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Background: To assess multicystic dysplastic kidneys (MCDK) in children, their complications and associated congenital genitourinary anomalies. Methods: Children with unilateral MCDK, evaluated between 2012 and 2020, were analyzed. In this retrospective study, data were obtained from electronic and paper health care records. Results: There were 80 children included. Follow-up time was 8.0 +/− 5.2 years (mean +/− standard deviation). None of them had hypertension. In total, 43.8% of the children had associated congenital genitourinary anomalies, most commonly cryptorchidism and vesicoureteral reflux (VUR), and 6.3% of these children had chromosomopathy. All of them had normal kidney function except one child with dysplasia of the contralateral kidney. Urinalysis was normal in 90% of children. Extrarenal malformations occurred in 22.5% of them. We observed spontaneous involution of MCDK in 38.8% of children in the observed period. Nephrectomy was performed in 12.5% of children, at an average age of 2.0 years. Conclusions: Children with a unilateral MCDK have a very good prognosis if the contralateral kidney is normal. Associated congenital genitourinary anomalies are common. Cryptorchidism was found to be the most common associated anomaly among boys, which is unique for this study. Most of these children do not suffer from hypertension, kidney dysfunction or other complications. [ABSTRACT FROM AUTHOR]

Although child-centred care is increasingly referred to within the nursing literature, a clear definition of child-centred care and clarity around the concept is yet to be achieved. The objectives of this review were to examine the following: (1) What constitutes the concept of child-centred care in healthcare? (2) How has the concept of child-centred care developed? (3) What is the applicability of child-centred care and what are its limitations? (4) How does the concept of child-centred care benefit and inform children's healthcare? In total, 2984 papers were imported for screening, and, following the removal of duplicates and screening, 21 papers were included in the scoping review. The findings suggest that child-centred care is an emerging, ambiguous poorly defined concept; no clear consensus exists about what constitutes child-centred care. Although it seems antithetical to argue against child-centred care, little robust evidence was identified that demonstrates the impact and benefit of child-centred care. If child-centred care is to be a sustainable, convincing model to guide practice and compete with other models of care, it needs to establish robust evidence of its effectiveness, the impact on children and their families, as well as the wider impacts on the healthcare system. [ABSTRACT FROM AUTHOR]

The research protocol described aims to examine and establish the health outcomes of children and young people living in Out-of-Home Care (OOHC) in Perth, Western Australia (WA) from the perspective of the care recipients and service providers. A Study Advisory Panel (SAP) will be established comprised of Aboriginal Elders (because of the over-representation of Aboriginal children in OOHC), health professionals and other relevant stakeholders to help co-design all phases of the study. Mixed methods will be used in data collection and analysis. In the quantitative phase, it is proposed to collect retrospective data from three WA Department of Communities (DOC) districts. The data proposed to be collected includes: the number of children who received initial and annual health assessments, the health needs identified, and interventions put in place to address these needs. The qualitative phase will consist of interviews with service recipients (young people who have exited OOHC and Carers), health service providers (Community Health Nurses, School Nurses, General Practitioners and Paediatricians) and OOHC Case Workers. The research will provide an overview of the current health needs of children and young people in OOHC in WA and the perspectives of these young people, their Carers and service providers on current processes for accessing healthcare. It is anticipated that the study will provide valuable evidence for quality improvement in health service delivery to better meet the health needs of children and young people in OOHC. [ABSTRACT FROM AUTHOR]

The healthcare industry is confronted with the challenge to offer an increasing variety of healthcare services while in the meantime controlling rapidly increasing healthcare costs. Mass customization has been proven to be an effective strategy to fulfill customers' individual specific needs with high efficiency and low cost in the manufacturing industry. This paper investigates the theoretical feasibility and practical applicability of adopting mass customization as a conceptual framework for designing a healthcare service delivery system. The nature of healthcare delivery systems and their evolution are discussed relative to those of manufacturing systems. Recent research in personalized medicine, consumer-driven healthcare, consumer healthcare informatics, and integrated healthcare delivery is reviewed as enabling technologies towards mass customization of healthcare services. By synthesizing these scattered efforts in different streams of literature, this paper concludes that mass customization can contribute to the redesign of healthcare service systems, and delineates a roadmap for future research. [ABSTRACT FROM AUTHOR]

When you are part of a community, especially a scientific one, you are required to contribute significantly to its welfare, because the community as a whole represents each individual within it and, in turn, determines the wellbeing of the participants themselves. Finally, a fourth review, which was a systematic review [[17]], investigated possible risk factors that associate cardiovascular diseases with obstructive sleep apnea, taking into account the fact that hypertension, obesity and diabetes mellitus may largely cause individuals to be at higher risk. Therefore, strategies such as reducing and/or controlling smoking, hypertension, alcoholism, diabetes mellitus, hyperlipidemia and overweight/obesity were suggested. References 1 Rakha E.A., Toss M., Shiino S., Gamble P., Jaroensri R., Mermel C.H., Chen P.C. Current and future applications of artificial intelligence in pathology: A clinical perspective. [Extracted from the article]

The purpose of this paper is to provide a description and evaluation of a graduate-level Health Services Research Methods course offered at the University of Arizona R. Ken Coit College of Pharmacy. This three-credit, round-table discussion-style course introduces students to fundamental concepts in healthcare study design and teaches them how to design and critique example studies for a variety of commonly encountered study designs. The course is assessed through essay-style examinations, development of a research proposal, and low-stakes weekly assignments. Twenty-seven students have completed the course in the past five years. Feedback from student course surveys was almost unanimously positive, with few meaningful suggestions for improvement. The description and evaluation of a graduate-level Health Services Research Methods course at one institution indicates that students had a largely favorable experience with the course. Considerations for future revisions to the course are discussed alongside other lessons learned. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Brain Sciences on recent advances in the pathophysiology and therapeutic approaches in epilepsy. The document highlights the global impact of epilepsy, with over 65 million people affected worldwide. It emphasizes the need for a deeper understanding of the biochemical, structural, physiological, and molecular changes in epileptic brains to develop novel therapeutic targets and treatment options. The special issue includes twelve papers, including research articles, review articles, and a systematic review, covering topics such as drug-resistant epilepsy, neuroinflammation, post-traumatic epilepsy, surgical treatments, status epilepticus, self-limited focal epilepsy, transition readiness, epilepsy networks, and interictal spikes. The articles aim to expand our understanding of epilepsy and contribute to the development of new treatment approaches. [Extracted from the article]

This document is a summary of a special issue of the journal Applied Sciences that focuses on eHealth innovative approaches and applications. It covers various topics including COVID-19 classification, AI in medical imaging, cyber security in healthcare, and the use of Large Language Models (LLMs) for coding diagnoses. The papers provide insights into the latest developments in eHealth and address issues such as data privacy, cyber security, and the challenges of implementing new technologies in healthcare. The document acknowledges the contributions of the authors and reviewers and expresses gratitude for their support. [Extracted from the article]

