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Proteinuria is a condition in which an excessive amount of protein is excreted in urine. It is, among others, an indicator of kidney disease or risk of cardiovascular disease. Rapid and reliable diagnosis and monitoring of proteinuria is of great importance for both patients and their physicians. For that reason, a paper-based sensor for proteinuria diagnosis was designed, optimized, and validated utilizing smartphone-assisted signal acquisition. In the first step, a few commonly employed protein assays were optimized and compared in terms of analytical performance on paper matrix. The tetrabromophenol blue method was selected as the one providing a sufficiently low limit of detection (39 mg·L−1) on the one hand and appropriate long-term stability (up to 3 months) on the other hand. The optimized assay was employed for protein-to-creatinine ratio (PCR) determination on a single paper-based sensor. For both analytes the linear ranges were within the clinically relevant range. The analytical usefulness of the developed sensors was demonstrated by a PCR recovery study in artificial urine. The obtained PCR recoveries were from ca. 80 to 150%. [ABSTRACT FROM AUTHOR]

The extraction of impacted third molars is one of the most common dental operations. When the impacted third molar is extracted, the operation plan is generally different because of the different impacted positions of the tooth. Therefore, judging the impacted type of the third molar is the basis of the third molar extraction operation. At present, oral health professionals usually analyze panoramic radiographs to determine the types of impacted third molars, but the diagnosis is easily affected by oral health professionals' subjective consciousnesses. Computer vision technology can help doctors analyze medical images faster and more accurately, so it is very desirable to use computer vision to detect and classify the impacted third molars. Based on the panoramic radiographs of the School of Stomatology, Lanzhou University, this paper establishes an object detection dataset containing six types of impacted third molars. On the basis of this dataset, the lightweight third molar impacted detection and classification model is studied in this paper. This study introduces the method of knowledge distillation on the basis of YOLOv5s and uses YOLOv5x as the teacher's model to guide YOLOv5s, which not only ensures the light weight of the model but also improves the accuracy of the model. Finally, the YOLOv5s-x model is obtained. The experimental results show that the introduction of knowledge distillation effectively improves the accuracy of the model while ensuring its light weight, the mAP of YOLOv5s-x is increased by 2.9% compared with the original model, and the amount of parameters and calculations is also reduced to a certain extent. Compared with mainstream object detection networks, including YOLOv8, YOLOv5s-x also has certain advantages, which can provide oral health professionals with better impacted third molar detection and classification services. [ABSTRACT FROM AUTHOR]

The role of a clinical nurse specialist in oncology varies greatly between healthcare systems, and implementing this healthcare role with its multifaceted and co-existing responsibilities may prove challenging. While already integrated into healthcare systems and services in several European countries, Asia, Canada, and the United States, other countries are just beginning to develop clinical nursing specialties. The current study aims to provide healthcare policymakers with up-to-date evidence that focuses on the diverse modes of oncology clinical nurse specialist role implementation across several healthcare systems and pertinent implementation challenges as described in the literature. A rapid evidence assessment was carried out in order to provide policymakers with a rigorous review in a condensed timescale. Initially, only items in the English language were included, and "grey literature" was excluded. We searched PubMed between 1 January 2022 and 28 February 2022 and two independent scholars reviewed items. Based on 64 papers, both non-scientific and papers that met the initial criteria of the rapid review, we describe the modes of implementation of the oncology clinical nurse specialist in the United States, Canada, United Kingdom, Japan, Brazil and Australia. Barriers to implementation include conflicts around role boundaries, skepticism and lack of organizational support, as well as fears that oncology clinical nurse specialists will "encroach" on doctors' powers. In contrast, an oncology clinical nurse specialist is found to be universally more accessible to patients and their families and can help physicians deal with difficult workloads, among other advantages. Conclusions: This role offers a myriad of gains for cancer patients, oncology physicians, and the healthcare system. The literature demonstrates that it is a necessary role, albeit one that brings specific implementation challenges. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper delves into the persistent issue of unequal access to medical imaging, with a particular focus on breast cancer screening and its impact on marginalized communities and racial/ethnic minorities. Central to our discussion is the role of scientific mobility among radiologists in fostering healthcare policy changes that promote diversity and cultural competence. We propose various strategies to bridge this gap, including cultural education, sensitivity training, and diversifying the radiology workforce. These measures aim to improve communication with diverse patient groups and reduce healthcare disparities. Additionally, we explore the challenges and advantages of teleradiology as a means to extend medical imaging services to underserved areas. In the context of artificial intelligence, we emphasize the critical need to validate algorithms across diverse populations to ensure unbiased and equitable healthcare outcomes. Overall, this paper underscores the importance of international collaboration in addressing global access barriers, presenting it as a key to mitigating disparities in medical imaging access and contributing to the pursuit of equitable healthcare. Access to medical imaging is pivotal in healthcare, playing a crucial role in the prevention, diagnosis, and management of diseases. However, disparities persist in this scenario, disproportionately affecting marginalized communities, racial and ethnic minorities, and individuals facing linguistic or cultural barriers. This paper critically assesses methods to mitigate these disparities, with a focus on breast cancer screening. We underscore scientific mobility as a vital tool for radiologists to advocate for healthcare policy changes: it not only enhances diversity and cultural competence within the radiology community but also fosters international cooperation and knowledge exchange among healthcare institutions. Efforts to ensure cultural competency among radiologists are discussed, including ongoing cultural education, sensitivity training, and workforce diversification. These initiatives are key to improving patient communication and reducing healthcare disparities. This paper also highlights the crucial role of policy changes and legislation in promoting equal access to essential screening services like mammography. We explore the challenges and potential of teleradiology in improving access to medical imaging in remote and underserved areas. In the era of artificial intelligence, this paper emphasizes the necessity of validating its models across a spectrum of populations to prevent bias and achieve equitable healthcare outcomes. Finally, the importance of international collaboration is illustrated, showcasing its role in sharing insights and strategies to overcome global access barriers in medical imaging. Overall, this paper offers a comprehensive overview of the challenges related to disparities in medical imaging access and proposes actionable strategies to address these challenges, aiming for equitable healthcare delivery. [ABSTRACT FROM AUTHOR]

Thyroid nodules in pediatric patients are less common than in adults but show a higher malignancy rate. Accordingly, the management of thyroid nodules in pediatric patients is more complex the younger the patient is, needing careful evaluation by physicians. In adult patients, specific ultrasound (US) features have been associated with an increased risk of malignancy (ROM) in thyroid nodules. Moreover, several US risk stratification systems (RSSs) combining the US features of the nodule were built to define the ROM. RSSs are developed for the adult population and their use has not been fully validated in pediatric patients. This study aimed to evaluate the available data about US features of thyroid nodules in pediatric patients and to provide a summary of the evidence regarding the performance of RSS in predicting malignancy. Moreover, insights into the management of thyroid nodules in pediatric patients will be provided. [ABSTRACT FROM AUTHOR]

Standard endovascular aortic repair (EVAR) has become the standard of care for treating infrarenal abdominal aortic aneurysms (AAAs) in patients with favorable anatomies, while patients with challenging AAA anatomies, and those with suprarenal or thoraco-abdominal aneurysms, still need alternative, more complex, solutions, including custom-made branched or fenestrated grafts, which are constrained by production delay and costs. To address urgent needs and complex cases, physicians have proposed modifying standard endografts by manually creating graft fenestrations. This allows for effective aneurysm exclusion and satisfactory patency of visceral vessels. Although physician-modified grafts (PMEGs) have demonstrated high technical success, standardized creation processes and long-term safety data are still lacking, necessitating further study to validate their clinical and legal standing. The aim of this article is to illustrate the state of the art with regard to this surgical technique, summarizing its origin, evolution, and the main clinical evidence supporting its effectiveness. The paper also aims to discuss the main medico-legal issues related to the use of PMEGs, with particular reference to the issue of safety related to the standardization of the surgical technique, medical liability profiles, and informed consent. [ABSTRACT FROM AUTHOR]

