Diagnostic marker for diagnosing ventilator-induced lung injury and application of diagnostic marker

Abstract

The invention relates to the technical field of biomedicine, in particular to a diagnostic marker for diagnosing ventilator-induced lung injury and application of the diagnostic marker. The invention provides application of a reagent for quantitatively detecting metabolites in a subject sample in preparation of a respirator induced lung injury diagnostic kit. The metabolites are selected from at least one of the following components: inosinic acid, guanosine, 3adenylate, arginine-aspartic acid, glutamyl-histidine, butyryl carnitine and phenylalanyl aspartic acid. And tryptophan-aspartic acid, and lysophosphatidylcholine (18: 0 / 0: 0). The metabolic marker for predicting and diagnosing is more representative. The metabolic markers can realize diagnosis only through blood sampling detection, do not need to additionally collect tissue specimens, can well replace or assist clinicians in timely adjustment of ventilation strategies, are beneficial to early prediction and recognition of breathing machine induced lung injury patients, and have good clinical use and popularization values.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to diagnostic markers for diagnosing ventilator-induced lung injury and applications thereof. Background Art

[0002] Acute respiratory distress syndrome (ARDS) is a common severe respiratory failure in the ICU, characterized by persistent hypoxemia. The pathogenesis of ARDS is complex, heterogeneous, and has a mortality rate of approximately 50%. Positive-pressure mechanical ventilation is the most important respiratory support method for the clinical treatment of ARDS. However, since its initial use, it has been noted that some patients experiencing lung damage have worsened. Pathological biopsies have revealed changes in lung tissue characterized by inflammatory cell infiltration, hyaline membrane formation, and increased vascular permeability. These changes are known as ventilator-induced lung injury (VILI).

[0003] The incidence of VILI is as high as 15-50%, significantly impacting the clinical efficacy and safety of mechanical ventilation. Furthermore, this complication is difficult to diagnose, severely impacting patient prognosis. VILI has been clearly demonstrated to be a major cause of death in patients with ARDS. With the widespread use of mechanical ventilation in intensive care units, the prevention and treatment of VILI has become a critical clinical issue that needs urgent attention.

[0004] Currently, VILI diagnosis relies primarily on imaging studies to identify patients experiencing barotrauma. However, early-stage injuries are often mistaken for underlying medical conditions and overlooked. Furthermore, the transportation of critically ill patients for imaging examinations carries certain risks. Therefore, a relatively non-invasive blood test is being developed to predict ventilator-induced lung injury. Summary of the Invention

[0005] The present invention covers the following technical solutions: One aspect of the present invention relates to the use of a reagent for quantitatively detecting metabolites in a subject sample in the preparation of a diagnostic kit for ventilator-induced lung injury: The metabolite is selected from at least one of the following components: inosinic acid, guanosine, 3'-adenylic acid, arginine-aspartic acid (Arg-Asp), glutamyl-histidine (Glu-His), butyrylcarnitine, phenylalanyl-aspartic acid (Phe-Asp), tryptophan-aspartic acid (Trp-Asp), and lysophosphatidylcholine (LPC 18:0 / 0:0).

[0006] Another aspect of the present invention relates to a system for diagnosing ventilator-induced lung injury, comprising: Patient information processing module and output module; The patient information processing module is used to receive information about potential patients with ventilator-induced lung injury, wherein the patient information includes at least a quantitative detection result of at least one metabolite in the patient's sample: Inosinic acid, guanosine, 3'-adenylic acid, arginine-aspartic acid, glutamyl-histidine, butyrylcarnitine, phenylalanyl-aspartic acid, tryptophan-aspartic acid, lysophosphatidylcholine (18:0 / 0:0); The output module is used to receive the information output by the patient information processing module and provide a judgment result on whether ventilator-induced lung injury exists.

[0007] Another aspect of the present invention relates to a computer-readable storage medium, which is used to store computer instructions, programs, code sets or instruction sets. When the computer-readable storage medium is run on a computer, it enables the computer to execute the functions corresponding to the patient information processing module and the output module in the system as described above.

[0008] Another aspect of the present invention relates to an electronic device, comprising: one or more processors; and Computer-readable storage medium, the computer storage medium is used to store computer instructions, programs, code sets or instruction sets, which, when running on a computer, enable the one or more processors to implement the functions corresponding to the patient information processing module and the output module in the system as described in any of the above items.

[0009] The predictive diagnostic metabolic markers constructed in this invention are even more representative. These metabolic markers can be diagnosed solely through blood sampling, without the need for additional tissue sampling, effectively replacing or assisting clinicians in timely adjustments to ventilation strategies. This simple, rapid, and relatively non-invasive method facilitates the early prediction and identification of patients with ventilator-induced lung injury, and holds great clinical and potential for widespread adoption. DETAILED DESCRIPTION

[0010] Reference will now be made in detail to embodiments of the present invention, one or more examples of which are described below. Each example is provided to illustrate, not to limit, the present invention. Indeed, it will be apparent to those skilled in the art that various modifications and variations may be made to the present invention without departing from the scope or spirit of the invention. For example, features illustrated or described as part of one embodiment may be used in another embodiment to produce further embodiments.

[0011] Unless otherwise indicated, all terms (including technical and scientific terms) used to disclose the present invention have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. By way of further guidance, the following definitions are provided to better understand the teachings of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0012] Unless otherwise indicated, scientific and technical terms used herein have the meanings commonly understood by those skilled in the art. Furthermore, terms and laboratory procedures related to protein and nucleic acid chemistry, molecular biology, cell and tissue culture, microbiology, and immunology used herein are those widely used in the respective fields and are standard procedures. To facilitate a better understanding of the present invention, definitions and explanations of relevant terms are provided below.

