Method for evaluating anti-haze pollution effect of raw materials by using 3D skin model

Abstract

The invention belongs to the technical field of daily cosmetics, and discloses a method for evaluating the anti-haze pollution effect of a raw material by using a 3D skin model, and the method comprises the following steps: incubating the 3D skin model, and respectively setting a blank control group, a negative control group, a positive control group and a sample group added with a sample to be detected; except the blank control group, the other groups are cultured under the PM condition; the expression of autophagy genes Beclin1, LC3A, LC3B and ATG7 in the 3D skin cells of each group is detected, and the content of inflammatory factors IL-1alpha and IL-8 in the culture solution is detected; calculating the relative expression quantity of the autophagy gene; calculating the ratio of the secretion amount of each group to the negative control group to obtain the relative expression amount of the inflammatory factors; by using the relative expression quantity of the autophagy gene and the relative expression quantity of the inflammatory factor, drawing and analyzing to obtain an anti-haze pollution effect result of the to-be-detected sample. A reliable evaluation method can be provided for early-stage verification and screening of anti-haze pollution raw materials of skin care products, and effective reference is provided for development and efficacy verification of the skin care products.

Description

Technical Field

[0001] The invention belongs to the technical field of daily cosmetics, and in particular relates to a method for evaluating the anti-haze pollution efficacy of raw materials by using a 3D skin model. Background Art

[0002] In the development of anti-pollution functional skin care products, the evaluation and screening of the anti-pollution efficacy of its active raw materials is one of the important steps in the early stage of formula design. At present, there are two main methods for testing the efficacy of anti-pollution active ingredients of cosmetics claiming to have anti-pollution efficacy on the market: in vivo and in vitro. Among them, the in vivo mouse exposure model is mostly used, which has the disadvantages of high requirements for instruments and equipment and greater difficulty in operation; the human exposure model experiment is costly and difficult to recruit volunteers. In vitro is mostly completed at the cellular level, where cells are cultured in a culture medium that simulates a polluted environment, and cells and culture medium are collected for corresponding testing. Since this environmental simulation is quite different from the normal polluted environment, it cannot simulate the in vitro pollution exposure environment well, and the single sensitivity of the cell is insufficient, so it is impossible to better simulate and test the efficacy of anti-pollution active ingredients. Constructing a new in vitro efficacy verification method will help to more efficiently and accurately realize the development and screening of anti-smog pollution raw materials for skin care products. Summary of the invention

[0003] In view of the above technical problems, the present invention provides a method for evaluating the anti-haze pollution efficacy of raw materials using a 3D skin model, such as Figure 2 As shown, the following steps are included:

[0004] Step S0-1, preparing samples to be tested;

[0005] Step S0-2, the 3D skin model is cultured for use in step S1;

[0006] Step S1, taking a 3D skin model, respectively setting up a blank control group, a negative control group, a positive control group and a sample group with a sample to be tested, and incubating them with a culture medium to obtain a 3D skin model after incubation;

[0007] Step S2, after incubation, the 3D skin model continues to be placed in the culture medium, and except for the blank control group, the other groups are cultured under PM conditions to obtain the 3D skin model after PM treatment;

[0008] Step S3, after step S2, detecting the expression of autophagy genes Beclin1, LC3A, LC3B, and ATG7 in the 3D skin model cells of each group, and detecting the content of inflammatory factors IL-1α and IL-8 in the culture medium;

[0009] Step S4, summarizing the results of the autophagy gene expression determination in step S3, and calculating the relative expression level of the autophagy gene;

[0010] Step S5, summarizing the results of the inflammatory factor content determination in step S3, calculating the ratio of the secretion amount of each group to the negative control group, and obtaining the relative expression amount of the inflammatory factor;

[0011] Step S6, using the relative expression levels of autophagy genes and the relative expression levels of inflammatory factors, a graphical analysis is performed to obtain the anti-smog pollution efficacy results of the sample to be tested.

[0012] In the present invention, step S0-1 and step S0-2 are pre-processing steps.

[0013] Preferably, the sample to be tested is a raw material of a predetermined concentration, which may be one or more than one.