Artificial intelligence (AI) algorithms are playing a crucial role in transforming healthcare by enhancing the quality, accessibility, and efficiency of medical care, research, and operations. These algorithms enable healthcare providers to offer more accurate diagnoses, predict outcomes, and customize treatments to individual patient needs. AI also improves operational efficiency by automating routine tasks and optimizing resource management. However, there are challenges to adopting AI in healthcare, such as data privacy concerns and potential biases in algorithms. Collaboration among stakeholders is necessary to ensure ethical use of AI and its positive impact on the field. AI also has applications in medical research, preventive medicine, and public health. It is important to recognize that AI should augment, not replace, the expertise and compassionate care provided by healthcare professionals. The ethical implications and societal impact of AI in healthcare must be carefully considered, guided by fairness, transparency, and accountability principles. Several research papers in this special issue explore the application of AI algorithms in various aspects of healthcare, such as gait analysis for Parkinson's disease diagnosis, human activity recognition, heart disease prediction, compliance assessment with clinical protocols, epidemic management, neurological complications identification, fall prevention, leukemia diagnosis, and genetic clinical pathways. These studies demonstrate the potential of AI in improving medical diagnostics, patient monitoring, and personalized care. [Extracted from the article]

The phenomenon of chemsex has emerged as an essential public health issue in recent years. This systematic review aimed to investigate currently available harm reduction strategies and to evaluate the efficacy of the corresponding interventions. Methods: A systematic review of the scientific literature related to harm and risk reduction strategies and the effectiveness of chemsex interventions. Between January 2024 and May 2024, the articles were retrieved from the electronic databases Pubmed, Web of Science, Scopus, PsycInfo, Cochrane, Dialnet, CUIDEN, and SciELO. The review protocol was registered in the PROSPERO database (registration number CRD42024508953). The inclusion criteria were as follows: (I) original studies published in peer-reviewed journals, (II) studies exploring harm reduction interventions for chemsex, and (III) studies reflecting the efficacy of harm reduction interventions for chemsex. Two reviewers independently selected articles by title, abstract, and full paper review and extracted data. Two authors described the selected studies and assessed their methodological quality. Results: The systematic review comprised six scientific papers that met the selection criteria and were obtained from five countries. Although a limited number of studies were included, it was observed that they presented a medium–high methodological quality. Programs evaluated interventions to reduce harm from chemsex, such as a web-based intervention that improved self-efficacy to refuse risky behaviors and accept HIV testing. The studies suggested that peer-led programs can be effective, especially with facilitators who have experienced chemsex dependence. Conclusion: Harm reduction strategies in chemsex are effective and should be promoted by health professionals. Interventions should be accessible, personalized, and non-judgmental to provide appropriate care and support, ensuring a comprehensive and effective public health response. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue in the journal Electronics, which focuses on emerging e-Health applications and medical information systems. The issue includes several research papers on various topics related to healthcare technology. Some of the topics covered include the use of machine learning and radar technology for COPD detection, an AI-based tool for detecting cognitive impairment, and a machine learning approach for preventing keratoconus. The papers highlight the potential of these technologies in improving healthcare delivery and offer insights into their advantages, limitations, and future developments. The authors express their gratitude to the contributors and reviewers of the special issue. [Extracted from the article]

Street food may be a vehicle of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) to humans. Foods contaminated with ARB entail serious problems or challenges in the fields of medical care, animal husbandry, food industry, and public health worldwide. The objectives of this systematic review were to identify and evaluate scientific reports associated with ARB isolated from various street foods. "Preferred reporting items for systematic reviews and meta-analysis" (PRISMA) guidelines were followed. The bibliographic material covers a period from January 2015 to April 2024. Six electronic scientific databases were searched individually for full-text articles; only those papers that met the inclusion and exclusion criteria were selected. Seventeen papers were included in this systematic review. This study highlighted the wide distribution of ARB resistant to β-lactams and other antibiotics, posing significant health risks to consumers. High resistance levels were observed for antibiotics such as ampicillin, ceftriaxone, and tetracycline, while some antibiotics, such as ceftazidime, clavulanic acid, cefoperazone, cotrimoxazole, doxycycline, doripenem, fosfomycin, vancomycin, and piperacillin-tazobactam, demonstrated 100% susceptibility. The prevalence of ARB in street foods varied between 5.2% and 70.8% among different countries. The multiple resistance of various bacteria, including Escherichia coli, Staphylococcus, Salmonella, and Klebsiella, to multiple classes of antibiotics, as well as environmental factors contributing to the spread of antibiotic resistance (AR), emphasize the urgent need for comprehensive approaches and coordinated efforts to confront antimicrobial resistance (AMR) under the "One Health" paradigm. [ABSTRACT FROM AUTHOR]

Background: Providing care to persons with complex problems is inherently difficult due to several factors, including the impacts of proximal determinants of health, treatment response, the natural emergence of comorbidities, and service system capacity to provide timely required services. Providing visibility into the dynamics of patients' engagement can help to optimize care for patients with complex problems. Method: In a previous work, graph machine learning and NLP methods were used to model the products of service system dynamics as atemporal entities, using a data model that collapsed patient encounter events across time. In this paper, the order of events is put back into the data model to provide topological depictions of the dynamics that are embodied in patients' movement across a complex healthcare system. Result: The results show that directed graphs are well suited to the task of depicting the way that the diverse components of the system are functionally coupled—or remain disconnected—by patient journeys. Conclusion: By setting the resolution on the graph topology visualization, important characteristics can be highlighted, including highly prevalent repeating sequences of service events readily interpretable by clinical subject matter experts. Moreover, this methodology provides a first step in addressing the challenge of locating potential operational problems for patients with complex issues engaging with a complex healthcare service system. [ABSTRACT FROM AUTHOR]

Background: Optimizing care for patients with complex problems entails the integration of clinically appropriate problem-specific clinical protocols, and the optimization of service-system-encompassing clinical pathways. However, alignment of service system operations with Clinical Practice Guidelines (CPGs) is far more challenging than the time-bounded alignment of procedures with protocols. This is due to the challenge of identifying longitudinal patterns of service utilization in the cross-continuum data to assess adherence to the CPGs. Method: This paper proposes a new methodology for identifying patients' patterns of service utilization (PSUs) within sparse high-dimensional cross-continuum health datasets using graph community detection. Result: The result has shown that by using iterative graph community detections, and graph metrics combined with input from clinical and operational subject matter experts, it is possible to extract meaningful functionally integrated PSUs. Conclusions: This introduces the possibility of influencing the reorganization of some services to provide better care for patients with complex problems. Additionally, this introduces a novel analytical framework relying on patients' service pathways as a foundation to generate the basic entities required to evaluate conformance of interventions to cohort-specific clinical practice guidelines, which will be further explored in our future research. [ABSTRACT FROM AUTHOR]

