Enhanced SpO2 estimation using explainable machine learning and neck photoplethysmography.

Reflectance-based photoplethysmogram (PPG) sensors provide flexible options of measuring sites for blood oxygen saturation (SpO2) measurement. But they are mostly limited by accuracy, especially when applied to different subjects, due to the diverse human characteristics (skin colors, hair density, etc.) and usage conditions of different sensor settings. This study addresses the estimation of SpO2 at non-standard measuring sites employing reflectance-based sensors. It proposes an automated construction of subject inclusion-exclusion criteria for SpO2 measuring devices, using a combination of unsupervised clustering, supervised regression, and model explanations. This is perhaps among the first adaptation of SHAP to explain the clusters gleaned from unsupervised learning methods. As a wellness application case study, we developed a pillow-based wearable device to collect reflectance PPGs from both the brachiocephalic and carotid arteries around the neck. The experiment was conducted on 33 subjects, each under totally 80 different sensor settings. The proposed approach addressed the variations of humans and devices, as well as the heterogeneous mapping between signals and SpO2 values. It identified effective device settings and characteristics of their applicable subject groups (i.e., subject inclusion-exclusion criteria). Overall, it reduced the root mean squared error (RMSE) by 16%, compared to an empirical formula and a plain SpO2 estimation model.

Expression of circulating tumour cells in oral squamous cell carcinoma: An ex vivo pilot study.

Introduction: Despite advances in diagnostics and therapeutics, two-thirds of oral cancer patients present with advanced disease, which increases both the morbidity and mortality risk. Circulating tumour cells (CTCs) are released in the circulation by primary tumours and have been demonstrated to have significant correlations between their occurrence and disease progression. Objectives: To characterize the circulating tumour cells in subjects with histologically diagnosed oral squamous cell carcinoma (OSCC). Materials and Methods: This pilot study was undertaken with ten fresh blood samples (6 ml each). Five samples from apparently healthy individuals and five OSCC samples were cultured and subjected to flow cytometric analysis for CD44 expression. Immunostaining was done using CD44 and EpCAM markers. Result: Several cells in OSCC samples showed EpCAM and CD44 positivity following immunostaining. However, flow cytometry performed with CD44 alone was not specific for OSCC samples. Hence, proving that CD44 and EpCAM when used in conjunction can help to characterize CTCs. Conclusion: The findings of our study suggest that the demonstration of CTCs is feasible and helps in understanding of disease progression and metastatic risk. Sensitive detection of CTCs from blood samples can serve as an implicit tool in early cancer diagnosis and prognosis through liquid biopsy which in itself is minimally invasive and time-saving.

Toward standardizing the clinical testing protocols of point-of-care devices for obstructive sleep apnea diagnosis.

PURPOSE: In recent years, point-of-care (POC) devices, especially smart wearables, have been introduced to provide a cost-effective, comfortable, and accessible alternative to polysomnography (PSG)-the current gold standard-for the monitoring, screening, and diagnosis of obstructive sleep apnea (OSA). Thorough validation and human subject testing are essential steps in the translation of these device technologies to the market. However, every device development group tests their device in their own way. No standard guidelines exist for assessing the performance of these POC devices. The purpose of this paper is to critically distill the key aspects of the various protocols reported in the literature and present a protocol that unifies the best practices for testing wearable and other POC devices for OSA. METHODS: A limited review and graphical descriptive analytics of literature-including journal articles, web sources, and clinical manuscripts by authoritative agencies in sleep medicine-are performed to glean the testing and validation methods employed for POC devices, specifically for OSA. RESULTS: The analysis suggests that the extent of heterogeneity of the demographics, the performance metrics, subject survey, hypotheses, and statistical analyses need to be carefully considered in a systematic protocol for testing POC devices for OSA. CONCLUSION: We provide a systematic method and list specific recommendations to extensively assess various performance criteria for human subject testing of POC devices. A rating scale of 1-3 is provided to encourage studies to put a focus on addressing the key elements of a testing protocol.

Deep Learning-Based Spermatogenic Staging Assessment for Hematoxylin and Eosin-Stained Sections of Rat Testes.

