Empowering healthcare education: A multilingual ontology for medical informatics and digital health (MIMO) integrated to artificial intelligence powered training in smart hospitals.

Objective: A comprehensive understanding of professional and technical terms is essential to achieving practical results in multidisciplinary projects dealing with health informatics and digital health. The medical informatics multilingual ontology (MIMO) initiative has been created through international cooperation. MIMO is continuously updated and comprises over 3700 concepts in 37 languages on the Health Terminology/Ontology Portal (HeTOP). Methods: We conducted case studies to assess the feasibility and impact of integrating MIMO into real-world healthcare projects. In HosmartAI, MIMO is used to index technological tools in a dedicated marketplace and improve partners' communication. Then, in SaNuRN, MIMO supports the development of a "Catalog and Index of Digital Health Teaching Resources" (CIDHR) backing digital health resources retrieval for health and allied health students. Results: In HosmartAI, MIMO facilitates the indexation of technological tools and smooths partners' interactions. In SaNuRN within CIDHR, MIMO ensures that students and practitioners access up-to-date, multilingual, and high-quality resources to enhance their learning endeavors. Conclusion: Integrating MIMO into training in smart hospital projects allows healthcare students and experts worldwide with different mother tongues and knowledge to tackle challenges facing the health informatics and digital health landscape to find innovative solutions improving initial and continuous education.

Thermal and emission analysis of waste plastic and microalgae biodiesel as a potential power source for diesel engines: A sustainable approach.

The future of diesel engines is greatly influenced by ongoing research on alternative fuels. Renewable fuels have been researched and adopted by several nations to encourage the production and use of biodiesel. This study examines the energy conversion of waste plastic biodiesel and Spirulina microalgae biodiesel at a 20% blending ratio to analyze the behavior of a one-cylinder, 4-stroke diesel engine running at 1500 rpm with a compression ratio of 17.5. The authors evaluated, analyzed, and compared the engine's combustion, performance, and emission at an incremental engine load of 25% from 25 to 100%. The findings demonstrated that the biodiesel fuel samples generated somewhat poorer efficiency but reduced emissions from the engine. At 100% load, the percentage differences between the diesel and blended fuel samples ranged from 2.4 to 7.3% for BTE, 2.9 to 16.5% for BSFC, and 1.0 to 4.62% for BSEC; however, the PM, SO2, and CO2 emissions were reduced by 1.6-28.8% except for NOx emissions, which are increased by up to 9.4%. Pure diesel exhibits the best performance characteristics; however, pure biodiesel exhibits the best emission characteristics. The 20% blended fuels showed promising results, as they exhibited comparable or slightly improved BTE values compared to their respective pure biodiesel fuels. The findings indicate that Spirulina microalgae biodiesel and waste plastic biodiesel have the potential to be utilized as substitute fuels for diesel engines; however, for the greatest performance and lowest emissions, their blending ratios and engine operating conditions must be optimized.

A perspective review on the role of engine sound in speed perception and control: state of the art and methodological suggestions.

In this review we focus on the role of in-car sound, specifically the artificial engine sounds, on drivers' speed perception and control, a topic that has received little attention so far. Previous studies indicate that removing or reducing engine sound leads drivers to underestimate speed and, consequently, to drive faster. Furthermore, evidence suggests that specific sound frequencies could play a role in this process, highlighting the importance of in-car sound features. First, we show that the amount of research in the field is scarce and rather outdated, and that this is largely due to the fact that industrial research is subject to very few publications. Then, we examine benefits and limitations of different research paradigms used and we propose a protocol to investigate systematically the phenomenon. In particular, we argue for the benefits of a wider use of psychophysical methods in speed perception, a field that has been typically explored by means of driving simulation. Finally, we highlight some methodological and statistical limitations that might impact the interpretation of the evidence considered. Our methodological considerations could be particularly useful for researchers aiming to investigate the impact of sound on speed perception and control, as well as for those involved in the design of in-car sounds. These are particularly relevant for the design of electric vehicles, which represent a challenge but also the ideal testing ground to advance the knowledge in the field.

Fusion of spectral and topographic features for land use mapping using a machine learning framework for a regional scale application.

