Application of sperm motility body fluid response characteristics in disease risk prediction

Abstract

The invention discloses application of sperm motility body fluid response characteristics in disease risk prediction. In the application, through quantitative analysis of motion parameters of mouse sperms after co-incubation with human serum from different sources, early prediction and diagnosis of high-risk diseases such as cerebral infarction (CI), myocardial infarction (MI) and pancreatitis can be realized, and the mouse sperm detection kit has the advantages of high accuracy, good sensitivity, low cost and the like. In specific implementation, after mouse sperms and patient serum are co-incubated, if the active sperm rate is lower than 14.3%, 10.9% or 20.6%, the active sperm rate can be respectively used as diagnosis thresholds of cerebral infarction, myocardial infarction or pancreatitis. Based on the technology, related products for high-risk disease diagnosis, such as diagnostic kits and diagnostic and screening equipment, can be further developed.

Description

Technical Field

[0001] This application relates to the field of disease diagnostic technology, specifically to the application of sperm motility and humoral response characteristics in disease risk prediction. Background Technology

[0002] High-risk diseases, such as cardiovascular and cerebrovascular diseases, malignant tumors, and severe inflammatory or metabolic diseases, have become a major challenge in global public health due to their high morbidity, mortality, and disability rates. These diseases often have long incubation periods and rapid progression, with obvious symptoms not appearing until the middle or late stages. At this point, treatment becomes more difficult, and irreversible damage to the body is often already inflicted, significantly exacerbating the health and economic burden. For example, the five-year survival rate for many cancers after reaching stage IV is typically less than 20%, while if detected before stage III, this figure can exceed 90%; timely intervention in acute cardiovascular events can reduce mortality by up to 30%. These data highlight the crucial role of early detection in improving patient prognosis and controlling healthcare costs. However, current mainstream disease diagnostic technologies still have significant limitations in achieving early, accurate, and accessible screening. Specifically:

[0003] 1. Imaging diagnostic methods: Although they play an important role in clinical diagnosis, they have several significant problems. First, their specificity is limited. For example, the false positive rate of low-dose CT in lung cancer screening exceeds 20% in some studies, which can easily lead to unnecessary invasive examinations and patient anxiety. Second, there is a risk of ionizing radiation. Third, the equipment is expensive and the examination costs are high, making it difficult to popularize in areas with scarce medical resources. Approximately 75% of the world's population lives in resource-limited areas where access to basic radiological services is limited.

[0004] 2. Biochemical marker detection methods, such as troponin in myocardial infarction and PSA detection in prostate cancer, often have insufficient sensitivity in the early stages of the disease; their diagnostic performance is also easily affected by cross-reactivity and individual physiological variations, resulting in low reliability in identifying early lesions.

[0005] Therefore, there is an urgent need to develop an innovative, economical, non-invasive, and easily scalable early disease screening and diagnostic technology to overcome the limitations of existing methods and improve the ability to detect and intervene in major diseases at an early stage. Summary of the Invention

[0006] Cell-based biosensing technologies are increasingly becoming an important supplement to traditional biomarker detection. These technologies utilize the natural response of living cells to changes in the microenvironment to identify subtle physiological alterations related to pathological states. Among numerous cell types, sperm cells, due to their unique and quantifiable motility characteristics, are ideal candidates for functional biosensing. The applicant's previous research on sperm sorting based on microfluidic technology has shown that sperm motility parameters are highly sensitive to minute changes in the surrounding medium. This dynamic response characteristic opens new avenues for developing novel, non-invasive, and low-cost early disease screening systems.

[0007] This application provides an application of sperm motility-based humoral response characteristics in disease risk prediction.

[0008] In the above applications, selected biosensors are co-incubated with the body fluid sample to be tested. By detecting changes in the activity, morphology, metabolism, or gene expression of the biosensors after incubation, a response model related to a specific disease state is established, which can then be used for disease prediction, disease typing and classification, disease stratification, or disease prognosis assessment.

