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Moungi G. Bawendi, Louis E. Brus and Alexei I. Ekimov are awarded the Nobel Prize in Chemistry 2023 for the discovery and development of quantum dots. These tiny particles have unique properties and now spread their light from television screens and LED lamps. They catalyse chemical reactions and their clear light can illuminate tumour tissue for a surgeon.

“Toto, I’ve a feeling we’re not in Kansas anymore,” is a classic quote from the film The Wizard of Oz. Twelve-year-old Dorothy faints onto her bed when her house is swept away by a powerful tornado, but when the house lands again and she steps outside the door, her dog Toto in her arms, everything has changed. Suddenly she is in a magical, technicolour world.

If an enchanted tornado were to sweep into our lives and shrink everything to nano dimensions, we would almost certainly be as astonished as Dorothy in the land of Oz. Our surroundings would be dazzlingly colourful and everything would change. Our gold earrings would suddenly glimmer in blue, while the gold ring on our finger would shine a ruby red. If we tried to fry something on the gas hob, the frying pan might melt. And our white walls – whose paint contains titanium dioxide – would start generating lots of reactive oxygen species.

Cancer immunotherapy drugs called PD-1 inhibitors are widely used to stimulate the immune system to fight cancer, but many patients either don’t respond or develop resistance to them. A new small-molecule drug candidate being tested in an early-stage clinical trial aims to improve patient responses to immunotherapy.

Now scientists have shown, in a study published in Nature, that the small molecule works through two different mechanisms to slow tumor growth and increase survival in lab animals.

Researchers from the Tumor Immunotherapy Discovery Engine (TIDE) at the Broad Institute of MIT and Harvard, AbbVie, and Calico Life Sciences report that the molecule simultaneously makes tumors more sensitive to immune attack and boosts the activity of immune cells to fight tumors in mice.

PhD candidate at UniSA’s Applied Chemistry and Translational Biomaterials (ACTB) Group, Cintya Dharmayanti, has taken out UniSA’s 2021 Three Minute Thesis (3MT) with a condensed presentation of her research about developing nanoparticles for cancer treatment, potentially leading to more effective treatments and reduced side effects. She will be competing in the 2023 FameLab National Finals with a presentation titled, “Behind enemy lines: Tiny assassins in the war against cancer.

For more from University of South Australia visit: https://www.unisa.edu.au/connect/alumni-network/alumni-news/…Track=true.

Continue reading “Using Nanoparticles to Treat Cancer” »

Within the last century different ways have been developed to fight cancer. The most recent type of therapy includes immunotherapy. Immunotherapy activates the immune system to recognize and target the tumor that was initially undetectable. The most common immunotherapy treatments include anti-programmed death-1 (anti-PD-1) and anti-cytotoxic T lymphocyte-associated antigen (anti-CTLA-4). The two therapies are known as checkpoint inhibitors because they block cell signaling between immune cells and cancer which allow immune cells to be activated and kill the tumor. For their work on both of these therapies, Dr. James Allison and Dr. Tasuku Honjo were awarded the Nobel Prize in Physiology or Medicine in 2018. Since the discovery of these checkpoint inhibitors, they have been used in multiple settings as single-agent therapies or in combination against many different cancers.

The use of checkpoint inhibitors before therapy or as a neoadjuvant has grown as a possible treatment in various cancer types. According to researchers at the Bloomberg-Kimel Institute for Cancer Immunotherapy and the Johns Hopkins Kimmel Cancer Center medicine, checkpoint inhibitors have just touched the surface in neoadjuvant immunotherapy. Researchers including Suzanne Topalian, Director of Johns Hopkins Melanoma/Skin Cancer Program, believe that cancer immunotherapy has a lot of rich untapped potential that can be used to further improve treatments.

Neoadjuvant immunotherapy, or therapy prior to surgery, has since been tested in various clinical trials. In some tumor types this form of therapy has resulted in complete tumor eradication. The report published in Cancer Cell highlights successful clinical trials in cancers such as lung, triple-negative breast, skin, and gastrointestinal. Although this therapy is still highly underdeveloped, the US Food and Drug Administration (FDA) has already approved neoadjuvant immunotherapy in triple-negative breast and lung cancer. It is expected that more approvals will follow for other cancer types.

Breast cancer screening guidelines recommend women should start getting a mammogram every other year beginning at age 40.

Artificial intelligence is becoming more common in many areas of our society. One area that we may start to see more of it is in the medical community, including when it comes to the management of chronic pain. Researchers recently put artificial intelligence to the test in helping people with managing their chronic pain, and the results turned up a promising outlook for those who may have difficulty accessing a therapist.

Cognitive pain therapy intervention can play an important role in helping people who suffer from chronic pain. Our thoughts regarding pain and what we are experiencing can influence the severity of pain that we experience and how well we manage through it. Having access to a therapist who can assist chronic pain patients with cognitive pain therapy can be a challenge for some people. This leads to people not receiving the therapy they could benefit from or not finishing treatment altogether.

Kristalyn Gallagher, DO, Kevin Chen, MD, and Shawn Gomez, EngScD, in the UNC School of Medicine have developed an AI model that can predict whether or not cancerous tissue has been fully removed from the body during breast cancer surgery.

Artificial intelligence (AI) and machine learning tools have received a lot of attention recently, with the majority of discussions focusing on proper use. However, this technology has a wide range of practical applications, from predicting natural disasters to addressing racial inequalities and now, assisting in cancer surgery.

A new clinical and research partnership between the UNC Department of Surgery, the Joint UNC-NCSU Department of Biomedical Engineering, and the UNC Lineberger Comprehensive Cancer Center has created an AI model that can predict whether or not cancerous tissue has been fully removed from the body during breast cancer surgery. Their findings were published in Annals of Surgical Oncology.

Summary: Pioneering artificial intelligence (AI) has astoundingly synthesized the design of a functional walking robot in a matter of seconds, illustrating a rapid-fire evolution in stark contrast to nature’s billion-year journey.

This AI, operational on a modest personal computer, crafts entirely innovative structures from scratch, distinguishing it from other AI models reliant on colossal data and high-power computing. The robot, emerging from a straightforward “design a walker” prompt, evolved from an immobile block to a bizarre, porously-holed, three-legged entity, capable of slow, steady locomotion.

Representing more than mere mechanical achievement, this AI-designed organism may mark a paradigm shift, offering a novel, unconstrained perspective on design, innovation, and potential applications in fields ranging from search-and-rescue to medical nanotechnology.

The investigators carried out animal trials with the engineered AsCas12f system, partnering it with other genes and administering it to live mice. The encouraging results indicated that engineered AsCas12f has the potential to be used for human gene therapies, such as treating hemophilia.

The team discovered numerous potentially effective combinations for engineering an improved AsCas12f gene-editing system, and acknowledged the possibility that the selected mutations may not have been the most optimal of all the available mixes. As a next step, computational modeling or machine learning could be used to sift through the combinations and predict which might offer even better improvements.

And as the authors noted, by applying the same approach to other Cas enzymes, it may be possible to generate efficient genome-editing enzymes capable of targeting a wide range of genes. “The compact size of AsCas12f offers an attractive feature for AAV-deliverable gRNA and partner genes, such as base editors and epigenome modifiers. Therefore, our newly engineered AsCas12f systems could be a promising genome-editing platform … Moreover, with suitable adaptations to the evaluation system, this approach can be applied to enzymes beyond the scope of genome editing.”