Ultra-weak bioluminescence detection application scheme in determination of qi stagnation and blood stasis syndromes and non-qi stagnation and blood stasis syndromes

Abstract

The invention discloses an ultra-weak bioluminescence detection application scheme in determination of qi stagnation and blood stasis syndromes and non-qi stagnation and blood stasis syndromes. According to the application, ultra-weak bioluminescence detection can be carried out on the hand of a patient, so that multi-data acquisition can be effectively carried out, and after optical signals are processed, the parameters of Strath, Intercept, Slope, FF, alpha, r, Theta, Phi, SSI and SSR and the three different fingers of the thumb, the middle finger and the little finger can be obtained; the difference between the three parameters SSI, theta and phi of the patients in the qi-stagnation and blood-stasis group and the patients in the non-qi-stagnation and blood-stasis group is mainly represented by the middle finger of the right hand, and the difference in the alpha parameter is represented by all ten fingers. By integrating a regularization result and a basic statistical analysis result, the thumb and the middle finger may be important parts for distinguishing patients in a qi-stagnation and blood-stasis group and a non-qi-stagnation and blood-stasis group, which indicates that the thumb and the middle finger have potential key effects in qi-stagnation and blood-stasis syndrome detection.

Description

Technical Field

[0001] The invention relates to the technical field of ultraweak bioluminescence detection, and in particular to an application scheme of ultraweak bioluminescence detection in determining qi stagnation and blood stasis syndrome and non-qi stagnation and blood stasis syndrome. Background Art

[0002] Syndrome differentiation and treatment, as the core feature of the theoretical system of traditional Chinese medicine, runs through the practice of traditional Chinese medicine and is also the basic principle of traditional Chinese medicine in understanding and treating diseases. "Syndrome" is the external reflection of the pathogenesis, reflecting the stage nature and characteristics of the disease. Syndrome differentiation occupies a very important position in the clinical diagnosis and treatment of traditional Chinese medicine. Therefore, syndrome research is particularly important in traditional Chinese medicine research. Since the 1950s, a large number of studies on traditional Chinese medicine syndromes have been carried out in China, and various explorations have been carried out from the aspects of genes, proteins, metabolites and pathological changes, but such methods still have certain deviations from the holistic concept of traditional Chinese medicine.

[0003] Even in the absence of any external stimulation, living systems will continue to radiate photons of very low energy levels to the outside world, which is called ultraweak bioluminescence. When an organism becomes ill, its photon radiation characteristics will change significantly. The research team's previous studies found that ultraweak bioluminescence is consistent with the holistic, dynamic, and connected characteristics of traditional Chinese medicine. The body's electromagnetic radiation can represent the "qi" of traditional Chinese medicine, and the quantum superposition state of the body's electromagnetic radiation field can represent traditional Chinese medicine syndromes.

[0004] This protocol uses a highly sensitive ultraweak bioluminescence detection system to detect photon radiation at the tips of the test subject's ten fingers. By performing multivariate statistical analysis on the photon radiation parameters, it is possible to use ultraweak bioluminescence to identify patients with and without Qi stagnation and blood stasis syndrome. Summary of the invention

[0005] The purpose of the present invention is to solve the deficiencies in the prior art and to propose an ultra-weak bioluminescence detection application scheme in the determination of qi stagnation and blood stasis syndrome and non-qi stagnation and blood stasis syndrome.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The ultra-weak bioluminescence detection application scheme in the determination of qi stagnation and blood stasis syndrome and non-qi stagnation and blood stasis syndrome includes the following steps:

[0008] S1. Hand cleaning: Before testing, clean and wipe your hands thoroughly and wait for 2-5 minutes; wear light-proof gloves and wait for 30 minutes in quiet dark light conditions to shield the delayed luminescence signal;

[0009] S2. Sealed test: clean the test equipment and test its light-shielding performance to avoid light transmission. Place the palm to be tested into the test equipment and seal the arm to ensure that the fingers are naturally straightened and spread out in the test equipment. Control the test equipment to perform ultra-weak bioluminescence tests on each finger in the order of thumb, index finger, middle finger, ring finger, and little finger, and collect the data.

[0010] S3, Data transmission: collect and upload the collected data to ensure accurate data transmission;

[0011] S4, data processing and comparison: Receive data and transmit it to the software in the device. The software can analyze and process the data, annotate the data, and convert the received data into charts;

[0012] S5. Data display: Display the charts converted from the data through the display device, focusing on the data of the thumb and middle finger on SSI, |α|, Theta, and Phi;

[0013] S6. Conclusion output: Determine the syndrome of the patient to be tested.

