Category: Statistics

J Innov Card Rhythm Manag. 2025 Jul 15;16(7):6355-6373. doi: 10.19102/icrm.2025.16075. eCollection 2025 Jul.

ABSTRACT

Atrial fibrillation (AF) is a major sequela after bioprosthetic valve replacement (BPVR) in patients with valvular heart disease. This study evaluates the data compiled from different meta-analyses in an umbrella review. We investigated the anticoagulation efficacy of direct oral anticoagulants (DOACs) versus vitamin K antagonists (VKAs) in patients with AF and BPVR. A comprehensive search of the Cochrane Database of Systematic Reviews, EMBASE, and PubMed was completed to find papers published up until June 2024 that could be included in this umbrella review. Randomized controlled trials (RCTs) and retrospective observational/cohort studies were primarily identified as the foundation of meta-analyses and peer-reviewed systematic reviews. The quality of the included publications was determined using the AMSTAR 2 tool and the Cochrane Collaboration’s risk-of-bias tool, while the overall certainty of the evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology. A total of 20 systematic reviews and meta-analyses of RCTs and observational studies were included in this umbrella review. Among the primary outcomes, the pooled analysis exhibited a significant reduction in all-cause mortality (risk ratio [RR], 0.95; 95% confidence interval [CI], 0.91-1.00; P = .05; I2 = 0%), risk of major/life-threatening bleeding (RR, 0.73; 95% CI, 0.66-0.82; P ≤ .00001; I2 = 66%), and stroke/thromboembolism (RR, 0.74; 95% CI, 0.67-0.82; P = .00001; I2 = 0%) in patients who were administered DOAC pharmacotherapy as compared to VKAs. The only primary outcome that demonstrated clinically insignificant results was all-cause stroke (RR, 0.9; 95% CI, 0.79-1.04; P = .16; I2 = 54%). Secondary outcomes such as intracranial bleeding, any bleeding, and minor or clinically insignificant bleeding all showed a significantly decreased risk in the DOAC group versus the VKA group. Only two outcomes revealed an increased risk of cardiovascular events and risk of ischemic stroke in patients who received DOACs; however, these outcomes were statistically insignificant. According to our analysis, DOACs exhibit a superior safety and efficacy profile to that of VKAs when it comes to treating patients with BPVR. DOACs do not require continuous monitoring; therefore, they could be an effective substitute for VKAs in these individuals.

PMID:40766961 | PMC:PMC12320913 | DOI:10.19102/icrm.2025.16075

Front Psychiatry. 2025 Jul 22;16:1589688. doi: 10.3389/fpsyt.2025.1589688. eCollection 2025.

ABSTRACT

BACKGROUND: Alcohol consumption behaviors and alcohol use disorder risk and presentation differ by sex, and are associated with blood concentrations of the steroid sex hormones, testosterone and estradiol, and their regulatory binding proteins, sex hormone binding globulin (SHBG) and albumin. Genetic variation is also associated with alcohol consumption, alcohol use disorder, and levels of these hormones and binding proteins.

METHODS: To assess the contribution of genetic factors to previously described phenotypic associations between alcohol-use traits and sex-hormone levels, we estimated genetic correlations (r_g) using summary statistics from prior published, large sample size genome-wide association studies (GWAS) of alcohol consumption, alcohol dependence, testosterone, estradiol, SHBG, and albumin. We defined statistical significance at p < 0.005 and trends at p < 0.05.

RESULTS: For alcohol consumption, we observed positive genetic correlation (i.e. genetic effects in the same direction) with SHBG in females (r_g = 0.089, p = 0.004) and a trend toward negative genetic correlation (i.e. genetic effects in opposite directions) with bioavailable testosterone (r_g = -0.064, p = 0.032); however there were only trends toward positive genetic correlation with total testosterone in males (r_g = 0.084, p = 0.007) and with albumin in a sex-combined cohort (r_g = 0.082, p = 0.015). For alcohol dependence, we observed trends toward negative genetic correlation with total testosterone in females (r_g = -0.106, p = 0.024) and positive genetic correlation with BMI-adjusted SHBG in males (r_g = 0.119, p = 0.017). Some of these genetic correlations were different than the corresponding phenotypic associations, and some may suggest differences between females and males.

