One of the most common statistical analyses in experimental psychology concerns the comparison of two means using the frequentist t test. However, frequentist t tests do not quantify evidence and require various assumption tests. Recently, popularized Bayesian t tests do quantify evidence, but these were developed for scenarios where the two populations are assumed to have the same variance. As an alternative to both methods, we outline a comprehensive t test framework based on Bayesian model averaging. This new t test framework simultaneously takes into account models that assume equal and unequal variances, and models that use t-likelihoods to improve robustness to outliers. The resulting inference is based on a weighted average across the entire model ensemble, with higher weights assigned to models that predicted the observed data well. This new t test framework provides an integrated approach to assumption checks and inference by applying a series of pertinent models to the data simultaneously rather than sequentially. The integrated Bayesian model-averaged t tests achieve robustness without having to commit to a single model following a series of assumption checks. To facilitate practical applications, we provide user-friendly implementations in JASP and via the RoBTT package in R . A tutorial video is available at https://www.youtube.com/watch?v=EcuzGTIcorQ.
- MeSH
- Bayes Theorem MeSH
- Psychology, Experimental * methods MeSH
- Data Interpretation, Statistical MeSH
- Humans MeSH
- Models, Statistical * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
BACKGROUND: Neuromuscular diseases (NMDs) are rare disorders characterized by progressive muscle fibre loss, leading to replacement by fibrotic and fatty tissue, muscle weakness and disability. Early diagnosis is critical for therapeutic decisions, care planning and genetic counselling. Muscle magnetic resonance imaging (MRI) has emerged as a valuable diagnostic tool by identifying characteristic patterns of muscle involvement. However, the increasing complexity of these patterns complicates their interpretation, limiting their clinical utility. Additionally, multi-study data aggregation introduces heterogeneity challenges. This study presents a novel multi-study harmonization pipeline for muscle MRI and an AI-driven diagnostic tool to assist clinicians in identifying disease-specific muscle involvement patterns. METHODS: We developed a preprocessing pipeline to standardize MRI fat content across datasets, minimizing source bias. An ensemble of XGBoost models was trained to classify patients based on intramuscular fat replacement, age at MRI and sex. The SHapley Additive exPlanations (SHAP) framework was adapted to analyse model predictions and identify disease-specific muscle involvement patterns. To address class imbalance, training and evaluation were conducted using class-balanced metrics. The model's performance was compared against four expert clinicians using 14 previously unseen MRI scans. RESULTS: Using our harmonization approach, we curated a dataset of 2961 MRI samples from genetically confirmed cases of 20 paediatric and adult NMDs. The model achieved a balanced accuracy of 64.8% ± 3.4%, with a weighted top-3 accuracy of 84.7% ± 1.8% and top-5 accuracy of 90.2% ± 2.4%. It also identified key features relevant for differential diagnosis, aiding clinical decision-making. Compared to four expert clinicians, the model obtained the highest top-3 accuracy (75.0% ± 4.8%). The diagnostic tool has been implemented as a free web platform, providing global access to the medical community. CONCLUSIONS: The application of AI in muscle MRI for NMD diagnosis remains underexplored due to data scarcity. This study introduces a framework for dataset harmonization, enabling advanced computational techniques. Our findings demonstrate the potential of AI-based approaches to enhance differential diagnosis by identifying disease-specific muscle involvement patterns. The developed tool surpasses expert performance in diagnostic ranking and is accessible to clinicians worldwide via the Myo-Guide online platform.
- MeSH
- Adult MeSH
- Internet MeSH
- Middle Aged MeSH
- Humans MeSH
- Magnetic Resonance Imaging * methods MeSH
- Neuromuscular Diseases * diagnosis diagnostic imaging MeSH
- Machine Learning * MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
In this study, we investigated the stability of the fully activated conformation of the orexin receptor 2 (OX2R) embedded in a pure POPC bilayer using MD simulations. Various thermodynamic ensembles (i.e., NPT, NVT, NVE, NPAT, μVT, and NPγT) were employed to explore the dynamical heterogeneity of the system in a comprehensive way. In addition, informational similarity metrics (e.g., Jensen-Shannon divergence) as well as Markov state modeling approaches were utilized to elucidate the receptor kinetics. Special attention was paid to assessing surface tension within the simulation box, particularly under NPγT conditions, where 21 nominal surface tension constants were evaluated. Our findings suggest that traditional thermodynamic ensembles such as NPT may not adequately control physical properties of the POPC membrane, impacting the plausibility of the OX2R model. In general, the performed study underscores the importance of employing the NPγT ensemble for computational investigations of membrane-embedded receptors, as it effectively maintains zero surface tension in the simulated system. These results offer valuable insights for future research aimed at understanding receptor dynamics and designing targeted therapeutics.
