Category: Statistics

J Radiat Res. 2026 Aug 21:rrag068. doi: 10.1093/jrr/rrag068. Online ahead of print.

ABSTRACT

Abnormally high tritium levels were identified at the Wolsong nuclear power plant (NPP) in 2019, prompting growing concern among nearby residents and public health authorities. The study aimed to investigate tritium internal exposure concentrations among residents and to identify potential internal exposure pathways. A total of 359 residents living within 15 km of the NPP with no occupational history at the facility were enrolled in this cross-sectional study in 2021. Participants were categorized into three distance-based zones. Tritium concentrations were measured in urine samples and local water resources. Statistical analyses were conducted using appropriate parametric tests and linear regression based on log-transformed data. The results showed that residents within 10 km of the NPP exhibited significantly higher urinary tritium levels (mean ± SD: 3.28 ± 2.28 Bq/L; 95% CI: 2.87-3.69; P < 0.05) compared with those living further away. Tap water within 5 km contained the highest tritium levels (6.6 ± 1.6 Bq/L), whereas groundwater in the 5-10 km zone showed elevated levels (2.0 ± 1.9 Bq/L). Participants who had visited the NPP in the previous 2 years presented higher urinary tritium levels than those who had not (P < 0.01). Overall, this study showed that urinary tritium concentrations varied according to residential distance and water resource. All measured urinary tritium levels were far below international safety limits. These findings establish essential baseline data for future surveillance and risk communication on nuclear-related public health issues.

PMID:42627965 | DOI:10.1093/jrr/rrag068

Public Health Nurs. 2026 Aug 21. doi: 10.1111/phn.70154. Online ahead of print.

ABSTRACT

BACKGROUND: Cognitive frailty (CF) is a prevalent geriatric syndrome associated with increased risks of falls, hospitalization, disability, and all-cause mortality among older adults. Emerging evidence suggests that chronic inflammation may contribute to the pathophysiology of CF, while dietary patterns can modulate inflammatory responses. The Dietary Inflammatory Index (DII) is a validated tool for quantifying the inflammatory potential of diet. Although higher DII scores have been associated with physical frailty and cognitive impairment, the association between DII and CF remains unclear.

AIM: To investigate the association between DII and CF in older adults.

METHODS: We conducted a secondary analysis of cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014 cycles, comprising 2,386 eligible participants. Participants were categorized into tertiles according to their DII scores: T1 (n = 709), T2 (n = 763), and T3 (n = 914). Survey-weighted logistic regression analyses were performed to examine the association between DII and CF. To examine potential nonlinear relationships, restricted cubic spline (RCS) regression was performed with three knots positioned at the 10th, 50th, and 90th percentiles of the DII distribution.

RESULTS: The prevalence of CF in the study sample was 14.9%, and the survey-weighted prevalence increased across DII tertiles, from 4.9% in the lowest tertile to 8.1% in the middle tertile and 16.6% in the highest tertile. Participants with CF had higher DII scores than those without CF (4.3 [IQR, 1.2 to 7.1] vs 1.5 [IQR, -2.3 to 4.7]). Participants with CF were also older and had higher white blood cell (WBC) counts and neutrophil-to-lymphocyte ratio (NLR) values than those without CF (all p < 0.05). In logistic regression analyses, each 1-unit increase in DII was associated with higher odds of CF in the the unadjusted model (OR 1.096, 95% CI 1.064-1.128), after sociodemographic adjustment (OR 1.056, 95% CI 1.026-1.088), and in the fully adjusted model (OR 1.039, 95% CI 1.001-1.080). Compared with participants in the lowest tertile, those in the highest tertile had higher odds of CF in both the unadjusted (OR 3.887, 95% CI 2.820-5.358) and fully adjusted (OR 2.254, 95% CI 1.522-3.337) analyses, whereas the association for the middle tertile was not statistically significant after full adjustment. RCS models indicated a nonlinear association (p for nonlinearity = 0.020), with the odds of CF increasing more steeply at DII values above approximately 2.4.