[0009] In some embodiments, the disease includes at least one of cardiovascular and cerebrovascular diseases, malignant tumors, autoimmune diseases, neurodegenerative diseases, metabolic diseases, infectious diseases, or inflammatory diseases.

[0010] In some preferred embodiments, the disease is at least one of cerebral infarction, myocardial infarction, or pancreatitis.

[0011] In some embodiments, the disease typing and classification include acute and chronic diseases, hereditary and sporadic diseases, local and systemic diseases, and classifications based on pathology or molecular typing (such as gene mutations, protein expression profiles).

[0012] In some implementations, the disease stratification is a risk stratification based on disease severity, rate of progression, risk of relapse, or treatment resistance;

[0013] In some implementations, the disease prognostic assessment is to predict the risk of disease recurrence, treatment responsiveness, risk of complications, or survival.

[0014] In some embodiments, the biosensing material is derived from at least one of the following a) to e):

[0015] a) Sperm from mammals, including those from humans, mice, rats, rabbits, cattle, pigs, sheep, cats, or dogs;

[0016] b) Other model organisms or their cells, including germ cells or somatic cells of nematodes, zebrafish, and fruit flies;

[0017] c) Microorganisms, including bacteria and yeast;

[0018] d) Cell lines cultured in vitro, including biochemical cell lines and primary cultured cells;

[0019] e) Specific functional cells derived from stem cell differentiation, including cardiomyocytes, neurons, hepatocytes, and pancreatic β cells derived from human pluripotent stem cells.

[0020] In some implementation methods China The body fluid is at least one of the following from the subject of the test: serum, plasma, urine, fecal extract, tears, sweat, nasal discharge, saliva, cerebrospinal fluid, pleural effusion, and ascites.

[0021] In some embodiments, the response characteristics of the biosensor include at least one of changes in activity, morphology, metabolic activity, gene expression profile, and proteomics.

[0022] In some implementations, the method for predicting disease risk based on sperm motility and humoral response characteristics includes the following steps:

[0023] S1: Prepare mouse sperm samples, mix them with human fallopian tube fluid to obtain a sperm suspension, and adjust the initial concentration and active sperm rate of the sperm suspension;

[0024] S2: Collect serum samples from the patient to be tested, mix the sperm suspension and serum at a certain volume ratio, and incubate them together;

[0025] S3: Perform motility analysis on the sperm after co-incubation, and assess the disease risk of the sample based on the percentage of viable sperm.

[0026] In some embodiments, in S1, the sperm suspension has a viable sperm percentage of 35%–45% and a sperm concentration of 0.5–1.5 × 10⁻⁶. 6 sperm count / mL; preferably, the active sperm rate is 40%, and the sperm concentration is 1×10⁶. 6 per mL.

[0027] In some embodiments, in S2, the volume ratio of the sperm suspension to the serum of the patient to be tested is 10:1, the co-incubation time is 10 minutes, and the co-incubation is carried out in an incubator environment of 37°C and 5% CO2.

[0028] In some embodiments, in step S3, a sperm quality analysis system is used to detect the motility of sperm after incubation.

[0029] In some implementations, in step S3, the method for determining disease risk is as follows: a threshold is set through a classification model, and cerebral infarction, myocardial infarction, and pancreatitis correspond to active sperm rates of less than 14.3%, 10.9%, and 20.6%, respectively; if the active sperm rate is less than the corresponding threshold, the sample is determined to be a high-risk individual for the corresponding disease.

[0030] Compared with the prior art, this application has at least the following advantages:

[0031] 1. High diagnostic accuracy and specificity: The technical solution of this application uses sperm as a natural, highly sensitive in vivo biosensor, capable of responding to changes in the overall microenvironment related to disease in human body fluids, which is directly reflected in changes in sperm motility. This detection mechanism based on cellular function responses can capture complex pathological information that is difficult to reflect by a single biomarker, effectively overcoming the limitations of traditional methods in early diagnosis, such as multiple cross-reactivity and weak signals, and providing a new approach to achieving high accuracy and high specificity in early diagnosis.

