Hydrogel-based organ-on-chip microfluidic method and device

Abstract

The application relates to the technical field of organ chips, in particular to a hydrogel-based organ chip microflow control method and device, wherein the method comprises the following steps: multi-modal stimulation synergistic regulation: at least two external stimulation signals are applied to a hydrogel microflow channel, the stimulation signals are selected from temperature, light, enzyme concentration or pH change, the physical properties of the hydrogel are changed to dynamically adjust the permeability, shear force or solute diffusion rate of the flow channel; multi-parameter fusion feedback: fluid dynamics parameters and cell physiological parameters in the flow channel are synchronously detected to generate multi-dimensional microenvironment state data; closed-loop control: based on the multi-dimensional microenvironment state data, the strength, timing or spatial distribution of the stimulation signals are adjusted through a dynamic algorithm to realize metabolic and fluid mechanics synergistic regulation across the organ chip. The device comprises a multi-modal stimulation synergistic regulation module, a multi-parameter fusion feedback module and a closed-loop control module. The application has the effect of improving simulation effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of organ chips, in particular to a hydrogel-based organ chip microfluidic control method and device. BACKGROUND

[0002] At present, an organ chip is an in vitro research platform simulating the physiological functions of human organs through microfluidic technology, which has important value in drug development, toxicity testing and disease modeling; traditional organ chips are mostly constructed with inert materials such as polydimethylsiloxane (PDMS) to build microfluidic channels, but PDMS lacks biological activity and is difficult to support the biomimetic functions of extracellular matrix (ECM); in recent years, hydrogels have become the core material of organ chips due to their similar physical and chemical properties to natural ECM (such as adjustable stiffness, permeability and biocompatibility).

[0003] In the prior art, the phase transition characteristics of poly-N-isopropylacrylamide (PNIPAM) are used to adjust the cross-sectional area of the flow channel through an external temperature control device, thereby controlling the fluid shear force. In a specific embodiment, the temperature control device is composed of discrete Peltier elements, which change the flow channel width by heating / cooling cycles (the flow channel cross-sectional area is 100 μm² at 25℃, and is reduced to 70 μm² at 37℃) or use a fixed flow channel design, relying on an external pump to drive fluid circulation.

[0004] In the above technology, a single stimulus (such as temperature) is relied on and the parameters are fixed, which cannot dynamically adapt to pathological and physiological changes (such as vascular leakage caused by inflammation); only a single fluid parameter such as flow rate or pressure is monitored, and real-time feedback of cell metabolic state (such as oxygen partial pressure and lactic acid concentration) is ignored; and the dynamic metabolic coupling between organs cannot be simulated, and the organ cannot dynamically simulate changes in response to various stimuli, resulting in poor simulation effect. SUMMARY

[0005] In order to improve the simulation effect, the present application provides a hydrogel-based organ chip microfluidic control method and device.

[0006] In a first aspect, the present application provides a hydrogel-based organ chip microfluidic control method and device, which adopts the following technical scheme:

[0007] A hydrogel-based organ chip microfluidic control method, comprising the following steps:

[0008] Multi-modal stimulation synergistic regulation: at least two external stimulation signals are applied to the hydrogel microfluidic channel, the stimulation signals are selected from temperature, light, enzyme concentration or pH change, and the permeability, shear force or solute diffusion rate of the flow channel are dynamically adjusted by changing the physical properties of the hydrogel;

[0009] Multi-parameter fusion feedback: Simultaneous detection of fluid dynamics parameters and cell physiological parameters in the flow channel, generating multi-dimensional microenvironment state data;

[0010] Closed-loop control: Based on the multi-dimensional microenvironment state data, the intensity, timing or spatial distribution of the stimulation signal is adjusted through a dynamic algorithm to achieve coordinated regulation of metabolism and fluid mechanics across the organ chip.

[0011] Traditional methods rely only on a single stimulus (such as temperature) and have limited regulation dimensions, making it difficult to simulate complex physiological scenarios. Existing technologies usually only monitor flow rate or pressure, ignoring feedback from cell metabolic state. Organ chips operate independently (such as Emulate liver chips), lacking cross-organ coordination logic, resulting in distorted pharmacokinetic simulations.

[0012] By using the above technical solutions, temperature (25℃→37℃) and light stimulation (405nm laser, 50mW / cm²) are applied to the liver chip to trigger PNIPAM hydrogel contraction (cross-sectional area reduction of 30%) and GelMA local permeability enhancement (pore size expansion to 50nm). Enzyme (MMP-9, 0.5μg / mL) and pH stimulation (pH6.0) are applied to the kidney chip to expand the flow channel permeability to 5×10 -12 m² through enzymatic action. The micro-resistance impedance spectrum sensor (1kHz-100kHz) measures the flow rate distribution (accuracy ±0.1mm / s). Fluorescent probes (rhodamine B labeled) detect lactic acid concentration (resolution ±0.1mM). The transendothelial electrical resistance (TEER) sensor evaluates barrier integrity (frequency 1MHz). Dynamic algorithm analyzes flow rate-metabolism correlation data, adjusts light stimulation intensity to 15mW / cm², and reduces enzyme injection rate to 0.3μg / mL. Multi-stimulation synergy reduces shear force control error and expands permeability adjustment range. Through multi-dimensional data fusion, the differences in fluid environment between liver sinusoidal zone (shear force 1-5dyn / cm²) and central venous zone (0.1-1dyn / cm²) are simulated. Cross-organ metabolic synchronization: liver-kidney drug metabolism half-life simulation error is reduced from ±30% to ±5%, significantly improving drug toxicity test accuracy. Through multi-external stimulation simulation and dynamic metabolic coupling between simulated organs, the simulation effect is improved, making it more realistic.

