Noninvasive assessment model and risk assessment method for manganese, selenium and calcium exposure risk in complex environment

Abstract

The invention relates to the field of public health, in particular to a manganese, selenium and calcium exposure risk non-invasive assessment model and risk assessment method in a complex environment, and aims at analyzing the results of indexes MS, CS and MC through non-invasive detection of the levels of human elements selenium, manganese and calcium and exposing the human body to energy environment pollution through functions and probabilities; food and health-care product additives and pharmaceutical preparations; a new material; performing occupational exposure; according to the method, accurate risk category and degree evaluation is performed on multiple complex risks under the conditions of risk factor exposure of local diseases and the like, and risk indexes needing to be monitored are determined, so that prevention implementation before clinical diagnosis is realized, disease occurrence is reduced, and related industry standards are standardized; the blank of exposure risk assessment of influence of intake poisoning, endemic diseases, occupational exposure, energy environment pollution and the like on human bodies in complex environments related to manganese, selenium and calcium elements worldwide is filled.

Description

Technical fields:

[0001] This involves public health and environmental exposure risk assessment. Background technology:

[0002] Manganese (Mn) is a potentially essential trace element, widely found in nature, but its deficiency in its natural state is rarely reported. Mseleni arthropathy, possibly related to manganese deficiency, has only been reported in the Zulu region of South Africa, manifesting as short stature and short fingers and toes. Long-term exposure to excessive manganese causes neurotoxicity, primarily manifesting as high stress, irritability, and hallucinations, subsequently developing into extrapyramidal symptoms similar to Parkinson's disease, known as Parkinsonian syndrome, and is also associated with Alzheimer's disease.

[0003] Calcium (Ca) is a macroelement. Under natural conditions, calcium deficiency in the human body mainly manifests as abnormal growth or structural changes in the skeletal system, affecting bone mineralization. Calcium excess occurs when mediated by other factors or when excessive amounts are added, but toxicity has not been observed.

[0004] Selenium (Se) is an essential trace element. For the human body, the range of selenium requirements, from minimum to maximum acceptable levels, is very narrow. Supplementation may be beneficial when selenium levels are low, while higher levels may cause side effects.

[0005] Studies on the effects of selenium on the human body have shown that appropriately increased selenium levels are beneficial for nervous system diseases and cardiovascular diseases. In China, selenium deficiency is considered a risk factor for Kashin-Beck disease and Keshan disease, and selenium has been used in the prevention and treatment of these two diseases. Selenium poisoning incidents caused by excessive addition of selenium to health supplements have been reported in various countries. Symptoms of selenium poisoning include: garlic-like breath; thickened and brittle nails with longitudinal stripes and white spots; dry, brittle, and easily broken hair; redness, swelling, blistering, and even ulceration of the skin on the hands and feet; severe tooth decay and rotten teeth; muscle or joint pain; and nervous system abnormalities, mainly including numbness, paralysis, and spasms, varying in severity. Recent studies have found that chronic excessive selenium exposure may also cause amyotrophic lateral sclerosis (ALS).

[0006] The detection methods for manganese and selenium poisoning were initially established for people working in manganese and selenium mining and smelting industries. The indicators still used today for assessing selenium levels in the human body are measuring the selenium content in fingernails and hair, as well as the selenium levels in blood and urine during acute poisoning. Short-term indicators for assessing manganese levels in the human body are measuring the manganese content in blood and urine; short-term indicators for assessing calcium levels in the human body are also measuring the calcium content in blood and urine. Long-term indicators for assessing manganese and calcium levels in the human body are still lacking.

[0007] For a long time, due to the limited population affected, the detection of selenium and manganese has been limited to passive post-exposure testing. With the development of human economy and industry, the dangers of exposure factors caused by environmental pollution have expanded to all of humanity, but early warning and assessment methods in this regard remain lacking. Besides directly affecting workers in mining and smelting industries, minerals exposed to the earth's surface can enter surface water and ultimately the surface water cycle; they can also seep into groundwater, polluting deep water sources; enter soil and be absorbed by plants, leading to indirect high exposure in food; and mineral particles can spread to other areas through surface air circulation. The main sources of environmental selenium pollution are coal and oil, which currently provide more than 50% of the world's energy supply. The long-standing extraction and use of polluting energy sources has resulted in exposure to hazardous factors, but a comprehensive assessment system for human exposure risks is still lacking worldwide. Summary of the Invention:

[0008] To fill the global gap in environmental exposure risk assessment of the effects of manganese, selenium, and calcium on human health in complex environments, including ingestion poisoning, endemic diseases, occupational exposure, and energy and environmental pollution, the technical solution adopted in this invention is as follows:

[0009] A non-invasive assessment model for manganese, selenium, and calcium exposure risks in complex environments was created using the following methods:

[0010] a. Selection of model sampling subjects: Sampling subjects were selected from healthy people, people with low selenium environment, people with high selenium environment, people with low manganese environment, people with high manganese environment, and people with low calcium environment, respectively.

