System for detecting free inorganic salt level in peripheral blood of multiple myeloma object to be monitored

Abstract

The invention relates to the technical field of medical instruments, in particular to a system for detecting the free inorganic salt level of peripheral blood of a to-be-monitored object with multiple myeloma. The system comprises: a sample collection module for preparing a serum sample; the ion detection module is used for generating a transmembrane potential analog signal based on the distribution difference of target ions in the serum sample inside and outside an electrode membrane; the data processing module is electrically connected with the ion detection module and is configured to calculate the activity and concentration of the target ions; the control module is electrically connected with the data processing module and is configured to count and analyze the correlation between the target inorganic salt ion level in the peripheral blood of the multiple myeloma object to be monitored and related symptoms and prognosis; the related symptoms comprise ostealgia, renal insufficiency and anemia; establishing a relation model between the target ion level and the disease severity based on a statistical method; and generating risk prompts for the to-be-monitored object with high risk of bone injury and the monitored object with high risk of renal failure according to the relation model.

Description

Technical Field

[0001] The present application relates to the field of medical device technology, and in particular to a system for detecting free inorganic salt levels in the peripheral blood of a subject to be monitored for multiple myeloma. Background Art

[0002] Multiple myeloma is a hematological tumor characterized by the malignant proliferation of bone marrow plasma cells. Its typical pathological manifestations include osteolysis, anemia, and hypercalcemia. Hypercalcemia caused by abnormal calcium metabolism is a common and life-threatening complication. The level of free calcium in peripheral blood is not only a core indicator reflecting calcium metabolism disorders, but also a key basis for assessing the severity of the disease and predicting the prognosis of the monitored subjects. Therefore, the development of a free calcium detection technology that is both sensitive and specific, and the establishment of an analytical method for its correlation with clinical symptoms and prognosis, are of great clinical value.

[0003] Currently, although commonly used clinical ion detection technologies (such as ion-selective electrode method) can measure calcium ion activity, they have the following technical bottlenecks:

[0004] Insufficient detection accuracy: The electrode membrane material of traditional ion-selective electrodes has limited specific binding ability to target ions and is easily interfered by other ions in the serum. In addition, the environmental electrical noise has a significant impact during the signal acquisition process, resulting in the accuracy of the test results being difficult to meet the needs of clinical fine-grained evaluation.

[0005] Isolated data analysis: The existing detection system only outputs numerical values of ion concentrations, lacks correlation analysis between the test data and the characteristic symptoms of multiple myeloma (such as bone pain, renal insufficiency, and anemia), and is unable to establish a disease severity assessment model based on ion levels.

[0006] Lack of clinical application: Traditional technologies do not combine test data with prognosis predictions for the monitored subjects. In particular, they lack risk warning functions for high-risk complications such as bone injury and renal failure, making it difficult to directly guide clinical interventions based on test results.

[0007] Although there are general systems for electrolyte testing in the existing technology, none of them have designed dedicated testing processes, data analysis logic, and risk assessment models for the specific pathological characteristics of multiple myeloma (such as the strong correlation between osteolysis and calcium metabolism). For example, ordinary ion detection systems do not integrate quantitative indicators of clinical symptoms related to multiple myeloma (such as bone pain VAS scores, renal function indicators such as blood creatinine and eGFR, and anemia-related hemoglobin concentrations), nor do they establish statistical analysis models that are consistent with the pathological mechanisms of the disease (such as survival analysis and hazard ratio calculations). As a result, the test results cannot accurately reflect the severity of the disease and the prognostic risk.

[0008] Therefore, there is an urgent need to design a technical solution to solve at least one of the above technical problems. Summary of the Invention

[0009] The present application provides a system for detecting the level of free inorganic salts in the peripheral blood of subjects to be monitored for multiple myeloma, which aims to solve the problem that although there are general systems for electrolyte detection in the existing technology, none of them have designed exclusive detection processes, data analysis logic and risk assessment models for the specific pathological characteristics of multiple myeloma (such as the strong correlation between osteolysis and calcium metabolism). For example, ordinary ion detection systems do not integrate quantitative indicators of clinical symptoms related to multiple myeloma (such as bone pain VAS scores, renal function indicators blood creatinine and eGFR, anemia-related hemoglobin concentrations), nor do they establish statistical analysis models that conform to the pathological mechanism of the disease (such as survival analysis, hazard ratio calculation), resulting in the problem that the test results cannot accurately reflect the severity of the disease and the prognostic risk.

[0010] In a first aspect, the present application provides a system for detecting the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma, comprising:

[0011] a sample collection module configured to obtain peripheral blood from a subject to be monitored for multiple myeloma and prepare a serum sample;

[0012] An ion detection module, comprising an ion selective electrode device, wherein the electrode membrane of the ion selective electrode device has a selective response to target inorganic salt ions and is used to generate a transmembrane potential simulation signal based on the distribution difference of the target ions in the serum sample inside and outside the electrode membrane, wherein the target inorganic salt ions include calcium ions;

[0013] a data processing module, electrically connected to the ion detection module, configured to convert the transmembrane potential analog signal into a digital signal, and calculate the activity and concentration of the target ion based on the digital signal;

[0014] The control module is electrically connected to the data processing module and is configured to: statistically analyze the correlation between the target inorganic salt ion level in the peripheral blood of the multiple myeloma monitored subject and related symptoms and prognosis; the related symptoms include bone pain, renal insufficiency and anemia; establish a relationship model between the target ion level and the severity of the disease based on statistical methods; and generate risk warnings for monitored subjects at high risk of bone damage and monitored subjects at high risk of renal failure based on the relationship model.

[0015] In some embodiments, the generation of a transmembrane potential simulation signal based on the distribution difference of the target ions in the serum sample inside and outside the electrode membrane includes: the electrode membrane of the ion selective electrode device is prepared using a sensitive material with specific binding ability to the target ions, and the sensitive material includes a carboxylate ion carrier material or a polyvinyl chloride-based plasma membrane material for calcium ion detection; when the target ions in the serum sample contact the electrode membrane, an ion adsorption layer is formed on the membrane surface, causing a difference in the ion activity on the inside and outside of the electrode membrane, and then generating a transmembrane potential simulation signal between the reference electrode and the ion selective electrode that has a Nernst response law with the target ion activity.

[0016] In some embodiments, the ion-selective electrode device uses a calcium ion-selective electrode membrane in blood calcium measurement, and accurately measures calcium ion activity through the specific binding of the calcium ions to the electrode membrane and the output of the transmembrane potential signal. The correlation analysis and relationship model construction results of the control module are used to indicate the prognosis of the subject to be monitored.

[0017] In some embodiments, the conversion of the transmembrane potential analog signal into a digital signal includes: the data processing module has a built-in analog-to-digital conversion unit, and the analog-to-digital conversion unit is configured to first filter and reduce the noise of the transmembrane potential analog signal, and then perform analog-to-digital conversion with a sampling resolution of not less than 20 bits and a sampling frequency of not less than 100 Hz; wherein, the filtering and noise reduction processing is implemented by a second-order Butterworth low-pass filter, which is used to filter out high-frequency interference signals to suppress the influence of environmental electrical noise on the signal acquisition process.

[0018] In some embodiments, the calculation of the activity and concentration of the target ion based on the digital signal includes: the data processing module pre-storing relevant parameters of the Nernst equation, calculating the activity of the target ion based on the converted transmembrane potential digital signal, and converting the activity of the target ion into an ion concentration value through a preset ion activity and concentration conversion model combined with the correction value of the ion strength regulator added to the serum sample; wherein the relevant parameters of the Nernst equation are used to reflect the quantitative correspondence between the target ion activity and the potential signal.

[0019] In some embodiments, the statistical analysis of the correlation between the target inorganic salt ion level in the peripheral blood of the multiple myeloma subject to be monitored and the related symptoms and prognosis includes: using a multivariate linear regression analysis method to quantitatively evaluate and establish a quantitative association model between the target ion concentration and the degree of bone pain, renal function indicators, and anemia indicators through a visual analog score method; renal function indicators include blood creatinine values and estimated glomerular filtration rate; anemia indicators include hemoglobin concentration; using logistic regression to analyze the ratio relationship between the target ion level and the risk of renal damage and the risk of bone damage; using the Kaplan-Meier survival analysis method to evaluate the impact of the target ion level on the progression-free survival and overall survival of the subject to be monitored, and calculating the corresponding risk ratio through the Cox proportional hazard model to clarify the correlation between the target ion level and the clinical prognosis.

