The potential of artificial intelligence-based applications in kidney pathology.

PURPOSE OF REVIEW: The field of pathology is currently undergoing a significant transformation from traditional glass slides to a digital format dependent on whole slide imaging. Transitioning from glass to digital has opened the field to development and application of image analysis technology, commonly deep learning methods (artificial intelligence [AI]) to assist pathologists with tissue examination. Nephropathology is poised to leverage this technology to improve precision, accuracy, and efficiency in clinical practice. RECENT FINDINGS: Through a multidisciplinary approach, nephropathologists, and computer scientists have made significant recent advances in developing AI technology to identify histological structures within whole slide images (segmentation), quantification of histologic structures, prediction of clinical outcomes, and classifying disease. Virtual staining of tissue and automation of electron microscopy imaging are emerging applications with particular significance for nephropathology. SUMMARY: AI applied to image analysis in nephropathology has potential to transform the field by improving diagnostic accuracy and reproducibility, efficiency, and prognostic power. Reimbursement, demonstration of clinical utility, and seamless workflow integration are essential to widespread adoption.

Antithrombin nanoparticles inhibit stent thrombosis in ex vivo static and flow models.

OBJECTIVE: Despite significant advances in intravascular stent technology, safe prevention of stent thrombosis over prolonged periods after initial deployment persists as a medical need to decrease device failure. The objective of this project was to assess the potential of perfluorocarbon nanoparticles (NP) conjugated with the direct thrombin inhibitor D-phenylalanyl-L-prolyl-L-arginyl chloromethylketone (PPACK-NP) to inhibit stent thrombosis. METHODS: In a static model of stent thrombosis, 3 × 3-mm pieces of stainless steel coronary stents were cut and adsorbed with thrombin to create a procoagulant surface that would facilitate thrombus development. After treatment with PPACK-NP or control NP, stents were exposed to platelet-poor plasma (PPP) or platelet-rich plasma (PRP) for set time points up to 60 minutes. Measurements of final clot weight in grams were used for assessing the effect of NP treatment on limiting thrombosis. Additionally, groups of stents were exposed to flowing plasma containing various treatments (saline, free PPACK, control NP, and PPACK-NP) and generated thrombi were stained and imaged to investigate the treatment effects of PPACK-NP under flow conditions. RESULTS: The static model of stent thrombosis used in this study indicated a significant reduction in thrombus deposition with PPACK-NP treatment (0.00067 ± 0.00026 g; n = 3) compared with control NP (0.0098 ± 0.0015 g; n = 3; P = .026) in PPP. Exposure to PRP demonstrated similar effects with PPACK-NP treatment (0.00033 ± 0.00012 g; n = 3) vs control NP treatment (0.0045 ± 0.00012 g; n = 3; P = .000017). In additional studies, stents were exposed to both PRP pretreated with vorapaxar and PPACK-NP, which illustrated adjunctive benefit to oral platelet inhibitors for prevention of stent thrombosis. Additionally, an in vitro model of stent thrombosis under flow conditions established that PPACK-NP treatment inhibited thrombus deposition on stents significantly. CONCLUSIONS: This study demonstrates that antithrombin perfluorocarbon NPs exert marked focal antithrombin activity to prevent intravascular stent thrombosis and occlusion.

Delivery of a Protease-Activated Cytolytic Peptide Prodrug by Perfluorocarbon Nanoparticles.

Melittin is a cytolytic peptide derived from bee venom that inserts into lipid membranes and oligomerizes to form membrane pores. Although this peptide is an attractive candidate for treatment of cancers and infectious processes, its nonspecific cytotoxicity and hemolytic activity have limited its therapeutic applications. Several groups have reported the development of cytolytic peptide prodrugs that only exhibit cytotoxicity following activation by site-specific proteases. However, systemic administration of these constructs has proven difficult because of their poor pharmacokinetic properties. Here, we present a platform for the design of protease-activated melittin derivatives that may be used in conjunction with a perfluorocarbon nanoparticle delivery system. Although native melittin was substantially hemolytic (HD50: 1.9 µM) and cytotoxic (IC50: 2.4 µM), the prodrug exhibited 2 orders of magnitude less hemolytic activity (HD50: > 100 µM) and cytotoxicity (IC50: > 100 µM). Incubation with matrix metalloproteinase-9 (MMP-9) led to cleavage of the prodrug at the expected site and restoration of hemolytic activity (HD50: 3.4 µM) and cytotoxicity (IC50: 8.1 µM). Incubation of the prodrug with perfluorocarbon nanoparticles led to stable loading of 10,250 peptides per nanoparticle. Nanoparticle-bound prodrug was also cleaved and activated by MMP-9, albeit at a fourfold slower rate. Intravenous administration of prodrug-loaded nanoparticles in a mouse model of melanoma significantly decreased tumor growth rate (p = 0.01). Because MMPs and other proteases play a key role in cancer invasion and metastasis, this platform holds promise for the development of personalized cancer therapies directed toward a patient's individual protease expression profile.

