Tag: statistics

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

Sci Rep. 2025 Aug 5;15(1):28617. doi: 10.1038/s41598-025-11357-4.

ABSTRACT

Optimization techniques are particularly useful when designing different free-form surfaces and manufacturing products in the engineering and CAD/CAM fields. Recently, many real-world problems utilize optimization techniques with objective functions to get their desired solution. In this paper, the shape optimization of GHT-Bézier developable surfaces by using a meta-heuristic technique called Improved-Grey Wolf Optimization (I-GWO, in short) technique is presented. This Grey Wolf optimization algorithm impersonates the hunting tactics of grey wolves in nature. Three optimization models arc length (AL), minimum energy (En), and curvature variation energy (CVEn) of dual and interpolation curves, are used to formulate this technique. The shape control parameters are considered as optimization variables. So, our aim is to find the optimal shape control parameters by applying the I-GWO algorithm to the optimization models through an iterative process. By using the duality principle between the points and planes, the construction of GHT-Bézier developable surfaces is formulated by the optimal choice of shape parameters obtained from I-GWO technique. Furthermore, the developability degree of GHT-Bézier developable surfaces is also determined. Some applications of the proposed method are given and the effectiveness of this method is illustrated.

PMID:40764629 | DOI:10.1038/s41598-025-11357-4

Parasit Vectors. 2025 Aug 5;18(1):335. doi: 10.1186/s13071-025-06988-5.

ABSTRACT

BACKGROUND: Onchocerca lupi is a zoonotic, vector-borne filarioid nematode that mainly infects wild and domestic canids in the Southwestern USA, Europe, Asia, and Africa. Clinical canine infections are associated with ocular disease, characterized by the presence of nodules and conjunctivitis. Subclinical cases can be challenging to diagnose, even with evaluation of cutaneous tissues for microfilariae. Current diagnostic tests include conventional polymerase chain reaction (cPCR) to detect O. lupi DNA, and, alternatively, real-time PCR (qPCR), which provides more rapid results and higher throughput. The objectives of this study were to: I) optimize a novel qPCR assay that detects O. lupi and II) to assess the prevalence of O. lupi in shelter dogs from Albuquerque, NM, USA.

METHODS: This probe-based qPCR was optimized with a detection threshold of 0.33 pg for DNA of an adult female O. lupi. We further optimized the assay by performing a dynamic range test to determine the ideal dilution factor and inclusion of an internal positive control. We collected skin snips from the interscapular region of 404 dogs between January and September 2023. Demographics were recorded, including age, sex, American Kennel Club breed groups, and coat color. Dogs were separated into age groups, including juveniles ≤ 1 year old (n = 120; 29.7%), adults > 1-7 years old (n = 260; 64.3%), and seniors > 7 years old (n = 24; 5.9%). Of those, 194 were female, and 210 were male. We also had nine different American Kennel Club breed groups represented, as well as two coat colors: single (33.0%) and mixed (67.0%). Genomic DNA was subjected to cPCR followed by Sanger sequencing and our probe-based qPCR. Both PCRs targeted a fragment of the cytochrome oxidase c subunit 1 (cox1) of the mitochondrial DNA. We performed statistical analysis to assess any association between exposure factors, such as age, sex, breed, and coat color and the outcome, whether O. lupi was present.

RESULTS: Overall, eight (1.9%; 95% confidence interval (CI) 0.8-3.8%) dogs tested O. lupi-positive via qPCR and five (1.2%; 95% CI 0.4-2.8%) via cPCR. Of the qPCR-positive dogs, six were adults and two were juveniles. Age (P = 0.704), sex (P = 0.910), breed groups (P = 0.217), and coat color (P = 0.781) were not statistically associated with a qPCR-positive result with a cutoff of P < 0.2. In addition, 20 dogs tested positive for Cercopithifilaria bainae via cPCR and sequencing, but these did not cross-react with our qPCR.

CONCLUSIONS: This is the first epidemiological study on O. lupi in a canine population from an urban center within an endemic area in North America. Active surveillance using reliable diagnostic tools can better elucidate the epidemiology of this zoonotic parasite and enable the implementation of strategies for control and prevention.

