Ultrasound-based Control of Micro-Bubbles for Exosome Delivery in Treating COVID-19 Lung Damage
Bruna Fonseca, Caio Fonseca, Michael Barros, Mark White, Vinay Abhyankar, David A. Borkholder, Sasitharan Balasubramaniam
UUltrasound-based Control of Micro-Bubbles forExosome Delivery in Treating COVID-19 LungDamage
Bruna Fonseca , ∗ Caio Fonseca , ∗ Michael Barros , ‡ Mark White , † Vinay Abhyankar , § David A. Borkholder , § Sasitharan Balasubramaniam ∗∗ Telecommunication Software & Systems Group, Waterford Institute of Technology, Waterford, Ireland † Research, Innovation & Graduate Studies, Waterford Institute of Technology, Waterford, Ireland ‡ School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK § Kate Gleason College of Engineering, Rochester Institute of Technology, Rochester, USAEmails: [email protected], [email protected], [email protected], [email protected],[email protected], [email protected], [email protected]
Abstract —The recent COVID-19 pandemic has resulted in highfatality rates, especially for patients who suffer from underlyinghealth issues. One of the more serious symptoms exhibited frompatients suffering from an acute COVID-19 infection is breathingdifficulties and shortness of breath, which is largely due to theexcessive fluid (cellular leakage and cytokine storm) and mucoiddebris that have filled lung alveoli, and reduced the surfactanttension resulting in heavy and stiff lungs. In this paper we proposethe use of micro-bubbles filled with exosomes that can be releasedupon exposure to ultrasound signals as a possible rescue therapyin deteriorating COVID-19 patients. Recent studies have shownthat exosomes can be used to repair and treat lung damage forpatients who have suffered from the viral infection. We haveconducted simulations to show the efficacy of the ultrasoundsignals that will penetrate through layers of tissues reachingthe alveoli that contains the micro-bubbles. Our results haveshown that ultrasound signals with low frequencies are requiredto oscillate and rupture the polymer-based micro-bubbles. Ourproposed system can be used for patients who require immediaterescue treatments for lung damage, as well as for recoveredpatients who may suffer from viral relapse infection, where themicro-bubbles will remain dormant for a temporary therapeuticwindow until they are exposed to the ultrasound signals.
Index Terms —COVID-19, Ultrasound Communication,Polymer-based Encapsulated Micro-bubbles.
I. I
NTRODUCTION
The recent COVID-19 pandemic has resulted in many newchallenges for humanity in the 21st century. According to theWorld Health Organization (WHO), more than a 100 millionpeople worldwide have been infected with the SARS-CoV-2virus, and unfortunately 2.2% of those infected have resultedin death, this mortality rate was obtained from the number ofconfirmed deaths divided by the number of confirmed casesfrom the data made available by the WHO in the time ofwriting this paper. While the virus does not have a high fatalityrate, the risk lies with people who have underlying healthcondition such as respiratory disorders and chronic diseases,as well as the elderly age group due to their frailty. During 2020, numerous studies have been carried out to gain moreknowledge about this unknown virus, and in particular inunderstanding its infection process, as well as novel treatmenttechniques.The research findings related to the infection process foundthat people infected with the SARS-CoV-2 virus can progressthrough four different stages of infection [1]. The first stageis described as the incubation period where the virus maynot be detected and the patient will not show any types ofsymptoms. The second stage is the period in which the patientwill start showing mild symptoms such as fever, malaise anddry cough, and during this stage the patients can be in thepulmonary phase infection period resulting in pneumonia andpulmonary inflammation. During this stage the virus can bedetected through testing [1]. The third stage is when the patientstarts to exhibit severe symptoms similar to Acute RespiratoryDistress syndrome (ARDS) with a high viral load [1]. Thefourth and final stage is when the patient start to recover fromthe infection and is highly dependent on their immune systemresponse and the treatments that have been applied.When the immune