Comparison Of Models Research Articles

Exploring the Current Applications and Effectiveness of ChatGPT in Nursing: An Integrative Review

ABSTRACTAimsTo systematically review the current application status of ChatGPT in nursing and explore its application effects.DesignAn integrative review.MethodsFollowing inclusion and exclusion criteria, two researchers summarised the selected literature and conducted a quality appraisal, followed by narrative synthesis.Data SourcesPubMed, Web of Science and Scopus were searched from January 2022 to June 2024.ResultsA total of 31 papers met the inclusion criteria. Fifteen empirical studies were rated as grade 5, while five were rated as grade 4. The references of a minireview were not recently published and lacked ChatGPT‐related articles, and a systematic review was of low quality. The review focused on three main topics: (1) The subsidiary role of ChatGPT in nursing; (2) Comparison of different models' effectiveness and (3) Existing challenges.ConclusionsWhile adopting new technologies such as ChatGPT, it is important to maintain a balanced perspective on both its benefits and limitations. Nursing professionals must actively address these deficiencies and explore solutions to improve ChatGPT's utility in the field.Implications to the Profession and Patient CareThis review synthesised evidence on ChatGPT's application and highlighted existing challenges in nursing. Nursing researchers, educators and practitioners can further validate these findings to explore its potential in various aspects of nursing practice.ImpactFor researchers, ChatGPT can enhance language quality and summarise findings effectively, but adherence to research standards is crucial. For educators, ChatGPT can serve as an effective information source for students, though caution should be taken to avoid overreliance. For practitioners, ChatGPT can offer useful suggestions for clinical practice, but these should be critically evaluated and not followed blindly, as issues of inaccuracy must be addressed.Reporting MethodThis review adhered to Preferred Reporting Items for Systematic Reviews and Meta‐Analyses.Patient or Public ContributionNo patient or public contribution.

Socioeconomic deprivation and suicide in Appalachia: The use of three socioeconomic deprivation indices to explain county-level suicide rates

Objective West Virginia’s (WV) suicide rate is 50% higher than the national average and is the highest in the Appalachian Region. Appalachia has several social factors that have contributed to greater socioeconomic deprivation, a known contributor of suicide. Given WV’s high prevalence of suicide and poverty, the current study aims to examine the relationship between socioeconomic deprivation and suicide rates in WV. Methods The Townsend Deprivation Index (TDI), Social Deprivation Index (SDI), and Social Vulnerability Index (SVI) measured socioeconomic deprivation. Negative binomial regression models assessed the relationship between socioeconomic deprivation scores, individual index items, and suicide rates. Model comparisons evaluated the indices’ ability to assess suicide rates. A backward selection strategy identified additional key items for examining suicide rates. Results There was a significant increase in suicide rates for every 10% increase in TDI (β = 0.04; p < 0.01), SDI (β = 0.03; p = 0.04), and SVI scores (β = 0.05; p < 0.01). Household overcrowding and unemployment had a positive linear relationship with suicide in TDI (β = 0.04, p = 0.02; β = 0.07, p = 0.01), SDI (β = 0.10, p = 0.02; β = 0.01, p<0.01), and the SVI (β = 0.10, p = 0.02; β = 0.03, p<0.01). The backwards selection strategy identified additional key items included by the SVI when assessing suicide. Conclusion Greater socioeconomic deprivation, measured by the TDI, SDI, and SVI, was significantly associated with higher suicide rates. Expanding unemployment benefits and increasing the availability of affordable housing, especially in rural areas, may be useful in reducing suicide rates. Our results suggest racial and ethnic minorities and adults living with a disability may benefit from targeted suicide prevention strategies.

