Aerosol delivery system toxicology scoring method and storage medium

Abstract

The present application relates to a kind of aerosol delivery system toxicology scoring method and storage medium, method includes: setting target group, the test biological of target group is in aerosol exposure environment;Blood physiological index and multi-omics data of control group are obtained, the test biological of control group is in air exposure environment;Blood physiological index of target group is measured, and first toxicology score is determined according to the difference of blood physiological index of target group and control group;Multi-omics data of target group is measured, and second toxicology score is determined according to the difference of multi-omics data of target group and control group;According to first toxicology score and second toxicology score, determine the comprehensive toxicology score of target group;Blood physiological index includes blood biochemistry, coagulation function and hematology index;Multi-omics data includes transcriptomics, proteomics and metabolomics data.The present application is evaluated from macroscopic physiological level and microscopic molecular level by the above setting, the toxicological effect of aerosol such as electronic cigarette liquid etc. is evaluated comprehensively.

Description

Technical Field

[0001] This invention relates to the field of aerosol toxicology scoring technology, and in particular to a scoring method and storage medium for the toxicology of an aerosol delivery system. Background Technology

[0002] The main components of e-liquids are propylene glycol (PG) and vegetable glycerin (VG). Studies have shown that propylene glycol and vegetable glycerin have toxicological effects on human bronchial epithelial cells in their vaporized state, although their cytotoxicity is lower than that of traditional cigarette smoke. Existing literature also indicates that the toxicological effects of propylene glycol and vegetable glycerin on the lungs are complex. 1,3-Propanediol (PDO), derived from environmentally friendly glycerin, has been proposed as a potential alternative to propylene glycol in e-liquids due to its superior thermal stability, nicotine delivery, and flavor properties. However, the evidence that PDO is safer than propylene glycol in e-cigarettes remains insufficient, and a reliable evaluation system is needed to compare the in vitro toxicological effects of these two e-cigarette carriers. Furthermore, the widespread adoption of e-cigarettes has led to a rapid increase in the types and quantities of e-liquids, and the diverse range of e-liquids on the market also requires comprehensive toxicological analysis.

[0003] Currently, the toxicological assessment of atomized substances in e-cigarettes mainly refers to the OECD's classification of chemicals for toxicological assessment, including acute toxicity and skin and eye irritation, which is rather general. There are no detailed differentiated assessment standards for the toxicological assessment of inhaled substances such as e-cigarettes. Therefore, there is a lack of a standardized toxicological method that can quantitatively assess and differentiate the toxicity levels of various e-cigarette liquids. Summary of the Invention

[0004] To address the aforementioned shortcomings, this invention proposes a toxicological scoring method and storage medium for aerosol delivery systems.

[0005] The technical solution adopted in this invention is a toxicological scoring method for an aerosol delivery system, the method comprising:

[0006] S100. Set up a target group, wherein the test organisms in the target group are in an aerosol exposure environment;

[0007] S200. Obtain blood physiological indicators and multi-omics data of the control group, wherein the experimental organisms in the control group are in an air-exposed environment;

[0008] S300. Measure the blood physiological indicators of the target group and determine the first toxicology score based on the difference in blood physiological indicators between the target group and the control group.

[0009] S400. Measure the multi-omics data of the target group, and determine the second toxicology score based on the difference between the multi-omics data of the target group and the control group;

[0010] S500. Determine the comprehensive toxicological score of the target group based on the first toxicological score and the second toxicological score;

[0011] The blood physiological indicators include blood biochemistry, coagulation function, and hematological indicators; the multi-omics data include transcriptomics, proteomics, and metabolomics data.

[0012] Furthermore, the S300 method determines the first toxicology score based on the difference in blood physiological indicators between the target group and the control group, specifically including:

[0013] The Z-scores of blood physiological indicators of the target group relative to the control group were calculated. Indicators with harmful increases higher than those of the control group were assigned positive Z-scores, and indicators with harmful decreases lower than those of the control group were assigned negative Z-scores. Indicators within the normal range were represented by the absolute value of the Z-score.

[0014] The first toxicology score was determined based on the Z-score of the blood physiological parameters of the target group relative to the control group.

