Tag: pubmed

Sci Rep. 2025 Dec 6. doi: 10.1038/s41598-025-29419-y. Online ahead of print.

ABSTRACT

Rural areas worldwide face unprecedented challenges from rapid urbanization, necessitating transformative development pathways to rural revitalization. Here, we present a theoretical framework positioning new quality productivity (NQP)-a productivity paradigm driven by innovation-as a promising lever for rural revitalization. Using panel data from 282 cities in China spanning 2010-2021, we construct fixed effects models and two-stage least squares models to examine NQP’s causal impact on rural revitalization outcomes. Our analysis reveals that NQP significantly enhances rural revitalization with lasting effects. Moderating effect models demonstrate that NQP’s impact is more pronounced in economically developed eastern regions and under stronger government support. Spatial econometric models uncover substantial positive spillover effects extending to both geographically and economically proximate cities. These findings underscore the importance of tailoring NQP to local conditions, enhancing government guidance, and fostering cross-regional collaboration. Our study provides a systematic framework and robust evidence for the penetration of innovation-driven productivity into rural areas, offering actionable insights for public policies aimed at fostering sustainable rural development.

PMID:41353513 | DOI:10.1038/s41598-025-29419-y

Sci Rep. 2025 Dec 6. doi: 10.1038/s41598-025-31501-4. Online ahead of print.

ABSTRACT

The efficacy of three-dimensional (3D) magnetic resonance imaging (MRI)-based registration techniques in femur models of osteoarthritis (OA) with respect to OA severity was investigated in this study. MRI data of 58 OA femurs (23 Kellgren-Lawrence grade 2, 20 grade 3, and 15 grade 4) and 31 normal femurs were analyzed. Distal femurs were segmented and converted into 3D reconstructed models. Several registration techniques (fiducial registration and automated landmarking using point-cloud alignment and correspondence analysis [ALPACA]), were applied to OA femur models. Fit quality and volume differences between the reference and OA models were assessed with respect to OA severity. Generalized Procrustes analysis (GPA) and principal component analysis (PCA) explored important bone-shape features of OA femurs. Deformable ALPACA registration exhibited the best fit. Significant differences were observed in the quality of fit of our techniques and volume differences between the reference and OA models in the OA severity groups. The mean OA model demonstrated bony enlargement at the edges of the cartilage plate in 3D statistical shape analysis (SSA). This shape variation was a major component associated with OA severity in the GPA-aligned PCA. This novel 3D MRI-based registration technique and SSA is useful to differentiate OA severity grades.

PMID:41353498 | DOI:10.1038/s41598-025-31501-4

Eur Arch Otorhinolaryngol. 2025 Dec 6. doi: 10.1007/s00405-025-09852-5. Online ahead of print.

ABSTRACT

PURPOSE: Training and refining both custom and pre-trained convolutional neural network (CNN) models for calculation of intracochlear positional index (ICPI) is as effective as manual calculation. The ICPI is a position factor that is known to influence cochlear implant performance, however manual calculation on computed tomography (CT) imaging is labour-intensive and prone to calculation errors. Automation of this process with machine learning via a custom built CNN model aims to reduce the difficulty in obtaining this position factor. Increasing the number of training epochs will improve accuracy. Our study aims to develop a validated CNN for ICPI calculation, which may improve surgical electrode positioning.

METHODS: Custom built CNN model and pre-trained ResNet 50 model trained and validated on 34 images, and tested on eight CT images of temporal bones with cochlear implants. The ground truth was manually established by calculating the distance from modiolus to electrode (DE) and lateral wall (DL), and applied to derive the ICPI.

RESULTS: The pre-trained ResNet-50 model outperformed the custom-built CNN, with improvement statistically significant on evaluation metrics. The ResNet-50 model has lower mean absolute error and root mean squared error (RMSE). In both models, increasing the number of training epochs from ten to 100 improves accuracy of the ICPI calculation.

CONCLUSION: Our machine learning models successfully achieved automation of ICPI calculation, with increasing accuracy increasing training epochs to 100 iterations. Future studies should explore optimizing these models and validating them on broader datasets to enhance their applicability in real-world scenarios by comparison to speech and audiometric outcomes.