Deep Learning-Based Postoperative Glioblastoma Segmentation and Extent of Resection Evaluation: Development, External Validation, and Model Comparison

Abstract Background The pursuit of automated methods to assess the extent of resection (EOR) in glioblastomas is challenging, requiring precise measurement of residual tumor volume. Many algorithms focus on preoperative scans, making them unsuitable for postoperative studies. Our objective was to develop a deep learning-based model for postoperative segmentation using magnetic resonance imaging (MRI). We also compared our model's performance with other available algorithms. Methods To develop the segmentation model, a training cohort from three research institutions and three public databases was used. Multiparametric MRI scans with ground truth labels for contrast enhancing tumor, edema, and surgical cavity, served as training data. The models were trained using MONAI and nnU-Net frameworks. Comparisons were made with currently available segmentation models using an external cohort from a research institution and a public database. Additionally, the model's ability to classify EOR was evaluated using the RANO-Resect classification system. To further validate our best-trained model, an additional independent cohort was used. Results The study included 586 scans: 395 for model training, 52 for model comparison, and 139 scans for independent validation. The nnU-Net framework produced the best model with median Dice scores of 0.81 for contrast enhancing tumor, 0.77 for edema, and 0.81 for surgical cavity. Our best-trained model classified patients into maximal and submaximal resection categories with 96% accuracy in the model comparison dataset and 84% in the independent validation cohort. Conclusion Our nnU-Net-based model outperformed other algorithms in both segmentation and EOR classification tasks, providing a freely accessible tool with promising clinical applicability.

Experimental study on the shear behaviour of high-strength lightweight concrete beams incorporating graphene oxide.

Graphene oxide (GO) has achieved significant progress in the material behaviour of cement-based materials. However, research on its structural behaviour in high-strength lightweight concrete (HSLWC) structures is limited, which restricts its engineering applications. This study focused on investigating the effects of low contents of GO on the shear behaviour of HSLWC beams. A total of four HSLWC beams with GO contents of 0, 0.01, 0.03 and 0.05% (by weight of cement) were designed to observe failure modes, load‒deformation curves, shear capacities, crack behaviour and load‒strain curves under four-point loading by a 300 kN servo loading device. The results revealed that all the beams exhibited shear compression failure. GO improved the shear capacity of the HSLWC beams, and this strengthening effect increased with increase in GO content. When the content of GO was 0.05%, the ultimate load of the beam reached a maximum, which was 39.2% greater than that of the control beam. GO can endow the HSLWC beams with a certain degree of ductility. In addition, a modified JGJ 12-2006 model was proposed to predict the shear capacity of HSLWC beams containing different GO contents on the basis of a comparison of typical models. This study can provide exploratory engineering practice for evaluating and designing GO-reinforced HSLWC structures.

A comparison of statistical and machine learning models for spatio-temporal prediction of ambient air pollutant concentrations in Scotland

AbstractThe spatio-temporal prediction of air pollutant concentrations is vital for assessing regulatory compliance and for producing exposure estimates in epidemiological studies. Numerous approaches have been utilised for making such predictions, including land use regression models, additive models, spatio-temporal smoothing models and machine learning prediction algorithms. However, relatively few studies have compared the predictive performance of these models thoroughly, which is one of the novel contributions of this paper. For the specific challenge of predicting monthly average concentrations of NO$$_{2}$$ 2 , PM$$_{10}$$ 10 and $$\hbox {PM}_{2.5}$$ PM 2.5 in Scotland, we find that random forests typically outperform (or are as good as) more traditional statistical prediction approaches. Additionally, we utilise the best performing model to provide a new data resource, namely, predictions of monthly average concentrations (with uncertainty quantification) of the above pollutants on a regular 1 km$$^{2}$$ 2 grid for all of Scotland between 2016 and 2020.

