Tag: pubmed

Cardiovasc Diabetol. 2025 May 21;24(1):220. doi: 10.1186/s12933-025-02768-8.

ABSTRACT

BACKGROUND AND OBJECTIVE: Atherogenicity indices have emerged as promising markers for cardiometabolic disorders, yet their relationship with prediabetes risk remains unclear. This study aimed to comprehensively evaluate the associations between six atherogenicity indices and prediabetes risk in a Chinese population, and explore the predictive value of these atherosclerotic parameters for prediabetes.

METHODS: This retrospective cohort study included 97,151 participants from 32 healthcare centers across China, with a median follow-up of 2.99 (2.13, 3.95) years. Six atherogenicity indices were calculated: Castelli’s Risk Index-I (CRI-I), Castelli’s Risk Index-II (CRI-II), Atherogenic Index of Plasma (AIP), Atherogenic Index (AI), Lipoprotein Combine Index (LCI), and Cholesterol Index (CHOLINDEX). To address the natural relationships between the atherogenicity indices and risk of prediabetes, we applied Cox proportional hazards regression with cubic spline functions and smooth curve fitting, using a recursive algorithm to calculate inflection points. Machine learning approach (XGBoost and Boruta methods) to address the high collinearity among indices and assess their relative importance, combined with time-dependent ROC analysis to evaluate the predictive performance at 3-, 4-, and 5-year follow-up.

RESULTS: During follow-up, 11,199 participants developed prediabetes (incidence rate: 3.71 per 100 person-years). Significant nonlinear associations were observed between all atherogenicity indices and prediabetes risk. Through Z-score standardization of atherogenicity indices and comprehensive Cox proportional hazards regression and advanced machine learning techniques, we identified AIP as the most significant predictor of prediabetes [HR = 1.057 (95% CI 1.035-1.080, P < 0.0001)], with LCI emerging as a secondary important marker [HR = 1.020 (95% CI 1.002-1.038, P = 0.0267)]. Our innovative XGBoost and Boruta analysis uniquely validated these findings, providing robust evidence of AIP and LCI’s critical role in prediabetes risk assessment. Time-dependent ROC analysis further validated these findings, with LCI and AIP demonstrating comparable discrimination, with overlapping AUC ranges of 0.5952-0.6082. Notably, the combined indices model achieved enhanced predictive performance (AUC: 0.6753) compared to individual indices, suggesting the potential benefit of using multiple atherogenicity indices for prediabetes risk prediction.

CONCLUSION: This study identifies statistically significant associations between atherogenicity indices and prediabetes risk, highlighting their nonlinear relationships and combined effects. While the predictive performance of these indices is modest (AUC 0.55-0.68), these findings may contribute to improved risk stratification when incorporated into comprehensive assessment strategies.

PMID:40399916 | DOI:10.1186/s12933-025-02768-8

Arthritis Res Ther. 2025 May 21;27(1):111. doi: 10.1186/s13075-025-03576-x.

ABSTRACT

BACKGROUND: To develop a predictive model for bone destruction in patients with rheumatoid arthritis (RA), based on the characteristics of plasma metabolites and common clinical indicators.

METHODS: The cohort comprised 60 patients with RA, with baseline metabolite features identified using the liquid chromatograph-mass spectrometer system. Radiographic outcomes were assessed using the van der Heijde-modified total Sharp score (mTSS) following a one-year follow-up period to quantify bone destruction. The longitudinal association between metabolites and radiographic progression was analyzed using several machine learning algorithms, and the significance of core metabolites was calculated. A new model incorporating metabolites and clinical indicators was created to evaluate its predictive performance for radiographic progression; the model was compared with other prediction models.

RESULTS: The median increase in mTSS was 3.50. Of the 774 detected metabolites, 77 differed between patients with different outcomes. Core metabolites identified using the Gaussian Naive Bayes algorithm included mangiferic acid, O-acetyl-L-carnitine, 5,8,11-eicosatrienoic acid, and 16-methylheptadecanoic acid. A standardized bone erosion risk score (BERS) was developed based on these core metabolite features for assessing the radiographic progression outcome. Individuals with a high BERS exhibited a lower risk of rapid radiographic progression than those with a lower score (OR = 0.01, 95% CI = 0.01-0.03, P = 0.003). The “China-Japan Friendship Hospital-BERS Model” (CjBM), combining BERS with clinical features (methotrexate and C-reactive protein), produced an area under the receiver operating characteristic curve of 0.800. Moreover, compared with the reported models, the CjBM showed near statistical significance in identifying rapid radiographic progression; adding BERS can improve the discrimination of the original reported model (P_DeLong=0.035).

