Antibiotic risk assessment method

Abstract

The invention provides an antibiotic risk assessment method, and belongs to the technical field of microorganisms. According to the method, the minimum selection concentration of each antibiotic in multiple antibiotics is directly calculated according to the resistance of microbial flora in a water body to be detected to multiple antibiotics, and then the antibiotic risk is evaluated by using the minimum selection concentration of each antibiotic in multiple antibiotics. By means of the method, antibiotic risk assessment can be conducted on the situation that the antibiotic content is low but the bacterial drug resistance level is high in the water environment, the problem that an existing antibiotic risk assessment method mainly depends on toxicity assessment and cannot effectively reflect the antibiotic risk is solved, and therefore the water environment with the antibiotic risk is effectively managed.

Description

Technical Field

[0001] The present invention relates to the field of microbial technology, and in particular to an antibiotic risk assessment method. Background Art

[0002] Antibiotic risk assessment is a process involving medicine, pharmacology, microbiology and public health, which aims to systematically analyze the health, environmental and social risks that may be caused by the use of antibiotics.

[0003] When assessing the antibiotic risk of aquatic environments (such as rivers), existing technologies do not take into account the situation in which the antibiotic content in aquatic environments is low but the bacterial resistance level is high. As a result, existing antibiotic risk assessment methods are unable to assess the hazards of the above situation, and the assessment results of existing antibiotic risk assessment methods are not comprehensive, resulting in the inability to effectively manage the water environment where antibiotic risks exist in the above situation. Summary of the Invention

[0004] The present invention proposes an antibiotic risk assessment method, which can perform antibiotic risk assessment in situations where the antibiotic content is low but the bacterial resistance level is high, which is often present in water environments. It solves the problem that existing antibiotic risk assessment methods mainly rely on toxicity assessment and the assessment results are not comprehensive. It can effectively manage water environments where antibiotic risks exist in the above situations.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] An antibiotic risk assessment method includes: first, conducting a microecological experiment on the microbial flora in the water to be tested, and measuring the number of resistant and sensitive bacteria in the microbial flora before and after the microecological experiment. The microecological experiment simulates the environment of the water to be tested and cultivates the microbial flora in the water to be tested. Resistant bacteria are resistant to at least one of the multiple antibiotics contained in the water to be tested, while sensitive bacteria are not resistant to multiple antibiotics. Based on the number of resistant and sensitive bacteria in the microbial flora before and after the microecological experiment and the MSCpre model, the minimum selective concentration of each of the multiple antibiotics is determined; the MSCpre model is constructed based on the microbial flora's resistance to multiple antibiotics. The antibiotic risk assessment result for the water to be tested is then determined based on the minimum selective concentration of each of the multiple antibiotics and the concentrations of the multiple antibiotics in the water to be tested.

[0007] In an antibiotic risk assessment method provided by the present invention, a microecological experiment is first carried out using the microbial flora in the water body to be detected, and the number of resistant bacteria and the number of sensitive bacteria in the above-mentioned microbial flora before and after the microecological experiment and the MSCpre model are used to calculate the minimum selection concentration of each antibiotic in the multiple antibiotics, taking into account the resistance of the microbial flora to multiple antibiotics. Based on the minimum selection concentration of each antibiotic in the multiple antibiotics and the concentration of multiple antibiotics in the water body to be detected under actual circumstances, the antibiotic risk assessment result of the water body to be detected is evaluated. In the above method, the minimum selection concentration of each antibiotic in the multiple antibiotics is directly calculated based on the resistance of the microbial flora in the water body to be detected to multiple antibiotics, and then the minimum selection concentration is used to evaluate the antibiotic risk. It is possible to perform antibiotic risk assessment on the situation where the antibiotic content is low but the bacterial resistance level is high in the water environment, so that the assessment results of the existing antibiotic risk assessment method are not comprehensive enough, and thus the water environment with antibiotic risks in the above-mentioned situation can be effectively managed.

