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Prediction model for extrathyroidal extension in thyroid papillary carcinoma based on ultrasound radiomics
Abstract This study aimed to construct preoperative prediction models for extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC) based on ultrasonic radiomics and explore their clinical application value. This retrospective study included PTC patients treated across three centers from 2015 to 2023. Data for 609 cases from two centers were utilized for model construction and divided 4:1 into a training set (n = 487; 144 with ETE and 343 without ETE) and test set (n = 122; 58 with ETE and 64 without ETE). The external validation set comprised 109 PTC patients from the third center (n = 109; 55 with ETE and 54 without ETE). Image features were extracted using Pyradiomics. Feature selection and dimensionality reduction were performed using the least absolute shrinkage and selection operator and principal component analysis to construct radiomics models. Model performance was evaluated by receiver operating characteristic (ROC) curve analysis, and clinical benefit was assessed by decision curve analysis. A total of 806 radiomics features were extracted from the training set data. After feature selection and dimensionality reduction, six significant features were included in the models, including one gray-level size zone matrix feature, one shape feature, one first-order feature, one gray-level run-length matrix feature, and two gray-level co-occurrence matrix features. The extreme gradient boosting (XGB) model showed the best performance in both the test and external validation sets, with area under the ROC curve values of 0.841 and 0.814, respectively. In conclusion, the XGB preoperative ETE prediction model for PTC based on ultrasonic radiomics offers good clinical application value for decision-making regarding therapeutic strategies.
Comparison the impact of IFN-β alone or in combination with vitamin D on critical pathways involved in AML progression
Background Acute myeloid leukemia (AML) is a malignant disorder characterized by the accumulation of immature myeloid cells which can be developed and exacerbated through inflammation. Interferon-β (IFN-β) and vitamin D (Vit D) are known for their immunomodulatory and anti-proliferative effects. Key molecules like IL-1β, Gal-9, β-catenin, and NF-κB play important roles in AML progression. This study examines the effects of IFN-β alone and in combination with Vit D on U937 cell proliferation and the regulation of these key molecules. Methods Cell counting kit-8 (CCK-8) was applied to assess the effect of IFN-β and Vit D on U937 cells proliferation. Real-time PCR was carried out to evaluate the impact of IFN-β and Vit D on IL-1β, IL-10, Gal-9, β-catenin, and NF-κB genes. ELISA was performed to estimate the protein levels of IL-1β and IL-10. Western-blotting was applied to evaluate the NF-κB signaling pathway. Results The proliferation of U937 cells reduced significantly in the presence of IFN-β, while it did not show a remarkable change following treatment with Vit D. IL-1β gene expression was reduced following either IFN-β or Vit D treatment, while IL-10 gene expression underwent a gentl increase following treatment with IFN-β, which was in contrast to Vit D treatment. IFN-β in contrast to Vit D decreased Gal-9 gene expression. β-catenin gene expression increased after IFN-β or Vit D treatment. NF-κB gene expression reduced following either IFN-β or Vit D treatment, except for the highest concentration of Vit D. At protein level, IFN-β and Vit D treatment leads to a reduction of both IL-1β and IL-10 as well as p-NF-κB. Conclusion Our findings suggest that IFN-β effectively inhibits U937 cell proliferation and modulates key inflammatory and immune-related molecules involved in AML pathogenesis. These results highlight IFN-β as a promising agent for targeting critical pathways in AML and suggest a modulatory, but less potent role for Vit D.
