Search

Although online learning has become ubiquitous worldwide, earlier research has neglected the relationship between its actual use and security concerns. Learners' lack of security awareness while using learning technologies remains rarely studied. This paper integrates Delone and McLean's information system success (D&M-ISS) model with the security triangle framework. Data from 2,451 higher education students at different universities and a wide variety of disciplines in Iraq were collected. In addition to the effectiveness of the D&M-ISS factors, the research findings based on the structural equation model suggest that the three constructs of the security triangle framework--namely, confidentiality, integrity, and availability--were significant predictors of students' use of online learning. This research can thus help academic organizations understand factors that can lead to the successful implementation of online learning and learners' security awareness.

For early-stage hormone receptor (HR)-positive and HER2-negative breast cancer, tools to estimate treatment benefit include free and publicly available algorithms (e.g., PREDICT 2.1) and expensive molecular assays (e.g., Oncotype DX). There remains a need to identify patients who de-rive the most benefit from molecular assays and where this test may be of poor value. In this multicenter prospective cohort study, we evaluated whether use of PREDICT 2.1 would impact physician decision making. For the first 6 months of the study, data on physician use of both PREDICT 2.1 and Oncotype DX ordering were collected on all newly diagnosed patients eligible for molecular testing. After 6 months, an educational intervention was undertaken to see if providing physicians with PREDICT 2.1 results affects the frequency of Oncotype DX requests. A total of 602 patients across six cancer centers in Ontario, Canada were recruited between March 2020 and November 2021. Providing PREDICT 2.1 results and an educational intervention did not alter the ordering of an Oncotype DX. For patients with low clinical risk, either by clinico-pathologic features or by PREDICT 2.1, the probability of obtaining a high Oncotype DX recurrence score was substantially lower compared to patients with high-clinical-risk disease. The introduction of an educational intervention had no impact on molecular assay requests. However, routine ordering of molecular assays for patients with low-clinical-risk disease is of poor value.

The utility of DNA sequencing in diagnosing and prognosis of diseases is vital for assessing the risk of genetic disorders, particularly for asymptomatic individuals with a genetic predisposition. Such diagnostic approaches are integral in guiding health and lifestyle decisions and preparing families with the necessary foreknowledge to anticipate potential genetic abnormalities. The present study explores implementing a define-by-run deep learning (DL) model optimized using the Tree-structured Parzen estimator algorithm to enhance the precision of genetic diagnostic tools. Unlike conventional models, the define-by-run model bolsters accuracy through dynamic adaptation to data during the learning process and iterative optimization of critical hyperparameters, such as layer count, neuron count per layer, learning rate, and batch size. Utilizing a diverse dataset comprising DNA sequences from two distinct groups: patients diagnosed with breast cancer and a control group of healthy individuals. The model showcased remarkable performance, with accuracy, precision, recall, F1-score, and area under the curve metrics reaching 0.871, 0.872, 0.871, 0.872, and 0.95, respectively, outperforming previous models. These findings underscore the significant potential of DL techniques in amplifying the accuracy of disease diagnosis and prognosis through DNA sequencing, indicating substantial advancements in personalized medicine and genetic counseling. Collectively, the findings of this investigation suggest that DL presents transformative potential in the landscape of genetic disorder diagnosis and management.

The popularity of 3D printed flexible sensors has increased as a result of recent breakthroughs in the Internet of Things (IOTs) and advances in the field of flexible and wearable electronics. Regardless of the field they serve, sensors are becoming an indispensable part of our everyday lives. Physical and biosensors for monitoring a variety of stimuli, including temperature, strain, glucose, and other biomarkers, may be the most widely used applications. Furthermore, the boon observed in the miniaturization of electronic devices and accommodation of a large number of electronic components necessitates the need for multi-sensor arrays with different sensors being fabricated into the same device. The need for complex and pliable sensors has led to the utilization of 3D printing technology in their production. 3D printing technology is a versatile process that may be utilized to easily fabricate intricate, multipurpose architecture. This review primarily focus on the recent advancements in developing 3D printed flexible sensors for biomedical applications with a special focus on physical, electrochemical and biosensors. The different types of 3D printing techniques used in fabrication of biomedical sensors along with their existing challenges and future perspectives are also discussed in this review.

Hybrid nanomaterials with unique physiochemical properties have received a lot of attention, making them attractive for application in different fields like cancer treatment. This study was designed to investigate the combined effects of single-walled carbon nanotubes (SWCNTs) hybridized with silver titanium dioxide composite (SWCNTs@Ag–TiO2). Transmission electron microscopy and field emission scanning electron microscopy images demonstrated the accumulation of SWCNTs with Ag–TiO2 due to an increased main grain size with functionalization to 40 nm. The D and G bands in SWCNTs @Ag–TiO2 shifted to 1,366 and 1,534 cm−1, respectively. SWCNTs@Ag-TiO2 were assessed for their cytotoxicity and autophagy induction in liver cancer cells (Hep-G2) using the lactate dehydrogenase assay, MTT assay, and flow cytometry methods. The results showed that SWCNTs and SWCNTs@Ag–TiO2 exhibited strong anti-cancer activity in vitro against Hep-G2 cells by inducing apoptosis and autophagy in liver cancer cells via controlling the AKT and JNK mitogen-activated protein kinase pathways. The results show that SWCNTs and SWCNTs coated with silver/titanium dioxide (SWCNTs@Ag–TiO2) reduce the cells’ viability and proliferation. It was shown that an excessive amount of reactive oxygen species was a crucial mediator of both the cell death caused by SWCNTs and the cell death caused by SWCNTs combined with Ag–TiO2. Based on these findings, it appears that SWCNTs and SWCNTs@Ag–TiO2 have the potential to be developed as nanotherapeutics for the treatment of liver cancer cells.