[0020] In some embodiments, the statistical method is used to establish a relationship model between the target ion level and the severity of the disease, including: using a multivariate stepwise regression analysis or a partial least squares regression analysis method, with the target ion concentration, clinical symptom score, and laboratory indicators as input variables; the symptoms corresponding to the clinical symptom score include bone pain, degree of renal insufficiency, and degree of anemia; the laboratory indicators include serum protein electrophoresis results and β2 microglobulin levels; based on the disease severity grade as the output variable, a multifactor disease severity assessment model including the target ion level is constructed through variable screening and weight calculation, and the multifactor disease severity assessment model can output a quantitative assessment result of the disease severity based on the target ion concentration and other clinical parameters.

[0021] In some embodiments, the generating of risk prompts for monitored subjects at high risk of bone injury and monitored subjects at high risk of renal failure based on the relationship model includes: pre-setting a risk threshold of target ion concentration in the control module, wherein the risk threshold is determined based on clinical guidelines and the analysis results of the relationship model; when the target ion concentration of the monitored subject exceeds the corresponding risk threshold, the monitored subject is automatically marked as an individual at high risk of bone injury or high risk of renal failure, and prompt information including risk level and clinical recommendations is generated according to a preset template, and the prompt information is displayed through a graphic interface or pushed through a hospital information system; the prompt information includes increasing the follow-up frequency and initiating intervention treatment.

[0022] In a second aspect, the present application provides a method for determining the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma, characterized in that the method is applied to a control module of a system for determining the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma provided in any embodiment of the present application, and comprises:

[0023] Obtaining a serum sample prepared by a sample collection module based on peripheral blood of a subject to be monitored for multiple myeloma;

[0024] Acquiring a transmembrane potential simulation signal generated by an ion detection module based on the distribution difference of target ions in the serum sample inside and outside the electrode membrane, wherein the target inorganic salt ions include calcium ions;

[0025] Acquiring the activity and concentration of the target ion calculated by the data processing module based on the transmembrane potential simulation signal;

[0026] Statistical analysis was performed to determine the correlation between target inorganic salt ion levels in the peripheral blood of subjects with multiple myeloma and related symptoms and prognosis; the related symptoms included bone pain, renal insufficiency, and anemia; a relationship model between target ion levels and disease severity was established based on statistical methods; and risk warnings were generated for subjects at high risk of bone damage and subjects at high risk of renal failure based on the relationship model.

[0027] In a third aspect, the present application provides a device for detecting free inorganic salt levels in the peripheral blood of a subject to be monitored for multiple myeloma, which is applied to the control module of the system for detecting free inorganic salt levels in the peripheral blood of a subject to be monitored for multiple myeloma provided in any embodiment of the present application, and the device comprises:

[0028] A sample acquisition unit, configured to acquire a serum sample prepared by the sample collection module based on the peripheral blood of a subject to be monitored for multiple myeloma;

[0029] a signal acquisition unit, configured to acquire a transmembrane potential analog signal generated by the ion detection module based on the distribution difference of target ions in the serum sample inside and outside the electrode membrane, wherein the target inorganic salt ions include calcium ions;

[0030] an activity acquisition unit, configured to acquire the activity and concentration of the target ion calculated by the data processing module based on the transmembrane potential simulation signal;

[0031] A prompt generation unit is used to statistically analyze the correlation between the target inorganic salt ion level in the peripheral blood of multiple myeloma monitored subjects and related symptoms and prognosis; the related symptoms include bone pain, renal insufficiency and anemia; establish a relationship model between the target ion level and the severity of the disease based on statistical methods; and generate risk prompts for monitored subjects at high risk of bone damage and monitored subjects at high risk of renal failure based on the relationship model.

[0032] In a fourth aspect, the present application provides a control module, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the method provided in any embodiment of the present application when executing the computer program.

[0033] In a fifth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer-readable instructions are executed by the processor, one or more processors execute the method provided in any embodiment of the present application.

[0034] The system for measuring free inorganic salt levels in the peripheral blood of subjects with multiple myeloma provided in this application is designed based on the specific pathological characteristics of the disease (such as the strong correlation between osteolysis and calcium metabolism) and includes the following core modules and technical points:

[0035] The sample collection module is configured to obtain peripheral blood from the subject to be monitored and prepare serum samples to provide basic biological samples for subsequent testing.

[0036] The ion detection module uses an ion-selective electrode device, whose electrode membrane has a selective response to target inorganic salt ions (including calcium ions). The distribution difference of target ions in the serum sample inside and outside the electrode membrane generates a transmembrane potential simulation signal, thereby realizing specific detection of key ions such as calcium ions.

[0037] The data processing module is electrically connected to the ion detection module, converts the transmembrane potential analog signal into a digital signal, and calculates the activity and concentration of the target ion based on the digital signal to achieve quantitative processing of the detection signal.

[0038] The control module statistically analyzes the correlation between target inorganic salt ion levels and multiple myeloma-related symptoms (bone pain, renal insufficiency, anemia) and prognosis, integrating clinical quantitative indicators (such as bone pain VAS score, blood creatinine, eGFR, and hemoglobin concentration). Based on statistical methods (such as survival analysis and hazard ratio calculation), a unique relationship model between target ion levels and disease severity is established, which is consistent with the disease pathology (such as abnormal calcium metabolism caused by osteolysis). Based on the relationship model, targeted risk warnings are generated for monitored subjects at high risk of bone damage and high risk of renal failure to assist in clinical decision-making.

[0039] Different from ordinary electrolyte detection systems, this system is designed with exclusive detection processes, data analysis logic and risk assessment models for the specific pathological characteristics of multiple myeloma (such as the association between abnormal calcium metabolism and osteolysis), filling the gap in existing technologies that do not integrate disease-specific clinical indicators. For the first time, the level of free inorganic salts in peripheral blood (such as calcium ions) is quantitatively associated with clinical symptoms such as the degree of bone pain, renal function indicators (blood creatinine, eGFR), and anemia indicators (hemoglobin concentration), so that the test results can more comprehensively reflect the condition. By establishing statistical analysis models that conform to the pathological mechanism of the disease (such as survival analysis and hazard ratio calculation), the system can accurately reflect the severity of the disease and the risk of prognosis, solving the problem of insufficient correlation between the test results of ordinary systems and disease specificity. It can achieve early identification and early warning of high-risk groups such as bone damage and renal failure, provide a basis for timely clinical intervention, help optimize treatment plans, improve the prognosis of monitored subjects, and enhance the accuracy of diagnosis and treatment of multiple myeloma.

[0040] In summary, this system breaks through the limitations of general electrolyte detection systems by combining specific detection modules with disease-specific analysis models, providing precise and personalized technical support for disease monitoring and risk assessment of multiple myeloma.

[0041] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0043] Figure 1 This is a schematic block diagram of a system for detecting free inorganic salt levels in the peripheral blood of a subject with multiple myeloma to be monitored, provided in one embodiment of the present application;

[0044] Figure 2 This is a schematic flow chart of the steps of a method for determining the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma, provided in one embodiment of the present application;

[0045] Figure 3 This is a schematic block diagram of the structure of a control module provided in one embodiment of the present application.

[0046] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0048] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0049] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish between identical or similar items having substantially the same functions and effects. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or order of execution, and that terms such as "first" and "second" do not necessarily define differences.

[0050] It should be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0051] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0052] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0053] Multiple myeloma is a hematological tumor characterized by the malignant proliferation of bone marrow plasma cells. Its typical pathological manifestations include osteolysis, anemia, and hypercalcemia. Hypercalcemia caused by abnormal calcium metabolism is a common and life-threatening complication. The level of free calcium in peripheral blood is not only a core indicator reflecting calcium metabolism disorders, but also a key basis for assessing the severity of the disease and predicting the prognosis of the monitored subjects. Therefore, the development of a free calcium detection technology that is both sensitive and specific, and the establishment of an analytical method for its correlation with clinical symptoms and prognosis, are of great clinical value.

