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Category: biotech/medical – Page 2

Senescent cells dodge cell death by rewiring fat metabolism, study shows

In response to stress or damage, cells undergo senescence and stop dividing. However, if senescent cells accumulate in tissues over the long term, chronic inflammation occurs and the risk of cancer increases. Researchers at the German Cancer Research Center (DKFZ) have now discovered a previously unknown mechanism by which senescent cells protect themselves from oxidative stress and a specific form of cell death known as ferroptosis.

In the long term, these findings could provide new avenues for cancer therapies and the treatment of age-related diseases. The research is published in the journal Cell Death & Differentiation.

Senescence occurs when cells respond to stress or harmful changes and permanently cease their growth. This process is considered a protective mechanism against cancer. For example, cells that carry an oncogene permanently activated by mutations are effectively “frozen” before they can proliferate uncontrollably—a biological emergency program. However, problems arise when senescent cells accumulate in tissue, where they promote chronic inflammation and thus facilitate tumor development. Scientists are therefore searching for ways to eliminate senescent cells before they can cause harm.

Critical Thalamocortical Coordination Dynamics Track Conscious State Transitions

Abstract Despite substantial progress in identifying neural correlates of consciousness, no unified quantitative framework currently derives a formally specified order parameter for conscious-state organisation from established neurophysiological principles, or links thalamocortical coordination dynamics to measurable state transitions across pharmacological, pathological, and perturbational conditions through a single computational formalism. We propose a neurocomputational theoretical framework in which conscious states are associated with metastable regimes of large-scale thalamocortical coordination operating near critical dynamical boundaries. The framework is formalised through a dynamic coordination functional Φ(t), defined as a surface integral over the thalamocortical interface and directly operationalisable from high-density EEG as a weighted combination of gamma-band power spectral density, thalamocortical coherence, and theta-gamma phase-amplitude coupling. The thalamic reticular nucleus (TRN) is identified as the anatomical implementation of the control parameter governing proximity to the critical point, grounded in a Wilson-Cowan model of TRN inhibitory gating whose bifurcation structure is characterised computationally. Numerical simulation of the linearised field equation on the thalamocortical boundary demonstrates internal consistency: the simulated system produces power-law recovery dynamics tau_rec proportional to | θ — θ _c|^v with nu consistent with model A universality class [0.5, 1.5], and a Kuramoto mean-field derivation establishes that Φ(t) emerges as the natural order parameter of coupled thalamocortical oscillators rather than being postulated. The joint (|Φ(t)|, Var[|Φ(t)|]) phase space correctly separates simulated waking, anaesthetic, ictal, and minimally conscious regimes without parameter fitting to empirical data. All simulation code is publicly available. Six quantitatively specific, independently falsifiable predictions are derived across five experimental domains: power-law Gamma Dip scaling in near-threshold EEG with a specific exponent range; causal disruption of thalamocortical coherence by selective TRN silencing; opposite EEG scaling exponent deviations in ASD versus schizophrenia; systematic Φ_est collapse under propofol anaesthesia correlated with PCI; Φ_est as a real-time consciousness biomarker in disorders of consciousness; and clinical validity of Φ_est in disorders of consciousness and ictal state discrimination by the metastability index. Each prediction is stated with quantitative thresholds and a pre-specified falsification criterion. The framework provides: the first anatomically specified and formally derived order parameter for conscious-state organisation directly operationalisable from passive EEG; a mechanistically grounded identification of the TRN as the dynamical control parameter, testable by a single optogenetic experiment; and a computationally validated, pre-registerable programme of six falsifiable predictions defining a tractable empirical agenda. Φ_est would constitute a candidate real-time consciousness biomarker if the framework’s predictions are confirmed in purpose-designed experiments.

Neuroproteasomes regulate endogenous tau paired helical filament formation in an APOE genotype- and age-dependent manner

A cellular explanation for how tau aggregates into fibrils in Alzheimer’s disease has been elusive. This paper identifies the failure of ‘neuroproteasomes’ as sufficient to convert tau into paired helical filaments, a process regulated by ApoE and aging.

NIH-funded study suggests that testosterone suppresses brain tumor growth in males

Findings may warrant exploration of the hormones as glioblastoma treatment.

In a new National Institutes of Health (NIH)-funded study, scientists at Cleveland Clinic discovered that hormones associated with male development may play a key role in limiting the growth of brain tumors in men. The research team found that the loss of androgen hormones, such as testosterone, in a preclinical model of glioblastoma drove tumor growth by inducing local inflammation and triggering the production of stress hormones. In an analysis of data from more than 1,300 men with glioblastoma, the authors found that supplemental testosterone was significantly associated with improved survival, which was consistent with their preclinical experiments.

“This outcome is a welcome surprise and may potentially offer a lead for new treatments for a kind of cancer that is deadlier in men,” said Anthony Letai, M.D., Ph.D., director of NIH’s National Cancer Institute (NCI).

