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.

Quantitative Analysis of the Reduction Kinetics of a Pt(Ⅱ) Precursor in the Context of Pt Nanocrystal Synthesis

In this letter, we report a quantitative analysis of how a Pt（II） precursor is reduced to atoms at different temperatures for the formation of Pt nanocrystals with different morphologies and sizes. Our results suggest that in the early stage of a synthesis, the Pt（II） precursor is reduced to atoms exclusively in the solution phase, followed by homogeneous nucleation to generate nuclei and then seeds. At a relatively low reaction temperature such as 22℃, the growth of the seeds is dominated by autocatalytic surface reduction that involves the adsorption and then reduction of the Pt（II） precursor on the surface of the just-formed seeds. This particular growth pathway results in relatively large assemblies of Pt nanocrystals. When the reaction temperature is increased to 100 ℃, the dominant reduction pathway will be switched from surface to solution phase, producing much smaller asselnblies of Pt nanocrystals. Our results also demonstrate that a similar trend applies to the seed-rnediated growth of Pt nanocrystals in the presence of Pd nanocubes.

Mechanochemitry:A molecular biomechanics view of mechanosensing

The development of molecular biomechanics parallels the development of molecular biology.As biological research moves towards understanding the molecular mechanisms of cellular functions,biomechanics research also moves to smaller and smaller scales from tissues to cells to molecules.In many ways,molecular biology and molecular biomechanics represent similar reductionist approaches that attempt to explain the complex cell by examining its constituent molecules in hope that their assemblies would help elucidate the cellular behavior.The development of molecular biomechanics is also driven,at least in part,by the development of molecu-

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.

Challenges and Opportunities Modeling the Dynamic Tumor Matrisome

We need novel strategies to target the complexity of cancer and,particularly,of metastatic disease.As an example of this complexity,certain tissues are particularly hospitable environments for metastases,whereas others do not contain fertile microenvironments to support cancer cell growth.Continuing evidence that the extracellular matrix(ECM)of tissues is one of a host of factors necessary to support cancer cell growth at both primary and secondary tissue sites is emerging.Research on cancer metastasis has largely been focused on the molecular adaptations of tumor cells in various cytokine and growth factor environments on 2-dimensional tissue culture polystyrene plates.Intravital imaging,conversely,has transformed our ability to watch,in real time,tumor cell invasion,intravasation,extravasation,and growth.Because the interstitial ECM that supports all cells in the tumor microenvironment changes over time scales outside the possible window of typical intravital imaging,bioengineers are continuously developing both simple and sophisticated in vitro controlled environments to study tumor(and other)cell interactions with this matrix.In this perspective,we focus on the cellular unit responsible for upholding the pathologic homeostasis of tumor-bearing organs,cancer-associated fibroblasts(CAFs),and their selfgenerated ECM.The latter,together with tumoral and other cell secreted factors,constitute the“tumor matrisome”.We share the challenges and opportunities for modeling this dynamic CAF/ECM unit,the tools and techniques available,and how the tumor matrisome is remodeled(e.g.,via ECM proteases).We posit that increasing information on tumor matrisome dynamics may lead the field to alternative strategies for personalized medicine outside genomics.

Tryptophan 2,3-dioxygenase-positive matrix fibroblasts fuel breast cancer lung metastasis via kynurenine-mediated ferroptosis resistance of metastatic cells and T cell dysfunction

