A wound simulation system and method

Abstract

This application provides an injury simulation system and method, belonging to the field of computer technology. The injury simulation system includes: a trauma simulation device, multiple organ-on-a-chips, a microelectrode array, and a processing unit. The trauma simulation device can apply different injury signals to the multiple organ-on-a-chips to simulate different injury conditions of the subject. The multiple organ-on-a-chips are interconnected, and each organ-on-a-chip can simulate the organ structure of the subject and collect corresponding physiological parameters. The processing unit is used to obtain multi-organ injury analysis results based on the injury signals and the physiological parameters detected by the organ-on-a-chips.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to an injury simulation system and method. Background Technology

[0002] Combined injuries (especially craniocerebral-thoracic-abdominal injuries) require more sophisticated research models due to their complex and coupled injury mechanisms and extremely short treatment time windows. However, existing technologies suffer from three major bottlenecks: models cannot simulate organ interactions, monitoring methods cannot capture early cellular events, and research has failed to connect the microscopic to macroscopic injury mechanisms, severely hindering the study of the comprehensive pathological process of combined injuries. Summary of the Invention

[0003] One of the technical problems this application aims to solve is: how to overcome the chain-like bottleneck of "lack of organ interaction simulation, fragmented dynamic monitoring, unclear cross-scale mechanisms, and lack of verification data".

[0004] To address the aforementioned technical problems, embodiments of this application provide an injury simulation system and method.

[0005] This application provides an injury simulation system, which includes: a trauma simulation device, multiple organ-on-a-chips, and a processing unit. The trauma simulation device can apply different injury signals to the multiple organ-on-a-chips to simulate different injury conditions of the subject. The multiple organ-on-a-chips are interconnected, and each organ-on-a-chip can simulate the organ structure of the subject and collect corresponding physiological parameters. The processing unit is used to call a first model, which can determine target correlations and obtain multi-organ injury analysis results based on the target correlations. The target correlations can characterize the correlation between injury signals and physiological parameters detected by organ-on-a-chips at different times.

[0006] In some embodiments, the system further includes a microelectrode array for connecting multiple organ-on-a-chips and capable of simulating the physiological interactions of multiple organs of the subject; after the trauma simulation device applies a damage signal to any organ-on-a-chip, the microelectrode array can transmit the damage signal to another connected organ-on-a-chip.

[0007] In some embodiments, the processing unit is further configured to determine the propagation path of the injury signal based on the injury signal, and map the propagation path of the injury signal and the physiological parameters to the same coordinate system to determine the target correlation.

[0008] In some embodiments, an organ-on-a-chip includes: a base layer in which a sensor is disposed, the sensor being used to collect corresponding physiological parameters; an intermediate layer disposed on the base layer, used to simulate transmembrane material exchange regions with other organ-on-a-chips; a chamber disposed on the intermediate layer, used to simulate the structure and function of a corresponding organ; and a control layer for connecting the chamber to other organ-on-a-chips.

[0009] In some embodiments, the processing unit is further configured to invoke a second model, which is capable of predicting the probability of multi-organ failure in the subject based on the results of multi-organ injury analysis.

[0010] In some embodiments, the system further includes a monitoring unit, which is configured to generate a prompt signal if the probability of multi-organ failure of the test subject generated by the second model is greater than a monitoring threshold and / or the physiological parameters are greater than a physiological threshold, and the prompt signal is used to prompt the user that the test subject is abnormal.

[0011] This application also provides an injury simulation method, including: collecting corresponding physiological parameters through an organ-on-a-chip; determining target correlation through a first model; and obtaining multi-organ injury analysis results based on the target correlation, wherein the target correlation can characterize the correlation between injury signals and physiological parameters detected by organ-on-a-chip at different times.

[0012] In some embodiments, determining the target association using the first model includes: determining the propagation path of the injury signal based on the injury signal using the first model; and mapping the propagation path of the injury signal and physiological parameters to the same coordinate system to determine the target association.

[0013] In some embodiments, the method further includes: predicting the probability of multi-organ failure in the subject based on the results of multi-organ injury analysis using a second model.

[0014] This application provides a computer device, including a processor and a memory storing a computer program. When the processor executes the program, it implements the injury simulation method provided in the above embodiments.

[0015] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of any of the above-described injury simulation methods.

