In Vitro Measurements of Shear-Mediated Platelet Adhesion Kinematics as Analyzed through Machine Learning

Sheriff, Jawaad; Wang, Peineng; Zhang, Peng; Zhang, Ziji; Deng, Yuefan; Bluestein, Danny

doi:10.1007/s10439-021-02790-3

Cited by 8 publications

(6 citation statements)

References 58 publications

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“…To improve the simulation speed, a multiscale model concurrently considering components at their own characteristic scales ( Zhu et al, 2021 ) and an intelligent time-stepping algorithm ( Han et al, 2021 ) can effectively relieve the computing load and shorten the simulation time. Advances on machine learning based techniques also have been made towards intelligent image processing for simulation parameter determination ( Zhang et al, 2021b ; Sheriff et al, 2021 ) and dynamics prediction ( Zhang et al, 2021a ), which enables long-term study with affordable efforts.…”

Section: Discussionmentioning

confidence: 99%

Modeling of the thermal properties of SARS-CoV-2 S-protein

Niu

Hasegawa²,

Deng³

et al. 2022

Front. Mol. Biosci.

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We calculate the thermal and conformational states of the spike glycoprotein (S-protein) of SARS-CoV-2 at seven temperatures ranging from 3°C to 95°C by all-atom molecular dynamics (MD) µs-scale simulations with the objectives to understand the structural variations on the temperatures and to determine the potential phase transition while trying to correlate such findings of the S-protein with the observed properties of the SARS-CoV2. Our simulations revealed the following thermal properties of the S-protein: 1) It is structurally stable at 3°C, agreeing with observations that the virus stays active for more than two weeks in the cold supply chain; 2) Its structure varies more significantly at temperature values of 60°C–80°C; 3) The sharpest structural variations occur near 60°C, signaling a plausible critical temperature nearby; 4) The maximum deviation of the receptor-binding domain at 37°C, corroborating the anecdotal observations that the virus is most infective at 37°C; 5) The in silico data agree with reported experiments of the SARS-CoV-2 survival times from weeks to seconds by our clustering approach analysis. Our MD simulations at µs scales demonstrated the S-protein’s thermodynamics of the critical states at around 60°C, and the stable and denatured states for temperatures below and above this value, respectively.

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Section: Discussionmentioning

confidence: 99%

Modeling of the thermal properties of SARS-CoV-2 S-protein

Niu

Hasegawa²,

Deng³

et al. 2022

Front. Mol. Biosci.

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“…Using a convolutional neural network (CNN) to identify subtle morphological features, Zhou et al classified platelet aggregates activated by different agonists [135], while Kempster et al used a CNN to automate analysis of spreading platelets captured under differential interference contrast (DIC) microscopy [136]. Semi-unsupervised learning based on CNN has been used to categorize morphology from platelets dynamics in microchannels [46,[137][138][139]. Several researchers have attempted to classify cellular information using features from diverse data types.…”

Section: Platelet Mechanobiology Modelling In the Age Of Datamentioning

confidence: 99%

Platelet Biorheology and Mechanobiology in Thrombosis: In Silico Perspectives

Tuna,

Yi,

Crespo Cruz

et al. 2024

Preprint

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Thrombosis is the pathological clot formation under abnormal hemodynamic conditions, which can result in vascular obstruction, causing ischemic strokes and myocardial infarction. Thrombus growth under moderate to low shear (10,000 s$^{-1}$) is predominantly driven by unactivated platelet binding and aggregating mediated by von Willebrand factor (VWF), while platelet activation and coagulation are secondary in supporting and reinforcing the thrombus. Given the molecular and cellular level information it can access, multiscale computational modelling informed by biology can provide new pathological mechanisms that are otherwise not accessible experimentally, holding promise for novel first principle based therapeutics. In this review, we summarize the key aspects of platelet mechanobiology, focusing on the molecular and cellular scale events and how they build up all the way to thrombosis through platelet adhesion and aggregation in the presence or absence of platelet activation. In particular, we highlight the recent advancements in multiscale modelling of platelet biorheology and mechanobiology and their role in predicting thrombus formation. Recent applications of artificial intelligence in modelling platelet mechanobiology and thrombosis have also been briefly discussed.

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“…In the current paper, our focus returns to identification and assessment of sources of thrombogenicity in contemporary clinical and also experimental mechanical and bioprosthetic heart valves with particular attention to the central role of conspicuous transient fluid velocities during valve closure. Flow velocity with intrinsic velocity gradients sufficient to induce blood damage can prompt clot formation from shear forces in vascular disease processes and prosthetic cardiovascular devices [16, 17]. Evidence comparing dynamic behavior across a range of both clinical and experimental devices then stimulated provocative conclusions regarding development of less thrombogenic yet durable prosthetic valves.…”

Section: Introductionmentioning

confidence: 99%

Canary in the cardiac-valve coal mine. Flow velocity and inferred shear during prosthetic valve closure –predictors of blood damage and clotting

Scotten¹,

Siegel²,

Blundon

et al. 2022

Preprint

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Objective: To demonstrate a clear link between predicted blood shear forces during valve closure and thrombogenicity that explains the thrombogenic difference between tissue and mechanical valves and provides a practical metric to develop and refine prosthetic valve designs for reduced thrombogenicity. Methods: Pulsatile and quasi-steady flow systems were used for testing. The instantaneous valve flow area was measured using analog opto-electronics with output calibrated to the projected dynamic valve area. Flow velocity during the open and closing periods was determined from the instantaneous volumetric flow rate divided by valve flow area. For the closed valve interval, data obtained from quasi-steady back pressure/flow tests was used. Performance ranked by the derived valvular flow velocity and maximum negative closing flow velocity for all valves is experimental evidence for potential clinical thrombogenicity. Clinical, prototype and control valves were tested. Results: Establishment of a link between blood shear force and thrombogenicity led to optimization of a prototype mechanical bi-leaflet valve. The flow velocity metric was used to empirically design a 3-D printed model (BV3D) for softer valve closure dynamics which implicates reduced thrombogenic potential. Conclusions: The relationship between leaflet geometry, flow velocity and predicted shear at valve closure illuminated an important source of prosthetic valve thrombogenicity. With an appreciation for this relationship and based on our experiment generated comparative data, we achieved optimization of valve prototypes with potential for reduced thrombogenicity.

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In Vitro Measurements of Shear-Mediated Platelet Adhesion Kinematics as Analyzed through Machine Learning

Cited by 8 publications

References 58 publications

Modeling of the thermal properties of SARS-CoV-2 S-protein

Modeling of the thermal properties of SARS-CoV-2 S-protein

Platelet Biorheology and Mechanobiology in Thrombosis: In Silico Perspectives

Canary in the cardiac-valve coal mine. Flow velocity and inferred shear during prosthetic valve closure –predictors of blood damage and clotting

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