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INTRODUCTION: The cornerstone in the management of type 2 diabetes (T2D) is lifestyle modification including a healthy diet, typically one in which carbohydrate provides 45%-60% of total energy intake (E%). Nevertheless, systematic reviews and meta-analyses of trials with low carbohydrate diets (which are increased in protein and/or fat) for T2D have found improved glycaemic control in the first months relative to comparator diets with higher carbohydrate content. Studies lasting ≥1 year are inconclusive, which could be due to decreased long-term dietary adherence. We hypothesise that glucometabolic benefits can be achieved following 12 months of carbohydrate-restricted dieting, by maximising dietary adherence through delivery of meal kits, containing fresh, high-quality ingredients for breakfast, dinner and snacks, combined with nutrition education and counselling. METHODS AND ANALYSIS: This protocol describes a 12-month investigator-initiated randomised controlled, open-label, superiority trial with two parallel groups that will examine the effect of a carbohydrate-reduced high-protein (CRHP) diet compared with a conventional diabetes (CD) diet on glucometabolic control (change in glycated haemoglobin being the primary outcome) in 100 individuals with T2D and body mass index (BMI) >25 kg/m2. Participants will be randomised 1:1 to receive either the CRHP or the CD diet (comprised 30/50 E% from carbohydrate, 30/17 E% from protein and 40/33 E% from fat, respectively) for 12 months delivered as meal kits, containing foods covering more than two-thirds of the participants' estimated daily energy requirements for weight maintenance. Adherence to the allocated diets will be reinforced by monthly sessions of nutrition education and counselling from registered clinical dietitians. ETHICS AND DISSEMINATION: The trial has been approved by the National Committee on Health Research Ethics of the Capital Region of Denmark. The trial will be conducted in accordance with the Declaration of Helsinki. Results will be submitted for publication in international peer-reviewed scientific journals. TRIAL REGISTRATION NUMBER: NCT05330247. PROTOCOL VERSION: The trial protocol was approved on 9 March 2022 (study number: H-21057605). The latest version of the protocol, described in this manuscript, was approved on 23 June 2023.

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Weight loss is often followed by weight regain. Characterizing endocrine alterations accompanying weight reduction and regain may disentangle the complex biology of weight-loss maintenance. Here, we profile energy-balance-regulating metabokines and sphingolipids in adults with obesity undergoing an initial low-calorie diet-induced weight loss and a subsequent weight-loss maintenance phase with exercise, glucagon-like peptide-1 (GLP-1) analog therapy, both combined, or placebo. We show that circulating growth differentiation factor 15 (GDF15) and C16:0-C18:0 ceramides transiently increase upon initial diet-induced weight loss. Conversely, circulating fibroblast growth factor 21 (FGF21) is downregulated following weight-loss maintenance with combined exercise and GLP-1 analog therapy, coinciding with increased adiponectin, decreased leptin, and overall decrements in ceramide and sphingosine-1-phosphate levels. Subgroup analyses reveal differential alterations in FGF21-adiponectin-leptin-sphingolipids between weight maintainers and regainers. Clinically, cardiometabolic health outcomes associate with selective metabokine-sphingolipid remodeling signatures. Collectively, our findings indicate distinct FGF21, GDF15, and ceramide responses to diverse phases of weight change and suggest that weight-loss maintenance involves alterations within the metabokine-sphingolipid axis.

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Determining the pK a values of various C-H sites in organic molecules offers valuable insights for synthetic chemists in predicting reaction sites. As molecular complexity increases, this task becomes more challenging. This paper introduces pKalculator, a quantum chemistry (QM)-based workflow for automatic computations of C-H pK a values, which is used to generate a training dataset for a machine learning (ML) model. The QM workflow is benchmarked against 695 experimentally determined C-H pK a values in DMSO. The ML model is trained on a diverse dataset of 775 molecules with 3910 C-H sites. Our ML model predicts C-H pK a values with a mean absolute error (MAE) and a root mean squared error (RMSE) of 1.24 and 2.15 pK a units, respectively. Furthermore, we employ our model on 1043 pK a-dependent reactions (aldol, Claisen, and Michael) and successfully indicate the reaction sites with a Matthew's correlation coefficient (MCC) of 0.82.