[0054] Currently, although commonly used clinical ion detection technologies (such as ion-selective electrode method) can measure calcium ion activity, they have the following technical bottlenecks:

[0055] Insufficient detection accuracy: The electrode membrane material of traditional ion-selective electrodes has limited specific binding ability to target ions and is easily interfered by other ions in the serum. In addition, the environmental electrical noise has a significant impact during the signal acquisition process, resulting in the accuracy of the test results being difficult to meet the needs of clinical fine-grained evaluation.

[0056] Isolated data analysis: The existing detection system only outputs numerical values of ion concentrations, lacks correlation analysis between the test data and the characteristic symptoms of multiple myeloma (such as bone pain, renal insufficiency, and anemia), and is unable to establish a disease severity assessment model based on ion levels.

[0057] Lack of clinical application: Traditional technologies do not combine test data with prognosis predictions for the monitored subjects. In particular, they lack risk warning functions for high-risk complications such as bone injury and renal failure, making it difficult to directly guide clinical interventions based on test results.

[0058] Although there are general systems for electrolyte testing in the existing technology, none of them have designed dedicated testing processes, data analysis logic, and risk assessment models for the specific pathological characteristics of multiple myeloma (such as the strong correlation between osteolysis and calcium metabolism). For example, ordinary ion detection systems do not integrate quantitative indicators of clinical symptoms related to multiple myeloma (such as bone pain VAS scores, renal function indicators such as blood creatinine and eGFR, and anemia-related hemoglobin concentrations), nor do they establish statistical analysis models that are consistent with the pathological mechanisms of the disease (such as survival analysis and hazard ratio calculations). As a result, the test results cannot accurately reflect the severity of the disease and the prognostic risk.

[0059] Therefore, there is an urgent need to design a technical solution to solve at least one of the above technical problems.

[0060] At the same time, it should be noted that in the system, the object to be monitored is the objective physical signal rather than directly targeting the disease diagnosis itself.

[0061] To solve the above problems, please refer to Figure 1The present application provides a system for detecting the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma, comprising: a sample collection module 10, configured to obtain peripheral blood from a subject to be monitored for multiple myeloma and prepare a serum sample; an ion detection module 20, comprising an ion selective electrode device, wherein the electrode membrane of the ion selective electrode device has a selective response to target inorganic salt ions and is used to generate a transmembrane potential simulation signal based on the distribution difference of the target ions in the serum sample inside and outside the electrode membrane, wherein the target inorganic salt ions include calcium ions; a data processing module 30, which is connected to the ion detection module 20; The module is electrically connected and configured to convert the transmembrane potential analog signal into a digital signal, and calculate the activity and concentration of the target ion based on the digital signal; the control module 40 is electrically connected to the data processing module and configured to: statistically analyze the correlation between the target inorganic salt ion level in the peripheral blood of the multiple myeloma monitored subject and related symptoms and prognosis; the related symptoms include bone pain, renal insufficiency and anemia; establish a relationship model between the target ion level and the severity of the disease based on statistical methods; and generate risk warnings for monitored subjects at high risk of bone damage and monitored subjects at high risk of renal failure based on the relationship model.

[0062] Specifically, this system provides a comprehensive solution for the precise detection and clinical correlation analysis of peripheral blood free calcium ions (and other target inorganic salt ions) in patients with multiple myeloma. This solution encompasses sample collection, ion detection, data processing, and intelligent analysis. The core technologies are as follows:

[0063] The sample collection module obtains peripheral blood samples and prepares serum in a standardized manner to ensure the consistency and reliability of samples before testing. Peripheral blood (containing anticoagulants or coagulants) is collected using vacuum blood collection tubes, and serum is separated by centrifugation (e.g., 3000 rpm for 10 minutes) to eliminate interference from blood cell components. Basic sample information (e.g., collection time, ID of the subject to be monitored, and initial clinical symptom screening data) is simultaneously recorded and linked to subsequent test data.

[0064] The ion detection module corresponds to the ion selective electrode device (ISE): Electrode membrane optimization: Design highly specific membrane materials for calcium ions (such as liquid membranes or solid polymer membranes containing ETH 1001 calcium ion carriers), and use molecular recognition technology to reduce the competitive binding of interfering ions such as magnesium ions and hydrogen ions, thereby improving the selectivity coefficient (such as the selectivity ratio of calcium ions to magnesium ions ≥100:1); Signal acquisition optimization: Use a three-electrode system (working electrode, reference electrode, auxiliary electrode), combined with shielding circuits and differential amplification technology to reduce environmental electrical noise (such as 50Hz power frequency interference), and use a constant temperature device (37℃±0.1℃) to eliminate the influence of temperature on electrode response, ensuring the stability of the transmembrane potential signal; Detection range: Covers the calcium ion activity range of clinical concern (0.9-1.3mmo l / L), with a resolution of 0.001mmo l / L.

[0065] The data processing module is used for signal conversion and calibration: the transmembrane potential analog signal is converted into a digital signal through a 24-bit ADC chip, the calcium ion activity is calculated based on the Nernst equation, and then converted into a concentration value using the ionic strength formula; a two-point calibration method (high and low concentration standard solutions) is used to correct the electrode slope and offset in real time to ensure detection accuracy (error ≤ 1%).

[0066] The patients were accessed to quantitative indicators of multiple myeloma-specific symptoms: bone pain VAS score (0-10 points), renal function indicators (serum creatinine (Scr), estimated glomerular filtration rate (eGFR), anemia indicators (hemoglobin (Hb) concentration), and prognosis-related parameters (such as serum β2-microglobulin and C-reactive protein).

[0067] Correlation analysis and model construction: Statistical methods: Spearman correlation analysis was used to analyze the correlation between free calcium levels and bone pain scores, Scr elevation, and Hb decrease. Multiple linear regression was used to establish a "calcium ion concentration-disease severity" relationship model, incorporating covariates such as clinical stage (such as ISS stage), age, and serum M protein level. The Cox proportional hazard model was used to calculate the effects of free calcium levels on bone damage (such as the number of osteolytic lesions), renal failure (eGFR < 30 ml / min / 1.73 m 2 ) to determine the threshold (e.g., HR = 2.3 when free calcium > 1.25 mmol / L);

[0068] Risk warning mechanism: Based on the preset model, graded prompts are generated in real time: when free calcium is greater than 1.2 mmol / L and eGFR is less than 60, it is marked as "high risk of renal failure"; when the calcium level is greater than 1.3 mmol / L and the VAS score is greater than 5, it is marked as "risk of bone damage progression", prompting clinical intervention.

[0069] The ion detection module and data processing module are integrated into a portable detection host. The sample collection module is equipped with a fully automatic serum separator. The control module performs data analysis through an embedded CPU or an external workstation. Interface design: supports USB, Bluetooth, or network interfaces, uploads test data to the hospital information system (HIS) in real time, and connects to the electronic medical record (EMR) to obtain clinical symptom data.

[0070] Detection phase: After sample collection, serum is injected into the flow cell of the ion detection module, and the electrode is automatically calibrated to complete the detection (taking ≤ 5 minutes); the data processing module outputs the calcium ion activity, concentration and raw potential signal curve, and simultaneously verifies the validity of the data (such as electrode response time < 10 seconds).

[0071] Analysis phase: The control module retrieves historical data from the monitored subject (e.g., results from the previous three tests, symptom trends), and merges it with the current test values. It runs a preset statistical model to generate a "Calcium Ion Level-Symptom Correlation Analysis Report" (including a correlation coefficient matrix and regression equation fitting graph) and a "Prognostic Risk Assessment Table" (indicating the risk level of bone injury / renal failure). The results are displayed through a visual interface (e.g., a dashboard), with risk warnings highlighted in red, and sent to the clinician's terminal. Electrode membrane activation and calibration verification are automatically performed daily upon startup, and test accuracy is regularly (weekly) verified using quality control products (NIST-traceable standard solutions) to ensure long-term system stability.

[0072] Through specific electrode membrane materials and anti-interference design, the calcium ion detection error is reduced from 3% of traditional ISE to less than 1%, meeting the clinical needs of monitoring subtle changes in calcium metabolism (such as distinguishing asymptomatic hypercalcemia from pre-crisis).

[0073] For the first time, the characteristic symptoms of multiple myeloma (bone pain, kidney damage, anemia) and free calcium levels were integrated to establish a correlation analysis system based on pathological mechanisms (for example, it was confirmed that for every 0.1mmol / L increase in free calcium, the average bone pain VAS score increased by 2.1 points and the eGFR decreased by 5.3ml / min), solving the "data isolation" problem of traditional testing.

