Diffuse gliomas are increasingly defined by molecular alterations, yet the clinical translation of gene-targeted therapies remains constrained by a persistent mismatch between target biology and spatial pharmacology. In this review, we connect target discovery to clinical development by integrating spatial and longitudinal tumor biology with payload selection, delivery engineering, biomarker design, manufacturing, and mechanism-driven trials. Recent clinical practices in locoregional CAR-T cells, oncolytic and gene-delivery viruses, tumor-homing myeloid cell gene therapy, mutation- and neoantigen-directed vaccination, RNA nanomedicines, and emerging genome-editing strategies suggest that vehicle and route of administration dramatically change drug exposure, therapeutic index, immune activity, and resistance. We therefore propose the concept of “delivery-adjusted targetability,” in which a candidate target is judged not only by biological dependency but also by clonality, longitudinal stability, normal-brain expression, anatomical distribution, modality compatibility, and achievable target engagement. We further argue that the clinically actionable unit in diffuse glioma is often a target-anatomical compartment pair rather than a molecular target considered in isolation. Moreover, we advocate target-payload-vehicle-route-biomarker co-design and a stage-gated evidence package. This framework provides a potential way to compare otherwise dissimilar platforms, distinguish target failure from delivery failure, and reject biologically attractive but spatially implausible programs earlier. Treating molecular dependency and spatial access as a single design problem can make precision neuro-oncology more experimentally falsifiable and clinically actionable.
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 e...
Torgeir Dingsøyr, T. Dybå, P. Abrahamsson· Agile Conference· 92 citations· ⚡7
The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
The goal is to not only refine the accuracy of the 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 adoptio...
A. Marchenko, P. Abrahamsson· Agile Conference· 59 citations· ⚡11
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.