A Machine Learning Approach for Detection of Phished Websites Using Neural Networks

Phishing is a means of obtaining confidential information through fraudulent website that appear to be legitimate .On detection of all the criteria ambiguities and certain considerations involve hence neural network techniques are used to build an effective tool in identifying phished websites There are many phishing detection techniques available, but a central problem is that web browsers rely on a black list of known phishing website, but some phishing website has a lifespan as short as a few hours. These website with a shorter lifespan are known as zero day phishing website. Thus, a faster recognition system needs to be developed for the web browser to identify zero day phishing website. To develop a faster recognition system, a neural network technique is used which reduces the error and increases the performance. This paper describes a framework to better classify and predict the phishing sites

Assessing readiness to manage medical emergencies among dental students at four dental schools

Background and Purpose Dentists treat a wide range of patients, including patients with compromised health conditions. While rendering treatment, various medical emergencies can and do occur. To help increase the knowledge required to manage such emergencies, dental students must be trained while in dental school. This study aims to assess the level of medical emergency preparedness and knowledge among dental students at four dental schools. Material and Methods The participating dental schools were IUSD, Case Western Reserve University School of Dentistry, Marquette University School of Dentistry, and the University of Alabama School of Dentistry. Groups were designed to include 20 dental students from Years 1 to 4. Students were asked to fill out a survey and were then tested on 10 clinical medical emergency scenarios. Results A total of 331 dental students participated in the study. The scores based on 10 case scenarios presented with a range of 4.35–8.02. There was no statistically significant difference in the level of preparedness when dental schools were compared. However, Year 1 and Year 2 dental students had significantly lower total scores than those of Years 3 and 4. The students in Years 1 and 2 demonstrated less confidence in their current knowledge to manage medical emergencies. Satisfaction with the training received ranged from 38% to 84%. Conclusion The results from this study indicate that students’ preparedness to manage medical emergencies at these four dental schools is statistically similar. Additional yearly training could enhance students’ preparedness in the management of medical emergencies in the dental setting

Human resource management in the age of generative artificial intelligence::Perspectives and research directions on ChatGPT

ChatGPT and its variants that use generative artificial intelligence (AI) models have rapidly become a focal point in academic and media discussions about their potential benefits and drawbacks across various sectors of the economy, democracy, society, and environment. It remains unclear whether these technologies result in job displacement or creation, or if they merely shift human labour by generating new, potentially trivial or practically irrelevant, information and decisions. According to the CEO of ChatGPT, the potential impact of this new family of AI technology could be as big as “the printing press”, with significant implications for employment, stakeholder relationships, business models, and academic research, and its full consequences are largely undiscovered and uncertain. The introduction of more advanced and potent generative AI tools in the AI market, following the launch of ChatGPT, has ramped up the “AI arms race”, creating continuing uncertainty for workers, expanding their business applications, while heightening risks related to well‐being, bias, misinformation, context insensitivity, privacy issues, ethical dilemmas, and security. Given these developments, this perspectives editorial offers a collection of perspectives and research pathways to extend HRM scholarship in the realm of generative AI. In doing so, the discussion synthesizes the literature on AI and generative AI, connecting it to various aspects of HRM processes, practices, relationships, and outcomes, thereby contributing to shaping the future of HRM research

Chitosan-based biomaterials for the treatment of bone disorders

Bone is an alive and dynamic organ that is well-differentiated and originated from mesenchymal tissues. Bone undergoes continuous remodeling during the lifetime of an individual. Although knowledge regarding bones and their disorders has been constantly growing, much attention has been devoted to effective treatments that can be used, both from materials and medical performance points of view. Polymers derived from natural sources, for example polysaccharides, are generally biocompatible and are therefore considered excellent candidates for various biomedical applications. This review outlines the development of chitosan-based biomaterials for the treatment of bone disorders including bone fracture, osteoporosis, osteoarthritis, arthritis rheumatoid, and osteosarcoma. Different examples of chitosan-based formulations in the form of gels, micro/nanoparticles, and films are discussed herein. The work also reviews recent patents and important developments related to the use of chitosan in the treatment of bone disorders. Although most of the cited research was accomplished before reaching the clinical application level, this manuscript summarizes the latest achievements within chitosan-based biomaterials used for the treatment of bone disorders and provides perspectives for future scientific activities. © 202