PMID:40764591 | DOI:10.1186/s13071-025-06988-5

BMC Med. 2025 Aug 5;23(1):458. doi: 10.1186/s12916-025-04282-w.

ABSTRACT

BACKGROUND: Parental obesity has been identified as one of the most important early risk factors for childhood obesity, but molecular mechanisms driving this greater predisposition remain to be elucidated.

METHODS: In this study, we recruited a cohort comprising children with obesity (body mass index over two z-scores above the age/sex-adjusted mean of the Spanish reference population, age range: 6-12 years), born to parents with obesity (N = 18) or without obesity (N = 41), as well as matched healthy controls (N = 26). Plasma and erythrocyte samples were collected for comprehensive biochemical and metabolomics analyses, this latter by applying high-throughput liquid chromatography-mass spectrometry. Then, a combination of multivariate and univariate statistical tools was applied to unravel the molecular pathogenic impairments that parental obesity may imprint in the offspring.

RESULTS: Interestingly, we found parental obesity to be associated with exacerbated unhealthy metabolic outcomes in the offspring with obesity, as mirrored in higher fasting insulin levels (p = 2.8 × 10^-8) and HOMA-IR scores (p = 1.3 × 10^-8). This was in turn accompanied by altered concentrations in 87 plasma and 51 erythroid metabolites (p < 0.05) involved in a variety of obesity-related pathways that are known to be tightly regulated by insulin action, namely energy-related metabolism, branched-chain amino acids, nitrogen homeostasis, redox systems, and steroid synthesis (i.e., steroid hormones, bile acids). Additional analyses demonstrated that most metabolomics associations were largely attenuated after adjusting for the HOMA-IR scores.

CONCLUSIONS: Therefore, we hypothesize that insulin resistance could be a major driving force in mediating deleterious programming mechanisms induced by parental obesity in the offspring.

PMID:40764579 | DOI:10.1186/s12916-025-04282-w

BMC Vet Res. 2025 Aug 6;21(1):505. doi: 10.1186/s12917-025-04946-y.

ABSTRACT

BACKGROUND: Canine obesity is a common disorder accompanied by a low-grade chronic inflammation and is considered a risk factor for liver and heart diseases. The present study aimed to investigate whether an olive oil-derivative enriched in N-acylethanolamines (Olaliamid^®, OLA) may protect dogs against obesity-induced comorbidities.

RESULTS: Twenty-seven dogs of mixed breed and size with a body condition score ≥ 7/9 were included in the trial, once provided they were otherwise healthy. Dogs were fed a commercial maintenance diet for two weeks before enrolment, and randomized in two groups, i.e., OLA (n = 14) and placebo (OLA vehicle; n = 13). Both treatments were administered orally by the owners in a liquid form at 0.7 ml/5kg body weight, once a day for three months. At baseline and three months later dogs underwent physical examination, blood draw, and echocardiography. At the same timepoints, owners were given a questionnaire about their dog’s general condition. OLA prevented the increase in leptin observed in the placebo group (P = 0.011), decreased IL-6 (P = 0.043) and derivatives-reactive oxygen metabolites (d-ROMs, P = 0.008), and increased biological antioxidant potential (BAP) compared to the placebo group (P = 0.032). Moreover, OLA protected the liver, with ALT levels being decreased in the OLA group compared to the placebo one (P = 0.005) and bilirubin levels being decreased in the OLA group (P = 0.030) but not in the placebo one. OLA showed a cardioprotective effect, with a significant decrease of IVSdN (P = 0.028), LVPWdN (P = 0.047), IVSd/LVIDd (P = 0.015) and LVPWd/LVIDd (P = 0.034) compared to the placebo group. According to dog owners, the difficulty rising from lying down significantly increased in the placebo group (P = 0.039) but not in the OLA one.

CONCLUSION: Overall, OLA improved obesity-induced meta-inflammation and oxidative status and helped to ameliorate liver and heart health as well.

PMID:40764576 | DOI:10.1186/s12917-025-04946-y

BMC Health Serv Res. 2025 Aug 6;25(1):1031. doi: 10.1186/s12913-025-13207-5.

