Search Results (480)

Background/Objectives: Inflammation-based markers have emerged as potential prognostic tools in hepatocellular carcinoma (HCC), but comparative data with classical prognostic factors in untreated HCC are limited. This study aimed to evaluate and compare the prognostic performance of inflammatory and conventional markers using Harrell’s concordance index (C-index). Methods: This retrospective study included 250 patients with untreated HCC. Prognostic variables included age, BCLC stage, Child–Pugh classification, Milan criteria, MELD score, AFP, albumin, Charlson comorbidity index, and the inflammation-based markers neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), Systemic Inflammation Response Index (SIRI), and Systemic Immune-inflammation Index (SIII). Survival was analyzed using Cox regression. Predictive performance was assessed using the C-index, Akaike Information Criterion (AIC), and likelihood ratio tests. Results: Among the classical markers, BCLC showed the highest predictive performance (C-index: 0.717), while NLR ranked highest among the inflammatory markers (C-index: 0.640), above the MELD score and Milan criteria. In multivariate analysis, NLR ≥ 2.3 remained an independent predictor of overall survival (HR: 1.787; 95% CI: 1.264–2.527; p < 0.001), along with BCLC stage, albumin, Charlson index, and Milan criteria. Including NLR in the model modestly improved the C-index (from 0.781 to 0.794) but significantly improved model fit (Δ–2LL = 10.75; p = 0.001; lower AIC). Conclusions: NLR is an accessible, cost-effective, and independent prognostic marker for overall survival in untreated HCC. It shows discriminative power comparable to or greater than most conventional predictors and may complement classical stratification tools for HCC. Full article

This thesis pioneers the development of q-Rung Neutrosophic Fuzzy Rough Sets (q-RNFRSs), establishing the first theoretical framework that integrates q-Rung Neutrosophic Sets with rough approximations to break through the conventional ${\mu }^q+{\eta }^q+{\nu }^q\le 1$ constraint of existing fuzzy–rough hybrids, achieving unprecedented capability in extreme uncertainty representation through our generalized model (${\mathcal{T}}^q+{\mathcal{I}}^q+{\mathcal{F}}^q\le 3$). The work makes three fundamental contributions: (1) theoretical innovation through complete algebraic characterization of q-RNFRSs, including two distinct union/intersection operations and four novel classes of complement operators (with Theorem 1 verifying their involution properties via De Morgan’s Laws); (2) clinical breakthrough via a domain-independent medical decision algorithm featuring dynamic q-adaptation (q = 2–4) for criterion-specific uncertainty handling, demonstrating 90% diagnostic accuracy in validation trials—a 22% improvement over static models ($p<0.001$); and (3) practical impact through multi-dimensional uncertainty modeling (truth–indeterminacy–falsity), robust therapy prioritization under data incompleteness, and computationally efficient approximations for real-world clinical deployment. Full article

The Qinghai–Xizang Plateau and its surrounding regions have experienced intense tectonic activity, resulting in complex geostress environments that cause frequent and distinctive rockburst disasters in plateau tunnel engineering. In this study, numerical simulations were conducted to investigate the distribution characteristics and patterns of tunnel rockbursts in high-altitude regions, using geostress orientation, lateral pressure coefficient, and tunnel depth as the primary independent variables. Secondary development of FLAC3D 7.00.126 was carried out using FISH language to enable the recording and visualization of tangential stress, the Russense rockburst criterion, and elastic strain energy. Based on this, the influence mechanisms of these key geostress parameters on the location, extent, and intensity of rockbursts within tunnel cross sections were analyzed. Results indicate that geostress orientation predominantly affects the location of rockbursts, with the surrounding rock in the direction of the minimum principal stress on the tunnel cross section being particularly prone to rockburst risks. The lateral pressure coefficient primarily influences the rockburst intensity and pit range within local stress concentration zones, with higher values leading to greater rockburst intensity. Notably, when structural stress is sufficiently large, rockbursts may occur even in tunnels with shallow burial depths. Tunnel depth determines the magnitude of geostress, mainly affecting the overall risk and potential extent of rockbursts within the cross section, with greater depths leading to higher rockburst intensities and a wider affected area. Full article

