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Amidst rapidly ageing European populations there is a growing need to develop pension systems that help support well-being in later life. Using German reunification as a unique case study of large and mostly exogenous pension increases in East but not in West Germany, we examine whether increases in pension income are associated with changes in health and well-being among pensioners in East vs West Germany. Our data come from the nationally representative German Socio-Economic Panel survey covering years 1984-2005 for West Germany, with data for East available from 1992. The analysis sample includes a cohort of retired individuals at the time of reunification, resulting in 15 991 observations. We use linear fixed effects models to quantify the within-person association between pensions and health and life satisfaction in East Germany - where pensions became more generous - vs West Germany where pensions remained stable. We find a small but robust conditional association of increasing pension income on life satisfaction, such that for every 10% increase in pension income, life satisfaction increases by 0.03 points on a 0-10 scale (95% CI 0.01 to 0.06) in East versus West Germany, with effects stronger for men. We find no effects on health satisfaction. Our results are robust to accounting for health-related attrition. Although the association between life satisfaction and increased pension income is modest, the substantial pension increases observed during the convergence period imply that the cumulative impact over time may correspond to a meaningful improvement in well-being at the population level.

Salvage biomass harvesting is increasingly recognized as a strategic approach to reduce emissions associated with spruce budworm (SBW) damage in eastern Canada by diverting biomass that would otherwise decompose in forests toward bioenergy and harvested wood products (HWP). However, its effects on overall system carbon dynamics remain poorly understood. We assessed how SBW-related salvage harvesting influences long-term (2010-2110) forest-sector carbon dynamics in the Côte-Nord region of Québec, Canada, considering net sector production (NSP), which is the sum of net carbon sequestration by ecosystem and HWP less operational emissions (harvesting, forwarding, transport, sawing, chipping, pelletization). This study evaluates the biophysical carbon balance and sector-level carbon dynamics of salvage harvesting, without accounting for substitution. We used the LANDIS-II model, its Forest Carbon Succession extension, and additional modules to simulate wind, fire, and SBW disturbances under multiple climate pathways. A no-additional-salvage reference (S0) was compared to increasing salvage areas (S1-S3) of dead snag stems. All additional salvaged biomass was directed to one of three pathways: bioenergy, sawnwood with a 35-year half-life, or sawnwood with a 60 year half-life. Salvage harvesting had minimal impact on ecosystem carbon pools but slightly reduced heterotrophic respiration by removing decomposing deadwood. The net carbon outcome depended strongly on product pathway: directing salvaged biomass to bioenergy resulted in immediate emissions and negative balance relative to the reference scenario; short-lived sawnwood (35-year half-life) increased HWP storage but accumulated decomposition emissions over time, also resulting in negative carbon balance; only long-lived sawnwood (60-year half-life) reduced total emissions sufficiently to approach/exceed carbon neutrality-and only under warmer climate scenarios (RCP4.5-RCP8.5) by late century. Operational emissions from harvesting, transport, and processing remained small relative to ecosystem and HWP carbon fluxes. Within the modeled system boundary (excluding substitution effects), the forest-sector carbon outcome of salvage harvesting depended strongly on target area, product type and their longevity; however, any benefits for reduced carbon emissions are likely to come from substitution effects unless warming dramatically increases decomposition and HWP have above-expected half-lives. The online version contains supplementary material available at 10.1007/s10980-026-02376-1.

Across many high-income countries, the ownership structures of residential long-term care (LTC) providers have come under increased scrutiny. While chain ownership is commonly recognised as a structural determinant of care quality, evidence on its effects remains mixed. Australia's highly concentrated, nationally regulated LTC system provides a useful setting to test whether effects vary by chain characteristics. To examine the association between chain characteristics of for-profit and non-profit residential long-term care (LTC) homes, including chain affiliation, chain size and geographic clustering on the quality of care delivered in those homes. A cross-sectional retrospective panel design was used, using de-identified datasets obtained under the legal authority of the Royal Commission into Aged Care Quality and Safety. Sample comprised 2596 unique Australian residential LTC homes between 2015 and 2019. Multivariable analyses estimated associations between chain characteristics and the rates of potentially preventable hospitalisations and complaints lodged with the national regulator, adjusting for home-specific characteristics. Multivariable analysis shows that homes operated by larger chains had higher rates of hospitalisations and complaints, controlling for other covariates influencing these outcomes. However, chain affiliated homes operating in a geographical clustering within Aged Care Planning Region had lower rates of preventable hospitalisations and complaints. Chain affiliation added limited additional explanatory power beyond other chain characteristics. These findings suggest that commonly used binary measures of chain affiliation may mask meaningful heterogeneity across providers. Policy and regulatory approaches aimed at monitoring market consolidation may benefit from considering more granular chain characteristics, particularly chain size and geographic clustering, rather than affiliation alone.

