Hasil untuk "Other systems of medicine"

J. Zinsstag, E. Schelling, D. Waltner-Toews et al.

Faced with complex patterns of global change, the inextricable interconnection of humans, pet animals, livestock and wildlife and their social and ecological environment is evident and requires integrated approaches to human and animal health and their respective social and environmental contexts. The history of integrative thinking of human and animal health is briefly reviewed from early historical times, to the foundation of universities in Europe, up to the beginning of comparative medicine at the end of the 19th century. In the 20th century, Calvin Schwabe coined the concept of “one medicine”. It recognises that there is no difference of paradigm between human and veterinary medicine and both disciplines can contribute to the development of each other. Considering a broader approach to health and well-being of societies, the original concept of “one medicine” was extended to “one health” through practical implementations and careful validations in different settings. Given the global health thinking in recent decades, ecosystem approaches to health have emerged. Based on complex ecological thinking that goes beyond humans and animals, these approaches consider inextricable linkages between ecosystems and health, known as “ecosystem health”. Despite these integrative conceptual and methodological developments, large portions of human and animal health thinking and actions still remain in separate disciplinary silos. Evidence for added value of a coherent application of “one health” compared to separated sectorial thinking is, however, now growing. Integrative thinking is increasingly being considered in academic curricula, clinical practice, ministries of health and livestock/agriculture and international organizations. Challenges remain, focusing around key questions such as how does “one health” evolve and what are the elements of a modern theory of health? The close interdependence of humans and animals in their social and ecological context relates to the concept of “human-environmental systems”, also called “social-ecological systems”. The theory and practice of understanding and managing human activities in the context of social-ecological systems has been well-developed by members of The Resilience Alliance and was used extensively in the Millennium Ecosystem Assessment, including its work on human well-being outcomes. This in turn entails systems theory applied to human and animal health. Examples of successful systems approaches to public health show unexpected results. Analogous to “systems biology” which focuses mostly on the interplay of proteins and molecules at a sub-cellular level, a systemic approach to health in social-ecological systems (HSES) is an inter- and trans-disciplinary study of complex interactions in all health-related fields. HSES moves beyond “one health” and “eco-health”, expecting to identify emerging properties and determinants of health that may arise from a systemic view ranging across scales from molecules to the ecological and socio-cultural context, as well from the comparison with different disease endemicities and health systems structures.

Robert D Truog, Margaret L Campbell, J. Curtis et al.

Richard J. Jones

M. Sporn, A. Roberts, D. Goodman

Joy Wolfram, Motao Zhu, Yong Yang et al.

Jae‐Woong Jeong, J. McCall, G. Shin et al.

Juan P Wachs

The CARE Workshop on Robotics and AI in Medicine, held on December 1, 2025 in Indianapolis, convened leading researchers, clinicians, industry innovators, and federal stakeholders to shape a national vision for advancing robotics and artificial intelligence in healthcare. The event highlighted the accelerating need for coordinated research efforts that bridge engineering innovation with real clinical priorities, emphasizing safety, reliability, and translational readiness with an emphasis on the use of robotics and AI to achieve this readiness goal. Across keynotes, panels, and breakout sessions, participants underscored critical gaps in data availability, standardized evaluation methods, regulatory pathways, and workforce training that hinder the deployment of intelligent robotic systems in surgical, diagnostic, rehabilitative, and assistive contexts. Discussions emphasized the transformative potential of AI enabled robotics to improve precision, reduce provider burden, expand access to specialized care, and enhance patient outcomes particularly in undeserved regions and high risk procedural domains. Special attention was given to austere settings, disaster and relief and military settings. The workshop demonstrated broad consensus on the urgency of establishing a national Center for AI and Robotic Excellence in medicine (CARE). Stakeholders identified priority research thrusts including human robot collaboration, trustworthy autonomy, simulation and digital twins, multi modal sensing, and ethical integration of generative AI into clinical workflows. Participants also articulated the need for high quality datasets, shared test beds, autonomous surgical systems, clinically grounded benchmarks, and sustained interdisciplinary training mechanisms.

Mingxuan Li, Wei Wei, Yin Xu et al.

