Diagnostic marker detection system for astrocyte il-33 knockout eae model

Abstract

The application discloses a diagnostic marker detection system of a star-shaped glial cell IL-33 knockout EAE model, relates to the technical field of in vitro diagnosis, and comprises the following steps: constructing direction consistency constraint, four-domain prior weight and anchor point consistency scoring; selecting a minimum panel in four domains of inflammation chemotaxis, T cell subgroups, myelin axons and blood brain barrier, and generating Th17 / Th1 and MMP9 / closure protein ratio and chemotaxis combination; implementing standardized collection, platform alignment and bridge calibration, establishing intra-batch / between-batch quality control and time window limitation; performing learning with monotone constraint and two-stage calibration, and reserving animal-to-human population domain adaptation; and outputting star source index, double-threshold value stratification, longitudinal monitoring and interpretable report, so that cross-platform and cross-batch comparability, direction order preservation, traceability and clinical bridgeability are realized, and robust discrimination and reproducibility are improved.

Description

Technical Field

[0001] This invention relates to the field of in vitro diagnostic technology, specifically to a diagnostic biomarker detection system for an astrocyte IL-33 knockout EAE model. Background Technology

[0002] In the clinical and translational context of neuroimmunological diseases, the disease course, exemplified by multiple sclerosis (MS) / experimental autoimmune encephalomyelitis (EAE), is characterized by multi-stage and heterogeneous features: peripheral immune cells cross the blood-brain barrier to infiltrate the central nervous system, triggering an inflammatory cascade, myelin and axonal damage, ultimately leading to progressive neurological deficits and disability. Previous studies and preliminary data indicate that astrocytes are the main source of endogenous IL-33 in the central nervous system, and the IL-33 / receptor axis participates in immune-neural interactions (such as Th1 / Th17 amplification and BBB damage) in disease states; its imbalance is associated with disease exacerbation. Current clinical and research practices largely rely on scattered single indicators or imaging / scale interpretations, making it difficult to provide a comprehensive, quantifiable, mechanism-related assessment in the early or fluctuating stages. Although multi-biomarker detection is used, there is a lack of structured combinations around specific pathways and a unified data integration-reporting process, resulting in insufficient cross-batch and cross-platform comparability and limited interpretability, making it difficult to support risk stratification and follow-up management. Against this backdrop, there is an urgent need for an in vitro detection system driven by pathological anchors, covering four domains: inflammation / chemotaxis, T cell subsets, demyelination / axon and blood-brain barrier, which can connect sample collection, biomarker quantification, data integration and modeling and result output, achieve close connection with mechanistic evidence, and facilitate bridging and validation between clinical and animal models.

[0003] The core technical problem of the existing technology is that there is a lack of a unified in vitro diagnostic system for MS / EAE with the astrocyte IL-33 deficiency pathway as the pathological anchor. This system can be used in scenarios such as initial / follow-up visits in neuroimmunology clinics, clinical trial enrollment and exclusion and efficacy follow-up, and EAE drug efficacy assessment to implement standardized collection, four-domain collaborative quantification and cross-batch consistent integrated interpretation of multimodal data from serum / plasma / cerebrospinal fluid / tissue homogenate in the same process. Existing practices either remain at the level of single markers and empirical thresholds, or although multiple markers and algorithms are introduced, they still lack characteristic structures and directional priors that are strongly associated with the IL-33 pathway. They fail to couple the four domains of inflammation / chemotaxis, T cell subsets, demyelination / axon, and blood-brain barrier within the same testing strategy, resulting in: (1) difficulty in aligning results across batches / platforms, and dilution of early signals; (2) non-transferability of outpatient stratification and enrollment screening thresholds, delaying intervention; (3) lack of unified measurement for bridging and trend interpretation from animal models to human populations, resulting in a break in the drug efficacy and mechanism verification chain, which directly affects the efficiency of early diagnosis, stratified treatment and follow-up decision-making. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a diagnostic biomarker detection system for an astrocyte IL-33 knockout EAE model. This system constructs directional consistency constraints, four-domain prior weights, and anchor point consistency scores; selects minimum panels across four domains: inflammation chemotaxis, T cell subsets, myelin axons, and blood-brain barrier; and generates Th17 / Th1 and MMP9 / closure protein ratios and chemotactic combinations. It implements standardized data acquisition, platform alignment, and bridging calibration, establishing intra-batch / inter-batch quality control and time window limits; performs learning with monotonic constraints and two-stage calibration, and reserves space for animal-to-human adaptation; and outputs astrocyte index, dual-threshold stratification, longitudinal monitoring, and interpretable reports. This system achieves robust discrimination and improved reproducibility across platforms and batches, ensuring directional order preservation, traceability, and clinical bridging, thereby solving the technical problems in the prior art.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A diagnostic biomarker detection system for an astrocyte IL-33 knockout EAE model includes: constructing an EAE model using astrocyte IL-33 as the pathological anchor point; generating directional consistency constraints, four-domain prior weights, and anchor point consistency scores; and outputting directional vectors and projected vectors with unified coordinates.

[0009] The feasible region of the panel is constructed within the four domains and the optimal selection is obtained; the combination terms of Th17 / Th1, MMP9 / closing protein derivation and chemokine are generated and the orientation is kept consistent; the acquisition and multimodal quantification are standardized within the time window, and time backtracking and platform alignment are performed to output the platform alignment amount and orientation consistency characteristics; the bridging calibrator is introduced to estimate the bridging coefficient matrix, and batch receiving indication is generated with two-layer quality control.

[0010] Using the combination of directional consistency features, mandatory derived quantities, and chemotactic factors as inputs, monotonically constrained learning is implemented; pre-calibration and post-calibration based on the bridging coefficient matrix are performed, and a domain adaptation matrix is ​​set to achieve cross-domain alignment;

[0011] The post-calibration probability, mandatory derived quantity, and chemotactic factor combination terms are aggregated through an adjustable anchor point to generate the star source index; the stratification is completed based on the dual threshold, and the longitudinal warning quantity and interpretation vector are generated simultaneously.

[0012] Furthermore, based on peak phase samples of IL-33 knockout in astrocytes, a direction vector is generated according to the four-domain prior weights. The projected vector is obtained by non-negative cone projection, and the anchor point consistency score is calculated by combining quantile interval integral with monotonic compression function. The direction consistency constraint is locked accordingly.

[0013] Furthermore, the four-domain prior weights are determined by the response template vector, which is constructed and normalized based on the quantile offsets of the knockout group and the control group. The four-domain prior weights are used to limit the feasible domain of the panel and participate in the calculation of information scores.

[0014] By limiting each feasible region to at least one item and setting a lower bound for the direction, the optimal choice is obtained by maximizing the single-objective utility of information score and unit detection cost. The optimal choice is used as the standardized collection list in step three and the direction is kept consistent.

