Model Assessment Research Articles

IMPROVING FRACTALS FINANCIAL CREDIT RISK EVALUATION BASED ON DEEP LEARNING TECHNIQUES AND BLOCKCHAIN-BASED ENCRYPTION

Predicting a client’s affluence is essential in financial services. This task is the unity of the most important danger factors in groups and additional economic institutions. Typically, credit risk evaluation relies on black box models. However, these models often need to clarify the hidden information within the data. Moreover, few clear models focus on being easy to understand and accessible. This paper proposes a fractal credit risk assessment model that uses deep techniques like self-attention generative adversarial networks (SA-GAN) and deep multi-layer perceptron (DMLP). We use blockchain technology with the Brakerski–Gentry–Vaikuntanathan (BGV) encryption method to bolster safekeeping. Additionally, the scheme is designed for the Edge-of-things network, enabling communication through a LoRaWAN server. The proposed solution was tested on the German retail credit dataset. We assessed its performance using accuracy, F1 score, precision, and recall as metrics. Notably, our hybrid deep model, which combines SA-GAN with DMLP, achieved an impressive accuracy of 97.8% — outperforming existing methods in works.

The Association Between Glucose Variability and Insulin Parameters in Gestational Diabetes Diagnosed After 24 Gestational Weeks

Background/Objectives: Recently, it was reported that glucose variability (GV) calculated using the 75 g oral glucose tolerance test (OGTT) is associated with adverse perinatal outcomes. However, its role in gestational diabetes mellitus (GDM) remains unclear. We investigated the association between GV and insulin parameters in Japanese women diagnosed with GDM after 24 weeks of gestation (late GDM). Methods: A total of 280 mothers with late GDM cared for at Keio University Hospital were included in this study. Using 75 g OGTT, the initial increase and subsequent decrease were calculated as the GV. Results: The initial increase was significantly positively associated with 1 h plasma glucose level (PG) and 2 h PG with 75 g OGTT (p < 0.001), but fasting PG, insulinogenic index (IGI), and homeostasis model assessment—insulin resistance were negatively associated with the initial increase (all p < 0.001). The subsequent decrease was significantly positively correlated with 1 h PG (p < 0.001) but negatively correlated with 2 h PG (p < 0.001), IGI (p = 0.009), and the whole-body insulin sensitivity index derived from the OGTT (p = 0.02). Insulin Secretion-Sensitivity Index-2 was not associated with an initial increase or subsequent decrease. Conclusions: Since the initial increase might reflect insulin secretion and the subsequent decrease might reflect insulin sensitivity in Japanese women with late GDM, GV could alter several insulin parameters. Further studies are required to investigate the usefulness of GV in the management of GDM.

Machine-learning model based on ultrasomics for non-invasive evaluation of fibrosis in IgA nephropathy.

To develop and validate an ultrasomics-based machine-learning (ML) model for non-invasive assessment of interstitial fibrosis and tubular atrophy (IF/TA) in patients with IgA nephropathy (IgAN). In this multi-center retrospective study, 471 patients with primary IgA nephropathy from four institutions were included (training, n = 275; internal testing, n = 69; external testing, n = 127; respectively). The least absolute shrinkage and selection operator logistic regression with tenfold cross-validation was used to identify the most relevant features. The ML models were constructed based on ultrasomics. The Shapley Additive Explanation (SHAP) was used to explore the interpretability of the models. Logistic regression analysis was employed to combine ultrasomics, clinical data, and ultrasound imaging characteristics, creating a comprehensive model. A receiver operating characteristic curve, calibration, decision curve, and clinical impact curve were used to evaluate prediction performance. To differentiate between mild and moderate-to-severe IF/TA, three prediction models were developed: the Rad_SVM_Model, Clinic_LR_Model, and Rad_Clinic_Model. The area under curves of these three models were 0.861, 0.884, and 0.913 in the training cohort, and 0.760, 0.860, and 0.894 in the internal validation cohort, as well as 0.794, 0.865, and 0.904 in the external validation cohort. SHAP identified the contribution of radiomics features. Difference analysis showed that there were significant differences between radiomics features and fibrosis. The comprehensive model was superior to that of individual indicators and performed well. We developed and validated a model that combined ultrasomics, clinical data, and clinical ultrasonic characteristics based on ML to assess the extent of fibrosis in IgAN. Question Currently, there is a lack of a comprehensive ultrasomics-based machine-learning model for non-invasive assessment of the extent of Immunoglobulin A nephropathy (IgAN) fibrosis. Findings We have developed and validated a robust and interpretable machine-learning model based on ultrasomics for assessing the degree of fibrosis in IgAN. Clinical relevance The machine-learning model developed in this study has significant interpretable clinical relevance. The ultrasomics-based comprehensive model had the potential for non-invasive assessment of fibrosis in IgAN, which helped evaluate disease progress.

