On-chip physiological mimicry of neurovascular unit: challenges and perspectives

Neurological disorders including neurodegenerative diseases,brain tumors,and stroke are the second leading cause of death and the greatest cause of disability worldwide.However,it remains challenging to achieve effective drug delivery to the central nervous system for treatments of neurological diseases due to the blood-brain barrier(BBB).The function of the BBB is regulated by the physiological interactions between various types of cells in the neurovascular unit(NVU).In the NVU,the brain vasculature of the BBB is surrounded by brain pericytes,brain astrocytes,neurons,and microglia(Figure 1).Moreover,the NVU at the levels of arteries and veins includes contractile smooth muscle cells(Schaeffer and Iadecola,2021).

Single-cell RNA-sequencing and subcellular spatial transcriptomics facilitate the translation of liver microphysiological systems for regulatory application

Reducing the use of animal models in drug development and safety assessment has long been supported by the U.S.Food and Drug Administration(FDA).The report by Royal Society for the Prevention of Cruelty to Animals indicates that in 2020,experiments involved the use of over 100 million animals,with the United States leading the list by utilizing 20 million animals.Beyond ethical considerations associated with animal testing and the costs in terms of time and money,animal models are not always effective in predicting human reactions to drug exposure.While animal testing has been the traditional method for assessing the safety and efficacy of drugs.

Advances in Wireless,Batteryless,Implantable Electronics for Real‑Time,Continuous Physiological Monitoring

This review summarizes recent progress in developing wireless,batteryless,fully implantable biomedical devices for real-time continuous physiological signal monitoring,focusing on advancing human health care.Design considerations,such as biological constraints,energy sourcing,and wireless communication,are discussed in achieving the desired performance of the devices and enhanced interface with human tissues.In addition,we review the recent achievements in materials used for developing implantable systems,emphasizing their importance in achieving multi-functionalities,biocompatibility,and hemocompatibility.The wireless,batteryless devices offer minimally invasive device insertion to the body,enabling portable health monitoring and advanced disease diagnosis.Lastly,we summarize the most recent practical applications of advanced implantable devices for human health care,highlighting their potential for immediate commercialization and clinical uses.

Machine Learning Model Comparison for Automatic Segmentation of Intracoronary Optical Coherence Tomography and Plaque Cap Thickness Quantification

Optical coherence tomography(OCT)is a new intravascular imaging technique with high resolution and could provide accurate morphological information for plaques in coronary arteries.However,its segmentation is still commonly performed manually by experts which is time-consuming.The aim of this study was to develop automatic techniques to characterize plaque components and quantify plaque cap thickness using 3 machine learning methods including convolutional neural network(CNN)with U-Net architecture,CNN with Fully convolutional DenseNet(FC-DenseNet)architecture and support vector machine(SVM).In vivo OCT and intravascular ultrasound(IVUS)images were acquired from two patients at Emory University with informed consent obtained.Eighteen OCT image slices which included lipid core and with acceptable image quality were selected for our study.Manual segmentation from imaging experts was used as the gold standard for model training and validation.Since OCT has limited penetration,virtual histology IVUS was combined with OCT data to improve reliability.A 3-fold cross-validation method was used for model training and validation.The overall tissue classification accuracy for the 18 slices studied(total classification database sample size was 8580096 pixels)was 96.36%and 92.72%for U-Net and FC-DenseNet,respectively.The best average prediction accuracy for lipid was 91.29%based on SVM,compared to 82.84%and 78.91%from U-Net and FC-DenseNet,respectively.The overall average accuracy(Acc)differentiating lipid and fibrous tissue were 95.58%,92.33%and 81.84%for U-Net,FC-DenseNet and SVM,respectively.The average errors of U-Net,FC-DenseNet and SVM from the 18 slices for cap thickness quantification were 8.83%,10.71%and 15.85%.The average relative errors of minimum cap thickness from 18 slices of U-Net,FC-DenseNet and SVM were 17.46%,13.06%and 22.20%,respectively.To conclude,CNN-based segmentation methods can better characterize plaque compositions and quantify plaque cap thickness on OCT images and are more likely to be used in the clinical arena.Large-scale studies are needed to further develop the methods and validate our findings.