Rapamycin nanoparticles target defective autophagy in muscular dystrophy to enhance both strength and cardiac function.

Duchenne muscular dystrophy in boys progresses rapidly to severe impairment of muscle function and death in the second or third decade of life. Current supportive therapy with corticosteroids results in a modest increase in strength as a consequence of a general reduction in inflammation, albeit with potential untoward long-term side effects and ultimate failure of the agent to maintain strength. Here, we demonstrate that alternative approaches that rescue defective autophagy in mdx mice, a model of Duchenne muscular dystrophy, with the use of rapamycin-loaded nanoparticles induce a reproducible increase in both skeletal muscle strength and cardiac contractile performance that is not achievable with conventional oral rapamycin, even in pharmacological doses. This increase in physical performance occurs in both young and adult mice, and, surprisingly, even in aged wild-type mice, which sets the stage for consideration of systemic therapies to facilitate improved cell function by autophagic disposal of toxic byproducts of cell death and regeneration.

Entropy vs. Energy Waveform Processing: A Comparison Based on the Heat Equation.

Virtually all modern imaging devices collect electromagnetic or acoustic waves and use the energy carried by these waves to determine pixel values to create what is basically an "energy" picture. However, waves also carry "information", as quantified by some form of entropy, and this may also be used to produce an "information" image. Numerous published studies have demonstrated the advantages of entropy, or "information imaging", over conventional methods. The most sensitive information measure appears to be the joint entropy of the collected wave and a reference signal. The sensitivity of repeated experimental observations of a slowly-changing quantity may be defined as the mean variation (i.e., observed change) divided by mean variance (i.e., noise). Wiener integration permits computation of the required mean values and variances as solutions to the heat equation, permitting estimation of their relative magnitudes. There always exists a reference, such that joint entropy has larger variation and smaller variance than the corresponding quantities for signal energy, matching observations of several studies. Moreover, a general prescription for finding an "optimal" reference for the joint entropy emerges, which also has been validated in several studies.

Programmable nanoparticle functionalization for in vivo targeting.

The emerging demand for programmable functionalization of existing base nanocarriers necessitates development of an efficient approach for cargo loading that avoids nanoparticle redesign for each individual application. Herein, we demonstrate in vivo a postformulation strategy for lipidic nanocarrier functionalization with the use of a linker peptide, which rapidly and stably integrates cargos into lipidic membranes of nanocarriers after simple mixing through a self-assembling process. We exemplified this strategy by generating a VCAM-1-targeted perfluorocarbon nanoparticle for in vivo targeting in atherosclerosis (ApoE-deficient) and breast cancer (STAT-1-deficient) models. In the atherosclerotic model, a 4.1-fold augmentation in binding to affected aortas was observed for targeted vs. nontargeted nanoparticles (P<0.0298). Likewise, in the breast cancer model, a 4.9-fold increase in the nanoparticle signal from tumor vasculature was observed for targeted vs. nontargeted nanoparticles (P<0.0216). In each case, the nanoparticle was registered with fluorine ((19)F) magnetic resonance spectroscopy of the nanoparticle perfluorocarbon core, yielding a quantitative estimate of the number of tissue-bound nanoparticles. Because other common nanocarriers with lipid coatings (e.g., liposomes, micelles, etc.) can employ this strategy, this peptide linker postformulation approach is applicable to more than half of the available nanosystems currently in clinical trials or clinical uses.

Synthesis of NanoQ, a copper-based contrast agent for high-resolution magnetic resonance imaging characterization of human thrombus.

A new site-targeted molecular imaging contrast agent based on a nanocolloidal suspension of lipid-encapsulated, organically soluble divalent copper has been developed. Concentrating a high payload of divalent copper ions per nanoparticle, this agent provides a high per-particle r1 relaxivity, allowing sensitive detection in T1-weighted magnetic resonance imaging when targeted to fibrin clots in vitro. The particle also exhibits a defined clearance and safety profile in vivo.

Lipid membrane editing with peptide cargo linkers in cells and synthetic nanostructures.

