Construction Of Model Research Articles

Development of the concept-process model and metacognition via FAR analogy-based learning approach in the topic of metabolism among second-year undergraduates

Abstract Metacognition is a critical cognitive skill in learning science. Numerous studies indicate that learners with high metacognitive abilities tend to excel in scientific skills, such as their capacity to construct scientific models. This study aims to compare students’ ability to construct concept-process models before and after participating in analogy-based learning using the Focus-Action-Reflection (FAR) method on the topic of metabolism. Additionally, the study investigates the relationship between students’ ability to construct conceptual process models and their metacognitive skills. The sample consisted of 137 second-year students enrolled in a basic biochemistry course, selected through cluster sampling. The research instruments included a process model construction assessment and the Metacognitive Awareness Inventory (MAI). The data were analyzed using paired sample t-tests and correlation analysis. The results showed significant improvement in students’ ability to construct conceptual process models, with an average learning gain of 16.5 %. Statistical analysis indicated a significant increase in test scores after the instructional activities. Furthermore, Spearman’s correlation analysis revealed a positive relationship between learning progress and metacognitive skills, particularly in the use of information management strategies (IMS) and debugging strategies (DS). These findings emphasize the importance of integrating metacognitive skill development into science education, particularly in fostering students’ ability to construct conceptual process models – an essential tool for enhancing science learning outcomes.

Construction of feature selection and efficacy prediction model for transformation therapy of locally advanced pancreatic cancer based on CT, 18F-FDG PET/CT, DNA mutation, and CA199

Construction of feature selection and efficacy prediction model for transformation therapy of locally advanced pancreatic cancer based on CT, 18F-FDG PET/CT, DNA mutation, and CA199

Construction of the bromodomain-containing protein-associated prognostic model in triple-negative breast cancer

BackgroundBromodomain-containing protein (BRD) play a pivotal role in the development and progression of malignant tumours. This study aims to identify prognostic genes linked to BRD-related genes (BRDRGs) in patients with triple-negative breast cancer (TNBC) and to construct a novel prognostic model.MethodsData from TCGA-TNBC, GSE135565, and GSE161529 were retrieved from public databases. GSE161529 was used to identify key cell types. The BRDRGs score in TCGA-TNBC was calculated using single-sample Gene Set Enrichment Analysis (ssGSEA). Differential expression analysis was performed to identify differentially expressed genes (DEGs): DEGs1 in key cells, DEGs2 between tumours and controls and DEGs3 in high and low BRDRGs score subgroups in TCGA-TNBC. Differentially expressed BRDRGs (DE-BRDRGs) were determined by overlapping DEGs1, DEGs2 and DEGs3. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) network analysis were conducted to investigate active pathways and molecular interactions. Prognostic genes were selected through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses to construct a risk model and calculate risk scores. TNBC samples from TCGA-TNBC were classified into high and low-risk groups based on the median risk score. Additionally, correlations with clinical characteristics, Gene Set Enrichment Analysis (GSEA), immune analysis, and pseudotime analysis were performed.ResultsA total of 120 DE-BRDRGs were identified by overlapping 605 DEGs1 from four key cell types, 10,776 DEGs2, and 4,497 DEGs3. GO analysis revealed enriched terms such as ‘apoptotic process,’ ‘immune response,’ and ‘regulation of the cell cycle,’ while 56 KEGG pathways, including the ‘MAPK signaling pathway,’ were associated with DE-BRDRGs. A risk model comprising six prognostic genes (KRT6A, PGF, ABCA1, EDNRB, CTSD and GJA4) was constructed. A nomogram based on independent prognostic factors was also developed. Immune cell abundance was significantly higher in high-risk group. In both risk groups, TP53 exhibited the highest mutation frequency. The expression of KRT6A, ABCA1, EDNRB, and CTSD went decreased progressively in pseudotime.ConclusionA novel prognostic model for TNBC associated with BRDRGs was developed and validated, providing fresh insights into the relationship between BRD and TNBC.