This study investigated the dynamics of land use and land cover (LULC) modelling, mapping, and assessment in the Kegalle District of Sri Lanka, where policy decision-making is crucial in agricultural development where LULC temporal datasets are not readily available. Employing remotely sensed datasets and machine learning algorithms, the work presented here aims to compare the accuracy of three classification approaches in mapping LULC categories across the time in the study area primarily using the Google Earth Engine (GEE). Three classifiers namely random forest (RF), support vector machines (SVM), and classification and regression trees (CART) were used in LULC modelling, mapping, and change analysis. Different combinations of input features were investigated to improve classification performance. Developed models were optimised using the grid search cross-validation (CV) hyperparameter optimisation approach. It was revealed that the RF classifier constantly outstrips SVM and CART in terms of accuracy measures, highlighting its reliability in classifying the LULC. Land cover changes were examined for two periods, from 2001 to 2013 and 2013 to 2022, implying major alterations such as the conversion of rubber and coconut areas to built-up areas and barren lands. For suitable classification with higher accuracy, the study suggests utilising high spatial resolution satellite data, advanced feature selection approaches, and a combination of several spatial and spatial-temporal data sources. The study demonstrated practical applications of derived temporal LULC datasets for land management practices in agricultural development activities in developing nations.

Investigation on nanostructure, surface functional groups, and oxidation activity of particulate along the exhaust after-treatment of a diesel engine fueled with waste cooking oil biodiesel blends.

In this study, the nanostructure, surface functional groups, and oxidation activity of soot particulate along the exhaust after-treatment system of a heavy-duty diesel engine fueled with waste cooking oil (WCO) biodiesel blends are investigated by TEM, XPS, and TGA respectively. The main findings are as follows: Along the exhaust after-treatment system, fringe length of primary particles of soot particulate emitted from tested heavy-duty diesel engine fueled with B0, B10, B20, and B100, i.e., 0%, 10%, 20%, and 100% ratio of WCO biodiesel blended into petroleum diesel in volume respectively increases, while fringe tortuosity and separation distance of primary particles reduces. The fringe length of B10, B20, and B100 is smaller, but the fringe tortuosity and separation distance are larger than that of B0. The O/C ratio of soot particulate tends to increase firstly and then decrease as the exhaust passes through DOC+cDPF and SCR+ASC in sequence. The O/C ratio of B10, B20, and B100 are also higher than that of B0. Soot particulate at cDPF outlet contains carborundum and biuret is found at SCR+ASC outlet. The sp3/sp2 ratio decreases along the exhaust after-treatment system, and B10, B20, and B100 tend to get higher sp3/sp2 ratio than B0. The C-OH and C=O content of soot particulate from different WCO biodiesel blends show generally similar trends along the exhaust after-treatment system, while the activation energy of soot particulate keeps increasing along the exhaust after-treatment system, but decreases with the increasing of blend ratio. These findings can provide useful references for optimizing the after-treatment system for WCO biodiesel blends.

Investigation of unswept ramp system with lobe shape nozzle for fuel mixing of hydrogen jet at a scramjet engine.

The role of efficient fuel mixing and a stable flame holder is crucial in enhancing the performance and capabilities of scramjet engines for high-speed flight. The present research paper has tried to disclose the fuel mixing efficiency of 3-lobe annular nozzle on the mixing mechanism of the fuel jet behind the strut. In addition, using internal air jet flow for increasing the circulation strength and fuel mixing behind the strut is also examined in this study. Numerical simulation of the flow and fuel jet behind the strut is done to reveal the main physics related to the mechanism of fuel mixing inside the combustor with the proposed injection system. The results of our simulation show that using annular 3-lobe fuel jet improve the fuel mixing via production of the multiple vortex pairs within the combustor behind the strut. The use of internal air jet also enhances the fuel mixing efficiency up to 90% in combustor of scramjet engine.

Online Patient Education in Obstructive Sleep Apnea: ChatGPT versus Google Search.