A deep learning framework for Ethiopian sign language recognition using skeleton-based representation
Development and validation of the arabic version of the social-ecological model questionnaire for patients undergoing maintenance hemodialysis
Background Patients undergoing maintenance hemodialysis (MHD) in Arabic-speaking regions, particularly in Palestine, face unique sociopolitical and cultural barriers affecting their care and quality of life. Existing assessment tools rarely address the multi-level determinants of support within these populations, highlighting the need for a culturally validated instrument based on the Social-Ecological Model (SEM). Objective To develop and validate an Arabic-language questionnaire grounded in the SEM to assess the multi-level (community, interpersonal, organizational, and policy) support factors influencing MHD patients in the Gaza Strip. Methods A cross-sectional study was conducted from November 2024 to February 2025 at three governmental dialysis centers in Gaza, enrolling 101 Arabic-speaking adult MHD patients through systematic random sampling. The validation process encompassed item analysis, content and face validity, and psychometric testing, including exploratory factor analysis (EFA), assessment of convergent and discriminant validity which includes average variance extracted, factor loadings, composite reliability, Cronbach’s alpha, Fornell-Larcker criterion, cross-loadings, and Heterotrait-Monotrait ratio. Results The EFA identified a four-factor structure corresponding to Community, Interpersonal, Organizational, and Policy Support, explaining 65.15% of the variance. The final validated questionnaire comprised 31 items, with the Community Support domain demonstrating the strongest psychometric properties (factor loadings: 0.821–0.923; Cronbach’s alpha = 0.972; AVE = 0.796). While Interpersonal and Organizational domains showed acceptable reliability and validity, the Policy Support domain displayed marginal construct validity. Overall, the instrument had strong content, face, convergent, and discriminant validity. Conclusion The Arabic SEM-based questionnaire is a reliable and valid tool for assessing multi-level support factors among MHD patients in Arabic-speaking contexts. It enables comprehensive evaluation for research and clinical practice. Future research should confirm its structure using confirmatory factor analysis, extend validation to diverse Arab populations, and examine temporal stability.
Chemical profile, antioxidant capacity, cytotoxicity, and dual enzymatic inhibition of Sloanea medusula K.Schum. & Pittier leaves for cosmeceutical applications
Anti-cancer activity of 7-methoxyheptaphylline from Clausena harmandiana against PANC-1 pancreatic cancer cells and its sustainable extraction method
Pancreatic cancer presents a significant therapeutic challenge characterized by poor survival rates, leading to development of innovative treatment strategies. This study evaluated the anti-cancer potential of 7-methoxyheptaphylline (7-MH), a carbazole alkaloid from Clausena harmandiana, against pancreatic cancer cells (PANC-1) and developed an environmentally sustainable extraction methodology using ultrasonic-assisted extraction (UAE). 7-MH demonstrated selective cytotoxicity against PANC-1 cells with preferential activity under nutrient deprivation conditions (PC50 = 4.54 μM) compared to nutrient-rich conditions (IC50 = 46.84 μM). This compound exhibited minimal toxicity toward normal MCE301 epithelial cells (IC50 = 83.4 μM). Live-cell imaging showed dose-dependent apoptotic morphology including membrane blebbing and cell shrinkage within 24 hours. At concentrations of 25 and 50 μM, the compound significantly inhibited wound closure and colony formation, suggesting antimetastatic properties. Mechanistic analysis exhibited that 7-MH suppressed the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) signaling pathway specially under nutrient deprived conditions. Western blot analysis showed 45% reduction in Akt expression, 43% decrease in mTOR phosphorylation, and complete inhibition of Akt phosphorylation at 20 µM concentration. For sustainable extraction of 7-MH, UAE using ethanol was optimized through response surface methodology (RSM) with central composite design (CCD). The optimal protocol (50 °C, 60 minutes, 0.40 g/10 mL plant/solvent ratio) achieved 1.26 ± 0.02% yield with only 3.55% deviation from predicted values. This developed extraction method provides an efficient and environmentally sustainable alternative to conventional halogenated solvent extraction methods. These findings offer valuable insights for advancing natural product-based cancer therapeutics and demonstrate the implementation of sustainable natural products extraction principles.
