A risk prediction model for efficient intubation in the emergency department: A 4-year single-center retrospective analysis.

Objective: To analyze the risk factors associated with intubated critically ill patients in the emergency department (ED) and develop a prediction model by machine learning algorithms. Methods: This study was conducted in an academic tertiary hospital in Hangzhou, China. Critically ill patients admitted to the ED were retrospectively analyzed from May 2018 to July 2022. The demographic characteristics, distribution of organ dysfunction, parameters for different organs' examination, and status of mechanical ventilation were recorded. These patients were assigned to the intubation and non-intubation groups according to ventilation support. We used the eXtreme Gradient Boosting (XGBoost) algorithm to develop the prediction model and compared it with other algorithms, such as logistic regression, artificial neural network, and random forest. SHapley Additive exPlanations was used to analyze the risk factors of intubated critically ill patients in the ED. Results: Of 14,589 critically ill patients, 10,212 comprised the training group and 4377 comprised the test group; 2289 intubated patients were obtained from the electronic medical records. The mean age, mean scores of vital signs, parameters of different organs, and blood oxygen examination results differed significantly between the two groups (p < 0.05). The white blood cell count, international normalized ratio, respiratory rate, and pH are the top four risk factors for intubation in critically ill patients. Based on the risk factors in different predictive models, the XGBoost model showed the highest area under the receiver operating characteristic curve (0.84) for predicting ED intubation. Conclusions: For critically ill patients in the ED, the proposed model can predict potential intubation based on the risk factors in the clinically predictive model.

Machine-learning-aided decision-making model for the discontinuation of continuous renal replacement therapy.

BACKGROUND: Continuous renal replacement therapy (CRRT) is a primary form of renal support for patients with acute kidney injury in an intensive care unit. Making an accurate decision of discontinuation is crucial for the prognosis of patients. Previous research has mostly focused on the univariate and multivariate analysis of factors in CRRT, without the capacity to capture the complexity of the decision-making process. The present study thus developed a dynamic, interpretable decision model for CRRT discontinuation. METHODS: The study adopted a cohort of 1234 adult patients admitted to an intensive care unit in the MIMIC-IV database. We used the extreme gradient boosting (XGBoost) machine learning algorithm to construct dynamic discontinuation decision models across four time points. Shapley additive explanation (SHAP) analysis was conducted to show the contribution of an individual feature to the model output. RESULTS: Of the 1234 included patients with CRRT, 596 (48.3%) successfully discontinued CRRT. The dynamic prediction by the XGBoost model produced an area under the curve of 0.848 and accuracy, sensitivity, and specificity of 0.782, 0.786, and 0.776, respectively. The XGBoost model was thus far superior to other test models. SHAP demonstrated that the features that contributed most to the model results were the sequential organ failure assessment score, serum lactate level, and 24-hour urine output. CONCLUSIONS: Dynamic decision models supported by machine learning are capable of dealing with complex factors in CRRT and effectively predicting the outcome of discontinuation.

In Silico Design of Heterogeneous Microvascular Trees Using Generative Adversarial Networks and Constrained Constructive Optimization.

OBJECTIVE: Designing physiologically adequate microvascular trees is of crucial relevance for bioengineering functional tissues and organs. Yet, currently available methods are poorly suited to replicate the morphological and topological heterogeneity of real microvascular trees because the parameters used to control tree generation are too simplistic to mimic results of the complex angiogenetic and structural adaptation processes in vivo. METHODS: We propose a method to overcome this limitation by integrating a conditional deep convolutional generative adversarial network (cDCGAN) with a local fractal dimension-oriented constrained constructive optimization (LFDO-CCO) strategy. The cDCGAN learns the patterns of real microvascular bifurcations allowing for their artificial replication. The LFDO-CCO strategy connects the generated bifurcations hierarchically to form microvascular trees with a vessel density corresponding to that observed in healthy tissues. RESULTS: The generated artificial microvascular trees are consistent with real microvascular trees regarding characteristics such as fractal dimension, vascular density, and coefficient of variation of diameter, length, and tortuosity. CONCLUSIONS: These results support the adoption of the proposed strategy for the generation of artificial microvascular trees in tissue engineering as well as for computational modeling and simulations of microcirculatory physiology.

