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⚡️📣👇Tremendously excited to share our new Cell article, where we develop TriPath, a method for analyzing 3D pathology samples using weakly supervised AI. Article: TriPath enables 3D computational pathology via 3D multiple instance learning allowing AI models to capture intricate morphological details from pathology volumes. Code: Blog post: Tested...

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Faisal Mahmood

6,996 subscribers

65,541 views • 2 years ago •via X (Twitter)

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David Ouyang, MD2 years ago

@CellCellPress Congrats!

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AAA2 years ago

@CellCellPress Incredible!

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⚡🎉 We are thrilled to introduce VORTEX, an AI-powered computational framework for predicting 3D Spatial Transcriptomics (ST) using 3D tissue images and minimal 2D ST! 🧬 By combining cutting-edge 3D non-destructive tissue imaging with AI, VORTEX imputes the 3D molecular landscape of large tissue samples in a cost-effective and scalable manner. 🧠💡Our approach: By pretraining on diverse 3D morphology–2D transcriptomic pairs from heterogeneous tissue samples, and then fine-tuning on minimal 2D ST data from a volume of interest, VORTEX leverages both generic tissue-specific and sample-specific morphomolecular correlates to predict 3D ST. Congratulations to our superstar co-leads Cristina Almagro Pérez and Andrew H. Song, this was an exciting collaboration with Jonathan Liu Sizun Jiang Ali Bashashati. Preprint: Demo: Read the excellent blog from our superstar grad student Cristina Almagro Pérez: Also see our previous work on 3D Computational Pathology from Andrew H. Song published in Cell last year: Stay tuned for more to come.
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⚡🎉 We are thrilled to introduce VORTEX, an AI-powered computational framework for predicting 3D Spatial Transcriptomics (ST) using 3D tissue images and minimal 2D ST! 🧬 By combining cutting-edge 3D non-destructive tissue imaging with AI, VORTEX imputes the 3D molecular landscape of large tissue samples in a cost-effective and scalable manner. 🧠💡Our approach: By pretraining on diverse 3D morphology–2D transcriptomic pairs from heterogeneous tissue samples, and then fine-tuning on minimal 2D ST data from a volume of interest, VORTEX leverages both generic tissue-specific and sample-specific morphomolecular correlates to predict 3D ST. Congratulations to our superstar co-leads Cristina Almagro Pérez and Andrew H. Song, this was an exciting collaboration with Jonathan Liu Sizun Jiang Ali Bashashati. Preprint: Demo: Read the excellent blog from our superstar grad student Cristina Almagro Pérez: Also see our previous work on 3D Computational Pathology from Andrew H. Song published in Cell last year: Stay tuned for more to come.

Faisal Mahmood

17,991 views • 1 year ago

From 3D imaging to therapy Using our DISCO tissue clearing and whole organ 3D imaging platform, we revealed skull to meninges microchannels connecting calvarial bone marrow with the brain borders in mice (Cai...Ertürk, Nature Neuroscience,2018, We then extended this biology to humans, discovering distinct skull bone marrow programs and structural skull meninges connections in health and neurological disease (Kolabas...Ertürk, Cell, 2023). Now, a new study reports a first therapeutic application of this route: intracalvariosseous (calvarial) injection of drug loaded albumin nanoparticles to “hijack” calvarial immune cells, which then migrate via skull meninges microchannels to CNS lesions, bypassing the blood brain barrier and improving outcomes in stroke models, with encouraging clinical feasibility data (Gao et al., Cell, 2026) We are proud that DISCO enabled biology that is now turning into concrete therapeutic strategies for devastating neurological diseases. More to come. #DISCO #TissueClearing #Neuroimmunology #Stroke #Nanomedicine #DrugDelivery #BBB #CNS Refs (for linking in comments): Cai et al. Nat Neuroscience, Kolabas et al. Cell 2023, the video from Gao et al. Cell 2026 (online Jan 16, 2026).
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From 3D imaging to therapy Using our DISCO tissue clearing and whole organ 3D imaging platform, we revealed skull to meninges microchannels connecting calvarial bone marrow with the brain borders in mice (Cai...Ertürk, Nature Neuroscience,2018, We then extended this biology to humans, discovering distinct skull bone marrow programs and structural skull meninges connections in health and neurological disease (Kolabas...Ertürk, Cell, 2023). Now, a new study reports a first therapeutic application of this route: intracalvariosseous (calvarial) injection of drug loaded albumin nanoparticles to “hijack” calvarial immune cells, which then migrate via skull meninges microchannels to CNS lesions, bypassing the blood brain barrier and improving outcomes in stroke models, with encouraging clinical feasibility data (Gao et al., Cell, 2026) We are proud that DISCO enabled biology that is now turning into concrete therapeutic strategies for devastating neurological diseases. More to come. #DISCO #TissueClearing #Neuroimmunology #Stroke #Nanomedicine #DrugDelivery #BBB #CNS Refs (for linking in comments): Cai et al. Nat Neuroscience, Kolabas et al. Cell 2023, the video from Gao et al. Cell 2026 (online Jan 16, 2026).

Ali Max Erturk

15,912 views • 6 months ago

Excited to share TERRA, a tissue world model 🧬 Over ~1.5 years we ran a large data-generation + modelling effort to build a world model for human tissues, pretrained on 112M cells from spatial transcriptomics (mostly Xenium 5000-plex + public data). It's built on one of the largest human spatial transcriptomics corpora assembled to date, spanning 20 tissues across development, health and 26 disease conditions, ~two-thirds newly generated in-house. Why a "world model" for tissue? Images have universal representations (ViT/DINOv3), so do proteins (ESM, Alex Rives) and pathology (UNI, Faisal Mahmood). We've worked hard to build something similar for human tissue: one model that captures its multi-scale logic, genes → cells → their native microenvironments. Like the JEPA approach Yann LeCun has championed, TERRA learns by prediction in embedding space, but for human tissue. How it works: it tokenises each cell together with its nearest neighbours into one sequence while keeping every gene's identity, then masks part of a neighbourhood and predicts the representation of the hidden part, not raw noisy counts. From one backbone it reads out three scales, gene embeddings (what a gene is doing in a cell and its niche), cell embeddings (cell type and state) and neighbourhood embeddings (the niche), and because it keeps gene-level resolution it can knock a gene out in silico and predict the response. Applied entirely zero-shot, TERRA maps and perturbs human tissue across unseen organs, diseases and technologies, outperforming existing spatial approaches. Three take-homes: 1️⃣ One model, any tissue. A single pretrained backbone provides tissue representations zero-shot, handling genes, cells and niches across organs and platforms, off the shelf. 2️⃣ New biology, development to clinic. We built a new spatial atlas of the developing human pancreas and found an islet-associated capillary state that looks like a precursor of mature islet vasculature. In kidney, TERRA's in silico knockouts predicted the tissue-injury programme from cancer immunotherapy (checkpoint blockade), confirmed in treated kidneys, detected in blood, and linked to declining kidney function. 3️⃣ A grammar of tissue architecture. By coupling each cell's state to its niche, TERRA defines recurring cross-organ "archetypes" of macrophage neighbourhoods, including a tumour-boundary niche that tracks poor survival in kidney cancer. TERRA is already in use: it powered our recent skin atlas of hidden immune-memory niches ( with more studies coming soon. This was an amazing collaboration between clinicians, machine-learning scientists and cell biologists 🙏 Led by Sebastian Birk, Vali Sanian Amirhosein Vahidi, Samuel Ogden, Daniyal Jafree, Adib Miraki, Carlo Leonardi and Arpit Merchant, with Lassi Paavolainen, Menna Clatworthy, Omer Ali Bayraktar, Muzlifah Haniffa, Tom Mitchell and Mostafa Bakhti. Huge thanks too to everyone who shared data and helped along the way. What excites me most is seeing how the community builds on this. The model, code and tutorials are all public, so anyone can run TERRA on their own tissues, extend it, or build new models on top. Huge thanks to the whole team across Wellcome Sanger Institute and our many collaborators. 📄 Paper: 💻 Code: 🤗 Model: #SpatialTranscriptomics #SpatialGenomics #FoundationModels #AI4Science #MachineLearning #ComputationalBiology #SingleCell #WorldModels
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Excited to share TERRA, a tissue world model 🧬 Over ~1.5 years we ran a large data-generation + modelling effort to build a world model for human tissues, pretrained on 112M cells from spatial transcriptomics (mostly Xenium 5000-plex + public data). It's built on one of the largest human spatial transcriptomics corpora assembled to date, spanning 20 tissues across development, health and 26 disease conditions, ~two-thirds newly generated in-house. Why a "world model" for tissue? Images have universal representations (ViT/DINOv3), so do proteins (ESM, Alex Rives) and pathology (UNI, Faisal Mahmood). We've worked hard to build something similar for human tissue: one model that captures its multi-scale logic, genes → cells → their native microenvironments. Like the JEPA approach Yann LeCun has championed, TERRA learns by prediction in embedding space, but for human tissue. How it works: it tokenises each cell together with its nearest neighbours into one sequence while keeping every gene's identity, then masks part of a neighbourhood and predicts the representation of the hidden part, not raw noisy counts. From one backbone it reads out three scales, gene embeddings (what a gene is doing in a cell and its niche), cell embeddings (cell type and state) and neighbourhood embeddings (the niche), and because it keeps gene-level resolution it can knock a gene out in silico and predict the response. Applied entirely zero-shot, TERRA maps and perturbs human tissue across unseen organs, diseases and technologies, outperforming existing spatial approaches. Three take-homes: 1️⃣ One model, any tissue. A single pretrained backbone provides tissue representations zero-shot, handling genes, cells and niches across organs and platforms, off the shelf. 2️⃣ New biology, development to clinic. We built a new spatial atlas of the developing human pancreas and found an islet-associated capillary state that looks like a precursor of mature islet vasculature. In kidney, TERRA's in silico knockouts predicted the tissue-injury programme from cancer immunotherapy (checkpoint blockade), confirmed in treated kidneys, detected in blood, and linked to declining kidney function. 3️⃣ A grammar of tissue architecture. By coupling each cell's state to its niche, TERRA defines recurring cross-organ "archetypes" of macrophage neighbourhoods, including a tumour-boundary niche that tracks poor survival in kidney cancer. TERRA is already in use: it powered our recent skin atlas of hidden immune-memory niches ( with more studies coming soon. This was an amazing collaboration between clinicians, machine-learning scientists and cell biologists 🙏 Led by Sebastian Birk, Vali Sanian Amirhosein Vahidi, Samuel Ogden, Daniyal Jafree, Adib Miraki, Carlo Leonardi and Arpit Merchant, with Lassi Paavolainen, Menna Clatworthy, Omer Ali Bayraktar, Muzlifah Haniffa, Tom Mitchell and Mostafa Bakhti. Huge thanks too to everyone who shared data and helped along the way. What excites me most is seeing how the community builds on this. The model, code and tutorials are all public, so anyone can run TERRA on their own tissues, extend it, or build new models on top. Huge thanks to the whole team across Wellcome Sanger Institute and our many collaborators. 📄 Paper: 💻 Code: 🤗 Model: #SpatialTranscriptomics #SpatialGenomics #FoundationModels #AI4Science #MachineLearning #ComputationalBiology #SingleCell #WorldModels