[0016] Through the aforementioned technical solutions, the injury simulation system and method provided in this application simulate the coupling of systemic circulation and metabolism within the human body by constructing multiple interconnected organ-on-a-chip devices. Furthermore, it enables the simultaneous induction of complex injuries, avoiding timing errors caused by step-by-step operations. This system can precisely control the intensity, location, and interaction of injuries, solving the "fragmented injury" problem of traditional methods. Simultaneously, by correlating injury signals with physiological parameters detected by organ-on-a-chip devices, it overcomes the chain-like bottleneck of "lack of organ interaction simulation - fragmented dynamic monitoring - unclear cross-scale mechanisms - lack of validation data," contributing to breakthroughs in the entire chain of research from injury mechanisms to clinical treatment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the injury simulation system disclosed in the embodiments of this application;

[0019] Figure 2 This is a schematic diagram of the structure of an injury simulation system disclosed in another embodiment of this application;

[0020] Figure 3 This is a schematic diagram of the structure of an injury simulation system disclosed in another embodiment of this application;

[0021] Figure 4 This is a flowchart illustrating the injury simulation method disclosed in the embodiments of this application;

[0022] Figure 5 This is a schematic diagram of the structure of a computer device disclosed in an embodiment of this application. Detailed Implementation

[0023] The embodiments of this application will be further described in detail below with reference to the accompanying drawings and examples. The detailed description of the following embodiments and the accompanying drawings are used to illustrate the principles of this application by way of example, but should not be used to limit the scope of this application. This application can be implemented in many different forms and is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

[0024] These embodiments are provided to make the application thorough and complete, and to fully express the scope of the application to those skilled in the art. It should be noted that, unless otherwise specifically stated, the relative arrangement of components and steps, material composition, numerical expressions, and values ​​illustrated in these embodiments should be interpreted as merely exemplary and not as limiting.

[0025] Furthermore, the terms "including" or "comprising" as used in this application mean that the element preceding the word covers the element listed after the word, and do not exclude the possibility that it may also cover other elements.

[0026] It should also be noted that, in the description of this application, unless otherwise expressly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this application depending on the specific circumstances. When a specific device is described as being located between a first device and a second device, an intermediary device may or may not be present between the specific device and the first or second device.

[0027] All terms used in this application have the same meaning as understood by one of ordinary skill in the art to which this application pertains, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant art, and not as idealized or highly formalized, unless expressly defined herein.

[0028] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0029] The injury mechanisms of combined injuries are complex. For example, combined craniocerebral-thoracic-abdominal injuries (such as blast injuries and traffic accidents) involve multiple pathological processes, including mechanical force transmission (shock wave), ischemia and hypoxia (vascular injury), and inflammatory storm (immune response). There are dynamic correlations between the damage to various organs (e.g., drastic changes in intrathoracic pressure affect cerebral blood flow). Furthermore, the treatment window is extremely short. For instance, thoracic-abdominal injuries easily lead to asphyxia (e.g., upper and middle airway obstruction), and craniocerebral injuries cause neurological failure; both require precise intervention within the "golden hour." Therefore, there is an urgent need for more comprehensive research methods to study combined injuries.

[0030] Table 1 below shows several schemes for simulating and studying complex injuries using the beagle complex injury animal model, along with representative cases for each scheme.

[0031] Table 1

[0032]

[0033] Although the schemes shown in Table 1 above can all simulate and analyze multiple injuries to a certain extent, they all have the following limitations:

[0034] 1. Phylogenetic differences hinder clinical translation

[0035] Animal models such as beagles have significant phylogenetic differences from humans (e.g., distribution of drug-metabolizing enzymes, intensity of immune response), resulting in a failure rate of over 70% in predicting injury mechanisms derived from animal experiments when applied to humans.

[0036] For example, although the pharmacokinetic (PK) parameters of cisplatin in canine models can be verified, they cannot be directly extrapolated to changes in blood-brain barrier penetration after human traumatic brain injury.

[0037] 2. Experiment on interference with the physiological characteristics of Beagle dogs

[0038] A predisposition to weight gain (as the literature indicates, "Bige is never full") leads to instability in the baseline cardiovascular condition, interfering with the reliability of post-traumatic hemodynamic data.

[0039] High frequency of gastrointestinal gas and large volume of excrement can cause abnormal fluctuations in chest and abdominal pressure monitoring data, masking true damage signals.

[0040] 3. Monitoring methods disrupt physiological homeostasis.

[0041] Traditional autopsy and ex vivo tissue sectioning endpoint analysis methods destroy evidence of organ-to-organ interactions (such as brain-gut axis communication within 2 hours after injury).

[0042] Repeated blood draws or imaging examinations (such as DSA) can induce stress responses and alter the natural course of trauma.

[0043] Table 2 below shows several schemes for simulating and studying complex injuries using organ-on-a-chip technology, along with representative cases for each scheme.