In preclinical toxicology studies, a "stage-aware" histopathological evaluation of testes is recognized as the most sensitive method to detect effects on spermatogenesis. A stage-aware evaluation requires the pathologist to be able to identify the different stages of the spermatogenic cycle. Classically, this evaluation has been performed using periodic acid-Schiff (PAS)-stained sections to visualize the morphology of the developing spermatid acrosome, but due to the complexity of the rat spermatogenic cycle and the subtlety of the criteria used to distinguish between the 14 stages of the cycle, staging of tubules is not only time consuming but also requires specialized training and practice to become competent. Using different criteria, based largely on the shape and movement of the elongating spermatids within the tubule and pooling some of the stages, it is possible to stage tubules using routine hematoxylin and eosin (H&E)-stained sections, thereby negating the need for a special PAS stain. These criteria have been used to develop an automated method to identify the stages of the rat spermatogenic cycle in digital images of H&E-stained Wistar rat testes. The algorithm identifies the spermatogenic stage of each tubule, thereby allowing the pathologist to quickly evaluate the testis in a stage-aware manner and rapidly calculate the stage frequencies.

Distribution of Pathogenic Yeasts in Different Clinical Samples: Their Identification, Antifungal Susceptibility Pattern, and Cell Invasion Assays.

INTRODUCTION: Species of genus Candida are part of the common microbiota of humans; however, some of the Candida species are known opportunistic pathogens. Formation of biofilms, resistance to antifungal drugs, and increase in asymptomatic infections demands more studies on isolation, identification and characterization of Candida from clinical samples. METHODS: The present manuscript deals with assessment of authentic yeast identification by three methods viz., DNA sequencing of 28S rRNA gene, protein profiles using MALDI-TOF MS, and colony coloration on chromogenic media. Antifungal susceptibility and in vitro cell invasion assays were performed to further characterize these isolates. RESULTS: Comparison of three methods showed that DNA sequence analysis correctly identified more than 99.4% of the isolates up to species level as compared to 89% by MALDI-TOF MS. In this study, we isolated a total of 176 yeasts from clinical samples and preliminary morphological characters indicated that these yeast isolates belong to the genus Candida. The species distribution of isolates was as follows: 75 isolates of Candida albicans (42.61%), 50 of C. tropicalis (28.40%), 22 of C. glabrata (12.5%), 14 of C. parapsilosis (7.95%) and 4 of Clavispora lusitaniae (2.27%). Other species like Cyberlindnera fabianii, Issatchenkia orientalis, Kluyveromyces marxianus, Kodamaea ohmeri, Lodderomyces sp., and Trichosporon asahii were less than 2%. Antifungal susceptibility assay performed with 157 isolates showed that most of the isolates were resistant to the four azoles viz., clotrimazole, fluconazole, itraconazole, and ketoconazole, and the frequency of resistance was more in non-albicans Candida isolates. The susceptibility to azole drugs ranged from 7% to 48%, while 75% of the tested yeasts were susceptible to nystatin. Moreover, 88 isolates were also tested for their capacity to invade human cells using HeLa cells. In vitro invasion assay showed that most of the C. albicans isolates showed epithelial cell invasion as compared to isolates belonging to C. glabrata, C. parapsilosis and C. tropicalis. DISCUSSION: The identification of yeasts of clinical origin by sequencing of 28S rRNA gene performed better than MALDI-TOF MS. The present study reiterates the world scenario wherein there is a shift from Candida strains to emerging opportunistic pathogens which were earlier regarded as environmental strains. The present study enlightens the current understanding of identification methods for clinical yeast isolates, increased antifungal drug resistance, epithelial cell invasion as a virulence factor, and diversity of yeasts in Indian clinical samples.

IL-3 Receptor Expression on Activated Human Th Cells Is Regulated by IL-4, and IL-3 Synergizes with IL-4 to Enhance Th2 Cell Differentiation.

IL-3, a cytokine secreted by activated T lymphocytes, is known to regulate the proliferation, survival, and differentiation of hematopoietic cells. However, the role of IL-3 in regulation of T cell functions is not fully delineated. Previously, we have reported that IL-3 plays an important role in development of regulatory T cells in mice. In this study, we investigated the regulation of IL-3R expression on human Th cells and also examined the role of IL-3 in effector functions of these cells. We found that human peripheral blood Th cells in resting state do not show surface expression of IL-3R; however, its expression was observed at transcript and intracellular protein levels. The functional IL-3R expression on the surface was seen only after antigenic stimulation. When naive Th cells were activated in the presence of various cytokines, we found that IL-4 significantly increases the surface expression of IL-3R and also increases the number of IL-3R+ Th cells. Interestingly, IL-3R+ cells exhibit a Th2 cell-like phenotype and show high GATA-3 expression. Moreover, Th2 cells in presence of IL-3 show increased expression of type 2 effector cytokines, such as IL-4, IL-5, and IL-13. Furthermore, IL-3R expressing and IL-3-secreting Th cells were high in house dust mite-allergic patients. Thus, to our knowledge, we provide the first evidence that the expression of IL-3R on activated human Th cells is modulated by IL-4, and IL-3 regulates the effector functions of Th2 cells. Our results suggest that IL-3 may play an important role in regulating allergic immune responses.

