Hair loss affects millions of people worldwide. Although treatments do exist, these solutions are costly and not always effective. Looking for a more lasting and effective solution, scientists have turned their attention to understanding the molecular mechanisms that regulate hair growth, leading to a new frontier in hair regeneration: dermal exosomes.
As the COVID-19 lockdown in 2020 stretched on, scientists watched for all sorts of unintended effects, from social to economic to environmental.
But the experts who predict disease transmission wondered specifically whether infectious diseases other than COVID-19 would surge after the prolonged isolation of the population. Would social distancing cause us to have less immunity to common diseases? Would those diseases rebound with deadly consequences?
In a paper published in Science, the University of Georgia’s Tobias Brett and Pejman Rohani explored which infectious diseases were impacted by COVID-19 control measures and, of those, which rebounded. They found airborne diseases were most likely to rebound—but not as much as some feared. Surprisingly, the incidence of sexually transmitted diseases remained low, even long after pandemic-era behaviors changed.
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).
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.
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 cancer research. 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 gene regulation.
“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).
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 code or would not help students with code comprehension tasks.