Tree-Related High-Impedance Fault in Distribution Systems: Modeling, Detection, and Ignition Risk Assessment (Review)

Tree-related high-impedance faults (THIFs) in medium voltage distribution systems represent a typical fault, especially where an overhead line crosses a forested area. The arc caused by THIFs could ignite nearby combustibles, significantly increasing the risk of forest fires. THIF detection remains a significant challenge because this type of fault has weak characteristics, as the fault impedance can reach hundreds of kΩ. Many previous studies have investigated reducing the risk of wildfires caused by THIFs. This paper reviews the existing literature on THIF modeling, detection, and ignition risk assessment. The modeling focuses on the distinctions and connections among electrical models of tree structures, traditional high-impedance fault (HIF) models, and THIF models. Detailed reviews and comparisons are conducted on THIF detection methods, encompassing fault analysis, fault feature extraction, and fault identification. The experiments and methods for assessing THIF ignition risk are also introduced and discussed. The review reveals critical research gaps. In modeling, there is a lack of frameworks that simultaneously elucidate underlying mechanisms and support detection algorithms. In detection algorithms, the existing methods have not been adequately validated under complex environmental conditions. In ignition risk assessment, current studies do not account for a comprehensive range of influencing variables. Finally, this paper proposes future research directions for THIF, aiming to provide a comprehensive reference for researchers and practitioners in this field.

Comparative study of DRASTIC-LU and radioactive isotope approaches for assessing groundwater vulnerability to pollution: the case study of Abuja, North Central Nigeria

Groundwater resources in Abuja, North Central Nigeria, are facing increasing vulnerability to pollution due to urbanization and anthropogenic activity. There are several methods of assessing groundwater vulnerability to contamination. Choosing the appropriate method for the study site, which is paramount for accurate vulnerability assessment, is sometimes very tasking. The DRASTIC-LU and 3H radioactive tracer methods were assessed in this study and applied to the Abuja aquifers. Water scientists have widely adopted these methods in vulnerability assessments. From the final DRASTIC-LU vulnerability map, it was observed that high to very high vulnerability areas were located in southwestern and northeastern parts of the area. 33% of the wells in the entire area exceed the nitrates statutory limits for drinking water, while 87% of the wells exceeding the nitrates statutory limits were located in high to very highly vulnerable areas. The radioactive tracers provided information on an Abuja aquifer-wide basis, showing areas of most recent recharge (post-nuclear) and older recharge (pre-nuclear), respectively. With the tracer approach, areas of preferential flow and diffuse flow indicating high vulnerability and low vulnerability, respectively, were mapped. This systematic review discusses in detail the advantages and limitations of these methods employing the origin-pathway-target model of vulnerability assessments as the basics of the study. Finally, the application of a dual approach involving the combination of the two is best for vulnerability assessments. This study therefore proposes the need for policymakers to adopt combined methodological approaches for sustainable groundwater management.