system does not react positively againstthe virus, it starts to proliferate throughout the body, affectingtissues and causing destruction. This is especially the case forcells that have high concentrations of Angiotensin-ConvertingEnzyme 2 (ACE2), which can be found in certain cells withinthe lungs, kidney, intestine, arteries and heart [2]. The damagedue to the inflammatory process in the lungs can result in life-threatening respiratory disorders and extraordinary therapeuticefforts are required to suppress the inflammation and repair thelungs tissues in a timely manner. For this reason, a numberof research challenges have been dedicated towards noveltreatments that can suppress the inflammatory process due tothe infection.Recent studies have shown that the Mesenchymal StemCells - Derived Exosomes (MSC-EXOS) presents the same a r X i v : . [ phy s i c s . m e d - ph ] F e b herapeutic benefits of MSCs [3] and can be used for thetreatment of lung damages resulting from the SARS-CoV-2infection due to their immunomodulatory functions that can beused for organs repair [4]. These extracellular vesicles need tobe carefully isolated and stored in special mediums duringthe treatment process, and further studies are required todetermine optimal dosage of growth factor production duringthe treatment. In particular, an outstanding research issue isthe ability to dynamically control the release depending onthe condition of the damaged tissue.This paper proposes a novel solution for an external devicethat can be used for automated control of MSC-EXOS releaseto treat patients that are suffering from lung damage due to theCOVID-19 infection. This automated mechanism is based onpolymer-based encapsulated micro-bubbles that houses MSC-EXOS that are placed in the lungs and remain temporarilyon a state of dormancy, where there is a therapeutic windowbefore they are removed by biological actions of the lungs [5],until it is activated to be released for treatment. Our proposedsolution is illustrated in Fig. 1, where the ultrasound sourcewill emit ultrasound waves that will travel deep into the lungsto break the micro-bubbles to release the extracellular vesicles.Our aim is to enable typical wireless body area networks tointerface the micro-bubbles within the body that can controlfunctions [6][7][8]. Besides applying this system to patientswho are placed on a ventilator, it can also be used for patientswho have recovered from COVID-19, but still remain viralpositive with a possibility of infection relapse. In the lattercase, the micro-bubbles will remain on a temporary therapeuticwindow of dormancy in the lungs, and once they are requiredto release MSC-EXOS, an ultrasound signal can be appliedand this could be from a portable device such as mobilephone [9]. Simulation has been conducted to demonstrate theconcept and in particular on the efficiency of the ultrasoundsignals that are emitted to break the micro-bubble to releasethe therapeutic molecules. Our simulation results have foundthat the ultrasound signal intensity and frequency required ishighly dependent on the materials within the alveoli region(e.g., the damage in the lungs that results excessive fluid ofmucoid debris (lung edema) caused by the damage).The paper is organized as follows: Section II presents theoverall system model of the ultrasound that is used to controlthe breakage of the micro-bubbles. Section III investigatesthe ultrasound propagation model, considering the attenuation,reflection, and refraction from the different tissue mediums andthis impacts on ultrasound intensity. Section IV presents thepolymer-based encapsulated micro-bubbles and the models forradius and its impact on a force applied leading to breakage.Section V presents the simulation results, while section VIpresents the conclusion.II. U LTRASOUND C ONTROL BASED M ICRO - BUBBLE S YSTEM A RCHITECTURE
Fig. 2 illustrates our proposed Ultrasound Control basedMicro-bubbles system for controlling the release of the ex-osomes for the tissue repair due to the COVID-19 infec-
Ultrasonic System
Micro-bubble after Ultrasonic StimulationMSC-EXOS released after Rupture of Micro-bubble Polymeric Shell LungsDamaged Tissue
Fig. 1: Ultrasound Control of micro-bubble containing exo-somes. The ultrasound signals will break the micro-bubbles torelease the exosomes.