Construction and validation of a predictive model for the risk of prolonged preoperative waiting time in patients with intertrochanteric fractures

Construction and validation of a predictive model for the risk of prolonged preoperative waiting time in patients with intertrochanteric fractures

The application of a clinical-multimodal ultrasound radiomics model for predicting cervical lymph node metastasis of thyroid papillary carcinoma

BackgroundPTC (papillary thyroid cancer) is a lymphotropic malignancy associated with cervical lymph node metastasis (CLNM, including central and lateral LNM), which compromises the effect of treatment and prognosis of patients. Accurate preoperative identification will provide valuable reference information for the formulation of diagnostic and treatment strategies. The aim of this study was to develop and validate a clinical-multimodal ultrasound radiomics model for predicting CLNM of PTC.MethodsOne hundred sixty-four patients with PTC who underwent treatment at our hospital between March 2016 and December 2021 were included in this study. The patients were grouped into a training cohort (n=115) and a validation cohort (n=49). Radiomic features were extracted from the conventional ultrasound (US), contrast-enhanced ultrasound (CEUS) and strain elastography-ultrasound (SE-US) images of patients with PTC. Multivariate logistic regression analysis was used to identify the independent risk factors. FAE software was used for radiomic feature extraction and the construction of different prediction models. The diagnostic performance of each model was evaluated and compared in terms of the area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV). RStudio software was used to develop the decision curve and assess the clinical value of the prediction model.ResultsThe clinical-multimodal ultrasound radiomics model developed in this study can successfully detect CLNM in PTC patients. A total of 3720 radiomic features (930 features per modality) were extracted from the ROIs of the multimodal images, and 15 representative features were ultimately screened. The combined model showed the best prediction performance in both the training and validation cohorts, with AUCs of 0.957 (95% CI: 0.918–0.987) and 0.932 (95% CI: 0.822–0.984), respectively. Decision curve analysis revealed that the combined model was superior to the other models.ConclusionThe clinical-multimodal ultrasound radiomics model constructed with multimodal ultrasound radiomic features and clinical risk factors has favorable potential and high diagnostic value for predicting CLNM in PTC patients.

Construction of a Compound Model to Enhance the Accuracy of Hepatic Fat Fraction Estimation with Quantitative Ultrasound

Background: we evaluated regression models based on quantitative ultrasound (QUS) parameters and compared them with a vendor-provided method for calculating the ultrasound fat fraction (USFF) in metabolic dysfunction-associated steatotic liver disease (MASLD). Methods: We measured the attenuation coefficient (AC) and the backscatter-distribution coefficient (BSC-D) and determined the USFF during a liver ultrasound and calculated the magnetic resonance imaging proton-density fat fraction (MRI-PDFF) and steatosis grade (S0–S4) in a combined retrospective–prospective cohort. We trained multiple models using single or various QUS parameters as independent variables to forecast MRI-PDFF. Linear and nonlinear models were trained during five-time repeated three-fold cross-validation in a retrospectively collected dataset of 60 MASLD cases. We calculated the models’ Pearson correlation (r) and the intraclass correlation coefficient (ICC) in a prospectively collected test set of 57 MASLD cases. Results: The linear multivariable model (r = 0.602, ICC = 0.529) and USFF (r = 0.576, ICC = 0.54) were more reliable in S0- and S1-grade steatosis than the nonlinear multivariable model (r = 0.492, ICC = 0.461). In S2 and S3 grades, the nonlinear multivariable (r = 0.377, ICC = 0.32) and AC-only (r = 0.375, ICC = 0.313) models’ approximated correlation and agreement surpassed that of the multivariable linear model (r = 0.394, ICC = 0.265). We searched a QUS parameter grid to find the optimal thresholds (AC ≥ 0.84 dB/cm/MHz, BSC-D ≥ 105), above which switching from a linear (r = 0.752, ICC = 0.715) to a nonlinear multivariable (r = 0.719, ICC = 0.641) model could improve the overall fit (r = 0.775, ICC = 0.718). Conclusions: The USFF and linear multivariable models are robust in diagnosing low-grade steatosis. Switching to a nonlinear model could enhance the fit to MRI-PDFF in advanced steatosis.

Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model.

Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individualized models through automatic machine learning (autoML) to predict the efficacy of immunotherapy in patients with inoperable advanced NSCLC. A total of 63 eligible participants were included and randomized into training and validation groups. Radiomics features were extracted from the volumes of interest of the tumor circled in the preprocessed computed tomography (CT) images. Golden feature, clinical, radiomics, and fusion models were generated using a combination of various algorithms through autoML. The models were evaluated using a multi-class receiver operating characteristic curve. In total, 1,219 radiomics features were extracted from regions of interest. The ensemble algorithm demonstrated superior performance in model construction. In the training cohort, the fusion model exhibited the highest accuracy at 0.84, with an area under the curve (AUC) of 0.89-0.98. In the validation cohort, the radiomics model had the highest accuracy at 0.89, with an AUC of 0.98-1.00; its prediction performance in the partial response subgroup outperformed that in both the clinical and radiomics models. Patients with low rad scores achieved improved progression-free survival (PFS); (median PFS 16.2 vs. 13.4, P = 0.009). autoML accurately and robustly predicted the short-term outcomes of patients with inoperable NSCLC treated with immune checkpoint inhibitor immunotherapy by constructing CT-based radiomics models, confirming it as a powerful tool to assist in the individualized management of patients with advanced NSCLC. This article highlights that autoML promotes the accuracy and efficiency of feature selection and model construction. The radiomics model generated by autoML predicted the efficacy of immunotherapy in patients with advanced NSCLC effectively. This may provide a rapid and non-invasive method for making personalized clinical decisions.

Financial contagion in the US, European and Chinese stock markets during global shocks

Under globalisation, integration, and financialisation of national economies, the financial markets’ interdependence tends to swell, which increases the probability of financial disturbances spreading between countries, especially during global shocks, and calls for the development of new standards of financial regulation. The article studies the financial contagion among stock markets of different countries during global shocks associated with the COVID-19 pandemic, the energy crisis, and the special military operation of Russia in Ukraine (SMO). The concept of financial contagion underlies the methodology of the research. The financial contagion is diag nosed based on the construction of DCC-GARCH models and the calculation of dy namic conditional beta coefficients. Causal relationships in the interaction of indices are established using the Granger test. The data are average daily indices: the Ameri can S&P 500, the European STOXX 600, and the Shanghai Stock Exchange (SSE) Composite Index – for December 2018 – March 2024 obtained from the financial portal Investing.com. The study reveals a high level of connectivity in normal times between the American and European stock markets with some autonomy of the Chinese stock market. However, during the 2020 pandemic, there was a short-term strong contagion of the S&P 500 and STOXX 600 from the SSE Composite Index, as well as longer-term, moderate cross-contagion between the S&P 500 and STOXX 600. During the 2021 energy crisis and SMO, there was strong and relatively long-lasting contagion of the S&P 500 from the STOXX 600 and their much weaker contagion from the SSE Composite Index. The findings may be useful for stock market players in managing investment portfolios, and for the state in formulating financial stabilisation policies during the impact of global shocks.