Machine learning based quantitative pain assessment for the perioperative period

This study developed and evaluated a model for assessing pain during the surgical period using photoplethysmogram data from 242 patients. Pain levels were measured at 2 min intervals using a numerical rating scale or clinical criteria: preoperative, before and after intubation, before and after skin incision, and postoperative. Key features from the photoplethysmography waveform were extracted to build XGBoost-based models for intraoperative and postoperative pain assessment. The combined perioperative model was compared with a commercial surgical pain index, yielding area under the receiver operating characteristics curve scores of 0.819 and 0.927 for intraoperative and postoperative periods, respectively, compared to the commercial index’s scores of 0.829 and 0.577. These results highlight the models’ effectiveness in pain assessment throughout the surgical process, identifying waveform skewness and diastolic phase rate decrease as critical for intraoperative pain assessment and systolic phase area or baseline fluctuation as significant for postoperative pain assessment.Clinical trial registration: Registration name: Clinical Research Information Service (CRIS). Registration site: http://cris.nih.go.kr. Number: KCT0005840. Principal Investigator: Dr. Byung-Moon Choi. Date of registration: January 28, 2021

Evaluating Sustainability in Post-Conflict Reconstruction: A Case Study of Blast-Damaged Buildings Without Structural Collapse Risk in Syria

In the context of post-war reconstruction, this study introduces a novel assessment model tailored for the preliminary management of blast-damaged reinforced concrete buildings without structural collapse risk. The model addresses the critical challenge of prioritizing reconstruction efforts within constraints of time and resources while integrating economic, environmental, and social dimensions. Developed using the Integrated Value Model for Sustainable Assessment and refined through the Delphi method, the model provides stakeholders with a practical tool to evaluate alternative reconstruction scenarios, including refurbishment, demolition, reconstruction with retained identity, and preservation for future work. Validation is carried out through a case study of a tourist hotel in the historic area of Damascus, where a report confirmed the building posed no structural collapse risk. The results indicate that “preservation for future work” is the most sustainable option environmentally, while “refurbishment” emerges as the most sustainable economically and socially. This study highlights the importance of comprehensive damage assessments and sustainability-oriented tools for resilient and sustainable post-war reconstruction strategies that respect heritage contexts.

A Holistic Maturity Model for Quality Assessment and Innovation in Peruvian Universities

This study proposes a holistic maturity model to evaluate and optimize the performance of Peruvian universities. It addresses key dimensions such as favorable governance, university talent (including students, faculty, and administrators), substantial resources, and results. It is based on the Design Science Research methodology and the Mettler framework. On the other hand, the Delphi method was selected for its ability to consolidate expert opinion. Aiken’s V coefficient was used to determine content validity, evaluating criteria such as clarity, relevance, and coherence, to ensure the reliability of the instrument. This model defines concrete practices for each maturity level, facilitating the progressive implementation of improvements in different university contexts. It contributes to Education 4.0 through the IT strategic alignment practices of the enabling governance dimension, promoting the implementation of personalized teaching methods and hybrid learning models. Regarding the Society 5.0 approach, the model prioritizes social impact and environmental sustainability through university social responsibility, ensuring that universities contribute to human and technological development. Finally, this proposal will support decision making in university management and educational policies in Peru and in international contexts.

Assessment of Mangrove Cover Change Based on Combining Remote Sensing Technique and Hydrodynamic Model Simulation

