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Adipokines as Clinically Relevant Therapeutic Targets in Obesity

Adipokines provide an outstanding role in the comprehensive etiology of obesity and may link adipose tissue dysfunction to further metabolic and cardiovascular complications. Although several adipokines have been identified in terms of their physiological roles, many regulatory circuits remain unclear and translation from experimental studies to clinical applications has yet to occur. Nevertheless, due to their complex metabolic properties, adipokines offer immense potential for their use both as obesity-associated biomarkers and as relevant treatment strategies for overweight, obesity and metabolic comorbidities. To provide an overview of the current clinical use of adipokines, this review summarizes clinical studies investigating the potential of various adipokines with respect to diagnostic and therapeutic treatment strategies for obesity and linked metabolic disorders.

Asimov Press (@asimovpress)

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Check it out here on Substack or on our custom website: https://read.asimov.com

Interfacing with the Brain: How Nanotechnology Can ContributeClick to copy article linkArticle link copied!

Interfacing artificial devices with the human brain is the central goal of neurotechnology. Yet, our imaginations are often limited by currently available paradigms and technologies. Suggestions for brain–machine interfaces have changed over time, along with the available technology. Mechanical levers and cable winches were used to move parts of the brain during the mechanical age. Sophisticated electronic wiring and remote control have arisen during the electronic age, ultimately leading to plug-and-play computer interfaces. Nonetheless, our brains are so complex that these visions, until recently, largely remained unreachable dreams. The general problem, thus far, is that most of our technology is mechanically and/or electrically engineered, whereas the brain is a living, dynamic entity. As a result, these worlds are difficult to interface with one another.

Dusty star-forming galaxy at high redshift discovered

An international team of astronomers reports the discovery of a new dusty star-forming galaxy at high redshift. The newfound galaxy, designated AC-2168, was detected using the Atacama Large Millimeter/submillimeter Array (ALMA) and the Northern Extended Millimeter Array (NOEMA). The finding was detailed in a paper published Nov. 11 on the pre-print server arXiv.

The so-called dusty star-forming galaxies (DSFGs) are highly obscured galaxies undergoing a period of intense star formation, with star-formation rates reaching even 1,000 solar masses per year. They represent the most intense starbursts in the universe and are crucial to improving our understanding of galaxy formation and evolution.

However, although many DSFGs are known, their nuclear structure, which can be essential to better understand the evolution of these galaxies, is still not fully explored. Hence, finding new DSFGs and investigating them in detail could shed more light on this matter.

Imaging of Skull Base Tumors

The skull base provides a platform for supporting the brain while serving as a conduit for major neurovascular structures. In addition to malignant lesions originating in the skull base, there are many benign entities and developmental variants that may simulate disease. Therefore, a basic understanding of the relevant embryology is essential. Lesions centered in the skull base can extend to the adjacent intracranial and extracranial compartments; conversely, the skull base can be secondarily involved by primary extracranial and intracranial disease. CT and MRI are the mainstay imaging methods and are complementary in the evaluation of skull base lesions. Advances in cross-sectional imaging have been crucial in the management of patients with skull base pathology, as this represents a complex anatomical area that is hidden from direct clinical exam.

How Taiwan’s Giant Genomics Project Is Rewriting the Future of Disease Prediction

A sweeping genomic effort in Taiwan has revealed something that global precision medicine has long overlooked, that the best way to predict disease is to study the people who will be living with its consequences. Researchers at Academia Sinica have now shown that building genetic risk tools tailored to Han Chinese populations can transform how common illnesses are forecast and understood.

In work published in Nature on October 15, 2025, scientists analyzed genomic and clinical data from more than half a million participants in the Taiwan Precision Medicine Initiative. By conducting the largest genome wide association analysis of Han Chinese individuals to date, they developed the first population specific polygenic risk score models for diseases ranging from type 2 diabetes to autoimmune disorders to heart disease, achieving markedly stronger accuracy than tools based on European data. “This project marks a milestone for precision medicine in East Asia,” said Dr. Cathy S. J. Fann, senior corresponding author at Academia Sinica. “By integrating large scale genomic and clinical data, we are building predictive models that truly reflect the real genetic architecture of our population.”

Explainable AI and turbulence: A fresh look at an unsolved physics problem

While atmospheric turbulence is a familiar culprit of rough flights, the chaotic movement of turbulent flows remains an unsolved problem in physics. To gain insight into the system, a team of researchers used explainable AI to pinpoint the most important regions in a turbulent flow, according to a Nature Communications study led by the University of Michigan and the Universitat Politècnica de València.

A clearer understanding of turbulence could improve forecasting, helping pilots navigate around turbulent areas to avoid passenger injuries or structural damage. It can also help engineers manipulate turbulence, dialing it up to help industrial mixing like water treatment or dialing it down to improve fuel efficiency in vehicles.

“For more than a century, turbulence research has struggled with equations too complex to solve, experiments too difficult to perform, and computers too weak to simulate reality. Artificial Intelligence has now given us a new tool to confront this challenge, leading to a breakthrough with profound practical implications,” said Sergio Hoyas, a professor of aerospace engineering at the Universitat Politècnica de València and co-author of the study.

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