[0013] Improve application scenario applicability: Drug liver toxicity test: dynamically adjust liver chip shear force (1-8dyn / cm²) and kidney chip filtration rate (permeability 1×10 -14 →5×10 -12 m²) to accurately simulate drug metabolism and excretion process. Inflammation model construction: synergistic degradation of hydrogel by enzyme + pH stimulation simulates nonlinear changes in blood vessel leakage rate (such as 3-fold increase in permeability taking 20 minutes to 10 minutes).

[0014] Optionally, in the multi-modal stimulation synergistic regulation step, the application of the temperature stimulation adopts a stepwise temperature rise.

[0015] By adopting the technical scheme, the prior art adopts linear temperature rise (1℃ / min), resulting in temperature overshoot (±5% error) and hydrogel structure thermal stress damage; temperature control precision is improved: stepwise temperature rise (initial 1℃ / min→subsequent 0.2℃ / min) reduces the cross-sectional area control error; PNIPAM hydrogel fatigue caused by thermal stress is reduced, and the cycle number is increased; stepwise temperature rise can reduce cell thermal damage.

[0016] Optionally, in the closed-loop control step, the dynamic algorithm adopts a hierarchical control architecture, a bottom-layer PID controller adjusts the temperature / light stimulation intensity, and a top-layer reinforcement learning model optimizes the enzyme / pH stimulation strategy once a period; the reinforcement learning model adopts transfer learning initialization, and a pre-training data set contains historical experimental data of several organ chips.

[0017] By adopting the technical scheme, the traditional method adopts single PID or reinforcement learning, which cannot balance real-time response and long-term optimization; a new organ chip needs to be trained from scratch (time-consuming >100 hours), and a large amount of data is required; control efficiency is improved: PID quickly responds to shear force fluctuations (adjustment time <1 second), and reinforcement learning optimizes cross-organ metabolic coupling (half-life error ±2%); transfer learning reduces the iteration number of new organ chip training.

[0018] Optionally, in the cross-organ synergistic regulation, there is a phase delay between the stimulation signal application of the upper chip and the lower chip, the temperature stimulation of the upper chip is triggered 1st phase delay time seconds in advance, and the light stimulation of the lower chip is triggered 2nd phase delay time seconds in advance.

[0019] By adopting the technical scheme, the liver chip (upper layer) and the kidney chip (lower layer) are connected in series through a microchannel, and the physical distance is 10 mm; the stimulation timing is set as follows: when the liver chip applies temperature stimulation (such as from 25℃ to 37℃), the system sets the 1st phase delay time of 5 seconds, that is, the temperature stimulation of the liver chip is triggered 5 seconds in advance; the light stimulation (such as 405nm laser irradiation) of the kidney chip is set to the 2nd phase delay time of 3 seconds, that is, the light stimulation of the kidney chip is started 3 seconds after the liver stimulation is triggered.

[0020] Dynamic regulation logic: In drug metabolism experiments, liver chip metabolites (such as drug intermediates) are transported to the kidney chip through the flow channel, with a delay time of 5 seconds and 3 seconds, ensuring that the metabolites are processed by the liver after the liver processing is completed, and the kidney chip has adjusted the permeability through light stimulation to match the filtration requirements; By delaying the kidney chip light stimulation, the time difference (physiological delay about 3-5 seconds) of real liver-kidney metabolism is simulated, and the drug metabolism half-life simulation error is reduced from ± 30% of the traditional fixed delay to ± 10%; If the kidney chip starts high permeability filtration too early (without delay), it may cause accumulation of incompletely metabolized toxic substances, and the delay mechanism improves cell survival rate from 70% to 85%, by setting the delay, it is more in line with the actual situation, improves the simulation authenticity, and improves the simulation effect.

[0021] Optionally, the first phase delay time , dynamically adjusted according to the hydrogel fatigue coefficient a and the metabolite concentration change rate β, the calculation model is as follows: ; wherein, is the physical transmission distance between the upper chip and the lower chip; represents the average diffusion rate of the drug or metabolite in the chip, represents the drug concentration gradient between the upper chip and the lower chip, is the reference concentration, is the time constant; is the number of hydrogel deformation cycles, and the fatigue coefficient , β is the metabolite concentration change rate, which is detected in real time by a fluorescent probe; is the drug concentration feedback gain coefficient, ; the second phase delay time The calculation model is as follows:

[0022] , wherein is a set value.

[0023] By adopting the above technical scheme, the existing organ chip adopts fixed delay (such as liver-kidney delay of 5 seconds), which cannot adapt to pathological state changes; the present scheme optimizes metabolism synchronization, and the liver-kidney delay time is dynamically adjusted according to the metabolic load (such as shortened to 3 seconds when β is high), and the metabolite clearance efficiency is improved; material protection mechanism: when the hydrogel is fatigued , the delay time is extended by 20%, reducing the material deformation caused by high-frequency stimulation and reducing structural damage; it can be used for kidney chip simulation, when the kidney chip filtration rate decreases , the liver chip delay is automatically extended to 8 seconds, simulating compensatory liver metabolism enhancement, accelerating the clearance of toxic metabolites, and improving cell survival rate.

[0024] Optionally, wherein Dynamic value, calculation model as follows: .

[0025] By adopting the above technical scheme, under high metabolic rate (such as acute toxicity test), the kidney chip is shortened to 4.64 seconds, and the metabolic substance clearance rate is increased by 40%; the structure protection mechanism: when , Automatically reduce, prolong the light stimulation interval, prevent hydrogel fatigue fracture.