[0044] Table 2

[0045]

[0046] Although the schemes shown in Table 2 above can all simulate and analyze multiple injuries to a certain extent, they all have the following limitations:

[0047] 1. Material defects cause prediction distortion

[0048] The current mainstream chip material PDMS (polydimethylsiloxane) has a strong adsorption capacity for biomolecules (such as inflammatory factors and drug metabolites), which significantly changes the concentration dynamics of key substances in the microenvironment, leading to a deviation between damage response data and the actual physiological state.

[0049] The thickness of PDMS films (typically >100μm) is much larger than that of in vivo basement membrane structures (such as the blood-brain barrier basement membrane, which is about 10-50nm), making it impossible to simulate the mechanical transmission process of shock waves in real tissues.

[0050] 2. Weak ability to simulate multi-organ interactions

[0051] Existing organ-on-a-chip systems mostly use static connections and lack biomimetic vascular networks and neuroendocrine signal transmission channels, making it impossible to simulate the cascade effects of autonomic nervous system disorders after traumatic brain injury on thoracic and abdominal organs (such as drastic changes in pulmonary vascular permeability).

[0052] The culture media of multi-organ microarrays are difficult to be compatible with the needs of different tissues (e.g., neurons require low shear stress while hepatocytes require high perfusion), leading to the failure of simulation of cross-organ metabolic pathways (e.g., liver-brain axis).

[0053] 3. The contradiction between long-term stability and complexity

[0054] Increasing the number of organ-on-a-chip devices significantly increases system complexity, but the ensuing problems such as culture medium contamination and accelerated cell apoptosis make it difficult to exceed 4 weeks for experimental cycles, thus failing to simulate chronic inflammatory responses after trauma.

[0055] 3D bioprinted organoids lack functional vascular networks, resulting in inefficient nutrient / metabolic waste exchange and affecting the sustainability of research on damage mechanisms.

[0056] In view of this, embodiments of this application provide an injury simulation system, which includes: a trauma simulation device 110, multiple organ-on-a-chip 120, and a processing unit 130, wherein...

[0057] The trauma simulation device 110 can apply different injury signals to multiple organ-on-a-chip 120s to simulate different injury conditions of the subject. For example, the trauma simulation device 110 can apply electromagnetic impact to the organ-on-a-chip 120 corresponding to the brain to simulate the pressure transmission of an explosive shock wave to the brain. Alternatively, the trauma simulation device 110 can apply pressure to the organ-on-a-chip 120 corresponding to the abdominal cavity to simulate the damage of an explosive shock wave to the thoracic and abdominal organs.

[0058] Multiple organ-on-a-chips 120 are interconnected. Each organ-on-a-chip 120 can simulate the organ structure of the subject and collect corresponding physiological parameters. Specifically, the organ-on-a-chip 120 can simulate the 3D structure, cell arrangement, and fluid dynamics (such as blood flow, respiratory peristalsis, etc.) of the subject's organs. For example, the lung chip simulates the alveolar-capillary interface, and the intestinal chip simulates the villus structure. Simultaneously, the organ-on-a-chip 120 also integrates sensors that can detect various physiological parameters such as metabolites, oxygen content, and electrical signals. Furthermore, after a injury signal is applied to the organ-on-a-chip 120 by the trauma simulation device 110, the sensors can detect physiological parameters at different times, facilitating subsequent analysis of the impact of the duration of the injury signal on these physiological parameters.

[0059] The processing unit 130 is used to invoke a first model, which can determine target correlations and obtain multi-organ injury analysis results based on the target correlations. The target correlations characterize the relationship between injury signals and physiological parameters detected by the organ-on-a-chip 120 at different times. Specifically, the processing unit 130 can correlate physiological parameters detected by the organ-on-a-chip at different times with injury signals. For example, it can correlate the peak pressure and duration of the shock wave (injury signal) with physiological parameters of the organ-on-a-chip 120 simulating the cranium and the organ-on-a-chip 120 simulating the abdominal cavity, respectively, thereby enabling analysis of the post-injury cascade response mechanism.

[0060] The injury simulation system provided in this application simulates the coupling of systemic circulation and metabolism within the human body by constructing multiple interconnected organ-on-a-chip 120s, and can simultaneously induce complex injuries, avoiding timing errors caused by step-by-step operations. This system can precisely control the intensity, location, and interaction of injuries, solving the "fragmented injury" problem of traditional methods. Furthermore, by correlating injury signals with physiological parameters detected by the organ-on-a-chip 120, it overcomes the chain-like bottleneck of "lack of organ interaction simulation - fragmented dynamic monitoring - unclear cross-scale mechanisms - lack of validation data," contributing to breakthroughs in the entire chain of research from injury mechanisms to clinical treatment.

