Microfluidic Platform for Stroke Risk Prediction: Evaluation of Blood Viscosity by Shear Rate Variations

doi:10.21203/rs.3.rs-4657162/v1

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Microfluidic Platform for Stroke Risk Prediction: Evaluation of Blood Viscosity by Shear Rate Variations

https://doi.org/10.21203/rs.3.rs-4657162/v1

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The incidence of stroke is on the rise globally, affecting one in every four individuals each year. An early warning and prevention systems are urgently required. Blood viscosity is a correlation factor that is worthy to study in the stroke risk evaluation model. For the first time, a microfluidic platform was used as the in-vitro blood property evaluation for stroke risk prediction. It can be also used to evaluate the variation of non-Newtonian fluid viscosity under different specific shear rate conditions. The rigorous microarray design is providing the meticulous shear rate which simulating the variable of blood viscosity during pulsation within blood vessels. Furthermore, the systolic blood viscosity (SBV) and diastolic blood viscosity (DBV) can be calculated by using the developed pulsatility flow concept.

The results demonstrate an impressive accuracy of 95% and excellent reproducibility while compared to traditional viscometers and rheometer within the human blood viscosity range of 1-10cP. This monitoring system is capable of being an indispensable component in the stroke risk evaluation platform.

Nonnewtonian fluid

microfluidic device

blood behavior flow

stroke risk prediction platform

In recent years, the integration of advanced biomedical knowledge and cutting-edge technologies has significantly enhanced the accuracy and scope of stroke prediction models (Arboix, 2015; Boehme, Esenwa, & Elkind, 2017; Letham, Rudin, McCormick, & Madigan, 2015). It looms as a leading cause of disability and mortality globally, relentlessly striking without warning and leaving a profound impact on individuals, families, and societies (Avan et al., 2019; Feigin, Norrving, & Mensah, 2017; Katan & Luft, 2018). A stroke occurs when the blood supply to the brain is abruptly interrupted or when a blood vessel in the brain bursts, leading to the death of brain cells. One critical factor in these advancements is the consideration of blood viscosity (Fisher & Meiselman, 1991; Song et al., 2017; Tikhomirova, Oslyakova, & Mikhailova, 2011), a key parameter reflecting the thickness and flow properties of blood (Furukawa et al., 2016; Gyawali et al., 2023; Gyawali et al., 2022). Emerging research has unveiled the intricate relationship between abnormal blood viscosity (BV) and the risk of stroke. High BV increases accordingly thromboembolic risk and is a correlation factor for cardiovascular disease. Those studies exhibit the associations between BV and ischemic stroke (Grotemeyer, Kaiser, Grotemeyer, & Husstedt, 2014; Han et al., 2019; T. Kim et al., 2020). There are many comprehensive factors to effective BV, such as blood sugar, erythrocyte amounts and thrombus (Baeckström, Folkow, Kendrick, Löfving, & ÖBerg, 1971; Cadroy, Horbett, & Hanson, 1989; Somer & Meiselman, 1993; Tamariz et al., 2008). A specify measurement to point out the relationship between stroke and BV is essential. The artery pulsatility is highly associated with blood viscosity in acute ischemic stroke within 24 hours of symptom onset (Han et al., 2019). An appropriate physical measurement model to evaluate the stroke happening risk is observing the viscosity of non-Newtonian property of blood by different shear rate conditions.

