Development Of Assessment Model Research Articles

A first step towards the development of load models for the vibration serviceability assessment of footbridges under running actions

A first step towards the development of load models for the vibration serviceability assessment of footbridges under running actions

Development of New Passenger and Freight Train Load Models for Dynamic Assessment of Railway Bridges

The dynamic response of a bridge to a train crossing needs to be assessed when planning new railway bridges on tracks with a line speed above or equal 120 km/h and when recalculating existing bridges for new train configurations planned to put into operation or when increasing the locally permissible line speed. In the assessment, the model trains HSLM-A and HSLM-B need to be applied as a sequence of moving loads on two- or three-dimensional mechanical models of the bridge structure as defined in Eurocode EN 1991-2. However, these load models were developed in the 1990s while in the last 25 years many new trains have been developed and put into operation with different configurations compared to the normative model trains in terms of axle distances and axle loads. As a result, the dynamic bridge response for actually operating trains can significantly exceed the permissible values, in particular the vertical bridge acceleration. The normatively regulated model trains HSLM-A and HSLM-B are therefore no longer adequate for the dynamic assessment of railway bridges, and there is an urgent need to develop new load models for passenger and freight trains, to cover the trains currently operating on the European railway network. For the reasons mentioned above, an international consortium consisting of TU Darmstadt, KU Leuven, AIT and REVOTEC was commissioned by the German Centre for Rail Transport Research (DZSF) in 2019 to develop new dynamic passenger and freight train load models for the dynamic calculation of railway bridges. This article presents the development steps carried out, the optimization methods used herein, as well as the validation of the newly developed load models on a large set of existing railway bridges.

Evaluation of a Microcontroller-based Smart Wearable Device in College Students' Sports Forging Application

INTRODUCTION: The widespread use of smart wearable devices in various fields, including healthcare and sports, underscores the importance of their application in enhancing physical exercise among college students. Recent advancements in technology have facilitated the development of sophisticated methods to assess and predict physical activity outcomes, making their evaluation increasingly critical.OBJECTIVES: This study aims to develop a reliable assessment model for smart wearable devices used in college students' sports activities. The objective is to accurately predict and evaluate the effectiveness of these devices in improving students' physical health and promoting lifelong sports habits. Ultimately, the research seeks to integrate advanced computational methods to enhance the accuracy of physical exercise assessments.METHODS: The research introduces a novel assessment model that combines a zebra behavior-based heuristic optimization algorithm with a convolutional neural network (CNN). By analyzing user behavior data from wearable devices, the model constructs an evaluation index system tailored for college sports activities. The approach optimizes the parameters of the CNN using the zebra optimization algorithm, ensuring enhanced prediction accuracy.RESULTS: The evaluation model demonstrated high accuracy, with a significant improvement in predicting the outcomes of physical exercises among college students. Comparative analyses with traditional methods revealed that the new model reduced prediction errors and increased real-time performance metrics. Specifically, the model achieved a lower root mean square error (RMSE) in simulation tests, indicating more precise assessments. Figures and statistical data provided in the study illustrate the model's superior performance across various parameters.CONCLUSION: The developed assessment model significantly advances the application of smart wearable devices in monitoring and enhancing college students' physical activities. By integrating cutting-edge algorithms, the study not only improves the accuracy of exercise assessments but also contributes to the broader understanding of technology's role in health and fitness education. Future research could further refine this model by incorporating additional sensors and data points to expand its applicability and robustness.