[0008] In one implementation of the present invention, the model formula of the MSCpre model is as follows:

[0009]

[0010] in, represents the rate of change of the number of resistant bacteria over time, M represents the chemical oxygen demand of the water to be tested, K M The half-saturation constant of chemical oxygen demand, represents the maximum bacterial growth rate of resistant bacteria, represents the minimum bacterial growth rate of resistant bacteria, a represents the concentration of an antibiotic in the microecological experiment, MIC R represents the minimum inhibitory concentration of resistant bacteria, k represents the Hill coefficient, R represents the number of resistant bacteria after the microecological experiment predicted by the MSCpre model, S represents the number of sensitive bacteria after the microecological experiment predicted by the MSCpre model, N represents the bacterial carrying capacity of the microecological experiment, and m represents the mortality rate of bacteria in the microecological experiment; represents the rate of change of the number of sensitive bacteria over time, represents the maximum bacterial growth rate of sensitive bacteria, Indicates the minimum bacterial growth rate of sensitive bacteria, MIC S represents the minimum inhibitory concentration of sensitive bacteria; SC represents the selection coefficient, R0 represents the number of resistant bacteria before the microecological experiment, and S0 represents the number of sensitive bacteria before the microecological experiment.

[0011] In one implementation of the present invention, determining the minimum selection concentration of each antibiotic in a plurality of antibiotics comprises:

[0012] For each antibiotic in the group of antibiotics:

[0013] The selection coefficient SC was obtained by substituting the number of resistant bacteria and the number of sensitive bacteria in the microbial flora before and after the microecological experiment into the model formula of the MSCpre model;

[0014] When the selection coefficient SC=1, the concentration of the antibiotic in the microecological experiment is determined as the minimum selection concentration of the antibiotic.

[0015] In one implementation of the present invention, determining the antibiotic risk assessment result of the water body to be tested includes:

[0016] For each of the multiple antibiotics: the minimum selection concentration of the antibiotic and the concentrations of the multiple antibiotics in the water to be tested are substituted into the antibiotic evaluation formula to calculate the antibiotic risk quotient; the antibiotic evaluation formula is as follows;

[0017]

[0018] Where RQ represents the risk quotient of the antibiotic, MEC represents the concentration of the antibiotic in the water to be tested, AF represents the risk assessment factor, and MSC represents the minimum selective concentration of the antibiotic;

[0019] The antibiotic risk assessment results of the water body to be tested are determined based on the risk quotient of each of the multiple antibiotics.

[0020] In one implementation of the present invention, the risk quotient RQ of each of the multiple antibiotics is:

[0021] When RQ is less than 0.01, the risk assessment result of antibiotics is no risk;

[0022] When 0.01≤RQ<0.1, the risk assessment result of antibiotics is low risk;

[0023] When 0.1≤RQ<1, the risk assessment result of the antibiotic is medium risk;

[0024] When RQ>1, the risk assessment result of the antibiotic is high risk.

[0025] In one implementation of the present invention, a microecological experiment is performed on the microbial flora in the water body to be tested, and the number of resistant bacteria and the number of sensitive bacteria in the microbial flora before and after the microecological experiment are measured, including:

[0026] Sampling the water body to be tested to obtain a water sample of the water body to be tested;

[0027] extracting microbial flora of the water body to be tested from a water sample of the water body to be tested;

[0028] Conduct microecological experiments on microbial flora, i.e. cultivate microbial flora in a simulated environment, including various antibiotics, nutrients contained in the water to be tested, and hydrodynamic conditions;

[0029] Metagenomic analysis was used to determine the number of resistant bacteria and sensitive bacteria in the microbial flora before and after the microecological experiment. Resistant bacteria are bacteria that contain resistance genes for at least one of the multiple antibiotics, and sensitive bacteria are bacteria that do not contain resistance genes for any of the multiple antibiotics.

[0030] In one implementation of the present invention, the plurality of antibiotics include β-lactams, quinolones, macrolides, tetracyclines and sulfonamides. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is one of the schematic diagrams of an antibiotic risk assessment method provided in the embodiments of the present application;

[0032] Figure 2 This is the second schematic diagram of an antibiotic risk assessment method provided in an embodiment of the present application;

[0033] Figure 3 This is the third schematic diagram of an antibiotic risk assessment method provided in an embodiment of the present application;

[0034] Figure 4 This is a heat map of antibiotic risk assessment for (a) SMX, (b) ERY, (c) NOR, and (d) TC using the 3M framework and existing assessment methods provided in the embodiments of the present application. DETAILED DESCRIPTION

[0035] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0036] In the description of the present invention, unless otherwise specified, "multiple" means two or more than two. For example, multiple antibiotics means two or more than two antibiotics.

