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Recently, powerful instruments for biomedical engineering research studies, including disease modeling, drug designing and nano-drug delivering, have been extremely investigated by researchers. Particularly, investigation in various microfluidics techniques and novel biomedical approaches for microfluidic-based substrate have progressed in recent years, and therefore, various cell culture platforms have been manufactured for these types of approaches. These microinstruments, known as tissue chip platforms, mimic in vivo living tissue and exhibit more physiologically similar vitro models of human tissues. Using lab-on-a-chip technologies in vitro cell culturing quickly caused in optimized systems of tissues compared to static culture. These chipsets prepare cell culture media to mimic physiological reactions and behaviors.

The authors used the application of lab chip instruments as a versatile tool for point of health-care (PHC) applications, and the authors applied a current progress in various platforms toward biochip DNA sensors as an alternative to the general bio electrochemical sensors. Basically, optical sensing is related to the intercalation between glass surfaces containing biomolecules with fluorescence and, subsequently, its reflected light that arises from the characteristics of the chemical agents. Recently, various techniques using optical fiber have progressed significantly, and researchers apply highlighted remarks and future perspectives of these kinds of platforms for PHC applications.

The authors assembled several microfluidic chips through cell culture and immune-fluorescent, as well as using microscopy measurement and image analysis for RNA sequencing. By this work, several chip assemblies were fabricated, and the application of the fluidic routing mechanism enables us to provide chip-to-chip communication with a variety of tissue-on-a-chip. By lab-on-a-chip techniques, the authors exhibited that coating the cell membrane via poly-dopamine and collagen was the best cell membrane coating due to the monolayer growth and differentiation of the cell types during the differentiation period. The authors found the artificial membrane, through coating with Collagen-A, has improved the growth of mouse podocytes cells-5 compared with the fibronectin-coated membrane.

The authors could distinguish the differences across the patient cohort when they used a collagen-coated microfluidic chip. For instance, von Willebrand factor, a blood glycoprotein that promotes hemostasis, can be identified and measured through these type-coated microfluidic chips.

This study aims to assess the nutritional status and physical fitness status of school children among major ethnic and Dalit populations, in Rajshahi, Bangladesh.

Data were collected through structured questionnaires administered to mothers or caregivers of the children, followed by physical fitness assessments. Anthropometric indicators were used to evaluate nutritional status, including body mass index-for-age, height-for-age, and weight-for-height, based on Centre for Disease Control guidelines. Physical fitness was assessed through tests for flexibility, abdominal, muscular, speed, and lower limb strength. Statistical analysis was performed using SPSS.

Approximately 45% of boys and 55% of girls had normal nutritional status. However, the study also identified varying percentages of children with stunted, underweight, wasted, and mixed nutritional issues, highlighting the prevalence of malnutrition among this population. Boys consistently outperformed girls in terms of physical fitness across all age groups, with 84% to 90% of boys meeting established fitness criteria. Girls face a 1.8-fold and 2.2-fold increased risk of wasted and underweight conditions compared to boys. Lower-income group children exhibit a 2.6-fold and 1.6-fold increased risk of wasted conditions and underweight conditions. Children consuming meals twice a day have a 2.1-fold and 2.5-fold elevated risk of wasted and underweight conditions compared to others.

This study highlights the need for interventions and strategies to improve the nutritional and health status of ethnic children in Rajshahi, Bangladesh, considering their diverse dietary habits and lifestyles. By addressing these issues, the authors can empower these communities to actively contribute to the social and economic development of the region.

This study aims to describe the current conceptualisation of self-management of dementia by family carers in the literature and from the views of dementia professionals and family carers, and to establish a more comprehensive concept of self-management of dementia by family carers.

A hybrid concept analysis included three phases: the theoretical phase reviewed the literature on self-management of dementia by family carers; the fieldwork phase interviewed professionals and family carers; and the analytical phase synthesised and discussed the findings from the previous two phases.

