Angelini has agreed to buy Catalyst Pharma for up to $4.1bn, its first acquisition since 2021, which would mark its entry into the US market.
Disclosure in the era of generative artificial intelligence
Generative artificial intelligence (AI) has rapidly become embedded in academic writing, assisting with tasks ranging from language editing to drafting text and producing evidence. Despite the wide range of AI use, the expectations for disclosure remain inconsistent. Several journals use binary disclosure statements that fail to distinguish minor language assistance from uses that have a […]
Alnylam rebuked by FDA over efficacy claims on Amvuttra website
The FDA has hit Alnylam with an untitled letter over the consumer website for Amvuttra, accusing the biotech of presenting open-label data that create a misleading impression of the drug’s effects.
Construction of patient trajectories to model clinical trial outcomes: application to myasthenia gravis
IntroductionAccurate prediction of patient outcomes in clinical trials is crucial for the timely assessment of treatment efficacy. This study proposes a novel approach to predict patient response using longitudinal clinical data.MethodsWe construct temporal trajectories from longitudinal data and extrapolate these trajectories to forecast individual patient outcomes. Additionally, we assess when new patients align with established […]
Effectiveness of the mobile application Holidaily in reducing work-related rumination when returning to work after vacation: a randomized controlled trial
BackgroundVacations reliably improve indicators of mental health, largely by providing relief from work-related stress. Low levels of work-related rumination, a key transdiagnostic factor linked to burnout and depression, are considered prerequisites for successful recovery both during vacations and in daily working life. However, such benefits are typically short-lived, with a rapid “fade-out” upon return to […]
A randomized factorial experiment to optimize the design of a culturally tailored breast cancer screening outreach chatbot intervention
IntroductionThe main objective of this study is to assess the effects of chatbot persona and communication style on trust and intention to use for scheduling breast cancer screening (BCS).MethodsWe conducted a mixed-methods analysis of a randomized factorial experiment to evaluate different chatbot designs for a BCS intervention. The study protocol is registered on ClinicalTrials.gov (NCT05472064). […]
Trustworthy intelligent rooms: integrating blockchain, federated learning, and data-centric AI for healthcare 4.0
IntroductionIntelligent room systems are experiencing a surge in demand within the Healthcare 4.0 ecosystem. The integration of Federated Learning (FL) and Data-Centric AI has led to substantial enhancements in the predictive capabilities of machine learning models while maintaining data privacy. However, centralized aggregation in FL remains a single point of failure and is vulnerable to […]
Effectiveness of digital and mobile-based interventions on sleep quality among nurses: a systematic review and meta-analysis
BackgroundNurses frequently endure diminished sleep quality, sleeplessness, and psychological distress due to high-intensity shifts and persistent work pressure. Digital health interventions are increasingly utilised to enhance sleep behaviour; however, systematic information about their real benefits on the nursing population remains insufficient.ObjectiveTo assess the efficacy of digital and mobile interventions on sleep and associated psychological consequences […]
Performance of large language models and prompt engineering strategies for data extraction in systematic reviews
BackgroundSystematic reviews depend on manual data extraction and synthesis, which are time-consuming and prone to human error. Although large language models (LLMs) have the potential to automate parts of this process, their accuracy, reproducibility, and efficiency across different models and prompt strategies remain insufficiently characterized.MethodsThis study evaluated the performance of three LLMs, including ChatGPT-4o, Claude […]
Predictive and Prescriptive AI toward Optimizing Wildfire Suppression
arXiv:2605.04510v1 Announce Type: cross Abstract: Intense wildfire seasons require critical prioritization decisions to allocate scarce suppression resources over a dispersed geographical area. This paper develops a predictive and prescriptive approach to jointly optimize crew assignments and wildfire suppression. The problem features a discrete resource-allocation structure with endogenous wildfire demand and non-linear wildfire dynamics. We formulate […]
Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning
arXiv:2605.04431v1 Announce Type: cross Abstract: Reinforcement fine-tuning (RFT) has become a core paradigm for post-training large language models, yet its training process remains highly fragile. Existing efforts mainly improve reliability at the system level or address specific issues in individual subproblems by modifying RFT algorithms. Despite their effectiveness, they largely overlook the problem of failure […]
CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation
arXiv:2605.04495v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) depends on document ranking to provide useful evidence for generation, but conventional reranking methods mainly optimize query-document relevance rather than generation usefulness. A relevant document may still introduce noise, while a lower-ranked document may better reduce the generator’s uncertainty. We propose CAR (Confidence-Aware Reranking), a query-guided, training-free, […]