CONCLUSION: Higher DII scores were associated with higher odds of CF among older adults in the United States, with a steeper increase in the odds of CF at DII values above approximately 2.4. Prospective cohort studies and dietary trials are needed to establish temporality and determine whether reducing the inflammatory potential of diet can prevent the onset or progression of CF.

PMID:42627964 | DOI:10.1111/phn.70154

Med Sci Sports Exerc. 2026 Aug 21. doi: 10.1249/MSS.0000000000004115. Online ahead of print.

ABSTRACT

OBJECTIVES: Healthcare costs are a major and growing burden in many societies and primarily driven by non-communicable diseases, which are strongly linked to physical fitness. In this study, we explored whether physical fitness is associated with healthcare costs, and whether this association is causal.

METHODS: We used data from the prospective Northern Finland Birth Cohort 1966 (NFBC1966, N=4,468, 55.5% women, age 46 years at baseline) for observational analysis. We measured cardiorespiratory fitness (CRF) via a step test and muscular fitness (MF) via handgrip strength and standardised both to minimal clinically important differences (MCID: one metabolic equivalent of task for CRF, 5 kg for MF). Healthcare costs, including primary and secondary care, and medication expenses, were obtained from national registers, log-transformed, and the analyses were conducted using linear regressions. As a robustness analysis, we conducted subgroup analyses of NFBC1966 participants with hypertension or overweight. To explore causal relations, we performed a two-sample Mendelian randomization (MR) analysis using genetic instruments from UK Biobank (CRF, MF) and FinnGen (healthcare costs).

RESULTS: The mean annual total healthcare costs per NFBC1966 participant were €933 over the six-year follow-up. One MCID unit higher CRF was associated with 11% lower annual costs (P<0.001), and higher MF with 5% lower costs (P=0.001). Similar trends were observed in subgroups and across cost categories, although statistical significance was primarily reached for CRF. The direction of the MR estimates was consistent, but statistical significance varied across outcomes (point estimate β= -0.01 for CRF, P=0.877; β= -0.07, P=0.015 for MF).

CONCLUSIONS: Our findings partially support the hypothesis that better CRF and MF reduce healthcare costs.

PMID:42627955 | DOI:10.1249/MSS.0000000000004115

Indian Dermatol Online J. 2026 Aug 21. doi: 10.4103/idoj.idoj_468_26. Online ahead of print.

ABSTRACT

BACKGROUND: Dermatological quackery is an increasing public health concern in India, driven by poor regulation, easy access to medications, and the growing influence of social media and unqualified practitioners.

AIM AND OBJECTIVES: To assess the patterns, outcomes, economic burden, awareness, and psychosocial impact of dermatological quackery among patients attending a dermatology camp.

PATIENTS AND METHODS: A cross-sectional study was conducted during a dermatology camp in Bengaluru, India (January 2026). Patients with prior exposure to unqualified practitioners were included. Data were collected using a structured, validated questionnaire. Descriptive statistics were computed, and associations were analyzed using Chi-square/Fisher’s exact tests with odds ratios (OR) and 95% confidence intervals.

RESULTS: Eighty-one patients were analyzed; most were young (55.6%), female (59.3%), from low-income households (79%), and from rural areas (60.5%). Common indications reported included skin lightening (96.3%), aesthetic concerns (91.3%), fungal infections (88.8%), and hair loss (83.9%) at various instances. Social media influencers (100%) and self-proclaimed skin/hair specialists without national medical commission-recognized postgraduate qualifications (93.8%) were major contributors. Topical treatments were used by 84.0% of participants; combination creams were used by 66.7%, while steroid-containing cream use was confirmed by 13.6% and considered possible by 44.4%. Adverse effects occurred in 76.5%, and 65.4% reported worsening. Most patients (90.1%) required dermatologist consultation, with delayed care (>1 month) in 59.3%. Side effects were strongly associated with worsening (OR 23.21, P < 0.001) and additional expenses (OR 15.50, P < 0.001). Awareness of legal aspects was low (27.2%), and reporting rates were poor (13.6%). Psychosocial effects included loss of confidence (66.7%) and emotional distress (16%).