The findings revealed that self-management of dementia by family carers encompasses four domains: supporting care recipients, self-care, sustaining a positive relationship with care recipients, and personal characteristics and skills.

The findings highlighted the essential elements of the construct of self-management of dementia by family carers. The findings can be used as a conceptual framework of self-management and are useful in designing and evaluating self-management support interventions for family carers.

A novel method has been proposed to reduce the false alarm rate of arrhythmia patients regarding life-threatening conditions in the intensive care unit. In this purpose, the atrial blood pressure, photoplethysmogram (PLETH), electrocardiogram (ECG) and respiratory (RESP) signals are considered as input signals.

Three machine learning approaches feed-forward artificial neural network (ANN), ensemble learning method and k-nearest neighbors searching methods are used to detect the false alarm. The proposed method has been implemented using Arduino and MATLAB/SIMULINK for real-time ICU-arrhythmia patients' monitoring data.

The proposed method detects the false alarm with an accuracy of 99.4 per cent during asystole, 100 per cent during ventricular flutter, 98.5 per cent during ventricular tachycardia, 99.6 per cent during bradycardia and 100 per cent during tachycardia. The proposed framework is adaptive in many scenarios, easy to implement, computationally friendly and highly accurate and robust with overfitting issue.

As ECG signals consisting with PQRST wave, any deviation from the normal pattern may signify some alarming conditions. These deviations can be utilized as input to classifiers for the detection of false alarms; hence, there is no need for other feature extraction techniques. Feed-forward ANN with the Lavenberg–Marquardt algorithm has shown higher rate of convergence than other neural network algorithms which helps provide better accuracy with no overfitting.

The present study aims at quantifying the likely impacts of an environmental tax on macroeconomic variables and pollution in Iran.

The computable general equilibrium model, which allows the prediction of the economy-wide effects of any change in policy instruments, is applied.

The main findings reveal that gross domestic product, private consumption and income in both urban and rural areas will follow a declining trend as a result of environmental tax imposition. In a scenario with the highest level of tax, the predicted percentage change to the gross domestic product and private consumption is estimated at −21.32 and −40.96, respectively. In the same scenario, pollution emissions would decrease by 12.4–22.6% for CO2, CH4 and N2O.

This study uses a general equilibrium model to examine the effects of the carbon tax on environmental issues and household welfare, considering the unique conditions and regulations of Iran. While the related literature examines the CO2 tax, the current study covers more pollutants, including CO2, CH4, N2O, CO, SO2 and NOx. In addition, a distinguishing feature of the current study is that it applies a modified version of the social accounting matrix (SAM) database, which includes the heavy subsidies of energy products. Another significant feature of the current study is that it examines tax policy while tax rates are exerted endogenously (compared to previous studies).

In the state of Hawaii, it has been shown that certain ethnic minority groups, such as Filipinos and Pacific Islanders, suffer disproportionally high rates of cardiovascular disease, evidence that local health-care systems and governing bodies fail to equally extend the human right to health to all. This study aims to examine whether these ethnic health disparities in cardiovascular disease persist even within an already globally disadvantaged group, the houseless population of Hawaii.

A retrospective chart review of records from Hawaii Houseless Outreach and Medical Education Project clinic sites from 2016 to 2020 was performed to gather patient demographics and reported histories of type II diabetes, obesity, hyperlipidemia, hypertension and other cardiovascular disease diagnoses. Reported disease prevalence rates were compared between larger ethnic categories as well as ethnic subgroups.

Unexpectedly, the data revealed lower reported prevalence rates of most cardiometabolic diseases among the houseless compared to the general population. However, multiple ethnic health disparities were identified, including higher rates of diabetes and obesity among Native Hawaiians and other Pacific Islanders and higher rates of hypertension among Filipinos and Asians overall. The findings suggest that even within a generally disadvantaged houseless population, disparities in health outcomes persist between ethnic groups and that ethnocultural considerations are just as important in caring for this vulnerable population.