Giami [[22]], in his concept paper, provides a very interesting analysis of the various forms of the medicalization of sexuality and gender, and demonstrates that medicalization is a very complex process, making a valuable addition to the literature. These articles represent the various forms of the medicalization of society, such as biobanks and biomedical research, the medicalization of pregnancy and childbirth, the medicalization and moralization of beauty and aging, the social construction of vaccination, the processes of medicalization and demedicalization, the depathologization and pharmacologization of sexuality, and the movement of medicalization critique, providing an insightful consideration of the range of complex aspects of medicalization processes and their implications for health and society. Medicalization has been a key concept in the field of the sociology of health and illness over the past 50 years, capturing the expanding social control of everyday life by medical experts [[1], [3]]. Cunha and Raposo [[23]], in their concept paper "A New Time of Reckoning, a Time for New Reckoning: Views on Health and Society, Tensions between Medicine and the Social Sciences, and the Process of Medicalization", provide an extensive and deep reflection with an enlarged knowledge-based orientation for standardizing the relationships between the health-illness-medicine complex and society. [Extracted from the article]

This paper presents an eye image segmentation-based computer-aided system for automatic diagnosis of ocular myasthenia gravis (OMG), called OMGMed. It provides great potential to effectively liberate the diagnostic efficiency of expert doctors (the scarce resources) and reduces the cost of healthcare treatment for diagnosed patients, making it possible to disseminate high-quality myasthenia gravis healthcare to under-developed areas. The system is composed of data pre-processing, indicator calculation, and automatic OMG scoring. Building upon this framework, an empirical study on the eye segmentation algorithm is conducted. It further optimizes the algorithm from the perspectives of "network structure" and "loss function", and experimentally verifies the effectiveness of the hybrid loss function. The results show that the combination of "nnUNet" network structure and "Cross-Entropy + Iou + Boundary" hybrid loss function can achieve the best segmentation performance, and its MIOU on the public and private myasthenia gravis datasets reaches 82.1% and 83.7%, respectively. The research has been used in expert centers. The pilot study demonstrates that our research on eye image segmentation for OMG diagnosis is very helpful in improving the healthcare quality of expert doctors. We believe that this work can serve as an important reference for the development of a similar auxiliary diagnosis system and contribute to the healthy development of proactive healthcare services. [ABSTRACT FROM AUTHOR]

In-flight medical incidents are becoming increasingly critical as passengers with diverse health profiles increase in the skies. In this paper, we reviewed how airlines, aviation authorities, and healthcare professionals respond to such emergencies. The analysis was focused on the strategies developed by the top ten airlines in the world by examining training in basic first aid, collaboration with ground-based medical support, and use of onboard medical equipment. Appropriate training of crew members, availability of adequate medical resources on board airplanes, and improved capabilities of dialogue between a flying plane and medical doctors on the ground will contribute to a positive outcome of the majority of medical issues on board airlines. In this respect, the adoption of advanced telemedicine solutions and the improvement of real-time teleconsultations between aircraft and ground-based professionals can represent the future of aviation medicine, offering more safety and peace of mind to passengers in case of medical problems during a flight. [ABSTRACT FROM AUTHOR]

COVID-19 is a lung disease caused by a coronavirus family virus. Due to its extraordinary prevalence and associated death rates, it has spread quickly to every country in the world. Thus, achieving peaks and outlines and curing different types of relapses is extremely important. Given the worldwide prevalence of coronavirus and the participation of physicians in all countries, information has been gathered regarding the properties of the virus, its diverse types, and the means of analyzing it. Numerous approaches have been used to identify this evolving virus. It is generally considered the most accurate and acceptable method of examining the patient's lungs and chest through a CT scan. As part of the feature extraction process, a method known as fractional Fourier transform (FrFT) has been applied as one of the time-frequency domain transformations. The proposed method was applied to a database consisting of 2481 CT images. Following the transformation of all images into equal sizes and the removal of non-lung areas, multiple combination windows are used to reduce the number of features extracted from the images. In this paper, the results obtained for KNN and SVM classification have been obtained with accuracy values of 99.84% and 99.90%, respectively. [ABSTRACT FROM AUTHOR]

Background and Objectives: Medical students represent the ideal target group for promoting mental health and mental wellbeing, being exposed to specific risk factors, such as the content of medical training, the exposure to sickness and death, and a stressful academic routine. Medical students report high levels of cynicism and emotional exhaustion, which represent two of the essential features of burnout syndrome. In this systematic review, studies assessing the levels of burnout among medical students through validated tools worldwide were analyzed. Materials and Methods: A systematic review has been performed in order to identify studies: (1) focusing on samples of medical students; (2) evaluating burnout syndrome using validated tools; (3) providing prevalence data on burnout; and (4) written in English. Results: Out of the 5547 papers initially obtained, 64 were finally included in the analysis. The sample sizes ranged from 51 to 2682 participants. Almost all studies had a cross-sectional design; the Maslach Burnout Inventory and its related versions were the most frequently used assessment tools. The prevalence of burnout, which was stratified based on gender and academic stage, ranged from 5.6 to 88%. Burnout was mostly predicted by thoughts of stopping medical education, negative life events, lack of support, dissatisfaction, and poor motivation. Conclusions: The prevalence of burnout syndrome in medical students is quite heterogeneous, reaching a peak of 88% in some countries. However, several predictors have been identified, including negative life events or poor motivation. These findings highlight the need to develop preventive interventions targeting the future generation of medical doctors, in order to improve their coping strategies and resilience styles. [ABSTRACT FROM AUTHOR]

Liver segmentation from abdominal computed tomography (CT) images is a primary step in the diagnosis and treatment of liver cancer, but previous liver segmentation methods have the problems of excessive demand for prior knowledge, under- and oversegmentation, and boundary leakage. To solve these problems, this paper proposes a new method of liver segmentation to assist doctors in medical judgment. Firstly, a liver reconstruction algorithm is proposed to obtain the approximate initial region of the liver, which reduces the requirement of prior knowledge and can reconstruct the liver region closer to the liver boundary. Then, we refine the edge of the liver region based on the reaction diffusion level set (RD level set). This edge refinement method can effectively deal with the weak boundary problem, prevent under- and oversegmentation, and obtain a more accurate liver region. Our method is verified on the clinical and public datasets, respectively. The segmentation results in terms of mean VOE, RVD, ASD, RMSD, and MSD on dataset Sliver07 are 5.1%, −0.1%, 1.0 mm, 2.0 mm, and 18.2 mm, and on dataset 3Dircadb are 8.1%, −0.2%, 1.5 mm, 2.4 mm, and 20.8 mm, respectively. Compared with the previous algorithms, the experiment results show that this method has a great improvement in accuracy with less prior knowledge. The liver reconstruction algorithm proposed in this paper can obtain a more accurate initial liver region, reduce the requirement for prior knowledge, and reduce time costs compared with the level set algorithm. [ABSTRACT FROM AUTHOR]