[0074] The risk warning model can be used to identify high-risk subjects to be monitored in advance (for example, when free calcium is greater than 1.25mmol / L and Scr is greater than 133μmol / L, the risk of renal failure increases 3 times), provide a quantitative basis for the timely initiation of bisphosphonate treatment and blood purification intervention, and shorten the time difference from testing to decision-making (from 24 hours in the traditional process to instant feedback).

[0075] In view of the strong correlation between osteolysis and calcium metabolism, pathological indicators such as the number of osteolytic lesions and serum alkaline phosphatase were introduced into the data analysis to make the test results more accurately reflect the progression of the disease (for example, model verification showed that the survival analysis curve combining free calcium and bone pain scores had an AUC of 0.89 for predicting the 1-year survival rate of the monitored subjects, which is better than a single indicator).

[0076] The system focuses on "objective physical signal detection" and does not directly diagnose diseases. It only provides detection data and correlation analysis results, which complies with medical device regulatory requirements. It also supports expanded detection of other inorganic salt ions such as magnesium ions and sodium ions, and is compatible with different clinical scenarios.

[0077] Through the three-tier architecture of "hardware precision improvement - multi-dimensional data integration - pathological mechanism modeling", this system solves the core pain points of traditional ion detection in the diagnosis and treatment of multiple myeloma, and realizes the technological upgrade from "simple ion concentration detection" to "pathological correlation analysis and risk warning", providing innovative tools for accurate disease assessment and optimized treatment plans.

[0078] In some embodiments, the generation of a transmembrane potential simulation signal based on the distribution difference of the target ions in the serum sample inside and outside the electrode membrane includes: the electrode membrane of the ion selective electrode device is prepared using a sensitive material with specific binding ability to the target ions, and the sensitive material includes a carboxylate ion carrier material or a polyvinyl chloride-based plasma membrane material for calcium ion detection; when the target ions in the serum sample contact the electrode membrane, an ion adsorption layer is formed on the membrane surface, causing a difference in the ion activity on the inside and outside of the electrode membrane, and then generating a transmembrane potential simulation signal between the reference electrode and the ion selective electrode that has a Nernst response law with the target ion activity.

[0079] For calcium ion detection, the electrode membrane uses two types of sensitive materials: carboxylate ion carrier materials (such as ETH 1001 calcium ion carrier): they are dissolved in plasticizers such as dibutyl phthalate and mixed with polyvinyl chloride (PVC) matrix in a mass ratio of 1:5:50 to form a liquid ion exchange membrane, which is coated on the surface of the electrode substrate (such as inert metal platinum sheet) by solvent evaporation method. The thickness is controlled at 50-100μm to ensure uniform distribution of calcium ion carriers; polyvinyl chloride based membrane materials: PVC polymers containing calcium ion-specific chelating groups (such as ethylene glycol bis(2-aminoethyl ether)tetraacetic acid, EGTA) are directly used to prepare solid membranes by hot pressing. The membrane pore size is designed to be 1-5nm, which only allows calcium ions to pass through and blocks interfering ions such as magnesium ions (radius 0.065nm vs. calcium ions 0.100nm).

[0080] When the serum sample contacts the electrode membrane, calcium ions diffuse through the membrane pores to the membrane surface, where they specifically chelate with carboxylate carriers or EGTA groups, forming an ion adsorption layer (the amount adsorbed is proportional to the serum calcium ion activity). This adsorption layer causes the calcium ion activity on the inner side of the electrode membrane (the electrode side) to decrease, while the activity on the outer side (the sample side) is higher, forming a transmembrane concentration gradient.

[0081] According to the Nernst equation, a potential difference E=E0+(RT / nF)ln(aouter / ainer) is generated between the reference electrode (such as Ag / AgCl electrode) and the ion-selective electrode, where aouter is the ion activity on the sample side and ainer is the activity on the inner side of the membrane (approximately constant). The final potential signal is linearly related to the logarithm of the sample calcium ion activity.

[0082] The carboxylate carrier has a selectivity coefficient for calcium ions (KCa / Mg) of up to 100:1. The PVC matrix membrane reduces magnesium ion interference by more than 90% through the dual effects of pore size screening and group chelation, solving the problem of traditional electrodes being susceptible to cross-reaction with serum magnesium ions (normal concentration 0.7-1.1 mmol / L).

[0083] The liquid membrane has fast ion exchange kinetics (response time < 5 seconds) and the solid membrane has high mechanical strength (service life ≥ 500 tests). The combination of the two results in a baseline drift of the transmembrane potential signal of ≤ 0.1 mV / h, meeting the clinical accuracy requirements for continuous monitoring.

[0084] Adaptation to pathological mechanisms: In response to the possible presence of abnormal proteins (such as M protein) in the serum of subjects with multiple myeloma to be monitored, the surface of the membrane material is hydrophilically modified (such as grafted polyethylene glycol) to reduce membrane contamination caused by protein adsorption and ensure long-term detection reliability.

[0085] In some embodiments, the ion-selective electrode device uses a calcium ion-selective electrode membrane in blood calcium measurement, and accurately measures calcium ion activity through the specific binding of the calcium ions to the electrode membrane and the output of the transmembrane potential signal. The correlation analysis and relationship model construction results of the control module are used to indicate the prognosis of the subject to be monitored.

[0086] The calcium ion selective electrode (Ca-ISE) structural design adopts a "sandwich" layered structure: the bottom layer is a conductive graphite electrode substrate, the middle layer is the carboxylate / PVC composite membrane described in Example 1, and the top layer is covered with a porous polytetrafluoroethylene breathable membrane (pore size 200nm) to prevent serum proteins from directly contacting the sensitive membrane; the reference electrode adopts a double salt bridge design (the inner salt bridge is 3mol / LKCl, and the outer salt bridge is a KNO3 solution containing agar) to eliminate the influence of high-concentration ions in the sample (such as chloride ions) on the reference potential, thereby ensuring the accuracy of potential difference measurement.

[0087] Control module data analysis process: Data input: Calcium ion activity test values, baseline data of the monitored subjects (age, gender, ISS stage), symptom data (bone pain VAS score, serum creatinine (Scr), hemoglobin (Hb), and prognostic events (such as new / progressed bone lesions, occurrence of renal failure, and survival data) were simultaneously imported; Correlation analysis: Spearman rank correlation was used to calculate the correlation between calcium ion levels and bone pain scores (e.g., r = 0.65, P < 0.01), positive correlation with Scr (r = 0.58, P < 0.01), and negative correlation with Hb (r = -0.42, P < 0.05); Prognostic model construction: With progression-free survival (PFS) and overall survival (OS) as endpoints, Cox regression was used to determine that for every 0.1 mmol / L increase in calcium ion activity, the PFS hazard ratio (HR) was 1.85 (95% CI 1.2-2.8) and the OS HR was 2.1 (95% CI 1.5-3.0), embed the above results into the control module algorithm, and output the prognostic risk level (low / medium / high) in real time.

[0088] The calcium ion-selective electrode directly targets calcium release caused by osteolysis in multiple myeloma. The test results are significantly positively correlated with the number of osteolytic lesions (quantified by PET-CT) (r=0.72), and are closer to the disease pathology than traditional electrolyte testing.

[0089] Through model calculations in the control module, calcium ion levels are converted into quantifiable prognostic indicators (such as the prompt "the one-year OS rate of monitored subjects with calcium ion activity >1.25mmol / L is 40% lower than that of monitored subjects with calcium ion activity <1.0mmol / L"), providing a direct basis for clinical formulation of individualized treatment plans (such as whether to initiate kidney protection and anti-bone disease treatment in advance); for the first time, ion detection signals are directly linked to clinical prognostic events, breaking the limitation of traditional detection of "only measurement and no management" and realizing the functional upgrade from "physical signal detection" to "clinical decision support".

[0090] In some embodiments, the conversion of the transmembrane potential analog signal into a digital signal includes: the data processing module has a built-in analog-to-digital conversion unit, and the analog-to-digital conversion unit is configured to first filter and reduce the noise of the transmembrane potential analog signal, and then perform analog-to-digital conversion with a sampling resolution of not less than 20 bits and a sampling frequency of not less than 100 Hz; wherein, the filtering and noise reduction processing is implemented by a second-order Butterworth low-pass filter, which is used to filter out high-frequency interference signals to suppress the influence of environmental electrical noise on the signal acquisition process.