Mangrove ecosystems along Vietnam's coastline face significant degradation due to human activities, despite their crucial role in coastal protection against natural hazards. This study aims to assess the spatial and temporal changes in mangrove coverage along Vietnam's southern coast by integrating remote sensing techniques with hydrodynamic model simulations. The research methodology combines the Collect Earth tool analysis of Spot-4 and Planet satellite imagery (2000-2020) with Mike 21-HD two-dimensional (2D) hydrodynamic modeling to evaluate mangrove coverage changes by simulating shoreline erosion. Results analysis reveals that a significant increase of 109.83 ha in mangrove area within Vinh Chau Town of Soc Trang Province during the period 2010-2020, predominantly in the eastern region. Hydrodynamic simulations demonstrate that the coastal zone is primarily influenced by the interaction of nearshore currents, East Sea tides, and seasonal monsoon wave patterns. The model results effectively capture the complex interactions between these hydrodynamic factors and mangrove distribution. These findings not only validate the effectiveness of combining remote sensing and hydrodynamic modeling for mangrove assessment but also provide crucial insights for sustainable coastal ecosystem management. The study's integrated approach offers a robust framework for monitoring mangrove dynamics and developing evidence-based conservation strategies, highlighting the importance of maintaining these vital ecosystems for coastal protection.

Incidence trends and prognostic factors in head and neck MALT lymphoma.

Primary head and neck mucosa-associated lymphoid tissue lymphoma (HN-MALT) is a rare lymphoma with unknown incidence and prognosis. We allocated HN-MALT data from the Self-Surveillance, Epidemiology, and End Results database (2000-2021) into training and validation cohorts at a 7:3 ratio. A joinpoint regression analysis was used to examine sex-specific and age-group morbidities, and independent prognostic factors were identified through multivariate Cox analysis to construct a nomogram prediction model and verify the accuracy of prediction. A total of 2,517 patients with HN-MALT were randomly divided into training (1,762) and validation (755) groups. The incidence of HN-MALT in men and women decreased from 2007 to 2021, with an annual percent change (APC) of -3.04% (95%: -4.4 - -1.6, P < 0.05), while in the ≥ 60 years old group, the incidence decreased significantly from 2016 to 2021, with an APC of -8.20 (95%: -13.9 - -2.1, P < 0.05). The multivariate Cox analysis revealed that male, ≥ 60 years old, white, and divorced/separated/widowed were independent risk factors of the overall survival and lymphoma-specific survival. A nomogram prediction model was constructed based on the Cox regression results for multiple factors, the areas under the curve, the calibration curve, and decision curve analysis results of which indicated that the nomogram prediction model performed very well. HN-MALT cells exhibit distinctive clinical and pathological characteristics. Furthermore, we developed a nomogram model for the prognostic assessment to offer valuable guidance for clinical decision-making.

Bayesian views of generalized additive modelling

Abstract Generalized additive models (GAMs) are a frequently used, flexible framework applied to many problems in statistical ecology. They are commonly used to incorporate smooth effects into models via splines, including spatial components in species distribution models. GAMs are often considered to be a purely frequentist framework (‘generalized linear models with wiggly bits’), however links between frequentist and Bayesian approaches to these models were highlighted early‐on in the literature. From a practical perspective, Bayesian thinking underlies many parts of the implementation in the popular R package mgcv, so understanding these underpinnings can be informative during model building and assessment. This article aims to highlight useful links (and differences) between Bayesian and frequentist approaches to smoothing, as detailed in the statistical literature, in accessible way, with a focus on the mgcv implementation. By harnessing these links we can expand the set of modelling tools we have at our disposal, as well as our understanding of how existing methods work. Two important topics for quantitative ecologists are covered in detail: model term selection and uncertainty estimation. Taking Bayesian viewpoints for these problems makes them much more tractable in many applied settings. Examples are given using data from the NOAA Alaska Fisheries Science Center's groundfish assessment program.