The widespread implementation of artificial intelligence technologies provides an appealing alternative to traditional search engines for online patient healthcare education. This study assessed ChatGPT-3.5's capabilities as a source of obstructive sleep apnea (OSA) information, using Google Search as a comparison. Ten frequently searched questions related to OSA were entered into Google Search and ChatGPT-3.5. The responses were assessed by two independent researchers using the Global Quality Score (GQS), Patient Education Materials Assessment Tool (PEMAT), DISCERN instrument, CLEAR tool, and readability scores (Flesch Reading Ease and Flesch-Kincaid Grade Level). ChatGPT-3.5 significantly outperformed Google Search in terms of GQS (5.00 vs. 2.50, p < 0.0001), DISCERN reliability (35.00 vs. 29.50, p = 0.001), and quality (11.50 vs. 7.00, p = 0.02). The CLEAR tool scores indicated that ChatGPT-3.5 provided excellent content (25.00 vs. 15.50, p < 0.001). PEMAT scores showed higher understandability (60-91% vs. 44-80%) and actionability for ChatGPT-3.5 (0-40% vs. 0%). Readability analysis revealed that Google Search responses were easier to read (FRE: 56.05 vs. 22.00; FKGL: 9.00 vs. 14.00, p < 0.0001). ChatGPT-3.5 delivers higher quality and more comprehensive OSA information compared to Google Search, although its responses are less readable. This suggests that while ChatGPT-3.5 can be a valuable tool for patient education, efforts to improve readability are necessary to ensure accessibility and utility for all patients. Healthcare providers should be aware of the strengths and weaknesses of various healthcare information resources and emphasize the importance of critically evaluating online health information, advising patients on its reliability and relevance.

Improvement of Hydrogen-Resistant Gas Turbine Engine Blades: Single-Crystal Superalloy Manufacturing Technology.

This paper presents the results of an analysis of resistance to hydrogen embrittlement and offers solutions and technologies for manufacturing castings of components for critical applications, such as blades for gas turbine engines (GTEs). The values of the technological parameters for directional crystallization (DC) are determined, allowing the production of castings with a regular dendritic structure of the crystallization front in the range of 10 to 12 mm/min and a temperature gradient at the crystallization front in the range of 165-175 °C/cm. The technological process of making GTE blades has been improved by using a scheme for obtaining disposable models of complex profile castings with the use of 3D printing for the manufacture of ceramic molds. The ceramic mold is obtained through an environmentally friendly technology using water-based binders. Short-term tensile testing of the samples in gaseous hydrogen revealed high hydrogen resistance of the CM-88 alloy produced by directed crystallization technology: the relative elongation in hydrogen at a pressure of 30 MPa increased from 2% for the commercial alloy to 8% for the experimental single-crystal alloy.

An e-learning platform for clinical reasoning in cardiovascular diseases: a study reporting on learner and tutor satisfaction.

BACKGROUND: Medical students gain essential skills through hospital training and internships, which complement their theoretical education. However, virtual patient platforms have been shown to effectively promote clinical reasoning and enhance learning outcomes. This study evaluates a web-based platform designed for learning clinical reasoning in cardiovascular diseases, detailing its functionalities and user satisfaction. METHODS: The Virtual Patient platform presents medical students with clinically valid scenarios, encompassing stages such as patient description, anamnesis, objective examination, presumptive diagnosis, health investigations, treatment planning, complications, differential and final diagnoses, and prognosis. Scenarios are generated either automatically or manually by professors, based on labeled and annotated clinical data. The Virtual Patient contains two types of medical cases: simple scenarios describing patients with one pathology, and complex scenarios describing patients with several related pathologies. The platform was evaluated by a total of 210 users: 178 medical students, 7 professors, and 25 engineering students, using questionnaires adjusted for each evaluation round to assess satisfaction and gather feedback. The evaluation by medical students was performed in four rounds, each round corresponding to successive enhancements of the platform functionalities and addition of new cases, with a total number of 1,098 evaluation sessions. RESULTS: The platform was evaluated at different implementation stages, involving simple and complex scenarios for various heart diseases. The majority of students found the platform very useful (82.58%), with significant appreciation for its features and functionalities, for example the dialogue module supporting natural language interactions in Romanian and English or the feed-back obtained during interaction. Professors highly valued the platform's flexibility in scenario generation, real-time feedback provision, and data management capabilities. They appreciated the possibility to provide feedback and score student performance in real-time or after the session, though some professors suggested improving the explainability of the scores. CONCLUSIONS: The Virtual Patient platform enables medical students to virtually replicate hospital interactions, diagnose patients, and plan treatments in clinically valid scenarios for cardiovascular diseases. User evaluations demonstrated high satisfaction and appreciation for the platform's features. Future work will focus on expanding medical cases, enhancing the dialogue module, improving scenario generation for complex cases, and extending the synthetic data generation component to produce additional types of medical investigations.