Absorption, CavitationReflection AttenuationScatteringMicro-bubbles Alveoli Bronchiole
Skin
SubcutaneousFatPectoralisMuscles
Lung
Rib CageBlood VesselsPleural Space
Fig. 2: Diagram representing ultrasound propagation throughthe tissue layers reaching the micro-bubbles within the Alveoliand Bronchiole.tion. The ultrasound transducers are placed externally on thechest of the patients, where it emits ultrasound signals thatpenetrate through layers of tissue to reach previously placedmicro-bubbles. The ultrasound signals will travel throughthe adipose tissue, pectoralis muscle, pleural space, as wellas gaps between the rib cage, and then through the lungtissue to reach the alveoli region where the micro-bubbles arelocated. Patients can inhale the micro-bubbles containing theexosomes that propagate through the Bronchial tubes reachingand ultimately residing in the vicinity of the alveoli air sacks.The inhalation process can be assisted through the use ofthe ventilators in endotrachial intubated patients who are ina critical condition. The micro-bubbles are made of polymermaterial that breaks upon the exposure to sound waves whichhas been previously observed in [10][11][12][13].n this paper, we will focus mainly on two aspects of theoverall system: the intra-body ultrasound propagation modelas well as the micro-bubbles breaking process. The externaltransducer that will generate and radiate the ultrasound signalsas well as the inhalation process of the micro-bubbles areoutside the scope of this paper. Our main objective with thefollowing models is to evaluate and quantify the micro-bubblesbreakage process in order to release the exosomes for thetreatment.III. I
NTRA - BODY U LTRASOUND P ROPAGATION M ODEL
Ultrasound technology has a number of attractive prop-erties that make them ideal for medical applications. Thisincludes their radiation signals that do not cause hazards tothe body if they are below certain recommended intensity, andcan be integrated into compact low-cost miniature transducerdevices that can be easily used. The ultrasound parametersthat can be measured such as the signal speed, attenuation,acoustic impedance, and dispersion, can be used and designedappropriately to match different tissue characteristics [14].According to the United States - Food and Drug Adminis-tration (US-FDA), the highest known acoustic field emissions I SP T A. for diagnostic ultrasound devices used for peripheralvessels is I SP T A. = 720 mW/cm , for the Cardiac regionis I SP T A. = 430 mW/cm , for Fetal Imaging & Otheris I SP T A. = 94 mW/cm , and for Ophthalmic usage is I SP T A. = 17 mW/cm [15].The main challenge for our proposed system is to optimizethe ultrasound acoustic pressure that is sufficient enough toreach deep into the alveoli region where the micro-bubbles areresiding and to break them in order to activate the treatment.We, thereafter, need to consider the ultrasound attenuationcaused by the tissue layers that will act as the pathway wherethe signal will propagate, as well as the acoustic properties ofeach tissue such as the density, speed of sound and attenuationcoefficient. At the same time, we also need to consider othermaterials that will reside in the alveoli due to the lung damageas this will also affect the signal attenuation. A. Attenuation
Ultrasound signals will face attenuation as they propagatethrough biological tissue due to the absorbance of energy bythe fluids, cells composition and structure of different tissuelayers. The attenuation affects higher frequency transmissions,thus resulting in the signal travelling lower distances in thetissue [14]. As shown in Fig. 2, the signals will face a numberof effects, and this includes reflection and attenuation as thesignals propagate between the different impedance level ofeach tissue layer.The attenuation model for the ultrasound signal, which getsattenuated due to the frequency and distance, is representedas follows [14][16]: I d = I s − ( αfd ) , (1)where I d and I s are the ultrasound intensity levels at a distance d from a source s , respectively, α is the attenuation coefficient of the various tissue type, f is the ultrasound wave frequency,and d is the distance between the source and the target micro-bubbles.For the micro-bubbles radius oscillation model developed byHoff, which will be explained in section IV in this article, heused