[0061] This application also provides an injury simulation system, which includes: a trauma simulation device 110, multiple organ-on-a-chip 120, a microelectrode array 140, and a processing unit 130.

[0062] The trauma simulation device 110 can apply different injury signals to multiple organ-on-a-chip 120s to simulate different injury conditions of the subject. For example, the trauma simulation device 110 can apply electromagnetic impact to the organ-on-a-chip 120 corresponding to the brain to simulate the pressure transmission of an explosive shock wave to the brain. Alternatively, the trauma simulation device 110 can apply pressure to the organ-on-a-chip 120 corresponding to the abdominal cavity to simulate the damage of an explosive shock wave to the thoracic and abdominal organs.

[0063] Multiple organ-on-a-chips 120 are interconnected, each capable of simulating the organ structure of the subject and acquiring corresponding physiological parameters. Specifically, the organ-on-a-chip 120 can simulate the 3D structure, cell arrangement, and fluid dynamics (such as blood flow, respiratory peristalsis, etc.) of organs. For example, the lung chip simulates the alveolar-capillary interface, and the intestinal chip simulates the villus structure. Simultaneously, the organ-on-a-chip 120 integrates sensors that can detect various physiological parameters such as metabolites, oxygen content, and electrical signals. Furthermore, after a injury signal is applied to the organ-on-a-chip 120 by the trauma simulation device 110, the sensors can detect physiological parameters at different times, facilitating subsequent analysis of the impact of the duration of the injury signal on these physiological parameters.

[0064] In this embodiment, a multi-organ simulation system (e.g., a combined simulation system of the brain and thoracic / abdominal organs) is constructed by interconnecting multiple organ-on-a-chip systems to simulate the physiological interactions of multiple organs in the human body (such as blood flow and metabolite transport) and dynamically monitor pathological changes after injury. Compared to traditional single-organ models, this embodiment can achieve linked injury simulation of the brain (highly neurosensitive) and the thoracic / abdominal organs (core of cardiopulmonary function), dynamically verifying the "trauma-immunity-metabolism" interaction network. This not only more closely resembles the complex injury mechanisms in actual combat but also provides a mechanistic basis for "preventive treatment."

[0065] In one specific embodiment, the organ-on-a-chip 120 includes:

[0066] The basal layer contains sensors for collecting corresponding physiological parameters. For example, the basal layer is a glass slide substrate on which various sensors (such as sensors for measuring pH, dissolved oxygen, glucose, lactate, impedance, and transmembrane resistance (TEER)) are integrated for real-time, non-invasive monitoring of cell state, barrier function, and metabolic activity.

[0067] An intermediate layer, positioned on top of the basal layer, is used to simulate transmembrane exchange regions with other organ-on-a-chip 120. Exemplarily, this intermediate layer is a porous membrane, such as one made of PDMS, polycarbonate, or collagen hydrogel. Different cell types (such as epithelial cells and endothelial cells) grow on either side of the membrane, forming physiological barriers (such as the alveolar-capillary barrier, intestinal barrier, and blood-brain barrier). This porous membrane allows for the exchange of nutrients, gases, and metabolites and can be used to study barrier function and substance transport. Extracellular matrix proteins (such as collagen, fibronectin, and laminin) can be coated onto the porous membrane to provide a scaffold for cell adhesion and growth, mimicking the basement membrane in vivo.

[0068] The chambers, located on the middle layer, are used to simulate the structure and function of the corresponding organs.

[0069] For example, the chambers can accommodate and culture living cells to simulate key structural and functional units of an organ. For instance, the chambers of the organ-on-a-chip 120 simulating a lung contain airway channels and vascular channels simulating alveoli, separated by a porous membrane. The chambers of the organ-on-a-chip 120 simulating a liver contain co-cultured hepatocytes (parenchymal cells) and non-parenchymal cells (such as endothelial cells and stellate cells) to simulate liver lobule structures or bile ducts. The organ-on-a-chip 120 simulating an intestine contains chambers simulating intestinal villi structures, with epithelial cell layers and endothelial cell layers separated by a porous membrane, introducing fluid shear forces and peristaltic mechanical forces.