Virtual Staining,Segmentation,and Classification of Blood Smears for Label-Free Hematology Analysis

Objective and Impact Statement.We present a fully automated hematological analysis framework based on single-channel(single-wavelength),label-free deep-ultraviolet(UV)microscopy that serves as a fast,cost-effective alternative to conventional hematology analyzers.Introduction.Hematological analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel,costly chemical reagents,and lengthy protocols.Label-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler workflow.In this work,we leverage the unique capabilities of deep-UV microscopy as a label-free,molecular imaging technique to develop a deep learning-based pipeline that enables virtual staining,segmentation,classification,and counting of white blood cells(WBCs)in single-channel images of peripheral blood smears.Methods.We train independent deep networks to virtually stain and segment grayscale images of smears.The segmented images are then used to train a classifier to yield a quantitative five-part WBC differential.Results.Our virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining,the gold standard in hematology.The trained cellular and nuclear segmentation networks achieve high accuracy,and the classifier can achieve a quantitative five-part differential on unseen test data.Conclusion.This proposed automated hematology analysis framework could greatly simplify and improve current complete blood count and blood smear analysis and lead to the development of a simple,fast,and low-cost,point-of-care hematology analyzer.

Predicting Coronary Plaque Morphology Changes Based on Multimodality FSI Models Using Follow-Up IVUS and OCT Data

Background Current bottleneck of patient-specific coronary plaque model construction is the resolution of in vivo medical imaging.The threshold of cap thickness of vulnerable coronary plaques is 65 microns,while the resolution of in vivo coronary intravascular ultrasound(IVUS)images is 150-200 microns,which is not enough to identify vulnerable plaques with thin caps and construct accurate biomechanical plaque models.Optical coherence tomography(OCT)with a 15-20μm resolution has the capacity to identify thin fibrous cap.IVUS and OCT images could complement each other and provide for more accurate plaque morphology,especially,fibrous cap thickness measurements.A modeling approach combining IVUS and OCT was introduced in our previous publication for cap thickness quantification and more accurate cap stress/strain calculations.In this paper,patient baseline and follow-up IVUS and OCT data were acquired and multimodality image-based Fluidstructure interaction(FSI)models combining 3D IVUS,OCT,angiography were constructed to better quantify human coronary atherosclerotic plaque morphology and plaque stress/strain conditions and investigate the relationship of plaque vulnerability and morphological and mechanical factors.Methods Baseline and 10-Month follow-up in vivo IVUS and OCT coronary plaque data were acquired from one patient with informed consent obtained.Co-registration and segmentation of baseline and follow-up IVUS and OCT images were performed for modeling use.Baseline and follow-up 3D FSI models based on IVUS and OCT were constructed to simulate the mechanical factors which integrating plaque morphology were employed to predict plaque vulnerability.These 3D models were solved by ADINA(ADINA R&D,Watertown,MA,USA).The quantitative indices of cap thickness,lipid percentage were classified according to histological literatures and denoted as Cap Index and Lipid Index.Cap Index,Lipid Index and Morphological Plaque Vulnerability Index(MPVI)were chosen to quantify plaque vulnerability,respectively.Random forest(RF)which was based 13 extracted features including morphological and mechanical factors was used for plaque vulnerability classification and prediction.Over sampling scheme and a 5-fold crossvalidation procedure was employed in all 45 slices for training and testing sets.Single and all different combinations of morphological and mechanical risk factors were used for plaque progression prediction.Results When Cap Index was used as the measurement,minimum cap thickness(MCT)was the best single predictor which area under curve(AUC)is 0.782 0;the combination of MCT,critical plaque wall strain(CPWSn),critical wall shear stress(CWSS)and cap wall shear stress(CapWSS)was the best predictor with ACU=0.868 6.When Lipid Index was used as the measurement,the lipid percentage(LP)was the best single predictor which AUC value is 0.857 8;the combination of Mean cap thickness(MeanCT),LP,CWSS and cap plaque wall stress(CapPWS)and was the best predictor with ACU=0.9821.When MPVI was used as the measurement,MCT was the best single predictor which AUC value is 0.782 9;the combination of MCT,LP,plaque area(PA),CPWSn and CapWSS was the best predictor with ACU=0.872 9.Conclusions Combinations of morphological and mechanical risk factors had higher prediction accuracy,compared to the prediction of single factors and other combination of morphological factors.