Extreme events such as earthquakes pose significant threats to integrated electricity-gas distribution systems (IEGDS) by causing widespread damage. Existing restoration approaches typically assume full awareness of damage, which may not be true if monitoring and communication infrastructures are impaired. In such circumstances, field inspection is necessary. This paper presents a novel adaptive restoration framework for IEGDS, considering dynamic damage assessment and repair. The restoration problem is formulated as a partially observable Markov decision process (POMDP), capturing the gradually revealed contingency and the evolving impact of field crew actions. To address the computational challenges of POMDPs in real-time applications, an advanced belief tree search (BTS) algorithm is introduced. This algorithm enables crew members to continuously update their actions based on evolving belief states, leveraging comprehensive simulations to evaluate potential future trajectories and identify optimal inspection and repair strategies. Based on the BTS algorithm, a unified real-time decision-making framework is developed for IEGDS restoration. Case studies on two distinct IEGDS systems demonstrate the effectiveness and scalability of the proposed method. The results indicate that the proposed approach achieves an outage cost comparable to the ideal solution, and reduces the total outage cost by more than 15% compared to strategies based on stochastic programming and heuristic methods.

M. Lionetto, R. Caricato, A. Calisi et al.

Acetylcholinesterase (AChE) is a key enzyme in the nervous system. It terminates nerve impulses by catalysing the hydrolysis of neurotransmitter acetylcholine. As a specific molecular target of organophosphate and carbamate pesticides, acetylcholinesterase activity and its inhibition has been early recognized to be a human biological marker of pesticide poisoning. Measurement of AChE inhibition has been increasingly used in the last two decades as a biomarker of effect on nervous system following exposure to organophosphate and carbamate pesticides in occupational and environmental medicine. The success of this biomarker arises from the fact that it meets a number of characteristics necessary for the successful application of a biological response as biomarker in human biomonitoring: the response is easy to measure, it shows a dose-dependent behavior to pollutant exposure, it is sensitive, and it exhibits a link to health adverse effects. The aim of this work is to review and discuss the recent findings about acetylcholinesterase, including its sensitivity to other pollutants and the expression of different splice variants. These insights open new perspective for the future use of this biomarker in environmental and occupational human health monitoring.

Babita Yadav, BCS Rao, Rajiv Narang et al.

Haolong He, Jingying Zhou, Sihui Cao et al.

Abstract Background Inflammatory bowel disease (IBD) involves pathological mechanical forces transduced by mechanosensitive Piezo1 channels. While electroacupuncture (EA) alleviates IBD injury, its relationship with Piezo1-mediated ferroptosis remains unknown. Methods Dextran sulfate sodium (DSS)-induced IBD mice and mechanically stressed HIEC-6 intestinal epithelial cells received EA or pharmacological modulators. Pathological scoring, transmission electron microscopy (TEM), inflammatory cytokine assays, Western blotting, and immunofluorescence evaluated mitochondrial dynamics and ferroptosis markers to elucidate the Piezo1-ferroptosis axis and EA's regulatory role. Results EA significantly reduced disease activity index (DAI), histopathological scores, colon shortening, and pro-inflammatory cytokines in IBD mice. By inhibiting fission, indicated by a decrease in dynamin-related protein 1 (DRP1), and mitophagy, shown by a reduction in Parkinson protein 2 (PARK2), EA maintained mitochondrial homeostasis. This effect was similar to ferroptosis inhibitor ferrostatin-1 (Fer-1). Moreover, EA lessened RSL3-induced exacerbation of ferroptosis. In vitro, mechanical stress or the Piezo1 agonist Yoda1 induced ferroptosis, which was evident from increased acyl-CoA synthetase Long-chain family member 4 (ACSL4), reactive oxygen species (ROS), malondialdehyde (MDA) and Fe2⁺ levels, while decreased glutathione peroxidase 4 (GPX4), ferritin (FTH) and glutathione (GSH) levels. Critically, EA inhibited Piezo1 activation and counteracted Yoda1-aggravated epithelial ferroptosis in vivo. Conclusion Piezo1-mediated mitochondrial dyshomeostasis critically drives intestinal epithelial ferroptosis in IBD. EA regulates Piezo1 to maintain mitochondrial homeostasis and suppresses ferroptosis, offering a potential therapeutic strategy for IBD. Graphical Abstract

Eric J. Roseen, André Bussières, Rocky Reichman et al.

