Optimization Method for Adsorption of Bovine Serum Endotoxin Based on Feedback Parameter Regulation

Abstract

The present invention relates to the technical field of biopharmaceuticals, and discloses an optimization method for endotoxin adsorption in bovine serum based on feedback parameter adjustment, including: identifying an optimized environment for bovine serum endotoxin, wherein the optimized environment includes: a bovine serum sample set, an endotoxin adsorbent set, a shaking mechanism, a freezing mechanism, an irradiation mechanism, a monitoring unit, a centrifugation mechanism, a melting mechanism, and a filtration mechanism; constructing an initial sample database based on the optimized environment; optimizing the initial sample database to obtain an optimized sample database; obtaining the volume of the target bovine serum and the concentration of endotoxin in the target bovine serum based on the pre-obtained target bovine serum; identifying a target adsorption data sequence in the optimized sample database based on the relevant adsorption parameters; and realizing the adsorption of endotoxin in the target bovine serum based on the target adsorption data sequence. The main purpose of the present invention is to solve the problems of inaccuracy and resource waste caused by the adsorption of endotoxin in bovine serum.

Description

Technical Field

[0001] The present invention relates to an optimization method for endotoxin adsorption in bovine serum based on feedback parameter adjustment, belonging to the technical field of biopharmaceuticals. Background Art

[0002] Newborn bovine serum is a key raw material commonly used in biopharmaceuticals and cell culture. However, endotoxins that can affect cell growth and experimental results often exist in newborn bovine serum. Correspondingly, how to remove endotoxins from bovine serum has become an urgent problem to be solved.

[0003] Currently, when removing endotoxins from bovine serum, a large margin is often preset to ensure that the endotoxins in bovine serum can be removed completely.

[0004] Although the above method can achieve the removal of endotoxins in bovine serum, before removing the endotoxins in bovine serum, the ability of each adsorbent to adsorb endotoxins is not considered, resulting in inaccurate adsorption of endotoxins in bovine serum and waste of resources. Summary of the Invention

[0005] The present invention provides an optimization method, device and computer-readable storage medium for endotoxin adsorption in bovine serum based on feedback parameter adjustment, and its main purpose is to solve the problems of inaccurate adsorption and resource waste in the adsorption of endotoxins in bovine serum.

[0006] To achieve the above object, an optimization method for endotoxin adsorption in bovine serum based on feedback parameter adjustment provided by the present invention includes:

[0007] Identifying an optimization environment for bovine serum endotoxin, where the optimization environment includes: a bovine serum sample set, an endotoxin adsorbent set, a shaking mechanism, a freezing mechanism, an irradiation mechanism, a monitoring unit, a centrifugation mechanism, a melting mechanism and a filtering mechanism;

[0008] Constructing an initial sample database based on the optimization environment;

[0009] Optimizing the initial sample database to obtain an optimized sample database;

[0010] Obtaining relevant adsorption parameters based on the pre-acquired target bovine serum, where the relevant adsorption parameters include: the volume of the target bovine serum and the concentration of endotoxins in the target bovine serum. Based on the relevant adsorption parameters, a target adsorption data sequence is identified in the optimized sample database, and the adsorption of endotoxins in the target bovine serum is realized based on the target adsorption data sequence.

[0011] Optionally, the constructing an initial sample database based on the optimization environment includes:

[0012] Obtain a first initial sample group set based on the bovine serum sample set, where the first initial sample group set includes a plurality of first initial sample groups, and each first initial sample group includes a plurality of bovine serum samples;

[0013] Obtain the reference range and the first average score value of endotoxin, and obtain a plurality of first endotoxin concentrations based on the first average score value and the reference range;

[0014] Perform the following operations on each of the plurality of first endotoxin concentrations:

[0015] Obtain a first target sample set by using the first endotoxin concentration and the first initial sample group set, where the first target sample set includes a plurality of first target sample groups;

[0016] Obtain an endotoxin adsorbent set for adsorbing endotoxin, where the endotoxin adsorbent set includes a plurality of endotoxin adsorbents with different adsorption parameters, and the adsorption parameters are composed of the adsorbent type, the action mechanism of the adsorbent, and the specific surface area of endotoxin;

[0017] Perform an identification operation on the endotoxin adsorbent based on the adsorption parameters to obtain an identified adsorbent;

[0018] Summarize the identified adsorbents by using the adsorbent type and the action mechanism of the adsorbent respectively to obtain a plurality of identified adsorbent sets;

[0019] Perform the following operations on each of the plurality of identified adsorbent sets:

[0020] Perform a sorting operation on the identified adsorbents in the identified adsorbent set in ascending order of specific surface area to obtain an identified adsorbent sequence;

[0021] Obtain a first target test sample set based on the identified adsorbent sequence and the first target sample set, where the first target test sample set includes a plurality of first target test sample groups, and the first target test sample groups correspond one-to-one with the identified adsorbents in the identified adsorbent set, and the mass of the identified adsorbent when in use is preset;

[0022] Construct an initial sample database based on the plurality of first target test sample groups.

[0023] Optionally, the constructing an initial sample database based on the plurality of first target test sample groups includes:

[0024] Perform the following operations on each of the plurality of first target test sample groups:

[0025] Perform a shaking operation on the first target test sample group using the shaking mechanism to obtain a shaken experimental group, where the first shaking time, the first shaking temperature, and the first shaking speed of the shaking mechanism are preset;

[0026] After using the freezing mechanism and the monitoring unit to confirm that the shaken experimental group is a frozen experimental group, perform an irradiation operation on the frozen experimental group using the irradiation mechanism to obtain an irradiated sample group, where the irradiation dose of the irradiation mechanism is preset;

[0027] Perform a thawing operation on the irradiated sample group using the thawing mechanism to obtain a thawed sample group;

[0028] Perform a centrifugation operation on the thawed sample group based on the centrifugation mechanism to obtain a target sample group;

[0029] Sequentially extract target samples from the target sample group, and perform the following operations on the extracted target samples:

[0030] Perform a filtration operation on the extracted target samples using the filtration mechanism to obtain a supernatant, and obtain the detected endotoxin content based on the supernatant;

[0031] Summarize the detected endotoxin content to obtain a detected endotoxin content set, and obtain the mean endotoxin content using the detected endotoxin content set, where the mean endotoxin content is the mean of the detected endotoxin contents in the detected endotoxin content set;

[0032] Construct an initial sample database based on the mean endotoxin content.

[0033] Optionally, the step of using the freezing mechanism and the monitoring unit to confirm that the shaken experimental group is a frozen experimental group includes:

[0034] Perform a freezing operation on the shaken experimental group using the freezing mechanism, and obtain a monitored freezing temperature time series using the monitoring unit and the shaken experimental group during freezing, where the acquisition frequency of the monitoring unit is preset, and the monitored freezing temperature time series includes multiple monitored freezing temperatures, and the number corresponding to the monitored freezing temperatures in the monitored freezing temperature time series is preset;

[0035] Obtain the freezing temperature variance and the freezing temperature mean based on the monitored freezing temperature time series, where the freezing temperature variance is the variance of the multiple monitored freezing temperatures in the monitored freezing temperature time series, and the freezing temperature mean is the mean of the multiple monitored freezing temperatures in the monitored freezing temperature time series;

[0036] Compare the freezing temperature variance with a preset freezing variance threshold and the freezing temperature mean with a preset freezing temperature threshold respectively;

[0037] If the variance of the freezing temperature is greater than the freezing variance threshold or the mean value of the freezing temperature is greater than the freezing temperature threshold, the monitored freezing temperatures are sequentially excluded from the monitored freezing temperature time series at the acquisition frequency, and the updated freezing temperatures obtained by the monitoring unit are supplemented into the monitored freezing temperature time series to obtain an updated freezing temperature sequence;

[0038] Taking the updated freezing temperature sequence as the monitored freezing temperature time series, return to the step of obtaining the freezing temperature variance and the mean value of the freezing temperature based on the monitored freezing temperature time series, until the freezing temperature variance is less than or equal to the freezing variance threshold and the mean value of the freezing temperature is less than or equal to the freezing temperature threshold, and then confirm that the shaking experimental group is the freezing experimental group.