Background:Tumor metastasis is a major threat to cancer patient survival.The organ-specific niche plays a pivotal role in tumor organotropic metas-tasis.Fibroblasts serve as a vital component of the metastatic microenviron-ment,but how heterogeneous metastasis-associated fibroblasts(MAFs)promote organotropic metastasis is poorly characterized.Here,we aimed to decipher the heterogeneity of MAFs and elucidate the distinct roles of these fibroblasts in pulmonary metastasis formation in breast cancer.Methods:Mouse models of breast cancer pulmonary metastasis were estab-lished using an in vivo selection method of repeated injections of metastatic cells purified from the mouse lung.Single-cell RNA-sequencing(scRNA-seq)was employed to investigate the heterogeneity of MAFs.Transgenic mice were used to examine the contribution of tryptophan 2,3-dioxygenase-positive matrix fibroblasts(TDO2^(+)MFs)in lung metastasis.Results:We uncovered 3 subtypes of MAFs in the lung metastatic microenviron-ment,and their transcriptome profiles changed dynamically as lung metastasis evolved.As the predominant subtype,MFs were exclusively marked by platelet-derived growth factor receptor alpha(PDGFRA)and mainly located on the edge of the metastasis,and T cells were enriched around MFs.Notably,high MF sig-natures were significantly associated with poor survival in breast cancer patients.Lung metastases were markedly diminished,and the suppression of T cells was dramatically attenuated in MF-depleted experimental metastatic mouse mod-els.We found that TDO2^(+)MFs controlled pulmonary metastasis by producing kynurenine(KYN),which upregulated ferritin heavy chain 1(FTH1)level in dis-seminated tumor cells(DTCs),enabling DTCs to resist ferroptosis.Moreover,TDO2^(+)MF-secreted chemokines C-C motif chemokine ligand 8(CCL8)and C-C motif chemokine ligand 11(CCL11)recruited T cells.TDO2^(+)MF-derived KYN induced T cell dysfunction.Conditional knockout of Tdo2 in MFs diminished lung metastasis and enhanced immune activation.Conclusions:Our study reveals crucial roles of TDO2^(+)MFs in promoting lung metastasis and DTCs’immune evasion in the metastatic niche.It suggests that targeting the metabolism of lung-specific stromal cells may be an effective treatment strategy for breast cancer patients with lung metastasis.

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.

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.

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.

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.

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.

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.

利用乳腺钼靶对纳米探针的成像预测纳米化疗药物的疗效

目的 利用数字化乳腺钼靶机检测肿瘤内部X线纳米探针对比剂的摄取量，前瞻性地预测临床使用的纳米化疗药物的疗效。方法 对所有动物的操作程序均获得了动物保护和使用委员会的许可。首先构建有较长循环时间的100nm大小的注射用脂质体探针，载碘量为155mg／mL。

Mechanosensing via Immunereceptors

The immune response is orchestrated by a variety of immune cells,the function of which then is determined by the collective signals from different immunoreceptors.Recent studies have highlighted the presence of mechanical force on these receptor-ligand pairs and its important role in regulating antigen recognition/discrimination and function.In this perspective,we use the T cell receptor as an example to review the current understanding of the mechanosensing properties of immunoreceptors.We discuss the types of forces that immunoreceptors may encounter,the effects on ligand recognition,conformational changes and mechanosensing mechanisms,as well as the consequences in downstream signal transduction and function.

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.

Soft wearable flexible bioelectronics integrated with an anklefoot exoskeleton for estimation of metabolic costs and physical effort

Activities and physical effort have been commonly estimated using a metabolic rate through indirect calorimetry to capture breath information.The physical effort represents the work hardness used to optimize wearable robotic systems.Thus,personalization and rapid optimization of the effort are critical.Although respirometry is the gold standard for estimating metabolic costs,this method requires a heavy,bulky,and rigid system,limiting the system’s field deployability.Here,this paper reports a soft,flexible bioelectronic system that integrates a wearable ankle-foot exoskeleton,used to estimate metabolic costs and physical effort,demonstrating the potential for real-time wearable robot adjustments based on biofeedback.Data from a set of activities,including walking,running,and squatting with the biopatch and exoskeleton,determines the relationship between metabolic costs and heart rate variability root mean square of successive differences(HRV-RMSSD)(R=−0.758).Collectively,the exoskeleton-integrated wearable system shows potential to develop a field-deployable exoskeleton platform that can measure wireless real-time physiological signals.