[0015] Furthermore, determining the first toxicology score based on the Z-score of the blood physiological indicators of the target group relative to the control group specifically includes: calculating the average Z-score of the blood physiological indicators of the target group relative to the control group to determine the first toxicology score.

[0016] further,

[0017] The acquisition of multi-omics data of the control group in S200 specifically includes: acquiring low-dimensional data after dimensionality reduction processing of the multi-omics data of the control group;

[0018] The second toxicology score determined by S400 based on the differences in multi-omics data between the target group and the control group specifically includes:

[0019] Dimensionality reduction is performed on the multi-omics data of the target group to obtain low-dimensional data;

[0020] The second toxicology score was determined based on the differences in low-dimensional data from multi-omics data between the target group and the control group.

[0021] Furthermore, the dimensionality reduction processing of the multi-omics data of the target group specifically includes: using the t-distributed random neighbor embedding algorithm to reduce the dimensionality of the multi-omics data of the target group.

[0022] further,

[0023] The acquisition of low-dimensional data after dimensionality reduction processing of the multi-omics data of the control group in S200 specifically includes: acquiring the geometric center of the projection region of the low-dimensional data after dimensionality reduction processing of the multi-omics data of the control group.

[0024] The step of determining the second toxicology score based on the difference in low-dimensional data of multi-omics data between the target group and the control group specifically includes: calculating the distance from the low-dimensional data of multi-omics data of the target group to the geometric center of the projection region determined by S200, which is the second toxicology score.

[0025] Furthermore, S500 specifically includes:

[0026] S510. Calculate the Z-score of the first toxicological score of the target group relative to the control group; calculate the Z-score of the second toxicological score of the target group relative to the control group.

[0027] S520. Based on the two Z scores determined in S510, determine the comprehensive toxicological score of the target group.

[0028] Furthermore, S520 specifically includes: calculating the average of the two Z scores determined in S510 to determine the comprehensive toxicology score of the target group.

[0029] Furthermore, the acquisition of blood physiological indicators and multi-omics data of the control group in S200 is specifically achieved through:

[0030] Database retrieval, or

[0031] Database and computation acquisition, or

[0032] Obtained through experiments and calculations.

[0033] The present invention also proposes a storage medium storing a computer program, which, when executed by a processor, implements the above-described method for scoring the toxicology of an aerosol delivery system.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. The toxicological scoring method for aerosol delivery systems disclosed in this invention comprehensively considers blood physiological indicators and multi-omics data, and evaluates the toxicological effects of aerosols such as e-cigarette liquids from both macroscopic physiological and microscopic molecular levels, avoiding the limitations of single-dimensional evaluation and making the evaluation results more comprehensive and reliable.

[0036] 2. The toxicological scoring method for the aerosol delivery system disclosed in this invention can quantify the aerosol toxicity of different e-liquids through specific scoring calculations. This facilitates a clear and precise comparison of the toxicity differences of different aerosols, such as e-liquids containing different components (e.g., PG and PDO) or at different concentrations, which helps in screening for relatively safer e-liquid products. Furthermore, it can also analyze the biological effects and molecular alteration pathways of aerosols such as e-liquids, thereby optimizing the components of e-liquids.

[0037] 3. Currently, there is a lack of detailed and reliable standards for the toxicological assessment of aerosols such as e-liquids. The toxicological scoring method for aerosol delivery systems disclosed in this invention provides a standardized toxicological assessment scheme, offering a unified method and scale for the safety assessment of e-liquids, and helping to regulate the e-cigarette market. Attached Figure Description

[0038] The present invention will now be described in detail with reference to the embodiments and accompanying drawings, wherein:

[0039] Figure 1 This is a flowchart of a scoring method for the toxicology of an aerosol delivery system;

[0040] Figure 2 This is a block diagram of a scoring method for the toxicology of an aerosol delivery system;

[0041] Figure 3 This is a visualization of the dimensionality reduction of the multi-omics data from the first set of samples.

[0042] Figure 4 This is a visualization of the dimensionality reduction of the multi-omics data from the second set of samples.