Mo Lotfollahi

49,648 views • 10 days ago

Axel update. We received the amended pathology report from CSU. The mass removed from Axel’s femur is now being classified as most consistent with osteosarcoma, an aggressive bone cancer. There was some good news in the report. The lymph node they tested showed no cancer cells, and the pelvic tissue samples they examined did not show tumor invasion. The margins in the pelvic sections they reviewed were clear. The primary tumor was removed, and the areas they tested did not show visible spread. The difficult reality is that osteosarcoma is known for microscopic spread that often cannot be detected early. While the scans, lymph node, and tissue samples they examined were clear, there is no way to know whether microscopic cancer cells exist elsewhere in the body. The pathology report states that dogs with appendicular osteosarcoma treated with amputation alone had a median survival time of 19.2 weeks, with an estimated one year survival rate of 11.5% and a two year survival rate of 2%. That’s the reality of what we were told. At the same time, Axel is recovering well from surgery. He’s eating, healing, moving around, and still showing the same stubborn fight that everyone who knows him would expect. Now we have some difficult decisions to make moving forward regarding treatment options, monitoring, quality of life, and what gives Axel the best chance possible. Thank you to everyone who has prayed, donated, shared, checked in, and supported Axel through this fight. We truly appreciate it more than words can express.
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Axel update. We received the amended pathology report from CSU. The mass removed from Axel’s femur is now being classified as most consistent with osteosarcoma, an aggressive bone cancer. There was some good news in the report. The lymph node they tested showed no cancer cells, and the pelvic tissue samples they examined did not show tumor invasion. The margins in the pelvic sections they reviewed were clear. The primary tumor was removed, and the areas they tested did not show visible spread. The difficult reality is that osteosarcoma is known for microscopic spread that often cannot be detected early. While the scans, lymph node, and tissue samples they examined were clear, there is no way to know whether microscopic cancer cells exist elsewhere in the body. The pathology report states that dogs with appendicular osteosarcoma treated with amputation alone had a median survival time of 19.2 weeks, with an estimated one year survival rate of 11.5% and a two year survival rate of 2%. That’s the reality of what we were told. At the same time, Axel is recovering well from surgery. He’s eating, healing, moving around, and still showing the same stubborn fight that everyone who knows him would expect. Now we have some difficult decisions to make moving forward regarding treatment options, monitoring, quality of life, and what gives Axel the best chance possible. Thank you to everyone who has prayed, donated, shared, checked in, and supported Axel through this fight. We truly appreciate it more than words can express.

John “TIG” Tiegen

14,332 views • 2 months ago

It Wasn't Supposed To Be There, Let Alone Replicate: Bombshell Analysis of a Cancer Biopsy Reveals 'Shocking' Levels of Pfizer's Vaccine Sequence, Suggesting a Mechanism Far Different Than What Was Promised. In an interview with Sharyl Attkisson, Dr. Kevin McKernan, a seasoned expert in genomics, revealed a discovery concerning COVID-19 mRNA vaccines that warrants immediate and serious investigation. The Finding: Researchers analyzed tissue from a patient who developed an aggressive colon cancer and died just 30 days after diagnosis. This individual had received four doses of the Pfizer vaccine. Using advanced genomic sequencing, they went looking for a trace of the vaccine in the cancerous tissue. The Shock: Not only was the Pfizer vaccine sequence present, but the quantity was described as "a lot of it." As Dr. McKernan states, this was "not expected." The volume of genetic material suggests it did not just passively reside there; the data indicates it may have gotten into the cells and started replicating. The Critical Concern: While a direct causal link to cancer cannot be definitively established from this single case, the presence of replicating vaccine sequences in a tumor is profoundly concerning. Dr. McKernan explains that there are known biological mechanisms where this could play a role in oncogenesis (the formation of cancer). The Call to Action: This is where public awareness becomes crucial. Dr. McKernan's urgent message is that evidence is being lost. "Too many of these tumor biopsies are getting thrown away." If a family member develops an unexplained cancer, it is imperative to: 1. Get a pathologist involved. 2. Ensure a slide or tissue sample is preserved and retained for potential future study. This is not about spreading fear, but about preserving data. To understand if this is an isolated tragedy or a wider pattern, the scientific community needs samples to investigate. The answers lie in the tissue, and we must not let that evidence disappear.
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It Wasn't Supposed To Be There, Let Alone Replicate: Bombshell Analysis of a Cancer Biopsy Reveals 'Shocking' Levels of Pfizer's Vaccine Sequence, Suggesting a Mechanism Far Different Than What Was Promised. In an interview with Sharyl Attkisson, Dr. Kevin McKernan, a seasoned expert in genomics, revealed a discovery concerning COVID-19 mRNA vaccines that warrants immediate and serious investigation. The Finding: Researchers analyzed tissue from a patient who developed an aggressive colon cancer and died just 30 days after diagnosis. This individual had received four doses of the Pfizer vaccine. Using advanced genomic sequencing, they went looking for a trace of the vaccine in the cancerous tissue. The Shock: Not only was the Pfizer vaccine sequence present, but the quantity was described as "a lot of it." As Dr. McKernan states, this was "not expected." The volume of genetic material suggests it did not just passively reside there; the data indicates it may have gotten into the cells and started replicating. The Critical Concern: While a direct causal link to cancer cannot be definitively established from this single case, the presence of replicating vaccine sequences in a tumor is profoundly concerning. Dr. McKernan explains that there are known biological mechanisms where this could play a role in oncogenesis (the formation of cancer). The Call to Action: This is where public awareness becomes crucial. Dr. McKernan's urgent message is that evidence is being lost. "Too many of these tumor biopsies are getting thrown away." If a family member develops an unexplained cancer, it is imperative to: 1. Get a pathologist involved. 2. Ensure a slide or tissue sample is preserved and retained for potential future study. This is not about spreading fear, but about preserving data. To understand if this is an isolated tragedy or a wider pattern, the scientific community needs samples to investigate. The answers lie in the tissue, and we must not let that evidence disappear.