[0037] The method provided in the embodiments of the present application relates to antibiotic risk assessment, and can be used to perform antibiotic risk assessment in situations where the antibiotic content is low but the bacterial resistance level is high, which is common in water environments.

[0038] It is understandable that antibiotics, as medical compounds for humans and animals, are generally characterized by low persistence, low bioaccumulation and low toxicity. Compared with humans and animals, antibiotics have a more profound impact on bacteria. The long-term presence of antibiotics in the environment will make bacteria in the environment increasingly resistant to antibiotics.

[0039] The environmental harm posed by high levels of bacterial resistance is manifested in the widespread spread and persistent persistence of resistance genes within ecosystems. These genes, through pharmaceutical wastewater, agricultural emissions, and medical waste, pollute the water and soil environment, disrupting the function of soil microbiota (e.g., reducing fertility and weakening pollutant degradation capacity) and disturbing the ecological balance of water bodies (leading to toxic effects on aquatic organisms). Resistance genes can also spread through horizontal transfer between environmental bacteria and pathogens, threatening human and animal health through the food chain (e.g., crop absorption and aquaculture), and even spread to remote areas through migratory birds, creating transregional ecological risks. In the long term, the accumulation of resistance genes in the environment could trigger irreversible imbalances in microbial communities, exacerbate the degradation of ecological services, and force humanity to invest higher costs in pollution remediation, ultimately threatening global ecological security and sustainable development.

[0040] Therefore, in the process of antibiotic risk assessment, bacteria should be used as test species, and bacterial resistance (also known as resistance) should be used as an important indicator to assess antibiotic risks in the environment.

[0041] In order to solve the problem that the existing antibiotic risk assessment method in the background technology is unable to assess the hazards of the above-mentioned situation, thereby making the assessment results of the existing antibiotic risk assessment method not comprehensive, resulting in the inability to effectively manage the water environment with antibiotic risks in the above-mentioned situation, the embodiment of the present application provides an antibiotic risk assessment method, which directly calculates the minimum selection concentration of each antibiotic in multiple antibiotics based on the resistance of the microbial flora in the water body to be tested to multiple antibiotics, and then uses the minimum selection concentration to assess the antibiotic risk. It can perform antibiotic risk assessment on the situation where the antibiotic content is low but the bacterial resistance level is high, which often exists in the water environment, so that the assessment results of the existing antibiotic risk assessment method are not comprehensive, and thus can effectively manage the water environment with antibiotic risks in the above-mentioned situation.

[0042] In one implementation, Figure 1 As shown, an antibiotic risk assessment method provided in an embodiment of the present application includes S101-S103.

[0043] S101. Conduct a microecological experiment on the microbial flora in the water body to be tested, and measure the number of resistant bacteria and the number of sensitive bacteria in the microbial flora before and after the microecological experiment.

[0044] The microecological experiment simulates the environment of the water to be tested and cultivates the microbial flora in the water to be tested. The resistant bacteria are resistant to at least one of the multiple antibiotics contained in the water to be tested, while the sensitive bacteria are not resistant to multiple antibiotics.

[0045] Exemplary, the multiple antibiotics included in the above-mentioned water body to be detected can include beta-lactams, quinolones, macrolides, tetracyclines and sulfonamides, such as ampicillin (AMP), sulfamethoxazole (SMX), erythromycin (ERY), tetracycline (TC) and norfloxacin (NOR) etc. The nutrients included in the above-mentioned water body to be detected can include carbon source, nitrogen source and phosphorus source etc. The above-mentioned hydrodynamic conditions are factors such as water flow velocity, direction, energy, wave action, tidal activity of the water body to be detected. It should be noted that the type included in the above-mentioned multiple antibiotics, the above-mentioned nutrients and the above-mentioned hydrodynamic conditions can also be set according to actual conditions, and the present application embodiment is not limited here.

[0046] Optionally, combined Figure 1 ,like Figure 2 As shown, the above S101 includes S1011-S1014.