The physical blood flow parameters, shear rate and shear stress, were identified in the early 1970's and subsequently investigated for their potential impact on arterial thrombus formation (Sakariassen, Orning, & Turitto, 2015). For normal vascular flow, narrowing of the arterial diameter (stenosis) while maintaining blood flow rate constantly increases the wall shear rates and shear stresses. That depend on the extent of reduction of the vessel lumen in a manner that is inversely proportional to the cube of the vessel diameter (Barstad, Roald, Cui, Turitto, & Sakariassen, 1994; Sakariassen, Houdijk, Sixma, Aarts, & de Groot, 1983). In previous investigations and experiments the factor associated with BV, people utilized the rotational viscometer to observe the non-Newtonian characteristics of blood (H. Kim et al., 2013; A. J. Lee et al., 1998; Lowe, Lee, Rumley, Price, & Fowkes, 1997; Rosenson, Mccormick, & Uretz, 1996). But the testing conditions employed by traditional rotational viscometers and rheometers are highly unsuitable for biological experiments. The limitation arises due to the lowest torque that can be reliably measured. This particularly affects acquisition of shear viscosity data at low shear rates (Gupta, Wang, & Vanapalli, 2016). Therefore, people started development the microfluidic platform to study blood property (Kang & Lee, 2018; Wang, Abaci, & Shuler, 2017; Zilberman-Rudenko et al., 2018). A parallel laminar flow microchip for Newtonian fluid viscosity measurement is provided (B. J. Kim, Lee, Jee, Atajanov, & Yang, 2017; Solomon & Vanapalli, 2014; Vanapalli, Van den Ende, Duits, & Mugele, 2007). The measurement model of the parallel laminar flow is a powerful method for accuracy and precision viscosity detection. Here we used specific microgeometry to create the blood viscosity measurements with artery pulsatility flow which are simulated by systolic blood viscosity (SBV) and diastolic blood viscosity (DBV) (Han et al., 2019). That provide the in-vitro evaluation system to observing the variable of blood viscosity, one of shear rate conditions, in particular vessel wall for stroke risk factor. An available quantify viscosity could help the clinical research to construct the threshold of blood viscosity for the prediction platform, that would be capable for evaluating the people's risk of stroke.

2.1 Parallel laminar flow microchip system

The experimental setup involves a two-phase fluid system where both the experimental sample and reference fluid are simultaneously injected into the chip, shown in Fig. 1(a). The flow input condition is provided by an input system which is consist of two syringe pump (KDS 1000, KD scientific, USA) and two flowmeters (FLU-M, FLUIGENT, France). The flow observation is relying on the inverted microscopy (CKX53, Olympus, Japan). The precision of the fluid control system is pivotal as any error directly impacts the measurement accuracy of the experimental platform. Consequently, the meticulous construction and rigorous verification of the system play a crucial role in ensuring the reliability and accuracy of the experimental results. An alignment standard relies on the vibration viscometer (VM-10L, SEKONIC, Japanese) and Rheometer (AR2000, TA Instrument, UK).

2.2 Sources of Newtonian and non-Newtonian flow sample in the experiments

The shear thinning and viscoelasticity of blood in viscoelasticity flow are closely related to its microscopic structure (Perktold, Peter, Resch, & Langs, 1991). The Aqueous Xanthan gum solutions have proven to be one of the more successful blood analog fluids (Brookshier & Tarbell, 1993). We prepare Xanthan gum (X0048, MP SINGAPORE, Singapore) solution as our non-Newtonian sample. A concentrated solution of glycerin (Glycerol, Merck, USA) and artificial blood dry (SIMULATED BLOOD, VATA, USA) in water was used as the Newtonian control fluid.

Table 1

the character of Newtonian sample
	Viscosity (cP)	Density (kg/m³)
Glycerin	5.21	1270
Artificial blood dry	4.9	1100

2.3 Chip design and fabrication

A polydimethylsiloxane (PDMS) Sylgard 184 silicone elastomer base and Sylgard 184 elastomer curing agent were purchased from Dow Corning Corporation, USA. The silicone mold is fabricated based on conventional photolithography techniques. Using spin-coating to create the mold of 50µm height of design pattern on a silicon wafer by negative photoresist (SU-8 3050, Kayaku Advanced Materials, MA, USA). The microfluidic chip is replica with a typical soft lithography technique. A polydimethylsiloxane (PDMS; Sylgard 184 A/B, Dow Corning, Corporation, USA) was irrigated to the silicon mold with typical mixing ratio of Sylgard 184 A and B. The mold was degassed in a vacuum desiccator until all air bubbles were removed, then cured in an isothermal oven for 24 hours at room temperature. The PDMS slab was bonded to a flat glass slide after via oxygen plasma treatment that was for 3 min, and then the microfluidic chip was put under a hydraulic press to insure complete sealing. The microfluidic chip consists of 50 microarray channels; each channel is 50 µm in height, 100 to 250 µm in width, and 500 to 4500 µm in length, shown in Fig. 1(b). The microarray channels are providing specific shear rate design to simulate the systolic and diastolic blood flow condition which is representation the artery pulsatility flow in vessel environment (Han et al., 2019). More experimental chips are easily presenting the viscosity value by counting the number of channels filled.