Camus

112,276 views • 10 months ago

Introducing the 10Ka Team 🤝 We've curated a team of exceptionally skilled individuals, each a trailblazer in their respective fields. Together, we possess all the necessary ingredients to realize our vision of becoming the premier project for digital fashion. Let's dive in 👇🏼 Diana Cañas: Breaking Barriers: Diana Cañas Leads the Way in NFT and Digital Fashion Empowerment. As the founder and CEO of 10ka NFT, Diana has taken the industry by storm, brilliantly combining her love for fashion with cutting-edge technologies like NFTs and NFCs. This unique fusion enables her to push the boundaries of the digital fashion realm. Ronin Studio: Ronin is a versatile creative studio specializing in corporate identity and character creation, along with UI/UX design. “Driven by a dedication to emerging trends and artistic/technological innovation, we draw inspiration from both the past and the future of design. Our approach is inherently disruptive, always encouraging us to think beyond conventional boundaries.” Tripton: We are a dynamic creative studio with expertise in 3D production, Visual Shows, and Virtual and Augmented Reality. “Fueled by a passion for pioneering technologies and groundbreaking ideas, we draw inspiration from eminent artists across advertising design, 3D animation, and the broader audiovisual realm.” Virtuozo: Virtuozo is a studio renowned for crafting immersive 3D worlds and captivating virtual experiences. At the helm is our lead 3D generalist, a virtuoso in every sense of the word. With mastery over the craft, he meticulously crafts realistic and aesthetically pleasing 3D models, textures, and animations using state-of-the-art technology. Cristian Torrijos: Blockchain Developer (Solidity, Smart Contracts, Web3) and expert in PHP, MySQL, and ReactJS. “Specializing in the development of Web3 and Web2 projects, we ensure the quality and longevity of our creations, delivering projects that are not only innovative but also enduring.” Domi Vakero: Digital Artist from the South of Spain proficient in 2D, 3D, and AI. With extensive expertise in the illustration and modeling of both 2D and 3D characters, we bring visions to life with creativity and precision. This is just some of the extensive team behind 10KA. We are dedicated to building & becoming one of the leading projects on Ethereum 🫡
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Introducing the 10Ka Team 🤝 We've curated a team of exceptionally skilled individuals, each a trailblazer in their respective fields. Together, we possess all the necessary ingredients to realize our vision of becoming the premier project for digital fashion. Let's dive in 👇🏼 Diana Cañas: Breaking Barriers: Diana Cañas Leads the Way in NFT and Digital Fashion Empowerment. As the founder and CEO of 10ka NFT, Diana has taken the industry by storm, brilliantly combining her love for fashion with cutting-edge technologies like NFTs and NFCs. This unique fusion enables her to push the boundaries of the digital fashion realm. Ronin Studio: Ronin is a versatile creative studio specializing in corporate identity and character creation, along with UI/UX design. “Driven by a dedication to emerging trends and artistic/technological innovation, we draw inspiration from both the past and the future of design. Our approach is inherently disruptive, always encouraging us to think beyond conventional boundaries.” Tripton: We are a dynamic creative studio with expertise in 3D production, Visual Shows, and Virtual and Augmented Reality. “Fueled by a passion for pioneering technologies and groundbreaking ideas, we draw inspiration from eminent artists across advertising design, 3D animation, and the broader audiovisual realm.” Virtuozo: Virtuozo is a studio renowned for crafting immersive 3D worlds and captivating virtual experiences. At the helm is our lead 3D generalist, a virtuoso in every sense of the word. With mastery over the craft, he meticulously crafts realistic and aesthetically pleasing 3D models, textures, and animations using state-of-the-art technology. Cristian Torrijos: Blockchain Developer (Solidity, Smart Contracts, Web3) and expert in PHP, MySQL, and ReactJS. “Specializing in the development of Web3 and Web2 projects, we ensure the quality and longevity of our creations, delivering projects that are not only innovative but also enduring.” Domi Vakero: Digital Artist from the South of Spain proficient in 2D, 3D, and AI. With extensive expertise in the illustration and modeling of both 2D and 3D characters, we bring visions to life with creativity and precision. This is just some of the extensive team behind 10KA. We are dedicated to building & becoming one of the leading projects on Ethereum 🫡

10KA👟

39,304 views • 2 years ago

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.
1:05

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

Khurram Javed

52,110 views • 8 months ago

Want to create an avatar from a single image? FlexAvatar is a transformer model that creates full 360°, high-quality, and expressive 3D head avatar from just a single portrait image in minutes. Real-time Demo: FlexAvatar's lightweight architecture allows both animation and rendering in real-time, enabling interactive user experiences. To create a new 3D head avatar, only one image is required, e.g., from a webcam. The final avatar is ready after 2 minutes. Architecture: Under the hood, FlexAvatar adopts a transformer-based encoder-decoder design. The encoder maps the input image onto a latent avatar space, while the decoder produces 3D Gaussian attribute maps by incorporating the animation signal via cross-attention. The model learns all facial animations directly from the data without relying on pre-built 3D face models. This equips the avatars with realistic facial expressions. The internal avatar latent space can be conveniently used to integrate additional observations of a person via fitting. This enables use-cases where more than one image of a person is available, e.g., from a phone scan of the person. We train jointly on 2D monocular videos and multi-view data. However, in monocular videos, the animation signal leaks the target viewpoint, causing the model to produce incomplete 3D heads. We call this phenomenon entanglement of driving signal and target viewpoint. To prevent entanglement, we introduce bias sinks. These are learnable tokens that indicate whether a training sample stems from a monocular or a multi-view dataset. During training, the model learns to produce incomplete 3D heads only when the monocular token is present. During inference, FlexAvatar then always uses the multi-view token for which the model has learned to produce complete 3D heads. This simple design allows to combine the generalizability from monocular data with the quality of multi-view data. FlexAvatar summary: - Input: Single-image, phone scan, or monocular video - Output: Full 360° head avatar - Expressive animations - Real-time rendering and animation - Generalization to any portrait - Create a new avatar in 2 minutes - Use bias sinks to combine 2D and 3D data 🏠 🌍 🎥 Great work by Tobias Kirschstein and Simon Giebenhain!
0:30

Want to create an avatar from a single image? FlexAvatar is a transformer model that creates full 360°, high-quality, and expressive 3D head avatar from just a single portrait image in minutes. Real-time Demo: FlexAvatar's lightweight architecture allows both animation and rendering in real-time, enabling interactive user experiences. To create a new 3D head avatar, only one image is required, e.g., from a webcam. The final avatar is ready after 2 minutes. Architecture: Under the hood, FlexAvatar adopts a transformer-based encoder-decoder design. The encoder maps the input image onto a latent avatar space, while the decoder produces 3D Gaussian attribute maps by incorporating the animation signal via cross-attention. The model learns all facial animations directly from the data without relying on pre-built 3D face models. This equips the avatars with realistic facial expressions. The internal avatar latent space can be conveniently used to integrate additional observations of a person via fitting. This enables use-cases where more than one image of a person is available, e.g., from a phone scan of the person. We train jointly on 2D monocular videos and multi-view data. However, in monocular videos, the animation signal leaks the target viewpoint, causing the model to produce incomplete 3D heads. We call this phenomenon entanglement of driving signal and target viewpoint. To prevent entanglement, we introduce bias sinks. These are learnable tokens that indicate whether a training sample stems from a monocular or a multi-view dataset. During training, the model learns to produce incomplete 3D heads only when the monocular token is present. During inference, FlexAvatar then always uses the multi-view token for which the model has learned to produce complete 3D heads. This simple design allows to combine the generalizability from monocular data with the quality of multi-view data. FlexAvatar summary: - Input: Single-image, phone scan, or monocular video - Output: Full 360° head avatar - Expressive animations - Real-time rendering and animation - Generalization to any portrait - Create a new avatar in 2 minutes - Use bias sinks to combine 2D and 3D data 🏠 🌍 🎥 Great work by Tobias Kirschstein and Simon Giebenhain!

Matthias Niessner

96,186 views • 8 months ago

We’re thrilled to share that our MERFISH+ preprint is now live on bioRxiv!👉 In this work, the Bintu and Zhu labs (UCSD) developed MERFISH+, a next-generation spatial genomics platform that combines genome-wide RNA and epigenetic imaging over a large field of view. By introducing acrydite-modified probes covalently anchored to hydrogels, MERFISH+ achieves remarkable imaging stability and enables >1,800-gene, multi-modal, and multi-month experiments. With this platform, they, together with the Chi lab at UCSD, profiled a whole developing human heart at 12 post-conception week with merely two slides, resulting in a total of 53 slides, 3.1 million single cells and more than 30 cell types. Building upon our previous 3D reconstruction and modeling framework, Spateo ( we reconstruct the 3D human heart that nicely captures the anatomical structure of the heart, including the intricate vasculature network. Sophisticated analyses provide a holistic view of an entire organ and enable systematic characterization of 3D cellular neighborhoods and transcriptional gradients of substructures such as the descending arteries. Furthermore, using a generative integration framework for spatial multimodal data (Spateo-VI), we harmonized these MERFISH+ transcriptomic and chromatin data to reconstruct a 3D spatially-resolved multi-omics atlas of the developing human heart, shared at and MERFISH+ thus sets a new standard for large-format, multi-omic spatial profiling, enabling holistic, 3D characterization of organs at subcellular resolution. Huge congratulations to first authors Colin Kern, qingquan Zhang, @YifanLu2024 , and Jacqueline Eschbach, and to all collaborators from the Bintu, Zhu, Chi, and Qiu labs for this amazing team effort. Thanks for your diligence, creativity, and hard work on this project. We’re grateful for support from Arc Institute and our generous donors. Our lab is expanding—if you’re excited about building the next generation of single-cell and spatial genomics techniques and predictive single cell and spatial foundation models, we’re hiring! If you are interested, please reach out to me via direct message or email at xiaojie@stanford.edu. We are excited for any potential collaborations along this line of research in Stanford, UCSF and Berkeley and other labs as well.
0:42