[0047] S1011. Sampling the water body to be tested to obtain a water sample of the water body to be tested.

[0048] S1012. Extract the microbial flora of the water body to be tested from the water sample of the water body to be tested.

[0049] Specifically, taking the water body to be tested as a river as an example, first use a filter membrane with a pore size of 0.22μm to filter 500m of the river water sample, thoroughly clean the filtered filter membrane, collect the microbial flora of the water body to be tested, and remove the antibiotics contained in the water sample.

[0050] S1013. Conduct microecological experiments on microbial flora.

[0051] The above-mentioned microecological experiment is to cultivate microbial flora in a simulated environment, which includes a variety of antibiotics, nutrients contained in the water to be tested, and hydrodynamic conditions.

[0052] The following describes the specific process of the microecological experiment in detail, taking the above-mentioned water body to be tested as a river as an example.

[0053] Cut the filtered membrane into small pieces and vigorously vortex them in 20 mL of PBS buffer along with sterile glass beads for 20 minutes to facilitate the transfer of the microbial flora from the test water to the PBS buffer. After discarding the membrane and glass beads, transfer the supernatant containing the bacteria to five 1-L sterile Erlenmeyer flasks.

[0054] Each of the five 1L sterile conical flasks was used as an experimental container (also called a microecological system) for the microecological experiment, and four antibiotic dilutions were added to the five microecological systems according to a concentration gradient of 0.01μg / L, 0.1μg / L, 1μg / L, 10μg / L and 100μg / L. Nutrients reflecting the natural level of the river were then added to each microecological system, and the liquid in the microecological system was gently stirred and cultured at room temperature for 14 days to simulate the hydrodynamic conditions of the water body to be tested. On this basis, the microecological experiment also had a control group without antibiotics under the same conditions. Three control groups were set up for each microecological system.

[0055] Among them, the four antibiotics are MMX, ERY, NOR and TC, and the nutrients include glucose (carbon source), ammonium chloride (nitrogen source) and potassium dihydrogen phosphate (phosphorus source). Since the PBS buffer is a common technical means in the art, the formula of the PBS buffer is not further described in the present embodiment.

[0056] S1014. Determine the number of resistant bacteria and sensitive bacteria in the microbial flora before and after the microecological experiment through metagenomic analysis.

[0057] The resistant bacteria are bacteria that contain a resistance gene for at least one antibiotic among a plurality of antibiotics, and the sensitive bacteria are bacteria that do not contain a resistance gene for any antibiotic among a plurality of antibiotics.

[0058] Specifically, using the aforementioned river as an example, for each of the five microecosystems, metagenomic analysis was performed on the microbial flora in the microecosystem before and after the microecosystem experiment. During the metagenomic analysis, strains containing MMX, ERY, NOR, or TC resistance genes in the microecosystem were classified as resistant bacteria, and the number of resistant bacteria before and after the microecosystem experiment (R0) was counted. Strains without MMX, ERY, NOR, or TC resistance genes were classified as sensitive bacteria, and the number of sensitive bacteria before and after the microecosystem experiment (S0) was counted.

[0059] It is understandable that metagenomic analysis is a technology that directly performs high-throughput sequencing and bioinformatics analysis on all genetic material in environmental samples without the need to isolate and culture a single microorganism. Therefore, by performing metagenomic analysis on the microbial flora, it is possible to distinguish between resistant and resistant bacteria, and then directly obtain the number of resistant and sensitive bacteria. Since metagenomic analysis is a common technical means in this technical field, the specific process of metagenomic analysis in this application embodiment is not described in detail.

[0060] S102. Determine the minimum selective concentration of each of the multiple antibiotics based on the number of resistant bacteria and the number of sensitive bacteria in the microbial flora before and after the microecological experiment and the MSCpre model.

[0061] In the examples of the present application, the MSCpre model is constructed based on the resistance of microbial flora to multiple antibiotics. The model formula of the MSCpre model is as follows.

