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Abstract
Aging is usually accompanied by decrease in limb motor function and change in muscle metabolism patterns. However, few studies have investigated the aging effect on muscle hemodynamics of the upper extremity. This study aims to explore the aging effect on muscle metabolism patterns during upper limb's exercise. Twelve middle-aged and elderly subjects and 12 young subjects were recruited, and muscle oxygenation signals from these subjects’ biceps brachii muscles were collected during active and passive upper limb's encircling exercise with near-infrared spectroscopy (NIRS). The old group showed stronger muscle hemodynamic metabolism than the young group. The multiscale fuzzy approximate entropy and multiscale transfer entropy analyses indicated higher complexity and stronger interlimb coupling of the muscle oxygenation signals for the old group. Based on the selected muscle metabolism features, the constructed support vector machine model showed a high accuracy rate for classifying the two groups of subjects: 91.6% for the passive mode and 87.5% for the active mode. Our results proved the specific muscle metabolism patterns in the upper limb's exercise for old subjects, promoting the understanding of the aging effect on muscle hemodynamics.
© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
The World Population Prospective 2019 suggested that the aged population is enlarging at an unprecedented speed [1]. Aging usually accompanies with physical changes in the human body, causes influence on various organs and systems and induces corresponding functional deficits, such as deficits in motor, sensory and executive functions. For motor function, the walking ability and balance control decline for the elderly [2,3]. Of note, the decline of motor function is closely associated with increased mortality and poor life quality in the elderly [4,5]. Along with the aggravation of our aging societies, studies on motor function change caused by aging are of great significance.
Along with aging, the muscle metabolism patterns change during exercise. It is shown that aging is associated with muscle mass reduction and muscle strength loss [6]. The oxidative capacity of skeletal muscle is disrupted in older age. Accumulated studies have indicated lower oxygen supply rate during exercise and extended oxygen recovery period after exercise in older age [7,8]. The declining motor function can trigger the potential compensatory mechanism which underlies the intermuscular coupling to complement the motor ability for the elderly [9]. It was found that muscle synergies for locomotive sites were higher reulting in stronger intermuscular coupling and interlimb coordination in the same motor task for older compared with younger adults [10–12]. All these suggest the compensary mechasnism in the human body for aging.
During exercise, the muscle’s physiological information, such as hemoglobin and electromyography (EMG) signals, are dynamic and exhibits a substantial degree of complexity at wide range of temporal scales [13,14]. Many studies have emplyed entropy to characterize complexity and investigated aging effect on the complexity of motor-related physiological signals since entropy was coined [15,16]. However, entropy analysis at a single scale ignored the multiple time scales which are intrinsic for physiologic systems. The multi-scale entropy (MSE) algorithm proposed by Costa et al. [17,18] can measure entropy values over multiple time scales, obtain MSE curves and exactly depict the signal complexity by assigning low values to completely random and highly deterministic signals. The volume of MSE research has greatly increased [19] and previous studies have found age-related complexity changes in postural control [14], finger flexion and abduction [20,21]. Kang et al. used MSE to analyze age-related complexity changes in EMG and found that the EMG complexity in the vastus lateralis and biceps femoris decreased and EMG complexity in the gastrocnemius increased for old adults compared to young adults [13].Wu et al. observed aging effect on complexities of the force and EMG signals during handgrip control [22].Muscle hemoglobin metabolism patterns change with aging. Nonetheless, few studies have investigated the aging effect on muscle hemodynamics of the upper extremity. Thus, this study used near-infrared spectroscopy (NIRS) and aimed to explore the aging effect on muscle metabolism patterns during upper limb's exercise with MSE.
We used NIRS to measure the muscle hemodynamics of biceps brachii during active and passive upper limb's encircling exercise in young and old adults. NIRS has little constraint for subjects during measurement and could obtain results with high ecological validity [23,24]. Besides, NIRS is non-invasive, low-cost, easily operated, and suitable for long-term continuous monitoring and repeated measurements during exercise [25–27]. We used the multiscale fuzzy approximate entropy (MSfApEn) to analyze the complexity of muscle hemodynamics, and multiscale transfer entropy (MSTE) to quantitatively evaluate the coupling strength between the left and right upper limbs. To verify if there was a specific muscle metabolism pattern for older age, we also chose muscle metabolism, MSfApEn and MSTE characteristics as features and constructed a support vector machine (SVM) model to classify young and old subjects.