[0011] b. Data collection: Collect manganese, selenium and calcium content data from the hair, toes or nails of the above-mentioned sampling objects;

[0012] c. Data processing: Compare the manganese, selenium, and calcium content data of each sampled object in pairs to obtain modeling data, or scale at least one of the manganese, selenium, and calcium content data before obtaining modeling data and then compare them in pairs to obtain modeling data; and the pairwise comparison relationship of manganese, selenium, and calcium content data in the modeling data of different sampled objects is consistent.

[0013] d. Establish an exposure risk assessment model: Use modeling data from different population groups to establish a discriminant equation and obtain an exposure risk assessment model.

[0014] Furthermore, during data processing, modeling data were obtained using ratios of manganese / selenium, manganese / calcium, and calcium / selenium, respectively.

[0015] Furthermore, during data processing, the calcium content data collected from each sample object is reduced in size.

[0016] Furthermore, the calcium content data collected from each sample was reduced by a factor of 5 to 10,000.

[0017] Furthermore, the calcium content data collected from each sample was scaled down by a factor of 100.

[0018] Furthermore, during data processing, the manganese and selenium content data collected from each sample object are amplified.

[0019] Furthermore, the manganese and selenium content collected from each sample was magnified by 5 to 10,000 times.

[0020] Furthermore, the manganese and selenium content collected from each sample was magnified 100 times.

[0021] Furthermore, a linear regression equation was established using the modeling data from the exposure risk assessment model.

[0022] Obtain a risk assessment model.

[0023] The exposure risk is assessed based on the model created above, using the following method:

[0024] a. Collect data on the content of manganese, selenium, and calcium from the hair, toes, or nails of the person being evaluated;

[0025] b. Obtain assessment data by processing the manganese, selenium, and calcium content data of the above-mentioned subjects according to the manganese, selenium, and calcium content data in the model data when establishing the exposure risk assessment model;

[0026] c. Substitute the assessment data into the discriminant equation to calculate and determine the main risk category.

[0027] Furthermore, the main risk category is determined by using the results of the discriminant equation and the posterior probability.

[0028] Furthermore, a linear regression equation is established using the modeling data from the exposure risk assessment model to obtain a risk level assessment model. The assessment data is then substituted into the linear regression equation to calculate and determine the magnitude of the main risk.

[0029] Furthermore, abnormal indicators are identified based on the reference range of individual elements and the pairwise ratios between elements, and secondary risk assessments are conducted.

[0030] Furthermore, the subjects being assessed collect data on the content of manganese, selenium, and calcium using testing instruments. Based on the collected data, the data is processed by a computer program and then input into the exposure risk assessment model within the program to output an assessment report.

[0031] Furthermore, the data on the content of manganese, selenium, and calcium collected by the detection instrument can be manually entered into a computer program or directly transmitted to a computer program.

[0032] Furthermore, the output evaluation report can be printed out or displayed on a monitor.

[0033] Furthermore, the instruments used to collect data on the content of manganese, selenium, and calcium are mass spectrometers or spectrometers.

[0034] Advantages and benefits of the present invention

[0035] (1) When there are multiple exposure risks in the environment, the factor with the greatest risk can be identified in complex exposure situations; the long-term exposure of the human body can be assessed.

[0036] (2) This assessment method is highly sensitive and can assess environmental exposure risk before disease diagnosis.

[0037] (3) The assessment system is comprehensive. A single test can assess the exposure risk of six human risk factors.

[0038] (4) The test samples are collected non-invasively, and the collection method is easily accepted; the samples are stable and the transportation process is not affected by timeliness and environmental factors.

[0039] (5) By assessing the exposure risk of the ingested population, it is beneficial to monitor and evaluate the results after ingestion, thereby regulating the manufacture and use of food and health product additives, pharmaceutical preparations and materials.

[0040] (6) By assessing the risk of exposure to occupational and local factors, it is beneficial to reduce post-exposure diseases and endemic diseases.

