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Colorectal cancer (CRC) is the third-most leading cause of cancer-related deaths in the United States. To advance the understanding of CRC tumor progression, models which mimic the tumor microenvironment (TME) and have translatable study outcomes are urgently needed. CRC patient-derived xenografts (PDXs) are promising tools for their ability to recapitulate tumor heterogeneity and key patient tumor characteristics, such as molecular characteristics. However, as in vivo models, CRC PDXs are costly and low-throughput, which leads to a need for equivalent in vitro models. To address this need, we previously established an in vitro model using a tissue engineering toolset with CRC PDX cells. However, it is unclear whether tissue engineering has the capacity to maintain patient- and/or cancer stage-specific tumor heterogeneity. To address this gap, we employed three PDX tumor lines, originated from stage II, III-B, and IV CRC tumors, in the formation of 3D engineered CRC PDX (3D-eCRC-PDX) tissues and performed an in-depth comparison between the 3D-eCRC-PDX tissues and the original CRC-PDX tumors. To form the tissues, CRC-PDX tumors were expanded in vivo and dissociated. The isolated cells were encapsulated within poly(ethylene glycol)-fibrinogen hydrogels and remained viable and proliferative post encapsulation over the course of 29 days in culture. To gain molecular insight into the maintenance of PDX line stage heterogeneity, we performed a transcriptomic analysis using RNA seq to determine the extent to which there were similarities and differences between the CRC-PDX tumors and the 3D-eCRC-PDX tissues. We observed the greatest correspondence in overlapping differentially expressed human genes, gene ontology, and Hallmark gene set enrichment between the 3D-eCRC-PDX tissues and CRC-PDX tumors in the stage II PDX line, while the least correspondence was observed in the stage IV PDX line. The Hallmark gene set enrichment from murine mapped RNA seq transcripts was PDX line-specific which suggested that the stromal component of the 3D-eCRC-PDX tissues was maintained in a PDX line-dependent manner. Consistent with our transcriptomic analysis, we observed that tumor cell subpopulations, including human proliferative (B2M+Ki67+) and CK20+ cells, remained constant for up to 15 days in culture even though the number of cells in the 3D-eCRC-PDX tissues from all three CRC stages increased over time. Yet, tumor cell subpopulation differences in the stage IV 3D-eCRC-PDX tissues were observed starting at 22 days in culture. Overall, our results demonstrate a strong correlation between our in vitro 3D-eCRC-PDX models and the originating in vivo CRC-PDX tumors, providing evidence that these engineered tissues may be capable of mimicking patient- and/or cancer stage-specific heterogeneity. 

Hardware security creates a hardware-based security foundation for secure and reliable operation of systems and applications used in our modern life. The presence of design for security, security assurance, and general security design life cycle practices in product life cycle of many large semiconductor design and manufacturing companies these days indicates that the importance of hardware security has been very well observed in industry. However, the high cost, time, and effort for building security into designs and assuring their security - due to using many manual processes - is still an important obstacle for economy of secure product development. This paper presents several promising directions for automation of design for security and security assurance practices to reduce the overall time and cost of secure product development. First, we present security verification challenges of SoCs, possible vulnerabilities that could be introduced inadvertently by tools mapping a design model in one level of abstraction to its lower level, and our solution to the problem by automatically mapping security properties from one level to its lower level incorporating techniques for extension and expansion of the properties. Then, we discuss the foundation necessary for further automation of formal security analysis of a design by incorporating threat model and common security vulnerabilities into an intermediate representation of a hardware model to be used to automatically determine if there is a chance for direct or indirect flow of information to compromise confidentiality or integrity of security assets. Finally, we discuss a pre-silicon-based framework for practical and time-and-cost effective power-side channel leakage analysis, root-causing the side-channel leakage by using the automatically generated leakage profile of circuit nodes, providing insight to mitigate the side-channel leakage by addressing the high leakage nodes, and assuring the effectiveness of the mitigation by reprofiling the leakage to prove its acceptable level of elimination. We hope that sharing these efforts and ideas with the security research community can accelerate the evolution of security-aware CAD tools targeted to design for security and security assurance to enrich the ecosystem to have tools from multiple vendors with more capabilities and higher performance. 

Medical Cyber-physical Systems (MCPS) are vulnerable to accidental or malicious faults that can target their controllers and cause safety hazards and harm to patients. This paper proposes a combined model and data-driven approach for designing context-aware monitors that can detect early signs of hazards and mitigate them in MCPS. We present a framework for formal specification of unsafe system context using Signal Temporal Logic (STL) combined with an optimization method for patient-specific refinement of STL formulas based on real or simulated faulty data from the closed-loop system for the generation of monitor logic. We evaluate our approach in simulation using two state-of-the-art closed-loop Artificial Pancreas Systems (APS). The results show the context-aware monitor achieves up to 1.4 times increase in average hazard prediction accuracy (F1score) over several baseline monitors, reduces false-positive and false-negative rates, and enables hazard mitigation with a 54% success rate while decreasing the average risk for patients. 

Medical Cyber-physical Systems (MCPS) are vul- nerable to accidental or malicious faults that can target their controllers and cause safety hazards and harm to patients. This paper proposes a combined model and data-driven approach for designing context-aware monitors that can detect early signs of hazards and mitigate them in MCPS. We present a framework for formal specification of unsafe system context using Signal Temporal Logic (STL) combined with an optimization method for patient-specific refinement of STL formulas based on real or simulated faulty data from the closed-loop system for the gener- ation of monitor logic. We evaluate our approach in simulation using two state-of-the-art closed-loop Artificial Pancreas Systems (APS). The results show the context-aware monitor achieves up to 1.4 times increase in average hazard prediction accuracy (F1- score) over several baseline monitors, reduces false-positive and false-negative rates, and enables hazard mitigation with a 54% success rate while decreasing the average risk for patients.