the acoustic pressure as excitation for the micro-bubblesinstead of the ultrasound intensity, and this pressure can beexpressed as Eq. (2) [16], where P d is the pressure at a distance d from a source s , I d is the intensity at the same distanceand Z medium is the acoustic impedance of the medium wherethe micro-bubble is inserted which can be calculated from theproduct between the density ( ρ ) and the speed of sound ( c ) ofthe medium. P d = ( I d Z medium ) , (2)Both Eq. (1) and Eq. (2) are going to be used for the simulationprocess of the ultrasound intensity attenuation through thetissue layers and to determine the acoustic pressure reachingthe micro-bubbles at the specified distance within the lungsrespectively. B. Reflection
As the ultrasound signals penetrate through the different lay-ers of the tissue, it will encounter different acoustic impedance,which can contribute to the reflection of the signals as well.Based on two materials Z and Z with incident ultrasoundsignal I s and the received signal I r at distance d , we canrepresent this relationship as [16] I r I s = ( Z − Z ) ( Z + Z ) . (3)However, we are interested in the transmitted ultrasoundsignal I t , that will continue propagating through the differenttissue layers eventually reaching the micro-bubbles, and it canbe obtained from Eq. (3) as follows [16]: I t = 1 − I r I s = 4 Z Z ( Z + Z ) . (4)IV. P OLYMER -B ASED M ICRO - BUBBLES FOR E XOSOMES E NCAPSULATION
Over the years numerous studies have been carried outto incorporate drugs and therapeutic molecules within theencapsulated micro-bubbles to help with different treatmentsthat require targeted drug-delivery [17] [10] [18]. For example,micro-bubbles with 0.1 µm to 10 µm diameter, have been usedas ultrasound contrast agents (UCAs) to improve the qualityof the images that use ultrasound technologies. The mostcommon approach used for micro-bubble based drug-deliveryis by either attaching or inserting the substances into theencapsulating shells (this could be in a two-coated shell).However, our application requires that we rupture and breakthe micro-bubble, which will have the shell’s thickness asone of the main factors to overcome, and so adding anotherlayer, a viscoelastic layer for example, to the shells will limitthe micro-bubble’s oscillation amplitude, which makes the SC-EXOS + HydrogelGaseous Void
Polymeric Shell
Fig. 3: Polymer-based micro-bubble for MSC-EXOS encapsu-lation with a gaseous nucleus.rupture process very difficult. In particular, when we focus onthe release of the exosomes at low acoustic amplitude [19].Therefore, an alternative solution is to insert the exosomes,contained in the hydrogel fluid, into the gaseous nucleus ofthe micro-bubble, as illustrated in Fig. 3, which in turn willtransform it into an anti-bubble .According to Kotopoulies et al. [19], the dynamics of theanti-bubble is similar to the dynamics of a gas bubble if theliquid core is less than 50% of the micro-bubble’s diameter[19]. This way, we can use a simplified version of Church’smodel [20] that was developed by Hoff et al. [11] for theoscillations of polymeric micro-bubbles considering the effectsof structure that utilizes an encapsulating shell. The oscillationbehaviour of the micro-bubble is represented as: ρ L (cid:18) ¨ R R + 32 ˙ R (cid:19) = p ge (cid:18) R e R (cid:19) κ − p ∞ ( t ) − µ L ˙ R R − µ S R e d Se R ˙ R R − G s R e d Se R (cid:18) − R e R (cid:19) , (5)where R and R are the inner and outer shell radii, ρ L isthe density of the surrounding liquid, p ge is the gas equilibriumpressure within the micro-bubble, R e and R e are the innerand outer shell radii at equilibrium, d Se is the shell thicknessat equilibrium, p ∞ ( t ) is the pressure in the liquid far fromthe micro-bubble, κ is the gas polythropic exponent, µ L isthe shear viscosity of the surrounding liquid, and µ S and G s are the shear viscosity and shear modulus, respectively, of thepolymer micro-bubble shell [11].The Eq. (5) is represented as a function of the inner andouter radii of the micro-bubble. However, Hoff modified Eq.(5) into a function of the outer radius R = R ( t ) . To do that,he used the following expression [11]: R e R ≈ R e R (cid:18) (cid:18) d Se R e − d S R (cid:19)(cid:19) ≈ R e R = R e R (6)Hoff also considered that at equilibrium, the pressure in the gasinside the bubble can be assumed to be equal to the hydrostaticpressure of the surrounding liquid ( p ge = p ) [11], whichmeans that there is no tension in the shell at equilibrium. In addition, he also considered that the pressure far from thebubble ( p ∞ ) is the sum of the surrounding liquid pressure p and the acoustic pressure that is being applied p i ( t ) [11].These assumptions resulted in Eq. (7), which represents themicro-bubble motion equation, described as follows: ρ L (cid:18) ¨ RR + 32 ˙ R (cid:19) = p (cid:18) R e R (cid:19) κ − p i ( t ) − µ L ˙ RR − µ S R e d Se R ˙ RR − G s R e d Se R (cid:18) − R e R (cid:19) (7)V. S IMULATIONS