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Abnormalities within the hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system have been implicated in depression. Studies have reported glucocorticoid insensitivity and reduced heart rate variability (HRV) in depressive disorders. However, little is known about the effects of cortisol on HRV and resting-state functional connectivity (rsFC) of the central autonomic network (CAN) in depression. We collected resting-state fMRI and cardiac data for women with different depression histories (n = 61) after administration of cortisol and placebo using a double-blind crossover design. We computed rsFC for R-amygdala and L-amygdala seeds and assessed the change in HRV after cortisol (cortisol-placebo). Analyses examined the effects of acute cortisol administration on HRV and rsFC of the R-amygdala and L-amygdala. There was a significant interaction between HRV and treatment for rsFC between the amygdala and CAN regions. We found lower rsFC between the L-amygdala and putamen for those with a greater decrease in HRV after cortisol. There was also reduced rsFC between the R-amygdala and dorsomedial prefrontal cortex, putamen, middle cingulate cortex, insula, and cerebellum in those with lower HRV after cortisol. These results remained significant after adjusting for depression symptoms, age, and race. Our findings suggest that the effect of cortisol on CAN connectivity is related to its effects on HRV. Overall, these results could inform transdiagnostic interventions targeting HRV and the stress response systems across clinical and non-clinical populations.

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Previous resting-state functional connectivity (rsFC) research has identified several brain networks impacted by depression and cortisol, including default mode (DMN), frontoparietal (FPN), and salience networks (SN). In the present study, we examined the effects of cortisol administration on rsFC of these networks in individuals varying in depression history and severity. We collected resting-state fMRI scans and self-reported depression symptom severity for 74 women with and without a history of depression after cortisol and placebo administration using a double-blind, crossover design. We conducted seed-based rsFC analyses for DMN, FPN, and SN seeds to examine rsFC changes after cortisol vs. placebo administration in relation to depression history group and severity. Results revealed a main effect of depression group, with lower left amygdala (SN)-middle temporal gyrus connectivity in women with a history of depression. Cortisol administration increased insula (SN)-inferior frontal gyrus and superior temporal gyrus connectivity. We also found that greater depression severity was associated with increased PCC (DMN)-cerebellum connectivity after cortisol. These results did not survive Bonferroni correction for seed ROIs and should be interpreted with caution. Our findings indicate that acute cortisol elevation may normalize aberrant connectivity of DMN and SN regions, which could help inform clinical treatments for depression.

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Short-range functional connectivity in the limbic network is increased in patients with temporal lobe epilepsy (TLE), and recent studies have shown that cortical myelin content correlates with fMRI connectivity. We thus hypothesized that myelin may increase progressively in the epileptic network. We compared T1w/T2w gray matter myelin maps between TLE patients and age-matched controls and assessed relationships between myelin and aging. While both TLE patients and healthy controls exhibited increased T1w/T2w intensity with age, we found no evidence for significant group-level aberrations in overall myelin content or myelin changes through time in TLE.

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The monitoring and assessment of data quality is an essential step in the acquisition and analysis of functional MRI (fMRI) data. Ideally data quality monitoring is performed while the data are being acquired and the subject is still in the MRI scanner so that any errors can be caught early and addressed. It is also important to perform data quality assessments at multiple points in the processing pipeline. This is particularly true when analyzing datasets with large numbers of subjects, coming from multiple investigators and/or institutions. These quality control procedures should monitor not only the quality of the original and processed data, but also the accuracy and consistency of acquisition parameters. Between-site differences in acquisition parameters can guide the choice of certain processing steps (e.g., resampling from oblique orientations, spatial smoothing). Various quality control metrics can determine what subjects to exclude from the group analyses, and can also guide additional processing steps that may be necessary. This paper describes a combination of qualitative and quantitative assessments to determine the quality of fMRI data. Processing is performed using the AFNI data analysis package. Qualitative assessments include visual inspection of the structural T1-weighted and fMRI echo-planar images, functional connectivity maps, functional connectivity strength, and temporal signal-to-noise maps concatenated from all subjects into a movie format. Quantitative metrics include the acquisition parameters, statistics about the level of subject motion, temporal signal-to-noise ratio, smoothness of the data, and the average functional connectivity strength. These measures are evaluated at different steps in the processing pipeline to catch gross abnormalities in the data, and to determine deviations in acquisition parameters, the alignment to template space, the level of head motion, and other sources of noise. We also evaluate the effect of different quantitative QC cutoffs, specifically the motion censoring threshold, and the impact of bandpass filtering. These qualitative and quantitative metrics can then provide information about what subjects to exclude and what subjects to examine more closely in the analysis of large datasets.

