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Scientists discover a trigger for cancer cells’ self-destruction

Researchers have found that when cancer cells are compressed or tightly confined, they begin a process that results in their self destruction.

Experts at Fudan University, Sun Yat-sen University and the Chinese Academy of Sciences in Shanghai used three dimensional models to uncover how cancer cells responded when mechanically confined for prolonged periods of time.

They saw that the cells, when trapped in tight spaces, underwent a kind of chronic stress response that affected their metastatic behavior — which enables them to spread — and resulted in cell death.

Lipid Infusion Enhances Sympathetic Transduction via Redox-Sensitive Pathways

BACKGROUND: Elevated circulating lipids increase oxidative stress and impair vascular function.

But their effects on sympathetic-vascular control in humans are not well understood.

We tested the hypothesis that intravenous lipid infusion enhances sympathetic-vascular.

Transduction and beat-to-beat arterial pressure variability in humans, and that these.

Undergraduate student may have solved puzzle regarding isolated galaxies that have stopped producing stars

An undergraduate astrophysics student at UC Berkeley has found three unusual galaxies—cataloged as Shapiro Dwarf Galaxies I, II and III. Each galaxy is far from other galaxies and has stopped making stars, a combination that is extremely rare. The discovery, published in The Astrophysical Journal, provides compelling observational evidence of a long-held astronomical theory.

“Dwarf galaxies exist both in isolation and as satellites around larger galaxies like our own Milky Way,” said Julian Shapiro, the study’s sole author, who conducted his research while still in high school. “In isolation, these galaxies are expected to be actively forming stars, as there are no larger galaxies to strip their star-forming gas, but the galaxies I discovered break this rule.

My paper argues that they may be among the first resolved ‘backsplash’ galaxy candidates, or galaxies that once passed close to a large neighbor, whose pressure removed their gas, before being flung outward.

‘Smart bomb’ combination attacks triple-negative breast cancer in preclinical study

A new preclinical study suggests that attacking triple-negative breast cancer (TNBC) in two ways at once can kill cancer cells, slow tumor growth and limit the cancer’s spread. Researchers at The Ohio State University Comprehensive Cancer Center—Arthur G. James Cancer Hospital and Richard J. Solove Research Institute (OSUCCC—James) developed the approach. The findings could help guide new treatments for this hard-to-treat form of breast cancer.

“This study points to a possible new way to fight TNBC by pairing a targeted ‘smart bomb’ drug with a treatment that shuts down the cancer cells’ energy factories,” said Xiaoguang “Margaret” Liu, Ph.D., the study’s corresponding author and a member of the OSUCCC—James Translational Therapeutics Program. Liu also is a professor in Ohio State’s College of Engineering. “These results are an early step. If the approach continues to work in laboratory studies and clinical trials, it could lead to a more effective treatment with fewer side effects than current options.”

The findings were published Aug. 18, 2026, in Cell Reports Medicine. The study helps scientists better understand how triple-negative breast cancer may survive treatment. It suggests the cancer may be more vulnerable when one treatment delivers a drug directly to cancer cells and another cuts off their energy supply.

Machine learning reveals potential warning signal for polar vortex disruptions

The polar vortex, a swirling mass of cold air encircling the North Pole, is sometimes disrupted by sudden stratospheric warming (SSW) events, unleashing waves of frigid air that penetrate farther south than usual. Temperatures across North America and Europe drop precipitously when this happens, but such events remain hard to predict, meaning we can’t easily warn people when SSW is imminent.

Boscu and team used a machine learning (ML) technique to learn SSW behavior from a physics-based simulation that reproduces important atmospheric dynamics, accelerating polar vortex simulations. They went a step further by analyzing the ML model itself to understand how it represents various aspects of the polar vortex. The work is published in the Journal of Geophysical Research: Machine Learning and Computation.

The authors trained their model on the Holton-Mass model of stratospheric variability, which produces two metastable regimes for the polar vortex: one weak and one strong. A transition from the strong to the weak regime is what causes SSW events.