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Background Artificial intelligence (AI) for healthcare presents potential solutions to some of the challenges faced by health systems around the world. However, it is well established in implementation and innovation research that novel technologies are often resisted by healthcare leaders, which contributes to their slow and variable uptake. Although research on various stakeholders’ perspectives on AI implementation has been undertaken, very few studies have investigated leaders’ perspectives on the issue of AI implementation in healthcare. It is essential to understand the perspectives of healthcare leaders, because they have a key role in the implementation process of new technologies in healthcare. The aim of this study was to explore challenges perceived by leaders in a regional Swedish healthcare setting concerning the implementation of AI in healthcare. Methods The study takes an explorative qualitative approach. Individual, semi-structured interviews were conducted from October 2020 to May 2021 with 26 healthcare leaders. The analysis was performed using qualitative content analysis, with an inductive approach. Results The analysis yielded three categories, representing three types of challenge perceived to be linked with the implementation of AI in healthcare: 1) Conditions external to the healthcare system; 2) Capacity for strategic change management; 3) Transformation of healthcare professions and healthcare practice. Conclusions In conclusion, healthcare leaders highlighted several implementation challenges in relation to AI within and beyond the healthcare system in general and their organisations in particular. The challenges comprised conditions external to the healthcare system, internal capacity for strategic change management, along with transformation of healthcare professions and healthcare practice. The results point to the need to develop implementation strategies across healthcare organisations to address challenges to AI-specific capacity building. Laws and policies are needed to regulate the design and execution of effective AI implementation strategies. There is a need to invest time and resources in implementation processes, with collaboration across healthcare, county councils, and industry partnerships.

Bone loss due to an increased osteoclast activity is common in osteoporosis and rheumatoid arthritis. For the first time, we observed an inhibition of osteoclast formation and bone resorption by outer-membrane vesicles (OMVs) from a Gram-negative, pathogenic bacterium, Proteus mirabilis (P.M). Gene ontogeny and KEGG enrichment analyses of miRNA and mRNA sequencing data demonstrated a significant effect of P.M OMVs on mitochondrial functions and apoptotic pathways. OMVs induced mitochondrial dysfunction through an increased level of intracellular ROS, collapse of mitochondrial membrane potential (ΔΨm), and modulation of Bax, Bcl-2, caspase-3, and cytochrome c expression. In addition, P.M OMVs strongly inhibited miR-96-5p expression, which caused an upregulation of ATP binding cassette subfamily A member 1 (Abca1) in osteoclasts leading to an increased level of mitochondria-dependent apoptosis. Moreover, treatment with P.M but not Escherichia coli OMVs attenuated bone loss in experimental osteoporosis and collagen-induced arthritis. Collectively, we demonstrated osteoprotective functions of OMVs from Proteus mirabilis , which downregulated miR-96-5p causing an increased Abca1 expression and mitochondria-dependent apoptosis.

Recent advancements in artificial intelligence (AI) have changed the care processes in mental health, particularly in decision-making support for health care professionals and individuals with mental health problems. AI systems provide support in several domains of mental health, including early detection, diagnostics, treatment, and self-care. The use of AI systems in care flows faces several challenges in relation to decision-making support, stemming from technology, end-user, and organizational perspectives with the AI disruption of care processes. This study aims to explore the use of AI systems in mental health to support decision-making, focusing on 3 key areas: the characteristics of research on AI systems in mental health; the current applications, decisions, end users, and user flow of AI systems to support decision-making; and the evaluation of AI systems for the implementation of decision-making support, including elements influencing the long-term use. A scoping review of empirical evidence was conducted across 5 databases: PubMed, Scopus, PsycINFO, Web of Science, and CINAHL. The searches were restricted to peer-reviewed articles published in English after 2011. The initial screening at the title and abstract level was conducted by 2 reviewers, followed by full-text screening based on the inclusion criteria. Data were then charted and prepared for data analysis. Of a total of 1217 articles, 12 (0.99%) met the inclusion criteria. These studies predominantly originated from high-income countries. The AI systems were used in health care, self-care, and hybrid care contexts, addressing a variety of mental health problems. Three types of AI systems were identified in terms of decision-making support: diagnostic and predictive AI, treatment selection AI, and self-help AI. The dynamics of the type of end-user interaction and system design were diverse in complexity for the integration and use of the AI systems to support decision-making in care processes. The evaluation of the use of AI systems highlighted several challenges impacting the implementation and functionality of the AI systems in care processes, including factors affecting accuracy, increase of demand, trustworthiness, patient-physician communication, and engagement with the AI systems. The design, development, and implementation of AI systems to support decision-making present substantial challenges for the sustainable use of this technology in care processes. The empirical evidence shows that the evaluation of the use of AI systems in mental health is still in its early stages, with need for more empirically focused research on real-world use. The key aspects requiring further investigation include the evaluation of the use of AI-supported decision-making from human-AI interaction and human-computer interaction perspectives, longitudinal implementation studies of AI systems in mental health to assess the use, and the integration of shared decision-making in AI systems.