[0014] More preferably, the samples to be tested are prepared as follows: raw materials with a solubility of ≥1000 μg / mL in water are directly dissolved in the culture medium for subsequent experiments; raw materials with a solubility of <1000 μg / mL in water are dissolved in dimethyl sulfoxide or ethanol solvents for subsequent experiments. When dimethyl sulfoxide or ethanol is used as a solvent for the raw materials, the volume percentage concentration of the solvent shall not exceed 1% of the total volume of the culture medium, and the same volume ratio of solvent shall be added to the control sample culture medium and the culture medium of other samples to be tested to ensure a single variable.

[0015] Preferably, in step S0-2, a matching special culture medium is used for gas-liquid two-phase culture, and all culture conditions are 37±5°C, (5±1)% CO2 concentration, and saturated humidity.

[0016] More preferably, the specific operation of step S0-2 is: put the 3D skin model into a 6-well plate, add 1 ml of culture medium to each well, and the culture conditions are 37°C, 5% CO2 and saturated humidity, and use it after stable culture for 12 hours. The role of step S0-2 is to stabilize the skin culture before the experiment, and then conduct subsequent experiments. However, it is necessary to replace fresh culture medium in step S1 to carry out the experiment.

[0017] Preferably, in step S1, the positive control group is added with the positive drug autophagy inhibitor 3-MA at a concentration of 1 mM to 5 mM. The negative control group uses only the 3D skin model, and the rest is not treated. The blank control group is only incubated in step S1.

[0018] In step S2, the PM condition is: a haze simulator is used to simulate the PM environment, and the PM2.5 concentration is set to 150 μg / m 3 ~500 μg / m 3 .

[0019] Preferably, in step S3, the specific operation of detecting the expression of autophagy genes Beclin1, LC3A, LC3B, and ATG7 is: collecting a 3D skin model, extracting RNA from the 3D skin model tissue using the Trizol method, performing reverse transcription on the extracted RNA using a kit, and then using real-time fluorescence quantitative PCR to detect the expression levels of the autophagy genes Beclin1, LC3A, LC3B, and ATG7.

[0020] Preferably, in step S3, an ELISA kit is used to detect the content of the inflammatory factors IL-1α and IL-8.

[0021] In the present invention, the culture medium in step S1 is a fresh maintenance medium, and the culture medium used for sampling and testing in step S3 is the culture medium after step S2, that is, the maintenance medium after incubation.

[0022] Preferably, in step S4, 2 ^-△△Ct Methods The relative expression levels of autophagy genes Beclin1, LC3A, LC3B, and ATG7 were calculated.

[0023] Preferably, in step S6, the relative expression of the autophagy gene and the relative expression of the inflammatory factor are used to draw a graph using GraphPad Prism, and the result is expressed as Mean±SD; One-way ANOVA statistical analysis is used for comparison between groups; all statistical analyses are two-tailed;

[0024] p<0.05 is considered to have a slightly significant difference, indicated by "*", indicating that the sample to be tested has a slight inhibitory effect, with a score of 0.3;

[0025] p<0.01 is considered to be significantly different, indicated by "**", indicating that the sample to be tested has a certain inhibitory effect, with a score of 0.5;

[0026] p<0.001 is considered to be very significantly different, indicated by "***", indicating that the sample to be tested has a significant inhibitory effect, with a score of 0.7;

[0027] p<0.0001 is considered to have an extremely significant difference, represented by "****", indicating that the sample to be tested has an extremely significant inhibitory effect, with a score of 1.0;

[0028] The scores of autophagy genes and inflammatory factors are summarized according to the above-mentioned scoring criteria, and whether the sample to be tested has anti-smog pollution efficacy is evaluated and screened.

[0029] In the present invention, a raw material can be selected to prepare samples of different concentrations to be tested, so as to detect whether the raw material has anti-smog pollution efficacy and screen out the optimal concentration of the raw material for anti-smog pollution efficacy. Different raw materials can also be selected to prepare samples of the same concentration, or corresponding samples can be prepared according to their optimal use concentrations to detect whether the raw material has anti-smog pollution efficacy and screen out the raw material with the best anti-smog pollution efficacy. Similarly, the above two can also be combined to evaluate whether the raw material has anti-smog pollution efficacy, screen out the best anti-smog pollution efficacy concentration of the raw material, and screen out the best anti-smog pollution efficacy raw material.

