collagen membrane. A total of 20 partially edentulous patients (Group-I n = 10; Group-II n = 10) that indicated GBR were included, and the surgical procedure was carried out. In Group-I, the sticky bone and collagen membrane were placed in ridge-deficient sites and Group-II received only sticky bone. At the end of 6 months, 20 patients (Group-I (n = 10); Group-II (n = 10)) completed the follow-up period. A CBCT examination was performed to assess changes in the horizontal ridge width (HRW) and vertical bone height (VBH). A statistically significant increase in HRW (p < 0.05) was observed in both groups with mean gains of 1.35 mm, 1.55 mm, and 1.93 mm at three levels (crest, 3 mm, and 6 mm) in Group-I and 2.7 mm, 2.8 mm, and 2.6 mm at three levels in Group-II. The intergroup comparison revealed statistical significance (p < 0.05) with respect to HRW and KTW (Keratinised tissue width) gains of 0.775 at the 6-month follow-up. Sticky-bone (Xenogenic-bone graft + i-PRF) served as a promising biomaterial in achieving better horizontal bone width gain.

Salivary Biomarkers in Periodontitis Post Scaling and Root Planing.

OBJECTIVES: This study was conducted to evaluate the levels of salivary uric acid and arginase in patients with periodontitis, generalized gingivitis, and in healthy individuals. Then, the effects of non-surgical periodontal therapy on levels of salivary arginase and uric acid were also investigated. METHODS: A total of 60 subjects were divided into three groups based on periodontal health: group I comprised 20 healthy individuals; group II comprised 20 subjects who had generalized gingivitis; group III comprised 20 subjects who had generalized periodontitis. On day 0, the clinical examination of periodontal status was recorded, following which saliva samples were collected. Group II and group III subjects underwent non-surgical periodontal therapy. These patients were recalled on day 30 to collect saliva samples. The periodontal parameters were reassessed on day 90, and saliva samples were collected for analysis of salivary arginase and uric acid levels. RESULTS: Group II and group III showed improvement in clinical parameters following non-surgical periodontal therapy on the 90th day. The MGI score, PPD, and CAL showed improvement. On day 0, at baseline, salivary arginase levels in group III and group II were higher than those in healthy subjects, whereas on day 0, salivary uric acid levels in group III and group II were lower than those in healthy subjects. Both on day 0 and day 90, the salivary arginase level showed a positive correlation with the periodontal parameters, whereas the salivary uric acid level was positively correlated with the periodontal parameters on day 90. CONCLUSION: the level of salivary arginase was a pro-inflammatory marker and a raised level of salivary uric acid was an anti-inflammatory marker following periodontal therapy, suggesting their pivotal role in assessing periodontal status and evaluation of treatment outcome.

Ensemble Deep-Learning-Based Prognostic and Prediction for Recurrence of Sporadic Odontogenic Keratocysts on Hematoxylin and Eosin Stained Pathological Images of Incisional Biopsies.

(1) Background: Odontogenic keratocysts (OKCs) are enigmatic developmental cysts that deserve special attention due to their heterogeneous appearance in histopathological characteristics and high recurrence rate. Despite several nomenclatures for classification, clinicians still confront challenges in its diagnosis and predicting its recurrence. This paper proposes an ensemble deep-learning-based prognostic and prediction algorithm, for the recurrence of sporadic odontogenic keratocysts, on hematoxylin and eosin stained pathological images of incisional biopsies before treatment. (2) Materials and Methods: In this study, we applied a deep-learning algorithm to an ensemble approach integrated with DenseNet-121, Inception-V3, and Inception-Resnet-V3 classifiers. Around 1660 hematoxylin and eosin stained pathologically annotated digital images of OKC-diagnosed (60) patients were supplied to train and predict recurrent OKCs. (3) Results: The presence of SEH (p = 0.004), an incomplete epithelial lining, (p = 0.023), and a corrugated surface (p = 0.049) were the most significant histological parameters distinguishing recurrent and non-recurrent OKCs. Amongst the classifiers, DenseNet-121 showed 93% accuracy in predicting recurrent OKCs. Furthermore, integrating and training the traditional ensemble model showed an accuracy of 95% and an AUC of 0.9872, with an execution time of 192.9 s. In comparison, our proposed model showed 97% accuracy with an execution time of 154.6 s. (4) Conclusions: Considering the outcome of our novel ensemble model, based on accuracy and execution time, the presented design could be embedded into a computer-aided design system for automation of risk stratification of odontogenic keratocysts.