[0014] Compared with the prior art, the present invention can collect multi-dimensional data by performing ultra-weak bioluminescence detection on the patient's fingers, and obtain the following parameters after processing the optical signal: Strength, Intercept, Slope, FF, |α|, r, Theta, Phi, SSI, SSR; principal component analysis and partial least squares discriminant analysis can clearly distinguish patients with qi stagnation and blood stasis syndrome from those without qi stagnation and blood stasis syndrome; regularization analysis (Lasso regression, ridge regression and elastic network) and basic statistical analysis (t-test, Nonparametric test and effect size calculation), Strength, SSI, |α|, Theta, Phi were screened out as important parameters for identifying patients with Qi stagnation and blood stasis syndrome; at the same time, it was analyzed that the thumb and middle finger may be important parts to distinguish patients in the Qi stagnation and blood stasis group from those in the non-Qi stagnation and blood stasis group, and these two fingers are the ends of the hand Taiyin lung meridian and the hand Jueyin pericardium meridian, respectively, which proves that this scheme is highly consistent with the pathogenesis characteristics of Qi stagnation and blood stasis syndrome; this scheme can realize the use of ultra-weak bioluminescence to distinguish patients with Qi stagnation and blood stasis syndrome from those without Qi stagnation and blood stasis syndrome.

[0015] Preferably, the data collection in S2 includes Strength, Intercept, Slope, FF, |α|, r, Theta, Phi, SSI and SSR data, and the samples of the data collection are ten fingers of the patient; the data collection is 100 UPE photon count distribution characteristic parameters.

[0016] Preferably, the analysis in S4 uses a two-independent-sample t-test for parameters that meet normality and homogeneity of variance, and uses a non-parametric Mann-Whitney U test for parameters that do not meet normal distribution.

[0017] The main focus of the UPE parameter is on the photon intensity, that is, the number of photons detected in a time interval.

[0018] Preferably, the UPE parameter is mainly concerned with the photon intensity, that is, the number of photons detected within a time interval.

[0019] Preferably, the |α|, r, Theta, and Phi constitute compression state parameters; the |α| and r are amplitude parameters, describing the strength and variation range of the signal; and the Theta and Phi are angle parameters, associated with the phase and directionality of the signal.

[0020] The |α| represents the amplitude of the compressed state of the UPE photon signal, and its magnitude reflects the intensity of the light field.

[0021] Preferably, the r is also a key parameter of the compressed state, representing the degree of compression, indicating another intensity of the UPE photon signal in the compressed state.

[0022] Preferably, the Theta provides angular information about the quantum state, defining the direction in which compression and anti-compression occur.

[0023] Preferably, the Phi is a global phase parameter, which reflects the overall phase of the compressed state and is closely related to the interference and coherence properties of the light field. It defines the angle or phase of the compressed state together with Theta.

[0024] Preferably, the SSR is another property parameter obtained from the estimation of the compression state parameters; it can provide relevant information about the non-compression state in the human UPE signal, can quantify the deviation or difference between the signal and the compression state model, and basically quantify the part of the UPE signal that does not conform to this quantum state model.

[0025] The SSI is a weighted sum of normality estimates for the parameters in the validity test.

[0026] Preferably, the analysis also includes principal component analysis (PCA), orthogonal partial least squares regression discriminant analysis (OPLS-DA), and subgroup analysis; the subgroup analysis includes subgroup analysis PCA analysis and subgroup analysis OPLS-DA analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1A step diagram of the ultraweak bioluminescence detection application scheme in the determination of Qi stagnation and blood stasis syndrome and non-Qi stagnation and blood stasis syndrome proposed by the present invention;

[0028] Figure 2 The UPE intensity diagram of each finger of the left hand of the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the present invention;

[0029] Figure 3 The UPE intensity diagram of each finger of the right hand of the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the present invention;

[0030] Figure 4 This is a graph showing the proportion of UPE light intensity of each finger on the left hand in the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the present invention;

[0031] Figure 5 This is a graph showing the proportion of UPE light intensity of each finger on the right hand in the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the present invention;

[0032] Figure 6 It is a Slop parameter diagram of the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the present invention;

[0033] Figure 7 It is a comparison diagram of the UPE compression state parameter |α| of the left hand fingers of the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the present invention;

[0034] Figure 8 A comparison diagram of the UPE compression state parameter |α| of the right hand fingers of the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the present invention;