Multi-organ spatiotemporal information aware model for sepsis mortality prediction.

BACKGROUND: Sepsis is a syndrome involving multi-organ dysfunction, and the mortality in sepsis patients correlates with the number of lesioned organs. Precise prognosis models play a pivotal role in enabling healthcare practitioners to administer timely and accurate interventions for sepsis, thereby augmenting patient outcomes. Nevertheless, the majority of available models consider the overall physiological attributes of patients, overlooking the asynchronous spatiotemporal interactions among multiple organ systems. These constraints hinder a full application of such models, particularly when dealing with limited clinical data. To surmount these challenges, a comprehensive model, denoted as recurrent Graph Attention Network-multi Gated Recurrent Unit (rGAT-mGRU), was proposed. Taking into account the intricate spatiotemporal interactions among multiple organ systems, the model predicted in-hospital mortality of sepsis using data collected within the 48-hour period post-diagnosis. MATERIAL AND METHODS: Multiple parallel GRU sub-models were formulated to investigate the temporal physiological variations of single organ systems. Meanwhile, a GAT structure featuring a memory unit was constructed to capture spatiotemporal connections among multi-organ systems. Additionally, an attention-injection mechanism was employed to govern the data flowing within the network pertaining to multi-organ systems. The proposed model underwent training and testing using a dataset of 10,181 sepsis cases extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. To evaluate the model's superiority, it was compared with the existing common baseline models. Furthermore, ablation experiments were designed to elucidate the rationale and robustness of the proposed model. RESULTS: Compared with the baseline models for predicting mortality of sepsis, the rGAT-mGRU model demonstrated the largest area under the receiver operating characteristic curve (AUROC) of 0.8777 ± 0.0039 and the maximum area under the precision-recall curve (AUPRC) of 0.5818 ± 0.0071, with sensitivity of 0.8358 ± 0.0302 and specificity of 0.7727 ± 0.0229, respectively. The proposed model was capable of delineating the varying contribution of the involved organ systems at distinct moments, as specifically illustrated by the attention weights. Furthermore, it exhibited consistent performance even in the face of limited clinical data. CONCLUSION: The rGAT-mGRU model has the potential to indicate sepsis prognosis by extracting the dynamic spatiotemporal interplay information inherent in multi-organ systems during critical diseases, thereby providing clinicians with auxiliary decision-making support.

A dynamic spatiotemporal model for fall warning and protection.

Early detection of falls is important for reducing fall injuries. However, existing fall detection strategies mostly focus on reducing impact injuries rather than avoiding falls. This study proposed the concept of identifying "Imbalance Point" to warn the body imbalance, allowing sufficient time to recover balance. And if falling cannot be avoided, an impact sign is released by detecting the "Fall Point" prior to the impact. To achieve this goal, motion prediction model and balance recovery model are integrated into a spatiotemporal framework to analyze dynamic and kinematic features of body motion. Eight healthy young volunteers participated in three sets of experiment: Normal trial, Recovery trial and Fall trial. The body motion in the trials was recorded using Microsoft Azure Kinect. The results show that the developed algorithm for Fall Point detection achieved 100% sensitivity and 98.6% specificity, along with an average lead time of 297 ms. Moreover, Imbalance Point was successfully detected in all Fall trials, and the average time interval between Imbalance Point and Fall Point was 315 ms, longer than reported step reaction time for elderly (approximately 270 ms). The experiment results demonstrate that the developed algorithm have great potential for fall warning and protection in the elderly.

Reinforcement Learning Model for Managing Noninvasive Ventilation Switching Policy.