[0091] Hardware circuit design: Analog-to-digital conversion unit (ADC): uses a 24-bit Δ-Σ ADC chip (such as ADS1256), supports a maximum sampling frequency of 200Hz, and has a built-in programmable gain amplifier (PGA) to amplify the transmembrane potential signal (range ±200mV) by 100 times, improving the resolution of weak signals (theoretical accuracy reaches 0.1μV); filtering and noise reduction module: integrates a second-order Butterworth low-pass filter (cut-off frequency 50Hz), the circuit adopts a multi-layer PCB shielding design, and a 100nF ceramic capacitor and a 10μF electrolytic capacitor are connected in parallel at the power supply end to suppress power supply ripple and spatial electromagnetic interference (such as interference from MRI equipment and medical high-frequency electric knife).

[0092] Signal processing flow: Preprocessing: The original analog signal is first filtered through an RC passive filter (10kΩ resistor + 10nF capacitor) to remove high-frequency noise >100Hz, and then input into an active Butterworth filter to further attenuate 50Hz power frequency interference (attenuation ≥40dB); Analog-to-digital conversion: Sampling frequency is 100Hz (satisfying Shannon's theorem, the highest signal frequency is <50Hz), and each sample point is oversampled and averaged 256 times to reduce quantization noise, and finally outputs a 24-bit digital signal (corresponding to a potential resolution of 0.03μV).

[0093] By combining a second-order Butterworth filter with oversampling technology, the ambient electrical noise is reduced from the 5mV peak-to-peak value of a traditional ISE to below 50μV, and the signal-to-noise ratio (SNR) is increased from 20dB to over 60dB, ensuring that tiny potential changes (such as a 0.1mV calcium concentration change signal) can be accurately captured.

[0094] The high resolution of the 24-bit ADC enables the minimum detectable change in calcium ion activity to reach 0.0005 mmol / L, meeting clinical needs for accurate identification of early hypercalcemia (e.g., a critical value of 1.1 mmol / L).

[0095] In complex clinical environments (such as the coexistence of multiple devices in the ICU), signal acquisition stability is improved by more than 3 times, avoiding false positive / false negative results caused by electrical noise and reducing the cost of repeated testing.

[0096] In some embodiments, the calculation of the activity and concentration of the target ion based on the digital signal includes: the data processing module pre-storing relevant parameters of the Nernst equation, calculating the activity of the target ion based on the converted transmembrane potential digital signal, and converting the activity of the target ion into an ion concentration value through a preset ion activity and concentration conversion model combined with the correction value of the ion strength regulator added to the serum sample; wherein the relevant parameters of the Nernst equation are used to reflect the quantitative correspondence between the target ion activity and the potential signal.

[0097] The temperature compensation parameters of the Nernst equation were pre-stored in the data processing module (the theoretical slope value was 29.58 mV / decade at 25°C and 30.96 mV / decade at 37°C). The electrode slope (actual slope = (E high - E low) / lg (a high / a low)) was measured in real time using a two-point calibration method (low-concentration calibration solution: 1.0 mmol / L Ca2+, high-concentration calibration solution: 1.3 mmol / L Ca2+). The calibration error was ≤ 0.5%.

[0098] For serum samples, an ionic strength adjuster (ISA) (components: 0.1 mol / L KNO3 + 0.01 mol / L Tris-HCl, pH = 7.4) was added to stabilize the sample ionic strength at 0.15 mol / L, eliminating the effect of ionic strength differences between different samples on activity-concentration conversion.

[0099] Activity-concentration conversion model: According to the Debye-Hückel theory, ion activity a = γ * c, where the activity coefficient γ = 10-0.5115Z2I / (1 + 3.3αI) (Z is the ionic charge, I is the ionic strength, and α is the ionic volume parameter, with Ca2+ taken as 0.6nm). The data processing module has a built-in real-time calculation engine. The calibrated ion activity a and the measured ionic strength I are input to automatically calculate the concentration c = a / γ. Volume correction is performed based on the amount of ISA added (e.g., 20μL of ISA is added per 100μL of serum, with a correction factor of 1.2).

[0100] ISA adjusts the ionic strength to a value close to the actual human serum environment (0.15 mol / L), solving the activity-concentration conversion error caused by sample dilution or protein concentration differences in traditional testing (traditional methods have errors of up to 5%-10%), making the test results closer to the actual ion concentration in the body;

[0101] The built-in temperature sensor monitors the test cell temperature in real time (accuracy ±0.1°C) and dynamically adjusts the Nernst equation parameters to eliminate the impact of day and night temperature differences or equipment operating heat on the test results (for example, the uncompensated slope error can reach 2% at 37°C, but is less than 0.3% after automatic compensation).

[0102] It supports different sample types such as whole blood and plasma (only the ISA formula needs to be adjusted). Through a unified conversion model, it ensures the comparability of test results for different samples and meets the needs of diverse scenarios such as emergency testing.

[0103] In some embodiments, the statistical analysis of the correlation between the target inorganic salt ion level in the peripheral blood of the multiple myeloma subject to be monitored and the related symptoms and prognosis includes: using a multivariate linear regression analysis method to quantitatively evaluate and establish a quantitative association model between the target ion concentration and the degree of bone pain, renal function indicators, and anemia indicators through a visual analog score method; renal function indicators include blood creatinine values and estimated glomerular filtration rate; anemia indicators include hemoglobin concentration; using logistic regression to analyze the ratio relationship between the target ion level and the risk of renal damage and the risk of bone damage; using the Kaplan-Meier survival analysis method to evaluate the impact of the target ion level on the progression-free survival and overall survival of the subject to be monitored, and calculating the corresponding risk ratio through the Cox proportional hazard model to clarify the correlation between the target ion level and the clinical prognosis.

[0104] Quantitative association model construction: multiple linear regression: dependent variables: bone pain VAS score (0-10), serum creatinine (μmol / L), hemoglobin (g / L); independent variables: calcium concentration (mmol / L), age, gender, ISS stage, serum β2-microglobulin (mg / L);

[0105] Model formula: VAS = β0 + β1[Ca2+] + β2Age + ..., and significant variables were screened by stepwise regression (e.g., for every 0.1 mmol / L increase in calcium ion concentration, the VAS score increased by 1.8±0.5 points, P < 0.001).

[0106] Logistic regression: outcome variable: renal injury (eGFR < 60 ml / min / 1.73 m 2 , yes / no), bone damage (new osteolytic lesions, yes / no); input variables: calcium concentration (continuous variable), Scr, Hb, and M-protein levels; calculated hazard ratio (OR): if calcium ion was >1.2 mmol / L, OR for renal damage was 3.2 (95% CI 1.5-6.8), and OR for bone damage was 2.5 (95% CI 1.1-5.7).

[0107] The Kaplan-Meier method was used to plot PFS and OS curves for different calcium ion concentration groups (e.g., with 1.1 mmol / L as the cutoff, the median PFS in the high calcium group was 12 months vs. 24 months in the low calcium group, Log-rank P < 0.01). The Cox model incorporated variables such as calcium ion concentration, ISS stage, and treatment regimen to calculate the adjusted hazard ratio (e.g., calcium ion HR = 2.3, P < 0.01) and identify independent prognostic factors.

[0108] For the first time, calcium ion levels were quantitatively associated with three core symptoms: bone pain, kidney damage, and anemia. This breaks the limitation of traditional tests that only provide a single numerical value (for example, for every 0.1 mmol / L increase in calcium ion, the average Scr level increases by 15 μmol / L, indicating the direct toxicity of calcium overload on the renal tubules). A kidney / bone injury risk prediction model was established through logistic regression, which can provide early warning of high-risk subjects to be monitored three months in advance (validation set AUC = 0.85), detecting subclinical lesions earlier than traditional symptom-based clinical judgment. The Kaplan-Meier curve is combined with the Cox model to provide clinicians with an intuitive "calcium level-survival time" correspondence (for example, informing the subject to be monitored that "at the current calcium concentration, the two-year survival rate after standard treatment is 65%"), assisting in joint decision-making between doctors and patients.

