Traditional Evaluation Research Articles

#264 : 3D Time-Lapse Refractive-Index Imaging of Intact Mouse Embryos for Non-Invasive Evaluation of the Embryonic Development

Background and Aims: Time-lapse technology is an emerging assisted reproductive technology (ART) that enables simultaneous culture and monitoring of embryos in vitro during their development. It provides extra information on key events during embryo development, which can assist embryologists in selecting embryos alongside traditional morphology evaluations. Various imaging technologies have been utilised to visualise and evaluate embryos such as brightfield microscopy, darkfield microscopy, and Hoffman modulation contrast microscopy. Although these imaging technologies allow for non-invasive imaging of human embryos in a clinical setting, 3D imaging of a whole embryo with subcellular resolution remains a challenge. Method: We demonstrate 3D time-lapse label-free imaging of live mouse embryos using the 3D refractive index (RI) imaging technique. The proposed method is a quantitative phase imaging technique that measures multiple-intensity images of transmitted light through the embryos via axial scanning to reconstruct the 3D RI map of the embryos. Results: Using the proposed method, live mouse embryos were monitored at different developmental stages, ranging from 2-cell embryos to expanded blastocysts. The measured 3D RI maps of the developing embryos reveal 3D landscape of subcellular features inside the embryos (Fig. 1). In the quantitative assessment of embryonic development, various morphokinematic parameters were extracted from the measured 3D RI maps of the embryos. Conclusion: Based on the results, we envision that our method has broad applications where non-invasive and quantitative assessment of embryonic development is required.

The Prognostic Impact of Measurable Residual Disease Dynamics in Mantle Cell Lymphoma

Introduction Measurable residual disease (MRD) technologies have greatly enhanced our ability to guide treatment duration and predict outcome in various hematologic malignancies. However, drawing definitive conclusions on the benefits of achieving MRD-negative status in patients with mantle cell lymphoma (MCL) has been challenging due to the limited availability of large-scale MRD detection series. To address this, our study aimed to determine the optimal timing for MRD detection and explore the prognostic value of MRD dynamics in MCL. Methods In present study, we included 102 patients diagnosed with advanced MCL demonstrating clonal B cell involvement in bone marrow (BM) detectable by multiparametric flow cytometry (MFC). 60/102 (58.8%) patients received high dose cytarabine-based intensive immunochemotherapy. BM aspirates were collected and MRD assessment was conducted at specific time points, including the initiation of cycle 3, cycle 5, at the end of induction, and every 6 months during maintenance therapy. Results 77/102 (75.4%) patients reached MRD negativity throughout induction treatment. Of those with MRD − vs MRD + status , 23.4% vs 68.0% were aged >65 years respectively, which was likely a consequence of the greater MRD − rates observed in patients who received intensive immunochemotherapy and auto-transplantation. Besides, elevated lactate dehydrogenase (LDH) level, high-risk MIPI, blastoid/pleomorphic pathology type, and non-cytarabine containing regimen were associated with a lower rate of MRD negativity. Follow-up data revealed that MRD-negative patients had significantly better survival (P<0.001). In subgroup analyses, MRD negativity was prognostic in almost all subgroups. MRD detection serves as a valuable complement to the traditional response evaluation system, particularly benefiting patients who achieve partial remission (PR). This indicated the importance of assessing MRD during induction therapy for PR patients. However, for patients who achieve complete remission (CR), the presence or absence of MRD has minimal impact on prognosis (P=0.921 for progression-free survival (PFS), and P=0.399 for overall survival (OS). Then we evaluated the optimum timing of MRD detection and impact of rapidity of MRD negativity. 31 of the 77 patients (40.3%) had a rapid reduction of tumor burden and achieved MRD negativity after two cycles of treatment. 35 patients (45.5%) were MRD negative after four cycles of treatment. In 14.2% of the patients, MRD turned negative only at the evaluation after six cycles of induction therapy or during maintance duration. Comparing MRD status at different time points, the MRD status after four cycles of therapy was the strongest predictor of survival prognosis (HR=5.37, C-index=0.673). However, the speed of reaching MRD negativity was not significantly correlated with survival prognosis. Patients with high-risk features, such as high-risk MIPI, elevated LDH levels and complex karyotype, tend to achieve MRD negativity at a faster speed, often within two treatment cycles. However, faster rate of achieving MRD negativity did not confer any prognostic benefit to these patients. The overall MRD negativity rate was relatively low, and the prognosis for these patients was worse compared to other patients. In the high dose cytarabine-based intensive immunochemotherapy group, patients who achieved a rapid MRD negativity within two cycles of treatment showed a slightly longer PFS compared to those who achieved MRD negativity after 4-6 cycles (P=0.017). However, there was no significant difference in OS between the two groups. Conclusions In summary,the optimal timing for MRD testing is after four treatment cycles, as this time point demonstrated the best predictive ability for survival prognosis. However, the speed of achieving MRD negativity was not found to be linked with survival prognosis. Although certain high-risk features may lead to faster achievement of MRD negativity, this rapidity did not confer any additional prognostic benefit to these patients. Importantly, our results suggest that the value of MRD negativity as a prognostic marker may be limited in patients who have achieved complete remission and those with blastoid/pleomorphic morphologies. These findings underscore the need for tailored approaches to MRD testing and interpretation in MCL, in alignment with individual patient characteristics and treatment responses.