This collection of research papers addresses fundamental questions concerning the nature of myalgic encephalomyelitis/ chronic fatigue syndrome (ME/CFS), the problem of disbelief and lack of knowledge and understanding of the condition among many doctors and the origins of this problem, and its impact on patients and their families. We report briefly the growing knowledge of the underlying pathological processes in ME/CFS, and the development of new organizations, including Doctors with ME, the US ME/CFS Clinical Coalition and EUROMENE, to address aspects of the challenges posed by the illness. We discuss the implications of COVID-19, which has much in common with ME/CFS, with much overlap of symptoms, and propose a new taxonomic category, which we are terming post-active phase of infection syndromes (PAPIS) to include both. This collection of papers includes a number of papers reporting similar serious impacts on the quality of life of patients and their families in various European countries. The advice of EUROMENE experts on diagnosis and management is included in the collection. We report this in light of guidance from other parts of the world, including the USA and Australia, and in the context of current difficulties in the UK over the promulgation of a revised guideline from the National Institute for Health and Care Excellence (NICE). We also consider evidence on the cost-effectiveness of interventions for ME/CFS, and on the difficulties of determining the costs of care when a high proportion of people with ME/CFS are never diagnosed as such. The Special Issue includes a paper which is a reminder of the importance of a person-centred approach to care by reviewing mind–body interventions. Finally, another paper reviews the scope for prevention in minimizing the population burden of ME/CFS, and concludes that secondary prevention, through early detection and diagnosis, could be of value. [ABSTRACT FROM AUTHOR]

Chronic diseases, such as type 2 diabetes (T2D), are difficult to manage because they demand continuous therapeutic review and monitoring. Beyond achieving the target HbA1c, new guidelines for the therapy of T2D have been introduced with the new groups of antidiabetics, glucagon-like peptide-1 receptor agonists (GLP-1ra) and sodium-glucose cotransporter-2 inhibitors (SGLT2-in). Despite new guidelines, clinical inertia, which can be caused by physicians, patients or the healthcare system, results in T2D not being effectively managed. This opinion paper explores the shift in T2D treatment, challenging assumptions and evidence-based recommendations, particularly for family physicians, considering the patient's overall situation in decision-making. We looked for the possible reasons for clinical inertia and the poor application of guidelines in the management of T2D. Guidelines for antidiabetic drugs should be more precise, providing case studies and clinical examples to define clinical contexts and contraindications. Knowledge communication can improve confidence and should include clear statements on areas of decision-making not supported by evidence. Precision medicine initiatives in diabetes aim to identify subcategories of T2D patients (including frail patients) using clustering techniques from data science applications, focusing on CV and poor treatment outcomes. Clear, unconditional recommendations for personalized T2D management may encourage drug prescription, especially for family physicians dealing with diverse patient contexts and clinical settings. [ABSTRACT FROM AUTHOR]

Due to cannabidiol's health benefits and absence of serious side effects, its use is constantly growing. This is a survey-based cross-sectional study that was conducted to determine Croatian pharmacists', physicians', and students' knowledge and attitudes about cannabidiol (CBD). Two questionnaires were created, one for students and the other for physicians and pharmacists. Our participants (in total 874: 473 students and 401 physicians and pharmacists) generally had positive attitudes towards CBD therapy as approximately 60% of them believe that CBD treatment is generally efficacious. Participants had positive attitudes toward the therapeutic value of CBD, especially pharmacists and pharmacy students (63.8% and 72.2%, respectively). Pharmacists were significantly more convinced that CBD could reduce the use of opioids prescribed for chronic pain (p < 0.05). Only 17.5% of students had read scientific papers about CBD, compared to a significantly higher percentage of physicians and pharmacists (43.0% and 47.8%, respectively) (p < 0.05). This study revealed a gap in knowledge regarding CBD, since 89.3% of pharmacists and physicians, as well as 84.8% of students, believe they need more education about CBD. We conclude that it is important to improve the educational curricula so that medical professionals can recommend CBD use to their patients when needed. [ABSTRACT FROM AUTHOR]

Jordanian healthcare institutes have launched several programs since 2009 to establish health information systems (HISs). Nowadays, the generic expectation is that the use of HIS resources is performed on daily basis among healthcare staff. However, there can be still a noticeable barrier due to a lack of knowledge if medical doctors do not receive proper training on existing HISs. Moreover, the lack of studies on this area hinders the clarity about the received versus the required training skills among medical doctors. To support this research initiative, survey data have been collected from specialized medical doctors who are currently affiliated with five Jordanian universities to assess their need for HIS training. The results also aim to explore the extent of medical doctors' use of HIS resources in Jordan. Moreover, they examine whether medical doctors require additional training on using HIS resources or not, as well as the main areas of required training programs. Specifically, this paper highlights the main topics that can be suitable subjects for enhanced training programs. The results show that most respondents use HISs in their daily clinical practices. However, most of them have not taken professional training on such systems. Hence, most of the respondents reported the need for additional training programs on several aspects of HIS resources. Moreover, based on the survey results, the most significant areas that require training are biomedical data analysis, artificial intelligence in medicine, health care management, and recent advances in electronic health records, respectively. Therefore, specialized medical doctors in Jordan need training on extracting useful and potential features of HISs. Education and training professionals in healthcare are recommended to establish training programs in Jordanian healthcare centers, which can further improve the quality of healthcare. [ABSTRACT FROM AUTHOR]

Simple Summary: The closeness of dogs to humans, in addition to poor veterinary care and zoonotic disease awareness, poses an increased risk of zoonotic disease transmission, resulting in a significant threat to human and animal populations and food security and safety. The diversity and burden of zoonotic pathogens harbored by dogs are underreported due to poor surveillance, limited diagnostic capacity, and limited data on the disease. The purpose of this review was to assemble relevant information from published peer-reviewed literature to assess the incidence of zoonotic pathogens associated with dogs in Nigeria in order to determine the risks involved and identify priority diseases to focus on, knowledge gaps, and areas for surveillance, control, and research in Nigeria. Knowledge of the prevalence and potential role of dogs in disease outbreaks will help heighten awareness, improve veterinary care, and support policy development, including government involvement in control strategies. Dogs live in close contact with humans as pets, for hunting, for security, and as a source of income. In addition, the meat is also consumed by some tribes in Nigeria. Dogs could therefore serve as reservoirs, carriers, and transmitters of zoonotic diseases. This review evaluates the literature from 2000 to 2020 for the prevalence and incidence of zoonotic pathogens associated with dogs in Nigeria. The results obtained show that overall, parasites constituted a majority (64%) of the zoonotic pathogens (or species) identified in dog hosts. A good number of studies have examined the role of ticks in the transmission of tick-borne pathogens. Further, bacteria make up 22% of the zoonotic pathogens. From this study, it appears that rabies is the major pathogen in dogs for which there is reliable evidence linking contact between humans and dogs. Oyo State in southwestern Nigeria and Plateau State in north-central Nigeria were the most frequently studied states, while prevalence/disease surveillance studies constituted 80% of the overall papers assessed. Interdisciplinary collaborations as well as research and diagnosis policy amendments are missing links to fully appreciate the role of dogs in the transmission of zoonotic diseases in Nigeria. Policies should integrate a one health approach in the Nigerian health system, whereby diagnostic screening of humans and animals by physicians and veterinarians includes zoonotic pathogens for more accurate diagnosis and control. [ABSTRACT FROM AUTHOR]

Cystinosis is a rare lysosomal storage disease that is tightly linked with the name of the American physician and scientist Dr. Jerry Schneider. Dr. Schneider (1937–2021) received his medical degree from Northwestern University, followed by a pediatrics residency at Johns Hopkins University and a fellowship in inherited disorders of metabolism. He started to work on cystinosis in J. Seegmiller's laboratory at the National Institutes of Health (NIH) and subsequently moved to the UC San Diego School of Medicine, where he devoted his entire career to people suffering from this devastating lysosomal storage disorder. In 1967, Dr. Schneider's seminal Science paper 'Increased cystine in leukocytes from individuals homozygous and heterozygous for cystinosis' opened a new era of research towards understanding the pathogenesis and finding treatments for cystinosis patients. His tremendous contribution transformed cystinosis from a fatal disorder of childhood to a treatable chronic disease, with a new generation of cystinosis patients being now in their 40th and 50th years. Dr. Schneider wrote a fascinating 'Personal History of Cystinosis' highlighting the major milestones of cystinosis research. Unfortunately, he passed away before this manuscript could be published. Fifty-five years after his first paper on cystinosis, the 'Personal History of Cystinosis' by Dr. Schneider is a tribute to his pioneering discoveries in the field and an inspiration for young doctors and scientists who have taken over the torch of cystinosis research towards finding a cure for cystinosis. [ABSTRACT FROM AUTHOR]

Image-guided focal therapy has increased in popularity as a treatment option for patients with primary and locally recurrent prostate cancer. This review will cover the basic indications, evaluation, treatment algorithm, and follow-up for patients undergoing image-guided ablation of the prostate. Additionally, this paper will serve as an overview of some technical approaches to cases so that physicians can familiarize themselves with working in this space. While the focus of this paper is prostate cryoablation, readers will obtain a basic literature overview of some of the additional available image-guided treatment modalities for focal prostate therapy.