CONCLUSIONS: The CjBM was developed for early prediction of bone destruction in patients with RA, and the evaluation of BERS emphasizes the significance of metabolite features.

PMID:40399914 | DOI:10.1186/s13075-025-03576-x

BMC Oral Health. 2025 May 21;25(1):748. doi: 10.1186/s12903-025-06124-w.

ABSTRACT

BACKGROUND: This study evaluated the penetration, interfacial adaptation, and push-out bond strength of four bioceramic-based root canal sealers (iRoot SP, Well-Root ST, C-Root SP, and KP-Root SP).

METHODS: A total of ninety mandibular first premolar teeth were used in this study, with eighty teeth randomly divided into eight groups (n = 10). Four groups were designated for sealer penetration analysis, using each of the four sealers mentioned above mixed with 0.1% rhodamine B and applied using the single-cone technique. Horizontal root sections were prepared at 2 mm (apical), 5 mm (middle), and 8 mm (coronal) from the root apex, resulting in a total of 120 slices. Penetration was evaluated using confocal laser scanning microscopy. The other four groups were used for marginal adaptation analysis, with the same sealers applied without rhodamine B, and adaptation was assessed using scanning electron microscopy on sections prepared at the same depths. The remaining ten teeth were used to evaluate push-out bond strength, with 30 dental slices prepared from the middle third, each drilled with four 1 mm diameter holes and randomly filled with one of the four sealers; bond strength was measured using a universal testing machine.

RESULTS: There was no statistically significant difference in the depth and circumference of dentin tubule penetration between different materials (P > 0.05). However, the coronal third was significantly higher than the apical third (P < 0.001). For iRoot SP, the percentage of dentin tubule penetration circumference at the middle third was significantly higher than that at the apical third (P < 0.05). Additionally, Well-Root ST demonstrated superior adaptability for interfacial adaptation than C-Root SP at all the sites (P < 0.05). However, the adaptability of iRoot SP was superior to C-Root SP at the coronal and middle thirds (P < 0.05). Moreover, the push-out bond strength conformed to the following order: Well-Root ST > iRoot SP > KP-Root SP > C-Root SP, with notable variations (P < 0.05).

CONCLUSION: The Well-Root ST sealer demonstrated the best interface adaptation and push-out bonding strength, as well as iRoot SP showed better permeability.

PMID:40399906 | DOI:10.1186/s12903-025-06124-w

BMC Oral Health. 2025 May 21;25(1):756. doi: 10.1186/s12903-025-06163-3.

ABSTRACT

BACKGROUND: With the growing capabilities of language models like ChatGPT to process text and images, this study evaluated their accuracy in detecting supernumerary teeth on periapical radiographs. A customized GPT-4V model (CGPT-4V) was also developed to assess whether domain-specific training could improve diagnostic performance compared to standard GPT-4V and GPT-4o models.

METHODS: One hundred eighty periapical radiographs (90 with and 90 without supernumerary teeth) were evaluated using GPT-4 V, GPT-4o, and a fine-tuned CGPT-4V model. Each image was assessed separately with the standardized prompt “Are there any supernumerary teeth in the radiograph above?” to avoid contextual bias. Three dental experts scored the responses using a three-point Likert scale for positive cases and a binary scale for negatives. Chi-square tests and ROC analysis were used to compare model performances (p < 0.05).

RESULTS: Among the three models, CGPT-4 V exhibited the highest accuracy, detecting supernumerary teeth correctly in 91% of cases, compared to 77% for GPT-4o and 63% for GPT-4V. The CGPT-4V model also demonstrated a significantly lower false positive rate (16%) than GPT-4V (42%). A statistically significant difference was found between CGPT-4V and GPT-4o (p < 0.001), while no significant difference was observed between GPT-4V and CGPT-4V or between GPT-4V and GPT-4o. Additionally, CGPT-4V successfully identified multiple supernumerary teeth in radiographs where present.

CONCLUSIONS: These findings highlight the diagnostic potential of customized GPT models in dental radiology. Future research should focus on multicenter validation, seamless clinical integration, and cost-effectiveness to support real-world implementation.