Comparison of multiple machine learning models for predicting prognosis of pancreatic ductal adenocarcinoma based on contrast-enhanced CT radiomics and clinical features

ObjectiveThe aim of this study was to evaluate the prognostic potential of combining clinical features and radiomics with multiple machine learning (ML) algorithms in pancreatic ductal adenocarcinoma (PDAC).MethodsA total of 116 patients with PDAC who met the eligibility criteria were randomly assigned to a training or validation cohort. Seven ML algorithms, including Supervised Principal Components, stepwise Cox, Random Survival Forest, CoxBoost, Least absolute shrinkage and selection operation (Lasso), Ridge, and Elastic network, were integrated into 43 algorithm combinations. Forty-three radiomics models were constructed separately using radiomics features extracted from arterial phase (AP), venous phase (VP), and combined arterial and venous phase (AP+VP) images. The concordance index (C-index) of each model was calculated. The model with the highest mean C-index was identified as the best model for calculating the radiomics score (Radscore). Univariate and multivariate Cox analyses were used to identify independent prognostic indicators and create a clinical model for prognosis prediction. The multivariable Cox regression was used to combine Radscore with clinical features to create a combined model. The efficacy of the model was evaluated using the C-index, calibration curves, and decision curve analysis (DCA).ResultsThe model based on the Lasso+StepCox[both] algorithm constructed using AP+VP radiomics features showed the best predictive ability among the 114 radiomics models. The C-indices of the model in the training and validation cohorts were 0.742 and 0.722, respectively. Based on the results of the univariate and multivariate Cox regression analyses, sex, Tumor-Node-Metastasis (TNM) stage, and systemic inflammation response index were included to build the clinical model. The combined model, incorporating three clinical factors and AP+VP-Radscore, achieved the highest C-indices of 0.764 and 0.746 in the training and validation cohorts, respectively. In terms of preoperative prognosis prediction for PDAC, the calibration curve and DCA showed that the combined model had a good consistency and greatest net benefit.ConclusionA combined model of clinical features and AP+VP-Radscore screened using multiple ML algorithms has an excellent ability to predict the prognosis of PDAC and may provide a noninvasive and effective method for clinical decision-making.

Comparison of treatment models for single primary advanced gallbladder cancer

PurposeTreatment for advanced gallbladder cancer (GBC) remains controversial, with various recommendations regarding the choice and combination of surgery and adjuvant therapy. The present article is targeting for the exploration of optimal treatment models for advanced GBC.MethodsAJCC (American Joint Committee on Cancer, 8th edition) stage III and stage IV GBC, were defined as advanced GBC. Patients with advanced GBC were identified using the Surveillance, Epidemiology, and End Results (SEER) database and departmental cohort. Because of the most representative, only gallbladder adenocarcinoma (GBAC) patients were selected. Based on their surgical status (No, Non-radical and Radical surgery), chemotherapy status (Chemotherapy, No chemotherapy), and radiotherapy status (Radiotherapy, No radiotherapy), treatment models were categorized. For the purposes of evaluating the treatment outcomes of various treatment models and determining the risk element for cancer-specific survival (CSS), Cox regression analysis was applied. Kaplan-Meier curves were used before and after adjusting for covariates, with log-rank tests used to analyze discrepancies between curves. Immunotherapy was analyzed using clinical data from departmental cohort. Finally, to compensate for the limitations of the database, a review examines the progress in treatment models for advanced GBC.Results5,154 patients aged over 18 years with solitary primary advanced GBC were identified from the SEER database. In advanced GBC patients, the treatment model has emerged as a significant prognostic factor. “Radical surgery + Chemotherapy + Radiotherapy” models maximally improved the CSS of advanced GBC before and after adjusting for covariates, while “No surgery + No chemotherapy + No radiotherapy” model had the lowest CSS. The present conclusions were supported even after subgroup analysis by AJCC stage. The efficacy of immunotherapy was demonstrated in the departmental cohort analysis. Additionally, this article provides a comprehensive overview of recent advancements in various emerging treatment strategies.ConclusionEven when optimal treatment model cannot be pursued, providing comprehensive combinations of treatments to advanced GBC patients whenever possible is always beneficial for their survival.

Classification of compact objects and model comparison using EOS knowledge

Classification of compact objects and model comparison using EOS knowledge

Neural reward system reflects individual value comparison strategy in cost-benefit decisions.