PMID:41353482 | DOI:10.1007/s00405-025-09852-5

Sci Rep. 2025 Dec 6. doi: 10.1038/s41598-025-30237-5. Online ahead of print.

ABSTRACT

Accelerometer data are commonly reduced into epoch summary measures (ESMs) for analysis, e.g. ENMO (Euclidean Norm Minus One), MAD (Mean Amplitude Deviation), MIMS (Monitor Independent Movement Summary) or Counts. We compared associations with all-cause mortality of the volume and intensity of physical activity when derived from those four measures in the Whitehall II and UK Biobank cohorts. Volume (Average Acceleration, AvAcc) and intensity (Intensity Gradient, IG) were derived from each ESM. Associations with mortality were estimated using Cox models. 3733 (25.1% female, median age 68.3 years) and 89,848 (56.4% female, 63.5 years) participants were included from Whitehall II and UK Biobank, respectively. Median (IQR) follow-up was 11.0 (10.7, 11.3) and 8.0 (7.5, 8.5) years, with 563 (15.1%) and 3656 (4.1%) deaths. Associations with mortality were largely consistent between ESMs with the lowest mortality risk for those high (above the median) in both AvAcc and IG (Whitehall: HR = 0.59-0.68; Biobank: 0.55-0.61, reference: low/low), and IG associated with lower mortality risk, irrespective of AvAcc. AvAcc was associated with lower mortality irrespective of IG in Biobank only. In conclusion, associations of AvAcc and IG with mortality are broadly consistent across common ESMs, supporting comparability of activity-health findings across studies using different ESMs.

PMID:41353463 | DOI:10.1038/s41598-025-30237-5

Sci Rep. 2025 Dec 6. doi: 10.1038/s41598-025-30659-1. Online ahead of print.

ABSTRACT

It is unknown whether non-dominant handgrip strength is more strongly associated with health-related quality of life than dominant handgrip strength. This analytical cross-sectional study aimed to determine the comparative association of dominant versus non-dominant handgrip strength with health-related quality of life. Bilateral handgrip strength assessments were performed, and health-related quality of life was assessed using the Short Form-36 questionnaire. A total of 229 participants were evaluated (49.6 ± 5.2 years; 29.9 ± 4.73 kg/m2; 52.8% female; 96.1% right-handed). Dominant handgrip strength (33.00 ± 10.66 kg) was significantly stronger than non-dominant handgrip strength (31.30 ± 9.74 kg; p < 0.001). Notably, in the total population, 32.3% (n = 74; 33.0 ± 10.1 kg) obtained their maximum grip strength in the non-dominant hand, whereas 67.7% (n = 155; 34.2 ± 10.9 kg) obtained it in the dominant hand. Both dominant (r_ω = 0.31; p < 0.001) and non-dominant (r_ω = 0.31; p < 0.001) handgrip strength showed significant and near-identical positive correlations with health-related quality of life. Linear regression models were statistically significant and showed that non-dominant (β = 0.32; p < 0.001) and dominant handgrip strength (β = 0.31; p < 0.001) explained 9.8% and 10.2% of the variance in health-related quality of life, respectively. In conclusion, non-dominant handgrip strength was significantly associated with health-related quality of life, with a magnitude almost identical to that of dominant handgrip strength. These findings challenge the common clinical and investigative practice of assessing only the dominant hand; given that nearly one-third (32.3%) of participants were strongest in their non-dominant hand, unilateral assessment protocols risk systematically underestimating true maximum strength and misclassifying individuals.

PMID:41353458 | DOI:10.1038/s41598-025-30659-1

AIDS Res Ther. 2025 Dec 7. doi: 10.1186/s12981-025-00822-9. Online ahead of print.

ABSTRACT

BACKGROUND: In India, despite significant advancements in Antiretroviral Therapy (ART), stigma and discrimination remain major barriers for people living with HIV (PLHIV), often hindering ART adherence and compromising treatment outcomes. This study aimed to assess the determinants and contributing factors of HIV-related stigma among PLHIV in Mizoram, a northeastern state with one of the highest HIV prevalence rates in the country.

METHODS: A cross-sectional study was conducted among 300 PLHIV attending the ART Center, in Aizawl, Mizoram. Descriptive statistics, Chi-square tests, and binary logistic regression were used to assess factors associated with stigma and treatment adherence.