Simple Summary: A multidisciplinary group researched and defined criteria to describe a sarcoma specialist treatment center or network. The project is led by Sarcoma Patient Advocacy Global Network (SPAGN), involves patient groups from around the world and is supported by clinical specialists and leading researchers. The paper identifies Core Principles and Key Features which define a specialist center/network. They are supported by evidence and experience. A primary aim is that new patients and their families can identify a specialist center which can provide curative treatment. A secondary aim is that countries where specialist treatment is not yet available can identify what they have to achieve to meet that ambition. The Core Principles allow a center or network to attain accreditation as a Sarcoma Intelligent Specialist Network. The Key Features are more aspirational and are expressed in a way that allows local needs, legal considerations and budgetary pressures to be taken into account. This is the first time an expert multidisciplinary group has defined specialism in cancer treatment in a worldwide context. The management of sarcomas in specialist centers delivers significant benefits. In much of the world, specialists are not available, and the development of expertise is identified as a major need. However, the terms 'specialist' or 'expert' center are rarely defined. Our objective is to offer a definition for patient advocates and a tool for healthcare providers to underpin improving the care of people with sarcoma. SPAGN developed a discussion paper for a workshop at the SPAGN 2023 Conference, attended by 75 delegates. A presentation to the Connective Tissue Oncology Society (CTOS) and further discussion led to this paper. Core Principles were identified that underlie specialist sarcoma care. The primary Principle is the multi-disciplinary team discussing every patient, at first diagnosis and during treatment. Principles for optimal sarcoma management include accurate diagnosis followed by safe, high-quality treatment, with curative intent. These Principles are supplemented by Features describing areas of healthcare, professional involvement, and service provision and identifying further research and development needs. These allow for variations because of national or local policies and budgets. We propose the term 'Sarcoma Intelligent Specialist Network' to recognize expertise wherever it is found in the world. This provides a base for further discussion and local refinement. [ABSTRACT FROM AUTHOR]

Smart healthcare is altering the delivery of healthcare by combining the benefits of IoT, mobile, and cloud computing. Cloud computing has tremendously helped the health industry connect healthcare facilities, caregivers, and patients for information sharing. The main drivers for implementing effective healthcare systems are low latency and faster response times. Thus, quick responses among healthcare organizations are important in general, but in an emergency, significant latency at different stakeholders might result in disastrous situations. Thus, cutting-edge approaches like edge computing and artificial intelligence (AI) can deal with such problems. A packet cannot be sent from one location to another unless the "quality of service" (QoS) specifications are met. The term QoS refers to how well a service works for users. QoS parameters like throughput, bandwidth, transmission delay, availability, jitter, latency, and packet loss are crucial in this regard. Our focus is on the individual devices present at different levels of the smart healthcare infrastructure and the QoS requirements of the healthcare system as a whole. The contribution of this paper is five-fold: first, a novel pre-SLR method for comprehensive keyword research on subject-related themes for mining pertinent research papers for quality SLR; second, SLR on QoS improvement in smart healthcare apps; third a review of several QoS techniques used in current smart healthcare apps; fourth, the examination of the most important QoS measures in contemporary smart healthcare apps; fifth, offering solutions to the problems encountered in delivering QoS in smart healthcare IoT applications to improve healthcare services. [ABSTRACT FROM AUTHOR]

Cost-effectiveness analyses of new cancer treatments in real-world settings (e.g., post-clinical trials) inform healthcare decision makers about their healthcare investments for patient populations. The results of these analyses are often, though not always, presented with statistical uncertainty. This paper identifies five ways to characterize statistical uncertainty: (1) a 95% confidence interval (CI) for the incremental cost-effectiveness ratio (ICER); (2) a 95% CI for the incremental net benefit (INB); (3) an INB by willingness-to-pay (WTP) plot; (4) a cost-effectiveness acceptability curve (CEAC); and (5) a cost-effectiveness scatterplot. It also explores their usage in 22 articles previously identified by a rapid review of real-world cost effectiveness of novel cancer treatments. Seventy-seven percent of these articles presented uncertainty results. The majority those papers (59%) used administrative data to inform their analyses while the remaining were conducted using models. Cost-effectiveness scatterplots were the most commonly used method (34.3%), with 40% indicating high levels of statistical uncertainty, suggesting the possibility of a qualitatively different result from the estimate given. Understanding the necessity for and the meaning of uncertainty in real-world cost-effectiveness analysis will strengthen knowledge translation efforts to improve patient outcomes in an efficient manner. [ABSTRACT FROM AUTHOR]

With the shifting healthcare environment, the importance of public healthcare systems is being emphasized, and the efficiency of public healthcare systems has become a critical research agenda. We reviewed recent research on the efficiency of public healthcare systems using DEA, which is one of the leading methods for efficiency analysis. Through a systematic review, we investigated research trends in terms of research purposes, specific DEA techniques, input/output factors used for models, etc. Based on the review results, future research directions are suggested. The results of this paper provide valuable information and guidelines for future DEA research on public healthcare systems. [ABSTRACT FROM AUTHOR]

This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods, particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and medical settings. It emphasizes the critical role of interpretability and transparency in AI systems for diagnosing diseases, predicting patient outcomes, and creating personalized treatment plans. While acknowledging the complexities and inherent trade-offs between interpretability and model performance, our work underscores the significance of local XAI methods in enhancing decision-making processes in healthcare. By providing granular, case-specific insights, local XAI methods like LORE enhance physicians' and patients' understanding of machine learning models and their outcome. Our paper reviews significant contributions to local XAI in healthcare, highlighting its potential to improve clinical decision making, ensure fairness, and comply with regulatory standards. [ABSTRACT FROM AUTHOR]

Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the Journal of Clinical Medicine titled "Spinal Cord Injuries: Advances in Rehabilitation." The special issue focuses on the challenges faced by individuals with spinal cord injuries (SCI) and the need for improved rehabilitation treatments and strategies. It highlights the importance of multidisciplinary collaboration in SCI rehabilitation and presents thirteen high-quality papers covering topics such as innovative rehabilitation approaches, improvements in screening and follow-up strategies, and research into novel treatments. The papers offer exciting advances in SCI rehabilitation and provide preliminary evidence for potential solutions to gaps in understanding and management of SCI. The ultimate goal is to enhance the quality of life for people living with SCI worldwide. [Extracted from the article]