This study introduces the Skeptical Optimism Scale (SkO) and presents preliminary evidence of its content, construct, and criterion validity. Skeptical optimism refers to dispositional tendencies of having general positive expectations about the future, conditional on critical analysis and in-depth exploration of (potential negative) outcomes. We developed an initial pool of 31 items that explore positive expectations in three main life domains (finding solutions to difficult problems, mastering novel and challenging tasks, and effectively dealing with general life challenges) that were subject to content analysis by eight independent raters. The remaining items were tested for criterion and predictive validity in two samples (N = 198 and N = 417 participants). Factor analyses supported a three-factor structure and the refined 17-item version of the scale showed good reliability and validity. To support applications in settings requiring brief instruments, we also developed a 9-item version, preserving the factorial structure and psychometric qualities of the original scale. The results show that the 17 as well as 9-item SkO scales have a good criterion validity as they positively and significantly correlate with the core self-evaluation scale, critical thinking disposition, and grit. Moreover, our results show that the SkO has good predictive validity as it is the only significant predictor of the creativity quotient in our sample. Full article

Background/Objectives: Prehabilitation is a multimodal intervention introduced in preparation for various surgical procedures. The most effective treatment for obesity is bariatric surgery. Physiotherapy during prehabilitation for bariatric surgery may be an effective method of functional capacity improvement. We aimed to evaluate the impact of an individual outpatient 12-week, exercise-based physiotherapy program featuring prehabilitation on functional status, exercise tolerance, everyday mobility, and fatigue among patients qualified for bariatric surgery. Methods: The completion of an individual outpatient 12-week, exercise-based physiotherapy program during prehabilitation was an inclusion criterion for the study group. Participants included in the study and control groups were assessed twice, after enrollment into the prehabilitation program (the first assessment) and after prehabilitation but before surgery (the second assessment). Both assessments involved functional tests (a six-minute walking test [6MWT], a timed up and go test [TUG], a chest mobility test, anthropometric measures, a mobility index [Barthel], and a modified Borg scale). The collected anthropometric data and values from the 6MWT were used to create original linear models. This study followed STROBE recommendations. Results: The study group and control group did not differ statistically in terms of their anthropometric data. Statistically significant results were obtained between the first and second assessments in both groups in terms of body weight and waist circumference. However, only the study group showed improved results in the TUG test (p = 0.0001) and distance in the 6MWT (p = 0.0005). The study group presented with the normalization of blood pressure (BP) after exertion in the second assessment (systolic BP p = 0.0204; diastolic BP p = 0.0377), and the 6MWT results were close to the norms. According to the original linear model used to predict performance in the 6MWT, the primary modifiable determinant of exercise tolerance was the participant’s weight, while gender served as a non-modifiable determinant. Conclusions: Exercise-based physiotherapy in prehabilitation was associated with improved functional capacity in patients preparing for bariatric surgery, contributing to the improvement in 6MWT results in relation to the norms as well as exercise tolerance. Body weight may be an independent factor determining distance in the 6MWT for patients undergoing prehabilitation for bariatric surgery. Full article

We prove that for the odd integers $n\in \{5,7,9,\dots ,25\}$, the Riemann zeta value $\zeta \left(n\right)$ is not a Liouville number. Our method applies a general strategy pioneered by Wadim Zudilin and D.V. Vasilyev. Specifically, we construct families of high-dimensional integrals that expand into rational linear combinations of odd zeta values, eliminate lower-order terms to isolate $\zeta \left(n\right)$, and apply Nesterenko’s linear independence criterion. We verify the required asymptotic growth and decay conditions for each odd $n\le 25$, establishing that $\mu \left(\zeta \right(n\left)\right)<\infty$, and thus that $\zeta \left(n\right)\notin \mathcal{L}$. This is the first unified proof covering all odd zeta values up to $\zeta \left(25\right)$ and highlights the structural barriers to extending the method beyond this point. We also give rigorous upper bounds on $\mu \left(\zeta \right(n\left)\right)$ for all odd integers $n\in \{5,7,\dots ,25\}$, using multiple integral constructions due to Vasilyev and Zudilin, elimination of lower zeta terms, and the quantitative version of Nesterenko’s criterion. Full article

Background and Objectives: For many patients, the induction-to-delivery interval is shorter with Propess than with Prostin E2. However, some patients also require Prostin E2 to sufficiently boost their dinoprostone levels to achieve cervical change and vaginal delivery. In this study, we compared the efficacy of different timings of Prostin E2 administration after Propess use. Materials and Methods: This single-institution retrospective cohort study was conducted between January 2020 and August 2023. The inclusion criterion was an unfavorable cervix (Bishop score ≤ 6) after Propess use for 8 h. We divided the patients into three groups based on the addition of Prostin E2 at the 8th (group 1), 12th (group 2), and 24th (group 3) hour after Propess insertion. The primary outcome was the cesarean section rate. The secondary outcomes were the induction-to-birth interval, Bishop score at 24 h, neonatal outcomes, and the predictors of labor induction duration. Results: A total of 63 patients were analyzed across three groups based on the timing of Prostin E2 administration (8, 12, and 24 h). The gestational age differed significantly between groups (p < 0.001), with the highest age being observed in the 24 h group. The 8 h group had the shortest induction-to-birth interval (p < 0.001) and the highest Bishop scores after 24 h of Propess use (p < 0.001). Blood loss was lowest in the 12 h group (p = 0.027). No significant differences were found in relation to the mode of birth, tachysystole, neonatal birth weight, or Apgar scores. A multivariable analysis identified gestational age (β = 3.33; p = 0.015) and Bishop score after 24 h of PGE2 (β = −1.99; p < 0.001) as being independent predictors of labor duration. Conclusions: administering Prostin E2 to patients who had a poor response after Propess use was safe; additionally, adding it at the 8th hour after Propess initiation could result in a shorter induction-to-birth interval. Full article

