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Introduction Cluster analysis has previously revealed five reproducible subgroups of diabetes, differing in risks of diabetic complications. We aimed to examine the clusters’ predictive ability for vascular complications as compared with established risk factors in a general adult diabetes population.Research design and methods Participants from the second (HUNT2, 1995–1997) and third (HUNT3, 2006–2008) surveys of the Norwegian population-based Trøndelag Health Study (HUNT Study) with adult-onset diabetes were included (n=1899). To identify diabetes subgroups, we used the same variables (age at diagnosis, body mass index, HbA1c, homeostasis model assessment estimates of beta cell function and insulin resistance, and glutamic acid decarboxylase antibodies) and the same data-driven clustering technique as in previous studies. We used Cox proportional hazards models to investigate associations between clusters and risks of vascular complications and mortality. We estimated the C-index and R2 to compare predictive abilities of the clusters to those of established risk factors as continuous variables. All models included adjustment for age, sex, diabetes duration and time of inclusion.Results We reproduced five subgroups with similar key characteristics as identified in previous studies. During median follow-up of 9–13 years (differing between outcomes), the clusters were associated with different risks of vascular complications and all-cause mortality. However, in prediction models, individual established risk factors were at least as good predictors as cluster assignment for all outcomes. For example, for retinopathy, the C-index for the model including clusters (0.65 (95% CI 0.63 to 0.68)) was similar to that of HbA1c (0.65 (95% CI 0.63 to 0.68)) or fasting C-peptide (0.66 (95% CI 0.63 to 0.68)) alone. For chronic kidney disease, the C-index for clusters (0.74 (95% CI 0.72 to 0.76)) was similar to that of triglyceride/high-density lipoprotein ratio (0.74 (95% CI 0.71 to 0.76)) or fasting C-peptide (0.74 (95% CI 0.72 to 0.76)), and baseline estimated glomerular filtration rate yielded a C-index of 0.76 (95% CI 0.74 to 0.78).Conclusions Cluster assignment did not provide better prediction of vascular complications or all-cause mortality compared with established risk factors.

Introduction While cognitive-behavioural therapy (CBT) for obsessive-compulsive disorder (OCD) and anxiety disorders (ADs) has been proven to be effective and is commonly recommended, a considerable proportion of patients remain symptomatic, do not respond to treatment or discontinue it. Thus, augmentation strategies aimed at enhancing CBT outcomes are essential to reduce the burden of OCD and ADs on patients and society. Various augmentation strategies for CBT in OCD and ADs have been investigated, yet it remains unclear if they show robust beneficial effects beyond first-line CBT. With this systematic review and meta-analysis, we will provide an overview and critically assess the efficacy of non-pharmacological augmentation strategies in addition to first-line CBT treatment for symptom reduction, response rates and dropout rates in individuals with OCD or ADs.Methods and analysis We will screen PubMed, Embase, PsycArticles, PsycInfo, CINAHL, PSYNDEX and Cochrane Register of Controlled Trials without restrictions on publication dates or languages. Additionally, forward, and backward searches of included studies and systematic reviews will be conducted. Two reviewers will independently screen the studies, extract data and assess the methodological quality of the studies. We will exclusively include randomised controlled trials. The primary outcomes will be symptom severity and response rates. Dropout rates will serve as a secondary outcome. Moreover, we will provide a narrative review of the results. We will use subgroup and meta-regression analyses to identify potential moderators and sources of between-study heterogeneity. We will use the Grading of Recommendations Assessment, Development and Evaluation system to assess the overall quality of evidence.Ethics and dissemination Ethical approval is not required. Results will be published in a peer-reviewed journal.PROSPERO registration number CRD42024561027.

Study region: Eight unregulated basins in the headwaters of five mid-latitude mountain ranges in Central Europe, including the Šumava Mts. (Vydra, Blanice), Krkonoše (Mumlava, Úpa), Orlické Mts. (Zdobnice), Jeseníky Mts. (Branná), and Beskydy Mts. (Čeladenka, Vsetínská Bečva). Study focus: This study examines the impacts of climate warming and forest disturbances on hydrological alterations in montane headwater basins. Using the MIKE SHE distributed hydrological model, scenario-based simulations assessed changes in runoff seasonality, evapotranspiration, streamflow, and variability. Hydrological alteration indicators were applied to disentangle the contributions of these drivers and their interactions under varying environmental conditions. New hydrological insights for the region: Climate warming is the primary driver of hydrological change, causing shifts in runoff seasonality, increased evapotranspiration, and reduced streamflow. Forest disturbances amplify these effects during dry conditions, intensifying runoff variability, increasing low-flow frequency, and modifying peak flows. Regional differences show greater sensitivity in steeper eastern basins due to limited snow accumulation and higher runoff variability. This study highlights the interconnected impacts of climate warming and forest disturbances, with warming driving systemic shifts and disturbances acting as amplifiers in extreme conditions. The findings provide a framework for disentangling the effects of climate and land-cover changes on hydrology, offering insights for managing sensitive montane ecosystems and water resources under changing environmental conditions.