AI system automates scientific software design, outperforming human-written code in key benchmarks

A research team at Google co-led by Michael Brenner, Catalyst Professor of Applied Mathematics and Physics at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and Google research scientist, has produced a new artificial intelligence system that can automatically write scientific software programs that surpass the performance of human-written programs. The paper is published in the journal Nature.

How the ERA system came together The system is called Empirical Research Assistance (ERA), and the project was co-led by Brenner and Shibl Mourad from Google DeepMind. Harvard Ph.D. students Qian-Ze Zhu, Ryan Krueger, and Sarah Martinson contributed as Google student researchers while working in Brenner’s group. The research was done in Brenner’s capacity as a Catalyst Professor, a position established by the University to enhance relationships between academia and the private sector by supporting senior faculty in research roles at external companies.

Across modern science, customized software is constantly used to test specific hypotheses or interpret complex data. The authors refer to this type of computer program as “empirical software”—a program whose sole purpose is to maximize how well it does on a scientific task, like making weather predictions or forecasting hospitalizations during a disease outbreak. Any problem that can be expressed as a numerical value—its “score”—is called a scorable task.

Terahertz imaging maps spatial chirality in materials with 100-micrometer resolution

In nature, there exist structures that are mirror images of each other but cannot be perfectly superimposed. These are known as chiral objects, derived from the Greek word for “hand,” since left and right hands share the same relationship. Although similar in structure, chiral molecules exhibit different behaviors, and chirality is central to life itself. DNA has a twisted chiral structure, and living organisms prefer one handedness over the other. This distinction is equally important in drug design, materials science, and nanotechnology.

One way to distinguish chiral molecules is by measuring their response to circularly polarized light in the terahertz (THz) region. THz waves lie between microwaves and infrared light and are especially sensitive to subtle collective motions and twisting structures in materials. However, conventional THz measurements average the signal across an entire sample, making it impossible to determine how chirality varies across different locations.

Now, researchers from Chiba University, Japan, and Tohoku University, Japan, have shown that this limitation can be overcome, allowing chirality to be visualized as two-dimensional images, much like creating a map of chirality across a material. The work appears in ACS Photonics.

Proteins can be selectively controlled with radio waves

In a significant advance in biological quantum sensing, a research team led by the Technical University of Munich (TUM) has discovered and tested a new mechanism of action in which proteins can be controlled with radio waves. In doing so, they influence a sensitive quantum state known as spin and make it visible via light. In the future, such findings could help detect and even direct biochemical processes in cells simply from the outside using radio waves.

Until now, quantum sensing has primarily been known from solid-state materials such as diamonds with deliberately introduced tiny defects. The researchers are now transferring this principle to proteins —biological molecules that can be genetically produced and specifically tailored. In the future, this could allow quantum sensors to be built directly into cells or tissue.

These protein-based sensors are potentially particularly well suited for biosensing—that is, for imaging living cells, tissues, or organs. In theory, they sit directly where measurement is needed, making them suitable for studies in organisms—unlike bulky solid-state sensors.

First human SMUG1 atomic snapshots reveal how cells repair DNA

Researchers have captured the first atomic structures of human SMUG1, an enzyme that helps cells repair damaged DNA. The findings provide new insight into how cells recognize and remove harmful DNA bases, and may support future efforts to develop drugs that target this DNA repair pathway.

“These structures give us the first detailed view of how human SMUG1 engages damaged DNA and carries out the first steps of repair,” says professor Pål Stenmark, who led the study.

DNA is constantly damaged by normal processes in our cells, as well as by environmental factors and cancer treatments. If the damage is not repaired, it can lead to permanent mutations.

Deep brain stimulation boosts myelination and shifts brain networks linked to depression

Researchers from the Icahn School of Medicine at Mount Sinai have uncovered the first direct evidence that deep brain stimulation (DBS) can remodel white matter pathways in the brain and alter communication across large-scale neural networks, revealing a previously unrecognized mechanism that may explain how the therapy helps patients recover from severe depression. The study, published June 1 in Nature Neuroscience, provides critical insight into the biological basis of DBS, an emerging therapy for treatment-resistant depression and other neuropsychiatric disorders.

Deep brain stimulation, approved by the U.S. Food and Drug Administration to treat essential tremor, Parkinson’s disease, epilepsy, and obsessive-compulsive disorder, is a neurosurgical procedure involving placement of a neurostimulator (sometimes referred to as a “brain pacemaker”), which sends high-frequency electrical impulses through implanted electrodes deep in the brain to specific areas responsible for the symptoms of each disorder.

Although DBS has shown sustained clinical benefit for many patients with severe depression who do not respond to medications, psychotherapy, and electroconvulsive therapy, the mechanisms underlying its therapeutic effects have remained poorly understood.

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