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The majority of people in the world lack basic access to breast diagnostic imaging resulting in delay to diagnosis of breast cancer. In this study, we tested a volume sweep imaging (VSI) ultrasound protocol for evaluation of palpable breast lumps that can be performed by operators after minimal training without prior ultrasound experience as a means to increase accessibility to breast ultrasound.Medical students without prior ultrasound experience were trained for less than 2 hours on the VSI breast ultrasound protocol. Patients presenting with palpable breast lumps for standard of care ultrasound examination were scanned by a trained medical student with the VSI protocol using a Butterfly iQ handheld ultrasound probe. Video clips of the VSI scan imaging were later interpreted by an attending breast imager. Results of VSI scan interpretation were compared to the same-day standard of care ultrasound examination.Medical students scanned 170 palpable lumps with the VSI protocol. There was 97% sensitivity and 100% specificity for a breast mass on VSI corresponding to 97.6% agreement with standard of care (Cohen's κ = 0.95, P .0001). There was a detection rate of 100% for all cancer presenting as a sonographic mass. High agreement for mass characteristics between VSI and standard of care was observed, including 87% agreement on Breast Imaging-Reporting and Data System assessments (Cohen's κ = 0.82, P .0001).Breast ultrasound VSI for palpable lumps offers a promising means to increase access to diagnostic imaging in underserved areas. This approach could decrease delay to diagnosis for breast cancer, potentially improving morbidity and mortality.

Elastography researchers have utilized several rheological models to characterize soft tissue viscoelasticity over the past thirty years. Due to the frequency-dependent behavior of viscoelastic parameters as well as the different techniques and frequencies employed in various studies of soft tissues, rheological models have value in standardizing disparate techniques via explicit mathematical representations. However, the important question remains: which of the several available models should be considered for widespread adoption within a theoretical framework? We address this by evaluating the performance of three well established rheological models to characterize ex vivo bovine liver tissues: the Kelvin-Voigt (KV) model as a 2-parameter model, and the standard linear solid (SLS) and Kelvin-Voigt fractional derivative (KVFD) models as 3-parameter models. The assessments were based on the analysis of time domain behavior (using stress relaxation tests) and frequency domain behavior (by measuring shear wave speed (SWS) dispersion). SWS was measured over a wide range of frequency from 1 Hz to 1 kHz using three different tests: (i) harmonic shear tests using a rheometer, (ii) reverberant shear wave (RSW) ultrasound elastography scans, and (iii) RSW optical coherence elastography scans, with each test targeting a distinct frequency range. Our results demonstrated that the KVFD model produces the only mutually consistent rendering of time and frequency domain data for liver. Furthermore, it reduces to a 2-parameter model for liver (correspondingly to a 2-parameter "spring-pot" or power-law model for SWS dispersion) and provides the most accurate predictions of the material viscoelastic behavior in time (98% accuracy) and frequency (96% accuracy) domains. STATEMENT OF SIGNIFICANCE: Rheological models are applied in quantifying tissues viscoelastic properties. This study is unique in presenting comprehensive assessments of rheological models.

An ensemble of in vitro cardiac tissue models has been developed over the past several decades to aid our understanding of complex cardiovascular disorders using a reductionist approach. These approaches often rely on recapitulating single or multiple clinically relevant end points in a dish indicative of the cardiac pathophysiology. The possibility to generate disease-relevant and patient-specific human induced pluripotent stem cells has further leveraged the utility of the cardiac models as screening tools at a large scale. To elucidate biological mechanisms in the cardiac models, it is critical to integrate physiological cues in form of biochemical, biophysical, and electromechanical stimuli to achieve desired tissue-like maturity for a robust phenotyping. Here, we review the latest advances in the directed stem cell differentiation approaches to derive a wide gamut of cardiovascular cell types, to allow customization in cardiac model systems, and to study diseased states in multiple cell types. We also highlight the recent progress in the development of several cardiovascular models, such as cardiac organoids, microtissues, engineered heart tissues, and microphysiological systems. We further expand our discussion on defining the context of use for the selection of currently available cardiac tissue models. Last, we discuss the limitations and challenges with the current state-of-the-art cardiac models and highlight future directions.