RESULTS: A total of 300 PLHIV were enrolled in the study, comprising 176 (58.7%) males and 124 (41.3%) females. In the internalized stigma domain, males experienced significantly higher stigma compared to females (aOR = 2.394, CI = 1.294-4.426, p = 0.005). In the felt-normative stigma domain, participants aged 41-50 years reported higher stigma levels compared to aged 51 years and above (aOR = 0.329, CI = 0.110-0.985, p = 0.047). Regarding medication adherence, 208 (69.3%) participants demonstrated optimal adherence to ART, while 92 (30.7%) had sub-optimal adherence.

CONCLUSION: Most PLHIV in our study reported low to moderate stigma across domains. To reduce HIV related stigma among PLHIV, one should prioritize patient centric counselling, educational interventions in the form of mass communication, printed media etc., to ensure their psychological well-being and to create educational awareness involving the community and healthcare professionals to promote more positive thoughts on HIV which will reduce HIV related stigma in the society.

PMID:41353439 | DOI:10.1186/s12981-025-00822-9

Arthritis Res Ther. 2025 Dec 7. doi: 10.1186/s13075-025-03694-6. Online ahead of print.

ABSTRACT

BACKGROUND: Evidence on the association between sleep disorders and rheumatoid arthritis (RA) remains limited. This study aimed to investigate this relationship in U.S. adults using data from the National Health and Nutrition Examination Survey (NHANES).

METHODS: This cross-sectional study included adults aged ≥ 18 years from the 2005-2018 NHANES cycles. A total of 28,040 participants were included. Weighted multivariate logistic regression models were employed to assess the association between sleep disorders and RA. Three models were constructed: an unadjusted model, a minimally adjusted model controlling for demographic variables, and a fully adjusted model incorporating additional lifestyle and clinical covariates. Subgroup analyses were performed to assess the consistency of associations across different population strata, and sensitivity analyses were conducted to confirm the robustness of the results.

RESULTS: Of the 28,040 participants, 4168 (14.32%) were identified as having sleep disorders, and 1589 (4.28%) reported having RA. In the fully adjusted model, sleep disorders were significantly associated with increased odds of RA (OR = 1.76, 95% CI: 1.46-2.13, P < 0.001). Subgroup analyses showed that this positive association persisted across all examined strata, with no significant interactions (P for interaction > 0.05).

CONCLUSIONS: In conclusion, our findings indicate a statistically significant association between sleep disorders and the prevalence of RA in U.S. adults. However, given the limitations of the cross-sectional design, causal inferences cannot be made. Future longitudinal and mechanistic studies are warranted to clarify the temporal direction and biological pathways underlying this association.

PMID:41353432 | DOI:10.1186/s13075-025-03694-6

BMC Med Inform Decis Mak. 2025 Dec 6. doi: 10.1186/s12911-025-03272-5. Online ahead of print.

ABSTRACT

The integration of clinical research data across various institutions faces hurdles due to differing definitions, inconsistent terminologies, and inadequate support for interoperable metadata. While biomedical ontologies offer valuable tools for structuring clinical data, they have not yet been fully utilized for creating comprehensive metadata descriptors, such as variable semantics, statistical summaries, and governance elements essential for data discovery and alignment. We present the Clinical Metadata Exploration Ontology (CMEO) that builds upon well-established ontologies to provide a cohesive representation of study designs, data elements, exploratory statistics, and data reuse permissions. CMEO facilitates semantic querying for study exploration and comparison of data elements across studies, particularly when individual-level data cannot be shared. We demonstrate its utility using metadata from five studies: four heart-failure studies and one wearable-based type 1 diabetes study. After serializing, we executed SPARQL queries that operationalized study-level discovery, variable alignment across studies, and governance-constrained reuse. This FAIR-compliant, metadata-driven integration across heterogeneous sources enables scalable, privacy-conscious research and underpins federated clinical data exploration.

PMID:41353418 | DOI:10.1186/s12911-025-03272-5

BMC Oral Health. 2025 Dec 6. doi: 10.1186/s12903-025-07425-w. Online ahead of print.

ABSTRACT

BACKGROUND: This in vitro study aimed to evaluate and compare preclinical effectiveness of two whitening toothpastes hydrogen peroxide based and activated charcoal based on cigarette smoke-induced discoloration in three restorative materials: a micro-hybrid composite, a nano ceramic composite, and a 3D printed ceramic based resin material.