LIMITATIONS: Camp-based sampling and self-reported data may limit generalizability.

CONCLUSIONS: Dermatological quackery leads to significant clinical harm, delayed care, and psychosocial burden, highlighting the need for stricter regulation and public awareness.

PMID:42627937 | DOI:10.4103/idoj.idoj_468_26

Sci Adv. 2026 Aug 21;12(34):eaeh5460. doi: 10.1126/sciadv.aeh5460. Epub 2026 Aug 21.

ABSTRACT

More than half of global primary energy is dissipated as low-grade waste heat, yet thermoelectric conversion remains constrained by the intrinsic coupling between phonon and charge transport. Here, we introduce graded interfacial size distribution as a thermodynamic design variable that breaks translational symmetry in multilayers, enabling anisotropic regulation of phonon-carrier transport. Using bismuth telluride (Bi₂Te₃)/metal [gold, silver, and platinum (Pt)] multilayers as a model system, we demonstrate that multiscale interface distributions induce broadband phonon suppression through the coexistence of interfacial scattering, coherent interference, and localization. This yields an ultralow cross-plane thermal conductivity of 0.22 watts per meter per kelvin and a high room-temperature ZT of 1.51 in Bi₂Te₃/Pt films. Concurrently, asymmetric metal-semiconductor interfaces create quasi-two-dimensional accumulation channels that enhance in-plane carrier mobility while preserving energy filtering, delivering a power factor of 176.2 microwatts per centimeter per square kelvin at 300 kelvin. The graded architecture enables high performance in both vertical and flexible planar devices, illustrating a general strategy in which interface distribution, not merely composition, governs anisotropic heat-charge transport. Our findings establish statistical interface engineering as a platform for thermoelectric energy harvesting and solid-state cooling.

PMID:42627918 | DOI:10.1126/sciadv.aeh5460

Sci Adv. 2026 Aug 21;12(34):eaec0156. doi: 10.1126/sciadv.aec0156. Epub 2026 Aug 21.

ABSTRACT

Coccidioidomycosis (Valley fever) is a growing public health concern in the western U.S., with California reporting more than an eightfold rise in cases over the past two decades. As climate change, groundwater regulation, and prolonged drought drive major agricultural land-use shifts in the state, including widespread retirement of cultivated lands (i.e., fallowing), understanding their effects on this soil-borne fungal infection is critical. We linked 65,657 confirmed, geolocated cases (2008-2021) to high-resolution maps distinguishing natural vegetation, crop types, and fallowed fields to quantify how land use and land cover were associated with disease rates. Flexible statistical models showed that natural land covers, shrubland, barren ground, and grassland, were associated with higher incidence. Some agricultural uses (grain/hay, field crops, corn, and cotton) were associated with higher incidence, whereas others (orchards, rice, and truck crops/berries) were associated with lower incidence. Recently fallowed land (1 to 4 years) was linked to higher incidence, but the effect depended on prior cultivation. These findings indicate that land use changes may inadvertently impact coccidioidomycosis risk, underscoring the need for dust control and other measures to protect communities.

PMID:42627914 | DOI:10.1126/sciadv.aec0156

PLoS One. 2026 Aug 21;21(8):e0356238. doi: 10.1371/journal.pone.0356238. eCollection 2026.