We’re thrilled to share that our MERFISH+ preprint is now live on bioRxiv!👉 In this work, the Bintu and Zhu labs (UCSD) developed MERFISH+, a next-generation spatial genomics platform that combines genome-wide RNA and epigenetic imaging over a large field of view. By introducing acrydite-modified probes covalently anchored to hydrogels, MERFISH+ achieves remarkable imaging stability and enables >1,800-gene, multi-modal, and multi-month experiments. With this platform, they, together with the Chi lab at UCSD, profiled a whole developing human heart at 12 post-conception week with merely two slides, resulting in a total of 53 slides, 3.1 million single cells and more than 30 cell types. Building upon our previous 3D reconstruction and modeling framework, Spateo ( we reconstruct the 3D human heart that nicely captures the anatomical structure of the heart, including the intricate vasculature network. Sophisticated analyses provide a holistic view of an entire organ and enable systematic characterization of 3D cellular neighborhoods and transcriptional gradients of substructures such as the descending arteries. Furthermore, using a generative integration framework for spatial multimodal data (Spateo-VI), we harmonized these MERFISH+ transcriptomic and chromatin data to reconstruct a 3D spatially-resolved multi-omics atlas of the developing human heart, shared at and MERFISH+ thus sets a new standard for large-format, multi-omic spatial profiling, enabling holistic, 3D characterization of organs at subcellular resolution. Huge congratulations to first authors Colin Kern, qingquan Zhang, @YifanLu2024 , and Jacqueline Eschbach, and to all collaborators from the Bintu, Zhu, Chi, and Qiu labs for this amazing team effort. Thanks for your diligence, creativity, and hard work on this project. We’re grateful for support from Arc Institute and our generous donors. Our lab is expanding—if you’re excited about building the next generation of single-cell and spatial genomics techniques and predictive single cell and spatial foundation models, we’re hiring! If you are interested, please reach out to me via direct message or email at [email protected]. We are excited for any potential collaborations along this line of research in Stanford, UCSF and Berkeley and other labs as well.

evo-devo

42,268 views • 9 months ago

📢📢 𝐀𝐯𝐚𝐭𝟑𝐫 📢📢 Avat3r creates high-quality 3D head avatars from just a few input images in a single forward pass with a new dynamic 3DGS reconstruction model. Video: Project: Our core idea is to make Gaussian Reconstruction Models animatable. We find that a simple cross-attention to an expression code sequence is already sufficient to model complex facial expressions. We then incorporate position maps from DUSt3R and feature maps from Sapiens to facilitate the prediction task. While DUSt3R's position maps act as a pixel-aligned initialization for the Gaussians' positions, the Sapiens feature maps help the cross-view transformer to match corresponding image tokens in the 4 input images. One major challenge in creating a 3D head avatar from smartphone images comes from inconsistent facial expressions when the subject could not remain perfectly static during the capture. We eliminate this static requirement by simply showing our model input images with different facial expressions during training. This technique makes our model robust to inconsistent input images later on. Finally, we show that despite the model has been trained with 4 input images, one can even create a 3D head avatar when only a single image is available. To achieve this, we employ a pre-trained 3D GAN to lift the single image to 3D and then render the 4 input images for our model. This allows us to create 3D head avatars from single images and even highly out-of-distribution examples like AI generated faces, paintings or statues. Great work by Tobias Kirschstein from his internship at Meta with Javier Romero, Artem Sevastopolsky, and Shunsuke Saito
0:38

📢📢 𝐀𝐯𝐚𝐭𝟑𝐫 📢📢 Avat3r creates high-quality 3D head avatars from just a few input images in a single forward pass with a new dynamic 3DGS reconstruction model. Video: Project: Our core idea is to make Gaussian Reconstruction Models animatable. We find that a simple cross-attention to an expression code sequence is already sufficient to model complex facial expressions. We then incorporate position maps from DUSt3R and feature maps from Sapiens to facilitate the prediction task. While DUSt3R's position maps act as a pixel-aligned initialization for the Gaussians' positions, the Sapiens feature maps help the cross-view transformer to match corresponding image tokens in the 4 input images. One major challenge in creating a 3D head avatar from smartphone images comes from inconsistent facial expressions when the subject could not remain perfectly static during the capture. We eliminate this static requirement by simply showing our model input images with different facial expressions during training. This technique makes our model robust to inconsistent input images later on. Finally, we show that despite the model has been trained with 4 input images, one can even create a 3D head avatar when only a single image is available. To achieve this, we employ a pre-trained 3D GAN to lift the single image to 3D and then render the 4 input images for our model. This allows us to create 3D head avatars from single images and even highly out-of-distribution examples like AI generated faces, paintings or statues. Great work by Tobias Kirschstein from his internship at Meta with Javier Romero, Artem Sevastopolsky, and Shunsuke Saito

Matthias Niessner

74,763 views • 1 year ago

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:
1:07

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:

SpAItial AI

176,105 views • 8 months ago

🎉 The best way to start the week is to find out that our MedSAM is finally published today in Nature Communications! **Segment anything in medical images** Paper: arXiv: Data & Code: MedSAM is the first promotable foundation model for medical image segmentation. **Highlights**: ⭐ Before its formal publication, we have received 220 citations and 1400+ GitHub stars 🙏🙏❤️‍🔥❤️‍🔥❤️‍🔥 📊 We curated a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. 🚀 Built on top of SAM (AI at Meta ) with transfer learning, we have significantly enhanced its segmentation performance of medical images. 📈 Comprehensive evaluations of 86 internal validation tasks and 60 external validation tasks demonstrate its better accuracy and robustness than modality-wise specialist models. **What is Next? --- Clinical Translation!!** 🍕Our next goal is to make the model deployable on laptops (CPUs) or other edge devices without reliance on GPUs. We have distilled a lightweight model, LiteMedSAM, offering a speed boost of 10x while maintaining accuracy. Plus, we have integrated it into the 3D Slicer plugin, providing an efficient tool for medical image segmentation. 🌐 To further promote developments in this field, we organize a competition on #CVPR2026: Segment Anything in Medical Images on Laptop! An out-of-the-box baseline has been released to reduce the entry barriers. Welcome to join us to push the boundary further: 🙏 Massive thanks to MetaAI AI at Meta for their open-source project SAM and many reviewers/users for their invaluable feedback. A huge shoutout to my postdoc Jun Ma (JunMa) for his leadership on this project!! UHN AI Hub Vector Institute Peter Munk Cardiac Centre AI Department of Laboratory Medicine & Pathobiology U of T Department of Computer Science University of Toronto University Health Network Brad Wouters 🇨🇦 Barry Rubin MD, PhD, FRCSC Shaf Keshavjee
0:59

🎉 The best way to start the week is to find out that our MedSAM is finally published today in Nature Communications! **Segment anything in medical images** Paper: arXiv: Data & Code: MedSAM is the first promotable foundation model for medical image segmentation. **Highlights**: ⭐ Before its formal publication, we have received 220 citations and 1400+ GitHub stars 🙏🙏❤️‍🔥❤️‍🔥❤️‍🔥 📊 We curated a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. 🚀 Built on top of SAM (AI at Meta ) with transfer learning, we have significantly enhanced its segmentation performance of medical images. 📈 Comprehensive evaluations of 86 internal validation tasks and 60 external validation tasks demonstrate its better accuracy and robustness than modality-wise specialist models. **What is Next? --- Clinical Translation!!** 🍕Our next goal is to make the model deployable on laptops (CPUs) or other edge devices without reliance on GPUs. We have distilled a lightweight model, LiteMedSAM, offering a speed boost of 10x while maintaining accuracy. Plus, we have integrated it into the 3D Slicer plugin, providing an efficient tool for medical image segmentation. 🌐 To further promote developments in this field, we organize a competition on #CVPR2026: Segment Anything in Medical Images on Laptop! An out-of-the-box baseline has been released to reduce the entry barriers. Welcome to join us to push the boundary further: 🙏 Massive thanks to MetaAI AI at Meta for their open-source project SAM and many reviewers/users for their invaluable feedback. A huge shoutout to my postdoc Jun Ma (JunMa) for his leadership on this project!! UHN AI Hub Vector Institute Peter Munk Cardiac Centre AI Department of Laboratory Medicine & Pathobiology U of T Department of Computer Science University of Toronto University Health Network Brad Wouters 🇨🇦 Barry Rubin MD, PhD, FRCSC Shaf Keshavjee