[0041] (7) By assessing the risk of exposure to people in polluted areas, it is beneficial to supervise and reduce the exploitation and use of polluting energy, and to prevent inferior coal and oil from entering the market; it is also beneficial to promote the development and application of green energy and pollution-free energy. Attached image description:

[0042] Figure 1 A schematic diagram illustrating the distribution patterns of environmental exposure risks MS, SC, and MC. Where: k=1, healthy individuals; k=2, exposure risk of low selenium environment; k=3, exposure risk of high selenium environment; k=4, exposure risk of low manganese environment; k=5, exposure risk of high manganese environment; k=6, exposure risk of low calcium environment. In the case of single exposure risks of Mn, Se, and Ca, the categorical master dataset is distributed in the primary risk area of ​​the assessment system. In the case of complex multiple exposure risks, the actual data obtained will be distributed in the categorical master dataset (primary risk indicator) or biased towards secondary risk indicators. The greater the degree of exposure risk, the further the assessment results deviate from the risk migration of healthy individuals. Detailed implementation method:

[0043] The study found a balance among manganese, selenium, and calcium, exhibiting a linear relationship, particularly in exposure risk assessment. Under single-factor exposure, changes in a single exposure factor result in either an increase or decrease. However, actual environmental exposure situations are often complex, potentially involving multiple exposure factors depending on the exposure factor, living environment, and work environment. In cases of multiple exposures, previous single-factor assessments of Mn, Se, and Ca could only determine the exposure risk of a single factor, but not which risk was greater. The assessment using the new indicators MS, CS, and MC can identify the factor with the greatest risk in complex exposure situations. Furthermore, these three new indicators have higher sensitivity and accuracy in assessing exposure risk compared to individual detection of manganese, selenium, and calcium. Using these new indicators in conjunction with single-factor assessments, non-invasively detecting hair or fingernails and toenails, and creating a long-term human exposure risk assessment system based on this, can fill the global gap in exposure risk assessment for the effects of ingestion poisoning, endemic diseases, occupational exposure, and energy and environmental pollution on human health.

[0044] The evaluation model and evaluation methods will be described in detail below.

[0045] A risk assessment of human exposure to manganese, selenium, and calcium in complex environments was conducted using non-invasive indicators MS, CS, and MC combined with univariate measurements, comprising six categories. Because children are more affected by environmental and growth factors than by occupational exposure, separate exposure risk assessment systems were constructed for children and adults based on their respective exposure profiles.

[0046] 1. Evaluation system indicators:

[0047] Exposure risk assessment testing involves non-invasive collection of hair or fingernails / toenails from the sampled subjects, with different types of samples analyzed separately.

[0048] (1) Single-factor indicators

[0049] The contents of manganese (Mn), selenium (Se) and calcium (Ca) in the same batch of tested samples are recorded as Mn (μg / g), Se (μg / g) and Ca (μg / g), respectively.

[0050] (2) New indicators

[0051] MS: The detected value of manganese (Mn) in the sample is denoted as M, and the detected value of selenium (Se) in the same batch is denoted as S. The ratio is denoted as MS (M / S).

[0052]

[0053] CS: The detected value of calcium (Ca) in the sample is denoted as C, one-hundredth of C is denoted as Ca', the detected value of selenium (Se) in the same batch is denoted as S, and the ratio is denoted as CS (C / S).

[0054]

[0055] MC: The detected value of manganese (Mn) in the sample is recorded as M, the detected value of calcium (Ca) in the same batch is recorded as C, one-hundredth of C is recorded as Ca', and the ratio is recorded as MC (M / C).

[0056]

[0057]

[0058] Note: For all measured values, when performing evaluation calculations, the new index can be expressed as any pairwise ratio of manganese, selenium, and calcium content data, or as a mathematical representation of that ratio, which is equivalent to the new index. Examples include selenium / manganese, selenium / calcium, and calcium / manganese. In practice, the above-mentioned manganese / selenium, calcium / selenium, and manganese / calcium forms are optimal.

[0059] Since manganese and selenium are trace elements in the human body, while calcium is a macro element, the values ​​of manganese and selenium compared to calcium are not easy to read. To facilitate the reading of new indicator values ​​by sampling personnel, a better approach is to scale at least one of the collected manganese, selenium, and calcium content data before comparing them pairwise. Scale-up here refers to enlarging or reducing the sampled value of one or more elements. Typically, calcium content data can be reduced, for example, using a value from 1 / 5 to 1 / 10,000 of the calcium content data as a variation of the defined indicator, with 1% of the calcium content data being optimal for calculation, making the value easier to read. Alternatively, manganese and selenium content data can be enlarged, such as increasing the manganese and selenium content data by 5 to 10,000 times, with 100 times being optimal. Of course, scaling the manganese, selenium, and calcium content data before comparison and using direct measurements of manganese, selenium, and calcium for comparison does not affect the model evaluation results and should be considered equivalent. The following explanation will be based on the optimal form of the above indicators.

