Tag: statistics

J Hum Hypertens. 2026 Jun 2. doi: 10.1038/s41371-026-01170-5. Online ahead of print.

ABSTRACT

Acute psychological stress may contribute to cardiovascular disease risk through transient vascular dysfunction. In a randomized crossover study of healthy young adults, the Trier Social Stress Task increased brachial-femoral pulse wave velocity compared with a neutral time- and speech-matched control condition. Using an experimental design and statistical approach that addressed limitations identified in prior studies, these findings support meta-analytic evidence and suggest that transient arterial stiffening may represent a mechanism linking repeated stress exposure to long-term cardiovascular risk.

PMID:42230806 | DOI:10.1038/s41371-026-01170-5

Commun Biol. 2026 Jun 3. doi: 10.1038/s42003-026-10357-y. Online ahead of print.

ABSTRACT

Traditionally, the electroencephalogram (EEG) has been understood as arising from rhythmic neuronal oscillators with varying degrees of synchronisation. Alternative insights, however, highlight the arrhythmic nature of the EEG, primarily inferred from broadband properties like the ubiquitous 1/f spectrum. From the analysis of EEG simulations based on stochastic pulse superposition, we identified mathematical relations between the statistical features of the superposition signal and the shape of the underlying pulse(s), allowing us to develop a new method for recovering EEG transient components from their stochastic interference. Applying this approach to spontaneous mouse EEG recordings sampled at 5 kHz during the sleep-wake cycle, we discovered unique patterns that unambiguously identified all major behavioural states. These patterns are composed of fast transients with temporal features comparable to those observed in Local Field Potentials, which may help us to unify our understanding of neuronal dynamics across spatiotemporal scales.

PMID:42230781 | DOI:10.1038/s42003-026-10357-y

Sci Rep. 2026 Jun 2. doi: 10.1038/s41598-026-53264-2. Online ahead of print.

ABSTRACT

The upcoming 6G networks require digital signal processing (DSP) systems which need to adapt their operations for unanticipated changes in communication channel conditions and system environmental factors because the demand for high-speed communication demands both quick response times and dependable service. The fixed processing chains of traditional DSP systems which engineers built for 5G networks together with their requirement for specific hardware usage create limits that prevent effective operation under changing environmental conditions. The paper presents ADaPT-6 (Adaptive DSP for Progressive Transceivers in 6G) as a solution to these obstacles through its entire software-based AI-powered digital signal processing system which enables real-time transceiver operation changes through intelligent learning. The ADaPT-6 system operates through two fundamental components which use FlexiTune Modulation Adaptation to enable systems to select their best modulation methods based on current channel conditions while the Signal State Evolution Engine predicts system behavior to modify system operations of filtering and equalization and synchronization. The framework achieves better spectral efficiency through its implementation of adaptive modulation together with its ability to predict signal states which improves system reliability and operational efficiency. The research shows through extensive testing in practical 6G fronthauling situations that both traditional fixed DSP systems and DSP systems with limited adaptation capabilities face performance difficulties across multiple performance metrics including bit error rate and statistical reliability and throughput and energy efficiency and latency and outage probability. The evaluation process used SNR values ranging from 0 to 20 dB while measuring EVM in percentage terms and ACLR in decibels and average throughput in Mbps and latency in milliseconds and energy efficiency in bits per Joule. Quantitative evaluation demonstrates that ADaPT-6 achieves significant performance improvements over conventional DSP frameworks, including a ~ 20.8% increase in throughput (57.37 Mbps vs. 47.47 Mbps), ~ 23.7% reduction in latency (20.52 ms vs. 26.88 ms), and ~ 44.5% improvement in energy efficiency (0.678 vs. 0.469 bits/Joule). Additionally, the framework consistently achieves lower BER and EVM across the entire SNR range (0-20 dB), confirming its robustness under dynamic channel conditions. The results confirm that ADaPT-6 operates as an effective digital signal processing solution which works on any hardware platform and can be implemented at any scale. The system functions as the best solution for future AI-native 6G transceiver systems.

PMID:42230775 | DOI:10.1038/s41598-026-53264-2

Sci Rep. 2026 Jun 2. doi: 10.1038/s41598-026-55279-1. Online ahead of print.

ABSTRACT

Under the background of the child-friendly city initiative, school commuting routes serve as the core daily space connecting families and schools for children aged 6-14, and the design of their street environments directly impacts children’s travel experiences and healthy development. Existing studies mostly focus on the macro planning of child-friendly spaces, lacking systematic decomposition and quantitative analysis of children’s visual preferences for specific environmental elements in school commuting routes. To fill this gap, this study uses eye-tracking technology (Tobii Eye Tracker 5) and mathematical statistical analysis (Friedman test, Spearman correlation analysis) to explore the patterns and key influencing factors of children’s visual preferences for four core elements (architecture, ground, greening, and facilities) of street spaces along school commuting routes. Sixteen children aged 6-14 were selected as participants, who observed 12 screened street view photos of school commuting routes (including historical style, high greening-dominated, facility-dominated, and conventional comprehensive types). Data such as eye-tracking trajectories, heat maps, fixation duration and frequency were collected, and a comprehensive analysis was conducted combined with questionnaire feedback. This study reveals children’s implicit preferences for micro-environmental elements in school commuting routes through quantitative methods and constructs a “factor indicator-visual preference” correlation model, making up for the deficiency of insufficient attention to the micro-level in existing studies. The research conclusions can provide scientific support for the refined design of school commuting routes in the construction of child-friendly cities, helping to create safe, comfortable, and child-centered daily travel spaces. Meanwhile, it enriches the research methods and theoretical achievements in the fields of child environmental psychology and urban planning.

