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Functionally dominant hotspot mutations of mitochondrial ribosomal RNA genes in cancer

To study selection for somatic single nucleotide variants (SNVs) in tumor mtDNA, we identified somatic mtDNA variants across primary tumors from the GEL cohort (n = 14,106). The sheer magnitude of the sample size in this dataset, in conjunction with the high coverage depth of mtDNA reads (mean = 15,919×), enabled high-confidence identification of mtDNA variants to tumor heteroplasmies of 5%. In total, we identified 18,104 SNVs and 2,222 indels (Supplementary Table 1), consistent with previously reported estimates of approximately one somatic mutation in every two tumors1,2,3. The identified mutations exhibited a strand-specific mutation signature, with a predominant occurrence of CT mutations on the heavy strand and TC on the light strand in the non-control region that was reversed in the control region2 (Extended Data Fig. 1a, b). These mutations occur largely independently of known nuclear driver mutations, with the exception of a co-occurrence of TP53 mutation and mtDNA mutations in breast cancer (Q = 0.031, odds ratio (OR) = 1.43, chi-squared test) (Extended Data Fig. 2a and Supplementary Table 4).

Although the landscape of hotspot mutations in nuclear-DNA-encoded genes is relatively well described, a lack of statistical power has impeded an analogous, comprehensive analysis in mtDNA16,17. To do so, we applied a hotspot detection algorithm that identified mtDNA loci demonstrating a mutation burden in excess of the expected background mutational processes in mtDNA (Methods). In total, we recovered 138 unique statistically significant SNV hotspots (Q 0.05) across 21 tumor lineages (Fig. 1a, b and Supplementary Table 2) and seven indel hotspots occurring at homopolymeric sites in complex I genes, as previously described by our group (Extended Data Fig. 2b and Supplementary Table 3). SNV hotspots affected diverse genetic elements, including protein-coding genes (n = 96 hotspots, 12 of 13 distinct genes), tRNA genes (n = 8 hotspots, 6 of 22 distinct genes) and rRNA genes (n = 34 hotspots, 2 of 2 genes) (Fig. 1b, c, e).

Microgravity Muscle Printing Paves Way for Space Biomanufacturing

The study notes in its conclusions, “We have presented G-FLight printing as an effective tool for the rapid gravity-independent fabrication of aligned tissues, focusing on muscle tissue as an application.”

Can muscle tissue be 3D-printed in outer space to improve astronaut health? This is what a recent study published in Advanced Science hopes to address as a team of scientists investigated how human tissue can be manufactured in space. This study has the potential to help scientists, researchers, and the public better understand new methods for not only aiding in long-term space travel but also combating diseases on Earth.

For the study, the researchers used a series of parabolic flights to test G-FLight (Gravity-independent Filamented Light), which is a novel 3D printing biomanufacturing system capable of producing muscle cells and fibers in a matter of seconds. The purpose of the parabolic flights was to simulate microgravity, which is produced by the airplane sharply diving after gradually rising in altitude. The goal of the study was to ascertain if G-Flight could successfully 3D-print muscle fibers under microgravity conditions. In the end, the researchers found that G-FLight successfully produced muscle fibers under microgravity conditions during parabolic flights.

Evolutionary comparison points to pigs as superior models for human pancreas and diabetes research

Pancreas development in pigs resembles humans much more closely than does the established mouse model. An international team headed by Helmholtz Munich and the German Center for Diabetes Research (DZD) has now produced a comprehensive evolutionary comparison of single-cell atlases of pancreas development. The results open up new prospects for regenerative therapies.

For decades, the pancreas and its development have been a major focus of diabetes and . Until now, the science was almost exclusively based on mouse models. However, mice differ from humans in many respects—from developmental duration to metabolism and .

“Particularly for complex diseases such as diabetes mellitus, we need models that truly resemble humans,” therefore emphasizes Prof. Heiko Lickert. The DZD researcher is the director of the Institute of Diabetes and Regeneration Research at Helmholtz Munich and professor at the Technical University of Munich (TUM).

Student trust in AI coding tools grows briefly, then levels off with experience

How much do undergraduate computer science students trust chatbots powered by large language models like GitHub Copilot and ChatGPT? And how should computer science educators modify their teaching based on these levels of trust?

These were the questions that a group of U.S. computer scientists set out to answer in a study that will be presented at the Koli Calling conference Nov. 11 to 16 in Finland. In the course of the study’s few weeks, researchers found that trust in generative AI tools increased in the short run for a majority of students.

But in the long run, students said they realized they needed to be competent programmers without the help of AI tools. This is because these tools often generate incorrect or would not help students with code comprehension tasks.

The right dose for the brain: Selenomethionine’s role in protecting dopaminergic neurons

Dopamine is often called the brain’s “motivation molecule,” but for me, it represents something deeper, a window into how fragile our neurons can be. The cells that produce dopamine, known as dopaminergic neurons, are among the first to die in Parkinson’s disease, leading to the motor symptoms that gradually rob patients of movement and independence.

To understand what makes these neurons so vulnerable, I used an in-vitro model where I exposed N27 dopaminergic cells to 6-hydroxydopamine (6-OHDA), a toxin that triggers oxidative stress, like what occurs in the Parkinsonian brain. Then, I introduced Selenomethionine (SeMet), an organic form of selenium, to test whether this compound could counteract the damage and help the neurons survive.

Selenium has long intrigued scientists for its paradoxical nature. It is a trace element essential for antioxidant defense, yet in excess it can become toxic. I wanted to see whether a specific range of SeMet concentrations could offer meaningful protection without tipping that balance. My study, carried out at Charles University and the National Institute of Mental Health (NUDZ) in the Czech Republic, set out to define that “safe and effective window.” It is published in the journal In vitro models.

Fintech’s New Power Couple: AI And Trust

At Money20/20 I learned that Fintech has a new power couple, AI and Trust.

The combination of the two is the payment protocol of tomorrow. Come along with me as I share my findings from the world’s #1 fintech show.

There is bunch of interviews and images forthcoming from the event.

Thanks again to Tedd Huff of Fintech Confidential for inviting me to participate in the event. It allowed me to share My Instant AI with event attendees.

(https://www.linkedin.com/pulse/fintechs-new-power-couple-ai-…urke-eirte)

Tedd Huff asked me to be a confidential informant in Las Vegas recently. But, wait before you go down conspiracy theory rabbit hole, please let me explain.

Continue reading “Fintech’s New Power Couple: AI And Trust” | >

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