Current strategies for deploying synthetic nanocarriers involve the creation of agents that incorporate targeting ligands, imaging agents, and/or therapeutic drugs into particles as an integral part of the formulation process. Here we report the development of an amphipathic peptide linker that enables postformulation editing of payloads without the need for reformulation to achieve multiplexing capability for lipidic nanocarriers. To exemplify the flexibility of this peptide linker strategy, 3 applications were demonstrated: converting nontargeted nanoparticles into targeting vehicles; adding cargo to preformulated targeted nanoparticles for in vivo site-specific delivery; and labeling living cells for in vivo tracking. This strategy is expected to enhance the clinical application of molecular imaging and/or targeted therapeutic agents by offering extended flexibility for multiplexing targeting ligands and/or drug payloads that can be selected after base nanocarrier formulation.

Development and Validation of a Deep Learning Model to Quantify Glomerulosclerosis in Kidney Biopsy Specimens.

Importance: A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate is directly associated with biopsy specimen evaluation, which shows poor reproducibility among pathologists. A deep learning algorithm for measuring percent global glomerulosclerosis (an important predictor of outcome) on images of kidney biopsy specimens could enable pathologists to more reproducibly and accurately quantify percent global glomerulosclerosis, potentially saving organs that would have been discarded. Objective: To compare the performances of pathologists with a deep learning model on quantification of percent global glomerulosclerosis in whole-slide images of donor kidney biopsy specimens, and to determine the potential benefit of a deep learning model on organ discard rates. Design, Setting, and Participants: This prognostic study used whole-slide images acquired from 98 hematoxylin-eosin-stained frozen and 51 permanent donor biopsy specimen sections retrieved from 83 kidneys. Serial annotation by 3 board-certified pathologists served as ground truth for model training and for evaluation. Images of kidney biopsy specimens were obtained from the Washington University database (retrieved between June 2015 and June 2017). Cases were selected randomly from a database of more than 1000 cases to include biopsy specimens representing an equitable distribution within 0% to 5%, 6% to 10%, 11% to 15%, 16% to 20%, and more than 20% global glomerulosclerosis. Main Outcomes and Measures: Correlation coefficient (r) and root-mean-square error (RMSE) with respect to annotations were computed for cross-validated model predictions and on-call pathologists' estimates of percent global glomerulosclerosis when using individual and pooled slide results. Data were analyzed from March 2018 to August 2020. Results: The cross-validated model results of section images retrieved from 83 donor kidneys showed higher correlation with annotations (r = 0.916; 95% CI, 0.886-0.939) than on-call pathologists (r = 0.884; 95% CI, 0.825-0.923) that was enhanced when pooling glomeruli counts from multiple levels (r = 0.933; 95% CI, 0.898-0.956). Model prediction error for single levels (RMSE, 5.631; 95% CI, 4.735-6.517) was 14% lower than on-call pathologists (RMSE, 6.523; 95% CI, 5.191-7.783), improving to 22% with multiple levels (RMSE, 5.094; 95% CI, 3.972-6.301). The model decreased the likelihood of unnecessary organ discard by 37% compared with pathologists. Conclusions and Relevance: The findings of this prognostic study suggest that this deep learning model provided a scalable and robust method to quantify percent global glomerulosclerosis in whole-slide images of donor kidneys. The model performance improved by analyzing multiple levels of a section, surpassing the capacity of pathologists in the time-sensitive setting of examining donor biopsy specimens. The results indicate the potential of a deep learning model to prevent erroneous donor organ discard.

Deep learning quantification of percent steatosis in donor liver biopsy frozen sections.

BACKGROUND: Pathologist evaluation of donor liver biopsies provides information for accepting or discarding potential donor livers. Due to the urgent nature of the decision process, this is regularly performed using frozen sectioning at the time of biopsy. The percent steatosis in a donor liver biopsy correlates with transplant outcome, however there is significant inter- and intra-observer variability in quantifying steatosis, compounded by frozen section artifact. We hypothesized that a deep learning model could identify and quantify steatosis in donor liver biopsies. METHODS: We developed a deep learning convolutional neural network that generates a steatosis probability map from an input whole slide image (WSI) of a hematoxylin and eosin-stained frozen section, and subsequently calculates the percent steatosis. Ninety-six WSI of frozen donor liver sections from our transplant pathology service were annotated for steatosis and used to train (n = 30 WSI) and test (n = 66 WSI) the deep learning model. FINDINGS: The model had good correlation and agreement with the annotation in both the training set (r of 0.88, intraclass correlation coefficient [ICC] of 0.88) and novel input test sets (r = 0.85 and ICC=0.85). These measurements were superior to the estimates of the on-service pathologist at the time of initial evaluation (r = 0.52 and ICC=0.52 for the training set, and r = 0.74 and ICC=0.72 for the test set). INTERPRETATION: Use of this deep learning algorithm could be incorporated into routine pathology workflows for fast, accurate, and reproducible donor liver evaluation. FUNDING: Mid-America Transplant Society.