[0070] A control layer is used to connect the chamber to other organ-on-a-chip 120s. For example, the control layer has channels connecting the chamber to other organ-on-a-chip 120s, allowing continuous flow of culture medium (simulating blood or interstitial fluid) to simulate the transport of blood, lymph, etc., providing nutrition and mechanical stimulation (such as shear force), and enabling nutrient supply, waste removal, and drug / compound delivery.

[0071] It is understood that the structure of the organ-on-a-chip 120 in this application embodiment is not limited to the structure provided in the above embodiments. The organ-on-a-chip 120 can simulate any organ in the human body, such as the brain, heart, lungs, liver, kidneys, etc., and the structure of the organ-on-a-chip 120 can be adjusted according to different simulated organs and different tracked physiological parameters. For example, the organ-on-a-chip 120 simulating the brain uses a 3D-printed blood-brain barrier structure (basement membrane thickness ≤50nm), integrates astrocytes and neurons, and simulates the phosphorylation response of Tau protein. The organ-on-a-chip 120 simulating the chest and abdomen uses alveolar-capillary units co-cultured with hepatic sinusoidal endothelium, embedding micromechanical sensors to capture the shock wave conduction path.

[0072] The microelectrode array 140 is used to connect multiple organ-on-a-chip 120s and is capable of simulating the physiological interactions of multiple organs of the subject. After the trauma simulation device 110 applies a damage signal to any organ-on-a-chip 120, the microelectrode array 140 can transmit the damage signal to another connected organ-on-a-chip 120.

[0073] Considering that existing multi-organ-on-a-chip 120s mainly rely on circulating culture medium to deliver soluble factors (hormones, cytokines, metabolites), but severely lack rapid, directional, and precise neural electrical signal transmission, while neural regulation is crucial for immune, metabolic, and cardiovascular functions, this application employs a microelectrode array 140 connected between multiple organ-on-a-chip 120s to simulate the physiological interactions of multiple organs in the subject. For example, the microelectrode array 140 can replace the afferent and efferent fibers of the vagus nerve, connecting the organ-on-a-chip 120 simulating the brain and the organ-on-a-chip 120 simulating the lungs. This microelectrode array 140 can both record abnormal electrical signals generated by the organ-on-a-chip 120 simulating the brain (simulating injury signals transmitted to the vagus nerve nucleus) and apply precise electrical pulse sequences to the target points of the organ-on-a-chip 120 simulating the lungs (simulating vagus nerve efferent excitation). This can simulate the chain reaction of "brain injury → vagus nerve excitation → alveolar macrophage activation," solving the problem of missing organ interactions. It can enhance the ability of injury simulation systems to simulate complex neuroimmune interactions, providing a mechanistic basis for the research and treatment of brain injury-related complications.

[0074] In a specific embodiment, the processing unit 130 is used to invoke a first model, which can determine the target correlation and obtain multi-organ injury analysis results based on the target correlation. The target correlation can characterize the correlation between the injury signal and the physiological parameters detected by the organ-on-a-chip at different times.

[0075] Specifically, the processing unit 130 can invoke the first model, which can determine the target correlation. In some specific examples, the first model maps the physiological parameters detected by organ detection at different times to the same coordinate system as the injury signal. For example, the peak pressure and duration of the shock wave (injury signal) can be mapped to the same coordinate system as the physiological parameters (electrophysiological signals (neuronal discharge)) of the organ-on-a-chip 120 simulating the cranium and the physiological parameters (metabolite concentration gradient) of the organ-on-a-chip 120 simulating the abdominal cavity, respectively. This can determine the target correlation, so as to facilitate the subsequent analysis of the post-injury cascade response mechanism.

[0076] Understandably, the first model can be a model trained beforehand using different injury signals and their corresponding physiological parameters at different times, possessing the ability to analyze injury signals and physiological parameters. The first model can be an artificial intelligence (AI) model, a neural network model, or other model capable of inferring the correlation between corresponding targets based on injury signals and physiological parameters.

[0077] The following example illustrates how mechanical fluctuations (shock wave propagation), electrophysiological signals (neuronal firing), and metabolite concentration gradients are mapped to a unified coordinate system.

[0078] Considering that the propagation speed of shock waves (≈1500 m / s) is thousands of times faster than that of electrical signals (≈1-100 m / s), and that metabolite diffusion is even slower (µm / s), there are spatiotemporal scale differences among the three. To address this issue, embodiments of this application employ a layered time axis, capturing shock waves with microsecond precision (piezoelectric sensor), recording electrical signals with millisecond precision (microelectrode array 140), and sampling metabolites with second-level precision (electrochemical sensor). Then, signals with different sampling rates are aligned, and event timing (such as the arrival of the shock wave at the neuron's location and the starting point of discharge) is matched through nonlinear stretching / compression.

