Safety Assessment Approach Research Articles

Safety Landscape of Therapeutic Nanozymes and Future Research Directions.

Oxidative stress and inflammation are at the root of a multitude of diseases. Treatment of these conditions is often necessary but current standard therapies to fight excessive reactive oxygen species (ROS) and inflammation are often ineffective or complicated by substantial safety concerns. Nanozymes are emerging nanomaterials with intrinsic enzyme-like properties that hold great promise for effective cancer treatment, bacterial elimination, and anti-inflammatory/anti-oxidant therapy. While there is rapid progress in tailoring their catalytic activities as evidenced by the recent integration of single-atom catalysts (SACs) to create next-generation nanozymes with superior activity, selectivity, and stability, a better understanding and tuning of their safety profile is imperative for successful clinical translation. This review outlines the current applied safety assessment approaches and provides a comprehensive summary of the safety knowledge of therapeutic nanozymes. Overall, nanozymes so far show good in vitro and in vivo biocompatibility despite considerable differences in their composition and enzymatic activities. However, current safety investigations mostly cover a limited set of basic toxicological endpoints, which do not allow for a thorough and deep assessment. Ultimately, remaining research gaps that should be carefully addressed in future studies are highlighted, to optimize the safety profile of therapeutic nanozymes early in their pre-clinical development.

Safety Assessment Approach to UAVs Based on Profust Safety Index and HIL Simulation

Safety Assessment Approach to UAVs Based on Profust Safety Index and HIL Simulation

Consideration of special effects for the application of an optimized fracture mechanics approach for the RPV safety assessment (CAMERA)

A comprehensive fracture mechanics test program called CAMERA was carried out for seven RPV base and weld materials in unirradiated and irradiated conditions. The objective was to establish an application-oriented completion of the fracture mechanics approach for the RPV safety assessment over the entire relevant temperature range up to operating temperature. For this purpose, fracture toughness tests in the ductile-brittle transition region with and without warm pre-stress (WPS), and in the ductile region (upper shelf) were performed in order to complete the fracture toughness curves of the material concerned. The test program was completed by various analytical and numerical calculations and microstructural analyses. The benefit of the WPS effect was confirmed for both the material (higher apparent fracture toughness values) and the load path (no initiation after maximum loading). By fracture toughness tests in the ductile regime the fracture toughness curve could be completed up to the operating temperature and the T0 based criterion temperature TUS above which no brittle fracture occurs was determined and successfully verified. Based on the results obtained by the Master Curve, WPS and crack resistance tests it was demonstrated how to integrate the criterion temperature TUS in the fracture toughness-temperature diagram including the loading transient to an application window for the RPV safety assessment that is based on T0 only. For three out of nineteen data sets the homogeneity screening procedure described in ASTM E1921 did indicate a macroscopically inhomogeneous material for that a generally conservative reference temperature T0IN was calculated that is on average 11 K higher than the reference temperature T0. A higher susceptibility of weld materials for macroscopic material inhomogeneity compared to base materials was found. The suitability of the applied SE(B) and C(T) specimens for the specific fracture mechanics tests (WPS and/or crack resistance in ductile region) was proven. Micromechanical Local Approach damage models (Bordet and Gurson) were successfully applied for test design and prediction of fracture toughness. The overall results reveal even so some open gaps remaining for future work that are addressed as well.

Update of the list of qualified presumption of safety (QPS) recommended microbiological agents intentionally added to food or feed as notified to EFSA 20: Suitability of taxonomic units notified to EFSA until March 2024.