[0026] Optionally, the first phase delay time and the second phase delay time The regulation result input organ digital twin model, solves the porous medium flow equation and solute diffusion equation based on the finite element method, real-time predicts the metabolic trajectory after delay adjustment, and triggers the control parameter reset if the prediction error is greater than the set error.

[0027] By adopting the above technical scheme, the metabolic trajectory prediction deviation is compressed from ±20% of the offline model to ±8%, avoiding control failure caused by model mismatch; fast recovery capability: parameter reset makes the system recover to steady state within 30 seconds, reducing the experimental interruption time.

[0028] Optionally, the digital twin model integrates a microfluid channel deformation prediction module, which calculates the channel geometry change in real time based on the viscoelastic constitutive equation of the hydrogel, and triggers a reduction in global stimulation intensity if the predicted deformation exceeds the safety threshold.

[0029] By adopting the above technical scheme, based on the Kelvin-Voigt model (elastic modulus E=5kPa, viscosity coefficient), the deformation of the flow channel cross-sectional area is predicted; safety threshold judgment: if the predicted deformation > 35%, trigger the global stimulation intensity reduction; reduce the temperature stimulation from 37℃ to 34℃, and the light intensity from 50mW / cm² to 25mW / cm²; the metabolic trajectory prediction deviation is compressed from ±20% of the offline model to ±8%, avoiding control failure caused by model mismatch; parameter reset makes the system recover to steady state within 30 seconds, reducing the experimental interruption time.

[0030] Deformation risk early warning: predict the collapse risk (such as the cross-sectional area is reduced to the critical value) 5 minutes in advance, and the channel recovery rate is increased from 40% to 90% after intervention; cross-organ protection: global stimulation reduction avoids cascading organ damage (such as cell necrosis rate from 25% to 5% due to flow channel blockage in kidney chip).

[0031] Optionally, in the viscoelastic constitutive equation of the digital twin model, a fatigue degree-metabolism coupling correction factor is introduced, and the calculation model is as follows: ;

[0032] Wherein, as a metabolic rate reference value, as a fatigue degree coefficient, as a real-time metabolic substance concentration change rate.

[0033] By adopting the technical scheme, the elastic modulus is adjusted through the introduction of the correction factor, so that the deformation prediction accuracy is improved in high load experiments, the service life is significantly prolonged, and the chip still maintains 90% performance after 2000 cycles through dynamic compensation of the gamma factor, and the service life is prolonged.

[0034] In a second aspect, the device provided by the application adopts the following technical scheme:

[0035] The device comprises the following modules:

[0036] The multi-modal stimulation synergistic regulation module is used to apply external stimulation, dynamically adjust the flow channel permeability, shear force or solute diffusion rate by changing the physical properties of the hydrogel;

[0037] The multi-parameter fusion feedback module is connected to the output end of the multi-modal stimulation synergistic regulation module, and is used to synchronously detect the fluid dynamics parameters and cell physiological parameters in the flow channel, and generate multi-dimensional microenvironment state data;

[0038] The closed-loop control module is connected to the output end of the multi-parameter fusion feedback module, and is used to adjust the intensity, timing or spatial distribution of the stimulation signal according to the multi-dimensional microenvironment state data through a dynamic algorithm, so as to realize the metabolic and fluid mechanics synergistic regulation of the cross-organ chip.

[0039] By adopting the technical scheme,

[0040] In summary, the application has at least one of the following beneficial technical effects:

[0041] 1. By simulating multiple external stimuli and simulating dynamic metabolic coupling between organs, the simulation effect is improved, and the simulation is more realistic.

[0042] 2. By setting a delay, the simulation is more realistic, the simulation is more realistic, and the simulation effect is improved. DETAILED DESCRIPTION

[0043] The application will be further described below.

[0044] The embodiment discloses a hydrogel-based organ chip microfluid control method.

[0045] Embodiment 1: The hydrogel-based organ chip microfluid control method comprises the following steps:

[0046] Multi-modal stimulation synergy: at least two external stimuli signals are applied to the microfluidic channel of the hydrogel, which are selected from temperature, light, enzyme concentration or pH change, and the physical properties of the hydrogel are dynamically adjusted to change the permeability, shear force or solute diffusion rate of the channel;

[0047] Specifically, the selection and application of the stimulation signal: temperature stimulation: a micro-thermocouple array (pitch 200 μm) is used in combination with a PID temperature control module to locally heat / cool the temperature-sensitive hydrogel (PNIPAM) channel, the temperature range is 25-40℃, the resolution is ±0.1℃, and the application of the temperature stimulation uses a stepwise temperature rise.

[0048] Initial warming stage: the temperature is raised from 25℃ to 35℃ at a rate of 1℃ / min, triggering the phase transition shrinkage of the PNIPAM hydrogel (the cross-sectional area is reduced from 100 μm² to 80 μm²);

[0049] Slow maintenance stage: the temperature is raised to the target temperature of 37℃ at a rate of 0.2℃ / min, and the cross-sectional area of the channel is kept stable at 70 μm² (error ±1.5%); reducing the temperature shock-induced stress damage to the hydrogel structure, and reducing the expression of cell heat shock proteins by 50%.

[0050] Light stimulation: a digital micromirror device (DMD) is used to generate a dynamic light spot (wavelength 405 nm / 650 nm, power 10-50 mW / cm²) to irradiate the photosensitive hydrogel (GelMA) region; shrinkage control: a 405 nm light spot (15 mW / cm²) is used to locally irradiate the photosensitive GelMA region, the aperture is reduced to 30 nm, and the permeability is reduced to 1×10 -14 m²; expansion control: a 650 nm light spot (20 mW / cm²) expands the aperture to 50 nm, and the permeability rises to 3×10 -12 m²; technical effect: light intensity gradient control realizes shear force distribution (0.1-10 dyn / cm²), simulates the turbulent flow effect of blood vessel branches.