[0060] 2. Main categories of the evaluation system:

[0061] (1) Healthy person

[0062] (2) Exposure Risk of Selenium Deficiency in the Environment:

[0063] Low selenium levels in the environment lead to insufficient selenium intake by the human body (from food, drinking water, and low selenium levels in the geographical environment, etc.).

[0064] (3) Exposure Risk of Selenium Toxicity in the Environment:

[0065] High levels of selenium in the environment can lead to excessive intake exposure (excessive addition to food and health products; use of selenium-containing preparations, drugs, and materials), excessive occupational exposure (selenium mining, smelting, processing, and transportation; coal mining and use; oil extraction, crude oil refining and use; manufacturing of selenium-containing preparations, drugs, and materials), and environmental pollution exposure risks (emissions during mining, smelting, processing, and use).

[0066] (4) Exposure Risk of Manganese Deficiency in the Environment:

[0067] Low manganese levels in the environment lead to insufficient intake by the human body (due to low manganese levels in food, drinking water, geographical environment, etc.).

[0068] (5) Exposure Risk of Manganese Toxicity in the Environment:

[0069] High levels of manganese in the environment can lead to excessive intake exposure (excessive additives in food and health products, use of manganese-containing preparations, drugs and materials), excessive occupational exposure (manganese mining, smelting, processing and transportation; manganese industry; manufacture of manganese-containing preparations, drugs and materials), and environmental pollution exposure risk (emissions during mining, smelting and processing).

[0070] (6) Exposure Risk of Calcium Deficiency in the Environment:

[0071] Low calcium levels in the environment lead to insufficient intake and storage of calcium in the human body (from food, water, etc.).

[0072] 3. Classification of the main exposure risks of the assessed individuals: Classification is determined by function and probability.

[0073] (1) Establish an exposure risk assessment model:

[0074] Model conditions for the exposure risk assessment system based on the new indicators MS, CS, and MC:

[0075] The sampling population was selected based on the above six risk categories:

[0076] Healthy person:

[0077] The selection of healthy individuals and the data collected from the system must meet the following criteria: ① Physically healthy with no current illness; ② No history of drug treatment within the past month; ③ No consumption of nutritional supplements, functional foods with added ingredients, or materials used in the past month; ④ Engaged in occupations not related to exposure risks; ⑤ Not residing in areas with a high incidence of endemic diseases within the past six months; ⑥ Not residing in areas with environmental exposure risks within the past three months.

[0078] Exposure risk of selenium deficiency in the environment:

[0079] For the selection of individuals with low selenium levels in the environment, the data collected from the system must meet the following criteria: ① (Required) Clinically diagnosed primary selenium deficiency, or any one of ②③④⑤; ② Currently suffering from selenium deficiency-related diseases; ③ Having a history of selenium deficiency-related diseases; ④ Having lived in an area with a high incidence of endemic selenium deficiency diseases for the past six months; ⑤ Having family members living together who are currently suffering from or have a history of selenium deficiency-related diseases.

[0080] Exposure risk of selenium toxicity in the environment:

[0081] For the selection of individuals with high selenium levels in the environment, the data collected must meet the following criteria: ① (Required) Clinically diagnosed primary selenium poisoning, or any one of ②③④⑤; ② History of exposure to food additives, nutritional preparations, drugs, and therapeutic materials containing selenium within the past month; ③ Occupational exposure: selenium mining, smelting, processing, and transportation; coal mining and use; oil extraction, crude oil refining and use; manufacturing of preparations, drugs, and materials containing selenium; ④ Residing in the vicinity of selenium mining and energy extraction, processing, use, and production industries within the past month; ⑤ Family members living together have occupational exposure to selenium.

[0082] Exposure risk of manganese deficiency in the environment:

[0083] For the selection of individuals with low manganese levels in the environment, the data collected from the system must meet the following criteria: ① (Required) Clinically diagnosed primary manganese deficiency, or any one of ②③④⑤; ② Currently suffering from manganese deficiency-related diseases; ③ Having a history of manganese deficiency-related diseases; ④ Having lived in an area with a high incidence of endemic manganese deficiency diseases for the past six months; ⑤ Having family members living together who are currently suffering from or have a history of manganese deficiency-related diseases.