[0032] 2. Simple operation, low cost, and easy to promote: The technical solution of this application does not rely on expensive large-scale imaging equipment or complex molecular detection platforms. The core steps only involve the co-incubation of sperm with body fluids and subsequent motility analysis. The process is simple and requires minimal professional background from operators. Furthermore, sperm sources are widely available and extremely inexpensive, which facilitates the promotion and application of this technology in primary healthcare institutions, communities, and resource-limited areas, improving the accessibility of advanced diagnostic technologies and alleviating the current problem of uneven distribution of diagnostic resources.

[0033] 3. Potential to become a universal early screening tool: Based on the high sensitivity of sperm to the body fluid microenvironment, this technology can respond to early serological changes in a variety of diseases (such as malignant tumors, inflammation, metabolic abnormalities, etc.). It is not only suitable for the diagnosis of specific diseases, but also has the potential to be developed into a low-cost, non-invasive universal early screening tool, applicable to large-scale population health screening, promoting "early detection and early intervention", and effectively reducing the social medical burden. Attached Figure Description

[0034] Figure 1 A flowchart illustrating the application of mouse sperm motility serum response characteristics in disease risk prediction, as provided in this application.

[0035] Figure 2 The image shows the sperm motility trajectory of mouse sperm after co-incubation with serum from different sources, as provided in the embodiments of this application.

[0036] Figure 3 Comparison of active sperm rates between patients with cerebral infarction, myocardial infarction, and pancreatitis and a control group, as provided in the embodiments of this application.

[0037] Figure 4The receiver operating characteristic curves for the classification of cerebral infarction, myocardial infarction, and pancreatitis provided in the embodiments of this application are shown.

[0038] Figure 5 Precise recall curves for the classification of cerebral infarction, myocardial infarction, and pancreatitis provided in the embodiments of this application.

[0039] Figure 6 The key classification evaluation index provided for embodiments of this application at the optimal active sperm rate threshold. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0041] The materials used in the following embodiments are not limited to those listed below, and other similar materials may be used instead. Unless otherwise specified, the instruments shall be used under conventional conditions or as recommended by the manufacturer. Those skilled in the art should have relevant knowledge of the use of conventional materials and instruments.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the subject matter pertains. Before a detailed description of the invention, the following definitions are provided to better understand it.

[0043] To better understand this teaching and without limiting its scope, all figures and other numerical values ​​used in the specification and claims to express quantities, percentages, or proportions should, in all cases, be understood to be modified by the term "about." Therefore, unless otherwise stated, the numerical parameters set forth in the following specification and appended claims are approximate values ​​that may vary depending on the desired properties sought. At a minimum, each numerical parameter should be interpreted based at least on the reported significant figures and by applying common rounding techniques.

[0044] In this application, the term "preferred" is used only to describe a more effective implementation or embodiment, and should be understood not to limit the scope of protection of this invention.

[0045] In this application, the term "active sperm rate" refers to the percentage of active sperm (i.e., sperm with motility) out of the total sperm count, and is a key quantitative indicator for measuring sperm motility. "Sperm motility" is categorized as follows: forward-motile sperm (sperm actively swimming forward), non-motile sperm (sperm moving but without direction (e.g., spinning in place, swaying), and immobile sperm (sperm with no motility). The formula for calculating the "active sperm rate" is: Active sperm rate = (Number of forward-motile sperm + Number of non-motile sperm) / Total sperm count.

[0046] In this application, the term "threshold" refers to the critical value used in disease diagnosis or risk assessment to classify a test result as "positive" (indicating disease or risk) or "negative" (indicating no disease or low risk). It is a judgment standard set based on diagnostic indicators (such as biomarker concentration, scores, and signal intensity). For example, the diagnostic threshold for a certain blood indicator might be 10 ng / mL; a test value ≥10 ng / mL is considered "positive" (possibly indicating disease), while 10 ng / mL is considered "negative" (possibly indicating no disease). The setting of the diagnostic threshold directly affects diagnostic performance: a higher threshold may reduce false positives (misdiagnosis) but increase false negatives (missed diagnosis); a lower threshold may reduce false negatives but increase false positives. In practical applications, the optimal threshold needs to be determined by combining clinical needs (such as disease severity, risk trade-off between missed and misdiagnosis) and statistical analysis (such as ROC curves) to balance sensitivity and precision, thereby improving diagnostic accuracy and practicality.