PMID:40399904 | DOI:10.1186/s12903-025-06163-3

Cardiovasc Diabetol. 2025 May 21;24(1):218. doi: 10.1186/s12933-025-02783-9.

ABSTRACT

BACKGROUND: The triglyceride-glucose (TyG) index serves as a crucial indicator for evaluating insulin resistance (IR) and cardiovascular risk among patients with type 2 diabetes mellitus (T2DM). Concurrently, hyperuricemia (HUA) strongly correlates with adverse cardiovascular outcomes. However, the prognostic value of the TyG index, particularly in patients exhibiting both conditions, remains inadequately defined. This study assessed the association between TyG index measurements and the incidence of major adverse cardiovascular events (MACEs) among patients simultaneously diagnosed with T2DM and HUA.

METHODS: This retrospective, single-center cohort study included 628 patients diagnosed with both T2DM and HUA at the Chaohu Hospital (Anhui Medical University) between 2019 and 2024. Participants were stratified into tertiles based on their TyG index values. Kaplan-Meier survival curves with log-rank tests estimated the risk of MACEs, and Cox regression analyses calculated hazard ratios. The additional predictive contribution of the TyG index was evaluated using C statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) metrics.

RESULTS: During the 38.00 ± 8.78 months follow-up period, 74 MACEs were recorded. A significant proportional relationship emerged between the TyG index and cardiovascular events-patients in the highest tertile demonstrated markedly increased risk compared with those in the lowest tertile (HR = 2.45, 95% CI 1.23-4.95). A pivotal threshold was identified at TyG > 8.40, beyond which each standard deviation increase corresponded to a 66% higher probability of MACEs (HR = 1.66, 95% CI 1.36-2.36, P = 0.014). Integrating the TyG index into traditional risk models significantly improved predictive performance (C statistic increase: 0.64 → 0.67, P = 0.029; NRI = 0.14, IDI = 0.02, both P < 0.05).

CONCLUSION: The TyG index constitutes an autonomous MACE predictor specifically within the distinctive cohort of patients manifesting both T2DM and HUA. This study is the first to validate the TyG > 8.40 threshold in T2DM patients with HUA and identify a synergistic interaction between serum uric acid (SUA) and TyG, providing a novel stratification tool for managing dual metabolic disorders.

PMID:40399902 | DOI:10.1186/s12933-025-02783-9

Mar Environ Res. 2025 May 16;209:107225. doi: 10.1016/j.marenvres.2025.107225. Online ahead of print.

ABSTRACT

This study examined the effect of aquaculture on benthic macrophytes growing on coastal hard substrates across Greece. Stations were categorized into ‘near’ (60-80 m from fish farms), ‘far’ (more than 80 m), and ‘no farm’ (potential future aquaculture sites). Photographic samples were taken between July and September 2021, followed by analysis of macrophyte identification, diversity indices, and ecological assessments. Additionally, the composition of epiphytic microalgae on macrophytes was studied. The overall observation of macrophyte communities revealed a difference between “near” and “far”/”no farm” stations, and a possible shift of the algal communities driven by the aquacultures. Based on the EEI-c index, all – except one – stations, were found to be in a Good or High Ecological Status, in accordance with the provisions of the Water Framework Directive. However, diversity indices, such as the Shannon-Wiener index, provided more nuanced insights into biodiversity changes, revealing differences. For all indicators, but also for the composition of the biocommunities of macrophytes, the effect of the geographical area emerged as statistically significant, but it was not found to show interactions with the distance factor. The photographic sampling method used in the study is easy, economical, and non-destructive, allowing cost effective, long-term monitoring, while improvement actions for the better application for the calculation of EEI-c were suggested. Regarding epiphytic microalgae, the analysis revealed no statistically significant differences between “near”/”far” sites, but no definitive conclusion can be drawn on the impact of aquaculture, as nutrient availability, especially the N:P ratio, exerts a stronger influence on community composition. This study highlights the importance of combining multiple ecological tools and indices to gain a comprehensive understanding of aquaculture’s impact on coastal ecosystems, as reliance on a single indicator may lead to misleading conclusions.

PMID:40398006 | DOI:10.1016/j.marenvres.2025.107225

Eur J Radiol. 2025 May 14;189:112167. doi: 10.1016/j.ejrad.2025.112167. Online ahead of print.