2.4 Principle of viscosity measurement

In viscosity measurement, a two-phase laminar flow microfluidic chip was designed for measuring viscosity of fluid. Initial step, two fluids are injected into the wafer at the same time as the Fig. 1(a). One fluid with a known viscosity is injected into the wafer and named the reference fluid; the other fluid which is our sample has an unknown viscosity and named the sample fluid for experimental measurement. Fluids are parallelly flowing to measurement zone. Two different function design zone on microfluid chip which are transient are and microarray area. The sample and reference fluid are inputted to transient area of chip with specific flow conditions, Q_s and Q_r. The fluid resistance of microarray area (R_m) is designed much large than transient area (R_t), that is designed as R_m$\gg$ R_t. The flow resistance is followed by the Poiseuille's law as shown in Fig. 1(b).

the resistance of each channel is calculated using Eqs. (1) and (2).

$${R}_{t}=\frac{12\bullet \mu \bullet {L}_{t}}{{w}_{t}{h}^{3}}\bullet {(1-\frac{192}{{\pi }^{5}} \frac{h}{w}\sum _{n=\text{1,3},5\dots }^{\infty }\text{t}\text{a}\text{n}\text{h}\left(\frac{n\pi \omega }{2h}\right))}^{-1}$$

$${R}_{m}=\frac{12\bullet \mu \bullet {L}_{m}}{{w}_{m}{h}^{3}N}\bullet {(1-\frac{192}{{\pi }^{5}} \frac{h}{w}\sum _{n=\text{1,3},5\dots }^{\infty }\text{t}\text{a}\text{n}\text{h}\left(\frac{n\pi \omega }{2h}\right))}^{-1}$$

where ${R}_{t}$ is the flow resistance of transient area, ${R}_{m}$ is the flow resistance of microarray area, $\mu$ is the viscosity of fluid which passes through the transient area, ${L}_{t}$ and ${L}_{m}$ are the length of transient area and microarray area, ${w}_{t}$ and ${w}_{m}$ are the width of transient area and microarray area, N is the number of microarray channel, $h$ is the height of channel of chip. Under the condition that the two-phase flow was fully developed, then the reference and sample fluids can be flown into the microarray channels with fully developed flow phenomena. The pressure variable is described as:

$$\varDelta {P}_{s}={Q}_{s}\bullet {R}_{s}$$

$$\varDelta {P}_{r}={Q}_{r}\bullet {R}_{r}$$

The pressure drops of each microarray channel of the sample and reference fluids are the same, and the fluids can meet the same pressure drop (ΔP_s = ΔP_r), which is determined by the hydraulic pressure as shown in Fig. 2.

Based on hydraulic analogy analysis, the pressure drop relationship of fluid can be derived with the equity of pressure drop, ΔP_s = ΔP_r. Therefore, the relationship of viscosity is representing as:

$${\mu }_{s}=\frac{{Q}_{r}}{{Q}_{s}}\bullet {\mu }_{r}\bullet \frac{{R}_{s}}{{R}_{r}}$$

Variable in the relative concentrations of the reference fluid and sample fluid will affect the interface position of the two-phase flow. The expression method of the interface position can be simplified by the number of channels in the index flow channel area, so the calculation method of viscosity measurement can be simplified as:

$${\mu }_{s}=\frac{{Q}_{r}}{{Q}_{s}}\bullet {\mu }_{r}\bullet \frac{{N}_{s}}{{N}_{r}}$$

The viscosity of sample can be evaluated by the Eq. (6), based on observing the channel numbers on microarray chip.

2.5 Microarray design

In this experiment, the different microarray design were used to simulate the fluid behavior of blood transporting in the vessel to provide the different shear rate. The blood viscosity is altering when the nonnewtonian fluid pass through the microarray channel. Defined the different shear rate for different application is essential specially at blood viscosity measurement. A regular flow channel is proposed to describe the shear by Yang group (Hardeman, Goedhart, & Shin, 2007; Kang & Yang, 2013).

$$\dot{\gamma }=\left(\frac{6{Q}_{s}}{{w}_{m}{h}^{2}{N}_{s}}\right)\left(\frac{\alpha }{\beta }\right)$$

$$\alpha =1-\frac{192}{{\pi }^{5}} \frac{h}{{w}_{m}}\sum _{n=\text{1,3},5\dots }^{\infty }\frac{1}{{n}^{5}}\text{tanh}\left(\frac{n\pi {w}_{m}}{2h}\right)$$

$$\beta =1-\frac{16}{{\pi }^{3}} \frac{h}{{w}_{m}}\sum _{n=\text{1,3},5\dots }^{\infty }\frac{1}{{n}^{3}}\text{t}\text{a}\text{n}\text{h}\left(\frac{n\pi {w}_{m}}{2h}\right)$$