The qualified presumption of safety (QPS) process was developed to provide a safety assessment approach for microorganisms intended for use in food or feed chains. In the period covered by this statement, no new information was found that would change the status of previously recommended QPS TUs. The TUs in the QPS list were updated based on a verification, against their respective authoritative databases, of the correctness of the names and completeness of synonyms. A new procedure has been established to ensure the TUs are kept up to date in relation to recent taxonomical insights. Of 83 microorganisms notified to EFSA between October 2023 and March 2024 (47 as feed additives, 25 as food enzymes or additives, 11 as novel foods), 75 were not evaluated because: 15 were filamentous fungi, 1 was Enterococcus faecium, 10 were Escherichia coli, 1 was a Streptomyces (all excluded from the QPS evaluation) and 48 were TUs that already have a QPS status. Two of the other eight notifications were already evaluated for a possible QPS status in the previous Panel Statement: Heyndrickxia faecalis (previously Weizmannia faecalis) and Serratia marcescens. One was notified at genus level so could not be assessed for QPS status. The other five notifications belonging to five TUs were assessed for possible QPS status. Akkermansia muciniphila and Actinomadura roseirufa were still not recommended for QPS status due to safety concerns. Rhizobium radiobacter can be recommended for QPS status with the qualification for production purposes. Microbacterium arborescens and Burkholderia stagnalis cannot be included in the QPS list due to a lack of body of knowledge for its use in the food and feed chain and for B. stagnalis also due to safety concerns. A. roseirufa and B. stagnalis have been excluded from further QPS assessment.

An Engine-Level Safety Assessment Approach of Sustainable Aviation Fuel Based on a Multi-Fidelity Aerodynamic Model

Safety is essential for sustainable aviation fuels (SAFs). However, evaluating SAFs’ impacts on aero-engine safety is challenging because it involves multiple space scales and the strongly coupled relationships of aero-engine components. Aiming at addressing this problem, a model-based approach is proposed to establish the relationship between the fuel-level physical properties and engine-level safety parameters. Firstly, a unified modeling criterion is proposed to consider the interrelations of aero-engine components. Under this criterion, aero-engine secondary air system (SAS) components are included in SAF safety assessment, since they have non-neglectable influences on aero-engine safety. Secondly, this paper proposes a surrogate-based iteration strategy to embed the combustor’s high-dimensional computational fluid dynamics (CFD) model into the aero-engine flow network model. Then, the proposed model-based safety assessment approach is applied to a Fischer–Tropsch hydro-processed synthesized paraffinic kerosine (F-T SPK) safety assessment case. The effects of fuel flow and blending ratio are considered. The results indicate the necessity to evaluate SAFs’ safety at the aero-engine level and consider the influences of SAS components. The proposed model-based approach may provide a preliminary screening before SAFs’ certification tests. This convenience may be beneficial for reducing the cost and accelerating SAFs’ application.

The rat acute oral toxicity of trifluoromethyl compounds (TFMs): a computational toxicology study combining the 2D-QSTR, read-across and consensus modeling methods.

All areas of the modern society are affected by fluorine chemistry. In particular, fluorine plays an important role in medical, pharmaceutical and agrochemical sciences. Amongst various fluoro-organic compounds, trifluoromethyl (CF3) group is valuable in applications such as pharmaceuticals, agrochemicals and industrial chemicals. In the present study, following the strict OECD modelling principles, a quantitative structure-toxicity relationship (QSTR) modelling for the rat acute oral toxicity of trifluoromethyl compounds (TFMs) was established by genetic algorithm-multiple linear regression (GA-MLR) approach. All developed models were evaluated by various state-of-the-art validation metrics and the OECD principles. The best QSTR model included nine easily interpretable 2D molecular descriptors with clear physical and chemical significance. The mechanistic interpretation showed that the atom-type electro-topological state indices, molecular connectivity, ionization potential, lipophilicity and some autocorrelation coefficients are the main factors contributing to the acute oral toxicity of TFMs against rats. To validate that the selected 2D descriptors can effectively characterize the toxicity, we performed the chemical read-across analysis. We also compared the best QSTR model with public OPERA tool to demonstrate the reliability of the predictions. To further improve the prediction range of the QSTR model, we performed the consensus modelling. Finally, the optimum QSTR model was utilized to predict a true external set containing many untested/unknown TFMs for the first time. Overall, the developed model contributes to a more comprehensive safety assessment approach for novel CF3-containing pharmaceuticals or chemicals, reducing unnecessary chemical synthesis whilst saving the development cost of new drugs.