Noninvasive ventilation (NIV) has been recognized as a first-line treatment for respiratory failure in patients with chronic obstructive pulmonary disease (COPD) and hypercapnia respiratory failure, which can reduce mortality and burden of intubation. However, during the long-term NIV process, failure to respond to NIV may cause overtreatment or delayed intubation, which is associated with increased mortality or costs. Optimal strategies for switching regime in the course of NIV treatment remain to be explored.For the goal of reducing 28-day mortality of the patients undergoing NIV, Double Dueling Deep Q Network (D3QN) of offline-reinforcement learning algorithm was adopted to develop an optimal regime model for making treatment decisions of discontinuing ventilation, continuing NIV, or intubation. The model was trained and tested using the data from Multi-Parameter Intelligent Monitoring in Intensive Care III (MIMIC-III) and evaluated by the practical strategies. Furthermore, the applicability of the model in majority disease subgroups (Catalogued by International Classification of Diseases, ICD) was investigated. Compared with physician's strategies, the proposed model achieved a higher expected return score (4.25 vs. 2.68) and its recommended treatments reduced the expected mortality from 27.82% to 25.44% in all NIV cases. In particular, for these patients finally received intubation in practice, if the model also supported the regime, it would warn of switching to intubation 13.36 hours earlier than clinicians (8.64 vs. 22 hours after the NIV treatment), granting a 21.7% reduction in estimated mortality. In addition, the model was applicable across various disease groups with distinguished achievement in dealing with respiratory disorders. The proposed model is promising to dynamically provide personalized optimal NIV switching regime for patients undergoing NIV with the potential of improving treatment outcomes.

Comprehensive breathing variability indices enhance the prediction of extubation failure in patients on mechanical ventilation.

BACKGROUND AND OBJECTIVE: Despite the numerous studies on extubation readiness assessment for patients who are invasively ventilated in the intensive care unit, a 10-15% extubation failure rate persists. Although breathing variability has been proposed as a potential predictor of extubation failure, it is mainly assessed using simple statistical metrics applied to basic respiratory parameters. Therefore, the complex pattern of breathing variability conveyed by continuous ventilation waveforms may be underexplored. METHODS: Here, we aimed to develop novel breathing variability indices to predict extubation failure among invasively ventilated patients. First, breath-to-breath basic and comprehensive respiratory parameters were computed from continuous ventilation waveforms 1 h before extubation. Subsequently, the basic and advanced variability methods were applied to the respiratory parameter sequences to derive comprehensive breathing variability indices, and their role in predicting extubation failure was assessed. Finally, after reducing the feature dimensionality using the forward search method, the combined effect of the indices was evaluated by inputting them into the machine learning models, including logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost). RESULTS: The coefficient of variation of the dynamic mechanical power per breath (CV-MPd[J/breath]) exhibited the highest area under the receiver operating characteristic curve (AUC) of 0.777 among the individual indices. Furthermore, the XGBoost model obtained the best AUC (0.902) by combining multiple selected variability indices. CONCLUSIONS: These results suggest that the proposed novel breathing variability indices can improve extubation failure prediction in invasively ventilated patients.

Enhanced assessment of human dynamic stability by eliminating the effect of body height: modeling and experiment study.

Margin of stability (MOS) is one of the essential indices for evaluating dynamic stability. However, there are indications that MOS was affected by body height and its application in identifying factors on dynamic stability other than body height is restricted. An inverted pendulum model was used to simulate human walking and investigate the relevance between MOS and body height. Eventually, a height-independent index in dynamic stability assessment (named as Angled Margin of Stability, AMOS) was proposed. For testing, fifteen healthy young volunteers performed walking trials with normal arm swing, holding arms, and anti-normal arm swing. Kinematic parameters were recorded using a gait analysis system with a Microsoft Kinect V2.0 and instrumented walkway. Both simulation and test results show that MOS had a significant correlation with height during walking with normal arm swing, while AMOS had no such significant correlation. Walking with normal arm swing produced significantly larger AMOS than holding arms and anti-normal arm swing. However, no significant difference showed up in MOS between normal arm swing and holding arms. The results suggest that AMOS is not affected by body height and has the potential to identify the variations in dynamic stability caused by physiological factors other than body height.

A model of the pulmonary acinar circulatory system with gas exchange function to explore the influence of alveolar diameter.