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BACKGROUND: Vagus nerve stimulation (VNS) is a FDA approved therapy regularly used to treat a variety of neurological disorders that impact the central nervous system (CNS) including epilepsy and stroke. Putatively, the therapeutic efficacy of VNS results from its action on neuromodulatory centers via projections of the vagus nerve to the solitary tract nucleus. Currently, there is not an established large animal model that facilitates detailed mechanistic studies exploring how VNS impacts the function of the CNS, especially during complex behaviors requiring motor action and decision making. METHODS: We describe the anatomical organization, surgical methodology to implant VNS electrodes on the left gagus nerve and characterization of target engagement/neural interface properties in a non-human primate (NHP) model of VNS that permits chronic stimulation over long periods of time. Furthermore, we describe the results of pilot experiments in a small number of NHPs to demonstrate how this preparation might be used in an animal model capable of performing complex motor and decision making tasks. RESULTS: VNS electrode impedance remained constant over months suggesting a stable interface. VNS elicited robust activation of the vagus nerve which resulted in decreases of respiration rate and/or partial pressure of carbon dioxide in expired air, but not changes in heart rate in both awake and anesthetized NHPs. CONCLUSIONS: We anticipate that this preparation will be very useful to study the mechanisms underlying the effects of VNS for the treatment of conditions such as epilepsy and depression, for which VNS is extensively used, as well as for the study of the neurobiological basis underlying higher order functions such as learning and memory.

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BACKGROUND: Identifying and reducing cardiometabolic risks driven by obesity remains a healthcare challenge. The metabolic syndrome is associated with abdominal obesity and inflammation and is predictive of long-term risk of developing type 2 diabetes and cardiovascular disease in otherwise healthy individuals living with obesity. Therefore, we investigated the effects of adherent exercise, a glucagon-like peptide 1 receptor agonist (GLP-1 RA), or the combination on severity of metabolic syndrome, abdominal obesity, and inflammation following weight loss. METHODS: This was a randomized, double-blinded, placebo-controlled trial. During an 8-week low-calorie diet (800 kcal/day), 195 adults with obesity and without diabetes lost 12% in body weight. Participants were then evenly randomized to four arms of one-year treatment with: placebo, moderate-to-vigorous exercise (minimum of 150 min/week of moderate-intensity or 75 min/week of vigorous-intensity aerobic physical activity or an equivalent combination of both), the GLP-1 RA liraglutide 3.0 mg/day, or a combination (exercise + liraglutide). A total of 166 participants completed the trial. We assessed the prespecified secondary outcome metabolic syndrome severity z-score (MetS-Z), abdominal obesity (estimated as android fat via dual-energy X-ray absorptiometry), and inflammation marker high-sensitivity C-reactive protein (hsCRP). Statistical analysis was performed on 130 participants adherent to the study interventions (per-protocol population) using a mixed linear model. RESULTS: The diet-induced weight loss decreased the severity of MetS-Z from 0.57 to 0.06, which was maintained in the placebo and exercise groups after one year. MetS-Z was further decreased by liraglutide (- 0.37, 95% CI - 0.58 to - 0.16, P < 0.001) and the combination treatment (- 0.48, 95% CI - 0.70 to - 0.25, P < 0.001) compared to placebo. Abdominal fat percentage decreased by 2.6, 2.8, and 6.1 percentage points in the exercise, liraglutide, and combination groups compared to placebo, respectively, and hsCRP decreased only in the combination group compared with placebo (by 43%, P = 0.03). CONCLUSION: The combination of adherent exercise and liraglutide treatment reduced metabolic syndrome severity, abdominal obesity, and inflammation and may therefore reduce cardiometabolic risk more than the individual treatments. Trial registration EudraCT number: 2015-005585-32, ClinicalTrials.gov: NCT04122716.

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STUDY OBJECTIVES: Insufficient sleep may attenuate weight loss, but the role of sleep in weight loss maintenance is unknown. Since weight regain after weight loss remains a major obstacle in obesity treatment, we investigated whether insufficient sleep predicts weight regain during weight loss maintenance. METHODS: In a randomized, controlled, two-by-two factorial study, 195 adults with obesity completed an 8-week low-calorie diet and were randomly assigned to 1-year weight loss maintenance with or without exercise and liraglutide 3.0 mg/day or placebo. Sleep duration and quality were measured before and after the low-calorie diet and during weight maintenance using wrist-worn accelerometers (GENEActiv) and Pittsburgh Sleep Quality Index (PSQI). To test associations between insufficient sleep and weight regain, participants were stratified at randomization into subgroups according to sleep duration (5). RESULTS: After a diet-induced 13.1 kg weight loss, participants with short sleep duration at randomization regained 5.3 kg body weight (p = .0008) and had less reduction in body fat percentage compared with participants with normal sleep duration (p = .007) during the 1-year weight maintenance phase. Participants with poor sleep quality before the weight loss regained 3.5 kg body weight compared with good quality sleepers (p = .010). During the weight maintenance phase, participants undergoing liraglutide treatment displayed increased sleep duration compared with placebo after 26 weeks (5 vs. -15 min/night) but not after 1 year. Participants undergoing exercise treatment preserved the sleep quality improvements attained from the initial weight loss. CONCLUSIONS: Short sleep duration or poor sleep quality was associated with weight regain after weight loss in adults with obesity.