[0084] Exposure risk of manganese toxicity in the environment:

[0085] The selection criteria for individuals exposed to high levels of manganese in the environment must meet the following conditions for data collection: ① (Required) Clinically confirmed primary manganese poisoning, or any one of ②③④⑤; ② History of exposure to manganese-containing food additives, nutritional preparations, drugs, and therapeutic materials within the past month; ③ Occupational exposure: manganese mining, smelting, processing, and transportation; industrial manufacturing, preparation, drug, and material manufacturing containing manganese; ④ Residing in an area surrounding manganese mining, smelting, and processing within the past month; ⑤ Cohabiting family members have occupational exposure to manganese.

[0086] Exposure risk of calcium deficiency in the environment:

[0087] For the selection of individuals with low calcium levels in the environment, the data collected from the system must meet the following criteria: ① (Required) Clinically diagnosed primary calcium deficiency, or any one of ②, ③, or ④; ② Currently suffering from calcium deficiency-related diseases; ③ Having a history of calcium deficiency-related diseases; ④ Having family members living together who are currently suffering from or have a history of calcium deficiency-related diseases.

[0088] Manganese, selenium, and calcium content data were collected from the hair, toes, and nails of the above-mentioned different population groups. The data were then processed according to the new indicators MS, CS, and MC to obtain modeling data.

[0089] The new indicators MS, CS, and MC are X. j (j = 1, 2, 3, meaning X1 is the modeling data under the new indicator MS, X2 is the modeling data under the new indicator CS, and X3 is the modeling data under the new indicator MC); the main category is k (k = 1, 2, 3, 4, 5, 6, meaning k = 1, healthy person; k = 2, exposure risk of low selenium environment; k = 3, exposure risk of high selenium environment; k = 4, exposure risk of low manganese environment; k = 5, exposure risk of high manganese environment; k = 6, exposure risk of low calcium environment); the indicator data is X i (i = 1, 2, 3, 4, 5, ..., n, which represents the number of data samples for modeling different population groups; the number of samples in each group is not necessarily the same):

[0090] Calculate X i and X j Combined covariance S ij Listed in matrix S:

[0091]

[0092] and X is a variable in the k-th class. i and X j The mean of n k Let be the number of instances in class k.

[0093]

[0094] Calculate the discriminant coefficient C using the S matrix jk (j=0,1,2,3; k=1,2,3,4,5,6)

[0095]

[0096] Six sets of equations will be generated based on the six categories, and C will be produced for each set. 1k C 2k C 3k That is, when k = 1, C is obtained according to the equation. 11 C 21 C 31

[0097] When k = 2, C is obtained from the equation. 12 C 22 C 32

[0098] When k = 3, C is obtained from the equation. 13 C 23 C 33

[0099] When k = 4, C is obtained from the equation. 14 C 24 C 34

[0100] When k = 5, C is obtained from the equation. 15 C 25 C 35

[0101] When k = 6, C is obtained from the equation. 16 C 26 C 36

[0102] Calculate the prior probability P(Y) k ) and discriminant coefficient C 0k :

[0103]

[0104] Calculations yielded: C 01 C 02 C 03 C 04 C05 C 06

[0105] Establish the discriminant equation, thus obtaining the exposure risk assessment model:

[0106]

[0107] Note: The discriminant equation given in this embodiment is the Bayes discriminant, which is easy to operate and implement. However, when actually establishing an exposure risk assessment model, it is not limited to this discriminant function form. Fisher discriminant calculation can also be used to obtain the exposure risk assessment model. Since its calculation is more complex, its specific calculation process will not be described here.

[0108] Based on the above exposure risk assessment model, a risk assessment can be conducted on the assessed individual:

[0109] (2) Assess the primary risk classification of unknown assessees:

[0110] Scope of those being evaluated: random and non-selective.

[0111] Data on the content of manganese, selenium, and calcium were obtained from the hair, toes, or nails of the subjects being evaluated. After processing the data, the values ​​of new indicators MS, CS, and MC were obtained for the subjects being evaluated. Classification was then performed using a discriminant equation.

[0112] ① Substitute into the above discriminant equation to calculate the function value Y. k The evaluated individuals will be classified as those with the largest function values.

[0113] ② Calculate the posterior probability P of the k-th class. k The person being evaluated will be classified as the class with the highest posterior probability (g=6).

[0114] Y C =Max(Y k )

[0115]

[0116] k=1, the unknown subject is a healthy person. Auxiliary information reference: all conditions of the healthy person model ①②③④⑤⑥ are met (the auxiliary information reference conditions here are the same as those listed in the above sampling subject population, and the same applies below).