Sicurpest: A Prototype of a User-Friendly Tool for Preventive Risk Assessment of Pesticide Use in Agriculture

In recent years, there has been a significant increase in attention towards the use of pesticides, as evidenced by the introduction of regulations aimed at ensuring their safe and environmentally responsible application. Although this stricter legislative framework offers potential benefits, the challenges faced by farmers, particularly those in small-scale operations, in complying with occupational health and safety (OHS) requirements are considerable. To address this issue, a research project was promoted by the Italian Workers’ Compensation Authority (INAIL) aimed at developing a user-friendly software tool to support farmers in planning pesticide applications in safe conditions. This study summarizes the results of the research, which consisted of three main phases: the definition of the main parameters that characterize the farmers’ exposure based on the analysis of the literature; the development of a risk assessment model that integrates these determinants with data provided by producers for each authorized pesticide; and the development of software tool (called SICURPEST) for a preliminary risk assessment when using pesticides. This tool serves as a preliminary risk assessment method, offering a simplified model for practical use by farmers. Its initial verification was conducted through a case study and the results represent a step towards promoting safer pesticide practices, providing a solid basis for further validation.

Development and validation of a prediction model for estimating patency rates after treatment of haemodialysis access dysfunction.

This study aims to develop a risk assessment model for predicting haemodialysis access dysfunction and to construct a nomogram. The clinical data of patients with haemodialysis access dysfunction treated at our hospital from October 2020 to January 2024 were retrospectively analysed. The least absolute shrinkage and selection operator regression method was used to filter variables and select predictors, while Cox regression was applied to filter variables and construct a nomogram. The discriminatory ability of the model was determined by calculating the area under the curve (AUC). Calibration was evaluated using bootstrap internal validation and the Hosmer-Lemeshow test. The clinical utility and applicability of the model were assessed through decision curve analysis (DCA) and the clinical impact curve (CIC). Subgroup analysis of risk factors for haemodialysis access dysfunction was performed using Kaplan-Meier survival curves. The study included 423 patients, and seven variables were used to construct the risk prediction model and nomogram for haemodialysis access dysfunction. The C-index of the prediction model was 0.783, and the time-dependent AUC (>0.8) at 6, 12, 18 and 24 months post-surgery indicated strong discriminatory ability. The calibration curve and Hosmer-Lemeshow test demonstrated good agreement between the prediction of the nomogram and the observed values. The DCA and CIC curves further confirmed the clinical practicability of the model. A risk assessment model and nomogram for haemodialysis access dysfunction based on seven variables were successfully constructed. This model demonstrates good discrimination and calibration, offering valuable guidance for clinical decision-making and significant clinical utility.

Incidental nanoparticle characterisation in industrial settings to support risk assessment modelling.

Research on nanoparticle (NP) release and potential exposure can be assessed through experimental field campaigns, laboratory simulations, and prediction models. However, risk assessment models are typically designed for manufactured NP (MNP) and have not been adapted for incidental NP (INP) properties. A notable research gap is identifying NP sources and their chemical, physical, and toxicological properties, especially in real-world settings. This work aims to provide insights into the release and physico-chemical properties of INP while contributing to improving models for INP release. INP release was evaluated through a case study in a ceramic tile firing facility, where aerosol (10nm - 10μm) properties were determined. The Control Banding (CB) Nanotool model was applied to test outputs based on provided input parameters. RESULTS: demonstrate the constant generation and release of INP during tile firing, with NP concentrations up to 68711/cm³ and mean diameters of 37nm, with 95% smaller than 100nm. Particle morphology was mostly spherical, suggesting nucleation from precursor gases as the main formation mechanism. INP chemical composition was driven by primary ceramic components, while trace elements like Ni and Ti exhibited size-dependent patterns. In vitro cell viability tests indicated low to medium cytotoxicity of PM2 aerosols, decreasing human alveolar epithelial cell viability in a concentration-dependent manner. Applying the risk model with varying input parameters revealed that the risk level (RL) based on severity scores decreased when aerosol size distribution data were used, illustrating the model's sensitivity to input variables. We conclude on the need for comprehensive experimental datasets to support risk assessment models and achieve effective risk management strategies in real-world scenarios.