[0051] Enzyme / pH stimulation: matrix metalloproteinase (MMP-9, concentration 0.1-1 μg / mL) and acidic buffer (pH 5.0-6.5) are injected through the microfluidic valve to adjust the cross-linking density of the hydrogel. Enzyme / pH stimulation:

[0052] Enzymatic degradation control: the injection of MMP-9 (0.5 μg / mL) degrades the cross-linking points of the hydrogel, and the permeability is increased by 5 times (1×10 -14 →5×10 -12 m²) within 10 minutes; pH synergy: pH 5.5 buffer enhances enzyme activity, and the reduction rate of cross-linking density is increased by 2 times; technical effect: simulates the vascular leakage under inflammatory conditions (nonlinear change of permeability, R²>0.95);

[0053] Dynamic response mechanism: Temperature response: PNIPAM phase transition shrinks at 37℃, the flow channel cross-sectional area shrinks by 30% (e.g. from 100 μm² to 70 μm²), and the shear force increases from 1 dyn / cm² to 3 dyn / cm². Light response: 405 nm light triggers GelMA shrinkage (pore size shrinks to 20 nm), and 650 nm light triggers expansion (pore size expands to 50 nm), with a permeability change range of ± 5%. Enzyme / pH response: MMP-9 enzyme degrades the cross-linking points of the hydrogel, combined with a decrease in pH (pH 6.0→5.5), and the permeability increases by 5 times (1×10 -14 →5×10 -12 m²).

[0054] Multi-parameter fusion feedback: Simultaneous detection of fluid dynamics parameters and cell physiological parameters in the flow channel, generating multi-dimensional microenvironment state data;

[0055] Specifically, fluid dynamics parameter detection: Micro particle image velocimetry (μPIV): Fluorescent nanoparticles (200 nm, excitation wavelength 488 nm) are injected into the flow channel, and a high-speed CMOS camera (frame rate 1000 fps) captures particle trajectories to calculate flow velocity distribution (accuracy ± 0.1 mm / s) and shear force gradient.

[0056] Micro electrical impedance spectroscopy sensing: Interdigital electrodes (spacing 50 μm) measure impedance changes in the 1 kHz-1 MHz frequency band, and the local shear force in the flow channel is inverted (accuracy ± 0.05 dyn / cm²).

[0057] Cell physiological parameter detection: Transendothelial electrical resistance (TEER): 1 MHz high-frequency impedance detects endothelial barrier integrity, and a 10% decrease in impedance triggers an early warning.

[0058] Fluorescent metabolic probe: Rhodamine B labeled glucose (excitation / emission wavelength 560 nm / 585 nm) detects metabolic rate (resolution ± 0.1 mM, sampling rate 10 Hz).

[0059] Data fusion and synchronization: Hardware: FPGA chip synchronizes multi-sensor data in real time (time deviation <1 ms); Software: Construct a multi-dimensional state matrix (flow velocity, shear force, metabolite concentration, TEER), and eliminate noise through Kalman filtering.

[0060] Closed-loop control: Based on the multi-dimensional microenvironment state data, the intensity, timing or spatial distribution of the stimulation signal is adjusted through a dynamic algorithm to achieve metabolic and fluid mechanics coordinated regulation across the organ-on-chip.

[0061] Specifically, hierarchical control architecture: bottom layer PID control: temperature regulation: thermocouple power adjusted at 10 Hz frequency (±0.1 °C), response to shear fluctuation (e.g. from 1.5→2.0 dyn / cm², adjustment time <1 s). Light intensity regulation: dynamically match target permeability, light intensity adjustment step ±2 mW / cm².

[0062] Top layer reinforcement learning optimization: policy generation: every 5 minutes, based on metabolite clearance rate, cell viability, barrier integrity, etc., optimize enzyme / pH stimulation parameters (e.g. MMP-9 concentration from 0.5→0.3 μg / mL).

[0063] Transfer learning initialization: pre-trained dataset contains 100 groups of liver chip historical experimental data (shear, metabolic rate, deformation cycle number), after transfer to kidney chip, the number of training iterations is reduced by 70%.

[0064] Cross-organ synergistic logic: dynamic phase delay: liver chip temperature stimulation triggers 5 seconds in advance, i.e. temperature stimulation starts 5 seconds before drug injection is completed; kidney chip light stimulation is delayed for 4.5 seconds, i.e. light stimulation is applied 4.5 seconds after temperature stimulation is applied, ensuring that metabolite transport and filtration rate match.

[0065] Digital twin verification: based on Brinkman equation and Fick's law to predict metabolic trajectory, if error >10%, reset control parameters; cross-organ synergy: ensure that when liver chip metabolites reach kidney chip, kidney flow channel permeability has been adjusted to target value (e.g. 3×10 -12 m²).

[0066] For example, chip preparation and initialization: materials: upper liver chip: PNIPAM hydrogel (LCST=32 °C), flow channel cross-sectional area 100 μm², inner wall seeded with primary hepatocytes; lower kidney chip: GelMA hydrogel (photo-crosslinked), flow channel modified with renal tubular endothelial cells.

[0067] Sensor integration: μPIV imaging window, TEER electrode array, fluorescent probe microfluidic injection channel.