[0013] The terms "and / or", "or / and", and "and / or" used herein include any one of two or more related listed items, and also include any and all combinations of the related listed items, wherein the any and all combinations include any combination of two related listed items, any more related listed items, or all related listed items. It should be noted that when at least three items are connected by at least two conjunctions selected from "and / or", "or / and", and "and / or", it should be understood that in the present invention, the technical solution undoubtedly includes technical solutions connected by "logical and" and also undoubtedly includes technical solutions connected by "logical or". For example, "A and / or B" includes three parallel solutions of A, B and A+B. For example, the technical solution of "A, and / or, B, and / or, C, and / or, D" includes any one of A, B, C, and D (that is, the technical solution of all being connected by "logical OR"), and also includes any and all combinations of A, B, C, and D, that is, the combination of any two or any three of A, B, C, and D, and also includes the four-item combination of A, B, C, and D (that is, the technical solution of all being connected by "logical AND").

[0014] As used herein, the terms "comprising," "including," and "comprising" are synonymous and are inclusive or open-ended and do not exclude additional, unrecited members, elements, or method steps.

[0015] The recitation of numerical ranges herein by endpoints includes all numbers and fractions subsumed within the range, as well as the recited endpoints.

[0016] The term "about" or "approximately" used in the present invention means within 20% of a given value or range, preferably within 10%, more preferably within 5%. It also includes specific numbers, for example, about 20 includes 20.

[0017] In addition, when describing representative embodiments of the present invention, this specification may present the method and / or process of the present invention as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of steps set forth herein, the method or process should not be limited to the specific order of steps described. As one of ordinary skill in the art will appreciate, other sequences of steps are also possible. Therefore, the specific order of steps set forth in the specification should not be interpreted as limiting the claims. In addition, claims to the method and / or process of the present invention should not be limited to the execution of the steps in the order in which they are written, and those skilled in the art will readily recognize that the sequence can be changed and still remain within the spirit and scope of the present invention.

[0018] Concentration values ​​used in this invention include fluctuations within a certain range. For example, fluctuations within a certain precision range are permitted. For example, for a 2% value, fluctuations within a range of ±0.1% are permitted. For larger values ​​or values ​​that do not require overly precise control, greater fluctuations are permitted. For example, for 100 mM, fluctuations within ranges of ±1%, ±2%, ±5%, etc. are permitted. Regarding molecular weight, fluctuations within a range of ±10% are permitted.

[0019] As used herein, the singular articles "a," "an," and "the" include plural referents unless otherwise indicated.

[0020] In the present invention, descriptions such as "plurality" and "multiple" refer to quantities greater than or equal to 2 unless otherwise specified.

[0021] In the present invention, the technical features described in an open manner include closed technical solutions composed of the listed features, and also include open technical solutions containing the listed features.

[0022] In the present invention, "preferably", "better", "more preferably", and "suitably" are merely descriptions of preferred implementation methods or examples, and should be understood to not limit the scope of protection of the present invention. In the present invention, "optionally", "optional", and "optional" refer to being optional, that is, to being selected from either of the two parallel options of "with" or "without". If multiple "options" appear in a technical solution, unless otherwise specified and without contradiction or mutual restriction, each "optional" is independent.

[0023] In the present invention, the term "subject" or "patient" may refer to an animal that is treated with the kits or methods described herein for diagnosis of a disease, condition, or symptom described herein. Subjects include warm-blooded animals, such as mammals, such as pandas and elephants, primates (chimpanzees, orangutans, gibbons, macaques, and marmosets), and preferably humans. Non-human primates are also individuals. The term "individual" includes domesticated animals, such as cats and dogs, livestock (e.g., cattle, horses, pigs, sheep, goats, etc.), and laboratory animals (e.g., mice, rabbits, rats, gerbils, guinea pigs, etc.).

[0024] In the present invention, the term "diagnosis" refers to the process of systematically analyzing an individual's clinical manifestations, physiological parameters, biochemical indicators, imaging results, histopathology, genomic or proteomic data, medical history information, and other medical testing methods to identify diseases, pathological conditions, functional abnormalities, pathological types, or risk trends. Diagnosis includes, but is not limited to, preliminary diagnosis, definitive diagnosis, differential diagnosis, typing diagnosis, and prognostic diagnosis. The term "prognostic diagnosis" specifically refers to the assessment of the future development trend of the disease, possible complications, treatment responsiveness, and long-term outcomes, and especially includes the predictive assessment of respiratory support-related diseases, such as the risk assessment of ventilator-induced lung injury (VILI), the prediction of the course of the disease, and the judgment of its interaction with primary lung diseases (such as emphysema, ARDS, etc.). This definition is applicable to various technical fields such as artificial intelligence-assisted diagnosis systems, biomarker screening methods, image analysis algorithms, and risk stratification models.

[0025] The first aspect of the present invention relates to the use of a reagent for quantitatively detecting metabolites in a subject sample in the preparation of a diagnostic kit for ventilator-induced lung injury: The metabolite is selected from at least one of the following components: inosinic acid, guanosine, 3'-adenosine, arginine-aspartic acid, glutamyl-histidine, butyrylcarnitine, phenylalanyl aspartic acid, tryptophan-aspartic acid, and lysophosphatidylcholine (18:0 / 0:0).

[0026] The present invention adopts the above metabolites as diagnostic markers for ventilator-induced lung injury.