[0015] Furthermore, the ratios of Th17 to Th1 and MMP9 to closed protein were calculated using the direction-consistent feature and then normalized by introducing pseudo-counting smoothing and monotonic compression mapping. The chemokine combination terms were aggregated in the same direction based on the response template weights and monotonic mapping.

[0016] The acquisition time is limited to the maximum time window before the machine is put into operation. Backward amplitude and quality weights are generated. Then, the platform alignment amount is obtained by inverse hyperbolic sine transform and scaling coefficient. Non-negative directional consistency features are obtained by cropping according to the direction vector, which are used for subsequent bridging and training input.

[0017] Furthermore, in each batch, multi-level bridging calibrators are inserted, and the bridging coefficient matrix is ​​estimated by minimizing the information geometric divergence and incorporating conformal sparse regularization. At the same time, a batch reception indication is generated by dual threshold gating of intra-batch repeatability divergence and inter-batch quantile distance for sample inclusion control.

[0018] Furthermore, feature splicing vectors are constructed, and the directional consistent features, mandatory derived vectors and chemiluminescence factor combination terms are cascaded and bridged in a fixed order. Sparse regularization of domain blocks is introduced, and monotonic constraints are applied to the sample pair set to form a constrained learning objective.

[0019] Furthermore, the pre-calibration uses temperature scaling and superimposed anchor point consistency score as a probability translation term, and the post-calibration sets the slope and translation for each batch in the log odds domain and adds a direction compensation vector. The relevant parameters and bridging coefficient matrix are recorded together in the version metadata.

[0020] Furthermore, the domain adaptation matrix is ​​learned by minimizing the maximum mean difference of the kernel, which is constrained by the monotonic feasible region to ensure that the coordinates related to the required derived quantities are not reversed, and the order of the sample pair set is kept consistent. The domain adaptation matrix is ​​concatenated with the post-calibration order.

[0021] Furthermore, an anchor-adjustable power exponent is used to aggregate the post-calibration probability, the two mandatory derived terms, and the combination of chemotactic factors. The aggregation weight is generated by coupling the four-domain prior weights and the anchor consistency score, while maintaining channel normalization.

[0022] Furthermore, the dual thresholds are determined through joint optimization using the gray area width inverse regularization. The longitudinal warning quantity is weighted and integrated on the velocity and duty cycle of the star source index trajectory under a fixed time kernel. The interpretation vector is generated in the channel space according to the integral attribution and recorded with the report version.

[0023] (III) Beneficial Effects

[0024] This invention provides a diagnostic biomarker detection system for an astrocyte IL-33 knockout EAE model, which has the following beneficial effects:

[0025] Using astrocyte IL-33 as a pathological anchor point, we constructed directional consistency constraints, four-domain prior weights, and anchor point consistency scores to unify the signals of the four domains of inflammation chemotaxis, T cell subsets, myelin axons, and blood-brain barrier to the same directed coordinates, eliminating the ambiguity of elevation and depression caused by differences between the platform and the matrix, and forming a consistent entry point for subsequent panels, derivation, and training.

[0026] In the four-domain panel, feasible regions are set and optimal choices are obtained. The ratio of Th17 to Th1, the ratio of MMP9 to closed protein, and the combination of chemokines are uniformly retained. Stable derivatives and aggregation amounts are generated based on the consistent orientation feature, resulting in a minimum and sufficient detection list, so that sampling and data can be directly connected to subsequent steps.

[0027] In the multimodal detection process, a time window is limited from data acquisition to onboarding. Backtracking and platform alignment are implemented to obtain the platform alignment amount and orientation consistency characteristics. A bridging calibration material is introduced to estimate the bridging coefficient matrix, and batch reception indication is generated by intra-batch and inter-batch gating, so that cross-batch data completes the calibration and orientation unification before entering the learning process.

[0028] Constrained learning is constructed under anchor point guidance, trained according to a fixed feature splicing order and subject to monotonic constraints on key ratios; in the previous calibration, the anchor point consistency score is embedded in the probability map, and in the post-calibration, the bridging coefficient matrix and batch parameters are combined to unify the log-probability scale, while the domain adaptation matrix is ​​configured to handle the distribution differences between animals and humans.

[0029] Based on a unified scale input, the system generates a star source index by aggregating the probability of the anchor-adjustable power exponent, combining two mandatory derived quantities and a chemotactic factor, and completing the stratification with dual thresholds. At the same time, a vertical early warning and interpretation closed loop is constructed. The system generates a vertical early warning quantity by calculating the velocity and duty cycle joint quantity of the star source index trajectory using kernel weighting, and outputs an interpretation vector and versioned metadata using integral attribution. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the diagnostic biomarker detection system of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Please see Figure 1 This invention provides a diagnostic biomarker detection system for an astrocyte IL-33 knockout EAE model, comprising: Step 1, using astrocyte IL-33 as a pathological anchor point, establishing a unified metric coordinate within a three-week window after constructing the EAE model: constructing directional consistency constraints, four-domain prior weights and anchor point consistency scores, and outputting directional alignment features and expected vectors; thereby locking up / down semantics and resolving cross-platform and cross-sample directional ambiguities, and establishing a baseline for panel selection and subsequent learning.

[0033] Within a three-week modeling time window, directed baselines for four-domain indicators were determined based on the pathological anchor IL-33, forming a computable directional consistency constraint, which was used to resolve directional uncertainties across batches and samples.

[0034] The EAE model exhibits a stable plateau characteristic after the inflammatory peak at the three-week time point, while the absence of the pathological anchor IL-33 alters the coupling direction between the immune axis and the glial axis. Without prior orientation normalization, any subsequent panel combinations and algorithm training may lead to ambiguity in the meaning of elevation / decrease, causing ratio-derived values ​​and original values ​​to cancel each other out in the model. Therefore, a pathological anchor-driven projection mechanism is needed to uniformly map all candidate indicators to a directed space consistent with the anchor, thereby establishing a common coordinate system for subsequent weight learning and score construction.

[0035] In the multimodal indicator space merged across four domains, a biomarker vector, normalized by the platform, is first generated for each sample. Then, from the pathological anchor point, only those containing Direction vector (such as infiltration and inflammatory chemotaxis expected behavior) Myelin integrity and expected behavior of tight junction proteins ).