[0030] from Figure 1 It can be seen that PM2.5, the culprit of haze, is a kind of skin invader that cannot be ignored. It stays in the atmosphere for a long time and is transported over a long distance, which invisibly adds to the burden on the skin. The small molecular harmful substances (PAHs, etc.) adsorbed by it can use hair follicles as channels to enter deeper layers of the skin. A large amount of inorganic carbon and heavy metals cannot be degraded by autophagy, thus causing abnormal increase in the body's autophagy, directly or indirectly stimulating the body's immune system, activating inflammatory cells, and promoting the expression and release of cytokines. Compared with the prior art, the present invention has at least the following beneficial effects:

[0031] 1. The 3D skin model has a similar physiology and structure to normal human skin, and the commercially available 3D skin models are produced in a unified manner, with relatively stable structural characteristics. A certain degree of haze can cause abnormal increase in skin cell autophagy and induce inflammatory response. The expression of autophagy-related genes Beclin1, LC3A, LC3B, ATG7, etc. in skin cells was detected, and the secretion of inflammatory factors IL-1a and IL-8 was detected. The 3D skin model was used to simulate the state of real skin when it was polluted by haze, and whether cosmetic raw materials can reduce skin inflammatory response through autophagy inhibition pathways was detected. The anti-haze pollution efficacy of cosmetic raw materials was evaluated, which is closer to the real situation. It can provide a reliable evaluation method for the early verification and screening of anti-haze pollution raw materials for skin care products, and provide an effective reference for the development and efficacy verification of skin care products.

[0032] 2. Comprehensively evaluate the impact of PM on the skin from the two dimensions of autophagy and inflammation, more accurately determine whether the target raw materials have anti-smog pollution effects, screen the optimal usage concentration of the target raw materials, and compare the anti-smog pollution effects of different target raw materials at their respective optimal concentrations. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The process by which PM enters cells and affects cell autophagy and inflammatory factors;

[0034] Figure 2This is a flow chart of anti-pollution efficacy screening of the method for screening the anti-smog pollution efficacy of the 3D skin model of cosmetic raw materials of the present invention;

[0035] Figure 3a is the relative expression level of the autophagy gene Beclin1 after PM-induced RHE 3D skin model contamination in the example;

[0036] Figure 3b is the relative expression level of the autophagy gene LC3A after PM-induced RHE 3D skin model contamination in the example;

[0037] Figure 3c is the relative expression level of the autophagy gene LC3B after PM-induced RHE 3D skin model contamination in the example;

[0038] Figure 3d is the relative expression level of the autophagy gene ATG7 after PM-induced RHE 3D skin model contamination in the example;

[0039] Figure 4a The relative secretion amount of inflammatory factor IL-1a after PM-induced RHE 3D skin model pollution in the example;

[0040] Figure 4b The relative secretion amount of inflammatory factor IL-8 after PM-induced RHE 3D skin model contamination in the example;

[0041] Figure 5 Relative expression levels of related autophagy genes after PM-induced RHE 3D skin model pollution in the comparative example. DETAILED DESCRIPTION

[0042] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and through specific implementation methods. However, the following examples are only simplified examples of the present invention and do not represent or limit the scope of protection of the present invention. The scope of protection of the present invention shall be subject to the claims.

[0043] Unless otherwise specified, the raw materials and materials used in the examples of the present invention are purchased through general commercial channels.

[0044] The sources of the raw materials, materials and instruments involved in the following examples are as follows:

[0045] 1. Experimental Materials

[0046] 1. Experimental Materials and Instruments

[0047] 1.13D Skin Model

[0048] The 3D skin model is derived from a commercially available functionally stable human reconstructed 3D epidermal model. In this embodiment, the RHE 3D skin model was purchased from Shanghai Si'anfunuo Biotechnology Co., Ltd.

[0049] 1.2 Main Reagents

[0050] (1) 3D skin model related reagents: The culture medium was RHE maintenance medium purchased from Shanghai Sianfuno Biotechnology Co., Ltd., and Dulbecco's phosphate buffered solution (DPBS) was purchased from Gibco, USA;

[0051] (2) Chemical reagents: 3-methyladenine (3-MA) ​​was purchased from TargetMol, DMSO, isopropanol, and anhydrous ethanol were purchased from Sinopharm Chemical Reagent Co., Ltd., and Trizol was purchased from Thermo Fisher Scientific.