Simple Summary: This paper presents a novel approach to produce virtual contrast enhanced (VCE) images for nasopharyngeal cancer (NPC) without the use of contrast agents, which carry certain risks. This model uses pixelwise gradient term to capture the shape and a GAN terms to capture the texture of the real contrast enhanced T1C images With similar accuracy to existing models, our method shows an advantage in reproducing texture closer to the realistic contrast-enhanced images. This results are tested by various measures, including mean absolute error (MAE), mean square error (MSE) and structural similarity index (SSIM) for similarity accuracy; total mean square variation per mean intensity (TMSVPMI), the total absolute vari-ation per mean intensity (TAVPMI), Tenengrad function per mean intensity (TFPMI) and variance function per mean intensity (VFPMI) Various variations of the model, including fine-tuning of the hyperparameters, normalization methods on the images and using single modality, have also been investigated to test the optimal performance. Background: The development of advanced computational models for medical imaging is crucial for improving diagnostic accuracy in healthcare. This paper introduces a novel approach for virtual contrast enhancement (VCE) in magnetic resonance imaging (MRI), particularly focusing on nasopharyngeal cancer (NPC). Methods: The proposed model, Pixelwise Gradient Model with GAN for Virtual Contrast Enhancement (PGMGVCE), makes use of pixelwise gradient methods with Generative Adversarial Networks (GANs) to enhance T1-weighted (T1-w) and T2-weighted (T2-w) MRI images. This approach combines the benefits of both modalities to simulate the effects of gadolinium-based contrast agents, thereby reducing associated risks. Various modifications of PGMGVCE, including changing hyperparameters, using normalization methods (z-score, Sigmoid and Tanh) and training the model with T1-w or T2-w images only, were tested to optimize the model's performance. Results: PGMGVCE demonstrated a similar accuracy to the existing model in terms of mean absolute error (MAE) (8.56 ± 0.45 for Li's model; 8.72 ± 0.48 for PGMGVCE), mean square error (MSE) (12.43 ± 0.67 for Li's model; 12.81 ± 0.73 for PGMGVCE) and structural similarity index (SSIM) (0.71 ± 0.08 for Li's model; 0.73 ± 0.12 for PGMGVCE). However, it showed improvements in texture representation, as indicated by total mean square variation per mean intensity (TMSVPMI) (0.124 ± 0.022 for ground truth; 0.079 ± 0.024 for Li's model; 0.120 ± 0.027 for PGMGVCE), total absolute variation per mean intensity (TAVPMI) (0.159 ± 0.031 for ground truth; 0.100 ± 0.032 for Li's model; 0.153 ± 0.029 for PGMGVCE), Tenengrad function per mean intensity (TFPMI) (1.222 ± 0.241 for ground truth; 0.981 ± 0.213 for Li's model; 1.194 ± 0.223 for PGMGVCE) and variance function per mean intensity (VFPMI) (0.0811 ± 0.005 for ground truth; 0.0667   ± 0.006 for Li's model; 0.0761   ± 0.006 for PGMGVCE). Conclusions: PGMGVCE presents an innovative and safe approach to VCE in MRI, demonstrating the power of deep learning in enhancing medical imaging. This model paves the way for more accurate and risk-free diagnostic tools in medical imaging. [ABSTRACT FROM AUTHOR]

In the realm of Parkinson's Disease (PD) research, the integration of wearable sensor data with personal health records (PHR) has emerged as a pivotal avenue for patient alerting and monitoring. This study delves into the complex domain of PD patient care, with a specific emphasis on harnessing the potential of wearable sensors to capture, represent and semantically analyze crucial movement data and knowledge. The primary objective is to enhance the assessment of PD patients by establishing a robust foundation for personalized health insights through the development of Personal Health Knowledge Graphs (PHKGs) and the employment of personal health Graph Neural Networks (PHGNNs) that utilize PHKGs. The objective is to formalize the representation of related integrated data, unified sensor and PHR data in higher levels of abstraction, i.e., in a PHKG, to facilitate interoperability and support rule-based high-level event recognition such as patient's missing dose or falling. This paper, extending our previous related work, presents the Wear4PDmove ontology in detail and evaluates the ontology within the development of an experimental PHKG. Furthermore, this paper focuses on the integration and evaluation of PHKG within the implementation of a Graph Neural Network (GNN). This work emphasizes the importance of integrating PD-related data for monitoring and alerting patients with appropriate notifications. These notifications offer health experts precise and timely information for the continuous evaluation of personal health-related events, ultimately contributing to enhanced patient care and well-informed medical decision-making. Finally, the paper concludes by proposing a novel approach for integrating personal health KGs and GNNs for PD monitoring and alerting solutions. [ABSTRACT FROM AUTHOR]

In 2020 it is estimated that 281 million people were international migrants. Migrants constitute a potentially vulnerable population in terms of facing discrimination, poor living and housing conditions, and insufficient access to healthcare services. Due to the armed conflict in Ukraine in 2022, almost 10 million people crossed the Polish border within a year of the outbreak of the conflict. The objective of this paper is to present the use of primary healthcare services by people migrating from Ukraine to Poland and identify the barriers in access to healthcare by this group of persons. This study used a qualitative research technique in the form of an expert interview using individual in-depth interviews (IDI). The study group consisted of professionally active primary healthcare providers (doctors, nurses, and facility managers) in Poland. Research was carried out in the areas regarding the availability of healthcare, the potential threats and challenges, and possible system solutions. The results showed that the most common cause for doctor's appointments among migrants are respiratory infections, including COVID-19. Many cases were related to back pain, mainly resulting from the physical work of the patients. Additionally, some barriers to access and the provision of healthcare services for patients from Ukraine were identified. The majority (75%) of respondents indicated language as a significant barrier when providing services. Based on the study results, we recommend creating a dedicated website and telephone hotline for this group of persons as well as the use of traditional media to distribute information about access to healthcare services. It is also essential to focus on assistance for older people, since they may experience more difficulties with language and navigating the healthcare system. [ABSTRACT FROM AUTHOR]

Introduction: Digitalization in medicine is steadily increasing. Complex treatments, scarce personnel resources and a high level of documentation are a constant burden on healthcare systems. The balancing between correct manual documentation in the digital records and limited staff resources is rarely successful. The aim of this study is to evaluate the adherence between documentation and lack of documentation in the treatment of critical care patients. Material and Methods: For the evaluation, data from the hospital information system (HIS) of several intensive care units (ICU) were examined in conjunction with data collected from a checklist. All boluses of sedatives, analgesics and catecholamines were documented paper based across all shifts and all weekdays and compared with corresponding digital data from the HIS (2019–2022) of previous years. Results: 939 complete digital patient records revealed a massive under-documentation of the medication administration compared to that applied according to the checklist. Only 12% of all administered catecholamines, 11% of α2-agonists, 33% of propofol, 92% of midazolam and 46% of opioids were found in the digital recordings. In comparison, the effect was more pronounced on weekdays compared to weekends. In addition, the highest documentation gap was found in the comparison of early shifts. Comparing neurosurgical vs. internal vs. anesthesiologic ICUs there was a highly significant difference between anesthesiologic ICUs compared with other disciplines (p < 0.0001). Discussion: Our data shows that there is a remarkable documentation gap and incongruence in the area of applied boli. Automated documentation by connecting syringe pumps that enter data directly into the HIS can not only reduce the workload, but also lead to comprehensive and legally required documentation of all administered medication. [ABSTRACT FROM AUTHOR]

Detecting brain tumors is crucial in medical diagnostics due to the serious health risks these abnormalities present to patients. Deep learning approaches can significantly improve localization in various medical issues, particularly brain tumors. This paper emphasizes the use of deep learning models to segment brain tumors using a large dataset. The study involves comparing modifications to U-Net structures, including kernel size, number of channels, dropout ratio, and changing the activation function from ReLU to Leaky ReLU. Optimizing these parameters has notably enhanced brain tumor segmentation in MR images, achieving a Global Accuracy of 99.4% and a dice similarity coefficient of 90.2%. The model was trained, validated, and tested on many magnetic resonance images, with a training time not exceeding 19 min on a powerful GPU. This approach can be extended in medical care and hospitals to assist radiologists in identifying tumor locations and suspicious regions, thereby improving diagnosis and treatment effectiveness. The software could also be integrated into MR equipment protocols. [ABSTRACT FROM AUTHOR]