Insight of effects of air quality and sustainable aviation fuel blend on energy saving and emission reduction in airport.

Air quality in airport attracts a widespread attention due to the emission of GHGs and pollutants related with aircraft flight. Sustainable aviation fuel (SAF) has confirmed PM2.5 reduction due to free of aromatics and sulphur, and thus air quality improvement in airport is prospected by SAF blend. Two types of SAF were assessed the potential of energy saving and emission reduction by ZF850 jet engine. FT fuel is characterized with only paraffins without aromatics and cycloparaffins while HCHJ fuels is characterized with no aromatics. The descend of air quality and SAF blend were both investigated the effect on the engine performance and emission characteristic. The critical parameters were extracted from fuel compositions and air pollutants. Ambient air with a higher PM2.5 could lead to the rise of engine emission especially in UHC and PM2.5 despite at the low thrust setting and high thrust setting, and even couple with 3.2% rise in energy consumption and 1% reduction in combustion efficiency. CO, NO and NO2 in ambient air show less influence on engine performance and emission characteristic than PM2.5. Both types of SAF blend were observed significant reductions in PM2.5 and UHC. PM2.5 reduction obtained 37.9%-99.8% by FT blend and 0.64%-93.9% by HCHJ blend through the whole trust settings. There are almost 6.67% positive benefit in TSFC through the whole thrust setting by 7% FT blend. The effects of air quality and SAF blend on engine emission present significant changes on PM and UHC but the slight change on CO and NOx. By SAF blend, the energy saving and pollutant reduction obtained could be both benefit for air quality improvement in airport and further reduce engine emission as the feedback of less pollutants in ambient air.

A smart system for student performance assessment (SPA).

The proposed smart system for Student Performance Assessment (SPA) is a system that evaluates students' knowledge and skill attainment in a specific course by measuring their achievements of the Course Learning Outcomes (CLOs). The instructor defines the aspects, weights, and rating scale used by SPA to analyze each course. The system calculates the average of students' marks in each learning outcome and compares them with the CLO targets and scores to determine the effectiveness of the teaching and learning methods used. The system uses facts and rules extracted from the course syllabus and Bloom's Taxonomy to build its knowledge base. This paper presents the development of the SPA inference engine, which is used to find CLO targets based on the course level. The inference engine uses efficient procedures and a prediction process to determine the correct target and score, providing a reliable and understandable methodology for reasoning about the information in the knowledge base and formulating conclusions. SPA is a highly responsive and intelligent system that can be a valuable tool for measuring students' achievements. Its characteristics include high performance, reliability, and intelligibility, and its combination of cognitive systems and cognitive theory has led to remarkable progress in measuring student performance. Limitations include dependency on accurate course content and initial setup time, potential bias in CLO weight assignments, challenges in integrating SPA with existing institutional databases, the need for continuous updates to the knowledge base to reflect curriculum changes, and potential resistance from educators to adopt new technologies. Future improvements could involve adaptive learning integrations, enhanced user interfaces, and broader applicability across diverse educational settings.

Performance and emission analysis of cassava peel waste pyrolysis oil-hydrogen-diesel blends in a compression ignition engine.