[0079] Furthermore, since the sensors are distributed across different organ-on-a-chip 120 units, a common spatial reference frame needs to be established. The chip modules are meshed (e.g., at 100µm resolution), with each mesh assigned 3D coordinates; fluorescent positioning markers (e.g., quantum dots) are implanted, and sensor positions are calibrated in real-time using microscopic imaging; the shock wave propagation path is simulated based on the brain-on-a-chip material properties (Young's modulus, density) to infer the signal origin location. Monitoring nodes and corresponding parameters (e.g., inflammatory factor concentrations, metabolite changes) are marked on the time axis at 0h, 6h, and 24h post-injury.

[0080] Then, the multimodal signals are fused to generate a multi-organ injury propagation thermogram. Because the physical units of the three signals are different (pressure Pa, voltage µV, concentration µM), they cannot be directly superimposed. Therefore, it is necessary to normalize their biological effects. For example, the mechanical signals are converted into tissue strain energy density (J / m³). 3 It quantifies the degree of cellular mechanical damage; converts electrical signals into neuronal abnormal discharge entropy values ​​to characterize neuroexcitotoxicity; and converts metabolite concentrations into inflammatory indices (such as the TNF-α / IL-10 ratio) to assess the level of immune activation.

[0081] Therefore, the damage path of the shock wave to the object under test can be analyzed as follows:

[0082] Local effects: The shock wave directly penetrates the skull, causing brain tissue deformation;

[0083] Systemic effects: compression of the chest and abdomen → pressure transmission in large blood vessels → obstruction of intracranial venous return → secondary brain injury.

[0084] In some embodiments, the processing unit 130 is further configured to invoke a second model, which is capable of predicting the probability of multi-organ failure of the subject based on the results of multi-organ injury analysis.

[0085] In this embodiment, the second model is pre-trained based on different multi-organ injury analysis results and corresponding actual cases of multi-organ failure. Therefore, this second model can extract physiological parameters (heart rate, lactate, creatinine, etc.) at multiple post-injury time points (e.g., 0h, 6h, 24h) corresponding to each organ chip 120 based on the multi-organ injury analysis results corresponding to the injury signal, and predict the probability of multi-organ failure in the test subject based on these physiological parameters. For example, if abnormal lactate / glucose metabolism is detected by the sensors of the organ chip 120, an early warning of organ failure is issued.

[0086] For example, the second model includes a data perception layer and an LSTM prediction engine. The data perception layer can detect physiological parameters corresponding to multiple organ-on-a-chip 120s, such as heart rate, blood pressure, central venous pressure, lactate, and vasoactive drug scores corresponding to the cardiovascular organ-on-a-chip 120; and bilirubin, transaminase, and coagulation function (INR, PTT) corresponding to the liver organ-on-a-chip 120. The LSTM prediction engine can capture acute compensatory responses over 6-12 hours based on the corresponding physiological parameters and learn 24-hour failure inflection point characteristics, thereby obtaining the probability of multi-organ failure in the subject.

[0087] In some embodiments, the second model can also predict the probability of multiple organ failure in the subject using physiological parameters collected by the organ-on-a-chip 120. Specific steps can be found in the prediction process of the second model based on the results of multi-organ injury analysis in the above embodiments, and will not be repeated in this application.

[0088] In some embodiments, the injury simulation system further includes a display device capable of simultaneously displaying physiological parameters and dynamic curves collected in real time from multiple organ-on-a-chip 120s, such as those from the brain (intracranial pressure, electroencephalogram) and chest and abdomen (blood oxygen saturation, liver enzyme levels). These dynamic curves allow for comparison of organ function indicators (such as brain tissue pathology score vs. hepatocyte survival rate) at different time points after injury, facilitating technicians' observation of the responses of different organ-on-a-chip 120s after the application of injury signals.

[0089] In some specific embodiments, the injury simulation system further includes a monitoring unit 150, which is used to generate a prompt signal if the probability of multi-organ failure of the test subject generated by the second model is greater than a monitoring threshold and / or the physiological parameters are greater than a physiological threshold. The prompt signal is used to prompt the user that the test subject is abnormal.

[0090] In this embodiment, the monitoring unit 150 monitors the prediction results of the second model and the physiological thresholds collected by the organ-on-a-chip 120 in real time. When it is predicted that the subject may have organ failure or abnormal physiological parameters, the technicians can be alerted in time.