[0027] As used herein, the term "biomarker" or "marker" generally refers to a molecule (including a gene, protein, amino acid, carbohydrate structure, or glycolipid) that is expressed or secreted in mammalian tissues, cells, or body fluids, or on mammalian tissues or cells and can be detected by known methods (or methods disclosed herein) and that predicts or can be used to predict (or assist in predicting) the occurrence and progression of ventilator-induced lung injury. In some embodiments, it predicts (or assists in predicting) an individual's responsiveness to a treatment regimen.

[0028] In some embodiments, the metabolites are selected from at least 2, 3, 4, 5, 6, 7, 8, or 9 of the following: inosinic acid, guanosine, 3'-adenosine, arginine-aspartic acid, glutamyl-histidine, butyrylcarnitine, phenylalanyl-aspartic acid, tryptophan-aspartic acid, and lysophosphatidylcholine (18:0 / 0:0).

[0029] In some embodiments, the metabolites comprise at least glutamyl-histidine.

[0030] In some embodiments, the metabolites comprise at least arginine-aspartate.

[0031] In some embodiments, the metabolite comprises at least 3'-adenylic acid.

[0032] In some embodiments, the metabolites comprise at least tryptophan-aspartate.

[0033] In some embodiments, the metabolites include at least one of glutamyl-histidine, arginine-aspartic acid, 3'-adenosine, and tryptophan-aspartic acid.

[0034] In some embodiments, the sample is selected from blood, serum or plasma. Preferably, the blood is peripheral blood.

[0035] In some embodiments, the reagent is used to implement any of the following methods: Liquid chromatography-tandem mass spectrometry, ultra-performance liquid chromatography-high-resolution mass spectrometry, capillary electrophoresis-mass spectrometry, high-performance liquid chromatography coupled with detector, gas chromatography, nuclear magnetic resonance, infrared spectroscopy and Raman spectroscopy, biosensor arrays, microfluidic chip-electrochemical detection, ion mobility spectrometry-mass spectrometry (IMS-MS), paper spray mass spectrometry (Paper Spray-MS), mass spectrometry imaging (MSI), enzyme-linked immunosorbent assay, microdialysis coupled analysis, and chemiluminescence immunoassay.

[0036] A second aspect of the present invention relates to a system for diagnosing ventilator-induced lung injury, comprising: Patient information processing module and output module; The patient information processing module is used to receive information about potential patients with ventilator-induced lung injury, wherein the patient information includes at least a quantitative detection result of at least one metabolite in the patient's sample: Inosinic acid, guanosine, 3'-adenosine, arginine-aspartic acid, glutamyl-histidine, butyrylcarnitine, phenylalanyl-aspartic acid, tryptophan-aspartic acid, lysophosphatidylcholine (18:0 / 0:0); The output module is used to receive the information output by the patient information processing module and provide a judgment result on whether ventilator-induced lung injury exists.

[0037] In some embodiments, the patient information also includes one or more of the patient's photo, age, gender, height, weight, eating habits, medication history, mood, time from symptom onset to medical treatment, family history of genetic diseases, smoking frequency, exercise type and frequency.

[0038] Provided that there is no logical contradiction, the description of the first aspect of the present invention is also applicable to the second aspect of the present invention.

[0039] The third aspect of the present invention also relates to a computer-readable storage medium, which is used to store computer instructions, programs, code sets or instruction sets. When the computer is run on a computer, it enables the computer to execute the functions corresponding to the patient information processing module and the output module in the system as described above.

[0040] Any combination of one or more computer-readable media may be employed. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0041] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0042] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0043] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, Python, Objective-C, Swift, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0044] A fourth aspect of the present invention further relates to an electronic device, comprising: one or more processors; and Computer-readable storage medium, the computer storage medium is used to store computer instructions, programs, code sets or instruction sets, which, when running on a computer, enable the one or more processors to implement the functions corresponding to the patient information processing module and the output module in the system as described in any of the above items.

[0045] In some embodiments, the electronic device may further include a transceiver. The processor and the transceiver are connected, for example, via a bus. It should be noted that in actual applications, the number of transceivers is not limited to one, and the structure of the electronic device does not constitute a limitation on the embodiments of the present application.

[0046] The processor may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. A processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0047] A bus may include a path for transmitting information between the above components. A bus may be a PCI bus or an EISA bus, etc. A bus may be divided into an address bus, a data bus, a control bus, etc.

[0048] The embodiments of the present invention will be described in detail below with reference to the examples. It should be understood that these examples are intended to illustrate the present invention only and are not intended to limit the scope of the invention. For experimental methods in the following examples where specific conditions are not specified, reference is made to the guidance provided in the present invention, and may also be made to experimental manuals or conventional conditions in the art, other experimental methods known in the art, or conditions recommended by the manufacturer.

[0049] In the following specific examples, the measured parameters of raw material components may have slight deviations within the range of weighing accuracy unless otherwise specified. For temperature and time parameters, acceptable deviations caused by instrument testing accuracy or operational accuracy are allowed.

[0050] The experimental instrument information in the embodiment is shown in Table 1 Table 1

[0051] Example 1 Construction of a plasma-specific metabolite ion pair database for acute respiratory distress syndrome patients This embodiment provides a method for constructing a plasma-specific metabolite ion pair database of patients with acute respiratory distress syndrome, comprising the following steps: S1, sample collection This study, with consent from the patients or their families, collected continuous peripheral venous plasma from 420 patients aged 18 years or older with confirmed acute respiratory distress syndrome (ARDS) according to the Berlin definition. All groups were matched for age and sex. Blood was collected in the early morning, fasting.

[0052] All plasma samples were centrifuged and stored in a −80°C refrigerator. During the study, plasma samples were taken out and thawed for subsequent analysis.