[0047] The technical solution and its effects are detailed below through more specific embodiments. This application provides a clinical trial study using mouse sperm motility in human serum to predict or screen for critical diseases, including cerebral infarction, myocardial infarction, and pancreatitis. The study was approved by the clinical trial review committee, and all participating subjects signed informed consent forms before enrollment.

[0048] 1. Participant Sources: The participants in this study came from two collaborating hospitals. The cohort at the First Affiliated Hospital of Wannan Medical College included 28 patients with cerebral infarction and 19 patients with myocardial infarction. The other hospital was Renmin Hospital of Wuhan University, which included 28 controls with cerebral infarction and 17 controls with myocardial infarction. Their gender and age were matched with those of the participants from the First Affiliated Hospital of Wannan Medical College. In addition, 11 patients with pancreatitis and 11 healthy controls were also collected from Renmin Hospital of Wuhan University.

[0049] 2. Serum Sample Collection: All blood samples were collected after clinical diagnosis and before any treatment intervention. 5 mL of peripheral blood was drawn from each subject and allowed to stand at room temperature for 30 minutes to avoid hemolysis, followed by centrifugation at 4000 rpm for 5 minutes at 4°C. 1 mL of the supernatant serum was collected, aliquoted into sterile labeled tubes, sealed with sealing film, and stored at -80°C for subsequent analysis. All sample processing procedures followed standardized protocols to minimize technical errors and ensure the consistency and reliability of the experiment.

[0050] 3. Sperm Sample Preparation: The sperm samples used in this application were obtained from 3-month-old male C57BL / 6 mice. After euthanasia by cervical dislocation, the epididymal tail was removed and placed in a preheated HTF (Vitrolife, G-IVF PLUS) container to collect the motile sperm, and the concentration was adjusted to 1×10⁻⁶. 6 Animals were incubated at 37°C and 5% CO2 for 1 hour to achieve capacitation. All animal experiments were conducted in accordance with institutional ethical standards and were approved by the Experimental Animal Management Committee of Wuhan Sports University (Animal Use Agreement No.: 2025007). The number of animals used and their suffering were minimized as much as possible during the experiments.

[0051] 4. Co-incubation of sperm suspension with serum: Use sperm with 40% motility and a concentration of 1×10⁻⁶. 6 A sperm suspension of 10 sperm cells / mL was mixed with the serum sample at a volume ratio of 10:1 in a sterile EP tube and incubated at 37°C in a 5% CO2 humidified incubator for 10 minutes. This standardized procedure helps to achieve controlled biochemical interactions under physiological conditions and ensures the reproducibility of the experiment.

[0052] 5. Sperm motility analysis: A sperm quality analysis system (BEION, V4.20) was used to quantitatively analyze sperm motility trajectories. This system, based on image recognition and particle tracking algorithms, can identify and track sperm head movements frame by frame, accurately quantifying multiple kinematic parameters, including:

[0053] MR (Morbidity): The percentage of sperm with motility, reflecting overall sperm motility;

[0054] Curve rate (VCL): The speed at which sperm travels along its actual trajectory;

[0055] Linear velocity (VSL): The linear velocity of a sperm cell from its starting point to its ending point;

[0056] Mean path rate (VAP): The speed at which sperm travels along its average trajectory;

[0057] Linearity (LIN): The ratio of VSL to VCL, reflecting the straightness of the sperm motility trajectory;

[0058] Forward motility (STR): The ratio of VSL to VAP, indicating the efficiency of forward sperm motility;

[0059] Wobble (WOB): The ratio of VAP to VCL, describing the amplitude of sperm head oscillation;

[0060] Lateral swing amplitude (ALH): The maximum lateral deviation of the sperm head from its path;

[0061] Whiplash frequency (BCF): The number of times the sperm head crosses the average trajectory per unit time, reflecting the frequency of flagellar waving.