During doctor's visits, fundamental decisions regarding a patient's diagnosis and therapy are taken. However, consultations often take place within a limited time frame and are characterized by an asymmetric interaction. Therefore, patients' questions can remain unanswered and concerns unspoken. A "Patients' Guide for Doctor's Visit" (DocVISITguide) was developed to prepare patients for their visits, supporting them to take an active role in the communication and leave the consultation well-informed. This paper describes the development of the DocVISITguide and its first small-scale evaluation based on a sequential explanatory mixed methods design from the patients' perspective. For this purpose, a small sample of patients completed a pre- and post-questionnaire, and two in-depth interviews were conducted. Most participants found the DocVISITguide easy to understand. The guide helped them to take a closer look at their own health situation and be better prepared for the visit. More than three-quarters (82%) of the participants would probably use the guide again, and all (100%) would recommend it to family and friends. However, some patients felt unsure about using the guide within the consultation and showing it to their physician. To counteract this uncertainty, physicians should be actively involved in the use of such guides in the future.

At present, people and hospital management are wasting their time and wealth due to a lack of a smart system in the pre-examination process for patients. This paper discusses an approach to designing a healthcare system that collects necessary health condition data using a microcontroller-based smart system, the information from which is referred to an authorized device. The smart Health Care System is an economical healthcare device for a hospital; it obtains measurements of vital signs like temperature using a DS18B20 Digital Temperature Sensor, heartbeat and SpO2 level using pulse oximetry and a health rate sensor, and the height of a patient via an ultrasonic sensor, etc. The system contains a microcontroller, an Arduino UNO, and all the collected information from sensors is sent to the Arduino UNO. The LCD screen coupled with the Arduino UNO displays all measurements from the sensors. This project has an alert system that works according to the patient's condition; if there is an emergency, the RED LED will light up, and the system will generate an alert through a buzzer and send the information to the doctor using a Bluetooth system. This system can be placed before entering triage, used in the intensive care unit, or used as a personal device to monitor the health of a patient. [ABSTRACT FROM AUTHOR]

Simple Summary: Patients with advanced non-small cell lung cancer (NSCLC) with epidermal growth factor receptor gene (EGFR) Exon 20 insertion mutations (Exon20ins) used to have poor outcomes after the failure of platinum-based therapies. Amivantamab, a drug that targets two proteins involved in NSCLC (EGFR/MET), was developed and studied in the CHRYSALIS clinical trial. This paper summarizes an analysis comparing outcomes for amivantamab from CHRYSALIS to the outcomes for a mix of treatments that were used in real-world clinical practice (real-world physician's choice [RWPC]). RWPC data were collected from hospitals across Europe in the CATERPILLAR real-world evidence (RWE) study. The outcomes of 114 patients from CHRYSALIS were compared to 55 lines of treatment (LOTs) from CATERPILLAR-RWE, accounting for differences in patient characteristics between the two populations. Overall, the results show that amivantamab was at least twice as good as RWPC for platinum pre-treated patients in terms of tumor response and survival rates. Patients with advanced non-small cell lung cancer (NSCLC) with epidermal growth factor receptor gene (EGFR) Exon 20 insertions (Exon20ins) at the second line and beyond (2L+) have an unmet need for new treatment. Amivantamab, a bispecific EGFR- and MET-targeted antibody, demonstrated efficacy in this setting in the phase 1b, open-label CHRYSALIS trial (NCT02609776). The primary objective was to compare the efficacy of amivantamab to the choices made by real-world physicians (RWPC) using an external control cohort from the real-world evidence (RWE) chart review study, CATERPILLAR-RWE. Adjustment was conducted to address differences in prognostic variables between cohorts using inverse probability weighting (IPW) and covariate adjustments based on multivariable regression. In total, 114 patients from CHRYSALIS were compared for 55 lines of therapy from CATERPILLAR-RWE. Baseline characteristics were comparable between the amivantamab and IPW-weighted RWPC cohorts. For amivantamab versus RWPC using IPW adjustment, the response rate ratio for the overall response was 2.14 (p = 0.0181), and the progression-free survival (PFS), time-to-next-treatment (TTNT) and overall survival (OS) hazard ratios (HRs) were 0.42 (p < 0.0001), 0.47 (p = 0.0063) and 0.48 (p = 0.0207), respectively. These analyses provide evidence of clinical and statistical benefits across multiple outcomes and adjustment methods, of amivantamab in platinum pre-treated patients with advanced NSCLC harboring EGFR Exon20ins. These results confirm earlier comparisons versus pooled national registry data. [ABSTRACT FROM AUTHOR]

This article analyzes how and why a near-total abortion ban was recently secured by a populist ruling party with support from Catholic Church leaders and lay groups following earlier passage of one of the most restrictive abortion laws in Europe. The paper further examines the public health threats posed by these measures, which have long been under-researched. These policy shifts are situated within the deeply embedded context of Poland's abortion conflict and a setting that has long been challenging for reproductive health. The 1993 ban led to the resurgence of clandestine abortions, a near-total privatization of abortion services, and more women seeking abortion services overseas. In late 2020, the exemption for severe fetal anomalies that made up 97% of all legal abortion cases in prior years was eliminated. Neither ban has significantly reduced the number of women obtaining abortions, nor have they increased birth rates as anticipated by proponents who championed traditional family values that they equated with Catholicism and Polish national identity. The new blanket ban on abortion constitutes a health risk and a punitive measure achieved via a judgement of the Constitutional Tribunal stacked with loyalists by the ruling party. It did not reflect popular will, although societal attitudes on abortion are markedly less permissive than in much of Europe. Although the populist and religious right have realized their long-held goal of further restricting, if not eliminating, women's access to abortion services, compelling criticisms have been raised about how this move compromises women's health and autonomy. Ironically, the realization of this goal, which many Poles view as unduly extreme, may also undermine long-term support for both the political right and the Church. The wisdom of their move was widely questioned, sparked the largest protests since the end of Communist rule, and drew international criticism. It proved a polarizing action that alienated many young adults and may have accentuated a secularizing shift. Women and their partners are finding new ways to navigate many public health threats by increasingly traveling beyond Poland for safe abortion care and resorting to newly available medication abortion methods. [ABSTRACT FROM AUTHOR]