[0043] Figure 5 This is a visualization of the dimensionality reduction of the multi-omics data from the third set of samples;

[0044] Figure 6 This is a visualization of the dimensionality reduction of the multi-omics data from the fourth set of samples;

[0045] Figure 7 This is a comparison chart of toxicity assessments between different aerosol exposure groups and the air group;

[0046] Figure 8 This is a comparison chart of characteristic distances between different aerosol exposure groups and the air group;

[0047] Figure 9 This is a linear relationship between the toxicity assessment of PDO / VG aerosols and the characteristic distance;

[0048] Figure 10 This is a comparison chart of aerosol toxicity scores between different aerosol exposure groups and the air group. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar components or components having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0050] To compare the in vitro toxicological effects of e-liquids with 1,3-propanediol (PDO) as the main component and e-liquids with propylene glycol (PG) as the main component, to conduct comprehensive toxicological analysis of various other e-liquids, and to conduct comprehensive toxicological analysis of aerosols other than e-liquids, this application proposes a scoring method and storage medium for the toxicology of aerosol delivery systems.

[0051] To assess the systemic toxicity levels following PDO / VG and PG / VG aerosol nebulization exposure, traditional toxicology scoring methods were first integrated and quantified. After measuring rat body weight, food and water intake, organ weight, organ morphology and pathological evaluation, blood biochemistry, coagulation function, and hematological parameters, no significant changes were observed in the first four measurements, but significant changes were found in the latter three. Further multi-omics analysis of lung tissue, including transcriptomics, proteomics, and metabolomics, revealed alterations in immune-related pathways in rats with high-dose PDO / VG and PG / VG exposure. By combining traditional assessment methods with multi-omics analysis, this application establishes a precise and reliable scoring method for the toxicology of aerosol delivery systems.

[0052] In one embodiment, a toxicological scoring method for an aerosol delivery system is described in [reference needed]. Figure 1-2 The method includes the following steps:

[0053] S100. Set up a target group, wherein the test organisms in the target group are in an aerosol exposure environment.

[0054] Target groups are set up, and test organisms are placed in an aerosol exposure environment. In one specific embodiment, the aerosol can be generated by the vaporization of e-cigarette liquid. Different target groups can use different types or concentrations of e-cigarette liquid, such as e-cigarette liquids containing propylene glycol (PG) and 1,3-propanediol (PDO), respectively. This allows for the study of the effects of e-cigarette liquid aerosols with different components on test organisms. Different target groups can also have different exposure times to study the effects of different exposure times of e-cigarette liquid aerosols on test organisms. Test organisms can be selected from animals such as rats and rabbits, or cell lines such as human bronchial epithelial cells. They are placed in a specific experimental device and continuously exposed to the aerosol environment generated by vaporization.

[0055] S200. Obtain blood physiological indicators and multi-omics data of the control group, wherein the experimental organisms in the control group are in an air-exposed environment.

[0056] Data from the control group were obtained. The experimental organisms in the control group were placed in an air-exposed environment, i.e., a normal, unpolluted environment. In a specific embodiment, blood physiological indicators, including blood biochemistry (such as blood glucose, blood lipids, liver and kidney function-related enzyme indicators, etc.), coagulation function (such as prothrombin time, partial thromboplastin time, etc.), and hematological indicators (such as red blood cell count, white blood cell count, platelet count, etc.), were obtained through experimental methods such as RNA sequencing to obtain transcriptomics data, proteomics analysis to obtain proteomics data, and metabolite detection to obtain metabolomics data, thus obtaining multi-omics data. These data represent the physiological and molecular characteristics of the experimental organisms under normal conditions.

[0057] In another specific embodiment, the blood physiological indicators and multi-omics data of the control group can also be obtained directly from the database.

[0058] S300. Measure the blood physiological parameters of the target group, and determine the first toxicology score based on the difference in blood physiological parameters between the target group and the control group. Blood samples are also collected from the test organisms in the target group, and their blood physiological parameters are measured. The first toxicology score is determined based on the difference in blood physiological parameters between the target group and the control group.