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Alternative strategies are needed for patients with B-cell malignancy relapsing after CD19-targeted immunotherapy. Here, cell surface proteomics revealed CD72 as an optimal target for poor-prognosis KMT2A/MLL1-rearranged (MLLr) B-cell acute lymphoblastic leukemia (B-ALL), which we further found to be expressed in other B-cell malignancies. Using a recently described, fully in vitro system, we selected synthetic CD72-specific nanobodies, incorporated them into chimeric antigen receptors (CAR), and demonstrated robust activity against B-cell malignancy models, including CD19 loss. Taking advantage of the role of CD72 in inhibiting B-cell receptor signaling, we found that SHIP1 inhibition increased CD72 surface density. We establish that CD72-nanobody CAR-T cells are a promising therapy for MLLr B-ALL.Significance:Patients with MLLr B-ALL have poor prognoses despite recent immunotherapy advances. Here, surface proteomics identifies CD72 as being enriched on MLLr B-ALL but also widely expressed across B-cell cancers. We show that a recently described, fully in vitro nanobody platform generates binders highly active in CAR-T cells and demonstrate its broad applicability for immunotherapy development.This article is highlighted in the In This Issue feature, p. 1861

Supplementary Data from Surface Proteomics Reveals CD72 as a Target for In Vitro–Evolved Nanobody-Based CAR-T Cells in KMT2A/MLL1-Rearranged B-ALL

Supplementary Figure from Surface Proteomics Reveals CD72 as a Target for In Vitro–Evolved Nanobody-Based CAR-T Cells in KMT2A/MLL1-Rearranged B-ALL

SummaryCRISPR-Cas9 genome editing has enabled advanced T cell therapies, but occasional loss of the targeted chromosome remains a safety concern. To investigate whether Cas9-induced chromosome loss is a universal phenomenon and evaluate its clinical significance, we conducted a systematic analysis in primary human T cells. Arrayed and pooled CRISPR screens revealed that chromosome loss was generalizable across the genome and resulted in partial and entire loss of the chromosome, including in pre-clinical chimeric antigen receptor T cells. T cells with chromosome loss persisted for weeks in culture, implying the potential to interfere with clinical use. A modified cell manufacturing process, employed in our first-in-human clinical trial of Cas9-engineered T cells,1dramatically reduced chromosome loss while largely preserving genome editing efficacy. Expression of p53 correlated with protection from chromosome loss observed in this protocol, suggesting both a mechanism and strategy for T cell engineering that mitigates this genotoxicity in the clinic.

Biohybrid systems have been developed to better understand the design principles and coordination mechanisms of biological systems. We consider whether two functional regulatory features of the heart—mechanoelectrical signaling and automaticity—could be transferred to a synthetic analog of another fluid transport system: a swimming fish. By leveraging cardiac mechanoelectrical signaling, we recreated reciprocal contraction and relaxation in a muscular bilayer construct where each contraction occurs automatically as a response to the stretching of an antagonistic muscle pair. Further, to entrain this closed-loop actuation cycle, we engineered an electrically autonomous pacing node, which enhanced spontaneous contraction. The biohybrid fish equipped with intrinsic control strategies demonstrated self-sustained body–caudal fin swimming, highlighting the role of feedback mechanisms in muscular pumps such as the heart and muscles.

The remarkable specificity of antibodies has enabled precision cancer immunotherapies, including chimeric antigen receptor T cells and antibody-drug conjugates. In parallel, single-cell genomics technologies present the possibility of a comprehensive annotation of antigen expression throughout tissues of the human body and on cancer cells. We reflect on the rationale for antigen targets currently used in immunotherapies, their adverse effects revealed in the clinic, and the opportunity to utilize large genomics datasets to de-risk potential targets and nominate optimal antigens for therapy.