(1) Background: This paper focuses on the development of a community assessment for telehealth using an interprofessional lens, which sits at the intersection of public health and urban planning using multistakeholder input. The paper analyzes the process of designing and implementing a telemedicine plan for the City of Brownsville and its surrounding metros. (2) Methods: We employed an interprofessional approach to CBPR which assumed all stakeholders as equal partners alongside the researchers to uncover the most relevant and useful knowledge to inform the development of telehealth community assessment. (3) Results: Key findings include that: physicians do not have the technology, financial means, or staff to provide a comprehensive system for telemedicine; and due to language and literacy barriers, many patients are not able to use a web-based system of telemedicine. We also found that all stakeholders believe that telehealth is a convenient tool that has the capacity to increase patient access and care. (4) Conclusions: Ultimately, the use of an interprofessional community-based participatory research (CBPR) design allowed our team to bring together local knowledge with that of trained experts to advance the research efforts. [ABSTRACT FROM AUTHOR]

An important issue in medical robotics is communication between physicians and robots. Speech-based communication is of particular advantage in robot-assisted surgery. It frees the surgeon's hands; hence, he can focus on the principal tasks. Man-machine voice communication is the subject of research in various domains (industry, social robotics), but medical robots are very specific. They must precisely synchronize their activities with operators. Voice commands must be possibly short. They must be executed without significant delays. An important factor is the use of a vision system that provides visual information in direct synchronization with surgeon actions. Its functions could be also controlled using speech. The aim of the research presented in this paper was to develop a method facilitating creation of voice-controlled medical robotic systems, fulfilling the mentioned requirements and taking into account possible scenarios of man-machine collaboration in such systems. A robot skill description (RSD) format was proposed in order to facilitate programming of voice control applications. A sample application was developed, and experiments were conducted in order to draw conclusions regarding the usefulness of speech-based interfaces in medical robotics. The results show that a reasonable selection of system functions controlled by voice may lead to significant improvement of man-machine collaboration. [ABSTRACT FROM AUTHOR]

Purpose: Effective antimicrobial use enhances care quality and combats antibiotic resistance. Yet, non-guideline factors influence potentially inappropriate prescribing. This study explores psycho-socio-organisational factors in antimicrobial prescribing as perceived by physicians across primary, secondary, and tertiary care. Methods: Adhering to PRISMA guidelines, a systematic review was conducted using PubMed and Scopus databases from 1 January 2000, to 8 March 2023, with an update search until 30 January 2024. Inclusion criteria focused on studies in Europe exploring psycho-socio-organisational factors for antibiotic prescribing from physicians' perspectives in hospital, inpatient, or primary care settings. Exclusion criteria targeted out-of-office prescriptions or low-quality studies. To evaluate the latter, several quality and risk-of-bias checklists were used. Data were extracted on study characteristics, study design, and methods and identified determinants of antibiotic prescribing. Data was analysed using a narrative synthesis method. Results: Among 8370 articles, 58 met inclusion criteria, yielding 35 articles from 23 countries. Three main themes emerged: personal, psychological, and organisational factors, encompassing seven determinants including work experience, knowledge, guideline adherence, uncertainty management, perceived pressure, time constraints, and diagnostic resource availability. Uncertainty management was key, with work experience and knowledge mitigating it. No additional factors emerged in the updated search. Conclusion: Enhanced uncertainty management decreases perceived patient and/or parental pressure to prescribe antibiotics, contributing to reducing potentially inappropriate prescribing (PIP). Therefore, it is imperative to educate physicians on effectively managing uncertainty. Interventions to improve antibiotic prescribing should be tailored to the specific needs and preferences of the different prescribing physicians. [ABSTRACT FROM AUTHOR]

Background and Objectives: Checking and correctly preparing the patient for endoscopic procedures is a mandatory step for the safety and quality of the interventions. The aim of this paper is to emphasize the importance and necessity of a "team time out" as well as the implementation of a customized "checklist" before the actual procedure. Material and Methods: We developed and implemented a checklist for the safe conduct of endoscopies and for the entire team to thoroughly know about the patient's medical history. The subjects of this study were 15 physicians and 8 endoscopy nurses who performed overall 572 consecutive GI endoscopic procedures during the study period. Results: This is a prospective pilot study performed in the endoscopy unit of two tertiary referral medical centers. We customized a safety checklist that includes the steps to be followed before, during and after the examination. It brings together the whole team participating in the procedure in order to check the key points during the following three vital phases: before the patient falls asleep, before the endoscope is inserted and before the team leaves the examination room. The perception of team communication and teamwork was improved after the introduction of the checklist. The checklist completion rates, identity verification rates of patients by the endoscopist, adequate histological labeling management and explicit recording of follow-up recommendations are some of the parameters that improved post-intervention. Conclusions: Using a checklist and adapting it to local conditions is a high-level recommendation of the Romanian Ministry of Health. In a medical world where safety and quality are essential, a checklist could prevent medical errors, and team time out can ensure high-quality endoscopy, enhance teamwork and offer patients confidence in the medical team.

Background: The oncology setting is characterized by various complexities, and healthcare professionals may experience stressful conditions associated with ethical decisions during daily clinical practice. Moral distress (MD) is a condition of distress that is generated when an individual would like to take action in line with their ethical beliefs but in conflict with the healthcare facility's customs and/or organization. This study aims to describe the MD of oncology health professionals in different care settings., Methods: Descriptive quantitative study was conducted in the Operating Units of the Istituti Fisioterapici Ospitalieri in Rome between January and March 2022. The investigated sample consisted of the medical and nursing staff on duty at the facility, who were given a questionnaire through a web survey. Besides a brief sociodemographic form, the MD Scale-Revised questionnaire was used for data collection., Results: The sample consisted of nurses (51%) and physicians (49%), predominantly working in surgeries (48%), and having 20-30 years of service (30%). MD was higher among healthcare professionals, in medicine than that ing in corporate organizations, surgeries, or outpatient clinics ( p = 0.007). It was not related to the profession ( p = 0.163), gender ( p = 0.103), or years of service ( p = 0.610)., Conclusions: This paper outlines the prevalence of MD in care settings and describes its relationship with profession, gender, and seniority. There is no patient care without the care of health professionals: knowing and fighting MD improves the safety of the treatments provided and the quality perceived by patients.

The application of machine learning (ML) has made an unprecedented change in the field of medicine, showing a significant potential to automate tasks and to achieve objectives that are closer to human cognitive capabilities. Human gait, in particular, is a series of continuous metabolic interactions specific for humans. The need for an intelligent recognition of dynamic changes of gait enables physicians in clinical practice to early identify impaired gait and to reach proper decision making. Because of the underlying complexity of the biological system, it can be difficult to create an accurate detection and analysis of imbalanced gait. This paper proposes a novel Criticality Analysis (CA) methodology as a feasible method to extract the dynamic interactions involved in human gait. This allows a useful scale-free representation of multivariate dynamic data in a nonlinear representation space. To quantify the effectiveness of the CA methodology, a Support Vector Machine (SVM) algorithm is implemented in order to identify the nonlinear relationships and high-order interactions between multiple gait data variables. The gait features extracted from the CA method were used for training and testing the SVM algorithm. The simulation results of this paper show that the implemented SVM model with the support of the CA method increases the accuracy and enhances the efficiency of gait analysis to extremely high levels. Therefore, it can perform as a robust classification tool for detection of dynamic disturbances of biological data patterns and creates a tremendous opportunity for clinical diagnosis and rehabilitation. [ABSTRACT FROM AUTHOR]