[0059] In one specific embodiment, S300 specifically includes:

[0060] S310. Measure the blood physiological parameters of the target group. Blood physiological parameters include blood biochemistry, coagulation function, and hematological parameters. Blood biochemistry parameters include liver function, kidney function, blood glucose, and blood lipids, which are usually measured using a biochemical analyzer to quantitatively detect the corresponding components in the blood, and then the corresponding parameter values ​​are calculated based on the test results. Coagulation function parameters include prothrombin time and activated partial thromboplastin time, which are usually measured using a coagulation analyzer to detect some parameters in the blood coagulation process, and then the corresponding parameter values ​​are calculated based on the test results. Hematological parameters include white blood cell count, red blood cell count, and platelet count, which are usually measured using a blood cell analyzer to count and classify white blood cells, red blood cells, and platelets in the blood, and then the corresponding values ​​are calculated.

[0061] S320. Calculate the Z-scores of blood physiological indicators in the target group relative to the control group. Indicators with harmful increases higher than those in the control group are assigned positive Z-scores, and indicators with harmful decreases lower than those in the control group are assigned negative Z-scores. Indicators within the normal range are represented by the absolute value of their Z-scores. Compare these indicators with the corresponding indicators in the control group and calculate the Z-score for each indicator to measure the degree of deviation from the control group.

[0062] Specifically, the mean is calculated first. For each hematological indicator, the mean μ of that indicator is calculated across the entire sample set (including the target group and the control group). Then, the standard deviation σ is calculated, which reflects the dispersion of the data. Finally, standardization is performed: for each sample's indicator value x, its Z-score is calculated using the formula z = (x - μ) / σ. After this step, all samples' hematological indicator values ​​are transformed into values ​​following a standard normal distribution with a mean of 0 and a standard deviation of 1.

[0063] Different blood physiological indicators originally had different dimensions and numerical ranges. For example, the value of blood glucose might be a few mmol / L, while the value of white blood cell count might be on the order of 10. 9 / L. By calculating the Z-score, all indicators are transformed into values ​​under a standard normal distribution with a mean of 0 and a standard deviation of 1, making different indicators comparable and facilitating subsequent comprehensive analysis. Combined with rules for assigning positive or negative values ​​or taking absolute values ​​to different risk factor indicators, abnormal indicators showing increased or decreased harmful levels can be quickly screened out. Indicator values ​​processed by Z-score calculation can be more scientifically and rationally incorporated into the calculation of the first toxicology score.

[0064] S330. Determine the first toxicology score based on the Z-scores of the blood physiological indicators of the target group relative to the control group. In a specific embodiment, the average Z-score of the blood physiological indicators of the target group relative to the control group is calculated to determine the first toxicology score. This score reflects the overall deviation of the target group from the control group in terms of blood physiological indicators; the larger the absolute value of the score, the greater the impact of aerosol exposure on the blood physiological level of the test organism. Using the mean Z-score can reflect the average difference level of the indicators while preserving the data distribution characteristics, preventing the score from being too small or too large when there are many test indicators, and thus better homogenizing the results.

[0065] In another specific embodiment, the varying importance of different blood physiological indicators in reflecting aerosol toxicological effects can be considered. Each indicator can be assigned a corresponding weight, and then a weighted average can be calculated as the first toxicological score. In yet another specific embodiment, the Z-scores of blood physiological indicators in the target group relative to the control group can be processed using machine learning-based methods, hierarchical scoring methods, etc., to determine the first toxicological score.

[0066] S400. Measure the multi-omics data of the target group, and determine the second toxicology score based on the differences in multi-omics data between the target group and the control group. Analyzing the differences in multi-omics data between the target group and the control group allows for a comprehensive understanding of the impact of aerosol mist exposure on the molecular level of the body from multiple perspectives.

[0067] In a specific embodiment, S200, acquiring multi-omics data of the control group, specifically includes: acquiring low-dimensional data of the control group after dimensionality reduction processing. This data can be directly obtained from a database, or low-dimensional data can be obtained by performing dimensionality reduction processing on transcriptomics, proteomics, and metabolomics data acquired from a database / experiment.