A Finite Element Analysis-Unascertained Measure Theory-Based Hybrid Approach to Safety Assessment for Pipelines Subject to Landslide Disasters

Abstract Pipeline safety faces a prevalent threat in mountainous areas due to landslides. The advent of landslides introduces the risk of pipeline leaks or ruptures, posing a significant threat to the environment, with the potential for casualties. Throughout the occurrence of landslides, uncertainties abound, yet few studies have addressed the incorporation of uncertainties in assessing pipeline safety. This work proposes a novel hybrid approach to the safety assessment for pipelines under landslides. The use of finite element analysis (FEA) models the pipeline under the action of landslides. The numerical outcomes, combined with unascertained measure theory (UMT), develop a multi-indicator unascertained measure (UM) matrix. Random forest (RF) algorithm is employed to determine the weight of indicators in the matrix. The hybrid application of set pair theory and the UM evaluation vector finally determine the pipeline safety degree and level. The proposed methodology has been well-validated through a case study on an in-service pipeline. The results indicate that the case pipeline safety degree is 2.777, 2.132, 3.132, 3.904, and 2.240, respectively. The corresponding safety level is III, II, III, IV, and II, respectively, which is consistent with the pipeline's actual condition. Different from the conventional safety assessment approach, the proposed methodology demonstrates the enhanced effectiveness, facilitating a more precise evaluation of the pipeline's safety condition.

A comparison of traditional road safety assessment methods and the newly developed 'road safety deserts' approach

Road fatalities were labelled a pandemic as early as 1973 (BMJ 1973). The number of road fatalities reached 1.35 million in 2016. Currently over 3 500 people perish every day on the world's roads. South Africa has one of the highest road fatality rates in the world, with a fatality rate of 25.9 deaths per 100 000 population (WHO 2018). In order to understand and improve the road safety situation of a region, effective road safety assessments must be carried out. This paper presents a comparison of four different road safety assessment approaches, both traditional and novel, and serves as a proof of concept for the 'road safety desert' methodology, a new technique adapted from the 'transit desert' concept. This new approach to road safety assessment explores the possibility of geo-coded supply and demand comparisons to identify 'road safety deserts' - areas that have a comparatively higher road safety risk. This paper shows that there are several unique and effective ways to assess road safety, and that each approach incorporates different characteristics within their methodologies. It is recommended that road safety analysis is conducted using a multitude of methods, so as to improve understanding and intervention selection.

Dynamic Submodular-Based Learning Strategy in Imbalanced Drifting Streams for Real-Time Safety Assessment in Nonstationary Environments.

The design of real-time safety assessment (RTSA) approaches in nonstationary environments is meaningful to reduce the possibility of significant losses. However, several challenging problems are needed to be well considered. The performance of existing approaches will be negatively affected in the settings of imbalanced drifting streams. In this case, the model design with the incremental update should also be explored. Furthermore, the query strategy should also be well-designed. This article investigates a dynamic submodular-based learning strategy to address such issues. Specifically, an efficient incremental update procedure is designed with the structure of the broad learning system (BLS), which is beneficial to the detection of concept drift. Furthermore, a novel dynamic submodular-based annotation with an activation interval strategy is proposed to select valuable samples in imbalanced drifting streams. The lower bound of annotation value is also proven theoretically with a novel drift adaption mechanism. Numerous experiments are conducted with the realistic data of JiaoLong deep-sea manned submersible. The experimental results show that the proposed approach can achieve better assessment accuracy than typical existing approaches.

An Image Processing Approach for Real-Time Safety Assessment of Autonomous Drone Delivery

The aim of producing self-driving drones has driven many researchers to automate various drone driving functions, such as take-off, navigation, and landing. However, despite the emergence of delivery as one of the most important uses of autonomous drones, there is still no automatic way to verify the safety of the delivery stage. One of the primary steps in the delivery operation is to ensure that the dropping zone is a safe area on arrival and during the dropping process. This paper proposes an image-processing-based classification approach for the delivery drone dropping process at a predefined destination. It employs live streaming via a single onboard camera and Global Positioning System (GPS) information. A two-stage processing procedure is proposed based on image segmentation and classification. Relevant parameters such as camera parameters, light parameters, dropping zone dimensions, and drone height from the ground are taken into account in the classification. The experimental results indicate that the proposed approach provides a fast method with reliable accuracy based on low-order calculations.