2. Material and methods
2.1 Subjects
Twenty-four healthy subjects were recruited for this study: 12 healthy young in the age range 20-24 years (young group) and 12 healthy older in the age range 55-65 years (old group). All subjects were right-handed and did not have any cardiac or musculoskeletal disorders. The subcutaneous adipose tissue thickness (ATT) was measured in the range of 2-3.75 mm for young subjects and 2-5 mm for old subjects. There was no significant different in ATT between these two groups (p = 0.122). Before the experiment, they were informed of the experimental process and task requirements and gave their signed informed consent. This study was reviewed and approved by the Ethics Committee of Foshan University.
2.2 Experiment
The experiment was conducted in a quiet room. The subjects seated in the comfortable non-slip chair with back support and performed upper limb's encircling exercise with hands rotating. Before the formal experiment, subjects performed a 3-min passive exercise (the machine drives the subject to exercise) as warm-up to prevent muscle strain. After 5-min rest, subjects followed the order of passive exercise for 3 minutes, and then active exercise for 3 minutes. During the passive exercise mode, the subjects passively moved with the machine set to 50 r/min. Afterwards, the speed of the machine returned to zero and the subjects were asked to have a rest and avoid any unnecessary movements. This rest period lasted 2 minutes and could be longer if necessary [28]. During the active exercise mode, the subjects turned hands at a speed of 50 r/min without resistance. The speed feedback by the front flat-panel display helped keep the velocity at around 50 r/min. The experimental procedure is shown in Fig. 1(a).
2.3 Data acquisition
A real-time wireless Artinis device, OctaMon M, was used to measure the muscle hemodynamic signals. The device contains two probes, and each probe consists of four light sources and one detector forming four detection channels. The distance between the light source and the detector is 30 mm. The light source wavelengths are 760 nm and 850 nm. With these two wavelengths, OctaMon M can measure concentration changes of oxyhemoglobin (Δ[HbO2]) and deoxyhemoglobin (Δ[Hb]) according to the modified Lambert–Beer's law. A fixed differential pathlength factor of six was used for muscle tissue based on manufacturer recommendations. During the experiment, the NIRS probes were placed on the belly of the biceps brachii muscles of left and right upper limbs with the axes of the light source and the detector parallel to the arm. The NIRS probes were reinforced with a bandage to prevent movement and block light. NIRS data were collected at a sampling rate of 10 Hz. The experiment scene is shown in Fig. 1(b).
2.4 Data preprocessing
We used the MATLAB software to preprocess the muscle oxygenation data. First, we removed artificial signals based on moving standard deviation and cubic spline interpolation [29]. Then, the mean metabolism of the baseline (30-s pre-exercise rest) was subtracted to obtain the variation trend of muscle oxygenation for each motor task [30]. The negative slope and MSE obtained from each channel of each arm showed little difference with that from average of the four channels of each arm. Thus, muscle oxygenation data from four right channels and four left channels were averaged to obtain the muscle oxygenation in the right and left upper limbs respectively, which were used for subsequent metabolism and entropy analyses.
2.5 Muscle metabolism analyses
2.5.1 Rotation period
We first obtained the rotation period by analyzing the fluctuation period of the muscle oxygenation to confirm that subjects performed exercise at about 50 r/min. The fluctuation period analysis includes two steps: (1) find local peaks for each fluctuation period in muscle oxygenation during the motor task, and define them as L, L = {L1, L2, …, LN}; (2) calculate the fluctuation period T by a differential operation (Eq. (1)).
2.5.2 Negative slope
We used the slope method to obtain the muscle oxygenation metabolism during exercise [31]. We scanned the motor-related oxygenation responses for the two groups of subjects and found that the Δ[HbO2] and Δ[Hb] responses reached the maximum at about 10 s after the exercise beginning. In this case, the slope method can effectively explain the metabolism level of the detected skeletal muscle [32]. Therefore, we performed linear regression based on the first 10-s motor-related Δ[HbO2] and Δ[Hb] responses for each subject under each exercise mode and obtained the slope coefficients through the linear equation to evaluate the muscle metabolism.
2.6 Complexity and interlimb coupling analyses
2.6.1 Multiscale fuzzy approximate entropy
We applied fuzzy approximate entropy (fApEn) to describe the nonlinear dynamic characteristics of muscle oxygenation since it is more robust and can effectively avoid statistical instability [33]. MSfApEn was used [22] and the algorithm consists of two steps: (1) generate signals at each time scale through a coarse-grained process; (2) calculate the fApEn at each time scale.