PMID:42230757 | DOI:10.1038/s41598-026-55279-1

NPJ Digit Med. 2026 Jun 2. doi: 10.1038/s41746-026-02837-6. Online ahead of print.

ABSTRACT

Large language models (LLMs) are being deployed in clinical settings despite an underdeveloped evidence base regarding their real-world effectiveness. This study employed systematic evidence mapping to characterize outcome measures used in published studies and registered clinical trials (Jan 2022-Jun 2025) evaluating LLM performance. Analysis of 55 included studies revealed a predominance of human-AI collaborative designs (65.5%) for decision support and symptom management. LLM-only interventions focused on functional performance and operational or process impact outcomes (e.g., accuracy and time saving), whereas LLM-assisted interventions showed positive clinical effects, particularly in psychological health endpoints. Critical evidence gaps persist: diagnostic accuracy in randomized trials was notably lower and more variable (range 0.65-0.88) compared to non-randomized studies (typically ≥ 0.80); clinical efficiency impacts were inconsistent, and reporting quality was suboptimal (78.8% mean CONSORT-AI adherence), with critical omissions in handling data quality and performance errors. These findings indicate a heterogeneous and insufficient evidence landscape, necessitating standardized core outcome sets, mandatory use of specialized reporting guidelines, and robust clinical trials to ensure the safe integration of LLMs.

PMID:42230743 | DOI:10.1038/s41746-026-02837-6

Sci Rep. 2026 Jun 3. doi: 10.1038/s41598-026-55791-4. Online ahead of print.

ABSTRACT

At the present stage, the development of renewable energy sources, particularly solar energy, is considered one of the key directions for ensuring energy security and sustainable development. In this context, training specialists with well-developed research and engineering thinking in the field of solar energy within the higher education system has become an urgent issue. The purpose of this study is to theoretically and empirically examine the effectiveness of developing students’ research and engineering activities through the implementation of a STEM Project-Based Learning (PjBL) approach in solar energy education. The study was conducted using a sequential explanatory design, consisting of two phases. In the first phase, a systematic literature review and meta-analysis were carried out based on studies published between 2015 and 2025 and selected from Web of Science, Scopus, and Google Scholar in accordance with the PRISMA methodology. The meta-analysis was performed using Comprehensive Meta-Analysis 4.0 software and included 15 empirical studies. In the second phase, a pedagogical experiment was conducted at Khoja Akhmet Yassawi International Kazakh-Turkish University involving 88 students divided into control and experimental groups. An independent samples t-test was applied to analyze the collected data. The meta-analysis results indicated a large overall effect size (d = 1.75; 95% CI [1.29, 2.22]; p < 0.001), suggesting the strong potential of STEM-PjBL in enhancing students’ research and engineering skills. The experimental results showed no statistically significant difference between the control and experimental groups at the pre-test stage (t = 0.1425; p = 0.887), while a significant difference was observed at the post-test stage (t = 5.8550; p < 0.001), with a large effect size (Cohen’s d = 1.27). The findings suggest that the implementation of STEM Project-Based Learning in solar energy education can effectively support the development of students’ research autonomy, engineering thinking, and their ability to connect theoretical knowledge with real-world applications.

PMID:42230742 | DOI:10.1038/s41598-026-55791-4

Sci Rep. 2026 Jun 2. doi: 10.1038/s41598-026-56353-4. Online ahead of print.

ABSTRACT

Psoriasis and inflammatory bowel disease (IBD) share genetic and immunological features, but the impact of concomitant psoriasis on the clinical course of IBD is not well defined. We investigated whether concomitant psoriasis is associated with increased treatment burden and worse outcomes in patients with IBD. This retrospective cohort study included 75 patients with IBD from two academic centers in South Korea (IBD with psoriasis [IBD-PS], n = 25; IBD-only, n = 50). The IBD-PS group included only patients in whom psoriasis was diagnosed prior to IBD. Demographic data and clinical outcomes were analyzed. The primary endpoints were biologic and immunomodulator (IMM) use, and secondary endpoints included IBD-related surgery and emergency room (ER) visits. Time-to-event outcomes were assessed using Kaplan-Meier methods. Biologic and IMM use were significantly higher in the IBD-PS group than in the IBD-only group (biologics: 40% vs. 16%; IMMs: 60% vs. 20%). Treatment escalation to biologics or IMMs occurred earlier in patients with concomitant psoriasis (log-rank P = 0.0021). ER visits were numerically more frequent among patients with severe psoriasis, and the time to first hard clinical event (IBD-related surgery or ER visit) tended to be shorter in the IBD-PS group, although this difference was not statistically significant. Overall, concomitant psoriasis may be associated with increased treatment intensity and a more aggressive IBD course. These findings support the need for coordinated multidisciplinary care and further validation in prospective multicenter studies.

