CN114324662A - Serum biomarker for diagnosing or preventing diabetes, detection reagent and application thereof - Google Patents
Serum biomarker for diagnosing or preventing diabetes, detection reagent and application thereof Download PDFInfo
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Abstract
The invention relates to a serum biomarker for diagnosing or preventing diabetes, a detection reagent and application thereof. The serum biomarker for diagnosing or preventing diabetes provided by the invention comprises one or more of 2-hydroxycaproic acid, phenyllactic acid, phenylalanine, glycolic acid, hydroxypropionic acid, valine, acetyltryptophan, histidine, nonadecanoic acid, linoleic acid, oxalic acid, isoleucine, leucine, picolinic acid, methylmalonic acid, acetic acid, p-hydroxyphenylacetic acid, o-hydroxyphenylacetic acid, glyceric acid, glycine, alanine, aspartic acid, lysine, methionine, proline, serine, tyrosine or pelargonic acid. The serum biomarker provided by the invention has high sensitivity and specificity and good predictability, and can be used for early diagnosis or prevention of diabetes patients, particularly people who are exposed to diabetes by combining long-term low-dose radiation and high-sugar high-fat diet.
Description
Technical Field
The invention designs a serum biomarker and a detection reagent for diagnosing or preventing diabetes and application thereof.
Technical Field
With the widespread use of nuclear power, isotopes, and radiation therapy, there are more and more opportunities for long-term low-dose ionizing radiation exposure. Meanwhile, the change of the dietary structure leads the population suffering from diabetes mellitus caused by the high-sugar and high-fat diet mode to be increased continuously. The health impact of long-term low-dose radiation in combination with high-sugar, high-fat diets is becoming a growing concern.
There are considerable radioactive ore resources and radioactive ore waste residues on the earth, and various rays released by the radioactive ore resources and the radioactive ore waste residues cause long-term potential low-dose radiation damage to microorganisms, animals, plants and human beings through ionization and excitation. For example, China has a plurality of places where solid wastes such as uranium waste stones and uranium tailings are stacked, the uranium waste stones and the uranium tailings contain various radionuclides, and the radionuclides in the environment can release various rays through ionization and excitation to cause radiation damage to organisms and can enter the organisms to cause internal irradiation damage to the organisms, so that health is threatened. Furthermore, there is an increasing risk of long term exposure of humans to low doses of radiation. Medical personnel in the radiotherapy department operate X-rays for a long time to carry out physical examination or diagnosis on normal people and examine patients; military personnel carry out weapon armor and metal quality detection for a long time; and the security personnel of the country carry out security inspection and the like.
According to the 1986 report of the united nations committee on atomic radiation effects science (unc), low LET (energy transmission linear density) radiation at a dose within 0.2Gy and high LET radiation at a dose within 0.05Gy, with radiation at a dose rate within 0.05mGy/min, are defined as low-level radiation with respect to human irradiation. In practical studies, radiation with an irradiation dose meeting the above conditions and a dose rate higher than 0.05mGy/min is called low dose ionizing radiation (LDR). Research shows that long-term low-dose radiation exposure can cause irreversible damage to organisms and possibly cause adverse reactions such as malignant tumors, blood system diseases, inflammation fibrosis and the like. After exposure to low doses of ionizing radiation, individuals may not develop obvious symptoms for hours or days, or may not exhibit progressive damage to the hematopoietic and immune systems and carcinogenic effects until years have passed.
Diabetes Mellitus (DM) is a metabolic disease characterized by elevated blood sugar due to insufficient insulin secretion or insulin resistance, which is influenced by environmental and genetic factors. At present, the death rate caused by diabetes is high, and the diabetes is second to cardiovascular diseases and malignant tumors in developed countries. Complications from long-term diabetes can lead to organ dysfunction and failure. Researches show that long-term low-dose radiation exposure can promote the occurrence and development of diabetes, the traditional diabetes diagnosis usually pays attention to the establishment of blood sugar standards, high-sugar and high-fat diet is taken as a risk factor to influence the onset of the diabetes, and the influence of physical factors such as ionizing radiation in the environment on the organism is ignored. Therefore, the development of a diabetes diagnosis serum biomarker simultaneously considering the exposure of environmental ionizing radiation and high-sugar and high-fat factors has great significance for the evaluation of the risk of early low-dose radiation and the diabetes management of exposed people.