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Rumination is a common feature of depression and predicts the onset and maintenance of depressive episodes. Maladaptive and adaptive subtypes of rumination contribute to distinct outcomes, with brooding worsening negative mood and reflection related to fewer depression symptoms in healthy populations. Neuroimaging studies have implicated several cortical midline and lateral prefrontal brain regions in rumination. Recent research indicates that blood oxygen level-dependent (BOLD) signal variability may be a novel predictor of cognitive flexibility. However, no prior studies have investigated whether brooding and reflection are associated with distinct patterns of BOLD signal variability in depression. We collected resting-state fMRI data for 79 women with different depression histories: no history, past history, and current depression. We examined differences in BOLD signal variability (BOLDSD) related to rumination subtypes for the following regions of interest previously implicated in rumination: amygdala, medial prefrontal, anterior cingulate, posterior cingulate, and dorsolateral prefrontal cortices (dlPFC). Rumination subtype was associated with BOLDSD in the dlPFC, with greater levels of brooding associated with lower BOLDSD in the dlPFC, even after controlling for depression severity. Depression history was related to BOLDSD in the dlPFC, with reduced BOLDSD in those with current depression versus no history of depression. These findings provide a novel demonstration of the neural circuitry associated with maladaptive rumination in depression and implicate decreased prefrontal neural signal variability in the pathophysiology of depression.

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Metastable microstates in electro- and magnetoencephalographic (EEG and MEG) measurements are usually determined using modified k-means accounting for polarity invariant states. However, hard state assignment approaches assume that the brain traverses microstates in a discrete rather than continuous fashion. We present multimodal, multisubject directional archetypal analysis as a scale and polarity invariant extension to archetypal analysis using a loss function based on the Watson distribution. With this method, EEG/MEG microstates are modeled using subject- and modality-specific archetypes that are representative, distinct topographic maps between which the brain continuously traverses. Archetypes are specified as convex combinations of unit norm input data based on a shared generator matrix, thus assuming that the timing of neural responses to stimuli is consistent across subjects and modalities. The input data is reconstructed as convex combinations of archetypes using a subject- and modality-specific continuous archetypal mixing matrix. We showcase the model on synthetic data and an openly available face perception event-related potential data set with concurrently recorded EEG and MEG. In synthetic and unimodal experiments, we compare our model to conventional Euclidean multisubject archetypal analysis. We also contrast our model to a directional clustering model with discrete state assignments to highlight the advantages of modeling state trajectories rather than hard assignments. We find that our approach successfully models scale and polarity invariant data, such as microstates, accounting for intersubject and intermodal variability. The model is readily extendable to other modalities ensuring component correspondence while elucidating spatiotemporal signal variability.

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Aberrant activity and connectivity in default mode (DMN), frontoparietal (FPN), and salience (SN) network regions is well-documented in depression. Recent neuroimaging research suggests that altered variability in the blood oxygen level-dependent (BOLD) signal may disrupt normal network integration and be an important novel predictor of psychopathology. However, no studies have yet determined the relationship between resting-state BOLD signal variability and depressive disorders nor applied BOLD signal variability features to the classification of depression history using machine learning (ML). We collected resting-state fMRI data for 79 women with different depression histories: no history, past history, and current depressive disorder. We tested voxelwise differences in BOLD signal variability related to depression group and severity. We also investigated whether BOLD signal variability of DMN, FPN, and SN regions could predict depression history group using a supervised random forest ML model. Results indicated that individuals with any history of depression had significantly decreased BOLD signal variability in the left and right cerebellum and right parietal cortex (pFWE <0.05). Furthermore, greater depression severity was also associated with reduced BOLD signal variability in the cerebellum. A random forest model classified participant depression history with 74% accuracy, with the ventral anterior cingulate cortex of the DMN as the most important variable in the model. These findings provide novel support for resting-state BOLD signal variability as a marker of neural dysfunction in depression and implicate decreased neural signal variability in the pathophysiology of depression.

