Biomedical Applications of Iron Oxide Nanoparticles: Current Insights Progress and Perspectives

The enormous development of nanomaterials technology and the immediate response of many areas of science, research, and practice to their possible application has led to the publication of thousands of scientific papers, books, and reports. This vast amount of information requires careful classification and order, especially for specifically targeted practical needs. Therefore, the present review aims to summarize to some extent the role of iron oxide nanoparticles in biomedical research. Summarizing the fundamental properties of the magnetic iron oxide nanoparticles, the review’s next focus was to classify research studies related to applying these particles for cancer diagnostics and therapy (similar to photothermal therapy, hyperthermia), in nano theranostics, multimodal therapy. Special attention is paid to research studies dealing with the opportunities of combining different nanomaterials to achieve optimal systems for biomedical application. In this regard, original data about the synthesis and characterization of nanolipidic magnetic hybrid systems are included as an example. The last section of the review is dedicated to the capacities of magnetite-based magnetic nanoparticles for the management of oncological diseases.

A Primer on Incorporating Sex as a Biological Variable into the Conduct and Reporting of Basic and Clinical Research Studies

The recent move to require sex as a biological variable (SABV), which includes gender, into the reporting of research published by the American Journal of Physiology - Heart and Circulatory Physiology follows a growing, and much-needed, trend by journals. Understandably, there is concern over how to do this without adding considerable work, especially if one's primary research focus is not on elucidating sex/gender differences. The purpose of this article is to provide additional guidance and examples on how to incorporate SABV into the conduct and reporting of basic and clinical research. Using examples from our research, which includes both studies focused and not focused on sex/gender differences, we offer suggestions for how to incorporate SABV into basic and clinical research studies.

Implementing effective community-based surveillance in research studies of maternal, newborn and infant outcomes in low resource settings

Background Globally adopted health and development milestones have not only encouraged improvements in the health and wellbeing of women and infants worldwide, but also a better understanding of the epidemiology of key outcomes and the development of effective interventions in these vulnerable groups. Monitoring of maternal and child health outcomes for milestone tracking requires the collection of good quality data over the long term, which can be particularly challenging in poorly-resourced settings. Despite the wealth of general advice on conducting field trials, there is a lack of specific guidance on designing and implementing studies on mothers and infants. Additional considerations are required when establishing surveillance systems to capture real-time information at scale on pregnancies, pregnancy outcomes, and maternal and infant health outcomes. Main body Based on two decades of collaborative research experience between the Kintampo Health Research Centre in Ghana and the London School of Hygiene and Tropical Medicine, we propose a checklist of key items to consider when designing and implementing systems for pregnancy surveillance and the identification and classification of maternal and infant outcomes in research studies. These are summarised under four key headings: understanding your population; planning data collection cycles; enhancing routine surveillance with additional data collection methods; and designing data collection and management systems that are adaptable in real-time. Conclusion High-quality population-based research studies in low resource communities are essential to ensure continued improvement in health metrics and a reduction in inequalities in maternal and infant outcomes. We hope that the lessons learnt described in this paper will help researchers when planning and implementing their studies.

The Feasibility of a Novel Recruitment Protocol for Collecting Survivor Data via Disclosure Recipient Referrals

The current arricle describes a novel recruitment protocol for collecting data from sexual assault and intimate partner violence survivors referred to research studies by individuals to whom they had previously disclosed. Challenges in both recruiting participants and interpreting data are described. Only 35.8% of cases had usable data for both survivors and disclosure recipients, suggesting that this referral method had limited success in recruiting matched pairs. Suggestions for modifications to improve the protocol for future research are offered. Potential advantages and drawbacks of various methods for recruiting dyads are described in order to facilitate future research on survivors’ disclosure processes, social reactions, and the influence of social reactions on survivor recovery.