[0053] S2, sample pretreatment Remove the sample from the -80℃ freezer and thaw on ice until there is no ice in the sample (all subsequent operations are required to be performed on ice); after the sample is thawed, vortex for 10 seconds to mix, take 50uL of the sample and add it to the corresponding numbered centrifuge tube; add 300ml of pure methanol internal standard extract respectively; vortex for 5 minutes, let it stand for 24 hours, and centrifuge at 12000r / min and 4℃ for 10 minutes; draw 270uL of the supernatant and concentrate it for 24 hours: add 100ml of reconstitution solution (made by mixing acetonitrile and water in a volume ratio of 1:1), and take 50uL of each sample to mix into the mix detection solution.

[0054] S3, database construction process Liquid chromatography-tandem mass spectrometry (LC-MS / MS) facilitates the entire process, from chromatographic separation to mass spectrometric identification. Based on a broadly targeted metabolomics approach, a database of plasma-specific metabolite ion pairs from patients with ventilator-induced lung injury (VLI) was constructed using the aforementioned mixed test solution. Metabolite ion pairs were primarily derived from four sources: MIM-EPI acquisition, time-of-flight acquisition, a reference material database, and metabolites from relevant literature. Ultimately, 2,640 plasma-specific metabolite ion pairs for VLI were identified.

[0055] Example 2 Screening for metabolic biomarkers associated with ventilator-induced lung injury This embodiment provides a method for screening metabolic markers related to ventilator-induced lung injury, comprising the following steps: S1, Collection of clinical patient samples For this study, with patient consent, peripheral venous plasma was collected from 135 patients with acute respiratory distress syndrome (ARDS) in the medical intensive care units of five independent clinical centers. Forty-seven patients were already receiving mechanical ventilation at the time of ARDS diagnosis, while 88 were not. Age and sex were matched across all groups. Blood was collected in the early morning, fasting, and all plasma samples were centrifuged and stored at -80°C. Plasma samples were thawed and subsequently analyzed.

[0056] S2, plasma broad targeted metabolomics analysis (1) Sample pretreatment Remove the sample collected in step S1 from the -80℃ freezer and thaw on ice until there is no ice in the sample (all subsequent operations are required to be performed on ice); after the sample is thawed, vortex for 10 seconds to mix, take 50mL of the sample and add it to the corresponding numbered centrifuge tube, add 300μL of pure methanol internal standard extract (containing 100ppm concentration of L-2-phenylalanine, [2H3]-L-carnitine-d3 hydrochloride 4-fluoro-L-2-phenylglycine, L-phenylalanine, [2H5]-hippuric acid, [2H5]-kynuric acid, [2H5]-phenoxyacetic acid internal standard); vortex for 5 minutes, let it stand for 24 hours, and then centrifuge at 12000r / min and 4℃ for 10 minutes; aspirate 270uL of the supernatant and concentrate it for 24 hours; then add it to 100mL of a reconstitution solution composed of acetonitrile and water in a volume ratio of 1:1 for LC-MS / MS analysis. Take 20 mL of each sample and mix it into quality control sample (QC), and collect it once every 15 samples.

[0057] (2) Sample metabolite detection and analysis Table 2 Experimental reagents

[0058] The liquid chromatography conditions were determined as follows: Chromatographic column: Waters ACQUITY UPLC HSS T3 C18 1.8 μm, 2,1 mm*100 mm; column temperature: 40 °C; injection volume: 2 μL.

[0059] Mobile phase: Phase A was an aqueous solution containing 0.1% acetic acid, and phase B was an acetonitrile solution containing 0.1% acetic acid. The elution gradient was as follows: 0 min, 95:5 volume ratio of phase A to phase B; 11.0 min, 10:90 volume ratio of phase A to phase B; 12.0 min, 10:90 volume ratio of phase A to phase B; 12.1 min, 95:5 volume ratio of phase A to phase B; 14.0 min, 95:5 volume ratio of phase A to phase B (v / v). Flow rate: 0.4 mL / min.

[0060] The mass spectrometry conditions were determined as follows: The electrospray ionization (ESI) source temperature was 500°C, the mass spectrometer voltage was 5500 V (positive) or −4500 V (negative), the ion source gas I (GS I) was 55 psi, the gas II (GS II) was 60 psi, the curtain gas (CUR) was 25 psi, and the collision-activated dissociation (CAD) parameter was set to high.

[0061] In the triple quadrupole (Qtrap), each ion pair is detected in MRM mode according to the optimized declustering potential (DP) and collision energy (CE).

[0062] The samples were analyzed and tested according to the determined liquid chromatography conditions and mass spectrometry conditions.

[0063] (3) Spectral peak area preprocessing and integration Based on a database of plasma-specific metabolites from patients with acute respiratory distress syndrome, qualitative and quantitative mass spectrometry analysis of metabolites in the samples was performed. Liquid chromatography can separate metabolites of varying molecular weights. Characteristic ions for each substance were screened using triple quadrupole multiple reaction monitoring (MRM) mode, and the characteristic ion signal intensity (CPS) was obtained in the detector. The mass spectrometry files of the samples were opened using MultiQuant 3.0.3 software, and chromatographic peak integration and calibration were performed. The peak area (Area) of each chromatographic peak represented the relative content of the corresponding substance. The S / N ratio was set to >5, and peaks with a retention time offset of no more than 0.2 min were retained. Finally, all chromatographic peak area integration data were exported and stored.