Background: Data accessibility remains a significant barrier in healthcare AI due to privacy constraints and logistical challenges. Synthetic data, which mimics real patient information while remaining both realistic and non-identifiable, offers a promising solution. Large Language Models (LLMs) create new opportunities to generate high-fidelity clinical conversations between patients and physicians. However, the value of this synthetic data depends on careful evaluation of its realism, accuracy, and practical relevance. Objective: To assess the performance of four leading LLMs: ChatGPT 4.5, ChatGPT 4o, Claude 3.7 Sonnet, and Gemini Pro 2.5 in generating synthetic transcripts of patient–physician interactions in plastic surgery scenarios. Methods: Each model generated transcripts for ten plastic surgery scenarios. Transcripts were independently evaluated by three clinically trained raters using a seven-criterion rubric: Medical Accuracy, Realism, Persona Consistency, Fidelity, Empathy, Relevancy, and Usability. Raters were blinded to the model identity to reduce bias. Each was rated on a 5-point Likert scale, yielding 840 total evaluations. Descriptive statistics were computed, and a two-way repeated measures ANOVA was used to test for differences across models and metrics. In addition, transcripts were analyzed using automated linguistic and content-based metrics. Results: All models achieved strong performance, with mean ratings exceeding 4.5 across all criteria. Gemini 2.5 Pro received mean scores (5.00 ± 0.00) in Medical Accuracy, Realism, Persona Consistency, Relevancy, and Usability. Claude 3.7 Sonnet matched the scores in Persona Consistency and Relevancy and led in Empathy (4.96 ± 0.18). ChatGPT 4.5 also achieved perfect scores in Relevancy, with high scores in Empathy (4.93 ± 0.25) and Usability (4.96 ± 0.18). ChatGPT 4o demonstrated consistently strong but slightly lower performance across most dimensions. ANOVA revealed no statistically significant differences across models (F(3, 6) = 0.85, p = 0.52). Automated analysis showed substantial variation in transcript length, style, and content richness: Gemini 2.5 Pro generated the longest and most emotionally expressive dialogues, while ChatGPT 4o produced the shortest and most concise outputs. Conclusions: Leading LLMs can generate medically accurate, emotionally appropriate synthetic dialogues suitable for educational and research use. Despite high performance, demographic homogeneity in generated patients highlights the need for improved diversity and bias mitigation in model outputs. These findings support the cautious, context-aware integration of LLM-generated dialogues into medical training, simulation, and research. Full article

Soil organic carbon (SOC) monitoring is central to carbon farming Monitoring, Reporting, and Verification (MRV), yet high laboratory costs and sparse sampling limit its scalability. We present the first independent field validation of the Stenon FarmLab multi-sensor probe across 100 temperate European arable-soil samples, benchmarking its default outputs and a simple pH-corrected model against three laboratory reference methods: acid-treated TOC, temperature-differentiated TOC (SoliTOC), and total carbon dry combustion. Uncorrected FarmLab algorithms systematically overestimated SOC by +0.20% to +0.27% (SD = 0.25–0.28%), while pH adjustment reduced bias to +0.11% and tightened precision to SD = 0.23%. Volumetric moisture had no significant effect on measurement error (r = −0.14, p = 0.16). Bland–Altman and Deming regression demonstrated improved agreement after pH correction, but formal equivalence testing (accuracy, precision, concordance) showed that no in-field model fully matched laboratory standards—the pH-corrected variant passed accuracy and concordance evaluation yet failed the precision criterion (p = 0.0087). At ~EUR 3–4 per measurement versus ~EUR 44 for lab analysis, FarmLab facilitates dense spatial sampling. We recommend a hybrid monitoring strategy combining routine, pH-corrected in-field mapping with laboratory-based recalibrations alongside expanded calibration libraries, integrated bulk density measurement, and adaptive machine learning to achieve both high-resolution and certification-grade rigor. Full article