The Truth About Co-Benefits: A Multidimensional Feasibility Assessment for Thailand and Beyond

Abstract Research has yielded increasingly robust estimates of the co-benefits from mitigating climate change while reducing air pollution, improving health, and meeting other development needs. Though quantifying these often hidden benefits could ease cost concerns and lower technological constraints for development-friendly climate solutions, achieving co-benefits frequently requires overcoming difficult-to-measure social and institutional barriers. This study extends insights from research focusing on quantitatively assessing the feasibility of a 1.5oC future to build a multidimensional framework for measuring different barriers to achieving co-benefits. The framework offers a novel yet generalizable approach for bringing context-appropriate assessments of different dimensions of feasibility into the integrated assessment modelling that underpins work on co-benefits. It then outlines five steps for applying that framework to evaluate the size of different barriers for transport, agricultural and residential energy co-benefit solutions in Thailand. The results demonstrate that the sum of the delays from social/institutional barriers exceed economic/technological barriers for four out of six studied solutions. These delays also lead to increases of 24% to 31% in PM2.5 emissions relative to a no-barriers effective implementation scenario between 2015 and 2030 and 2040. The feasibility framework can be integrated into not only national policy scenarios but also project assessments, following trends in carbon finance. An international barriers database as well as strengthening links to work on barriers and technological diffusion, transaction costs, and multi-level transitions can also help spread multi-dimensional feasibility assessments across countries and scales.

Risk assessment and management strategies for peri-implantitis in high-risk populations⁠

Peri-implantitis is a multifactorial condition characterized by inflammation and progressive bone loss surrounding dental implants, often leading to implant failure. High-risk populations, including patients with systemic conditions such as diabetes or undergoing cancer treatments, face an elevated susceptibility due to impaired immune responses and altered bone metabolism. Understanding the pathophysiological mechanisms underlying the disease is essential for effective risk assessment and management. Dysbiosis of the microbial biofilm, immune system dysregulation, and systemic factors like oxidative stress and cytokine imbalances are critical contributors to disease progression. These factors necessitate the development of targeted strategies for prevention and intervention. Risk assessment models have evolved to include advanced technologies such as artificial intelligence and decision tree regression to predict peri-implantitis in vulnerable individuals accurately. These tools incorporate systemic, local, and implant-specific parameters to enable personalized treatment planning and early intervention. Innovative management strategies have also emerged, ranging from adjunctive antimicrobial therapies, such as glycine powder air-polishing and localized drug delivery systems, to host modulation therapies and regenerative techniques. The use of advanced biomaterials, such as implants coated with bioactive peptides or antimicrobial nanoparticles, has further improved osseointegration and reduced infection rates. Regenerative approaches, including the application of growth factors like platelet-derived growth factor and bone morphogenetic proteins, have shown significant potential in repairing peri-implant defects and restoring functional stability. Emerging technologies, such as nanotechnology and bioengineered implants, offer new possibilities for real-time health monitoring and sustained therapeutic delivery at implant sites. These advancements, coupled with a deeper understanding of peri-implantitis etiology, provide a framework for optimizing outcomes in high-risk populations, addressing both the local and systemic challenges posed by the disease.

The association of visceral and subcutaneous fat areas with phenotypic age in non-elderly adults, mediated by HOMA-IR and HDL-C