Surface plastic flow in polishing of rough surfaces.

We present experimental evidence for a new mechanism for how smooth surfaces emerge during repetitive sliding contacts, as in polishing. Electron microscopy observations of Ti-6Al-4V surface with a spherical asperity structure-realized via additive manufacturing-during successive polishing stages suggest that asperity-abrasive contacts exhibit viscous behavior, where the asperity material flows in the form of thin (1-10 µm) fluid-like layers. Subsequent bridging of these layers among neighboring asperities results in progressive surface smoothening. Using analytical asperity-abrasive contact temperature modeling and microstructural characterization, we show that the sliding contacts encounter flash temperatures of the order of 700-900 K which is in the range of the dynamic recrystallization temperature of the material considered, thus supporting the experimental observations. Besides providing a new perspective on the long-held mechanism of polishing, our observations provide a novel approach based on graph theory to quantitatively characterize the evolution of surface morphology. Results suggest that the graph representation offers a more efficient measure to characterize the surface morphology emerging at various stages of polishing. The research findings and observations are of broad relevance to the understanding of plastic flow behavior of sliding contacts ubiquitous in materials processing, tribology, and natural geological processes as well as present unique opportunities to tailor the microstructures by controlling the thermomechanics of the asperity-abrasive contacts.

Multi class disorder detection of magnetic resonance brain images using composite features and neural network.

Brain disorder recognition has becoming a promising area of study. In reality, some disorders share similar features and signs, making the task of diagnosis and treatment challenging. This paper presents a rigorous and robust computer aided diagnosis system for the detection of multiple brain abnormalities which can assist physicians in the diagnosis and treatment of brain diseases. In this system, we used energy of wavelet sub bands, textural features of gray level co-occurrence matrix and intensity feature of MR brain images. These features are ranked using Wilcoxon test. The composite features are classified using back propagation neural network. Bayesian regulation is adopted to find the optimal weights of neural network. The experimentation is carried out on datasets DS-90 and DS-310 of Harvard Medical School. To enhance the generalization capability of the network, fivefold stratified cross validation technique is used. The proposed system yields multi class disease classification accuracy of 100% in differentiating 90 MR brain images into 18 classes and 97.81% in differentiating 310 MR brain images into 6 classes. The experimental results reveal that the composite features along with BPNN classifier create a competent and reliable system for the identification of multiple brain disorders which can be used in clinical applications. The Wilcoxon test outcome demonstrates that standard deviation feature along with energies of approximate and vertical sub bands of level 7 contribute the most in achieving enhanced multi class classification performance results.

Multi class disorder detection of magnetic resonance brain images using composite features and neural network

Brain disorder recognition has becoming a promising area of study. In reality, some disorders share similar features and signs, making the task of diagnosis and treatment challenging. This paper presents a rigorous and robust computer aided diagnosis system for the detection of multiple brain abnormalities which can assist physicians in the diagnosis and treatment of brain diseases. In this system, we used energy of wavelet sub bands, textural features of gray level co-occurrence matrix and intensity feature of MR brain images. These features are ranked using Wilcoxon test. The composite features are classifi ed using back propagation neural network. Bayesian regulation is adopted to fi nd the optimal weights of neural network. The experimentation is carried out on datasets DS-90 and DS-310 of Harvard Medical School. To enhance the generalization capability of the network, fi vefold stratifi ed cross validation technique is used. The proposed system yields multi class disease classifi cation accuracy of 100% in diff erentiating 90 MR brain images into 18 classes and 97.81% in diff erentiating 310 MR brain images into 6 classes. The experimental results reveal that the composite features along with BPNN classifi er create a competent and reliable system for the identifi cation of multiple brain disorders which can be used in clinical applications. The Wilcoxon test outcome demonstrates that standard deviation feature along with energies of approximate and vertical sub bands of level 7 contribute the most in achieving enhanced multi class classifi cation performance results.

Correction: Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets.

[This corrects the article DOI: 10.1371/journal.pone.0164406.].