METHODS: Seventy-two disc-shaped samples (n = 8 per subgroup) were fabricated from micro-hybrid composite, nano ceramic composite and a 3D-printed ceramic resin-based material. After baseline color measurements, all specimens were stained using a standardized cigarette smoke exposure protocol (four cycles, 10 min each). Subsequently, the specimens were brushed with either a hydrogen peroxide containing toothpaste, an activated charcoal containing toothpaste, or distilled water (control), using an electric brushing simulator at 2 N force. Color measurements (ΔE₀₀) were obtained spectrophotometrically at three timepoints: baseline (T₀), post-smoking (T₁), and post-brushing (T₂). Color differences (ΔE₀₀) were analyzed using three-way repeated measures ANOVA for intra-group comparisons over time and one-way ANOVA with Tukey’s post hoc test for inter-group comparisons at each time point (p < 0.05).

RESULTS: All materials exhibited clinically unacceptable discoloration after cigarette exposure (ΔE₀₀ >1.8), with the 3D printed resin-based material group showing the highest color change (ΔE₀₀ ≈ 15.3). Post brushing, both hydrogen peroxide and activated charcoal toothpastes significantly reduced discoloration in micro-hybrid composite resin and nano ceramic composite resin groups (p < 0.001), with hydrogen peroxide showing greater efficacy. No statistically significant improvement was observed in the 3D printed ceramic resin-based material group regardless of the toothpaste used (p > 0.05). Overall, ΔE₀₀ values remained above clinical acceptability after brushing in all groups.

CONCLUSION: 3D-printed ceramic-based resin exhibited the highest discoloration after cigarette smoke exposure, while the nano-ceramic composite showed the lowest. Both whitening toothpastes significantly improved color recovery following cigarette smoke-induced discoloration. However, they remained ineffective in fully reversing discoloration, particularly in 3D-printed ceramic resin-based materials.

PMID:41353363 | DOI:10.1186/s12903-025-07425-w

BMC Oral Health. 2025 Dec 6. doi: 10.1186/s12903-025-07477-y. Online ahead of print.

ABSTRACT

BACKGROUND: Removable dentures are one of the common treatment modalities used to restore missing teeth. Denture stomatitis, caused by the adhesion of Candida albicans, is considered a major complication of these prostheses. The purpose of this article is to evaluate the impact of adding Halloysite Nanotubes (HNTs) on the surface roughness (Ra, µm), color change (∆E*), and Candida albicans adhesion in 3D-printed denture base resins (PDBRs).

METHODS: Three concentrations (0.3%,0,6%, and 0.9%wt.) of HNTs were added to two PDBRs, ASIGA and NextDent, while the control group remained without HNTs addition. The specimens were printed in a disk shape (15 × 2 mm). After printing, specimens were thermocycled (5000 cycles). Surface roughness was measured using a non-contact profilometer. Color change (∆E*) was measured using the Commission International de l’Eclairage (CIE) system coordinates (L*, a*, b*) method. C. albicans adhesion was evaluated using a colony-forming unit (CFU/mL). The collected data were statistically analyzed using ANOVA and a post-hoc Tukey test (α = 0.05).

RESULTS: The addition of HNTs showed no significant difference in surface roughness between all tested groups (P ˃0.05). Adding HNTs resulted in a slight color change at 0.3% and 0.6%, while 0.9% significantly increased the color change (P < 0.001) with a noticeable difference. Adding HNTs significantly decreased the C. albicans adhesion to PDBRs compared to pure resins (P < 0.001). Regarding the concentration effect, the effect was concentration-dependent, with 0.9% showing the lowest Candida colony count for ASIGA resin (1018.9 ± 80.0) and NextDent resins (911.1 ± 89.3).

CONCLUSION: Adding HNTs to PDBRs did not change the surface roughness and significantly decreased C. albicans adhesion. The color was changed with HNTs to noticeable with 0.9%HNTs. Due to the antifungal activities of PDBRs containing HNTs, these PDBRs could be recommended as a method for preventing denture stomatitis.

PMID:41353358 | DOI:10.1186/s12903-025-07477-y