Automatic Segmentation for Intracoronary OCT Image Based on Convolutional Neural Network and Support Vector Machine Methods

Background Cardiovascular diseases are closely associated with atherosclerotic plaque development and rupture.Traditional medical imaging techniques such as magnetic resonance imaging(MRI)and intravascular ultrasound(IVUS)were unable to identify vulnerable plaques due to their limited resolution.Fortunately,optical coherence tomography(OCT)is an advanced intravascular imaging technique developed in recent years which has high resolution approximately 10 microns and could provide more accurate morphology of coronary plaque.In particular,it has the ability to identify plaques with fibrous cap thickness<65μm,an accepted threshold value for vulnerable plaques.However,segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective.To overcome time consumption,several methodologies have been proposed for automatic segmentation of OCT images but most of these methods were still limited by intricate image preprocessing and expensive computation.In this research,two automatic segmentation methods for intracoronary OCT image based on support vector machine(SVM)and convolutional neural network(CNN)were performed to identify the plaque region and characterize plaque components.Methods In vivo IVUS and OCT coronary plaque data from 5 patients were acquired at Emory University with patient’s consent obtained.OCT were obtained from ILUMIEN OPTIS System(St.Jude,Minnesota,MN).The OCT catheter was traversed to the segment of interest and the catheter pullback was limited at a rate of 20 mm/sec.Following the OCT image acquisition,the IVUS catheter was traversed distally though the artery to the same coronary segment(Volcano Therapeutics,Rancho Cordova)and the catheter pullback speed was at a standard rate of 0.5 mm/sec.Seventy-seven matched IVUS and OCT slices with good image quality and lipid cores were selected for our segmentation study.Manual OCT segmentation was performed by experts and used as gold standard in the automatic segmentations.VH-IVUS was used as references and guide by the experts in the manual segmentation process.Three plaque component tissue classes were identified from OCT images in this work:lipid tissue(LT),fibrous tissue(FT)and background(BG).Procedures using two machine learning methods(CNN and SVM)were developed to segment OCT images,respectively.For CNN method,the U-Net architecture was selected due to its good performance in very different biomedical segmentation and very few annotated images.For SVM method,local binary patterns(LBPs),gray level co-occurrence matrices(GLCMs)which contains contrast,correlation,energy and homogeneity,entropy and mean value were calculated as features and assembled to feed SVM classifier.The accuracies of two segmentation methods were evaluated and compared using the OCT dataset.Segmentation accuracy is defined as the ratio of the number of pixels correctly classified over the total number of pixels.Results The overall classification accuracy based CNN method reached 95.8%,and the accuracies for LT,FT and BG were 86.8%,83.4%,and 98.2%,respectively.The overall classification accuracy based SVM was 71.9%,and per-class accuracy for LT,FT and BG was 75.4%,78.3%,and67.0%,respectively.Conclusions The two methods proposed can automatically identify plaque region and characterize plaque compositions for OCT images and potentially reduce the time spent by doctors in segmenting and evaluating coronary plaque OCT images.CNN provided better segmentation accuracies compared to those achieved by SVM.

A bio-instructive parylene-based conformal coating suppresses thrombosis and intimal hyperplasia of implantable vascular devices

Implantable vascular devices are widely used in clinical treatments for various vascular diseases. However, current approved clinical implantable vascular devices generally have high failure rates primarily due to their surface lacking inherent functional endothelium. Here, inspired by the pathological mechanisms of vascular device failure and physiological functions of native endothelium, we developed a new generation of bioactive parylene (poly(p-xylylene))-based conformal coating to address these challenges of the vascular devices. This coating used a polyethylene glycol (PEG) linker to introduce an endothelial progenitor cell (EPC) specific binding ligand LXW7 (cGRGDdvc) onto the vascular devices for preventing platelet adhesion and selectively capturing endogenous EPCs. Also, we confirmed the long-term stability and function of this coating in human serum. Using two vascular disease-related large animal models, a porcine carotid artery interposition model and a porcine carotid artery-jugular vein arteriovenous graft model, we demonstrated that this coating enabled rapid generation of self-renewable “living” endothelium on the blood contacting surface of the expanded polytetrafluoroethylene (ePTFE) grafts after implantation. We expect this easy-to-apply conformal coating will present a promising avenue to engineer surface properties of “off-the-shelf” implantable vascular devices for long-lasting performance in the clinical settings.