[0039] Optionally, the construction of the initial sample database based on the mean value of endotoxin content includes:

[0040] Mapping the mean value of the endotoxin content to a pre-constructed content relationship coordinate system to obtain a set of content relationship points, where the horizontal axis of the content relationship coordinate system is the specific surface area corresponding to the mean value of the endotoxin content, and the vertical axis of the content relationship coordinate system is the mean value of the endotoxin content;

[0041] Obtain a set of fitting function models for curve fitting, where the set of fitting function models includes multiple fitting function models. Sequentially extract the fitting function models from the fitting function models, and perform the following operations on the extracted fitting function models:

[0042] Obtain a fitting curve based on the extracted fitting function model and the set of content relationship points, calculate the fitting evaluation value using a pre-constructed fitting evaluation relationship and the fitting curve, summarize the fitting evaluation values to obtain a set of fitting evaluation values, confirm the target fitting curve using the set of fitting evaluation values, and construct the initial sample database based on the target fitting curve, where the target fitting curve is the fitting curve corresponding to the smallest fitting evaluation value in the set of fitting evaluation values, and the fitting evaluation relationship is as follows:

[0043]

[0044] where Z represents the fitting evaluation value, d 1i represents the mean value of the endotoxin content corresponding to the i-th first target test sample group in the fitting curve, d 0i represents the mean value of the endotoxin content corresponding to the i-th first target test sample group in the set of content relationship points, and n represents the number of the first target test sample groups.

[0045] Optionally, the construction of the initial sample database based on the target fitting curve includes:

[0046] Identify the target content points in the target fitting curve using a pre-constructed screening relationship, where the screening relationship is as follows:

[0047] S = min(αf′ B1j + βB 1j )

[0048] where S represents the screening relationship, α and β are both preset coefficients, B 1j represents the j-th specific surface area in the fitting curve, and f' B1j represents the derivative value corresponding to the j-th specific surface area point in the fitting curve;

[0049] Identify the initial screening range based on the target content points, where the initial screening range is the range corresponding to the two specific surface areas adjacent to the target content point in the target fitting curve;

[0050] Divide the initial screening range evenly using a preset specific surface area division value to obtain multiple initial screening points, use the multiple initial screening points to obtain the target screening points, and calculate the adsorption threshold of the endotoxin adsorbent using the target screening points. The calculation formula is as follows:

[0051]

[0052] where M represents the adsorption threshold, Z represents the mass of the labeled adsorbent during use, V represents the specific surface area, n1 represents the first endotoxin concentration in the first target test sample group corresponding to the target fitting curve, n0 represents the average endotoxin content in the first target test sample group corresponding to the target fitting curve, and G represents the volume of the first target test sample in the first target test sample group corresponding to the target fitting curve;

[0053] Summarize the adsorption thresholds to obtain an adsorption threshold set, and perform a standardization operation on the adsorption thresholds in the adsorption threshold set to obtain a standardized adsorption threshold set, where the standardized adsorption threshold set includes multiple standardized adsorption thresholds;

[0054] Construct an initial sample database based on the standardized adsorption threshold set.

[0055] Optionally, the constructing the initial sample database based on the standardized adsorption threshold set includes:

[0056] Use the first equal division value and the reference range to obtain multiple first equal division range segments;

[0057] Perform the following operations on each of the multiple first equal division range segments:

[0058] Use the first equal division range segment and a preset second equal division value to obtain multiple second endotoxin concentrations;

[0059] Randomly extract multiple second endotoxin concentrations from the multiple second endotoxin concentrations, the number of which is the same as the preset extraction numerical quantity, to obtain multiple third endotoxin concentrations;

[0060] Based on the multiple third endotoxin concentrations, obtain multiple target endotoxin content means, and use the multiple third endotoxin concentrations and the standardized adsorption threshold to obtain multiple reference endotoxin content means, wherein the target endotoxin content means and the reference endotoxin content means correspond one by one;

[0061] Based on the multiple target endotoxin content means and the multiple reference endotoxin content means, obtain multiple reference absolute differences, wherein the reference absolute difference is the absolute difference between the target endotoxin content mean and the reference endotoxin content mean corresponding to the target endotoxin content mean;

[0062] Associate the standardized adsorption thresholds in the standardized adsorption threshold set with the identification adsorbent to obtain first associated data;

[0063] If multiple reference absolute differences are all less than or equal to the preset reference absolute threshold, then summarize the first associated data to obtain an initial sample database;

[0064] If at least one reference absolute difference among the multiple reference absolute differences is greater than the reference absolute threshold, then update the first endotoxin concentration with the second endotoxin concentration corresponding to the at least one reference absolute difference to obtain multiple updated first endotoxin concentrations, use the multiple updated first endotoxin concentrations as the multiple first endotoxin concentrations, and return to the step of performing the following operations on each of the multiple first endotoxin concentrations until an initial sample database is obtained.

[0065] Optionally, the optimizing the initial sample database to obtain an optimized sample database includes:

[0066] Obtain a reference mass gradient set of the identification adsorbent, a shaking time gradient set of the shaking mechanism, a shaking speed gradient set, and a shaking temperature gradient set;

[0067] Use the pre - constructed control variable method, the reference mass gradient set, the shaking time gradient set, the shaking speed gradient set, and the shaking temperature gradient set to obtain supplementary associated data;

[0068] Use the supplementary associated data to update the initial sample database to obtain an optimized sample database.

[0069] Optionally, the confirming a target adsorption data sequence in the optimized sample database based on the relevant adsorption parameters includes:

[0070] Obtain an initial adsorption data set based on the relevant adsorption parameters and the optimized sample database, where the initial adsorption data set includes one or more initial adsorption data;

[0071] Perform the following operations on the initial adsorption data in the initial adsorption data set:

[0072] Calculate the comprehensive evaluation value by using the pre-constructed comprehensive evaluation relation and the initial adsorption data;

[0073] Summarize the comprehensive evaluation values respectively based on the types of adsorbents of the added endotoxin adsorbents to obtain multiple comprehensive evaluation value sets, and use the multiple comprehensive evaluation value sets to obtain a target evaluation value set, where the target evaluation value set is a set of the smallest comprehensive evaluation values in each of the multiple comprehensive evaluation value sets;

[0074] Perform a sorting operation on the comprehensive evaluation values in the target evaluation value set in ascending order to obtain a target adsorption data sequence.

[0075] Optionally, the comprehensive evaluation relation is as follows:

[0076]

[0077] where N m represents the comprehensive evaluation value corresponding to the m-th initial adsorption data, h m represents the energy consumption value of the endotoxin adsorbent corresponding to the m-th initial adsorption data, T represents the shaking temperature, s represents the shaking speed, f(T, s) represents the unit energy consumption function of the shaking mechanism, and this unit energy consumption function is related to the shaking time, shaking temperature and shaking speed, t m represents the shaking time corresponding to the m-th initial adsorption data, and t represents time.

[0078] To solve the above problems, the present invention also provides an electronic device, and the electronic device includes:

[0079] At least one processor; and,

[0080] A memory communicatively connected to the at least one processor; wherein,

[0081] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the above-mentioned optimization method for bovine serum endotoxin adsorption based on feedback parameter adjustment.

[0082] To solve the above problems, the present invention also provides a computer-readable storage medium storing at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-described optimization method for bovine serum endotoxin adsorption based on feedback parameter adjustment.