Lung diseases such as acute respiratory distress syndrome affect the patient's lung compliance, which in turn affects the ability of gas exchange. Changes in alveolar diameter relate to local lung compliance. How alveolar diameter affects gas exchange, particularly oxygen concentrations in alveolar capillaries, is a topic of concern for researchers, and can be studied using mathematical models. The level of small-scale mathematical models of the pulmonary circulatory system was the alveolar capillaries, but existing models do not consider the gas-exchange function and fail to reflect the influence of alveolar diameter. Therefore, we proposed a pulmonary acinar capillary model with gas exchange function, and most importantly, introduced alveolar diameter into the model, to analyze the effect of alveolar diameter on the gas exchange function of the pulmonary acini. The model was tested by three respiratory function simulation experiments. According to the simulation results of changing diameter, we found that the alveolar diameter mainly affects the alveolar gas exchange function of lung acinar inlets and the middle section compared with the peripheral section.

Pulsatility damping in the microcirculation: Basic pattern and modulating factors.

Blood flow pulsatility is an important determinant of macro- and microvascular physiology. Pulsatility is damped largely in the microcirculation, but the characteristics of this damping and the factors that regulate it have not been fully elucidated yet. Applying computational approaches to real microvascular network geometry, we examined the pattern of pulsatility damping and the role of potential damping factors, including pulse frequency, vascular viscous resistance, vascular compliance, viscoelastic behavior of the vessel wall, and wave propagation and reflection. To this end, three full rat mesenteric vascular networks were reconstructed from intravital microscopic recordings, a one-dimensional (1D) model was used to reproduce pulsatile properties within the network, and potential damping factors were examined by sensitivity analysis. Results demonstrate that blood flow pulsatility is predominantly damped at the arteriolar side and remains at a low level at the venular side. Damping was sensitive to pulse frequency, vascular viscous resistance and vascular compliance, whereas viscoelasticity of the vessel wall or wave propagation and reflection contributed little to pulsatility damping. The present results contribute to our understanding of mechanical forces and their regulation in the microcirculation.

Visualizing the spatiotemporal pattern of yolk sac membrane vascular network by enhanced local fractal analysis.

OBJECTIVE: To establish methods for providing a comprehensive and detailed description of the spatial distribution of the vascular networks, and to reveal the spatiotemporal pattern of the yolk sac membrane vascular network during the angiogenic procedure. METHODS: Addressing the limitations in the conventional local fractal analysis, an improved approach, named scanning average local fractal dimension, was proposed. This method was conducted on 6 high-resolution vascular images of the yolk sac membrane for 3 eggs at two stages (E3 and E4) to characterize the spatial distribution of the complexity of the vascular network. RESULTS: With the proposed method, the spatial distribution of the complexity of the yolk sac membrane vascular network was visualized. From E3 to E4, the local fractal dimension increased in 3 eggs, 1.80 ± 0.02 vs. 1.85 ± 0.02, 1.72 ± 0.03 vs. 1.83 ± 0.02, and 1.77 ± 0.03 vs. 1.82 ± 0.02, respectively. The mean local fractal dimension in the most distal area from the embryo proper was the lowest at E3 while the highest at E4. At E3, the most peaks of the local fractal dimension were located in the vein territories and shifted to artery territories at E4. CONCLUSIONS: The spatial distribution of the complexity of the yolk sac membrane vascular network exhibited diverse patterns at different stages. In addition from E3 to E4, the increment of complexity at the intersection areas between arteries and sinus terminalis was with the most advance. This is consistent with the physiologic evidence. The present work provides a potential approach for investigating the spatiotemporal pattern of the angiogenic process.

Development of Novel Fractal Method for Characterizing the Distribution of Blood Flow in Multi-Scale Vascular Tree.