Disclosure of Invention
Aiming at the problems in the prior art, the inventor of the invention carries out metabonomics analysis on mouse serum by an ultra-high performance liquid chromatography-tandem mass spectrometry technology to obtain a serum biomarker for diagnosing or preventing diabetes, has high sensitivity and specificity and good predictability, and can be used for early diagnosis or prevention of diabetes patient groups, particularly groups exposed to diabetes by combining long-term low-dose radiation and high-sugar high-fat diet.
To this end, a first aspect of the present invention provides a serum biomarker for diagnosing or preventing diabetes, the serum biomarker comprising one or more of 2-hydroxyhexanoic acid, phenyllactic acid, phenylalanine, glycolic acid, hydroxypropionic acid, valine, acetyltryptophan, histidine, nonadecanoic acid, linoleic acid, oxalic acid, isoleucine, leucine, picolinic acid, methylmalonic acid, acetic acid, p-hydroxyphenylacetic acid, o-hydroxyphenylacetic acid, glyceric acid, glycine, alanine, aspartic acid, lysine, methionine, proline, serine, tyrosine, tryptophan, arachidonic acid, hydroxyphenyllactic acid, adipic acid, 2-hydroxybutyric acid, phenylacetic acid or pelargonic acid.
According to some embodiments of the invention, the serum biomarker comprises one or more of 2-hydroxycaproic acid, phenyllactic acid, phenylalanine, glycolic acid, hydroxypropionic acid, valine, acetyltryptophan, histidine, nonadecanoic acid, linoleic acid, oxalic acid, isoleucine, leucine, picolinic acid, methylmalonic acid, acetic acid, p-hydroxyphenylacetic acid, o-hydroxyphenylacetic acid, glyceric acid, glycine, alanine, aspartic acid, lysine, methionine, proline, serine, tyrosine or pelargonic acid.
According to some embodiments of the invention, the serum biomarker comprises 2-hydroxycaproic acid, phenyllactic acid, phenylalanine, glycolic acid, hydroxypropionic acid, valine, acetyltryptophan, histidine, nonadecanoic acid, linoleic acid, oxalic acid, isoleucine, leucine, picolinic acid, methylmalonic acid, acetic acid, p-hydroxyphenylacetic acid, o-hydroxyphenylacetic acid, glyceric acid, glycine, alanine, aspartic acid, lysine, methionine, proline, serine, tyrosine and nonanoic acid.
According to some embodiments of the invention, the serum biomarker further comprises tryptophan and arachidonic acid.
According to some embodiments of the invention, the serum biomarker further comprises hydroxyphenyllactic acid, adipic acid, 2-hydroxybutyrate, and phenylacetic acid.
According to some embodiments of the invention, the serum biomarker is derived from serum.
According to some embodiments of the invention, the serum biomarkers are obtained by a metabolomics approach based on combined liquid chromatography-mass spectrometry analysis.
According to some embodiments of the invention, the diabetes is diabetes resulting from chronic low dose radiation exposure in combination with a high-sugar, high-fat diet.
The inventor of the invention conducts metabonomics analysis on mouse serum by an ultra-high performance liquid chromatography-tandem mass spectrometry technology, and conducts model construction and discriminant analysis on response intensity data of substance peaks in all samples, thereby finding characteristic differential metabolites between normal mice and mice exposed by combination of long-term low-dose radiation and high-sugar and high-fat diet, further analyzing content change of the characteristic differential metabolites, and finding specific differential metabolites caused by the combination exposure of long-term low-dose radiation and high-sugar and high-fat diet, namely early diagnosis molecular markers of diabetes caused by the combination exposure of long-term low-dose radiation and high-sugar and high-fat diet. The method for obtaining the serum biomarker has the characteristics of convenience and rapidness, can accurately reflect the metabolic spectrum difference between a mouse exposed by combining long-term low-dose radiation and high-sugar and high-fat diet and a normal mouse, and has high specificity.
In a second aspect of the present invention, there is provided a detection reagent for a serum biomarker for diagnosing or preventing diabetes, the detection reagent comprising a reagent for detecting the serum biomarker described in the first aspect.
In a third aspect of the present invention, there is provided a test kit for serum biomarkers for diagnosing or preventing diabetes, which comprises the test reagent of the second aspect.