Bo Wang

140,229 views • 2 years ago

PHOTON COUNTING CT is NOT a better CT It is a NEW imaging modality Photon Counting CT (PCCT) represents a transformative leap in medical imaging, not only as a molecular imaging modality but also as a technology offering ultra-high resolution and functional imaging capabilities. It is fundamentally more than just an enhanced version of traditional CT—PCCT introduces new ways of seeing and understanding the human body, providing critical insights at the molecular, structural, and functional levels. This positions PCCT as a unique imaging modality that requires a fresh approach to technical implementation, operational workflows, and financial planning. Despite the larger upfront investment, PCCT’s ability to drastically reduce downstream healthcare costs makes it a highly valuable investment in the long run. 1. Technical Innovations • Molecular Imaging and Energy Discrimination: Unlike traditional CT, which simply measures the total absorbed energy, PCCT counts individual X-ray photons and differentiates their energy levels. This allows for precise molecular imaging, revealing the composition of tissues and materials at a biochemical level. By distinguishing between different tissue types and contrast agents, PCCT opens up new diagnostic possibilities, such as identifying molecular biomarkers in tumors or distinguishing between stable and unstable plaque in coronary arteries. This capability shifts the focus of imaging from purely anatomical to both anatomical and molecular, offering more comprehensive diagnostic information. • Ultra-High Spatial Resolution: PCCT features significantly smaller detector elements compared to conventional CT scanners, allowing for ultra-high resolution imaging. This means clinicians can visualize fine structures such as microcalcifications in arteries, small lesions in soft tissues, or the intricate architecture of bones. This level of detail was previously unattainable with traditional CT. When combined with molecular imaging, this ultra-high resolution allows for the precise localization and characterization of disease at very early stages, which is essential for early diagnosis and intervention. • Functional Imaging Capabilities: PCCT also excels as a functional imaging modality. By capturing energy-resolved information, PCCT can provide insights into tissue functionality and dynamic physiological processes. For instance, it can detect changes in blood flow, tissue perfusion, and oxygenation without the need for additional contrast agents or scans. This functionality allows for real-time assessment of physiological processes, making it particularly valuable in cardiology, oncology, and neurology for evaluating organ function and monitoring disease progression. • Reduced Noise and Artifact Reduction: Photon-counting technology dramatically reduces electronic noise and imaging artifacts, such as beam hardening, resulting in clearer and more accurate images. The ability to deliver ultra-high resolution images with minimal artifacts improves diagnostic accuracy, reducing the need for repeat scans and ensuring that even subtle abnormalities are detected. 2. Operational Considerations • New Workflow for Molecular, High-Resolution, and Functional Imaging: The integration of molecular, ultra-high resolution, and functional imaging into routine clinical workflows introduces complexity that requires adaptation. Radiologists and technicians need specialized training to interpret and analyze multi-energy datasets that include molecular and functional information. PCCT produces a vast amount of detailed data, requiring clinicians to adopt new imaging protocols and refine their diagnostic approaches to fully leverage its capabilities. • Post-Processing and Data Management: PCCT generates richer, more complex datasets, which necessitates advanced post-processing tools and data management systems. Existing PACS and imaging software may not be equipped to handle such large volumes of data or to process functional and molecular information effectively. This means healthcare institutions must invest in robust IT infrastructure, including upgraded software and storage solutions, as well as provide additional training for staff on new imaging analysis techniques. • Revised Clinical Protocols: The molecular, functional, and ultra-high resolution imaging capabilities of PCCT will likely prompt changes in clinical protocols. For instance, the need for contrast agents may be reduced, simplifying patient preparation and decreasing the risk of adverse reactions. Additionally, the ability to monitor physiological functions in real-time through functional imaging could lead to more dynamic diagnostic procedures, such as assessing the effectiveness of interventions or treatments in real-time. 3. Financial Impact • Higher Initial Investment: PCCT systems are more expensive than traditional CT scanners due to their advanced technology, which includes photon-counting detectors and the computational power required for high-resolution, molecular, and functional imaging. While this upfront cost is significant, it is crucial to view it in the broader context of the downstream benefits and cost reductions that PCCT offers. • Downstream Cost Reductions: Although the initial capital investment is higher, PCCT’s ability to combine molecular, functional, and ultra-high resolution imaging leads to substantial reductions in downstream healthcare costs. Its superior diagnostic accuracy minimizes the need for follow-up tests, repeat scans, or invasive diagnostic procedures, such as diagnostic coronary angiographies. For example, in cardiology, PCCT can precisely differentiate between types of coronary plaque, reducing the need for invasive procedures to assess risk. • Lower Overall Healthcare Expenditures: By enabling earlier, more accurate diagnoses, PCCT can reduce the overall cost of patient care. Early detection of disease, particularly through its molecular and functional imaging capabilities, allows for more targeted treatments, potentially preventing the need for more aggressive and expensive interventions down the line. For instance, early-stage tumor detection via molecular imaging could lead to less invasive treatments, reducing hospital stays and improving patient outcomes, ultimately driving down healthcare costs. • Increased ROI Through Enhanced Patient Outcomes: Over time, the combination of molecular, functional, and ultra-high resolution imaging enhances diagnostic precision, which translates into better patient outcomes. Improved diagnostic accuracy reduces the incidence of unnecessary procedures, minimizes treatment delays, and results in more personalized and effective care. This leads to increased patient satisfaction, better healthcare outcomes, and greater patient throughput—all factors that improve the institution’s return on investment (ROI). • Competitive Advantage and New Revenue Streams: By adopting PCCT, healthcare institutions position themselves at the forefront of advanced imaging technologies. The ability to offer molecular, functional, and ultra-high resolution imaging creates a competitive advantage, attracting more complex and high-value cases. This can boost the institution’s reputation for excellence in diagnostics, leading to increased referrals, new patient populations, and expanded revenue opportunities. Summary Photon Counting CT (PCCT) is not just an evolution of existing CT technology—it is a molecular, ultra-high resolution, and functional imaging modality that fundamentally transforms the diagnostic landscape. Its ability to capture detailed molecular data, visualize minute anatomical structures with ultra-high resolution, and provide real-time functional imaging opens new possibilities for earlier and more precise diagnoses. While the financial investment in PCCT is larger, the reduction in downstream healthcare costs through improved diagnostic accuracy, fewer unnecessary interventions, and earlier disease detection far outweighs the initial expense. For institutions committed to advancing patient care and improving long-term financial outcomes, PCCT is an essential investment in the future of medical imaging. The video attached shows a patient accessing the Hospital for ACS. PCCT can provide ALL the imaging information of the concurrent imaging modalities (CXR, CAG, Echo, CMR) that you see around it... that's a lot! #PhotonCountingCT #MolecularImaging #UltraHighResolution #FunctionalImaging #FutureOfImaging #AdvancedMedicalImaging #EarlyDiseaseDetection #InnovativeCT #CuttingEdgeHealthcare #PrecisionDiagnostics #HealthcareInnovation #MedicalTechnology #CostEffectiveImaging #NextGenCT #PatientCareRevolution
1:10