[0052] (3) ELISA test: Human IL-1α ELISA kit and human IL-8 ELISA kit were purchased from Xinbosheng Biotechnology Co., Ltd.;

[0053] (4) Verification materials: Terminalia chebula fruit extract was purchased from Shanghai Boshuo Industrial Co., Ltd., Camellia extract was purchased from Shanghai Ruimu Chemical Technology Co., Ltd., and anti-pollution polysaccharide was purchased from Shanghai Zhirou Chemical Co., Ltd.;

[0054] (5) Gene detection related reagents: reverse transcription kit PrimeScriptTM RT Master Mix was purchased from TaKaRa, real-time fluorescence quantitative PCR reagent FastStart Essential DNA Green Master was purchased from Roche, and primers were purchased from Shanghai Jierui Bioengineering Co., Ltd.

[0055] The primer sequences are shown in Table 1:

[0056] Gene name Forwardprimer(5'->3') Reverseprimer(5'->3') Beclin1 GAGCTAAAGGAGCTGGCACT TCTGCCACTATCTTGCGGTT LC3A TGTCAACATGAGCGAGTTGGT GCTCGTAGATGTCCGCGATG LC3B TTCAGGTTCACAAAACCCGC TCTCACACAGCCCGTTTACC ATG7 AGAACCAGAAAGGAGGCATGG GCTCCAAGCAGCAGACATTTG ATG5 GGACAGTTGCACACACTAGG CAATCTGTTGGCTGTGGGATG ATG12 AGGCAGAAAAGGTTAGGCGT TGTCCTATGTGCTTGCTCTCC

[0057] 1.3 Instruments

[0058] Microplate reader (Thermo, USA), CO2 incubator (Thermo, USA), fluorescence quantitative PCR instrument (Roche, Switzerland), electric thermostatic water bath (Shanghai Yiheng Scientific Instrument Co., Ltd., China), biological safety cabinet (Thermo, USA), high-speed low-temperature centrifuge (Thermo, USA).

[0059] <Example>

[0060] This embodiment describes in detail the method for evaluating the anti-haze pollution efficacy of raw materials using a 3D skin model. In the following steps, except for the necessary steps with a certain order, such as culturing the 3D skin model, preparing the test substance, and PM-induced 3D skin model pollution, the remaining steps without a certain order can be adjusted.

[0061] Step S0-1, preparing samples to be tested.

[0062] Three raw materials with known anti-pollution effects were selected to verify the feasibility of the experimental method, including active ingredient A: Terminalia chebula fruit extract, active ingredient B: Camellia extract (1,3-propylene glycol, water, Camellia extract), and active ingredient C: anti-pollution polysaccharides (water, butylene glycol, Alteromonas fermentation product extract). The optimal concentration was screened and verified using the recommended formula addition amount and the concentration after upward / downward dilution.

[0063] The solubility of the test substances A, B, and C in water was ≥1000 μg / mL, so the three raw materials A, B, and C were directly dissolved in the culture medium for subsequent experiments.

[0064] For each raw material sample (test substance) in the sample group, based on the optimal addition amount recommended by the manufacturer, other concentrations were prepared with culture medium upward (×10) or downward (×0.1), namely: the concentration of test substance A was 10ug / mL, 1ug / mL, and 0.1ug / mL, and the concentrations of test substances B and C were 20mg / mL and 2mg / mL, respectively.

[0065] Step S0-2, 3D skin model cultivation.

[0066] The commercially available 3D epidermal model was placed in a 6-well plate for stable culture. 1 ml of RHE maintenance medium was added to each well. The culture conditions were 37° C., 5% CO 2 and saturated humidity. After stable culture for 12 hours, it was used in step S1.

[0067] Step S1, preparation of test substances.

[0068] The culture plate with the 3D skin model in S0-2 was taken out and placed in a biosafety cabinet, and the 3D skin models were moved one by one into the well plate with fresh RHE maintenance medium.

[0069] In the positive control group, 80 μL of 1-5 mM 3-MA was added to the 3D skin model per well; in the sample group, 80 μL of the sample to be tested was added to the 3D skin model per well. The blank control group and the negative control group were not treated. The 3D skin models of all groups were incubated at 37°C, 5% CO2 and saturated humidity for 2 h to obtain the incubated 3D skin models.