[0062] Mean angular displacement (MAD): the average time value of the instantaneous turning angle of the sperm head along its trajectory. The larger the value, the more unstable the direction of movement.

[0063] 6. Statistical Analysis Methods: This application used the Wilcoxon test to compare sperm motility parameters of patient serum samples and healthy control groups after incubation. Significance levels were set as *p < 0.05, p < 0.01, *p < 0.001, and ****p < 0.0001. To comprehensively evaluate the performance of the diagnostic system, several statistical indicators were introduced. The specific evaluation roles of each indicator are as follows:

[0064] Area under the receiver operating characteristic curve (AUC-ROC): Used to assess the overall ability of a model to distinguish between different categories (such as patients and healthy individuals).

[0065] Area under the precision-recall curve (AUC-PR): Applicable to imbalanced datasets (such as those with a small proportion of patient samples), focusing more on the model's performance in identifying minority classes (such as patients).

[0066] Sensitivity: Reflects the proportion of positive samples correctly identified by the model out of all actual positive samples, demonstrating the ability to "not miss diagnoses".

[0067] Precision: This represents the proportion of samples that the model predicts to be positive but are actually positive, reflecting the ability to "avoid misdiagnosis".

[0068] Accuracy: Measures the proportion of samples (including true positives and true negatives) that the model correctly predicts out of the total sample.

[0069] F1 score: The harmonic mean of accuracy and sensitivity, used to comprehensively evaluate the model's performance in balancing the two, especially suitable for imbalanced class scenarios.

[0070] 7. Experimental Results

[0071] (1) Serum-induced changes in sperm motility

[0072] Figure 2 The study demonstrates the motility trajectories of mouse sperm after co-incubation with serum from different sources. These trajectories were analyzed using a sperm quality analysis system, yielding the data summarized in Table 1. Table 1 includes basic information for some patients (including sex, age, and disease type) and various sperm motility parameters after co-incubation.

[0073] Table 1

[0074] Sample name Sex Age MR VCL VSL VAP MAD ALH BCF LIN WOB STR Pancreatitis-1 Male 58 20.6 91.7 31.3 37.2 64.02 3.5 5 30.3 39.7 76 Pancreatitis-2 Male 63 25.8 59.4 14 19.3 26.51 1.4 5.2 22 30.8 67.8 Pancreatitis-3 Male 58 14.7 45.2 12.5 17.7 41.08 1.2 3.5 26.3 37.1 60.5 Pancreatitis-4 Female 77 8.8 46.2 26.2 31.2 13.95 1.8 3.1 69.7 76 90.1 Pancreatitis-5 Male 52 15.2 96.7 37.8 43.4 23.49 3.4 4.5 39.1 46.3 80.9 Pancreatitis-6 Male 71 20 55.5 12.9 19.4 24.08 1.9 3.8 20.2 32.7 57.6 Pancreatitis-7 Female 57 14.3 70.3 21.5 29.1 40.18 2.5 4.2 31.1 40.1 77.3 Pancreatitis-8 Female 62 19.4 88 33.5 40.2 48.8 3.1 5.1 31.7 42.8 67.2 Pancreatitis-9 Male 63 14.1 61.4 20.9 28.1 48.73 1.8 4.3 29.2 47.9 60.2 Pancreatitis-10 Male 77 24.1 47.4 9.9 14.4 33.5 1.3 4.8 19.7 29.3 60.3 Pancreatitis-11 Male 73 18.4 48.8 12.5 18.7 25.36 1.4 4.6 29.8 43 70.3 Control-1 Male 57 29.2 66 23.3 27.2 27.51 2.7 4.1 32.1 39.2 74.1 Control-2 Male 63 17.8 117.8 29.6 37.8 64.84 3.7 5.3 29.2 35.2 80.3 Control-3 Male 58 27.9 77.9 27.7 32.8 49.52 2.9 4.7 31.7 38.6 78 Control-4 Female 76 11.5 76.4 24.2 30.3 28.21 2.1 4.9 29 36.5 72 Control-5 Male 52 22.2 87.2 30.3 41.3 52.15 3.1 5.3 33 45.4 69.6 Control-6 Male 69 22.9 60.9 18.4 24.1 48.52 2.1 4.3 23.4 33.3 65.1 Control-7 Female 57 31 71.7 21 29 33.25 2.4 4.9 27.3 40.2 65.8 Control-8 Female 64 19.2 71.2 28.2 38.3 45.94 2.7 4.3 32.1 49.5 57.2 Control-9 Male 62 19.8 60.9 22.9 27.5 36.3 1.8 5.5 30.8 40.6 67.5 Control-10 Male 78 18.5 65.7 20.8 26.8 53.67 2.2 4.2 29.4 41.6 66.1 Control-11 Male 74 26.7 84.9 34.9 39.2 44.56 2.6 4.9 38.4 45.4 75.9