The shear rate is defined by some factors which are the delivery flow rate of the sample fluid is represented by Q_s, the number of microarray channels filled with the sample fluid is represented by Ns, and the width of the indicator channel is represented by Wm and the flow channel height is represented by h. Followed by the description of flow resistance and shear rate of microarray. the high influence of factors is proposed as:

$$\frac{{R}_{m}}{{R}_{t}}\propto \frac{{L}_{m}}{{w}_{m}}\times \frac{{w}_{t}}{{L}_{t}}$$

The parameter design of physical description of measurement applications is concluded at Eq. (10). The $\frac{{R}_{m}}{{R}_{t}}$ is indicative of evaluating situation of full development flow of nonnewtonian fluid. The $\frac{{w}_{t}}{{w}_{m}}$ and $\frac{{L}_{m}}{{L}_{t}}$ is the factor that we create the geometry model of blood vessel.

3.1 Physical numerical simulating result

The analysis of theoretical and physical models involves utilizing the versatile COMSOL Multiphysics 6.1 universal engineering CAE simulation software platform. The finite element method is particularly advantageous for handling irregular shapes, intricate physical couplings, and complex boundary conditions, making it well-suited for studying the rheological state and flow field of non-Newtonian fluids, such as blood, under a variety of fluid boundary conditions. Given the intricate nature of blood as a non-Newtonian fluid, numerous models have been proposed in the field of blood research. These models, including Carreau-Yasuda, Casson, and Power law, aim to accurately predict the relationship between sample viscosity and shear rate. Among these, the power law model has been selected for its ability to provide a high degree of freedom in describing fluid behavior within specific shear rate ranges. The model's parameters can be adeptly fitted by using MATLAB, enhancing the precision of predictions regarding blood viscosity, and enabling more accurate analyses of blood flow dynamics in microarray chip design.

We used the power law to construct the blood behavior model and simulate the two-phase flow of sample with reference fluid. The interface distribution of two fluid is provide a verify method for model accuracy by Eq. (6). The blood viscosity database is referred to E. WELLS group (Wells & Merrill, 1962). They contribute the blood viscosity variable of shear rate and hematocrit. The hematocrit blood viscosity model is built by Power law fitted, shown as the Fig. 3.

The R square value was used to evaluate the appropriate K and n values. Only results with R square above than 0.95 would be adopted, as shown in Fig. 4.

The hematocrit levels considered within the healthy normal range are typically 40–54% for adult men and 35–48% for adult women. In numerical simulation, a hematocrit value of 40% is selected as the standard for viscosity simulation samples. The microarray chip is systematically divided into distinct functional areas: the flow channel entrance area, confluence area, measurement area, and flow channel exit area, as illustrated in Fig. 5. Those used parameters and settle boundary conditions are listed on Table 2. Following the injection of the fluid into the channel during the simulation, each of these functional areas is precisely defined to establish specific physical boundary condition settings by COMSOL.

Table 2

The boundary condition and explanation of geometry channel
Function design		Setting condition
$\alpha$	Inlet flow Zone	Flow Model	Laminar flow
$\beta$	Parallel flow Zone	Inlet velocity	10 ($\mu$l/min)
$\gamma$	Measurement Zone	Outlet pressure	0 (Pa)
$\delta$	Outlet flow Zone	A	Inlet of reference fluid
		B	Inlet of simulation blood
		C	Outlet of pipe geometry
		a	Width of Inlet flow Zone
		b	length of Parallel flow Zone
		c	Length of Outlet flow Zone

Table 3

Microarray parallel flow zone and flow channel outlet area length design, unit: mm
Design regions	a	b	c
Initial	6	1.29	3.5
$\varvec{\beta }1$	6	3	3.5
$\varvec{\beta }2$	6	4.5	3.5
$\varvec{\delta }1$	6	1.29	7
$\varvec{\delta }2$	6	4.5	7

The fully development flow of nonnewtonian fluid is estimated as laminar flow from the simulation (Poole & Ridley, 2007). The fluid is in a fully developed flow state while flowing in the microarray channel. Therefore, the 2D physical module is adopted for calculation of a lesser number of elements. Beyond alterations in the flow field, a critical focus of observation lies on the interface between the sample and the reference fluid. The parallel zone of the initial design steam line shows the fluid is squeezed from the distribution and slope of the steam line, as shown in Fig. 6(a), which represents the lack of the distance of the parallel zone in original design. The interface of two fluid is locating at the parallel zone of top of chip, which is the reference fluid side, as shown in Fig. 6(b). Following Eq. (6), both fluids are injected under identical flow rate conditions, resulting in the high-viscosity fluid occupying a larger area compared to the other. According to Eq. (6), the reference and sample fluids occupy within 19% and 81% of the microarray channel, respectively, in line with the simulation results.