[0070] Step S2, PM induces contamination of the 3D skin model.

[0071] The haze generator temperature is set to 37°C and the PM2.5 concentration is set to 150μg / m 3 ~500 μg / m 3 Store smoke.

[0072] After the smoke storage is completed, the door of the haze generator is opened. Except for the blank control group, the culture plate with the 3D skin model in step S1 (without cover) is taken out and placed in the haze generator for PM treatment for 6 hours to obtain the 3D skin model after PM treatment.

[0073] Step S3, detecting the expression of autophagy genes Beclin1, LC3A, LC3B, and ATG7 in the 3D skin model cells of each group, and detecting the content of inflammatory factors IL-1α and IL-8 in the culture medium.

[0074] (1) Real-time fluorescence quantitative PCR detection of autophagy gene expression in skin cells

[0075] After 6 hours of PM treatment, the culture plate with the 3D skin model (without cover) was taken out of the haze generator, and the 3D skin model was rinsed with DPBS buffer. The residual liquid on the surface of each 3D skin model was gently absorbed with a cotton swab, and the 3D skin model was cut with scissors and transferred to a centrifuge tube, and the RNA in the skin tissue was extracted using the Trizol method.

[0076] The extracted RNA was prepared according to the kit instructions, and reverse transcription was performed in a water bath at 37°C for 15 min and 85°C for 5 s to obtain cDNA.

[0077] Prepare PCR reaction solution according to 20 μL system:

[0078] 0.7 μL cDNA,

[0079] 0.8 μL primer,

[0080] 10μLFastStart Essential DNA GreenMaster 2×conc,

[0081] 8.5 μL HO.

[0082] The reaction solution is added to a dedicated 96-well plate and placed in a fluorescent quantitative PCR instrument for amplification reaction. The instrument program settings are:

[0083] Pre-incubation: 95℃ for 300s, 1 cycle

[0084] 3-StepAmplification: 95℃ for 10s, 60℃ for 10s, 72℃ for 10s, 40 cyclesMelting: 95℃ for 10s; 65℃ for 60s, 97℃ for 1s, 1 cycle

[0085] Cooling: 37℃ for 30s, 1 cycle.

[0086] Obtain the Ct values ​​corresponding to different genes in different samples.

[0087] (2) ELISA detection of inflammatory factor secretion in culture medium

[0088] After the culture plate (without cover) with the 3D skin model is taken out from the haze generator, the 3D skin model is subjected to the above-mentioned test in (1), and the culture medium is maintained in the well plate for inflammatory factor detection.

[0089] After the culture medium in the well plate is evenly pipetted, it is collected into a centrifuge tube, and the content of IL-1α and IL-8 is detected using an ELISA kit. The operation steps are consistent with the instructions of the kit, and the optical density (OD value) corresponding to different inflammatory factors of different samples is obtained.

[0090] Step S4, PCR data processing.

[0091] Collect Ct data using 2 ^-△△Ct The relative expression of the gene to be tested in the sample was calculated by the method, and the calculation formula is as follows:

[0092] △Ct=Ct(target gene)-Ct(reference gene)

[0093] △△Ct=△Ct(sample group)-△Ct(negative control group)

[0094] Relative expression ratio = 2 ^-△△Ct .

[0095] The relative expression levels of Beclin1, LC3A, LC3B, and ATG7 are shown in Figure 3a~3d As shown. Among them, Figure 3a is the relative expression level of Beclin1 mRNA, Figure 3b is the relative expression level of LC3A mRNA, Figure 3c is the relative expression level of LC3B mRNA, Figure 3d Relative expression of ATG7 mRNA. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001 are the comparison results between the other groups and the negative control group.

[0096] from Figure 3a~3d It can be seen that the expression of Beclin1, LC3A, LC3B, and ATG7 genes in the negative control group was significantly higher than that in the blank control group, indicating that autophagy was significantly enhanced after PM treatment; the expression of Beclin1, LC3A, LC3B, and ATG7 genes in the positive control group was significantly lower than that in the negative control group, indicating that autophagy was significantly inhibited after positive drug treatment, and the model was successfully constructed.

[0097] After adding different concentrations of three active ingredients, active ingredient A at 10× and 0.1× concentrations could not inhibit the expression of Beclin1, LC3A, LC3B, and ATG7 genes; active ingredient A at different concentrations could simultaneously inhibit the expression of Beclin1, LC3A, LC3B, and ATG7 genes, with different degrees of inhibition.