<i>DNMT3B7</i> Expression Promotes Tumor Progression to a More Aggressive Phenotype in Breast Cancer Cells

Epigenetic changes, such as DNA methylation, have been shown to promote breast cancer progression. However, the mechanism by which cancer cells acquire and maintain abnormal DNA methylation is not well understood. We have previously identified an aberrant splice form of a DNA methyltransferase, DNMT3B7, expressed in virtually all cancer cell lines but at very low levels in normal cells. Furthermore, aggressive MDA-MB-231 breast cancer cells have been shown to express increased levels of DNMT3B7 compared to poorly invasive MCF-7 cells, indicating that DNMT3B7 may have a role in promoting a more invasive phenotype. Using data gathered from The Cancer Genome Atlas, we show that DNMT3B7 expression is increased in breast cancer patient tissues compared to normal tissue. To determine the mechanism by which DNMT3B7 was functioning in breast cancer cells, two poorly invasive breast cancer cell lines, MCF-7 and T-47D, were stably transfected with a DNMT3B7 expression construct. Expression of DNMT3B7 led to hypermethylation and down-regulation of E-cadherin, altered localization of β-catenin, as well as increased adhesion turnover, cell proliferation, and anchorage-independent growth. The novel results presented in this study suggest a role for DNMT3B7 in the progression of breast cancer to a more aggressive state and the potential for future development of novel therapeutics.</p

DNMT3B7 expression promotes tumor progression to a more aggressive phenotype in breast cancer cells.

Epigenetic changes, such as DNA methylation, have been shown to promote breast cancer progression. However, the mechanism by which cancer cells acquire and maintain abnormal DNA methylation is not well understood. We have previously identified an aberrant splice form of a DNA methyltransferase, DNMT3B7, expressed in virtually all cancer cell lines but at very low levels in normal cells. Furthermore, aggressive MDA-MB-231 breast cancer cells have been shown to express increased levels of DNMT3B7 compared to poorly invasive MCF-7 cells, indicating that DNMT3B7 may have a role in promoting a more invasive phenotype. Using data gathered from The Cancer Genome Atlas, we show that DNMT3B7 expression is increased in breast cancer patient tissues compared to normal tissue. To determine the mechanism by which DNMT3B7 was functioning in breast cancer cells, two poorly invasive breast cancer cell lines, MCF-7 and T-47D, were stably transfected with a DNMT3B7 expression construct. Expression of DNMT3B7 led to hypermethylation and down-regulation of E-cadherin, altered localization of β-catenin, as well as increased adhesion turnover, cell proliferation, and anchorage-independent growth. The novel results presented in this study suggest a role for DNMT3B7 in the progression of breast cancer to a more aggressive state and the potential for future development of novel therapeutics

<i>DNMT3B7</i> expression in breast cancer.

<p><i>DNMT3B7</i> reads per kilobase per million (RPKM) values of A) normal tissue versus primary tumors in unmatched patient samples or B) matched normal tissue versus primary tumor. C) <i>DNMT3B7</i> expression in all primary tumors segregated by respective tumor stages. D) <i>DNMT3B7</i> expression in all primary tumors segregated by respective molecular subtype: luminal A (ER and/or PR positive and HER2 negative), luminal B (ER and/or PR positive and HER2 positive), triple negative/basal-like (ER negative, PR negative, HER2 negative), and HER2 type (ER negative, PR negative, HER2 positive). Statistical significance is indicated on the graphs. Immunoblot analysis of nuclear lysates of (E) MCF-7 and (F) T-47D cells stably transfected with a DNMT3B7-expression construct or an empty control vector. Topoisomerase was utilized as a nuclear lysate loading control.</p

<i>DNMT3B7</i> regulates cell adhesion, proliferation, and growth in soft agar.

<p>A-C) MCF-7 or (D-F) T-47D cells stably expressing DNMT3B7 or a control vector were measured for changes in adhesion, proliferation, and growth in soft agar. A, D) Adhesion was measured as the number of cells that could adhere to a 6-well dish in 1 hour normalized to the control (100%). B, E) Cells were plated on a dish and counted twice a week for 3 weeks to measure proliferative ability. C, F) Anchorage-independent growth was determined after cells were grown in soft agar for 2 weeks, stained with crystal violet to visualize colonies, counted, and normalized to the control vector. Statistical significance is indicated on the graphs. * represents statistical significance, <i>p</i><0.05.</p