Agriculture is widely acknowledged as a dangerous industry for workers. Agriculture workers are also older compared with other industries, which further increases the risks of occupational injuries. In recent years, a wide range of new technologies has come into use in the agriculture industry, so researchers need to consider how these technological advances can be used to improve occupational safety and health outcomes for agriculture workers. This scoping review aims to summarize the current evidence for the use of robotics and autonomous technologies in improving occupational safety and health outcomes for agricultural workers. A systematic literature search was performed on June 4 and 14, 2024, across the following databases - MEDLINE, Embase, IEEE, CAB Abstracts and Google Scholar. Two independent reviewers screened for eligibility in Covidence. Eligibility criteria included studies in the English language from 2015 onwards reporting on robotics or technologies for health and safety in agriculture. Literature reviews were excluded. The PRISMA Scoping Review statement guided the reporting. The search resulted in 845 studies. Of the 26 included studies, 13 studied robots or automated machines, four studied exoskeletons, three studied wearable sensors, four investigated the use of artificial intelligence and five studied other autonomous technologies. Automated milking systems (AMS) were the most studied autonomous technology in the review, with three studies finding farmers perceive a reduction in physically demanding labor when using AMS, and another finding a correlation between using AMS and improved mental health. Most included papers focused on occupational safety and physical health. Of the included studies, 12 were product development studies, meaning there is need for primary evidence studies (e.g. randomized trials, observational studies). There was also only one study that focused on mental health and improving accessibility for farmers with mobility impairments, so these are directions for future research. The results of this review show that there is some evidence for the use of a variety of autonomous technologies in improving farmer health and safety, although more work is required, especially regarding mental health and farmers with mobility impairments.

Asthma is a complex inflammatory disease where oxidative stress and immune metabolic dysfunction coexist. As master regulators of this interface, macroautophagy/autophagy and nitric oxide (NO) signaling govern immune polarization, metabolic flux, and mitochondrial integrity. While NO functions as a redox messenger that affects both protective and pathogenic outcomes, autophagy maintains cellular homeostasis through coordinated degradation and recycling processes. These pathways come together to form a regulatory triad that controls T-cell differentiation, macrophage activation, and airway remodeling. Here, we outline the ways in which autophagy-NO interactions alter immune metabolism to fuel inflammation in asthma and investigate their potential as a combined therapeutic target. We offer a systems-level perspective of immune reprogramming by mapping important molecular nodes, including those involving MTOR, AMPK, BECN1, NOS2/iNOS, and NOS3/eNOS, and connecting them to metabolic checkpoints. Finally, as a potential avenue that can offer improved efficacy and durability in the management of asthma, we highlight translational strategies that combine autophagy modulators, NO donors or inhibitors, and metabolic regulators.Abbreviations: ASM: airway smooth muscle; COPD: chronic obstructive pulmonary disease; DC: dendritic cell; FAO: fatty acid oxidation; HIF1A/HIF-1α: hypoxia inducible factor 1 subunit alpha; L-NIL: L-N6-(1-Iminoethyl)lysine (selective NOS2 inhibitor); M1 and M2: macrophage pro-inflammatory and anti-inflammatory polarization states; NO: nitric oxide; NOS2/iNOS: nitric oxide synthase 2; NOS3/eNOS: nitric oxide synthase 3; OXPHOS: oxidative phosphorylation; PPARGC1A/PGC-1α: PPARG coactivator 1 alpha; ROS: reactive oxygen species; TGFB1/TGF-β1: transforming growth factor beta 1; Treg: regulatory T cell; TSLP: thymic stromal lymphopoietin.

