Knowledge of Dental Students and Practitioners About Medication-Related Osteonecrosis of the Jaw in the Central Region of Saudi Arabia.

BACKGROUND: Bisphosphonates (BPs) are often used in treating benign and malignant disorders. Medication-related osteonecrosis of the jaw (MRONJ) is a significant problem that arises from the long-term use of BPs. OBJECTIVE: In this study, we assessed the knowledge of students and dentists about MRONJ in the central region of Saudi Arabia. METHODS: A cross-sectional study was conducted to collect information from dental students and practitioners from the central region of Saudi Arabia. A valid, reliable, and structured questionnaire was used to gather data using a non-probability convenient sampling technique. IBM SPSS Statistics for Windows, Version 22.0 (Released 2013; IBM Corp., Armonk, New York, United States) was used to analyse the data. The descriptive data were expressed as frequencies and percentages to evaluate the association between dentists and students concerning overall knowledge related to osteonecrosis of the jaw, and a chi-squared test was applied. RESULTS: In total, 250 individuals completed the questionnaire. The general knowledge of antiresorptive/antiangiogenic medications showed that most dentists (87.5%) and students (68.4%) knew about BP medications. A general lack of understanding about the therapeutic uses of antiangiogenic and antiresorptive medications was demonstrated by the participants. A significant proportion of dentists (58.8%) and students (50.9%) were not convinced that invasive dental procedures can be safely performed on patients receiving intravenous BP therapy. A significant proportion of the participants in the sample were unclear of the principal diseases that antiresorptive and antiangiogenic medications target. A mere 22% of respondents were aware of the accurate definition of medications-related MRONJ. CONCLUSION: There is insufficient knowledge about MRONJ among students and practitioners. Therefore, these findings suggest increased emphasis should be placed on educating dentists and students about this condition to ensure patients receive the best possible care.

EDLM: Ensemble Deep Learning Model to Detect Mutation for the Early Detection of Cholangiocarcinoma.

The most common cause of mortality and disability globally right now is cholangiocarcinoma, one of the worst forms of cancer that may affect people. When cholangiocarcinoma develops, the DNA of the bile duct cells is altered. Cholangiocarcinoma claims the lives of about 7000 individuals annually. Women pass away less often than men. Asians have the greatest fatality rate. Following Whites (20%) and Asians (22%), African Americans (45%) saw the greatest increase in cholangiocarcinoma mortality between 2021 and 2022. For instance, 60-70% of cholangiocarcinoma patients have local infiltration or distant metastases, which makes them unable to receive a curative surgical procedure. Across the board, the median survival time is less than a year. Many researchers work hard to detect cholangiocarcinoma, but this is after the appearance of symptoms, which is late detection. If cholangiocarcinoma progression is detected at an earlier stage, then it will help doctors and patients in treatment. Therefore, an ensemble deep learning model (EDLM), which consists of three deep learning algorithms-long short-term model (LSTM), gated recurrent units (GRUs), and bi-directional LSTM (BLSTM)-is developed for the early identification of cholangiocarcinoma. Several tests are presented, such as a 10-fold cross-validation test (10-FCVT), an independent set test (IST), and a self-consistency test (SCT). Several statistical techniques are used to evaluate the proposed model, such as accuracy (Acc), sensitivity (Sn), specificity (Sp), and Matthew's correlation coefficient (MCC). There are 672 mutations in 45 distinct cholangiocarcinoma genes among the 516 human samples included in the proposed study. The IST has the highest Acc at 98%, outperforming all other validation approaches.

iDHU-Ensem: Identification of dihydrouridine sites through ensemble learning models.

Background: Dihydrouridine (D) is one of the most significant uridine modifications that have a prominent occurrence in eukaryotes. The folding and conformational flexibility of transfer RNA (tRNA) can be attained through this modification. Objective: The modification also triggers lung cancer in humans. The identification of D sites was carried out through conventional laboratory methods; however, those were costly and time-consuming. The readiness of RNA sequences helps in the identification of D sites through computationally intelligent models. However, the most challenging part is turning these biological sequences into distinct vectors. Methods: The current research proposed novel feature extraction mechanisms and the identification of D sites in tRNA sequences using ensemble models. The ensemble models were then subjected to evaluation using k-fold cross-validation and independent testing. Results: The results revealed that the stacking ensemble model outperformed all the ensemble models by revealing 0.98 accuracy, 0.98 specificity, 0.97 sensitivity, and 0.92 Matthews Correlation Coefficient. The proposed model, iDHU-Ensem, was also compared with pre-existing predictors using an independent test. The accuracy scores have shown that the proposed model in this research study performed better than the available predictors. Conclusion: The current research contributed towards the enhancement of D site identification capabilities through computationally intelligent methods. A web-based server, iDHU-Ensem, was also made available for the researchers at https://taseersuleman-idhu-ensem-idhu-ensem.streamlit.app/.