[0035] Fig. 9 The compressed state parameter r diagram of the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the present invention;

[0036] Fig.10 Theta diagram of compression state parameters of the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the present invention;

[0037] Fig.11 It is the compression state parameter φ diagram of the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the present invention;

[0038] Fig.12 This is the first SSR parameter diagram of the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the present invention;

[0039] Fig.13 This is the SSR parameter diagram of the second Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the present invention;

[0040] Fig.14 This is a graph of SSI parameters for the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the present invention;

[0041] Fig.15 The PCA sub-graph of the human body UPE characteristic parameters used in the present invention to distinguish the Qi stagnation and blood stasis group from the non-Qi stagnation and blood stasis group;

[0042] Fig.16 OPLS-DA diagram of the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the present invention;

[0043] Fig.17 This is a cross-validation diagram of the OPLS-DA model of the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the present invention;

[0044] Fig.18 This is a PCA analysis diagram for subgroup analysis in the present invention;

[0045] Fig.19 This is the OPLS-DA analysis diagram of the subgroup analysis in the present invention;

[0046] Fig. 20 This is a cross-validation diagram of OPLS-DA for subgroup analysis in the present invention. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0048] Reference Figure 1 The ultra-weak bioluminescence detection application scheme in the determination of qi stagnation and blood stasis syndrome and non-qi stagnation and blood stasis syndrome includes the following steps:

[0049] S1. Hand cleaning: Before testing, clean and wipe your hands thoroughly and wait for 2-5 minutes; wear light-proof gloves and wait for 30 minutes in quiet dark light conditions to shield the delayed luminescence signal;

[0050] S2. Sealed test: clean the test equipment and test its light-shielding performance to avoid light transmission. Place the palm to be tested into the test equipment and seal the arm to ensure that the fingers are naturally straightened and spread out in the test equipment. Control the test equipment to perform ultra-weak bioluminescence tests on each finger in the order of thumb, index finger, middle finger, ring finger, and little finger, and collect the data.

[0051] S3, Data transmission: collect and upload the collected data to ensure accurate data transmission;

[0052] S4, data processing and comparison: Receive data and transmit it to the software in the device. The software can analyze and process the data, annotate the data, and convert the received data into charts;

[0053] S5. Data display: Display the charts converted from the data through the display device, focusing on the data of the thumb and middle finger on SSI, |α|, Theta, and Phi;

[0054] S6. Conclusion output: Determine the syndrome of the patient to be tested.

[0055] In actual verification: Comparison of UPE light intensity and photon count distribution parameters between the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group:

[0056] The distribution characteristics of ultraweak bioluminescence photon counts at each test site of the subject are described by 10 parameters, namely: Strength, Intercept, Slope, FF, |α|, r, Theta, Phi, SSI, SSR. Ten fingers were tested, and 100 UPE photon count distribution characteristic parameters were obtained for each subject. First, basic statistical analysis was performed on all 100 parameters in the qi stagnation and blood stasis group and the non-qi stagnation and blood stasis group. For parameters that met normality and homogeneity of variance, two independent sample t tests were used, and parameters that did not meet normal distribution were tested using the non-parametric Mann-WhitneyU test. Due to the large number of parameters, only statistically significant parameters are shown in Table 1. As shown in Table 1, the UPE parameters with statistical differences between the two groups were: StrengthL1—L5, StrengthR1—R5, SlopL3, |α|L1—L5, |α|R1—R5, rR3, ThetaR3, PhiR3, SSRL1—L5, SSRR1—R5, and SSIR3.

[0057] Table 1 The distribution characteristic parameters of ultraweak bioluminescence photon counts with statistical differences between the qi stagnation and blood stasis group and the non-qi stagnation and blood stasis group

[0058]

[0059]

[0060]

[0061] Note: *P<0.05, **P<0.01, ***P<0.001

[0062] Analysis of the light intensity characteristics of human ultraweak bioluminescence in the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group

[0063] For the analysis of human UPE parameter characteristics, the main focus is on the photon intensity, that is, the number of photons detected within a time interval, referred to as light intensity, or Strength parameter. The finger light intensity characteristics of the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group are shown in Table 2. Figure 2 , Figure 3As shown. The box plot results show that the light intensity in the Qi stagnation and blood stasis group is significantly higher than that in the non-Qi stagnation and blood stasis group, and the differences in the light intensity of the ten fingers in the two groups are statistically significant (P<0.001). In the box plot, there are more outliers in the non-Qi stagnation and blood stasis group. Outliers usually represent atypical points in the data set. More outliers may indicate that there are far extreme values ​​in the data distribution, indicating that the group has greater variability, which is also consistent with the data distribution characteristics of the non-Qi stagnation and blood stasis group.