[0068] Step S400 specifically includes:

[0069] S410. Obtain multi-omics data for the target group. Multi-omics data includes transcriptomics, proteomics, and metabolomics data. Specifically, transcriptomics data can be obtained through RNA sequencing, proteomics data through proteomic analysis, and metabolite data through metabolite detection.

[0070] S420. Dimensionality reduction is performed on the multi-omics data of the target group to obtain low-dimensional data. In a more specific embodiment, the t-distributed random neighbor embedding algorithm can be used to reduce the dimensionality of the multi-omics data of the target group. In other more specific embodiments, the dimensionality reduction methods in S200 and S420 can also employ principal component analysis (PCA), linear discriminant analysis (LDA), isomap, or deep learning-based dimensionality reduction methods, etc.

[0071] S430. Determine the second toxicology score based on the differences in low-dimensional data from the multi-omics data of the target group and the control group. Dimensionally reduced low-dimensional data effectively avoids the curse of dimensionality, making analysis more efficient and easier to detect subtle changes, thus improving the accuracy of the toxicology score.

[0072] In a more specific embodiment, S200, obtaining the low-dimensional data after dimensionality reduction of the multi-omics data of the control group, specifically includes: obtaining the geometric center of the projection region of the low-dimensional data after dimensionality reduction of the multi-omics data of the control group. This can be obtained directly from a database, or by calculating the geometric center of the projection region of the low-dimensional data after dimensionality reduction of the multi-omics data of the control group from transcriptomics, proteomics, and metabolomics data obtained from a database / experiment. For the projection region of the control group, its geometric center is a feature point representing the location of that region. The calculation method is usually to average the coordinates of all data points within that region; the resulting coordinate point is the geometric center.

[0073] Step S430 specifically includes: calculating the distance from the low-dimensional data of the multi-omics data of the target group to the geometric center of the projection region determined in S200, which is the second toxicology score. This distance can be calculated using various distance metrics, such as Euclidean distance, Manhattan distance, etc. In the dimensionality-reduced data space, the distance from each sample to the geometric center of the control group projection region is calculated, and this distance is used as the omics toxicology score. The geometric center of the control group projection region represents the data distribution center under normal conditions. The farther the sample is from this center, the greater the impact of aerosol atomization exposure on the sample, and the higher the toxicity may be.

[0074] S500. Based on the first toxicology score and the second toxicology score, determine the comprehensive toxicology score for the target group. Combining the first and second toxicology scores, the comprehensive toxicology score for the target group is obtained. This score integrates information from both hematological and molecular omics levels, enabling a more comprehensive and accurate assessment of the toxicological effects of aerosol exposure on the test organisms.

[0075] In a more specific embodiment, step S500 specifically includes:

[0076] S510. Calculate the Z-score of the first toxicology score of the target group relative to the control group; calculate the Z-score of the second toxicology score of the target group relative to the control group. Specifically, this refers to determining the Z-score of each aerosol exposure sample relative to the control group using the mean and standard deviation of the control group. Toxicity assessment is relative to the control group level; therefore, the data distribution must be based on the control group, i.e., the Z-score of each sample is calculated using the control group as the benchmark. Z-score for any sample = (sample expression level - mean expression level within the control group) / standard deviation within the control group. The purpose is to compare the first toxicology score (based on blood physiological indicators) and the second toxicology score (based on multi-omics data) with the control group on the corresponding dimensions. By calculating the Z-score, the toxicology scores obtained from the two different levels are converted into standardized values ​​that measure the degree of deviation from the control group mean, facilitating subsequent comprehensive consideration on the same scale.

[0077] S520. Based on the two Z-scores determined in S510, determine the comprehensive toxicology score of the target group. Specifically, this includes calculating the average of the two Z-scores determined in S510 to determine the comprehensive toxicology score of the target group. Using the mean of the Z-scores as the final indicator better meets the requirements of numerical standardization and is simpler and more efficient to calculate. The method of calculating the average of two Z-scores to determine the comprehensive toxicology score is simple and easy to understand, and the results are intuitive and clear, facilitating researchers' understanding and use, and also enabling rapid comparison and analysis between different target groups. In other embodiments, weighted average methods, machine learning algorithms, analytic hierarchy process (AHP), etc., can also be used to process the two Z-scores determined in S510 to determine the comprehensive toxicology score of the target group, improving the accuracy and adaptability of the comprehensive toxicology score.