A new method for safety assessment of complex avionic systems

In this article, we propose a new approach for safety assessment of safety-critical systems. This approach, so-called Synthesis, is dedicated to the Preliminary System Safety Assessment included within the process of Safety Certification of avionic systems. The central idea consists in decomposing the assessment into two parts aiming at studying respectively the functional and the physical characteristics of the system under study. The whole approach is supported by a fully operational tool chain, dedicated to probabilistic safety assessment, which includes the AltaRica 3.0 integrated modeling environment, and a tool dedicated to the synthesis of functional minimal cutsets into physical minimal cutsets, making possible their quantitative assessment. We illustrate the benefits of the approach by means of a concrete avionic case study.

Update of the list of qualified presumption of safety (QPS) recommended microbiological agents intentionally added to food or feed as notified to EFSA 19: Suitability of taxonomic units notified to EFSA until September 2023.

The qualified presumption of safety (QPS) process was developed to provide a safety assessment approach for microorganisms intended for use in food or feed chains. The QPS approach is based on an assessment of published data for each taxonomic unit (TU), with respect to its taxonomic identity, the body of relevant knowledge and safety concerns. Safety concerns identified for a TU are, where possible, confirmed at the species/strain or product level and reflected by 'qualifications'. In the period covered by this Statement, no new information was found that would change the status of previously recommended QPS TUs. Of 71 microorganisms notified to EFSA between April and September 2023 (30 as feed additives, 22 as food enzymes or additives, 7 as novel foods and 12 from plant protection products [PPP]), 61 were not evaluated because: 26 were filamentous fungi, 1 was Enterococcus faecium, 5 were Escherichia coli, 1 was a bacteriophage (all excluded from the QPS evaluation) and 28 were TUs that already have a QPS status. The other 10 notifications belonged to 9 TUs which were evaluated for a possible QPS status: Ensifer adhaerens and Heyndrickxia faecalis did not get the QPS recommendation due to the limited body of knowledge about their occurrence in the food and/or feed chains and Burkholderia ubonensis also due to its ability to generate biologically active compounds with antimicrobial activity; Klebsiella pneumoniae, Serratia marcescens and Pseudomonas putida due to safety concerns. K. pneumoniae is excluded from future QPS evaluations. Chlamydomonas reinhardtii is recommended for QPS status with the qualification 'for production purposes only'; Clostridium tyrobutyricum is recommended for QPS status with the qualification 'absence of genetic determinants for toxigenic activity'; Candida oleophila has been added as a synonym of Yarrowia lipolytica. The Panel clarifies the extension of the QPS status for genetically modified strains.

Enabling novel paradigms: a biological questions-based approach to human chemical hazard and drug safety assessment.

Throughput needs, costs of time and resources, and concerns about the use of animals in hazard and safety assessment studies are fueling a growing interest in adopting new approach methodologies for use in product development and risk assessment. However, current efforts to define "next-generation risk assessment" vary considerably across commercial and regulatory sectors, and an a priori definition of the biological scope of data needed to assess hazards is generally lacking. We propose that the absence of clearly defined questions that can be answered during hazard assessment is the primary barrier to the generation of a paradigm flexible enough to be used across varying product development and approval decision contexts. Herein, we propose a biological questions-based approach (BQBA) for hazard and safety assessment to facilitate fit-for-purpose method selection and more efficient evidence-based decision-making. The key pillars of this novel approach are bioavailability, bioactivity, adversity, and susceptibility. This BQBA is compared with current hazard approaches and is applied in scenarios of varying pathobiological understanding and/or regulatory testing requirements. To further define the paradigm and key questions that allow better prediction and characterization of human health hazard, a multidisciplinary collaboration among stakeholder groups should be initiated.