[0062]

[0063] in, represents the rate of change of the number of resistant bacteria over time, M represents the chemical oxygen demand of the water to be tested, K M The half-saturation constant of chemical oxygen demand, represents the maximum bacterial growth rate of resistant bacteria, represents the minimum bacterial growth rate of resistant bacteria, a represents the concentration of an antibiotic in the microecological experiment, MIC R represents the minimum inhibitory concentration of resistant bacteria, k represents the Hill coefficient, R represents the number of resistant bacteria after the microecological experiment predicted by the MSCpre model, S represents the number of sensitive bacteria after the microecological experiment predicted by the MSCpre model, N represents the bacterial carrying capacity of the microecological experiment, and m represents the mortality rate of bacteria in the microecological experiment; represents the rate of change of the number of sensitive bacteria over time, represents the maximum bacterial growth rate of sensitive bacteria, Indicates the minimum bacterial growth rate of sensitive bacteria, MIC S represents the minimum inhibitory concentration of sensitive bacteria; SC represents the selection coefficient, R0 represents the number of resistant bacteria before the microecological experiment, and S0 represents the number of sensitive bacteria before the microecological experiment.

[0064] Specifically, in the above model formula, the number R of resistant bacteria after the microecological experiment predicted by the MSCpre model is optimized based on the number of resistant bacteria actually detected after the microecological experiment, and the number S of sensitive bacteria after the microecological experiment predicted by the MSCpre model is optimized based on the number of sensitive bacteria actually detected after the microecological experiment. M The value can be taken within 50-150 mg / L, the Hill coefficient k can be taken within 1-10, and the bacterial carrying capacity N of the microecological experiment can be taken within 10 7 -10 9 The maximum bacterial growth rate of resistant bacteria Minimum bacterial growth rate of resistant bacteria Minimum inhibitory concentration (MIC) of resistant bacteriaR , the maximum bacterial growth rate of sensitive bacteria Minimum bacterial growth rate of sensitive bacteria and the minimum inhibitory concentration (MIC) of sensitive bacteria S The value of can be obtained by querying relevant technical documents in this technical field or set according to actual conditions.

[0065] Continuing to explain the parameters of the above model formula, the above selection coefficient SC refers to the ratio between the adaptability of resistant bacteria to the test water environment and the adaptability of sensitive bacteria to the test water environment. When the selection coefficient SC = 1, it means that the adaptability of resistant bacteria to the test water environment is comparable to that of sensitive bacteria. As you can imagine, when the concentration of the antibiotic increases, the adaptability of resistant bacteria to the test water environment is stronger than that of sensitive bacteria. This means that when the selection coefficient SC = 1, the concentration of the antibiotic has reached the minimum selective concentration MSC of the antibiotic.

[0066] The specific process of the above S103 is described below.

[0067] For an antibiotic in the microecological experiment, the chemical oxygen demand M of the water to be tested and the concentration a of an antibiotic in the microecological experiment are determined, and the half-saturation constant K of the chemical oxygen demand is calculated. M , the maximum bacterial growth rate of resistant bacteria Minimum bacterial growth rate of resistant bacteria Minimum inhibitory concentration (MIC) of resistant bacteria R , Hill coefficient k, bacterial carrying capacity N of microecological experiment, bacterial mortality rate m in microecological experiment, maximum bacterial growth rate of sensitive bacteria Minimum bacterial growth rate of sensitive bacteria Minimum inhibitory concentration (MIC) of sensitive bacteria S Get the value.

[0068] Based on the values of the above parameters, the above MSCpre model and By combining the formulas, we can get f(a,t)=R and g(a,t)=S.

[0069] By substituting the time t of the microecological experiment and the concentration a of an antibiotic in the microecological experiment into the above f(a, t) = R and g(a, t) = S, the number R of resistant bacteria after the microecological experiment predicted by the MSCpre model and the number S of sensitive bacteria after the microecological experiment predicted by the MSCpre model can be obtained.

[0070] According to the number of resistant bacteria R' actually detected after the microecological experiment and the number of resistant bacteria R predicted by the MSCpre model after the microecological experiment, the number of sensitive bacteria S' actually detected after the microecological experiment and the number of sensitive bacteria S predicted by the MSCpre model after the microecological experiment, the Hill coefficient k, the bacterial mortality rate m in the microecological experiment, and the maximum bacterial growth rate of resistant bacteria were adjusted. Minimum bacterial growth rate of resistant bacteria Maximum bacterial growth rate of sensitive bacteria and the minimum bacterial growth rate of sensitive bacteria The adjusted f'(a, t)=R and g'(a, t)=S are obtained.