Application of aluminum oxide nanoparticles in asphalt cement toward non-polluted green environment using linear regression.

In order to decrease the greenhouse gas emissions generated by regular Portland cement (OPC), additional cementitious ingredients have been frequently employed, even while building road bases. OPC's susceptibility to moisture and lack of flexibility make it ineffective for stabilizing road bases. This research used alkali-activated materials (AAM) with fly ash to investigate the mechanical properties of cold asphalt binder (freeze-thaw cycles) including the compressive, flexural strength, workability and porosity of cement. Dry specimens and specimens in distilled water have both been used in the experiments to study these temperature correlations. One sample was tested at 20 °C, and the other was frozen and thawed five times at a temperature of -5 °C (cold region environment). The resulting mixtures' morphologies and microstructures were analyzed via SEM images. During the 7 to 28-day curing period, the mixture's growth ratio rose. The combination registered both the greatest and lowest robust elastic modulus. The total compressive strength of the material decreased as the water-to-cement ratio increased due to the greater amount of free water accessible with a higher cationic asphalt emulsion (CAE) content. The moderate loss of flexural strength with increasing CAE concentration after 7 and 28 days of curing was seen. There is not a major impact on flexural strength in the materials by looking at the very modest gaps in flexural strength between 7 and 28 days curing periods. Due to the particle shape and size of this precursor, FA's inclusion allowed for a lower water to binder rate while maintaining a similar level of workability. The porosity and water absorption values rose with FA substitutions. Further studies might clarify the lower flexural strength observed in this study by adding other hybrids plus fly ash such as lime or nanoparticles.

RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features.

To save lives from cancer, it is very crucial to diagnose it at its early stages. One solution to early diagnosis lies in the identification of the cancer driver genes and their mutations. Such diagnostics can substantially minimize the mortality rate of this deadly disease. However, concurrently, the identification of cancer driver gene mutation through experimental mechanisms could be an expensive, slow, and laborious job. The advancement of computational strategies that could help in the early prediction of cancer growth effectively and accurately is thus highly needed towards early diagnoses and a decrease in the mortality rates due to this disease. Herein, we aim to predict clear cell renal carcinoma (RCCC) at the level of the genes, using the genomic sequences. The dataset was taken from IntOgen Cancer Mutations Browser and all genes' standard DNA sequences were taken from the NCBI database. Using cancer-associated information of mutation from INTOGEN, the benchmark dataset was generated by creating the mutations in original sequences. After extensive feature extraction, the dataset was used to train ANN+ Hist Gradient boosting that could perform the classification of RCCC genes, other cancer-associated genes, and non-cancerous/unknown (non-tumor driver) genes. Through an independent dataset test, the accuracy observed was 83%, whereas the 10-fold cross-validation and Jackknife validation yielded 98% and 100% accurate results, respectively. The proposed predictor RCCC_Pred is able to identify RCCC genes with high accuracy and efficiency and can help scientists/researchers easily predict and diagnose cancer at its early stages.

DHU-Pred: accurate prediction of dihydrouridine sites using position and composition variant features on diverse classifiers.

Background: Dihydrouridine (D) is a modified transfer RNA post-transcriptional modification (PTM) that occurs abundantly in bacteria, eukaryotes, and archaea. The D modification assists in the stability and conformational flexibility of tRNA. The D modification is also responsible for pulmonary carcinogenesis in humans. Objective: For the detection of D sites, mass spectrometry and site-directed mutagenesis have been developed. However, both are labor-intensive and time-consuming methods. The availability of sequence data has provided the opportunity to build computational models for enhancing the identification of D sites. Based on the sequence data, the DHU-Pred model was proposed in this study to find possible D sites. Methodology: The model was built by employing comprehensive machine learning and feature extraction approaches. It was then validated using in-demand evaluation metrics and rigorous experimentation and testing approaches. Results: The DHU-Pred revealed an accuracy score of 96.9%, which was considerably higher compared to the existing D site predictors. Availability and Implementation: A user-friendly web server for the proposed model was also developed and is freely available for the researchers.

Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations.