Evaluating the influence of functional unit on life cycle assessment (LCA) reliability of concrete

The linear lifecycle of concrete, from production through usage to disposal, highlights its status as one of the most carbon-, energy-, and resource-intensive processes in the industry. The development of life cycle assessment (LCA) models to quantify concrete’s embodied and buildings’ operational emissions is strongly influenced by the selection of the functional unit (FU) since it dictates the magnitude and significance of the environmental burdens from concrete and buildings' life span. The chosen FU should comprehensively capture the concrete's functionalities and construction parameters. Nonetheless, establishing a FU that encompasses spatial, temporal, and intensity aspects of cement/concrete and its applications remains a significant limitation in the practice of current LCA models. This study analyzed the FU influence on the environmental footprint of concrete samples with specific compressive strengths (material level) via LCA. The LCA was conducted in OneClick LCA using the environmental product declaration (EPD) provided by the software for each ready-mix concrete sample. From the LCA results, the C 30/37 concrete mix (lowest compressive strength) exhibited the lowest carbon footprint for all the environmental indicators when a volume-based FU was considered. In contrast, the results were more favorable for the C 65/70 concrete specimen when the compressive strength (28 curing days) was chosen because less cement is needed to achieve one MPa of strength. This behavior was also observed in the modeling of a column in a building, where the mixture with the highest compressive strength (C 65/70) showed the lowest environmental impact for all the indicators. However, this assessment does not represent the actual building system's footprint. Thus, regionally specific functional units need to be established, considering not just concrete functionalities but also national regulations, stakeholder needs, and local resource availability.

Development of an Interpretable Deep Learning Model for Pathological Tumor Response Assessment After Neoadjuvant Therapy.

Neoadjuvant therapy followed by surgery has become the standard of care for locally advanced esophageal squamous cell carcinoma (ESCC) and accurate pathological response assessment is critical to assess the therapeutic efficacy. However, it can be laborious and inconsistency between different observers may occur. Hence, we aim to develop an interpretable deep-learning model for efficient pathological response assessment following neoadjuvant therapy in ESCC. This retrospective study analyzed 337 ESCC resection specimens from 2020-2021 at the Pudong-Branch (Cohort 1) and 114 from 2021-2022 at the Puxi-Branch (External Cohort 2) of Fudan University Shanghai Cancer Center. Whole slide images (WSIs) from these two cohorts were generated using different scanning machines to test the ability of the model in handling color variations. Four pathologists independently assessed the pathological response. The senior pathologists annotated tumor beds and residual tumor percentages on WSIs to determine consensus labels. Furthermore, 1850 image patches were randomly extracted from Cohort 1 WSIs and binarily classified for tumor viability. A deep-learning model employing knowledge distillation was developed to automatically classify positive patches for each WSI and estimate the viable residual tumor percentages. Spatial heatmaps were output for model explanations and visualizations. The approach achieved high concordance with pathologist consensus, with an R^2 of 0.8437, a RAcc_0.1 of 0.7586, a RAcc_0.3 of 0.9885, which were comparable to two senior pathologists (R^2 of 0.9202/0.9619, RAcc_0.1 of 8506/0.9425, RAcc_0.3 of 1.000/1.000) and surpassing two junior pathologists (R^2 of 0.5592/0.5474, RAcc_0.1 of 0.5287/0.5287, RAcc_0.3 of 0.9080/0.9310). Visualizations enabled the localization of residual viable tumor to augment microscopic assessment. This work illustrates deep learning's potential for assisting pathological response assessment. Spatial heatmaps and patch examples provide intuitive explanations of model predictions, engendering clinical trust and adoption(Code and data will be available at https://github.com/WinnieLaugh/ESCC_Percentage once the paper has been conditionally accepted). Integrating interpretable computational pathology could help enhancethe efficiency and consistency of tumor response assessment and empower precise oncology treatment decisions.

Fully automated deep learning system for osteoporosis screening using chest computed tomography images.