[0091] It should be noted that the processing unit 130 and the monitoring unit 150 mentioned above can be integrated into the same server or into different servers. This server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0092] This application also provides an injury simulation method, which includes:

[0093] S10: Apply different injury signals to multiple organ-on-a-chips to simulate different injury conditions of the subject; the multiple organ-on-a-chips are interconnected, and each organ-on-a-chip can simulate the organ structure of the subject.

[0094] S20: Collect corresponding physiological parameters through organ-on-a-chip;

[0095] S30: Using the first model, determine the target correlation and obtain the multi-organ injury analysis results based on the target correlation. The target correlation can characterize the relationship between the injury signal and the physiological parameters obtained by organ-on-a-chip detection at different times.

[0096] The injury simulation method provided in this application constructs multiple interconnected organ-on-a-chip devices to simulate the coupling of systemic circulation and metabolism within the human body, and can simultaneously induce complex injuries, avoiding timing errors caused by step-by-step operations. This method can precisely control the intensity, location, and interaction of injuries, solving the "injury fragmentation" problem of traditional methods. Furthermore, by correlating injury signals with physiological parameters detected by organ-on-a-chip devices, it can reveal the post-injury cascade response mechanism, providing a mechanistic basis for "preventive treatment."

[0097] In some embodiments, determining the target association using the first model includes: determining the propagation path of the injury signal based on the injury signal using the first model; and mapping the propagation path of the injury signal and physiological parameters to the same coordinate system to determine the target association.

[0098] In some embodiments, the method further includes: predicting the probability of multi-organ failure in the subject based on the results of multi-organ injury analysis using a second model.

[0099] In some embodiments, the method further includes: if the probability of multiple organ failure of the test subject generated by the second model is detected to be greater than a monitoring threshold and / or the physiological parameters are greater than a physiological threshold, generating a prompt signal, the prompt signal being used to alert the user that the test subject is abnormal.

[0100] Based on the above embodiments, this application also provides a computer device. Figure 5 A schematic diagram of a computer device structure is provided for an embodiment of this application, such as... Figure 5 As shown, it includes: processor 501, communication interface 502, memory 503 and communication bus 504, wherein processor 501, communication interface 502 and memory 503 communicate with each other through communication bus 504.

[0101] The memory 503 stores a computer program. When the program is executed by the processor 501, the processor 501 performs the following steps:

[0102] S10: Apply different injury signals to multiple organ-on-a-chips to simulate different injury conditions of the subject; the multiple organ-on-a-chips are interconnected, and each organ-on-a-chip can simulate the organ structure of the subject.

[0103] S20: Collect corresponding physiological parameters through organ-on-a-chip;

[0104] S30: Using the first model, determine the target correlation and obtain the multi-organ injury analysis results based on the target correlation. The target correlation can characterize the relationship between the injury signal and the physiological parameters obtained by organ-on-a-chip detection at different times.

[0105] In some embodiments, the processor 501 performs the following steps: determining the target association relationship through the first model includes: determining the propagation path of the injury signal based on the injury signal through the first model; mapping the propagation path of the injury signal and the physiological parameters to the same coordinate system to determine the target association relationship.

[0106] In some embodiments, the processor 501 performs the following steps: predicting the probability of multi-organ failure of the subject based on the results of multi-organ injury analysis using a second model.

[0107] In some embodiments, the processor 501 performs the following steps: if the probability of multi-organ failure of the test object generated by the second model is detected to be greater than the monitoring threshold and / or the physiological parameters are greater than the physiological threshold, a prompt signal is generated, which is used to prompt the user that the test object is abnormal.

[0108] The communication bus mentioned in the above computer equipment can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0109] Communication interface 502 is used for communication between the aforementioned computer equipment and other equipment.

[0110] The memory may include RAM (Random Access Memory) or NVM (Non-Volatile Memory), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0111] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be DSPs (Digital Signal Processors), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0112] Based on the above embodiments, this application provides a computer-readable storage medium storing a computer program executable by a computer device. When the program is run on the computer device, the computer device performs the following steps:

[0113] S10: Apply different injury signals to multiple organ-on-a-chips to simulate different injury conditions of the subject; the multiple organ-on-a-chips are interconnected, and each organ-on-a-chip can simulate the organ structure of the subject.

[0114] S20: Collect corresponding physiological parameters through organ-on-a-chip;

[0115] S30: Using the first model, determine the target correlation and obtain the multi-organ injury analysis results based on the target correlation. The target correlation can characterize the relationship between the injury signal and the physiological parameters obtained by organ-on-a-chip detection at different times.