The use of real-world data (RWD) for healthcare decision-making is complicated by concerns regarding whether RWD is fit-for-purpose or is of sufficient validity to support the creation of credible RWE. An efficient mechanism for screening the quality of RWD is needed as regulatory agencies begin to use real-world evidence (RWE) to inform decisions about treatment effectiveness and safety. First, we provide an overview of RWD and RWE. Data quality frameworks (DQFs) in the US and EU were examined, including their dimensions and subdimensions. There is some convergence of the conceptual DQFs on specific assessment criteria. Second, we describe a list of screening criteria for assessing the quality of RWD sources. The curation and analysis of RWD will continue to evolve in light of developments in digital health and artificial intelligence (AI). In conclusion, this paper provides a perspective on the utilization of RWD and RWE in healthcare decision-making. It covers the types and uses of RWD, data quality frameworks (DQFs), regulatory landscapes, and the potential impact of RWE, as well as the challenges and opportunities for the greater leveraging of RWD to create credible RWE. [ABSTRACT FROM AUTHOR]

Digital therapeutics refers to smartphone applications, software, and wearable devices that provide digital solutions to improve healthcare delivery. We developed a digital platform to support the GYM (Grow Your Muscle) study, an ongoing 48-week randomized, controlled trial on reduction of sarcopenia through a home-based, app-monitored physical exercise intervention. The GYM platform consists of a smartphone application including the exercise program and video tutorials of body-weight exercises, a wearable device to monitor heart rate during training, and a website for downloading training data to remotely monitor the exercise. The aim of this paper is to describe the platform in detail and to discuss the technical issues emerging during the study and those related to usability of the smartphone application through a retrospective survey. The main technical issue concerned the API level 33 upgrade, which did not enable participants using the Android operating systems to use the wearable device. The survey revealed some problems with viewing the video tutorials and with internet or smartphone connection. On the other hand, the smartphone application was reported to be easy to use and helpful to guide home exercising. Despite the issues encountered during the study, this digital-supported physical exercise intervention could provide useful to improve muscle measures of sarcopenia. [ABSTRACT FROM AUTHOR]

The importance of the IoT is increasing in every field of life, and it especially has a significant role in improving the efficiency of the healthcare system. Its demand further increased during COVID-19 to facilitate the patient remotely from their home digitally. Every time the COVID-19 patient visited the doctor for minor complications, it increased the risk of spreading the virus and the cost for the patient. Another alarming situation arose when a patient was in a critical position and may not claim an emergency service from the nearby healthcare system, increasing the death rate. The IoT uses healthcare services to properly monitor COVID-19 patients by using the interconnected network to overcome these issues. Through the IoT, the patient is facilitated by the health care system without spreading the virus, decreasing the death ratio during COVID-19. This paper aims to discuss different applications, technologies, and challenges of the IoT healthcare system, related to COVID-19. Different databases were searched using keywords in PubMed, ResearchGate, Scopus, ACM, Springer, Elsevier, Google Scholar, etc. This paper is trying to discuss, identify, and highlight the useful applications of the IoT healthcare system to provide guidelines to the researchers, healthcare institutions, and scientists to overcomes the hazards of COVID-19 pandemics. Hence, IoT is beneficial by identifying the symptoms of COVID-19 patients and by providing better treatments that use the healthcare system efficiently. At the end of the paper, challenges and future work are discussed, along with useful suggestions through which scientists can benefit from the IoT healthcare system during COVID-19 and in a severe pandemic. The survey paper is not limited to the healthcare system and COVID-19, but it can be beneficial for future pandemics or in a worse situation. [ABSTRACT FROM AUTHOR]

Near-eye display (NED) devices are required to provide visual instructions in the fields of education, navigation, military operations, construction, healthcare, etc. The issues with conventional NEDs are the form factor and vergence–accommodation conflict (VAC). The Maxwellian display alleviates the VAC in NEDs by providing consistently focused virtual images to the viewer, regardless of the depth of focus of the human eye. The main limitation of the Maxwellian display is its limited exit pupil size. Due to misalignment of the device or eyeball rotation, the user may miss the eye box, and the image will become lost. To mitigate this limitation, exit pupil expansion can be obtained either statically or dynamically. This paper reviews the various techniques employed to expand the exit pupil. The review includes the principle, advantages, and drawbacks of various techniques for expanding the exit pupil of the Maxwellian display. The structure of the paper starts with an introduction and the principle of the Maxwellian display, followed by a discussion of the main limitations that arise with various techniques, along with potential solutions. [ABSTRACT FROM AUTHOR]

In recent years, the globe has faced a series of topics of growing concern, such as the COVID-19 pandemic, the international financial crisis, rising socio-economic inequalities, the negative outcomes of greenhouse gas emissions, which resulted in climate change, and many others. Organizations worldwide have confronted these new challenges of sustainable finance by incorporating environmental, social, and corporate governance (ESG) factors and digital transformation (DT) in their innovation business strategies. The healthcare sector represents a large share of the global economy (about 10% of global economic output), employs a large number of workers, and needs to rely more on an open innovation model where interested parties, especially patients, are going to have a say in their own well-being. Thus, it is imperative that healthcare providers be efficient, effective, resilient, and sustainable in the face of significant challenges and risks. At the same time, they must offer sustainable development goals and digital transformation to healthcare users through limited governmental resources. This study investigates the role, importance, and correlation of ESG factors and digital transformation to the sustainable finance of healthcare systems through an innovative model. The main purpose of the paper is to present the already implemented ESG and DT factors in the healthcare sector and to propose a mutual and combined implementation strategy based on common evaluation tools, methods, and actions. A set of proposed actions and strategies are presented for the sustainability and resilience of the healthcare sector. [ABSTRACT FROM AUTHOR]

Atrial septal defects (ASDs) present a significant healthcare challenge, demanding accurate and timely diagnosis and precise management to ensure optimal patient outcomes. Artificial intelligence (AI) applications in healthcare are rapidly evolving, offering promise for enhanced medical decision-making and patient care. In the context of cardiology, the integration of AI promises to provide more efficient and accurate diagnosis and personalized treatment strategies for ASD patients. In interventional cardiology, sometimes the lack of precise measurement of the cardiac rims evaluated by transthoracic echocardiography combined with the floppy aspect of the rims can mislead and result in complications. AI software can be created to generate responses for difficult tasks, like which device is the most suitable for different shapes and dimensions to prevent embolization or erosion. This paper reviews the current state of AI in healthcare and its applications in cardiology, emphasizing the specific opportunities and challenges in applying AI to ASD diagnosis and management. By exploring the capabilities and limitations of AI in ASD diagnosis and management. This paper highlights the evolution of medical practice towards a more AI-augmented future, demonstrating the capacity of AI to unlock new possibilities for healthcare professionals and patients alike. [ABSTRACT FROM AUTHOR]