[0064] Table 2. Ultraweak bioluminescence intensity of human body in the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group

[0065]

[0066] Note: *P<0.05, **P<0.01, ***P<0.001.

[0067] The P values ​​of the differences between the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the ten fingers were all <0.0001. In order to screen the fingers with more significant differences, the effect size of each was calculated again, and the results are shown in Table 3. Effect size is a statistical concept that can be used to quantify the size of the difference between two groups. The effect size indicator used here is "Vargha and Delaney's A" in the non-parametric effect size, which measures the probability that the observed value of one group is greater than the observed value of the other group. The range of "Vargha and Delaney's A" values ​​is 0-1. If the value of A is close to 1 or 0, it means that the effect size is large and the difference between the two groups is significant. From Table X, it can be seen that among the five fingers of the left hand, the middle finger has the highest effect size index, while among the five fingers of the right hand, the thumb has the highest effect size index, which indicates that the differences between the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group in the middle finger of the left hand and the thumb of the right hand are more significant.

[0068] Table 3. Effect size of ultraweak bioluminescence intensity parameters in the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group

[0069]

[0070] Note: L stands for left hand and R stands for right hand.

[0071] Comparison of UPE intensity ratio of each finger between Qi stagnation and blood stasis group and non-Qi stagnation and blood stasis group:

[0072] In order to eliminate individual differences in human UPE intensity, we calculated the ratio of the light intensity of each finger of each subject to the total light intensity of the palm, and compared whether there was any difference in the finger luminescence ratio between the two groups. The ratio of the light intensity of each finger in the two groups is shown in Table 4. Figure 4 and Figure 5As shown in the figure, the luminous ratio of the left middle finger and the right middle finger in the Qi stagnation and blood stasis group was significantly higher than that in the non-Qi stagnation and blood stasis group, and the difference was statistically significant (P<0.05).

[0073] Table 4. The ratio of ultra-weak bioluminescence intensity of fingers in the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group

[0074]

[0075]

[0076] Note: *P<0.05, **P<0.01, ***P<0.001.

[0077] Fano factor curve analysis of Qi stagnation and blood stasis group and non-Qi stagnation and blood stasis group:

[0078] The Fano factor curve was fitted with a quadratic polynomial to obtain two important characteristic parameters of the Fano factor curve, namely intercept and slope. The Fano factor curve is subject-specific and has a certain degree of variability. The intercept implies the quantum nature of the UPE signal, and the slope provides information to distinguish different time series with the same statistical moment and photon count distribution. The comparison of the characteristics of the slope between the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group is shown in the figure. Figure 6 As shown; (a) is the left hand (b) is the right hand, Figure 6 It can be seen that the difference in the slop of the left middle finger between the two groups was statistically significant (P<0.05), which suggests that the slop value of the left middle finger may be a meaningful parameter for distinguishing the syndrome of qi stagnation and blood stasis.

[0079] Comparison of compression parameters between the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group:

[0080] The compression effect is an important feature of quantum optics, which is reflected by a set of specific compressed state parameters (|α|, r, Theta, Phi). These parameters are derived from the photon count distribution and can effectively characterize the compressed state properties of photon signals. They are of great significance for in-depth understanding and interpretation of the essence of life activities of organisms from an optical perspective. Among these parameters, |α| and r are amplitude parameters that describe the intensity and range of variation of the signal; while Theta and Phi are angle parameters that are related to the phase and directionality of the signal.

[0081] (1) The compressed state parameter |α| represents the amplitude of the compressed state of the UPE photon signal, and its magnitude reflects the intensity of the light field. The Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group showed significant statistical differences in |α| of the ten fingers (P<0.001), as shown in Table 5. Figure 7 , Figure 8 shown.

[0082] Table 5. Ultraweak bioluminescence compression state parameters of fingers in the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group |α|

[0083]

[0084] Note: *P<0.05, **P<0.01, ***P<0.001.

[0085] In order to screen the fingers with more significant differences, the effect size was calculated, as shown in Table 6. Among the five fingers of the left hand, the middle finger had the highest effect size index, and among the five fingers of the right hand, the thumb had the highest effect size index, which indicated that the differences between the two groups of patients in the left middle finger and the right thumb were more significant.

[0086] Table 6. Effect size of compressed state parameters of finger ultraweak bioluminescence |α parameters in the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group

[0087]

[0088]

[0089] Note: Left represents the left hand and Right represents the right hand.