Osteoporosis, a disease stemming from bone metabolism irregularities, affects approximately 200 million people worldwide. Timely detection of osteoporosis is pivotal in grappling with this public health challenge. Deep learning (DL), emerging as a promising methodology in the field of medical imaging, holds considerable potential for the assessment of bone mineral density (BMD). This study aimed to propose an automated DL framework for BMD assessment that integrates localization, segmentation, and ternary classification using various dominant convolutional neural networks (CNNs). In this retrospective study, a cohort of 2,274 patients underwent chest computed tomography (CT) was enrolled from January 2022 to June 2023 for the development of the integrated DL system. The study unfolded in 2 phases. Initially, 1,025 patients were selected based on specific criteria to develop an automated segmentation model, utilizing 2 VB-Net networks. Subsequently, a distinct cohort of 902 patients was employed for the development and testing of classification models for BMD assessment. Then, 3 distinct DL network architectures, specifically DenseNet, ResNet-18, and ResNet-50, were applied to formulate the 3-classification BMD assessment model. The performance of both phases was evaluated using an independent test set consisting of 347 individuals. Segmentation performance was evaluated using the Dice similarity coefficient; classification performance was appraised using the receiver operating characteristic (ROC) curve. Furthermore, metrics such as the area under the curve (AUC), accuracy, and precision were meticulously calculated. In the first stage, the automatic segmentation model demonstrated excellent segmentation performance, with mean Dice surpassing 0.93 in the independent test set. In the second stage, both the DenseNet and ResNet-18 demonstrated excellent diagnostic performance in detecting bone status. For osteoporosis, and osteopenia, the AUCs were as follows: DenseNet achieved 0.94 [95% confidence interval (CI): 0.91-0.97], and 0.91 (95% CI: 0.87-0.94), respectively; ResNet-18 attained 0.96 (95% CI: 0.92-0.98), and 0.91 (95% CI: 0.87-0.94), respectively. However, the ResNet-50 model exhibited suboptimal diagnostic performance for osteopenia, with an AUC value of only 0.76 (95% CI: 0.69-0.80). Alterations in tube voltage had a more pronounced impact on the performance of the DenseNet. In the independent test set with tube voltage at 100 kVp images, the accuracy and precision of DenseNet decreased on average by approximately 14.29% and 18.82%, respectively, whereas the accuracy and precision of ResNet-18 decreased by about 8.33% and 7.14%, respectively. The state-of-the-art DL framework model offers an effective and efficient approach for opportunistic osteoporosis screening using chest CT, without incurring additional costs or radiation exposure.

The Dueling Ambitions of High Achieving Women: A Family Development Pilot Case Study

At some point prior to their children turning 18, roughly 70% of mothers will hold the position of breadwinner in their family. The increasing number of breadwinning mothers is reflective of the shifting roles of women in families. Although a family role shift is evident for mothers, the centrality of women in the development of families across the lifespan remains unchanged. The centrality of mothers to family development is obvious in the family life cycle. Embedded in the traditional family structure, the family life cycle is a commonly used assessment model for family lifespan development despite its incongruence with professional counseling ethics and curriculum standards. The incongruence highlights the need for a family development model appropriate for diverse family structures. Therefore, a pilot case study approach guided this study as an initial exploration into the family development of high achieving women. An in-depth semi-structured interview with a single participant was conducted and then analyzed using a phenomenological approach. Four themes emerged from the participant's experience: self-efficacy and hustle, supportive relationships, resources used, and barriers faced. The findings from this study demonstrated a clear need for additional research. Additionally, the researcher recommends that counselor educators and family counselors should be cautious in their utilization of existing family development assessment models with diverse families.

Assessment approaches in undergraduate health professions education: towards the development of feasible assessment approaches for low-resource settings