[0064] (4) Experimental quality control By overlaying and analyzing the total ion chromatograms (TICs) from mass spectrometry analysis of different quality control (QC) samples, the reproducibility of metabolite extraction and detection, known as technical replicates, can be determined. The high stability of the instrument provides a crucial guarantee for data reproducibility and reliability. The coefficient of variation (CV), or coefficient of variation, is the ratio of the standard deviation of the raw data to the mean, reflecting the degree of data dispersion. The empirical cumulative distribution function (ECDF) can be used to analyze the frequency of CVs of substances with values ​​less than the reference value. A higher proportion of substances with low CV values ​​in QC samples indicates more stable experimental data. A proportion of substances with CV values ​​less than 0.5 in QC samples exceeding 85% indicates relatively stable experimental data, while a proportion of substances with CV values ​​less than 0.3 exceeding 75% indicates very stable experimental data. The CV values ​​of all internal standards are also monitored during the assay process. A change in the CV value of an internal standard less than 20% indicates good instrument stability.

[0065] (5) Data processing and analysis Peak area integration data were used to analyze differential metabolites between the two groups. The variable importance in projection (VIP) value greater than 1, fold change greater than 1, and P value less than 0.05 were set as the significance criteria for the difference. A total of 305 differential metabolites were screened as candidate metabolic biomarkers for predicting ventilator-induced lung injury.

[0066] S3, Animal Model Validation (1) Construction of mouse model of acute respiratory distress syndrome Male 6-8 week old wild-type C57BL / 6 mice were anesthetized (intraperitoneal anesthesia with sodium pentobarbital at a dose of 30 mg / kg). Tracheal intubation was performed using a 20 G intravenous cannula instead of a tracheal tube under laryngoscope visualization. LPS (1 mg / kg) was instilled into the lungs. A control group received an equal volume of saline or PBS to simulate clinical acute respiratory distress syndrome and establish a mouse model.

[0067] (2) Construction of a ventilator-induced lung injury mouse model Male 6-8-week-old wild-type C57BL / 6 mice were anesthetized (intraperitoneally with sodium pentobarbital at a dose of 30 mg / kg). A midline incision was performed on the anterior neck skin, and the subcutaneous tissue was bluntly dissected to expose the trachea. The trachea was surgically exposed and a 20-gauge intravenous cannula was inserted to replace the endotracheal tube. The cannula was inserted through the tracheal wall and the stylet was removed. The cannula was then inserted into the trachea just below the cricoid cartilage and secured with surgical suture to prevent air leaks. Mechanical ventilation was provided using a small animal ventilator (Harvard). The mice were ventilated in a volume-controlled mode with a FiO2 of 0.4, an inspiratory-expiratory time ratio of 1:1, a tidal volume of 20 ml / kg, positive end-expiratory pressure (PEEP) of 0 cmH2O, and a respiratory rate of 80 breaths / min. Anesthesia was maintained with 200 μL of fluids per hour. Mechanical ventilation was continued for 4 hours before weaning from the ventilator.

[0068] (3) Tissue specimen collection and fixation The entire lung was dissected and removed using scissors. Lung tissue from different lobes proximal to the trachea and bronchi was dissected and preserved. After fresh tissue from the right lower lobe was removed, it was immediately rinsed with pre-chilled PBS (0.1M, pH 7.4) to remove any surface blood and briefly immersed in 4°C pre-chilled OCT embedding medium (Optimal Cutting Temperature Compound, Sakura® Finetek) for approximately 10-15 seconds to remove any surface air bubbles. A plastic embedding mold was pre-chilled on dry ice. A small amount of OCT embedding medium was injected into the bottom to form a base layer approximately 1 mm thick. The mold was left to stand for 30 seconds until semi-solidified (milky white). Using pre-chilled forceps, the tissue was placed in the center of the mold, oriented according to the intended section, and gently pressed to secure. OCT embedding medium was rapidly injected to completely cover the tissue (thickness ≥ 3 mm), avoiding the formation of air bubbles. The mold was immediately transferred to a pre-chilled isopentane bath at -80°C for quick freezing until the OCT was completely solidified. Remove the solidified embedded block from the mold, mark the sample number, wrap it in aluminum foil, and store it in a -80℃ ultra-low temperature freezer for long-term storage.

[0069] S2, Spatial metabolomics analysis of mouse lung tissue (1) Sample pretreatment Tissues were cryosectioned using a Leica CM1950 cryostat, with a thickness of 10 μm. Tissues were removed from the -80°C freezer and equilibrated in the microtome at -20°C for 1 hour. The sample was secured to the specimen holder and the angle and orientation of the sample were adjusted. The specimen holder was then secured to the positionable specimen head of the microtome. Sectioning was performed according to the microtome's instruction manual. The cut sections were transferred to a pre-chilled ITO glass slide using a pre-chilled brush. The back of the slide was placed against the back of the hand, and the tissue sections were melted with the warmth of the back of the hand until transparent. Once transparent, the back of the slide was rubbed with the fingers. The warmth from the back evaporated the moisture on the tissue surface, causing the tissue to turn from transparent to white. After sectioning, the ITO slide containing the tissue sections was dried in a vacuum dryer for 30 minutes.

[0070] (2) Determine the testing conditions and conduct the test A 15 mg / mL DHB (2,5-dihydroxybenzoic acid) solution was prepared using a 90%:10% acetonitrile:water solution. The DHB matrix solution was evenly sprayed onto the ITO glass slide containing the tissue section using a TM-Sprayer matrix sprayer. The instrument parameters were: temperature 60°C, flow rate 0.12 mL / min, pressure 10 psi, and a total of 30 gas spray cycles with a 5 s drying time between each cycle.