[0068] Multi-modal stimulation application and feedback: liver chip regulation: stepwise heating to 37 °C, flow channel cross-sectional area reduced to 70 μm², shear increased to 3 dyn / cm²; 405 nm laser (15 mW / cm²) local irradiation, permeability reduced to 1×10 -14 m².

[0069] Kidney chip regulation: injection of MMP-9 (0.5 μg / mL, pH 6.0), permeability increased to 3×10 -12 m²; 650 nm laser (20 mW / cm²) expands filtration pore size to 50 nm.

[0070] Data collection: μPIV detection flow rate 1.2 ± 0.1 mm / s, TEER impedance drop 15%, lactate concentration detection value 5.2 mM.

[0071] Closed-loop control and optimization: PID real-time adjustment: temperature dropped to 34℃, cross-sectional area restored to 85 μm², shear force stabilized at 1.5 dyn / cm²; kidney chip light intensity increased to 35 mW / cm², permeability increased to 5 × 10 -12 m². Reinforcement learning optimization: based on historical data to generate MMP-9 injection strategy (0.3 μg / mL), pH adjusted to 5.8.

[0072] Metabolic synchrony: drug half-life simulation value 12 hours, measured value 12.3 hours (error ± 2.5%); cell survival rate: liver cell survival rate 92% (traditional method 68%), kidney cell survival rate 89%; system stability: after 1000 cycles of circulation test, the attenuation rate of flow passage cross-sectional area is less than 5% (traditional chip > 30%).

[0073] Through dynamic regulation, the shear force control error is reduced, the permeability regulation range is expanded, combined with cross-organ synergy, the metabolic half-life error is reduced, the cell survival rate is improved, and through the use of digital twin, the material life is prolonged.

[0074] In the cross-organ synergistic regulation of embodiment 2, there is a phase delay in the application of stimulation signals of the upper chip and the lower chip, the temperature stimulation of the upper chip is triggered first a first phase delay time seconds in advance, and the light stimulation of the lower chip is triggered first a second phase delay time

[0075] seconds in advance. The first phase delay time is dynamically adjusted according to the hydrogel fatigue coefficient α and the metabolic concentration change rate β, and the calculation model is as follows: wherein, is the average diffusion rate of drugs or metabolites in the chip, is the drug concentration gradient between the upper chip and the lower chip, is the reference concentration (1 μg / mL), is the time constant (such as 60 s); is the number of hydrogel deformation cycles, and the fatigue coefficient is the metabolic concentration change rate, which is detected in real time by a fluorescent probe; is the drug concentration feedback gain coefficient, ; the second phase delay time is calculated according to the following calculation model:

[0076] wherein is a set value.

[0077] Specifically, chip layout and distance measurement: measure the physical transmission distance between the upper layer (liver chip) and the lower layer (kidney chip) flow channel , for example, set ; calibration: verify the flow channel geometry parameters through the microfluidic chip design drawings, ensure that the spacing error is <1%.

[0078] Metabolic rate calibration: equipment: fluorescent tracer (such as rhodamine B labeled drug) and high-speed imaging system. Inject fluorescent labeled drug (concentration 1 μM), track the diffusion process of the drug from the liver chip to the kidney chip through microscopic imaging (frame rate 1000 fps).

[0079] Calculate the average diffusion rate , for example, the measured transmission time is 6.67 seconds, then ;

[0080] Sensor calibration: fluorescent probe: calibrate the metabolic substance concentration change rate (β) detection range (0-10 mM / s), resolution ±0.1 mM. Pressure sensor: record the number of times of hydrogel deformation cycles , accuracy ±1 times.

[0081] Real-time parameter acquisition: metabolic substance concentration change rate (β): the fluorescent probe detects the lactic acid concentration every 10 seconds, and calculates the change rate (for example, β=0.1 mM / s).

[0082] Hydrogel fatigue degree ( ): pressure sensor accumulates deformation times (for example ), fatigue degree coefficient α=0.002.

[0083] Drug concentration gradient ( ): microelectrode measures the concentration difference between the liver chip outlet and the kidney chip inlet (for example ).

[0084] Calculate the first phase delay time ,

[0085] ;

[0086] Set ;

[0087] Then .

[0088] Upper layer liver chip: trigger temperature stimulation (25℃→37℃) 7.377 seconds in advance before drug injection, the flow channel cross-sectional area is reduced to 70 μm², and the shear force is increased to 3 dyn / cm².

[0089] Lower kidney chip: light stimulus (405 nm, 15 mW / cm2) triggered 5.377 seconds after temperature stimulus application, permeability adjusted to 3 x 10 -12 m².

[0090] Dynamic adjustment example: scenario 1 (high metabolic load): if β rises to 4 mM / s, recalculate , , metabolic impact is limited.

[0091] Scenario 2 (material fatigue): if , , , extend the stimulus interval to protect the hydrogel.

[0092] Verification and feedback: metabolic synchrony verification: equipment: LC-MS detects liver chip metabolites (such as drug intermediates) and kidney chip filtrate. Index: half-life error (target value 12 hours, actual value 12.3 hours, error ± 2.5%). Cell physiological state monitoring: survival rate detection: Calcein-AM / PI double staining method, liver cell survival rate 92% (control group 68%). Barrier function: TEER impedance value maintained > 200 Ω·cm2 (damage threshold < 150 Ω·cm2).

[0093] Material performance evaluation: fatigue test: after 1000 cycles of loading, the attenuation rate of the cross-sectional area of the hydrogel is < 5% (traditional materials > 30%).

[0094] Specific implementation case: liver-kidney chip drug metabolism simulation

[0095] Drug: test compound X (hepatic metabolism toxic drug, EC 50 = 10 μM).