Abstract Introduction Limited adoption of first line treatments for low back pain (LBP) in primary care settings may contribute to an overreliance on pain medications by primary care providers (PCPs). While chiropractic care typically includes recommended nonpharmacologic approaches (e.g., manual therapy, exercise instruction, advice on self-care), implementation strategies to increase adoption of chiropractic care for LBP in primary care clinics are understudied, particularly in underserved communities. Methods We will use a stepped-wedge cluster randomized controlled pilot trial design to evaluate the feasibility of a multi-level implementation strategy to increase adoption of chiropractic care for LBP in primary care clinics at community health centers. Key barriers and facilitators identified by site champions and other key stakeholders will help us to develop and tailor implementation strategies including educational materials and meetings, developing a network of local chiropractors, and modifying the electronic health record to facilitate referrals. Three primary care clinics will be randomized to receive the implementation strategy first, second, or third over a fourteen-month study period. At our first clinic, we will have a four-month pre-implementation period, a two-month implementation deployment period, and a subsequent eight-month follow-up period. We will stagger the start of our implementation strategy, beginning in a new clinic every two months. We will evaluate the proportion of patients with LBP who receive a referral to chiropractic care in the first 21 days after their index visit with PCP. We will also evaluate adoption of other guideline concordant care (e.g., other nonpharmacologic treatments) and non-guideline concordant care (e.g., opioids, imaging) over the study period. Discussion LBP is currently the leading cause of disability worldwide. While there are several treatment options available for individuals with LBP, patients in underserved populations do not often access recommended nonpharmacologic treatment options such as chiropractic care. The results from this study will inform the development of practical implementation strategies that may improve access to chiropractic care for LBP in the primary care context. Furthermore, results may also inform policy changes needed to expand access to chiropractic care in underserved communities. Clintrials.gov NCT# NCT06104605.

Naomi Zukerman, Naomi Zukerman, Emily Bottone et al.

BackgroundChildren with neurodevelopmental disabilities (NDDs, e.g., cerebral palsy) and their caregivers face lifelong and impactful challenges, particularly during life-transition periods such as adolescence. One's resilience emerges as an essential ability to navigate this vulnerable phase. Resilience is a complex concept that embeds multiple factors on various levels. Little is known about what resilience factors are pivotal in youth with NDDs and their families as they transition into adolescence and how these are addressed as part of existing targeted interventions.ObjectivesThis review explored the concept of resilience in youth with NDDs and their families. Specific aims included describing salient resilience factors in adolescents with NDDs and their families and to describe how resilience is addressed as part of targeted interventions.MethodsUsing the Arskey and O'Malley framework, six steps were undertaken, including a comprehensive literature search (n = 5 databases), transparent study selection, detailed data extraction with a coding scheme (n = 46 factors), results' collating with numerical and inductive content analysis, and consultation with three key stakeholders.ResultsThe study screened 1,191 publications, selecting fifty-eight (n = 58; n = 52 observational and n = 6 intervention) studies. Findings revealed that resilience in this context is closely linked to more than forty factors across four levels (individual; family; school/peers; and community). Pivotal factors include social and emotional competence, optimism, and family/peer relationships. While existing interventions targeting resilience show promising results, few programs are available and generalizable to different NDDs. Stakeholders highlighted the importance of addressing resilience factors that are not targeted in existing interventions: caregivers' self-efficacy and self-esteem, as well as youth's and caregiver's confidence. Preferences for and advantages of online delivery for support programs and individual/group features also emerged.ConclusionThe review emphasizes the need for a holistic approach to support youth with NDDs and their families during adolescence transition. To enhance their resilience, recognizing caregivers' roles, customizing interventions, and exploring new implementation formats are avenues that align with the current evidence and opportunities for practical development in this field.

Anubhuti Sood, Deepika Mishra, Varun Surya

The last decade has been marked by several transformative works based on Artificial Intelligence (AI), machine learning and/or deep learning models in the healthcare ecosystem. As the world gears to adapt itself to this technology, the low- and middle-income countries face significant challenges in this field. The integration of AI with the health ecosystem could improve patient services, reduce clinician’s load, and enable mass remote screening for noncommunicable diseases. The paradigm shift presents both unique learning and employment opportunities provided that the healthcare personnel are primed for a major skill overhaul, collaborative efforts and challenge-based learning.

Gabriel C. M. da Silva, Victor F. Monteiro, Diego A. Sousa et al.

As the number of user equipments increases in fifth generation (5G) and beyond, it is desired to densify the cellular network with auxiliary nodes assisting the base stations. Examples of these nodes are integrated access and backhaul (IAB) nodes, network-controlled repeaters (NCRs) and reconfigurable intelligent surfaces (RISs). In this context, this work presents a system level overview of these three nodes. Moreover, this work evaluates through simulations the impact of network planning aiming at enhancing the performance of a network used to cover an outdoor sport event. We show that, in the considered scenario, in general, IAB nodes provide an improved signal to interference-plus-noise ratio and throughput, compared to NCRs and RISs. However, there are situations where NCR outperforms IAB due to higher level of interference caused by the latter. Finally, we show that the deployment of these nodes in unmanned aerial vehicles (UAVs) also achieves performance gains due to their aerial mobility. However, UAV constraints related to aerial deployment may prevent these nodes from reaching results as good as the ones achieved by their stationary deployment.