Microstructural changes in Oxisols under long-term different management systems

ABSTRACT There has long been a discussion about the effects of soil management on its structure. Since changes can occur due to management and time of use, more accurate assessments can be achieved if carried out in long-term experiments. This study investigated the long-term effects of soil management on the physical quality of a Cerrado Oxisol (Latossolo Vermelho), focusing on microstructural changes. Micromorphology and computed tomography techniques were used to assess the soil's microstructure. The study compared areas under long-term and different soil management practices, including disc plowing, no-tillage, and disc harrow+subsoiler. A native Cerrado area was considered as the reference. Micromorphology revealed some changes in the pedological features of soil aggregates, but the granular structure showed good resistance even after two decades of use and management. It also indicated a decrease in larger pores and an increase in the surface soil layer micropores for the disc plowing and no-tillage treatments. These results were consistent with traditional laboratory evaluations of soil porosity. Computed tomography was limited due to increased soil bulk density in the cultivated treatments, but it showed potential for assessing soil porosity and pore connectivity. We concluded that micromorphology effectively identifies microstructural changes in Oxisols with small and strong granular structures, and the granular soil aggregates displayed resilience even after long-term management. The micromorphometric evaluation corroborates with traditional methods and suggests loss of pores associated with the disc harrow+subsoiler treatment.

Characterizing the fault performance of real-time precise satellite orbit and clock correction products

The a priori fault probability of the real-time precise satellite orbit and clock correction products is the critical parameter for integrity monitoring of precise point positioning (PPP). The traditional fault probability evaluation methods use the worst-case instantaneous user ranging error (IURE) as the conservative test statistic. However, the systematic biases of IURE contained in the worst-case IURE barely affect the PPP accuracy, which will undermine the statistical distribution of test statistic and reduce the sensitivity of fault detection. The fault probability will be estimated over-conservatively for the traditional methods. By clarifying the sources of the systematic biases, a new test statistic is constructed by deliberately removing the systematic biases of IURE originated from satellite orbit and clock errors. One-year Global Positioning System correction products evaluation results have demonstrated that the constructed test statistic follows the Gaussian distribution with the decreased uncertainty and the improved fault detection sensitivity. The real-world data experiments have shown that the a priori probabilities of the satellite fault and the constellation fault are at the order of 10−4 and 10−5 levels, respectively.

The Construction of a Grammatical System for Japanese Linguistics in the Context of Big Data

Abstract This paper first outlines the concept of teaching Japanese grammar according to the Japanese language talent cultivation standard and the current dilemma in teaching Japanese language in colleges and universities. Secondly, on the basis of big data technology, considering the effective availability of the initial data of Japanese grammar, it is necessary to preprocess the language grammar learning data and, at the same time, according to the fuzzy comprehensive evaluation, determine the Japanese grammar evaluation affiliation function. Then, for the problem of an incomplete and inaccurate traditional fuzzy comprehensive evaluation, they proposed to construct a comprehensive evaluation model of the Japanese linguistics grammar system based on the bat algorithm and carried out research and analysis on the Japanese linguistics grammar system. The results show that on the evaluation model, the comprehensive evaluation score of the Japanese language grammar system is C=2.65, which belongs to the E2 level. On the statistical analysis, the mean value of all the scores of the concise class was higher than that of the standard Japanese class, and all of them showed significant differences (p=0.002, 0.000, and 0.047, all less than 0.05).