Simple Summary: Structure name standardization is a critical problem in Radiotherapy planning systems to correctly identify the various Organs-at-Risk, Planning Target Volumes and 'Other' organs for monitoring present and future medications. We propose a deep neural network-based approach on the multimodal vision-language prostate cancer patient data that provides state-of-the-art results for structure name standardization. Our framework considers for the first time, both the bony anatomy along with radiation dose information and the textual physician-given names of the structures present in the prostate of cancer patients. The pipeline presented here, helps in automatic standardization of structure names given by physicians with high accuracy. Our pipeline can successfully standardize the Organs-at-Risk and the Planning Target Volumes, which are of utmost interest to the clinicians and simultaneously, performs very well on the 'Other' organs. We performed comprehensive experiments by varying input data modalities to show that using masked images and masked dose data with text outperforms the combination of other input modalities. We also undersampled the majority class, i.e., the 'Other' class, at different degrees and conducted extensive experiments to demonstrate that a small amount of majority class undersampling is essential for superior performance. Overall, our proposed integrated, deep neural network-based architecture for prostate structure name standardization can solve several challenges associated with multimodal data. Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and 'Other' organs is a vital problem. This paper presents novel deep learning methods on structure sets by integrating multimodal data compiled from the radiotherapy centers of the US Veterans Health Administration (VHA) and Virginia Commonwealth University (VCU). These de-identified data comprise 16,290 prostate structures. Our method integrates the multimodal textual and imaging data with Convolutional Neural Network (CNN)-based deep learning approaches such as CNN, Visual Geometry Group (VGG) network, and Residual Network (ResNet) and shows improved results in prostate radiotherapy structure name standardization. Evaluation with macro-averaged F1 score shows that our model with single-modal textual data usually performs better than previous studies. The models perform well on textual data alone, while the addition of imaging data shows that deep neural networks achieve better performance using information present in other modalities. Additionally, using masked images and masked doses along with text leads to an overall performance improvement with the CNN-based architectures than using all the modalities together. Undersampling the majority class leads to further performance enhancement. The VGG network on the masked image-dose data combined with CNNs on the text data performs the best and presents the state-of-the-art in this domain. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) overcomes the flaws in the traditional healthcare system by enabling remote administration, more effective use of resources, and the mobility of medical devices to fulfil the patient's needs. The IoMT makes it simple to review the patient's cloud-based medical history in addition to allowing the doctor to keep a close eye on the patient's condition. However, any communication must be secure and dependable due to the private nature of patient medical records. In this paper, we proposed an authentication method for the IoMT based on hyperelliptic curves and featuring dual signatures. The decreased key size of hyperelliptic curves makes the proposed scheme efficient. Furthermore, security validation analysis is performed with the help of the formal verification tool called Scyther, which shows that the proposed scheme is secure against several types of attacks. A comparison of the proposed scheme's computational and communication expenses with those of existing schemes reveals its efficiency. [ABSTRACT FROM AUTHOR]

The digitization of German hospitals is proceeding continuously, leading to the implementation of new digital technologies, such as electronic health records (EHRs) or other technologies, used for the purpose of medical documentation tasks. Even though the replacement of paper documentation through digitized documentation in general promises to come along with plenty of benefits, the daily utilization of technologies might also lead to stresses and strains among the medical staff, eventually possibly leading to the development of different negative work and health-related outcomes. This study, therefore, aims at identifying persisting digitization-associated stressors and resources among medical hospital staff, examining their influences on different work and health-related outcomes, and finally, identifying potential needs for preventive measures. A quantitative study in the form of an online questionnaire survey was conducted among physicians working in the medical field of neuro- and vascular surgery in German hospitals. The study was carried out between June and October 2022 utilizing an online questionnaire based on several standardized scales, such as the technology acceptance model (TAM) and the technostress model, as well as on several scales from the Copenhagen Psychosocial Questionnaire (COPSOQ). The study found medium levels of technostress among the participating physicians (n = 114), as well as low to medium levels of persisting resources. The queried physicians, on average, reported low levels of burnout symptoms, generally described their health status as good, and were mostly satisfied with their job. Despite the prevalence of technostress and the low levels of resources among the surveyed physicians, there is little awareness of the problem of digital stress, and preventive measures have not been widely implemented yet in the clinics, indicating a needs gap and the necessity for the strategic and quality-guided implementation of measures to effectively prevent digital stress from developing. [ABSTRACT FROM AUTHOR]

Modern medical devices connected to public and private networks require additional layers of communication and management to effectively and securely treat remote patients. Wearable medical devices, for example, can detect position, movement, and vital signs; such data help improve the quality of care for patients, even when they are not close to a medical doctor or caregiver. In healthcare environments, these devices are called Medical Internet-of-Things (MIoT), which have security as a critical requirement. To protect users, traditional risk assessment (RA) methods can be periodically carried out to identify potential security risks. However, such methods are not suitable to manage sophisticated cyber-attacks happening in near real-time. That is the reason why dynamic RA (DRA) approaches are emerging to tackle the inherent risks to patients employing MIoT as wearable devices. This paper presents a systematic literature review of RA in MIoT that analyses the current trends and existing approaches in this field. From our review, we first observe the significant ways to mitigate the impact of unauthorised intrusions and protect end-users from the leakage of personal data and ensure uninterrupted device usage. Second, we identify the important research directions for DRA that must address the challenges posed by dynamic infrastructures and uncertain attack surfaces in order to better protect users and thwart cyber-attacks before they harm personal (e.g., patients' home) and institutional (e.g., hospital or health clinic) networks. [ABSTRACT FROM AUTHOR]

Background: The medical profession is associated with a heavy psychological and physical burden. Specific working conditions can negatively affect the assessment of physicians' quality of life. The lack of current studies prompted us to evaluate the life satisfaction of the physicians in the Silesian Province in relation to the selected factors (health status, professional preferences, family and material status)., Material and Methods: The study included 701 physicians and dentists from the Silesian Province aged between 25 and 80 years. It was conducted in 2018 using the Paper and Pencil Interview technique by obtaining non-personalized demographic, anthropometric, socioeconomic, occupational, health and lifestyle data. The following measures were used: the Satisfaction with Life Scale (SWLS), Occupational Satisfaction and the Hospital Anxiety and Depression Scale (HADS). Considering the environmental conditions, the total SWLS scores were analysed in terms of the significance of differences in the groups. Moreover, the SWLS scores underwent multivariate analysis of variance and the correlation analysis of job satisfaction and the presence of anxiety and/or depressive symptoms., Results: Life satisfaction among the physicians and dentists from the Silesian Province was at an average level. Significant predictors included age and economic status. Additionally, significant predictors in the younger subjects (25-50 years) included the Body Mass Index and practising sports. In turn, in the older subjects (50-80 years), these predictors were related to hospital work and being on sick leave. The study found a significant moderate association between life satisfaction and professional satisfaction. Additionally, a significantly lower level of life satisfaction was reported in the subjects who presented with anxiety and/or depressive symptoms., Conclusions: Due to its association with the profession, the mean level of life satisfaction among physicians and dentists prompts verification of crucial spheres related to the physical, emotional, social and material well-being and the activity of the professional group.

Minimally invasive surgical robots have the advantages of high positioning accuracy, good stability, and flexible operation, which can effectively improve the quality of surgery and reduce the difficulty for doctors to operate. However, in order to realize the translation of the existing RCM mechanism, it is often necessary to add a mobile unit, which is often bulky and occupies most space above the patient's body, thus causing interference to the operation. In this paper, a new type of planar RCM mechanism is proposed. Based on this mechanism, a 3-DOF robotic arm is designed, which can complete the required motion for surgery without adding a mobile unit. In this paper, the geometric model of the mechanism is first introduced, and the RCM point of the mechanism is proven during the motion process. Then, based on the establishment of the geometric model of the mechanism, a kinematics analysis of the mechanism is carried out. The singularity, the Jacobian matrix, and the kinematic performance of the mechanism are analyzed, and the working space of the mechanism is verified according to the kinematic equations. Finally, a prototype of the RCM mechanism was built, and its functionality was tested using a master-slave control strategy.