Genes are composed of DNA and each gene has a specific sequence. Recombination or replication within the gene base ends in a permanent change in the nucleotide collection in a DNA called mutation and some mutations can lead to cancer. Breast adenocarcinoma starts in secretary cells. Breast adenocarcinoma is the most common of all cancers that occur in women. According to a survey within the United States of America, there are more than 282,000 breast adenocarcinoma patients registered each 12 months, and most of them are women. Recognition of cancer in its early stages saves many lives. A proposed framework is developed for the early detection of breast adenocarcinoma using an ensemble learning technique with multiple deep learning algorithms, specifically: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bi-directional LSTM. There are 99 types of driver genes involved in breast adenocarcinoma. This study uses a dataset of 4127 samples including men and women taken from more than 12 cohorts of cancer detection institutes. The dataset encompasses a total of 6170 mutations that occur in 99 genes. On these gene sequences, different algorithms are applied for feature extraction. Three types of testing techniques including independent set testing, self-consistency testing, and a 10-fold cross-validation test is applied to validate and test the learning approaches. Subsequently, multiple deep learning approaches such as LSTM, GRU, and bi-directional LSTM algorithms are applied. Several evaluation metrics are enumerated for the validation of results including accuracy, sensitivity, specificity, Mathew's correlation coefficient, area under the curve, training loss, precision, recall, F1 score, and Cohen's kappa while the values obtained are 99.57, 99.50, 99.63, 0.99, 1.0, 0.2027, 99.57, 99.57, 99.57, and 99.14 respectively.

Evaluation of deep learning techniques for identification of sarcoma-causing carcinogenic mutations.

The abnormal growth of human healthy cells is called cancer. One of the major types of cancer is sarcoma, mostly found in human bones and soft tissue cells. It commonly occurs in children. According to a survey of the United States of America, there are more than 17,000 sarcoma patients registered each year which is 15% of all cancer cases. Recognition of cancer at its early stage saves many lives. The proposed study developed a framework for the early detection of human sarcoma cancer using deep learning Recurrent Neural Network (RNN) algorithms. The DNA of a human cell is made up of 25,000 to 30,000 genes. Each gene is represented by sequences of nucleotides. The nucleotides in a sequence of a driver gene can change which is termed as mutations. Some mutations can cause cancer. There are seven types of a gene whose mutation causes sarcoma cancer. The study uses the dataset which has been taken from more than 134 samples and includes 141 mutations in 8 driver genes. On these gene sequences RNN algorithms Long and Short-Term Memory (LSTM), Gated Recurrent Units and Bi-directional LSTM (Bi-LSTM) are used for training. Rigorous testing techniques such as Self-consistency testing, independent set testing, 10-fold cross-validation test are applied for the validation of results. These validation techniques yield several metrics such as Area Under the Curve (AUC), sensitivity, specificity, Mathew's correlation coefficient, loss, and accuracy. The proposed algorithm exhibits an accuracy of 99.6% with an AUC value of 1.00.

Enhanced artificial intelligence for electrochemical sensors in monitoring and removing of azo dyes and food colorant substances.

It is necessary to determine whether synthetic dyes are present in food since their excessive use has detrimental effects on human health. For the simultaneous assessment of tartrazine and Patent Blue V, a novel electrochemical sensing platform was developed. As a result, two artificial azo colorants (Tartrazine and Patent Blue V) with toxic azo groups (-NN-) and other carcinogenic aromatic ring structures were examined. With a low limit of detection of 0.06 µM, a broad linear concentration range 0.09µM to 950µM, and a respectable recovery, scanning electron microscopy (SEM) was able to reveal the excellent sensing performance of the suggested electrode for patent blue V. The electrochemical performance of an electrode can be characterized using cyclic and differential pulse voltammetry, and electrochemical impedance spectroscopy. Moreover, the classification model was created by applying binary classification assessment using enhanced artificial intelligence comprises of support vector machine (SVM) and Genetic Algorithm (GA), respectively, a support vector machine and a genetic algorithm, which was then validated using the 50 dyes test set. The best binary logistic regression model has an accuracy of 83.2% and 81.1%, respectively, while the best SVM model has an accuracy of 90.3% for the training group of samples and 81.1% for the test group (RMSE = 0.644, R2 = 0.873, C = 205.41, and = 5.992). According to the findings, Cu-BTC MOF (copper (II)-benzene-1,3,5-tricarboxylate) has a crystal structure and is tightly packed with hierarchically porous nanomaterials, with each particle's edge measuring between 20 and 37 nm. The suggested electrochemical sensor's analytical performance is suitable for foods like jellies, condiments, soft drinks and candies.