Machine learning based classification of yogurt aroma types with flavoromics

Traditional sensory evaluation, relying on human assessors, is vulnerable to subjective error and lacks automation. Nonetheless, the complexity of human sensation makes it challenging to develop a computational method in place of human sensory evaluation. To tackle this challenge, this study constructed logistic regression classification models that could predict yogurt aroma types based on aroma-active compound concentrations with high classification accuracy (AUC ROC > 0.8). Furthermore, indicator compounds discovered from feature importance analysis of classification models led to the derivation of classification criteria of yogurt aroma types. Through constructing and analyzing machine learning models on yogurt aroma types, this study provides an automated pipeline to monitor sensory properties of yogurts.

Optimization of Adadiya - A Milk-Black Gram based Traditional Sweet Delicacy and Evaluation of Combined Effect of Vacuum Packaging and Refrigeration Storage on its Stability

Background: Adadiya is a milk-pulse based sweet traditionally manufactured in the Gujarat state, which combines goodness of milk solids, black gram and spices. Sensory attributes of most preferred adadiya possess rich flavour of spices, resembles brown to dark brown colour, coarse texture embedded with small pieces of almonds and cashew. In present study, process for manufacture of adadiya was developed and formulation was optimized. Methods: Brief method of preparation involves heating of dehulled black gram coarse flour along with ghee with continuous stirring, followed by the addition of khoa and further heating till desired colour is obtained. The mixture is then cooled and added with sugar syrup, edible gum, nuts and spices. Result: Optimum formulation of adadiya contained 89.9% ghee, 30% khoa and 117.8% sugar syrup (73o Bx), on flour basis, which gave overall acceptability score of 8.31 on a 9-point hedonic scale. Vacuum packed adadiya samples remained acceptable on 30th day of storage with overall acceptability score of 8.14. Consumer survey indicated wide acceptability of adadiya among consumers with rated score of ≥ 8 on a 9-point hedonic scale.

The Application and Potential of Artificial Intelligence Participating in College Teaching Evaluation

The dominance could be applied to many more areas as new artificial intelligence (AI) advances. There are great defects in the traditional evaluation methods of higher education. The evaluation of people is dominant, resulting in excessively subjective results and wasting human resources. These issues adversely affect many stakeholders. Therefore, the combination of the objectivity of artificial intelligence and the traditional method of teaching evaluation has an exploratory significance for the development of teaching and emphasizes the technical ability that the new artificial intelligence can realize. The purpose of this study is to explore the potential of a new method for artificial intelligence in higher education evaluation. Based on the analysis of existing experimental case reports and related studies by the method of literature synthesis, it is certain that the new artificial intelligence will help improve the quality of teaching evaluation in higher education. This has a positive impact on all fields, including schools, teachers, and students. The teaching objectives of the school can be better and more efficiently accomplished under the evaluation system. This study confirms the feasibility of incorporating artificial intelligence into university evaluation.

Process-driven susceptibility assessment of glacial lake outburst debris flow in the Himalayas under climate change

Global warming is causing glaciers to retreat and glacial lakes to expand in the Himalayas, which amplifies the risk of glacial lake outburst debris flows (GLODFs) and poses a significant threat to downstream lives and infrastructures. However, the complex interplay between GLODF occurrences and associated indicators, coupled with the lack of a comprehensive susceptibility indicator system that considers the entire GLODF process, presents a substantial challenge in assessing GLODF susceptibility in the Himalayas. This study proposes a process-driven GLODF susceptibility assessment indicator system responding to climate change that considers the complete process of GLODF formation, incorporating relevant parameters about upstream, themselves, and downstream of glacial lakes. Furthermore, to mitigate subjective factors associated with traditional evaluation methods, we developed three novel hybrid machine-learning models by integrating classic machine-learning algorithms with the whale optimization algorithm (WOA) to delineate the distribution of GLODF susceptibility in the Himalayas. All the hybrid models effectively predicted the GLODFs occurrence, with the WOA-SVC model demonstrating the highest prediction accuracy. Approximately 34% of the catchments exhibit high and very high susceptibility levels, primarily concentrated along the north and south sides of the Himalayan ridge, particularly in the eastern and central Himalayas. Indicators capturing the physical formation process of hazards, such as topographic potential (highest relative importance value of 40%), can precisely identify GLODF. A total of 128 catchments pose potential transboundary threats, with 24 classified as having a very high susceptibility level and 25 as having a high susceptibility level. Notably, the border region between China and Nepal is a prominent hotspot for transboundary threats of GLODF. These findings can provide valuable clues for disaster prevention, mitigation, and cross-border coordination in the Himalayas.