A. Simulation Model
We developed a simulation model using MATLAB, wherewe simulate the ultrasound signal intensity that propagatesthrough various layers of the tissue between the chest andthe alveoli, and this includes the materials within the alveolithat resulted from the lung tissue damage, as well as signalthat penetrates through the micro-bubbles. In our simulationstudy, we consider five different layers of tissues, and thisincludes the skin with an average thickness of . mm [25],the subcutaneous fat tissue with a thickness of . mm [25], muscle tissue with a thickness of . mm [26], theconnective tissue with . mm , and finally the lung tissue with cm thickness [27]. The acoustic properties of each tissue arepresented in Table I. We know that for patients who suffer fromsevere COVID-19 infections, the lung characteristics changes,where the density is larger when compared to a healthy lung.This expansion is due to the amount of inflammation andmucus produced during the infection and this will depend onthe stage as well as the severity of the infection. In cases wherethe patients suffer from respiratory distress, this expansion canincrease considerably. B. Ultrasound Propagation through COVID-19 Damaged Tis-sue
To run the simulations we developed an algorithm on MAT-LAB using Eq.(1) - Eq.(4) and the results are shown on Fig. 4.In the simulation results presented in Fig. 4, we tested differentultrasound frequencies to see how it penetrates through varyingdistances. The simulation also includes materials from theCOVID-19 patients in the alveoli. The results show that lowfrequencies are able to penetrate towards the alveoli comparedto higher frequencies, which suffer from very high attenuationwith distance. All tissue layers caused ultrasound intensityattenuation and reflection, however the layers before the Lung(from the source to . cm on Fig. 4) presented a lowerreflection and the ultrasound signal did propagate through allof them up to the lungs. When the signal reached the lungs(after . cm of distance), it suffered higher absorption andreflection for all the applied frequencies.ABLE I: Acoustic Properties of Different Tissues and Materials used in the Simulations. Tissue/Material Density ( kg/m ) Speed of Sound ( m/s ) Acoustic Impedance x ( kg/sm ) Attenuation ( dB/cm/MHz ) Skin [21] 1090 1615 1.76 0.35Soft Tissue [21] 950 1478 1.404 0.48Connective Tissue [21] 1120 1613 1.806 1.57Muscle [21] 1050 1575 1.654 1.09Healthy Lungs [22] 180 650 [23] 0.177 5.66COVID-19 fluids and debris [22] 220 650 [23] 0.143 4.93PLGA [24] 1190 23260 2.76 0.42Water [14] 1000 1480 1.48 0.002
Distance from the Ultrasound Source (cm) U l t r a s ound I n t en s i t y ( m W / c m ) Ultrasound Intensity as a Function of Distance I I I I M i c r o - bubb l e M i c r o - bubb l e M i c r o - bubb l e M i c r o - bubb l e M i c r o - bubb l e Fig. 4: Ultrasound attenuation through all the tissue layers. I1represents the interface between the skin and soft tissue. I2 isthe interface between soft tissue and muscles. I3 represents theinterface between muscles and connective tissues. I4 representsthe interface between connective tissues to the Lung. Theposition of the micro-bubbles is also represented in the figure.Variation of frequency from 0.1 MHz to 5 MHz.
C. Micro-bubble Radius Oscillation Simulations
To perform the simulations of the micro-bubbles radiusoscillations, the MATLAB ”Bubblesim” package developedby Hoff was used. For our study, we considered a micro-bubble with a polymeric shell of poly(lactic-co-glycolic acid)(PLGA), which has a
Shear Modulus of M P a , and
