Low molecular-weight heparin is better than warfarin for prevention of recurrent venous thromboembolism in cancer patients

A critical appraisal and clinical application of Lee AYY, Levin MN, Bake RI, et al. Low-molecular-weight heparin versus a coumarin for the prevention of recurrent venous thromboembolism in patients with cancer. N Engl J Med. 2003;349:146-153. doi: 10.1056/NEJMoa02531

A hybrid EAV-relational model for consistent and scalable capture of clinical research data

Many clinical research databases are built for specific purposes and their design is often guided by the requirements of their particular setting. Not only does this lead to issues of interoperability and reusability between research groups in the wider community but, within the project itself, changes and additions to the system could be implemented using an ad hoc approach, which may make the system difficult to maintain and even more difficult to share. In this paper, we outline a hybrid Entity-Attribute-Value and relational model approach for modelling data, in light of frequently changing requirements, which enables the back-end database schema to remain static, improving the extensibility and scalability of an application. The model also facilitates data reuse. The methods used build on the modular architecture previously introduced in the CURe project

Implementation and Effects of LDC and MDC in Kentucky Districts

This brief summarizes early evidence on the success of two tools Kentucky districts have used to support their teachers' transition to these more demanding goals: Literacy Design Collaborative (LDC) and Math Design Collaborative (MDC). With support from the Bill and Melinda Gates Foundation, LDC and MDC tools have been designed and implemented to embody the key shifts in teaching and learning that the new standards demand. By implementing the tools, teachers then engage in new pedagogy and address relevant learning goals of the Kentucky Core Academic Standards

Addressing the challenge of integrated care through digital technology

There is a need to constantly tackle a range of diverse and, sometimes, contradictory requirements of people with multiple chronic conditions. Integrated Care provides a potential solution to this need and digital technology can be the proposition for addressing its implementation challenge. Digital technology can support clinical teams to achieve care across all levels and provide independence in patients’ lives, by supporting them in enhanced and integrated activity within our societal structures

Myosin IIA-mediated forces regulate multicellular integrity during vascular sprouting

Angiogenic sprouting is a critical process involved in vascular network formation within tissues. During sprouting, tip cells and ensuing stalk cells migrate collectively into the extracellular matrix while preserving cell-cell junctions, forming patent structures that support blood flow. Although several signaling pathways have been identified as controlling sprouting, it remains unclear to what extent this process is mechanoregulated. To address this question, we investigated the role of cellular contractility in sprout morphogenesis, using a biomimetic model of angiogenesis. Three-dimensional maps of mechanical deformations generated by sprouts revealed that mainly leader cells, not stalk cells, exert contractile forces on the surrounding matrix. Surprisingly, inhibiting cellular contractility with blebbistatin did not affect the extent of cellular invasion but resulted in cell-cell dissociation primarily between tip and stalk cells. Closer examination of cell-cell junctions revealed that blebbistatin impaired adherens-junction organization, particularly between tip and stalk cells. Using CRISPR/Cas9-mediated gene editing, we further identified NMIIA as the major isoform responsible for regulating multicellularity and cell contractility during sprouting. Together, these studies reveal a critical role for NMIIA-mediated contractile forces in maintaining multicellularity during sprouting and highlight the central role of forces in regulating cell-cell adhesions during collective motility.R01 EB000262 - NIBIB NIH HHS; R01 HL115553 - NHLBI NIH HHSPublished versio

Leveraging Large Language Models for Automated Dialogue Analysis

Developing high-performing dialogue systems benefits from the automatic identification of undesirable behaviors in system responses. However, detecting such behaviors remains challenging, as it draws on a breadth of general knowledge and understanding of conversational practices. Although recent research has focused on building specialized classifiers for detecting specific dialogue behaviors, the behavior coverage is still incomplete and there is a lack of testing on real-world human-bot interactions. This paper investigates the ability of a state-of-the-art large language model (LLM), ChatGPT-3.5, to perform dialogue behavior detection for nine categories in real human-bot dialogues. We aim to assess whether ChatGPT can match specialized models and approximate human performance, thereby reducing the cost of behavior detection tasks. Our findings reveal that neither specialized models nor ChatGPT have yet achieved satisfactory results for this task, falling short of human performance. Nevertheless, ChatGPT shows promising potential and often outperforms specialized detection models. We conclude with an in-depth examination of the prevalent shortcomings of ChatGPT, offering guidance for future research to enhance LLM capabilities.Comment: Accepted to SIGDIAL 202