BackgroundAgeing results in diminished adaptability, as well as declines in physiological and psychological functions and resilience. The epigenetic clock ‘Phenotypic Age’ (PhenoAge) represents ‘preclinical ageing’. Phenotypic Age Acceleration (PhenoAgeAccel) is defined as the residual from a linear regression model predicting PhenoAge on the basis of chronological age. Abdominal subcutaneous adipose tissue, visceral adipose tissue, the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), and high-density lipoprotein cholesterol (HDL-C) have all been shown to correlate with ageing; however, the connections between these factors and PhenoAge are still insufficiently investigated.MethodsData for this study were sourced from the National Health and Nutrition Examination Survey (2015–2018), comprising 2580 participants. Complex survey designs were considered. To examine the association between body fat area and PhenoAgeAccel, logistic regression was applied. Additionally, subgroup analysis was used to identify variations in population characteristics. The dose‒response relationship between body fat area and PhenoAgeAccel was determined via restricted cubic spline analysis. Mediation and interaction analyses were further employed to investigate the roles of the HOMA-IR and HDL-C in this association.ResultsIn nonelderly adults, the relationships between body fat area and PhenoAgeAccel differed chronological age. For abdominal subcutaneous fat area (SFA), this relationship was nonlinear in individuals aged 18–44 years and 45–59 years, with thresholds of 2.969 m² and 3.394 m², respectively. In contrast, a nonlinear relationship of visceral fat area (VFA) with PhenoAgeAccel was observed in individuals aged 18–44 years, while this relationship was linear in individuals aged 45–59 years, with thresholds of 0.769 m² and 1.220 m², respectively. Mediation effect analysis revealed that the HOMA-IR had a more pronounced mediation effect in individuals aged 18–44 years, accounting for 13.4% of the relationship between VFA and PhenoAgeAccel and 6.9% of the relationship between SFA and PhenoAgeAccel. Conversely, HDL-C had a greater mediating effect in individuals aged 45–59 years, accounting for 21.7% of the relationship between VFA and PhenoAgeAccel and 11.6% of the relationship between abdominal SFA and PhenoAgeAccel. HOMA-IR ≥ 2.73 or VFA > 0.925 m², as well as HOMA-IR ≥ 2.73 or abdominal SFA > 3.137 m², accelerated PhenoAge, whereas 1.60 < HDL-C ≤ 3.90 mmol/L combined with abdominal SFA ≤ 3.137 m² or VFA ≤ 0.925 m² decelerated PhenoAge.ConclusionIn this study, the nonlinear relationships among abdominal SFA, VFA, and PhenoAgeAccel were elucidated, while characteristic thresholds across different age groups were identified. The results of this study emphasize the complex influence of fat distribution on the ageing process and refine the roles of HOMA-IR and HDL-C in various age cohorts. These findings provide a biological basis for future screening for accelerated ageing and appropriate intervention in high-risk populations and offer valuable insights for guiding personalized clinical interventions and health management strategies.

The role of ultrasound combined with water bath in the establishment of animal models of rat urethral stricture

Urinary tract injuries represent a significant clinical challenge, necessitating precise diagnosis and effective treatment strategies. Rat models are preferred for studying urinary tract injuries due to their size, visibility of external genitalia, and robust reproductive and growth capabilities. However, there is a lack of standardized methodologies for evaluating the endpoints of rat urinary tract injury models. This study aimed to investigate the methodology of model establishment, imaging evaluation techniques, and endpoint effectiveness. Twenty-four male Sprague-Dawley rats were randomly assigned to groups, including a blank control group and surgical groups. The surgical groups underwent urethral injury induced by recombinant transforming growth factor-β1 (TGF-β1) solution, followed by the random selection of one surgical group (n = 6) to receive treatment with mesenchymal stem cell (MSC) exosomes. High-frequency ultrasonography using a GE E10 device combined with a water bath method was employed to evaluate the urethral conditions of all rats. Ultrasonographic characteristics were scored for all surgical group rats, and an intergroup analysis was conducted between the surgical group and the control group rats. Statistical analysis was conducted using SPSS 22.0 software. Ultrasonographic assessment revealed significant differences in urethral echogenicity between the normal group and the surgical group, with noticeable changes in urethral morphology post-injury. Histopathological examination confirmed more severe urethral stenosis in the TGF group compared to the MSC group. High-frequency ultrasound could effectively differentiate between the two groups of rats. Additionally, there were significant differences in the scoring of ultrasonographic characteristics between the two groups. Urethral stricture presents a complex challenge in urology, often requiring invasive treatment modalities. The establishment of animal models plays a crucial role in understanding and addressing this condition. TGF-β1 induced fibrosis and MSC therapy represent promising avenues for urethral stricture management. High-frequency ultrasound combined with the immersion method offers a non-invasive and cost-effective approach for evaluating rat urethral stricture models. This study highlights the practicality of high-frequency ultrasound combined with the water bath method in evaluating rat urethral stricture models, as it can effectively differentiate between rats in the pure TGF-β1 induced group and those in the MSC treatment group based on urethral changes. The results underscore the importance of standardized methodologies for model assessment and the potential of non-invasive imaging techniques in preclinical research. Overall, this study contributes to advancing our understanding of urinary system injuries and holds implications for future therapeutic interventions in urology.