Shape-Controlled Synthesis of Copper Nanocrystals for Plasmonic,Biomedical,and Electrocatalytic Applications

CONSPECTUS:As a metal that can occur in nature in the elemental form,copper(Cu)has been used by humans since ca.8000 BC.With most properties matching those of Ag and Au,Cu has played a more significant role in commercial applications owing to its much higher(the 25th among all elements)abundance in Earth’s crust and thus more affordable price.In addition to its common use as a conductor of heat and electricity,it is a constituent of various metal alloys for hardware,coins,strain gauges,and thermocouples.

Deterministic Synthesis of Pd Nanocrystals Enclosed by High-Index Facets and Their Enhanced Activity toward Formic Acid Oxidation

Noble-metal nanocrystals enclosed by high-index facets are of growing interest due to their enhanced catalytic performance in a variety of reactions.Herein,we report the deterministic synthesis of Pd nanocrystals encased by high-index facets by controlling the rate of deposition(V_(deposition))relative to that of surface diffusion(V_(diffusion)).For octahedral seeds with truncated corners,a reduction rate(and thus deposition rate)faster than that of surface diffusion(i.e.,V_(deposition)/V_(diffusion)>1)led to the formation of concave trisoctahedra(TOH)with high-index facets.When the reduction was slowed down,in contrast,surface diffusion dominated the growth pathway.In the case of V_(deposition)/V_(diffusion)≈1,truncated octahedra with enlarged sizes were produced.When the reduction rate was between these two extremes,we obtained concave tetrahexahedra(THH)without or with truncation.Similar growth patterns were also observed for the cuboctahedral seeds.When the Pd octahedra,concave TOH,and concave THH were tested for electrocatalyzing the formic acid oxidation(FAO)reaction,those with high-index facets were advantageous over the conventional Pd octahedra enclosed by{111}facets.This work not only contributes to the understanding of surface diffusion and its role in nanocrystal growth but also offers a general protocol for the synthesis of nanocrystals enclosed by high-index facets.

Shape-controlled syntheses of rhodium nanocrystals for the enhancement of their catalytic properties

金老(Rh ) 是为许多化学转变过程的许多催化剂的一个批评部件。控制 Rh nanocrystals 的形状由于在催化活动 / 选择和表面之间的靠近的关联把一条有效线路提供给他们的催化表演的优化原子结构。它也帮助实质地减少装载数量并且因此完成这少见、宝贵的金属的持续使用。在这篇评论文章，我们旨在提高他们的催化性质在 Rh nanocrystals 的控制形状的合成集中于最近的进步。传统、最新开发的合成策略和生长机制将基于盖住代理人，减小动力学的操作，表面散开率的控制，蚀刻的氧化的管理，和电气化学的改变的表面的使用被讨论，包括那些。我们也使用二个例子加亮控制形状的合成为在催化应用提高这金属的使用提供的唯一的机会。策略能也被扩大到另外的宝贵金属以推进划算的催化剂的生产。

Cdc42 regulates the cellular localization of Cdc42ep1 in controlling neural crest cell migration

小 GTPases Cdc42 的 Rho 家庭的成员在房间极性和活动性起重要、保存的作用。Cdc42ep 家庭蛋白质被识别了绑在 Cdc42，然而，他们怎么与 Cdc42 交往调整房间移植，尚待被阐明。在这研究，我们集中于 Cdc42ep1，它在青蛙胚胎在高度迁移的神经的冠房间主要被表示。通过调停 morpholino 击倒，我们证明 Cdc42ep1 为头部的神经的冠房间的迁居被要求。Cdc42ep1 的损失导致更圆的房间形状和膜水泡的形成，与在肌动朊组织和焦点的粘附排列的观察混乱一致。作为结果， Cdc42ep1 是批评的让神经的冠房间在正确地方使用拖拉力量高效地移居。我们进一步证明 Cdc42ep1 对在神经的冠房间的二个区域局部性：在和 Cdc42 的膜伸出并且在 Cdc42 是不在的 perinuclear 补丁。Cdc42 直接与 Cdc42ep1 交往(通过 CRIB 域) 并且在 Cdc42 水平的变化转移在这二个 subcellular 地点之间的 Cdc42ep1 的分发，作为后果控制膜伸出的形成和移植的 directionality。这些结果建议 Cdc42ep1 在神经的冠房间详细描述 Cdc42 活动支持他们的有效迁居。