Selective laser etching fabrication of stacked microporous membranes for multisize particle separation in 3D microfluidics
Immuno-informatics voyage through molecular mimicry of Heat Shock Proteins: Potential IBD immunopathogenesis
Background The interplay between the gut microbiota axis and host immunity is pivotal in the pathogenesis of inflammatory bowel disease (IBD), an idiopathic inflammatory condition. Molecular mimicry may be at the root of autoimmune and auto-inflammatory diseases, such as IBD, when microbial antigens and host proteins share structural and molecular similarities. However, auto-inflammation can also occur through mechanisms independent of molecular mimicry. The present study focused on the possible involvement of intestinal bacterial heat shock proteins (HSPs) in the immunopathogenesis of IBD as a cutting-edge issue. Methods We employed an immuno-informatics approach to evaluate host-microbe interactions and predict the involvement of bacterial HSPs 60, 70, and 90 in IBD via molecular mimicry as our primary objective. The substantial evolutionary conservation of HSPs and their presence in inflammation scenarios propelled our research. To validate our approach, we performed docking and molecular dynamics (MD) simulations on selected HLA-epitope complexes. Results Our analysis revealed that all studied bacteria, compared to Homo sapiens, exhibited meaningful sequence similarity and identity of HSPs. Thirteen bacterial species and their corresponding thirteen epitopes derived from HSP counterparts were selected for further investigation. Finally, a specific epitope of human HSP60 and three epitopes of HSP70 demonstrated considerable sequence similarity to their bacterial counterparts, which was further corroborated through MD simulations as a primary outcome. The secondary outcomes encompassed various factors, including assessing residues in the epitope and receptor-binding grooves within the epitope-HLA complex. Based on the secondary data analysis, the co-expression findings suggested that HSP70 could serve as epitopes in eliciting T-cell-mediated autoimmune responses during infections. Conclusion The study provided evidence of molecular mimicry between microbial and human HSPs, which could serve as molecular targets for cross-reactive T cells. In addition to considering sequence similarity, our study emphasized the importance of structural interactions as essential factors in cell signaling and immunological pathways.
Prognostic impact of hand-foot skin reaction in regorafenib-treated adult-type diffuse gliomas: A multicenter Turkish Oncology Group study
Abstract Glioblastoma, IDH-wildtype is the most aggressive primary brain tumor in adults, and treatment options for recurrent disease are limited. Regorafenib, an oral multikinase inhibitor, has shown efficacy in glioblastoma at first recurrence, but its role in later recurrences and in other adult-type diffuse gliomas remains unclear. This multicenter retrospective study aimed to evaluate the safety and efficacy of regorafenib and to identify prognostic factors in patients with adult-type diffuse gliomas, including glioblastoma, who were treated at the second or subsequent recurrences. A total of 68 patients from 22 institutions were analyzed. The median overall survival (OS) was 3.84 months, and the median progression-free survival was 2.46 months. The objective response rate was 13%, and the disease control rate was 48%. Adverse events occurred in 80.9% of patients, most commonly fatigue, anemia, and elevated transaminases. Hand–foot skin reaction (HFSR) of any grade was observed in 36.4% of patients and was associated with significantly improved OS (HR: 0.41; p = 0.005). Regorafenib demonstrated a manageable safety profile and modest activity in this heavily pretreated population. The development of HFSR emerged as a potential prognostic marker for treatment benefit. Taken together, our findings support further exploration of regorafenib in this setting and suggest that HFSR may serve as a practical marker to guide treatment continuation.