First, for the muscle oxygenation signal X, X = {X1, X2, …, XN}, the overlapping coarse-grained method (Eq. (2)) was used to multiscale the signal and obtain Y (Eq. (3)). This method can overcome the imprecise or undefined entropy values caused by short time series at large scales [34].
Then, the fApEn values were calculated according to the following steps:
- Step 1: reconstruct the phase space of Y and generate a new set of m-dimensional vectors, from ${u_m}$(1) to ${u_m}$(M-m+1):
In the Eq. (4), τ is omitted for convenience of writing.
- Step 2: calculate the distance $d_{ij}^m$ between the vector ${u_m}(i)$ and ${u_m}(j)$.$$d_{ij}^m = d[{u_m}(i),{u_m}(j)] = {\max _{k \in (0,m - 1)}}|{y(i + k) - {y_0}(j) - y(j + k) - {y_0}(i)} |$$where y0(i) is the baseline value and defined as:${y_0}(i) = \frac{1}{m}\sum\limits_{j = 0}^{m - 1} {y(i + j)} ;i,j = 1,\ldots ,M - m + 1,j \ne i$.
Finally, the fApEn values are calculated at each scale. We set m = 2, n = 2, r = 0.15*std(signal) and $\tau$=10 based on previous studies [22,35,36].
2.6.2 Multiscale transfer entropy
We used MSTE to study the coupling between left and right upper limbs [37]. MSTE analyses include two steps: (1) generate information at different time scales by multiscale process; (2) calculate the transfer entropy at each time scale.
Firstly, define the muscle oxygenation signal from the right (left) upper limb as X (Y), X = {X1, X2, …, XN}, Y = {Y1, Y2, …, YN}. Multiscale X and Y according to Eq. (2).
Then, calculate the transfer entropy (TE) value of signal X to signal Y according to Eq. (9):
Similarly, the TE value of signal Y to signal X is calculated as follows:
2.6.3 Complexity index and coupling Index
To quantitatively describe the information under the MSE curve [13,17,18], we calculated the area under the multiscale entropy curve. The curve area of MSfApEn and MSTE were defined as the complexity index and coupling index, respectively. These parameters could quantify the complexity of the muscular oxygenation, and represent the coupling strength between upper limbs.
2.7 SVM Classification
To investigate whether old subjects had specific muscle metabolism patterns that differed from young subjects, we used SVM approach to classify subjects in the two groups. The goal of pattern recognition is to find a mapping relationship between the feature space and the interpretation space [38]. If old subjects have specific muscle metabolism patterns that distinguish them from young subjects, they can be reasonably separated by means of machine learning.
We used the SVM model for the classification since it works well for small sample size. The negative slope, complexity index and coupling index were used as candidate features. The grid search method was utilized to determine the optimal kernel function and penalty coefficient for the SVM model. We used leave-one out cross-validation to evaluate the SVM model by using the indicators of sensitivity (Eq. (1)1), specificity (Eq. (1)2), and classification accuracy (Eq. (1)3).
where true positive (TP) is the number of old subjects who are correctly classified, and true negative (TN) is the number of young subjects who are correctly classified. False positive (FP) is the number of young subjects who are misclassified, and true negative (FN) is the number of old subjects who are misclassified.The flow chart of the processing and analysis of NIRS data in this study is shown in Fig. 2.
2.8 Statistical analyses
In this study, the independent samples t-test was used for statistical analyses on MSfApEn, MSTE and rotation periods. The normal distribution for all data was verified before t tests via Shapiro–Wilk tests, and all t tests were corrected by FDR. Two-way ANOVA with Mode (passive, active) and Group (young, old) as factors were performed on the negative slope and complexity index. Three-way ANOVA with Mode (passive, active), Group (young, old) and Direction (right→left, left→right) as factors were performed on the coupling index. The GPower software was used to calculate the statistical power, and the Python software was used for SVM modeling and classification prediction. The statistical significance level was set at 0.05.