[0071] Substitute the above f'(a, t) = R and g'(a, t) = S into Then, let SC=1, solve for the concentration a of an antibiotic when SC=1, and determine the concentration a of an antibiotic when SC=1 as the minimum selection concentration of an antibiotic.

[0072] Furthermore, the embodiment of the present application also adopts the correlation coefficient R 2 The performance of the above MSCpre model was evaluated and the correlation coefficient R 2 It is in the range of 0.64 to 0.92, which proves the accuracy of the above MSCpre model.

[0073] For example, the above correlation coefficient R 2 The following formula is satisfied.

[0074]

[0075] Among them, Y actual It represents the actual selection coefficient value after the microecological system is subjected to microecological experiments under a certain antibiotic concentration; Y predict represents the selectivity coefficient value calculated by the above MSCpre model, Y mean Indicates Y actual The average value of .

[0076] The specific implementation process of the above S102 is described below.

[0077] For each of the multiple antibiotics, the number of resistant and sensitive bacteria in the microbial flora before and after the microecological experiment was substituted into the MSCpre model formula to calculate the selectivity coefficient (SC). When the selectivity coefficient (SC) = 1, the concentration of the antibiotic used in the microecological experiment was determined as the minimum selective concentration (SC). This yielded the minimum selective concentration for each of the multiple antibiotics.

[0078] S103. Determine an antibiotic risk assessment result of the water body to be tested based on the minimum selection concentration of each antibiotic in the multiple antibiotics and the concentrations of the multiple antibiotics in the water body to be tested.

[0079] In one application scenario, combined with Figure 2 ,like Figure 3 As shown, the above S103 includes S1031-S1032.

[0080] S1031. For each of the multiple antibiotics: substitute the minimum selection concentration of the antibiotic and the concentrations of the multiple antibiotics in the water to be tested into the antibiotic evaluation formula to calculate the risk quotient of the antibiotic.

[0081] Exemplarily, the antibiotic evaluation formula is as follows;

[0082]

[0083] Among them, RQ represents the risk quotient of antibiotics, MEC represents the concentration of antibiotics in the water to be tested, AF represents the risk assessment factor, and MSC represents the minimum selective concentration of antibiotics.

[0084] It can be understood that the above-mentioned method for detecting the concentration of antibiotics is a common technical means in this technical field, and the embodiment of this application does not elaborate on the detection process of the above-mentioned concentration of antibiotics.

[0085] S1032. Determine the antibiotic risk assessment result of the water body to be tested based on the risk quotient of each of the multiple antibiotics.

[0086] Specifically, for each of the multiple antibiotics, the risk quotient RQ is:

[0087] When RQ is less than 0.01, the risk assessment result of the antibiotic is no risk;

[0088] When 0.01≤RQ<0.1, the risk assessment result of the antibiotic is low risk;

[0089] When 0.1≤RQ<1, the risk assessment result of the antibiotic is medium risk;

[0090] When RQ>1, the risk assessment result of the antibiotic is high risk.

[0091] In one application scenario, taking multiple antibiotics including MMX, ERY, NOR, and TC as an example, after detecting the concentrations of MMX, ERY, NOR, and TC in the water body to be tested, the above-mentioned MSCpre model is used to calculate the minimum selection concentration of MMX, the minimum selection concentration of ERY, the minimum selection concentration of NOR, and the minimum selection concentration of TC, respectively. The above-mentioned antibiotic evaluation formula is then used to calculate the risk quotient of MMX, the risk quotient of ERY, the risk quotient of NOR, and the risk quotient of TC. The antibiotic risk assessment result of the water body to be tested is obtained by the above-mentioned risk quotient judgment method. The antibiotic risk assessment result of the water body to be tested then includes the risk assessment result of MMX, the risk assessment result of ERY, the risk quotient of NOR, and the risk quotient of TC.

[0092] Based on the above content, it can be seen that in the above-mentioned antibiotic risk assessment method provided in the embodiment of the present application, microbial flora (Microbiota) is used to conduct microecological experiments (Microcosms), and then the antibiotic risk assessment results are determined by the MSCpre model. Therefore, the above-mentioned antibiotic risk assessment method can be referred to as the 3M framework.

