High-throughput image analysis in the biomedical domain has gained significant attention in recent years, driving advancements in drug discovery, disease prediction, and personalized medicine. Organoids, specifically, are an active area of research, providing excellent models for human organs and their functions. Automating the quantification of organoids in microscopy images would provide an effective solution to overcome substantial manual quantification bottlenecks, particularly in high-throughput image analysis. However, there is a notable lack of open biomedical datasets, in contrast to other domains, such as autonomous driving, and, notably, only few of them have attempted to quantify annotation uncertainty. In this work, we present MultiOrg a comprehensive organoid dataset tailored for object detection tasks with uncertainty quantification. This dataset comprises over 400 high-resolution 2d microscopy images and curated annotations of more than 60,000 organoids. Most importantly, it includes three label sets for the test data, independently annotated by two experts at distinct time points. We additionally provide a benchmark for organoid detection, and make the best model available through an easily installable, interactive plugin for the popular image visualization tool Napari, to perform organoid quantification.

Organoids, specifically, are an active area of research, providing excellent models for human organs and their functions.

Pacific Grove, California--Pop a few human stem cells into culture, provide the right molecular signals, and before long a mock cerebral cortex or a cerebellum knockoff could be floating in the medium. These neural, or brain, organoids, typically just a few millimeters across, are not "brains in a dish," as some journalists have described them. But they are becoming ever more sophisticated and true to life, capturing more of the brain's cellular and structural intricacy. "It's surprising how far this [area] has advanced in the last year," says John Evans, a sociologist at the University of California San Diego who follows the research and public opinions on it. That progress has allowed researchers to delve deeper into how the human brain develops, functions, and goes awry in diseases, but it has also sharpened ethical questions.

The capacity of an embodied agent to understand, predict, and interact with its environment is fundamentally contingent on an internal world model. This paper introduces a novel framework for investigating the formation and adaptation of such world models within a biological substrate: human neural organoids. We present a curriculum of three scalable, closed-loop virtual environments designed to train these biological agents and probe the underlying synaptic mechanisms of learning, such as long-term potentiation (LTP) and long-term depression (LTD). We detail the design of three distinct task environments that demand progressively more sophisticated world models for successful decision-making: (1) a conditional avoidance task for learning static state-action contingencies, (2) a one-dimensional predator-prey scenario for goal-directed interaction, and (3) a replication of the classic Pong game for modeling dynamic, continuous-time systems. For each environment, we formalize the state and action spaces, the sensory encoding and motor decoding mechanisms, and the feedback protocols based on predictable (reward) and unpredictable (punishment) stimulation, which serve to drive model refinement. In a significant methodological advance, we propose a meta-learning approach where a Large Language Model automates the generative design and optimization of experimental protocols, thereby scaling the process of environment and curriculum design. Finally, we outline a multi-modal evaluation strategy that moves beyond task performance to directly measure the physical correlates of the learned world model by quantifying synaptic plasticity at electrophysiological, cellular, and molecular levels. This work bridges the gap between model-based reinforcement learning and computational neuroscience, offering a unique platform for studying embodiment, decision-making, and the physical basis of intelligence.

The toxic metal may have rewired early human brains--and sealed the Neanderthals' fate. Lead exposure may have negatively affected Neanderthal abilities for language and speech development. Breakthroughs, discoveries, and DIY tips sent every weekday. Today, lead exposure directly correlates to a post-industrialized world. However, new evidence indicates that exposure to the poisonous element is not necessarily a new issue.

It may have its roots in science fiction, but a small number of researchers are making real progress trying to create computers out of living cells. Welcome to the weird world of biocomputing. Among those leading the way are a group of scientists in Switzerland, who I went to meet. One day, they hope we could see data centres full of living servers which replicate aspects of how artificial intelligence (AI) learns - and could use a fraction of the energy of current methods. That is the vision of Dr Fred Jordan, co-founder of the FinalSpark lab I visited.

Organoids replicate organ structure and function, playing a crucial role in fields such as tumor treatment and drug screening. Their shape and size can indicate their developmental status, but traditional fluorescence labeling methods risk compromising their structure. Therefore, this paper proposes an automated, non-destructive approach to organoid segmentation and tracking. We introduced the LGBP-OrgaNet, a deep learning-based system proficient in accurately segmenting, tracking, and quantifying organoids. The model leverages complementary information extracted from CNN and Transformer modules and introduces the innovative feature fusion module, Learnable Gaussian Band Pass Fusion, to merge data from two branches. Additionally, in the decoder, the model proposes a Bidirectional Cross Fusion Block to fuse multi-scale features, and finally completes the decoding through progressive concatenation and upsampling. SROrga demonstrates satisfactory segmentation accuracy and robustness on organoids segmentation datasets, providing a potent tool for organoid research.

This study proposes a generalizable encoding strategy that maps tactile sensor data to electrical stimulation patterns, enabling neural organoids to perform an open-loop artificial tactile Braille classification task. Human forebrain organoids cultured on a low-density microelectrode array (MEA) are systematically stimulated to characterize the relationship between electrical stimulation parameters (number of pulse, phase amplitude, phase duration, and trigger delay) and organoid responses, measured as spike activity and spatial displacement of the center of activity. Implemented on event-based tactile inputs recorded from the Evetac sensor, our system achieved an average Braille letter classification accuracy of 61 percent with a single organoid, which increased significantly to 83 percent when responses from a three-organoid ensemble were combined. Additionally, the multi-organoid configuration demonstrated enhanced robustness against various types of artificially introduced noise. This research demonstrates the potential of organoids as low-power, adaptive bio-hybrid computational elements and provides a foundational encoding framework for future scalable bio-hybrid computing architectures.

Breakthroughs, discoveries, and DIY tips sent every weekday. Biomedical engineers have achieved a major breakthrough in organoid research, pushing us closer to a new era of neurophysiological analysis and treatments. A team at Johns Hopkins University has created some of the first whole-brain organoids that include interconnected, functional tissues from each region of the human brain. According to their paper published in the journal Advanced Science, these neuronal cell masses display activity similar to what's seen in a 40-day-old human fetus, and may soon allow for better, more effective drug treatments for diseases like Parkinson's and Alzheimer's. Brain organoid development is one of the most promising, complex, and often surreal biomedical frontiers.