Resolution of Murine Toxic Hepatic Injury Quantified With Ultrasound Entropy Metrics.

Image-based classification of liver disease generally lacks specificity for distinguishing between acute, resolvable injury and chronic irreversible injury. We propose that ultrasound radiofrequency data acquired in vivo from livers subjected to toxic drug injury can be analyzed with information theoretic detectors to derive entropy metrics, which classify a statistical distribution of pathologic scatterers that dissipate over time as livers heal. Here we exposed 38 C57BL/6 mice to carbon tetrachloride to cause liver damage, and imaged livers in vivo 1, 4, 8, 12 and 18 d after exposure with a broadband 15-MHz probe. Selected entropy metrics manifested monotonic recovery to normal values over time as livers healed, and were correlated directly with progressive restoration of liver architecture by histologic assessment (r2 ≥ 0.95, p < 0.004). Thus, recovery of normal liver microarchitecture after toxic exposure can be delineated sensitively with entropy metrics.

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections.

Transplantable kidneys are in very limited supply. Accurate viability assessment prior to transplantation could minimize organ discard. Rapid and accurate evaluation of intra-operative donor kidney biopsies is essential for determining which kidneys are eligible for transplantation. The criterion for accepting or rejecting donor kidneys relies heavily on pathologist determination of the percent of glomeruli (determined from a frozen section) that are normal and sclerotic. This percentage is a critical measurement that correlates with transplant outcome. Inter- and intra-observer variability in donor biopsy evaluation is, however, significant. An automated method for determination of percent global glomerulosclerosis could prove useful in decreasing evaluation variability, increasing throughput, and easing the burden on pathologists. Here, we describe the development of a deep learning model that identifies and classifies non-sclerosed and sclerosed glomeruli in whole-slide images of donor kidney frozen section biopsies. This model extends a convolutional neural network (CNN) pre-trained on a large database of digital images. The extended model, when trained on just 48 whole slide images, exhibits slide-level evaluation performance on par with expert renal pathologists. Encouragingly, the model's performance is robust to slide preparation artifacts associated with frozen section preparation. The model substantially outperforms a model trained on image patches of isolated glomeruli, in terms of both accuracy and speed. The methodology overcomes the technical challenge of applying a pretrained CNN bottleneck model to whole-slide image classification. The traditional patch-based approach, while exhibiting deceptively good performance classifying isolated patches, does not translate successfully to whole-slide image segmentation in this setting. As the first model reported that identifies and classifies normal and sclerotic glomeruli in frozen kidney sections, and thus the first model reported in the literature relevant to kidney transplantation, it may become an essential part of donor kidney biopsy evaluation in the clinical setting.

Molecular imaging with targeted perfluorocarbon nanoparticles: quantification of the concentration dependence of contrast enhancement for binding to sparse cellular epitopes.

Targeted, liquid perfluorocarbon nanoparticles are effective agents for acoustic contrast enhancement of abundant cellular epitopes (e.g., fibrin in thrombi) and for lower prevalence binding sites, such as integrins associated with tumor neovasculature. In this study, we sought to delineate the quantitative relationship between the extent of contrast enhancement of targeted surfaces and the density (and concentration) of bound perfluorocarbon (PFC) nanoparticles. Two dramatically different substrates were utilized for targeting. In one set of experiments, the surfaces of smooth, flat, avidin-coated agar disks were exposed to biotinylated nanoparticles to yield a thin layer of targeted contrast. For the second set of measurements, we targeted PFC nanoparticles applied in thicker layers to cultured smooth muscle cells expressing the transmembrane glycoprotein "tissue factor" at the cell surface. An acoustic microscope was used to characterize reflectivity for all samples as a function of bound PFC (determined via gas chromatography). We utilized a formulation of low-scattering nanoparticles having oil-based cores to compete against high-scattering PFC nanoparticles for binding, to elucidate the dependence of contrast enhancement on PFC concentration. The relationship between reflectivity enhancement and bound PFC content varied in a curvilinear fashion and exhibited an apparent asymptote (approximately 16 dB and 9 dB enhancement for agar and cell samples, respectively) at the maximum concentrations (approximately 150 microg and approximately 1000 microg PFOB for agar and cell samples, respectively). Samples targeted with only oil-based nanoparticles exhibited mean backscatter values that were nearly identical to untreated samples (<1 dB difference), confirming the oil particles' low-scattering behavior. The results of this study indicate that substantial contrast enhancement with liquid perfluorocarbon nanoparticles can be realized even in cases of partial surface coverage (as might be encountered when targeting sparsely populated epitopes) or when targeting surfaces with locally irregular topography. Furthermore, it may be possible to assess the quantity of bound cellular epitopes through acoustic means.