Construction of cuproptosis-related genes risk model predicts the prognosis of Uterine Corpus Endometrial Carcinoma

Cuproptosis, a recently discovered form of cell death, has emerged as a crucial player in tumor development, although its role in uterine corpus endometrial carcinoma (UCEC) remains inadequately explored. This study aims to identify prognostically relevant cuproptosis-related genes in endometrial cancer. Cuproptosis-related genes were sourced from previously published studies and the FerrDb database. UCEC gene expression profiles and clinical data were obtained from the TCGA database. Differential gene expression was determined using LIMMA analysis, and functional enrichment analysis was conducted on identified cuproptosis-related genes. A prognostic model for UCEC was developed using LASSO Cox regression analysis and a Nomogram, integrating survival data, status, and gene signatures. TIMER analysis assessed the impact of crucial cuproptosis-related genes on immune cell infiltration in UCEC. Validation of the selected genes, CDKN2A, GLS, and PPAT, was performed at both mRNA and protein levels. A total of 27 cuproptosis-related genes were identified, with 19 upregulated and 6 downregulated in UCEC. These genes were associated with key signaling pathways, including the TCA cycle, Pyruvate metabolism, Glycolysis/Gluconeogenesis, and Platinum drug resistance. The LASSO regression and Nomogram models demonstrated robust predictive performance for UCEC prognosis, identifying CDKN2A, GLS, and PPAT as critical prognostic genes. Furthermore, these genes played essential roles in immune cell infiltration in UCEC, confirming their significance. Validation at both mRNA and protein levels solidified the role of CDKN2A, GLS, and PPAT. The identified signature of CDKN2A, GLS, and PPAT demonstrates significant predictive value for UCEC prognosis, suggesting their potential as therapeutic targets, including their application in immunotherapy strategies.

Use of implementation logic models in the Quadruple Aim QUERI: conceptualization and evolution

BackgroundImplementation strategies are essential to deliver evidence-based programs that align with local context, resources, priorities, and preferences. However, it is not always clear how specific strategies are selected (vs. others) and strategies are not always operationalized clearly, distinctly, and dynamically. Implementation logic models provide one useful way to conceptualize the role and selection of implementation strategies, plan evaluation of their intended impacts on implementation and effectiveness outcomes, and to communicate key aspects of a project.MethodsThis paper describes our initial plans, experiences, and lessons learned from applying implementation logic models in the Quadruple Aim Quality Enhancement Research Initiative (QUERI) a large multi-study program funded by the Veterans Health Administration (VA). We began with two primary implementation strategies based on our earlier work (i.e., Iterative RE-AIM and Relational Facilitation) that were applied across three different health outcomes studies.ResultsOur implementation strategies evolved over time, and new strategies were added. This evolution and reasons for changes are summarized and illustrated with the resulting logic models, both for the overall Quadruple Aim QUERI and the three specific projects. We found that implementation strategies are often not discrete, and their delivery and adaptation is dynamic and should be guided by emerging data and evolving context. Review of logic models across projects was an efficient and useful approach for understanding similarities and differences across projects.ConclusionsImplementation logic models are helpful for clarifying key objectives and issues for both study teams and implementation partners. There are challenges in logic model construction and presentation when multiple strategies are employed, and when strategies change over time. We recommend presentation of both original and periodically updated project models and provide recommendations for future use of implementation logic models.

A Graph Neural Network-Based Approach to XANES Data Analysis.

The determination of three-dimensional structures (3D structures) is crucial for understanding the correlation between the structural attributes of materials and their functional performance. X-ray absorption near edge structure (XANES) is an indispensable tool to characterize the atomic-scale local 3D structure of the system. Here, we present an approach to simulate XANES based on a customized 3D graph neural network (3DGNN) model, XAS3Dabs, which takes directly the 3D structure of the system as input, and the inherent relation between the fine structure of spectrum and local geometry is considered during the model construction. It turns out to be faster than the traditional XANES fitting method when the simulation approach and XANES optimization algorithm are combined to fit the 3D structure of the given system. The geometric features of the system are included in the weighted message passing block of XAS3Dabs and their importance is investigated. XAS3Dabs model demonstrates superior accuracy in XANES prediction compared to most machine learning models. By extracting graphs constituted by edges related to the absorbing atom, our model reduces redundant information, thereby not only enhancing the model's performance but also improving its robustness across different hyperparameters. XAS3Dabs model can be generalized to simulate the spectra for the systems with the absorber having the designed absorption edge so as to meet the expectations of online data processing. The method is expected to be the key part of the online 3D structure analysis framework for the XAS-related beamlines of high-energy photon source (HEPS) now under construction.