3. Results
3.1 Muscle metabolism results
Figure 3 shows the trends of three hemoglobin parameters (Δ[HbO2], Δ[Hb] and Δ[tHb]) over time for both groups of subjects in both exercise modes. Both groups of subjects showed similar trends in muscle oxygenation signals under the same exercise mode. After the exercise onset, muscles began to consume more oxygen for cellular respiration. Δ[HbO2] decreased rapidly from the initial baseline and this decrease was even more pronounced in the active mode. Shortly after exercise, the body adjusted to meet the energy and oxygen demand, and Δ[HbO2] gradually increased. Δ[Hb] and Δ[tHb] decreased at the beginning of exercise and then increased. Since the muscle oxygenation trends were similar between the left and right upper limbs, the muscle oxygenation trends from the left and right upper limbs were averaged together and used to analyze the rotation period, negative slope, MSfApEn, MSTE and complexity index.
Fig. 3. Mean hemoglobin parameters for (a) young subjects in the passive mode, (b) old subjects in the passive mode, (c) young subjects in the active mode, (d) old subjects in the active mode.
3.1.1 Rotation period
Figure 4(a) shows the rotation periods for the two groups in the two exercise modes. There were no evident statistical differences in the rotation periods between the young group and the old group, either in the passive mode (t = 0.48, p = 0.638) or in the active mode (t = 0.87, p = 0.393). The rotation periods were 1.21 ± 0.05 s (passive: 1.21 ± 0.03 s; active: 1.212 ± 0.09 s) for the young group, and 1.20 ± 0.04 s for the old group (passive: 1.203 ± 0.03 s, active: 1.205 ± 0.05 s). It could be seen that subjects were able to maintain the exercise speed at about 50 r/min according to the experimental requirements.
Fig. 4. The rotation period (a) and negative slope (b) for the two groups. * represents p < 0.05. The negative slope values in (b) and (c) are shown as absolute values. The data are shown as mean ± standard error (SE).
3.1.2 Negative slope
Figure 4(b) shows the negative slope values of muscle oxygenation (Δ[HbO2]) for the two groups in the two exercise modes. The statistical analyses revealed significant Group (F(1, 44) = 11.364, p = 0.001, statistic power = 0.91) and Mode (F(1, 44) = 5.876, p = 0.017, statistic power = 0.67) effects. The interaction between Group and Mode was not significant (F(1, 44) = 0.012, p = 0.913, statistic power = 0.05). Further simple effect analyses showed that the negative slope values were larger for the old group compared with the young group in both exercise modes (p < 0.05).
Figure 4(c) shows the negative slope values of muscle oxygenation (Δ[Hb]) for the two groups in the two exercise modes. The statistical analyses revealed significant Group (F(1, 44) = 7.855, p = 0.006, statistic power = 0.79) and Mode (F(1, 44) = 4.61, p = 0.03, statistic power = 0.57) effects. The interaction between Group and Mode was not significant (F(1, 44) = 0.015, p = 0.904, statistic power = 0.05). Further simple effect analyses showed that the negative slope values were larger for the old group compared with the young group in both exercise modes (p < 0.05), indicating that muscle metabolism is stronger in the older group.
3.2 Multiscale entropy results
3.2.1 MSfApEn results
Figure 5(a) and 5(b) (Fig. 6(a) and 6(b)) show the MSfApEn values of Δ[HbO2] (Δ[Hb]) for the two groups in the two exercise modes. As the time scale increases, fApEn values decrease in both groups. The old group had higher fApEn values than the young group in both the passive and active modes. Figure 5(c) and Fig. 6(c) show the complexity indices for the two groups. For the complexity index, statistical analyses showed significant Group (Δ[HbO2]: F(1, 44) = 27.429, p = 0.000004, statistic power = 0.99; Δ[Hb]: F(1, 44) = 20.015, p = 0.000054, statistic power = 0.99) and Mode (Δ[HbO2]: F(1, 44) = 13.769, p = 0.00006, statistic power = 0.95) effects. The interaction between Group and Mode was not significant (p = 0.98). Further simple effect analyses showed that the complexity for the old group was greater than that for the young group in both active and passive exercise modes (Δ[HbO2]: p < 0.01; Δ[Hb]: p < 0.01). The complexity of the active mode was greater than that of the passive mode for both groups (Δ[HbO2]: p < 0.05).
Fig. 5. The MSfApEn obtained from Δ[HbO2] for the two groups in the passive (a) and active (b) exercise modes. (c) The complexity index for the two groups. Orange asterisk and * represent p < 0.05. Green asterisk and ** represent p < 0.01.
Fig. 6. The MSfApEn obtained from Δ[Hb] for the two groups in the passive (a) and active (b) exercise modes. (c) The complexity index for the two groups. Orange asterisk and * represent p < 0.05. Green asterisk and ** represent p < 0.01.