ShearViscosity of P a − s [28] with nm of thickness and . µm for the outer radius. For the first simulations, we consideredthe gas inside the micro-bubble to have the same propertiesof the air, and that the micro-bubbles were inserted into theLung with the medium characteristics of COVID-19 Lungtissue. Fig. 5 shows the amplitude of the oscillations withrespect to variations in the ultrasound frequency and pressurefor a single micro-bubble. The results comply with ultrasoundpropagation behaviour in Fig. 4, where we find that at thevery low frequency of . M Hz we will obtain high radiusoscillations of the micro-bubbles. At this particular frequency,the amplitude is large enough to rupture the micro-bubble.While at . M Hz we observe small oscillations, that is notsufficient to break the micro-bubble.The same simulation process was done for the other ultra-sound intensity limits specified by the US-FDA ( mW/cm , R ad i u s [ m ] Time [s] x 10 -5 Bubble Radius x 10 -6 Bubble Radius x 10 -6 Bubble Radius x 10 -6 Bubble Radius x 10 -6 Bubble Radius x 10 -6 R ad i u s [ m ] x 10 -5 R ad i u s [ m ] x 10 -4 Bubble Radius x 10 -6 x 10 -6 Time [s] x 10 -6 x 10 -6 R ad i u s [ m ] R ad i u s [ m ] R ad i u s [ m ] Fig. 5: Simulation of micro-bubble excitation with respect tovarying ultrasound frequency and intensity. This simulationis for a single micro-bubble. The dashed lines represents theideal values and the red lines represents the simulated values. mW/cm and mW/cm ) to detect the numbers ofmicro-bubbles that will break within the lung. The simulationconsidered five micro-bubbles inside a lung infected withCOVID-19, from which the reference values can be observedin Table I. The micro-bubbles are placed at a distance of . cm from each other at the same depth. The results from thesimulations are shown in Fig. 6. For the intensity source of mW/cm , three micro-bubbles ruptured and two oscillatedwith the applied frequency of . M Hz and for the otherfrequencies, part of the micro-bubbles oscillated or did notsuffer any excitation. When the micro-bubbles were applied mW/cm , four micro-bubbles ruptured and one oscillatedwith . M Hz , one micro-bubble ruptured and the rest os-cillated with . M Hz . At the same time some oscillationswere observed and for other micro-bubbles no excitation wasobserved for the other frequencies. For the ultrasound intensityof mW/cm , the micro bubbles showed the same resultsobtained for mW/cm with frequencies of . M Hz and . M Hz , and for the other frequencies some micro-bubblesoscillated and some did not suffer excitation.VI. C
ONCLUSION
The COVID-19 pandemic has challenged researchers frommany disciplines to develop new and novel treatment solutions icro-bubbles Oscillations as a function of Frequency and Ultrasound Intensity
Frequencies N u m be r o f M i c r o - bubb l e s Rupture Oscillation No excitationIs = 94 mW/cm --Is = 430 mW/cm --Is = 720 mW/cm -- Fig. 6: Number of micro-bubbles that ruptured, oscillatedor didn’t suffer any excitation when exposed with differentfrequencies and ultrasound intensities.in order to rescue patients who are infected and seriouslycompromised with the virus. In this paper, we proposed anultrasound-based control of exosomes release from micro-bubbles for treating lung damage from COVID-19 infection.Simulations have been conducted to validate the required in-tensity and frequency of ultrasound signals that will penetratedifferent layers of the tissue to reach the alveoli containing themicro-bubbles. The results from the simulations showed thatvery low frequency signals are more efficient in oscillatingand vibrating the micro-bubbles in order to break them torelease the exosomes. Our proposed approach can lead tofuture reactive and proactive treatments, where micro-bubblescan be ruptured to release the exosomes on demand.A
CKNOWLEDGMENT
The research emanated from this publication is funded bythe Waterford Institute of Technology Postgraduate Scholar-ship. R
EFERENCES[1] J. S. Ayres, “A metabolic handbook for the COVID-19 pandemic,”
Nature Metabolism , vol. 2, no. 7, pp. 572–585, 2020.[2] I. Hamming, W. Timens, M. L. Bulthuis, A. T. Lely, G. J. Navis,and H. van Goor, “Tissue distribution of ACE2 protein, the functionalreceptor for SARS coronavirus. A first step in understanding SARSpathogenesis,”