CONCLUSIONS: Shared genetic effects might contribute to positive associations of alcohol consumption with albumin and between alcohol dependence and SHBG in males; however, most of the phenotypic associations between alcohol-use traits and levels of sex hormones and their binding proteins did not correspond to broadly shared genetic effects in the same direction. Some even corresponded to genetic effects in the opposite direction. Future studies of these traits should include GWAS on larger cohorts by sex and investigation of localized correlations of genetic effects and the relative contributions of heritable and environmental factors.

PMID:40766921 | PMC:PMC12322815 | DOI:10.3389/fpsyt.2025.1589688

Clinicoecon Outcomes Res. 2025 Aug 1;17:537-546. doi: 10.2147/CEOR.S529853. eCollection 2025.

ABSTRACT

PURPOSE: Anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) are rare chronic autoimmune diseases, potentially fatal, with frequent relapses. They are associated with vital organ damage, especially renal, often resulting in end-stage renal disease. While current standard of care with immunosuppressants has improved renal function and survival, the main risks for patients under life-long immunosuppression are infections and other concomitant diseases. This study evaluated the burden of AAV using patient-level data from a disease-specific registry.

PATIENTS AND METHODS: The cohort of incident AVV patients (2013-2022) in the REDCap registry in a university hospital in Spain was studied. Patients with Granulomatosis with Polyangiitis (GPA), Microscopic Polyangiitis (MPA) and Eosinophilic Granulomatosis with Polyangiitis (EGPA) with at least one year of follow-up (or deceased during the period) were included. Clinical outcomes, including Birmingham Vasculitis Activity Score (BVAS) and healthcare resource consumption were analysed for the first year after diagnosis. Mean annual costs were calculated using unitary costs from the hospital accounting department.

RESULTS: Seventy-five patients (12% EGPA, 32% GPA, and 56% MPA) were included. Fifty-two percent were women. Mean age at diagnosis was 65.20±14.70 years. At baseline, mean BVAS was 17.35±5.70, 93.33% of patients showed renal affectation, mean estimated glomerular filtration rate was 33.32±29.93mL/min/1.73m². As induction treatment, 62.67% received methylprednisolone, 37.33% rituximab, 25.33% cyclophosphamide, 14.67% rituximab plus cyclophosphamide, 34.67% plasmapheresis. During the first year after diagnosis, 17.33% relapsed and 78.67% had at least 1 hospitalisation; 97.33% received steroids; 13.33% were on dialysis at some point; one patient received a kidney transplant; 46.67% presented infections and 28% suffered corticosteroid-associated complications; 4 patients died, being 50% of deaths treatment-related. The highest observed mean cost per patient for the first year was €11,647.95 for hospital care.

CONCLUSION: This study revealed a considerable burden of AAV, as evidenced by high rates of hospitalisation, relapses, and the need for intensive medical interventions.

PMID:40766903 | PMC:PMC12323866 | DOI:10.2147/CEOR.S529853

Appl Plant Sci. 2025 Mar 1;13(4):e70001. doi: 10.1002/aps3.70001. eCollection 2025 Jul-Aug.

ABSTRACT

Plant metabolomes are structurally diverse. One of the most popular techniques for sampling this diversity is liquid chromatography-mass spectrometry (LC-MS), which typically detects thousands of peaks from single organ extracts, many representing true metabolites. These peaks are usually annotated using in-house retention time or spectral libraries, in silico fragmentation libraries, and increasingly through computational techniques such as machine learning. Despite these advances, over 85% of LC-MS peaks remain unidentified, posing a major challenge for data analysis and biological interpretation. This bottleneck limits our ability to fully understand the diversity, functions, and evolution of plant metabolites. In this review, we first summarize current approaches for metabolite identification, highlighting their challenges and limitations. We further focus on alternative strategies that bypass the need for metabolite identification, allowing researchers to interpret global metabolic patterns and pinpoint key metabolite signals. These methods include molecular networking, distance-based approaches, information theory-based metrics, and discriminant analysis. Additionally, we explore their practical applications in plant science and highlight a set of useful tools to support researchers in analyzing complex plant metabolomics data. By adopting these approaches, researchers can enhance their ability to uncover new insights into plant metabolism.

PMID:40766901 | PMC:PMC12319716 | DOI:10.1002/aps3.70001

Appl Plant Sci. 2025 Jun 3;13(4):e70009. doi: 10.1002/aps3.70009. eCollection 2025 Jul-Aug.