[0096] Chip parameters: liver chip: PNIPAM flow channel, initial cross-sectional area 100 μm2, primary hepatocytes are planted; kidney chip: GelMA flow channel, initial permeability 1 x 10 -14 m², kidney tubular epithelial cells are planted.

[0097] Phase delay regulation process: parameter initialization: , , α = 0.003, k = 0.1.

[0098] Dynamic calculation and stimulus triggering:

[0099] Liver chip: temperature rises to 37°C 7 seconds in advance, cross-sectional area shrinks to 70 μm2;

[0100] Kidney chip: light stimulus is started 5 seconds later, permeability rises to 3 x 10-12 m2.

[0101] Data collection: μPIV shows liver-on-chip shear stress 3.0 ± 0.2 dyn / cm2;

[0102] Fluorescent probe detects kidney-on-chip filtration rate 1.2 μL / min.

[0103] Metabolic synchrony: drug intermediate reaches kidney-on-chip 7 seconds after liver-on-chip processing, filtration completed within 5 seconds (total delay 12 seconds), simulating physiological liver-kidney delay (10-15 seconds).

[0104] Toxicity control: unmetabolized drug residual amount reduced from 25% to 5% of traditional methods, cell necrosis rate reduced from 30% to 8%; system stability: flow channel function remains intact (cross-sectional area > 95 μm2) after 1000 cycles of testing.

[0105] Adjusting delay time in real time through fatigue degree (a) and metabolic rate (β) improves pathological simulation accuracy; phase delay matches liver-kidney metabolic timing, half-life error compressed to ± 2.5%; fatigue compensation mechanism extends chip life to 3 times that of traditional methods.

[0106] In other embodiments, wherein Dynamic value, calculation model as follows: .

[0107] Specifically, parameter collection: real-time detection , , ;

[0108] Calculation , if , take ; the above calculation is 0.635 > 0.5, directly using the calculated value.

[0109] By dynamically adjusting , reduce the possibility of excessive delay, reduce the possibility of too long delay time, optimize the response speed of the system, basic transmission time, fatigue attenuation factor, reflect the influence of material fatigue on delay, remove the influence of β, solve the unit conflict, focus on the coupling effect of fatigue and transmission time.

[0110] In other embodiments, the first phase delay time and the second phase delay time a regulatory result input organ digital twin model, solves a porous medium flow equation and a solute diffusion equation based on a finite element method, predicts a metabolic trajectory in real time after a delay adjustment, and triggers a control parameter reset if a prediction error is greater than a set error; the digital twin model integrates a microfluid channel deformation prediction module, calculates a channel geometry change in real time based on a viscoelastic constitutive equation of a hydrogel, and triggers a reduction of a global stimulation intensity if a predicted deformation exceeds a safety threshold; in the viscoelastic constitutive equation of the digital twin model, a fatigue degree-metabolism coupling correction factor is introduced , and the calculation model is as follows: ;

[0111] wherein, is a metabolic rate reference value, is a fatigue degree coefficient, is a real-time metabolic substance concentration change rate.

[0112] Specifically, a high-performance computing cluster is deployed, equipped with a GPU acceleration module, for real-time finite element calculation; a biological sensor array is integrated to collect organ metabolite concentration (β), fluid pressure and microfluid channel deformation data in real time.

[0113] Software architecture: build an organ digital twin model platform, integrate a multi-physical field solver (COMSOL / ANSYS), a viscoelastic constitutive equation solving module and a control logic unit.

[0114] Digital twin model construction: geometric modeling: reconstruct the three-dimensional structure of the target organ through medical image data (CT / MRI), divide the microfluid channel network and the porous medium area, and generate an unstructured tetrahedral mesh.

[0115] Physical field coupling: porous medium flow equation: Brinkman equation is used to describe fluid motion: , wherein is the fluid viscosity, is the permeability, is the pressure field.

[0116] Solute diffusion equation: coupling convection-diffusion effect: ; wherein, is the diffusion coefficient, is the metabolic source term.

[0117] Phase delay regulation and metabolic trajectory prediction:

[0118] Input parameters: input the first phase delay time (single channel regulation) and the second phase delay time (cross-channel collaborative regulation) into the model.

[0119] Finite element solution: Time discretization: implicit Euler method, time step adaptive adjustment (Δt = 1~10 ms).

[0120] Boundary conditions: According to Set the inlet pulsatile pressure waveform of the flow channel, and the outlet is free outflow.

[0121] Real-time solution: Calculate the flow field , concentration field and metabolite distribution .

[0122] Error feedback mechanism: Calculate the predicted metabolic trajectory and the root mean square error (RMSE) of the measured value :

[0123] ;

[0124] If RMSE> set threshold (such as 5%), trigger control parameter reset: back to the last stable state parameter, and start genetic algorithm optimization and .

[0125] Flow channel deformation prediction and stimulation intensity regulation: Viscoelastic constitutive equation: use generalized Maxwell model to describe the rheological properties of hydrogel: ; Where and are relaxation modulus and viscosity, which are calibrated by dynamic mechanical analysis (DMA) experiment.

[0126] Real-time deformation calculation: Update the flow channel wall displacement δ at each step, if the local deformation (safety threshold), it is determined that there is a risk of structural failure.

[0127] Trigger stimulation intensity attenuation: Global stimulation voltage / current is dynamically down-regulated according to (k is the attenuation coefficient).

[0128] Fatigue-metabolism coupling correction factor integration: parameter dynamic update:

[0129] : Statistics of microfluidic pressure cycle times (ΔP>10kPa is a cycle).

[0130] : Take the metabolic rate benchmark value from static culture.

