Category: Statistics

Sci Rep. 2025 Nov 27. doi: 10.1038/s41598-025-27743-x. Online ahead of print.

ABSTRACT

The current study demonstrates the intricate thermo-solutal transportation features of a nanofluid experiencing non-linear kinematics as it flows across a rough porous stretched interface. Previous work has typically been limited to smooth geometries, narrow parameter ranges, and few physical intuitions. However, this paper extends the analysis to include surface roughness, porosity effect, nonlinear stretching and essential physical phenomena like effect of magnetic field, Brownian motion special case thermophoresis effect and variable suction/injection. The resulting extension does not only reproduce realistic flow cases, but reveals extremely sensitive solution behaviors that have been completely untouched in the literature. Using scaling transformation approach, the governing non-linear partial differential equations (PDEs) for the transport of momentum, energy, and solutal in the transformed independent variables are translated into a set of coupled ordinary differential equations (ODEs). Numerical simulation of the above transport equations with ten dimensionless parameters is done using the MATLAB BVP4C (built in solver) approach, which ensures computational stability and high precision across broad parametric domains. Additionally, using an expanded parameter domain revealed previously unknown solution properties. For instance, as the thermophoretic limitation raised, the species concentration rose by 5% and fell by 12%. Additionally, sensitivity was demonstrated by the velocity profiles shifting by 20% in response to a small variation in the slip parameter. Finding the limits at which qualitatively reactions to system modifications and other non-physical solutions arise from the qualitative responses is notably innovative. Such findings will propel the development of more efficient coatings and temperature control techniques, offering helpful advice to greatly improve transportation effectiveness in actual nanofluid applications.

PMID:41310388 | DOI:10.1038/s41598-025-27743-x

Electromagn Biol Med. 2025 Nov 27:1-11. doi: 10.1080/15368378.2025.2593267. Online ahead of print.

ABSTRACT

This study aimed to examine the impacts of extremely low-frequency (ELF)-electromagnetic field (EMF) on cognitive functions and analgesia in terms of total oxidant status (TOS) and total antioxidant status (TAS) in the experimental pentylenetetrazole (PTZ)-induced epilepsy model. Twenty-four Wistar albino male rats were categorized into four groups: sham, EMF, PTZ, and EMF+PTZ. The rats were repeatedly exposed to alternating 50-Hz and 5-mT EMF for 165 min a day for 7 days. Epileptic seizures were induced with PTZ. The levels of oxidative stress markers were measured. Univariate multifactorial one-way analysis of variance and post hoc Tukey’s test were used for pairwise comparisons between groups. A statistically significant difference was observed in the learning and short-term memory levels in the EMF + PTZ group compared with the PTZ group (p < 0.001). Analgesia latency statistically significantly increased in the ELF-EMF and ELF-EMF+PTZ groups compared with both the control and epilepsy groups (p < 0.001). A statistically significant increase in TOS was found in the prefrontal cortex in the PTZ group compared with the sham group (p < 0.001). Also, TOS statistically significantly increased in the hippocampus in both PTZ and ELF-EMF+ PTZ groups compared with the sham group (p < 0.001). ELF-EMF decreased the increased TOS in the hippocampus of rats in the PTZ group.

PMID:41310382 | DOI:10.1080/15368378.2025.2593267

Sci Rep. 2025 Nov 27;15(1):42413. doi: 10.1038/s41598-025-26687-6.

ABSTRACT

We implement a framed field experiment to understand and rationalize previous contradictory calorie labeling findings showing mostly decreasing or null effects, but also some evidence of increasing calorie intake. Our study suggests that the numeric value of calorie information alone is not sufficient to explain the impact of information on food choice, but it is the gap between an individual calorie reference point expectation and the realized actual amount that influences food choices. We manipulate this gap in a carefully controlled experiment creating meals that look nearly identical but substantially differ in their calorie content. There is a sharp contrast in the literature with a large body of research only examining the effect of providing the calorie content for a meal while ignoring individual consumers’ expectations. Understanding the underlying mechanism driving calorie information response is crucial for designing and implementing effective calorie interventions and policies.