[0090] Finger UPE compression parameter r in the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group

[0091] r is also a key parameter of the compressed state, representing the degree of compression, indicating another intensity of the UPE photon signal in the compressed state. The difference in r between the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group is shown in Table 7 and Fig. 9 As shown, the difference in r value between the two groups in the right middle finger was statistically significant (P<0.05), which suggested that r value may be a potential parameter for distinguishing the two groups, and the right middle finger is an important detection site.

[0092] Table 7. Compression parameters r of the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group

[0093]

[0094] Note: *P<0.05, **P<0.01, ***P<0.001.

[0095] The compressed state parameter Theta provides information about the angle of the quantum state and defines the direction in which compression and anti-compression occur. In phase space, the compressed state usually appears as an elliptical uncertainty region, and Theta determines the direction of this ellipse. The difference in Theta between the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group is shown in Figure 2. Fig.10As shown in Table 8, the difference in the Theta parameter was statistically significant in the middle finger of the right hand (P<0.05). This suggests that the Theta value of the middle finger of the right hand may play an important role in distinguishing the Qi stagnation and blood stasis groups.

[0096] Table 8. Compression parameters Theta of the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group

[0097]

[0098] Note: *P<0.05, **P<0.01, ***P<0.001.

[0099] The compressed state parameter Phi is a global phase parameter that reflects the overall phase of the compressed state and is closely related to the interference and coherence properties of the light field. Together with Theta, it defines the angle or phase of the compressed state. The differences in Phi parameters between the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group are shown in Table 9 and Fig.11 As shown in the figure, the difference of the right middle finger was also statistically significant (P<0.05), which suggests that the Phi value of the right middle finger may be an important parameter for the identification of qi stagnation and blood stasis groups.

[0100] Table 9. Compression parameters Phi of Qi stagnation and blood stasis group and non-Qi stagnation and blood stasis group

[0101]

[0102]

[0103] Note: *P<0.05, **P<0.01, ***P<0.001.

[0104] SSR parameters of Qi stagnation and blood stasis group and non-Qi stagnation and blood stasis group:

[0105] The sum of squared residuals (SSR) is another attribute parameter obtained from the estimation of the compressed state parameters, which can provide relevant information about the non-compressed state in the ultra-weak bioluminescent signal of the human body, and can quantify the deviation or difference between the signal and the compressed state model, basically quantifying the part of the ultra-weak bioluminescent signal that does not conform to this quantum state model. Fig.12 , Fig.13 It can be seen that the differences in SSR values ​​of the ten finger detection sites between the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group were statistically significant, suggesting that the non-compressed state of the human body's ultra-weak bioluminescent signal also has the potential to distinguish Qi stagnation and blood stasis syndrome.

[0106] Table 10 provides the effect size indicators of the SSR parameters of the two groups of patients. It can be seen that among the five fingers of the left hand, the middle finger has the highest effect size indicator, and among the five fingers of the right hand, the ring finger has the highest effect size indicator. However, in overall comparison, the effect sizes of the middle finger and ring finger of the left hand are higher, which indicates that the differences between the two groups of patients in the middle finger and ring finger of the left hand are more significant.

[0107] Table 10. Effect size of SSR parameters in the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group

[0108]

[0109] Note: L stands for left hand and R stands for right hand.

[0110] SSI parameters of Qi stagnation and blood stasis group and non-Qi stagnation and blood stasis group:

[0111] The Squeezed State Index (SSI) is a weighted sum of the normal value estimates of the parameters in the validity test. As an empirical property, it describes the estimated results of the compressed state parameters in the ultra-weak bioluminescence signal of the human body in a compact and concise way. SSI essentially quantifies the quantum state parameters derived from the photon count distribution and reflects the degree of a normal compressed state in the ultra-weak bioluminescence signal of the human body. Fig.14 Data analysis showed that there was a significant statistical difference in the SSI value of the right middle finger between the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group (P<0.01), suggesting that the SSI value of the right middle finger may be a potential effective parameter for distinguishing Qi stagnation and blood stasis syndrome.

