The Life Sciences Institute (LSI) is a collaborative, independent research institution located on the campus of the University of Michigan in Ann Arbor. It encompasses 22 faculty-led teams from 12 schools and departments throughout U-M. The LSI brings together leading scientists from a variety of life science disciplines, working in a range of animal models and using a number of cutting-edge research tools, to accelerate breakthroughs and discoveries that will improve human health and lead to new treatments for diabetes, neurodegenerative disease, cancer and infectious disease. Of the university's $823 million in research expenditures, more than half is allocated for research in the life sciences, and the LSI is a cornerstone of this effort.
This study presents the development of a smart patient-care robot designed to assist healthcare workers by automating routine monitoring tasks and enhancing autonomous mobility in hospital wards. Unlike conventional systems that rely on multiple sensors, the proposed platform achieves reliable navigation using a single two-dimensional light detection and ranging sensor combined with an enhanced D* lite algorithm. A key contribution of this work is the introduction of virtual obstacle spaces in occupancy grid maps, which simplify path planning and significantly increase the success rate of passing through narrow doorways, one of the most collision-prone scenarios in ward environments. Experimental results show a navigation success improvement of over 30%, confirming enhanced safety and efficiency in mobility. This research establishes a practical foundation for deploying patient-care robots in real hospital settings, showcasing both technical advances in autonomous navigation and the potential to reduce healthcare staff workload while ensuring patient safety.
BACKGROUND:This study aims to evaluate the use of circulating tumor DNA (ctDNA) for identifying mutation profiles and monitoring dynamic changes in patients with metastatic colorectal cancer (mCRC) undergoing palliative chemotherapy. METHODS:This prospective observational study enrolled patients with mCRC treated at Asan Medical Center, Korea, between July 2018 and April 2020. ctDNA analysis was performed on plasma samples collected at baseline, first progression (PD), and second PD. Baseline ctDNA profiles were compared with matched tumor NGS data, and longitudinal changes in ctDNA were tracked over time. Clinical outcomes were correlated with ctDNA concentration and variant allele frequency (VAF). RESULTS:A total of 33 patients were included. ctDNA was detected in 93.9% of patients at baseline. The most frequent mutations were in APC and TP53 (81.8%), followed by KRAS (39.4%) and PIK3CA (30.3%). The median baseline ctDNA VAF was 17.7%, with significantly higher levels observed in patients with liver metastases. The overall concordance rate between plasma and tissue samples was 86.9%, with high accuracy in determining RAS/RAF mutation status. During treatment, acquired mutations emerged; notably, anti-EGFR therapy was associated with new RAS mutations (predominantly Q61 variants), while anti-VEGF therapy induced a broader range of genetic alterations. Elevated baseline ctDNA levels and higher maximum VAF at first PD were significantly correlated with shorter overall survival. CONCLUSIONS:ctDNA analysis offers a non-invasive approach for comprehensive genomic profiling in mCRC. Its ability to monitor genetic alterations and predict clinical outcomes underscores its potential utility in guiding personalized treatment strategies and monitoring resistance in clinical practice.
Background:Immunosuppressive breast cancer subtypes driven by regulatory T cells (Tregs) remain under-characterized, limiting precise identification of patients who may benefit from immunomodulatory therapies. Tregs are key mediators of immunosuppression within the tumor microenvironment (TME) and are closely associated with resistance to immune checkpoint inhibitors (ICIs). Therefore, defining and characterizing tumors with predominant Treg-mediated immunosuppression is essential for optimizing the use of Treg-targeted and combination immunotherapies. Methods:We applied an unsupervised multi-omics integration approach across four molecular layers - mRNA, miRNA, DNA methylation, and proteomics -to identify immunologically distinct subtypes of breast cancer. Autoencoder-based dimensionality reduction followed by consensus clustering revealed a subgroup characterized by high Treg infiltration and immunosuppressive signaling, referred to as the Treg-enriched subtype. To evaluate therapeutic strategies, we employed a spatial quantitative systems pharmacology (spQSP) model simulating tumor-immune dynamics and tested Treg-targeted and PD-1 blockade therapies both alone and in combination. In vivo efficacy studies were conducted using the EMT6 syngeneic breast tumor model, characterized by an immunosuppressive tumor microenvironment, assessing the antitumor effects of a CCR8-targeted small molecule (IPG7236) as monotherapy or in combination with anti-PD-L1 treatment. Results:The C2 cluster exhibited elevated Treg-related signatures and a highly immunosuppressive tumor microenvironment. A similar Treg-enriched cluster was also identified in an independent cohort, supporting the robustness and clinical relevance of this immunosuppressive subtype. In-silico simulations performed under a C2-like, immunosuppressive context predicted that combining Treg-targeted therapy with PD-1 blockade would substantially enhance immune activation and tumor control compared with monotherapy. To experimentally validate these predictions, combination treatment of a CCR8 inhibitor (IPG7236) and anti-PD-L1 antibody demonstrated greater tumor growth inhibition than either monotherapy in the EMT6 model, confirming the predicted therapeutic synergy in Treg-enriched, immune-suppressive tumors. Conclusion:This study identifies Treg-enriched and immunosuppressive breast cancer subtype through integrative multi-omics analysis and demonstrates, through both in-silico and in-vivo approaches, the therapeutic potential of combining Treg-targeted and PD-L1 blockade therapies. These findings highlight Treg-mediated immunosuppression as a key determinant of therapeutic responsiveness, providing a biological rationale for patient stratification and guiding the development of personalized combination strategies for clinical translation.
The use of AI in microservices (MSs) is an emerging field as indicated by a substantial number of surveys. However these surveys focus on a specific problem using specific AI techniques, therefore not fully capturing the growth of research and the rise and disappearance of trends. In our systematic mapping study, we take an exhaustive approach to reveal all possible connections between the use of AI techniques for improving any quality attribute (QA) of MSs during the DevOps phases. Our results include 16 research themes that connect to the intersection of particular QAs, AI domains and DevOps phases. Moreover by mapping identified future research challenges and relevant industry domains, we can show that many studies aim to deliver prototypes to be automated at a later stage, aiming at providing exploitable products in a number of key industry domains.
Internet gaming disorder is an increasing public health problem due to the widespread availability of online gaming. Social media platforms drive this trend by enabling gameplay sharing and increasing user engagement, potentially reinforcing addictive gaming behaviors. Understanding how gaming content exposure on social media affects brain activity in individuals with internet gaming disorder is crucial. This study aimed to investigate gaming content neural responses on social media in individuals with internet gaming disorder using functional magnetic resonance imaging. We aimed to determine differences in activation patterns that contribute to understanding the neurobiological underpinnings of internet gaming disorder by examining brain activity in these individuals and comparing it to healthy controls. Additionally, we investigated the association of brain activity with clinical characteristics (internet gaming disorder severity and illness duration). The participants with internet gaming disorder demonstrated increased bilateral orbitofrontal cortex, bilateral hippocampus, left precuneus, and right superior temporal gyrus activation in response to gaming-related cues on social media compared to healthy controls. Additionally, internet gaming disorder severity and illness duration correlated with left hippocampus activation levels. These results improve our understanding of how gaming-related content on social media affects individuals with internet gaming disorder. Our findings provide valuable information into the neurobiological features of internet gaming disorder and help develop effective treatment interventions.
