Japan Approves the World's First Treatment Made With Reprogrammed Human Cells Researchers in Japan pioneered reprogrammed cells 20 years ago. Now the country has given the first-ever authorizations to manufacture and sell medical products based on the technology. Human iPS cell colony established from fibroblasts. Its actual width is approximately 0.5 mm. On March 6, Japan's Ministry of Health, Labor and Welfare officially granted conditional and time-limited marketing authorization to two regenerative medical products derived from reprogrammed iPS cells, marking exactly 20 years since the creation of mouse iPS cells .

Modern science advances fastest when thought meets action. LabOS represents the first AI co-scientist that unites computational reasoning with physical experimentation through multimodal perception, self-evolving agents, and Extended-Reality(XR)-enabled human-AI collaboration. By connecting multi-model AI agents, smart glasses, and robots, LabOS allows AI to see what scientists see, understand experimental context, and assist in real-time execution. Across applications -- from cancer immunotherapy target discovery to stem-cell engineering and material science -- LabOS shows that AI can move beyond computational design to participation, turning the laboratory into an intelligent, collaborative environment where human and machine discovery evolve together.

This replica of the body's blood factory is made entirely with human cells. Breakthroughs, discoveries, and DIY tips sent every weekday. Without even thinking about it, the bone marrow in your body is churning out billions of cells every single day. Bone marrow is our body's strong and silent "blood factory," working hard in the background while heart pumps and brain controls. The spongy marrow really gets attention during a blood cancer diagnosis or when this crucial system stops working properly.

Taiwan University researchers developed a serum that regrew hair in mice within 20 days by using fatty acids to activate hair stem cells after skin injury.

Things to Do in L.A. Tap to enable a layout that focuses on the article. 'I just wanted to help.' Father turns to 9-year-old son for lifesaving stem cell donation Stephen Mondek became what Cedars-Sinai Medical Center believes is its youngest known stem cell donor. His father was dying of acute myeloid leukemia, a cancer that affects blood-forming cells in the bone marrow, and needed a donation to rebuild his immune system. This is read by an automated voice. Please report any issues or inconsistencies here .

Understanding crucial crop's genes can help feed a hungry world. Breakthroughs, discoveries, and DIY tips sent every weekday. Despite the 15 billion bushels grown in the United States last year alone, we still don't know much about corn's stem cells . That may seem like a minor issue, but these cells play a huge role dictating the important plant's growth, health, and hardiness . Identifying the specific genes responsible for these and other factors could help agricultural scientists craft more robust crops--a vital need in the face of food insecurity and climate change.

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are an important resource for the identification of new therapeutic targets and cardioprotective drugs. After differentiation iPSC-CMs show an immature, fetal-like phenotype. Cultivation of iPSC-CMs in lipid-supplemented maturation medium (MM) strongly enhances their structural, metabolic and functional phenotype. Nevertheless, assessing iPSC-CM maturation state remains challenging as most methods are time consuming and go in line with cell damage or loss of the sample. To address this issue, we developed a non-invasive approach for automated classification of iPSC-CM maturity through interpretable artificial intelligence (AI)-based analysis of beat characteristics derived from video-based motion analysis. In a prospective study, we evaluated 230 video recordings of early-state, immature iPSC-CMs on day 21 after differentiation (d21) and more mature iPSC-CMs cultured in MM (d42, MM). For each recording, 10 features were extracted using Maia motion analysis software and entered into a support vector machine (SVM). The hyperparameters of the SVM were optimized in a grid search on 80 % of the data using 5-fold cross-validation. The optimized model achieved an accuracy of 99.5 $\pm$ 1.1 % on a hold-out test set. Shapley Additive Explanations (SHAP) identified displacement, relaxation-rise time and beating duration as the most relevant features for assessing maturity level. Our results suggest the use of non-invasive, optical motion analysis combined with AI-based methods as a tool to assess iPSC-CMs maturity and could be applied before performing functional readouts or drug testing. This may potentially reduce the variability and improve the reproducibility of experimental studies.

Left: PHATE visualization of scRNA-seq data color coded by time intervals. Right: PHATE plot colored by diffusion curvature values. We applied diffusion curvature to a single-cell RNA-sequencing dataset of human embryonic stem cells [1]. These cells were grown as embryoid bodies over a period of 27 days, during which they start as human embryonic stem cells and differentiate into diverse cellular lineages including neural progenitors, cardiac progenitors, muscle progenitors, etc. This developmental process is visualized using PHATE in Figure A1 (left), where embryonic cells (at days 0-3, annotated in blue) progressively branch into the two large splits of endoderm (upper split) and ectoderm (lower split around day 6.

Single-cell sequencing is revolutionizing biology by enabling detailed investigations of cell-state transitions. Many biological processes unfold along continuous trajectories, yet it remains challenging to extract smooth, low-dimensional representations from inherently noisy, high-dimensional single-cell data. Neighbor embedding (NE) algorithms, such as t-SNE and UMAP, are widely used to embed high-dimensional single-cell data into low dimensions. But they often introduce undesirable distortions, resulting in misleading interpretations. Existing evaluation methods for NE algorithms primarily focus on separating discrete cell types rather than capturing continuous cell-state transitions, while dynamic modeling approaches rely on strong assumptions about cellular processes and specialized data. To address these challenges, we build on the Predictability-Computability-Stability (PCS) framework for reliable and reproducible data-driven discoveries. First, we systematically evaluate popular NE algorithms through empirical analysis, simulation, and theory, and reveal their key shortcomings, such as artifacts and instability. We then introduce NESS, a principled and interpretable machine learning approach to improve NE representations by leveraging algorithmic stability and to enable robust inference of smooth biological structures. NESS offers useful concepts, quantitative stability metrics, and efficient computational workflows to uncover developmental trajectories and cell-state transitions in single-cell data. Finally, we apply NESS to six single-cell datasets, spanning pluripotent stem cell differentiation, organoid development, and multiple tissue-specific lineage trajectories. Across these diverse contexts, NESS consistently yields useful biological insights, such as identification of transitional and stable cell states and quantification of transcriptional dynamics during development.

Extracting scientific evidence from biomedical studies for clinical research questions (e.g., Does stem cell transplantation improve quality of life in patients with medically refractory Crohn's disease compared to placebo?) is a crucial step in synthesising biomedical evidence. In this paper, we focus on the task of document-level scientific evidence extraction for clinical questions with conflicting evidence. To support this task, we create a dataset called CochraneForest, leveraging forest plots from Cochrane systematic reviews. It comprises 202 annotated forest plots, associated clinical research questions, full texts of studies, and study-specific conclusions. Building on CochraneForest, we propose URCA (Uniform Retrieval Clustered Augmentation), a retrieval-augmented generation framework designed to tackle the unique challenges of evidence extraction. Our experiments show that URCA outperforms the best existing methods by up to 10.3% in F1 score on this task. However, the results also underscore the complexity of CochraneForest, establishing it as a challenging testbed for advancing automated evidence synthesis systems.