In medical imaging, quantitative measurements have shown promise in identifying diseases by classifying normal vs. pathological parameters from tissues. The support vector machine (SVM) has shown promise as a supervised classification algorithm and has been widely used. However, the classification results typically identify a category of abnormal tissues, but do not necessarily differentiate progressive stages of a disease. Moreover, the classification result is typically provided independently as a supplement to medical images, which contributes to an overload of information sources in the clinic. Hence, we propose a new imaging method utilizing the SVM to integrate classification results into medical images. This framework is called disease-specific imaging (DSI) which produces a color overlaid highlight on B-mode ultrasound images indicating the type, location, and severity of pathology from different conditions. In this paper, the SVM training was performed to construct hyperplanes that can differentiate normal, fibrosis, steatosis, and pancreatic ductal adenocarcinoma (PDAC) metastases in livers based on ultrasound echoes. Also, cluster centroids for specific diseases define unique disease axes, and the inner product between measured features and any disease axis selected by the SVM quantifies the disease progression. The features were measured from 2794 ultrasound frames using the H-scan analysis, attenuation estimation, and B-mode image analysis. The performance of our proposed DSI method was evaluated for a pre-clinical model of steatosis (n = 400 frames). The contribution of each feature was assessed, and the results were compared with ground truth from histology. Moreover, the images generated by our DSI were compared with earlier imaging methods of B-mode, H-scan, and histology. The comparisons demonstrate that DSI images yield higher sensitivity to monitor progressive steatosis than B-mode and H-scan and provide a comparable performance with the histology. For the parameter comparison, DSI and H-scan resulted in similar correlation with histology (r(s)=0.83), but higher than attenuation (r(s)=0.73) and B-mode (r(s)=0.47). Therefore, we conclude that DSI utilizing the SVM applied to steatosis can visually represent the classification results with color highlighting, which can simplify the interpretation of classification compared to the traditional SVM result. We expect the proposed DSI can be used for any medical imaging modality that can estimate multiple quantitative parameters at high resolution.

The study of ultrasound tissue interactions in fatty livers has a long history with strong clinical potential for assessing steatosis. Recently we proposed alternative measures of first- and second-order statistics of echoes from soft tissues, namely, the H-scan, which is based on a matched filter approach, to quantify scattering transfer functions and the Burr distribution to model speckle patterns. Taken together, these approaches produce a multiparameter set that is directly related to the fundamentals of ultrasound propagation in tissue. To apply this approach to the problem of assessing steatotic livers, these analyses were applied to in vivo rat livers ( N = 21 ) under normal feeding conditions or after receiving a methionine- and choline-deficient diet that produces steatosis within a few weeks. Ultrasound data were acquired at baseline and again at weeks 2 and 6 before applying the H-scan and Burr analyses. Furthermore, a classification technique known as the support vector machine was then used to find clusters of the five parameters that are characteristic of the different steatotic liver conditions as confirmed by histologic processing of excised liver tissue samples. With the in vivo multiparametric ultrasound measurement approach and determination of clusters, steatotic can be discriminated from normal livers with 100% accuracy in a rat animal model.

The art of population modelling is to incorporate factors essential for capturing a population's dynamics while otherwise keeping the model as simple as possible. However, it is unclear how optimal model complexity should be assessed, and whether this optimal complexity has been affected by recent advances in modelling methodology. This issue is particularly relevant to small populations because they are subject to complex dynamics but inferences about those dynamics are often constrained by small sample sizes. We fitted Bayesian hierarchical models to long-term data on vital rates (survival and reproduction) for the toutouwai Petroica longipes population reintroduced to Tiritiri Matangi, a 220-ha New Zealand island, and quantified the performance of those models in terms of their likelihood of replicating the observed population dynamics. These dynamics consisted of overall growth from 33 (±0.3) to 160 (±6) birds from 1992-2018, including recoveries following five harvest events for further reintroductions to other sites. We initially included all factors found to affect vital rates, which included inbreeding, post-release effects (PRE), density-dependence, sex, age and random annual variation, then progressively removed these factors. We also compared performance of models where data analysis and simulations were done simultaneously to those produced with the traditional two-step approach, where vital rates are estimated first then fed into a separate simulation model. Parametric uncertainty and demographic stochasticity were incorporated in all projections. The essential factors for replicating the population's dynamics were density-dependence in juvenile survival and PRE, i.e. initial depression of survival and reproduction in translocated birds. Inclusion of other factors reduced the precision of projections, and therefore the likelihood of matching observed dynamics. However, this reduction was modest when the modelling was done in an integrated framework. In contrast, projections were much less precise when done with a two-step modelling approach, and the cost of additional parameters was much higher under the two-step approach. These results suggest that minimization of complexity may be less important than accounting for covariances in parameter estimates, which is facilitated by integrating data analysis and population projections using Bayesian methods.