Construction of a Simplified Bone Age Assessment Model in Chinese Children Aged 3-18 Years.

Bone age assessment (BAA) is critical for pediatric endocrinology. Traditional methods are complex and time-consuming, and current computer-aided systems have made progress but still lacking in robustness. Develop simplified BAA methods to aid pediatricians in quick clinical assessments. Overall, 5,551 left-hand X-ray images were collected from a cross-sectional survey in 2022- 2023. Bone age (BA) was assessed using the Tanner-Whitehouse 3-China/radius-ulna-short bone (TW3-C-RUS) method. The linear regression models were constructed with BA as the dependent variable and 13 RUS bones' grades as independent variables. A cross-sectional survey in Beijing, China. 5,551 children aged 3-18 years. Model accuracy was evaluated by R², residuals, and root mean square error (RMSE), and BA with an error margin ≤0.5 years. When bone grades were consistent, a single or few bones may serve as proxies for BA, such as metacarpals and phalanges reached grade 6, the BA for boys and girls were 13.0-13.5 years and 10.9-11.5 years, respectively. When bone grades were inconsistent, regression models were needed. Comparatively, the three-bone models have advantages and are proposed for clinically simplified BAA. The simplified three-bone model (radius, ulna, and metacarpal I) yielded an R² > 0.94 and an RMSE < 0.5 years. When considering puberty stages, specific bone combination models can further improve assessment accuracy. These three-bone models could be useful for rapid BAA, with improved accuracy when puberty stages is included. Further validation is warranted to test their robustness across populations and scenarios.

Construction and validation of a prediction model for fall risk in hospitalized older adults with osteoporosis

ObjectiveThe aim of this study is to develop and validate a prediction model for fall risk factors in hospitalized older adults with osteoporosis.MethodsA total of 615 older adults with osteoporosis hospitalized at a tertiary (grade 3A) hospital in Nantong City, Jiangsu Province, China, between September 2022 and August 2023 were selected for the study using convenience sampling. Fall risk factors were identified using univariate and logistic regression analyses, and a predictive risk model was constructed and visualized through a nomogram. Model performance was evaluated using the area under the receiver operator characteristic curve (AUC), Hosmer-Lemeshow goodness-of-fit test, and clinical decision curve analysis, assessing the discrimination ability, calibration, and clinical utility of the model.ResultsBased on logistic regression analysis, we identified several significant fall risk factors for older adults with osteoporosis: gender of the study participant, bone mineral density, serum calcium levels, history of falls, fear of falling, use of walking aids, and impaired balance. The AUC was 0.798 (95% CI: 0.763–0.830), with a sensitivity of 80.6%, a specificity of 67.9%, a maximum Youden index of 0.485, and a critical threshold of 121.97 points. The Hosmer-Lemeshow test yielded a χ2 value of 8.147 and p = 0.419, indicating good model calibration. Internal validation showed a C-index of 0.799 (95% CI: 0.768–0.801), indicating the model’s high discrimination ability. Calibration curves showed good agreement between predicted and observed values, confirming good calibration. The clinical decision curve analysis further supported the model’s clinical utility.ConclusionThe prediction model constructed and verified in this study was to predict fall risk for hospitalized older adults with osteoporosis, providing a valuable tool for clinicians to implement targeted interventions for patients with high fall risks.

