Enhancing heart disease prediction using a self-attention-based transformer model

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction

Agnostic Energy Consumption Models for Heterogeneous GPUs in Cloud Computing

The adoption of cloud computing has grown significantly among individuals and in organizations. According to this growth, Cloud Service Providers have continuously expanded and updated cloud-computing infrastructures, which have become more heterogeneous. Managing these heterogeneous resources in cloud infrastructures while ensuring Quality of Service (QoS) and minimizing energy consumption is a prominent challenge. Therefore, unifying energy consumption models to deal with heterogeneous cloud environments is essential in order to efficiently manage these resources. This paper deeply analyzes factors affecting power consumption and employs these factors to develop power models. Because of the strong correlation between power consumption and energy consumption, the influencing factors on power consumption, with the addition of other factors, are considered when developing energy consumption models to enhance the treatment in heterogeneous infrastructures in cloud computing. These models have been developed for two Virtual Machines (VMs) containing heterogeneous Graphics Processing Units (GPUs) architectures with different features and capabilities. Experiments evaluate the models through a cloud testbed between the actual and predicted values produced by the models. Deep Neural Network (DNN) power models are validated with shallow neural networks using performance counters as inputs. Then, the results are significantly enhanced by 8% when using hybrid inputs (performance counters, GPU and memory utilization). Moreover, a DNN energy-agnostic model to abstract the complexity of heterogeneous GPU architectures is presented for the two VMs. A comparison between the standard and agnostic energy models containing common inputs is conducted in each VM. Agnostic energy models with common inputs for both VMs show a slight enhancement in accuracy with input reduction

Naproxen Based 1,3,4-Oxadiazole Derivatives as EGFR Inhibitors: Design, Synthesis, Anticancer, and Computational Studies

A library of novel naproxen based 1,3,4-oxadiazole derivatives (8–16 and 19–26) has been synthesized and screened for cytotoxicity as EGFR inhibitors. Among the synthesized hybrids, compound2-(4-((5-((S)-1-(2-methoxynaphthalen-6-yl)ethyl)-1,3,4-oxadiazol-2-ylthio)methyl)-1H-1,2,3-triazol-1-yl)phenol(15) was the most potent compound against MCF-7 and HepG2cancer cells with IC50 of 2.13 and 1.63 µg/mL, respectively, and was equipotent to doxorubicin (IC50 1.62 µg/mL) towards HepG2. Furthermore, compound 15 inhibited EGFR kinase with IC50 0.41 μM compared to standard drug Erlotinib (IC50 0.30 μM). The active compound induces a high percentage of necrosis towards MCF-7, HePG2 and HCT 116 cells. The docking studies, DFT and MEP also supported the biological data. These results demonstrated that these synthesized naproxen hybrids have EGFR inhibition effects and can be used as leads for cancer therapy

Cell Cycle Arrest and Apoptosis-Inducing Ability of Benzimidazole Derivatives: Design, Synthesis, Docking, and Biological Evaluation

In the current study, new benzimidazole-based 1,3,4-oxadiazole derivatives have been synthesized and characterized by NMR, IR, MS, and elemental analysis. The final compounds were screened for cytotoxicity against MDA-MB-231, SKOV3, and A549 cell lines and EGFR for inhibitory activities. Compounds 10 and 13 were found to be the most active against all the tested cell lines, comparable to doxorubicin, and exhibited significant inhibition on EGFR kinase, with IC50 0.33 and 0.38 μM, respectively, comparable to erlotinib (IC50 0.39 μM). Furthermore, these two compounds effectively suppressed cell cycle progression and induced cell apoptosis in MDA-MB-231, SKOV3, and A549 cell lines. The docking studies revealed that these compounds showed interactions similar to erlotinib at the EGFR site. It can be concluded that the synthesized molecules effectively inhibit EGFR, can arrest the cell cycle, and may trigger apoptosis and therefore, could be used as lead molecules in the development of new anticancer agents targeting EGFR kinase

Egyptian mandarin peel oil's anti-scabies potential via downregulation-of-inflammatory/immune-cross-talk: GC–MS and PPI network studies

Abstract The current study investigated the scabicidal potential of Egyptian mandarin peel oil (Citrus reticulata Blanco, F. Rutaceae) against sarcoptic mange-in-rabbits. Analysis of the oil's GC–MS identified a total of 20 compounds, accounting for 98.91% of all compounds found. Mandarin peel oil topical application improved all signs of infection, causing a scabicidal effect three days later, whereas in vitro application caused complete mite mortality one day later. In comparison to ivermectin, histopathological analysis showed that the epidermis' inflammatory-infiltration/hyperkeratosis-had disappeared. In addition to TIMP-1, the results of the mRNA gene expression analysis showed upregulation of I-CAM-1-and-KGF and downregulation of ILs-1, 6, 10, VEGF, MMP-9, and MCP-1. The scabies network was constructed and subjected to a comprehensive bioinformatic evaluation. TNF-, IL-1B, and IL-6, the top three hub protein-coding genes, have been identified as key therapeutic targets for scabies. From molecular docking data, compounds 15 and 16 acquired sufficient affinity towards the three screened proteins, particularly both possessing higher affinity towards the IL-6 receptor. Interestingly, it achieved a higher binding energy score than the ligand of the docked protein rather than displaying proper binding interactions like those of the ligand. Meanwhile, geraniol (15) showed the highest affinity towards the GST protein, suggesting its contribution to the acaricidal effect of the extract. The subsequent, MD simulations revealed that geraniol can achieve stable binding inside the binding site of both GST and IL-6. Our findings collectively revealed the scabicidal ability of mandarin peel extract for the first time, paving the way for an efficient, economical, and environmentally friendly herbal alternative for treating rabbits with Sarcoptes mange

Future Acceptability of Respiratory Virus Infection Control Interventions in General Population to Prevent Respiratory Infections

Background and Objectives: In both pandemic and non-pandemic situations, nonpharmaceutical public health measures may offer easy, low-cost, and effective means of reducing the spread and impact of acute respiratory infections. It is unknown whether such measures would be acceptable to the Saudi community beyond the current pandemic. Materials and Methods: A validated survey was used to test community acceptance of the measures. Respondents were asked which infection control practices they planned to maintain and which they believed should be policies for the community as a whole after the COVID-19 pandemic has subsided. Results: The survey was completed by 2057 people (95% completion rate), 1486 (72%) of whom were female, 259 (12.5%) of whom were current smokers, and 72 (3.5%) of whom had chronic lung disease. The most prevalent age groups were 18–30 years (933; 45.4%) and 31–40 years (483; 23.5%), with 641 individuals over 40 years old. Of the responses, 93% indicated that they would continue washing their hands more often; 92% wanted both clinicians and patients to wear masks in hospitals; 86% would continue avoiding smoking in indoor and outdoor areas; 73% would continue wearing a face covering on public transportation; 70% indicated that they would continue wearing a face covering in indoor public places. Regarding the respiratory virus infection control measures, 85% (11/13) received significant support (≥70% acceptability level) for continuation as policies in the future. Wearing face coverings outdoors and social distancing outdoors received little support (45% and 66%, respectively). Of the respiratory virus infection control measures, 54% received less support from current smokers than non-smokers (acceptability level < 70%). People with chronic respiratory disease supported 77% of the measures being regarded as policies in the future. Conclusion: The Saudi community supports nonpharmacological respiratory infection control measures that reduce the likelihood of infection. Public health campaigns should target smokers to increase awareness of the importance of these measures in lowering infections. Based on the findings of this study, nonpharmacological treatments should be presented and included in future recommendations for both the public and patients diagnosed with chronic respiratory diseases