BackgroundFeasible and effective assessment approaches to measuring competency in health sciences are vital in competency-based education. Educational programmes for health professions in low- and middle-income countries are increasingly adopting competency-based education as a strategy for training health professionals. Importantly, the organisation of assessments and assessment approaches must align with the available resources and still result in the fidelity of implementation. A review of existing assessment approaches, frameworks, models, and methods is essential for the development of feasible and effective assessment approaches in low-resource settings.MethodsPublished literature was sourced from 13 electronic databases. The inclusion criteria were literature published in English between 2000 and 2022 about assessment approaches to measuring competency in health science professions. Specific data relating to the aims of each study, its location, population, research design, assessment approaches (including the outcome of implementing such approaches), frameworks, models, and methods were extracted from the included literature. The data were analysed through a multi-step process that integrated quantitative and qualitative approaches.ResultsMany articles were from the United States and Australia and reported on the development of assessment models. Most of the articles included undergraduate medical or nursing students. A variety of models, theories, and frameworks were reported and included the Ideal model, Predictive Learning Assessment model, Amalgamated Student Assessment in Practice (ASAP) model, Leadership Outcome Assessment (LOA) model, Reporter-Interpreter-Manager-Educator (RIME) framework, the Quarter model, and the model which incorporates four assessment methods which are Triple Jump Test, Essay incorporating critical thinking questions, Multistation Integrated Practical Examination, and Multiple Choice Questions (TEMM) model. Additional models and frameworks that were used include the Entrustable Professional Activities framework, the System of Assessment framework, the Reporter-Interpreter-Manager-Educator (RIME) framework, the Clinical Reasoning framework (which is embedded in the Amalgamated Student Assessment in Practice (ASAP) model), Earl’s Model of Learning, an assessment framework based on the Bayer–Fetzer Kalamazoo Consensus Statement, Bloom's taxonomy, the Canadian Medical Education Directions for Specialists (CanMEDS) Framework, the Accreditation Council for Graduate Medical Education (ACGME) framework, the Dreyfus Developmental Framework, and Miller's Pyramid.ConclusionAn analysis of the assessment approaches, frameworks, models, and methods applied in health professions education lays the foundation for the development of feasible and effective assessment approaches in low-resource settings that integrate competency-based education.Trial registrationThis study did not involve any clinical intervention. Therefore, trial registration was not required.

Development of a Performance Assessment Model for Contractors in Saudi Arabian Construction Projects

Saudi Arabia leads the Gulf Cooperation Council countries in the construction industry, with 63% of the region’s future projects and $1.4 trillion in building and transportation projects. Previous studies have shown that 60% of construction projects are behind the schedule. One of the reasons behind this delay is the inappropriate selection of contractors for construction projects; therefore, the development of a contractor performance assessment model is needed for construction projects in Saudi Arabia. The proposed model includes the major key performance indicators (KPIs) used to measure contractors’ performance in construction projects. Afterward, a questionnaire is conducted with construction professionals to recommend the important KPIs to consider while developing the model. The analytic hierarchy process decision-making technique is applied for assessing relative priorities among the identified KPIs based on expert or decision-maker consensus. The results show that the time category is the most important in selecting the best contractor, emphasizing the significance of timely project completion.

Comprehensive investigation into the influence of glycosylation on head and neck squamous cell carcinoma and development of a prognostic model for risk assessment and anticipating immunotherapy.

It has been well established that glycosylation plays a pivotal role in initiation, progression, and therapy resistance of several cancers. However, the correlations between glycosylation and head and neck squamous cell carcinoma (HNSCC) have not been elucidated in detail. The paramount genes governing glycosylation were discerned via the utilization of the Protein-Protein Interaction (PPI) network and correlation analysis, coupled with single-cell RNA sequencing (scRNA-seq) analysis. To construct risk models exhibiting heightened predictive efficacy, cox- and lasso-regression methodologies were employed, and the veracity of these models was substantiated across both internal and external datasets. Subsequently, an exploration into the distinctions within the tumor microenvironment (TME), immunotherapy responses, and enriched pathways among disparate risk cohorts ensued. Ultimately, cell experiments were conducted to validate the consequential impact of SMS in Head and Neck Squamous Cell Carcinoma (HNSCC). A total of 184 genes orchestrating glycosylation were delineated for subsequent scrutiny. Employing cox- and lasso-regression methodologies, we fashioned a 3-gene signature, proficient in prognosticating the outcomes for patients afflicted with HNSCC. Noteworthy observations encompassed distinctions in the Tumor Microenvironment (TME), levels of immune cell infiltration, and the presence of immune checkpoint markers among divergent risk cohorts, holding potentially consequential implications for the clinical management of HNSCC patients. The prognosis of HNSCC can be proficiently anticipated through risk signatures based on Glycosylation-related genes (GRGs). A thorough delineation of the GRGs signature in HNSCC holds the potential to facilitate the interpretation of HNSCC's responsiveness to immunotherapy and provide innovative strategies for cancer treatment.