ABSTRACT

OBJECTIVE: To assess the clinical value of the deep learning image reconstruction (DLIR) algorithm compared with conventional adaptive statistical iterative reconstruction-Veo (ASiR-V) in image quality, diagnostic confidence, and intestinal lesion detection in 100-kVp CT enterography (CTE) for patients with wide-range body mass index (BMI).

METHODS: A total of 84 patients underwent 100-kVp dual-phase CTE were included. Images were reconstructed using filtered back projection (FBP), ASiR-V 30 %, ASiR-V 60 %, and DLIR with low, medium, and high levels (DLIR-L, DLIR-M, and DLIR-H). The CT value, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of small and large intestines were compared using repeated measures analysis of variance with the Bonferroni correction or Friedman test. The correlation between relative CNR increment and BMI was analyzed using Pearson’s correlation coefficient. The overall image quality and diagnostic confidence scores were evaluated. Additionally, lesion detection of intestinal disease was conducted by three readers with different experience and compared between DLIR-M and ASiR-V 60 % images using McNemar’s test.

RESULTS: SD decreased sequentially from FBP, ASiR-V 30 %, DLIR-L, ASiR-V 60 %, DLIR-M, to DLIR-H, which corresponded with improvements in CNR and SNR (all p < 0.001). The relative CNR increment of DLIR exhibited a significantly positive linear correlation with BMI (r:0.307-0.506, all p ≤ 0.005). For overall image quality scores, the ranking was: FBP < ASiR-V 30 % < ASiR-V 60 % ≈DLIR-L < DLIR-M ≈ DLIR-H. DLIR-M outperformed ASiR-V 60 % in diagnostic confidence (p ≤ 0.018 for all three readers). In lesion detection, for the two junior readers, DLIR-M exhibited higher sensitivity for inflammatory lesions compared to ASiR-V 60 % (0.700 (95 % confidence interval [95 % CI]: 0.354-0.919) vs. 0.300 (95 % CI: 0.081-0.646) for reader 1 and 0.700 (95 %CI: 0.354-0.919) vs. 0.500 (95 % CI: 0.201-0.799) for reader 2), though no statistical significance was reached.

CONCLUSION: DLIR effectively reduces noise and improves image quality in 100-kVp dual-phase CTE for wide-range BMIs. DLIR-M exhibits superior performance in image quality and diagnostic confidence, also provide potential value in improving intestinal inflammatory lesion detection in junior readers and sheds lights on benefiting clinical decision making, which needs further investigation.

PMID:40398003 | DOI:10.1016/j.ejrad.2025.112167

Rheumatology (Oxford). 2025 May 21:keaf265. doi: 10.1093/rheumatology/keaf265. Online ahead of print.

ABSTRACT

OBJECTIVES: ANCA-associated vasculitis (AAV) is a group of systemic autoimmune diseases affecting small blood-vessels. Class-II HLA genes often reported as major genetic determinants. We conducted a systematic review and meta-analysis to evaluate the susceptibility conferred by HLA genes in AAV and five sub-types i.e. PR3+AAV, MPO+AAV, Granulomatosis with polyangiitis (GPA), Microscopic polyangiitis (MPA) and Eosinophilic granulomatosis with polyangiitis (EGPA).

METHODS: Relevant articles were retrieved until March 2024, from electronic databases using appropriate keywords. Eligible studies were included following inclusion-exclusion criteria. Funnel plots, Newcastle-Ottawa Scale and GRADE tools were used to evaluate the quality of evidence and research findings. Statistical analyses were performed by RevMan 5.4.1. The meta-odds ratio and Z test p-value were considered to check the HLA associations.

RESULTS: Meta-analysis of HLA-alleles identified 30 significant associations with AAV and its sub-types of which 17 withstood Bonferroni corrections. rs9277554-C from HLA-DPB1 (Meta-OR = 3.92(3.27-4.69)), rs1049072-A from HLA-DQB1 (Meta-OR = 1.39(1.27-1.52)) and rs9277341-C from HLA-DPA1 (Meta-OR = 0.41(0.03-0.57)) were significantly associated (p < 0.00001) with AAV and GPA respectively. DRB1*09:01 was significantly (p < 0.00001) predisposing allele in AAV (Meta-OR = 1.72(1.46-2.03)) and MPO+AAV (Meta-OR = 1.65(1.41-1.93)) and MPA (Meta-OR = 1.75(1.41-2.19)). Significant association (p ≤ 0.0005) was also observed for DPB1*01:01 (Meta-OR = 0.38(0.24-0.62)) and DRB1*11:01 (Meta-OR = 2.11(1.39-3.20)) for AAV and MPA respectively. Sensitivity analysis identified additional significant (p ≤ 0.001) predisposing alleles DPB1*04:01 and DPB1*02:01 in AAV and more than one sub-types.

