Background . There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been limited review into how to make the most of SBDH information from EHRs using AI approaches. Methods . A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided. Results . Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, natural language processing (NLP) approaches for extracting SBDH from clinical notes, and predictive models using SBDH for health outcomes. Discussion . The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using NLP technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues. Conclusion . Despite known associations between SBDH and diseases, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, ultimately promoting health and health equity.
Photoacoustic computed tomography of mechanical HIFU-induced vascular injury
Mechanical high-intensity focused ultrasound (HIFU) has been used for cancer treatment and drug delivery. Existing monitoring methods for mechanical HIFU therapies such as MRI and ultrasound imaging often suffer from high cost, poor spatial-temporal resolution, and/or low sensitivity to tissue’s hemodynamic changes. Evaluating vascular injury during mechanical HIFU treatment, therefore, remains challenging. Photoacoustic computed tomography (PACT) is a promising tool to meet this need. Intrinsically sensitive to optical absorption, PACT provides high-resolution imaging of blood vessels using hemoglobin as the endogenous contrast. In this study, we have developed an integrated HIFU-PACT system for detecting vascular rupture in mechanical HIFU treatment. We have demonstrated singular value decomposition for enhancing hemorrhage detection. We have validated the HIFU-PACT performance on phantoms and in vivo animal tumor models. We expect that PACT-HIFU will find practical applications in oncology research using small animal models.
Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)
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Current clinically used ultrasound elastography is obtained when tissue undergoes small deformation (<2% strain). However, small deformation may not be large enough to characterize tissues that exhibit similar elasticity properties to normal tissue at small deformation but behave differently from normal tissue at large deformation (>10% strain). Therefore, tissue elasticity properties measured at large deformation may provide better diagnostic differentiation. Combining the quasi-static and shear wave ultrasound elastography methods, we propose a noninvasive method to estimate the nonlinear elasticity property of tissue. To test this method, cubic tissue-mimicking phantoms were constructed. We progressively compressed the phantom to produce a series of stepwise stress states. At every stress state, we conducted shear wave measurements in which two acoustic radiation force pushes generated shear waves, which were measured using ultrafast imaging in order to estimate the local shear modulus. For every compression step, we tracked incremental tissue displacements between two successive frames and then accumulated these displacements for all steps to obtain the cumulative displacements and strains. Using the shear modulus and cumulative strain images we calculated the stress map under an incompressibility assumption. Finally, we obtained the local stress-strain curve that reflected the nonlinear elasticity of the tested material.
Repeatability of linear and nonlinear quantitative compression elastography in the breast
Compression elastography allows the precise measurement of large deformations of soft tissue in vivo. From a measured large deformation, an inverse problem for both the linear and nonlinear elastic moduli distributions can be solved. As part of a larger clinical study to evaluate NEMs in breast cancer, we evaluate the repeatability of linear and nonlinear modulus maps from repeat measurements. Within the cohort of 31 subjects scanned to date, several had repeated scans. These repeated scans were processed to evaluate NEM repeatability. In vivo data were acquired by a custom, digitally controlled, uniaxial compression device with force feedback. RF-data were acquired using plane wave imaging, at a frame-rate of 200 Hz, with a ramp-and-hold compressive force of 8N, applied at 8 N/s. A two-dimensional (2D) block-matching algorithm was used to obtain sample-level displacement fields which were then tracked at subsample resolution using 2D cross correlation. Linear and nonlinear elasticity parameters in the Blatz model of tissue elasticity are estimated using iterative optimization. Repeatability between both modes and elastic modulus maps is measured and compared. Preliminary results indicate that when images are acquired in the same region of tissue, the modulus maps are consistent. [Work supported by NIH R01CA195527.]