Recomendaciones del grupo de trabajo para la valoración preanestésica de la cirugía de cataratas

Introduction and objectivesCataract surgery is one of the most common procedures in outpatient surgery units. The use of information and communication technologies (ICT) in clinical practice and the advent of new health scenarios, such as the Covid pandemic, have driven the development of pre-anaesthesia assessment models that free up resources to improve access to cataract surgery without sacrificing patient safety. The approach to cataract surgery varies considerably among public, subsidised and private hospitals. This raises the need for guidelines to standardise patient assessment, pre-operative testing, management of background medication, patient information and informed consent. ResultsIn this document, the SEDAR Clinical Management Division together with the Major Outpatient Surgery Division put forward a series of consensus recommendations on pre-anaesthesia testing based on the use of ITCs, health questionnaires, patient information, and informed consent supervised and evaluated by an anaesthesiologist. ConclusionsThis consensus document will effectivise pre-anaesthesia assessment in cataract surgery while maintaining the highest standards of quality, safety and legality.

Pre-anaesthesia assessment in cataract surgery: Recommendations from the SEDAR Working Group

Introduction and objectivesCataract surgery is one of the most common procedures in outpatient surgery units. The use of information and communication technologies (ICT) in clinical practice and the advent of new health scenarios, such as the Covid pandemic, have driven the development of pre-anaesthesia assessment models that free up resources to improve access to cataract surgery without sacrificing patient safety. The approach to cataract surgery varies considerably among public, subsidised and private hospitals. This raises the need for guidelines to standardise patient assessment, pre-operative tests, management of background medication, patient information and informed consent. ResultsIn this document, the SEDAR Clinical Management Division together with the Major Outpatient Surgery Division SEDAR Working Group put forward a series of consensus recommendations on pre-anaesthesia testing based on the use of ITCs, health questionnaires, patient information and informed consent supervised and evaluated by an anaesthesiologist. ConclusionsThis consensus document will effectivise pre-anaesthesia assessment in cataract surgery while maintaining the highest standards of quality, safety and legality.

Evaluating and analyzing student labor literacy in China's higher vocational education: an assessment model approach

IntroductionThis study addresses the gap in evaluating labor literacy amongst vocational students in China's higher vocational education system. It aims to develop a comprehensive framework for assessing essential labor competencies, thereby contributing to a nuanced understanding of vocational education's role in skill development.MethodsEmploying a multifaceted research methodology, this study integrates questionnaire surveys, econometric analyses, and the Delphi method to assess labor literacy among 749 students from three leading vocational institutions. A pioneering labor literacy assessment model is introduced: S = 0.5466B1+0.1816B2+0.1623B3+0.1095B4, where S denotes the overall labor literacy score. Here, B1 represents labor concepts, B2 denotes habits and qualities, B3 signifies knowledge and skills, and B4 encapsulates emotions and attitudes, illustrating a comprehensive approach to measuring labor literacy.ResultsOur findings reveal pronounced disparities in labor literacy across the identified dimensions, with particular deficiencies in labor concepts. The study also identifies six determinants—gender, political profile, academic performance, internship and training base utilization, inclination towards innovation and entrepreneurship, and labor education evaluation mechanisms—that significantly influence labor literacy outcomes.DiscussionHighlighting the imperative for a contextually informed and holistic approach to labor literacy, this study's insights advocate for educational strategies that are both aligned with labor market demands and cognizant of socio-cultural nuances. The developed assessment model not only propels the theoretical discourse in vocational education forward but also provides a pragmatic guide for educators and policy makers, aiming to mitigate disparities and enhance labor competencies through refined educational practices.