Sensitive ultrasonic detection of dystrophic skeletal muscle in patients with duchenne muscular dystrophy using an entropy-based signal receiver.

The dystrophinopathies comprise a group of X-linked genetic diseases that feature dystrophin deficiency. Duchenne and Becker muscular dystrophy are characterized by progressive weakness and wasting of skeletal, smooth, and/or cardiac muscle. Duchenne muscular dystrophy (DMD) is the most severe dystrophinopathy, with an incidence of 1:3500 male births. Despite understanding the structural and genetic basis for DMD, the pathogenesis and clinical basis for more severe involvement in specific skeletal muscle groups and the heart are poorly understood. Current techniques, such as strength testing for monitoring progress of disease and therapy in DMD patients, are imprecise and physically demanding for test subjects. Ultrasound is well-suited to detect changes in structure and organization in muscle tissue in a manner that makes low demands on the patient. Therefore, we investigated the use of ultrasound to quantitatively phenotype the remodeling process in patients with DMD. Beam-formed radio-frequency (RF) data were acquired from the skeletal muscles of nine DMD and five normal subjects imaged with a clinical imaging system (HDI5000 w/7 MHz probe applied above left biceps muscle). From these data, images were reconstructed using B-mode (log of analytic signal magnitude) and information-theoretic receivers (H(f)-receiver). H(f) images obtained from dystrophic muscle contained extensive "mottled" regions (i.e., areas with heterogeneous image contrast) that were not readily apparent from the B-Mode images. The 2-D autocorrelation of DMD H(f) images have broader peaks than those of normal subjects, which is indicative of larger scatterer sizes, consistent with pathologic changes of fibers, edema and fatty infiltration. Comparison of the relative peak widths (full width measured at 60% maximum) of the autocorrelation of the DMD and normal H(f) images shows a quantitative difference between the two groups (p < 0.005, student two-tailed paired t-test). Consequently, these imaging techniques may prove useful for longitudinal monitoring of disease progression and therapy.

Sensitive ultrasonic delineation of steroid treatment in living dystrophic mice with energy-based and entropy-based radio frequency signal processing.

Duchenne muscular dystrophy is a severe wasting disease, involving replacement of necrotic muscle tissue by fibrous material and fatty infiltrates. One primary animal model of this human disease is the X chromosome-linked mdx strain of mice. The goals of the present work were to validate and quantify the capability of both energy and entropy metrics of radio-frequency ultrasonic backscatter to differentiate among normal, dystrophic, and steroid-treated skeletal muscle in the mdx model. Thirteen 12-month-old mice were blocked into three groups: 4 treated mdx-dystrophic that received daily subcutaneous steroid (prednisolone) treatment for 14 days, 4 positive-control mdx-dystrophic that received saline injections for 14 days, and 5 negative-control animals. Biceps muscle of each animal was imaged in vivo using a 40-MHz center frequency transducer in conjunction with a Vevo-660 ultrasound system. Radio-frequency data were acquired (1 GHz, 8 bits) corresponding to a sequence of transverse images, advancing the transducer from "shoulder" to "elbow" in 100-micron steps. Data were processed to generate both "integrated backscatter" (log energy), and "entropy" (information theoretic receiver, H(f)) representations. Analyses of the integrated-backscatter values delineated both treated-and untreated-mdx biceps from normal controls (p < 0.01). Complementary analyses of the entropy images differentiated the steroid-treated and positive-control mdx groups (p < 0.01). To our knowledge, this study represents the first reported use of quantitative ultrasonic characterization of skeletal muscle in mdx mice. Successful differentiation among dystrophic, steroid-treated, and normal tissues suggests the potential for local noninvasive monitoring of disease severity and therapeutic effects.

Acoustic activation of targeted liquid perfluorocarbon nanoparticles does not compromise endothelial integrity.