Based on the β2 and δ1 design, parameters b and c contribute to a low variability and high stability flow field respectively in lengthening the parallel flow and the outlet flow zone. Here, the specific parameter designs from β2 and δ1 to δ2, are shown in Fig. 7(a). We observe flow circuit connected in series at these designs, which is expected to provide a well-stabilized fully developed flow condition in the measurement zone. A Simulation result, from Fig. 7(b), demonstrate that the changing parts simultaneously does not have a mutual influence, each contributing to a stable flow field in their respective b and c region.

Following the numerical result of various functional zone designs described as Fig. 7, the $\delta 2$ design has been identified as offering a smooth and minimal squeezing phenomenon in the confluence area, extending these benefits to the outlet end as well.

3.2 Implementation of measurement zone design of microarray chip

The viscosity measurement was conducted at a room temperature of 26.0 ± 0.5 degrees Celsius. The viscosity sample uses artificial blood dye and is a Newtonian fluid with a viscosity of 4.8 cP. Investigating flow rate ratio is crucial to accuracy of the system for fluid dynamics and flow behaviors within our experimental setup (B. J. Kim et al., 2017). The flow ratio 1 to 7 was used as our operation parameter to find the optimizing solution. We proposed five chip design, different geometry structure of channels in the measurement zone, derived from the formula Eq. (10). The flow resistance ratio between the confluence and measurement zones is altered while keeping the length ratio fixed, by fine-tuning the width. Four different designs (Design X₁ to X₄) are proposed to simulate different diameter vessel flow resistance conditions and shear rates, as shown in Fig. 8(a). We focus on the flow resistance ratio from the parallel flow zone (R_t) to the measurement zone (R_m) under different width ratios, where a higher ratio indicates stronger resistance and shear rate at the measurement zone, affecting flow field stability. Furthermore, we fine-tune the length under fixed width ratios to simulate the impact of different lengths of vessel flow resistance. The flow resistance ratios provided by designs X₃ and Y₂ offer different $\frac{{R}_{m}}{{R}_{t}}$ values under the $\frac{{w}_{t}}{{w}_{m}}$ ratio, as shown in Fig. 8(b).

Through the experiment designs described above, the experiments have been conducted, that can be used to define the interface location between the reference and sample fluids via image analysis. Finally, the viscosity value can be converted by Eq. (6). By using fluids with known viscosities, the experimental results can be used to evaluate measurement errors with standard value. By applying experimental designs from Fig. 8, the experimental results are shown in Fig. 9.

As shown in Fig. 9(a), the adjusted W_m design has a different accuracy model of viscosity measurement that error comparing with standard value with − 2.3–1.36%. The model indicates that the X₂ design parameter minimizes errors within different flow ratio in the system. As shown in Fig. 9(b), the adjusted microgeometry length has enlarged system error from − 2.5% to -5.4%, which showed the result was underpriced.

A noteworthy observation from our findings is the inverse relationship between flow rate ratio and measurement stability, that higher ratios correlate with reduced stability and accuracy. Conversely, the flow ratio approaching one yield superior and more stable outcomes. Despite the proposed width of microarray channel in the design, discerning changes in accuracy model proves challenging. However, alterations in the length design significantly impact measured data across varying flow rate ratios, leading to values lower than the actual viscosity. We decide to use X₂ design of measurement zone and low flow ratio condition in implementation of Newtonian and Non-Newtonian measurement.

3.3 Implementation of Newtonian sample viscosity measurement

The samples were prepared from aqueous of glycerin in concentrations ranging from 10wt% to 40wt%. Concurrently, different flow ratio conditions were applied to have high accuracy measurement. The outcomes of measurement were concurrently compared with the conventional vibration viscometer, as illustrated in Fig. 10. The results of measuring viscosity in Newtonian fluids show that the traditional viscometer and the measurement from the designed chip have a high similarity trend in Newtonian test under the different flow ratio condition. The maximum difference between viscometer and the measurement from microarray chip is lower than 2% in flow ratio to be 1 and 3. In the Newtonian fluid tests, the operation flow rate condition would not significantly affect the accuracy and precision of microarray chip viscosity measurement.

