Oct 2026· Journal of Dementia and Alzheimer's Disease· 0 citations· 61 references
Alzheimer's disease research and treatments
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, and women are disproportionately affected. This review examines the evidence that estrogen signaling contributes to this disparity and asks what a molecularly targeted therapeutic would require. We first distinguish prevalence from incidence in the gender-difference literature and review the genetic, epigenetic, and ascertainment factors that structure female-specific risk, noting that the associations localize consistently to tau rather than to amyloid pathology. We then examine estrogen receptor (ER) biology in the brain, reconciling apparently contradictory accounts of ERα and ERβ function by resolving them at the level of downstream pathway and cell type rather than receptor identity, and review evidence that DNA methylation at ER genes predicts cognitive decline and tangle density through mechanisms independent of receptor expression. The consistent failure of systemic hormone therapy in clinical trials is interpreted not as a refutation of this biology but as a constraint defining three design requirements: receptor engagement resolved by subtype, cell type, and tissue compartment; sufficient central nervous system exposure; and delivery timed to the perimenopausal window. Against these requirements we review AI-driven approaches to ER modulator design, spanning diffusion-based backbone generation and protein language models for peptides, and virtual screening and generative frameworks for small molecules. We argue that the principal barrier is data rather than model architecture: ERβ-selective binding data and peptide blood–brain barrier permeability data are both scarce, and no AI-designed ER modulator has been evaluated in an AD model. We conclude by identifying the epigenetic layer as a target class that current design pipelines do not address, and for which the necessary editing tools are technically available but validated against the wrong readout.
Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...
O'Grady, Jake, Gürhan, Asena Isik, Chee, Fong Ting et al.· Zenodo (CERN European Organi...· 465 citations
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
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The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
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 “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
MIT News · Artificial Intelligence· news.mit.eduAug 18, 2026
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.
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