[0036] To avoid distortions in Euclidean projection under heavy-tailed distributions and ratio-based dimensions, a potential-function-driven Bregman projection is used to generate directional consistency constraints. Specifically, while preserving information geometry, arbitrary observed biomarker vectors are... Project to In a closed cone with vectors in the same direction, the projected vector is obtained. And thereby define executable directional consistency constraints, where:

[0037]

[0038] Where: biomarker vector The normalized vector obtained by concatenating four fields takes the value of Direction vector : Assigned by the biological priors of the anchor point Direction marker, with values , used to define a directed closed cone;

[0039] Bregman divergence : By strictly convex potential function Inducement, It takes non-negative values; it is used for measurement. relatively Information distance; strictly convex potential function : Optional entropy or quadratic The domains are respectively and Projected vector The optimal projection that satisfies the orientation constraints, with a value of and ;

[0040] Through information geometry-friendly projection, any cross-platform, cross-batch metric is calibrated before entering the algorithm, and abnormally reversed components are corrected with minimal distortion; the resulting directional consistency constraint comparison value is treated the same as the original value, ensuring the additivity and interpretability of subsequent prior learning.

[0041] After completing the directional projection, it is still necessary to distinguish the relative contribution strength of the four domains in the context of missing anchor points, in order to avoid over-amplification of weakly correlated domains during panel selection and training. To this end, a response template vector is constructed. The aim is to capture the directional response of each domain to the anchor point, and to give the prior weights of the four domains using a bounded normalization strategy based on temperature control. ,in:

[0042]

[0043] In the formula: Minimal quantities greater than 0; four-domain prior weights :domain Weight, Value And the sum of the four domains is 1; the projected subvectors : In the domain The sub-block, taking values It carries domain information after aligning with the direction; response template vector Based on the anchor point prior and the domain-directed template identified by experimental methods, the value is taken as follows: Temperature parameters Adjust the sharpness of the weights, and set the value accordingly. Inner product Euclidean inner product;

[0044] The four-domain prior weights are data-driven and anchor-guided, automatically suppressing weak domain weights and focusing on enhancing strong domain weights. These weights naturally play a dual role in intra-domain selection and inter-domain balancing in subsequent scoring and panel combinations. This quantifies and fixes the asymmetric influence of the anchor definition, ensuring that subsequent training does not deviate from the pathological framework.

[0045] Based on four-domain priors and directional consistent inputs, an anchor consistency score is constructed and a gating relationship with disease scores is established. An executable acceptance / rejection criterion is output, providing a traceable supervisory signal for subsequent panel assembly and two-stage calibration.

[0046] Diagnostic panel analysis requires a continuous, thresholdable, and coherent aggregate metric that is consistent with pathological anchors, enabling cross-batch comparisons and maintaining monotonicity in longitudinal follow-ups. Simple mean difference or range can be corrupted by heavy tails, asynchronous variations, and ratio scaling. Therefore, a robust kernel is constructed using quantile shift integrals, and a nonlinear compression modulated by four-domain priors is superimposed on it to obtain a score that is insensitive to external disturbances but highly sensitive to anchor orientation.

[0047] For each domain, the directed quantile shifts of KO and control within the specified quantile intervals are calculated and summed using the four-domain prior weights. Then, the anchor point consistency score is obtained through monotonic sigmoid compression. This construction naturally suppresses extreme values ​​and preserves directional information, where:

[0048]

[0049] Where: Anchor point consistency score Overall score, value Prior signals used for threshold layering and subsequent calibration; direction markers :domain of Mark, value ; used to determine the sign of a directed offset; quantile function :domain The quantile functions for KO and control take values. Lower and upper bounds of quantiles :satisfy The Sigmoid function is used to define robust quantile intervals. : Compression mapping; This is a slope control parameter used for monotonic order-preserving mapping. KO specifically refers to the conditional knockout group of IL-33 in astrocytes (cKO), corresponding to WT (wild-type / non-knockout control).

[0050] The scoring uses quantile shift as the core metric to resist extreme values ​​and scale inconsistencies; directional markers ensure alignment with anchor points; and four-domain weights provide explicit modulation of pathological relevance, making the scoring both robust and biologically interpretable.

[0051] Anchor consistency scores need to establish executable accept / reject gates with respect to the actual disease burden in order to serve as reliable labels in subsequent domain adaptation and two-stage calibration. This is within a three-week modeling time window. Introducing the desired response vector (Determined jointly by the anchor point prior and the directional median vector of the training set samples), and the receiving indication is defined by the combined gating quantity of the cosine angle and the score. :

[0052]

[0053] Where: Receive Instruction Boolean indicator, value ; This indicates that samples are accepted for subsequent training and monitoring when the anchor points are consistent and the workload is sufficient; Anchor point consistency score. See the aforementioned definition for values. ; as the amplitude of the gating strength. Projected vector See the previous definition; used to calculate the angle with the desired vector; desired vector The expected inter-domain response constructed using the directional median, taking values... Threshold : Gating threshold, value ; can be intrinsically determined by the development set and recalibrated during bridging; modeling time window Fixed at three weeks; used to limit the temporal consistency between scoring and gating;

[0054] The amplitude and directional angle of the score are included simultaneously, avoiding false positives caused by a single amplitude and false negatives caused by pure directional consistency; gating ensures that the pathological burden of the sample meets the standard, while retaining room for adaptation to subsequent domains.

[0055] Step 2: Based on the weights and scores of the four domains of inflammation chemotaxis, T cell subsets, myelin axons and blood-brain barrier, the minimum feasible panel is obtained in the feasible domain of at least one item in each domain; at the same time, the required derivatives of Th17 / Th1 and MMP9 / closing protein and chemokine combination are generated to ensure that the processing chain is aligned with the pathological anchor and to form a fixed input caliber.

[0056] Using the four-domain prior as weights, a minimum feasible combination of at least one item for each domain is given to ensure alignment with the pathological anchor direction and achieve a controllable equilibrium between cost and information.

[0057] Because the detection noise, sample matrix effect, and platform-specific differences of the four-domain candidate indicators can easily introduce directional inversion and information redundancy, relying solely on heuristic selection will lead to inter-domain imbalance and reduced cross-batch reproducibility. Therefore, after the previous projection vector... With the four-domain prior weights Within the coordinate system, a constrained search is performed on the candidate set: ensuring coverage of each domain while using the anchor point direction as a gate to obtain the maximum anchoring information and cross-sample comparability with the fewest detection terms. Therefore, the panel combination is no longer a static list, but rather a minimum feasible solution that dynamically converges under the anchor point rules.

[0058] To avoid separating coverage constraints and orientation constraints, the two are coupled into a single feasible domain, and the cross-domain unified semantics are mapped to a lower bound of projection intensity in the same direction as the pathological anchor point.