ASPP1/2-PP1 complexes are required for chromosome segregation and kinetochore-microtubule attachments
Abstract We have previously reported that the protease activity of uPA delays tumor progression in a syngeneic model of breast cancer, and that the inhibitory effects are due to the protease activity of uPA (Merchan et. al, JNCI, 2006: 7; 98(11)). We also reported that changes in the uPA/PAI-1 balance favoring the proteolytic activity of uPA are associated with delayed tumor growth and reduction in lung metastases in vivo (Jing, et al, AACR 2009 meeting, #1124). The aim of this study was to extend the above findings in other in vivo syngeneic cancer models and to characterize the changes in gene expression mediated by uPA overexpression. We generated stably uPA overexpressing murine renal cancer (RENCA) and murine colon cancer (MC-38) cell lines using lentiviral vectors carrying m-uPA and selecting high uPA expressing clones for in vivo studies. uPA protein expression and in vitro growth was similar between the uPA overexpressing and control cancer cells. Similar with our findings in the 4T1 model, mice harboring RENCA tumors overexpressing uPA had reduced tumor growth than controls. Conversely, mice carrying uPA-overexpressing MC-38 tumors had significantly enhanced tumor growth compared to controls. To understand the causes for the opposite in vivo effects of uPA overexpressing RENCA and MC-38 tumors, we first compared uPA's proteolytic activity vs. protein concentration between both cell lines. To our surprise, the uPA proteolytic activity of (uPA overexpressing) MC-38 conditioned medium (CM) was significantly lower (> 15-fold) than that of stable RENCA cell lines even though uPA protein levels were comparable. To assess if the differences in uPA proteolytic activity between MC38 and RENCA transfectants was due to differential endogenous PAI-1 expression, we measured PAI-1 levels in MC38 and RENCA transfectants CM (ELISA). We found that PAI-1 expression in MC 38 was significantly higher (30-fold) than RENCA cells. The above data strongly suggest that in MC-38 tumors, uPA overexpression promoted tumor growth in part due to high endogenous PAI-1 levels. On the other hand, in the RENCA model, uPA overexpression (and low PAI-1 expression) was associated with significantly enhanced proteolytic activity and delayed tumor growth. Gene expression arrays of early murine mammary tumors overexpressing active uPA, showed significant tumor down regulation of several members of the MMP family, which may explain the in vivo antitumor effects. The above data suggest that the pro-or anti tumorigenic/metastatic effects of uPA overexpression depend on its proteolytic activity, and indicate that the status of endogenous tumor PAI-1 may regulate uPA's effects on tumor progression and metastases. The above data may help reconcile contradictory experimental and clinical findings on the role of proteases and protease inhibitors on tumor progression. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 516.
Tumor and Vascular Targeting of a Novel Oncolytic Measles Virus Retargeted against the Urokinase Receptor
Abstract Oncolytic measles virus (MV) induces cell fusion and cytotoxicity in a CD46-dependent manner. Development of fully retargeted oncolytic MVs would improve tumor selectivity. The urokinase-type plasminogen activator receptor (uPAR) is a tumor and stromal target overexpressed in multiple malignancies. MV-H glycoproteins fully retargeted to either human or murine uPAR were engineered and their fusogenic activity was determined. Recombinant human (MV-h-uPA) and murine (MV-m-uPA) uPAR–retargeted MVs expressing enhanced green fluorescent protein (eGFP) were rescued and characterized. Viral expression of chimeric MV-H was shown by reverse transcription-PCR and Western blot. In vitro viral replication was comparable to MV-GFP control. The receptor and species specificity of MV-uPAs was shown in human and murine cells with different levels of uPAR expression. Removal of the NH2-terminal fragment ligand from MV-uPA by factor X(a) treatment ablated the MV-uPA functional activity. Cytotoxicity was shown in uPAR-expressing human and murine cells. MV-h-uPA efficiently infected human endothelial cells and capillary tubes in vitro. I.v. administration of MV-h-uPA delayed tumor growth and prolonged survival in the MDA-MB-231 breast cancer xenograft model. Viral tumor targeting was confirmed by immunohistochemistry. MV-m-uPA transduced murine mammary tumors (4T1) in vivo after intratumor administration. MV-m-uPA targeted murine tumor vasculature after systemic administration, as shown by dual (CD31 and MV-N) staining of tumor capillaries in the MDA-MB-231 model. In conclusion, MV-uPA is a novel oncolytic MV associated with potent and specific antitumor effects and tumor vascular targeting. This is the first retargeted oncolytic MV able to replicate in murine cells and target tumor vasculature in a uPAR-dependent manner. [Cancer Res 2009;69(4):1459–68]