CT辐射量降低和投影数目研究（英文）

改进CT技术的最终目标是用较低的辐射剂量重建出更高质量的图像以降低患癌症的风险。近年来受压缩感知理论的启发,减少投影角度重建一直是减少辐射量的一个热门课题。但是,当辐射剂量固定,减少投影角度并不总是意味着更好的图像质量。本文研究固定辐射剂量下图像质量和扫描角度数目的关系。数值实验表明对于固定的辐射剂量,起初图像质量随着扫描角度数目的增加而提高,但是当扫描角度数目足够大之后图像质量反而下降了。在等角全扫描模式下,对于我们测试的辐射剂量和图像,产生最佳图像质量的最佳扫描角度数目是300,这对于实际的应用具有借鉴意义.

Facile synthesis of Pd concave nanocubes： From kinetics to mechanistic understanding and rationally designed protocol

We report a rationally designed one-pot method for the facile synthesis of Pd concave nanocubes in an aqueous solution at room temperature by manipulating the reduction kinetics through the selection of a proper combination of a salt precursor （PdBr4^2-） and reductant （sodium ascorbate）. Our kinetic analysis demonstrates that, through this selection, the nucleation and growth of Pd nanocrystals could be effectively separated into two kinetic regimes involving distinctive reduction pathways： i） solution reduction for the initial formation of single-crystal seeds and ii） surface reduction for the formation of concave nanocrystals via autocatalytic growth from the single-crystal seeds. The suppressed surface diffusion at room temperature, when coupled with the capping effect of bromide ions, ultimately leads to the formation of concave nanocubes with an asymmetric shape and high-index facets, whose synthesis would otherwise require multiple steps and the use of elevated temperatures.

基于多尺度计算方法的活体磁共振谱自动相位矫正和代谢物定量分析(英文)

活体多片磁共振谱成像(MRSI)产生大量的波谱数据,因此需要使用自动的谱数据分析方法来获得不同组织代谢产物的定量分布图.然而,活体波谱通常产生严重的谱和基线变形,使得基于曲线拟合的谱定量数据分析方法失效.该文应用多尺度分析(Multiscale)方法自动确定兴趣代谢物在频率空间的谱峰特征(位置和线宽),然后通过叠代运算对该代谢物对应的谱峰进行独立的自动相位矫正和线型拟合.大脑波谱成像的实验结果表明,该方法可以方便、有效的获得代谢产物在大脑的分布,特别适宜于多片磁共振谱成像的代谢产物定量分析.

Spatial control of robust transgene expression in mouse artery endothelium under ultrasound guidance

Dear Editor,Dysfunctional vascular endothelial cells(ECs)contribute to the pathophysiology of several cardiovascular diseases,such as atherosclerosis and its life-threatening complications.1 Gene therapy can be a valuable approach to modulate endothelial cell function for the prevention of atherosclerosis.However,there is still a lack of method for transgene expression in vascular endothelium.2 Herein,an effective and specific strategy was established for noninvasive spatial control of transgene expression into the target region of the mouse artery without systemic spillover using an ultrasound and microbubble(MB)guided adenoassociated viral vector(UMGAAV)(Supplementary Fig.S1).To the best of our knowledge,this is the first report on noninvasive spatial control of transgene expression in arterial endothelium in vivo.

Differentiation of human mesenchymal stem cell spheroids under microgravity conditions

To develop and characterize a novel cell culture method for the generation of undifferentiated and differentiated human mesenchymal stem cell 3D structures,we utilized the RWV system with a gelatin-based scaffold.3×10^(6) cells generated homogeneous spheroids and maximum spheroid loading was accomplished after 3 days of culture.Spheroids cultured in undifferentiated spheroids of 3 and 10 days retained expression of CD44,without expression of differentiation markers.Spheroids cultured in adipogenic and osteogenic differentiation media exhibited oil red O staining and von Kossa staining,respectively.Further characterization of osteogenic lineage,showed that 10 day spheroids exhibited stronger calcification than any other experimental group corresponding with significant expression of vitamin D receptor,alkaline phosphatase,and ERp60.In conclusion this study describes a novel RWV culture method that allowed efficacious engineering of undifferentiated human mesenchymal stem cell spheroids and rapid osteogenic differentiation.The use of gelatin scaffolds holds promise to design implantable stem cell tissue of various sizes and shapes for future regenerative treatment.