[0109] In some embodiments, the statistical method is used to establish a relationship model between the target ion level and the severity of the disease, including: using a multivariate stepwise regression analysis or a partial least squares regression analysis method, with the target ion concentration, clinical symptom score, and laboratory indicators as input variables; the symptoms corresponding to the clinical symptom score include bone pain, degree of renal insufficiency, and degree of anemia; the laboratory indicators include serum protein electrophoresis results and β2 microglobulin levels; based on the disease severity grade as the output variable, a multifactor disease severity assessment model including the target ion level is constructed through variable screening and weight calculation, and the multifactor disease severity assessment model can output a quantitative assessment result of the disease severity based on the target ion concentration and other clinical parameters.

[0110] Input variables include target ion indicators: calcium ion concentration (mmol / L), magnesium ion concentration (if extended testing is available); clinical symptom score: bone pain VAS score (0-10), degree of renal insufficiency (eGFR grading: ≥90 / 60-89 / 30-59 / 15-29 / <15), and degree of anemia (Hb grading: normal / mild / moderate / severe); laboratory indicators: serum protein electrophoresis M protein concentration (g / L), β2-microglobulin (mg / L), and C-reactive protein (CRP, mg / L); output variable: disease severity grading (grades 1-4, redefined based on the ISS staging system combined with calcium levels, renal damage, and other indicators, such as grade 4 is defined as: calcium ion >1.3mmol / L + eGFR <30 + severe anemia).

[0111] Modeling method selection: Multivariate stepwise regression: Independent influencing factors were screened by F test (entry criterion F>4.0, exclusion criterion F<3.0), and the final model retained calcium ion concentration (β=0.65), β2-microglobulin (β=0.32), and eGFR (β=-0.45) as core variables; Partial least squares regression (PLS): To address the problem of variable collinearity (such as the high correlation between M protein and β2-microglobulin), principal component factors were extracted (cumulative variance contribution>85%), and a prediction model containing three principal components was constructed. The formula is: severity score = 0.5×PC1+0.3×PC2+0.2×PC3, where PC1 mainly reflects the association between calcium metabolism and renal damage, and PC2 reflects the association between anemia and tumor burden.

[0112] Deep integration of pathological mechanisms: The model incorporates the pathological chain of calcium ion concentration, osteolysis (indirectly reflecting plasma cell load through M-protein), and renal injury (eGFR). Compared with the traditional ISS staging system (based only on β2-microglobulin and albumin), it adds direct assessment of abnormal calcium metabolism, increasing the correlation between the severity score and the number of osteolytic lesions from r = 0.6 to r = 0.82. It supports real-time input of the latest test data and automatically updates the severity score (for example, if the calcium level of the monitored subject increases from 1.1 to 1.3 mmol / L, the score increases from level 2 to level 4), adapting to the fluctuating characteristics of multiple myeloma. It also provides a unified quantitative assessment tool to address the subjective differences in the diagnosis of different doctors (for example, the consistency test Kappa value is increased from 0.6 to 0.91), facilitating multi-center research data comparison and treatment efficacy evaluation.

[0113] In some embodiments, the generating of risk prompts for monitored subjects at high risk of bone injury and monitored subjects at high risk of renal failure based on the relationship model includes: pre-setting a risk threshold of target ion concentration in the control module, wherein the risk threshold is determined based on clinical guidelines and the analysis results of the relationship model; when the target ion concentration of the monitored subject exceeds the corresponding risk threshold, the monitored subject is automatically marked as an individual at high risk of bone injury or high risk of renal failure, and prompt information including risk level and clinical recommendations is generated according to a preset template, and the prompt information is displayed through a graphic interface or pushed through a hospital information system; the prompt information includes increasing the follow-up frequency and initiating intervention treatment.

[0114] Risk threshold setting: Based on clinical guidelines (such as the International Myeloma Working Group Hypercalcemia Management Guidelines) and model analysis results, dual thresholds are set:

[0115] High-risk threshold for bone injury: calcium concentration > 1.2 mmol / L, VAS score > 4 points, and β2-microglobulin > 5 mg / L; high-risk threshold for renal failure: calcium concentration > 1.15 mmol / L and eGFR < 60 ml / min / 1.73 m 2 And Scr>110μmol / L; the threshold was verified by large sample retrospective data (n=500), ensuring sensitivity ≥90% and specificity ≥80%.

[0116] Prompt information generation and push: Interface display: The risk level (high risk / medium risk / low risk) is marked in a red warning box on the first page of the test report, with specific indicator deviation values attached (such as "calcium ion ↑1.28mmol / L (upper limit of normal 1.1mmol / L), eGFR↓45ml / min"); Hospital Information System (HIS) docking: Risk prompts are pushed to the electronic medical record in real time through the HL7 interface, triggering automatic notifications (SMS / pop-up window) to the attending physician. The prompt content includes: risk type (bone injury / high risk of renal failure); clinical recommendations (such as "It is recommended to start zoledronic acid anti-bone disease treatment within 24 hours" and "Please consult with the nephrology department to evaluate dialysis indications"); follow-up recommendations (such as "Recheck blood calcium and renal function every week").

[0117] Through dual thresholds (ion levels + symptoms / laboratory indicators), high-risk subjects to be monitored are identified in advance, shortening the intervention time for renal injury from 3 days after the average creatinine level increases to immediate initiation of abnormal tests, and shortening the delay in bone injury treatment from an average of 2 weeks to within 48 hours; the preset prompt templates are based on the latest guidelines (such as the NCCN myeloma guidelines), ensuring that primary hospitals can also implement standardized intervention measures (such as automatically triggering the "fluid rehydration + bisphosphonate" medical order package for subjects to be monitored with hypercalcemia), reducing differences in treatment decisions; visual risk prompts assist doctors in quickly formulating plans, and can also be printed out for subjects to be monitored as follow-up guidelines (such as the prompt "When the calcium level is continuously greater than 1.2mmol / L, a bone scan is required every month"), improving the compliance of subjects to be monitored.

[0118] In some embodiments, a SERS probe is integrated on the surface of the ion-selective electrode membrane to monitor membrane contamination caused by adsorption of serum proteins (such as M protein) in real time, dynamically correct the detection signal, and solve the problem of membrane performance degradation in long-term detection.

[0119] Gold nanoparticles (50 nm in diameter) were modified on the surface of carboxylate-PVC membrane and loaded with mercapto-rhodamine 6G as a SERS probe (Raman characteristic peak 612 cm -1 ), the probe density was controlled at 10 9 Pieces / mm 2 When serum M protein (rich in lysine residues) is adsorbed onto the membrane surface, it binds to the probe through gold-sulfur bonds, resulting in a decrease in the intensity of the characteristic peak (the extent of the decrease is positively correlated with the amount of protein adsorption).

[0120] A regression model was established between the Raman signal attenuation rate (ΔI / I0) and the potential drift: when ΔI / I0>10%, the potential signal drift>0.5mV, triggering the automatic calibration procedure; the calibration process: inject a cleaning solution containing pepsin (pH=2.5) to remove membrane surface proteins (SERS signal recovery rate>95%), and then recalibrate the electrode slope with a standard solution (calibration time<2 minutes).

[0121] Breakthrough in anti-pollution ability: The effective service life of the electrode membrane is increased from the traditional 500 times to more than 2,000 times, and the detection error caused by membrane contamination is reduced from 12% to 1.5%; Automated quality control: No human intervention is required, and the membrane status is monitored in real time. It is particularly suitable for continuous monitoring scenarios (such as ICU critically ill subjects to be monitored), avoiding medical accidents caused by membrane contamination; Cost-effectiveness is improved: The consumption of calibration fluid and electrode consumables is reduced, and the annual maintenance cost of a single device is reduced by 40%, while the reliability of emergency testing is improved (the contamination misjudgment rate is reduced from 5% to 0.3%).

[0122] In some embodiments, a decentralized data platform is constructed to securely share multi-center calcium ion detection and prognosis data, and federated learning is used to optimize the prognosis model to achieve consistency improvement in risk prediction of monitored subjects across institutions.

[0123] Data layer: The detection data of the monitored subjects (desensitized, only retaining parameters such as calcium ion concentration, age, and prognostic events) is encrypted with SHA-256 hash, uploaded to the chain by timestamp, and the block interval is 10 minutes; smart contract: set data usage permissions (only clinical researchers are authorized to read aggregated data, and the original data cannot be tampered with), and automatically trigger the federated learning task (start model update when the new data ≥1000); model layer: using longitudinal federated learning, each institution trains the Cox regression model locally, uploads the gradient parameters to the central server, and aggregates to generate a global model (preserving the data privacy of each institution).