PMID:42230724 | DOI:10.1038/s41598-026-56353-4

Comput Methods Programs Biomed. 2026 May 22;285:109458. doi: 10.1016/j.cmpb.2026.109458. Online ahead of print.

ABSTRACT

BACKGROUND: Ventricular late potentials (VLPs) are markers of arrhythmogenic substrate, but conventional assessment using signal-averaged ECG (SAECG) requires prolonged acquisition and operator-dependent artifact handling, limiting scalability and ambulatory use. Single-beat detection of VLP-like activity from standard surface ECG remains insufficiently validated.

OBJECTIVE: To evaluate the technical feasibility of interpretable single-beat detection of VLP-like perturbations from standard ECG leads without signal averaging.

METHODS: Using the MIMIC-IV-ECG database, we analyzed 120,000 beats from leads II, V2, and V6. Because large public datasets with beat-level clinically adjudicated VLP labels are not currently available, physiologically constrained synthetic VLP-like signals were injected into a subset of beats to create a controlled feasibility benchmark. For each beat, more than 200 features were extracted, including time-domain statistics, frequency-domain measures, wavelet coefficients, autocorrelation features, and localized windowed summaries. Ten classifiers were optimized using nested patient-wise cross-validation and evaluated in five settings: single-lead detection, cross-lead generalization, mixed-lead training, reduced training size, and class-imbalance robustness.

RESULTS: Gradient-boosted ensembles, particularly XGBoost and CatBoost, achieved strong discrimination on held-out single-beat data (AUC > 0.99; F1 > 0.93), while remaining stable with 10% of the training data and 5% positive-class prevalence. Performance was also robust in lead-transfer experiments. SHAP analysis identified localized entropy, dispersion, and related high-frequency descriptors in late post-R windows as the dominant predictors.

CONCLUSION: These findings support the methodological feasibility of interpretable single-beat detection of VLP-like signatures from routine surface ECG under controlled synthetic conditions. Validation on clinically adjudicated cohorts and external datasets is required before clinical translation.

PMID:42229036 | DOI:10.1016/j.cmpb.2026.109458

J Anxiety Disord. 2026 May 28;122:103188. doi: 10.1016/j.janxdis.2026.103188. Online ahead of print.

ABSTRACT

The 12-item Intolerance of Uncertainty Scale (IUS-12) is a widely used measure in anxiety disorders research and clinical practice, although recent research efforts have recommended the development of a briefer measure to reduce response burdens. The current study was designed to develop a short form of the IUS-12. We administered the IUS-12 to two samples (cross-sectional, n = 3952; longitudinal, n = 190) of Canadian Public Safety Personnel through an online self-report survey. Exploratory (EFA) and confirmatory (CFA) factor analyses assessed the factor structure of the IUS-12 and facilitated item reduction and optimization. Convergent and concurrent validity, as well as pre-post rank-order consistency were also assessed. The IUS-12 was reduced to a two-factor, six-item short form (i.e., IUS-6) that retains the factor structure of the IUS-12 with statistically comparable validity and consistency, which appears to retain the intended latent content for each subscale. Very strong statistically significant positive correlations were observed between IUS-6 and IUS-12 subscales (rs = .90-.94) and total scores (r = .95) for all samples, suggesting excellent construct validity. Moderate to strong statistically significant associations were also observed among the IUS-6 and IUS-12 total and subscales and measures of generalized anxiety, social anxiety, and anxiety sensitivity, suggesting good convergent validity (rs = .23-.64), with only small differences in correlation strengths (i.e., r < .14). The IUS-6 may be used as an ultra-short version of the IUS-12 for assessing self-reported intolerance of uncertainty in both cross-sectional and longitudinal research designs, potentiating reduced response burdens, which may be important in various research and clinical settings.

PMID:42229033 | DOI:10.1016/j.janxdis.2026.103188

Cogn Neurosci. 2026 Jun 2:1-2. doi: 10.1080/17588928.2026.2682170. Online ahead of print.

ABSTRACT

Slotnick (2026) provides a large number of simulations to demonstrate that statistical power in fMRI can be improved by including the sample size N when calculating an appropriate cluster extent threshold for thresholding statistical maps. I argue that the problems acknowledged by Slotnick can instead be solved using threshold free cluster enhancement (TFCE) and a permutation test, which together apply a large number of cluster forming thresholds and implicitly model the sample size as well as the spatial autocorrelation. Furthermore, I briefly mention some other approaches for increasing statistical power in fMRI.

PMID:42228992 | DOI:10.1080/17588928.2026.2682170