Can AI really replace scientists? Robin, an AI system, identified a potential drug for vision loss, but scientists are skeptical of full research automation.
Category: biotech/medical – Page 3
This 70-year-old doctor is stronger than ever, and here is HOW he achieved his fitness (no, not just through cardio)
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Dr. Eric Topol, a 70-year-old cardiologist, challenges conventional aging perceptions by embracing strength training. Abandoning cardio, he discovered that building muscle mass significantly improves health span. His regimen of simple exercises at home led to increased strength, balance, mental focus, and confidence, proving that aging can be a period of renewal, not decline.
Since the time of the ancient Greek physician Hippocrates, cancer has been recognized as being sensitive to heat. Today, this principle forms the basis of hyperthermia treatment—a promising cancer therapy that uses controlled heat to kill tumor cells while sparing healthy ones.
Unlike chemotherapy or radiation, hyperthermia works by heating cancerous tissue to temperatures around 50°C, causing cancer cell death while simultaneously activating the body’s immune system against the tumor. This approach holds particular promise when combined with immunotherapy, as heat-killed cancer cells can trigger a stronger anti-tumor immune response.
However, a few major challenges have limited hyperthermia’s clinical success. One of the main hurdles is the limited understanding of the biological mechanisms behind heat sensitivity in cancer cells. Researchers have discovered that some cancer cells—even those from the same organ—react differently to heat shock, with some surprisingly more heat-resistant than others.
CAR-T cells are specialized immune cells genetically modified to recognize and attack cancer cells. Researchers at Nagoya University in Japan and their collaborators have developed new CAR-T cells to target malignant tumors. While similar treatments have worked well for blood cancers, treating solid tumors is more difficult. Their method targeted a protein found in high amounts on many types of cancer cells (Eva1) and successfully eliminated tumors in lab mice.
A protein that appears on malignant tumors may hold the key to successful treatment.
In mice, engineered immune cells shrink pancreatic and other tumours bearing a mutant version of the KRAS protein.
Facial morphology is a distinctive biometric marker, offering invaluable insights into personal identity, especially in forensic science. In the context of high-throughput sequencing, the reconstruction of 3D human facial images from DNA is becoming a revolutionary approach for identifying individuals based on unknown biological specimens. Inspired by artificial intelligence techniques in text-to-image synthesis, it proposes Difface, a multi-modality model designed to reconstruct 3D facial images only from DNA. Specifically, Difface first utilizes a transformer and a spiral convolution network to map high-dimensional Single Nucleotide Polymorphisms and 3D facial images to the same low-dimensional features, respectively, while establishing the association between both modalities in the latent features in a contrastive manner; and then incorporates a diffusion model to reconstruct facial structures from the characteristics of SNPs. Applying Difface to the Han Chinese database with 9,674 paired SNP phenotypes and 3D facial images demonstrates excellent performance in DNA-to-3D image alignment and reconstruction and characterizes the individual genomics. Also, including phenotype information in Difface further improves the quality of 3D reconstruction, i.e. Difface can generate 3D facial images of individuals solely from their DNA data, projecting their appearance at various future ages. This work represents pioneer research in de novo generating human facial images from individual genomics information.
(Repost)
This study has introduced Difface, a de novo multi-modality model to reconstruct 3D facial images from DNA with remarkable precision, by a generative diffusion process and a contrastive learning scheme. Through comprehensive analysis and SNP-FACE matching tasks, Difface demonstrated superior performance in generating accurate facial reconstructions from genetic data. In particularly, Difface could generate/predict 3D facial images of individuals solely from their DNA data at various future ages. Notably, the model’s integration of transformer networks with spiral convolution and diffusion networks has set a new benchmark in the fidelity of generated images to their real images, as evidenced by its outstanding accuracy in critical facial landmarks and diverse facial feature reproduction.
Difface’s novel approach, combining advanced neural network architectures, significantly outperforms existing models in genetic-to-phenotypic facial reconstruction. This superiority is attributed to its unique contrastive learning method of aligning high-dimensional SNP data with 3D facial point clouds in a unified low-dimensional feature space, a process further enhanced by adopting diffusion networks for phenotypic characteristic generation. Such advancements contribute to the model’s exceptional precision and ability to capture the subtle genetic variations influencing facial morphology, a feat less pronounced in previous methodologies.
Despite Difface’s demonstrated strengths, there remain directions for improvement. Addressing these limitations will require a focused effort to increase the model’s robustness and adaptability to diverse datasets. Future research should aim to incorporating variables like age and BMI would allow Difface to simulate age-related changes, enabling the generation of facial images at different life stages an application that holds significant potential in both forensic science and medical diagnostics. Similarly, BMI could help the model account for variations in body composition, improving its ability to generate accurate facial reconstructions across a range of body types.
Scientists at St. Jude Children’s Research Hospital, the National Center for Genomic Analysis and the University of Adelaide have created a single-cell RNA analysis method that is 47 times cheaper and more scalable than other techniques.
Single-cell RNA sequencing provides scientists with important information about gene expression in health and disease. However, the technique is expensive and often prohibits analysis of large numbers of cells.
Scientists from St. Jude Children’s Research Hospital, the National Center for Genomic Analysis and the University of Adelaide have created a method that combines microscopy with single-cell RNA analysis to overcome these limitations. The technique called Single-Cell Transcriptomics Analysis and Multimodal Profiling through Imaging (STAMP) can look at millions of single cells for a fraction of the cost of existing approaches.
Scientists have mapped how over 140,000 mutations affect the formation of amyloid beta fibrils, offering an unprecedented look at early events in Alzheimer’s disease.
One of the biggest mysteries of evolution is how species first developed complex vision. Jellyfish are helping scientists solve this puzzle, as the group has independently evolved eyes at least nine separate times. Different species of jellyfish have strikingly different types of vision, from simple eyespots that detect light intensity to sophisticated lens eyes similar to those in humans.
Biologists have studied jellyfish eye structure, light sensitivity, and visual behavior for decades, but the exact genes involved in jellyfish eye formation remain unknown.
Aide Macias-Muñoz, a professor of ecology and evolutionary biology, is exploring how eyes and light detection evolved using genetic tools. Her lab has just completed a high-quality genome sequence of Bougainvillia cf. muscus, a small jellyfish-like animal in the Hydrozoa group that has an astonishing 28 eyes.