In a fourth aspect, the present invention provides a use of a serum biomarker for the manufacture of a kit for diagnosing or preventing diabetes, wherein the serum biomarker comprises one or more of 2-hydroxycaproic acid, phenyllactic acid, phenylalanine, glycolic acid, hydroxypropionic acid, valine, acetyltryptophan, histidine, nonadecanoic acid, linoleic acid, oxalic acid, isoleucine, leucine, picolinic acid, methylmalonic acid, acetic acid, p-hydroxyphenylacetic acid, o-hydroxyphenylacetic acid, glyceric acid, glycine, alanine, aspartic acid, lysine, methionine, proline, serine, tyrosine, tryptophan, arachidonic acid, hydroxyphenyllactic acid, adipic acid, 2-hydroxybutyric acid, phenylacetic acid or nonanoic acid.
According to some embodiments of the invention, the serum biomarker comprises one or more of 2-hydroxycaproic acid, phenyllactic acid, phenylalanine, glycolic acid, hydroxypropionic acid, valine, acetyltryptophan, histidine, nonadecanoic acid, linoleic acid, oxalic acid, isoleucine, leucine, picolinic acid, methylmalonic acid, acetic acid, p-hydroxyphenylacetic acid, o-hydroxyphenylacetic acid, glyceric acid, glycine, alanine, aspartic acid, lysine, methionine, proline, serine, tyrosine or pelargonic acid.
According to some embodiments of the invention, the serum biomarker comprises 2-hydroxycaproic acid, phenyllactic acid, phenylalanine, glycolic acid, hydroxypropionic acid, valine, acetyltryptophan, histidine, nonadecanoic acid, linoleic acid, oxalic acid, isoleucine, leucine, picolinic acid, methylmalonic acid, acetic acid, p-hydroxyphenylacetic acid, o-hydroxyphenylacetic acid, glyceric acid, glycine, alanine, aspartic acid, lysine, methionine, proline, serine, tyrosine and nonanoic acid.
According to some embodiments of the invention, the serum biomarker further comprises tryptophan and arachidonic acid.
According to some embodiments of the invention, the serum biomarker further comprises hydroxyphenyllactic acid, adipic acid, 2-hydroxybutyrate, and phenylacetic acid.
According to some embodiments of the invention, the kit comprises a substance for detecting the serum biomarker and optionally a standard for the serum biomarker.
According to some embodiments of the invention, the level of the serum biomarker is determined by a metabolomics method based on combined liquid chromatography-mass spectrometry analysis.
According to some embodiments of the invention, the level of the serum biomarker in the serum sample of the subject is determined by a metabolomics method based on combined liquid chromatography-mass spectrometry analysis.
According to some embodiments of the invention, the diabetes is diabetes resulting from chronic low dose radiation exposure in combination with a high-sugar, high-fat diet.
The invention also provides a using method of the kit, which comprises the following steps:
1) collecting a serum sample from a subject, and carrying out pretreatment to obtain a sample to be detected, wherein the sample can be subjected to marker determination;
2) determining the level of the serum biomarker in the test sample obtained in step 1);
3) comparing the level of the serum biomarker obtained in step 2) with a reference value for a healthy control.
According to some embodiments of the invention, the healthy control is a standard for the serum biomarker.
Compared with the prior art, the invention has the following beneficial effects:
(1) the serum biomarker for early diagnosis or prevention of diabetes, particularly diabetes caused by combined exposure of long-term low-dose radiation and high-sugar high-fat diet, provided by the invention, has sensitivity and specificity of more than 0.9, and area under the curve (AUC) of more than 0.9, and shows that the serum biomarker provided by the invention is good in prediction.
(2) The invention carries out metabonomics analysis on the mouse serum by the ultra-high performance liquid chromatography-tandem mass spectrometry technology, is convenient and quick, can accurately reflect the metabolic spectrum difference between a long-term low-dose radiation and high-sugar high-fat diet combined exposed mouse and a normal mouse, has high specificity, and can be used for early diagnosis or prevention of diabetic patients, particularly the patients with long-term low-dose radiation and high-sugar high-fat diet combined exposed diabetes.
