Healthcare workers' knowledge and attitudes regarding artificial intelligence adoption in healthcare: A cross-sectional study.

Background: The convergence of healthcare and artificial intelligence (AI) introduces a transformative era in medical practice. However, the knowledge and attitudes of healthcare workers concerning the adoption of artificial intelligence in healthcare are currently unknown.

Aims: The primary objective was to investigate the knowledge and attitudes of healthcare professionals in Dhaka city, Bangladesh, regarding the adoption of AI in healthcare.

Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare.

Background: With the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption.

Objectives: This study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings.

Perceptions and attitudes of nurse practitioners toward artificial intelligence adoption in health care.

Background: With the ever-increasing integration of artificial intelligence (AI) into health care, it becomes imperative to gain an in-depth understanding of how health care professionals, specifically nurse practitioners, perceive and approach this transformative technology.

Objectives: This study aimed to gain insights into nurse practitioners' perceptions and attitudes toward AI adoption in health care.

Methods: This qualitative research employed a descriptive and phenomenological approach using in-depth interviews.

The long-term effects of childhood circumstances on older individuals: A systematic review.

Childhood experiences are known to shape individuals' development and can influence various aspects of life later on. Understanding the long-term effects is crucial for informing interventions and policies aimed at promoting healthy aging. This review aimed to explore the long-term effects of childhood experiences on older individuals.

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities.

In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare.

Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals - A descriptive qualitative study.

Background: The healthcare landscape is rapidly evolving, with artificial intelligence (AI) emerging as a transformative force. In this context, understanding the viewpoints of nursing professionals regarding the integration of AI in future nursing care is crucial.

Aims: This study aimed to provide insights into the perceptions of nursing professionals regarding the role of AI in shaping the future of healthcare.

Human placenta releases extracellular vesicles carrying corticotrophin releasing hormone mRNA into the maternal blood.

The human placenta releases diverse extracellular vesicles (EVs), including microvesicles (100-1000 nm) and exosomes (30-150 nm), into the maternal blood for feto-maternal communication. Exosomes and microvesicles contribute to normal pregnancy physiology and major pregnancy pathologies. Differences in miRNA expressions and protein content in placental exosomes have been reported in complicated pregnancies.

Overcoming Uncertainties in Electrogram-Based Atrial Fibrillation Mapping: A Review.

In clinical rhythmology, intracardiac bipolar electrograms (EGMs) play a critical role in investigating the triggers and substrates inducing and perpetuating atrial fibrillation (AF). However, the interpretation of bipolar EGMs is ambiguous due to several aspects of electrodes, mapping algorithms and wave propagation dynamics, so it requires several variables to describe the effects of these uncertainties on EGM analysis. In this narrative review, we critically evaluate the potential impact of such uncertainties on the design of cardiac mapping tools on AF-related substrate characterization.

4SQR-Code: A 4-state QR code generation model for increasing data storing capacity in the Digital Twin framework.

Introduction: The usage of Quick Response (QR) Codes has become widely popular in recent years, primarily for immense electronic transactions and industry uses. The structural flexibility of QR Code architecture opens many more possibilities for researchers in the domain of the Industrial Internet of Things (IIoT). However, the limited storage capacity of the traditional QR Codes still fails to stretch the data capacity limits.

Geographical variation in the association between physical violence and sleep disturbance among adolescents: A population-based, sex-stratified analysis of data from 89 countries.

Objective: To examine geographical variations in involvement in physical violence and sleep disturbance among adolescents.

Design: Cross-sectional study.

Setting: Eighty-nine low- to middle-income and high-income countries PARTICIPANTS: Adolescents 13-17 years of age.

Accurate brain tumor detection using deep convolutional neural network.

Detection and Classification of a brain tumor is an important step to better understanding its mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging technique that helps the radiologist find the tumor region. However, it is a time taking process and requires expertise to test the MRI images, manually.

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues.

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system.

Geographical variations in the association between bullying victimization and sleep loss among adolescents: a population-based study of 91 countries.

Objective: To examine geographical variations in the association between bullying victimization and sleep loss over worry among adolescents.

Methods: We used data from the Global School-based Student Health Survey conducted between 2003 and 2017 in 91 countries across the globe. Sleep loss was categorised as: none/minimum (reported never or rarely sleep loss), moderate (reported sometimes), and severe (reported most of the time or always).

Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World.

This paper shows the efficacy of a novel urban categorization framework based on deep learning, and a novel categorization method customized for cities in the global south. The proposed categorization method assesses urban space broadly on two dimensions-the states of urbanization and the architectural form of the units observed. This paper shows how the sixteen sub-categories can be used by state-of-the-art deep learning modules (fully convolutional network FCN-8, U-Net, and DeepLabv3+) to categorize formal and informal urban areas in seven urban cities in the developing world-Dhaka, Nairobi, Jakarta, Guangzhou, Mumbai, Cairo, and Lima.

SDN-IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic.

The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage the deployment of contactless Industry 4.

Integrating human rights approaches into public health practices and policies to address health needs amongst Rohingya refugees in Bangladesh: a systematic review and meta-ethnographic analysis.

Background: The Rohingya people of Myanmar are one of the most persecuted communities in the world and are forced to flee their home to escape conflict and persecution. Bangladesh receives the majority of the Rohingya refugees. On arrival they experience a number of human rights issues and the extent to which human rights approaches are used to inform public health programs is not well documented.

Effects of individual, household and community characteristics on child nutritional status in the slums of urban Bangladesh.

Background: Bangladesh urban population is expected to overtake rural population by 2040, and a significant part of the increase will be in slums. Wide disparities between urban slums and the rest of the country can potentially push country indicators off track unless the specific health and nutrition needs of the expanding slum communities are addressed. The study aims at describing the individual, household and community determinants of undernutrition status among children living in major urban strata, viz.