3.4 Implementation of Non-Newtonian sample viscosity measurement

The blood sample by using xanthan gum aqueous, is patient pending as the non-Newtonian measurement. The rheometer is used as an alignment standard for non-Newtonian fluid measurements. The viscosity measurement shown in Fig. 11, can be used to compare the measurement difference between rheometer’s and microarray chips.

The measurement between rheometer and microarray chip also showed a very high similarity in this non-Newtonian test. The measurement difference between rheometer and microarray chip is lower than 5% in flow rate to be 1. The general blood flow velocity in the common carotid artery (CCA) for patient is 12.15 cm/s and 36.46 cm/s during diastole and systole (W. Lee, 2014). Along with the CCA reference diameter of the vessel, is used to calculate the shear rate(Meng et al., 2009). The shear rate was determined as follows (Cho & Kensey, 1991):

$$\dot{\gamma }=4\times \frac{V}{D}$$

where V and D are the fluid velocity and CCA diameter, respectively. Therefore, the shear rate is started from the 20s^-1 to the 100s^-1 as DBV and SBV. The measurement result with flow ratio to be 3, gave the unstable performance compared with the one by using other conditions. However, the trend of SBV and DBV is high simulating to rheometer. The operation flow rate condition is significantly affecting the accuracy and precision of microarray chip viscosity measurement in the non-Newtonian fluid tests.

By conducting viscosity measurements on the Newtonian and the non-Newtonian fluids, the concept of a resistance design method has been preliminarily validated, as the chip test demonstrated provided by the stable fully developed flow field. This lays the foundation for our chip to achieve good stability and accuracy when facing different characteristics of fluids. The microarray chip exhibits the performance as a rheometer in measurement within the blood viscosity spectrum (1-10cP). In the blood viscosity spectrum, the microarray chip avoids the physical obstacles of traditional viscometer which are the low torque that can be reliably measured, small sample volumes and interfacial artifacts. The non-Newtonian fluid sample tests resulted in measured SBV and DBV values of 6 cP and 11 cP, respectively. Notably, our approach requires less than 4% of the sample volume compared the rheometer for superior-quality testing. Employing specific shear rate designs, our in-vitro vessel simulating module accurately measures the blood viscosity. The clinical research indicates a correlation between blood viscosity and stroke. Illustration: the SBV and DBV measurement value is 4.45 cP and 13 cP but using a traditional viscometer. Our system reduces the measurement error of blood viscosity spectrum to 5% within low shear rate condition. These findings validate the effectiveness of our proposed in vitro blood viscosity measurement model. Furthermore, the high correlation between stroke and mean middle cerebral artery(mMCA) pulsatility index (PI), the stroke and mMCA PI are monitored by blood viscosity at the same time. We expect the platform to exhibit the potential to be a foundation for integrating more detection sensors of stroke factors. To complete the high specificity stroke prediction platform.

Author Contribution

Yii-Nuoh Chang is MS graduate student, who conduct the experiments and data collection and analysis. He also prepared the draft for this manuscript. Dr. Da-Jeng Yao applied the research fund and guide Mr. Chang for the research. He also revised this manuscript and to be the corresponding author for submission into Microfluidics and Nanofluidics.