PHOTON COUNTING CT is NOT a better CT It is a NEW imaging modality Photon Counting CT (PCCT) represents a transformative leap in medical imaging, not only as a molecular imaging modality but also as a technology offering ultra-high resolution and functional imaging capabilities. It is fundamentally more than just an enhanced version of traditional CT—PCCT introduces new ways of seeing and understanding the human body, providing critical insights at the molecular, structural, and functional levels. This positions PCCT as a unique imaging modality that requires a fresh approach to technical implementation, operational workflows, and financial planning. Despite the larger upfront investment, PCCT’s ability to drastically reduce downstream healthcare costs makes it a highly valuable investment in the long run. 1. Technical Innovations • Molecular Imaging and Energy Discrimination: Unlike traditional CT, which simply measures the total absorbed energy, PCCT counts individual X-ray photons and differentiates their energy levels. This allows for precise molecular imaging, revealing the composition of tissues and materials at a biochemical level. By distinguishing between different tissue types and contrast agents, PCCT opens up new diagnostic possibilities, such as identifying molecular biomarkers in tumors or distinguishing between stable and unstable plaque in coronary arteries. This capability shifts the focus of imaging from purely anatomical to both anatomical and molecular, offering more comprehensive diagnostic information. • Ultra-High Spatial Resolution: PCCT features significantly smaller detector elements compared to conventional CT scanners, allowing for ultra-high resolution imaging. This means clinicians can visualize fine structures such as microcalcifications in arteries, small lesions in soft tissues, or the intricate architecture of bones. This level of detail was previously unattainable with traditional CT. When combined with molecular imaging, this ultra-high resolution allows for the precise localization and characterization of disease at very early stages, which is essential for early diagnosis and intervention. • Functional Imaging Capabilities: PCCT also excels as a functional imaging modality. By capturing energy-resolved information, PCCT can provide insights into tissue functionality and dynamic physiological processes. For instance, it can detect changes in blood flow, tissue perfusion, and oxygenation without the need for additional contrast agents or scans. This functionality allows for real-time assessment of physiological processes, making it particularly valuable in cardiology, oncology, and neurology for evaluating organ function and monitoring disease progression. • Reduced Noise and Artifact Reduction: Photon-counting technology dramatically reduces electronic noise and imaging artifacts, such as beam hardening, resulting in clearer and more accurate images. The ability to deliver ultra-high resolution images with minimal artifacts improves diagnostic accuracy, reducing the need for repeat scans and ensuring that even subtle abnormalities are detected. 2. Operational Considerations • New Workflow for Molecular, High-Resolution, and Functional Imaging: The integration of molecular, ultra-high resolution, and functional imaging into routine clinical workflows introduces complexity that requires adaptation. Radiologists and technicians need specialized training to interpret and analyze multi-energy datasets that include molecular and functional information. PCCT produces a vast amount of detailed data, requiring clinicians to adopt new imaging protocols and refine their diagnostic approaches to fully leverage its capabilities. • Post-Processing and Data Management: PCCT generates richer, more complex datasets, which necessitates advanced post-processing tools and data management systems. Existing PACS and imaging software may not be equipped to handle such large volumes of data or to process functional and molecular information effectively. This means healthcare institutions must invest in robust IT infrastructure, including upgraded software and storage solutions, as well as provide additional training for staff on new imaging analysis techniques. • Revised Clinical Protocols: The molecular, functional, and ultra-high resolution imaging capabilities of PCCT will likely prompt changes in clinical protocols. For instance, the need for contrast agents may be reduced, simplifying patient preparation and decreasing the risk of adverse reactions. Additionally, the ability to monitor physiological functions in real-time through functional imaging could lead to more dynamic diagnostic procedures, such as assessing the effectiveness of interventions or treatments in real-time. 3. Financial Impact • Higher Initial Investment: PCCT systems are more expensive than traditional CT scanners due to their advanced technology, which includes photon-counting detectors and the computational power required for high-resolution, molecular, and functional imaging. While this upfront cost is significant, it is crucial to view it in the broader context of the downstream benefits and cost reductions that PCCT offers. • Downstream Cost Reductions: Although the initial capital investment is higher, PCCT’s ability to combine molecular, functional, and ultra-high resolution imaging leads to substantial reductions in downstream healthcare costs. Its superior diagnostic accuracy minimizes the need for follow-up tests, repeat scans, or invasive diagnostic procedures, such as diagnostic coronary angiographies. For example, in cardiology, PCCT can precisely differentiate between types of coronary plaque, reducing the need for invasive procedures to assess risk. • Lower Overall Healthcare Expenditures: By enabling earlier, more accurate diagnoses, PCCT can reduce the overall cost of patient care. Early detection of disease, particularly through its molecular and functional imaging capabilities, allows for more targeted treatments, potentially preventing the need for more aggressive and expensive interventions down the line. For instance, early-stage tumor detection via molecular imaging could lead to less invasive treatments, reducing hospital stays and improving patient outcomes, ultimately driving down healthcare costs. • Increased ROI Through Enhanced Patient Outcomes: Over time, the combination of molecular, functional, and ultra-high resolution imaging enhances diagnostic precision, which translates into better patient outcomes. Improved diagnostic accuracy reduces the incidence of unnecessary procedures, minimizes treatment delays, and results in more personalized and effective care. This leads to increased patient satisfaction, better healthcare outcomes, and greater patient throughput—all factors that improve the institution’s return on investment (ROI). • Competitive Advantage and New Revenue Streams: By adopting PCCT, healthcare institutions position themselves at the forefront of advanced imaging technologies. The ability to offer molecular, functional, and ultra-high resolution imaging creates a competitive advantage, attracting more complex and high-value cases. This can boost the institution’s reputation for excellence in diagnostics, leading to increased referrals, new patient populations, and expanded revenue opportunities. Summary Photon Counting CT (PCCT) is not just an evolution of existing CT technology—it is a molecular, ultra-high resolution, and functional imaging modality that fundamentally transforms the diagnostic landscape. Its ability to capture detailed molecular data, visualize minute anatomical structures with ultra-high resolution, and provide real-time functional imaging opens new possibilities for earlier and more precise diagnoses. While the financial investment in PCCT is larger, the reduction in downstream healthcare costs through improved diagnostic accuracy, fewer unnecessary interventions, and earlier disease detection far outweighs the initial expense. For institutions committed to advancing patient care and improving long-term financial outcomes, PCCT is an essential investment in the future of medical imaging. The video attached shows a patient accessing the Hospital for ACS. PCCT can provide ALL the imaging information of the concurrent imaging modalities (CXR, CAG, Echo, CMR) that you see around it... that's a lot! #PhotonCountingCT #MolecularImaging #UltraHighResolution #FunctionalImaging #FutureOfImaging #AdvancedMedicalImaging #EarlyDiseaseDetection #InnovativeCT #CuttingEdgeHealthcare #PrecisionDiagnostics #HealthcareInnovation #MedicalTechnology #CostEffectiveImaging #NextGenCT #PatientCareRevolution

Dr. Filippo Cademartiri

11,849 views • 1 year ago

Excited to share our new work on building a multimodal atlas of human skin in health and inflammatory disease — a project I’m especially proud of, bringing together AI, high-throughput genomics, and clinical science to accelerate discovery. Over the past decade, single-cell genomics has transformed how we map cells in human tissues. But a major challenge remains: can we systematically decode how cells organize into functional niches in situ — including those invisible to standard histopathology? To address this, we integrated large-scale scRNA-seq, spatial transcriptomics, histopathology, and AI-driven modeling frameworks to build an in situ atlas of human skin across health and disease. Led by Lloyd Steele, an MD/PhD student working between Haniffa Lab and my lab at Wellcome Sanger Institute and Cambridge University . Another amazing collaboration with Muzz Haniffa, the mastermind behind the work as part of Human Cell Atlas. A key part of this study is that we didn’t build everything from scratch — we leveraged and combined AI methods that actually work! and showed how they can be used together to extract biological insight at scale. We used: • scArches to build and map into a reference scRNA-seq atlas of human skin: • NicheCompass to identify and characterize spatial niches: • MINT-Flow to extract microenvironment-induced cell states and gene programs: Together, these enabled an end-to-end workflow from atlas construction to spatial mapping, niche discovery, and cell state decoding. At scale, we integrated ~5 million cells and 100+ spatial sections, enabling a systematic view of tissue organization. Using this framework, we identified 26 niches in skin, including known histopathologic structures as well as hidden disease-associated niches not visible on H&E. Among the most striking findings were a resident memory T cell-rich sebaceous gland niche and a plasma cell-rich sweat gland niche, suggesting that appendageal structures act as active immunological microenvironments and may contribute to inflammatory memory and disease persistence. Importantly, this atlas is not just descriptive — it is usable. It can support mapping of new datasets, resolve finer cell types and niches, extract microenvironment-driven programs, and enable predictive analyses at scale. More broadly, this work shows what becomes possible when AI, spatial genomics, and atlas-scale data are integrated end-to-end: not just mapping tissues, but systematically decoding them. This was a massive collaboration, and I’m very grateful to the amazing scientists April Foster, Kenny Roberts, and Chloe Admane. Lloyd is an amazing scientist, and I’m especially excited for the community to see more of his work soon — stay tuned. The data and pre-trained models will be released soon. Preprint:
1:09

Excited to share our new work on building a multimodal atlas of human skin in health and inflammatory disease — a project I’m especially proud of, bringing together AI, high-throughput genomics, and clinical science to accelerate discovery. Over the past decade, single-cell genomics has transformed how we map cells in human tissues. But a major challenge remains: can we systematically decode how cells organize into functional niches in situ — including those invisible to standard histopathology? To address this, we integrated large-scale scRNA-seq, spatial transcriptomics, histopathology, and AI-driven modeling frameworks to build an in situ atlas of human skin across health and disease. Led by Lloyd Steele, an MD/PhD student working between Haniffa Lab and my lab at Wellcome Sanger Institute and Cambridge University . Another amazing collaboration with Muzz Haniffa, the mastermind behind the work as part of Human Cell Atlas. A key part of this study is that we didn’t build everything from scratch — we leveraged and combined AI methods that actually work! and showed how they can be used together to extract biological insight at scale. We used: • scArches to build and map into a reference scRNA-seq atlas of human skin: • NicheCompass to identify and characterize spatial niches: • MINT-Flow to extract microenvironment-induced cell states and gene programs: Together, these enabled an end-to-end workflow from atlas construction to spatial mapping, niche discovery, and cell state decoding. At scale, we integrated ~5 million cells and 100+ spatial sections, enabling a systematic view of tissue organization. Using this framework, we identified 26 niches in skin, including known histopathologic structures as well as hidden disease-associated niches not visible on H&E. Among the most striking findings were a resident memory T cell-rich sebaceous gland niche and a plasma cell-rich sweat gland niche, suggesting that appendageal structures act as active immunological microenvironments and may contribute to inflammatory memory and disease persistence. Importantly, this atlas is not just descriptive — it is usable. It can support mapping of new datasets, resolve finer cell types and niches, extract microenvironment-driven programs, and enable predictive analyses at scale. More broadly, this work shows what becomes possible when AI, spatial genomics, and atlas-scale data are integrated end-to-end: not just mapping tissues, but systematically decoding them. This was a massive collaboration, and I’m very grateful to the amazing scientists April Foster, Kenny Roberts, and Chloe Admane. Lloyd is an amazing scientist, and I’m especially excited for the community to see more of his work soon — stay tuned. The data and pre-trained models will be released soon. Preprint:

Mo Lotfollahi

11,813 views • 4 months ago

🔴 Finally! NVIDIA has finally made the code for Neuralangelo public! It has the ability to transform any video into a highly detailed 3D environment, and it's a technology related to but DIFFERENT from NeRF. 💡 Here's how it works: It takes a 2D video as input, showing an object, monument, building, landscape, etc., from various perspectives and analyzes details such as depth, size, and the shapes of objects. From this, the AI sketches an initial 3D model, similar to how an artist molds a figure. This representation is then refined to highlight more details, just as an artist would make the final touches when sculpting. The result is a 3D environment/model, perfect for use in any environment. Imagine the applications it will have for video games, cinema, virtual environments, VR, and more! 📽️🎮 💡 More details: A year ago, an article was presented on a groundbreaking technique called NVIDIA's Instant NeRF. This technique turns images into stunning 3D scenes in a short time, ideal for creating realistic models for video games and other applications. Although Instant NeRF had a lot of potential, the generated models were not perfect and often lacked detailed structures, appearing somewhat cartoonish. A year on, NVIDIA releases a new technique based on Instant NeRF, named Neuralangelo. This enhances the fidelity of surface structures. While NeRF reconstructs real objects in virtual environments from images or videos, Instant NeRF speeds up this process, and Neuralangelo further improves the quality, making the generated objects appear even more realistic when examined up close. Neuralangelo improves Instant NeRF's approach in two key ways related to the hash grid encoding technique: 1⃣ Numerical gradients have been used to compute higher-order derivatives as a smoothing operation. This optimizes the "hash grid" encoding using numerical rather than analytical gradients, providing a smoother input to the network that produces the 3D model. 2⃣ A "coarse-to-fine" optimization has been implemented in the hash grids to control different levels of detail. That is, they first focus on a smoothed version of the scene, and then refine it with more detailed updates. Well, as Arthur C. Clarke said, "Any sufficiently advanced technology is indistinguishable from magic."
0:50