ABSTRACT

PREMISE: With the advent of inexpensive nucleic acid sequencing and automated annotation at the level of basic functionality, the central problem of enzyme discovery is no longer finding active sequences, it is determining which ones are suitable for further study. This requires annotation that goes beyond sequence similarity to known enzymes and provides information at the sequence and structural levels.

METHODS: Here we introduce a workflow for generating highly informative, richly annotated sequence alignments from protein sequence data. Computer-Assisted Sequence Annotation (CASA) is a freely available Python-based workflow designed to automate portions of novel protein characterization, while producing a human-interpretable final output.

RESULTS: We demonstrate CASA using one enzyme from the Drosera capensis genome. The workflow generates detailed annotations providing comparisons to known reference sequences. In addition to sequence similarity and predicted function, user-specified features such as active site residues, disulfide bonds, and substrate-binding residues can be displayed, and these can then be combined with downstream analyses to gain new insights into enzyme structure and function.

DISCUSSION: This work demonstrates the utility of detailed annotations and protein structure prediction for choosing protein targets for biochemistry or structural biology from nucleic acid sequence data. The toolchain is freely available along with instructions and representative examples.

PMID:40766899 | PMC:PMC12319702 | DOI:10.1002/aps3.70009

ArXiv [Preprint]. 2025 Jul 31:arXiv:2508.00191v1.

ABSTRACT

Animals employ different strategies for relating sensory input to behavioral output to navigate sensory environments, but what strategy to use, when to switch and why remain unclear. In C. elegans, navigation is composed of ‘steering’ and ‘turns’, corresponding to small heading changes and large reorientation events, respectively. It is unclear whether transitions between these elements are driven solely by sensory input or are influenced by internal states that persist over time. It also remains unknown how worms accomplish seemingly surprising feats of navigation–for example, worms appear to exit turns correctly oriented toward a goal, despite their presumed lack of spatial awareness during the turn. Here, we resolve these questions using detailed measurements of sensory-guided navigation and a novel statistical model of state-dependent navigation. We show that the worm’s navigation is well described by a sensory-driven state-switching model with two distinct states, each persisting over many seconds and producing different mixtures of sensorimotor relations. One state is enriched for steering, while the other is enriched for turning. This hierarchical, temporal organization of strategies challenges the previous assumption that strategies are static over time and driven solely by immediate sensory input. Sensory input causally drives transitions between these persistent internal states, and creates the appearance of ‘directed turns.’ Genetic perturbations and a data-constrained reinforcement learning model demonstrate that state-switching enhances gradient-climbing performance. By combining measurement, perturbation, and modeling, we show that state-switching plays a functionally beneficial role in organizing behavior over time–a principle likely to generalize across species and contexts.

PMID:40766885 | PMC:PMC12324552

J Multivar Anal. 2025 May;207:105400. doi: 10.1016/j.jmva.2024.105400. Epub 2024 Dec 14.

ABSTRACT

We address the challenge of estimation in the context of constant linear effect models with dense functional responses. In this framework, the conditional expectation of the response curve is represented by a linear combination of functional covariates with constant regression parameters. In this paper, we present an alternative solution by employing the quadratic inference approach, a well-established method for analyzing correlated data, to estimate the regression coefficients. Our approach leverages non-parametrically estimated basis functions, eliminating the need for choosing working correlation structures. Furthermore, we demonstrate that our method achieves a parametric $\sqrt{n}$ -convergence rate, contingent on an appropriate choice of bandwidth. This convergence is observed when the number of repeated measurements per trajectory exceeds a certain threshold, specifically, when it surpasses $n^{a_0}$ , with $n$ representing the number of trajectories. Additionally, we establish the asymptotic normality of the resulting estimator. The performance of the proposed method is compared with that of existing methods through extensive simulation studies, where our proposed method outperforms. Real data analysis is also conducted to demonstrate the proposed method.

PMID:40766879 | PMC:PMC12320752 | DOI:10.1016/j.jmva.2024.105400

Sci Rep. 2025 Aug 5;15(1):28484. doi: 10.1038/s41598-025-14319-y.