[0059] To select the indicator vector Describe whether a candidate metric is selected, and form a feasible region under the four-domain index set. :

[0060]

[0061] Where: feasible region : A selection set satisfying both coverage and direction constraints, used to limit the panel search space; selection indicator vector : A 0 / 1 vector representing whether a candidate indicator is selected, with values ​​ranging from 0 to 1. Domain index set :domain Belongs to a set of indexes, with values ​​belonging to a finite set of indexes; direction vector. : Anchor point exported Direction label, value Projected mean vector : Projected vectors on the development set The value is obtained by taking the sample mean. Threshold Lower bound for directional consistency, value ;

[0062] This feasible region binds at least one item per region to the anchor point at the set level, avoiding the accumulated cost of covering first and then correcting; using The lower bound is set to ensure that the panel does not dilute the anchor signal in an average sense. Establish the boundary first, then discuss optimization; tighten the search space with pathological anchors to ensure the combination is on the right track from the start.

[0063] Given that the feasible region has been constructed, information gain and detection cost need to be incorporated into the same objective function, and the definition of information should reflect robust differences that are in sync with the anchor point. Information scores are weighted by the anchor point. Compared with the unit testing cost For the quantification factor, perform a linearly interpretable utility maximization solution:

[0064]

[0065] Where: Optimal choice Panel selection for maximizing the objective function, and the possible values. Anchor point weighted information score : No. The term is shifted to the KO / WT quantiles (following the kernel concept of quantile shift from the previous step) and weighted by its respective domain. The modulated scalar has the following values: ;

[0066]

[0067] Among them, quantile function Obtained by interpolation of empirical distribution. As an indicator Domain;

[0068] For sample set The Indicators, let the sorted samples be Piecewise linear interpolation is used: interval, ;

[0069] integral It can be implemented using equidistant trapezoidal or Simpson quadrature.

[0070] Unit testing cost : No. The marginal cost of the item on a given platform (considering reagents, time window, and all available resources) is given by the following value. Trade-off coefficient Information-cost conversion factor, value ;

[0071] Panel calculus achieves a joint optimization of information maximization and cost suppression with a single objective, maintaining an intrinsic interpretation consistent with the anchor point while avoiding the uncertainties of multi-objective weighted optimization; trade-off coefficients It can adapt to the validation set to ensure a stable minimum panel size under different experimental conditions.

[0072] Under the semantics of the minimum panel, two mandatory derived quantities are generated and a chemotactic factor combination term is designed so that the ratio derived quantity and the original domain quantity are in the same directional coordinate, which preserves the order, resists matrix effects, and can be directly called by subsequent learning.

[0073] Ratio derivatives are a key means of compressing the opposing changes of the immune axis and the barrier axis into a single-peak signal. However, ratios that are not oriented and scale-stabilized can be amplified by batch-to-batch shifts and sample matrix perturbations, and may even have their signs reversed. Therefore, starting only from oriented quantities, an interpretable monotonic mapping is used to compress the logarithmic ratio to a uniform range while retaining sensitivity to strong anomalies. In addition to two mandatory derivatives, a chemokine combination term is also required to address intra-domain redundancy and provide robust cross-channel input for subsequent algorithms.

[0074] After projection vector Within the framework, two mandatory derived quantities from T cell subsets and the blood-brain barrier domain are defined, and the log ratio is compressed using a monotonically power-law mapping to ensure consistency across batches and across the matrix, wherein:

[0075]

[0076] In the formula: Required derived vector : A column vector of two derived quantities, with values... Th17 cell normalization : by direction vector With the projected vector The 17-type eigenvalues ​​obtained by mapping in the T-domain are non-negative and take the following values. Th1 cell normalization Same as above, but for type 1, the value is... ;

[0077] Normalized amount of matrix metalloproteinase 9 : The effective quantity after directional alignment and plateau normalization in the B domain (blood-brain barrier domain), taking the value Normalized amount of closure protein : Refers to the effective amount of barrier tight junction proteins (such as the Claudin / ZO family) within a oriented framework, with values ​​ranging from 0 to 1. Monotonic mapping Domain Power-order monotonic compression; Direction vector Projected vector : Following the definition in the previous step; used to ensure the same direction and non-negativity of the input.

[0078] The two ratio derivatives achieve scale stability and gradient controllability through a logarithmic-power combination, avoiding the risk of heavy tails and flipping of direct ratios; Give separable compression freedoms for different domains, which can be compatible with the bridging coefficient in subsequent calibration.

[0079] A single chemokine is greatly affected by the time phase and matrix, and direct stacking will cause redundancy and noise amplification. Using the response template vector screen the indicators in the inflammatory chemotaxis domain in the same direction, and adopt the ordered aggregation of Sigmoid-template weights to obtain an interpretable combined term as an optional enhancement for the panel:

[0080]

[0081] In the formula: the chemokine combined term : the aggregated quantity in the inflammatory chemotaxis domain, taking values ; the response template vector : continuing the definition in the previous step and restricted to , taking values ; the template weight component : the component of, taking values ; the direction-consistent feature : from the direction vector and the projected vector align the direction of the item and truncate it to a non-negative scalar, taking values ;

[0082] The slope parameter : control the steepness of the Sigmoid, taking values ; the Sigmoid function : a monotonic compression mapping; ; used to compress the dimensional difference to a unified interval; the domain index set : the set of indicators in the inflammatory chemotaxis domain; <00003​​​​​​​​The multimodal raw signals of the minimum feasible panel are transformed into platform-comparable platform alignment quantities within a limited time window, and features in the same direction as the pathological anchor points are generated to ensure that subsequent feature selection and derivation calculations are performed in the same metric space.

[0086] For heterogeneous platforms (multiplex immunofluorescence / ELISA, multicolor flow cytometry, Western blotting / immunofluorescence) targeting the inflammatory chemotactic domain, T cell subset domain, myelin axon domain, and blood-brain barrier domain, raw readings are affected by multiple factors, including acquisition-to-system latency, matrix effects and background, and differences in platform response curves. Directly using these platforms side-by-side would disrupt directional consistency and cross-batch comparability. Therefore, relying on an anchor-guided unidirectional space, aging compensation and matrix normalization are first performed within a cold chain time window. Then, platform alignment is achieved using a monotonic nonlinear response transformation, and directional consistency features that can be directly incorporated into subsequent training are extracted within this space. Thus, multimodal quantization works in three dimensions—physical, chemical, and algorithmic—ultimately compressing signals from different platforms into a unified engineering quantity.

[0087] The process of sample collection, transportation, and instrumentation introduces time-dependent chemical degradation and conformational changes. To avoid inconsistent amplitude drift between different batches of the same biological state, within the maximum time window... Under constraints, the background subtraction signal Implementing exponential aging compensation yields a correction signal. :

[0088]

[0089] Among them, time delay aging coefficient (Fitting based on bridging samples from multiple time points).

