This study investigated the associations between neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, and systemic immune-inflammation index with progression of CSVD.
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Anyone interested in the morality of suicide reads David Hume’s essay on the subject even today. There are numerous reasons for this, but the central one is that it sets up the starting point for contemporary debate about the morality of suicide, namely, the debate about whether some condition of life could present one with a morally acceptable reason for autonomously deciding to end one’s life. We shall only be able to have this debate if we think that at least some acts of suicide can be moral, and we shall only be able to think this if we give up the blanket condemnation of suicide that theology has put in place. I look at this strategy of argument in the context of the wider eighteenth-century attempt to develop a non-theologically based ethic. The result in Hume’s case is a very modern tract on suicide, with voluntariness and autonomy to the fore and with reflection on the condition of one’s life and one’s desire to carry on living a life in that condition the motivating circumstance.
Can a large language model (LLM) provide insights on the history of chatbots and their clinical applications? 🤖
In this episode of JAMA+ AI Conversations, JAMA+ AI Editor in Chief Roy Perlis, MD, MSc, interviews OpenAI’s ChatGPT (GPT-4o, voice mode) about the development and legacy of the first clinical chatbots, ELIZA and PARRY.
The discussion explores differing perspectives of their creators, as well as how foundational debates about technology and ethics continue to inform the present landscape of AI in mental health care.
🎧 Listen now.
JAMA+ AI Editor in Chief Roy Perlis, MD, MSc, conducted an interview with ChatGPT about the history of chatbots and their clinical applications, for JAMA+ AI Conversations.
A privacy-first AI can diagnose a life-shortening hormone disorder—just from a photo of your hand.
Researchers at Kobe University have developed an artificial intelligence system that can identify a rare endocrine disorder by examining photos of the back of a person’s hand and their clenched fist. By avoiding facial images, the approach was designed with privacy in mind. The team believes this tool could help doctors refer patients to specialists more efficiently and help narrow gaps in access to care.
Acromegaly and Delayed Diagnosis.
Nishada Ramphal, Ari Waisman et al. (Johannes Gutenberg-Universität Mainz) reveal that NIK drives neuroantigen-specific T cell priming by regulating antigen presentation and IL-23 production, identifying NIK as a key orchestrator of myeloid-driven CNS autoimmunity.
Neuroinflammation.
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Ease see my latest Forbes article and have a great weekend! Chuck Brooks by Chuck Brooks.
#artificialIntelligence #ai #future #tech Forbes
AI is redefining power, productivity, security, and sovereignty. Dual-use, convergent, and autonomous AI is the 21st-century force multiplier. Not only is technology advancing, but civilization is about to change.
The 1956 Dartmouth Conference invented the term “artificial intelligence.” Alan Turing and other pioneers shaped the conceptualization of AI. The first systems used symbolic logic and determinism. Certain expert systems excelled but struggled in dynamic, uncertain environments. Fragility, computational capacity, and data accessibility caused “AI winters.”
Review of International AI Safety Report 2026.
Heliox unpacks the 2026 International AI Safety Report — the definitive global scientific consensus on AI risk — in forty minutes of evidence-grounded, empathetically framed conversation. From jagged AI genius to geopolitical fracture to cognitive atrophy, this episode makes the most consequential technology document of 2026 genuinely accessible.
Drug discovery and development requires integrating diverse evidence across biological scales and data modalities. However, relevant data, tools, and expertise remain fragmented across teams and organizations, making integration difficult. To address these challenges, we introduce the Virtual Biotech, a coordinated team of AI agents that mirrors the structure of human therapeutic research organizations to support end-to-end computational discovery. The Virtual Biotech is led by a Chief Scientific Officer agent that receives scientific queries, delegates them to domain-specialized scientist agents, and integrates their outputs through data-driven reasoning. Scientist agents leverage complementary tools and knowledge sources spanning statistical genetics, functional genomics, pathways and interactions, chemoinformatics, disease biology, and clinical data. We showcase the Virtual Biotech across three translational applications. First, the agents autonomously annotated and analyzed outcomes from 55,984 clinical trials to identify genomic features of drug targets associated with trial success. More than 37,000 clinical-trialist agents curated structured trial outcomes and linked targets to multi-omic annotations, including cell-type-specific features derived by the agents from single-cell RNA-sequencing atlases. The agents discovered that drugs targeting cell-type-specific genes were 40% more likely to progress from Phase I to Phase II and 48% more likely to reach market (Phase IV), while exhibiting 32% lower adverse event rates. Second, the Virtual Biotech evaluated B7-H3 as a lung cancer target, integrating statistical genetics, single-cell, spatial, and clinicogenomic evidence to propose an antibody–drug conjugate strategy while identifying key liabilities and differentiation opportunities. Third, the platform analyzed a terminated ulcerative colitis trial targeting OSMR β to infer potential failure mechanisms and proposed biomarker-guided enrollment strategies to address precision-medicine gaps. Together, these results illustrate how the Virtual Biotech can enable more transparent, efficient, and comprehensive multi-scale therapeutic analyses, helping to accelerate early-stage drug discovery workflows while keeping human scientists in the loop.
The authors have declared no competing interest.
Which Careers Are Most At Risk from AI Impact.
Artificial intelligence is reshaping the global labor market, with white-collar workers, especially those with higher education, facing the highest risk of job displacement.
Routine and structured tasks in administration, customer service, translation, and content production are most vulnerable, while roles requiring empathy, creativity, or physical skill, such as doctors, teachers, and electricians, remain relatively protected.
By 2026, AI is expected to handle up to 75% of customer service interactions, while 40% of the global workforce will need reskilling. Governments and companies must prioritize training and social protection to prevent widening labor and social inequality.
CHAPTERS:
0:12 Safest Jobs.
0:37 AI-Proof Careers.
1:05 Jobs AI Cannot Replace.
1:49 Future-Proof Jobs.
2:26 Tech Job Market.
3:01 AI and Employment.
3:44 Most Secure Careers.
4:22 Jobs Safe from Automation.
4:59 Jobs Safe from Automation 2025
5:18 Artificial Intelligence Impact.
6:57 Stable Tech Careers.
Produced by: Samantha Harvey.