A hybrid spherical fuzzy AHP-MARCOS model for evaluating the condition of saltwater pipes in Hong Kong

PurposeWater pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.Design/methodology/approach As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.FindingsAnalytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.Originality/valueIt can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.

Scenario development for safety assessment in deep geologic disposal of high-level radioactive waste and spent nuclear fuel: A review.

Radiation and radioactive substances result in the production of radioactive wastes which require safe management and disposal to avoid risks to human health and the environment. To ensure permanent safe disposal, the performance of a deep geological repository for radioactive waste is assessed against internationally agreed risk-based standards. Assessing postclosure safety of the future system's evolution includes screening of features, events, and processes (FEPs) relevant to the situation, their subsequent development into scenarios, and finally the development and execution of safety assessment (SA) models. Global FEP catalogs describe important natural and man-made repository system features and identify events and processes that may affect these features into the future. By combining FEPs, many of which are uncertain, different possible future system evolution scenarios are derived. Repository licensing should consider both the reference or "base" evolution as well as alternative futures that may lead to radiation release, pollution, or exposures. Scenarios are used to derive and consider both base and alternative evolutions, often through production of scenario-specific SA models and the recombination of their results into an assessment of the risk of harm. While the FEP-based scenario development process outlined here has evolved somewhat since its development in the 1980s, the fundamental ideas remain unchanged. A spectrum of common approaches is given here (e.g., bottom-up vs. top-down scenario development, probabilistic vs. bounding handling of uncertainty), related to how individual numerical models for possible futures are converted into a determination as to whether the system is safe (i.e., how aleatoric uncertainty and scenarios are integrated through bounding or Monte Carlo approaches).

Development of micro grid model for stability assessment

This paper is qualified for the assessment of cooperation and interaction of micro grids (furthermore referred to as MG that means a micro network with about zero transfer power) and power system (furthermore referred to as PS that means the connection and synchronous operation of each generators and consumer districts through the electric network). Primarily it examines the effect of the continuously increasing integration of the intelligent energy distribution networks concerning the stability of the PS. In the paper the model is published, used for simulating the MGs and how to apply them for stability assessment. With the help of a model network the results of the simulations are presented and also the conclusions can be drawn are evaluated. The expectation is that the stability of the PS will increase in consequence of the growing number of the MGs.

Modeling climate change adaptation for sustainable coastal zones using GIS and AHP

The world is currently confronting one of its biggest environmental challenges: combating climate change. Coastal zones are one of the areas thought to be most sensitive to current and future climate change threats. The paper integrates Remote Sensing (RS), Geographic Information System (GIS) techniques, and Multi-Criteria Decision Analysis (MCDA) to detect vulnerable areas from climate change impacts in coastal zones in order to recommend adaptation systems in new coastal zones that can withstand various climatic changes. The proposed decision-making framework was developed in three phases: 1) climate data collection and processing; 2) Coastal Climate Impact Assessment (CCIA) model development; and 3) implementation and adaptation system selection. The climate data collection and processing phase involves determining the most significant climate change parameters and their indicators that affect coastal zone stability, extracting climatic data indicators from different climate database sources, and prioritizing the selected indicators. The indicators’ weights were estimated using the Analytical Hierarchy Process (AHP) through a questionnaire survey shared with experts in climate change impacts. A CCIA model development phase involves the formulation of the proposed model using GIS technique to discover the vulnerable areas according to the most dominant impact. The implementation and adaptation system selection phase involves the application of the framework to Al-Alamein New City in Egypt. A sensitivity analysis was conducted to measure the behavior of several climate change parameters to identify the most critical parameter for climate change in Al-Alamein New City. The results showed that the geology of the region is the most crucial component influenced by climate change. It is capable of producing a very sensitive area in the coastal zone while also taking other factors into account. When creating new urban neighborhoods, the erosion of the shoreline is the least important factor to consider. This is because coastal deterioration is caused by both the influence of metrological data on the region and the impact of human activity. Shoreline deterioration will be reduced if climate conditions are maintained while limiting the impact of human activities. To adapt to the long-term effects of climate change on coastal zones, a combination of soft and hard protection systems should be considered.