Deep Learning-based Automated Knee Joint Localization in Radiographic Images Using Faster R-CNN.

Osteoarthritis is a condition that poses a risk to the knee joint, resulting in pain and impaired function. However, traditional knee X-ray evaluations using the Kellgren-Lawrence grading system have proven to be inefficient. These evaluations are subjective, time-consuming, and labor-intensive, particularly in busy hospital settings. The objective of this research was to present a deep learning-based approach that can detect knee joint regions in medical images. By addressing the limitations of traditional methods, the aim was to develop a more efficient and automated approach for knee joint analysis. The proposed method utilizes the Faster R-CNN model, which consists of a region proposal network (RPN) and Fast R-CNN. The RPN generates region proposals that potentially contain knee joint regions, while the Fast R-CNN network categorizes and extracts features from these proposals. To train the model, a dataset of knee joint images was employed. The performance of the model was evaluated using metrics, such as accuracy, precision, recall, F1-score, and mean IoU (Intersection Over Union). The results demonstrated the high accuracy of the proposed method in detecting knee joint regions. The model achieved a mean IoU of 94.5, indicating a strong overlap between the predicted and ground truth regions. These findings highlight the potential of deep learning-based approaches in automating medical image analysis, specifically in the diagnosis and management of knee joint disorders. This study emphasizes the significance of leveraging advanced technologies, such as deep learning, in medical imaging. By developing more efficient and accurate methods for identifying knee joint regions in medical images, it becomes feasible to enhance patient outcomes and healthcare delivery. The proposed deep learning-based approach showcases promising results, paving the way for further advancements in the field of medical image analysis and contributing to improved diagnostic capabilities for knee joint disorders.

Evaluating a Situational Judgment Test for Use in Medical School Admissions: Two Years of AAMC PREview Exam Administration Data.

To examine the relationship between the Association of American Medical Colleges (AAMC) Professional Readiness Exam (PREview) scores and other admissions data, group differences in mean PREview scores, and whether adding a new assessment tool affected the volume and composition of applicant pools. Data from the 2020 and 2021 PREview exam administrations were analyzed. Two U.S. schools participated in the PREview pilot in 2020 and 6 U.S. schools participated in 2021. PREview scores were paired with data from the American Medical College Application Service (undergraduate grade point averages [GPAs], Medical College Admission Test [MCAT] scores, race, and ethnicity) and participating schools (interview ratings). Data included 19,525 PREview scores from 18,549 unique PREview examinees. Correlations between PREview scores and undergraduate GPAs ( r = .16) and MCAT scores ( r = .29) were small and positive. Correlations between PREview scores and interview ratings were also small and positive, ranging between .09 and .14 after correcting for range restriction. Small group differences in mean PREview scores were observed between White and Black or African American and White and Hispanic, Latino, or of Spanish origin examinees. The addition of the PREview exam did not substantially change the volume or composition of participating schools' applicant pools. Results suggest the PREview exam measures knowledge of competencies that are distinct from those measured by other measures used in medical school admissions. Observed group differences were smaller than group differences observed with traditional academic assessments and evaluations. The addition of the PREview exam did not substantially change the overall volume of applications or the proportions of out-of-state, underrepresented in medicine, or lower socioeconomic status applicants. While more research is needed, these results suggest the PREview exam may provide unique information to the admissions process without adversely affecting applicant pools.

Intercultural Communication Competence Improvement of Students under the International Education Management Model

With the acceleration of globalization, international education has gradually become the core content of higher education. In this context, students have encountered lots of intercultural challenges, making the evaluation and improvement of their intercultural communication competence (ICC) a key issue. It is difficult for traditional evaluation methods, such as questionnaire surveys, interviews, and teacher evaluations, to meet the needs of current educational institutions because they are subjective, time-consuming, and inefficient. After describing the ICC evaluation process of students under the international education management model, this study proposed a new automatic evaluation method based on the dual-scale convolutional neural network (CNN) model and the bi-directional long-short-term memory (Bi-LSTM) model of characters and words. The new method evaluated students’ ICC more accurately and objectively, providing a new perspective and tool for future research and educational training.