[0098] Active substance B at the added concentration can inhibit the expression of Beclin1, LC3A, LC3B, and ATG7 genes at the same time; active substance B at the added concentration of 0.1× can only inhibit the expression of Beclin1 gene, and has no effect on the expression of LC3A, LC3B, and ATG7 genes.

[0099] Active substance C at a concentration of 10× can only inhibit the expression of Beclin1 and LC3B genes, and has no effect on the expression of LC3A and ATG7 genes; Active substance C at a concentration of 10× can inhibit the expression of Beclin1, LC3A, LC3B, and ATG7 genes at the same time.

[0100] Step S5, use the ELISA Calc regression / fitting calculation program to calculate the standard curve, subtract the OD value of the background well from the OD value of the sample to be tested and insert it into the standard curve to calculate the content of IL-1α and IL-8. The negative control group is taken as 100% to calculate the relative expression of inflammatory factors, and the calculation formula is as follows:

[0101] Relative expression level = sample group content / negative control group content.

[0102] The relative secretion of IL-1α and IL-8 Figure 4a and Figure 4b As shown. Among them, Figure 4a This is the relative secretion of inflammatory factor IL-1a in PM-induced 3D skin model; Figure 4b The relative secretion of IL-8, an inflammatory factor, in the PM-induced 3D skin model. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001 are the comparisons between the sample group and the PM negative control group.

[0103] from Figure 4a and Figure 4b It can be seen that the expression of IL-1α and IL-8 in the negative control group was significantly higher than that in the blank control group, indicating that PM treatment significantly enhanced inflammation; the expression of IL-1α and IL-8 in the positive control group was significantly lower than that in the negative control group, indicating that the positive drug treatment inhibited inflammation through the autophagy inhibition pathway, and the model was successfully constructed.

[0104] After adding different concentrations of three active ingredients, active ingredient A at 10× and 0.1× concentrations could not inhibit the expression of IL-1α and IL-8; active ingredient A at the added concentration could inhibit the expression of IL-1α and IL-8 at the same time.

[0105] The added concentration of active substance B can inhibit the expression of IL-1α and IL-8 at the same time, with different degrees of inhibition; the added concentration of active substance B at 0.1× can only inhibit the expression of IL-8, and has no effect on the expression of IL-1α.

[0106] Active substance C at a concentration of 10× can only inhibit the expression of IL-8 and has no effect on the expression of IL-1α; active substance C at a concentration of 10× can inhibit the expression of IL-1α and IL-8 at the same time.

[0107] Step S6, result analysis.

[0108] The relative expression of autophagy genes and inflammatory factors was used to draw a graph using GraphPad Prism, and the results were expressed as Mean±SD. One-way ANOVA statistical analysis was used for comparison between groups. All statistical analyses were two-tailed. p<0.05 was considered to have a slightly significant difference, indicated by "*"; p<0.01 was considered to have a significant difference, indicated by "**"; p<0.001 was considered to have a very significant difference, indicated by "***"; p<0.0001 was considered to have an extremely significant difference, indicated by "****".

[0109] The experimental significant difference results of the 7 sample groups tested in this example at the 3D skin model level are summarized in Table 2, and the evaluation and scoring are performed according to the standards in Table 3. The evaluation results of the comprehensive anti-pollution efficacy of the samples are shown in Table 4.

[0110] Table 2 Statistical difference results of the tested substances

[0111] Serial number Sample name Sample concentration Beclin1 LC3A LC3B ATG7 IL-1a IL-8 1 Active ingredient A 10×addition amount NS NS NS * NS NS 2 Active ingredient A Addition amount **** **** ** **** ** ** 3 Active ingredient A 0.1×addition amount NS NS NS NS NS NS 4 Active ingredient B Addition amount **** **** **** **** ** **** 5 Active ingredient B 0.1×addition amount **** NS NS NS NS **** 6 Active Ingredients C 10×addition amount **** NS * NS NS **** 7 Active Ingredients C Addition amount **** **** **** **** **** ****

[0112] Table 3 Scoring rules

[0113]