The relationship of future anxiety with a multidimensional framework of well-being among undergraduate students: optimism and pessimism as mediators

BackgroundEnhancing and maintaining well-being is of great importance for university students to improve their future lives. Therefore, determining the factors that promote well-being is essential. This study aimed to examine the mediating roles of optimism and pessimism in the association between future anxiety and well-being after controlling gender, age, faculty, and department.Methods1024 undergraduate students participated in the study. The mean age of students was 20.27 years (SD = 2.02). Preliminary analyses were conducted to determine descriptive statistics, Cronbach’s alpha coefficients, normality, and correlation coefficients between study variables. Mediation analyses were performed to investigate the mediating roles of optimism and pessimism in the association between future anxiety and well-being.ResultsThe results of the study showed that future anxiety was positively associated with pessimism, and negatively associated with optimism, positive emotions, engagement, positive relationships, meaning and accomplishment. Optimism had a positive relationship with the constructs of well-being model, and pessimism had a negative relationship with the pillars of well-being. Optimism and pessimism served as mediators in the link between future anxiety and well-being.ConclusionThe findings of this study indicate that students who are anxious about the future are less optimistic and more pessimistic, which in turn leads to lower well-being. The current study suggests that future-oriented thinking and individual strengths, including optimism, should be incorporated into the mental health management of university administrations to promote the well-being of university students.

Application of a 3D Fusion Model to Evaluate the Efficacy of Clear Aligner Therapy in Malocclusion Patients: Prospective Observational Study.

Investigating the safe range of orthodontic tooth movement is essential for maintaining oral and maxillofacial stability posttreatment. Although clear aligners rely on pretreatment digital models, their effect on periodontal hard tissues remains uncertain. By integrating cone beam computed tomography-derived cervical and root data with crown data from digital intraoral scans, a 3D fusion model may enhance precision and safety. This study aims to construct a 3D fusion model based on artificial intelligence software that matches cone beam computed tomography and intraoral scanning data using the Andrews' Six Element standard. The model will be used to assess the 3D effects of clear aligners on tooth movement, to provide a reference for the design of pretreatment target positions. Between May 2022 and May 2024, a total of 320 patients who completed clear aligner therapy at our institution were screened; 136 patients (aged 13-35 years, fully erupted permanent dentition and periodontal pocket depth <3 mm) met the criteria. Baseline ("simulation") and posttreatment ("fusion") models were compared. Outcomes included upper core discrepancy (UCD), upper incisors anteroposterior discrepancy (UAP), lower Spee curve deep discrepancy (LSD), upper anterior teeth width discrepancy (UAW), upper canine width discrepancy (UCW), upper molar width discrepancy (UMW), and total scores. Subanalyses examined sex, age stage (adolescent vs adult), and treatment method (extraction vs nonextraction). The study was funded in May 2022, with data collection beginning the same month and continuing until May 2024. Of 320 initial participants, 136 met the inclusion criteria. Data analysis is ongoing, and final results are expected by late 2024. Among the 136 participants, 90 (66%) were female, 46 (34%) were male, 64 (47%) were adolescents, 72 (53%) were adults, 38 (28%) underwent extraction, and 98 (72%) did not. Total scores did not differ significantly by sex (mean difference 0.01, 95% CI -0.13 to 0.15; P=.85), age stage (mean difference 0.03, 95% CI -0.10 to 0.17; P=.60), or treatment method (mean difference 0.07, 95% CI -0.22 to 0.07; P=.32). No significant differences were found in UCD (mean difference 0.001, 95% CI -0.02 to 0.01; P=.90) or UAP (mean difference 0.01, 95% CI -0.03 to 0.00; P=.06) by treatment method. However, adolescents exhibited smaller differences in UCD, UAW, UCW, and UMW yet larger differences in UAP and LSD (df=134; P<.001). Extraction cases showed smaller LSD, UAW, and UCW but larger UMW differences compared with nonextraction (df=134; P<.001). The 3D fusion model provides a reliable clinical reference for target position design and treatment outcome evaluation in clear aligner systems. The construction and application of a 3D fusion model in clear aligner orthodontics represent a significant leap forward, offering substantial clinical benefits while establishing a new standard for precision, personalization, and evidence-based treatment planning in the field. Chinese Clinical Trial Registry ChiCTR2400094304, https://www.chictr.org.cn/hvshowproject.html?id=266090&v=1.0.