Influencing Factors and Realization Strategies of Teachers’ Professional Development in Higher Vocational Colleges and Universities Based on the Background of Deep Learning

Abstract The fuzzy comprehensive evaluation algorithm is employed in this paper to evaluate teachers’ professional competence and improve the traditional evaluation system to construct a comprehensive evaluation system. According to the Zagan theory, twenty-three influencing factors of teachers’ professional development were screened, and five main influencing factors were derived after exploratory factor analysis and structural equation test. Countermeasures and suggestions were proposed after analyzing the factors that affect the professional development of teachers in higher vocational colleges. The results show that the model KMO value is 0.923, Bartlett’s spherical test chi-square value is 2712.752, and the significance probability value P=0.000<0.005 is statistically significant. The correlation coefficients between the five main factors are in line with the condition of correlation attribute defined among the factor analysis variables, with a range of 0.37 to 1.00. The optimization countermeasures proposed in this paper have important theoretical significance and practical significance for the professional development of teachers in higher vocational colleges.

Peer Group Tari Tradisional dalam Mencegah Gangguan Gerak Tubuh Orang dengan Gangguan Jiwa

Stigma and movement disorders due to drugs in people with mental disorders create problems in the community. Efforts that can be made to prevent through preventive efforts are empowerment in the community. Peer groups are a form of empowerment between peer groups. The aim of this program is to train youth groups in helping ODGJ reduce movement disorders caused by mental disorders drugs. The method used is through FGD and measuring partners' knowledge and dancing skills. The program will be implemented in June 2023. The stages include pre-program, module prescription testing, program socialization, traditional dance peer group training, and evaluation and follow-up plans. The pre-program results agreed regarding advocacy activities for people with mental disorders, the results of the perception equality test (ICC: 0.945), the knowledge value of (p= 0.000), the value of traditional dance peer group skills (mean 75), and the results of the FGD with partners making innovation program in mental health posyandu. The conclusion is that traditional dance peer group training in preventing movement disorders for ODGJ can be used as health promotion in mentally healthy posyandu.

What Matters in the Effectiveness of Airborne Collision Avoidance Systems? Monte Carlo Simulation of Uncertainties for TCAS II and ACAS Xa

TCAS II is a rule-based airborne collision avoidance system (ACAS) that is used in current commercial air transport operations, and ACAS Xa is a new optimization-based system. Operational validation studies have mainly used deterministic simulations of ACAS performance using various sets of encounters. Recently a new approach was developed, which employs Monte Carlo (MC) simulation of agent-based models to evaluate the impact of sensor errors and pilot response variability. This paper contrasts the results of both approaches in a comparison of TCAS II and ACAS Xa for various types of synthetic encounters. It was found that conventional estimates of near mid-air collision (NMAC) probabilities are often lower than the estimates achieved using MC simulation, and that the biases in the P(NMAC) estimates are consistently larger for ACAS Xa than for TCAS II. Contributions to unresolved risk are largest for pilot performance, then for encounter types, and finally for sensor errors. The contribution of non-responding pilots is much larger than the differences between TCAS II and ACAS Xa. It is concluded that the agent-based MC simulation overcomes the limitations in traditional evaluation of altimetry errors and pilot response, providing an independent means to effectively analyze the robustness of ACASs.

Hepato-renal toxicity of low dose metal(oid)s mixture in real-life risk simulation in rats: Effects on Nrf2/HO-1 signalling and redox status

The understanding that humans are exposed to a low level of toxic metals and metalloids in their lifetime has resulted in a shift in scientific and regulatory perspectives from the traditional evaluation of single metal toxicity to complex mixtures, relevant to real-life exposure. Therefore, the aim of this study was to examine the impact of real-life, 90-days exposure to mixture of toxic metal(oid)s, Cd, Pb, Ni, Cr, As and Hg, on the nuclear factor erythroid 2–related factor 2 and hemoxygenase-1 (Nrf2/HO-1) signalling and redox status by assessing total sulfhydryl groups (SH), glutathione (GSH), superoxide dismutase activity (SOD), malondialdehyde (MDA), and ischemia modified albumin (IMA) in the liver and kidney of Wistar rats. Animals (20 males and 20 females) were randomized in 2 control and 6 treated groups that received by oral gavage mixture of metal(oid)s solutions in doses that reflect blood metal(oid) levels determined in previous human biomonitoring study as benchmark dose (F/M _BMD), median (F/M _MED), and 95th percentile (F/M _95). Our results have shown that metal(oid)s mixture impaired the activation of the Nrf2/HO-1 pathway in the kidney and liver of male rats and kidney of female rats, followed by depletion of GSH levels in males. Additionally, in males elevated levels of IMA in the liver were observed, while in both genders increased MDA levels were observed in the kidney. Interestingly, the effects were more pronounced in male than in female rats. This study is among the first that examined hepato-renal toxic mechanisms of real-life metal mixture exposure, while our results might be of immense importance for assessing the risk of exposure to mixtures of toxic substances.