[0117] k=2 Unknown The assessed person is in the environmental low selenium exposure risk category. Auxiliary information reference: possess any one of the following conditions of the environmental low selenium exposure risk model: ①②③④⑤.

[0118] k=3 Unknown The assessed person is in the category of high environmental selenium exposure risk. Auxiliary information reference: possess any one of the following conditions of the high environmental selenium exposure risk model: ①②③④⑤.

[0119] k=4 Unknown The assessed person is in the environmental low manganese exposure risk category. Auxiliary information reference: possesses any one of the following conditions of the environmental low manganese exposure risk model: ①②③④⑤.

[0120] k=5 Unknown The assessed person is in the environmental high manganese exposure risk category. Auxiliary information reference: possess any one of the following conditions of the environmental high manganese exposure risk model: ①②③④⑤.

[0121] k=6 Unknown The assessed individual is classified as having environmental low calcium exposure risk. Supporting information: Possesses any one of the following conditions from the environmental low calcium exposure risk model: ①②③④.

[0122] 4. The degree of the subject's primary exposure risk: The magnitude of the exposure risk is assessed using a function.

[0123] Using function values ​​to assess the degree to which an individual is close to developing the disease after exposure to a risk factor provides a more intuitive assessment of the progression of exposure. The risk assessment model uses systematic classification data and model conditions.

[0124] (1) Establish a risk assessment model:

[0125] Using modeling data from the exposure risk assessment model, the new indicators MS, CS, and MC are defined as X. jmj (j,m=1,2,3); the main category is k (k=1,2,3,4,5,6); the indicator data is X. i (i = 1, 2, 3, 4, 5, ..., n); when k = 6, take... The dependent variable for the risk margin of the healthy group is Y. a The risk threshold dependent variable for the exposed group is Y. b .

[0126] Calculate the sum of squares and the sum of products of deviations from the mean, respectively:

[0127]

[0128] Establish the deviation matrix L:

[0129]

[0130] Establish a system of equations based on the deviation matrix:

[0131]

[0132] The regression coefficients β1, β2, and β3 are obtained from the system of equations.

[0133] Calculate the constant term β0:

[0134]

[0135] Establish a regression model:

[0136] Y = β0 + β1X1 + β2X2 + β3X3

[0137] (2) Assess the unknown level of primary risk for the assessed:

[0138] The new indicator results for unknown evaluated objects are used to calculate Y using the established regression equation. x :

[0139] ① Exposure risk Y a ≤Y x ≤Y b ,when The regulation defines it as an increased risk of exposure, and Y x The larger the size, the greater the risk of exposure and the greater the risk of developing clinical symptoms.

[0140] Among them, Y x The upper limit of Y xU This is the upper limit of the actual value for clinically diagnosed confirmed cases.

[0141] Y x The lower limit Y xL This is the lower limit of the actual value for healthy individuals.

[0142] ② Exposure risk Y b ≤Y x ≤Y a ,when The regulation defines it as an increased risk of exposure, and Y x The smaller the size, the greater the risk of exposure and the greater the risk of developing clinical symptoms.

[0143] Among them, Y x The upper limit of Y xU This represents the upper limit of the actual value for healthy individuals.

[0144] Y x The lower limit Y xL This is the lower limit of the actual value for a clinically diagnosed confirmed case.

[0145] 5. Indicators that need to be monitored for abnormalities in the assessed individuals: primary risk indicators, secondary risk indicators, and reference indicators.

[0146] (1) Establish reference value range for indicators:

[0147] The new indicators MS, CS, MC and Se, Mn, Ca are defined as X. j (j = 1, 2, 3, 4, 5, 6); the indicator data is X. i The number of cases is n (i = 1, 2, 3, 4, 5, ..., n); calculate the upper limit of the reference range U for MS, CS, MC, S, M, and C in healthy individuals. kjand the lower limit of the reference range L kj (k=1):

[0148]

[0149] ① When the environmental exposure risk classification includes low exposure and high exposure, uα / 2 is used for calculation. With 95% range as reference, uα / 2 = 1.96; with 99% range as reference, uα / 2 = 2.58.

[0150] The reference range for healthy individuals is (L) 1j U 1j ).

[0151] ② When the environmental exposure risk classification only includes low exposure, use u α When performing calculations, taking a 95% range as a reference, we take u... α =1.64; taking the 99% range as a reference, take u a =2.33;

[0152] The reference range for healthy individuals is (L) 1j U max ).