Drawings
FIG. 1 shows the principal component analysis plots of metabolites obtained from mice of different treatment groups according to example 1 of the present invention, PC1 represents the first principal component, PC2 represents the second principal component, PC3 represents the third principal component, and the percentages represent the contribution rates of the principal components to the differences in the samples.
FIG. 2 shows a cluster analysis graph of differentially expressed metabolites obtained according to example 1 of the present invention.
FIG. 3 shows a functional predictive analysis of differentially expressed metabolites obtained according to example 1 of the present invention.
FIG. 4 shows the deterioration of diabetes-related indices in mice of different treatment groups obtained according to example 1 of the present invention.
Figure 5 shows different treatment groups of mice in example 2 according to the invention.
Fig. 6 shows representative electron microscopy pictures (20000X) of intestinal structures of mice of different treatment groups obtained according to example 2 of the present invention.
FIG. 7 shows mRNA-related expression levels of different treatment groups of intestinal Claudin ZO-1, Occuludin, Claudin-1 and Claudin-2 obtained according to example 2 of the present invention.
FIG. 8 shows immunohistochemical detection results (scale bar: 100. mu.M) of the tight junction proteins Occludin and ZO-1 in intestinal tissues of different treatment groups obtained according to example 2 of the present invention.
FIG. 9 shows the effect of different concentrations of proline on the expression level of intestinal claudin mRNA in HCT116 cell line obtained according to example 2 of the present invention.
Detailed Description
The following further description of specific embodiments of the invention, taken in conjunction with the examples and the accompanying drawings, is intended to illustrate, but not limit the invention.
Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art. The reagents and materials used in the following examples are all commercially available products.
In the invention, orthogonal partial least squares discriminant analysis (OPLS-DA) can decompose X matrix information into two types of information which are related to Y and irrelevant, and screen difference variables by removing irrelevant differences. The specific process is as follows: carrying out logarithmic transformation and UV formatting on the data by using SIMCA software, firstly carrying out OPLS-DA modeling analysis on the first main component, and checking the quality of the model by 7-fold cross validation (7-fold cross validation); then judging the effectiveness of the model by using R2Y (model interpretation degree of the classification variable Y) and Q2 (model predictability) obtained after cross validation; finally, through a permutation test (permationtest), the arrangement sequence of the classification variables Y is changed for 200 times randomly to obtain different random Q2 values, so that the validity of the model is further tested.
In the present invention, one-way ANOVA (one-way ANOVA), also called F-test, is a statistical inference method for inferring whether the overall mean represented by two or more sample means is different by analyzing the variation of data. Simply stated, it is a method to check whether different levels of the same influencing factor have an effect on the factor. Like the conventional statistical inference problem, the task of ANOVA is to first propose the original hypothesis H0 and the alternative hypothesis H1 according to the actual situation, and then to find the appropriate test statistic for hypothesis testing.
In the present invention, the P value, i.e., the probability, reflects the probability of occurrence of a certain event. The significance of the P value obtained by statistics according to the significance test method is generally that P is less than 0.05, and P is less than 0.01, which means that the probability of the difference between samples caused by sampling errors is less than the probability of the P value, namely the probability, and reflects the possibility of certain event.
Example 1
(1) Experiments were performed with 6-8 week old healthy mice divided into four experimental groups of 3 mice each, respectively: normal control group (Con), low dose radiation group (LDR), high sugar and high fat diet group (HFD), high sugar and high fat diet in combination with low dose radiation group (LDR + HFD); the normal control group was not treated, and the mice in the low-dose radiation group were continuously irradiated for 10 weeks by 0.05Gy of cobalt 60 radiation for 1 time per week, while the mice in the high-sugar and high-fat diet group were fed with the high-sugar and high-fat diet combined with the low-dose radiation group of high-sugar and high-fat diet and 1 cobalt 60 radiation for 1 time per week, at a dose of 0.05Gy, for 10 weeks. The mice were housed for 3 months.