The number of obstetric departments in German hospitals has declined in the last decades. In particular, rural hospitals are challenged to sustain their delivery services. In this paper, we analyse the role of variation and overheads of obstetric departments from the perspective of current and future German hospital financing. For this purpose, we develop a Monte Carlo simulation model that analyses the workload of the labour room and the obstetric ward. The results show that a hospital with less than 640 deliveries per year cannot break even. In order to offer services 24 h per day, 365 days per year, five nurses, five midwives, and five gynaecologists are needed. This results in high fixed costs. At the same time, the variation coefficient of the labour room and the obstetric ward declines with an increasing number of deliveries. Consequently, small hospitals have a higher risk of over- and under-utilization in the course of the year. This paper acknowledges that economics is not the only decision dimension. The quality of the institution and the transport to the hospital have to be considered, as well as the population's wish for nearby services. However, the simulations clearly demonstrate that unless the hospital financing system is changed fundamentally, the decline in the number of hospitals offering delivery services will continue. [ABSTRACT FROM AUTHOR]

The brain and its health are essential for our (physical mental, social, and spiritual) wellbeing, for being able to realize our potential as individuals, and also for a fair, well-functioning, and productive society. However, today the world is facing a healthcare crisis related to the very high (and increasing) burden of brain disorders. As a response to this crisis, the “Swiss Brain Health Plan” (SBHP) was conceptualized in the context of other initiatives launched to value, promote, and protect brain health over the entire life course. In the first section of this position paper, the following fundamental considerations of the SBHP are discussed: (1) the high (and increasing) burden of brain disorders in terms of prevalence (>50% of the population suffers from a brain disorder), disability, mortality, and costs; (2) the prevention of brain disorders; (3) the operational definition of brain health; (4) determinants of brain health; (5) international initiatives to promote brain (including mental) health including the World Health Organization (WHO) intersectorial global action plan on epilepsy and other neurological disorders (NDs) (IGAP) and the WHO comprehensive mental health action plan. In the second section of the paper, the five strategic objectives of the SBHP, which has the vision of promoting brain health for all across the entire life course, are presented: (1) to raise awareness; (2) strengthen cross-disciplinary and interprofessional training/educational programs for healthcare professionals; (3) foster research on brain health determinants and individualized prevention of brain disorders; (4) prioritize a holistic (non-disease-specific), integrated, person-centered public health approach to promote brain health and prevent brain disorders through collaborations across scientific, health care, commercial, societal and governmental stakeholders and insurance providers; (5) support, empower, and engage patients, caregivers, and patient organizations, and reduce the stigma and discrimination related to brain disorders. In the third section of the paper, the first (2024) steps in the implementation of the SHBP, which will be officially launched in Zurich on 22 November 2023, are presented: (1) a definition of the overall organization, governance, specific targets, and action areas of the SBHP; (2) the patronage and/or co-organization of events on such specific topics as brain research (Lausanne), dementia (Geneva), stroke (Basel), neurohumanities (Bellinzona), sleep (Lugano), and psychiatry (Zurich); (3) the conduction of a new study on the global burden of brain disorders in Switzerland; (4) the launching of an international Certificate of Advanced Studies (CAS) on Brain Health at the University of Bern. In the fourth section of the paper, there is a concise executive summary of the SBHP [ABSTRACT FROM AUTHOR]

This study proposes the adoption of the IoT technology for the home monitoring of the health status of frail patients. Such a solution is thought to be part of the forthcoming Italian COT; the latter is an organizational model which is devoted to integrate the current national healthcare network. The prevalent deployment model of IoT systems is the Cloud, which offers powerful services and unlimited storage/computing capacity on-demand; unfortunately, connecting smart devices to the Cloud poses severe issues. First of all, connected devices create large volumes of data, which will drive inevitably to performance and network congestion challenges. Secondly, there are security, bandwidth, and reliability concerns that make the Cloud-only solution not suitable for all the potential real-world applications. The Fog computing paradigm has been introduced to bridge the gap between the Cloud and IoT devices. This paper gives a twofold contribution: (a) a Cloud–Fog architecture is proposed using a three-tiers solution where the Fog computing layer constitutes the middle tier; (b) simulations have been carried out in order to compare Cloud–Fog computing as an alternative to the Cloud-only solution. The experimental results demonstrate a remarkable degradation of the latency in the first solution with respect to the second one. The measured benefit indicates that the best way to implement the COTs consists in placing inside them the fog layer mentioned above. The iFogSim open-source toolkit has been used to carry out the experiments. [ABSTRACT FROM AUTHOR]

Systemic steroids are frequently used in critically ill patients for their anti-inflammatory properties. Potential benefits of these agents should be balanced against their known side effects. In this paper, we review trials assessing the use of systemic steroids in common conditions requiring admission to the intensive care unit. These include septic shock, the acute respiratory distress syndrome, severe pneumonia, COVID-19, and hypercapnic respiratory failure due to chronic obstructive pulmonary disease. We will mainly focus on well-conducted randomized controlled trials to determine whether steroids should be administered to critically ill patients presenting with these conditions. [ABSTRACT FROM AUTHOR]

Over the past decade, the use of telehealth has garnered increasing attention. The focus on quality aspects has seen significant growth in tandem with the telehealth expansion. Having useful indicators in this area is becoming increasingly strategic for fully integrating the technology into the health domain. These indicators can help monitor and evaluate the quality of telehealth services, guiding improvements and ensuring that these digital solutions meet the necessary standards for effective healthcare delivery. The purpose of this study is to analyze Key performance indicators (KPIs) in telehealth within institutional websites and the scientific dissemination world by means of a narrative review. A narrative review was proposed with these two specific points of view based on a standardized checklist and a quality control procedure for including scientific papers in the analysis. Results from scientific studies emphasize KPIs such as patient outcomes, operational efficiency, technical reliability, and cost-effectiveness. These include measures like improvements in condition management, patient satisfaction, consultation numbers, waiting times, and cost savings. Institutional documents from entities like the WHO also show diverse perspectives, focusing on equitable access, clinical excellence, patient prioritization, response times, and patient and staff satisfaction. The findings suggest that adopting a comprehensive set of KPIs and continuously monitoring and evaluating telehealth services can enhance their effectiveness, efficiency, and equity, ultimately improving healthcare outcomes and accessibility. [ABSTRACT FROM AUTHOR]

Apart from reverse-transcription polymerase chain reaction (RT-PCR) testing, chest radiographs (CXR) and computed tomography (CT) scans were employed as crucial diagnostic methods for detecting the 2019 new coronavirus disease (COVID-19). Our objective is to examine three notable COVID-19 instances from patients across the globe, along with their CXR and CT data. The evaluation of the imaging characteristics of the reported instances was the primary objective of a methodical examination of the literature. We located more than several articles that had been published between 2020 and 2023. After the papers were examined, three major cases were chosen, including a COVID-19 assessment of imaging features (chest X-ray and CT scan). Corona viral diseases (COVID-19) pose a significant risk to healthcare facilities, especially when the patient has additional medical issues. It is challenging to understand the various chest radiography results because of the use of specialized and ambiguous terminology such as "airspace disease", "pneumonia", "infiltrates", "patchy opacities", and "hazy opacities". The current investigation considered peer-reviewed case reports with Images features. Study designs, including reporting cases, were considered for imaging feature analysis. [ABSTRACT FROM AUTHOR]

