BioMed Research International

Humans are familiar with “diabetes,” a chronic metabolic disease that causes resistance to insulin in the human body, and about 425 million cases worldwide. Diabetes is a hazard to human health since it can gradually cause significant damage to the heart, blood vessels, eyes, kidneys, and nerves. As a result, it is critical to recognize diabetes early on to minimize its negative consequences. Over the years, artificial intelligence (AI) technology and data mining methods are playing a crucial role in detecting diabetic patients. Considering this opportunity, we present a fine-tuned random forest algorithm with the best parameters (RFWBP) that is used with the RF algorithm and feature engineering to detect diabetes patients at an early stage. We have employed several data processing techniques (e.g., normalization, conversion into numerical data) to raw data during the prepossessing phase. After that, we further applied some data mining techniques, adding related characteristics to the primary dataset. Finally, we train the proposed RFWBP and conventional methods like the AdaBoost algorithm, support vector machine, logistic regression, naive Bayes, multilayer perceptron, and a regular random forest with the dataset. Furthermore, we also utilized 5-fold cross-validation to enhance the performance of the RFWBP classifier. The proposed RFWBP achieved an accuracy of 95.83% and 90.68% with and without 5-fold cross-validation, respectively. Moreover, the proposed RFWBP is compared with conventional machine learning methods to evaluate the performance. The experimental results confirm that the proposed RFWBP outperformed conventional machine learning methods.

The virus responsible for the coronavirus viral pandemic is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Emerging SARS-CoV-2 variants caused by distinctive mutations within the viral spike glycoprotein of SARS-CoV-2 are considered the cause for the rapid spread of the disease and make it challenging to treat SARS-CoV-2. The manufacturing of appropriate efficient vaccines and therapeutics is the only option to combat this pandemic. Nanomedicine has enabled the delivery of nucleic acids and protein-based vaccines to antigen-presenting cells to produce protective immunity against the coronavirus. Nucleic acid-based vaccines, particularly mRNA nanotechnology vaccines, are the best prevention option against the SARS-CoV-2 pandemic worldwide, and they are effective against the novel coronavirus and its multiple variants. This review will report on progress made thus far with SARS-CoV-2 vaccines and beyond employing nanotechnology-based nucleic acid vaccine approaches.

Almost 17.9 million people are losing their lives due to cardiovascular disease, which is 32% of total death throughout the world. It is a global concern nowadays. However, it is a matter of joy that the mortality rate due to heart disease can be reduced by early treatment, for which early-stage detection is a crucial issue. This study is aimed at building a potential machine learning model to predict heart disease in early stage employing several feature selection techniques to identify significant features. Three different approaches were applied for feature selection such as chi-square, ANOVA, and mutual information, and the selected feature subsets were denoted as SF1, SF2, and SF3, respectively. Then, six different machine learning models such as logistic regression (C1), support vector machine (C2), K-nearest neighbor (C3), random forest (C4), Naive Bayes (C5), and decision tree (C6) were applied to find the most optimistic model along with the best-fit feature subset. Finally, we found that random forest provided the most optimistic performance for SF3 feature subsets with 94.51% accuracy, 94.87% sensitivity, 94.23% specificity, 94.95 area under ROC curve (AURC), and 0.31 log loss. The performance of the applied model along with selected features indicates that the proposed model is highly potential for clinical use to predict heart disease in the early stages with low cost and less time.

Purpose. Evaluate the effect of different mini-implant numbers on overdenture retention and evaluate attachment wear following one year of simulated placement/removal. Material and Methods. Nine models simulating atrophic mandibles held 27 mini dental implants in three groups of 2, 3, and 4 mini-implants. A total of 1080 simulated placement/removal cycles were carried out, and a digital force gauge was used to measure the overdenture dislodgment force. The means of the retention forces were analyzed using SPSS with one-way ANOVA and post hoc (). The inner diameter of attachment inserts was evaluated using a light microscope before and after testing. A paired -test was used to compare the mean of inner ring diameters (). Results. The retention was significantly reduced regardless of the mini dental implant number, but the number affected overdenture retention. The placement of 4 mini dental implants provided higher retention and less reduction in retentiveness. However, no significant difference was found when 3 mini dental implants were compared to 2 mini dental implants (). Microscopic examination showed abrasion wear in all inserts following testing. However, the inserts of the 4 mini dental implants showed less wear than those used for 2 or 3 mini dental implants with and , respectively. Conclusion. Mini dental implant overdenture retention force and attachment wear could improve by increasing the mini dental implants to 4. However, there was no difference in retention force or attachment wear when 2 or 3 mini dental implant overdentures were compared.

Heart disease (HD) has become a dangerous problem and one of the most significant mortality factors worldwide, which requires an expensive and sophisticated detection process. Most people are affected due to the failure of the heart which seriously threatens their lives due to high morbidity and mortality. Therefore, accurate prediction and diagnosis are needed for early prevention, detection, and treatment to reduce the death threats to human life. However, an early and accurate prediction of HD is still a challenging task to be addressed. In this work, we propose a machine learning-based prediction model (MLbPM) that exploits a combination of the data scaling methods, the split ratios, the best parameters, and the machine learning algorithms for predicting HD. The performance of the proposed model is tested by performing experiments on a University of California Irvine HD dataset to indicate the presence or absence of HD. The results show that the proposed MLbPM provides an accuracy of 96.7% when logistic regression, robust scaler, best parameter, and 70 : 30 as a split ratio of the dataset are considered. In addition, MLbPM outperforms other compared works in terms of accuracy.

Imaging studies in dentistry and maxillofacial pathology have recently concentrated on detecting the inferior alveolar nerve (IAN) canal. In spite of the minor dimensions of 3D maxillofacial datasets, deep learning-based algorithms have shown encouraging consequences in this study area. This study describes a mandibular cone-beam CT (CBCT) dataset with 2D and 3D hand comments. It is huge and freely available. It was possible to utilise this dataset by applying the residual neural network (IANSegNet), which consumed less GPU memory and computational complexity. As an encoder, IANSegNet uses the computationally efficient 3D ShuffleNetV2 network to reduce graphics processing unit (GPU) memory usage and improve efficiency. After that, a decoder with leftover blocks is added to keep the quality high. To address network convergence and data inequity, Dice’s loss and cross-entropy loss were created. Optimized postprocessing techniques are also recommended for fine-tuning the coarse segmentation findings that are generated by IANSegNet. The results of the validation show that IANSegNet outperformed other deep learning models in a variety of criteria.