[0112] Multivariate analysis:

[0113] (1) Principal component analysis (PCA) was used to evaluate the ability of ultraweak bioluminescence characteristic parameters to distinguish between the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group:

[0114] Through the above comprehensive basic statistical analysis, it was found that the ultra-weak bioluminescent signal intensity of each finger of the human body, the ratio of ultra-weak bioluminescent signal intensity of different fingers, the slope of the fano factor curve, the compression state parameters |α|, r, Theta, Phi, the compression state index SSI and the residual sum of squares SSR, etc., can characterize the characteristics of the ultra-weak bioluminescent signal of the human body to a certain extent, and have a certain significance for reflecting the difference in ultra-weak bioluminescent signals between the qi stagnation and blood stasis group and the non-qi stagnation and blood stasis group. In addition to the above parameters, although the statistical differences of other parameters are not significant enough, considering that even statistically insignificant parameters may contain valuable information, these parameters may play an important role and significance under specific conditions or in interaction with other variables. In view of this, we combined all 100 parameters of the ten fingers of each subject to comprehensively characterize the characteristics of the ultra-weak bioluminescent signal, and used the principal component analysis (PCA) method to reduce the dimension of the data. At the same time, the characteristic parameters of the human body's ultra-weak bioluminescent signal were evaluated for the ability to distinguish between qi stagnation and blood stasis syndrome and non-qi stagnation and blood stasis syndrome. The results are as follows: Fig.15 As shown. It can be seen that the ultra-weak bioluminescent signal characteristic parameters after dimensionality reduction have the ability to distinguish the Qi stagnation and blood stasis group from the non-Qi stagnation and blood stasis group, and the distribution of the two groups is roughly distributed on both sides with x=0 as the axis. This also suggests that the combination of all 100 ultra-weak bioluminescent characteristic parameters has the potential to distinguish the Qi stagnation and blood stasis group from the non-Qi stagnation and blood stasis group.

[0115] Light color represents the group with Qi stagnation and blood stasis, and dark color represents the group without Qi stagnation and blood stasis:

[0116] Orthogonal partial least squares regression discriminant analysis (OPLS-DA) was used to evaluate the ability of ultraweak bioluminescence characteristic parameters to distinguish between the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group:

[0117] In this study, unsupervised principal component analysis was first used to explore the characteristics of the data. It can reduce the dimension of the data and reveal the main sources of variation in the data. However, the PCA method also has some limitations. For example, it cannot distinguish between variations that are useful for classification and noise or other irrelevant variations, which may lead to a reduction in the explanatory and predictive capabilities of the model. In addition, the effectiveness of PCA for data sets that do not follow a normal distribution may be limited. Most of the parameters of this data set do not follow a normal distribution, so it is impossible to use conventional discriminant analysis and Bayesian discriminant analysis to discriminate the data, but the OPLS-DA method is not affected by the type of data distribution. Therefore, it is very appropriate to use the OPLS-DA method to reduce the dimension of the data, remove parameter information that is misused for classification, and observe the distribution of the two groups. The visualization results are shown below. Fig.16 As shown in Figure 2, it can be seen that the distribution of the two groups is clearly different. The predictive performance of the model is evaluated using R 2 and Q2 The value of R 2 Y, Q 2 The values ​​are 0.601 and 0.485 respectively, indicating that the model has good predictability and reliability.

[0118] Due to the large number of parameters, the model may be overfitted, so a cross-validation analysis was performed with the number of permutations set to 200 to test the stability of the model by randomly rearranging the labels of the response variables. Fig.17 As shown in the figure, from the intercept and the position of the data after replacement, it can be seen that the OPLS-DA model fitted to this data set does not have the phenomenon of overfitting. This model can be used to effectively identify and explain the differences in UPE signal characteristic parameters between patients in the Qi stagnation and blood stasis group and those in the non-Qi stagnation and blood stasis group.

[0119] Subgroup analysis:

[0120] The above PCA and OPLS-DA analysis has verified that the ultra-weak bioluminescence characteristic parameters have a certain ability to distinguish between the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group. Considering that the subjects' underlying diseases are distributed in several different subgroups, in order to explore and verify the identification ability of ultra-weak bioluminescence characteristic parameters in different subgroup disease systems, subgroup analysis based on different disease systems was further performed. Through PCA and OPLS-DA analysis of Qi stagnation and blood stasis and non-Qi stagnation and blood stasis in the endocrine system, cardiovascular and cerebrovascular system, skeletal muscle system, and cardiovascular and cerebrovascular and endocrine combined disease system, the ability of ultra-weak bioluminescence signal characteristic parameters to distinguish in each subgroup system was carefully observed. The relevant analysis results are shown in the figure below. Fig.18 As shown in the figure; (a) endocrine system diseases; (b) cardiovascular and cerebrovascular system diseases; (c) skeletal muscle system diseases; (d) cardiovascular and cerebrovascular and endocrine combined diseases. From the figure, we can see the PCA two-dimensional graph of each subgroup. The four subgroups can be well distinguished between patients with qi stagnation and blood stasis and those without qi stagnation and blood stasis by PCA method.