The reproduction number, R 0 , is an important parameter in epidemic models. It is interpreted as the average number of new cases resulted from each infected individual during the course of infection. In this paper, the R 0 estimates since the outbreak of COVID-19 till 10 August 2020 for eight countries were computed using the package R{eSIR}. The computed values were examined and compared with the daily R 0 estimates obtained by a static SIR model by aligning the days of infection, assuming a fixed number of days for the infected person to become confirmed/recover/die. The results showed that running R{eSIR} to obtain R 0 estimates provided an easy mean of exploring epidemic data. Care must be taken in the interpretation of R 0 as a measure of severity of the spread of an epidemic. Other factors, such as imported cases, need to be considered. [ABSTRACT FROM AUTHOR]

Universal health coverage has been proposed as a strategy to improve health in low- and middle-income countries, but this depends on a good provision of health services. Under-5 mortality (U5M) reflects the quality of health services, and its reduction has been a milestone in modern society, reducing global mortality rates by more than two-thirds between 1990 and 2020. However, despite these impressive achievements, they are still insufficient, and most deaths in children under 5 can be prevented with the provision of timely and high-quality health services. The aim of this paper is to conduct a literature review on amenable (treatable) mortality in children under 5. This indicator is based on the concept that deaths from certain causes should not occur in the presence of timely and effective medical care. A systematic and exhaustive review of available literature on amenable mortality in children under 5 was conducted using MEDLINE/PubMed, Cochrane CENTRAL, OVID medline, Scielo, Epistemonikos, ScienceDirect, and Google Scholar in both English and Spanish. Both primary sources, such as scientific articles, and secondary sources, such as bibliographic indices, websites, and databases, were used. Results: The main cause of amenable mortality in children under 5 was respiratory disease, and the highest proportion of deaths occurred in the perinatal period. Approximately 65% of avoidable deaths in children under 5 were due to amenable mortality, that is, due to insufficient quality in the provision of health services. Most deaths in all countries and around the world are preventable, primarily through effective and timely access to healthcare (amenable mortality) and the management of public health programs focused on mothers and children (preventable mortality). [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is a significant cause of female cancer-related deaths in the US. Checking how severe the cancer is helps in planning treatment. Modern AI methods are good at grading cancer, but they are not used much in hospitals yet. We developed and utilized ensemble deep learning algorithms for addressing the tasks of classifying (1) breast cancer subtype and (2) breast cancer invasiveness from whole slide image (WSI) histopathology slides. The ensemble models used were based on convolutional neural networks (CNNs) known for extracting distinctive features crucial for accurate classification. In this paper, we provide a comprehensive analysis of these models and the used methodology for breast cancer diagnosis tasks. Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens. [ABSTRACT FROM AUTHOR]

Healthcare is undergoing a fundamental shift in which digital health tools are becoming ubiquitous, with the promise of improved outcomes, reduced costs, and greater efficiency. Healthcare professionals, patients, and the wider public are faced with a paradox of choice regarding technologies across multiple domains. Research is continuing to look for methods and tools to further revolutionise all aspects of health from prediction, diagnosis, treatment, and monitoring. However, despite its promise, the reality of implementing digital health tools in practice, and the scalability of innovations, remains stunted. Digital health is approaching a crossroads where we need to shift our focus away from simply looking at developing new innovations to seriously considering how we overcome the barriers that currently limit its impact. This paper summarises over 10 years of digital health experiences from a group of researchers with backgrounds in physical therapy—in order to highlight and discuss some of these key lessons—in the areas of validity, patient and public involvement, privacy, reimbursement, and interoperability. Practical learnings from this collective experience across patient cohorts are leveraged to propose a list of recommendations to enable researchers to bridge the gap between the development and implementation of digital health tools. [ABSTRACT FROM AUTHOR]

Iatrogenic amyloidosis results from medical therapeutic interventions, leading to the misfolding and aggregation of proteins into amyloid fibrils or to their direct deposition in different tissues. This review aims to provide a comprehensive overview of the iatrogenic amyloidosis pathology, underlying the possible molecular mechanisms, associated pathological manifestations, and clinical implications within modern medicine. By conducting a systematic analysis of the current literature, this paper highlights the diverse instances of iatrogenic amyloidosis triggered by medical procedures such as dialysis, organ and tissue transplantation, and therapeutic drugs. Exploring the intricate molecular pathways and contributing factors involved in protein misfolding and amyloidogenesis, and uncovering the pathological consequences observed in various tissues and organs, allows us to establish appropriate nomenclature and to gain a more profound understanding of the condition, working towards improved medical interventions and treatments. [ABSTRACT FROM AUTHOR]

Medical data sharing is pivotal in enhancing accessibility and collaboration among healthcare providers, researchers, and institutions, ultimately leading to enhanced patient outcomes and more efficient healthcare delivery. However, due to the sensitive nature of medical information, ensuring both privacy and confidentiality is paramount. Access control-based data sharing methods have been explored to address these issues, but data privacy concerns still remain. Therefore, this paper proposes a secure and privacy-preserving data sharing scheme that achieves an equilibrium between data confidentiality and privacy. By leveraging key aggregate encryption and private set intersection techniques, our scheme ensures secure data sharing while protecting against the exposure of sensitive information related to data. We conduct informal and formal security analyses, including Burrow–Abadi–Needham logic and Scyther, to demonstrate its resilience against potential adversarial attacks. We also implement the execution time for cryptographic operations using multiprecision integer and a rational arithmetic cryptographic library and perform comparative analysis with existing related schemes in terms of security, computational cost, and time complexity. Our findings demonstrate a high level of security and efficiency, demonstrating that the proposed scheme contributes to the field by providing a solution that protects data privacy while enabling secure and flexible sharing of medical data. [ABSTRACT FROM AUTHOR]

The delivery of genetic services in developing countries is faced with significant challenges, despite medical and technological advances globally. The Philippines, being an archipelago, faces even more challenges, with significant disparities in access to healthcare, and tertiary medical centers and specialists being concentrated in the major cities. The utilization of different networks for the integration of genetic services in the existing public health delivery system has been valuable. Using the well-established network of the national newborn screening program, genetic services have been successfully integrated into the delivery of healthcare, even at the grassroot level. Equitable access to healthcare, including genetic services, was highlighted and supported by the enactment of the Rare Disease Law in 2016. The support of the academe to assure the sustainability of services was evident in the establishment of a genetic counseling program to augment the work of a handful of clinical geneticists. Professional societies and support groups have been instrumental in identifying genetic conditions to be prioritized and lobbying for increased public awareness, leading to national programs and policies. This paper primarily discusses the value of networks in the delivery of genetic services, specifically newborn screening, programs for rare diseases, birth defects, and genetic counseling. [ABSTRACT FROM AUTHOR]