[0078] The toxicological scoring method for aerosol delivery systems disclosed in this application comprehensively considers blood physiological indicators and multi-omics data, evaluating the toxicological effects of aerosols such as e-liquids from both macroscopic physiological and microscopic molecular levels. This avoids the limitations of single-dimensional assessments, making the evaluation results more comprehensive and reliable. Through specific scoring calculations, the toxicity of aerosols produced by different e-liquids can be quantified, facilitating clear and precise comparisons of the toxicity differences between different aerosols, such as those containing different components (e.g., PG and PDO) or at different concentrations. This helps in screening for relatively safer e-liquid products. Furthermore, it allows for the analysis of the biological effects and molecular alteration pathways of aerosols such as e-liquids, thereby optimizing the components of e-liquids. Currently, there is a lack of detailed and reliable standards for the toxicological evaluation of aerosols such as e-liquids. This method provides a standardized toxicological evaluation scheme, offering a unified method and scale for the safety evaluation of e-liquids, and contributing to the regulation of the e-cigarette market.

[0079] The following is a detailed experiment conducted to obtain the scoring method for the toxicology of the aforementioned aerosol delivery system. This experiment evaluated the 28-day inhalation toxicity of PDO / VG and PG / VG in rats. After the exposure period, routine toxicological analyses were performed, along with multi-omics assessments of lung tissue using transcriptomics, proteomics, and metabolomics. Subsequent comparative analyses were conducted to assess the biosafety of PDO and PG in e-cigarettes. The results indicate that low doses of PDO / VG exhibited relatively low toxicity when assessed using this method. These results provide a new paradigm for assessing the toxicity of e-cigarettes, namely the aforementioned scoring method for the toxicology of aerosol delivery systems, and enable more effective comparison of the toxicity of different e-liquid formulations.

[0080] The experiment consisted of four experimental groups: purple represented the air group (i.e., the control group), light blue represented the low-concentration PDO / VG aerosol exposure group (PDO / VG-L), light green represented the high-concentration PDO / VG aerosol exposure group (PDO / VG-H), and orange represented the PG / VG aerosol exposure group (PG / VG). The red pentagram represented the geometric center of the air group (i.e., the control group). The geometric center of the control group's projection area represented the data distribution center under normal conditions. The farther the sample was from this center, the greater the impact of aerosol atomization exposure on the sample, and the higher the potential toxicity.

[0081] See Figure 3-6 This set of images is a visualization of the dimensionality reduction of multi-omics data from different groups of samples using the t-distributed random neighbor embedding (t-SNE) algorithm, used to analyze the impact of different aerosol exposures on the samples. Figure 3-6 The figures show the t-SNE dimensionality reduction results of transcriptomics, proteomics, gas-phase non-target metabolomics, and liquid-phase non-target metabolomics of lung tissue from four different aerosol exposure groups, as well as the geometric center identification of the confidence ellipse in the control group (air exposure group). All four figures were obtained by analyzing the four omics data matrices using the sklearn.manifold, numpy, and SciPy packages in a Python 3.11 environment. Figure 3-6 The x and y coordinates of (i.e., B, C, D, E) represent a group in t-SNE. The t-SNE algorithm maps high-dimensional multi-omics data to a two-dimensional space to intuitively present the distribution relationship between different groups of sample data.

[0082] Each point in the graph represents a sample. Points of the same color belong to the same group, and semi-transparent areas of different colors roughly outline the distribution range of each group of samples. By observing the distribution of sample points and the overlap of distribution areas between different groups, the degree of difference between different aerosol exposure groups and the control group can be analyzed. If the sample points of a certain aerosol exposure group are far away from the sample points of the air group, and the distribution areas overlap little, it indicates that the aerosol exposure has a greater impact on the characteristics of the sample data, and the difference from the air group is obvious; conversely, if there is more overlap, it indicates that the difference is smaller. For example, comparing... Figure 3-6 The distribution relationship between the orange PG / VG group and the purple air group shows their differences across different dimensions. The relationship between different concentrations of PDO / VG (light blue and light green) and the air group can also be used to determine the impact of aerosol concentration on sample characteristics through point distribution and regional extent.