🔴 Finally! NVIDIA has finally made the code for Neuralangelo public! It has the ability to transform any video into a highly detailed 3D environment, and it's a technology related to but DIFFERENT from NeRF. 💡 Here's how it works: It takes a 2D video as input, showing an object, monument, building, landscape, etc., from various perspectives and analyzes details such as depth, size, and the shapes of objects. From this, the AI sketches an initial 3D model, similar to how an artist molds a figure. This representation is then refined to highlight more details, just as an artist would make the final touches when sculpting. The result is a 3D environment/model, perfect for use in any environment. Imagine the applications it will have for video games, cinema, virtual environments, VR, and more! 📽️🎮 💡 More details: A year ago, an article was presented on a groundbreaking technique called NVIDIA's Instant NeRF. This technique turns images into stunning 3D scenes in a short time, ideal for creating realistic models for video games and other applications. Although Instant NeRF had a lot of potential, the generated models were not perfect and often lacked detailed structures, appearing somewhat cartoonish. A year on, NVIDIA releases a new technique based on Instant NeRF, named Neuralangelo. This enhances the fidelity of surface structures. While NeRF reconstructs real objects in virtual environments from images or videos, Instant NeRF speeds up this process, and Neuralangelo further improves the quality, making the generated objects appear even more realistic when examined up close. Neuralangelo improves Instant NeRF's approach in two key ways related to the hash grid encoding technique: 1⃣ Numerical gradients have been used to compute higher-order derivatives as a smoothing operation. This optimizes the "hash grid" encoding using numerical rather than analytical gradients, providing a smoother input to the network that produces the 3D model. 2⃣ A "coarse-to-fine" optimization has been implemented in the hash grids to control different levels of detail. That is, they first focus on a smoothed version of the scene, and then refine it with more detailed updates. Well, as Arthur C. Clarke said, "Any sufficiently advanced technology is indistinguishable from magic."

Javi Lopez ⛩️

689,180 views • 3 years ago

🚀 The Segment Anything Model (SAM) has been upgraded to SAM2, featuring an efficient image encoder for segmenting images and videos. But does SAM2 outperform SAM1 in medical image and video segmentation? We're thrilled to present our paper "Segment Anything in Medical Images and Videos: Benchmark and Deployment"! We comprehensively benchmark SAM2 across 11 medical image modalities and videos. 📄 Paper: 💻 Code: **Highlights:** 1. SAM2 doesn’t always outperform SAM1 in 2D medical images, but excels in video segmentation, making it more accurate and efficient for 3D images, such as CT and MR scans. 2. MedSAM still outperforms SAM2 on most 2D modalities, but SAM2 surpasses MedSAM for 3D image segmentation in a slice-by-slice approach. 3. Segmentation performance varies with model size; sometimes the smallest model outperforms larger ones. 4. Fine-tuning SAM2 significantly boosts its performance for medical image segmentation. While SAM2 may struggle with challenging objects that have unclear boundaries or low contrast, it excels in generating good initial segmentation masks for common medical images and videos. However, the official interface doesn’t support medical data formats and has limitations on video length. To address this, we've developed a 3D Slicer Plugin and Gradio API for efficient 3D medical image and video segmentation. We invite you to try them out and provide feedback! 🔧 Deployment: - 3D Slicer Plugin: - Gradio API: (Note: Due to GPU limitations, the online API is available for only 12 hours and may be slow. We highly recommend deploying the Gradio API with your own computing resources: A big shoutout to Jun Ma (JunMa) who recently joined our UHN AI hub (UHN AI Hub) as Machine Learning Lead, and kudos to all co-authors: Sumin Kim, Feifei Li, Mohammed Baharoon (Mohammed Baharoon), Reza Asakereh, and Hongwei Lyu! This is true teamwork! Looking forward to collaborating with the community to advance 3D medical image and video segmentation foundation models! University Health Network U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology Temerty Centre for AI in Medicine (T-CAIREM) Vector Institute #MedTech #AIinHealthcare #DeepLearning #MedicalImaging #SAM2 #MedSAM #AIResearch
0:22

🚀 The Segment Anything Model (SAM) has been upgraded to SAM2, featuring an efficient image encoder for segmenting images and videos. But does SAM2 outperform SAM1 in medical image and video segmentation? We're thrilled to present our paper "Segment Anything in Medical Images and Videos: Benchmark and Deployment"! We comprehensively benchmark SAM2 across 11 medical image modalities and videos. 📄 Paper: 💻 Code: **Highlights:** 1. SAM2 doesn’t always outperform SAM1 in 2D medical images, but excels in video segmentation, making it more accurate and efficient for 3D images, such as CT and MR scans. 2. MedSAM still outperforms SAM2 on most 2D modalities, but SAM2 surpasses MedSAM for 3D image segmentation in a slice-by-slice approach. 3. Segmentation performance varies with model size; sometimes the smallest model outperforms larger ones. 4. Fine-tuning SAM2 significantly boosts its performance for medical image segmentation. While SAM2 may struggle with challenging objects that have unclear boundaries or low contrast, it excels in generating good initial segmentation masks for common medical images and videos. However, the official interface doesn’t support medical data formats and has limitations on video length. To address this, we've developed a 3D Slicer Plugin and Gradio API for efficient 3D medical image and video segmentation. We invite you to try them out and provide feedback! 🔧 Deployment: - 3D Slicer Plugin: - Gradio API: (Note: Due to GPU limitations, the online API is available for only 12 hours and may be slow. We highly recommend deploying the Gradio API with your own computing resources: A big shoutout to Jun Ma (JunMa) who recently joined our UHN AI hub (UHN AI Hub) as Machine Learning Lead, and kudos to all co-authors: Sumin Kim, Feifei Li, Mohammed Baharoon (Mohammed Baharoon), Reza Asakereh, and Hongwei Lyu! This is true teamwork! Looking forward to collaborating with the community to advance 3D medical image and video segmentation foundation models! University Health Network U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology Temerty Centre for AI in Medicine (T-CAIREM) Vector Institute #MedTech #AIinHealthcare #DeepLearning #MedicalImaging #SAM2 #MedSAM #AIResearch