CONCLUSION: Multiple alleles from HLA-DRB1 and DPB1 were found to provide predisposition to AAV and sub-types. Predisposition by DPB1*04:01 and protection by DPB1*02:01 were specific for AAV, PR3+AAV and GPA. Predisposition by DRB1*09:01 was observed among AAV, MPO+AAV and MPA.

PMID:40397991 | DOI:10.1093/rheumatology/keaf265

Rheumatology (Oxford). 2025 May 21:keaf272. doi: 10.1093/rheumatology/keaf272. Online ahead of print.

ABSTRACT

OBJECTIVES: The role of neutrophil extracellular traps (NETs) in immunoglobulin A vasculitis (IgAV) pathogenesis is emerging, with NETosis-associated markers potentially linked to disease activity. This study aimed to explore the relationship between NETosis biomarkers and IgAV disease phases.

METHODS: A longitudinal study involving 33 pediatric IgAV patients and 26 healthy controls was conducted. Blood and urine samples were collected from healthy controls and patients during active and inactive disease phases. NETosis markers, including cell-free DNA (cf-DNA), neutrophil elastase (NE), myeloperoxidase (MPO), and citrullinated histone H3 (cit-H3) were measured using ELISA kits. Statistical analyses were conducted to compare differences for NETosis markers between groups and to evaluate correlations among variables using appropriate statistical tests.

RESULTS: There was no significant difference in gender and age between the patient and control groups. The serum cf-DNA level was significantly higher in the active patient group compared with the control and inactive patient groups (p= 0.04; p= 0.04, respectively). In urine, MPO levels were significantly lower in the active phase of patients than controls (p= 0.009), while cit-H3 levels were higher in both active and inactive phases compared with controls (p= 0.01 and p= 0.03, respectively). A cf-DNA threshold of 935 ng/ml was identified, which achieved a sensitivity of 93% (correctly identifying 93% of active patients) and a specificity of 72% (correctly identifying 72% of healthy controls).

CONCLUSION: Elevated serum cf-DNA and urine cit-H3 suggest a potential role for NETosis in IgAV activity, highlighting these markers as potential indicators for disease monitoring. Further studies are warranted to establish standardized protocols for NETosis marker assessment in IgAV.

PMID:40397989 | DOI:10.1093/rheumatology/keaf272

J Clin Ethics. 2025 Summer;36(2):121-131. doi: 10.1086/734771.

ABSTRACT

AbstractContext: As advance directives (ADs) become more frequently utilized, opportunities increase for conflict between a patient’s designated healthcare power of attorney (POA) and the treatment preferences outlined in their living will (LW). Little is known about patient preferences regarding how to resolve these conflicts.

OBJECTIVES: To assess patient preferences regarding whether their POA or LW should have authority in times of conflict.

METHODS: In this mixed methods study, we completed a retrospective chart review to analyze patient selections in their AD, including selections in a novel section of the AD called the “Binding Guidance” section that gives patients the ability to designate whether their POA or LW should have authority when there is conflict between the two. Additionally, willing patient participants were asked two interview questions about their selections to further elucidate their perspectives.

RESULTS: Out of 143 patients, 48.3 percent (n = 69) chose to have their LW followed over their POA and 51.7 percent (n = 74) chose to have their POA followed over their LW. Several statistically significant associations were identified regarding binding guidance selections. Seventy-four (51.75%) of these patients also answered the additional interview questions, with the participants evenly distributed (n = 37 each) in their binding guidance selections.

CONCLUSION: Patients have varying preferences regarding whether their POA or LW should have authority in times of conflict. ADs should reflect this variation in preferences and allow patients the ability to designate whether they prefer their POA or LW to have ultimate authority when in conflict.

PMID:40397975 | DOI:10.1086/734771