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Artificial intelligence can transform medicine in a myriad of ways, including its promise to act as a trusted diagnostic aide to busy clinicians.

Over the past two years, proprietary AI models, also known as closed-source models, have excelled at solving hard-to-crack medical cases that require complex clinical reasoning. Notably, these closed-source AI models have outperformed ones, so-called because their source code is publicly available and can be tweaked and modified by anyone.

Has open-source AI caught up?

“Anyone with lungs can get lung cancer.” Kelly thought it couldn’t happen to her—until it did. Having worked at Pfizer for over 25 years, she knew how an unexpected diagnosis could suddenly turn someone’s life upside down, but nothing could have prepared her for her own cancer journey.

After developing a chronic cough, Kelly was looking for answers. She had appointments with several healthcare providers and specialists who diagnosed her with everything from gastroesophageal reflux disease to long COVID. Nothing gave her relief. Kelly’s symptoms only got worse; her cough soon became debilitating, and she developed shortness of breath. After months of frustration, she decided to take matters into her own hands and called a friend who works as a pulmonologist. After hearing her symptoms, he booked Kelly for a CT scan right away. Minutes after the test was complete, she received news that turned her life upside down—she had cancer.

Despite significant advancements, millions of people across the globe face a cancer diagnosis each year. For the Pfizer Oncology team, these individuals are more than a statistic—they are parents, children, friends and colleagues.

In this Review, Ahmad et al. examine how antibiotics influence bacterial metabolism and how metabolism, in turn, affects drug efficacy and the emergence and evolution of antimicrobial resistance. They also explore the role of bacterial metabolism in clinical contexts and the potential for metabolic-based therapies to improve antibacterial treatment.

Surprising research from Spain has demonstrated the uniqueness of human consciousness, as a team of scientists say they have shown how the human brain stores memories differently than other species.

Neurons in a human brain record information separate from context, allowing humans to process more complex and abstract information relationships than other species. Dr. Rodrigo Quian Quiroga, group leader of the Neural Mechanisms of Perception and Memory Research Group at the Hospital del Mar Research Institute, led the groundbreaking research into human consciousness.

Master AI avatars, video automation, AI graphics, and monetization 👉👉🔗 https://www.skool.com/aicontentlab/about 🚀 New content added monthly!

Scientists have created a groundbreaking AI that uses living human brain cells instead of traditional silicon chips, allowing it to learn and adapt faster than any existing artificial intelligence. Developed by Cortical Labs, this new technology, called Synthetic Biological Intelligence (SBI), combines human neurons and electrodes to create a self-learning system that could revolutionize drug discovery, robotics, and computing. The CL1 AI unit, unveiled in March 2025, operates with minimal energy, doesn’t require an external computer, and is available through Wetware-as-a-Service (WaaS), enabling researchers to run experiments on biological neural networks from anywhere in the world.

🔍 KEY TOPICS
Scientists create an AI using living human brain cells, redefining intelligence and learning.
Cortical Labs’ CL1 unit combines neurons and electrodes for faster, more efficient AI
Breakthrough in Synthetic Biological Intelligence (SBI) with real-world applications in medicine, robotics, and computing.

🎥 WHAT’S INCLUDED
How human neurons power AI, enabling it to learn and adapt faster than any chip.
The revolutionary CL1 system, a self-contained AI unit that doesn’t need an external computer.
The potential impact of biological AI on drug discovery, robotics, and future technology.

📊 WHY IT MATTERS
This video explores how AI built with human neurons could reshape computing, making systems smarter, more energy-efficient, and capable of human-like learning, raising new possibilities and ethical debates.

DISCLAIMER

Most cells in the body send out little messengers called extracellular vesicles that carry proteins, lipids, and other bioactive molecules to other cells, playing an important role in intercellular communication. But healthy cells are not the only ones that rely on extracellular vesicles. Cancer cells do, too. Small extracellular vesicles that are shed from tumor cells contribute to how cancer spreads to healthy tissue.

These small messengers could be a key to developing new cancer-fighting drugs and therapies, but it has been unclear how exactly the recipient cells absorb the extracellular vesicles and their cargo. Recent research used state-of-the-art imaging to observe the uptake of tumor-derived small extracellular vesicles by target cells. The results were published in Nature Communications on March 12, 2025.