Interleukin-3 enhances the migration of human mesenchymal stem cells by regulating expression of CXCR4.

BACKGROUND: Mesenchymal stem cells (MSCs) represent an important source for cell therapy in regenerative medicine. MSCs have shown promising results for repair of damaged tissues in various degenerative diseases in animal models and also in human clinical trials. However, little is known about the factors that could enhance the migration and tissue-specific engraftment of exogenously infused MSCs for successful regenerative cell therapy. Previously, we have reported that interleukin-3 (IL-3) prevents bone and cartilage damage in animal models of rheumatoid arthritis and osteoarthritis. Also, IL-3 promotes the differentiation of human MSCs into functional osteoblasts and increases their in-vivo bone regenerative potential in immunocompromised mice. However, the role of IL-3 in migration of MSCs is not yet known. In the present study, we investigated the role of IL-3 in migration of human MSCs under both in-vitro and in-vivo conditions. METHODS: MSCs isolated from human bone marrow, adipose and gingival tissues were used for in-vitro cell migration, motility and wound healing assays in the presence or absence of IL-3. The effect of IL-3 preconditioning on expression of chemokine receptors and integrins was examined by flow cytometry and real-time PCR. The in-vivo migration of IL-3-preconditioned MSCs was investigated using a subcutaneous matrigel-releasing stromal cell-derived factor-1 alpha (SDF-1α) model in immunocompromised mice. RESULTS: We observed that human MSCs isolated from all three sources express IL-3 receptor-α (IL-3Rα) both at gene and protein levels. IL-3 significantly enhances in-vitro migration, motility and wound healing abilities of MSCs. Moreover, IL-3 preconditioning upregulates expression of chemokine (C-X-C motif) receptor 4 (CXCR4) on MSCs, which leads to increased migration of cells towards SDF-1α. Furthermore, CXCR4 antagonist AMD3100 decreases the migration of IL-3-treated MSCs towards SDF-1α. Importantly, IL-3 also induces in-vivo migration of MSCs towards subcutaneously implanted matrigel-releasing-SDF-1α in immunocompromised mice. CONCLUSIONS: The present study demonstrates for the first time that IL-3 has an important role in enhancing the migration of human MSCs through regulation of the CXCR4/SDF-1α axis. These findings suggest a potential role of IL-3 in improving the efficacy of MSCs in regenerative cell therapy.

Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets.

Recent advances in sensor technologies and predictive analytics are fueling the growth in point-of-care (POC) therapies for obstructive sleep apnea (OSA) and other sleep disorders. The effectiveness of POC therapies can be enhanced by providing personalized and real-time prediction of OSA episode onsets. Previous attempts at OSA prediction are limited to capturing the nonlinear, nonstationary dynamics of the underlying physiological processes. This paper reports an investigation into heart rate dynamics aiming to predict in real time the onsets of OSA episode before the clinical symptoms appear. A prognosis method based on a nonparametric statistical Dirichlet-Process Mixture-Gaussian-Process (DPMG) model to estimate the transition from normal states to an anomalous (apnea) state is utilized to estimate the remaining time until the onset of an impending OSA episode. The approach was tested using three datasets including (1) 20 records from 14 OSA subjects in benchmark ECG apnea databases (Physionet.org), (2) records of 10 OSA patients from the University of Dublin OSA database and (3) records of eight subjects from previous work. Validation tests suggest that the model can be used to track the time until the onset of an OSA episode with the likelihood of correctly predicting apnea onset in 1 min to 5 mins ahead is 83.6 ± 9.3%, 80 ± 8.1%, 76.2 ± 13.3%, 66.9 ± 15.4%, and 61.1 ± 16.7%, respectively. The present prognosis approach can be integrated with wearable devices, enhancing proactive treatment of OSA and real-time wearable sensor-based of sleep disorders.

Random Forests Are Able to Identify Differences in Clotting Dynamics from Kinetic Models of Thrombin Generation.