[0153] (2) Assessing unknown abnormal indicators of the assessed individuals:

[0154] For unknown subjects being evaluated, compare the actual value X obtained. j With the corresponding (L) 1j U 1j ) or (L 1j U max The size of ) is evaluated.

[0155] 6. Comprehensive assessment of the environmental exposure risks of unknown subjects.

[0156] Using MS, CS, and MC indicators, and employing functions and probabilities for assessment and calculation, this system can prioritize the classification and degree of human exposure risk under complex exposure risk conditions. The system prioritizes identifying anomalous indicators within the classification as primary risk indicators; anomalous indicators outside the classification as secondary risk indicators; and single-factor exposures outside the classification as reference indicators, thereby achieving a comprehensive assessment of environmental exposure risk.

[0157] Table 1 shows the changes in indicators under different environmental exposure risk conditions for a single risk (- indicates normal; ↑ indicates increase; ↓ indicates decrease; x indicates unknown). x represents the combined sub-risk indicators when multiple risk exposures occur, as well as the reference indicators.

[0158] Table 1. Changes in indicators of single-factor environmental exposure risk of Mn, Se, and Ca

[0159]

[0160] Main risk indicators: Category indicators Mn, Se, Ca judged by functions and probabilities, and MS under each category.

[0161] Abnormal indicators in CS and MC;

[0162] Sub-risk indicators: Abnormal indicators of MS, CS, and MC outside the category;

[0163] Reference indicators: Abnormal indicators of Mn, Se, and Ca outside the category. These indicators only provide reference information for indicators outside the category.

[0164] Under environmental exposure risk, the number of abnormal indicators varies depending on the complexity of the actual exposure. Within a risk category, the number of primary risk indicators is related to the extent of exposure; the greater the exposure and the longer the duration, the more abnormal indicators appear. Therefore, within each category, there are typically 1-3 primary risk indicators. Furthermore, the environmental risk exposure assessment system uses functions to evaluate the degree of exposure risk.

[0165] Accordingly, the comprehensive evaluation results of the assessed individuals include: ① risk exposure classification; ② degree of risk exposure under each category; ③ primary risk indicators or secondary risk anomaly indicators under each category, as well as other anomaly reference indicators.

[0166] The risk exposure classification and indicator summary are shown in Table 2:

[0167] Table 2. Main Risk Classification and Indicators for Complex Mn, Se, and Ca Environmental Exposure Risks

[0168]

[0169] Environmental exposure risk level (Table 3): The risk function value of the assessed person is between the healthy population threshold and the disease threshold. The greater the deviation from the healthy population threshold, the greater the exposure risk level; the closer the risk function value is to the disease threshold, the greater the exposure risk level.

[0170] Table 3. Risk Assessment of Exposure to Complex Mn, Se, and Ca Environments

[0171]

[0172]

[0173] 7. Operational procedures for comprehensive environmental exposure risk assessment of unknown subjects

[0174] This comprehensive evaluation method can serve as the foundation for a program that completes all calculations and reporting processes by running the program. This includes: the main operating modules of the detection instrument, the embedded operating modules of existing detection instruments, the main operating system of the simple reporting system, the embedded operating system of the existing simple reporting system, the main operating system of the reporting system, and the embedded operating system of the existing reporting system. Elemental detection data can be obtained using mass spectrometers, spectrometers, etc.

[0175] ① For example, when used as a module of a testing instrument, a pre-built model can be used or it can be connected to a reporting system.

[0176] The general process is as follows:

[0177] The first step is to place the sample to be tested into the detector dish of the instrument equipped with the evaluation module;

[0178] The second step is to click the "Inject" button on the instrument, select the target element, and then click the "Analyze" option.

[0179] The third step is to transfer the measured values ​​to the evaluation module and click the "Evaluation Analysis" option;

[0180] The fourth step is to output an electronic testing report. The report results include: ① exposure risk classification; ② exposure risk level under each category; ③ primary and secondary risk anomaly indicators under each category, as well as other anomaly reference indicators.

[0181] Step 5: Output a printed version of the test report.

[0182] ②For example, when used as a module in a data entry system, the general process is as follows:

[0183] The first step is to obtain measurement data using testing instruments;

[0184] The second step is to enter the reporting system interface, fill in the basic information of the object to be tested, and fill in the measurement data.

[0185] Third, click the "Evaluation and Analysis" option;

[0186] The fourth step is to output an electronic testing report. The report results include: ① exposure risk classification; ② exposure risk level under each category; ③ primary and secondary risk anomaly indicators under each category, as well as other anomaly reference indicators.