[0083] Compared with the problems described in the background art, the present invention first receives and identifies an optimization environment for bovine serum endotoxin, where the optimization environment includes: a bovine serum sample set, an endotoxin adsorbent set, a shaking mechanism, a freezing mechanism, an irradiation mechanism, a monitoring unit, a centrifugation mechanism, a melting mechanism, and a filtering mechanism, and constructs an initial sample database based on the optimization environment. It can be seen that the present invention first formulates an initial sample database through the identified optimization environment. Among them, when formulating the initial sample database, the problem of resource waste is considered. Therefore, the experimental data is fitted into a fitting curve to reduce the resource waste caused during the construction of the initial sample database. After fitting the fitting curve, a target fitting curve is identified from multiple fitting curves to improve the accuracy of evaluating the adsorption capacity of the endotoxin adsorbent. When identifying the target fitting curve, experimental points for verification are selected from the target fitting curve to improve the accuracy of the target fitting curve. Furthermore, the embodiments of the present invention optimize the initial sample database to obtain an optimized sample database. It can be seen that the embodiments of the present invention also consider factors that may affect the adsorption effect of the endotoxin adsorbent. Therefore, the initial sample database is optimized so that the optimized sample database can more accurately express the actual adsorption capacity of each endotoxin adsorbent to improve the accuracy of endotoxin adsorption in bovine serum. The embodiments of the present invention obtain relevant adsorption parameters based on the pre-acquired target bovine serum, where the relevant adsorption parameters include: the volume of the target bovine serum and the concentration of endotoxin in the target bovine serum, and identify a target adsorption data sequence in the optimized sample database based on the relevant adsorption parameters, and implement the adsorption of endotoxin in the target bovine serum based on the target adsorption data sequence. It can be seen that the embodiments of the present invention also consider that the diversity of data may cause inconvenience in querying the energy consumption of each endotoxin adsorbent during actual use. Therefore, a target adsorption data sequence is formulated to improve the convenience of reference. Therefore, the optimization method, device, electronic device, and computer-readable storage medium for bovine serum endotoxin adsorption based on feedback parameter adjustment proposed by the present invention mainly aim to solve the problems of inaccurate endotoxin adsorption in bovine serum and resource waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 It is a schematic flowchart of an optimization method for bovine serum endotoxin adsorption based on feedback parameter adjustment provided by an embodiment of the present invention;

[0085] Figure 2Schematic structural diagram of an electronic device for implementing the optimization method of endotoxin adsorption in bovine serum based on feedback parameter adjustment provided by an embodiment of the present invention.

[0086] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0087] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0088] The embodiment of the present application provides an optimization method for endotoxin adsorption in bovine serum based on feedback parameter adjustment. The execution subject of the optimization method for endotoxin adsorption in bovine serum based on feedback parameter adjustment includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the optimization method for endotoxin adsorption in bovine serum based on feedback parameter adjustment can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0089] Embodiment 1:

[0090] Referring to Figure 1 As shown, it is a flowchart of an optimization method for endotoxin adsorption in bovine serum based on feedback parameter adjustment provided by an embodiment of the present invention. In this embodiment, the optimization method for endotoxin adsorption in bovine serum based on feedback parameter adjustment includes:

[0091] S1. Identify the optimization environment for bovine serum endotoxin, where the optimization environment includes: a bovine serum sample set, an endotoxin adsorbent set, a shaking mechanism, a freezing mechanism, an irradiation mechanism, a monitoring unit, a centrifugation mechanism, a melting mechanism, and a filtering mechanism.

[0092] It should be understood that the optimization environment is a necessary environment for constructing an initial sample database. The bovine serum sample set refers to samples of bovine serum with known endotoxin concentrations and is used to prepare bovine serum with different endotoxin contents. Optionally, a shaker is selected as the shaking mechanism. Optionally, a refrigerator is selected as the freezing mechanism.

[0093] It should be explained that the irradiation mechanism refers to an institution for irradiating bovine serum. In the embodiment of the present invention, the monitoring unit is a unit or mechanism capable of monitoring temperature. Optionally, a temperature sensor is selected as the monitoring unit, and the same effect can be achieved by using other technologies, which will not be elaborated here. Optionally, a centrifuge is selected as the centrifugation mechanism, a constant temperature water bath is selected as the melting mechanism, and a filter containing a microporous membrane is selected as the filtering mechanism.

[0094] S2. Construct an initial sample database based on the optimized environment.

[0095] It can be understood that constructing the initial sample database based on the optimized environment includes:

[0096] Obtain a first initial sample group set based on the bovine serum sample set. Among them, the first initial sample group set includes multiple first initial sample groups, and each first initial sample group includes multiple bovine serum samples;

[0097] Obtain the reference range and the first average score value of endotoxin, and obtain multiple first endotoxin concentrations based on the first average score value and the reference range;

[0098] Perform the following operations on each of the multiple first endotoxin concentrations:

[0099] Obtain a first target sample set by using the first endotoxin concentration and the first initial sample group set. Among them, the first target sample set includes multiple first target sample groups;

[0100] Obtain a set of endotoxin adsorbents for adsorbing endotoxin. Among them, the endotoxin adsorbent set includes multiple endotoxin adsorbents with different adsorption parameters, where the adsorption parameters are composed of the adsorbent type, the action mechanism of the adsorbent, and the specific surface area of endotoxin;

[0101] Perform an identification operation on the endotoxin adsorbent based on the adsorption parameters to obtain an identified adsorbent;

[0102] Summarize the identified adsorbents by using the adsorbent type and the action mechanism of the adsorbent respectively to obtain multiple identified adsorbent sets;

[0103] Perform the following operations on each of the multiple identified adsorbent sets:

[0104] Perform a sorting operation on the identified adsorbents in the identified adsorbent set in ascending order of specific surface area to obtain an identified adsorbent sequence;

[0105] Obtain a first target test sample set based on the identified adsorbent sequence and the first target sample set. Among them, the first target test sample set includes multiple first target test sample groups, and the first target test sample groups correspond one-to-one with the identified adsorbents in the identified adsorbent set, and the mass of the identified adsorbent when in use is preset;

[0106] Construct an initial sample database based on the multiple first target test sample groups.

[0107] It should be explained that the reference range refers to the range of endotoxin concentrations commonly found in bovine serum. For example: the reference range is 200 - 300 EU / ml. The first average value is a value set for dividing the reference range, and when dividing the reference range, the equal division method is used to divide the reference range. For example: using the equal division method to divide the reference range of 200 - 300 EU / ml into multiple first endotoxin concentrations, and the first average value is 20 EU / ml, then 200 - 300 EU / ml can be divided into: 200 EU / ml, 220 EU / ml, 240 EU / ml, 260 EU / ml, 280 EU / ml, and 300 EU / ml.

[0108] It should be understood that the first target sample set refers to multiple first target sample groups with the same endotoxin content as the first endotoxin concentration in bovine serum. Optionally, by adding a certain concentration of endotoxin to the first initial sample group, the first initial sample group after adding the certain concentration of endotoxin is configured as the first target sample group. Due to the occasional nature of the testing process, it is necessary to ensure that the first target sample set includes multiple first target sample groups.