Arboix A (2015) Cardiovascular risk factors for acute stroke: Risk profiles in the different subtypes of ischemic stroke. World J Clin Cases: WJCC 3(5):418
Avan A, Digaleh H, Di Napoli M, Stranges S, Behrouz R, Shojaeianbabaei G, Spence JD (2019) Socioeconomic status and stroke incidence, prevalence, mortality, and worldwide burden: an ecological analysis from the Global Burden of Disease Study 2017. BMC Med 17(1):1–30
Baeckström P, Folkow B, Kendrick E, Löfving B, ÖBerg B (1971) Effects of vasoconstriction on blood viscosity in vivo. Acta Physiol Scand 81(3):376–384
Barstad RM, Roald HE, Cui Y, Turitto VT, Sakariassen KS (1994) A perfusion chamber developed to investigate thrombus formation and shear profiles in flowing native human blood at the apex of well-defined stenoses. Arterioscler Thrombosis: J Vascular Biology 14(12):1984–1991
Boehme AK, Esenwa C, Elkind MS (2017) Stroke risk factors, genetics, and prevention. Circul Res 120(3):472–495
Brookshier K, Tarbell J (1993) Evaluation of a transparent blood analog fluid: aqueous xanthan gum/glycerin. Biorheology 30(2):107–116
Cadroy Y, Horbett T, Hanson S (1989) Discrimination between platelet-mediated and coagulation-mediated mechanisms in a model of complex thrombus formation in vivo. J Lab Clin Med 113(4):436–448
Cho YI, Kensey KR (1991) Effects of the non-Newtonian viscosity of blood on flows in a diseased arterial vessel. Part 1: Steady flows. Biorheology 28(3–4):241–262
Feigin VL, Norrving B, Mensah GA (2017) Global burden of stroke. Circul Res 120(3):439–448
Fisher M, Meiselman HJ (1991) Hemorheological factors in cerebral ischemia. Stroke 22(9):1164–1169
Furukawa K, Abumiya T, Sakai K, Hirano M, Osanai T, Shichinohe H, Houkin K (2016) Increased blood viscosity in ischemic stroke patients with small artery occlusion measured by an electromagnetic spinning sphere viscometer. J Stroke Cerebrovasc Dis 25(11):2762–2769
Grotemeyer KC, Kaiser R, Grotemeyer K-H, Husstedt IW (2014) Association of elevated plasma viscosity with small vessel occlusion in ischemic cerebral disease. Thromb Res 133(1):96–100
Gupta S, Wang WS, Vanapalli SA (2016) Microfluidic viscometers for shear rheology of complex fluids and biofluids. Biomicrofluidics, 10(4)
Gyawali P, Lillicrap TP, Esperon CG, Bhattarai A, Bivard A, Spratt N (2023) Whole Blood Viscosity and Cerebral Blood Flow in Acute Ischemic Stroke. Paper presented at the Seminars in Thrombosis and Hemostasis
Gyawali P, Lillicrap TP, Tomari S, Bivard A, Holliday E, Parsons M, Spratt N (2022) Whole blood viscosity is associated with baseline cerebral perfusion in acute ischemic stroke. Neurol Sci, 1–7
Han JE, Kim T, Park JH, Baik JS, Kim JY, Park JH, Yu H-J (2019) Middle cerebral artery pulsatility is highly associated with systemic blood viscosity in acute ischemic stroke within 24 hours of symptom onset. J Neurosonology Neuroimaging 11(2):126–131
Hardeman M, Goedhart P, Shin S (2007) Methods in hemorheology. Handbook of Hemorheology and Hemodynamics. In: IOS
Kang YJ, Lee S-J (2018) In vitro and ex vivo measurement of the biophysical properties of blood using microfluidic platforms and animal models. Analyst 143(12):2723–2749
Kang YJ, Yang S (2013) Integrated microfluidic viscometer equipped with fluid temperature controller for measurement of viscosity in complex fluids. Microfluid Nanofluid 14:657–668
Katan M, Luft A (2018) Global burden of stroke. Paper presented at the Seminars in neurology
Kim BJ, Lee SY, Jee S, Atajanov A, Yang S (2017) Micro-viscometer for measuring shear-varying blood viscosity over a wide-ranging shear rate. Sensors 17(6):1442
Kim H, Cho YI, Lee D-H, Park C-M, Moon H-W, Hur M, Yun Y-M (2013) Analytical performance evaluation of the scanning capillary tube viscometer for measurement of whole blood viscosity. Clin Biochem 46(1–2):139–142
Kim T, Oh J, Han JE, Park JH, Baik JS, Kim JY, Kim E-G (2020) The relationship between changes in systemic blood viscosity and transcranial Doppler pulsatility in lacunar stroke. J Neurosonology Neuroimaging 12(2):67–72
Lee AJ, Mowbray PI, Lowe GD, Rumley A, Fowkes FGR, Allan PL (1998) Blood viscosity and elevated carotid intima-media thickness in men and women: the Edinburgh Artery Study. Circulation 97(15):1467–1473
Lee W (2014) General principles of carotid Doppler ultrasonography. Ultrasonography 33(1):11
Letham B, Rudin C, McCormick TH, Madigan D (2015) Interpretable classifiers using rules and bayesian analysis. Building a better stroke prediction model
Lowe G, Lee A, Rumley A, Price J, Fowkes F (1997) Blood viscosity and risk of cardiovascular events: the Edinburgh Artery Study. Br J Haematol 96(1):168–173
Meng W, Yu F, Chen H, Zhang J, Zhang E, Dian K, Shi Y (2009) Concentration polarization of high-density lipoprotein and its relation with shear stress in an in vitro model. BioMed Research International, 2009
Perktold K, Peter RO, Resch M, Langs G (1991) Pulsatile non-Newtonian blood flow in three-dimensional carotid bifurcation models: a numerical study of flow phenomena under different bifurcation angles. J Biomed Eng 13(6):507–515
Poole R, Ridley B (2007) Development-length requirements for fully developed laminar pipe flow of inelastic non-Newtonian liquids
Rosenson RS, Mccormick A, Uretz EF (1996) Distribution of blood viscosity values and biochemical correlates in healthy adults. Clin Chem 42(8):1189–1195
Sakariassen KS, Houdijk WP, Sixma JJ, Aarts PA, de Groot PG (1983) A perfusion chamber developed to investigate platelet interaction in flowing blood with human vessel wall cells, their extracellular matrix, and purified components. J Lab Clin Med 102(4):522–535
Sakariassen KS, Orning L, Turitto VT (2015) The impact of blood shear rate on arterial thrombus formation. Future Sci OA, 1(4)
Solomon DE, Vanapalli SA (2014) Multiplexed microfluidic viscometer for high-throughput complex fluid rheology. Microfluid Nanofluid 16:677–690
Somer T, Meiselman HJ (1993) Disorders of blood viscosity. Ann Med 25(1):31–39
Song SH, Kim JH, Lee JH, Yun Y-M, Choi D-H, Kim HY (2017) Elevated blood viscosity is associated with cerebral small vessel disease in patients with acute ischemic stroke. BMC Neurol 17(1):1–10
Tamariz LJ, Young JH, Pankow JS, Yeh H-C, Schmidt MI, Astor B, Brancati FL (2008) Blood viscosity and hematocrit as risk factors for type 2 diabetes mellitus: the atherosclerosis risk in communities (ARIC) study. Am J Epidemiol 168(10):1153–1160
Tikhomirova IA, Oslyakova AO, Mikhailova SG (2011) Microcirculation and blood rheology in patients with cerebrovascular disorders. Clin Hemorheol Microcirc 49(1–4):295–305
Vanapalli S, Van den Ende D, Duits M, Mugele F (2007) Scaling of interface displacement in a microfluidic comparator. Appl Phys Lett, 90(11)
Wang YI, Abaci HE, Shuler ML (2017) Microfluidic blood–brain barrier model provides in vivo-like barrier properties for drug permeability screening. Biotechnol Bioeng 114(1):184–194
Wells RE, Merrill EW (1962) Influence of flow properties of blood upon viscosity-hematocrit relationships. J Clin Investig 41(8):1591–1598
Zilberman-Rudenko J, White RM, Zilberman DA, Lakshmanan HH, Rigg RA, Shatzel JJ, McCarty OJ (2018) Design and utility of a point-of-care microfluidic platform to assess hematocrit and blood coagulation. Cell Mol Bioeng 11:519–529