As the world moves away from fossil fuels and embraces sustainable energy sources, the need for sustainable fuels for transportation becomes paramount. This study investigates the effects of pyrolysis oil derived from cassava peel waste (CPO), hydrogen (H), and diesel (D) blends as a partial substitute for low-displacement compression ignition engines. We tested three blends - CPO25, CPO25H5, and CPO25H10 - against neat diesel operation at engine speeds of 3400 rpm, 3600 rpm, and 3800 rpm and torques of 4 Nm, 6 Nm, and 8 Nm. Our findings reveal that while energy efficiency decreased with CPO25 compared to D100 operation, adding H2 increased energy efficiency. The highest increase was 7.8 % for CPO25H5 and 16 % for CPO25H10 compared to CPO25. Exergy efficiency also decreased with CPO25 compared to D100, but adding H2 compensated for this reduction. The highest increase was 8.0 % for CPO25H5 and 17 % for CPO25H10 compared to D100. CPO25H10 showed an increase of 8.1 % in combustion pressure and 9.9 % in heat release rate compared to CPO25. Emissions analysis also revealed that CO emissions were considerably lower with CPO and H2 than with D100, with the highest decrease of 11 % with CPO25H10. CO2 and hydrocarbon emissions followed the same trend as CO. Although NOx emissions slightly increased, the benefits of using pyrolysis oil-H2-diesel blends as a partial substitution fuel for low-displacement compression ignition engines are evident.

Influence of engine oil-infused multi-walled carbon nanotubes and titania nanoparticles on a vertically inclined porous surface.

This study analyses the flow of a hybrid nanofluid, combining engine oil with multi-walled carbon nanotubes and titania nanoparticles. The flow occurs over a vertically inclined, electrically conducting, heat-producing/absorbing surface that is both permeable and expanding/contracting. The analysis incorporates influential factors such as buoyancy forces, heat source/sink effects, and convective conditions with Cattaneo-Christov theory and Hamilton-Crosser model. The mathematical model is numerically solved using the bvp4c solver in MATLAB. The expansion/contraction of the surface significantly impacts the boundary layer thickness, leading to changes in velocity, temperature, and various physical parameters. This study is significant due to the nanoparticles' enhanced optical and mechanical properties, offering potential applications in diverse fields. A notable finding is the reduced fluid velocity and temperature within a porous medium with permeability. These findings present opportunities for enhancing heat and fluid transmission in various systems, including those related to energy storage.

Research on remote sensing ecological livability index based on Google Earth Engine: a case study from Urumqi-Changji-Shihezi urban cluster.

The U-Chang-Shi (Urumqi-Changji-Shihezi) urban cluster, located at the heart of Xinjiang, boasts abundant natural resources. Over the past two decades, rapid urbanization, industrialization, and climate change have significantly threatened the region's ecological livability. To comprehensively, scientifically, and objectively assess the ecological livability of this area, this study leverages the Google Earth Engine (GEE) platform and multi-source remote sensing data to develop a comprehensive evaluation metric: the Remote Sensing Ecological Livability Index (RSELI). This aims to examine the changes in the ecological livability of the U-Chang-Shi urban cluster from 2000 to 2020. The findings show that despite some annual improvements, the overall trend in ecological livability is declining, indicating that the swift pace of urbanization and industrialization has placed considerable pressure on the region's ecological environment. Land use changes, driven by urban expansion and the growth in agricultural and industrial lands, have progressively encroached upon existing green spaces and water bodies, further deteriorating the ecological environment. Additionally, the region's topographical features have influenced its ecological livability; large terrain fluctuations have made soil erosion and geological disasters common. Despite the central plains' vast rivers providing ample water resources, over exploitation and ill-conceived hydrological constructions have led to escalating water scarcity. The area near the Gurbantunggut Desert in the north, with its extremely fragile ecological environment, has long been unsuitable for habitation. This study provides a crucial scientific basis for the future development of the U-Chang-Shi urban cluster and hopes to offer theoretical support and practical guidance for the sustainable development and ecological improvement of the region.

Vegetation changes and influencing factors in different watersheds of Henan Province based on GEE and geographic detectors.