Trends and disparities in dilated cardiomyopathy related mortality among adults in the United States: A CDC WONDER analysis (1999–2023)
Background Dilated cardiomyopathy (DCM) is a progressive myocardial disease characterized by ventricular dilation and impaired systolic function, often leading to heart failure and increased mortality. This study aims to analyze DCM-related mortality trends among adults in the United States from 1999 to 2023. Methods Trends in DCM-related mortality among adults aged ≥ 25 years from 1999 to 2023 were analyzed using the CDC WONDER multiple-cause of death database. Age adjusted mortality rates (AAMRs) per 100,000 persons and annual percent change (APC) were calculated and stratified by year, sex, race, census region and urbanization status. Results From 1999 to 2023, there were 184,073 deaths in the United States attributed to DCM. Over this period, the AAMR declined from 5.19 in 1999 to 2.34 in 2023. Between 1999 and 2002, the AAMR decreased significantly from 5.19 to 4.38 (APC −6.20* [95% CI, −12.42 to −0.72; P = 0.0208]). The trend remained stable between 2002 and 2005, followed by a significant decline between 2005 and 2014, where the AAMR decreased from 4.96 to 2.66 (APC −6.84* [95% CI, −9.00 to −3.05; P = 0.0300]). The trend then remained relatively unchanged from 2014 to 2023. In 2023, males (3.4) averaged a considerably higher AAMR than females (1.38). Among racial groups, the highest AAMR in 2023 was reported in the Non-Hispanic (NH) Black group (3.77), followed by the NH White group (2.3), the Hispanics and Latinos (1.67) and the NH Others group (NH Asians and NH Native American Indian or Alaskan Native) at 1.23. Overall from 1999 to 2020, rural areas (3.52) averaged a significantly higher AAMR than urban areas (3.50). Regionally, in 2023, the Western region averaged the highest AAMR at 2.69, followed by the South at 2.31, the Midwest at 2.42 and lastly the Northeast at 1.83. Conclusion Our analysis revealed a significant decline in DCM-related mortality rates in the United States from 1999 to 2023, with the most substantial reductions occurring between 1999 and 2014. However, disparities persist, with higher mortality rates observed in males, NH Black individuals, and rural populations. Regional variations also highlight the need for targeted interventions to further reduce the burden of DCM.
Deep mobile profile control agent for iron-containing reservoirs prior to salinity-based ternary flooding
Clinical and pathological implications of the presence of MECA-79-expressing tumor cells in pathological stage IA lung adenocarcinoma
Approximately 15% of patients with resected pathological stage IA lung adenocarcinoma develop recurrent disease, indicating the formation of a cancer metastasis-promoting microenvironment, and highlighting the importance of identifying early prognostic biomarkers. The MECA-79 epitope is a glycan structure modulating immune response, normally expressed on high endothelial venules. Ectopic MECA-79 expression has been recently reported in several cancer cells and is associated with poor prognosis. In this retrospective cohort study, we aimed to investigate the clinical and pathological significance of tumoral MECA-79 expression in early-stage lung cancer. Immunohistochemical analysis for MECA-79 was performed in 195 patients with pathological stage IA lung adenocarcinoma undergoing lobectomy. Clinical, radiological, and pathological factors were assessed, and recurrence-free survival (RFS) was analyzed using Kaplan–Meier analysis and univariable Cox regression proportional hazards models. Multivariable Cox analyses were performed as exploratory analyses only due to the limited number of recurrence events. Tumoral MECA-79 expression was observed in 5.1% of cases (n = 10). Patients with MECA-79+ tumor cells exhibited a larger pathological invasive size (2.1 vs. 1.6 cm, P = 0.044), higher rates of vascular invasion (90.0% vs. 40.0%, P = 0.0023), and increased 5-year postoperative recurrence (40.0% vs. 7.6%, P = 0.0061). Kaplan–Meier analysis demonstrated significantly worse RFS for patients with MECA-79+ tumor cells (5-year rate: 54.9% vs. 87.4%, P = 0.003). The univariate Cox regression model identified body mass index, histological grade based on the International Association for the Study of Lung Cancer histological grading system, vascular invasion, spread through air spaces, and the presence of MECA-79+ tumor cells as prognostic factors. Our results indicate that tumoral MECA-79 expression is associated with the recurrence of resected pathological stage IA lung adenocarcinoma; however, these findings should be validated in multicenter, stage-matched cohorts.