[0083] See Figure 7-10 This set of images contains analysis results related to aerosol toxicity. Figure 7-10 The analysis presented different aspects.

[0084] Figure 7 The vertical axis represents toxicity assessment, and the horizontal axis displays different groups, including the air control group (Air), two PDO / VG aerosol exposure groups (PDO / VG-L and PDO / VG-H), and the PG / VG aerosol exposure group (PG / VG). The different colored bars in the figure show the toxicity assessment values ​​for each group. It can be seen that the toxicity assessment of the air group is relatively low, while the values ​​of the aerosol exposure groups are higher than those of the air group. The PDO / VG-H group has the highest toxicity assessment value, indicating that this group may have experienced the greatest toxic effects. The figure also indicates the p-values ​​for comparisons between different groups, showing that there are statistically significant differences in toxicity assessment between these groups (p-value less than 0.05 is generally considered statistically significant), meaning that different aerosol exposures have significantly different effects on toxicity.

[0085] Figure 8 The vertical axis refers to the feature distance, and the horizontal axis is the same. Figure 7 The characteristic distance of the air group is close to 0, indicating that its sample data characteristics serve as a reference benchmark. The characteristic distances of the PDO / VG-L, PDO / VG-H, and PG / VG groups are all greater than 0, with the PDO / VG-H group having the largest characteristic distance, meaning that its sample data characteristics differ most significantly from the air group. This is followed by the PG / VG and PDO / VG-L groups. The p-values ​​for inter-group comparisons show significant differences in data characteristics between the different aerosol exposure groups and the air group.

[0086] Figure 9 The x-axis represents toxicity assessment, and the y-axis represents characteristic distance. The scatter points in the graph represent sample data, and the straight line is the fitted line with the fitted equation "Y = 8.496 * X - 2.312" and a p-value "P = 0.0099", showing a significant linear relationship between toxicity assessment and characteristic distance. As the toxicity assessment value increases, the characteristic distance also increases accordingly, indicating that the stronger the toxicity of PDO / VG aerosol exposure, the greater the difference in sample data characteristics between the PDO / VG aerosol exposure and the air group.

[0087] Figure 10 The vertical axis represents the aerosol toxicity score, and the horizontal axis represents the aerosol toxicity score. Figure 7-8 Consistent. The bar chart shows the aerosol toxicity scores of different groups. The aerosol toxicity score of the air group is close to 0, while all aerosol exposure groups have positive scores. The PDO / VG-H group has the highest score, followed by the PG / VG group, and the PDO / VG-L group has a relatively low score. The p-values ​​for inter-group comparisons show significant differences in aerosol toxicity scores between the different aerosol exposure groups and the air group, further illustrating that different aerosol exposures produce different degrees of toxic effects.

[0088] The calculation of the toxicology scoring method for the aforementioned aerosol delivery system can be implemented using Python 3.11 and its related packages such as sklearn.manifold, numpy, and SciPy. Therefore, one embodiment of this application also discloses a storage medium storing a computer program that, when executed by a processor, implements the toxicology scoring method for the aerosol delivery system described in the above embodiment.

[0089] In the description of this specification, the use of terms such as "Embodiment 1," "this embodiment," or "in one embodiment" indicates that the specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example; moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in one or more embodiments or examples.

[0090] In the description of this specification, the terms "connection," "installation," "fixing," "setting," and "having" are interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0091] In the description of this specification, relational terms such as “first” and “second” are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0092] The above description of the embodiments is intended to enable those skilled in the art to understand and apply the technology of this invention. Those skilled in the art can easily make various modifications to these examples and apply the general principles described herein to other embodiments without creative effort. Therefore, this invention is not limited to the above embodiments. Modifications in the following situations should be within the scope of protection of this invention: ① New technical solutions implemented based on the technical solution of this invention and combined with existing common knowledge, where the technical effects of the new technical solution do not exceed the technical effects of this invention; ② Equivalent substitutions of some features of the technical solution of this invention using known technology, resulting in the same technical effects as those of this invention; ③ Extendable technical solutions based on the technical solution of this invention, where the substantive content of the extended technical solution does not exceed the technical solution of this invention; ④ Equivalent transformations made using the content of this specification and drawings, directly or indirectly applied to other related technical fields.