To the best of the authors’ knowledge, this is the first comprehensive study focusing on ethnic health disparities in cardiovascular disease and the structural processes that contribute to them, among a houseless population in the ethnically diverse state of Hawaii.

This study aims to synthesize the existing serious games designed to promote mental health in adolescents with chronic illnesses.

This study conducted a review following the guidelines of Joanna Briggs Institute and Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. Searches were conducted in databases PubMed, Scopus, Web of Science, Cochrane Library, cumulative index to nursing and allied health literature, PsycINFO, China national knowledge infrastructure Wanfang, VIP Database for Chinese Technical Periodicals and SinoMed from inception to February 12, 2023.

A total of 14 studies (describing 14 serious games) for improving the mental health of adolescents with chronic diseases were included. Of all the included games, 12 were not described as adopting any theoretical framework or model. The main diseases applicable to serious games are cancer, type 1 diabetes and autism spectrum disorder. For interventional studies, more than half of the study types were feasibility or pilot trials. Furthermore, the dosage of serious games also differs in each experiment. For the game elements, most game elements were in the category “reward and punishment features” (n = 50) and last was “social features” (n = 4).

Adolescence is a critical period in a person’s physical and mental development throughout life. Diagnosed with chronic diseases during this period will cause great trauma to the adolescents and their families. Serious game interventions have been developed and applied to promote the psychological health field of healthy adolescents. To the best of the authors’ knowledge, this study is the first to scope review the serious game of promoting mental health in the population of adolescents with chronically ill. At the same time, the current study also extracted and qualitatively analyzed the elements of the serious game.

The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.

This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).

Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.

The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.

This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.

Despite significant progress in Iran's immunization programs, vaccine policymaking in the country still faces various challenges and shortcomings. To address these issues and ensure sustained progress toward achieving comprehensive vaccination policies, it is essential to identify the critical factors influencing vaccine policies in Iran. Our study aims to provide evidence-based insights that can inform the development of effective and equitable vaccine strategies, leading to a more sustainable and efficient approach to vaccination in the country.

This mixed-method study aimed to analyze the factors influencing the future of human vaccine policy using Cross Impact Analysis. Firstly, a scoping review was conducted to identify the factors affecting the future of human vaccine development. Secondly, a semi-structured interview was conducted with experts in this field to add more factors and confirm the identified factors within the Iranian context. Finally, a Cross-Impact Analysis (CIA) approach was applied to comprehend the complex relationships between the identified factors. Thematic analysis was used for the qualitative data, and MICMAC analysis was applied to characterize the relationships between the factors.

Seventeen key driving force factors were identified through comprehensive review and interviews. These factors were assigned weighted values ranging from zero to three and subsequently analyzed using MICMAC software. Employing the Cross-Impact Analysis (CIA) technique, the study characterized the impact of each factor on vaccine policy and elucidated the intricate interactions between them. The findings underscored that robust leadership and governance, an innovative ecosystem, and well-established immunization information systems emerged as pivotal driving forces shaping vaccine policy in Iran.

While this study contributes valuable insights into the driving factors influencing vaccine policy in Iran, it is important to acknowledge several limitations. The results rely on the subjective perceptions of a diverse group of specialists, and future research could delve into additional factors in other countries to identify common themes and differences.

This study provides evidence to assist policymakers in making informed decisions regarding vaccines in Iran. The findings suggest that enhancing access to vaccines, fostering trust in the healthcare system, and prioritizing equity in distribution can contribute to increased vaccination rates and a reduction in vaccine-preventable diseases.

This study provides a unique contribution to the field of vaccine policy by utilizing the cross-impact analysis to examine the complex interactions among various factors. The results of this analysis demonstrate that these interactions can significantly impact the overall system, highlighting the need for policymakers to consider multiple factors when formulating effective strategies. By revealing the significance of these interactions, this research offers valuable insights into the development of successful policies that can shape a desirable future for vaccine policy in Iran. Future studies could ratify the findings from this research by applying other methodological approaches.