[0109] It can be understood that an endotoxin adsorbent refers to a substance that can adsorb endotoxin in bovine serum. For example: magnetic agarose microspheres, diatomaceous earth, polycaprolactone micro - and nano - particles, etc. The action mechanism of the adsorbent can be divided into: physical adsorption and chemical adsorption. Physical adsorption is to utilize the non - covalent interaction between the adsorbent and endotoxin in bovine serum to achieve the adsorption process. Chemical adsorption refers to the adsorption of endotoxin through strong interactions such as the formation of covalent bonds or coordination bonds between the endotoxin and the surface of the adsorbent. The type of adsorbent refers to the type of the adsorbent, and the specific surface area of the adsorbent refers to the total area possessed by a unit mass of the material. In the embodiments of the present invention, the specific surface area is used to represent the number of adsorption sites in the adsorbent, that is, the larger the specific surface area, the more adsorption sites. And for adsorbents with different types, different specific surface areas, and different action mechanisms, their adsorption abilities for endotoxin in bovine serum are generally different. Therefore, the purpose of performing the identification operation on the reference adsorbent using the adsorption parameter is: to distinguish adsorbents of different types and different action mechanisms, so as to be able to construct a more accurate initial sample database. For example: the endotoxin adsorbent set includes three endotoxin adsorbents, namely: the first reference adsorbent, the second reference adsorbent, and the third reference adsorbent, and the difference between each reference adsorbent lies in the different specific surface areas. Therefore, after performing the identification on the reference adsorbent using the adsorption parameter, three identified adsorbents are obtained. Among them, the first identified adsorbent is: diatomaceous earth - physical adsorption - 700m 2 / g, where 700m 2 / g represents the specific surface area.

[0110] Exemplarily, the endotoxin adsorbent concentrate includes a plurality of different type A adsorbents, namely 500-type A adsorbent, 700-type A adsorbent, and 1000-type A adsorbent. Among them, 500, 700, and 1000 all represent the specific surface area. Add 500-type A adsorbent, 700-type A adsorbent, and 1000-type A adsorbent with the same mass as that of the labeled adsorbent during use to three first target sample groups in the first target sample set respectively, obtaining three first target test sample groups.

[0111] Further, constructing the initial sample database based on multiple first target test sample groups includes:

[0112] Perform the following operations on each first target test sample group among multiple first target test sample groups:

[0113] Use the shaking mechanism to perform a shaking operation on the first target test sample group, obtaining a shaken experimental group, where the first shaking time, first shaking temperature, and first shaking speed of the shaking mechanism are preset;

[0114] After using the freezing mechanism and the monitoring unit to confirm that the shaken experimental group is a frozen experimental group, use the irradiation mechanism to perform an irradiation operation on the frozen experimental group, obtaining an irradiated sample group, where the irradiation dose of the irradiation mechanism is preset;

[0115] Use the thawing mechanism to perform a thawing operation on the irradiated sample group, obtaining a thawed sample group;

[0116] Based on the centrifugation mechanism, perform a centrifugation operation on the thawed sample group, obtaining a target sample group;

[0117] Sequentially extract target samples from the target sample group, and perform the following operations on the extracted target samples:

[0118] Use the filtering mechanism to perform a filtering operation on the extracted target sample, obtaining a supernatant, and obtain the detected endotoxin content based on the supernatant;

[0119] Summarize the detected endotoxin content, obtaining a detected endotoxin content set, and use the detected endotoxin content set to obtain the mean endotoxin content, where the mean endotoxin content is the mean of the detected endotoxin contents in the detected endotoxin content set;

[0120] Construct the initial sample database based on the mean endotoxin content.

[0121] It is understandable that the purpose of performing the shaking operation on the first target test sample group is to increase the adsorption rate of the endotoxin adsorbent to the endotoxin in the first target test sample group, and the technology of using the shaking mechanism to perform the shaking operation on the first target test sample group is an existing technology. The first shaking time, the first shaking temperature, and the first shaking speed respectively refer to: the duration of the shaking operation, the temperature of the shaking operation, and the speed of shaking during the shaking operation. The thawing operation, the centrifugation operation, and the filtration operation are all existing technologies and will not be elaborated here.

[0122] Furthermore, the supernatant refers to the bovine serum after filtering impurities, adsorbent, etc., and this bovine serum is the target product. Detecting the endotoxin content refers to the concentration of endotoxin contained in the supernatant. Optionally, the dynamic turbidity method is used to obtain the detected endotoxin content in the supernatant, and the same effect can be achieved by using other methods, which will not be elaborated here.

[0123] It should be explained that the use of the freezing mechanism and the monitoring unit to confirm that the shaking experimental group is a freezing experimental group includes:

[0124] Performing a freezing operation on the shaking experimental group using the freezing mechanism, and using the monitoring unit and the shaking experimental group during freezing to obtain the monitored freezing temperature time series. Among them, the acquisition frequency of the monitoring unit is preset, and the monitored freezing temperature time series includes multiple monitored freezing temperatures, and the number corresponding to the monitored freezing temperature in the monitored freezing temperature time series is preset;

[0125] Obtaining the freezing temperature variance and the freezing temperature mean based on the monitored freezing temperature time series. Among them, the freezing temperature variance is the variance of multiple monitored freezing temperatures in the monitored freezing temperature time series, and the freezing temperature mean is the mean of multiple monitored freezing temperatures in the monitored freezing temperature time series;

[0126] Comparing the freezing temperature variance with the preset freezing variance threshold and the freezing temperature mean with the preset freezing temperature threshold respectively;

[0127] If the freezing temperature variance is greater than the freezing variance threshold or the freezing temperature mean is greater than the freezing temperature threshold, the monitored freezing temperatures are sequentially excluded from the monitored freezing temperature time series at the acquisition frequency, and the updated freezing temperatures obtained by the monitoring unit are supplemented to the monitored freezing temperature time series to obtain an updated freezing temperature sequence;

[0128] Taking the updated freezing temperature sequence as the monitored freezing temperature time series, returning to the step of obtaining the freezing temperature variance and the freezing temperature mean based on the monitored freezing temperature time series, until the freezing temperature variance is less than or equal to the freezing variance threshold and the freezing temperature mean is less than or equal to the freezing temperature threshold, and then confirming that the shaking experimental group is a freezing experimental group.

[0129] It is understandable that monitoring the freezing temperature time series refers to the time series of temperature acquisition for the shaken experimental group during freezing using the said acquisition frequency. For example, if the acquisition frequency is once per second and the number of monitored freezing temperatures corresponding to the monitored freezing temperature time series is preset to be 10, then within 10 s, a total of 10 monitored freezing temperatures can be acquired. Sort the 10 monitored freezing temperatures in the order of acquisition time from first to last to obtain the monitored freezing temperature time series. When the variance of the freezing temperatures corresponding to the monitored freezing temperature time series is greater than the said freezing variance threshold or the mean of the freezing temperatures is greater than the said freezing temperature threshold, then the first monitored freezing temperature in the monitored freezing temperature time series is removed, and the monitored freezing temperature acquired at the 11th s is used as the 10th monitored freezing temperature in the monitored freezing temperature time series after removing the first monitored freezing temperature, and return to the step of obtaining the variance and mean of the freezing temperatures based on the said monitored freezing temperature time series. The monitored freezing temperature acquired at this 11th s is the updated freezing temperature.

[0130] It should be explained that when the shaken experimental group is completely frozen, the monitored freezing temperature time series corresponding to the shaken experimental group after freezing tends to be stable. Therefore, it is possible to judge whether the shaken experimental group is the frozen experimental group by comparing the variance of the freezing temperatures with the said freezing variance threshold and the mean of the freezing temperatures with the said freezing temperature threshold.

[0131] Furthermore, constructing the initial sample database based on the mean endotoxin content includes:

[0132] Mapping the said mean endotoxin content to a pre-constructed content relationship coordinate system to obtain a set of content relationship points. Among them, the horizontal axis of the content relationship coordinate system is the specific surface area corresponding to the mean endotoxin content, and the vertical axis of the content relationship coordinate system is the mean endotoxin content;

[0133] Obtain a set of fitting function models for fitting curves. Among them, the set of fitting function models includes multiple fitting function models. Extract the fitting function models from the said fitting function models in turn, and perform the following operations on the extracted fitting function models:

[0134] Obtain a fitting curve based on the extracted fitting function model and the said set of content relationship points, calculate the fitting evaluation value using a pre-constructed fitting evaluation relation formula and the fitting curve, summarize the said fitting evaluation values to obtain a set of fitting evaluation values, confirm the target fitting curve using the said set of fitting evaluation values, and construct the initial sample database based on the target fitting curve. Among them, the target fitting curve is the fitting curve corresponding to the smallest fitting evaluation value in the set of fitting evaluation values, and the said fitting evaluation relation formula is as follows:

[0135]

[0136] Among them, Z represents the fitting evaluation value, d 1i represents the average endotoxin content corresponding to the i-th first target test sample group in the fitting curve, d 0i represents the average endotoxin content corresponding to the i-th first target test sample group in the content relationship point set, and n represents the number of first target test sample groups.