The hippocampus (HPC) is essential for navigation and memory, tracking environmental continuity and change, including navigation relative to moving targets. CA1 ensembles expressing immediate-early gene (IEG) Arc and Homer1a RNA are contextually specific. While IEG expression correlates with HPC-dependent task demands, the effects of behavioral demands on IEG-expressing ensembles remain unclear. In three experiments, we investigated the effects of context switch, sustained presence, and task demands on dorso-proximal CA1 IEG+ ensembles in rats. Experiment 1 showed that the size of IEG+ (Arc, Homer1a RNA) ensembles dropped to baseline during uninterrupted 30-min exploration, reflecting familiarization, unless a context switch was present. Context-specificity of the ensembles depended on both environment identity and timing of the context switch. Experiment 2 found no effect of HPC-dependent mobile robot avoidance or HPC-independent avoidance of a stationary robot on IEG+ ensembles beyond mere exploration. Experiment 3 replicated these findings for c-Fos protein. The data suggest that IEG+ ensembles are driven by a context switch and shrink over time during sustained presence in the same environment. We found no evidence of task demands or their change affecting the size, stability over time, or task-specificity of IEG+ ensembles. These results shed light on the temporal dynamics of CA1 IEG+ ensembles, and their control by contextual and behavioral factors.
- MeSH
- Behavior, Animal physiology MeSH
- Cytoskeletal Proteins genetics metabolism MeSH
- CA1 Region, Hippocampal * metabolism physiology MeSH
- Homer Scaffolding Proteins * metabolism genetics MeSH
- Rats MeSH
- Genes, Immediate-Early * physiology MeSH
- Rats, Long-Evans * MeSH
- Nerve Tissue Proteins genetics metabolism MeSH
- Animals MeSH
- Check Tag
- Rats MeSH
- Male MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
BACKGROUND AND OBJECTIVES: Early treatment of multiple sclerosis (MS) reduces disease activity and the risk of long-term disease progression. Effectiveness of ocrelizumab is established in relapsing MS (RMS); however, data in early RMS are lacking. We evaluated the 4-year effectiveness and safety of ocrelizumab as a first-line therapy in treatment-naive patients with recently diagnosed relapsing-remitting MS (RRMS). METHODS: ENSEMBLE was a prospective, 4-year, international, multicenter, single-arm, open-label, phase IIIb study. Patients were treatment naive, aged 18-55 years, had early-stage RRMS with a disease duration ≤3 years, Expanded Disability Status Scale (EDSS) score ≤3.5, and ≥1 clinically reported relapse(s) or ≥1 signs of brain inflammatory activity on MRI in the prior 12 months. Patients received IV ocrelizumab 600 mg every 24 weeks. Effectiveness endpoints over 192 weeks were proportion of patients with no evidence of disease activity (NEDA-3; defined as absence of relapses, 24-week confirmed disability progression [CDP], and MRI measures, with prespecified MRI rebaselining at week 8), 24-week/48-week CDP and 24-week confirmed disability improvement, annualized relapse rate (ARR), mean change in EDSS score from baseline, and safety. Cognitive status, patient-reported outcomes, and serum neurofilament light chain (NfL) were assessed. Descriptive analysis was performed on the intention-to-treat population. RESULTS: Baseline characteristics (N = 678) were consistent with early-stage RRMS (n = 539 patients, 64.6% female, age 40 years and younger; median age: 31.0 years; duration since: MS symptom onset 0.78 years, RRMS diagnosis 0.24 years; mean baseline EDSS score [SD] 1.71 [0.95]). At week 192, most of the patients had NEDA-3 (n = 394/593, 66.4%), 85.0% had no MRI activity, 90.9% had no relapses, and 81.8% had no 24-week CDP over the study duration. Adjusted ARR at week 192 was low (0.020, 95% CI 0.015-0.027). NfL levels were reduced to and remained within the healthy donor range, by week 48 and week 192, respectively. No new or unexpected safety signals were observed. DISCUSSION: Disease activity based on clinical and MRI measures was absent in most of the patients treated with ocrelizumab over 4 years in the ENSEMBLE study. Safety was consistent with the known profile of ocrelizumab. Although this single-arm study was limited by lack of a parallel group for comparison of outcome measures, the positive benefit-risk profile observed may provide confidence to adopt ocrelizumab as a first-line treatment in newly diagnosed patients with early RMS. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that adult patients with early-stage MS who were treatment naive maintained low disease activity (NEDA-3) over 4 years with ocrelizumab treatment; no new safety signals were detected. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov Identifier NCT03085810; first submitted March 16, 2017; first patient enrolled: March 27, 2017; available at clinicaltrials.gov/ct2/show/NCT03085810.