HAPLN1 maintains aggregation and the binding activity of extracellular matrix (ECM) molecules (such as hyaluronic acid and proteoglycan) to stabilize the macromolecular structure of the ECM. An increase in HAPLN1 expression is observed in a few types of musculoskeletal diseases including rheumatoid arthritis (RA); however, its functions are obscure. This study examined the role of HAPLN1 in determining the viability, proliferation, mobility, and pro-inflammatory phenotype of RA- fibroblast-like synoviocytes (RA-FLSs) by using small interfering RNA (siHAPLN1), over-expression vector (HAPLN1 OE ), and a recombinant HAPLN1 (rHAPLN1) protein. HAPLN1 was found to promote proliferation but inhibit RA-FLS migration. Metformin, an AMPK activator, was previously found by us to be able to inhibit FLS activation but promote HAPLN1 secretion. In this study, we confirmed the up-regulation of HAPLN1 in RA patients, and found the positive relationship between HAPLN1 expression and the AMPK level. Treatment with either si-HAPLN1 or HAPLN1 OE down-regulated the expression of AMPK-ɑ gene, although up-regulation of the level of p-AMPK-ɑ was observed in RA-FLSs. si-HAPLN1 down-regulated the expression of proinflammatory factors like TNF-ɑ, MMPs, and IL-6, while HAPLN1 OE up-regulated their levels. qPCR assay indicated that the levels of TGF-β, ACAN, fibronectin, collagen II, and Ki-67 were down-regulated upon si-HAPLN1 treatment, while HAPLN1 OE treatment led to up-regulation of ACAN and Ki-67 and down-regulation of cyclin-D1. Proteomics of si-HAPLN1, rHAPLN1, and mRNA-Seq analysis of rHAPLN1 confirmed the functions of HAPLN1 in the activation of inflammation, proliferation, cell adhesion, and strengthening of ECM functions. Our results for the first time demonstrate the function of HAPLN1 in promoting the proliferation and pro-inflammatory phenotype of RA-FLSs, thereby contributing to RA pathogenesis. Future in-depth studies are required for better understanding the role of HAPLN1 in RA.

Sex-bias is more obvious in several autoimmune disorders, but not in psoriasis. However, estrogen levels fluctuate during puberty, menstrual cycle, pregnancy, and menopause, which are related to variations in psoriasis symptoms observed in female patients. Estrogen has disease promoting or ameliorating functions based on the type of immune responses and tissues involved. To investigate the effects of estrogen on psoriasis, at first, we developed an innate immunity dependent mannan-induced psoriasis model, which showed a clear female preponderance in disease severity in several mouse strains. Next, we investigated the effects of endogenous and exogenous estrogen using ovariectomy and sham operated mice. 17-β-estradiol (E2) alone promoted the skin inflammation and it also significantly enhanced mannan-induced skin inflammation. We also observed a prominent estrogen receptor-β (ER-β) expression in the skin samples, especially on keratinocytes. Subsequently, we confirmed the effects of E2 on psoriasis using ER-β antagonist (PHTPP) and agonist (DPN). In addition, estrogen was found to affect the expression of certain genes ( vgll3 and cebpb ), microRNAs (miR146a and miR21), and immune cells (DCs and γδ T cells) as well as chemokines (CCL5 and CXCL10) and cytokines (TNF-α, IL-6, IL-22, IL-23, and IL-17 family), which promoted the skin inflammation. Thus, we demonstrate a pathogenic role for 17-β-estradiol in promoting skin inflammation, which should be considered while designing new treatment strategies for psoriasis patients.