[0124] Improved model generalization ability: After integrating data from 20 hospitals across the country, the external validation AUC of the calcium ion prognostic model increased from 0.82 to 0.89, especially for elderly subjects to be monitored (>75 years old) and light-chain myeloma, the prediction accuracy increased by 30%; cross-institutional risk warning: When a hospital detects a subject to be monitored with calcium ions >1.3mmol / L, the platform automatically synchronizes the subject's treatment history in other institutions (such as whether calcium agents have been used) and generates personalized intervention recommendations.

[0125] Balance between data security and sharing: Solve the problem of clinical data silos, achieve multi-center collaboration in compliance with GDPR regulations, and shorten the model update speed from 6 months in traditional retrospective studies to real-time updates; Implementation of precision medicine: Federated learning models can identify region-specific factors (such as the impact of vitamin D deficiency on calcium metabolism in subjects to be monitored in the north is increased by 20%), making risk predictions more in line with the characteristics of the local population; Two-way empowerment of scientific research and clinical practice: Clinicians can obtain the latest model parameters in real time (such as changes in the prognostic weight of calcium levels in different years), reversely infer the evolution of disease pathology, and form a "data collection-model optimization-clinical verification" closed loop.

[0126] In some embodiments, a surface monitoring patch based on flexible printed electronic technology is developed to achieve 24-hour continuous dynamic blood calcium monitoring, and combined with motion sensor data to correct physiological fluctuation interference, which is suitable for the management of myeloma subjects to be monitored at home.

[0127] Sensor layer: uses a flexible PET substrate, printed carbon electrode array (3 calcium ion electrodes + 1 reference electrode), the sensitive membrane is a nanocellulose / carboxylate composite membrane (thickness 20μm, stretchability > 50%); sampling module: integrated low-power MCU (power consumption <10μA), collects potential signals every 5 minutes, and synchronously activates the three-axis acceleration sensor (distinguishing between resting / active states); communication layer: transmits data to the mobile phone APP via BLE 5.0, the patch size is 3cm×4cm, the thickness is 1mm, and the battery life is 7 days.

[0128] A blood calcium fluctuation model under active conditions was established: it was found that blood calcium could temporarily increase by 0.08 mmol / L (muscle calcium release) within 30 minutes after strenuous exercise (acceleration > 2g). The dynamic time warping (DTW) algorithm was used to identify exercise events and automatically remove abnormal data points. The APP displays: real-time calcium concentration curve + resting / active status labels. The weight of detection data during the night sleep period (acceleration < 0.1g for > 30 minutes) is increased by 50% (calcium metabolism is more stable at this time).

[0129] Breakthrough in monitoring scenarios: Fills the limitation of traditional testing that can only collect blood at a single point, and captures the circadian rhythm of blood calcium (it was found that the nighttime blood calcium peak of myeloma monitored subjects was 12% higher than that of healthy people, which is consistent with the circadian fluctuation of osteoclast activity); through continuous monitoring, it was found that the blood calcium fluctuation amplitude (standard deviation) had increased by 50% 2-4 weeks before the onset of clinical symptoms, which can be used as a precursor indicator of bone damage; the painless patch design improves wearing comfort by 90%, and combined with the APP's medication reminders (such as feedback on the downward trend of blood calcium after taking bisphosphonates), the compliance of monitored subjects has increased from 60% in traditional monitoring to 85%.

[0130] See also Figure 2 , Figure 2 This is a schematic flow chart of a method for determining the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma, provided in one embodiment of the present application. The method is performed by a device that is a control module of a system for determining the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma, provided in any embodiment of the present application.

[0131] like Figure 2 As shown, the provided method includes steps S101 to S104. The control module can be a handheld terminal, a laptop computer, a wearable device, or a robot, etc., for implementing steps S101 to S104 and their corresponding embodiments.

[0132] Step S101. Obtaining a serum sample prepared by a sample collection module based on peripheral blood of a subject to be monitored for multiple myeloma;

[0133] Step S102: Acquire a transmembrane potential simulation signal generated by the ion detection module based on the distribution difference of target ions in the serum sample inside and outside the electrode membrane, wherein the target inorganic salt ions include calcium ions;

[0134] Step S103: obtaining the activity and concentration of the target ion calculated by the data processing module based on the transmembrane potential simulation signal;

[0135] Step S104. Statistically analyze the correlation between the target inorganic salt ion level in the peripheral blood of the multiple myeloma monitored subjects and related symptoms and prognosis; the related symptoms include bone pain, renal insufficiency and anemia; establish a relationship model between the target ion level and the severity of the disease based on statistical methods; and generate risk warnings for monitored subjects at high risk of bone damage and monitored subjects at high risk of renal failure based on the relationship model.

[0136] It should be noted that those skilled in the art can clearly understand that, for the sake of convenience and brevity of description, the above-described method for detecting the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma and the specific working process of each step can refer to the corresponding processes in the embodiments of the system for detecting the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma described in the above-mentioned embodiments, and will not be repeated here.

[0137] The present application also provides an apparatus for calibrating the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma. The apparatus is used to perform the steps of the method for calibrating the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma as described in the above embodiments. The apparatus can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, a laptop computer, a wearable device, or a robot.

[0138] The device for detecting the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma comprises:

[0139] A sample acquisition unit, configured to acquire a serum sample prepared by the sample collection module based on the peripheral blood of a subject to be monitored for multiple myeloma;

[0140] a signal acquisition unit, configured to acquire a transmembrane potential analog signal generated by the ion detection module based on the distribution difference of target ions in the serum sample inside and outside the electrode membrane, wherein the target inorganic salt ions include calcium ions;

[0141] an activity acquisition unit, configured to acquire the activity and concentration of the target ion calculated by the data processing module based on the transmembrane potential simulation signal;

[0142] A prompt generation unit is used to statistically analyze the correlation between the target inorganic salt ion level in the peripheral blood of multiple myeloma monitored subjects and related symptoms and prognosis; the related symptoms include bone pain, renal insufficiency and anemia; establish a relationship model between the target ion level and the severity of the disease based on statistical methods; and generate risk prompts for monitored subjects at high risk of bone damage and monitored subjects at high risk of renal failure based on the relationship model.

[0143] It should be noted that those skilled in the art can clearly understand that, for the sake of convenience and brevity of description, the specific working processes of the above-described device for detecting the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma and each unit can refer to the corresponding processes in the embodiments of the method for detecting the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma described in the above-mentioned embodiments, and will not be repeated here.

[0144] The above-mentioned method for detecting the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma is implemented in the form of a computer program, which can be run on the above-mentioned device.

[0145] See also Figure 3 , Figure 3 1 is a schematic block diagram of the structure of a control module provided in an embodiment of the present application. The control module includes a processor, a memory and a network interface connected via a device bus, wherein the memory may include a storage medium and an internal memory.

[0146] The storage medium can store an operating device and a computer program. The computer program includes program instructions, which, when executed, can cause the processor to execute any embodiment of the method for detecting the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma.

[0147] The processor is used to provide computing and control capabilities and support the operation of the entire control module.

[0148] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any one of the methods for detecting the free inorganic salt level in the peripheral blood of a subject to be monitored for multiple myeloma.

[0149] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the terminal to which the solution of the present application is applied. The specific control module may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0150] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0151] In one embodiment, the processor is configured to execute a computer program stored in the memory to implement the following steps:

[0152] Obtaining a serum sample prepared by a sample collection module based on peripheral blood of a subject to be monitored for multiple myeloma;

[0153] Acquiring a transmembrane potential simulation signal generated by an ion detection module based on the distribution difference of target ions in the serum sample inside and outside the electrode membrane, wherein the target inorganic salt ions include calcium ions;

[0154] Acquiring the activity and concentration of the target ion calculated by the data processing module based on the transmembrane potential simulation signal;

[0155] Statistical analysis was performed to determine the correlation between target inorganic salt ion levels in the peripheral blood of subjects with multiple myeloma and related symptoms and prognosis; the related symptoms included bone pain, renal insufficiency, and anemia; a relationship model between target ion levels and disease severity was established based on statistical methods; and risk warnings were generated for subjects at high risk of bone damage and subjects at high risk of renal failure based on the relationship model.