Clarifying vegetation changes and the driving factors can provide references for ecological restoration and sustainable social development. We analyzed vegetation distribution and trend changes in Henan Province and its basin zoning (Haihe River basin zoning, Yellow River basin zoning, Huaihe River basin zoning, Yangtze River basin zoning), with fractional vegetation cover data from 2000 to 2020 based on the Google Earth Engine platform, and by combining Theil-Sen Median trend analysis, Mann-Kendall test, and Hurst index. We also utilized factor detection and factor interaction to explore the individual and mutual influences of natural and anthropogenic factors on vegetation at different scales. The results showed that the fractional vegetation cover (FVC) in Henan Province exhibited a distribution pattern of higher coverage in the south and lower in the north during the study period, predominantly characterized by moderate to high vegetation coverage. The Yangtze River basin zoning had the highest coverage. FVC in Henan Province and its zoning exhibited a consistent pattern of fluctuating upward trends, with all areas showing significant improvement. Particularly, the Yangtze River basin zoning had the largest area of improvement. According to the Hurst index, apart from the possibility of continued improvement in the Huaihe River basin zoning, other zoning would be likely to shift from improvement to degradation in the future. Vegetation changes in Henan Province and its zoning were the result of combined effects of anthropogenic and natural factors, with the influence of these factors changing over time and the dominant factors varying by region. Anthropogenic factors such as land use/cover type and nighttime lighting had a stronger impact on vegetation than natural factors like elevation, slope, and annual mean low temperature. The interaction between factors, particularly between anthropogenic and natural factors, exhibited a nonlinear enhancing pattern.

Assessment of ecological environment in arid region based on the improved remote sensing ecological index: A case study of Wuchuan County, Inner Mongolia at the northern foot of Yin Mountains.

Real-time assessment of ecological environment quality in arid and semi-arid regions is crucial for the sustainable development of ecological environments in China. In this study, we constructed a topsoil remote sensing ecological index (TRSEI) by coupling five indicators, greenness, wetness, dryness, topsoil grain size, and heat, with the Google Earth Engine (GEE). With the index, we evaluated the ecological environment quality of Wuchuan County from 1990 to 2020, and examined the spatio-temporal variations of ecological environment quality and its driving factors by using univariate linear regression, multiple regression residual analysis, and Hurst index. Results showed that the first principal component of the TRSEI in the study area contributed over 70%, with a mean eigenvalue of 0.148, indicating the effective integration of various ecological indicators by TRSEI. The topsoil grain size index was essential for the assessment of ecological environment quality in arid and semi-arid regions. From 1990 to 2020, the fluctuation range of TRSEI in the study area was between 0.289 and 0.458, showing an overall slight deterioration trend. The ecological environment quality of cropland and de-farming region had improved, with the improved area accounting for 47.9% of the total area. The grassland, barren land, and construction land areas had deteriorated, with the deteriorated area accounting for 52.1% of the total area. In the future, 36.9% of the regions would experience continuous improvement in ecological environment quality, while 41.4% might continue to dete-riorate. Human activities were the primary driving factor for the changes in ecological environment quality in arid and semi-arid regions, accounting for 88.6% of the total area. Climate change also had a significant impact, accounting for 11.4% of the total area. The TRSEI could effectively assess the ecological environment quality of arid and semi-arid regions, providing a scientific basis for ecological conservation and construction in these areas.

Extending the TOP Framework with an Ontology-Based Text Search Component.

INTRODUCTION: Constructing search queries that deal with complex concepts is a challenging task without proficiency in the underlying query language - which holds true for either structured or unstructured data. Medical data might encompass both types, with valuable information present in one type but not the other. METHOD: The TOP Framework provides clinical practitioners as well as researchers with a unified framework for querying diverse data types and, furthermore, facilitates an easier and intuitive approach. Additionally, it supports collaboration on query modeling and sharing. RESULTS: Having demonstrated its effectiveness with structured data, we introduce the integration of a component for unstructured data, specifically medical documents. CONCLUSION: Our proof-of-concept shows a query language agnostic framework to model search queries for unstructured and structured data.

Ecotoxicological effects, human and animal health risks of pollution and exposure to waste engine oils: a review.