Claims

1. A toxicological scoring method for an aerosol delivery system, characterized in that, The method includes: S100. Set up a target group, wherein the test organisms in the target group are in an aerosol exposure environment; S200. Obtain blood physiological indicators and multi-omics data of the control group, wherein the experimental organisms in the control group are in an air-exposed environment; S300. Measure the blood physiological indicators of the target group and determine the first toxicology score based on the difference in blood physiological indicators between the target group and the control group. S400. Measure the multi-omics data of the target group, and determine the second toxicology score based on the difference between the multi-omics data of the target group and the control group; S500. Determine the comprehensive toxicological score of the target group based on the first toxicological score and the second toxicological score; The blood physiological indicators include blood biochemistry, coagulation function, and hematological indicators; the multi-omics data include transcriptomics, proteomics, and metabolomics data.

2. The scoring method according to claim 1, characterized in that, The S300 determines the first toxicology score based on the difference in blood physiological indicators between the target group and the control group, specifically including: The Z-scores of blood physiological indicators of the target group relative to the control group were calculated. Indicators with harmful increases higher than those of the control group were assigned positive Z-scores, and indicators with harmful decreases lower than those of the control group were assigned negative Z-scores. Indicators within the normal range were represented by the absolute value of the Z-score. The first toxicology score was determined based on the Z-score of the blood physiological parameters of the target group relative to the control group.

3. The scoring method according to claim 2, characterized in that, The determination of the first toxicology score based on the Z-score of the blood physiological indicators of the target group relative to the control group specifically includes: calculating the average Z-score of the blood physiological indicators of the target group relative to the control group to determine the first toxicology score.

4. The scoring method according to any one of claims 1-3, characterized in that, The acquisition of multi-omics data of the control group in S200 specifically includes: acquiring low-dimensional data after dimensionality reduction processing of the multi-omics data of the control group; The second toxicology score determined by S400 based on the differences in multi-omics data between the target group and the control group specifically includes: Dimensionality reduction is performed on the multi-omics data of the target group to obtain low-dimensional data; The second toxicology score was determined based on the differences in low-dimensional data from multi-omics data between the target group and the control group.

5. The scoring method according to claim 4, characterized in that, The dimensionality reduction of the multi-omics data of the target group specifically includes: using the t-distributed random neighbor embedding algorithm to reduce the dimensionality of the multi-omics data of the target group.

6. The scoring method according to claim 4, characterized in that, The acquisition of low-dimensional data after dimensionality reduction processing of the multi-omics data of the control group in S200 specifically includes: acquiring the geometric center of the projection region of the low-dimensional data after dimensionality reduction processing of the multi-omics data of the control group. The step of determining the second toxicology score based on the difference in low-dimensional data of multi-omics data between the target group and the control group specifically includes: calculating the distance from the low-dimensional data of multi-omics data of the target group to the geometric center of the projection region determined by S200, which is the second toxicology score.

7. The scoring method according to claim 5 or 6, characterized in that, The S500 specifically includes: S510. Calculate the Z-score of the first toxicological score of the target group relative to the control group; calculate the Z-score of the second toxicological score of the target group relative to the control group. S520. Based on the two Z scores determined in S510, determine the comprehensive toxicological score of the target group.

8. The scoring method according to claim 7, characterized in that, S520 specifically includes: calculating the average of the two Z scores determined in S510 to determine the comprehensive toxicology score of the target group.

9. The scoring method according to any one of claims 1-3, 5-6, or 8, characterized in that, The acquisition of blood physiological indicators and multi-omics data of the control group in S200 is specifically achieved through: Database retrieval, or Database and computation acquisition, or Obtained through experiments and calculations.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the scoring method for the toxicology of the aerosol delivery system as described in any one of claims 1-9.

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