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Revolutionising cancer treatment: intracellular protein delivery using hybrid nanotubes

A new hybrid nanotube stamp system has been developed which revolutionises precision medicine with high efficiency and cell viability rates for cancer treatment.
Precision medicine and targeted therapies are gaining traction for their ability to tailor treatments to individual patients while minimising adverse effects. Conventional methods, such as gene transfer techniques, show promise in delivering therapeutic genes directly to cells to address various diseases.
However, these methods face significant drawbacks, hindering their efficacy and safety. Intracellular protein delivery offers a promising approach for developing safer, more targeted, and effective therapies. By directly transferring proteins into target cells, this method circumvents issues such as silencing during transcription and translation and the risk of undesirable mutations from DNA insertion. Additionally, intracellular protein delivery allows for precise distribution of therapeutic proteins within target cells without causing toxicity.
A group of researchers led by Professor Takeo Miyake at Waseda University, Japan in collaboration with the Mikawa Group at the RIKEN Institute have now developed a hybrid nanotube stamp system for intracellular delivery of proteins. This innovative technique enables the simultaneous delivery of diverse cargoes, including calcein dye, lactate oxidase (LOx) enzyme, and ubiquitin (UQ) protein, directly into adhesive cells for cancer treatment.
The researchers explored the therapeutic potential of delivering LOx enzyme for cancer treatment. “Through our innovative stamp system, we successfully delivered LOx into both healthy mesenchymal stem cells (MSC) and cancerous HeLa cells. While MSC cells remained unaffected, we observed significant cell death in HeLa cancer cells following LOx treatment with viabilities decreasing over time. Our findings highlight the promising efficacy of intracellularly delivered LOx in selectively targeting and killing cancer cells, while sparing healthy cells, offering a targeted therapeutic strategy for cancer treatment,” explains Miyake.
Finally, the team successfully delivered 15N isotope-labeled UQ proteins into HeLa cells using the HyNT stamp system. This delivery allowed for the analysis of complex protein structures and interactions within the cells. In addition, optical and fluorescence imaging confirmed the presence of delivered UQ in HeLa cells, and nuclear magnetic resonance spectroscopy matched the intracellular UQ protein concentration with that of a solution containing 15N-labeled UQ. These results demonstrate the effectiveness of the stamp system in delivering target proteins for subsequent analysis.
The results demonstrate the remarkable capability of the HyNT stamp system in delivering LOx and UQ into a substantial number of adhesive cells, as required for regenerative medicine applications. The system achieved a notably high delivery efficiency of 89.9%, indicating its effectiveness in transporting therapeutic proteins into the target cells with precision. Moreover, the cell viability rate of 97.1% highlights the system’s ability to maintain the health and integrity of the treated cells throughout the delivery process.
The HyNT stamp system offers transformative potential in intracellular protein delivery, with applications spanning from cancer treatment to molecular analysis. Beyond medicine, its versatility extends to agriculture and food industries, promising advancements in crop production and food product development. With precise cell manipulation and efficient delivery, the HyNT stamp system is poised to revolutionize biomedical research, clinical practice, and diverse industries, paving the way for personalized interventions and shaping the future of modern medicine.
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AI can predict Alzheimer’s with almost 93% accuracy, researchers say

Alzheimer’s AI can predict the disease with nearly 93 per cent accuracy using more than 800 brain scans, researchers say.
The system identified anatomical changes in the brain linked to the onset of the most common form of dementia, a condition that gradually damages memory and thinking.
The findings build on years of research suggesting AI could help spot early Alzheimer’s risk, predict disease and identify patients whose condition has not yet been diagnosed.
Benjamin Nephew, an assistant research professor at the Worcester Polytechnic Institute in Massachusetts, said: “Early diagnosis of Alzheimer’s disease can be difficult because symptoms can be mistaken for normal ageing.
“We found that machine-learning technologies, however, can analyse large amounts of data from scans to identify subtle changes and accurately predict Alzheimer’s disease and related cognitive states.”
The study used MRI scans, a type of detailed brain imaging, from 344 people aged 69 to 84.
The dataset included 281 scans showing normal mental function, 332 with mild cognitive impairment, an early stage of memory and thinking decline, and 202 with Alzheimer’s.
The scans covered 95 of the brain’s nearly 200 distinct regions and used an AI algorithm to predict patients’ health.
Being able to use AI to help diagnose Alzheimer’s earlier could give patients and doctors crucial time to prepare and potentially slow the progression of the disease.
The analysis showed that one of the top predictive factors was brain volume loss, or shrinkage, in the hippocampus, which helps form memories, the amygdala, which processes fear, and the entorhinal cortex, which helps provide a sense of time.
This pattern held across age and sex, with both men and women aged 69 to 76 showing volume loss in the right part of the hippocampus, suggesting it may be an important area for early diagnosis, the researchers noted.
However, the research also found that the way brain regions shrink differs by sex.
In females, volume loss occurred in the brain’s left middle temporal cortex, which is involved in language and visual perception. In males, it was mainly seen in the right entorhinal cortex
The researchers believe this could be linked to changes in sex hormones, including the loss of oestrogen in women and testosterone in men.
These conclusions could help improve methods of diagnosis and treatment going forward, Nephew said.
More than 7.2m Americans are living with Alzheimer’s, according to the Alzheimer’s Association.
More research is being done to reveal other impacting factors.
Nephew said: “The critical challenge in this research is to build a generalisable machine-learning model that captures the difference between healthy brains and brains from people with mild cognitive impairment or Alzheimer’s disease.”
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