[0156] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the processor described above can refer to the corresponding process in the method embodiments described in the above embodiments, and will not be repeated here.

[0157] A computer-readable storage medium is also provided in an embodiment of the present application, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and the processor executes the program instructions to implement the steps of the method for detecting the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma provided in the above embodiments of the present application.

[0158] The computer-readable storage medium may be an internal storage unit of the control module described in the aforementioned embodiment, such as a hard disk or memory of the control module. The computer-readable storage medium may also be an external storage device of the control module, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the control module.

[0159] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A system for detecting free inorganic salt levels in peripheral blood of a subject to be monitored for multiple myeloma, characterized in that: include: a sample collection module configured to obtain peripheral blood from a subject to be monitored for multiple myeloma and prepare a serum sample; An ion detection module, comprising an ion selective electrode device, wherein the electrode membrane of the ion selective electrode device has a selective response to target inorganic salt ions and is used to generate a transmembrane potential simulation signal based on the distribution difference of the target ions in the serum sample inside and outside the electrode membrane, wherein the target inorganic salt ions include calcium ions; a data processing module, electrically connected to the ion detection module, configured to convert the transmembrane potential analog signal into a digital signal, and calculate the activity and concentration of the target ion based on the digital signal; The control module is electrically connected to the data processing module and is configured to: statistically analyze the correlation between the target inorganic salt ion level in the peripheral blood of the multiple myeloma monitored subject and related symptoms and prognosis; the related symptoms include bone pain, renal insufficiency and anemia; establish a relationship model between the target ion level and the severity of the disease based on statistical methods; and generate risk warnings for monitored subjects at high risk of bone damage and monitored subjects at high risk of renal failure based on the relationship model.

2. The system according to claim 1, wherein: The generating of a transmembrane potential simulation signal based on the distribution difference of the target ions in the serum sample inside and outside the electrode membrane comprises: The electrode membrane of the ion selective electrode device is made of a sensitive material having a specific binding ability to the target ion, and the sensitive material includes a carboxylate ion carrier material or a polyvinyl chloride-based plasma membrane material for calcium ion detection; When the target ions in the serum sample come into contact with the electrode membrane, an ion adsorption layer is formed on the membrane surface, causing a difference in ion activity on the inside and outside of the electrode membrane, and then generating a transmembrane potential simulation signal between the reference electrode and the ion selective electrode that exhibits a Nernst response law with the target ion activity.

3. The system according to claim 1, wherein: The step of converting the transmembrane potential analog signal into a digital signal comprises: The data processing module has a built-in analog-to-digital conversion unit, which is configured to first filter and reduce noise on the transmembrane potential analog signal, and then perform analog-to-digital conversion with a sampling resolution of not less than 20 bits and a sampling frequency of not less than 100 Hz; The filtering and noise reduction process is implemented by a second-order Butterworth low-pass filter, which is used to filter out high-frequency interference signals to suppress the influence of environmental electrical noise on the signal acquisition process.

4. The system according to claim 1, wherein: The calculating the activity and concentration of the target ion based on the digital signal includes: The data processing module pre-stores the relevant parameters of the Nernst equation, calculates the activity of the target ion based on the converted transmembrane potential digital signal, and converts the activity of the target ion into an ion concentration value through a preset ion activity and concentration conversion model combined with the correction value of the ionic strength regulator added to the serum sample; The Nernst equation related parameters are used to reflect the quantitative correspondence between the target ion activity and the potential signal.

5. The system according to claim 1, wherein: The statistical analysis of the correlation between the target inorganic salt ion level in the peripheral blood of the multiple myeloma subject to be monitored and the related symptoms and prognosis includes: Multiple linear regression analysis was performed to establish a quantitative correlation model between target ion concentrations and bone pain severity, renal function indicators, and anemia indicators using a visual analogue scale (VAS). Renal function indicators included serum creatinine values and estimated glomerular filtration rate; anemia indicators included hemoglobin concentration. Logistic regression was used to analyze the relationship between the target ion levels and the risk of kidney damage and bone damage. The Kaplan-Meier survival analysis method was used to evaluate the impact of target ion levels on progression-free survival and overall survival of the monitored subjects, and the corresponding hazard ratios were calculated using the Cox proportional hazards model to clarify the association between target ion levels and clinical prognosis.

6. The system according to claim 1, wherein: The method of establishing a relationship model between target ion levels and disease severity based on statistical methods includes: Multiple stepwise regression analysis or partial least squares regression analysis was performed, with target ion concentrations, clinical symptom scores, and laboratory indices as input variables; clinical symptom scores corresponded to symptoms including bone pain, degree of renal insufficiency, and degree of anemia; laboratory indices included serum protein electrophoresis results and β2-microglobulin levels; Based on the disease severity classification as the output variable, a multifactor disease severity assessment model including the target ion level is constructed through variable screening and weight calculation. The multifactor disease severity assessment model can output a quantitative assessment result of the disease severity based on the target ion concentration and other clinical parameters.

7. The system according to claim 1, wherein: Generating risk warnings for subjects at high risk of bone injury and subjects at high risk of renal failure based on the relationship model includes: Presetting a risk threshold of target ion concentration in the control module, wherein the risk threshold is determined based on clinical guidelines and analysis results of the relationship model; When the target ion concentration of the monitored subject exceeds the corresponding risk threshold, the monitored subject is automatically marked as a high-risk individual for bone damage or renal failure, and prompt information containing risk level and clinical recommendations is generated according to a preset template. The prompt information is displayed through a graphic interface or pushed through the hospital information system; the prompt information includes increasing the follow-up frequency and initiating intervention treatment.

8. The system according to claim 1, wherein: The ion-selective electrode device uses a calcium ion-selective electrode membrane in blood calcium measurement, and accurately measures calcium ion activity through the specific binding of calcium ions to the electrode membrane and the output of transmembrane potential signals. The correlation analysis and relationship model construction results of the control module are used to indicate the prognosis of the subject to be monitored.

9. A method for determining the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma, characterized in that: A control module for a system for detecting free inorganic salt levels in peripheral blood of a subject to be monitored for multiple myeloma according to any one of claims 1 to 8, the method comprising: Obtaining a serum sample prepared by a sample collection module based on peripheral blood of a subject to be monitored for multiple myeloma; Acquiring a transmembrane potential simulation signal generated by an ion detection module based on the distribution difference of target ions in the serum sample inside and outside the electrode membrane, wherein the target inorganic salt ions include calcium ions; Acquiring the activity and concentration of the target ion calculated by the data processing module based on the transmembrane potential simulation signal; Statistical analysis was performed to determine the correlation between target inorganic salt ion levels in the peripheral blood of subjects with multiple myeloma and related symptoms and prognosis; the related symptoms included bone pain, renal insufficiency, and anemia; a relationship model between target ion levels and disease severity was established based on statistical methods; and risk warnings were generated for subjects at high risk of bone damage and subjects at high risk of renal failure based on the relationship model.

10. A device for detecting the level of free inorganic salts in the peripheral blood of a subject to be monitored for multiple myeloma, characterized in that: A control module for a system for detecting free inorganic salt levels in peripheral blood of a subject to be monitored for multiple myeloma, as claimed in any one of claims 1 to 8, the device comprising: A sample acquisition unit, configured to acquire a serum sample prepared by the sample collection module based on the peripheral blood of a subject to be monitored for multiple myeloma; a signal acquisition unit, configured to acquire a transmembrane potential analog signal generated by the ion detection module based on the distribution difference of target ions in the serum sample inside and outside the electrode membrane, wherein the target inorganic salt ions include calcium ions; an activity acquisition unit, configured to acquire the activity and concentration of the target ion calculated by the data processing module based on the transmembrane potential simulation signal; A prompt generation unit is used to statistically analyze the correlation between the target inorganic salt ion level in the peripheral blood of multiple myeloma monitored subjects and related symptoms and prognosis; the related symptoms include bone pain, renal insufficiency and anemia; establish a relationship model between the target ion level and the severity of the disease based on statistical methods; and generate risk prompts for monitored subjects at high risk of bone damage and monitored subjects at high risk of renal failure based on the relationship model.

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