[0137] It can be understood that the fitting function model is a model used to fit the content relationship point set into a function. For example, a polynomial is used as the fitting function model, and the same effect can be achieved by using other techniques, which will not be elaborated here. Generally, as the specific surface area increases, the average endotoxin content will decrease. Therefore, a fitting curve can be fitted in the constructed content relationship coordinate system.

[0138] Furthermore, the smaller the fitting evaluation value, the closer the fitting curve is to the content relationship point set. Therefore, in the embodiments of the present invention, the fitting curve with the smallest fitting evaluation value is used as the target fitting curve.

[0139] It should be explained that constructing the initial sample database based on the target fitting curve includes:

[0140] Using a pre-constructed screening relational expression to confirm target content points in the target fitting curve, where the screening relational expression is as follows:

[0141] S = min+(αf′ B1j +βB 1j )

[0142] Among them, S represents the screening relational expression, α and β are both preset coefficients, and B 1j represents the j-th specific surface area in the fitting curve, and f' B1j represents the derivative value corresponding to the j-th specific surface area point in the fitting curve;

[0143] Based on the target content points, an initial screening range is confirmed, where the initial screening range is the range corresponding to the two specific surface areas adjacent to the target content points on the target fitting curve;

[0144] Using a preset specific surface area division value to evenly divide the initial screening range to obtain multiple initial screening points, using the multiple initial screening points to obtain target screening points, and calculating the adsorption threshold of the endotoxin adsorbent using the target screening points. The calculation formula is as follows:

[0145]

[0146] Wherein, M represents the adsorption threshold, Z represents the mass of the identification adsorbent during use, V represents the specific surface area, n1 represents the first endotoxin concentration in the first target test sample group corresponding to the target fitting curve, n0 represents the average endotoxin content in the first target test sample group corresponding to the target fitting curve, and G represents the volume of the first target test sample in the first target test sample group corresponding to the target fitting curve;

[0147] Summarize the adsorption thresholds to obtain an adsorption threshold set, and perform a standardization operation on the adsorption thresholds in the adsorption threshold set to obtain a standardized adsorption threshold set, wherein the standardized adsorption thresholds include multiple standardized adsorption thresholds;

[0148] Construct an initial sample database based on the standardized adsorption threshold set.

[0149] It should be explained that as the specific surface area increases, the content of endotoxin adsorbed by the endotoxin adsorbent in bovine serum during adsorption is continuously increasing. Therefore, at a certain moment, there is a point that can completely absorb the endotoxin in bovine serum or reach the saturation point of endotoxin adsorption, and this point is the target content point. For example: the confirmed target content point is a specific surface area of 700 m 2 / g, then when the specific surface areas are 500 m 2 / g, 700 m 2 / g, and 1000 m 2 / g respectively, the initial screening range is the range in the target fitting curve corresponding to 500 m 2 / g to 1000 m 2 / g. When the specific surface area division value is 100 m 2 / g, 7 initial screening points can be obtained in this initial screening range.

[0150] It can be understood that the adsorption threshold set refers to the set of adsorption thresholds corresponding to the same endotoxin adsorbent under different specific surface areas, and this adsorption threshold is used to represent the maximum value that the endotoxin adsorbent can achieve for endotoxin adsorption in bovine serum under preset conditions. In the embodiment of the present invention, the standardization operation refers to converting the adsorption thresholds in the adsorption threshold set to between 0 and 1, so as to estimate the endotoxin adsorbent with an untested specific surface area.

[0151] It should be understood that constructing the initial sample database based on the standardized adsorption threshold set includes:

[0152] Obtain multiple first equal division range segments by using the first equal division numerical value and the reference range;

[0153] Perform the following operations on each of the multiple first equal division range segments:

[0154] Obtain multiple second endotoxin concentrations by using the first equal division range segment and a preset second equal division numerical value;

[0155] Randomly extract multiple second endotoxin concentrations from the multiple second endotoxin concentrations, the number of which is the same as the preset extraction numerical value, to obtain multiple third endotoxin concentrations;

[0156] Obtain multiple target endotoxin content means based on the multiple third endotoxin concentrations, and obtain multiple reference endotoxin content means by using the multiple third endotoxin concentrations and the standardized adsorption threshold, wherein the target endotoxin content means and the reference endotoxin content means correspond one by one;

[0157] Obtain multiple reference absolute differences based on the multiple target endotoxin content means and the multiple reference endotoxin content means, wherein the reference absolute difference is the absolute difference between the target endotoxin content mean and the reference endotoxin content mean corresponding to the target endotoxin content mean;

[0158] Associate the standardized adsorption thresholds in the standardized adsorption threshold set with the identification adsorbent to obtain first associated data;

[0159] If the multiple reference absolute differences are all less than or equal to a preset reference absolute threshold, then summarize the first associated data to obtain an initial sample database;

[0160] If at least one of the multiple reference absolute differences is greater than the reference absolute threshold, then update the first endotoxin concentration by using the second endotoxin concentration corresponding to the at least one reference absolute difference to obtain multiple updated first endotoxin concentrations, take the multiple updated first endotoxin concentrations as the multiple first endotoxin concentrations, and return to the step of performing the following operations on each of the multiple first endotoxin concentrations until an initial sample database is obtained.

[0161] It should be explained that the first equal division range segment refers to the range segment confirmed within the reference range with the first equal division numerical value as the difference. For example: the reference range is 200 - 300 EU / ml, and the first equal division numerical value is 20 EU / ml, then the first first equal division range segment is: 200 - 220 EU / ml, the second first equal division range segment is: 220 - 240 EU / ml... The fifth first equal division range segment is: 280 - 300 EU / ml, the second equal division numerical value is 5 / ml, then 3 second endotoxin concentrations can be obtained within each first equal division range segment, the preset extraction numerical value is 2, then 2 second endotoxin concentrations can be extracted from the 3 second endotoxin concentrations corresponding to each first equal division range segment, and the 2 extracted second endotoxin concentrations are 2 third endotoxin concentrations.

[0162] It should be understood that the mean value of the target endotoxin content is obtained in the same way as the mean value of the endotoxin content and can achieve the same effect, which will not be elaborated here. When multiple reference absolute differences are less than or equal to a preset reference absolute threshold, it can be determined that the identified target fitting curve conforms to the law of endotoxin adsorption by the endotoxin adsorbent. The standardized adsorption threshold is associated with the labeled adsorbent to express the ability of endotoxin adsorbents with different specific surface areas to adsorb endotoxin under the proposed conditions.

[0163] S3. Optimize the initial sample database to obtain an optimized sample database.

[0164] It can be understood that the optimizing the initial sample database to obtain an optimized sample database includes:

[0165] Obtain a reference mass gradient set of the labeled adsorbent, a shaking time gradient set, a shaking speed gradient set, and a shaking temperature gradient set of the shaking mechanism;

[0166] Use the pre-constructed method of controlling variables, the reference mass gradient set, the shaking time gradient set, the shaking speed gradient set, and the shaking temperature gradient set to obtain supplementary correlation data;

[0167] Update the initial sample database with the supplementary correlation data to obtain an optimized sample database.