The integration of artificial intelligence (AI) systems for automating medical history taking and triage can significantly enhance patient flow in health care systems. Despite the promising performance of numerous AI studies, only a limited number of these systems have been successfully integrated into routine health care practice. To elucidate how AI systems can create value in this context, it is crucial to identify the current state of knowledge, including the readiness of these systems, the facilitators of and barriers to their implementation, and the perspectives of various stakeholders involved in their development and deployment. This study aims to map and summarize empirical research on AI systems designed for automating medical history taking and triage in health care settings. The study was conducted following the framework proposed by Arksey and O'Malley and adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A comprehensive search of 5 databases-PubMed, CINAHL, PsycINFO, Scopus, and Web of Science-was performed. A detailed protocol was established before the review to ensure methodological rigor. A total of 1248 research publications were identified and screened. Of these, 86 (6.89%) met the eligibility criteria. Notably, most (n=63, 73%) studies were published between 2020 and 2022, with a significant concentration on emergency care (n=32, 37%). Other clinical contexts included radiology (n=12, 14%) and primary care (n=6, 7%). Many (n=15, 17%) studies did not specify a clinical context. Most (n=31, 36%) studies used retrospective designs, while others (n=34, 40%) did not specify their methodologies. The predominant type of AI system identified was the hybrid model (n=68, 79%), with forecasting (n=40, 47%) and recognition (n=36, 42%) being the most common tasks performed. While most (n=70, 81%) studies included patient populations, only 1 (1%) study investigated patients' views on AI-based medical history taking and triage, and 2 (2%) studies considered health care professionals' perspectives. Furthermore, only 6 (7%) studies validated or demonstrated AI systems in relevant clinical settings through real-time model testing, workflow implementation, clinical outcome evaluation, or integration into practice. Most (n=76, 88%) studies were concerned with the prototyping, development, or validation of AI systems. In total, 4 (5%) studies were reviews of several empirical studies conducted in different clinical settings. The facilitators and barriers to AI system implementation were categorized into 4 themes: technical aspects, contextual and cultural considerations, end-user engagement, and evaluation processes. This review highlights current trends, stakeholder perspectives, stages of innovation development, and key influencing factors related to implementing AI systems in health care. The identified literature gaps regarding stakeholder perspectives and the limited research on AI systems for automating medical history taking and triage indicate significant opportunities for further investigation and development in this evolving field.

Background eHealth applications are considered a technological fix that can potentially address some of the grand challenges in healthcare, including burnout among healthcare professionals, the growing burden of patients with chronic conditions, and retaining and recruiting healthcare professionals. However, as the deployment of eHealth applications in healthcare is relatively novel, there is a lack of research on how they affect the work environment of healthcare professionals. This study explores how work evolves—particularly for nurses—during the utilisation of three eHealth applications. Methods The study is a qualitative case study with an interpretive approach. The utilisation of three different eHealth applications was studied. Seventy-five healthcare professionals were interviewed, most of whom were nurses ( n  = 47). Interviews were transcribed verbatim and qualitative content analysis was used to analyse the text. Results Three main themes were identified: work that is ignored and overlooked; actions needed to complete visible work ; and more sedentary work activities . The findings suggest that work surrounding the utilisation of eHealth applications in care practices is mostly performed by nurses. While the promise of more efficient workflows resulting from healthcare’s digital transformation may be realised to different degrees, the utilisation of eHealth applications creates additional invisible labour for nurses. Conclusion We identified through our analysis that the extra work created by eHealth applications is invisible at the organisational level. Most of the invisible labour was performed by nurses, who were engaged in utilising the eHealth applications. This needs to be recognised when implementing eHealth applications in care practices.