Assessment of osteopontin as an early nephrotoxicity indicator in human renal proximal tubule cells and its application in evaluating lanthanum-induced nephrotoxicity

Nephrotoxicity is a common adverse effect induced by various chemicals, necessitating the development of reliable toxicity screening models for nephrotoxicity assessment. In this study, we assessed a group of nephrotoxicity indicators derived from different toxicity pathways, including conventional endpoints and kidney tubular injury biomarkers such as clusterin (CLU), kidney injury molecule-I (KIM-1), osteopontin (OPN), and neutrophil gelatinase-associated lipocalin (NGAL), using HK-2 and induced pluripotent stem cells (iPSCs)-derived renal proximal tubular epithelial-like cells (PTLs). Among the biomarkers tested, OPN emerged as the most discerning and precise marker. The predictive potential of OPN was tested using a panel of 10 nephrotoxic and 5 non-nephrotoxic compounds. The results demonstrated that combining OPN with the half-maximal inhibitory concentration (IC50) enhanced the diagnostic accuracy in both cellular models. Additionally, PTLs cells showed superior predictive efficacy for nephrotoxicity compared to HK-2 cells in this investigation. The two cellular models were utilized to evaluate the nephrotoxicity of lanthanum. The findings indicated that lanthanum possesses nephrotoxic properties; however, the degree of nephrotoxicity was relatively low, consistent with the outcomes of in vivo experiments.

Exploration of underlying patterns among conflict, socioeconomic and political factors.

The emergence of conflict is a complex issue with numerous drivers and interactions playing a role. Exploratory dimension-reduction techniques can reveal patterns of association in such complex data. In this study, an existing dataset was reanalyzed using factor analysis for mixed data to visualize the data in two-dimensional space to explore the conditions associated with high levels of conflict. The first dimension was strongly associated with resilience index, control of corruption, income, income inequality, and regime type, while the second dimension was strongly associated with oil production, regime type, conflict level, political terror level, and water stress. Hierarchical clustering from principal components was used to group the observations into five clusters. Country trajectories through the two-dimensional space provided examples of how movement in the first two dimensions reflected changes in conflict, political terror, regime type, and resilience index. These trajectories correspond to the evolution of themes in research on conflict, particularly in terms of considering the importance of climate or environmental variables in stimulating or sustaining conflict. Understanding conditions associated with high conflict can be helpful in guiding the development of future models for prediction and risk assessment.

Development of a piglet model for cerebrovascular autoregulation assessment with altered PaCO2

IntroductionCerebrovascular autoregulation (CA) capacity can be impaired in the aftermath of acute brain injuries. Altered physiological states, such as hypo- and hypercapnia, affect CA. Although these effects have been demonstrated in several animal experiments, the exact effect of PaCO₂ on the plateau of cerebral blood flow (CBF) across the spectrum of arterial blood pressures has not been fully disclosed. Research questionThe aim was to explore pial vasodynamics in response to changing PaCO₂ in a porcine cranial window model, as preparation for an experimental setup in which the CBF plateau position is investigated under different PaCO₂ conditions. Material and methodsFive piglets were brought under anesthesia, intubated, ventilated and instrumented with a cranial window through which pial arteriolar diameters could be microscopically observed. By changing ventilation to either hyper- or hypoventilation we were able to investigate a range of PaCO2 from 25 till 90 mmHg. ResultsAltering the respiratory rate to manipulate PaCO₂ by ventilation appeared to be feasible and reliable. Discussion and conclusionWe found that ETCO₂ reliably represents PaCO₂ in our model. Pial arteriolar diameter changes followed the direction of PaCO₂ changes, but the effect of PaCO₂ on the diameters was not linear. Only in the hypercapnia setting did we observe a clear and consistent vasodilation of the pial arterioles.