[0131] Real-time metabolic rate , obtained by sliding window filtering of sensor data.

[0132] Correction factor γ calculation: γ value is updated every 5 minutes, if γ > 2.0, trigger warning and reduce the weight coefficient of α (a = a x 0.9).

[0133] Embed γ into the constitutive equation to correct the relaxation modulus: .

[0134] Safety protection and data interaction: dual redundancy monitoring: two sets of solvers run in parallel, switch to backup solver when the difference exceeds 3%; API interface: provide RESTful API to interact with external medical devices, support JSON format transmission control parameters (such as , I) and alarm signals.

[0135] The embodiment of the application also discloses a device comprising the following modules:

[0136] Multi-modal stimulation synergistic regulation module: for applying external stimulation, dynamically adjusting the permeability of the flow channel, shear force or solute diffusion rate by changing the physical properties of the hydrogel;

[0137] Specifically, the temperature control unit: micro thermocouple array (pitch 200 μm, nickel-chromium alloy material): embedded in the side wall of the hydrogel flow channel, used for local heating / cooling.

[0138] PID temperature control circuit (precision ±0.1℃): integrated MAX31855 chip, supports 10Hz feedback regulation, matches the physiological temperature range (25-40℃).

[0139] Application scenario: trigger the phase transition of temperature-sensitive hydrogel (such as PNIPAM), adjust the cross-sectional area of the flow channel (such as from 100 μm² to 70 μm²).

[0140] Light regulation unit: digital micromirror device (DMD) (DLPLightCrafter4500): generates dynamic light spots (wavelength 405nm / 650nm), light intensity range 1-50mW / cm² (adjustable).

[0141] Optical lens group (NA=0.3): focus the light spot to the target area (diameter 50 μm), reduce light scattering loss.

[0142] Application scenario: 405nm light shrinks GelMA pore size (30nm→20nm), 650nm light expands pore size (20nm→50nm).

[0143] Enzyme / pH infusion unit: microfluidic syringe pump (HarvardApparatusPHDUltra): nanoscale flow control (0.1-10 μL / min), integrated micro electromagnetic valve (response time <10ms).

[0144] Multi-channel mixer: real-time mixing of MMP-9 (0.1-1 μg / mL) with buffer (pH 5.0-6.5) in microfluidic channels, gradient precision ±0.1 pH. Application scenario: simulate inflammatory microenvironment (e.g. MMP-9 degrades cross-linking points, permeability increases 5 times)

[0145] Multi-parameter fusion feedback module: input end connected with output end of multi-modal stimulation synergistic regulation module, for synchronous detection of fluid dynamics parameters and cell physiological parameters in flow channel, to generate multi-dimensional microenvironment state data;

[0146] Specifically, the function is to detect fluid dynamics parameters (flow rate, shear force) and cell physiological parameters (metabolism, barrier function) in real time, and generate a multi-dimensional data matrix.

[0147] Fluid dynamics sensor: micro-particle image velocimetry system (μPIV):

[0148] Fluorescent nanoparticles (200 nm, FluoSpheres™): injected into the flow channel, excitation wavelength 488 nm (CNI laser).

[0149] High-speed CMOS camera (Phantom VEO410L): frame rate 1000 fps, spatial resolution 1 μm / pixel, flow velocity field analyzed by MATLAB PIVlab.

[0150] Micro-impedance spectrometer (Agilent 4294A): interdigital electrodes (spacing 50 μm, platinum material): measure impedance from 1 kHz to 1 MHz, and invert local shear force (precision ±0.05 dyn / cm²).

[0151] Transendothelial electrical resistance (TEER) detection module: high-frequency impedance analyzer (ECIS Zθ): 1 MHz AC signal, detects endothelial barrier integrity (impedance drop >10% triggers early warning).

[0152] Fluorescent metabolic probe system: rhodamine B labeled glucose (Ex / Em=560 / 585 nm): fiber optic spectrometer (Ocean Insight FLAME) detects metabolic rate (resolution ±0.1 mM).

[0153] Microfluidic probe injection channel: integrated piezoelectric micropump (Bartels mp6), sampling rate 10 Hz.

[0154] Data synchronization unit: FPGA controller (Xilinx Zynq-7000): real-time synchronization of multi-sensor data (time deviation <1 ms), uploaded to host computer through PCIe interface.

[0155] Closed-loop control module: input connected with the output of the multi-parameter fusion feedback module, used to adjust the intensity, timing or spatial distribution of the stimulation signal through dynamic algorithm according to multi-dimensional microenvironment state data, to realize the metabolic and fluid mechanics collaborative regulation of the cross-organ chip.

[0156] Specifically, the stimulation parameters are dynamically adjusted based on the feedback data to realize metabolic-fluid mechanics collaborative regulation.

[0157] Bottom layer PID controller: temperature regulation: STM32F4 microcontroller, 10Hz update thermocouple power (PWM duty cycle ± 5%).

[0158] Light intensity regulation: DMD drive board (DLPC3478), light intensity step ± 2mW / cm², matching target permeability (such as 1×10 -14 →3×10 -12 m²).

[0159] Top layer reinforcement learning unit: NVIDIA Jetson AGXXavier: running PyTorch reinforcement learning model, optimizing enzyme / pH parameters every 5 minutes (such as MMP-9 concentration 0.5→0.3μg / mL).

[0160] Transfer learning initialization: pre-trained data set (100 groups of liver / kidney chip data) stored in SSD (Samsung 870EVO 1TB).

[0161] Dynamic phase delay controller: real-time clock module (DS3231): calibrating the timing of the stimulation signal, phase delay accuracy ± 0.1s (such as liver chip triggering temperature control 7.395s in advance).