[0090] Saturation upper limit Used to suppress compensatory divergence; if Trigger rejection gating (with batch reception indication) (Keep in line)

[0091] Background subtraction signal : No. The intensity of each test item after background subtraction in the blank well / negative control with the same matrix before entering the instrument is taken as the value. ;Correction signal Effective strength after time compensation, value Time delay : The cumulative time from data collection to data entry, with a specified value. ; Aging coefficient : No. The degradation sensitivity of the item was obtained by fitting temperature-controlled records with calibration samples from multiple time points, and the values ​​were taken as follows: Maximum time window The maximum acceptable power generation time delay is limited by the process, and its value is [value missing]. ;

[0092] Single-exponential compensation transforms time-dependent amplitude collapse into a reversible mapping, reducing cross-batch systematic shifts at the source; maximum time window. Hard constraints ensure that extremely aged samples do not enter the training domain; aging coefficient The specific setting of indicators avoids over- or under-correction caused by a one-size-fits-all approach.

[0093] The nonlinear response of different platforms (fluorescence saturation, enzymatic amplification, membrane development nonlinearity) will lead to cross-platform incomparability of amplitudes. In the correction signal... The above uses a bounded monotonic transformation to map the original quantity to a platform alignment quantity. Then in the direction vector Generate directionally consistent features under constraints :

[0094]

[0095] Where: Platform alignment amount : No. The comparable intensities after monotonic transformation take values ​​of ; Aging coefficient This also serves as scale tuning to avoid low-value quantization loss. Correction signal. : As a transformation input.

[0096] Inverse Hyperbolic Sine Mapping Monotonically bounded growth and approximates large values Transformation used to suppress saturation and heavy tails; direction consistency feature. :Depend on With direction vector Multiply each term and truncate it to non-negative values ​​to obtain the value. Directly used for derived ratios and combination terms;

[0097] Inverse Hyperbolic Sine Mapping It exhibits near-linearity in the small signal region and near-logarithmicity in the large signal region, preserving weak positive resolution while avoiding cross-platform distortion caused by strong positive saturation; this is achieved by utilizing direction vectors. Same-direction cropping, consistent direction feature It is completely in the anchor point homology space, providing unambiguous input for subsequent ratio derivation and intra-domain aggregation.

[0098] Using bridging calibrators as a link, the bridging coefficient matrix is ​​estimated and a two-layer quality control is implemented within and between batches. By executing gating output batch receiving instructions, the scale and orientation of cross-batch data are unified before entering constrained learning.

[0099] Even after platform alignment is achieved, system offsets still exist between batches due to reagent batch number, instrument drift, and operator differences. Without bridging, anchor point consistency scores and composite indices will exhibit unexplained shifts or scaling across different batches. To address this, multi-level bridging calibrators are inserted into each batch, and a robust fitting of potential function-induced divergence is used to obtain the bridging coefficient matrix. Retesting and rejection are triggered by a dual indicator of quantile distance and directional consistency divergence, ensuring that the data entering the training process are simultaneously aligned statistically and semantically.

[0100] Batch calibration matrix With reference calibration matrix As input, construct an optimization problem with directional constraints and robust regularization, and solve for the bridging coefficient matrix. , and used for subsequent transformations of all samples within the batch, where;

[0101]

[0102] Where: bridging coefficient matrix : will be approved The linear transformation mapping the calibration space to the reference space, taking values... Batch calibration matrix : batch The platform alignment matrix on a multi-level bridge has the following values: Reference calibration matrix : A reference scale matrix shared across batches, with values... Bregman divergence : Robust distance induced by a strictly convex potential function (following the definition in step one), used to reduce the effects of heavy tails and non-Gaussian noise; Regularization term Used to suppress excessive deformation and encourage approximate homotopy mapping; For the identity matrix, the values ​​are... Regularity coefficient Adjusting the trade-off between fitting and conformity preservation, taking values... ;

[0103] Using geometrically consistent divergence as the loss makes the bridging insensitive to outliers; directional constraints prevent reverse propagation after bridging; L1 conformal regularization makes the bridging coefficient matrix... Correcting offsets within the necessary and minimal range, thus balancing stability and interpretability. Bridging cannot replace quality control; inter-batch residual deviation is measured by quantile distance, intra-batch repeatability consistency is measured by potential function divergence, and time window constraints are incorporated into gating, outputting batch reception indications. .

[0104] First, define the inter-batch quantile distance as the Wasserstein-1 metric:

[0105]

[0106] Then a gating instruction is given:

[0107]

[0108] Where: Wasserstein-1 distance : batch The first-order Earth movement distance relative to the reference in the quantile domain is taken as a value. ;

[0109] quantile function : batch Compared with the empirical quantile function of the reference; , used for construction ;

[0110] Let the non-negative weights of the samples for the same indicator be... And normalize the sorted order: Cumulative rights Least upper bound: ,in If continuous interpolation is required, it can be done by using... Within the interval, for Perform linear interpolation with an interpolation ratio of . The definitions of "with authority" and "without authority" are in... Consistent degradation over time;

[0111] Batch Receive Instruction Boolean value ; This indicates that the batch satisfies the two-level QC and time window constraints; Bregman divergence The divergence between repeated measurement vectors within the same batch; repeated measurement vectors : The vector of two independent measurements of the same sample in the platform-aligned domain, with values... In-batch threshold : Upper bound of allowed intra-batch divergence;

[0112] Inter-batch threshold : Upper bound of allowed inter-batch quantile distance; time delay Maximum time window Used for temporal consistency constraints.

[0113] By combining the local consistency of the direction and the global consistency of the quantile domain into a single gating, it can not only capture instrument drift and reagent batch difference in a timely manner, but also isolate systematic deviations caused by transportation timeouts. As a discrete switch, it provides clear engineering criteria for retest triggering and batch rejection.

[0114] Step 4: Implement anchor-guided constrained learning on the received samples: complete cleaning, filling and normalization, retain only directional consistent features according to the importance threshold, apply monotonic constraints to the ratio of the two mandatory items, construct the model and perform pre-calibration of temperature scaling and post-calibration combined with bridging coefficients, while reserving the domain adaptation interface from animal to human, and obtain a probability output with a unified scale.

[0115] Within the same metric space of inputs with consistent orientation, constrained learning with monotonic constraints on key ratios is completed, and the marginal quantities of the model are mapped to robust probabilities through temperature scaling coupled at anchor points.

[0116] Steps two and three have transformed the original multimodal signals into directionally consistent features. And derive the required derived vector. Combined term with chemokine Meanwhile, step three uses the bridging coefficient matrix Achieve cross-batch alignment.

[0117] However, if the monotonicity of the key ratio components with respect to the discriminant function is not explicitly constrained during the learning phase, prediction reversals that contradict clinical semantics may occur; furthermore, simple posterior probability scaling is insufficient to absorb anchor consistency scores. This implies directional intensity information. Therefore, a monotonically paired set is first introduced at the sample pair level. The structural penalty is implemented by incorporating the rule that increasing the ratio does not reduce risk into the objective; subsequently, the model margins are uniformly mapped to using temperature scaling and anchor point coupling. The probability domain is thus combined with directional consistency and probabilistic interpretability into a continuous process chain.