PMID:41310375 | DOI:10.1038/s41598-025-26687-6

Sci Rep. 2025 Nov 27;15(1):42430. doi: 10.1038/s41598-025-26493-0.

ABSTRACT

Hematoxylin and eosin (H&E) staining is time-consuming, costly, hazardous, and prone to technician-dependent quality variations. This calls for fast, low-cost, and standardized computational alternatives. Lately, generative adversarial networks (GANs) have shown promising results by generating virtual stains from unstained tissue sections. However, no prior study has systematically benchmarked GANs for optimizing skin histology. Moreover, prior evaluations have focused mostly on the perceptual quality of virtual stains rather than their diagnostic utility. In this paper, we introduce VISGAB, a virtual staining-driven GAN benchmark. To our knowledge, it is the first to systematically evaluate and compare common GAN architectures for skin histology. We have also introduced a novel histology-specific fidelity index (HSFI), which focuses on diagnostic accuracy. VISGAB has been systematically applied to Cycle Consistent GAN (CycleGAN), Contrastive Unpaired Translation GAN (CUTGAN), and Dual Contrastive Learning GAN (DCLGAN) using the E-Staining DermaRepo skin histology dataset. The dataset contains 87 whole-slide images (WSIs) of normal, carcinoma, and inflammatory dermatoses tissues. VISGAB findings identify CycleGAN with superior structural fidelity (SSIM: 0.93, HSFI: 0.81), diagnostic sufficiency (75% nuclear atypia detection), and Turing test success (81%), despite higher mean inference time (~ 1.96 min) and mode collapse risk (~ 25%). Although CUTGAN and DCLGAN offer faster training, artifacts (blurring, overstaining, hallucinations) limit their diagnostic utility. Qualitative evaluations by experts and statistical rigor further substantiate our findings in favor of CycleGAN. This work supports AI-driven histopathology by addressing critical gaps in the literature.

PMID:41310372 | DOI:10.1038/s41598-025-26493-0

Sci Rep. 2025 Nov 27. doi: 10.1038/s41598-025-30141-y. Online ahead of print.

ABSTRACT

Modeling mineral and ore bodies from gravity anomalies remains challenging in geophysical exploration due to the ill-posed and non-unique nature of the inverse problem, particularly under conditions of noisy or sparse data. Established inversion methods, including local optimization and metaheuristic algorithms, often require extensive parameter tuning and may yield unstable or poorly constrained solutions. This study proposes a regularized ensemble Kalman inversion (EKI) framework enhanced by Tikhonov regularization to improve numerical stability and mitigate sensitivity to ensemble degeneracy, thereby enabling efficient uncertainty quantification through ensemble statistics. Controlled numerical experiments show that the ensemble size is larger than [Formula: see text] with moderate regularization, we can achieve an optimal balance between convergence stability and model resolution. Benchmarking against established metaheuristic algorithms (PSO, VFSA, and BA) suggests superior computational efficiency and stable convergence. Synthetic and real gravity data inversion (chromite, Pb-Zn, sulphide, and Cu-Au deposits) suggests that the regularized EKI yields stable, geologically consistent results with prior interpretations and drilling data. These results highlight the regularized EKI framework as a robust and efficient tool for mitigating mining risks and supporting strategic decision-making in mineral exploration.

PMID:41310348 | DOI:10.1038/s41598-025-30141-y

Sci Rep. 2025 Nov 27;15(1):42449. doi: 10.1038/s41598-025-26503-1.