[0075] Figure 3 The results show a comparison of the motile sperm rate between the patient group and the control group. As can be seen from the figure:

[0076] In patients with cerebral infarction and the control group, the average rate of viable sperm incubated with serum from healthy individuals was 23.98%, while that in the patient serum incubation group was 10.04%, and the difference was statistically significant (p = 6.04e-7, Wilcoxon test).

[0077] In patients with myocardial infarction and the control group, the average active sperm rate in the healthy serum incubation group was 17.66%, while that in the patient serum incubation group was 8.48%, and the difference was also highly statistically significant (p = 3.64e-4, Wilcoxon test).

[0078] The above results indicate that the motile sperm rate in patients with cerebral infarction and myocardial infarction was significantly lower than that in the corresponding control groups, suggesting that serum incubation significantly inhibits sperm motility. Furthermore, a decreasing trend in motile sperm rate was also observed in patients with pancreatitis (mean 22.43% in the healthy control group vs. mean 17.76% in the patient group), but the difference was not statistically significant (p = 0.082, Wilcoxon test). In conclusion, sperm motility analysis shows potential application value in distinguishing patients with certain complex diseases from healthy individuals.

[0079] (2) Evaluation of diagnostic performance.

[0080] Receiver operating characteristic (ROC) and exact recall curve analyses were used to further evaluate the diagnostic performance of sperm motility parameters. The results showed that sperm motility and sperm rate exhibited good classification ability. Figure 4 The ROC curves for the classification of cerebral infarction, myocardial infarction, and pancreatitis are shown. Figure 5 Precision-recall curves for each disease group are shown. The AUC values ​​for cerebral infarction, myocardial infarction, and pancreatitis were 0.888, 0.848, and 0.719, respectively, and the AUPRC values ​​were 0.908, 0.816, and 0.694, respectively.

[0081] The optimal classification thresholds for MR were determined using the Youden index as follows: the threshold for the active sperm rate after serum incubation in patients with cerebral infarction was 14.3%, and a value below this indicated a high risk of cerebral infarction; the threshold for the active sperm rate in patients with myocardial infarction was 10.9%, and a value below this indicated a high risk of myocardial infarction; the threshold for the active sperm rate in patients with pancreatitis was 20.6%, and a value below this indicated a high risk of pancreatitis.

[0082] Based on the above thresholds, the model exhibits robust diagnostic performance metrics. Figure 6 In cerebral infarction, myocardial infarction, and pancreatitis: the sensitivities were 85.7%, 82.4%, and 81.8%, respectively; the precisions were 88.9%, 82.4%, and 64.3%, respectively; the accuracy was 87.5%, 83.3%, and 68.2%, respectively; and the F1 scores were 0.873, 0.824, and 0.720, respectively.

[0083] In summary, sperm motility response characteristics to body fluids demonstrate good risk prediction and diagnostic performance in various diseases, and have the potential to identify diseased individuals.