A core assumption in decision neuroscience is that individuals decide between options by comparing option-specific subjective reward values. Psychological accounts challenge this view and suggest that decisions are better explained by comparisons between choice attributes than by comparisons between option-specific values, casting doubts on the interpretation of activation in the neural reward system as subjective value signals. Here, we provide neuroimaging and pharmacological evidence that value-related neural activity follows the value comparison strategy employed by an individual on the psychological level. Neural model comparisons reveal that activation in the striatum, rather than generally reflecting attribute-wise or option-wise value comparisons, reflects the value comparison strategy that provides the best explanation for an individual's choice behavior. Strikingly, manipulating activation in the dopaminergic reward system reveals that dopamine antagonism counteracts the engagement in an individual's dominant value comparison strategy. Together, our findings provide evidence for the biological plausibility of psychological accounts of decision making and emphasize the importance of neural model comparisons to prevent misinterpretations of brain activation.

Scallop fishing activity characterization in Southern New England: Offshore wind demands and fisheries-dependent methods.

Managers attempt to minimize spatial use conflicts in siting of offshore wind developments, but they must rely on available data to balance biological, commercial, and recreational needs. Marine spatial planning products are only as good as the data they are built upon and fishing data present major challenges due to their confidentiality and the difficulty in isolating true fishing activity. We present a methodology to increase the spatiotemporal resolution of fishing effort and exposure estimates for Southern New England scallop fishing activity using random decision forests to perform supervised classification on AIS data, with fallback to lower resolution datasets for vessels without AIS coverage. Final predictive accuracy of the tuned random forest AIS model was 97.9%, offering improvements of 24.7, 48.6, and 50% over VTR fishing footprints, and AIS and VMS speed cutoff methods, respectively, to predict whether vessel locations correspond to fishing activity. Comparison of the AIS model with VMS and VTR fallback to the VTR fishing footprints data product demonstrated that the increased precision of the AIS point data delineated as fishing dramatically changed how fishing effort, and therefore exposure in the form of fishery landings values, is distributed spatially in Southern New England wind energy areas. This is due to how the probability of fishing is distributed across location data points in the various products, which has implications for marine spatial planning and mitigation decision-making. Therefore, multiple data products should be considered when evaluating management options, as exposure estimates may differ depending on what inputs are used. The higher resolution AIS product may offer enhanced value in understanding exposure and impacts to individual vessels, especially once wind farms are under construction or operational.

Coupled infectious disease and behavior dynamics. A review of model assumptions.

To comprehend the dynamics of infectious disease transmission, it is imperative to incorporate human protective behavior into models of disease spreading. While models exist for both infectious disease and behavior dynamics independently, the integration of these aspects has yet to yield a cohesive body of literature. Such an integration is crucial for gaining insights into phenomena like the rise of infodemics, the polarization of opinions regarding vaccines, and the dissemination of conspiracy theories during a pandemic. 
We make a threefold contribution. First, we introduce a framework to describe models coupling infectious disease and behavior dynamics, delineating four distinct update functions. Reviewing existing literature, we highlight a substantial diversity in the implementation of each update function. This variation, coupled with a dearth of model comparisons, renders the literature hardly informative for researchers seeking to develop models tailored to specific populations, infectious diseases, and forms of protection.
Second, we advocate an approach to comparing models' assumptions about human behavior, the model aspect characterized by the strongest disagreement. Rather than representing the psychological complexity of decision-making, we show that "influence-response functions'' allow one to identify which model differences generate different disease dynamics and which do not, guiding both model development and empirical research testing model assumptions. 
Third, we propose recommendations for future modeling endeavors and empirical research aimed at selecting models of coupled infectious disease and behavior dynamics. We underscore the importance of incorporating empirical approaches from the social sciences to propel the literature forward.