No competing interests reported.

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Microfluidic Platform for Stroke Risk Prediction: Evaluation of Blood Viscosity by Shear Rate Variations

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Abstract

Figures

1. Introduction

2. Material and methods

2.1 Parallel laminar flow microchip system

2.2 Sources of Newtonian and non-Newtonian flow sample in the experiments

2.3 Chip design and fabrication

2.4 Principle of viscosity measurement

2.5 Microarray design

3. Results and discussion

3.1 Physical numerical simulating result

3.2 Implementation of measurement zone design of microarray chip

3.3 Implementation of Newtonian sample viscosity measurement

3.4 Implementation of Non-Newtonian sample viscosity measurement

4. Conclusion

Declarations

Author Contribution

References

Additional Declarations

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Function design		Setting condition
\(\alpha\)	Inlet flow Zone	Flow Model	Laminar flow
\(\beta\)	Parallel flow Zone	Inlet velocity	10 (\(\mu\)l/min)
\(\gamma\)	Measurement Zone	Outlet pressure	0 (Pa)
\(\delta\)	Outlet flow Zone	A	Inlet of reference fluid
		B	Inlet of simulation blood
		C	Outlet of pipe geometry
		a	Width of Inlet flow Zone
		b	length of Parallel flow Zone
		c	Length of Outlet flow Zone

Design regions	a	b	c
Initial	6	1.29	3.5
\(\varvec{\beta }1\)	6	3	3.5
\(\varvec{\beta }2\)	6	4.5	3.5
\(\varvec{\delta }1\)	6	1.29	7
\(\varvec{\delta }2\)	6	4.5	7