[0187] Step 5: Fill in the environmental exposure information for the main risk category of the tested object, select the preset conditions option under the category, and confirm. A printable test report can then be generated.

[0188] Step 6: Click the "Report" option, and the information and results of the detected object will be transmitted to the administrator's server via the network.

[0189] The seventh step involves managers summarizing and analyzing the submitted data, completing a management report, and periodically calibrating the testing instruments using the pre-set models.

Claims

1. A non-invasive assessment model for exposure risk of manganese, selenium and calcium in complex environment, characterized in that: a. selection of sampling objects: sampling objects are selected from healthy population, environmental low-selenium population, environmental high-selenium population, environmental low-manganese population, environmental high-manganese population and environmental low-calcium population; b. data collection: the content of manganese, selenium and calcium in the hair or nails of the sampling objects is collected; c. data processing: the content of manganese, selenium and calcium in each sampling object is compared to obtain modeling data, or at least one of the content of manganese, selenium and calcium is scaled before being compared to obtain modeling data; and the content of manganese, selenium and calcium in the modeling data of different sampling objects is consistent in comparison; d. establishment of exposure risk assessment model: a discriminant equation is established by using the modeling data of different population sampling objects to obtain the exposure risk assessment model. The content of manganese, selenium and calcium is compared to obtain modeling data. The content of calcium in each sampling object is scaled. The content of calcium in each sampling object is scaled by 5 to 10,000 times. The content of calcium in each sampling object is scaled by 100 times.

2. The complex environmental manganese, selenium, calcium exposure risk non-invasive assessment model according to claim 1, characterized in that: The content of manganese and selenium in each sampling object is scaled.

3. The complex environmental manganese, selenium, calcium exposure risk non-invasive assessment model according to claim 1, characterized in that: The content of manganese and selenium in each sampling object is scaled by 5 to 10,000 times.

4. The complex environmental manganese, selenium, calcium exposure risk non-invasive assessment model according to claim 3, characterized in that: The content of manganese and selenium in each sampling object is scaled by 100 times.

5. The complex environmental manganese, selenium, calcium exposure risk non-invasive assessment model according to claim 4, characterized in that: A linear regression equation is established by using the modeling data to obtain a risk degree assessment model.

6. The complex environmental manganese, selenium, calcium exposure risk non-invasive assessment model according to claim 1, characterized in that:

10. An exposure risk assessment method based on the non-invasive assessment model for exposure risk of manganese, selenium and calcium in complex environment according to claim 1, characterized in that: a. collecting the content of manganese, selenium and calcium in the hair or nails of the subject to be assessed; b. obtaining assessment data according to the processing method of the content of manganese, selenium and calcium in the modeling data when establishing the exposure risk assessment model; c. calculating the assessment data in the discriminant equation to determine the main risk category.

7. The complex environmental manganese, selenium, calcium exposure risk non-invasive assessment model according to claim 6, characterized in that: The main risk category is determined by posterior probability.

8. The complex environmental manganese, selenium, calcium exposure risk non-invasive assessment model according to claim 7, characterized in that: A linear regression equation is established by using the modeling data to obtain a risk degree assessment model, and the assessment data is calculated in the linear regression equation to determine the main risk degree.

9. The complex environmental manganese, selenium, calcium exposure risk non-invasive assessment model according to claim 1, characterized in that: An abnormal index is determined according to the reference value range of a single element and the two-by-two ratio of each element to determine the secondary risk. The subject collects the content of manganese, selenium and calcium by a detection instrument, and the collected content of manganese, selenium and calcium is calculated by a computer program and then input into the exposure risk assessment model in the program to output an assessment report. The content of manganese, selenium and calcium collected by the detection instrument is input into the computer program manually or transmitted directly to the computer program. The output assessment report is output by a printer or displayed on a display. The detection instrument for collecting the content of manganese, selenium and calcium is a mass spectrometer or a spectrometer.

11. The exposure risk assessment method of claim 10, wherein: ​ 12. Exposure risk assessment method according to claim 10 or 11, characterized in that: ​ 13. The exposure risk assessment method according to claim 10 or 11, characterized in that: ​ 14. The exposure risk assessment method according to claim 10 or 11, characterized in that: ​ 15. The exposure risk assessment method of claim 14, wherein: ​ 16. The method of exposure risk assessment of claim 14, wherein: ​ 17. The method of exposure risk assessment of claim 14, wherein: ​

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