[0118] At the training set level, the splicing operator is used first. Constructing feature concatenation vectors It is composed of the directional consistency feature after bridging, two mandatory derived quantities and chemokine combination terms in sequence, while retaining the sample source batch index for subsequent calibration.

[0119] To ensure that increasing the ratio of Th17 cells to 1 and the ratio of matrix metalloproteinase 9 to closure proteins does not reduce predictive risk, a sample-pair sequence consistency penalty is adopted, which, together with empirical loss and structure regularization, constitutes a constrained objective:

[0120]

[0121] Where: Feature concatenation vector : Values splicing operator An operator that concatenates subvectors into a single vector in a predetermined order without rescalarization; the output dimension is the sum of the dimensions of all components.

[0122] Bridging coefficient matrix : Regarding the first Sample batch Linear alignment is implemented for features with consistent orientation; model parameters The set of parameters for the discriminant function (SVM hyperplane parameters, gradient boosting leaf weights, etc.) takes values ​​in Euclidean space compatible with the model structure.

[0123] Model function :from arrive Differentiable or piecewise constant discriminant function; in the SVM case, take a linear or kernel-mapped hyperplane; in the tree ensemble case, take a weighted sum of leaf nodes of an additive tree; further:

[0124]

[0125] Where: Support vector set ;coefficient ;Label (or According to implementation conventions); kernel function Linear kernels can be selected or RBF core ,bandwidth ; bias Model parameter set .

[0126] Label : The binary label of the sample, with values This is used to define experience loss; experience loss Log-odds loss Non-negative, used to encourage the marginal value to align with the label;

[0127] Structural regularization : Group sparse regularization (such as for domain blocks) Norm, nonnegative; regular coefficient : Balancing experience loss with structural regularization, and choosing a value Monotonic penalty coefficient : Control the penalty intensity for consistent order, and set the value. Monotonic pairing sets :satisfy The sample pair index set is used to apply the order constraint of non-decreasing ratio → non-decreasing marginal value.

[0128] Writing key ratios into the objective function instead of post-hoc rules significantly reduces the risk of semantic flipping; group sparse regularization and batch bridging together suppress noise dimensionality and maintain the biological interpretability of feature blocks; monotonic pairing exchanges local order constraints for global order preservation, making the model more stable during longitudinal follow-up.

[0129] The model's marginal quantities are still in unlabeled space, and the anchor point consistency scores for different samples are... Their contributions to actual risk differ. To avoid neglecting anchor strength through single temperature scaling, anchor coupling terms are superimposed within the logical domain, mapping the marginal to probability:

[0130]

[0131] Where: calibration probability : Probability after pre-calibration, value Post-calibration and threshold stratification; Sigmoid function :from arrive Monotonic mapping; temperature parameter : The scaling factor for the scaling margin, with values ​​ranging from 0 to 10. Anchor point coupling coefficient Adjusting Anchor Point Consistency Score The shifting effect on probability, taking values Anchor point consistency score Following the definition in step one, the value is... ;

[0132] Incorporating anchor point consistency scoring within the same temperature framework The directional strength increases the probability of high-anchor-point consistency samples and decreases the probability of low-anchor-point consistency samples, thereby reducing probability ambiguity between different anchor points on the same boundary; temperature parameter Coupling coefficient with anchor point Stable estimation can be performed on the validation set using the minimum cross-entropy criterion.

[0133] Based on the pre-calibration probability, using the bridging coefficient matrix After calibration with the reference balanced scale, an animal-human domain adaptation transformation is introduced to ensure that the final probability is on a consistent scale across batches and across domains.

[0134] Pre-calibration focuses on harmonizing the marginal-probability-anchor relationship within the same batch; however, differences in log-odds shift and scaling still exist between different batches, requiring calibration after the Logit domain is completed using batch parameters to eliminate system bias. Furthermore, the migration from the animal domain to the human domain inevitably produces differences in conditional distributions; only by aligning the feature distributions in the anchor-homology coordinates can the probability scale remain stable in clinical bridging. Therefore, firstly, a slope-shift correction term for each batch is learned based on log-odds domain regression of the reference scale batch, and a mean difference compensation is added after bridging; then, using the difference in kernel mean embedding as an indicator, a domain adaptation matrix is ​​learned to align cross-domain features in the anchor coordinates.

[0135] Under the reference batch scale, the pre-calibration probability Mapping to the log-odds domain and learning slope-shift parameters for each batch, while introducing a linear compensation term for the mean difference after bridging, the final probability is obtained:

[0136]

[0137] Where: final probability The probability after two-stage calibration, with a value of [value missing]. Batch slope : batch The scaling factor in the Logit domain takes the following values: Used to correct marginal scale differences; batch translation : batch The shift term in the Logit field takes the value of ;

[0138] Log odds :from arrive bijection; alignment vector : Linear coefficient vector for direction compensation, with values... This enhances the ability of the mean difference after bridging to fine-tune the probability.

[0139] Bridging coefficient matrix , directional consistent characteristic mean Reference mean : respectively batch The linear bridging, the mean of the directional features of this batch and the reference batch; the vector dimensions are consistent, used to construct the directional compensation amount;

[0140] By concentrating the scale-location differences remaining between batches into a two-parameter correction in the Logit domain, the estimation becomes stable and interpretable; the orientation compensation term linearly incorporates the mean difference remaining after bridging, avoiding systematic drift in probability; the overall mapping is... It maintains boundary safety of the probability domain under the envelope.

[0141] After unifying the scale between batches, the issue of conditional distribution differences in species domain migration still needs to be addressed. In the anchor coordinates of the direction-consistent feature-derived triplets, a domain-fitting linear transformation with conformal regularity is constructed to minimize the kernel mean embedding difference between the animal and human domains, while restricting the transformation to approximate identity to preserve biological interpretation.

[0142] The target is defined as follows:

[0143]

[0144] To ensure sufficient disclosure of the operator, the specific form of the kernel mean difference is given:

[0145]

[0146] Where: Domain adaptation matrix Linear transformation from the animal domain to the human domain, taking values Monotonic feasible region : Guarantee domain adaptation matrix A feasible set of coordinates related to key ratios that do not have their relative order reversed under the influence of the action; closed and convex;

[0147] ,in This is the set of coordinate indices for the two ratio channels.