(2) Collecting serum samples of four experimental mice respectively, adding 90 μ L methanol into 10 μ L serum samples, mixing uniformly by vortex, precipitating protein at 4 deg.C overnight, centrifuging at 4 deg.C for 10min at 10000g, and collecting supernatant to obtain test solution; the samples were thawed on an ice bath to reduce sample degradation. Approximately 10mg of the sample was transferred to a 1.5mL centrifuge tube and milled for 3 minutes by adding 25. mu.L of water and zirconia beads. The metabolite was extracted by addition of 185. mu.L acetonitrile/methanol (8/2). After centrifugation at 18000g for 20min, the supernatant was transferred to a 96-well plate, 20. mu.L of derivatizing agent was added to each well, and derivatization was carried out at 30 ℃ for 60 min. Dilute by adding 350. mu.L of 50% ice cold methanol. Refrigerating at-20 deg.C for 20min, and centrifuging at 4000g and 4 deg.C for 30 min. Aspirate 135. mu.L of supernatant with 15. mu.L of internal standard in a new 96-well plate before on-machine detection.
(3) The test solution was detected by ultra performance liquid chromatography-tandem mass spectrometry for the quantification of metabolites in this example. Using conventional chromatographic conditions: pre-column: ACQUITY
BEHC18 column (2.1X 5mm, 1.7 μm, Waters Corp.), analytical column: ACQUITY
BEHC18 column (2.1X 100mm, 1.7 μm, Waters corporation, USA), sample size 5 μ L, column temperature 40 deg.C, mobile phase A-0.1% formic acid, mobile phase B-acetonitrile/IPA (90:10), flow rate 0.4 mL/min; the gradient elution procedure was as follows: 0-1min, 5% B; 1-12min, 5-80% B; 12-15min, 80-95% B; 15-16min, 95-100% B; 16-18min, 100% B; 18-18.1min, 100-5% B; 18.1-20min, 5% B. Mass spectrum MS conditions: electrospray ionization (ESI) source, positive and negative ion ionization mode. The source temperature is 150 ℃, the desolventizing temperature is 550 ℃, and the desolventizing airflow rate is 1000L/Hr.
(4) The differential metabolites were further screened using orthogonal partial least squares discriminant analysis (OPLS-DA) and the differential variables between groups were statistically analyzed by one-way anova with p < 0.05 indicating that the differences were statistically significant. Differential metabolites were considered when the variable importance in the projection (VIP) > 2, p < 0.05 and the minimum Coefficient of Variation (CV) ≧ 30. Table 1 shows differential metabolites obtained from the serum metabolomics analysis of mice, indicating the difference in expression of the different metabolites in the normal control group, the low-dose radiation group, the high-sugar high-fat diet group, and the high-sugar high-fat diet combined with the low-dose radiation group.
TABLE 1
FIG. 1 is a graph of principal component analysis of metabolites of mice in different treatment groups, wherein the abscissa PCA1 represents the first principal component, the ordinate PCA2 represents the second principal component, the ellipse represents the 95% confidence interval, the percentage represents the contribution of the principal component to the difference of the samples, the different colors represent that the samples belong to different groups, and the farther the distance between the two points is, the greater the difference of the metabolites of the two samples is. FIG. 2 is a graph of cluster analysis of the resulting differentially expressed metabolite profiles. FIG. 3 predicts cellular function associated with differentially expressed metabolites and provides a theoretical basis for further studies of the function of differentially expressed metabolites; wherein the X axis is based on pathwayimapactvalue (from the pathway topology analysis); the Y-axis is based on p-value (from the pathway enrichment analysis); the node color is based on the p value, from shallow to deep, the p value is from large to small, the node radius is based on the pathwayimapactvalue, from small to large, and the impact value is from small to large. Figure 4 shows the deterioration of diabetes related indicators P < 0.05 in the different treatment groups of mice.
Example 2
Mice were divided into four experimental groups to explore the effect of proline treatment on mice: normal control group (Con), low dose irradiation (1 irradiation of cobalt 60 ray per week at a dose of 0.05Gy for 10 consecutive weeks) combined with proline-treated group (LDR + PA), high-sugar high-fat diet group (HFD), and high-sugar high-fat diet combined with proline-treated group (HFD + PA), as shown in fig. 5. Fig. 6 shows representative electron microscopy images (20000X) of the intestinal structures of the four groups of mice, and the results show that the intestinal damage of the mice treated with low-dose radiation combined with proline is more severe than that of the normal control group, and the intestinal damage of the mice treated with high-sugar high-fat diet combined with proline is more severe than that of the high-sugar high-fat diet group.
FIGS. 7-9 show the effect of proline treatment on zon-1, Occuludin, Claudin-1 and Claudin-2, respectively, in mice from different treatment groups.