Community health workers (CHW) programs are effective at reducing hospitalizations, improving mental health, and supporting health-promoting behaviors. Research suggests awareness and use of CHW services remain uneven across communities nationally. This study describes awareness, use and satisfaction with a city-wide CHW program among racial/ethnic minority individuals in Philadelphia. Data come from a web-based health survey conducted February-March 2024. Participants reported sociodemographic characteristics, health conditions, barriers to social resources, and awareness and use of the city's CHW program. Among 522 participants, the majority were Black (55.4%), women (70.1%), with mean age 39 years. Less than half (44.6%) were aware of the CHW program; of those, 30.5% (13.6% of whole sample) had used it. Among users, 80.3% reported being satisfied with services. In multivariate models, Asian, Hispanic/Latine, privately insured, unemployed, those without barriers to social resources, those with chronic conditions, and West Philadelphia residents had lower odds of awareness. Education, income, and anxiety symptoms were not associated with program awareness. Awareness and use of this CHW program is low to moderate among key populations in Philadelphia. Given variations in awareness, scale-up of CHW programs should include evaluation of program reach, especially to individuals who are not employed and have chronic health conditions.

Simulation is an established evidence-based tool in healthcare. It is widely used for medical education (simulation-based medical education [SBME]), quality improvement, capacity building, audit and feedback, and probing healthcare systems for latent safety threats. Implementation science (IS) currently subsumes these functions under broad taxonomy categories such as "conduct ongoing training" or "cyclical tests of change." This framing fails to capture simulation's distinct mechanisms and its unique potential to close the evidence-to-practice gap. We argue that the time has come for implementation scientists and the healthcare simulation community to come together around a shared question: does simulation, as it is already practiced in healthcare, meet the criteria for a discrete, named implementation strategy within taxonomies such as the Expert Recommendations for Implementing Change (ERIC)? Drawing on the ERIC definition of an implementation strategy as "methods or techniques used to enhance the adoption, implementation, and sustainability of a clinical program or practice," we argue that simulation in healthcare already functions in this role across at least three distinct domains: developing individual and team competencies (SBME); probing healthcare systems for latent hazards and driving quality improvement; and operationalizing evidence-based interventions (EBIs) and clinical decision supports (CDS) under realistic conditions. We specify simulation using established IS frameworks-including the Consolidated Framework for Implementation Research (CFIR), the EPIS framework (Exploration, Preparation, Implementation, Sustainment), and the PRISM (Practical, Robust Implementation and Sustainability Model)-and propose a research agenda at the intersection of these two fields. High-reliability industries including aviation, astronautics, and nuclear power provide useful analogies, but the healthcare simulation literature has developed a robust and distinct evidence base in its own right, exemplified by translational simulation paradigms. Simulation is not simply a training technique. It is a multi-mechanism, multi-level strategy with proven applications in healthcare education, QI, and systems change. Formally recognizing simulation within IS strategy taxonomies would sharpen conceptual clarity, enable more precise evaluation, and open a productive dialogue between two scientific communities whose work is more convergent than current frameworks suggest.

Families of children with neurodisabilities often face significant child behavioral challenges that can affect overall family functioning and well-being. This randomized controlled trial (RCT) used family resilience theory as the investigative framework to examine the impact of an online parenting program designed to address child behavioral challenges. Parents of children with a range of neurodisabilities were randomized into three groups: a self-directed online parenting program, the same program with coaching support, and a control group. Family well-being was measured using the SCORE-15 at baseline, 5, and 10 months. The primary analysis included a per-protocol sample comprising self-directed participants who completed ≥ 8 of 12 modules, coached participants who completed ≥ 8 modules and ≥ 8 coaching sessions, and all control-group participants. Linear mixed models (LMMs) were used to analyze the repeated measures data, followed by standard linear regression models to assess the average intervention effect at each time point. Clinically significant changes were calculated via the reliable change index (RCI). Of the 454 families randomized, 422 participants had at least one available SCORE-15 assessment (self-directed: n = 140; coached: n = 139; control: n = 143). Descriptively, mean SCORE-15 scores remained relatively stable in both intervention groups but increased over time in the control group (lower scores indicate better family functioning). Broader results from the full-sample analysis are reported separately (publication forthcoming). The present analyses focus on the 248 participants who met the per-protocol adherence criteria (self-directed: n = 36; coached: n = 60; control: n = 152). Families that reported higher baseline family well-being were associated with greater adherence to the intervention. Participants in both intervention groups demonstrated significant improvements in family well-being relative to the control group. The coached version of the intervention demonstrated a more robust and persistent impact relative to the self-directed version, particularly with respect to communication-related subscales. Across both versions of the intervention 10%-28% of participants experienced reliable clinical change. The findings from our analysis suggest that both versions of the parenting program led to notable enhancements in aspects of family well-being, with the coached group exhibiting particularly remarkable improvements. Our study highlights that parenting interventions can contribute to the well-being of the entire family system, extending beyond individual child and parent outcomes typically addressed by these programs. This study looked at how effective online parenting programs are for families of children with neurodisabilities like autism and cerebral palsy. Some parents had extra help through coaching, while others did not. These programs taught parents how to handle challenging child behaviors and improve family life. The study found that while both versions of the program had some positive impact, families who received extra coaching seemed to benefit more, especially in how they communicate and solve problems together. Our results suggest that with the right support, families can get better at dealing with the challenges that come with parenting children with these conditions. Our hope is that these findings will help create better programs for families who need extra support.