Blood perfusion is an important index for the function of the cardiovascular system and it can be indicated by the blood flow distribution in the vascular tree. As the blood flow in a vascular tree varies in a large range of scales and fractal analysis owns the ability to describe multi-scale properties, it is reasonable to apply fractal analysis to depict the blood flow distribution. The objective of this study is to establish fractal methods for analyzing the blood flow distribution which can be applied to real vascular trees. For this purpose, the modified methods in fractal geometry were applied and a special strategy was raised to make sure that these methods are applicable to an arbitrary vascular tree. The validation of the proposed methods on real arterial trees verified the ability of the produced parameters (fractal dimension and multifractal spectrum) in distinguishing the blood flow distribution under different physiological states. Furthermore, the physiological significance of the fractal parameters was investigated in two situations. For the first situation, the vascular tree was set as a perfect binary tree and the blood flow distribution was adjusted by the split ratio. As the split ratio of the vascular tree decreases, the fractal dimension decreases and the multifractal spectrum expands. The results indicate that both fractal parameters can quantify the degree of blood flow heterogeneity. While for the second situation, artificial vascular trees with different structures were constructed and the hemodynamics in these vascular trees was simulated. The results suggest that both the vascular structure and the blood flow distribution affect the fractal parameters for blood flow. The fractal dimension declares the integrated information about the heterogeneity of vascular structure and blood flow distribution. In contrast, the multifractal spectrum identifies the heterogeneity features in blood flow distribution or vascular structure by its width and height. The results verified that the proposed methods are capable of depicting the multi-scale features of the blood flow distribution in the vascular tree and further are potential for investigating vascular physiology.

Dynamic prediction of late noninvasive ventilation failure in intensive care unit using a time adaptive machine model.

BACKGROUND: Noninvasive ventilation (NIV) failure is strongly associated with poor prognosis. Nowadays, plenty of mature studies have been proposed to predict early NIV failure (within 48 hours of NIV), however, the prediction for late NIV failure (after 48 hours of NIV) lacks sufficient research. Late NIV failure delays intubation resulting in the increasing mortality of the patients. Therefore, it is of great significance to expeditiously predict the late NIV failure. In order to dynamically predict late NIV failure, we proposed a Time Updated Light Gradient Boosting Machine (TULightGBM) model. MATERIAL AND METHODS: In this work, 5653 patients undergoing NIV over 48 hours were extracted from the database of Medical Information Mart for Intensive Care Ⅲ (MIMIC-Ⅲ) for model construction. The TULightGBM model consists of a series of sub-models which learn clinical information from updating data within 48 hours of NIV and integrates the outputs of the sub-models by the dynamic attention mechanism to predict late NIV failure. The performance of the proposed TULightGBM model was assessed by comparison with common models of logistic regression (LR), random forest (RF), LightGBM, eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and long short-term memory (LSTM). RESULTS: The TULightGBM model yielded prediction results at 8, 16, 24, 36, and 48 hours after the start of the NIV with dynamic AUC values of 0.8323, 0.8435, 0.8576, 0.8886, and 0.9123, respectively. Furthermore, the sensitivity, specificity, and accuracy of the TULightGBM model were 0.8207, 0.8164, and 0.8184, respectively. The proposed model achieved superior performance over other tested models. CONCLUSIONS: The TULightGBM model is able to dynamically predict the late NIV failure with high accuracy and offer potential decision support for clinical practice.

An adaptive fractal model for sublingual microcirculation.

The hemodynamic conditions and partial pressure of oxygen in microcirculation generally indicate the status of tissue perfusion, which provides essential information for the assessment and treatment of critical diseases such as sepsis. The human tongue is known to have abundant microcirculation and is an ideal window to observe the microcirculation. At present, the monitoring of sublingual microcirculation is mostly achieved using handheld vital microscopy (HVM). Microcirculation is organized and works as a network. However, HVM can obtain only limited view of few vessels and is not able to acquire information regarding the entire network. In this work, we proposed a method to construct a mathematical network model of sublingual microcirculation to solve the problems. The proposed method is based on fractal analysis to model and simulate the hemodynamic and functional activities of sublingual microcirculation. Specifically, the HVM technology is used to obtain the partial morphological and hemodynamic data of sublingual microcirculation, and fractal analysis is applied thereafter to establish the hemodynamic model of the network based on the data from few vessels. Further, the adaptive regulation mechanism of microcirculation is introduced to enhance the performance of the model. The model was validated by the experimental data and the results are consistent with the characteristics of microcirculation. The work demonstrates the potential of the proposed method in sublingual microcirculation research and for the further assessment of tissue perfusion.