Background: The words "hope" and "cure" were used in a greater number of articles and sentences in narrative and editorial papers than in primary research. Despite concomitant improvements in cancer outcomes, the related reluctance to use these terms in more scientifically oriented original reports may reflect a bias worthy of future exploration. This study aims to survey a group of physicians and cancer patients regarding their perception and use of the word cure., Materials and Method: An anonymous online and print survey was conducted to explore Italian clinicians' (the sample includes medical oncologists, radiotherapists, and oncological surgeons) and cancer patients' approach to the perception and use of the word "cure" in cancer care. The participants received an email informing them of the study's purpose and were invited to participate in the survey via a linked form. A portion, two-thirds, of questionnaires were also administered to patients in the traditional paper form., Results: The survey was completed by 224 clinicians (54 oncologists, 78 radiotherapists, and 92 cancer surgeons) and 249 patients. The results indicate a favourable attitude for patients in favour of a new language ("cured" vs. "complete remission") of the disease experience., Conclusions: The use of the word cured is substantially accepted and equally shared by doctors and patients. Its use can facilitate the elimination of metaphoric implications and toxic cancer-related connotations registered in all cultures that discourage patients from viewing cancer as a disease with varied outcomes, including cure.

Artificial intelligence (AI) with deep learning models has been widely applied in numerous domains, including medical imaging and healthcare tasks. In the medical field, any judgment or decision is fraught with risk. A doctor will carefully judge whether a patient is sick before forming a reasonable explanation based on the patient's symptoms and/or an examination. Therefore, to be a viable and accepted tool, AI needs to mimic human judgment and interpretation skills. Specifically, explainable AI (XAI) aims to explain the information behind the black-box model of deep learning that reveals how the decisions are made. This paper provides a survey of the most recent XAI techniques used in healthcare and related medical imaging applications. We summarize and categorize the XAI types, and highlight the algorithms used to increase interpretability in medical imaging topics. In addition, we focus on the challenging XAI problems in medical applications and provide guidelines to develop better interpretations of deep learning models using XAI concepts in medical image and text analysis. Furthermore, this survey provides future directions to guide developers and researchers for future prospective investigations on clinical topics, particularly on applications with medical imaging.

The medical service is a special credit commodity, and trust plays a very important role in patients' online medical choice behavior. By collecting information about the doctors on China's leading online medical platform (Platform A), a regression analysis model was constructed, based on the credibility theory model, which has the following three dimensions: ability trust, benevolence trust, and integrity trust. The results showed that the medical title of the doctors, their department's reputation, the number of gifts given to them, and the number of patients who registered with them after diagnosis, among other factors, had a significant, positive impact on the behavior of choosing doctors. Among these considerations, the number of patients registered after diagnosis had the greatest impact on the behavior of choosing doctors. This factor is the result of each doctor's personal brand management, which reflects their comprehensive ability, reputation and integrity. Compared with previous studies, this paper creatively analyzed the important influence of departmental reputation and the number of patients registered after diagnosis on medical choice behavior and puts forward that a doctor can use the number of patients registered after diagnosis to manage their personal brand. Based on the results of this study, we will also put forward suggestions from the perspectives of patients, doctors and the online medical community. [ABSTRACT FROM AUTHOR]

This paper presents the findings of detailed and comprehensive technical literature aimed at identifying the current and future research challenges of tactical autonomy. It discusses in great detail the current state-of-the-art powerful artificial intelligence (AI), machine learning (ML), and robot technologies, and their potential for developing safe and robust autonomous systems in the context of future military and defense applications. Additionally, we discuss some of the technical and operational critical challenges that arise when attempting to practically build fully autonomous systems for advanced military and defense applications. Our paper provides the state-of-the-art advanced AI methods available for tactical autonomy. To the best of our knowledge, this is the first work that addresses the important current trends, strategies, critical challenges, tactical complexities, and future research directions of tactical autonomy. We believe this work will greatly interest researchers and scientists from academia and the industry working in the field of robotics and the autonomous systems community. We hope this work encourages researchers across multiple disciplines of AI to explore the broader tactical autonomy domain. We also hope that our work serves as an essential step toward designing advanced AI and ML models with practical implications for real-world military and defense settings.

Private equity (PE) investments in health care have increased drastically over the last decade, and the profit interests of these companies have triggered a vivid discussion among medical professions. However, what are the key underlying perceptions among physicians regarding this trend? Unravelling the argumentative structure of this debate is the purpose of this paper. With physicians being a major stakeholder group in the outpatient health care setting, this paper explores physicians' perspectives regarding increasing PE activities. We systematically searched, selected, and synthesized existing knowledge in a scoping review and complemented the findings through 14 semi-structured interviews with physicians working in the outpatient health care sector in Germany. The results outline a complex network of arguments, concerns, and fears whereby the first intuitive perception of physicians is of critical nature. Arguments cluster around central perceptions of how PE involvement affects the individual autonomy of physicians in their daily work and decision-making, the impact on quality of care, work-life balance considerations, PE investment strategies, lack of medical vs. managerial expertise, and taxation issues. The high number of opinion papers among the literature underlines the actuality of the topic and emphasizes the need for empirical research., Competing Interests: Tim N. Nolte graduated from Erasmus University Rotterdam in 2021 and his graduation work formed the basis for this paper. As per August 2022 Tim N. Nolte is employed at Eterno Health GmbH, a commercial organization focusing on outpatient care. Felix Miedaner and Sandra S&uuml;lz declare no conflict of interest.

Trust is considered a critical factor in the physician-patient relationship. However, little is known about the development and impact of physicians trusting their patients. A model that is premised on the integrated model of organizational trust was proposed in this article to reveal the cognitive processes involved in physicians' trust, with perceived integrity and the ability of the patient as antecedents and the physicians' communication efficacy as the outcome. A cross-sectional survey of 348 physicians in Zhejiang province, China, revealed that a physician's trust in a patient mediated the relationship between the physicians' perception of the integrity and ability of the patient, and the physician's communication efficacy. The physicians' educational backgrounds and work experience were also found to moderate an indirect effect: a lower level of education and longer work experience intensified the impact of the perceived integrity and ability of the patient on the physician's trust, while shorter work experience made the association between the physician's trust and communication efficacy more salient. This paper provided implications for both physician and patient sides.

Machine learning (ML) models have proven their potential in acquiring and analyzing large amounts of data to help solve real-world, complex problems. Their use in healthcare is expected to help physicians make diagnoses, prognoses, treatment decisions, and disease outcome predictions. However, ML solutions are not currently deployed in most healthcare systems. One of the main reasons for this is the provenance, transparency, and clinical utility of the training data. Physicians reject ML solutions if they are not at least based on accurate data and do not clearly include the decision-making process used in clinical practice. In this paper, we present a hybrid human-machine intelligence method to create predictive models driven by clinical practice. We promote the use of quality-approved data and the inclusion of physician reasoning in the ML process. Instead of training the ML algorithms on the given data to create predictive models (conventional method), we propose to pre-categorize the data according to the expert physicians' knowledge and experience. Comparing the results of the conventional method of ML learning versus the hybrid physician-algorithm method showed that the models based on the latter can perform better. Physicians' engagement is the most promising condition for the safe and innovative use of ML in healthcare.

Doctor-patient relationships (DPRs) in China have been straining. With the emergence of the COVID-19 pandemic, the relationships and interactions between patients and doctors are changing. This study investigated how patients' attitudes toward physicians changed during the pandemic and what factors were associated with these changes, leading to insights for improving management in the healthcare sector. This paper collected 58,600 comments regarding Chinese doctors from three regions from the online health platform Good Doctors Online (haodf.com, accessed on 13 October 2022). These comments were analyzed using text mining techniques, such as sentiment and word frequency analyses. The results showed improvements in DPRs after the pandemic, and the degree of improvement was related to the extent to which a location was affected. The findings also suggest that administrative services in the healthcare sector need further improvement. Based on these results, we summarize relevant recommendations at the end of this paper.