[0121] We continued to conduct OPLS-DA analysis on each subgroup to check the ability of OPLS-DA method to distinguish each subgroup. The results are as follows: Fig.19 As shown in the figure; (a) endocrine system diseases; (b) cardiovascular and cerebrovascular system diseases; (c) skeletal muscle system diseases; (d) combined cardiovascular and cerebrovascular and endocrine system diseases, the patients with qi stagnation and blood stasis and those without qi stagnation and blood stasis in the four subgroups can be clearly distinguished by the OPLS-DA method. 2 Y were 0.711, 0.888, 0.727, 0.954 respectively; Q 2 The values ​​are 0.468, 0.575, 0.455, and 0.31 respectively.

[0122] To avoid overfitting of the model, we continued the cross-validation analysis and set the number of permutations to 200. The results of the correlation analysis are as follows: Fig. 20 As shown in the figure, it can be seen that the replacement test results of the cardiovascular and endocrine combined disease system Q 2 The value is too large, and there is a potential risk of overfitting, which may be due to the small sample size and large number of characteristic parameters in this subgroup.

[0123] Parameter screening:

[0124] Since there are many parameters obtained in this study, and although the PCA method and OPLS-DA method have reduced the dimension of the parameters, they are based on constructing new latent variables rather than screening the original variable parameters. Therefore, different parameter screening methods are used again to screen the variables to find the parameters and fingers that are more relevant to distinguishing Qi stagnation and blood stasis syndrome. The regularization method is used to screen the parameters, and the Lasso regression, ridge regression and elastic network analysis are carried out based on the "GLMNET" and "CARET" packages of the R language to screen the characteristic parameters, and the 10-fold cross validation is used to make the model more robust. The characteristics of Lasso regression, ridge regression, and elastic network screening are shown in Tables 11-13. Lasso regression screened out 34 characteristic parameters with non-zero coefficients, and the elastic network screened out 49 characteristic parameters with non-zero coefficients. Due to its algorithmic characteristics, ridge regression cannot classify the characteristic coefficients as 0, so the characteristic parameters with the top 44 coefficients are selected here.

[0125] Table 11 Coefficients of Lasso regression screening

[0126]

[0127] Table 12 Coefficients of ridge regression screening

[0128]

[0129]

[0130] Table 13. Parameters for elastic network screening

[0131]

[0132]

[0133] From Tables 11 to 13, we can see that the parameters selected by Lasso regression are mainly concentrated in |α|, SSI, Slope, Theta and other parameters, which are mainly reflected in the thumb, middle finger and little finger. The parameters selected by ridge regression are mainly concentrated in SSI, |α|, Slope, Theta, Phi and other parameters, which are mainly reflected in the thumb, middle finger and little finger. The parameters selected by elastic network are also concentrated in SSI, |α|, Theta, Phi and other parameters, and the fingers are also concentrated in the thumb, middle finger and little finger. The parameter results selected by the three different regularization methods are similar, with the coefficients of the thumb, middle finger and little finger being the most representative. The selected parameters are mainly related parameters (compressed state parameters and compressed state index) that represent the compressed state of UPE photon signals, which indicates that the information contained in the compressed state of UPE photon signals has potential application value for distinguishing different TCM syndromes.

[0134] After regularization screening of all 100 parameters, we found that the parameters with higher characteristic coefficients were mainly concentrated in SSI, |α|, Theta, Phi, and three different fingers: thumb, middle finger, and little finger. In the above basic statistical analysis, the differences in various parameters between the patients in the Qi stagnation and blood stasis group and the non-Qi stagnation and blood stasis group were more significant in the middle finger and thumb. Combining the regularization results and basic statistical analysis results, the thumb and middle finger may be important parts to distinguish the patients in the Qi stagnation and blood stasis group from the non-Qi stagnation and blood stasis group, which shows their potential key role in the detection of Qi stagnation and blood stasis syndrome. These two fingers are the termination points of the hand Taiyin lung meridian and the hand Jueyin pericardium meridian, respectively, which proves that this scheme is highly consistent with the pathogenesis characteristics of Qi stagnation and blood stasis syndrome.

