Computational modeling for the quantitative assessment of cardiac autonomic response to orthostatic stress.

The Autonomic Nervous System (ANS) plays a critical role in regulating cardiac functions. Early detection of ANS dysfunctions is crucial for preventing or slowing the progression of cardiovascular diseases. Current methods for analyzing ANS activity, such as heart rate variability analysis and muscle sympathetic nerve activity recording, face challenges such as poor temporal resolution, invasiveness, and insufficient sensitivity to individual physiological variations, which limit personalized health assessments. This study aims to introduce the open-loop Mathematical Model of Autonomic Regulation of the Cardiac System under Supine-to-stand Maneuver (MMARCS) to overcome the limitations of existing ANS analysis methods. The MMARCS model is designed to offer a balance between physiological fidelity and simplicity, focusing on the ANS cardiac control subsystems' input-output curve. The MMARCS model simplifies the complex internal dynamics of ANS cardiac control by emphasizing input-output relationships and utilizing sensitivity analysis and parameter subset selection to increase model specificity and eliminate redundant parameters. This approach aims to enhance the model's capacity for personalized health assessments. The application of the MMARCS model revealed significant differences in ANS regulation between healthy (14 females and 19 males) and diabetic subjects (8 females and 6 males). Parameters indicated heightened sympathetic activity and diminished parasympathetic response in diabetic subjects compared to healthy subjects (p<0.05), and also suggested a more sensitive and potentially more reactive sympathetic response among diabetic subjects (p<0.05). The MMARCS model represents an innovative computational approach for quantifying ANS functionality, offering potential benefits for clinical measurements of cardiovascular, disease progression monitoring, and home health monitoring via wearable technology. Its balance between physiological accuracy and model simplicity makes it a promising tool for personalized health assessments.

Model-based assessment of cardiopulmonary autonomic regulation in paced deep breathing.

Autonomic dysfunction can lead to many physical and psychological diseases. The assessment of autonomic regulation plays an important role in the prevention, diagnosis, and treatment of these diseases. A physiopathological mathematical model for cardiopulmonary autonomic regulation, namely Respiratory-Autonomic-Sinus (RSA) regulation Model, is proposed in this study. A series of differential equations are used to simulate the whole process of RSA phenomenon. Based on this model, with respiration signal and ECG signal simultaneously acquired in paced deep breathing scenario, we manage to obtain the cardiopulmonary autonomic regulation parameters (CARP), including the sensitivity of respiratory-sympathetic nerves and respiratory-parasympathetic nerves, the time delay of sympathetic, the sensitivity of norepinephrine and acetylcholine receptor, as well as cardiac remodeling factor by optimization algorithm. An experimental study has been conducted in healthy subjects, along with subjects with hypertension and coronary heart disease. CARP obtained in the experiment have shown their clinical significance.

Automatic Detection of the Cyclic Alternating Pattern of Sleep and Diagnosis of Sleep-Related Pathologies Based on Cardiopulmonary Resonance Indices.

The cyclic alternating pattern is the periodic electroencephalogram activity occurring during non-rapid eye movement sleep. It is a marker of sleep instability and is correlated with several sleep-related pathologies. Considering the connection between the human heart and brain, our study explores the feasibility of using cardiopulmonary features to automatically detect the cyclic alternating pattern of sleep and hence diagnose sleep-related pathologies. By statistically analyzing and comparing the cardiopulmonary characteristics of a healthy group and groups with sleep-related diseases, an automatic recognition scheme of the cyclic alternating pattern is proposed based on the cardiopulmonary resonance indices. Using the Hidden Markov and Random Forest, the scheme combines the variation and stability of measurements of the coupling state of the cardiopulmonary system during sleep. In this research, the F1 score of the sleep-wake classification reaches 92.0%. In terms of the cyclic alternating pattern, the average recognition rate of A-phase reaches 84.7% on the CAP Sleep Database of 108 cases of people. The F1 score of disease diagnosis is 87.8% for insomnia and 90.0% for narcolepsy.

Primitive muscle synergies reflect different modes of coordination in upper limb motions.

The motor system relies on the recruitment of motor modules to perform various movements. Muscle synergies are the modules used by the central nervous system to simplify the control of complex motor tasks. In this paper, we aim to explore the primitive synergies to reflect different modes of coordination in upper limb motions. Muscle synergies and corresponding activation coefficients were extracted via non-negative matrix factorization from the electromyography signals of three basic and four complex upper limb motions in sagittal plane and coronal plane. Similarities of muscle synergies and activation coefficients between different tasks and different subjects were compared. Moreover, we used network theory to assess the coordination between multiple muscles and to elucidate the neural implementation of muscle synergies. The results showed that the combination of different sets of primitive muscle synergies can achieve complex motions in different planes. The muscle synergy network topology differed significantly between different tasks. We also demonstrated the potential of this study for the understanding of human motor control mechanism and implications for neurorehabilitation.

Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images.

Rationale: This study aimed to use computed tomography (CT) images to assess PD-L1 expression in non-small cell lung cancer (NSCLC) and predict response to immunotherapy. Methods: We retrospectively analyzed a PD-L1 expression dataset that consisted of 939 consecutive stage IIIB-IV NSCLC patients with pretreatment CT images. A deep convolutional neural network was trained and optimized with CT images from the training cohort (n = 750) and validation cohort (n = 93) to obtain a PD-L1 expression signature (PD-L1ES), which was evaluated using the test cohort (n = 96). Finally, a separate immunotherapy cohort (n = 94) was used to assess the prognostic value of PD-L1ES with respect to clinical outcome. Results: PD-L1ES was able to predict high PD-L1 expression (PD-L1 ≥ 50%) with areas under the receiver operating characteristic curve (AUC) of 0.78 (95% confidence interval (CI): 0.75~0.80), 0.71 (95% CI: 0.59~0.81), and 0.76 (95% CI: 0.66~0.85) in the training, validation, and test cohorts, respectively. In patients treated with anti-PD-1 antibody, low PD-L1ES was associated with improved progression-free survival (PFS) (median PFS 363 days in low score group vs 183 days in high score group; hazard ratio [HR]: 2.57, 95% CI: 1.22~5.44; P = 0.010). Additionally, when PD-L1ES was combined with a clinical model that was trained using age, sex, smoking history and family history of malignancy, the response to immunotherapy could be better predicted compared to either PD-L1ES or the clinical model alone. Conclusions: The deep learning model provides a noninvasive method to predict high PD-L1 expression of NSCLC and to infer clinical outcomes in response to immunotherapy. Additionally, this deep learning model combined with clinical models demonstrated improved stratification capabilities.

Cardiopulmonary Resonance Function and Indices-A Quantitative Measurement for Respiratory Sinus Arrhythmia.

Respiratory sinus arrhythmia (RSA) represents a physiological phenomenon of cardiopulmonary interaction. It is known as a measure of efficiency of the circulation system, as well as a biomarker of cardiac vagal and well-being. In this article, RSA is modeled as modulation of heart rate by respiration in an interactive cardiopulmonary system with the most effective system state of resonance. By mathematically modeling of this modulation, we propose a quantitative measurement for RSA referred to as "Cardiopulmonary Resonance Function (CRF) and Cardiopulmonary Resonance Indices (CRI)," which are derived by disentanglement of the RR-intervals series into respiratory-modulation component, R-HRV, and the rest, NR-HRV using spectral G-causality. Evaluation of CRI performance in quantifying RSA has been conducted in the scenarios of paced breathing and in the different sleep stages. The preliminary experimental results have shown superior representation ability of CRF and CRI compared to Heart Rate Variability (HRV) and Cardiopulmonary Coupling index (CPC).

Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker.

BACKGROUND: Tumor mutational burden (TMB) is a significant predictor of immune checkpoint inhibitors (ICIs) efficacy. This study investigated the correlation between deep learning radiomic biomarker and TMB, including its predictive value for ICIs treatment response in patients with advanced non-small-cell lung cancer (NSCLC). METHODS: CT images from 327 patients with TMB data (TMB median=6.067 mutations per megabase (range: 0 to 42.151)) were retrospectively collected and randomly divided into a training (n=236), validation (n=26), and test cohort (n=65). We used 3D-densenet to estimate the target tumor area, which used 1020 deep learning features to distinguish High-TMB from Low-TMB patients and establish the TMB radiomic biomarker (TMBRB). The TMBRB was developed in the training cohort combined with validation cohort and evaluated in the test cohort. The predictive value of TMBRB was assessed in a cohort of 123 NSCLC patients who had received ICIs (survival median=462 days (range: 16 to 1128)). RESULTS: TMBRB discriminated between High-TMB and Low-TMB patients in the training cohort (area under the curve (AUC): 0.85, 95% CI: 0.84 to 0.87))and test cohort (AUC: 0.81, 95% CI: 0.77 to 0.85). In this study, the predictive value of TMBRB was better than that of a histological subtype (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.71, 95% CI: 0.66 to 0.76) or Radiomic model (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.74, 95% CI: 0.69 to 0.79). When predicting immunotherapy efficacy, TMBRB divided patients into a high- and low-risk group with distinctly different overall survival (OS; HR: 0.54, 95% CI: 0.31 to 0.95; p=0.030) and progression-free survival (PFS; HR: 1.78, 95% CI: 1.07 to 2.95; p=0.023). Moreover, TMBRB had a better predictive ability when combined with the Eastern Cooperative Oncology Group performance status (OS: p=0.007; PFS: p=0.003). Visual analysis revealed that tumor microenvironment was important for predicting TMB. CONCLUSION: By combining deep learning technology and CT images, we developed an individual non-invasive biomarker that could distinguish High-TMB from Low-TMB, which might inform decisions on the use of ICIs in patients with advanced NSCLC.