ABSTRACT

Breastfeeding is widely recognized as the optimal form of infant nutrition; however, exclusive breastfeeding (EBF) rates remain low worldwide. Psychological factors such as maternal self-efficacy and satisfaction play a key role in breastfeeding success. This randomized controlled trial evaluated whether a mixed-reality educational strategy could improve maternal self-efficacy and breastfeeding satisfaction. A total of 58 pregnant women in their third trimester were randomly assigned to receive either mixed reality plus traditional counseling or traditional counseling alone. Breastfeeding self-efficacy and satisfaction were measured one week postpartum using validated instruments. No statistically significant differences were found between the groups in self-efficacy (mean scores 63.3 vs. 63.1) or satisfaction (133.5 vs. 134.0). However, both groups demonstrated remarkably high rates of exclusive breastfeeding during the first week of life (93.1%), far exceeding the national and global average. Although the mixed-reality intervention did not yield superior outcomes within the short follow-up period, the findings highlight the potential benefits of structured prenatal education in enhancing breastfeeding practices. This low-cost immersive approach may be particularly relevant in middle- and low-income settings. Further research with a larger sample size and extended follow-up is required to assess the long-term impact and broader applicability of mixed reality in maternal health education.Clinical trial registration: https://ClinicalTrials.gov (NCT06800521; registered on 30/01/2025).

PMID:40764648 | DOI:10.1038/s41598-025-14319-y

Sci Rep. 2025 Aug 5;15(1):28571. doi: 10.1038/s41598-025-13821-7.

ABSTRACT

Fluoride toxicity has become a significant global public health concern, with drinking water being a major source of exposure. This study aimed to determine groundwater fluoride concentration and assess its non-carcinogenic health effects on human health. A longitudinal study design was applied to select water samples in dry and wet seasons from Adama City and Wenji Gefersa town of Ethiopia. Groundwater fluoride concentration was measured using an ion-selective electrode. Total hazard analysis was assessed based on the chronic daily oral intake and dermal absorbed dose of fluoride. Analyses were conducted using ArcGIS, an Excel spreadsheet and Statistical Packages for the Social Sciences (SPSS). This study reported that groundwater fluoride concentration ranged from 0.3 mg/L to 38 mg/L, with the mean annual concentrations of Adama City and Wenji Gefersa Town being 1.9 mg/L and 23 mg/L, respectively. Fluoride concentrations reported at 70% and 45% of groundwater samples during the wet season and dry season were above World Health Organization (WHO) guidelines for drinking water. Total hazard index values among sampled locations varied from 0.17 to 30.43. Three-fourths of infants, 99% of children, 62% of adolescents, and 45% of adults had a risk of developing a non-carcinogenic health effect. This study demonstrated fluoride contamination of groundwater sources pose the residents for higher probability of developing non-carcinogenic health effects on their lifetimes. Application of locally available defluorination technology is paramount to safeguard the community.

PMID:40764642 | DOI:10.1038/s41598-025-13821-7

Sci Rep. 2025 Aug 5;15(1):28599. doi: 10.1038/s41598-025-13419-z.

ABSTRACT

From cellular mechanotransduction to the formation of embryonic tissues and organs, mechanics has been shown to play an important role in the control of cell behavior and embryonic development. Most of our existing knowledge of how mechanics affects cell behavior comes from in vitro studies, mainly because measuring cell and tissue mechanics in 3D multicellular systems, and especially in vivo, remains challenging. Oil microdroplet sensors, and more recently gel microbeads, use surface deformations to directly quantify mechanical stresses within developing tissues, in vivo and in situ, as well as in 3D in vitro systems like organoids or multicellular spheroids. However, an automated analysis software able to quantify the spatiotemporal evolution of stresses and their characteristics from particle deformations is lacking. Here we develop STRESS (Surface Topography Reconstruction for Evaluation of Spatiotemporal Stresses), an analysis software to quantify the geometry of deformable particles of spherical topology, such as microdroplets or gel microbeads, that enables the automatic quantification of the temporal evolution of stresses in the system and the spatiotemporal features of stress inhomogeneities in the tissue. As a test case, we apply these new code to measure the temporal evolution of mechanical stresses using oil microdroplets in developing zebrafish tissues. Starting from a 3D timelapse of a droplet, the software automatically calculates the statistics of local anisotropic stresses, decouples the deformation modes associated with tissue- and cell-scale stresses, obtains their spatial features on the droplet surface and analyzes their spatiotemporal variations using spatial and temporal stress autocorrelations. We provide fully automated software in Matlab/Python and also in Napari (napari-STRESS), which allows the visualization of mechanical stresses on the droplet surface together with the microscopy images of the biological systems. The automated nature of the analysis will help users obtain quantitative information about mechanical stresses in a wide range of 3D multicellular systems, from developing embryos or tissue explants to organoids.

PMID:40764636 | DOI:10.1038/s41598-025-13419-z