Journal of Pathology , vol. 203, no. 2, pp. 631–637, 2004.[3] L. O’Driscoll, “Extracellular vesicles from mesenchymal stem cells asa Covid-19 treatment,” pp. 1124–1125, jul 2020.[4] K. Jayaramayya, I. Mahalaxmi, M. D. Subramaniam, N. Raj, A. A.Dayem, K. M. Lim, S. J. Kim, J. Y. An, Y. Lee, Y. Choi, A. Kirub-hakaran, S. G. Cho, and B. Vellingiri, “Immunomodulatory effect ofmesenchymal stem cells and mesenchymal stem-cell-derived exosomesfor COVID-19 treatment,” pp. 400–412, 2020.[5] A. Fern´andez Tena and P. Casan Clar`a, “Deposition of Inhaled Particlesin the Lungs,” pp. 240–246, jul 2012.[6] S. Misra and S. Sarkar, “Priority-based time-slot allocation in wirelessbody area networks during medical emergency situations: An evolution-ary game-theoretic perspective,”
IEEE Journal of Biomedical and HealthInformatics , vol. 19, no. 2, pp. 541–548, mar 2015. [7] S. Misra, A. Roy, C. Roy, and A. Mukherjee, “DROPS: Dynamic RadioProtocol Selection for Energy-Constrained Wearable IoT Healthcare,”
IEEE Journal on Selected Areas in Communications , vol. 39, no. 2, pp.338–345, feb 2021.[8] S. Misra, S. Moulik, and H. C. Chao, “A cooperative Bargaining solutionfor priority-based data-rate tuning in a Wireless Body Area Network,”
IEEE Transactions on Wireless Communications
The Journal of theAcoustical Society of America , vol. 107, no. 4, pp. 2272–2280, 2000.[12] G. Madras and V. Karmore, “Continuous distribution kinetics for ultra-sonic degradation of poly(methyl methacrylate),”
Polymer International ,vol. 50, no. 6, pp. 683–687, 2001.[13] C. R. Mayer, N. A. Geis, H. A. Katus, and R. Bekeredjian, “Ultrasoundtargeted microbubble destruction for drug and gene delivery,” pp. 1121–1138, oct 2008.[14] H. Azhari,
Basics of Biomedical Ultrasound for Engineers . Wiley-IEEEPress, 2010.[15] FDA, “Marketing Clearance of Diagnostic Ultrasound Systems andTransducers- Guidance for Industry and Food and Drug AdministrationStaff,”
U.S. Department of Health and Human Services: Food and DrugAdministration: Center for Devices and Radiological Health , pp. 18–34,2019.[16] J. L. Prince and J. M. Links,
Prince & Links, Medical Imaging Signalsand Systems— Pearson , 2nd ed. Pearson Education Inc., 2015.[17] A. Upadhyay and S. V. Dalvi, “Microbubble Formulations: Synthesis,Stability, Modeling and Biomedical Applications,” pp. 301–343, feb2019.[18] A. Bouakaz, A. Zeghimi, and A. A. Doinikov, “Sonoporation: Conceptand mechanisms,”
Advances in Experimental Medicine and Biology , vol.880, pp. 175–189, jan 2016.[19] S. Kotopoulis, K. Johansen, O. H. Gilja, A. T. Poortinga, andM. Postema, “Acoustically active antibubbles,”
Acta Physica PolonicaA , vol. 127, no. 1, pp. 99–102, 2015.[20] C. C. Church, “The effects of an elastic solid surface layer on the radialpulsations of gas bubbles,”
The Journal of the Acoustical Society ofAmerica , vol. 97, no. 3, pp. 1510–1521, mar 1995.[21] T. D. Mast, “Empirical relationships between acoustic parameters inhuman soft tissues,”
Acoustic Research Letters Online , vol. 1, pp. 37–42, nov 2000.[22] F. Shi, Y. Wei, L. Xia, F. Shan, Z. Mo, F. Yan, and D. Shen, “Lungvolume reduction and infection localization revealed in Big data CTimaging of COVID-19,”
International Journal of Infectious Diseases ,vol. 102, pp. 316–318, jan 2021.[23] S. K. EDELMAN, “Propagation Speed and Distance Measurement,”
Echocardiography , vol. 5, no. 1, pp. 71–77, jan 1988.[24] N. G. Parker, M. L. Mather, S. P. Morgan, and M. J. Povey, “Longitudinalacoustic properties of poly(lactic acid) and poly(lactic-co-glycolic acid),”
Biomedical Materials , vol. 5, no. 5, 2010.[25] J. G. B. Derraik, M. Rademaker, W. S. Cutfield, T. E. Pinto, S. Tregurtha,A. Faherty, J. M. Peart, P. L. Drury, and P. L. Hofman, “Effects of Age,Gender, BMI, and Anatomical Site on Skin Thickness in Children andAdults with Diabetes,”
PLoS ONE , vol. 9, no. 1, p. e86637, jan 2014.[26] R. Yoshida, K. Tomita, K. Kawamura, T. Nozaki, Y. Setaka, M. Monma,and H. Ohse, “Measurement of intercostal muscle thickness with ultra-sound imaging during maximal breathing,”
Journal of Physical TherapyScience , vol. 31, no. 4, pp. 340–343, 2019.[27] G. H. Kramer, K. Capello, B. Bearrs, A. Lauzon, and L. Normandeau,“Linear dimensions and volumes of human lungs obtained from CTimages,”
Health Physics , vol. 102, no. 4, pp. 378–383, apr 2012.[28] F. Coulouvrat, K. Astafyeva, J.-L. Thomas, N. Taulier, J.-M. Conoir, andW. Urbach, “Ultrasound Characterization of Mechanical Properties ofNanometric Contrast Agents with PLGA Shell in Suspension,”