Current methods for distinguishing acute coronary syndromes such as heart attack from stable coronary artery disease, based on the kinetics of thrombin formation, have been limited to evaluating sensitivity of well-established chemical species (e.g., thrombin) using simple quantifiers of their concentration profiles (e.g., maximum level of thrombin concentration, area under the thrombin concentration versus time curve). In order to get an improved classifier, we use a 34-protein factor clotting cascade model and convert the simulation data into a high-dimensional representation (about 19000 features) using a piecewise cubic polynomial fit. Then, we systematically find plausible assays to effectively gauge changes in acute coronary syndrome/coronary artery disease populations by introducing a statistical learning technique called Random Forests. We find that differences associated with acute coronary syndromes emerge in combinations of a handful of features. For instance, concentrations of 3 chemical species, namely, active alpha-thrombin, tissue factor-factor VIIa-factor Xa ternary complex, and intrinsic tenase complex with factor X, at specific time windows, could be used to classify acute coronary syndromes to an accuracy of about 87.2%. Such a combination could be used to efficiently assay the coagulation system.

Inferring sparse networks for noisy transient processes.

Inferring causal structures of real world complex networks from measured time series signals remains an open issue. The current approaches are inadequate to discern between direct versus indirect influences (i.e., the presence or absence of a directed arc connecting two nodes) in the presence of noise, sparse interactions, as well as nonlinear and transient dynamics of real world processes. We report a sparse regression (referred to as the l1-min) approach with theoretical bounds on the constraints on the allowable perturbation to recover the network structure that guarantees sparsity and robustness to noise. We also introduce averaging and perturbation procedures to further enhance prediction scores (i.e., reduce inference errors), and the numerical stability of l1-min approach. Extensive investigations have been conducted with multiple benchmark simulated genetic regulatory network and Michaelis-Menten dynamics, as well as real world data sets from DREAM5 challenge. These investigations suggest that our approach can significantly improve, oftentimes by 5 orders of magnitude over the methods reported previously for inferring the structure of dynamic networks, such as Bayesian network, network deconvolution, silencing and modular response analysis methods based on optimizing for sparsity, transients, noise and high dimensionality issues.

Adipose-Derived Mesenchymal Stem Cells Prevent Systemic Bone Loss in Collagen-Induced Arthritis.

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammatory synovitis leading to joint destruction and systemic bone loss. The inflammation-induced bone loss is mediated by increased osteoclast formation and function. Current antirheumatic therapies primarily target suppression of inflammatory cascade with limited or no success in controlling progression of bone destruction. Mesenchymal stem cells (MSCs) by virtue of their tissue repair and immunomodulatory properties have shown promising results in various autoimmune and degenerative diseases. However, the role of MSCs in prevention of bone destruction in RA is not yet understood. In this study, we investigated the effect of adipose-derived MSCs (ASCs) on in vitro formation of bone-resorbing osteoclasts and pathological bone loss in the mouse collagen-induced arthritis (CIA) model of RA. We observed that ASCs significantly inhibited receptor activator of NF-κB ligand (RANKL)-induced osteoclastogenesis in both a contact-dependent and -independent manner. Additionally, ASCs inhibited RANKL-induced osteoclastogenesis in the presence of proinflammatory cytokines such as TNF-α, IL-17, and IL-1ß. Furthermore, treatment with ASCs at the onset of CIA significantly reduced clinical symptoms and joint pathology. Interestingly, ASCs protected periarticular and systemic bone loss in CIA mice by maintaining trabecular bone structure. We further observed that treatment with ASCs reduced osteoclast precursors in bone marrow, resulting in decreased osteoclastogenesis. Moreover, ASCs suppressed autoimmune T cell responses and increased the percentages of peripheral regulatory T and B cells. Thus, we provide strong evidence that ASCs ameliorate inflammation-induced systemic bone loss in CIA mice by reducing osteoclast precursors and promoting immune tolerance.

High fat diet promotes achievement of peak bone mass in young rats.

The relationship between obesity and bone is complex. Epidemiological studies demonstrate positive as well as negative correlation between obesity and bone health. In the present study, we investigated the impact of high fat diet-induced obesity on peak bone mass. After 9 months of feeding young rats with high fat diet, we observed obesity phenotype in rats with increased body weight, fat mass, serum triglycerides and cholesterol. There were significant increases in serum total alkaline phosphatase, bone mineral density and bone mineral content. By micro-computed tomography (µ-CT), we observed a trend of better trabecular bones with respect to their microarchitecture and geometry. This indicated that high fat diet helps in achieving peak bone mass and microstructure at younger age. We subsequently shifted rats from high fat diet to normal diet for 6 months and evaluated bone/obesity parameters. It was observed that after shifting rats from high fat diet to normal diet, fat mass, serum triglycerides and cholesterol were significantly decreased. Interestingly, the gain in bone mineral density, bone mineral content and trabecular bone parameters by HFD was retained even after body weight and obesity were normalized. These results suggest that fat rich diet during growth could accelerate achievement of peak bone mass that is sustainable even after withdrawal of high fat diet.

