Cancer cachexia is a multifactorial syndrome characterized by progressive muscle wasting, metabolic dysfunction, and systemic inflammation, leading to reduced quality of life and increased mortality. Despite extensive research, no single definitive biomarker exists, as cachexia-related indicators such as serum biomarkers, skeletal muscle measurements, and metabolic abnormalities often overlap with other conditions. Existing composite indices, including the Cancer Cachexia Index (CXI), Modified CXI (mCXI), and Cachexia Score (CASCO), integrate multiple biomarkers but lack standardized thresholds, limiting their clinical utility. This study proposes a multimodal AI-based biomarker for early cancer cachexia detection, leveraging open-source large language models (LLMs) and foundation models trained on medical data. The approach integrates heterogeneous patient data, including demographics, disease status, lab reports, radiological imaging (CT scans), and clinical notes, using a machine learning framework that can handle missing data. Unlike previous AI-based models trained on curated datasets, this method utilizes routinely collected clinical data, enhancing real-world applicability. Additionally, the model incorporates confidence estimation, allowing the identification of cases requiring expert review for precise clinical interpretation. Preliminary findings demonstrate that integrating multiple data modalities improves cachexia prediction accuracy at the time of cancer diagnosis. The AI-based biomarker dynamically adapts to patient-specific factors such as age, race, ethnicity, weight, cancer type, and stage, avoiding the limitations of fixed-threshold biomarkers. This multimodal AI biomarker provides a scalable and clinically viable solution for early cancer cachexia detection, facilitating personalized interventions and potentially improving treatment outcomes and patient survival.

With an increasing focus in STEM education on critical thinking skills, science writing plays an ever more important role in curricula that stress inquiry skills. A recently published dataset of two sets of college level lab reports from an inquiry-based physics curriculum relies on analytic assessment rubrics that utilize multiple dimensions, specifying subject matter knowledge and general components of good explanations. Each analytic dimension is assessed on a 6-point scale, to provide detailed feedback to students that can help them improve their science writing skills. Manual assessment can be slow, and difficult to calibrate for consistency across all students in large classes. While much work exists on automated assessment of open-ended questions in STEM subjects, there has been far less work on long-form writing such as lab reports. We present an end-to-end neural architecture that has separate verifier and assessment modules, inspired by approaches to Open Domain Question Answering (OpenQA). VerAs first verifies whether a report contains any content relevant to a given rubric dimension, and if so, assesses the relevant sentences. On the lab reports, VerAs outperforms multiple baselines based on OpenQA systems or Automated Essay Scoring (AES). VerAs also performs well on an analytic rubric for middle school physics essays.

Since the Nintendo Switch launched back in 2017, it has been marred by reports of'Joy-Con drift', where the detachable controllers register movements without the users touching them. The bug can make video games unplayable and has left many consumers out of pocket having to buy expensive replacement controllers, which can cost around £70. Now, an experiment by Which? has tested faulty controllers, revealing a crucial design flaw that is to blame. Based on the findings, Which? is calling on Nintendo to act urgently to fix the issues. Since the Nintendo Switch launched back in 2017, it has been marred by reports of'Joy-Con drift', where the detachable controllers register movements without the users touching them Gamers who use the Nintendo Switch have complained of a defect in their controllers dubbed'Joy-Con Drift.' 'Drift' is an infuriating flaw which causes video game characters to move about on screen or cameras to zoom out awkwardly without the player even touching the control stick. It first surfaced as a problem soon after the console was launched in 2017.

If we look back at the human race, we can consider ourselves the luckiest when it comes to minimising mundane day-to-day tasks. We do not have to wait in long queues to pay bills, we do not have to be physically present for business meetings and we do not even have to step out for basic things like food, shelter and clothing! What more can we ask for? Guess what, this is just the beginning. New technology advances are bringing about drastic changes in almost every sector, including the complex world of healthcare and medicine.