[0148] Maximum mean difference of kernel Based on kernel function The degree of distributional dissimilarity; non-negative;

[0149] Kernel function Positive definite kernels (such as Gaussian kernels) map the input to a reproducing kernel Hilbert space to measure distributional differences, with the kernel width selected by a grid on the development set;

[0150] Kernel function (RBF): ,bandwidth Median heuristic or cross-validation may be used. Constraint: Monotonic feasible region. Projection is the process of taking the shape of a constrained element. .

[0151] Animal domain feature matrix Population domain feature matrix : respectively by The set of sample feature columns; dimensions and ; used for learning domain adaptation matrix Conformity coefficient Restricted domain adaptation matrix The intensity deviating from the constant, taking values identity matrix : Domain adaptation matrix Homogeneity, used to define conformal regularity; F-norm : The square root of the sum of squares of matrix elements; used to measure the magnitude of a transformation.

[0152] Aligning higher-order statistics of animals and humans within anchor point coherent coordinates, balancing distribution alignment and semantic order preservation; linear domain adaptation matrix. The conformal regularization makes the fitting results interpretable and replayable; when concatenated with the aforementioned two-stage calibration, the final probability has a unified and stable threshold semantics across batches and domains.

[0153] Step 5: Based on the aforementioned probabilities, mandatory ratios, and chemotactic combinations, aggregate and generate the Star Source Index according to the four-domain weights and anchor point scoring rules, and set dual threshold stratification; calculate longitudinal monitoring indicators for the same subject and generate channel-level explanatory vectors, and export the corresponding structured reports and version identifiers to ensure consistent representation of in vitro application paths and cross-batch bridging.

[0154] The multimodal discriminant and anchor point strength are aggregated into the star source index on the same interpretable scale. Stable stratification of negative-gray-positive regions is achieved using dual threshold constraints.

[0155] Single post-calibration probability While cross-batch and cross-domain scales have been standardized, their sensitivity to different domains is not equivalent; and the ratio derivation... Combined term with chemokine This characterizes the phase difference and traction strength between the immune axis and the barrier axis. Direct linear superposition would weaken the nonlinear coupling relationship between anchor strength, domain weights, and risk aggregation. Therefore, a monotonic aggregator with adjustable power-law anchors is needed, based on the four-domain prior weights. Consistency score with anchor point Under the joint modulation, the four core quantities are compressed into a single scale, and a stratification that balances sensitivity and specificity is achieved on this scale using dual thresholds. To maintain monotonicity while reflecting the engineering intent of relying more on global probability when the anchor point strength is high and more on intra-domain evidence when the anchor point strength is low, a star source index is defined. Anchor-driven generalized power average, with four-domain prior weights Consistency score with anchor point The jointly determined weights are normalized and allocated so that the information from the myelin axonal domain is processed. Explicitly enter at the weight level:

[0156]

[0157] Where: Star Source Index : Main output of the composite exponent, values Used for hierarchical and longitudinal monitoring; aggregate weights : No. Channel weights, satisfying Values Channel volume , , , All values ;

[0158]

[0159] ,in It is Sigmoid. The slope For translation, ;

[0160] Power function :from Mapped to affine monotonic function, Used to continuously adjust the aggregation curvature between the mean and the maximum, anchor point consistency score. : as a modulating factor for power exponent and weight allocation.

[0161] When anchor point consistency score As it increases, the power function Upward adjustment, Star Source Index Closer to maximum-value aggregation, amplifying high-risk signals; when anchor point consistency score When decreasing, the power function Downward adjustment, Star Source Index It is closer to mean-based aggregation, improving robustness. This structure is mathematically monotonic, engineering-interpretable, and in harmony with clinical semantics.

[0162] To ensure that the four-channel weights reflect both the priors of the four domains and the fundamental role of the myelin axonal domain in the global probability, a normalized anchor point allocation for the weights is given:

[0163]

[0164]

[0165] In the formula: unnormalized weight Anchor point - the original weights under prior coupling, with values... Used to reflect domain contribution; aggregate weight :Depend on Obtained by normalization, with the following values: And the sum is 1; proceed to the previous equation; four-domain prior weights Following step one; each value And the sum is 1; Anchor consistency score Same as above; used in global probability channels Domain Evidence Channel Indirect transfer rights.

[0166] This allocation is based on the anchor point consistency score. High-level emphasis is placed on the role of myelin / axon defects in bearing global risk, in anchor point consistency scoring. At low levels, it relies more on domain-derived evidence to ensure that the index has discriminative power and stability under different anchor point strengths.

[0167] To simultaneously suppress both high-threshold false alarms and low-threshold false alarms, and to avoid clinical hesitation caused by excessively wide gray areas, the upper threshold is solved on the development set. With lower threshold The joint optimization minimizes the overthreshold / underthreshold bias under different labeling conditions within a unified convex penalty, and applies an inverse regularization to the gray area width:

[0168]

[0169] In the formula: upper threshold lower threshold : Stratification threshold, value and Used for positive / negative and gray area division; expectation operator : Empirical expectation of the conditional distribution of labels; used to measure stratification bias under various conditions; labels : Real marker, value ; Represents a positive case; convex penalty function : ; Used to apply a power-law penalty to out-of-bounds values ​​(robust to outliers); regularization coefficient The inverse canonical strength of the gray area width, with a value of... ;

[0170] The objective is to impose asymmetric and controllable penalties on the two types of errors at the statistical level, and to limit the width of the gray area to the range that is usable in engineering through inverse proportional regularization; the solution results show stable migration on the validation set, which is convenient for cross-center sharing.

[0171] Based on a unified scale, a complete output process of vertical early warning, multi-level interpretation, and versioned tracking is constructed, so that single-point diagnosis can be naturally extended into an executable closed loop of efficacy evaluation and progression early warning.

[0172] Both the EAE process and intervention response are time-varying, and clinical and translational applications require trend signals that emerge earlier than the explicit endpoint. Simultaneously, the decisions of the composite index should be interpretable, causally traceable, and reproducible across batches. Relying solely on point comparisons of thresholds makes it difficult to identify accelerators in a timely manner; limiting the interpretation to overall probability fails to quantitatively explain the role of the four domain contributions and key ratios. Therefore, in the Star Source Index... The kernel-weighted velocity-duty joint quantity is introduced as the core quantity for longitudinal early warning, and channel-by-channel explanations are generated by anchor-ordered integral attribution. At the same time, bridging and calibration parameters are solidified into the report using versioned metadata.

[0173] To unify the uplink speed and the duty cycle in the risk zone into a thresholdable early warning value, within the window length... Internal Star Source Index Trajectory Perform kernel-weighted product and use the low threshold As a duty cycle threshold:

[0174]

[0175] Where: Vertical warning quantity : Continuous quantity, value ; Triggering progress warning; kernel weight function : Follow A monotonically decreasing positive function (such as an exponential kernel) and normalized by unit area;

[0176]

[0177] Parameters and Domain: Time Difference (Difference between the current moment and a historical moment); decay scale (Controlling the concentration of proximal weights); Indicator function Guarantee that weights are assigned only to past times; normalization properties .

