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Fever temperatures rev up immune cell metabolism, proliferation and activity, but they also — in a particular subset of T cells — cause mitochondrial stress, DNA damage and cell death, Vanderbilt University Medical Center researchers have discovered.

The findings, published Sept. 20 in the journal Science Immunology, offer a mechanistic understanding for how cells respond to heat and could explain how chronic inflammation contributes to the development of cancer.

The impact of fever temperatures on cells is a relatively understudied area, said Jeff Rathmell, PhD, Cornelius Vanderbilt Professor of Immunobiology and corresponding author of the new study. Most of the existing temperature-related research relates to agriculture and how extreme temperatures impact crops and livestock, he noted. It’s challenging to change the temperature of animal models without causing stress, and cells in the laboratory are generally cultured in incubators that are set at human body temperature: 37 degrees Celsius (98.6 degrees Fahrenheit).

A BREAKTHROUGH cancer treatment “could be the cure” for a “death sentence” form of the disease after making tumours disappear.

The experimental approach has seen remarkable success in some brain cancer patients — with experts saying it could be available on the NHS within five years.

All proteins are composed of chains of amino acids, which generally fold up into compact globules with specific shapes. The folding process is governed by interactions between the different amino acids—for example, some of them carry electrical charges—so the sequence determines the structure. Because the structure in turn defines a protein’s function, deducing a protein’s structure is vital for understanding many processes in molecular biology, as well as for identifying drug molecules that might bind to and alter a protein’s activity.

Protein structures have traditionally been determined by experimental methods such as x-ray crystallography and electron microscopy. But researchers have long wished to be able to predict a structure purely from its sequence—in other words, to understand and predict the process of protein folding.

For many years, computational methods such as molecular dynamics simulations struggled with the complexity of that problem. But AlphaFold bypassed the need to simulate the folding process. Instead, the algorithm could be trained to recognize correlations between sequence and structure in known protein structures and then to generalize those relationships to predict unknown structures.

However, the effect of the PIC on firing outputs also depends on its location in the dendritic tree. To investigate the interaction between PIC neuromodulation and PIC location dependence, we used a two-compartment model that was biologically realistic in that it retains directional and frequency-dependent electrical coupling between the soma and the dendrites, as seen in multi-compartment models based on full anatomical reconstructions of motoneurons. Our two-compartment approach allowed us to systematically vary the coupling parameters between the soma and the dendrite to accurately reproduce the effect of location of the dendritic PIC on the generation of nonlinear (hysteretic) motoneuron firing patterns. Our results show that as a single parameter value for PIC activation was either increased or decreased by 20% from its default value, the solution space of the coupling parameter values for nonlinear firing outputs was drastically reduced by approximately 80%. As a result, the model tended to fire only in a linear mode at the majority of dendritic PIC sites. The same results were obtained when all parameters for the PIC activation simultaneously changed only by approximately ±10%. Our results suggest the democratization effect of neuromodulation: the neuromodulation by the brainstem systems may play a role in switching the motoneurons with PICs at different dendritic locations to a similar mode of firing by reducing the effect of the dendritic location of PICs on the firing behavior.

Spinal motoneurons have large, highly branched dendrites and voltage-gated ion channels that generate strong persistent inward currents (PICs) (Schwindt and Crill, 1980). Over the past 30 years, the impact of PICs on the firing output of the motoneurons has been extensively investigated in various species, including turtles (Hounsgaard and Kiehn, 1985, 1989), rats (Bennett et al., 2001; Li and Bennett, 2003), mice (Carlin et al., 2000; Meehan et al., 2010) and cats (Lee and Heckman, 1998, 1999). There has been a consensus in the motoneuron physiology community that in the presence of monoamines (i.e., norepinephrine and serotonin), the activation of the L-type Ca²⁺ PIC channels is facilitated, producing a long-lasting membrane depolarization (i.e., plateau potential) (reviewed in Powers and Binder, 2001; Heckman et al., 2008).

I’m pretty much a subscriber to the computational theory of mind (broadly speaking), which holds that the mind is information in the brain. If this theory of mind is accurate, then there should be no barrier to someday uploading a copy of our mind into a computer, providing we can find a way to record it.

This is, of course, a controversial notion. There are many people who swear that uploading will never be accomplished. They list a lot of reasons, from the fact that the mind is inextricably entangled with the workings of the body, to the impossibility of ever making a fully accurate representation of the brain, to religious beliefs about mind / body dualism (which you won’t see me address in this post).

Regarding the notions about the mind being tangled with the body, I suspect the people who express these sentiments are underestimating what our ability will eventually be to virtualize these kinds of mechanisms. Sure, our mental states are tied to things like hormones, blood sugar level, the state of our gut, and many other body parameters. But many of these parameters are driven by the brain. And I don’t really see any reason why we wouldn’t eventually be able to simulate its effects on a virtual brain.

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In a recent study published in the journal Cell, researchers developed a deep learning model, “LucaProt,” a transformer-based AI model to detect highly divergent ribonucleic acid (RNA)-dependent RNA polymerase (RdRP) sequences in meta-transcriptomes from diverse ecosystems. They identified 180 RNA virus supergroups and 161,979 putative RNA virus species, showing that RNA viruses are widespread and present even in extreme environments.

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A Ludwig Cancer Research study has punctured a longstanding assumption about the source of the most common type of DNA mutation seen in the genome—one that contributes to many genetic diseases, including cancer.

Led by Ludwig Oxford Leadership Fellow Marketa Tomkova, postdoc Michael McClellan, Assistant Member Benjamin Schuster-Böckler and Associate Investigator Skirmantas Kriaucionis, the study has implications not only for basic cancer biology but also for such things as assessments of carcinogenic risk associated with environmental factors and our understanding of the emergence of drug resistance during cancer therapy. Its findings are reported in the current issue of Nature Genetics.

The mutation in question—in which cytosine ©, one of the four bases of DNA that spell out our genes, is erroneously switched to thymine (T)—was thought to be primarily the result of a spontaneous chemical reaction with water. This reaction, deamination, is about twice as likely to happen when a cytosine is chemically tagged by the addition of a molecule known as a methyl group to create 5-methylcytosine, which occurs in DNA at so-called “CpG” positions, where C is followed by the base guanine (G).

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