[0093] To verify that the 3M framework is capable of assessing situations where low levels of antibiotics are common in aquatic environments but bacterial resistance levels are high, the framework is compared with existing persistence, bioaccumulation, and toxicity (PBT) standards commonly used to assess antibiotic risks.

[0094] Taking a river as the water sample to be tested, MMX, ERY, NOR and TC as antibiotics, the 3M framework, the PBT standard-based assessment method (species sensitivity distribution assessment method SDD, risk assessment factor assessment method RAF) and other assessment methods (assessment method using MIC to estimate MSC, assessment method using mathematical model to predict MSC) were used to conduct antibiotic risk assessment on 48 water samples. The analysis results are as follows: Figure 4 shown.

[0095] Depend on Figure 4 It can be seen that for four antibiotics (MMX, ERY, NOR and TC) in 48 samples, SSD or RAF based on the PBT standard classified most samples as negligible or low risk. Specifically, only 8% to 23% of the SSD method showed potential risk. Similarly, RAF classified most samples as negligible or low risk, and only 8% of the samples showed potential risk. In contrast, the 3M framework determined a lower antibiotic threshold (13.8ng / L to 1.1μg / L), and determined that 44% of the antibiotics had moderate to high risks. Compared with the PBT standard, the sensitivity of the 3M framework was significantly improved.

[0096] Continue to refer Figure 4 The 3M framework is also superior to the previous MSC-related standards that used bacterial resistance to assess antibiotic risk. Among the 48 water samples, the assessment method using MIC to estimate MSC and the assessment method using mathematical models to predict MSC both assessed 23% of the samples as having potential risks, which is significantly lower than the 44% identified by the 3M framework. It is worth noting that the 3M framework classified 16% of the samples as high risk, while the previous method classified very few samples as high risk. The increase in sensitivity can be attributed to the focus on complex river microbial flora and the quantification of a wider range of antibiotic resistance genes (ARGs), thereby more accurately reflecting the resistance dynamics of complex microbial flora.

[0097] In summary, in an antibiotic risk assessment method provided by an embodiment of the present application, a microbial flora in a water body to be detected is first used to carry out a microecological experiment, and the number of resistant bacteria and the number of sensitive bacteria in the above-mentioned microbial flora before and after the microecological experiment and the MSCpre model are used. Taking into account the resistance of the microbial flora to multiple antibiotics, the minimum selection concentration of each antibiotic in the multiple antibiotics is calculated. Based on the minimum selection concentration of each antibiotic in the multiple antibiotics and the concentration of the multiple antibiotics in the water body to be detected under actual conditions, the antibiotic risk assessment result of the water body to be detected is evaluated. In the above method, the minimum selection concentration of each antibiotic in the multiple antibiotics is directly calculated based on the resistance of the microbial flora in the water body to be detected to multiple antibiotics, and then the minimum selection concentration is used to evaluate the antibiotic risk. It is possible to perform antibiotic risk assessment on the situation where the antibiotic content often present in the water environment is low but the bacterial resistance level is high, so that the assessment results of the existing antibiotic risk assessment method are not comprehensive enough, and thus the water environment with antibiotic risks in the above-mentioned situation can be effectively managed.

[0098] An embodiment of the present application further provides a computer-readable storage medium, which includes a computer program. When the computer program runs on a computer, the method described in the above embodiment is executed.

[0099] An embodiment of the present application further provides a computer program product, which includes computer program instructions. When the computer program instructions are run on a computer, the method described in the above embodiment is executed.

[0100] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for antibiotic risk assessment, characterized in that: include: Conducting a microecological experiment on the microbial flora in the water to be tested, and measuring the number of resistant bacteria and the number of sensitive bacteria in the microbial flora before and after the microecological experiment; wherein the microecological experiment is to simulate the environment of the water to be tested and culture the microbial flora in the water to be tested; the resistant bacteria are resistant to at least one of the multiple antibiotics contained in the water to be tested, and the sensitive bacteria are not resistant to the multiple antibiotics; determining the minimum selective concentration of each of the multiple antibiotics based on the number of resistant bacteria and the number of sensitive bacteria in the microbial flora before and after the microecological experiment and the MSCpre model; the MSCpre model is constructed based on the resistance of the microbial flora to the multiple antibiotics; The antibiotic risk assessment result of the water body to be tested is determined based on the minimum selection concentration of each antibiotic in the multiple antibiotics and the concentrations of the multiple antibiotics in the water body to be tested.