ABSTRACT

Reminiscence therapy and exercise therapy have both proven beneficial for individuals with dementia. However, there is limited information on the effects of combining these two approaches in older adults with dementia. Our study aimed to investigate the impact of combined group reminiscence therapy (GRT) and group exercise therapy (GET) on psychological well-being and functional fitness in this population. A total of 32 older adults with mild to moderate dementia living in care homes were randomly assigned into either intervention or usual care groups. The study was conducted from January to June 2021. Intervention: Participants in intervention group received weekly an hour session of GRT and biweekly 1.25-hour session of GET. Reminiscence therapy was based on Remembering Yesterday and Caring Today module, adapted and modified according to participants’ cultural background. GET consisted of stretching, strengthening, aerobic and multicomponent exercises. Outcome measures include the Quality of Life – Alzheimer’s Disease (QOL-AD), Addenbrooke’s Cognitive Examination-III (ACE-III), Beck Anxiety Inventory (BAI), Satisfaction with Life Scale (SWLS), Geriatric Depression Scale (GDS), and Functional Fitness MOT (FFMOT). Independent sample t-test and Mann-Whitney U test show that the participants from the GRT + GET group reported statistically significant higher quality of life and satisfaction with life, with a medium to large effect size. There are no other statistically significant results found for other psychosocial measures. FFMOT was found to deteriorate in both groups with a lesser amount in the intervention group. This study suggests that combined GRT and GET may induce some psychosocial benefits, in particular quality of life and some positive trend in deceleration of functional fitness deterioration among older adults with mild to moderate dementia. Preserving psychological and physical wellbeing is essential for older adults with dementia to maintain their functional independence for as long as possible.

PMID:41310327 | DOI:10.1038/s41598-025-26503-1

Int J Legal Med. 2025 Nov 28. doi: 10.1007/s00414-025-03681-1. Online ahead of print.

ABSTRACT

In the estimation of the Post-Mortem Interval (PMI), semi-quantitative methods have been proposed to overcome the challenges associated with determining the time of death. Among these, the Total Decomposition Score (TDS) method, developed by Gelderman et al., offers a systematic and semi-quantitative approach for estimating PMI. The aim of this study was to evaluate the reliability of the TDS by assessing its interoperator variability and comparing the results obtained with known reference data. A TDS-based questionnaire was administered to 100 participants – including forensic pathologists, residents in forensic medicine and professionals in forensic thanatology – using a dataset of six outdoor cadavers representing different decomposition stages. Data were analyzed using Fleiss’ Kappa (K) to assess inter-rater agreement and Spearman’s rank correlation to evaluate consistency. The results showed moderate overall agreement, with inter-rater reliability decreasing in cases with PMI exceeding 30 days. Linear regression analyses between estimated and actual post-mortem intervals yielded low coefficients of determination, with R² = 34.1% for the TDS-based model and R² = 20.5% for the ADD-based model, indicating that both methods explain only a limited portion of the variance in the actual PMI (PMIa). No statistically significant differences were observed among the professional categories, supporting the method’s applicability across different levels of expertise. While TDS shows promise as a practical tool for PMI estimation in field conditions, inter-operator variability remains a limiting factor in advanced decomposition stages.

PMID:41310302 | DOI:10.1007/s00414-025-03681-1

Pharmacotherapy. 2025 Nov 27. doi: 10.1002/phar.70087. Online ahead of print.

ABSTRACT

BACKGROUND: Falls and related injuries (FRI) pose a large burden among older adults with depression. Proactively identifying individuals at high FRI risk enables timely and tailored interventions, reducing unnecessary health care resource utilization. However, prior prediction models relied on fixed time intervals and failed to capture dynamic changes in health status over time.

OBJECTIVES: To develop and validate machine-learning algorithms (i.e., elastic net, random forest, and gradient boosting machine) for predicting 3-month FRI risk among older adults with depression.

METHODS: This prognostic modeling study included fee-for-service Medicare beneficiaries aged 65 years or older with a depression diagnosis in 2017. Beneficiaries were followed in 3-month episodes from the first depression diagnosis until the earliest of death, hospice services or nursing facility utilization, switching to Medicare Advantage plans, or the end of the study period (i.e., December 31, 2019). A total of 261 time-varying predictors, spanning patient-, provider-, health system- and region-related factors, were updated every 3 months to predict incident FRI risk in the subsequent 3 months. We assessed prediction performance using c-statistics and stratified patients into different risk subgroups using the best-performing model.

RESULTS: Among 274,268 eligible beneficiaries, the mean age was 74.6 (standard deviation [SD] = 7.2) years, 32.0% were male, 85.2% were White, and 15.1% experienced at least one FRI event throughout the study period. Using the random forest model (c-statistics = 0.68), 68.9% of the actual FRI cases were captured in the top three deciles of predicted risk. Individuals in the bottom seven deciles had a minimal FRI incidence (< 1.7%). Key predictors included frailty, age, prior FRI history, and daily dose of antidepressants.