3.2 Multiscale entropy results
3.2.2 MSTE results
Figure 7(a) and 7(b) (Fig. 8(a) and 8(b)) show the MSTE values of Δ[HbO2] (Δ[Hb]) for the two groups in the two exercise modes. With the time scale increases, TE decreases. Compared with the young group, MSTE between left and right upper limbs was greater for the old group. Figure 7(c) and Fig. 8(c) show the coupling index for the two groups. For the interlimb coupling index, the statistical analyses show that the Group effect was significant (Δ[HbO2]: F(1,88) = 16.932, p = 0.00018, statistic power = 0.98; Δ[Hb]: F(1, 88) = 12.765, p = 0.001, statistic power = 0.94) and the Mode effect was significant (Δ[Hb]: F(1, 88) = 4.008, p = 0.046, statistic power = 0.52). All interactions were not significant. Further simple effect analyses showed that the coupling index of the old group was greater than that of the young group under both the active (Δ[HbO2]: p < 0.05; Δ[Hb]: p < 0.05) and passive modes (Δ[HbO2]: p < 0.05). These results show that the old group has stronger interlimb coupling in the same exercise situation.
Fig. 7. The MSTE obtained from Δ[HbO2] for the two groups in the passive (a) and active (b) exercise modes. (c) The coupling index for the two groups. Orange asterisk and * represent p < 0.05, green asterisk and ** represent p < 0.01.
Fig. 8. The MSTE obtained from Δ[Hb] for the two groups in the passive (a) and active (b) exercise modes. (c) The coupling index for the two groups. Orange asterisk and * represent p < 0.05, green asterisk and ** represent p < 0.01.
3.3 SVM classification results
Based on above statistical results, the negative slope, complexity index and coupling index were used as features. Two SVM models were constructed to classify subjects in the two exercise modes. Table 1 presents the parameters and performance metrics for each model. The SVM models classified two groups of subjects with an accuracy rate of 91.6% and 87.5% under the passive and active exercise modes, respectively. The receiver operator characteristic (ROC) curves of the two SVM models are shown in Fig. 9, with the AUC reaching 0.94 and 0.95, respectively.
Table 1. The SVM model parameters and classification results.
4. Discussion
The present study aimed to examine the aging effect on muscle metabolism patterns during upper extremity exercise. We selected an upper limb circular exercise paradigm which is commonly used in clinical rehabilitation. The old group exhibited stronger muscle metabolism and larger muscle oxygenation complexity during exercise, accompanied by higher interlimb coupling in both the passive and active exercise modes. The old subjects presented these specific muscle metabolism patterns that were different from the young subjects through classification with machine learning.
During upper extremity exercise, the HbO2 concentration initially decreases since the muscle begins to use more oxygen. From the increase of Δ[Hb], it could be seen that muscle oxygen supply increased, resulting in slow rise of HbO2 concentration [39]. The muscle oxygenation of biceps brachii for the old group showed a higher negative slope and stronger metabolism in the upper limb exercise. Previous studies have shown that old-aged individuals require more muscle strength when accomplishing an action during the same exercise compared with young individuals, resulting in increased metabolic energy and oxygen consumption [6,7]. Consistently, we also revealed that the muscle oxygenation metabolism was higher for the older age in the same upper extremity exercise, especially in the active mode. Muscle metabolism increased after the beginning of the exercise, even in the passive mode. From this perspective, passive upper extremity exercise can be used as an effective rehabilitation approach. Especially, for patients with severely impaired motor function, the passive exercise is expected to promote blood and reduce muscle metabolic degeneration [40].
The muscle hemoglobin complexity for the old group was higher than that for the young group in both exercise modes. This might be due to the reduced elasticity of blood vessels in the elderly [41,42], resulting in greater fluctuation in muscle hemoglobin signals during the periodic muscle stretch and contraction caused by the periodic upper limb's encircling exercise. This explains the greater entropy of muscle hemoglobin signals for the old subjects and demonstrates that the complexity of physiological signals during exercise is closely correlated with aging [43,44]. This complexity increase may indeed a sign of age deterioration. For different types of physiological information, the aging may have diverse effects on the complexity. For example, Kang et al. found that EMG complexity in the vastus lateralis and biceps femoris decreased and EMG complexity in the gastrocnemius increased for old adults compared to young adults during treadmill walking [13]. These results indicate that it is necessary to combine multimodal physiological information to better investigate the aging effect on the dynamics of the physiological system.