Machine Learning-Based Financial Big Data Analysis and Forecasting: From Preprocessing to Deep Learning Models

Abstract: Machine learning technology has revolutionized the financial sector by allowing faster and more accurate analysis and forecasting of large-scale financial data. This paper focuses on how machine learning ( ML ), especially deep learning models, can help to deal with high dimensional, noisy, and non-stationary financial data. Essential methodologies such as data preprocessing, feature engineering, and dimensionality reduction are imperative for preparing the raw financial data to ML algorithms. Methods such as outlier detection, normalisation for preprocessing, and feature (variable) selection for dimensionality reduction improve the models accuracy and efficiency. The paper also examines how deep learning models, such as Recurrent Neural Networks ( RNN ) and Long Short-Term Memory ( LSTM ) networks, can overcome the issues of autoregressive integrated moving average ( ARIMA ) models for financial time series prediction. An in-depth comparison of the machine learning models, ranging from supervised to unsupervised methods, is also provided to discuss their pros and cons in the financial domain, including popular applications such as credit scoring, fraud detection, and market risk prediction. The study finally concludes by discussing how optimisation methods such as hyperparameter tuning and cross-validation are imperative for ML models in complex financial scenarios to ensure their generalisation capability and avoid overfitting.

Comparison of Different Keratinocyte Cell Line Models for Analysis of NLRP1 Inflammasome Activation

The NLRP1 (nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-1) inflammasome is the most important inflammasome in human keratinocytes. It plays a crucial role in regulating innate immunity in the skin. This study aimed to evaluate NLRP1 inflammasome activation and the corresponding levels of detection in different keratinocyte cell lines to identify a suitable in vitro model for analyzing inflammasome activation in keratinocytes. We compared NLRP1 inflammasome activation, expression, and cell death among primary keratinocytes and immortalized keratinocyte cell lines HaCaT, HaSKpw, and SVTERT upon stimulation with ultraviolet B (UVB) irradiation or talabostat. The effects of both NLRP1 inducers on cell death and the modification of NLRP1 molecules were examined using fluorescence-activated cell sorting analysis, Western blotting, and an enzyme-linked immunosorbent assay. The key inflammasome components had varied expression levels among the keratinocyte cell models, with the highest expression observed in primary keratinocytes. Moreover, our data showed that both UVB and talabostat triggered cell death, and NLRP1 inflammasome activation was readily detected in primary keratinocytes but not in the analyzed immortalized keratinocyte cell lines. Therefore, we do not recommend the use of the immortalized keratinocyte cell lines HaCaT, HaSKpw, and SVTERT for analyzing inflammasome activation in keratinocytes; we strongly recommend the use of primary keratinocytes for these studies.

Exploiting EEG to identify confused students in online learning: A machine learning model comparison

Abstract. The COVID-19 pandemic has forced most schools, including universities, to adopt online courses for teaching. Due to the breaking of the constraints of time and space, online courses still occupy a large proportion in education until now, and large-scale open online education courses will become a major trend in the future. Of course, problems also arise in this process. As teachers cannot receive feedback from students anytime and anywhere in online courses, such as micro expressions, body movements, etc., they cannot know whether students are confused. Therefore, how to effectively detect students' learning status has grown in popularity. In the last few years, machine learning has developed rapidly, and a large number of artificial intelligence have emerged, making it possible to detect whether students are confused by combining electroencephalography with machine learning. In order to determine whether students were confused, this study used a wireless single channel Mindset device to collect Electroencephalography (EEG) signals. Six machine learning modelsrandom forests, eXtreme Gradient boosting, K-nearest neighbors, gradient boosting machines, logistic regression, and support vector machineswere then chosen. The findings indicate that other machine learning models have a high accuracy rate in classifying students' bewilderment, with the exception of the logistic regression model. With a 99.69% accuracy rate, the eXtreme gradient boosting model performs better than a number of other models.