Compact malaria detectors for microcontrollers are almost always benchmarked on the NIH Malaria dataset with a per-cell random split. This leaks slide identity because the cells come from only about 200 slides and a random split mixes same-slide cells across training and testing. The leakage also distorts architectural conclusions: under a leakage-free slide-disjoint protocol, per-module ablation gains collapse to seed noise and an apparent cross-site robustness variant loses most of its advantage. Headline accuracy falls from 97.1% to 95.6%, a gap that sits within the cross-seed noise, and all eight tested architectures move the same way. The evidence is this unanimous direction, not the size of any single gap. This benchmarking finding is our main contribution. Two results survive. First, MalariaNet, our 21 K-parameter detector, reaches about 95.6% accuracy at 23.5 KB of INT8 weights, with a numerically faithful on-chip forward on an STM32H743 at a 1.2 FPS triage rate. Second, it is among the most interference-robust of the eight networks and the most robust microcontroller-deployable model. Scope is limited to single P. falciparum thin-smear cells. Slide-disjoint evaluation should become standard, and we provide MalariaNet as the first leakage-free, on-device-validated point-of-care malaria reference.

Duchenne muscular dystrophy (DMD) has, for the first time, several mutation-specific drugs in clinical use. Four exon-skipping antisense oligonucleotides and one adeno-associated virus (AAV) micro-dystrophin gene therapy carry US FDA approval, and one nonsense-readthrough agent (ataluren) holds a conditional European authorisation that has since been placed under review; ataluren has never been approved by the FDA. Regulatory approval, however, has not translated cleanly into demonstrable clinical benefit, and in resource-limited settings it has not translated into access at all. We compiled published efficacy estimates for each approved therapy: the EMBARK randomised trial for delandistrogene moxeparvovec (Elevidys), the STRIDE registry for ataluren, and the long-term extension studies of eteplirsen, golodirsen, viltolarsen and casimersen. We read these alongside mutation-spectrum and clinical data from a Kazakh DMD cohort (n = 34), one of the first Central Asian populations to be characterised systematically. Because these sources differ in design, population, endpoint and follow-up, the comparison is indirect and descriptive rather than pooled or statistical. On that basis we assembled three descriptive maps: biological versus clinical efficacy, eligibility versus access, and an order-of-magnitude estimate of cost per year of preserved ambulation. Across the approved therapies, larger biological dystrophin restoration did not correspond to longer preservation of walking. We describe this as the absence of the expected positive association rather than as evidence of a negative one: the comparison rests on a small number of drugs whose values come from heterogeneous studies. We did not-and with these data could not-test it as a formal correlation. Ataluren, with roughly 2% dystrophin restoration, was associated with a longer reported delay in loss of ambulation than Elevidys, which restores 34-51% micro-dystrophin. In the Kazakh cohort, 26.5% of patients were biologically eligible for exon-skipping and 11.8% for ataluren, yet realistic access to any mutation-specific therapy was effectively zero as of April 2026. Our order-of-magnitude cost estimates ran from about $12,000 per additional year of ambulation for standard of care to about $1.3 million for AAV gene therapy, a hundredfold range that did not track clinical effect. We argue that the conventional priority ordering (gene therapy first, exon-skipping second, standard care as background) does not hold up when weighed against patient-relevant outcomes and cost, and may reasonably be inverted for resource-limited systems. This is our interpretation of an indirect comparison, not an evidence-based clinical recommendation. On that reading, the highest-value investments for Central Asia are early molecular diagnosis, universal access to glucocorticoids and specialised physiotherapy, and individual-import pathways for ataluren, while AAV gene therapy is, in our view, a lower near-term priority until its durability and safety data improve.