ABSTRACT

Phage therapy has become an important strategy against the crisis of antibiotic resistance for its potential to specifically target pathogenic bacteria. However, the narrow host range of phage makes the screening of precise matches of clinical strains inefficient, while existing computational tools are difficult to capture the dynamics of phage-host interactions due to their reliance on single modal features (genome or proteins). In this paper, we propose FusionPHI, a phage-host interaction prediction model that adopts a fully connected neural network architecture and takes multi-modal features as input, including the k-mer statistics of genome sequences, the physicochemical properties of proteins, and the embedded representations of evolutionarily conserved gene motifs for a comprehensive understanding of phage-host interactions. Moreover, FusionPHI contains a dual-stage attention-drive feature fusion module that integrates a self-attention mechanism to optimize the correlation among the features within a single modality followed by a cross-attention mechanism to dynamically fuse genetic distribution patterns with the protein function information in global or local regions of sequences. The experiments of phage-host interaction prediction show that FusionPHI achieves 91% ROC AUC in cross-validation, demonstrating competitive performance compared to the evaluated baseline methods on our dataset, and ablation experiments further validate the necessity of multi-modal features and attention mechanism. The case study of E. coli infected by the M13K07 phage further validate the prediction ability of the proposed FusionPHI model.

PMID:42627866 | DOI:10.1371/journal.pone.0356238

PLoS Comput Biol. 2026 Aug 21;22(8):e1014578. doi: 10.1371/journal.pcbi.1014578. eCollection 2026 Aug.

ABSTRACT

Using contact tracing data provided by the Cyprus Ministry of Health, infection trees for the first four waves of the COVID-19 epidemic are constructed. In these trees, nodes represent infected individuals, while links indicate the direction of transmission between them. For each infection tree of N nodes, the hopcount distribution from the root node to all other nodes is calculated. The empirical distribution is then compared to the hopcount distribution of infection trees generated by a non-Markovian SI process on a complete graph, with Weibull infection times characterized by a shape parameter α. We compute the values of the shape parameter α that best fit the empirical distribution and find that only values of α>1 are obtained, while the Markovian case is characterized by α=1. A Weibull distributed infection time with shape parameter α>1 is characterized by a unimodal density function with a peak at finite time, consistent with previous findings in the literature. Our analysis therefore suggests that the spreading process is most likely governed by non-Markovian dynamics, and that non-Markovianity can be detected solely from the topology of the infection trees. Finally, we analyze the evolution of the empirical distribution of the number of secondary infections caused by each node in the infection trees across different time windows to estimate the effective reproduction number. In practice, the average number of secondary infections seems to often provide a lower bound of the reproduction number computed by the Cyprus Ministry of Health. When the last level of the trees, composed predominantly of terminal nodes that do not generate further infections, is excluded, the estimate reflects more accurately the dynamics of the epidemic.

PMID:42627864 | DOI:10.1371/journal.pcbi.1014578

J Int Soc Sports Nutr. 2026 Aug 21;23(sup1):2720733. doi: 10.1080/15502783.2026.2720733. Epub 2026 Aug 21.

ABSTRACT

BACKGROUND: This study aimed to investigate the effects of folic acid supplementation combined with resistance training on body composition, homocysteine (Hcy), and lipid-related biomarkers in adult males.

METHODS: Forty healthy male college students were recruited and randomly divided into four groups: control, resistance training (RT), folic acid supplementation (FA), and resistance training + folic acid supplementation (RF) condition (n = 10 each). The intervention lasted for 8 weeks, preceded by a 1-week testing period. The RT and RF followed the same resistance training program. The FA and RF participants consumed 0.4 mg folic acid daily, whereas the control participants received one appearance-matched placebo tablet daily. Body composition, Hcy concentration, blood lipids, and folic acid concentration were measured before and after the 8-week intervention. Data were analyzed using SPSS 22.0. Descriptive statistics are presented as mean ± standard deviation (SD). Paired t tests were used to compare pre- and postintervention data within groups, and one-way ANOVA was used to compare differences between groups. A significance level of P < 0.05 was used.