CONCLUSION: Using a nationally representative cohort and time-varying predictors, our model offers a practical approach for efficiently identifying older adults at high FRI risk, which can be updated over time. This approach can inform clinical decision-making and optimize the allocation of fall prevention resources.

PMID:41310296 | DOI:10.1002/phar.70087

Neotrop Entomol. 2025 Nov 27;54(1):121. doi: 10.1007/s13744-025-01341-y.

ABSTRACT

Baculoviruses are important bioinsecticides in integrated pest management, with in vivo production systems still predominant due to cost-effectiveness and scalability. However, inconsistencies in quality, such as viral infectivity and contamination, and polyhedra yield restrict their wider adoption. This study evaluated the infection dynamics of Spodoptera frugiperda multiple nucleopolyhedrovirus – Alphabaculovirus spofrugiperdae isolate 6 (SfMNPV6) in Spodoptera frugiperda larvae to determine the optimal harvest time for maximizing occlusion body (OB) yield. Larvae were exposed to three inoculum concentrations (1 × 10⁵, 1 × 10⁶, and 1 × 10⁷ OB/mL) and monitored daily from the third to the tenth day post-infection. We assessed larval survival, tegument color as an indicator of infection symptoms, and polyhedra yield. Results indicated dose-dependent variations in disease progression, with the infection peak occurring on days seven, eight, and ten for the highest to lowest inoculum concentrations, respectively. Pinkish tegument symptom was strongly correlated with maximum OB yield, making it a reliable visual indicator for harvest timing. Statistical modeling confirmed the relationship between tegument color and OB concentration, with pinkish larvae (symptomatic) significantly outperforming green (early infection stage) and gray (post-mortem period) larvae in virus production. This symptom-based monitoring provides a low-cost, non-invasive alternative to enhance timing in baculovirus harvest protocols. These findings suggest that optimizing harvest based on larval symptoms and dose-dependent infection dynamics can improve virus yield and product quality. This approach enhances the reliability of baculovirus-based bioinsecticides, providing a more effective production strategy to meet the increasing demand for biological control agents in sustainable agriculture, particularly as global pest pressures are intensified by climate change.

PMID:41310286 | DOI:10.1007/s13744-025-01341-y

Sports Med Open. 2025 Nov 27;11(1):150. doi: 10.1186/s40798-025-00947-1.

ABSTRACT

BACKGROUND: In baseball, batting performance can be measured using game and advanced statistics as well as hitting metrics. To date, the core set of individual characteristics or skills associated with superior batting performance remains to be identified. The aim of this scoping review was to identify and classify the individual characteristics or skills associated with baseball batting performance indicators and describe the methods used to assess these individual characteristics or skills and batting performance indicators.

METHODS: A scoping review design was chosen to conduct a systematic literature search. Electronic searches of MEDLINE, SPORTDiscus, and PsycINFO databases were undertaken from inception to August 2024. Cross-sectional studies that investigated the relationship between batting performance indicators and individual characteristics or skills in male or female baseball batters were selected.

RESULTS: Twenty-two cross-sectional studies investigating potential individual characteristics or skills of baseball batting performance met the inclusion criteria. The primary baseball batting performance indicators were grouped into three categories: game statistics, advanced statistics and hitting metrics. Anthropometric measures (height, weight), physical fitness tests (1-RM bench and squat, grip strength, jumps, medicine ball throws, sprint, trunk flexibility, etc.), visual skills (visual acuity, contrast sensitivity, etc.), perceptual skills (anticipation, visual recognition, etc.) and visuomotor skills (eye-hand coordination, reaction time, etc.) were the individual characteristics or skills associated with either game statistics, advanced statistics or hitting metrics.

CONCLUSIONS: Based on the studies included in this scoping review, the results show that several anthropometrics, physical, perceptual-cognitive, and visual skills were associated with superior game statistics, advanced statistics or hitting metrics. Greater height, weight, upper- and lower-body muscle strength, power, and speed, as well as oculomotor skills, visual system characteristics, anticipation, visual recognition, and visuomotor skills corresponded to better batting performance.

PMID:41310274 | DOI:10.1186/s40798-025-00947-1