[0084] The present application has been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present application. The descriptions of the embodiments above are only for the purpose of helping to understand the present application and its core ideas. It should be noted that those skilled in the art can make several improvements and modifications to the present application without departing from the principles of the present application, and these improvements and modifications also fall within the protection scope of the claims of the present application.

Claims

1. An application of sperm motility-based humoral response characteristics in disease risk prediction, characterized in that, In this application, selected biosensors are co-incubated with the body fluid sample to be tested. By detecting changes in the activity, morphology, metabolism, or gene expression of the biosensors after incubation, a response model related to a specific disease state is established, which can then be used for disease prediction, disease typing and classification, disease stratification, or disease prognosis assessment.

2. The application according to claim 1, characterized in that, The disease includes at least one of cardiovascular and cerebrovascular diseases, malignant tumors, autoimmune diseases, neurodegenerative diseases, metabolic diseases, infectious diseases, or inflammatory diseases; preferably, the disease is at least one of cerebral infarction, myocardial infarction, or pancreatitis. The disease classification and categorization include acute and chronic diseases, hereditary and sporadic diseases, local and systemic diseases, and classifications based on pathology or molecular typing. The disease stratification is based on risk stratification according to disease severity, progression rate, recurrence risk, or treatment resistance. The disease prognostic assessment is used to predict the risk of disease recurrence, treatment responsiveness, risk of complications, or survival.

3. The application according to claim 1, characterized in that, The biosensor material is derived from at least one of the following: a) Sperm from mammals, including those from humans, mice, rats, rabbits, cattle, pigs, sheep, cats, or dogs; b) Other model organisms or their cells, including germ cells or somatic cells of nematodes, zebrafish, and fruit flies; c) Microorganisms, including bacteria and yeast; d) Cell lines cultured in vitro, including biochemical cell lines and primary cultured cells; e) Specific functional cells derived from stem cell differentiation, including cardiomyocytes, neurons, hepatocytes, and pancreatic β cells derived from human pluripotent stem cells.

4. The application according to claim 1, characterized in that, The body fluid is at least one of the following from the subject of the test: serum, plasma, urine, fecal extract, tears, sweat, nasal discharge, saliva, cerebrospinal fluid, pleural effusion, and ascites.

5. The application according to claim 1, characterized in that, The response characteristics of the biosensor include at least one of the following: changes in activity, morphology, metabolic activity, gene expression profile, and proteomics.

6. The application according to claim 1, characterized in that, The method for predicting disease risk based on sperm motility and humoral response characteristics includes the following steps: S1: Prepare mouse sperm samples, mix them with human fallopian tube fluid to obtain a sperm suspension, and adjust the initial concentration and active sperm rate of the sperm suspension; S2: Collect serum samples from the patient to be tested, mix the sperm suspension and serum at a certain volume ratio, and incubate them together; S3: Perform motility analysis on the sperm after co-incubation, and assess the disease risk of the sample based on the percentage of viable sperm.

7. The application according to claim 6, characterized in that, In step S1, the sperm suspension has a viable sperm rate of 35%–45% and a sperm concentration of 0.5–1.5 × 10⁻⁶. 6 sperm count / mL; preferably, the active sperm rate is 40%, and the sperm concentration is 1×10⁶. 6 per mL.

8. The application according to claim 6, characterized in that, In S2, the volume ratio of sperm suspension to serum from the patient being tested is 10:1, and the co-incubation time is 10 minutes, conducted in an incubator at 37°C and 5% CO2.

9. The application according to claim 6, characterized in that, In step S3, a sperm quality analysis system is used to detect the motility of sperm after incubation.

10. The application according to claim 4, characterized in that, In S3, the method for determining disease risk is as follows: a threshold is set through a classification model, and cerebral infarction, myocardial infarction and pancreatitis correspond to active sperm rates of less than 14.3%, 10.9% and 20.6%, respectively; if the active sperm rate is less than the corresponding threshold, the sample is determined to be a high-risk individual for the corresponding disease.

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