Waste engine oils are hazardous waste oils originating from the transportation sector and industrial heavy-duty machinery operations. Improper handling, disposal, and miscellaneous misuses cause significant air, soil, sediments, surface water, and groundwater pollution. Occupational exposure by prolonged and repeated contact poses direct or indirect health risks, resulting in short-term (acute) or long-term (chronic) toxicities. Soil pollution causes geotoxicity by disrupting the biocenosis and physicochemical properties of the soil, and phytotoxicity by impairing plant growth, physiology and metabolism. Surface water pollution impacts aquatic ecosystems and biodiversity. Air pollution from incineration causes the release of greenhouse gases creating global warming, noxious gases and particulate matter eliciting pulmonary disorders. The toxicity of waste engine oil is due to the total petroleum hydrocarbons (TPH) composition, including polycyclic aromatic hydrocarbons (PAHs), benzene, toluene, ethylbenzene, xylene (BTEX), polychlorinated biphenyls (PCBs) congeners, organometallic compounds, and toxic chemical additives. The paper aims to provide a comprehensive overview of the ecotoxicological effects, human and animal health toxicology and exposure to waste engine oils. It highlights the properties and functions of engine oil and describes waste engine oil generation, disposal and recycling. It provides intensive evaluations and descriptions of the toxicokinetics, metabolism, routes of exposure and toxicosis in human and animal studies based on toxicological, epidemiological and experimental studies. It emphasises the preventive measures in occupational exposure and recommends risk-based remediation techniques to mitigate environmental pollution. The review will assist in understanding the potential risks of waste engine oil with significant consideration of the public health benefits and importance.

Adult zebrafish can learn Morris water maze-like tasks in a two-dimensional virtual reality system.

Virtual reality (VR) has emerged as a powerful tool for investigating neural mechanisms of decision-making, spatial cognition, and navigation. In many head-fixed VRs for rodents, animals locomote on spherical treadmills that provide rotation information in two axes to calculate two-dimensional (2D) movement. On the other hand, zebrafish in a submerged head-fixed VR can move their tail to enable movement in 2D VR environment. This motivated us to create a VR system for adult zebrafish to enable 2D movement consisting of forward translation and rotations calculated from tail movement. Besides presenting the VR system, we show that zebrafish can learn a virtual Morris water maze-like (VMWM) task in which finding an invisible safe zone was necessary for the zebrafish to avoid an aversive periodic mild electric shock. Results show high potential for our VR system to be combined with optical imaging for future studies to investigate spatial learning and navigation.

The key metabolic signatures and biomarkers of polycyclic aromatic hydrocarbon-induced blood glucose elevation in chinese individuals exposed to diesel engine exhaust.

Due to the complexity of environmental exposure factors and the low levels of exposure in the general population, identifying the key environmental factors associated with diabetes and understanding their potential mechanisms present significant challenges. This study aimed to identify key polycyclic aromatic hydrocarbons (PAHs) contributing to increased fasting blood glucose (FBG) concentrations and to explore their potential metabolic mechanisms. We recruited a highly PAH-exposed diesel engine exhaust testing population and healthy controls. Our findings found a positive association between FBG concentrations and PAH metabolites, identifying 1-OHNa, 2-OHPh, and 9-OHPh as major contributors to the rise in FBG concentrations induced by PAH mixtures. Specifically, each 10 % increase in 1-OHNa, 2-OHPh, and 9-OHPh concentrations led to increases in FBG concentrations of 0.201 %, 0.261 %, and 0.268 %, respectively. Targeted metabolomics analysis revealed significant alterations in metabolic pathways among those exposed to high levels of PAHs, including sirtuin signaling, asparagine metabolism, and proline metabolism pathway. Toxic function analysis highlighted differential metabolites involved in various dysglycemia-related conditions, such as cardiac arrhythmia and renal damage. Mediation analysis revealed that 2-aminooctanoic acid mediated the FBG elevation induced by 2-OHPh, while 2-hydroxyphenylacetic acid and hypoxanthine acted as partial suppressors. Notably, 2-aminooctanoic acid was identified as a crucial intermediary metabolic biomarker, mediating significant portions of the associations between the multiple different structures of OH-PAHs and elevated FBG concentrations, accounting for 16.73 %, 10.84 %, 10.00 %, and 11.90 % of these effects for 1-OHPyr, 2-OHFlu, the sum concentrations of 2- and 9-OHPh, and the sum concentrations of total OH-PAHs, respectively. Overall, our study explored the potential metabolic mechanisms underlying the elevated FBG induced by PAHs and identified 2-aminooctanoic acid as a pivotal metabolic biomarker, presenting a potential target for intervention.