Perfluorocarbon nanoparticles consisting essentially of liquid perfluoro-octyl bromide (PFOB) core surrounded by a lipid monolayer can serve as highly specific site-targeted contrast and therapeutic agents after binding to cellular biomarkers. Based on previous findings that ultrasound applied at 2 MHz and 1.9 mechanical index (MI) for a 5-min duration dramatically enhances the cellular interaction of targeted PFOB nanoparticles with melanoma cells in vitro without inducing apoptosis or other harmful effects to cells that are targeted, we sought to define mechanisms of interaction and the safety profile of ultrasound used in conjunction with liquid perfluorocarbon nanoparticles for targeted drug delivery, as compared with conventional microbubble ultrasound contrast agents under identical insonification conditions. Cell-culture inserts were used to grow a confluent monolayer of human umbilical vein endothelial cells. Definity in conjunction with continuous wave ultrasound (2.25 MHz for 1 and 5 min) increased the permeability of monolayer by four to six times above the normal, decreased transendothelial electrical resistance (a sign of reduced membrane integrity), and decreased cell viability by approximately 50%. Histological evaluation demonstrated extensive disruptions of cell monolayers. Nanoparticles (both nontargeted and targeted) elicited no changes in these different measures under similar insonification conditions and did not disrupt cell monolayers. We hypothesize that ultrasound facilitates drug transport from the perfluorocarbon nanoparticles not by cavitation-induced effects on cell membrane but rather by direct interaction with the nanoparticles that stimulate lipid exchange and drug delivery.

Characterization of digital waveforms using thermodynamic analogs: detection of contrast-targeted tissue in vivo.

We describe characterization of backscatter from tumor tissue targeted with a nanoparticle-based ultrasound contrast agent in vivo using analogs of thermodynamic quantities. We apply these waveform characteristics to detection of tumor neovasculature in tumors implanted in athymic nude mice, which were imaged using a research ultrasound scanner over a 2-hour period after injection of alpha upsilon beta3-targeted perfluorocarbon nanoparticles. Images were constructed from backscattered ultrasound using two different approaches: fundamental B-mode imaging and a signal receiver based on a thermodynamic analog (H(C)). The study shows that the thermodynamic analog is capable of detecting differences in backscattered signals that are not apparent with the B-mode approach.

Sonic activation of molecularly-targeted nanoparticles accelerates transmembrane lipid delivery to cancer cells through contact-mediated mechanisms: implications for enhanced local drug delivery.

Liquid perfluorocarbon nanoparticles serve as sensitive and specific targeted contrast and drug delivery vehicles by binding to specific cell surface markers. We hypothesized that application of acoustic energy at diagnostic power levels could promote nanoparticle-associated drug delivery by stimulating increased interaction between the nanoparticle's lipid layer and the targeted cell's plasma membrane. Ultrasound (mechanical index = 1.9) applied with a conventional ultrasound imaging system to nanoparticles targeted to alpha(v)beta3-integrins on C32 melanoma cancer cells in vitro produced no untoward effects. Within 5 min, lipid delivery from nanoparticles into cell cytoplasm was dramatically augmented. We also demonstrate the operation of a potential physical mechanism for this effect, the acoustic radiation force on the nanoparticles, which may contribute to the enhanced lipid delivery. Accordingly, we propose that local delivery of lipophilic substances (e.g., drugs) from targeted nanoparticles directly into cell cytoplasm can be augmented rapidly and safely with conventional ultrasound imaging devices through nondestructive mechanisms.

Characterization of digital waveforms using thermodynamic analogs: applications to detection of materials defects.

We describe characterization of digital signals using analogs of thermodynamic quantities: the topological entropy, Shannon entropy, thermodynamic energy, partition function, specific heat at constant volume, and an idealized version of Shannon entropy in the limit of digitizing with infinite dynamic range and sampling rate. We show that analysis based on these quantities is capable of detecting differences between digital signals that are undetectable by conventional methods of characterization based on peak-to-peak amplitude or signal energy. We report the results of applying thermodynamic quantities to a problem from nondestructive materials evaluation: detection of foreign objects (FO) embedded near the surface of thin graphite/epoxy laminates using backscattered waveforms obtained by C-scanning the laminate. The characterization problem was to distinguish waveforms acquired from the region containing the FO from those acquired outside. In all cases the thermodynamic analogs exhibit significant increases (up to 20-fold) in contrast and for certain types of FO materials permit detection when energy or amplitude methods fail altogether.