[0168] It should be explained that the adsorption ability of the endotoxin adsorbent to endotoxin is related to the time during shaking, the temperature during shaking, the speed during shaking, and the dosage of the endotoxin adsorbent. The methods for obtaining the reference mass gradient set, the shaking time gradient set, the shaking speed gradient set, and the shaking temperature gradient set are the same as the method for obtaining the multiple first endotoxin concentrations and can achieve the same effect, which will not be elaborated here. For example, only changing the shaking time and ensuring that the other variables remain unchanged, a data that changes with the shaking time can be obtained. Use this data to update the data in the initial sample database, so that the data in the initial sample database is associated with the shaking time. Furthermore, the accuracy of the data in the initial sample database is improved. The other variables can achieve the same effect, which will not be elaborated here.

[0169] S4. Obtain relevant adsorption parameters based on the pre-obtained target bovine serum. Among them, the relevant adsorption parameters include: the volume of the target bovine serum and the concentration of endotoxin in the target bovine serum. Based on the relevant adsorption parameters, confirm a target adsorption data sequence in the optimized sample database, and realize the adsorption of endotoxin in the target bovine serum based on the target adsorption data sequence.

[0170] It should be explained that the confirming the target adsorption data sequence in the optimized sample database based on the relevant adsorption parameters includes:

[0171] An initial adsorption data set is obtained based on the relevant adsorption parameters and the optimized sample database, where the initial adsorption data set includes one or more initial adsorption data;

[0172] Perform the following operations on the initial adsorption data in the initial adsorption data set:

[0173] Calculate a comprehensive evaluation value by using a pre-constructed comprehensive evaluation relation and the initial adsorption data;

[0174] Based on the types of adsorbents of the added endotoxin adsorbents, summarize the comprehensive evaluation values respectively to obtain a plurality of comprehensive evaluation value sets, and obtain a target evaluation value set by using the plurality of comprehensive evaluation value sets, where the target evaluation value set is a set of the minimum comprehensive evaluation values in each of the plurality of comprehensive evaluation value sets;

[0175] Perform a sorting operation on the comprehensive evaluation values in the target evaluation value set in ascending order to obtain a target adsorption data sequence.

[0176] It should be understood that the target bovine serum refers to the bovine serum to be subjected to endotoxin adsorption.

[0177] Further, the obtaining of the initial adsorption data set based on the relevant adsorption parameters and the optimized sample database includes:

[0178] Calculate the adsorbent mass based on the relevant adsorption parameters and a pre-constructed mass relation, where the mass relation is as follows:

[0179]

[0180] where y represents the adsorbent mass, and v1 and n1 respectively represent the volume of the target bovine serum and the concentration of endotoxin in the target bovine serum in the relevant adsorption parameters;

[0181] Obtain the initial adsorption data set based on the adsorbent mass.

[0182] It can be understood that the initial adsorption data set includes the adsorbent mass, specific surface area, shaking temperature, shaking time, and shaking speed of each type of endotoxin adsorbent.

[0183] It should be understood that the comprehensive evaluation relation is as follows:

[0184]

[0185] where N m represents the comprehensive evaluation value corresponding to the mth initial adsorption data, and h mdenotes the energy consumption value of the endotoxin adsorbent corresponding to the m-th initial adsorption data, T denotes the shaking temperature, s denotes the shaking speed, f(T, s) denotes the unit energy consumption function of the shaking mechanism, and this unit energy consumption function is related to the shaking time, shaking temperature, and shaking speed, t m denotes the shaking time corresponding to the m-th initial adsorption data, and t denotes time.

[0186] Exemplarily, there are 3 types of optimized sample data corresponding to 3 endotoxin adsorbents in the optimized sample database, and the 3 endotoxin adsorbents all include 5 types of initial adsorption data. Then, 15 initial adsorption data can be confirmed in the optimized sample database through relevant adsorption parameters, and only the initial adsorption data corresponding to 3 endotoxin adsorbents are included in the target adsorption data sequence, and they are arranged in ascending order according to the comprehensive evaluation values corresponding to these 3 endotoxin adsorbents.

[0187] Compared with the problems described in the background art, the present invention first receives an optimized environment for identifying endotoxin in bovine serum. The optimized environment includes: a bovine serum sample set, an endotoxin adsorbent set, a shaking mechanism, a freezing mechanism, an irradiation mechanism, a monitoring unit, a centrifugation mechanism, a melting mechanism, and a filtration mechanism. An initial sample database is constructed based on the optimized environment. It can be seen that the present invention first formulates an initial sample database through the identified optimized environment. Among them, when formulating the initial sample database, the problem of resource waste is considered. Therefore, the experimental data is fitted into a fitting curve to reduce the resource waste caused during the construction of the initial sample database. After fitting the fitting curve, a target fitting curve is identified from multiple fitting curves to improve the accuracy of evaluating the adsorption capacity of the endotoxin adsorbent. When identifying the target fitting curve, experimental points for verification are selected from the target fitting curve to improve the accuracy of the target fitting curve. Furthermore, the embodiment of the present invention optimizes the initial sample database to obtain an optimized sample database. It can be seen that the embodiment of the present invention also considers factors that may affect the adsorption effect of the endotoxin adsorbent. Therefore, the initial sample database is optimized so that the optimized sample database can more accurately express the actual adsorption capacity of each endotoxin adsorbent to improve the accuracy of endotoxin adsorption in bovine serum. The embodiment of the present invention obtains relevant adsorption parameters based on the pre-acquired target bovine serum. The relevant adsorption parameters include: the volume of the target bovine serum and the concentration of endotoxin in the target bovine serum. Based on the relevant adsorption parameters, a target adsorption data sequence is identified in the optimized sample database, and the adsorption of endotoxin in the target bovine serum is realized based on the target adsorption data sequence. It can be seen that the embodiment of the present invention also considers that the diversity of data may cause inconvenience in querying the energy consumption of each endotoxin adsorbent during actual use. Therefore, a target adsorption data sequence is formulated to improve the convenience of reference. Therefore, the optimization method, device, electronic device, and computer-readable storage medium for the endotoxin adsorption effect in bovine serum based on feedback parameter adjustment proposed by the present invention mainly aim to solve the problems of inaccurate endotoxin adsorption in bovine serum and resource waste.

[0188] Embodiment 2:

[0189] As Figure 2 shown, it is a schematic structural diagram of an electronic device for implementing the optimization method of the endotoxin adsorption effect in bovine serum based on feedback parameter adjustment provided by an embodiment of the present invention.

[0190] The electronic device 1 may include a processor 10, a memory 11, a bus 12, and a communication interface 13, and may further include a computer program stored in the memory 11 and executable on the processor 10, such as an optimization program for the endotoxin adsorption effect in bovine serum based on feedback parameter adjustment.

[0191] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disc, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as the mobile hard disk of the electronic device 1. In some other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 can be used not only to store application software installed on the electronic device 1 and various types of data, such as the code of the optimization program for bovine serum endotoxin adsorption based on feedback parameter adjustment, etc., but also to temporarily store data that has been output or will be output.

[0192] In some embodiments, the processor 10 may be composed of integrated circuits. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, connecting various components of the entire electronic device through various interfaces and lines, and by running or executing programs or modules stored in the memory 11 (such as the optimization program for bovine serum endotoxin adsorption based on feedback parameter adjustment, etc.), and calling data stored in the memory 11, to perform various functions of the electronic device 1 and process data.

[0193] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is set to enable connection and communication between the memory 11 and at least one processor 10, etc.

[0194] Figure 2 Only the electronic device with components is shown. Those skilled in the art can understand that, Figure 2The structure shown does not constitute a limitation on the electronic device 1, and it may include fewer or more components than those shown, or combine certain components, or have a different component arrangement.

[0195] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component. Preferably, the power source can be logically connected to the at least one processor 10 through a power management device, so as to implement functions such as charging management, discharging management, and power consumption management through the power management device. The power source may also include any components such as one or more DC or AC power sources, a recharge device, a power failure detection circuit, a power converter or inverter, and a power status indicator. The electronic device 1 may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0196] Furthermore, the electronic device 1 may further include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0197] Optionally, the electronic device 1 may further include a user interface. The user interface may be a display, an input unit (such as a keyboard), and optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visual user interface.