[0114] Table 4 Test object scoring results

[0115] Serial number Sample name Sample concentration Beclin1 LC3A LC3B ATG7 IL-1a IL-8 Summary 1 Active ingredient A 10×addition amount 0 0 0 0.3 0 0 0.3 2 Active ingredient A Addition amount 1 1 0.5 1 0.5 0.5 4.5 3 Active ingredient A 0.1×addition amount 0 0 0 0 0 0 0 4 Active ingredient B Addition amount 1 1 1 1 0.5 1 5.5 5 Active ingredient B 0.1×addition amount 1 0 0 0 0 1 2 6 Active Ingredients C 10×addition amount 1 0 0.3 0 0 1 2.3 7 Active Ingredients C Addition amount 1 1 1 1 1 1 6

[0116] According to the above results, the anti-smog pollution effects of each active substance at different concentrations are ranked respectively:

[0117] The strength of the efficacy of active substance A against smog pollution is ranked as follows: active substance A (addition amount) > active substance A (10X addition amount) > active substance A (0.1X addition amount), so the optimal anti-smog pollution concentration of active substance A is the addition amount concentration; the strength of the efficacy of active substance B against smog pollution is ranked as follows: active substance B (addition amount) > active substance B (0.1X addition amount), so the optimal anti-smog pollution concentration of active substance B is the addition amount concentration; the strength of the efficacy of active substance C against smog pollution is ranked as follows: active substance C (addition amount) > active substance C (10X addition amount), so the optimal anti-smog pollution concentration of active substance C is the addition amount concentration. This result shows that the three active substances have anti-smog pollution efficacy at specific concentrations, and the method has been verified, indicating that this method can verify whether the raw materials have anti-smog pollution efficacy; the optimal anti-smog pollution concentrations of the three active substances are all recommended addition amounts, and the method has been verified, indicating that this method can screen the optimal anti-smog pollution concentration of the raw materials.

[0118] The three active substances at the best anti-smog pollution concentration are ranked:

[0119] Active ingredient C (addition amount) > Active ingredient B (addition amount) > Active ingredient A (addition amount), so at the optimal concentration (addition amount concentration), Active ingredient C has the best anti-smog pollution effect. This result shows that the three active ingredients have their own optimal anti-smog pollution concentration, among which the optimal anti-smog pollution concentration of Active ingredient C is the best among the three active ingredients. The method has been verified, indicating that this method can screen the best anti-smog pollution raw materials.

[0120] <Comparative Example>

[0121] The autophagy genes Beclin1, LC3A, LC3B, ATG7, ATG5, and ATG12 were selected, and the relative expression levels of Beclin1, LC3A, LC3B, ATG7, ATG5, and ATG12 were obtained by using the operations of steps S0 to S4 and S6 in the embodiment. Figure 5 shown.

[0122] from Figure 5 It can be seen that the expression of Beclin1, LC3A, LC3B, ATG7, ATG5, and ATG12 genes in the negative control group after PM treatment was higher than that in the blank control group, indicating that autophagy was significantly enhanced after PM treatment; the added concentration of active substance C is known to have the effect of resisting haze pollution. The results show that the added concentration of active substance C is significantly lower than that in the negative control group in terms of Beclin1, LC3A, LC3B, and ATG7 gene expression, however, there is no significant change in the expression of ATG5 and ATG12 genes. Therefore, ATG5 and ATG12 genes have not been verified in this method and are not suitable for this method. Therefore, the autophagy genes selected are Beclin1, LC3A, LC3B, and ATG7 genes.

[0123] The applicant declares that the above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention are within the protection scope and disclosure scope of the present invention.

Claims

1. A method for evaluating the anti-haze pollution efficacy of raw materials using a 3D skin model, characterized in that: The following steps are involved: Step S1, taking a 3D skin model, respectively setting up a blank control group, a negative control group, a positive control group and a sample group with a sample to be tested, and incubating them with a culture medium to obtain a 3D skin model after incubation; Step S2, the incubated 3D skin model continues to be placed in the culture solution, and except for the blank control group, the other groups are cultured under PM conditions to obtain PM-treated 3D skin models; Step S3, after step S2, detecting the expression of autophagy genes Beclin1, LC3A, LC3B, and ATG7 in the 3D skin model cells of each group, and detecting the content of inflammatory factors IL-1α and IL-8 in the culture medium; Step S4, summarizing the autophagy gene expression measurement results in step S3, and calculating the relative expression level of the autophagy gene; Step S5, summarizing the results of the inflammatory factor content determination in step S3, calculating the ratio of the secretion amount of each group to the negative control group, and obtaining the relative expression amount of the inflammatory factor; Step S6, using the relative expression levels of the autophagy genes and the relative expression levels of the inflammatory factors, a graphical analysis is performed to obtain the anti-haze pollution efficacy results of the sample to be tested.