The present study showed that the transfer entropy between left and right upper limbs in the old-aged group was higher than that of the young group, suggesting a stronger interlimb coupling for older age. And this stronger interlimb coupling was bidirectional. Studies have proved that aging is commonly accompanied by loss of motor function (such as balance and gait [13]). Meanwhile, researchers found an intriguing phenomenon: the compensatory mechanism [11,45]. Aging might enhance the intermuscular couplings related to motor function which may be a strategy to compensate for natural degeneration, allowing old adults accomplishing better performance during exercise and reducing the aging effect. Previous studies have shown that synergistic patterns of proximal muscles in older adults tend to be strengthened compared to young adults, which is associated with the compensatory mechanism [11,45]. In our study, the biceps brachii belongs to the proximal muscles. Therefore, we speculate that the increased interlimb coupling in the old group is related to a compensatory mechanism.
Our results showed that old subjects manifested specific muscle metabolism patterns that differed from the young subjects. Based on the selected features, the constructed SVM model showed high sensitivity and specificity for both groups of subjects in both exercise modes, with sensitivity higher than specificity. The possible explanation was that the original NIRS data for two young adults had large fluctuation comparable to the data of middle-aged and old-aged adults, which caused large variance in the young group and affected the classification results. In SVM classification, too many features might lead to overfitting of the model resulting in reduced generalization ability of the model, while few features might lead to underfitting of the model resulting in reduced learning ability of the model. Therefore, the feature selection is extremely important to the performance of the model. In this study, we first conducted statistical analyses on features to help select the most appropriate features. Four features were selected. When all four features were integrated into the model, the classification effect reached the best, reaching to 91.7%. These results indicate that there are obviously different muscle metabolism patterns for the two groups.
There are several limitations in our study. First, the sample size is small. However, the statistical power proved the reliability of physiological characteristic differences between the two groups. It remains necessary to recruit more subjects to further solidate the conclusion. Second, we combined the dominant and non-dominant upper extremities together. We will conduct further research on the muscle metabolism of the dominant and non-dominant upper extremities separately to clarify the aging effect on motor function. Third, we did not correct the ATT influence on NIRS signals. Townsend et al. found that ATT would have a certain influence on the amplitude of NIRS signals, but would not affect the corresponding dynamic characteristics of muscle hemoglobin signals (such as the slope of the hemoglobin curve) [46]. This indicates that the dynamic characteristics of muscle hemoglobin signals could reduce the influence of ATT to a certain extent [47]. In this study, the negative slope and MSE of NIRS signals were analyzed, and these dynamic characteristics could reduce the influence of ATT. In the future, we will use previous methods to correct the ATT influence in NIRS signals and further verify our results.
5. Conclusion and perspective
In this study, we investigated the aging effect on the muscle metabolism patterns during exercise. The old subjects exhibited specific muscle metabolism patterns during upper limb's encircling exercise that differed from young subjects: larger muscle metabolism, higher muscle oxygenation complexity and stronger interlimb coupling. Our study confirmed the validity of multiscale entropy analyses on muscular oxygenation characteristics in different aged populations. NIRS has wide applications in clinical studies [25,48]. The selected features have great potential in evaluating motor ability. For example, when stroke patients are undergoing encircling rehabilitation training of upper limbs, it is possible to evaluate patients’ functional status of upper limbs and rehabilitation effect according to muscle metabolism and coupling characteristics, which provides novel perspectives and references for the assessment and treatment of motor dysfunction with NIRS.
Funding
National Key Research and Development Program of China (2020YFC2004300, 2020YFC2004302, 2020YFC2004301, 2020YFC2004303, 2020YFC2004304); National Natural Science Foundation of China (32000980, 82071970, 2171533); Basic and Applied Basic Research Foundation of Guangdong Province (2019A1515110427, 2020B1515120014); Guangdong Basic and Applied Basic Research Foundation Outstanding Youth Project (2021B1515020064); Key Laboratory Program of Guangdong Higher Education Institutes (2020KSYS001); Science and Technology Program of Guangzhou (202103000032); Science and Technology Innovation Project of Jianghan University (2021kjzx008).
Acknowledgements
We would like to thank all subjects for their participation.
Disclosures
The authors declare no conflicts of interest.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
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