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Introduction: Subject head motion is an ongoing challenge in functional magnetic resonance imaging, particularly in the estimation of functional connectivity. Infants (1-month old) scanned during nonsedated sleep often have occasional but large movements of several millimeters separated by periods with relatively little movement. This results in residual signal changes even after image realignment and can distort estimates of functional connectivity. A new motion correction technique, JumpCor, is introduced to reduce the effects of this motion and compared to other existing techniques. Methods: Different approaches for reducing residual motion artifacts after image realignment were compared both in actual and simulated data: JumpCor, regressing out the estimated subject motion, and regressing out the average white matter, cerebrospinal fluid (CSF), and global signals and their temporal derivatives. Results: Motion-related signal changes resulting from infrequent large motion were significantly reduced both by regressing out the estimated motion parameters and by JumpCor. Furthermore, JumpCor significantly reduced artifacts and improved the quality of functional connectivity estimates when combined with typical preprocessing approaches. Discussion: Motion-related signal changes resulting from occasional large motion can be effectively corrected using JumpCor and to a certain extent also by regressing out the estimated motion. This technique should reduce the data loss in studies where participants exhibit this type of motion, such as sleeping infants.

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BACKGROUND: Weight regain after weight loss is a major problem in the treatment of persons with obesity. METHODS: In a randomized, head-to-head, placebo-controlled trial, we enrolled adults with obesity (body-mass index [the weight in kilograms divided by the square of the height in meters], 32 to 43) who did not have diabetes. After an 8-week low-calorie diet, participants were randomly assigned for 1 year to one of four strategies: a moderate-to-vigorous-intensity exercise program plus placebo (exercise group); treatment with liraglutide (3.0 mg per day) plus usual activity (liraglutide group); exercise program plus liraglutide therapy (combination group); or placebo plus usual activity (placebo group). End points with prespecified hypotheses were the change in body weight (primary end point) and the change in body-fat percentage (secondary end point) from randomization to the end of the treatment period in the intention-to-treat population. Prespecified metabolic health-related end points and safety were also assessed. RESULTS: After the 8-week low-calorie diet, 195 participants had a mean decrease in body weight of 13.1 kg. At 1 year, all the active-treatment strategies led to greater weight loss than placebo: difference in the exercise group, -4.1 kg (95% confidence interval [CI], -7.8 to -0.4; P = 0.03); in the liraglutide group, -6.8 kg (95% CI, -10.4 to -3.1; P<0.001); and in the combination group, -9.5 kg (95% CI, -13.1 to -5.9; P<0.001). The combination strategy led to greater weight loss than exercise (difference, -5.4 kg; 95% CI, -9.0 to -1.7; P = 0.004) but not liraglutide (-2.7 kg; 95% CI, -6.3 to 0.8; P = 0.13). The combination strategy decreased body-fat percentage by 3.9 percentage points, which was approximately twice the decrease in the exercise group (-1.7 percentage points; 95% CI, -3.2 to -0.2; P = 0.02) and the liraglutide group (-1.9 percentage points; 95% CI, -3.3 to -0.5; P = 0.009). Only the combination strategy was associated with improvements in the glycated hemoglobin level, insulin sensitivity, and cardiorespiratory fitness. Increased heart rate and cholelithiasis were observed more often in the liraglutide group than in the combination group. CONCLUSIONS: A strategy combining exercise and liraglutide therapy improved healthy weight loss maintenance more than either treatment alone. (Funded by the Novo Nordisk Foundation and others; EudraCT number, 2015-005585-32; ClinicalTrials.gov number, NCT04122716.).

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BACKGROUND: Depression is associated with altered functional connectivity and altered cortisol sensitivity, but the effects of cortisol on functional connectivity in depression are unknown. Previous research shows that brief cortisol augmentation (CORT) has beneficial neurocognitive effects in depression. METHODS: We investigated the effects of CORT (20mg oral cortisol) on functional connectivity during emotion processing in women with depression. Participants included 75 women with no depression or a depressive disorder. In a double-blind, crossover study, we used functional magnetic resonance imaging to measure effects of CORT vs. placebo on task-based functional connectivity during presentation of emotionally-laden images. We performed psychophysiological interaction (PPI) to test interactions among depression severity, cortisol administration, and task-dependent functional connectivity using the hippocampus and amygdala as seeds. RESULTS: During the presentation of negative images, CORT (vs. placebo) increased functional connectivity between the hippocampus and putamen in association with depression severity. During the presentation of positive pictures CORT increased functional connectivity between the hippocampus and middle frontal gyrus as well as superior temporal gyrus in association with depression. LIMITATIONS: Because cortisol was pharmacologically manipulated, results cannot be extrapolated to endogenous increases in cortisol levels. The sample did not permit investigation of differences due to race, ethnicity, or sex. Co-morbidities such as anxiety or PTSD were not accounted for. CONCLUSIONS: The results suggest that CORT has normalizing effects on task-dependent functional connectivity in women with depression during emotion processing. Increasing cortisol availability or signaling may have therapeutic benefits within affective disorders.