[0116] In some embodiments, the computer device performs the following steps: determining the target association relationship through a first model includes: determining the propagation path of the injury signal based on the injury signal through the first model; mapping the propagation path of the injury signal and physiological parameters to the same coordinate system to determine the target association relationship.

[0117] In some embodiments, the computer device performs the following steps: based on a second model and based on the results of multi-organ damage analysis, predicts the probability of multi-organ failure in the subject.

[0118] In some embodiments, the computer device performs the following steps: if the probability of multiple organ failure of the test subject generated by the second model is greater than a monitoring threshold and / or the physiological parameters are greater than a physiological threshold, a prompt signal is generated, which is used to prompt the user that the test subject is abnormal.

[0119] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] The embodiments of this application have now been described in detail. To avoid obscuring the concept of this application, some details known in the art have not been described. Those skilled in the art can fully understand how to implement the technical solutions disclosed herein based on the above description.

[0121] While specific embodiments of this application have been described in detail by way of examples, those skilled in the art should understand that the above examples are for illustrative purposes only and are not intended to limit the scope of this application. Those skilled in the art should understand that modifications can be made to the above embodiments or equivalent substitutions can be made to some technical features without departing from the scope and spirit of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any manner.

Claims

1. A wound simulation system, characterized in that, include: The device includes a trauma simulation system, multiple organ-on-a-chip components, a microelectrode array, and a processing unit. The trauma simulation device can apply different injury signals to multiple organ chips to simulate different injury conditions of the subject. Multiple organ-on-a-chips are interconnected, and each organ-on-a-chip can simulate the organ structure of the subject under test and collect corresponding physiological parameters. The microelectrode array is used to connect multiple organ-on-a-chips and can simulate the physiological interaction of multiple organs of the subject under test; after the injury signal is applied to any of the organ-on-a-chips by the trauma simulation device, the microelectrode array can transmit the injury signal to another connected organ-on-a-chip. The processing unit is used to invoke a first model, which can determine the target correlation and obtain multi-organ injury analysis results based on the target correlation. The target correlation can characterize the correlation between the injury signal and the physiological parameters detected by the organ-on-a-chip at different times.

2. The system according to claim 1, characterized in that, The processing unit is further configured to determine the propagation path of the injury signal based on the injury signal, and map the propagation path of the injury signal and the physiological parameters to the same coordinate system to determine the target correlation.

3. The system according to claim 1, characterized in that, The organ-on-a-chip includes: A basal layer, in which sensors are disposed, for collecting corresponding physiological parameters; An intermediate layer, disposed on the base layer, is used to simulate transmembrane material exchange regions with other organ-on-a-chips; A cavity, which is disposed on the intermediate layer, is used to simulate the structure and function of the corresponding organ; A control layer is used to connect the chamber to other organ-on-a-chip devices.

4. The system according to any one of claims 1-3, characterized in that, The processing unit is also used to invoke a second model, which can predict the probability of multi-organ failure of the test subject based on the multi-organ injury analysis results.

5. The system according to claim 4, characterized in that, The system further includes a monitoring unit, which is configured to generate a prompt signal if the probability of multi-organ failure of the test subject generated by the second model is greater than a monitoring threshold and / or the physiological parameters are greater than a physiological threshold, and the prompt signal is used to alert the user that the test subject is abnormal.

6. A method for simulating injury, characterized in that, include: Different injury signals were applied to multiple organ-on-a-chip systems to simulate different injury conditions of the subjects under test; Multiple organ-on-a-chips are interconnected via a microelectrode array, each organ-on-a-chip being able to simulate the organ structure of the subject under test; the microelectrode array is used to connect multiple organ-on-a-chips and is able to simulate the physiological interaction of multiple organs of the subject under test; after the injury signal is applied to any organ-on-a-chip, the microelectrode array is able to transmit the injury signal to another connected organ-on-a-chip; The corresponding physiological parameters are collected through the organ-chip; The first model is used to determine the target correlation, and the multi-organ injury analysis results are obtained based on the target correlation. The target correlation can characterize the relationship between the injury signal and the physiological parameters detected by the organ-on-a-chip at different times.

7. The method according to claim 6, characterized in that, The determination of the target association through the first model includes: Based on the injury signal, the propagation path of the injury signal is determined using the first model. The propagation path of the injury signal and the physiological parameters are mapped to the same coordinate system to determine the target correlation.

8. The method according to claim 6 or 7, characterized in that, The method further includes: using a second model to predict the probability of multi-organ failure of the subject based on the results of the multi-organ injury analysis.

9. A computer device, comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the program, it runs the injury simulation method as described in any one of claims 6-8.

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