Proposing an effective approach for traffic safety assessment on heterogeneous traffic conditions using surrogate safety measures and speed of the involved vehicles

Objective The vehicular traffic in the cities is becoming more complex in developing countries like India due to the rising population and rapid urbanization. With the annually increasing road accidents, a study on the effective safety assessment of heterogeneous traffic conditions is needed. The study aims to evaluate the safety of heterogeneous traffic by spotting the critical conflicts with respect to the speed of the involved vehicles. Methods The current study proposed Critical Following Speed instead of using a single threshold value for safety assessment in mixed traffic. Critical Following Speed was proposed by comparing the stopping and the available distances between the involved vehicles and used to identify the critical conflicts. With this, the study uses the speeds of both the leading and the following vehicles to judge the nature of the conflict. Three unsignalized four-legged intersections (S-1, S-2, and S-3) and two straight road sections (S-4 and S-5) were selected as the study area in Trichy, India. Post Encroachment Time (PET) and Time to Collison (TTC) were used as surrogate indicators to assess the crossing or merging and rear-end conflicts, respectively. Results The average PET and TTC values were between 1.25 and 1.73 s in the study locations. The overall percentage of critical conflicts indicated the non-safer crossing and merging maneuvers in three locations. The other two locations were experiencing safe rear-end conflicts, as the percentages of critical conflicts were below 4%. Various combinations of leading and following vehicle types were examined for the contribution of critical conflicts. The proposed methodology was validated with the 4-year accident data and a good relation was obtained. Conclusions Fast-moving vehicles were responsible for the less safe maneuvers with a higher collision probability in all the study locations. The correlation between critical conflicts and road accidents shows the effectiveness of the proposed approach in the traffic safety assessments for mixed traffic. This approach could be employed even in countries with homogenous traffic conditions instead of using a single threshold value. The correlations also show the potential of the proposed Critical Following Speed as a surrogate safety indicator for safety evaluation in the future.

Event Sequence Based Fault Tree Analysis to Evaluate Minimal Combination of Event Sequences Leading to the Reactor Core Damage

Probabilistic safety assessment has been widely used to evaluate nuclear power plant safety systems. It couples fault tree (FT) analysis and event tree (ET) analysis. FT analysis is to develop failure scenarios, to quantify the failure probability and to identify critical components. ET analysis is to develop event sequences (ESs) leading to reactor core damage and quantify core damage frequency. Currently, there is no approach in probabilistic safety assessment that can be used to identify minimal combinations of safety system failures leading to reactor core damage. This study proposes an event sequence based fault tree analysis, which integrates FT model with ET model. The ET model is to identify the ESs leading to the reactor core damage. Meanwhile, the FT model is to identify minimal combinations of those ESs found in the ET model. The motivation of this study is on how to identify critical safety systems in nuclear power plants to mitigate disturbances caused by a group of postulated initiating events to avoid core damage. To confirm the feasibility and the applicability of the proposed approach, the performance of the AP1000 passive core cooling system is evaluated. It is found that if in-containment refueling water storage tank, automatic depressurization system - full, accumulator, core make-up tank, and passive residual heat removal work properly, the reactor core will intact. These results confirmed that the proposed approach can be applicable to identify minimal combinations of safety systems to keep the reactor core intact.

An Online Active Broad Learning Approach for Real-Time Safety Assessment of Dynamic Systems in Nonstationary Environments.

Real-time safety assessment of the complex dynamic systems in nonstationary environments is of great significance for avoiding the potential hazards. In this case, the update procedure with high assessment accuracy and training speed is crucial and meaningful in the dynamic streaming setting. Generally, the performance of most online learning approaches will be negatively affected by limited annotated samples in such a setting. Moreover, the time cost of advanced conventional methods with retaining procedures is relatively high, constraining their practicality. In this article, a novel online active broad learning approach, termed OABL, is proposed. In detail, the effectiveness of the broad learning system in the framework of online active learning is first revealed and verified. A reasonable dynamic asymmetric query strategy is then designed with a limited annotation budget to actively annotate the relatively valuable samples, which is beneficial to mitigating the negative effects of class imbalance. In this context, the advantage of the human-in-the-loop characteristic is also effectively used to control the evolution direction of the learner during the incremental update, which makes it better able to adapt to complex and nonstationary environments. Several related experiments are conducted with the realistic data of JiaoLong deep-sea manned submersible. Results show the effectiveness and practicality of the proposal compared with the existing advanced approaches.