This paper investigates the response of mid-latitude montane peatlands to climate warming, focusing on changes occurring in a montane peat bog during a drought period. Unmanned Aerial Systems (UAS) equipped with multispectral and thermal sensors were used for high-resolution monitoring to analyze qualitative changes within the peat bog and their spatial distribution. The study was conducted in the Rokytka mountain peat bog in Šumava National Park, Czech Republic, which is one of the largest mountain peat bog complexes in Central Europe. Monitoring took place during the 2019 vegetation season, coinciding with the peak of the 2015–2019 drought. The recurrent UAS imaging campaigns were complemented by continuous hydrological and hydropedological monitoring and in-situ calibration measurements. The findings revealed diverging responses of montane peatlands to climate change across different functional zones of the peat bog. UAS thermal mapping identified distinct land surface temperature variations across various vegetation categories under different conditions. Notably, ponds and waterlogged areas displayed a stabilizing effect on land surface temperature variability, though they exhibited different absolute temperatures. In contrast, shallow waterlogged areas exhibited surface temperatures akin to dry open peat areas. Multispectral UAS monitoring demonstrated significant transitions among the peat bog zones in response to heat and drought propagation. The most pronounced changes occurred in shallow waterlogged areas, which shrank notably from 22.8% to 4.5%, while bare peat expanded from 26.8% to 45.5% during the 2019 drought season. High-resolution thermal and multispectral monitoring has revealed the scope and magnitude of the intra-peatland responses to drought and heat waves and serves as a sensible indicator of environmental changes of peatlands. It has disclosed a large cumulative effect of change in an environment composed of highly heterogeneous and subtle structures. The results highlighted the effectiveness of UAS monitoring in understanding the extent of change in montane peatlands as a fragile environment exposed to the effects of climate change.

Objective: To investigate whether high-intensity lower extremity constraint-induced movement therapy can improve balance, leg strength, and dual-task ability. Design: A longitudinal cohort study in a real-world outpatient clinic. Patients: 147 community-dwelling participants in the subacute and chronic poststroke phases. Methods: Participants received lower extremity constraint-induced movement therapy for 6 hours/day during 2 consecutive weeks, including balance, strength, and functional training. The Berg Balance Scale (BBS), Single-Leg-Stance (SLS) bilaterally, one Repetition Maximum (1RM) in a leg press, symmetry of leg strength (Diff-1RM), Timed Up and Go (TUG), and the TUG Manual test were assessed before, after, and 3 months after lower extremity constraint-induced movement therapy. Results: Compared with preintervention data, statistically significant improvements after lower extremity constraint-induced movement therapy (p

Helena Backman,1,* Laxmi Bhatta,2,3,* Linnea Hedman,1 Ben Brumpton,2 Iida Vähätalo,4,5 Paul G Lassmann-Klee,6 Bright I Nwaru,7 Linda Ekerljung,7 Steinar Krokstad,8,9 Sigrid Anna Aalberg Vikjord,2 Anne Lindberg,10 Hannu Kankaanranta,7 Eva Rönmark,1,* Arnulf Langhammer8,* 1Section of Sustainable Health/ the OLIN Unit, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden; 2K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology NTNU, Trondheim, Norway; 3Division of Mental Health Care, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway; 4Department of Respiratory Medicine, Seinäjoki Central Hospital, Seinäjoki, Finland; 5Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; 6Clinical Physiology and Nuclear Medicine Unit, Helsinki University Hospital’s Diagnostic Center and University of Helsinki, Helsinki, Finland; 7Krefting Research Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 8HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway; 9Nord-Trøndelag Hospital Trust, Levanger Hospital, Levanger, Norway; 10Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden*These authors contributed equally to this workCorrespondence: Helena Backman, Email helena.backman@norrbotten.seBackground and Aim: The relationship between socioeconomic status (SES), asthma and mortality is complex and multifaceted, and it is not established if educational level modifies the association between asthma and mortality. The aim was to study the association between asthma and mortality in Sweden and Norway and to what extent educational level modifies this association.Participants and Methods: Within the Nordic EpiLung Study, > 56,000 individuals aged 30– 69 years participated in population-based surveys on asthma and associated risk factors in Sweden and Norway during 2005– 2007. Data on educational level and 10-year all-cause mortality were linked by national authorities. The fraction of mortality risk attributable to asthma was calculated, and Cox regression was used to estimate hazard ratios (HR) and 95% confidence intervals (95% CI) for mortality related to asthma, stratified by educational level.Results: In total, 5.5% of all deaths was attributed to asthma. When adjusted for potential confounders, the HR for mortality related to asthma was 1.71 (95% CI 1.52– 1.93). Those with primary level of education had higher hazard of all-cause death related to asthma than those with tertiary level (HR 1.80, 95% CI 1.48– 2.18, vs HR 1.39, 95% CI 0.99– 1.95).Conclusion: Asthma was associated with an overall 71% increased all-cause mortality and 5.5% of deaths can be attributed to asthma. Educational levels modified the risk of mortality associated with asthma, with the highest risk among those with primary education.Keywords: epidemiology, cohort, prognosis