Nanoporous-structured metasurface refractive index sensor employing quasi-bound states in the continuum
Re-weighted estimation of the transition probability density for second-order diffusion processes
The transition probability density of second-order diffusion processes plays a fundamental role in statistical inference and practical applications such as financial derivatives pricing. This paper combines nonparametric Nadaraya-Watson kernel smoothing and local linear smoothing techniques to devise a re-weighted estimator for the transition probability density of second-order diffusion processes. The proposed estimator effectively addresses the persistent boundary bias inherent in Nadaraya-Watson estimation while preserving the nonnegativity constraint essential for probability densities. Under standard regularity conditions, we establish the asymptotic properties of the proposed estimator, demonstrating its theoretical superiority over existing approaches. Furthermore, Monte Carlo simulations show that the new estimator has better performance than Nadaraya-Watson estimator and local linear estimator.
Machine learning identifies MiRNA biomarkers and immune mechanisms in active tuberculosis
Abstract Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a major global public health threat. The rising prevalence of HIV/TB co-infection and multidrug-resistant tuberculosis (MDR-TB) has further intensified this challenge. This study aims to explore the role of microRNAs (miRNAs) in the immune response to Mtb infection and to identify potential miRNA biomarkers for active TB diagnosis using machine learning techniques. miRNA expression profiles were retrieved from the Gene Expression Omnibus (GEO) database (accession number: GSE70425). Differential expression analysis between active TB and latent TB infection (LTBI) patients was conducted using the “limma” package, with a screening threshold of |logFC| > 0.25 and p-value < 0.05. Key differentially expressed miRNAs (DE-miRNAs) were further refined using machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) regression, support vector machine-recursive feature elimination (SVM-RFE), and Boruta. TargetScan was employed to predict miRNA target genes, and a regulatory network was visualized using Cytoscape. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to elucidate the functional roles of target mRNAs. Nine machine learning models were developed based on the selected miRNAs, and their predictive performance was assessed using metrics including AUROC, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In vitro experiments were performed using THP-1 macrophages to establish an Mtb infection model. Cells were transfected with miR-3607-3p mimics and negative controls, followed by flow cytometry for apoptosis detection, Western blot for protein expression analysis, and Quantitative Real-Time PCR (qRT-PCR) for validation of apoptosis-related gene expression. A total of 72 differentially expressed miRNAs were identified. Key miRNAs, including hsa-miR-3607-3p, hsa-miR-148b, and hsa-miR-519e, were identified through multiple machine learning methods, with hsa-miR-3607-3p emerging as the primary candidate miRNA. The nine machine learning models exhibited robust predictive performance in both the training and test sets. PPI network and functional enrichment analyses indicated that the target genes of hsa-miR-3607-3p are primarily associated with cell growth and apoptosis-related pathways. In vitro experiments further suggested that hsa-miR-3607-3p may modulate the apoptotic response of THP-1 cells to Mtb infection via a caspase-dependent mechanism. miR-3607-3p was upregulated in active TB patients and may modulate the apoptosis of THP-1 cells during Mtb infection via a caspase-dependent pathway. The mechanism of this miRNA offers preliminary insights into the immune regulation of tuberculosis. miR-3607-3p may serve as a potential biomarker for the early diagnosis and intervention of active TB; however, its clinical applicability necessitates further validation through larger sample sizes and multicenter studies.
Performance and carcass characteristics of Nellore cattle fed a high-concentrate diet with a phytogenic additive blend
This study evaluated the effects of a phytogenic feed additive – a blend of tannins, flavonoids, and essential oils – on intake, performance, and carcass traits of feedlot-finished Nellore cattle. The additive was hypothesized to enhance performance by modulating ruminal fermentation, favoring more efficient energy utilization pathways. Ninety-six intact Nellore bulls (357.4 ± 25.9 kg) were assigned to 24 pens (4 animals/pen) and fed high-concentrate diets for 116 days. Treatments included: 1) control diet and 2) control plus the phytogenic additive blend. Cattle receiving the phytogenic blend showed a significant reduction in dry matter intake (DMI) as a percentage of body weight (P = 0.04) without changes in final body weight, average daily gain, or carcass ultrasound traits. A numerical improvement in feed efficiency (3.3%; P = 0.19) was observed, along with increased selection for long particles (P = 0.02), potentially indicating altered feeding behavior. No differences were detected in fecal starch content or total starch digestibility. While the phytogenic blend did not significantly enhance performance or carcass traits, the reduction in intake without impairing productivity suggests a potential improvement in feed utilization efficiency. Further research is needed to refine dosing strategies and evaluate their effects under varying feeding conditions.