ADMNet: adaptive deformable convolution large model combining multi-level progressive fusion for Building Change Detection

ABSTRACT Building Change Detection (BCD) based on high-resolution Remote Sensing Images (RSI) simplifies urban surface monitoring. Nevertheless, the mainstream detection methods utilizing traditional convolution and attention mechanisms are often prone to errors due to the loss of edge detail information and underutilization of global context information. To address these issues, this paper presents a large model, namely ADMNet, which is built on adaptive deformable convolution and is designed to handles various types of building change information. First, we propose a Siamese neural network based on adaptive deformable convolution (ADC) modules. The ADC module incorporates spatial offset parameters into convolutional kernel sampling and mapping weights to capture irregularly varying edge features for local adaptive receptive fields. Second, we utilize a large model semantically driven to enhance model context awareness and construct long-range feature dependencies from multi-scale edge information, which are then integrated with locally adaptive edge structure features to achieve accurate edge localization. Furthermore, we design a Multi-Level Progressive Feature Fusion (MLPFF) module that enhances feature characterization capabilities to ensure internal integrity and improves model detection performance by integrating a priori knowledge from large-model transfer learning. To evaluate the effectiveness and generalizability of ADMNet, we conduct comparative experiments with current mainstream methods on two building datasets, LEVIR-CD and WHU-CD, and a land cover dataset, SYSU-CD. The results show that ADMNet outperforms all comparative methods. The source code is available at https://github.com/spaceYu180/ADMNet.

BMP6, a potential biomarker of inflammatory fibrosis and promising protective factor for dilated cardiomyopathy

BackgroundDilated cardiomyopathy (DCM) stands as one of the most prevalent and severe causes of heart failure. Inflammation plays a pivotal role throughout the progression of DCM to heart failure, while age acts as a natural predisposing factor for all cardiovascular diseases. These two factors often interact, contributing to cardiac fibrosis, which is both a common manifestation and a pathogenic driver of adverse remodeling in DCM-induced heart failure.MethodBulk RNA-seq, single-cell RNA-seq, Mendelian randomization analysis, animal model construction, and BMP6 knockdown were utilized to identify and validate potential specific markers and targets for intervention in DCM heart failure.ResultsWe found that DCM hearts exhibit pronounced inflammatory cell infiltration and fibrosis. Both bulk RNA-seq and single-cell RNA-seq analyses revealed aberrant BMP6 expression specifically in fibroblasts. The ROC curve underscores the high specificity of BMP6 in relation to DCM, while Mendelian randomization analysis further confirms BMP6 as a protective factor against DCM. Notably, BMP6 knockdown led to a decrease in SMAD6 expression and a marked elevation in COL1A1 expression levels, indicating its antifibrotic role.ConclusionBMP6 emerges as a promising biomarker for DCM, and its functional role in exerting an antifibrotic effect underscores its potential as a therapeutic target.

Biological characteristics, immune infiltration and drug prediction of PANoptosis related genes and possible regulatory mechanisms in inflammatory bowel disease