[0198] It should be understood that the above embodiments are only for illustration purposes and are not limited by this structure in the scope of the patent application.

[0199] The optimized program for bovine serum endotoxin adsorption based on feedback parameter adjustment stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can implement:

[0200] Identify the optimized environment for bovine serum endotoxin, where the optimized environment includes: a bovine serum sample set, an endotoxin adsorbent set, a shaking mechanism, a freezing mechanism, an irradiation mechanism, a monitoring unit, a centrifugation mechanism, a melting mechanism, and a filtration mechanism;

[0201] Construct an initial sample database based on the optimized environment;

[0202] Optimize the initial sample database to obtain an optimized sample database;

[0203] Obtain relevant adsorption parameters based on pre-acquired target bovine serum. Among them, the relevant adsorption parameters include: the volume of the target bovine serum and the concentration of endotoxin in the target bovine serum. Based on the relevant adsorption parameters, confirm a target adsorption data sequence in the optimized sample database, and realize the adsorption of endotoxin in the target bovine serum based on the target adsorption data sequence.

[0204] Specifically, for the specific implementation method of the above instructions by the processor 10, reference can be made to Figures 1 to 2 the description of the relevant steps in the corresponding embodiments, which will not be elaborated here.

[0205] Furthermore, if the modules / units integrated in the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM, Read-Only Memory).

[0206] The present invention also provides a computer-readable storage medium. The readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, it can implement:

[0207] Confirm an optimized environment for bovine serum endotoxin. Among them, the optimized environment includes: a bovine serum sample set, an endotoxin adsorbent set, a shaking mechanism, a freezing mechanism, an irradiation mechanism, a monitoring unit, a centrifugation mechanism, a melting mechanism, and a filtering mechanism;

[0208] Construct an initial sample database based on the optimized environment;

[0209] Optimize the initial sample database to obtain an optimized sample database;

[0210] Obtain relevant adsorption parameters based on pre-acquired target bovine serum. Among them, the relevant adsorption parameters include: the volume of the target bovine serum and the concentration of endotoxin in the target bovine serum. Based on the relevant adsorption parameters, confirm a target adsorption data sequence in the optimized sample database, and realize the adsorption of endotoxin in the target bovine serum based on the target adsorption data sequence.

[0211] The module described as a separation component may or may not be physically separated. The component shown as a module may or may not be a physical unit, that is, it may be located in one place or distributed over multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0212] In addition, each functional module in various embodiments of the present invention may be integrated in a processing unit, may also exist as individual physical units, or two or more units may be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a hardware plus software functional module.

[0213] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0214] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing the adsorption of bovine serum endotoxin based on feedback parameter adjustment, characterized in that: The method comprises: Identify the optimized environment of bovine serum endotoxin, wherein the optimized environment includes: a bovine serum sample set, an endotoxin adsorbent set, a shaking mechanism, a freezing mechanism, an irradiation mechanism, a monitoring unit, a centrifugation mechanism, a melting mechanism and a filtering mechanism; Building an initial sample database based on the optimized environment; Wherein, the constructing of an initial sample database based on the optimized environment includes: Acquire a first initial sample set based on the bovine serum sample set, wherein the first initial sample set includes a plurality of first initial sample groups, and each first initial sample group includes a plurality of bovine serum samples; Obtaining a reference range and a first average value of endotoxin, and obtaining a plurality of first endotoxin concentrations based on the first average value and the reference range; For each of the plurality of first endotoxin concentrations, the following operations are performed: Acquire a first target sample set using the first endotoxin concentration and the first initial sample set, wherein the first target sample set includes a plurality of first target sample sets; The endotoxin adsorbent set includes a plurality of endotoxin adsorbents with different adsorption parameters, wherein the adsorption parameters are composed of the type of adsorbent, the action mechanism of the adsorbent and the specific surface area of ​​the endotoxin; Performing a labeling operation on the endotoxin adsorbent based on the adsorption parameters to obtain a labeled adsorbent; The adsorbents are respectively identified by the type of adsorbent and the action mechanism of the adsorbent to obtain multiple sets of identified adsorbents; For each of the multiple identified sorbent sets, the following operations are performed: Sorting the identified adsorbents in the identified adsorbent set in ascending order of specific surface area to obtain an identified adsorbent sequence; Acquire a first target test sample set based on the identification adsorbent sequence and the first target sample set, wherein the first target test sample set includes a plurality of first target test sample groups, and the first target test sample groups correspond to the identification adsorbents in the identification adsorbent set one by one, and the quality of the identification adsorbents when in use is preset; constructing an initial sample database based on a plurality of first target test sample groups; The step of constructing an initial sample database based on a plurality of first target test sample groups includes: The following operations are performed on each of the plurality of first target test sample groups: Performing a shaking operation on the first target test sample group using the shaking mechanism to obtain a shaking experiment group, wherein a first shaking time, a first shaking temperature and a first shaking speed of the shaking mechanism are preset; After confirming that the shaking experiment group is a freezing experiment group by using the freezing mechanism and the monitoring unit, the freezing experiment group is irradiated by using the irradiation mechanism to obtain an irradiated sample group, wherein the irradiation dose of the irradiation mechanism is preset; Utilizing a thawing mechanism to perform a thawing operation on the irradiated sample group to obtain a thawed sample group; Performing a centrifugal operation on the thawed sample group based on the centrifugal mechanism to obtain a target sample group; Extract target samples from the target sample group in sequence, and perform the following operations on the extracted target samples: Performing a filtering operation on the extracted target sample by using a filtering mechanism to obtain a supernatant, and obtaining a detection endotoxin content based on the supernatant; Summarizing the detected endotoxin contents to obtain a detected endotoxin content set, and using the detected endotoxin content set to obtain an endotoxin content mean, wherein the endotoxin content mean is the mean of the endotoxin contents detected in the detected endotoxin content set; An initial sample database was constructed based on the mean endotoxin content; Optimizing the initial sample database to obtain an optimized sample database; Relevant adsorption parameters are obtained based on the pre-acquired target bovine serum, wherein the relevant adsorption parameters include: the volume of the target bovine serum and the concentration of endotoxin in the target bovine serum; based on the relevant adsorption parameters, a target adsorption data sequence is confirmed in the optimized sample database; and based on the target adsorption data sequence, adsorption of endotoxin in the target bovine serum is achieved.

2. The method for optimizing bovine serum endotoxin adsorption based on feedback parameter adjustment according to claim 1, characterized in that: The method of using the freezing mechanism and the monitoring unit to confirm that the shaking experiment group is a freezing experiment group includes: A freezing operation is performed on the shaking test group using a freezing mechanism, and a monitoring freezing temperature time sequence is obtained using a monitoring unit and the shaking test group in freezing, wherein the acquisition frequency of the monitoring unit is preset, and the monitoring freezing temperature time sequence includes a plurality of monitoring freezing temperatures, and the number corresponding to the monitoring freezing temperatures in the monitoring freezing temperature time sequence is preset; Based on the monitoring freezing temperature time series, a freezing temperature variance and a freezing temperature mean are obtained, wherein the freezing temperature variance is the variance of multiple monitored freezing temperatures in the monitoring freezing temperature time series, and the freezing temperature mean is the mean of multiple monitored freezing temperatures in the monitoring freezing temperature time series; Compare the freezing temperature variance with the preset freezing variance threshold, and the freezing temperature mean with the preset freezing temperature threshold; If the freezing temperature variance is greater than the freezing temperature variance threshold or the freezing temperature mean is greater than the freezing temperature threshold, the monitored freezing temperatures are sequentially eliminated in the monitoring freezing temperature time sequence at the acquisition frequency, and the updated freezing temperatures obtained by the monitoring unit are added to the monitoring freezing temperature time sequence to obtain an updated freezing temperature sequence; With the updated freezing temperature sequence as the monitored freezing temperature sequence, return to the step of obtaining the freezing temperature variance and the freezing temperature mean based on the monitored freezing temperature sequence, until the freezing temperature variance is less than or equal to the freezing variance threshold and the freezing temperature mean is less than or equal to the freezing temperature threshold, confirm that the shaking experiment group is the freezing experiment group.