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INTRODUCTION: Risk patients admitted to hospital wards may quickly develop haemodynamic deterioration and early recognition has high priority to allow preventive intervention. The peripheral perfusion index (PPI) may be an indicator of circulatory distress by assessing peripheral perfusion non-invasively from photoplethysmography. We aimed to describe the characteristics of PPI in hospitalized patients since this is not well-studied. MATERIALS AND METHODS: Patients admitted due to either acute exacerbation of chronic obstructive pulmonary disease (AECOPD) or after major abdominal cancer surgery were included in this study. Patients were monitored continuously up to 96 hours with a pulse oximeter. Comparisons between median PPI each day, time of day and admission type were described with mean difference (MD) and were analysed using Wilcoxon rank sum test and related to morbidity and mortality. RESULTS: PPI data from 291 patients were recorded for a total of 9279 hours. Median PPI fell from 1.4 (inter quartile range, IQR 0.9-2.3) on day 1 to 1.0 (IQR 0.6-1.6) on day 4. Significant differences occurred between PPI day vs evening (MD = 0.18, 95% CI 0.16-0.20, P = .028), day vs night (MD = 0.56, 95% CI 0.49-0.62, P < .0001) and evening vs night (MD = 0.38, 95% CI 0.33-0.42, P = .002). No significant difference in median PPI between AECOPD and surgical patients was found (MD = 0.15, 95% CI -0.08-0.38, P = .62). CONCLUSION: Lower PPI during daytime vs evening and night-time were seen for both populations. The highest frequency of serious adverse events and mortality was seen among patients with low median PPI. The clinical impact of PPI monitoring needs further confirmation.

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Tissue engineering is focusing research effort on search for new biomaterials that might be applied to create artificial urinary conduit. Nevertheless, the demanding biomechanical characteristics necessary for proper conduit function is difficult to be replicated. In this study, we are introducing novel marine biomaterial obtained by decellularization of squid mantle derived from Loligo vulgaris. Squid mantles underwent decellularization according to developed dynamic flow two-staged procedure. Efficacy of the method was confirmed by computational dynamic flow analysis. Subsequently Decellularized Squid Mantle (DSM) underwent extensive histological analysis and mechanical evaluation. Based on gained biomechanical data the computational modelling using finite element method was utilized to simulate behavior of DSM used as a urinary conduit. Taking into account potential application in reconstructive urology, the DSM was then evaluated as a scaffold for urothelial and smooth muscle cells derived from porcine urinary bladder. Conducted analysis showed that DSM created favorable environment for cells growth. In addition, due to polarized structure and natural external polysaccharide layer, it protected seeded cells from urine.

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In long-term individual-based field studies, several parameters need to be assessed repeatedly to fully understand the potential fitness effects on individuals. Often studies only evaluate capture stress that appears in the immediate weeks or breeding season and even long-term studies fail to evaluate the long-term effects of their capture procedures. We investigated effects of long-term repeated capture and handling of individuals in a large semi-aquatic rodent using more than 20 years of monitoring data from a beaver population in Norway. To investigate the effects, we corrected for ecological factors and analysed the importance of total capture and handling events, years of monitoring and deployment of telemetry devices on measures related to body condition, reproduction and survival of individual beavers. Body mass of dominant individuals decreased considerably with number of capture events (107 g per capture), but we found no statistically clear short or long-term effects of capture and handling on survival or other body condition indices. Annual litter size decreased with increasing number of captures among older individuals. Number of captures furthermore negatively affected reproduction in the beginning of the monitoring, but the effect decreased over the years, indicating habituation to repeated capture and handling. By assessing potential impacts on several fitness-related parameters at multiple times, we can secure the welfare of wild animal populations when planning and executing future conservation studies as well as ensure ecologically reliable research data.

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