Simple Summary: The current approach to breast cancer screening, which is based on a person's age, overlooks individual-level differences in breast cancer risk. As a result, many people are over- or under-screened according to their actual risk of breast cancer. Risk-stratified breast screening may overcome the limitations of age-based screening, but there are still many knowledge gaps regarding how best to implement it in the population setting. This study will generate the first Canadian evidence on the adoption of breast cancer risk assessment in the population setting, to support the future implementation of risk-stratified breast cancer screening. This study demonstrated that, while risk assessment for risk-stratified screening at the population level is feasible, an equity lens must be considered in implementation to ensure cancer-screening disparities are not widened. Risk-stratified breast screening has been proposed as a strategy to overcome the limitations of age-based screening. A prospective cohort study was undertaken within the PERSPECTIVE I&I project, which will generate the first Canadian evidence on multifactorial breast cancer risk assessment in the population setting to inform the implementation of risk-stratified screening. Recruited females aged 40–69 unaffected by breast cancer, with a previous mammogram, underwent multifactorial breast cancer risk assessment. The adoption of multifactorial risk assessment, the effectiveness of methods for collecting risk factor information and the costs of risk assessment were examined. Associations between participant characteristics and study sites, as well as data collection methods, were assessed using logistic regression; all p-values are two-sided. Of the 4246 participants recruited, 88.4% completed a risk assessment, with 79.8%, 15.7% and 4.4% estimated at average, higher than average and high risk, respectively. The total per-participant cost for risk assessment was CAD 315. Participants who chose to provide risk factor information on paper/telephone (27.2%) vs. online were more likely to be older (p = 0.021), not born in Canada (p = 0.043), visible minorities (p = 0.01) and have a lower attained education (p < 0.0001) and perceived fair/poor health (p < 0.001). The 34.4% of participants requiring risk factor verification for missing/unusual values were more likely to be visible minorities (p = 0.009) and have a lower attained education (p ≤ 0.006). This study demonstrates the feasibility of risk assessment for risk-stratified screening at the population level. Implementation should incorporate an equity lens to ensure cancer-screening disparities are not widened. [ABSTRACT FROM AUTHOR]

Simple Summary: Proton therapy is a crucial tool for head and neck and skull-base cancers, offering benefits over photon therapy by lowering the risk of adverse effects. However, its full potential could be further explored by better characterizing the uncertainties related to its relative biological effectiveness. Addressing these uncertainties is crucial for maximizing the potential of proton therapy. We explore the significance of proton therapy's biological impact in these cancers, review relative biological effectiveness uncertainties and modeling, and examine clinical outcomes and evidence linking specific biological factors to patient adverse effects. Additionally, we review the current clinical practices and provide insights into innovative developments and their future clinical implementation. Proton therapy has emerged as a crucial tool in the treatment of head and neck and skull-base cancers, offering advantages over photon therapy in terms of decreasing integral dose and reducing acute and late toxicities, such as dysgeusia, feeding tube dependence, xerostomia, secondary malignancies, and neurocognitive dysfunction. Despite its benefits in dose distribution and biological effectiveness, the application of proton therapy is challenged by uncertainties in its relative biological effectiveness (RBE). Overcoming the challenges related to RBE is key to fully realizing proton therapy's potential, which extends beyond its physical dosimetric properties when compared with photon-based therapies. In this paper, we discuss the clinical significance of RBE within treatment volumes and adjacent serial organs at risk in the management of head and neck and skull-base tumors. We review proton RBE uncertainties and its modeling and explore clinical outcomes. Additionally, we highlight technological advancements and innovations in plan optimization and treatment delivery, including linear energy transfer/RBE optimizations and the development of spot-scanning proton arc therapy. These advancements show promise in harnessing the full capabilities of proton therapy from an academic standpoint, further technological innovations and clinical outcome studies, however, are needed for their integration into routine clinical practice. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has placed enormous pressure on healthcare systems. In the first line of the "war" against the virus, hospitals tried to maintain their general operations, while supplementing their services with COVID-19 patient care. To tackle the new difficulties, hospitals had to develop strategic response plans, and were in need of finding the most effective management structure for their institution. The focus of this paper is the aforementioned management structures. After overviewing the international literature, we identified three different approaches and we named them the Horizontal, Vertical, and Intermedier Approach. The separation is based on the following features: decision making, information distribution, command forwarding, intra-hospital communication channels, hierarchy, and control. After summarizing the international experiences, we introduce a Hungarian centrum hospital's approach, as the institution was assigned to fulfil COVID-19 centrum hospital duties in the middle of the first wave. The North-Buda Szent János Central hospital was in need of restructuring their management structure, and the leadership decided to transform it into a hierarchical, vertical structure, operating with centralized decision making and personal control. This control–command system idea came from the hospital's Internist Head Coordinator Physician, who is the first author of our article, and had serious military medicine experiences (in Afghanistan). [ABSTRACT FROM AUTHOR]

By integrating energy-efficient AIoT-based biosensor networks, healthcare systems can now predict COVID-19 outbreaks with unprecedented accuracy and speed, revolutionizing early detection and intervention strategies. Therefore, this paper explores the rapid growth of electronic technology in today's environment, driven by the proliferation of advanced devices capable of monitoring and controlling various healthcare systems. However, these devices' limited resources necessitate optimizing their utilization. To tackle this concern, we propose an enhanced Artificial Intelligence of Things (AIoT) system that utilizes the networking capabilities of IoT biosensors to forecast potential COVID-19 outbreaks. The system aims to efficiently collect data from deployed sensor nodes, enabling accurate predictions of possible disease outbreaks. By collecting and pre-processing diverse parameters from IoT nodes, such as body temperature (measured non-invasively using the open-source thermal camera TermoDeep), population density, age (captured via smartwatches), and blood glucose (collected via the CGM system), we enable the AI system to make accurate predictions. The model's efficacy was evaluated through performance metrics like the confusion matrix, F1 score, precision, and recall, demonstrating the optimal potential of the IoT-based wireless sensor network for predicting COVID-19 outbreaks in healthcare systems. [ABSTRACT FROM AUTHOR]

Recent advances in the field of large language models (LLMs) underline their high potential for applications in a variety of sectors. Their use in healthcare, in particular, holds out promising prospects for improving medical practices. As we highlight in this paper, LLMs have demonstrated remarkable capabilities in language understanding and generation that could indeed be put to good use in the medical field. We also present the main architectures of these models, such as GPT, Bloom, or LLaMA, composed of billions of parameters. We then examine recent trends in the medical datasets used to train these models. We classify them according to different criteria, such as size, source, or subject (patient records, scientific articles, etc.). We mention that LLMs could help improve patient care, accelerate medical research, and optimize the efficiency of healthcare systems such as assisted diagnosis. We also highlight several technical and ethical issues that need to be resolved before LLMs can be used extensively in the medical field. Consequently, we propose a discussion of the capabilities offered by new generations of linguistic models and their limitations when deployed in a domain such as healthcare. [ABSTRACT FROM AUTHOR]