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About 10–15% of pregnancies fail after conception has been recognized, amounting to 23 million losses a year. Chromosomal anomalies underlie many embryonic and fetal losses, but their exact frequency and localization to the embryo or placenta are still unclear. A new study published in Nature Medicine reports on a chromosomal analysis of over 1,700 spontaneous early miscarriages.

The most common period of pregnancy loss is before the ninth week, though many may occur earlier and pass unrecognized. While about 11% of women have at least one miscarriage, the proportion goes down with two or three, at 2% and 0.7. respectively.

CU Cancer Center member, Michelle Springer, MS, CGC, presents “Genetic Testing for Family Members” at the Hereditary Cancer Conference.

Nervous systems are complex networks, comprised of billions of neurons connected by trillions of synapses. These connections are subject to specific wiring rules that are thought to result from competitive selection pressures to minimise wiring costs and promote complex, adaptive function. While most connections in the brain are short-range, a smaller subset of metabolically costly projections extend over long distances to connect disparate anatomical areas. These long-range connections support integrated brain function and are concentrated between the most highly connected network elements; the hubs of the brain. Hub connectivity thus plays a vital role in determining how a given nervous system negotiates the trade-off between cost and value, and natural.
selection may favour connections that provide high functional benefit for low cost.

Consistent with this view, Professor Alex Fornito will present evidence.
that hub connectivity is under strong genetic control. He will show that the strength of connectivity between hubs in the human brain is more heritable than connectivity between other nodes, and that the genetic variants influencing hub connectivity overlaps with those implicated in mental illness and intelligence. He will also discuss the progress and challenges of developing generative models that evaluate the role of different cost-value trade-offs in driving complex brain topology.

Continue reading “Brain network hubs: maps, molecules, and models” »

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Weaving piezoelectric polymers into nanofibers reveals a surprising pathway to boost stem cell growth naturally, without external power.

Our bodies are a complex tapestry of cells, woven into tissues and organs, like bones, muscle, and skin. All these cells begin as blank slates called stem cells, which are directed to become all the unique cell types in the body by a myriad of genetic and environmental cues.

To harness the biomedical potential of stem cells, researchers have long sought ways to untangle these factors and find a recipe to efficiently grow any desired cell type. Now, expertise from textile research is helping create a new platform to achieve this goal.

Investigators from the laboratory of Ali Shilatifard, Ph.D., the Robert Francis Furchgott Professor and chair of Biochemistry and Molecular Genetics, have discovered a new repeat gene cluster sequence that is exclusively expressed in humans and non-human primates.

The discovery, detailed in a study published in Science Advances, is a breakthrough for human genome biology and has wide-ranging implications for future research in transcriptional regulation, human evolution, and the study of repetitive DNA sequences, according to the authors.

Continue reading “New human gene cluster sequence discovered” »

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My name is Artem, I’m a computational neuroscience student and researcher. In this video we discuss engrams – fundamental units of memory in the brain. We explore what engrams are, how memory is allocated, where it is stored, and how different memories become linked with each other.

Continue reading “Building Blocks of Memory in the Brain” »

The groundbreaking gene-editing technology known as Crispr, which acts like a molecular pair of scissors that can be used to cut and modify a DNA sequence, has moved rather quickly from the pages of scientific journals to the medical setting. Earlier this month, about three years after Jennifer Doudna and Emmanuelle Charpentier won the Nobel Prize in Chemistry for describing how bacteria’s immune system could be used as a tool to edit genes, regulators in the U.K. approved the first Crispr-based treatment for sickle cell disease and beta-thalassemia patients. The treatment, from Vertex Pharmaceuticals and Crispr Therapeutics, could be approved by the U.S. Food and Drug Administration early next month for sickle cell patients.

While many obstacles lie ahead for the nascent field, such as how to pay for treatments that typically cost more than $1 million, these regulatory approvals are just the start as newer gene-editing technologies such as base and prime editing make their way through human studies. In an interview, Prof. Doudna says the approval is “a turning point in medicine because it really shows how genome editing can be used as a one-and-done cure for disease.”

Gene editing is part of a broader therapeutic revolution that encompasses genetic and cellular medicine. The pills and injections we are all familiar with generally target proteins or pathways in the body to treat disease. With gene and cell therapy, we can now target the root cause of disease, sometimes curing patients.

A research team from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences (CAS) has developed an analysis service platform called CRISPRimmunity, which was an interactive web server for identifying important molecular events related to CRISPR and regulators of genome editing systems. The study is published in Nucleic Acids Research.

The new CRISPRimmunity platform was designed for integrated analysis and prediction of CRISPR-Cas and anti-CRISPR systems. It includes customized databases with annotations for known anti-CRISPR proteins, anti-CRISPR-associated proteins, class II CRISPR-Cas systems, CRISPR array types, HTH structural domains and mobile genetic elements. These resources allow the study of molecular events in the co-evolution of CRISPR-Cas and anti-CRISPR systems.

To improve prediction accuracy, the researchers used strategies such as homology analysis, association analysis and self-targeting in prophage regions to predict anti-CRISPR proteins. When tested on data from 99 experimentally validated Acrs and 676 non-Acrs, CRISPRimmunity achieved an accuracy of 0.997 for anti-CRISPR protein prediction.