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At Science4Seniors we strive to take rigorous research published in Scientific Journals and make the core information accessible to all. If you want to support us please like and follow us on Facebook. In recent years, the intersection of medical science and technology has unfurled fascinating possibilities, especially in diagnostics. Among the many marvels we’ve been introduced to, medical artificial intelligence (AI) is reshaping how we detect and diagnose a plethora of health conditions. One area that stands out significantly in this transformation is the potential of AI in the analysis of retinal images.

In response to the increasing demand for medical services amid labor shortages and a rapidly aging population, Shanghai-based Fourier Intelligence is developing an innovative humanoid robot. The GR-1, as it is called, promises to transform healthcare facilities and offer vital assistance to the elderly.

Like many countries, China is confronting the challenge of an aging population. The number of individuals aged 60 and over is projected to rise from 280 million to over 400 million by 2035, according to estimates from the country’s National Health Commission. That’s more than the entire population of the United States projected for that year.

It’s not the sheer number of the elderly that is a problem, but rather their share of the overall population. By 2040, nearly 30% of China’s population will be 60 or older.

New research indicates that butterflies and moths share “blocks” of DNA

DNA, or deoxyribonucleic acid, is a molecule composed of two long strands of nucleotides that coil around each other to form a double helix. It is the hereditary material in humans and almost all other organisms that carries genetic instructions for development, functioning, growth, and reproduction. Nearly every cell in a person’s body has the same DNA. Most DNA is located in the cell nucleus (where it is called nuclear DNA), but a small amount of DNA can also be found in the mitochondria (where it is called mitochondrial DNA or mtDNA).

Numerous natural language processing (NLP) applications have benefited greatly from using large language models (LLMs). While LLMs have improved in performance and gained additional capabilities due to being scaled, they still have a problem with “hallucinating” or producing information inconsistent with the real-world facts detected during pre-training. This represents a significant barrier to adoption for high-stakes applications (such as those found in clinical and legal settings), where the generation of trustworthy text is essential.

The maximum likelihood language modeling target, which seeks to minimize the forward KL divergence between the data and model distributions, may be to blame for LMs’ hallucinations. However, this is far from certain. The LM may assign a non-zero probability to phrases that are not fully consistent with the knowledge encoded in the training data if this goal is pursued.

From the perspective of the interpretability of the model, studies have shown that the earlier layers of transformer LMs encode “lower level” information (such as part-of-speech tags). In contrast, the later levels encode more “semantic” information.

Scientists at Université Laval and the University of Lethbridge have succeeded in reversing certain cognitive manifestations associated with Alzheimer’s disease in an animal model of the disease. Their results have been published in the scientific journal Brain.

Although this has yet to be demonstrated in humans, we believe that the mechanism we have uncovered constitutes a very interesting therapeutic target, because it not only slows down the progression of the disease but also partially restores certain cognitive functions.

Many of the bacteria that ravage crops and threaten our food supply employ a shared tactic to induce disease: they inject a cocktail of harmful proteins directly into the plant’s cells.

For 25 years, biologist Sheng-Yang He and his senior research associate Kinya Nomura have been investigating this set of molecules that plant pathogens use to cause diseases in hundreds of crops globally, from rice to apple orchards.

Now, thanks to a team effort between three collaborating research groups, they may finally have an answer to how these molecules make plants sick — and a way to disarm them.

A study published in the journal PLOS Biology looked at the underlying pathways involved in breast cancer recurrence.

Researchers reported that a common chemotherapy treatment encouraged connective tissue cells to produce cytokines that helped reawaken dormant cancer cells.

The researchers say the results offer possible direction on adding other therapies to chemotherapy treatment to reduce the risk of breast cancer recurrence.


Researchers say a common chemotherapy may sometimes reactivate dormant breast cancer cells. They suggest supplemental therapies may be needed in these cases.