Strategies for comparison of modern probabilistic seismic hazard models and insights from the Germany and France border region

Abstract. The latest generation of national and regional probabilistic seismic hazard assessments (PSHAs) in Europe presents stakeholders with multiple representations of the hazard in many regions. This raises the question of why and by how much seismic hazard estimates between two or more models differ, not only where models overlap geographically but also where new models update existing ones. As modern PSHA incorporates increasingly complex analysis of epistemic uncertainty, the resulting hazard is represented not as a single value or spectrum but rather as probability distribution. Focusing on recent PSHA models for France and Germany, alongside the 2020 European Seismic Hazard Model, we explore the differences in model components and highlight the challenges and strategy for harmonising the different models into a common PSHA calculation software. We then quantify the differences in the source model and seismic hazard probability distributions using metrics based on information theory, illustrating their application to the Upper Rhine Graben region. Our analyses reveal the spatial variation in and complexity of model differences when viewed as probability distributions and highlight the need for more detailed transparency and replicability of the models when used as a basis for decision-making and engineering design.

Performance comparison of several explainable hybrid ensemble models for predicting carbonation depth in fly ash concrete

The carbonation of fly ash concrete critically impacts the lifespan of structures, necessitating precise prediction of carbonation depth for the construction industry. This study establishes an original database comprising 883 cases, which are divided into training and testing sets in a 4:1 ratio. The Sand Cat Swarm Algorithm (SCSO) was developed to optimize the hyperparameters of three ensemble models: Gradient Boosting Decision Trees (GBDT), Light Gradient Boosting Machine (LGBM), and Categorical Boosting (CatBoost), resulting in the development of three hybrid ensemble models: SCSO-GBDT, SCSO-LGBM, and SCSO-CatBoost. Five classic models were included in the comparison, and all models used five-fold cross validation. Models’ performance was rigorously evaluated using Correlation coefficient (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Variance Accounted For (VAF), with VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method and Taylor diagrams utilized for optimal model selection. The SCSO-CatBoost model demonstrated superior performance, with R2 = 0.9657, MAE = 2.0062, RMSE = 8.2147, and VAF = 96.6177. Shapley additive explanations (SHAP) analysis of the SCSO-CatBoost model identified time of exposure as the most significant factor influencing carbonation depth, followed by fly ash content and carbon dioxide concentration. To facilitate practical application by non-algorithm engineers, an intelligent program was developed, allowing for straightforward testing with the three hybrid ensemble models. This study presents three precise models for predicting the carbonation depth of fly ash concrete. These models serve as valuable tools for estimating the service life of concrete structures and can be utilized as simulation instruments in durability engineering.

Predicting temporomandibular disorders in adults using interpretable machine learning methods: a model development and validation study

IntroductionTemporomandibular disorders (TMD) have a high prevalence and complex etiology. The purpose of this study was to apply a machine learning (ML) approach to identify risk factors for the occurrence of TMD in adults and to develop and validate an interpretable predictive model for the risk of TMD in adults.MethodsA total of 949 adults who underwent oral examinations were enrolled in our study. 5 different ML algorithms were used for model development and comparison, and feature selection was performed by feature importance ranking and feature decreasing methods. Several evaluation indexes, including the area under the receiver-operating-characteristic curve (AUC), were used to compare the predictive performance. The precision-recall curve (PR), calibration curve, and decision curve analysis (DCA) further assessed the accuracy and clinical utility of the model.ResultsThe performance of the random forest (RF) model was the best among the 5 ML models. An interpretable RF model was developed with 7 features (gender, malocclusion, unilateral chewing, chewing hard substances, grinding teeth, clenching teeth, and anxiety). The AUCs of the final model on the training set, internal validation set, and external test set were 0.892, 0.854, and 0.857, respectively. Calibration and DCA curves showed high accuracy and clinical applicability of the model.DiscussionAn efficient and interpretable TMD risk prediction model for adults was successfully developed using the ML method. The model not only has good predictive performance, but also enhances the clinical application value of the model through the SHAP method. This model can provide clinicians with a practical and efficient TMD risk assessment tool that can help them better predict and assess TMD risk in adults, supporting more efficient disease management and targeted medical interventions.