2. The method according to claim 1, wherein The model formula of the MSCpre model is as follows: in, represents the rate of change of the number of resistant bacteria over time, M represents the chemical oxygen demand of the water to be tested, K M represents the half-saturation constant of the chemical oxygen demand, represents the maximum bacterial growth rate of resistant bacteria, represents the minimum bacterial growth rate of resistant bacteria, a represents the concentration of an antibiotic in the microecological experiment, MIC R represents the minimum inhibitory concentration of resistant bacteria, k represents the Hill coefficient, R represents the number of resistant bacteria after the microecological experiment predicted by the MSCpre model, S represents the number of sensitive bacteria after the microecological experiment predicted by the MSCpre model, N represents the bacterial carrying capacity of the microecological experiment, and m represents the bacterial mortality rate in the microecological experiment; represents the rate of change of the number of sensitive bacteria over time, represents the maximum bacterial growth rate of sensitive bacteria, Indicates the minimum bacterial growth rate of sensitive bacteria, MIC S represents the minimum inhibitory concentration of sensitive bacteria; SC represents the selection coefficient, R0 represents the number of resistant bacteria before the microecological experiment, and S0 represents the number of sensitive bacteria before the microecological experiment.

3. The method according to claim 2, wherein Determining the minimum selection concentration of each antibiotic in the plurality of antibiotics comprises: For each antibiotic in the plurality of antibiotics: Substituting the number of resistant bacteria and the number of sensitive bacteria in the microbial flora before and after the microecological experiment into the model formula of the MSCpre model, the selection coefficient SC is obtained; When the selection coefficient SC=1, the concentration of the antibiotic in the microecological experiment is determined as the minimum selection concentration of the antibiotic.

4. The method according to claim 3, wherein Determining the antibiotic risk assessment result of the water body to be tested includes: For each of the multiple antibiotics: the minimum selected concentration of the antibiotic and the concentrations of the multiple antibiotics in the water to be tested are substituted into an antibiotic evaluation formula to calculate a risk quotient of the antibiotic; the antibiotic evaluation formula is as follows; Wherein, RQ represents the risk quotient of the antibiotic, MEC represents the concentration of the antibiotic in the water to be tested, AF represents the risk assessment factor, and MSC represents the minimum selection concentration of the antibiotic; An antibiotic risk assessment result of the water body to be tested is determined according to the risk quotient of each of the multiple antibiotics.

5. The method according to claim 4, wherein The risk quotient RQ for each of the multiple antibiotics is: When RQ is less than 0.01, the risk assessment result of the antibiotic is no risk; When 0.01≤RQ<0.1, the risk assessment result of the antibiotic is low risk; When 0.1≤RQ<1, the risk assessment result of the antibiotic is medium risk; When RQ>1, the risk assessment result of the antibiotic is high risk.

6. The method according to claim 1, wherein The microbial flora in the water to be tested is subjected to a microecological experiment, and the number of resistant bacteria and the number of sensitive bacteria in the microbial flora before and after the microecological experiment are determined, including: Sampling the water body to be detected to obtain a water sample of the water body to be detected; extracting the microbial flora of the water body to be detected from the water sample of the water body to be detected; Conducting a microecological experiment on the microbial flora, that is, cultivating the microbial flora in a simulated environment, wherein the simulated environment includes the multiple antibiotics, nutrients contained in the water to be tested, and hydrodynamic conditions; The number of resistant bacteria and the number of sensitive bacteria in the microbial flora before and after the microecological experiment are determined by metagenomic analysis, wherein the resistant bacteria are bacteria containing a resistance gene for at least one of the multiple antibiotics, and the sensitive bacteria are bacteria that do not contain a resistance gene for any of the multiple antibiotics.

7. The method according to claim 1, wherein The various antibiotics include β-lactams, quinolones, macrolides, tetracyclines and sulfonamides.

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