[0162] Digital potentiometer (AD5293): adjusting the opening of the microfluidic valve, matching the metabolic substance transmission rate (such as =3mm / s).

[0163] Digital twin verification system: edge computing server (Intel NUC11): GPU acceleration module (NVIDIA RTXA4000): real-time solution of Brinkman equation (COMSOL LiveLink), predicting metabolic trajectory.

[0164] Safety threshold monitoring: if the deformation δ / δ_0>1.2, trigger the stimulation intensity attenuation through GPIO (such as light intensity reduced to 50%).

[0165] Dual-redundancy communication module: CAN bus (PCA82C250): transmitting control instructions to actuators (such as thermocouples, micropumps); Bluetooth 5.0 (TICC2640R2F): remote monitoring alarm signal (such as RMSE>5% when pushed to the mobile phone APP).

[0166] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application, and thus: any equivalent changes made according to the structure, shape, principle of the present application should be covered within the protection scope of the present application.

Claims

1. A microfluidic control method for organ-on-a-chip based on hydrogel, characterized in that: Includes the following steps: Multimodal stimulation synergistic regulation: At least two external stimulation signals are applied to the hydrogel microchannels, the stimulation signals being selected from temperature, light, enzyme concentration or pH changes, and the channel permeability, shear force or solute diffusion rate is dynamically adjusted by changing the physical properties of the hydrogel; Multi-parameter fusion feedback: Simultaneously detect fluid dynamics parameters and cell physiological parameters within the flow channel to generate multi-dimensional microenvironment state data; Closed-loop control: Based on the multidimensional microenvironment state data, the intensity, temporal sequence, or spatial distribution of the stimulation signal is adjusted through a dynamic algorithm to achieve cross-organ-on-a-chip metabolic and hydrodynamic coordinated regulation between the upper and lower layers of the chip; The upper chip is a liver chip, and the lower chip is a kidney chip; In the closed-loop control step, the dynamic algorithm adopts a hierarchical control architecture, with the bottom PID controller adjusting the temperature / light stimulation intensity and the top reinforcement learning model periodically optimizing the enzyme / pH stimulation strategy. In the aforementioned cross-organ coordinated regulation, there is a phase delay in the application of stimulation signals between the upper and lower layers of the chip, with the upper layer chip's temperature stimulation occurring before the first phase delay. Triggered in seconds, the lower-layer chip's light stimulation is advanced by the second phase delay time. Triggered in seconds; First phase delay time The calculation model is dynamically adjusted based on the hydrogel fatigue coefficient α and the metabolite concentration change rate β, as follows: ;in, This refers to the physical transmission distance between the upper-layer chip and the lower-layer chip. This indicates the average diffusion rate of a drug or metabolite within the chip. This represents the drug concentration gradient between the upper and lower layers of the chip. The reference concentration is τ, and the time constant is τ. The number of deformation cycles and fatigue coefficient of the hydrogel. β represents the rate of change in metabolite concentration, which is detected in real time using a fluorescent probe; This is the drug concentration feedback gain coefficient. The second phase delay time The calculation model is as follows: ,in Set value; in The value is dynamically selected, and the calculation model is as follows: 。 2. The organ-on-a-chip microfluidic control method based on hydrogel according to claim 1, characterized in that: In the multimodal stimulation synergistic regulation step, the application of the temperature stimulus adopts a stepwise temperature increase.

3. The organ-on-a-chip microfluidic control method based on hydrogel according to claim 1, characterized in that: The reinforcement learning model is initialized using transfer learning, and the pre-training dataset contains historical experimental data from several organ-on-a-chip systems.

4. The organ-on-a-chip microfluidic control method based on hydrogel according to claim 1, characterized in that: First phase delay time Second phase delay time The regulation results are input into the organ digital twin model, and the porous medium flow equation and solute diffusion equation are solved based on the finite element method. The metabolic trajectory after the delay adjustment is predicted in real time. If the prediction error is greater than the set error, the control parameters are reset.

5. The organ-on-a-chip microfluidic control method based on hydrogel according to claim 4, characterized in that: The digital twin model integrates a microchannel deformation prediction module, which calculates the channel geometry changes in real time based on the viscoelastic constitutive equation of hydrogel. If the predicted deformation exceeds the safety threshold, it triggers a reduction in the global stimulus intensity.

6. The organ-on-a-chip microfluidic control method based on hydrogel according to claim 5, characterized in that: In the viscoelastic constitutive equation of the digital twin model, a fatigue-metabolism coupling correction factor is introduced. The calculation model is as follows: ; in, This is the baseline value for metabolic rate. This is the fatigue coefficient. This represents the rate of change in real-time metabolite concentration.

7. A microfluidic control device, characterized in that: The method for implementing the hydrogel-based organ-on-a-chip microfluidic control method as described in any one of claims 1-6 includes the following modules: Multimodal stimulation synergistic regulation module: used to apply external stimuli and dynamically regulate channel permeability, shear force or solute diffusion rate by changing the physical properties of the hydrogel; Multi-parameter fusion feedback module: The input end is connected to the output end of the multimodal stimulation and coordinated regulation module, which is used to simultaneously detect fluid dynamic parameters and cell physiological parameters in the flow channel and generate multidimensional microenvironment state data; Closed-loop control module: The input end is connected to the output end of the multi-parameter fusion feedback module. It is used to adjust the intensity, temporal sequence or spatial distribution of the stimulation signal through dynamic algorithms based on multi-dimensional microenvironment state data, so as to realize the coordinated regulation of metabolism and fluid dynamics across organ-on-a-chip.

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