3. The method for optimizing bovine serum endotoxin adsorption based on feedback parameter adjustment according to claim 2, characterized in that: The initial sample database is constructed based on the mean value of endotoxin content, including: Mapping the mean endotoxin content to a pre-constructed content relationship coordinate system to obtain a content relationship point set, wherein the horizontal axis of the content relationship coordinate system is: the specific surface area corresponding to the mean endotoxin content, and the vertical axis of the content relationship coordinate system is the mean endotoxin content; A fitting function model set for fitting the curve is obtained, wherein the fitting function model set includes a plurality of fitting function models, fitting function models are sequentially extracted from the fitting function models, and the following operations are performed on the extracted fitting function models: A fitting curve is obtained based on the extracted fitting function model and the content relationship point set, a fitting evaluation value is calculated using a pre-constructed fitting evaluation relationship and the fitting curve, the fitting evaluation values ​​are summarized to obtain a fitting evaluation value set, a target fitting curve is confirmed using the fitting evaluation value set, and an initial sample database is constructed based on the target fitting curve, wherein the target fitting curve is a fitting curve corresponding to the smallest fitting evaluation value in the fitting evaluation value set, and the fitting evaluation relationship is as follows: Where Z represents the fitting evaluation value, d 1i represents the mean endotoxin content corresponding to the i-th first target test sample group in the fitting curve, d 0i represents the mean endotoxin content corresponding to the i-th first target test sample group in the content relationship point set, and n represents the number of first target test sample groups.

4. The method for optimizing bovine serum endotoxin adsorption based on feedback parameter adjustment according to claim 3, characterized in that: The constructing an initial sample database based on the target fitting curve comprises: The target content point is confirmed in the target fitting curve using a pre-built screening relationship, wherein the screening relationship is as follows: S=min+(αf′ B1j +βB 1j ) Wherein, S represents the screening relationship, α and β are preset coefficients, and B 1j represents the jth specific surface area in the fitting curve, f' B1j represents the derivative value corresponding to the j-th specific surface area point in the fitting curve; Based on the target content point, an initial screening range is determined, wherein the initial screening range is a range corresponding to two specific surface areas adjacent to the target fitting curve and the target content point; The initial screening range is evenly divided by a preset specific surface area division value to obtain a plurality of initial screening points, the target screening points are obtained by using the plurality of initial screening points, and the adsorption threshold of the endotoxin adsorbent is calculated by using the target screening points. The calculation formula is as follows: Wherein, M represents the adsorption threshold, Z represents the mass of the marker adsorbent when in use, V represents the specific surface area, n1 represents the first endotoxin concentration in the first target test sample group corresponding to the target fitting curve, n0 represents the mean endotoxin content in the first target test sample group corresponding to the target fitting curve, and G represents the volume of the first target test sample in the first target test sample group corresponding to the target fitting curve; Summarizing the adsorption thresholds to obtain an adsorption threshold set, performing a standardization operation on the adsorption thresholds in the adsorption threshold set to obtain a standardized adsorption threshold set, wherein the standardized adsorption thresholds include a plurality of standardized adsorption thresholds; An initial sample database is constructed based on the standardized adsorption threshold set.

5. The method for optimizing bovine serum endotoxin adsorption based on feedback parameter adjustment according to claim 4, characterized in that: The step of constructing an initial sample database based on the standardized adsorption threshold set includes: Obtaining a plurality of first averaged range segments using the first averaged value and a reference range; The following operation is performed on each of the plurality of first equally divided range segments: Obtaining a plurality of second endotoxin concentrations using the first average division range segment and a preset second average division value; Randomly extracting a plurality of second endotoxin concentrations having the same number as the preset extraction value from the plurality of second endotoxin concentrations to obtain a plurality of third endotoxin concentrations; Acquire multiple target endotoxin content means based on the multiple third endotoxin concentrations, and acquire multiple reference endotoxin content means using the multiple third endotoxin concentrations and the standardized adsorption threshold, wherein the target endotoxin content means correspond to the reference endotoxin content means one by one; Obtaining multiple reference absolute difference values ​​based on multiple target endotoxin content mean values ​​and multiple reference endotoxin content mean values, wherein the reference absolute difference value is the absolute difference between the target endotoxin content mean value and the reference endotoxin content mean value corresponding to the target endotoxin content mean value; Associating the standardized adsorption threshold value in the standardized adsorption threshold value set with the identification adsorbent to obtain first associated data; If the multiple reference absolute difference values ​​are all less than or equal to the preset reference absolute threshold, the first associated data are aggregated to obtain an initial sample database; If there is at least one reference absolute difference value among the multiple reference absolute difference values ​​that is greater than the reference absolute threshold value, the first endotoxin concentration is updated using the second endotoxin concentration corresponding to the at least one reference absolute difference value to obtain multiple updated first endotoxin concentrations, and the multiple updated first endotoxin concentrations are used as the multiple first endotoxin concentrations. The process returns to the step of performing the following operation on each of the multiple first endotoxin concentrations until an initial sample database is obtained.

6. The method for optimizing bovine serum endotoxin adsorption based on feedback parameter adjustment according to claim 1, characterized in that: The step of optimizing the initial sample database to obtain an optimized sample database includes: Obtaining a reference mass gradient set of the labeled adsorbent, a shaking time gradient set of the shaking mechanism, a shaking speed gradient set, and a shaking temperature gradient set; Supplementary correlation data were obtained using pre-built control variable methods, reference mass gradient sets, shaking time gradient sets, shaking velocity gradient sets, and shaking temperature gradient sets; The initial sample database is updated using the supplementary associated data to obtain an optimized sample database.

7. The method for optimizing bovine serum endotoxin adsorption based on feedback parameter adjustment according to claim 1, characterized in that: The step of confirming a target adsorption data sequence in an optimized sample database based on the relevant adsorption parameters includes: Acquiring an initial adsorption data set based on the relevant adsorption parameters and the optimized sample database, wherein the initial adsorption data set includes one or more initial adsorption data; Perform the following operations on the initial adsorption data in the initial adsorption data set: Calculate the comprehensive evaluation value using the pre-built comprehensive evaluation relationship and initial adsorption data; Based on the adsorbent type of the added endotoxin adsorbent, the comprehensive evaluation values ​​are respectively summarized to obtain multiple comprehensive evaluation value sets, and the target evaluation value set is obtained by using the multiple comprehensive evaluation value sets, wherein the target evaluation value set is a set of the minimum comprehensive evaluation values ​​in each comprehensive evaluation value set in the multiple comprehensive evaluation value sets; The comprehensive evaluation values ​​in the target evaluation value set are sorted in ascending order to obtain a target adsorption data sequence.

8. The method for optimizing bovine serum endotoxin adsorption based on feedback parameter adjustment according to claim 7, characterized in that: The comprehensive evaluation relationship is as follows: Among them, N m represents the comprehensive evaluation value corresponding to the mth initial adsorption data, h m represents the energy consumption value of the endotoxin adsorbent corresponding to the mth initial adsorption data, T represents the shaking temperature, s represents the shaking speed, f(T,s) represents the unit energy consumption function of the shaking mechanism, and the unit energy consumption function is related to the shaking time, shaking temperature and shaking speed, t m represents the shaking time corresponding to the mth initial adsorption data, and t represents time.

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