Noninvasive CT radiomic model for preoperative prediction of lymph node metastasis in early cervical carcinoma.

OBJECTIVE: To build and validate a CT radiomic model for pre-operatively predicting lymph node metastasis in early cervical carcinoma. METHODS AND MATERIALS: A data set of 150 patients with Stage IB1 to IIA2 cervical carcinoma was retrospectively collected from the Nanfang hospital and separated into a training cohort (n = 104) and test cohort (n = 46). A total of 348 radiomic features were extracted from the delay phase of CT images. Mann-Whitney U test, recursive feature elimination, and backward elimination were used to select key radiomic features. Ridge logistics regression was used to build a radiomic model for prediction of lymph node metastasis (LNM) status by combining radiomic and clinical features. The area under the receiver operating characteristic curve (AUC) and κ test were applied to verify the model. RESULTS: Two radiomic features from delay phase CT images and one clinical feature were associated with LNM status: log-sigma-2-0 mm-3D_glcm_Idn (p = 0.01937), wavelet-HL_firstorder_Median (p = 0.03592), and Stage IB (p = 0.03608). Radiomic model was built consisting of the three features, and the AUCs were 0.80 (95% confidence interval: 0.70 ~ 0.90) and 0.75 (95% confidence intervalI: 0.53 ~ 0.93) in training and test cohorts, respectively. The κ coefficient was 0.84, showing excellent consistency. CONCLUSION: A non-invasive radiomic model, combining two radiomic features and a International Federation of Gynecology and Obstetrics stage, was built for prediction of LNM status in early cervical carcinoma. This model could serve as a pre-operative tool. ADVANCES IN KNOWLEDGE: A noninvasive CT radiomic model, combining two radiomic features and the International Federation of Gynecology and Obstetrics stage, was built for prediction of LNM status in early cervical carcinoma.

Waist-to-Height Ratio Is a Better Predictor of Hyperuricemia than Body Mass Index and Waist Circumference in Chinese.

OBJECTIVE: Our study aimed to compare the predictive value of waist-to-height ratio (WHtR) for hyperuricemia with body mass index (BMI) and waist circumference (WC). METHODS: This is a cross-sectional study of 9,206 South China residents (male/female: 4,433/4,773) aged 18-89 years recruited during years 2009-2010 and 2014-2015. Anthropometric measurements, serum uric acid, blood pressure, and plasma glucose, lipid, lipoprotein, and transferase levels were measured. Receiver operating characteristic (ROC) curve and logistic regression analyses were applied to evaluate the predictive values of anthropometric indices for hyperuricemia. RESULTS: The prevalence of hyperuricemia increased significantly with higher quartiles of WHtR in both genders. The best cutoff points of WHtR to predict hyperuricemia are 0.52 for men and 0.49 for women and differed between different BMI and WC stratums. Although there was no significant difference between the area under the ROC curves, subjects in the top quartile of WHtR were at a highest risk of hyperuricemia (p for linear trend <0.001) and the adjusted ORs of WHtR (2.24-2.77 in men and 2.66-4.95 in women) were higher than those of BMI or WC in the multivariable regression model. CONCLUSIONS: WHtR was an independent and better predictor of hyperuricemia compared with BMI and WC.

Quantitative Assessment of Autonomic Regulation of the Cardiac System.

Autonomic neural system (ANS) regulates the circulation to provide optimal perfusion of every organ in accordance with its metabolic needs, and the quantitative assessment of autonomic regulation is crucial for personalized medicine in cardiovascular diseases. In this paper, we propose the Dystatis to quantitatively evaluate autonomic regulation of the human cardiac system, based on homeostatis and probabilistic graphic model, where homeostatis explains ANS regulation while the probability graphic model systematically defines the regulation process for quantitative assessment. The indices and measurement methods for three well-designed scenarios are also illustrated to evaluate the proposed Dystatis: (1) heart rate variability (HRV), blood pressure variability (BPV), and respiration synchronization (Synch) in resting situation; (2) chronotropic competence indices (CCI) in graded exercise testing; and (3) baroreflex sensitivity (BRS), sympathetic nerve activity (SNA), and parasympathetic nerve activity (PNA) in orthostatic testing. The previous clinical results have shown that the proposed method and indices for autonomic cardiac system regulation have great potential in prediction, diagnosis, and rehabilitation of cardiovascular diseases, hypertension, and diabetes.