Abstractive Arabic Text Summarization Based on Deep Learning

Text summarization (TS) is considered one of the most difficult tasks in natural language processing (NLP). It is one of the most important challenges that stand against the modern computer system’s capabilities with all its new improvement. Many papers and research studies address this task in literature but are being carried out in extractive summarization, and few of them are being carried out in abstractive summarization, especially in the Arabic language due to its complexity. In this paper, an abstractive Arabic text summarization system is proposed, based on a sequence-to-sequence model. This model works through two components, encoder and decoder. Our aim is to develop the sequence-to-sequence model using several deep artificial neural networks to investigate which of them achieves the best performance. Different layers of Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) have been used to develop the encoder and the decoder. In addition, the global attention mechanism has been used because it provides better results than the local attention mechanism. Furthermore, AraBERT preprocess has been applied in the data preprocessing stage that helps the model to understand the Arabic words and achieves state-of-the-art results. Moreover, a comparison between the skip-gram and the continuous bag of words (CBOW) word2Vec word embedding models has been made. We have built these models using the Keras library and run-on Google Colab Jupiter notebook to run seamlessly. Finally, the proposed system is evaluated through ROUGE-1, ROUGE-2, ROUGE-L, and BLEU evaluation metrics. The experimental results show that three layers of BiLSTM hidden states at the encoder achieve the best performance. In addition, our proposed system outperforms the other latest research studies. Also, the results show that abstractive summarization models that use the skip-gram word2Vec model outperform the models that use the CBOW word2Vec model.

Dispute over Grammatical Boundaries

This research studies the dispute among grammarians over some of the boundaries of grammatical categories by following the inductive-analytic approach. The selection of dispute issues is based on the objection by one of the dissenters with the linguistic inheritance. The disputes are classified in two sections, each of which embodies references you consult, and weighting what has been proven correct.

Impact of the COVID-19 pandemic on the oncologic activities (diagnosis, treatment, clinical trials enrollment) of a general hospital in a district with high prevalence of SARS-COV-2 in Italy

Purpose Little is known about the real impact of the COVID-19 outbreak on the qualitative and quantitative fall-out on the management of cancer patients. Our objective was to provide evidence of the effects of SARS-COV-2 on the management of cancer patients in the real world. Methods In a general hospital in a district in Italy with high prevalence of COVID-19 during the first wave, we retrospectively analyzed the data of oncologic activity, namely new cancer diagnosis, types of treatment (intravenous or by mouth), clinical research studies, and drug utilization, and compared the findings with those of 2019, before the pandemic. The data have been summarized in boxplot figures for median and interquartile range. Results In 2020, a significant reduction in new cancer diagnosis was demonstrated when compared with 2019, with 17.4% fewer cancer diagnoses, 84.5% fewer patients enrolled in clinical trials, a 10.6% reduction in intravenous antitumor treatment, and a 42.7% increase in oral anticancer treatment. Conclusion Our data indicate a significant reduction in cancer diagnosis, antitumor venous treatment, and patients enrolled in clinical research studies in 2020 compared with 2019, although there was a significant increase in oral treatment. These data suggest that the COVID-19 pandemic had a deep impact on the real-world management of cancer patients in a district of Italy with a high prevalence of COVID-19.

The ICECAP-O Measure

This chapter presents the ICECAP-O, a measure of capability tailored for older people. It briefly presents the original version of the ICECAP-O. In addition, it describes the work that has been done concerning the translation and cross-cultural adaptation of the ICECAP-O to the Swedish context. Finally, the chapter contains arguments for the use of the Swedish version of the ICECAP-O in health and social care, and in the evaluation of interventions and longitudinal research studies where older people’s capabilities are a focus.

Hybrid Filtering Recommendation System in an Educational Context

In education, the needs of learners are different in the majority of the time, as each has specificities in terms of preferences, performance and goals. Recommendation systems have proven to be an effective way to ensure this learning personalization. Already used and tested in other areas such as e-commerce, their adaptation to the educational context has led to several research studies that have tried to find the best approaches with the best expected results. This article suggests that a hybridization of recommendation systems filtering methods can improve the quality of recommendations. An experiment was conducted to test an approach that combines content-based filtering and collaborative filtering. The results proved to be convincing.