PANoptosis is one of several modes of programmed cell death (PCD) and plays an important role in many inflammatory and immune diseases. The role of PANoptosis in inflammatory bowel disease (IBD) is currently unknown. Differentially expressed PANoptosis-related genes (DE-PRGs) were identified, and pathway enrichment analyses were performed. LASSO regression model construction, a nomogram model, calibration curves, ROC and DCA curves were used to evaluate the predictive value of the model. Predicts transcription factors (TFs) and small-molecule drugs of DE-PRGs were analysed. Model genes and immuno-infiltration were analysed. The PANoptosis features of IBD include 12 genes: OGT, TLR2, GZMB, TLR4, PPIF, YBX3, CASP5, BCL2L1, CASP6, MEFV, GSDMB and BAX. The enrichment analysis suggested that these genes were related to TNF signalling, NF-κB, pyroptosis and necroptosis. Machine learning identified three model genes: OGT, GZMB and CASP5. The nomogram model, calibration curves, ROC and DCA curves have strong predictive value. Immuno-infiltration analysis revealed that immune cell infiltration was increased in patients with IBD, and the model genes were closely related to the infiltration of various immune cells. The TFs associated with DE-PRGs were RELA, NFKB1, HIF1A, TP53 and SP1. In addition, the Connectivity Map (CMap) database identified the top 10 small-molecule compounds, including buspirone, chloroquine, spectinomycin and chlortetracycline. This study indicate that DE-PRGs model genes have good predictive ability for IBD. Moreover, PANoptosis may mediate the process of IBD through TNF signalling, NF-κB, pyroptosis, necroptosis and immune mechanisms. These results present a new horizon for the research and treatment of IBD.

Development of a machine learning predictive model for central venous catheter-associated thrombosis in patients undergoing abdominal surgery.

Central venous catheters (CVCs) are placed where the vena cava meets the right atrium. Their common use raises the risk of catheter-related thrombosis (CRT), a potentially life-threatening complication. This study leverages machine learning to develop a CRT predictive model for abdominal surgery patients, aiming to refine clinical decisions and elevate treatment quality. The data were split into training and validation sets using the caret package in R. Decision Trees (DT), Extra Trees (ET), Ada Boost, Gradient Boosting (GB), Light Gradient Boosting Machine (LGBM), K Neighbours Classifier (KNN) and Random Forest (RF) algorithms were used for model construction. Receiver operating characteristic (ROC) curve, area under curve (AUC), accuracy, recall, precision, F1 score, sensitivity and specificity were used to evaluate the performance of the model. Decision curve analysis (DCA) was used to evaluate the clinical utility of each model. Among the 400 subjects, 184 had thrombosis, with an incidence of 46%. Basic characteristics analysis and univariate analysis showed that there were significant differences in the history of radiotherapy or chemotherapy, age, mobility score, retention time, D-dimer, fibrinogen and urea (p < .05). Among the models constructed by the seven algorithms, the performance of DT model was relatively balanced. The AUC of the validation set was 0.782, the sensitivity was 0.618, and the specificity was 0.781. The predictive model for CRT developed using machine learning algorithms demonstrates good discrimination and clinical applicability among abdominal surgery patients, offering valuable guidance for CRT prevention strategies. By integrating risk prediction models into the Hospital Information System (HIS), nurses can assess catheter status in a timely and accurate manner, understand the risks of thrombosis for patients, and implement targeted preventive measures. This approach can enhance the efficiency and accuracy of nursing care, holding clinical significance in critical care practice.

Enhancing Learning Efficiency and Critical Thinking Skills of Vocational Nursing Students: A Hybrid Instructional Approach Based on Cooperative Learning and Project-Based Learning

The purpose of this study is to examine the design and implementation process of Project-Based Cooperative Learning (PBCL) teaching models. The research initially introduces the theoretical framework, encompassing cooperative learning theory, project-based learning theory, and the theory of PBCL. Subsequent sections elaborate on the construction of the PBCL teaching model, focusing on the design of teaching objectives, integrating cooperative and project-based learning, the provisioning of learning resources and support, and the establishment of evaluation and feedback mechanisms. Building upon this foundation, the study delineates the steps to implement PBCL teaching methods, including pre-class preparations, project initiation, collaborative learning and project implementation, project summarization and evaluation, and post-class extension. Finally, the study analyzes the strengths and limitations of this teaching approach. The strengths include deep learning experiences, enhanced collaborative abilities, and the cultivation of critical thinking skills. However, limitations are evident in the greater practical input and resource demands, inadequacies in the capabilities of some students, and deficiencies in assessment effectiveness.