Alterations of Muscle Synergies During Voluntary Arm Reaching Movement in Subacute Stroke Survivors at Different Levels of Impairment.

Motor system uses muscle synergies as a modular organization to simplify the control of movements. Motor cortical impairments, such as stroke and spinal cord injuries, disrupt the orchestration of the muscle synergies and result in abnormal movements. In this paper, the alterations of muscle synergies in subacute stroke survivors were examined during the voluntary reaching movement. We collected electromyographic (EMG) data from 35 stroke survivors, ranging from Brunnstrom Stage III to VI, and 25 age-matched control subjects. Muscle synergies were extracted from the activity of 7 upper-limb muscles via nonnegative matrix factorization under the criterion of 95% variance accounted for. By comparing the structure of muscle synergies and the similarity of activation coefficients across groups, we can validate the increasing activation of pectoralis major muscle and the decreasing activation of elbow extensor of triceps in stroke groups. Furthermore, the similarity of muscle synergies was significantly correlated with the Brunnstrom Stage (R = 0.52, p < 0.01). The synergies of stroke survivors at Brunnstrom Stage IV-III gradually diverged from those of control group, but the activation coefficients remained the same after stroke, irrespective of the recovery level.

Improved Hill-type musculotendon models with activation-force-length coupling.

BACKGROUND: Hill-type musculotendon models are most commonly used in biomechanical simulations for their computational efficacy and efficiency. But these models are generally built for maximally-activated muscles and linearly scale muscle properties when applied to submaximal conditions. However, the precondition of this scaling, which is muscle activation and properties are independent each other, has been proven unreal in many studies. Actually, the maximal activation condition is not ubiquitous for muscles in vivo, so it is necessary to adapt the linear scaling approach to improve the model practicability. OBJECTIVE: This paper aimed at proposing two improved Hill-type musculotendon models that are better suited for submaximal conditions. METHOD: These two models were built by including the activation-force-length coupling and their biological accuracy and computation speed were evaluated by a series of benchmark simulations. RESULTS: Compared to experimental measurements, the percent root mean square errors of forces calculated by the two AFLC models were less than 13.98% and 13.81% respectively. However, the average running time of the second AFLC model was nearly 17 times that of the first one with only a little improvement in accuracy. CONCLUSION: The two AFLC models were validated more accurate than the common Hill-type model in submaximally activated conditions and the first one was recommended in the construction of upper-layer musculoskeletal models.

A Pilot Study of Individual Muscle Force Prediction during Elbow Flexion and Extension in the Neurorehabilitation Field.

This paper proposes a neuromusculoskeletal (NMS) model to predict individual muscle force during elbow flexion and extension. Four male subjects were asked to do voluntary elbow flexion and extension. An inertial sensor and surface electromyography (sEMG) sensors were attached to subject's forearm. Joint angle calculated by fusion of acceleration and angular rate using an extended Kalman filter (EKF) and muscle activations obtained from the sEMG signals were taken as the inputs of the proposed NMS model to determine individual muscle force. The result shows that our NMS model can predict individual muscle force accurately, with the ability to reflect subject-specific joint dynamics and neural control solutions. Our method incorporates sEMG and motion data, making it possible to get a deeper understanding of neurological, physiological, and anatomical characteristics of human dynamic movement. We demonstrate the potential of the proposed NMS model for evaluating the function of upper limb movements in the field of neurorehabilitation.

A single task assessment system of upper-limb motor function after stroke.

BACKGROUND: Nowadays, stroke is a leading cause of disability in adults. Assessment of motor performance has played an important role in rehabilitation for post stroke patients. Therefore, it is quite important to develop an automatic assessment system of motor function. OBJECTIVE: The purpose of this study is to assess the performance of the single task upper-limb movements quantitatively among stroke survivors. METHODS: Eleven normal subjects and thirty-five subjects with stroke were involved in this study. The subjects, who were wearing the micro-sensor motion capture system, performed shoulder flexion in a sitting position. The system recorded three-dimensional kinematics data of limb movements in quaternions. By extracting the significant features from these data, we built a linear model to acquire the functional assessment score (FAS). RESULTS: All of the kinematics features have a significant statistical difference (P < 0.05) between patients and healthy people, while the feature values have a high correlation with Fugl-Meyer (FM) scores (r > 0.5, p < 0.05), indicating that these features are able to reflect the level of motion impairment. Furthermore, most samples of the linear model locate in the confidence interval after regression, with the residual approaching a normal distribution. These results show that the FAS is capable of motor function assessment for stroke survivors. CONCLUSION: These findings represent an important step towards a system that can be utilized for precise single task motor evaluation after stroke, applicable to clinical research and as a tool for rehabilitation.