Lifeboat Foundation

Cold Spring Harbor Laboratory scientists developed an AI algorithm inspired by the genome’s efficiency, achieving remarkable data compression and task performance.

In a sense, each of us begins life ready for action. Many animals perform amazing feats soon after they’re born. Spiders spin webs. Whales swim. But where do these innate abilities come from? Obviously, the brain plays a key role as it contains the trillions of neural connections needed to control complex behaviors.

However, the genome has space for only a small fraction of that information. This paradox has stumped scientists for decades. Now, Cold Spring Harbor Laboratory (CSHL) Professors Anthony Zador and Alexei Koulakov have devised a potential solution using artificial intelligence.

A recent study from the Centre for Genomic Regulation (CRG) in Barcelona reveals that bacteria can adapt their ribosomes when exposed to widely used antibiotics, potentially playing a role in the development of antibiotic resistance. These small changes can modify the drug-binding sites on ribosomes, reducing the effectiveness of antibiotics.

The research focused on Escherichia coli (E. coli), a usually harmless bacterium that can lead to serious infections. The team exposed E. coli to two antibiotics, streptomycin and kasugamycin.

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Bio-Rad invites you to our 2nd India Cell Gene Therapy Symposium 2024 After many decades of effort, the future of cell and gene therapies (CGT) is incredibly promising. A flurry of recent successes has led to the approval of several life changing treatments for patients and many more therapies are in development. CGT seek to correct the root cause of an illness at the molecular level. These game changing medicines are reshaping how we address previously uncurable illnesses — transforming people’s lives.

Mayo Clinic researchers have developed new artificial intelligence (AI)-based tools to pinpoint specific regions of the brain with seizure hotspots more quickly and accurately in patients with drug-resistant epilepsy. Their study, published in Nature Communications Medicine, highlights the potential of AI to revolutionize epilepsy treatment by interpreting brain waves during electrode implantation surgery. This transformative approach could significantly reduce the time patients spend in the hospital, accelerating the identification and removal of seizure-generating brain regions.

“This innovative approach could enable more rapid and accurate identification of seizure-generating areas during stereo-electroencephalography (EEG) implantation surgery, potentially reducing the cost and risks of prolonged monitoring,” says Nuri Ince, Ph.D., senior author of the study and a consultant in the Mayo Clinic Department of Neurologic Surgery.

Drug-resistant epilepsy often requires surgical removal of the seizure-causing brain tissue. A first step in that treatment is typically a surgery that involves implanting electrodes in the brain and monitoring neural activity for several days or weeks to identify the location of the seizures.

VIEW VIDEOS Thank you for joining us. All sessions were recorded and will be available on the Globe Events YouTube channel. Visit Globe.com/events for future events Thank you for joining our first annual Future of Medicine, streaming live from Boston on November 13.

Artificial intelligence has the potential to improve the analysis of medical image data. For example, algorithms based on deep learning can determine the location and size of tumors. This is the result of AutoPET, an international competition in medical image analysis, where researchers of Karlsruhe Institute of Technology (KIT) were ranked fifth.

The seven best autoPET teams report in the journal Nature Machine Intelligence on how algorithms can detect tumor lesions in positron emission tomography (PET) and computed tomography (CT).

Imaging techniques play a key role in the diagnosis of cancer. Precisely determining the location, size, and type of tumor is essential for choosing the right therapy. The most important imaging techniques include positron emission tomography (PET) and computer tomography (CT).

Colorectal cancer (CRC) remains one of the most clinically challenging malignancies facing our public health system. CRC accounts for the second and third most common cancer in males and females, respectively. In addition, CRC represents one of the most deadly cancers, expected to result in over 50,000 mortalities in 2024.

Hereditary colorectal cancer (HCRC) occurs when a parent passes a cancer gene to a child. Unfortunately, we have not identified a single gene that causes the disease. Hereditary CRC syndromes, such as hereditary non-polyposis colorectal cancer (HNPCC; also known as Lynch syndrome) and familial adenomatous polyposis (FAP), describe a group of genetic diseases that confer a high risk of developing CRC. As our knowledge has expanded, we have learned about a growing number of genetic variants in the genes that predispose carriers to CRC. However, the precise role of some variants in the development of CRC cancer remains unclear. Uncovering more information about these variants, called variants of uncertain significance.

As our knowledge has expanded, we have learned about a growing number of genetic variants in the genes which predispose carriers to CRC. However, the precise role of some variants in the development of CRC cancer remains unclear. Uncovering more information about these variants, called variants of uncertain significance (VUS), can aid in optimizing screening and surveillance programs.