“In recent years, extracellular vesicles have attracted attention as a carrier of intercellular signaling. However, the mechanism of their internalization by target cells has not been well understood. We wanted to elucidate the pathway and mechanism of internalization of extracellular vesicles by target cells,” said Kenichi G. N. Suzuki, a professor at the Institute for Glyco-core Research at Gifu University in Gifu and a chief at the Division of Advanced Bioimaging, National Cancer Center Research Institute in Tokyo, Japan.

Does autoimmunity underlie minimal change disease?

Tobias B. Huber, Nicola M. Tomas & team report a direct pathogenic role of anti-nephrin autoantibodies in the development of podocytopathy with a minimal change disease phenotype:

The electron microscopy image shows moderate podocyte foot process effacement (without electron-dense deposits) in the anti-nephrin rabbit.


Address correspondence to: Tobias B. Huber or Nicola M. Tomas, III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, D-20246 Hamburg, Germany. Phone: 49.40.7410.53908; Email: [email protected] (TBH); [email protected] (NMT).

Molecular Nutrition In Health, Well-Being & Longevity — Dr. Courtney Millar, Ph.D. — Marcus Institute For Aging Research, Hebrew SeniorLife / Harvard Medical School


Dr. Courtney Millar, Ph.D. (https://www.marcusinstituteforaging.org/who-we-are/profiles/courtney-millar-phd) is an Assistant Scientist at the Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, and Instructor in Medicine, Harvard Medical School and Beth Israel Deaconess Medical Center.

Dr. Millar is a research scientist devoted to improving health and well-being of older adults through dietary interventions and her current research aims to test the ability of anti-inflammatory dietary strategies that promote both physical and emotional well-being in older adults.

Dr. Millar received her PhD in molecular nutrition at the University of Connecticut, where she developed a deep understanding of the relationship between dietary bioactive components and metabolic disease.

Dr. Millar’s post-doctoral fellowship focused on training related to conducting both nutritional epidemiological analyses and clinical interventions.

A 69-year-old man with metastatic prostate adenocarcinoma, treated with chemotherapy 3 years ago, presented with pancytopenia (white blood cells, 3.1 × 109/L; hemoglobin, 11.1 g/L; platelets, 47 × 109/L). A bone marrow aspirate revealed increased blasts with folded nuclei, deeply basophilic cytoplasm, prominent nucleoli, perinuclear hofs, and occasional salmon-colored cytoplasmic granules without Auer rods (panel A, Giemsa stain, original magnification ×1000, lens objective 100×). The blasts were positive for CD34, CD13, CD19, CD25 (partial), CD33, CD38 (decreased), CD45 (dim), CD64 (partial), CD79a (dim), CD117, CD123, HLA-DR (bright), and myeloperoxidase and negative for CD7, CD10, CD14, CD20, CD22, CD36, CD56, cytoplasmic IgM and terminal deoxynucleotidyl transferase (panel B). Next-generation sequencing detected a DNMT3A mutation (F794del, variant allelic frequency 2%), likely representing bystander clonal hematopoiesis. Cytogenetic analysis revealed an abnormal karyotype (46,XY,+1,der(1;15)(q10;q10), t(16;21)(q24;q22)[20]) (panel C). Result of dual-color fusion fluorescence in situ hybridization (FISH) was negative for RUNX1::RUNX1T1. Nevertheless, 23% of the cells exhibited 3 copies of RUNX1, suggesting RUNX1 rearrangement with another partner (panel D); RUNX1 rearrangement was confirmed by FISH using a break-apart probe (panel E). Optical genome mapping confirmed the presence of RUNX1::CBFA2T3 (panel F). Acute myeloid leukemia (AML) with RUNX1::CBFA2T3 is a rare AML with characteristic morphologic and immunophenotypic features that overlap with AML with RUNX1::RUNX1T1. This case highlights the morphologic and immunophenotypic similarities between these AML subtypes and raises awareness of this rare entity.


Wei J. Wang, Sanam Loghavi; Acute myeloid leukemia with RUNX1:: CBFA2T3 fusion. Blood 2025; 145 (11): 1226. doi: https://doi.org/10.1182/blood.2024027698

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