Ensuring that older adults perform chair-based exercises (CBEs) correctly is essential for improving physical outcomes and reducing the risk of injury, particularly in home and community rehabilitation settings. However, evaluating the correctness of movements accurately and objectively outside clinical environments remains challenging. In this study, camera-based methods have been used to evaluate practical exercise quality. A single-camera system utilizing MediaPipe pose estimation was used to capture joint angle data as twenty older adults performed eight CBEs. Simultaneously, surface electromyography (sEMG) recorded muscle activity. Participants were guided to perform both proper and commonly observed incorrect forms of each movement. Statistical analyses compared joint angles and sEMG signals, and a support vector machine (SVM) was trained to classify movement correctness. The analysis showed that correct executions consistently produced distinct joint angle patterns and significantly higher sEMG activity than incorrect ones (p < 0.001). After modifying the selection of joint angle features for Movement 5 (M5), the classification accuracy improved to 96.26%. Including M5, the average classification accuracy across all eight exercises reached 97.77%, demonstrating the overall robustness and consistency of the proposed approach. In contrast, high variability across individuals made sEMG less reliable as a standalone indicator of correctness. The strong classification performance based on joint angles highlights the potential of this approach for real-world applications. While sEMG signals confirmed the physiological differences between correct and incorrect executions, their individual variability limits their generalizability as a sole criterion. Joint angle data derived from a simple single-camera setup can effectively distinguish movement quality in older adults, offering a low-cost, user-friendly solution for real-time feedback in home and community settings. This approach may help support independent exercise and reduce reliance on professional supervision. Full article

The number of hologram points in near-field acoustical holography (NAH) for a vibro-acoustic system plays a vital role in conditioning the transfer function between the source and measuring points. The requirement for many overdetermined hologram points for extended sources to obtain high accuracy poses a problem for the practical applications of NAH. Furthermore, overdetermination does not generally ensure enhanced accuracy, stability, and convergence, owing to the problem of rank deficiency. To achieve satisfactory reconstruction accuracy with underdetermined hologram data, the best practice for choosing hologram points and regularization methods is determined by comparing cross-linked sets of data-sorting and regularization methods. Three typical data selection and treatment methods are compared: iterative discarding of the most dependent data, monitoring singular value changes during the data reduction process, and zero padding in the patch holography technique. To test the regularization method for inverse conditioning, which is used together with the data selection method, the Tikhonov method, Bayesian regularization, and the data compression method are compared. The inverse equivalent source method is chosen as the holography method, and a numerical test is conducted with a point-excited thin plate. The simulation results show that selecting hologram points using the effective independence method, combined with regularization via compressed sensing, significantly reduces the reconstruction error and enhances the modal assurance criterion value. The experimental results also support the proposed best practice for inverting underdetermined hologram data by integrating the NAH data selection and regularization techniques. Full article

Aims:This study aimed to identify the prognostic value of tumor invasion distance (TID) based on MRI findings in cervical-cancer (CC) patients treated with radiotherapy (RT). Methods: A total of 218 CC patients diagnosed at Fujian Cancer Hospital from December 2018 to December 2019 were included in the study. Cox regression analyses were conducted to identify independent prognostic factors for overall survival (OS), including low 1/3 vaginal involvement, a longer TID, and RT without chemotherapy. These factors were subsequently used to construct a nomogram for individualized risk prediction. Kaplan–Meier survival analysis was employed to evaluate survival outcomes and establish a risk stratification system. The performance of the new stratification was assessed using the linear trend χ² test, Akaike information criterion, and Harrell’s concordance index. Results: A longer TID was associated with worse 3-year OS (p < 0.001, HR: 3.42, 95% CI: 1.67–7.00). A longer TID, lower 1/3 vaginal involvement, and concurrent chemotherapy were independent prognostic survival factors for CC patients. Compared with the 2018 FIGO staging system, the new risk stratification system provided better monotonicity with a higher linear trend χ² value (28.03 vs. 9.35), better discriminatory ability with smaller Akaike information criterion (312 vs. 331), and a greater Harrell C statistic (0.74 vs. 0.65) for predicting 3-year OS. Conclusions: This was the first study to demonstrate the prognostic value of TID in CC patients who received RT. The new risk stratification system based on TID could complement the 2018 FIGO staging system in identifying high-risk patients for more intense treatment and care. Further prospective research with larger samples is warranted to confirm the significance of TID for CC patients treated with RT. Full article