Meticulous research for design of plasmonics sensors for cancer detection and food contaminants analysis via machine learning and artificial intelligence

Cancer is one of the leading causes of death worldwide, making early detection and accurate diagnosis critical for effective treatment and improved patient outcomes. In recent years, machine learning (ML) has emerged as a powerful tool for cancer detection, enabling the development of innovative algorithms that can analyze vast amounts of data and provide accurate predictions. This review paper aims to provide a comprehensive overview of the various ML algorithms and techniques employed for cancer detection, highlighting recent advancements, challenges, and future directions in this field. The main challenge is finding a safe, auditable and reliable analysis method for fundamental scientific publication. Food contaminant analysis is a process of testing food products to identify and quantify the presence of harmful substances or contaminants. These substances can include bacteria, viruses, toxins, pesticides, heavy metals, allergens, and other chemical residues. Machine learning (ML) and artificial intelligence (A.I) proposed as a promising method that possesses excellent potential to extract information with high validity that may be overlooked with conventional analysis techniques and for its capability in a wide range of investigations. A.I technology used in meta-optics can develop optical devices and systems to a higher level in future. Furthermore (M.L.) and (A.I.) play key roles as a health Approach for nano materials NMs safety assessment in environment and human health research. Beside, benefits of ML in design of plasmonic sensors for different applications with improved resolution and detection are convinced.

Programmatic model updating of damaged structures using modified SERR method

Vision inspection information of damages in civil engineering structures is hard to be transformed into an analysis-available resistance model to conduct in-service condition assessment. This paper proposes an updating crack-embedded finite element modeling framework using periodical vision inspection information, which offers a convenient crack embedding approach for safety assessment of cracked reinforced concrete (RC) frame structures. For this purpose, a modified stain energy release rate (SERR) method is proposed to enable the method for solid element with high accuracy. Consequently, an algorithm is proposed to map the crack information obtained by vision method in the FE model. The combination of modified SERR method and proposed mapping algorithm could achieve flexibility matrix updating of cracked element. In addition, the effectiveness and generality of the proposed framework are validated using three cracking configurations. This research can provide a feasible framework for the rapid condition assessment of in-service civil engineering structures, showing a great prospect of intelligent disaster prevention.

P24-40: Cosmetic products containing human skin microbiome modulating ingredients – A safety assessment approach

P24-40: Cosmetic products containing human skin microbiome modulating ingredients – A safety assessment approach

The safety assessment of tampons: illustration of a comprehensive approach for four different products.

We illustrate a comprehensive tampon safety assessment approach that assures products can be used safely. Material biocompatibility, vaginal mucosa assessment, vaginal microbiome evaluation, and in vitro assessment of potential risk of staphylococcal toxic shock syndrome expressed through growth of Staphylococcus aureus (S. aureus) and production of TSST-1 are the four essential portions of the approach. Post-marketing surveillance informs of possible health effects that warrant follow up. The approach meets or exceeds US and international regulatory guidance and is described through the example of four tampon products. Each product is comprised mostly of large molecular weight components (cotton, rayon, polymers) that cannot pass the vaginal mucosa, are widely used across the industry, and replete with a vast body of safety data and a long history of safe use in the category. Quantitative risk assessment of all small molecular weight components assured a sufficient margin of safety supporting their use. Vaginal mucosa assessment confirmed that pressure points, rough edges and/or sharp contact points were absent. A randomized cross-over clinical trial (ClinicalTrials.gov Identifier: NCT03478371) revealed favorable comfort ratings, and few complaints of irritation, burning, stinging, or discomfort upon insertion, wear, and removal. Adverse events were few, mild in severity, self-limited and resolved without treatment. Vaginal microbiota assessment in vitro presented no adverse effect on microbial growth. Culture-independent microbiome analyses from vaginal swab samples obtained during the clinical trial showed no differences attributable to tampon usage, but instead due to statistically significant subject-to-subject variability. Growth of S. aureus and TSST-1 toxin production in the presence of any of the four products in vitro were statistically significantly reduced when compared to medium control alone. The data from the four elements of the comprehensive safety assessment approach illustrated herein confirm that tampons evaluated using this system can be used safely for menstrual protection. A post-marketing surveillance system that monitors and responds to in-market experiences indicated in-use tolerability of the product among consumers, thus confirming the conclusions of the pre-marketing safety assessment.