2. The method for evaluating the anti-haze pollution efficacy of raw materials using a 3D skin model as claimed in claim 1, characterized in that: In step S6, the relative expression of the autophagy gene and the relative expression of the inflammatory factor were used to draw a graph using GraphPadPrism, and the results were expressed as Mean±SD; One-way ANOVA statistical analysis was used for comparison among the groups; all statistical analyses were two-tailed; p<0.05 is considered to have a slightly significant difference, indicated by "*", indicating that the sample to be tested has a slight inhibitory effect, with a score of 0.3; p<0.01 is considered to be significantly different, indicated by "**", indicating that the sample to be tested has a certain inhibitory effect, with a score of 0.5; p<0.001 is considered to have a very significant difference, represented by "***", indicating that the sample to be tested has a significant inhibitory effect, with a score of 0.7; p<0.0001 is considered to have an extremely significant difference, represented by "****", indicating that the sample to be tested has an extremely significant inhibitory effect, with a score of 1.0; The scores of autophagy genes and inflammatory factors are summarized according to the above-mentioned scoring criteria, and whether the sample to be tested has anti-smog pollution efficacy is evaluated and screened.

3. The method for evaluating the anti-haze pollution efficacy of raw materials using a 3D skin model as claimed in claim 1, characterized in that: The sample to be tested is a raw material with a predetermined concentration.

4. The method for evaluating the anti-haze pollution efficacy of raw materials using a 3D skin model as claimed in claim 3, characterized in that: Also included is a pre-processing step, which includes: Step S0-1, preparing samples to be tested; Step S0-2, the 3D skin model is cultured for use in step S1.

5. The method for evaluating the anti-haze pollution efficacy of raw materials using a 3D skin model as claimed in claim 4, characterized in that: in, The samples to be tested are prepared as follows: raw materials with a solubility in water of ≥1000 μg / mL are directly dissolved in culture medium for subsequent experiments; raw materials with a solubility in water of <1000 μg / mL are dissolved in dimethyl sulfoxide or ethanol and then subjected to subsequent experiments.

6. The method for evaluating the anti-haze pollution efficacy of raw materials using a 3D skin model as claimed in claim 4, characterized in that: The specific operation of step S0-2 is: put the 3D skin model into a 6-well plate, add 1 ml of culture medium to each well, culture conditions are 37° C., 5% CO2 and saturated humidity, and use it after stable culture for 12 hours.

7. The method for evaluating the anti-haze pollution efficacy of raw materials using a 3D skin model as claimed in claim 1, characterized in that: In step S3, the specific operation of detecting the expression of the autophagy genes Beclin1, LC3A, LC3B, and ATG7 is as follows: collecting a 3D skin model, extracting RNA from the 3D skin model tissue using the Trizol method, performing reverse transcription on the extracted RNA using a kit, and then using real-time fluorescence quantitative PCR to detect the expression levels of the autophagy genes Beclin1, LC3A, LC3B, and ATG7.

8. The method for evaluating the anti-haze pollution efficacy of raw materials using a 3D skin model as claimed in claim 7, characterized in that: In step S4, 2 ^-△△Ct Methods The relative expression levels of the autophagy genes were calculated.

9. The method for evaluating the anti-haze pollution efficacy of raw materials using a 3D skin model as claimed in claim 1, characterized in that: In step S1, the positive control group is added with the positive drug autophagy inhibitor 3-MA at a concentration of 1 mM to 5 mM; in step S2, the PM condition is: a haze simulator is used to simulate the PM environment, and the PM2.5 concentration is set to 150 μg / m 3 ~500 μg / m 3 .

10. The method for evaluating the anti-haze pollution efficacy of raw materials using a 3D skin model as claimed in claim 1, characterized in that: In step S3, an ELISA kit is used to detect the content of the inflammatory factors IL-1α and IL-8.

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