Comparison of Contact Angle Models in Two‐Phase Flow Simulations Using a Conservative Phase Field Equation

ABSTRACTIn phase field methods based on a second‐order Allen‐Cahn (AC) equation, contact angles are prescribed mostly via a geometric formulation. However, it is of great interest to utilize the surface‐energy formulation, which is often employed in the Cahn‐Hilliard (CH) phase field method, in the AC phase field method. This article thus put forward a surface‐energy formulation of contact angles. The model was compared with the geometric one in a number of impact problems, including both normal and oblique impacts. The governing equations were discretized using a finite difference method on a half‐staggered grid. The Navier–Stokes equation was tackled using an explicit projection method. The major findings are as follows. First, the geometric model can maintain a fixed contact angle throughout contact line motion, while the surface‐energy one predicts a changeable contact angle, with a fluctuation of about 5°. In the oblique drop impact, contact angle hysteresis was captured even if a static contact angle was applied in the surface‐energy formulation.

Comparativa de diferentes modelos radiómicos para la clasificación de lesiones adrenales indeterminadas diagnosticadas de forma incidental en TC con contraste

Purpose: A comparison of different machine learning models to discriminate adrenal incidentalomas by CT studies was performed. Methods: Sixty-two different features were obtained from a sample of 61 incidentalomas using the free license software LIFEx and 19 radiomic studies were performed with different models and feature selection methods to obtain the most efficient determination of possible malignancy. For all of them, four cross-validation methods were evaluated. Adenoma contouring was performed in duplicate by different radiologists evaluating all models in both groups. Results: ROC AUC between 0.42 (0.09-0.81) and 0.92 (0.63-1.00), and accuracy of the models between 0.63 (0.43-0.79) and 0.94 (0.82-1.00). The best-performing model was the balanced logistic regression applied to the 14 features with an intraclass coefficient greater than 0.9, with which accuracy of 0.94 (0.74-1.00), ROC AUC of 0.917 (0.63-1.00), benign recall of 0.92 (0.65-1.00) and malignant recall of 1.00 (0.71-1.00) were obtained. Conclusions: The evaluation and validation of different models has allowed us to obtain an efficient radiomic model for the discrimination of adrenal incidentalomas

The associations between exposure to mixed environmental endocrine disruptors and sex steroid hormones in men: a comparison of different statistical models

In recent years, worldwide fertility rates have continued to decrease. Humans are frequently exposed to a combination of environmental endocrine disruptors, which can cause male reproductive disorders. The study employed three distinct analytical models to examine the correlation between exposure to a combination of 25 chemicals and sex steroid hormone levels in adult males. This involved evaluating 12 chemicals and their metabolites from personal care and consumer products, as well as 13 metabolites linked to phthalates and plasticisers. The study analysed 25 chemicals and 3 measured sex steroid hormone outcomes, as well as two calculated hormonal outcomes (free androgen index, TT/E2 ratio) in 1262 adult men who participated in the National Health and Nutrition Examination Survey (NHANES) 2013–2016 in the United States. The study employed several statistical methods to estimate the relationships between single chemicals or chemical blends and sex hormones. These methods included linear regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine (BKMR) regression. The results of the linear regression analysis indicate that chemical exposure has a negative correlation with E2, TT, and FAI, and a positive correlation with SHBG and TT/E2. The mixture effect analyses using the WQS and BKMR models further confirmed that BP3, MECPP, and MECOP were the most highly weighted chemical mixtures. The analyses also suggested that there were differences in the effects of different concentrations of EDCs on sex steroid hormones. Exposure to environmental endocrine-disrupting chemicals (EDCs) has been found to have a negative correlation with estradiol and total testosterone, as well as FAI. Conversely, this exposure has been found to have a positive correlation with sex hormone binding globulin and the TT/E2 ratio. The study also revealed differences in the effects of different concentrations of EDCs.