[0071] An ITO glass slide coated with the matrix was placed on the mass spectrometer target. Using DataImaging (Bruker) software, the tissue region was selected and the imaging resolution was set to 50 μm (i.e., the minimum cell size of the two-dimensional array was 50 μm × 50 μm). The imaging region was divided into a two-dimensional array of points based on their size, and the imaging range was set to 50–1300 Da. Tissue samples were examined at the same laser energy. The laser beam, passing through a grating, was directed onto the tissue region on the target, continuously scanning the sample. Under the excitation of the laser beam, the tissue sample and the matrix were ionized and decomposed, and the released molecules were identified by the mass spectrometer. Raw data (mass-to-charge ratio (m / z) information and peak intensity were obtained for each pixel. The raw data were imported into SCiLS Lab software for root mean square (RMS) normalization. The relative intensity information for each spatial point at different m / z points was obtained and converted into pixel values ​​for the imaging heat map.

[0072] (3) Spectral peak area preprocessing and integration The raw imaging data were imported into SCiLS Lab software for reading and RMS normalization. For each tissue, the entire imaging area was selected to generate the average mass spectrum for each region, with the mass-to-charge ratio on the horizontal axis and the RMS-normalized peak intensity on the vertical axis representing the average value within that region.

[0073] Within the mass spectrometry peaks obtained through peak mapping, target peaks with higher intensity (MS primary information) were subjected to in situ secondary fragmentation on the tissue to generate secondary spectra (MS / MS secondary fragment peak information) collected on the tissue. The secondary spectra collected on the tissue were then compared against a self-built database and integrated public databases for substance identification. For target peaks (MS primary information) with lower intensity for which secondary spectra could not be collected, the primary molecular weights of the target peaks (MS primary information) were searched within a 10 ppm error range against the self-built database and integrated public databases to identify substances with molecular weights closest to those detected by the instrument. A total of 1,728 substances were identified in this project, including 566 substances identified through secondary identification.

[0074] (4) Data processing and analysis Peak area integration data were used to analyze differential metabolites between the two groups. The variable importance in projection (VIP) value greater than 1, fold change greater than 1, and P value less than 0.05 were set as the significance criteria for the difference. A total of 987 differential metabolites were screened as candidate metabolic biomarkers for predicting ventilator-induced lung injury.

[0075] The intersection of the differential metabolites screened from clinical samples and those screened from animal models resulted in a total of 9 metabolites: inosinic acid, guanosine, 3´-adenylic acid, arginine-aspartate (Arg-Asp), glutamyl-histidine (Glu-His), butyrylcarnitine, phenylalanyl-aspartate (Phe-Asp), tryptophan-aspartate (Trp-Asp), and lysophosphatidylcholine (LPC 18:0 / 0:0).

[0076] (6) Plasma metabolite analysis The metabolite markers screened by the above differential analysis were compared with the spectral information in the metabolite spectrum database to qualitatively identify the metabolites based on their retention time, primary and secondary inferred molecular mass and molecular formula.

[0077] Further non-isotopic standards of the metabolites identified above were purchased to verify the retention times of the metabolites in plasma samples and the corresponding non-isotopic standards in high performance liquid chromatography tandem mass spectrometry detection, as well as the consistency of primary and secondary mass spectrometric information, to determine the accuracy of metabolite qualitative identification.

[0078] Based on the above identification method, we successfully identified 9 plasma metabolic markers as predictive markers for ventilator-induced lung injury, as shown in Table 3: Table 3 Nine plasma metabolic markers of ventilator-induced lung injury

[0079] Example 3 Construction of a prediction model for metabolites related to ventilator-induced lung injury S1, sample collection For this study, 43 peripheral venous plasma samples were collected from 43 patients receiving mechanical ventilation for respiratory failure in the medical intensive care units of three independent clinical centers, with patient consent. Nine of these patients were diagnosed with barotrauma during mechanical ventilation. Samples were matched for age and sex. Blood was collected in the early morning, fasting, and all plasma samples were centrifuged and stored at -80°C. Plasma samples were thawed and subsequently analyzed.

[0080] S2, sample metabolite detection and analysis The experimental reagents used in this step are shown in Table 4 below. Table 4 Experimental reagents

[0081] (1) Sample pretreatment Remove samples from a -80°C freezer and thaw on ice until ice-free (all subsequent operations must be performed on ice). After thawing, vortex for 10 seconds to mix thoroughly. Add 50 μL of the sample to 150 μL of the extract (containing a 100 ppm isotope internal standard mixture). Vortex for 3 minutes, centrifuge at 12,000 rpm at 4°C for 10 minutes, and refrigerate overnight at -20°C. Centrifuge at 12,000 rpm at 4°C for 5 minutes. Collect 170 μL of the supernatant and transfer it sequentially to a 96-well plate. After protein precipitation, seal the plate and prepare for LC-MS / MS analysis. Take 20 μL of each sample and mix them to form a quality control (QC) sample. Collect samples every 15 samples.

[0082] (2) Determine the testing conditions and conduct the test Based on the differences in the properties of metabolite markers, targeted quantitative detection uses T3 column and Amide column methods to separate metabolites to ensure the accuracy of metabolite quantification.

[0083] Determine the T3 column liquid chromatography conditions: Chromatographic column: Waters ACQUITY UPLC HSS T3 C18 1.8 μm, 2.1 mm*100 mm; column temperature 40°C; injection volume 2 μL.

[0084] Mobile phase: Phase A: 0.04% acetic acid solution, Phase B: 0.04% acetic acid in acetonitrile solution; elution gradient: 0 min, Phase A:B volume ratio of 95:5; 11.0 min, Phase A:B volume ratio of 10:90; 12.0 min, Phase A:B volume ratio of 95:5; 14.0 min, Phase A:B volume ratio of 95:5 (v / v). Flow rate: 0.4 mL / min.