Preprocessing Unevenly Sampled RR Interval Signals to Enhance Estimation of Heart Rate Deceleration and Acceleration Capacities in Discriminating Chronic Heart Failure Patients from Healthy Controls.

Objective. The deceleration capacity (DC) and acceleration capacity (AC) of heart rate, which are recently proposed variants to the heart rate variability, are calculated from unevenly sampled RR interval signals using phase-rectified signal averaging. Although uneven sampling of these signals compromises heart rate variability analyses, its effect on DC and AC analyses remains to be addressed. Approach. We assess preprocessing (i.e., interpolation and resampling) of RR interval signals on the diagnostic effect of DC and AC from simulation and clinical data. The simulation analysis synthesizes unevenly sampled RR interval signals with known frequency components to evaluate the preprocessing performance for frequency extraction. The clinical analysis compares the conventional DC and AC calculation with the calculation using preprocessed RR interval signals on 24-hour data acquired from normal subjects and chronic heart failure patients. Main Results. The assessment of frequency components in the RR intervals using wavelet analysis becomes more robust with preprocessing. Moreover, preprocessing improves the diagnostic ability based on DC and AC for chronic heart failure patients, with area under the receiver operating characteristic curve increasing from 0.920 to 0.942 for DC and from 0.818 to 0.923 for AC. Significance. Both the simulation and clinical analyses demonstrate that interpolation and resampling of unevenly sampled RR interval signals improve the performance of DC and AC, enabling the discrimination of CHF patients from healthy controls.

[Prognostic model of small sample critical diseases based on transfer learning].

Aiming at the problem that the small samples of critical disease in clinic may lead to prognostic models with poor performance of overfitting, large prediction error and instability, the long short-term memory transferring algorithm (transLSTM) was proposed. Based on the idea of transfer learning, the algorithm leverages the correlation between diseases to transfer information of different disease prognostic models, constructs the effictive model of target disease of small samples with the aid of large data of related diseases, hence improves the prediction performance and reduces the requirement for target training sample quantity. The transLSTM algorithm firstly uses the related disease samples to pretrain partial model parameters, and then further adjusts the whole network with the target training samples. The testing results on MIMIC-Ⅲ database showed that compared with traditional LSTM classification algorithm, the transLSTM algorithm had 0.02-0.07 higher AUROC and 0.05-0.14 larger AUPRC, while its number of training iterations was only 39%-64% of the traditional algorithm. The results of application on sepsis revealed that the transLSTM model of only 100 training samples had comparable mortality prediction performance to the traditional model of 250 training samples. In small sample situations, the transLSTM algorithm has significant advantages with higher prediciton accuracy and faster training speed. It realizes the application of transfer learning in the prognostic model of critical disease with small samples.

A Long Short-Term Memory Ensemble Approach for Improving the Outcome Prediction in Intensive Care Unit.

In intensive care unit (ICU), it is essential to predict the mortality of patients and mathematical models aid in improving the prognosis accuracy. Recently, recurrent neural network (RNN), especially long short-term memory (LSTM) network, showed advantages in sequential modeling and was promising for clinical prediction. However, ICU data are highly complex due to the diverse patterns of diseases; therefore, instead of single LSTM model, an ensemble algorithm of LSTM (eLSTM) is proposed, utilizing the superiority of the ensemble framework to handle the diversity of clinical data. The eLSTM algorithm was evaluated by the acknowledged database of ICU admissions Medical Information Mart for Intensive Care III (MIMIC-III). The investigation in total of 18415 cases shows that compared with clinical scoring systems SAPS II, SOFA, and APACHE II, random forests classification algorithm, and the single LSTM classifier, the eLSTM model achieved the superior performance with the largest value of area under the receiver operating characteristic curve (AUROC) of 0.8451 and the largest area under the precision-recall curve (AUPRC) of 0.4862. Furthermore, it offered an early prognosis of ICU patients. The results demonstrate that the eLSTM is capable of dynamically predicting the mortality of patients in complex clinical situations.