Nima Mirzaei Alavijeh, Rahmat Khezri, Mohammadreza Mazidi et al.

This paper develops a novel mixed-integer linear programming (MILP) model for optimal participation of battery energy storage systems (BESSs) in the Swedish frequency containment reserve (FCR) markets. The developed model aims to maximize the battery owner's potential profit by considering battery degradation and participation in multiple FCR markets, i.e., FCR in normal operation (FCR-N), and FCR in disturbances (FCR-D) for up- and down-regulations. Accordingly, a precise formulation of a detailed battery degradation model and adherence to the technical requirements of the Swedish FCR markets are incorporated into the developed model. To achieve more practical results, simulations are conducted based on one minute time step realistic data for the whole year 2022. The results show a potential profit of 708 thousand Euros for a 1MW/1MWh BESS by participating in multi-FCR market. Analyzing the impact of considering degradation in the optimization problem has shown that the annual battery aging cost could decrease by 5%-29% without a significant effect on profit. The proposed model can be practically used by flexibility asset owners to achieve profitable and sustainable operation strategies that reduce battery degradation.

Christine P Lee

My research centers on the development of context-adaptive AI systems to improve end-user adoption through the integration of technical methods. I deploy these AI systems across various interaction modalities, including user interfaces and embodied agents like robots, to expand their practical applicability. My research unfolds in three key stages: design, development, and deployment. In the design phase, user-centered approaches were used to understand user experiences with AI systems and create design tools for user participation in crafting AI explanations. In the ongoing development stage, a safety-guaranteed AI system for a robot agent was created to automatically provide adaptive solutions and explanations for unforeseen scenarios. The next steps will involve the implementation and evaluation of context-adaptive AI systems in various interaction forms. I seek to prioritize human needs in technology development, creating AI systems that tangibly benefit end-users in real-world applications and enhance interaction experiences.

Emmanouil Nikolakakis, Joann Ching, Emmanouil Karystinaios et al.

Music therapy has been shown in recent years to provide multiple health benefits related to emotional wellness. In turn, maintaining a healthy emotional state has proven to be effective for patients undergoing treatment, such as Parkinson's patients or patients suffering from stress and anxiety. We propose fine-tuning MusicGen, a music-generating transformer model, to create short musical clips that assist patients in transitioning from negative to desired emotional states. Using low-rank decomposition fine-tuning on the MTG-Jamendo Dataset with emotion tags, we generate 30-second clips that adhere to the iso principle, guiding patients through intermediate states in the valence-arousal circumplex. The generated music is evaluated using a music emotion recognition model to ensure alignment with intended emotions. By concatenating these clips, we produce a 15-minute "music medicine" resembling a music therapy session. Our approach is the first model to leverage Language Models to generate music medicine. Ultimately, the output is intended to be used as a temporary relief between music therapy sessions with a board-certified therapist.

Xinyi Li, Sheng Lin, Yi Lin et al.

Objective: To explore the mechanism of Xi Shao Formula (XSF) in treating neuropathic pain (NP) through metabolomics and network pharmacology. Methods: A chronic compression injury (CCI) of the sciatic nerve model was established to simulate NP; following CCI induction, animals were gavaged with normal saline, XSF, or the positive control drug pregabalin for 21 days. Serum metabolomics methods and enrichment analysis were employed to identify significant serum metabolites and metabolic pathways influenced by XSF. Through network pharmacology and liquid chromatograph with mass spectrometer analyses, the active compounds and targets of XSF for treating NP were analyzed. Additionally, the serum metabolomics and network pharmacology analysis results were integrated, drawing network diagrams illustrating the relationships between “target, metabolic pathway, and metabolite” using Cytoscape 3.9.1. The findings were further validated through molecular docking. Results: Based on metabolomics, 11 differential endogenous metabolites were identified as potential biomarkers related to XSF for treating NP; functional enrichment analysis revealed eight metabolic pathways in XSF for treating NP. Based on integrated metabolomics and network pharmacology, the “monoamine oxidase A (MAOA)/MAOB/tyrosinase (TYR)–tyrosine metabolism pathway–gentisic acid” emerged as a significant network pathway in XSF for treating NP. The molecular docking results revealed high affinity and stable interaction between the active components of XSF and their respective target, namely MAOA, MAOB, and TYR (binding energy < −5 kcal/mol). Conclusion: Our findings supported and enhanced our current understanding of the therapeutic effects of XSF on NP, providing a scientific basis for the clinical application and mechanism research of XSF.

Sanjeev Sharma