RESULTS: Regarding body composition, the RF produced the greatest increase in skeletal muscle mass (SMM; + 4.79 ± 2.42 kg), which was significantly larger than the increases observed in the control (+0.96 ± 1.50 kg), FA (+1.03 ± 1.68 kg), and RT (+0.91 ± 1.12 kg) (all P < 0.01). Body fat mass (BFM) decreased most in the RF (-1.49 ± 1.40 kg), differing significantly from the control (+0.24 ± 1.71 kg; P < 0.01) and FA (+0.04 ± 1.18 kg; P < 0.05). Additionally, the RF showed a significantly greater decrease in body fat percentage (BFP) than the FA (P < 0.05). In terms of blood biomarkers, Hcy decreased significantly in the RF relative to the control and RT (both P < 0.05), and HDL-C increased more in the RF (+0.21 ± 0.24 mmol/L) than in the control (-0.01 ± 0.12 mmol/L; P < 0.01) and FA (+0.02 ± 0.16 mmol/L; P < 0.05). No significant between-group differences were detected for folic acid concentration, TC, TG, or LDL-C.

CONCLUSIONS: Folic acid supplementation combined with resistance training was associated with greater gains in skeletal muscle mass, greater reductions in body fat mass, lower Hcy, and higher HDL-C than either intervention alone. These favorable biomarker changes may contribute to a reduced risk of cardiovascular disease; however, these findings should be interpreted as preliminary rather than definitive evidence of cardiometabolic benefit.

PMID:42627860 | DOI:10.1080/15502783.2026.2720733

PLoS One. 2026 Aug 21;21(8):e0354976. doi: 10.1371/journal.pone.0354976. eCollection 2026.

ABSTRACT

Brain-computer interface (BCI) systems have advanced with deep learning, but they are still limited by designs tied to specific applications, poor scalability, weak portability, the need for user-specific adaptation, and privacy concerns. We present BELT, a modular Bayesian Edge-Cloud architecture based on three principles: (i) Bayesian priors and posteriors to balance generalization and subject-specific learning, (ii) lightweight classifiers suitable for embedded devices, and (iii) task-aware compression to reduce bandwidth and improve privacy in edge-cloud communication. To show feasibility, we implement BELT-lite as an instantiation of BELT, a lightweight version built only from linear time-invariant operations, making it directly compatible with digital signal processing hardware. Using the BCI Competition IV-2a and IV-2b motor imagery datasets (18 subjects total, ten-fold cross-validation), BELT-lite achieved strong posterior performance after subject-specific fine-tuning: mean accuracy of 87.9%±6.8% on Dataset B and 80.6%±8.6% on Dataset A with data augmentation. After adaptation, four subjects from Dataset B and two from Dataset A exceeded 90% accuracy. On ARM Cortex-A7 hardware, BELT-lite achieved a mean latency of 6.75 ms per sample, significantly faster than EEGNet’s 8.36 ms (p < 10-17)-a 21% speed improvement-at the cost of a modest but statistically significant accuracy reduction of approximately 2.7 percentage points compared to EEGNet. Network Tuning Blocks allowed partial parameter freezing: classifier-only fine-tuning incurred a modest 2-5% accuracy drop while substantially reducing training cost. Compression via the task-unaware autoencoder reduced data size by 3.3× while maintaining high accuracy: prior-model performance stayed within ≈1% of the uncompressed baseline (with slight improvements in some configurations), full posterior fine-tuning showed a ≈1% drop, and classifier-only fine-tuning incurred a ≈3% drop-an acceptable trade-off for privacy-preserving edge-cloud communication, where only a compressed latent representation is transmitted instead of raw EEG. Notably, this task-unaware autoencoder (trained solely to reconstruct the input) consistently outperformed autoencoders that also incorporated classification objectives (task-aware or task-only), providing the best accuracy-compression trade-off across all fine-tuning scenarios. These findings show that BELT provides a principled design for modular and scalable BCIs, while BELT-lite demonstrates that the approach supports accurate, efficient, and portable implementations. Together, they point toward BCI systems that are more practical, mass-producible, and privacy-aware, enabling wider use in real-world settings.

PMID:42627854 | DOI:10.1371/journal.pone.0354976