Artificial intelligence (AI) has become firmly established in radiology, with most current applications relying on task-specific convolutional neural networks (CNNs) such as U-Net architectures for segmentation, detection, and classification. The emergence of foundation models (FMs), however, marks a fundamental paradigm shift. These large-scale, pretrained models are designed to flexibly adapt to a wide range of radiological tasks, often requiring only minimal task-specific fine-tuning. In medical imaging, FMs are typically trained on large, heterogeneous, and multimodal data sets and enable novel applications such as zero-shot segmentation, prompt-based image exploration, and the integration of clinical context information. In parallel with academic developments, the first vendors have begun to incorporate FM-based approaches in commercial radiology products, gradually replacing narrowly specialized model architectures. For clinical practice, this technological shift offers potential benefits, including reduced false-positive findings, automated triage in emergency imaging, and improved detection of complex or atypical pathologies. At the same time, these advances come with increased demands on computational resources and costs, as well as unresolved challenges regarding validation, explainability, and regulatory approval. This review introduces the core principles of foundation models, discusses their technical and economic implications, and illustrates - using clinically relevant examples - how this new generation of models may fundamentally transform radiological image analysis. · Unlike conventional task-specific networks such as U-Net, foundation models can be flexibly applied and adapted to different radiological tasks.. · Foundation models may improve radiological and clinical performance, particularly in complex or data-limited settings.. · Radiologists and industry partners must critically validate foundation-model outputs and ensure their explainable integration into clinical workflows in accordance with regulatory requirements.. · Shahzadi I, Borggrefe J. Fundamentals of Foundation Models in Radiological Image Processing: Opportunities, Limitations, and Clinical Case Studies. Rofo 2026; DOI 10.1055/a-2899-1290. Künstliche Intelligenz (KI) ist in der Radiologie mittlerweile fest etabliert, wobei bislang vor allem aufgabenspezifische Convolutional Neural Networks (CNNs) wie U-Net-Architekturen für Segmentierung, Detektion und Klassifikation eingesetzt werden. Mit dem Aufkommen sogenannter Foundation Models (FMs) vollzieht sich jedoch ein grundlegender Paradigmenwechsel: Diese großskaligen, vortrainierten Modelle sind darauf ausgelegt, sich flexibel an eine Vielzahl radiologischer Aufgaben anzupassen, häufig mit nur minimalem aufgabenspezifischem Fine-Tuning. In der medizinischen Bildgebung basieren FMs typischerweise auf heterogenen, multimodalen Datensätzen und ermöglichen neue Anwendungen wie Zero-Shot-Segmentierung, prompt-basierte Bildexploration und die Integration klinischer Kontextinformationen. Parallel zur akademischen Entwicklung beginnen erste Hersteller, FM-basierte Ansätze in kommerzielle radiologische Produkte zu integrieren und klassische, eng spezialisierte Modellarchitekturen schrittweise abzulösen. Für die klinische Praxis ergeben sich daraus potenzielle Vorteile, etwa in der Reduktion falsch-positiver Befunde, der Automatisierung der Triage in der Notfalldiagnostik oder der verbesserten Detektion komplexer Pathologien. Demgegenüber stehen jedoch erhöhte Anforderungen an Rechenressourcen, Kosten sowie bislang ungelöste Fragen der Validierung, Erklärbarkeit und regulatorischen Einordnung. Diese Übersichtsarbeit stellt die konzeptionellen Grundlagen von Foundation Models vor, diskutiert deren technische und ökonomische Implikationen und illustriert anhand klinisch relevanter Anwendungsbeispiele, wie diese neue Modellgeneration die radiologische Bildanalyse nachhaltig verändern könnte. · Im Gegensatz zu konventionellen aufgabenspezifischen Netzwerken wie U-Net können Foundation Models flexibel für verschiedene radiologische Aufgaben eingesetzt und angepasst werden.. · Foundation Models können die radiologische und klinische Leistungsfähigkeit insbesondere bei komplexen oder datenarmen Fragestellungen verbessern.. · Radiologinnen und Radiologen sowie Industriepartner müssen die Ergebnisse von Foundation Models kritisch validieren und ihre nachvollziehbare Integration in klinische Arbeitsabläufe unter Einhaltung regulatorischer Anforderungen sicherstellen.. · Shahzadi I, Borggrefe J. Fundamentals of Foundation Models in Radiological Image Processing: Opportunities, Limitations, and Clinical Case Studies. Rofo 2026; DOI 10.1055/a-2899-1290.