Bo Wang

178,572 views • 2 years ago

Very powerful testimony by dr. Sabine Hazan "Thank you, senator. It's an honor to be here. The microbiome, our microbes in our guts, is our immunity and tells the story and will tell the story of COVID nineteen. And this is why as a gastroenterologist, I stepped into the pandemic. Through my experience, I will show you how difficult it was to conduct research and publish when the research goes against the national public health narrative." "Interference and delay in research happened and affects all of us. In early twenty twenty, my research genetic sequencing laboratory was the first lab to document the entire sequence of the virus in the stools as opposed to the PCR which is just a little piece of the virus." "We discovered that the virus lingered in the stools for up to forty five days. It took six months to publish this publication at a time where everybody needed to know that it was in your stools. My lab also showed that COVID nineteen in the stools was killed by hydroxychloroquine and azithromycin." "But unfortunately, azithromycin and hydroxychloroquine killed the microbiome. So therefore, vitamin c, d, and zinc was added. Three protocols were submitted to the FDA from our findings. Three studies were also put into in full transparencies to help doctors more effectively treat COVID because I knew data that nobody knew. 04/02/2020, FDA gave us an exempt letter for doing a clinical trial." "In other words, we did not need to do a clinical trial on hydroxychloroquine, z pack, vitamin c, d, and zinc as treatment or hydroxychloroquine, vitamin c, d, and zinc as prophylaxis. April 4, somebody must have called the FDA and said, I got another letter saying, I'm sorry, doctor Hazan. Exemption is denied. You must do a full on clinical trial. Here's the letter." "System pressures delayed us, and we got a green light to start recruiting by May 2020. By then, the media created fear around hydroxychloroquine. It was impossible to recruit. This drug was safely given for years for arthritis and lupus with no problems. My clinical trials companies were also banned and censored from advertising on Facebook, Instagram, and Twitter." "Remember, I do clinical trials for a living and never as a clinical trial doctor have I not been able to advertise to recruit for a trial on social media. I kept collecting stools of patients and noticed that patients with severe COVID had a certain bacteria that was missing compared to people that were highly exposed to COVID but never got COVID. That bacteria is called bifidobacteria. Bifidobacteria is an important and key microbe for immunity. It represents your trillion dollar industry of probiotics." "In fact, when you turn the bottle and you see the ingredient, it says bifidobacteria. It is present in newborns. This is why your newborns did not get a problem from COVID at the beginning, and it is absent in old people. The process of aging is loss of bifidobacteria. We published this paper, the lost microbes of COVID nineteen." "It took eight months to publish. If you follow the bifidobacteria like I did, you will notice, and we did notice anyways, that vitamin c actually increases bifidobacteria. This is why vitamin c is important when you take when you take care of viruses and, you know, you've all experienced taking vitamin c for a cold." "Well, we published this data where we showed vitamin c, if we give it to patients before and after, it increased the bifidobacteria. Ivermectin was also an interesting drug because Ivermectin, we noticed, also increased the bifidobacteria within twenty four hours of taking it." "Why Ivermectin? If you look at what Ivermectin is, it is a fermented product of a bacteria that is similar to bifidobacteria. In fact, they're in the same continent of microbes. They live. They're like sisters, brothers in the microbiome." "So I published. I knew that ivermectin increased bifidobacteria, but I said, nah. I can't go out there and start publishing that. That's gonna be too controversial. So I published a hypothesis that maybe what I was observing on the frontline treating patients with COVID, noticing that their oxygen saturation was increasing from ivermectin, was basically maybe ivermectin increased bifidobacteria." "The hypothesis on ivermectin was the most read hypothesis in the pandemic and was retracted after eight months of being on. When we cannot make a hypothesis, this is not science. December twenty twenty, at the same time that I was treating patients with COVID, I began collecting stools of my colleagues that were at home and started going into the hospital. And I said, can I get your stools before and after you get vaccinated? Because to me, this new technology of vaccines, I wanted to see what it was doing on the microbiome." "I discovered that messenger RNA vaccines killed the bifidobacteria. I knew I would never be able to publish this because it goes against the narrative. So I submitted it to my college, the American College of Gastroenterology, and presented it in October 2022. This abstract won a research award at the American College of Gastro beating 6,000 abstracts. That's from academic centers like Harvard and Mayo Clinic and MD Anderson." "This abstract got the attention of 18,000 GI doctors who all of a sudden started realizing maybe killing bifidobacteria is why I got COVID after my vaccine to begin with. Worse than that, and another abstract we presented, was the persistent damage of bifidobacteria from the vaccine." "What is going on here that the vaccine continues to kill the bifidobacteria? At the same time, we presented a link between loss of bifidobacteria and Crohn's disease, loss of bifidobacteria in Lyme disease, and loss of bifidobacteria in invasive cancer. It is nearly impossible to publish data that goes against the national public health narrative." "If doctors cannot publish the data, they cannot find solution to fix the problems. So in conclusion, I will finish with showing this. This represents clinical trials that I've done for pharmaceutical companies prior to COVID. Amongst them are vaccine studies. Yes." "I brought vaccines to the market. Proton pump inhibitors, cardiac drugs, biologics for all sorts of conditions. First, postpartum depression drug, drugs that never made it to the market because they killed people. Clinical trials doctors follow guidelines that allows the industry to provide safe drugs. These guidelines were not followed during the pandemic." "And because of that, everyone is affected. COVID should have been a time where humanity joined forces together and doctors needed to come together. It's a shame that it didn't happen. Interference with research affects all of us. This should not be political." "Science is a story that evolves. It's a multitude of experiments that allow us to see medicine, to give hopes to patients. Skepticism, challenging the current state of knowledge. Having an open mind must be allowed if we have any hope of moving science forward. What I saw this pandemic was not science. Thank you."
8:56

Very powerful testimony by dr. Sabine Hazan "Thank you, senator. It's an honor to be here. The microbiome, our microbes in our guts, is our immunity and tells the story and will tell the story of COVID nineteen. And this is why as a gastroenterologist, I stepped into the pandemic. Through my experience, I will show you how difficult it was to conduct research and publish when the research goes against the national public health narrative." "Interference and delay in research happened and affects all of us. In early twenty twenty, my research genetic sequencing laboratory was the first lab to document the entire sequence of the virus in the stools as opposed to the PCR which is just a little piece of the virus." "We discovered that the virus lingered in the stools for up to forty five days. It took six months to publish this publication at a time where everybody needed to know that it was in your stools. My lab also showed that COVID nineteen in the stools was killed by hydroxychloroquine and azithromycin." "But unfortunately, azithromycin and hydroxychloroquine killed the microbiome. So therefore, vitamin c, d, and zinc was added. Three protocols were submitted to the FDA from our findings. Three studies were also put into in full transparencies to help doctors more effectively treat COVID because I knew data that nobody knew. 04/02/2020, FDA gave us an exempt letter for doing a clinical trial." "In other words, we did not need to do a clinical trial on hydroxychloroquine, z pack, vitamin c, d, and zinc as treatment or hydroxychloroquine, vitamin c, d, and zinc as prophylaxis. April 4, somebody must have called the FDA and said, I got another letter saying, I'm sorry, doctor Hazan. Exemption is denied. You must do a full on clinical trial. Here's the letter." "System pressures delayed us, and we got a green light to start recruiting by May 2020. By then, the media created fear around hydroxychloroquine. It was impossible to recruit. This drug was safely given for years for arthritis and lupus with no problems. My clinical trials companies were also banned and censored from advertising on Facebook, Instagram, and Twitter." "Remember, I do clinical trials for a living and never as a clinical trial doctor have I not been able to advertise to recruit for a trial on social media. I kept collecting stools of patients and noticed that patients with severe COVID had a certain bacteria that was missing compared to people that were highly exposed to COVID but never got COVID. That bacteria is called bifidobacteria. Bifidobacteria is an important and key microbe for immunity. It represents your trillion dollar industry of probiotics." "In fact, when you turn the bottle and you see the ingredient, it says bifidobacteria. It is present in newborns. This is why your newborns did not get a problem from COVID at the beginning, and it is absent in old people. The process of aging is loss of bifidobacteria. We published this paper, the lost microbes of COVID nineteen." "It took eight months to publish. If you follow the bifidobacteria like I did, you will notice, and we did notice anyways, that vitamin c actually increases bifidobacteria. This is why vitamin c is important when you take when you take care of viruses and, you know, you've all experienced taking vitamin c for a cold." "Well, we published this data where we showed vitamin c, if we give it to patients before and after, it increased the bifidobacteria. Ivermectin was also an interesting drug because Ivermectin, we noticed, also increased the bifidobacteria within twenty four hours of taking it." "Why Ivermectin? If you look at what Ivermectin is, it is a fermented product of a bacteria that is similar to bifidobacteria. In fact, they're in the same continent of microbes. They live. They're like sisters, brothers in the microbiome." "So I published. I knew that ivermectin increased bifidobacteria, but I said, nah. I can't go out there and start publishing that. That's gonna be too controversial. So I published a hypothesis that maybe what I was observing on the frontline treating patients with COVID, noticing that their oxygen saturation was increasing from ivermectin, was basically maybe ivermectin increased bifidobacteria." "The hypothesis on ivermectin was the most read hypothesis in the pandemic and was retracted after eight months of being on. When we cannot make a hypothesis, this is not science. December twenty twenty, at the same time that I was treating patients with COVID, I began collecting stools of my colleagues that were at home and started going into the hospital. And I said, can I get your stools before and after you get vaccinated? Because to me, this new technology of vaccines, I wanted to see what it was doing on the microbiome." "I discovered that messenger RNA vaccines killed the bifidobacteria. I knew I would never be able to publish this because it goes against the narrative. So I submitted it to my college, the American College of Gastroenterology, and presented it in October 2022. This abstract won a research award at the American College of Gastro beating 6,000 abstracts. That's from academic centers like Harvard and Mayo Clinic and MD Anderson." "This abstract got the attention of 18,000 GI doctors who all of a sudden started realizing maybe killing bifidobacteria is why I got COVID after my vaccine to begin with. Worse than that, and another abstract we presented, was the persistent damage of bifidobacteria from the vaccine." "What is going on here that the vaccine continues to kill the bifidobacteria? At the same time, we presented a link between loss of bifidobacteria and Crohn's disease, loss of bifidobacteria in Lyme disease, and loss of bifidobacteria in invasive cancer. It is nearly impossible to publish data that goes against the national public health narrative." "If doctors cannot publish the data, they cannot find solution to fix the problems. So in conclusion, I will finish with showing this. This represents clinical trials that I've done for pharmaceutical companies prior to COVID. Amongst them are vaccine studies. Yes." "I brought vaccines to the market. Proton pump inhibitors, cardiac drugs, biologics for all sorts of conditions. First, postpartum depression drug, drugs that never made it to the market because they killed people. Clinical trials doctors follow guidelines that allows the industry to provide safe drugs. These guidelines were not followed during the pandemic." "And because of that, everyone is affected. COVID should have been a time where humanity joined forces together and doctors needed to come together. It's a shame that it didn't happen. Interference with research affects all of us. This should not be political." "Science is a story that evolves. It's a multitude of experiments that allow us to see medicine, to give hopes to patients. Skepticism, challenging the current state of knowledge. Having an open mind must be allowed if we have any hope of moving science forward. What I saw this pandemic was not science. Thank you."

Camus

127,765 views • 1 year ago