This paper investigates the use of machine learning algorithms to aid medical professionals in the detection and risk assessment of diabetes. The research employed a dataset gathered from individuals with type 2 diabetes in Ninh Binh, Vietnam. A variety of classification algorithms, including Decision Tree Classifier, Logistic Regression, SVC, Ada Boost Classifier, Gradient Boosting Classifier, Random Forest Classifier, and K Neighbors Classifier, were utilized to identify the most suitable algorithm for the dataset. The results of the present study indicate that the Random Forest Classifier algorithm yielded the most promising results, exhibiting a cross-validation score of 0.998 and an accuracy rate of 100%. To further evaluate the effectiveness of the selected model, it was subjected to a testing phase involving a new dataset comprising 67 patients that had not been previously seen. The performance of the algorithm on this dataset resulted in an accuracy rate of 94%, especially the study's notable finding is the algorithm's accurate prediction of the probability of patients developing diabetes, as indicated by the class 1 (diabetes) probabilities. This innovative approach offers a meticulous and quantifiable method for diabetes detection and risk evaluation, showcasing the potential of machine learning algorithms in assisting clinicians with diagnosis and management. By communicating the diabetes score and probability estimates to patients, the comprehension of their disease status can be enhanced. This information empowers patients to make informed decisions and motivates them to adopt healthier lifestyle habits, ultimately playing a crucial role in impeding disease progression. The study underscores the significance of leveraging machine learning in healthcare to optimize patient care and improve long-term health outcomes. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has given rise to all types of beliefs, theories, and explanations, whether scientific, religious, or conspiratorial. At the beginning of the pandemic, science did not yet have a medicinal product for this new disease, and alternative medicines offering "miracle cures" were acclaimed by some citizens looking for an effective treatment for COVID-19. This article aims to study a specific "miracle cure", namely, chlorine dioxide, a bleaching agent for textiles or paper that also has disinfectant properties (water, surfaces). The dissemination of information about chlorine dioxide to French-speaking people on the social network Twitter from 1 December 2019 to 30 November 2021 is analyzed using a graph network. The results show that messages promoting misinformation, even if they are likely to be quantitatively less numerous, spread more widely than those based on more reliable information. In addition, this article shows that chlorine dioxide was promoted as an effective cure by medical doctors and peer-reviewed articles, which consequently increased the dissemination of this belief in the social space. Consequently, the process of misinformation entered the sphere of scientific controversy. The boundary between science and misinformation is becoming blurred to the point that it is no longer possible until proven otherwise to call chlorine dioxide a "false miracle cure" but a controversial treatment against COVID-19. [ABSTRACT FROM AUTHOR]

Background: Telemedicine is the service of delivering medical care from a distance through the means of modern technology. It has many advantages, including improved access, decreased costs for both patients and clinics, more flexibility and availability, as well as more precise and individualized therapies. However, it is equally important to take into consideration all the challenges associated with this innovative way of providing care. This virtual technology has had an exponential growth, especially since the beginning of the COVID-19 pandemic, because it delivers great outcomes and suggests exciting future promises. Methods: The study involved the collection of responses from an online questionnaire comprising 26 questions that was distributed to healthcare professionals in Romania. Results: The questionnaire was completed by a number of 1017 healthcare professionals. We investigated and analyzed whether telehealth is seen as an important constituent of the healthcare system, if it is perceived as necessary, safe, well-managed by lawmakers, and easy to use, what advantages it has, what common practices specialists already employ and, additionally, the openness toward becoming more digitally educated for the purpose of streamlining the use of telemedicine. Conclusions: This paper reports on the perception of telemedicine among healthcare professionals in Romania, as constructive feedback represents an essential piece of the puzzle in assuring the smooth transition toward this facet of modern healthcare. [ABSTRACT FROM AUTHOR]

Urine output is an important indicator of renal function. In hospitals, urine is collected using a catheter connected to a urine collection bag that has volume gradation markings. This type of visual measurement has low levels of accuracy and is labor-intensive. This paper developed an Internet-of-Things enabled system that continuously monitors the urine volume collected via the urine collection system. The device is built utilizing a strain gauge load cell, an integrated circuit that contains an amplifier, analog-to-digital converter, and a WiFi-enabled microcontroller. The data is sent via wireless networking to a data collection and analysis server, which provides accurate analyses of urine output. A mobile application utilizing the Blynk.io system is used to display the data. This device and mobile application were built at a minimal cost of 26 USD. The device has been tested multiple times and reported urine output accurately, with minimal difference between actual versus measured volumes. In the future, further development of this device can provide hospitals and physicians worldwide with easy access to affordable, accurate, and real-time urine measurement, which would translate into better, life-saving medical care. [ABSTRACT FROM AUTHOR]

Networks form the backbone of any healthcare system. Various databases were searched with relevant keywords, data were abstracted, and numerous papers were appraised for this synthesis. This compiled systematic review gives a comprehensive overview of various networks that are found in healthcare, with a special reference to the treatment, referral, and best-practice care of patients. Special support networks, such as Clinical decision support systems, Physician collaboration networks, Telemedicine networks, and Shared healthcare record access, are also described, as these support networks play a pivotal role in improving the quality of healthcare for patients. [ABSTRACT FROM AUTHOR]

The WHO established that medication errors are the most common and preventable errors and represent an expenditure of 42 billion U.S. dollars annually. The risk of medication errors increases in transitions between levels of care, mainly from hospital care to primary healthcare after hospital discharge. In this context, communication is a key element in the safety of the medication reconciliation process. The aim of this paper was to describe the barriers to, and facilitators of, effective communication during the medication reconciliation process at hospital discharge in people over 65 years of age, from the perspective of primary healthcare professionals. A qualitative descriptive study was designed, and in-depth interviews were conducted with 21 individuals, of whom 13 were nurses and 8 were physicians. This study was carried out with healthcare professionals belonging to primary healthcare centres in Huelva (Spain). Following content analysis of the discourses we identified 19 categories, grouped into three areas: interlevel communication, communication between primary healthcare professionals, and communication between healthcare professionals and patients/caregivers. The barriers found mainly relate to the adequacy and use of technological tools, time available, workload and the level of collaboration of patients/caregivers. Facilitating elements for communication in medication reconciliation included technologies, such as computerized medical history, protocolization of clinical sessions, the presence of case management nurse and interdisciplinary teamwork. [ABSTRACT FROM AUTHOR]

Angiogenesis is the process of new blood vessels growing from existing vasculature. Visualizing them as a three-dimensional (3D) model is a challenging, yet relevant, task as it would be of great help to researchers, pathologists, and medical doctors. A branching analysis on the 3D model would further facilitate research and diagnostic purposes. In this paper, a pipeline of vision algorithms is elaborated to visualize and analyze blood vessels in 3D from formalin-fixed paraffin-embedded (FFPE) granulation tissue sections with two different staining methods. First, a U-net neural network is used to segment blood vessels from the tissues. Second, image registration is used to align the consecutive images. Coarse registration using an image-intensity optimization technique, followed by finetuning using a neural network based on Spatial Transformers, results in an excellent alignment of images. Lastly, the corresponding segmented masks depicting the blood vessels are aligned and interpolated using the results of the image registration, resulting in a visualized 3D model. Additionally, a skeletonization algorithm is used to analyze the branching characteristics of the 3D vascular model. In summary, computer vision and deep learning is used to reconstruct, visualize and analyze a 3D vascular model from a set of parallel tissue samples. Our technique opens innovative perspectives in the pathophysiological understanding of vascular morphogenesis under different pathophysiological conditions and its potential diagnostic role. [ABSTRACT FROM AUTHOR]