ABSTRACT

BACKGROUND: Artificial Intelligence (AI) can empower clinicians to make data-driven decisions, treatments and streamline administrative tasks. However, it is vital to understand their perception towards AI for seamless implementation in practice. Therefore, the study aimed to assess the attitude, receptivity and readiness of medical and dental practitioners towards the use of AI in clinical practice.

METHODS: A cross-sectional study employing non-probability convenience sampling was conducted from April to August 2024. A questionnaire was distributed among practitioners working in public and private sector hospitals. The questionnaire included a validated tool, the General Attitude towards Artificial Intelligence Scale (GAAIS), comprising of total 20 items with two subscales; positive and negative attitudes. They were rated on a 5-point Likert scale, ranging from strongly disagree (1) to strongly agree (5). The items of negative attitudes were reverse coded. Self-formulated questions to assess the readiness of medical and dental practitioners to incorporate AI in practice were also included. Data was analyzed using IBM SPSS v.25. Results were reported as frequencies and percentages. Statistical analysis was performed using Mann-Whitney U, and Chi-squared test for continuous and categorical variables. The association between two continuous variables was assessed through Spearman’s correlation.

RESULTS: A total of 451 responses were analyzed. The mean score for positive attitudes toward AI was 3.6 ± 0.54, whereas for negative attitudes it was estimated to be 2.8 ± 0.71. Approximately 187 (41.5%) respondents believed AI was superior to humans in routine jobs. About 190 (42.1%) respondents agreed that AI can make errors. Most respondents 334 (74.1%) were aware of AI applications in healthcare, and 329 (72.9%) reported familiarity with AI technologies. However, only 153 (33.9%) felt confident in operating AI systems. While 282 (62.5%) expressed eagerness to incorporate AI into diagnosis and treatment planning, a significant difference was observed between groups, with dental practitioners showing greater willingness (p = 0.004). Additionally, dentists exhibited higher confidence in using AI compared to medical practitioners (p = 0.047).

CONCLUSIONS: The findings suggest that most practitioners had a positive attitude towards AI and were receptive towards incorporating the technology as a beneficial tool in their practice. Ethical guidelines and extensive training can mitigate negative perceptions associated with the use of AI technology. It is also imperative that resources should be provided, specially to public-sector healthcare systems as they serve underprivileged communities. This will help practitioners gain familiarity with AI technology and will enable them to develop proficiency in AI use.

PMID:40764570 | DOI:10.1186/s12913-025-13207-5

BMC Psychiatry. 2025 Aug 5;25(1):761. doi: 10.1186/s12888-025-07237-w.

ABSTRACT

BACKGROUND: This study addresses the challenge of accurately classifying the severity of schizophrenia in patients through a clever approach. By leveraging electroencephalography (EEG) signals, we aim to establish a method for evaluating patient conditions, thereby contributing to the psychiatric diagnosis and treatment field.

METHODS: Our research methodology encompasses a comprehensive system framework designed to analyze EEG signals with the Positive and Negative Syndrome Scale (PANSS) for correlation analysis. The process involves: (1) administering the PANSS test to create a database of schizophrenia patients; (2) developing a visual concentration test system that measures EEG signals in real-time; (3) processing these signals to construct an EEG feature database; (4) employing support vector machine and decision tree methods for illness severity classification; (5) conducting statistical analysis to correlate PANSS scores with EEG features, assessing the effectiveness of these correlations in clinical applications.

RESULTS: The study successfully demonstrated the potential of a concentration detection system, integrating EEG signal analysis with PANSS scores, to classify schizophrenia severity accurately. Applying SVM and decision tree methods established significant correlations between EEG features and clinical scales, indicating the system’s efficacy in supporting psychiatric diagnosis.

CONCLUSIONS: Our findings suggest that the proposed analytical methods, focusing on EEG signals and employing a novel system framework, can effectively assist in classifying the severity of schizophrenia. This approach offers promising implications for enhancing diagnostic accuracy and tailoring treatment strategies for patients with schizophrenia.

PMID:40764561 | DOI:10.1186/s12888-025-07237-w