Digital dominance: assessing market definition and market power for online platforms under Article 102 TFEU

ABSTRACT The traditional, price-focused antitrust tools for assessing dominance under Article 102 TFEU and merger control are challenged by the business models and features of multisided digital platforms. This article demonstrates the importance of data in digital markets and how data enable the utilization of network effects and economies of scale in a manner that challenges the traditional dominance evaluation. Based on this, the article systematizes the solutions to the challenges caused by decreased accuracy of price-based tools through an analysis of various approaches to the issue. It is argued that potential competition should be emphasized more strongly throughout the dominance assessment and that different approaches can be chosen based on the case at hand. Thus the article contributes to the development of the dominance analysis for digital markets with a thorough overview of possible solutions and ties it to the proposed renewal of the Market Definition Notice.

Estimation of Fracture Size and Probability Density Function by Setting Scanlines in Rectangular Sampling Window

Abstract Rock masses are very important materials in geotechnical engineering. In engineering rock mass, fracture is the relatively weak part of mechanical strength in rock mass and is the most important factor controlling the deformation, damage, and permeability of rock mass. Therefore, investigating fractures is very important for characterizing rock mass. This paper proposed a new approach by using uniformly equidistant orthogonal scanlines. Within the study context, the solution formula of fracture size is derived by establishing the space intersection model of arbitrary fracture and scanline, rectangular window, and a rectangular box with a rectangular window. Then, fractures were randomly generated in a certain size cube and compared with the traditional Kulatilake trace length integral evaluation method. The study results have shown that the proposed method is more reasonable and accurate. Then, this method was applied to an adit of Songta Hydropower Station. Finally, a new fracture diameter probability density estimation method was proposed, the fracture diameter of the normal distribution was verified, and the parameters of the probability density function obtained by the scanlines method were in agreement with the initial set parameters. In summary, the proposed scanlines method can well estimate the mean value of the fracture diameter and the probability density function of the fracture size.

Tor network anonymity evaluation based on node anonymity

In order to address the shortcomings of traditional anonymity network anonymity evaluation methods, which only analyze from the perspective of the overall network and ignore the attributes of individual nodes, we proposes a dynamic anonymity model based on a self-built anonymous system that combines node attributes, network behavior, and program security monitoring. The anonymity of evaluation nodes is assessed based on stable intervals and behavior baselines defined according to their normal operating status. The anonymity of the network is evaluated using an improved normalized information entropy method that refines anonymity evaluation to the anonymity of each node and expands the dimensionality of evaluation features. This paper compares the effectiveness of our proposed method with static framework information entropy and single indicator methods by evaluating the degree of anonymity provided by a self-built Tor anonymous network under multiple operating scenarios including normal and under attack. Our approach utilizes dynamically changing network anonymity based on multiple anonymous attributes and better reflects the degree of anonymity in anonymous systems.

The mediating role of AKT/ERK/JNK signaling on the malignant phenotype of microcystin-LR in gastric adenocarcinoma cells

Microcystin-leucine arginine (MC-LR), a widely distributed and highly toxic environmental pollutant, plays crucial roles in cancer malignancy by activating characteristically toxic signaling pathways. Traditional animal-based toxicity evaluation methods have proven insufficient for identifying the specific role of these signaling pathways. Therefore, this study aimed to uncover the regulatory relationship between the toxic pathways and the progression of gastric cancer (GC). The findings provide novel avenues for conducting in vitro toxicity tests based on the investigated pathways. We found that MC-LR promoted the migration and invasion of SGC-7901 cells while simultaneously inhibiting their apoptosis in a dose-dependent manner. This observed cytotoxicity was primarily mediated through the AKT, JNK, and ERK signaling pathways. By using a mediation analysis model, we determined that AKT and ERK exhibited competitive effects in MC-LR-treated GC malignancy, while AKT and JNK acted independently from one another. This study establishes an in vitro toxicity test model of MC-LR based on toxicity-related pathways and underscores the pivotal roles of AKT, ERK, and JNK signaling in MC-LR toxicity. The findings offer a novel, fundamental framework for conducting chemical toxicity risk assessment.