Background: The World Health Organization (WHO) defines stillbirth as the loss of a fetus after 28 weeks of gestation. Annually, approximately 2 million stillbirths occur worldwide. Projections indicate that by 2030, this figure could rise to nearly 15.9 million, with half of these stillbirths expected to take place in Sub-Saharan Africa. In the global literature, causes include placental complications, birth defects, and maternal health issues, though often the cause is unknown. Stillbirths have significant emotional and financial impacts on families. Methods: The process involves using chi-square tests to identify candidate covariates for model building. The relative risk (RR) measures the association between variables using the sample data of 1435 mothers collected retrospectively. Since these tests are independent, covariates might be interrelated. The unadjusted RR from the bivariate analysis is then refined using stepwise logistic regression, guided by the Akaike Information Criterion (AIC), to select the best subset of covariates among the candidate variables. The logistic model’s regression coefficients provide the adjusted RR (aRR), indicating the strength of the association between a factor and stillbirth. Results: The model fit results reveal that heavy bleeding in the second or third trimester increases stillbirth risk by 4.69 times. Other factors, such as water breaking early in the third trimester (aRR = 3.22), severe back pain (aRR = 2.61), and conditions like anemia (aRR = 2.45) and malaria (aRR = 2.74), also heightened the risk. Further, mothers with a history of hypertension faced a 3.89-times-greater risk, while multifetal pregnancies increased risk by over 6 times. Conversely, proper mental and physical relaxation could reduce stillbirth risk by over 60%. Additionally, mothers aged 20 to 35 had a 40% lower risk than younger or older mothers. Conclusions: This research study identifies the significant predictors for forecasting stillbirth in pregnant women, and the results could help in the development of health monitoring strategies during pregnancy to reduce stillbirth risks. The research findings further support the importance of targeted interventions for high-risk groups. Full article

Background: While magnetic resonance imaging (MRI) is commonly used to identify meniscal tears, intraoperative assessment typically dictates repairability. This study evaluated whether a simplified MRI-based scoring system could reliably predict meniscal repair versus meniscectomy. Methods: Patients who underwent meniscectomy or meniscal repair between 2010 and 2018 were retrospectively identified. Preoperative MRIs were independently reviewed in a blinded fashion by two radiologists and one orthopedic sports surgeon. Reviewers scored images based on four arthroscopic criteria for tear repairability, with one point awarded for each of the following criteria—(1) proximity within 4 mm of the meniscosynovial junction, (2) length > 10 mm, (3) presence of intact inner meniscal segment, and (4) >50% meniscal thickness. Tears scoring four points were considered repairable. Accuracy, sensitivity, and positive and negative predictive values were calculated against the actual procedure performed. Inter- and intraobserver reliability were evaluated using kappa statistics. The predictive performance of each individual criterion was also analyzed. Results: A total of 202 meniscal tears were included (134 meniscectomies and 68 repairs). Reviewer accuracy in predicting repairability ranged from 48% to 76%. Intraobserver reliability was moderate to substantial (κ = 0.42–0.66), whereas interobserver reliability was poor to moderate (pairwise κ = 0.07–0.43; Fleiss’ κ = 0.11). Analysis of individual MRI criteria demonstrated limited predictive value, with most criteria achieving less than 50% accuracy across reviewers. Conclusions: MRI-based prediction of meniscal repairability using arthroscopic criteria demonstrated limited accuracy and poor interobserver reliability. Overall predictive reliability remains insufficient for clinical decision-making. Further investigation, integrating advanced imaging techniques and artificial intelligence, may improve the preoperative assessment of meniscal repairability. Full article

Graph autoencoders’ inherent capability to capture node feature correlations poses significant privacy risks through attackers inference. Previous feature decoupling approaches predominantly apply uniform privacy protection across nodes, disregarding the varying sensitivity levels inherent in graph structures. To solve the above problems, we propose a novel dual-path graph autoencoder incorporating attention-aware privacy adaptation. Firstly, we design an attention-driven metric learning framework to quantify node-specific privacy importance through attention weights and select important nodes to construct the privacy distribution, so that realizing the dynamically privacy decoupling and reducing utility loss. Then, we introduce Hilbert-Schmidt Independence Criterion (HSIC) to measure the dependence between privacy and non-privacy information, which avoids the deviations that occur when using approximate methods such as variational inference. Finally, we use the method of alternating training to comprehensively evaluate the privacy importance of nodes. Experimental results on three real-world datasets—Yale, Rochester, and Credit Defaulter—demonstrate that our proposed method significantly outperforms existing approaches like PVGAE, GAE-MI, and APGE, where the inference accuracy regarding privacy decreased by 25.5%, but the accuracy rate of link prediction achieved the highest 84.7% compared to other methods. Full article