Intracerebral and Subarachnoid Hemorrhage Research
Abstract
Intracerebral hemorrhage (ICH) is a life-threatening subtype of stroke characterized by the acute accumulation of blood within the brain parenchyma and progressive secondary brain injury. After ICH, dysregulated neuroinflammation drives a cascade of secondary injury processes that shape neurological deterioration and long-term recovery. However, the molecular mechanisms that determine the magnitude, temporal evolution, and resolution of immune-inflammatory responses after ICH remain incompletely understood, limiting the development of targeted therapeutic strategies. Accumulating evidence indicates that epigenetic regulation constitutes a critical layer controlling neuroinflammatory programs after ICH. Hematoma-derived stimuli, including hemoglobin degradation products, iron overload, oxidative stress, and damage-associated molecular patterns, create a unique inflammatory microenvironment that reshapes gene-regulatory landscapes in resident and infiltrating cells. DNA methylation remodeling, histone modification dynamics, chromatin accessibility alterations, and non-coding RNA regulatory networks collectively orchestrate cell-type–specific transcriptional reprogramming in microglia, astrocytes, endothelial cells, neurons, and infiltrating leukocytes. Rather than serving as passive consequences of tissue injury, these epigenetic processes actively modulate innate immune activation, cytokine production, leukocyte recruitment, blood–brain barrier integrity, and the balance between neurotoxic and reparative inflammatory states. In this review, we synthesize current evidence on the immuno-epigenetic regulation of neuroinflammation after ICH, with emphasis on cell-specific mechanisms, temporal dynamics, and immune–vascular interactions. We discuss how epigenetic reprogramming contributes to inflammatory amplification, glial phenotypic transitions, endothelial dysfunction, and the potential persistence of maladaptive inflammatory memory. Finally, we highlight emerging precision strategies, including locus-specific epigenome editing and RNA-based therapeutics, that may enable targeted modulation of neuroinflammation after ICH. An immuno-epigenetic perspective may provide a conceptual framework for developing precision neurotherapeutics for ICH.
Some claim that especially in the field of agile software development the research lags years behind of the practice. In this paper, we characterize the status and main challenges for research on agile software development, and propose a preliminary roadmap, focusing on providing more empirical research, primarily on experienced agile teams and organizations, connecting better to existing streams of research in more established fields, giving more attention to management-oriented approaches, and finally give more emphasis to the core ideas in agile software development in order to increase our understanding. We hope that this preliminary roadmap serves as a starting point for creating a common research agenda and enables the generation of fruitful discussions and research results from the field.
Torgeir Dingsøyr, T. Dybå, P. Abrahamsson· Agile Conference· 91 citations· ⚡7
The happy-productive worker thesis states that happy workers are more productive. Recent research in software engineering supports the thesis, and the ideal of flourishing happiness among software developers is often expressed among industry practitioners. However, the literature suggests that a cost-effective way to foster happiness and productivity among workers could be to limit unhappiness. Psychological disorders such as job burnout and anxiety could also be reduced by limiting the negative experiences of software developers. Simultaneously, a baseline assessment of (un)happiness and knowledge about how developers experience it are missing. In this paper, we broaden the understanding of unhappiness among software developers in terms of (1) the software developer population distribution of (un)happiness, and (2) the causes of unhappiness while developing software. We conducted a large-scale quantitative and qualitative survey, incorporating a psychometrically validated instrument for measuring (un)happiness, with 2 220 developers, yielding a rich and balanced sample of 1318 complete responses. Our results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains. We also identified 219 factors representing causes of unhappiness while developing software. Our results, which are available as open data, can act as guidelines for practitioners in management positions and developers in general for fostering happiness on the job. We suggest considering happiness in future studies of both human and technical aspects in software engineering.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
To compete in this age of disruption, large companies cannot rely on cost efficiency, lead time reduction and quality improvement. They are now looking for ways to innovate like startups. Meanwhile, the awareness and use of the Lean startup approach have grown rapidly amongst the software startup community in recent years. This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors. A multiple case study approach is followed in the investigation. Two software product innovation projects from two large companies are examined, using a conceptual framework that is based on the method-in-action framework and extended with the previously developed Lean-Internal Corporate Venture model. Seven face-to-face in-depth interviews of the employees with different roles are conducted. Within-case analysis and cross-case comparison are applied to draw the findings from the cases. A generic process flow summarises the common key processes of Lean internal startups. The findings suggest that an internal startup that is initiated management or employees faces different challenges. A list of enablers of applying Lean startup in large companies are identified, including top management support and cross-functional team. Both cases face different inhibitors due to the different process of inception, objective of the team and type of the product. Our contributions are threefold. First, this study is one of the first attempt to investigate the use of Lean startup approach in large companies empirically. Second, the study shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context. The third is a general process of Lean internal startup and the evidence of the enablers and inhibitors of implementing it, which are both theory-informed and empirically grounded.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
In this paper, we present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model designed to review code and identify potential issues. Our proposed LLM-based AI agent model is trained on large code repositories. This training includes code reviews, bug reports, and documentation of best practices. It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code. Unlike traditional static code analysis tools, our LLM-based AI agent has the ability to predict future potential risks in the code. This supports a dual goal of improving code quality and enhancing developer education by encouraging a deeper understanding of best practices and efficient coding techniques. Furthermore, we explore the model's effectiveness in suggesting improvements that significantly reduce post-release bugs and enhance code review processes, as evidenced by an analysis of developer sentiment toward LLM feedback. For future work, we aim to assess the accuracy and efficiency of LLM-generated documentation updates in comparison to manual methods. This will involve an empirical study focusing on manually conducted code reviews to identify code smells and bugs, alongside an evaluation of best practice documentation, augmented by insights from developer discussions and code reviews. Our goal is to not only refine the accuracy of our LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoption of Agile methods in general, and Scrum in particular. Little, if anything, is empirically known about the application and adoption of Scrum in a multi-team and multi-project situation. The authors carried out an ethnographically informed longitudinal case study in industrial settings and closely followed how the Scrum method was adopted in a 20-person department, working in a simultaneous multi-project R&D environment. Altogether 10 challenges pertinent to the case of multi-team multi-project Scrum adoption were identified in the study. The authors contend that these results carry great relevance for other industrial teams. Future research avenues arising from the study are indicated.
A. Marchenko, P. Abrahamsson· Agile Conference· 59 citations· ⚡11
Systematic literature review (SLR) is foundational to evidence-based research, enabling scholars to identify, classify, and synthesize existing studies to address specific research questions. Conducting an SLR is, however, largely a manual process. In recent years, researchers have made significant progress in automating portions of the SLR pipeline to reduce the effort and time required for high-quality reviews; nevertheless, there remains a lack of AI-agent-based systems that automate the entire SLR workflow. To this end, we introduce a novel multi-AI-agent system designed to fully automate SLRs. Leveraging large language models (LLMs), our system streamlines the review process to enhance efficiency and accuracy. Through a user-friendly interface, researchers specify a topic; the system then generates a search string to retrieve relevant academic papers. Next, an inclusion/exclusion filtering step is applied to titles relevant to the research area. The system subsequently summarizes paper abstracts and retains only those directly related to the field of study. In the final phase, it conducts a thorough analysis of the selected papers with respect to predefined research questions. This paper presents the system, describes its operational framework, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision. The code for this project is available at: https://github.com/GPT-Laboratory/SLR-automation .
Malik Abdul Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 43 citations· ⚡2
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