- MeSH
- Adult MeSH
- Antibodies, Monoclonal, Humanized * therapeutic use adverse effects administration & dosage MeSH
- Immunologic Factors * therapeutic use adverse effects administration & dosage MeSH
- Middle Aged MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Adolescent MeSH
- Young Adult MeSH
- Disability Evaluation MeSH
- Disease Progression MeSH
- Prospective Studies MeSH
- Multiple Sclerosis, Relapsing-Remitting * drug therapy diagnostic imaging MeSH
- Treatment Outcome MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Clinical Trial, Phase III MeSH
- Multicenter Study MeSH
BACKGROUND: The interaction between joint kinematics and kinetics is usually assessed by linear correlation analysis, which does not imply causality. Understanding the causal links between these variables may help develop landing interventions to improve technique and create joint-specific strengthening programs to reduce reaction forces and injury risk. OBJECTIVE: Therefore, the aim of this study was to analyze the causal interaction between lower limb sagittal kinematics and vertical ground reaction force (VGRF) during single-leg jump landing in children who are jumpers (volleyball and gymnastics) and non-jumpers, using the causal empirical decomposition method. Our hypothesis is that children who participate in jumping sports, compared to those who do not, employ a different joint strategy to regulate ground reaction forces during landing, particularly at the ankle level. METHODS: Two groups were compared: the jumpers group (n = 14) and the non-jumpers (control group, n = 11). The causal interaction between sagittal kinematics and VGRF was assessed using ensemble empirical mode decomposition (EEMD) and time series instantaneous phase dependence in bi-directional causality. The relative causal strength (RCS) between the time series was quantified as the relative ratio of absolute cause strength between kinematics and VGRF. RESULTS: A significant interaction between joint and group was found for RCS (p = 0.035, η2p = 0.14). The post-hoc analysis showed the jumpers group had higher ankle-to-VGRF RCS than the control group (p = 0.017, d = 1.03), while in the control group the hip-to-VGRF RCS was higher than the ankle-to-VGRF RCS (p = 0.004, d = 0.91). CONCLUSION: Based on the causal decomposition approach, our results indicate that practicing jumping sports increases the causal effect of ankle kinematics on ground reaction forces in children. While non-jumper children rely more on the hip to modulate reaction forces, jumper children differ from non-jumpers by their greater use of the ankle joint. These findings could be used to develop specific training programs to improve landing techniques according to practice level, potentially helping to reduce the risk of injury in both athletes and non-athletes.
- MeSH
- Biomechanical Phenomena physiology MeSH
- Child MeSH
- Lower Extremity physiology MeSH
- Gymnastics * physiology MeSH
- Ankle Joint physiology MeSH
- Humans MeSH
- Volleyball physiology MeSH
- Check Tag
- Child MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
OBJECTIVES: In this study, the trends and current situation of the injury burden as well as attributable burden to injury risk factors at global, regional, and national levels based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 are presented. STUDY DESIGN: To assess the attributable burden of injury risk factors, the data of interest on data sources were retrieved from the Global Health Data Exchange (GHDx) and analyzed. METHODS: Cause-specific death from injuries was estimated using the Cause of Death Ensemble model in the GBD 2019. The burden attributable to each injury risk factor was incorporated in the population attributable fraction to estimate the total attributable deaths and disability-adjusted life years. The Socio-demographic Index (SDI) was used to evaluate countries' developmental status. RESULTS: Globally, there were 713.9 million (95% uncertainty interval [UI]: 663.8 to 766.9) injuries incidence and 4.3 million (UI: 3.9 to 4.6) deaths caused by injuries in 2019. There was an inverse relationship between age-standardized disability-adjusted life year rate and SDI quintiles in 2019. Overall, low bone mineral density was the leading risk factor of injury deaths in 2019, with a contribution of 10.5% (UI: 9.0 to 11.6) of total injuries and age-standardized deaths, followed by occupational risks (7.0% [UI: 6.3-7.9]) and alcohol use (6.8% [UI: 5.2 to 8.5]). CONCLUSION: Various risks were responsible for the imposed burden of injuries. This study highlighted the small but persistent share of injuries in the global burden of diseases and injuries to provide beneficial data to produce proper policies to reach an effective global injury prevention plan.