[0085] Amide column liquid chromatography conditions: Chromatographic column: Waters ACQUITY UPLC BEH Amide 1.7 μm, 2.1 mm*100 mm; column temperature 40°C; injection volume 2 μL.

[0086] Mobile phase: Phase A: ultrapure water (10 mM ammonium acetate + 0.3% ammonia + 1 mg methylene diphosphonic acid), Phase B: 90% acetonitrile in water (containing 1 mg methylene diphosphonic acid); elution gradient: 0 min, phase A: phase B volume ratio of 10:90; 9.0 min, phase A: phase B volume ratio of 40:60; 10.0 min, phase A: phase B volume ratio of 60:40; 11.0 min, phase A: phase B volume ratio of 60:40; 11.1 min, phase A: phase B volume ratio of 10:90; 15.0 min, phase A: phase B volume ratio of 10:90. Flow rate: 0.4 mL / min.

[0087] Mass spectrometry conditions: Mass spectrometry acquisition conditions for the T3 and Amide columns were identical, including: electrospray ionization (ESI) source temperature of 500°C, mass spectrometer voltages of 5500 V (positive) and -4500 V (negative), ion source gas I (GS I) at 55 psi, gas II (GS II) at 60 psi, curtain gas (CUR) at 25 psi, and collision-activated dissociation (CAD) parameters set to high. Each ion transition was detected in MRM mode using a triple quadrupole (Qtrap) scan with optimized declustering potential (DP) and collision energy (CE).

[0088] (3) Spectral peak area preprocessing and integration MultiQuant 3.0.3 software was used to process the mass spectrometry data. The retention time and peak shape information of the standard were used as reference, and the mass spectrometry peaks detected in different samples were integrated and calibrated to ensure the accuracy of qualitative and quantitative analysis.

[0089] All samples were qualitatively and quantitatively analyzed. The peak area of ​​each chromatographic peak represented the relative content of the corresponding substance. Substituting it into the linear equation and calculation formula, the qualitative and quantitative analysis results of the analytes in all samples were finally obtained.

[0090] (4) Calculation of metabolite concentration Standard solutions of varying concentrations (0.01 ng / mL, 0.05 ng / mL, 0.1 ng / mL, 0.5 ng / mL, 1 ng / mL, 5 ng / mL, 10 ng / mL, 50 ng / mL, 100 ng / mL, 200 ng / mL, and 500 ng / mL) were prepared. Mass spectrometry peak intensity data for the corresponding quantitative signals were obtained for each concentration. Standard curves for each metabolite were plotted using the external standard to internal standard concentration ratio (concentration ratio) as the abscissa and the external standard to internal standard peak area ratio as the ordinate. The integrated peak area ratios for all detected samples were substituted into the linear equation of the standard curve for calculation. After further substitution into the calculation formula, the dilution factor was set to 3 in MultiQuant 3.0.3. The final concentration value (ng / mL) obtained by substituting the integrated peak area ratios of the samples into the standard curve was the actual content data for the substance.

[0091] (5) Experimental quality control By overlaying and analyzing the total ion chromatograms (TICs) from mass spectrometry analysis of different quality control (QC) samples, the reproducibility of metabolite extraction and detection, known as technical replicates, can be determined. The high stability of the instrument provides a crucial guarantee for data reproducibility and reliability. The coefficient of variation (CV), or coefficient of variation, is the ratio of the standard deviation of the raw data to the mean, reflecting the degree of data dispersion. The empirical cumulative distribution function (ECDF) can be used to analyze the frequency of CVs of substances with values ​​less than the reference value. A higher proportion of substances with low CVs in QC samples indicates more stable experimental data. A CV value of less than 0.3 for all substances in the QC samples indicates relatively stable experimental data. A proportion of substances with CVs less than 0.2 in greater than 90% of the QC samples indicates very stable experimental data. The CV value of the isotopic internal standard (IS) was also monitored during the assay. A change of less than 20% in the IS CV indicated good instrument stability.

[0092] (6) Data processing and analysis The 9 metabolites screened in Example 2 were classified using the random forest algorithm to construct a model to predict ventilator-induced lung injury. The 9 metabolites were individually highly capable of predicting ventilator-induced lung injury, with the area under the ROC curve (AUC) greater than 0.8, which is of clinical diagnostic significance. When the 9 metabolites were combined for prediction, the AUC was further improved, and the ACU for the combined prediction of ventilator-induced lung injury by the 9 metabolites reached 0.936. The results of using a single metabolite marker to predict ventilator-induced lung injury are shown in Table 5.

[0093] Table 5 AUC values ​​of individual metabolites for predicting ventilator-induced lung injury

[0094] Example 4 Construction of a diagnostic model for ventilator-induced lung injury using two plasma metabolic markers The research subjects and detection and analysis methods of this embodiment are the same as those of Example 3, except that any two of the above-mentioned plasma metabolic markers are used in the binary logistic regression modeling in step (6).

[0095] After statistical analysis of the constructed model, it was found that any two metabolic markers had a strong ability to predict ventilator-induced lung injury, with the area under the receiver operating characteristic (ROC) curve (AUC) greater than 0.85, indicating clinical diagnostic significance. Some statistical results are shown below: The AUC of tryptophan-aspartate and inosinic acid combined to predict ventilator-induced lung injury was 0.871; The AUC of guanosine and 3'-adenylic acid combined to predict ventilator-induced lung injury was 0.902; The AUC of butyrylcarnitine and lysophosphatidylcholine (18:0 / 0:0) combined to predict ventilator-induced lung injury was 0.882; The AUC of butyrylcarnitine and phenylalanylaspartate combined to predict ventilator-induced lung injury was 0.884; The AUC of arginine-aspartate and glutamyl-histidine combined to predict ventilator-induced lung injury was 0.902.