Abstract Introduction In epidemiological studies, the age at asthma onset is often defined by patients’ self-reported age at diagnosis. The reliability of this report might be questioned. Our objective was to evaluate the agreement between self-reported and registered age at asthma diagnosis and assess features contributing to the agreement. Methods As part of the FinEsS respiratory survey in 2016, randomly selected population samples of 13,435 from Helsinki and 8000 from Western Finland were studied. Self-reported age at asthma diagnosis was compared to age at asthma diagnosis registered in the Finnish register on special reimbursement for asthma medication. The reimbursement right is based on lung function criteria according to GINA and Finnish guidelines. If the difference was less than 5 years, self-reported diagnosis was considered reliable. Features associated with the difference between self-reported and registered age at asthma diagnosis were evaluated. Results Altogether 197 subjects from Helsinki and 144 from Western Finland were included. Of these, 61.9% and 77.8%, respectively, reported age at diagnosis reliably. Median difference between self-reported and registered age at diagnoses was − 2.0 years (IQR − 9.0 to 0) in Helsinki and − 1.0 (IQR − 4.3 to 0) in Western Finland indicating earlier self-reported age at diagnosis. More reliable self-report was associated with non-allergic subjects and subjects who reported having asthma diagnosis more recently. Conclusions Agreement between self-reported and registered age at asthma diagnosis was good especially with adult-onset asthma patients. Poor agreement in early-onset asthma could be related to delay in registration due to reimbursement criteria.

Abstract Although highly effective on average, exposure-based treatments do not work equally well for all patients with anxiety disorders. The identification of pre-treatment response-predicting patient characteristics may enable patient stratification. Preliminary research highlights the relevance of inhibitory fronto-limbic networks as such. We aimed to identify pre-treatment neural signatures differing between exposure treatment responders and non-responders in spider phobia and to validate results through rigorous replication. Data of a bi-centric intervention study comprised clinical phenotyping and pre-treatment resting-state functional connectivity (rsFC) data of n = 79 patients with spider phobia (discovery sample) and n = 69 patients (replication sample). RsFC data analyses were accomplished using the Matlab-based CONN-toolbox with harmonized analyses protocols at both sites. Treatment response was defined by a reduction of >30% symptom severity from pre- to post-treatment (Spider Phobia Questionnaire Score, primary outcome). Secondary outcome was defined by a reduction of >50% in a Behavioral Avoidance Test (BAT). Mean within-session fear reduction functioned as a process measure for exposure. Compared to non-responders and pre-treatment, results in the discovery sample seemed to indicate that responders exhibited stronger negative connectivity between frontal and limbic structures and were characterized by heightened connectivity between the amygdala and ventral visual pathway regions. Patients exhibiting high within-session fear reduction showed stronger excitatory connectivity within the prefrontal cortex than patients with low within-session fear reduction. Whereas these results could be replicated by another team using the same data (cross-team replication), cross-site replication of the discovery sample findings in the independent replication sample was unsuccessful. Results seem to support negative fronto-limbic connectivity as promising ingredient to enhance response rates in specific phobia but lack sufficient replication. Further research is needed to obtain a valid basis for clinical decision-making and the development of individually tailored treatment options. Notably, future studies should regularly include replication approaches in their protocols.

This study investigates the performance of two lumped hydrological models, BILAN and GR2M, in simulating runoff across six catchments in the Gambia River Basin (Senegal) over a 30-year period employing a 7-year sliding window under different climatic conditions. The results revealed differences in overall performance and variable sensitivity of the models to hydrological conditions and calibration period lengths, stemming from their different structure and complexity. In particular, the BILAN model, which is based on a more complex set of parameters, showed better overall results in simulating dry conditions, while the GR2M model had superior performance in wet conditions. The study emphasized the importance of the length of the calibration period on model performance and on the reduction of uncertainty in the results. Extended calibration periods for both models narrowed the range of the Kling-Gupta Efficiency (KGE) values and reduced the loss of performance during the parameter transfer from calibration to validation. For the BILAN model, a longer calibration period also significantly reduced the variability of performance metric values. Conversely, for the GR2M model, the variability rate did not decrease with the length of the calibration periods. Testing both models under variable conditions underscored the crucial role of comprehending model structure, hydrological sensitivity, and calibration strategy effects on simulation accuracy and uncertainty for reliable results.

Abstract Environmental humidity variations are ubiquitous and high humidity characterizes fuel cell and electrolyzer operation conditions. Since hydrogen-air mixtures are highly flammable, humidity tolerant H2 sensors are important from safety and process monitoring perspectives. Here, we report an optical nanoplasmonic hydrogen sensor operated at elevated temperature that combined with Deep Dense Neural Network or Transformer data treatment involving the entire spectral response of the sensor enables a 100 ppm H2 limit of detection in synthetic air at 80% relative humidity. This significantly exceeds the

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.