CRISPR perturbations are valuable tools for studying the functional effects of the genome. However, existing methods are limited in their utility for studying noncoding elements and genetic interactions. Here, we develop a system for bidirectional epigenetic editing (CRISPRai), in which orthogonal activating (CRISPRa) and repressive (CRISPRi) perturbations are applied simultaneously to multiple loci the same cell. We developed dual-gRNA-capture single-cell Perturb-seq to study the established interaction between SPI1 and GATA1, two hemopoietic lineage transcription factors, and discovered novel context-specific regulation modes for co-regulated genes. Extending CRISPRai to noncoding elements, we addressed how multiple enhancers interact to modulate expression of a shared target gene, Interleukin-2, in T cells. We found that enhancer function was primarily additive and enabled fine-tuning of gene expression, yet a clear hierarchy existed among enhancers in strength of gene expression control. The promoter was dominant over most enhancers in controlling gene expression; however, a small subset of enhancers exhibited strong functional effects, or gatekeeper function, and could turn off the gene despite promoter activation. Integration of these functional data with histone ChIP-seq and TF motif enrichment suggests the existence of multiple modes of enhancer-mediated gene regulation. Our method, CRISPRai for bidirectional epigenetic editing, provides an approach for identifying novel genetic interactions that may be overlooked when studied without bidirectional perturbations and can be applied to both genes and noncoding elements.

PURPOSE: The ability of ultrasound to assess pathology is increasing with the development of quantitative parameters. Among these are a set of parameters derived from the recent H-scan analysis of subresolvable scattering. The emergence of these quantitative measures of tissue/ultrasound interactions now enables a study of the unique trajectories of multiparametric features in multidimensional space, representing the progression of specific diseases over time. We develop the mathematical and visual tools that are effective for classifying, quantifying, and visualizing the steady progression of several diseases from independent studies, all within a uniform framework. METHODS: After applying the H-scan analysis of ultrasound echoes, we trained a support vector machine (SVM) to classify the unique trajectories of progressive liver disease from fibrosis, steatosis, and pancreatic ductal adenocarcinoma (PDAC) metastasis. Our approaches include the development of trajectory maps and disease-specific color imaging stains. RESULTS: The multidimensional SVM image classification reached 100% accuracy across the three different studies. CONCLUSION: H-scan trajectories can be useful to track the progression of multiple classes of diseases, improving diagnosis, staging, and assessing the response to therapy.

Alternative strategies are needed for patients with B-cell malignancy relapsing after CD19-targeted immunotherapy. Here, cell surface proteomics revealed CD72 as an optimal target for poor-prognosis KMT2A/MLL1-rearranged (MLLr) B-cell acute lymphoblastic leukemia (B-ALL), which we further found to be expressed in other B-cell malignancies. Using a recently described, fully in vitro system, we selected synthetic CD72-specific nanobodies, incorporated them into chimeric antigen receptors (CAR), and demonstrated robust activity against B-cell malignancy models, including CD19 loss. Taking advantage of the role of CD72 in inhibiting B-cell receptor signaling, we found that SHIP1 inhibition increased CD72 surface density. We establish that CD72-nanobody CAR-T cells are a promising therapy for MLLr B-ALL. Significance: Patients with MLLr B-ALL have poor prognoses despite recent immunotherapy advances. Here, surface proteomics identifies CD72 as being enriched on MLLr B-ALL but also widely expressed across B-cell cancers. We show that a recently described, fully in vitro nanobody platform generates binders highly active in CAR-T cells and demonstrate its broad applicability for immunotherapy development. This article is highlighted in the In This Issue feature, p. 1861