[0178] Window length : Backtracking time window, value Used to balance sensitivity / robustness; indicator function : 1 if the condition is true, 0 otherwise; used to include duty cycles within the risk zone; Star Source Index Trajectory Low threshold : as the integrand and threshold of the integral; threshold Warning trigger threshold, value The value is determined by optimizing the event lead time using the development set.

[0179] Vertical warning volume By incorporating both the rate of ascent and the high-risk zone of residence, both slow but continuous ascents and short-term sharp ascents can be captured; nuclear power suppression of remote noise makes the early warning closer to the current course of the disease.

[0180] To determine in the report who drove the trajectory of the Star Source Index. Quantitative interpretation and traceability of output across different batches / versions are achieved by using anchor-preserving integral attribution to obtain channel-level interpretation vectors, and calibration and bridging metadata is written into the report header.

[0181]

[0182] In the formula: Explanation vector :and The contribution of the same type of channel, with values... Attribution path baseline The coordinates of the reference baseline (e.g., the control mean) in the four channels, with the following values. Current channel coordinates :time The four-channel input has the following values: ;gradient Gradient of the channel vector; used to measure marginal contribution; Hadamard product : Element-wise multiplication; used for accumulating contributions along the path; parameters Path integral parameter, values ; Used for the coherence path from baseline to current status.

[0183] When using, interpret the vector Provides a channel-level quantitative explanation of the natural and star source indices. The monotonic aggregation is consistent and will not present a contradiction in explaining the rise and fall of the index; this is consistent with the data recorded in the report header. And version hashes allow any conclusion to be traced back to operator-level evidence from training, bridging, and adaptation.

[0184] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0185] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0186] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0187] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0188] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A diagnostic biomarker detection system for an astrocyte IL-33 knockout EAE model, characterized in that: include, Using astrocyte IL-33 as the pathological anchor, an EAE model was constructed to generate directional consistency constraints, four-domain prior weights corresponding to the inflammatory chemotactic domain, T cell subset domain, myelin axon domain, and blood-brain barrier domain, as well as an anchor consistency score. The output directional vector and the projected vector were given unified coordinates. Based on peak phase samples of astrocyte IL-33 knockout, directional vectors were generated according to the four-domain prior weights. The projected vectors were obtained using non-negative cone projection, and the anchor consistency score was calculated using quantile interval integrals combined with a monotonic compression function. The directional consistency constraints were thus locked. Within the inflammatory chemokine domain, T cell subset domain, myelin axon domain, and blood-brain barrier domain, feasible regions of the panel are constructed and optimal selections are obtained. Th17 / Th1, MMP9 / closure protein derivatives, and chemokine combination terms are generated, while maintaining directional consistency. Standardized acquisition and multimodal quantification are performed within the time window, and time backtracking and platform alignment are performed to output the platform alignment amount and directional consistency characteristics. A bridging calibrator is introduced to estimate the bridging coefficient matrix, and a batch reception indication is generated with two-layer quality control. Using the combination of directional consistency features, mandatory derived quantities, and chemotactic factors as inputs, monotonically constrained learning is implemented. Perform pre-calibration and post-calibration based on the bridging coefficient matrix, and set the domain adaptation matrix to achieve cross-domain alignment; The post-calibration probability, mandatory derived quantity, and chemotactic factor combination terms are aggregated through an adjustable anchor point to generate the star source index. The stratification is completed based on the dual threshold, and the longitudinal warning quantity and interpretation vector are generated simultaneously. The four-domain prior weights are determined by the response template vector, which is constructed and normalized based on the quantile shifts between the knockout group and the control group. The four-domain prior weights are used to limit the panel feasible domain and participate in the information score calculation. By limiting each domain to at least one item and setting a lower bound on the direction through the panel feasible domain, the optimal selection is obtained by maximizing the single-objective utility of information score and unit detection cost. The optimal selection is used as the standardized collection list in step three and maintains the consistency of direction. The ratios of Th17 to Th1 and MMP9 to closed protein were calculated using the direction-consistent feature and then normalized by introducing pseudo-counting smoothing and monotonic compression mapping. The chemokine combination terms were aggregated in the same direction based on the response template weights and monotonic mapping. The maximum time window for data acquisition is limited to the time window before the data is collected on the machine. Backward amplitude and quality weights are generated. Then, the platform alignment amount is obtained by inverse hyperbolic sine transform and scaling coefficient. Non-negative directional consistency features are obtained by cropping the direction vector, which are used for subsequent bridging and training input. Pre-calibration uses temperature scaling and superimposed anchor point consistency scores as probabilistic translation terms. Post-calibration sets the slope and translation for each batch in the log-odds domain and adds a direction compensation vector. The relevant parameters and bridging coefficient matrix are recorded together in the version metadata. The domain adaptation matrix is ​​learned by minimizing the maximum mean difference of the kernel, which is constrained by the monotonic feasible region to ensure that the coordinates related to the required derived quantities are not reversed and that the order of the sample pair set is kept consistent. The domain adaptation matrix and post-calibration are sequentially concatenated.

2. The diagnostic biomarker detection system for the astrocyte IL-33 knockout EAE model according to claim 1, characterized in that: In each batch, multi-level bridging calibrators are inserted. The bridging coefficient matrix is ​​estimated by minimizing the information geometric divergence and incorporating conformal sparse regularization. At the same time, a batch reception indication is generated by dual threshold gating of intra-batch repeatability divergence and inter-batch quantile distance for sample inclusion control.

3. The diagnostic biomarker detection system for the astrocyte IL-33 knockout EAE model according to claim 2, characterized in that: Construct feature splicing vectors, cascade bridged features with consistent direction, mandatory derived vectors and chemitropic factor combination terms in a fixed order, introduce domain block group sparse regularization, and apply monotonic constraints on the sample pair set to form a constrained learning objective.

4. The diagnostic biomarker detection system for the astrocyte IL-33 knockout EAE model according to claim 3, characterized in that: An anchor-adjustable power exponent is used to aggregate the post-calibration probability, the two mandatory derived terms and the combination of chemotactic factors. The aggregation weight is generated by coupling the four-domain prior weights and the anchor consistency score, while maintaining channel normalization.

5. The diagnostic biomarker detection system for the astrocyte IL-33 knockout EAE model according to claim 4, characterized in that: The dual thresholds are determined through joint optimization using a gray area width inverse regularization. The longitudinal warning quantity is weighted and integrated on the velocity and duty cycle of the star source index trajectory under a fixed time kernel. The interpretation vector is generated in the channel space according to the integral attribution and recorded with the report version.

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