Construction and evaluation of a triage assessment model for patients with acute non-traumatic chest pain: mixed retrospective and prospective observational study

BackgroundAcute non-traumatic chest pain is one of the common complaints in the emergency department and is closely associated with fatal disease. Triage assessment urgently requires the use of simple, rapid tools to screen patients with chest pain for high-risk condition to improve patient outcomes.MethodsAfter data preprocessing and feature selection, univariate and multiple logistic regression analyses were performed to identify potential predictors associated with acute non-traumatic chest pain. A nomogram was built based on the predictors, and an internal evaluation was performed using bootstrap resampling methods. The model was also externally validated in this center. Furthermore, the model results were risk-stratified using the decision tree analysis to explore the corresponding triage level. Subsequently, we developed an online visualization tool based on the model to assess the risk of high risk in patients with chest pain.ResultsMultiple logistic regression analysis showed that age, smoking, coronary heart disease, hypertension, diabetes, hyperlipidemia, pain site, concomitant symptoms, and electrocardiograph, all of which are independent predictors of high-risk chest pain patients. The AUC of our model in the development and validation groups was 0.919 (95%CI: 0.891 ~ 0.974) and 0.904 (95%CI: 0.855 ~ 0.952). Moreover, our model demonstrated better outcomes in terms of accuracy/sensitivity in both cohorts (81.9%/85.2% and 94.8%/78.5%). The calibration curve shows a high degree of agreement between the predicted and actual probabilities. Decision curve analysis clarified that our model had higher net gains across the entire range of clinical thresholds. Afterward, we developed an online tool, which is used in the triage link to facilitate nurses to screen people with high-risk chest pain.ConclusionWe proposed an accurate model to predict the high-risk populations with chest pain, based on which a simple and rapid online tool was developed and provided substantial support for its application as a decision-making tool for the emergency department.RegistrationThe study protocol was approved by the Ethics Committee Board of Fujian Provincial Hospital. Clinical trial registration number: ChiCTR2200061918.

A New Approach to Risk Assessment Model and Management for Jetty Fuel Terminal Industrywide Using Risk Priority Number

Jetty is an essential part of a fuel terminal and oil distribution process in terms of loading and unloading oil from and to tanker. Jetty has been widely used in fuel supply point facilities in archipelago countries like Indonesia. The increase in the importance given to maritime transportation has not only resulted in the development of the structure of safety issues but has also led to new investigations into the design of ships, the configuration of ports, and the operations conducted, all of which are essential components. However, it is not possible to ignore the risk inherent in jetty fuel terminal operations. Most of the reseach have pre-dominantly focused on the operation and impact of ship maneuvers and ship operations. But focusing on the jetty fuel risk itself is also very important as a whole part of reliability of a fuel receiving facility. FMEA is frequently used as a risk analysis method in determining risks arising from oil and gas sector. Each category of Severity, Occurance, and Detection from each risk factor can be rated to obtained the Risk Priority Number value. Mooring facilities assessment furthermore provides the implementation of assessment (grading) of operational suitability of jetties, based on the results of a visual assessment and special inspections. This paper aims to identify the risk associated with and to analyze these risk using Risk Priority Number ranking.