Investigation into the diversity in the fractal dimensions of arterioles and venules in a microvascular network - A quantitative analysis.

Fractal dimension is a robust fractal parameter for estimating the morphology of vascular networks. It reflects the property of vascular networks that may vary and thus, differentiate between individual networks and/or identify physiological and pathological conditions. As such, fractal dimension differs also between arteriolar and venular compartments, yet the underlying reason is so far unclear. In order to understand the mechanisms behind these differences, we quantitatively analyzed the impacts of vessel attributes on the fractal dimension. Fractal dimension and vessel attributes given by vessel density (VD), vessel length density (VL), and diameter index (DI=VD/VL) were analyzed in three microvascular networks of the rat mesentery, which were reconstructed from experimental data. The results show that differences in diameter between arterioles and venules are primarily responsible for arterio-venous differences in fractal dimension. Moreover, multiple linear regression analysis demonstrates that the sensitivity of the variation of fractal dimension to vessel length and diameter varies with the type of the vessels. While the change of vessel length contributes 57.8 ±â€¯3.4% to the variation of arteriolar dimension, vessel diameter contributes 63.9 ±â€¯4.8% to the variation of venular dimension. The present study provides an explanation for the different fractal dimension and dimension variation in arteriolar and venular compartments. It highlights the importance of estimating the fractal dimensions of arterioles and venules separately, which will enhance the ability of feature extraction by fractal analysis in physiological and clinical application.

Pulse wave velocity in the microcirculation reflects both vascular compliance and resistance: Insights from computational approaches.

OBJECTIVE: PWV is the speed of pulse wave propagation through the circulatory system. mPWV emerges as a novel indicator of hypertension, yet it remains unclear how different vascular properties affect mPWV. We aim to identify the biomechanical determinants of mPWV. METHODS: A 1D model was used to simulate PWV in a rat mesenteric microvascular network and, for comparison, in a human macrovascular arterial network. Sensitivity analysis was performed to assess the relationship between PWV and vascular compliance and resistance. RESULTS: The 1D model enabled adequate simulation of PWV in both micro- and macrovascular networks. Simulated arterial PWV changed as a function of vascular compliance but not resistance, in that arterial PWV varied at a rate of 0.30 m/s and -6.18 × 10-3  m/s per 10% increase in vascular compliance and resistance, respectively. In contrast, mPWV depended on both vascular compliance and resistance, as it varied at a rate of 2.79 and -2.64 cm/s per 10% increase in the respective parameters. CONCLUSIONS: The present study identifies vascular compliance and resistance in microvascular networks as critical determinants of mPWV. We anticipate that mPWV can be utilized as an effective indicator for the assessment of microvascular biomechanical properties.

[Analysis of the relevance of age and toe out angle of normal adults' gait].

Due to the decline of motor ability and the impact of the diseases, abnormalities in gait is common in the elderly population, which will raise the risk of fall and cause serious injury. This study focuses on the analysis of the gait kinematics parameters of normal adults' gait, aiming to investigate the characteristics of gait parameters in different age groups and to explore the role of gait parameters in motor function assessment and clinical diagnosis. Based on the gait data gained by electronic walkway, the relationship among the toe out angles and their correlation with age and gender etc. were quantitatively analyzed. The results show that most normal subjects walk with positive toe out angles, and the angles increase with age. Such changes are slow in the young and middle age groups. However, the elevations of the left out toe angle and the angles between the feet are statistically significant after entering elder age ( >60 years). The results also suggest that the angle between the feet is a kind of practical gait parameter for varying applications. This study concludes that feet angle analysis is potential to provide a convenient and quantitative tool for the assessment of lower limb motor ability and the diagnosis of knee joint diseases.