- MeSH
- Global Health * statistics & numerical data MeSH
- Child MeSH
- Adult MeSH
- Global Burden of Disease * trends MeSH
- Incidence MeSH
- Infant MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Cost of Illness MeSH
- Disability-Adjusted Life Years * MeSH
- Child, Preschool MeSH
- Cause of Death MeSH
- Wounds and Injuries * epidemiology mortality MeSH
- Risk Factors MeSH
- Aged MeSH
- Check Tag
- Child MeSH
- Adult MeSH
- Infant MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Child, Preschool MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
PURPOSE: Chronic obstructive pulmonary disease (COPD) is a prevalent and preventable condition that typically worsens over time. Acute exacerbations of COPD significantly impact disease progression, underscoring the importance of prevention efforts. This observational study aimed to achieve two main objectives: (1) identify patients at risk of exacerbations using an ensemble of clustering algorithms, and (2) classify patients into distinct clusters based on disease severity. METHODS: Data from portable medical devices were analyzed post-hoc using hyperparameter optimization with Self-Organizing Maps (SOM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Isolation Forest, and Support Vector Machine (SVM) algorithms, to detect flare-ups. Principal Component Analysis (PCA) followed by KMeans clustering was applied to categorize patients by severity. RESULTS: 25 patients were included within the study population, data from 17 patients had the required reliability. Five patients were identified in the highest deterioration group, with one clinically confirmed exacerbation accurately detected by our ensemble algorithm. Then, PCA and KMeans clustering grouped patients into three clusters based on severity: Cluster 0 started with the least severe characteristics but experienced decline, Cluster 1 consistently showed the most severe characteristics, and Cluster 2 showed slight improvement. CONCLUSION: Our approach effectively identified patients at risk of exacerbations and classified them by disease severity. Although promising, the approach would need to be verified on a larger sample with a larger number of recorded clinically verified exacerbations.
- Publication type
- Journal Article MeSH
The complexity of omes - the key cellular ensembles (genome and epigenome, transcriptome, proteome, and metabolome) - is becoming increasingly understood in terms of big-data analysis, the omics. Amongst these, proteomics provides a global description of quantitative and qualitative alterations of protein expression (or protein abundance in body fluids) in response to physiologic or pathologic processes while metabolomics offers a functional portrait of the physiological state by quantifying metabolite abundances in biological samples. Here, we summarize how different techniques of proteomic and metabolic analysis can be used to define key biochemical characteristics of pheochromocytomas/paragangliomas (PPGL). The significance of omics in understanding features of PPGL biology that might translate to improved diagnosis and treatment will be highlighted.
- MeSH
- Pheochromocytoma * metabolism diagnosis MeSH
- Humans MeSH
- Metabolomics * methods MeSH
- Adrenal Gland Neoplasms * metabolism diagnosis MeSH
- Paraganglioma * metabolism diagnosis MeSH
- Proteomics * methods MeSH
- Translational Research, Biomedical MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Despite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have emerged as a potential neural correlate of fundamental cognitive processes. High frequency oscillations are detected in the human mesial temporal lobe and neocortical intracranial recordings spanning gamma/epsilon (60-150 Hz), ripple (80-250 Hz) and higher frequency ranges. Separate from other non-oscillatory activities, these brief electrophysiological oscillations of distinct duration, frequency and amplitude are thought to be generated by coordinated spiking of neuronal ensembles within volumes as small as a single cortical column. Although the exact origins, mechanisms and physiological roles in health and disease remain elusive, they have been associated with human memory consolidation and cognitive processing. Recent studies suggest their involvement in encoding and recall of episodic memory with a possible role in the formation and reactivation of memory traces. High frequency oscillations are detected during encoding, throughout maintenance, and right before recall of remembered items, meeting a basic definition for an engram activity. The temporal coordination of high frequency oscillations reactivated across cortical and subcortical neural networks is ideally suited for integrating multimodal memory representations, which can be replayed and consolidated during states of wakefulness and sleep. High frequency oscillations have been shown to reflect coordinated bursts of neuronal assembly firing and offer a promising substrate for tracking and modulation of the hypothetical electrophysiological engram.
- MeSH
- Electroencephalography MeSH
- Cognition * physiology MeSH
- Humans MeSH
- Brain physiology MeSH
- Brain Waves physiology MeSH
- Memory physiology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