[0135] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. The ultra-weak bioluminescence detection application scheme in the determination of Qi stagnation and blood stasis syndrome and non-Qi stagnation and blood stasis syndrome includes the following steps: S1. Hand cleaning: Before testing, clean and wipe your hands thoroughly and wait for 2-5 minutes; wear light-proof gloves and wait for 30 minutes in quiet dark light conditions to shield the delayed luminescence signal; S2. Sealed test: clean the test equipment and test its light-shielding performance to avoid light transmission. Place the palm to be tested into the test equipment and seal the arm to ensure that the fingers are naturally straightened and spread out in the test equipment. Control the test equipment to perform ultra-weak bioluminescence tests on each finger in the order of thumb, index finger, middle finger, ring finger, and little finger, and collect the data. S3, Data transmission: collect and upload the collected data to ensure accurate data transmission; S4, data processing and comparison: Receive data and transmit it to the software in the device. The software can analyze and process the data, annotate the data, and convert the received data into charts; S5. Data display: Display the charts converted from the data through the display device, focusing on the data of the thumb and middle finger on SSI, |α|, Theta, and Phi; S6. Conclusion output: Determine the syndrome of the patient to be tested.

2. The ultraweak bioluminescence detection application scheme in the determination of Qi stagnation and blood stasis syndrome and non-Qi stagnation and blood stasis syndrome according to claim 1 is characterized by: The data collection in S2 includes Strength, Intercept, Slope, FF, |α|, r, Theta, Phi, SSI and SSR data, and the samples of the data collection are ten fingers of the patient; the data collection is 100 UPE photon count distribution characteristic parameters.

3. The ultraweak bioluminescence detection application scheme in the determination of Qi stagnation and blood stasis syndrome and non-Qi stagnation and blood stasis syndrome according to claim 1 is characterized by: The analysis in S4 was performed by using two independent sample t-tests for parameters that met normality and homogeneity of variance, and using the non-parametric Mann-Whitney U test for parameters that did not meet normal distribution.

4. The ultraweak bioluminescence detection application scheme in the determination of Qi stagnation and blood stasis syndrome and non-Qi stagnation and blood stasis syndrome according to claim 3 is characterized by: The main focus of the UPE parameter is on the photon intensity, that is, the number of photons detected in a time interval.

5. The ultraweak bioluminescence detection application scheme in the determination of Qi stagnation and blood stasis syndrome and non-Qi stagnation and blood stasis syndrome according to claim 1, characterized in that: The |α|, r, Theta, and Phi constitute compression state parameters; the |α| and r are amplitude parameters that describe the strength and variation range of the signal; the Theta and Phi are angle parameters that are related to the phase and directionality of the signal; The |α| represents the amplitude of the compressed state of the UPE photon signal, and its magnitude reflects the intensity of the light field.

6. The ultraweak bioluminescence detection application scheme in the determination of Qi stagnation and blood stasis syndrome and non-Qi stagnation and blood stasis syndrome according to claim 5, characterized in that: The r is also a key parameter of the compressed state, representing the degree of compression and indicating another intensity of the UPE photon signal in the compressed state.

7. The ultraweak bioluminescence detection application scheme in the determination of Qi stagnation and blood stasis syndrome and non-Qi stagnation and blood stasis syndrome according to claim 6, characterized in that: Theta provides angular information about the quantum state, defining the direction in which compression and anti-compression occur.

8. The ultraweak bioluminescence detection application scheme for determining Qi stagnation and blood stasis syndrome and non-Qi stagnation and blood stasis syndrome according to claim 7, characterized in that: The Phi is a global phase parameter that reflects the overall phase of the compressed state and is closely related to the interference and coherence properties of the light field. Together with Theta, it defines the angle or phase of the compressed state.

9. The ultraweak bioluminescence detection application scheme in the determination of Qi stagnation and blood stasis syndrome and non-Qi stagnation and blood stasis syndrome according to claim 1, characterized in that: The SSR is another property parameter obtained from the estimation of the compression state parameters; it can provide relevant information about the non-compression state in the human UPE signal, and can quantify the deviation or difference between the signal and the compression state model, basically quantifying the part of the UPE signal that does not conform to this quantum state model; The SSI is a weighted sum of normality estimates for the parameters in the validity test.

10. The ultraweak bioluminescence detection application scheme in the determination of Qi stagnation and blood stasis syndrome and non-Qi stagnation and blood stasis syndrome according to claim 1, characterized in that: The analysis also includes principal component analysis (PCA), orthogonal partial least squares regression discriminant analysis (OPLS-DA), and subgroup analysis; the subgroup analysis includes subgroup analysis PCA analysis and subgroup analysis OPLS-DA analysis.

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