Figure 7 shows that intestinal claudin mRNA levels were significantly reduced in the low-dose radiation plus proline treated group compared to the low-dose radiation group; the intestinal claudin mRNA level was significantly reduced in the high-sugar, high-fat diet plus proline-treated group compared to the high-sugar, high-fat diet group.
FIG. 8 shows that the levels of the intestinal tight junction proteins Occuludin and ZO-1 protein were significantly reduced in the low dose radiation plus proline treated group compared to the control group; the intestinal tight junction proteins Occuludin and ZO-1 protein levels were significantly reduced in the high-sugar, high-fat diet plus proline treated group compared to the high-sugar, high-fat diet group.
FIG. 9 shows that 50. mu.M, 100. mu.M, and 150. mu.M proline treatment resulted in significantly reduced and concentration-dependent reduction of mRNA expression levels of zon-1, Claudin-1, and Claudin-2 in HCT116 cells, but did not have a significant effect on the mRNA expression level of Occuludin, as compared to the non-proline group.
It should be noted that the above-mentioned embodiments are only for explaining the present invention, and do not constitute any limitation to the present invention. The present invention has been described with reference to exemplary embodiments, but the words which have been used herein are words of description and illustration, rather than words of limitation. The invention can be modified, as prescribed, within the scope of the claims and without departing from the scope and spirit of the invention. Although the invention has been described herein with reference to particular means, materials and embodiments, the invention is not intended to be limited to the particulars disclosed herein, but rather extends to all other methods and applications having the same functionality.
Claims (10)
1. A serum biomarker for diagnosing or preventing diabetes, the serum biomarker comprising one or more of 2-hydroxyhexanoic acid, phenyllactic acid, phenylalanine, glycolic acid, hydroxypropionic acid, valine, acetyltryptophan, histidine, nonadecanoic acid, linoleic acid, oxalic acid, isoleucine, leucine, picolinic acid, methylmalonic acid, acetic acid, p-hydroxyphenylacetic acid, o-hydroxyphenylacetic acid, glyceric acid, glycine, alanine, aspartic acid, lysine, methionine, proline, serine, tyrosine, tryptophan, arachidonic acid, hydroxyphenyllactic acid, adipic acid, 2-hydroxybutyric acid, phenylacetic acid, or pelargonic acid.
2. The serum biomarker according to claim 1, wherein the serum biomarker is derived from serum.
3. The serum biomarker according to claim 1 or 2, characterized in that it is obtained by a metabolomics approach based on combined liquid chromatography-mass spectrometry analysis.
4. The serum biomarker according to any one of claims 1 to 3, wherein the diabetes is diabetes resulting from long-term exposure to a combination of low-dose radiation and a high-sugar, high-fat diet.
5. A detection reagent for a serum biomarker for diagnosing or preventing diabetes, the detection reagent comprising a reagent for detecting the serum biomarker according to any one of claims 1 to 4.
6. A test kit for a serum biomarker for diagnosing or preventing diabetes, comprising the test reagent of claim 5.
7. Use of a serum biomarker in the manufacture of a kit for diagnosing or preventing diabetes, wherein the serum biomarker comprises one or more of 2-hydroxyhexanoic acid, phenyllactic acid, phenylalanine, glycolic acid, hydroxypropionic acid, valine, acetyltryptophan, histidine, nonadecanoic acid, linoleic acid, oxalic acid, isoleucine, leucine, picolinic acid, methylmalonic acid, acetic acid, p-hydroxyphenylacetic acid, o-hydroxyphenylacetic acid, glyceric acid, glycine, alanine, aspartic acid, lysine, methionine, proline, serine, tyrosine, tryptophan, arachidonic acid, hydroxyphenyllactic acid, adipic acid, 2-hydroxybutanoic acid, phenylacetic acid, or nonanoic acid.
8. Use according to claim 7, wherein the kit comprises a substance for detecting the serum biomarker and optionally a standard for the serum biomarker.
9. Use according to claim 7 or 8, characterized in that the level of serum biomarkers is determined by a metabolomics method based on combined liquid chromatography-mass spectrometry analysis.
10. The use according to any one of claims 7 to 9, wherein the diabetes is diabetes resulting from a combination of long-term exposure to low-dose radiation and a high-sugar, high-fat diet.
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