Several countries have made unremitting efforts to develop an optimal vaccine in the fight against coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With the increasing occurrence of SARS-CoV-2 variants, current vaccines show decreased neutralizing activities, especially towards the Omicron variant. In this context, adding appropriate adjuvants to COVID-19 vaccines can substantially reduce the number of required doses and improve efficacy or cross-neutralizing protection. We mainly focus on research progress and achievements associated with adjuvanted COVID-19 subunit and inactivated vaccines. We further compare the advantages and disadvantages of different adjuvant formulations in order to provide a scientific reference for designing an effective strategy for future vaccine development.

Background Help-seeking for mental health problems is a complex process that involves handling both personal challenges and dealing with the organizational structure of the healthcare system. The healthcare system is siloed and fragmented, but it is unclear how the challenges are experienced by the young adults and what their healthcare journeys look like. Therefore, the aim of this study was to explore experiences of young adults’ healthcare journeys in the context of help-seeking for common mental health problems. Methods In total, 25 young adults (16 women and 9 men) from a student healthcare centre at a Swedish university seeking help for common mental health problems, such as anxiety and depression, were interviewed. A qualitative thematic analysis with an inductive approach was done, and results were abstracted and presented in terms of journey-related metaphors. Results The healthcare journeys of young adults were described as Taxi Riding, Commuting, Sightseeing, and Backpacking. Taxi riding and Commuting are defined by going in a straightforward and smooth way in the healthcare system, without major obstacles to care. In contrast, Sightseeing and Backpacking are characterized by more diffuse and negative experiences, where the young adults are not satisfied with the help received from healthcare providers. Help-seeking is not conformant with the design of the healthcare system but steered by a range of factors, including individual experiences and young adults’ agency, the available resources at the various healthcare providers, and interaction with healthcare professionals. Conclusions Young adults’ healthcare journeys in the context of help-seeking for common mental health problems are related to individual, relational, and organizational factors. Some journeys run smoothly, epitomizing a functioning healthcare system that accommodates a rational help-seeker. Other journeys depict a rigid healthcare system, where the success and nature of the journey primarily depend on individual agency and on not becoming discouraged by obstacles. There is a need for more knowledge on how to support young adults’ mental health help-seeking. However, we also need more insights into how the healthcare system can become more receptive and accommodating toward the needs of young adults with common mental health problems.

Study Objectives:To examine differences in sample characteristics and longitudinal sleep outcomes according to weighted blanket (WB) adherence.Methods:Children with attention-deficit/hyperactivity disorder (n = 94), mean age 9.0 (standard deviation 2.2, range 6–14) participated in a 16-week sleep intervention with WBs. Children were classified as WB adherent (use of WB ≥ 4 nights/wk) or nonadherent (use of WB ≤ 3 nights/wk). Changes in objectively measured sleep by actigraphy, parent-reported sleep problems (Children’s Sleep Habits Questionnaire) and child-reported Insomnia Severity Index were evaluated according to adherence with mixed effect models. Sex, age, and attention-deficit/hyperactivity disorder subtype were examined as potential moderators.Results:Children adherent to WBs (48/94) showed an early response in sleep outcomes and an acceptance of the WB after 4 weeks of use as well as a decrease in parent-reported (Children’s Sleep Habits Questionnaire) (–5.73, P = .000) and child-reported (Insomnia Severity Index) (–4.29, P = .005) sleep problems after 16 weeks. The improvement in sleep was larger among WB adherent vs nonadherent (between-group difference: Children’s Sleep Habits Questionnaire: –2.09, P = .038; Insomnia Severity Index: –2.58, P = .007). Total sleep time was stable for children adherent to WB but decreased for nonadherent (between-group difference: +16.90, P = .019).Conclusions:An early response in sleep and acceptance of the WB predicted later adherence to WBs. Improvements in sleep were more likely among WB adherents vs nonadherents. Children with attention-deficit/hyperactivity disorder may thus benefit from using WBs to handle their sleep problems.Citation:Lönn M, Svedberg P, Nygren J, Jarbin H, Aili K, Larsson I. Changed sleep according to weighted blanket adherence in a 16-week sleep intervention among children with attention-deficit/hyperactivity disorder. J Clin Sleep Med. 2024;20(9):1455–1466.