High gain and high efficiency soft switching quadratic boost converter for renewable energy applications
Abstract Quadratic boost converter (QBC) is crucial in immediate technologies, including renewable energy, electric vehicles (EV), DC microgrids and EV charging stations, where efficient and dependable power conversion is essential. This article introduces a high-gain, high-efficiency QBC operating with soft-switching capabilities explicitly tailored for renewable energy sources that can be used in charging stations for EVs. The design achieves high voltage gain (VGN) by incorporating a coupled inductor (CIN) with a restricted duty cycle, which minimizes the need for extreme duty cycle adjustments that often impact efficiency and cause component stress. The leakage inductances of the CIN’s enable zero voltage switching (ZVS) for the power switches at turn-on and ZVS turn-on and zero current switching (ZCS) turn-off for the diodes. This approach mitigates the switches’ losses and enhances the efficiency. Furthermore, an active clamp circuit is employed, allowing the converter to operate with reduced voltage stress across semiconductor components, thus improving their durability and reliability. The converter operated in continuous conduction mode (CCM) is extensively analysed to assess its performance across various operating conditions. This converter compares VGN, efficiency, and stress on components with several recent QBCs for a comprehensive performance assessment. The converter’s 250 W hardware prototype has also been built and tested, demonstrating its practical suitability and effectiveness for high-demand renewable energy applications. Experimental findings affirm the converter’s high efficiency and reliable performance in real-world situation.
EGCN: Entropy-based graph convolutional network for anomalous pattern detection and forecasting in real estate markets
Real estate markets are inherently dynamic, influenced by economic fluctuations, policy changes and socio-demographic shifts, often leading to emergence of anomalous—regions, where market behavior significantly deviates from expected trends. Traditional forecasting models struggle to handle such anomalies, resulting in higher errors and reduced prediction stability. In order to address this challenge, we propose EGCN, a novel cluster-specific forecasting framework that first detects and clusters anomalous regions separately from normal regions, and then applies forecasting models. This structured approach enables predictive models to treat normal and anomalous regions independently, leading to enhanced market insights and improved forecasting accuracy. Our evaluations on the UK, USA, and Australian real estate market datasets demonstrates that the EGCN achieves the lowest error both anomaly-free (baseline) methods and alternative anomaly detection methods, across all forecasting horizons (12, 24, and 48 months). In terms of anomalous region detection, our EGCN identifies 182 anomalous regions in Australia, 117 in the UK and 34 in the US, significantly more than the other competing methods, indicating superior sensitivity to market deviations. By clustering anomalies separately, forecasting errors are reduced across all tested forecasting models. For instance, when applying Neural Hierarchical Interpolation for Time Series Forecasting, the EGCN improves accuracy across forecasting horizons. In short-term forecasts (12 months), it reduces MSE from 1.3 to 1.0 in the US, 9.7 to 6.4 in the UK and 2.0 to 1.7 in Australia. For mid-term forecasts (24 months), EGCN achieves the lowest errors, lowering MSE from 3.1 to 2.3 (US), 14.2 to 9.0 (UK), and 4.5 to 4.0 (Australia). Even in long-term forecasts (48 months), where error accumulation is common, EGCN remains stable; decreasing MASE from 6.9 to 5.3 (US), 12.2 to 8.5 (UK), and 16.0 to 15.2 (Australia), highlighting its robustness over extended periods. These results highlight how separately clustering anomalies allows forecasting models to better capture distinct market behaviors, ensuring more precise and risk-adjusted predictions.