[0096] Example 5 Detection Kit This embodiment provides a detection kit prepared based on the above-mentioned metabolic markers, and the kit includes the following components: Metabolic marker standards: inosinic acid, guanosine, 3'-adenylic acid, arginine-aspartic acid, glutamyl-histidine, butyrylcarnitine, phenylalanyl-aspartic acid, tryptophan-aspartic acid, lysophosphatidylcholine (18:0 / 0:0).

[0097] Metabolite extraction and: 100% pure methanol and 50% acetonitrile aqueous solution were used for sample preparation; 50% acetonitrile aqueous solution was used as the solvent for dissolving standards.

[0098] Internal standards: [2H3]-L,-carnitine-d3 hydrochloride, 4-fluoro-L-2-phenylglycine, L-phenylalanine, [2H5]-hippuric acid, [2H5]-kynurenine, [2H5]-phenoxyacetic acid.

[0099] Of course, when designing a test kit, it is not necessary to include all the nine markers mentioned above. Only a few of them can be used, or some or all of them can be combined with other markers. These standards can be packaged individually or as a mixture.

[0100] The detection kit provided in this embodiment can be used to predict the occurrence of ventilator-induced lung injury.

[0101] The above-described embodiments merely represent several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the spirit of the present invention, and these modifications and improvements fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be based on the appended claims, and the description may be used to interpret the content of the claims.

Claims

1. Application of a reagent for quantitatively detecting metabolites in a subject's sample in the preparation of a diagnostic kit for ventilator-induced lung injury: The metabolite is selected from at least one of the following components: inosinic acid, guanosine, 3'-adenosine, arginine-aspartic acid, glutamyl-histidine, butyrylcarnitine, phenylalanyl aspartic acid, tryptophan-aspartic acid, and lysophosphatidylcholine (18:0 / 0:0).

2. The use according to claim 1, wherein the metabolites are selected from at least 2, 3, 4, 5, 6, 7, 8 or 9 of the following components: inosinic acid, guanosine, 3'-adenosine monophosphate, arginine-aspartic acid, glutamyl-histidine, butyrylcarnitine, phenylalanyl-aspartic acid, tryptophan-aspartic acid, and lysophosphatidylcholine (18:0 / 0:0).

3. The use according to claim 1, wherein the sample is selected from blood, serum or plasma.

4. The use according to any one of claims 1 to 3, wherein the reagent is used to implement any one of the following methods: Liquid chromatography-tandem mass spectrometry, ultra-high performance liquid chromatography-high resolution mass spectrometry, capillary electrophoresis-mass spectrometry, high performance liquid chromatography coupled with detector, gas chromatography, nuclear magnetic resonance, infrared spectroscopy and Raman spectroscopy, biosensor array, microfluidic chip-electrochemical detection, ion mobility spectrometry-mass spectrometry, paper spray mass spectrometry, mass spectrometry imaging, enzyme-linked immunosorbent assay, microdialysis coupled analysis, and chemiluminescence immunoassay.

5. Ventilator-induced lung injury diagnostic system, including: Patient information processing module and output module; The patient information processing module is used to receive information about potential patients with ventilator-induced lung injury, wherein the patient information includes at least a quantitative detection result of at least one metabolite in the patient's sample: Inosinic acid, guanosine, 3'-adenylic acid, arginine-aspartic acid, glutamyl-histidine, butyrylcarnitine, phenylalanyl-aspartic acid, tryptophan-aspartic acid, lysophosphatidylcholine (18:0 / 0:0); The output module is used to receive the information output by the patient information processing module and provide a judgment result on whether ventilator-induced lung injury exists.

6. The system of claim 5, wherein the metabolites are selected from at least 2, 3, 4, 5, 6, 7, 8, or 9 of the following: inosinic acid, guanosine, 3'-adenosine, arginine-aspartic acid, glutamyl-histidine, butyrylcarnitine, phenylalanyl-aspartic acid, tryptophan-aspartic acid, and lysophosphatidylcholine (18:0 / 0:0). The system according to claim 5 , wherein the sample is selected from blood, serum or plasma.

8. The system according to claim 5, wherein the quantitative detection is obtained by any one of the following methods: liquid chromatography-tandem mass spectrometry, ultra-performance liquid chromatography-high-resolution mass spectrometry, capillary electrophoresis-mass spectrometry, high-performance liquid chromatography coupled with a detector, gas chromatography, nuclear magnetic resonance, infrared spectroscopy and Raman spectroscopy, biosensor array, microfluidic chip-electrochemical detection, ion mobility spectrometry-mass spectrometry, paper spray mass spectrometry, mass spectrometry imaging, enzyme-linked immunosorbent assay, microdialysis coupled analysis, and chemiluminescence immunoassay.

9. A computer-readable storage medium, characterized in that The computer storage medium is used to store computer instructions, programs, code sets or instruction sets, which, when run on a computer, enable the computer to perform the functions corresponding to the patient information processing module and the output module in the system as described in any one of claims 5 to 8.

10. An electronic device, characterized in that: include: one or more processors; as well as A computer-readable storage medium, wherein the computer storage medium is used to store computer instructions, programs, code sets or instruction sets, which, when executed on a computer, enable the one or more processors to implement the functions corresponding to the patient information processing module and the output module in the system according to any one of claims 5 to 8.

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