Deep UV Microscopy Identifies Prostatic Basal Cells:An Important Biomarker for Prostate Cancer Diagnostics

Objective and Impact Statement.Identifying benign mimics of prostatic adenocarcinoma remains a significant diagnostic challenge.In this work,we developed an approach based on label-free,high-resolution molecular imaging with multispectral deep ultraviolet(UV)microscopy which identifies important prostate tissue components,including basal cells.This work has significant implications towards improving the pathologic assessment and diagnosis of prostate cancer.Introduction.One of the most important indicators of prostate cancer is the absence of basal cells in glands and ducts.However,identifying basal cells using hematoxylin and eosin(H&E)stains,which is the standard of care,can be difficult in a subset of cases.In such situations,pathologists often resort to immunohistochemical(IHC)stains for a definitive diagnosis.However,IHC is expensive and time-consuming and requires more tissue sections which may not be available.In addition,IHC is subject to false-negative or false-positive stains which can potentially lead to an incorrect diagnosis.Methods.We leverage the rich molecular information of label-free multispectral deep UV microscopy to uniquely identify basal cells,luminal cells,and inflammatory cells.The method applies an unsupervised geometrical representation of principal component analysis to separate the various components of prostate tissue leading to multiple image representations of the molecular information.Results.Our results show that this method accurately and efficiently identifies benign and malignant glands with high fidelity,free of any staining procedures,based on the presence or absence of basal cells.We further use the molecular information to directly generate a high-resolution virtual IHC stain that clearly identifies basal cells,even in cases where IHC stains fail.Conclusion.Our simple,low-cost,and label-free deep UV method has the potential to improve and facilitate prostate cancer diagnosis by enabling robust identification of basal cells and other important prostate tissue components.

Two-photon MINFLUX with doubled localization precision

Achieving localization with molecular precision has been of great interest for extending fluorescence microscopy to nanoscopy.MINFLUX pioneers this transition through point spread function(PSF)engineering,yet its performance is primarily limited by the signal-to-background ratio.Here we demonstrate theoretically that two-photon MINFLUX(2p-MINFLUX)could double its localization precision through PSF engineering by nonlinear effect.Cramér-Rao Bound(CRB)is studied as the maximum localization precision,and CRB of two-photon MINFLUX is halved compared to single-photon MINFLUX(1p-MINFLUX)in all three dimensions.Meanwhile,in order to achieve same localization precision with 1p-MINFLUX,2p-MINFLUX requires only 1/4 of fluorescence photons.Exploiting simultaneous two-photon excitation of multiple fluorophore species,2p-MINFLUX may have the potential for registration-free nanoscopy and multicolor tracking.

Color-coded perfluorocarbon nanodroplets for multiplexed ultrasound and photoacoustic imaging

Laser-activated perfluorocarb on n anodroplets are an emerging class of phase-cha nge, dual-c ontrast age nts that can be utilized in ultraso und and photoacoustic imaging. Through the ability to differe ntiate subpopulations of nano droplets via laser activatio n at differe nt wavelengths of n ear-infrared light, optically-triggered color-coded perfluorocarb on nano droplets prese nt themselves as an attractive tool for multiplexed ultrasound and photoacoustic imaging. In particular, laser-activated droplets can be used to provide quantitative spatiotemporal information regarding distinct biological targets, allowing for their potential use in a wide range of diagnostic and therapeutic applications. In the work prese nted, laser-activated color-coded perfluorocarb on nan odroplets are syn thesized to selectively resp ond to laser irradiati on at corresp on ding wavele ngths. The dyn amic ultraso und and photoacoustic signals produced by laser-activated perfluorocarbon nano droplets are evaluated in situ prior to implementation in a murine model. In vivo, these particles are used to distinguish unique particle trafficking mechanisms and are show n to provide ultraso und and photoacoustic contrast for up to 72 hours within lymphatics. Overall, the con ducted studies show that laser-activated color-coded perfluorocarbo n nano droplets are a promising agent for multiplexed ultraso und and photoacoustic imaging.