Optimization of selective production media for enhanced production of xylanases in submerged fermentation by Thielaviopsis basicola MTCC 1467 using L16 orthogonal array.

Enzymes have been the centre of attention for researchers/industrialists worldwide due to their wide range of physiological, analytical, food/feed and industrial based applications. Among the enzymes explored for industrial applications, xylanases play an instrumental role in food/feed, textile/detergent, paper and biorefinery based application sectors. This study deals with the statistical optimization of xylanase production by Thielaviopsis basicola MTCC 1467 under submerged fermentation conditions using rice straw, as sole carbon source. Different fermentation parameters such as carbon source, nitrogen source, inorganic salts like KH2PO4, MgSO4 and pH of the medium were optimized at the individual and interactive level by Taguchi orthogonal array methodology (L16). All selected fermentation parameters influenced the enzyme production. Rice straw, the major carbon source mainly influenced the production of xylanase (~34 %). After media optimization, the yield of enzyme improved from 38 to ~60 IU/ml (161.5 %) indicating the commercial production of xylanase by T. basicola MTCC 1467. This study shows the potential of T. basicola MTCC 1467 for the efficient xylanase production under the optimized set of conditions.

Detection of sleep apnea events via tracking nonlinear dynamic cardio-respiratory coupling from electrocardiogram signals.

Obstructive sleep apnea (OSA) is a common sleep disorder that causes increasing risk of mortality and affects quality of life of approximately 6.62% of the total US population. Timely detection of sleep apnea events is vital for the treatment of OSA. In this paper, we present a novel approach based on extracting the quantifiers of nonlinear dynamic cardio-respiratory coupling from electrocardiogram (ECG) signals to detect sleep apnea events. The quantifiers of the cardio-respiratory dynamic coupling were extracted based on recurrence quantification analysis (RQA), and a battery of statistical data mining techniques were to enhance OSA detection accuracy. This approach would lead to a cost-effective and convenient means for screening of OSA, compared to traditional polysomnography (PSG) methods. The results of tests conducted using data from PhysioNets Sleep Apnea database suggest excellent quality of the OSA detection based on a thorough comparison of multiple models, using model selection criteria of validation data: Sensitivity (91.93%), Specificity (85.84%), Misclassification (11.94%) and Lift (2.7).

Topology and random-walk network representation of cardiac dynamics for localization of myocardial infarction.

While detection of acute cardiac disorders such as myocardial infarction (MI) from electrocardiogram (ECG) and vectorcardiogram (VCG) has been widely reported, identification of MI locations from these signals, pivotal for timely therapeutic and prognostic interventions, remains a standing issue. We present an approach for MI localization based on representing complex spatiotemporal patterns of cardiac dynamics as a random-walk network reconstructed from the evolution of VCG signals across a 3-D state space. Extensive tests with signals from the PTB database of the PhysioNet databank suggest that locations of MI can be determined accurately (sensitivity of ∼88% and specificity of ∼92%) from tracking certain consistently estimated invariants of this random-walk representation.

Real-time lumped parameter modeling of cardiovascular dynamics using electrocardiogram signals: toward virtual cardiovascular instruments.

We present an approach to deriving a real-time, lumped parameter cardiovascular dynamics model that uses features extracted from online electrocardiogram (ECG) signal recordings to generate certain surrogate hemodynamic signals. The model represents the coupled dynamics of the heart chambers, valves, and pulmonary and systemic blood circulation loops in the form of nonlinear differential equations. The features extracted from ECG signals were used to estimate the timings and amplitudes of the atrioventricular activation input functions as well as other model parameters that capture the effect of cardiac morphological and physiological characteristics. The model was tested using hemodynamic signals from the PhysioNet MGH/MF Waveform database. The results suggest that the model can capture the salient time and frequency patterns of the measured central venous pressure, pulmonary arterial pressure, and respiratory impedance signals (R(2) > 0.65). We have developed a method based on Anderson-Darling statistic and Kullback-Leibler divergence to compare the clinical measures (i.e., systolic and diastolic pressures) estimated from model waveform-extrema with those from actual measurements. The test statistics of the model waveform-extrema were statistically indistinguishable from the measured values with beat-to-beat rejection rates of 10%. The results indicate the potential of a virtual instrument that uses the model-derived signals for clinical diagnosis in lieu of expensive instrumentation.