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Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis

Advances in high-throughput technologies, digital phenotyping, and increased accessibility of publicly available datasets offer opportunities for big data to be applied in infectious disease surveillance, diagnosis, treatment, and outcome prediction. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to analyze complex clinical and molecular data. However, it remains unclear which AI or ML models are most suitable for infectious disease management, as most existing studies use non-scoping literature reviews to recommend AI and ML models for data analysis. This scoping literature review thus examines the ML models and applications that are most relevant for infectious disease management, with a proposed actionable workflow for implementing ML models in clinical practice.

Scientists discover a two-stage aging process that may cause cancer and arthritis

Inherited genetic mutations may also stay silent for decades before increasing the risk of diseases such as cancer or fibrosis later in life.

Evolutionary Biology and Aging Research

The researchers say their model builds on long-standing evolutionary theories of aging. One influential idea is that natural selection becomes weaker later in life, allowing harmful biological processes to emerge with age because they have less impact on reproduction and survival earlier in life.

New antibody may boost KRAS-targeted lung cancer treatment after resistance emerges

An experimental antibody treatment that binds to a protein known as PCDH7 shrank tumors in preclinical models of non-small cell lung cancer (NSCLC), including those resistant to a targeted therapy, a study led by UT Southwestern Medical Center researchers showed. The findings, published in Science Advances, could eventually lead to a new class of drugs to treat NSCLC and potentially other cancers.

“Overcoming resistance to molecularly targeted therapies is a critical unmet need for lung cancer patients. We are excited that these antibodies may open another therapeutic avenue for lung cancer, especially for patients whose cancers have become resistant to KRAS inhibitors,” said Kathryn O’Donnell, Ph.D., associate professor of molecular biology and a member of the Harold C. Simmons Comprehensive Cancer Center at UT Southwestern. O’Donnell co-led the study with first author Nicole Novaresi, Ph.D., a postdoctoral researcher in the O’Donnell Lab, and collaborators at the University of Texas Health Science Center at Houston.

NSCLC accounts for about 85% of lung cancer cases in the U.S. and is the leading cause of cancer-related deaths. The O’Donnell Lab focuses on identifying and characterizing proteins on the surface of NSCLC and other cancer cells because of their potential as therapeutic targets. In 2017, O’Donnell and her colleagues identified PCDH7 as a driver of NSCLC, especially in tumors with mutations in a gene called KRAS. Found in about 25% of NSCLC cases, these mutations cause uncontrolled cell proliferation that propels tumor growth.

Novel gene therapy platform restores muscle function in Duchenne muscular dystrophy model

A new treatment platform developed by researchers at the University of Texas MD Anderson Cancer Center was able to deliver messenger RNA (mRNA) of the full-length DMD gene into preclinical models of Duchenne muscular dystrophy, successfully restoring the production of an important muscle protein, dystrophin, and dramatically improving muscle strength, endurance and function in vivo.

The study, published in Nature Biomedical Engineering, was co-led by Betty Kim, M.D., Ph.D., professor of neurosurgery and core member of the James P. Allison Institute, and Wen Jiang, M.D., Ph.D., associate professor of CNS Radiation Oncology.

The approach uses engineered extracellular vesicles (EVs)—natural nanoscale delivery particles—that offer distinct benefits over current viral-based gene therapies, including reduced side effects and the ability to transfer the entire DMD gene. The researchers engineered the EVs with special tags that directly target skeletal muscles after injection into the bloodstream.

LIS1 Is Critical for Axon Integrity in Adult Mice

Mutations in human LIS1 cause lissencephaly, a severe developmental brain malformation. Although most studies focus on development, LIS1 is also expressed in adult mouse tissues. We previously induced LIS1 knock-out (iKO) in adult mice using a Cre-Lox approach with an actin promoter driving CreERT2 expression. This proved to be rapidly lethal, with evidence pointing toward nervous system dysfunction. CreERT2 activity was observed in astrocytes, brainstem and spinal motor neurons, and axons and Schwann cells in the sciatic and phrenic nerves, suggesting dysfunctional cardiorespiratory and motor circuits. However, it is unclear how LIS1 knock-out in these different cell types contributes to the lethal phenotype.

How the body remembers the tumor?

While we tend to quickly forget having been ill or having received a vaccine, the immune system remembers remarkably well. It has memory B cells – “trained” immune cells that circulate throughout the body in search of harmful invaders they have encountered previously; these cells can rapidly deploy targeted weapons when faced with a pathogen again. Now, researchers report that activated memory B cells can also recognize an internal enemy: cancer cells.

In patients with ovarian cancer, the researchers identified memory cells that are capable of homing in on the tumor, springing into action and producing effective antibodies against it. The new study, whose findings were published in Immunity, advances the development of vaccines and therapies based on immune memory against cancer.

The immune system’s arsenal contains hundreds of millions of B cell clones, each producing a unique antibody against a specific pathogen. These antibodies are proteins that identify their target and either neutralize it or recruit other immune cells to attack it. When a clone encounters its target for the first time, its antibody binds weakly and elicits a limited response. But some of these cells enter “training camps” – structures called germinal centers in the lymph nodes – where they undergo genetic changes and rigorous selection, emerging with much more effective antibodies. Some of these trained cells immediately become active antibody producers; others develop into memory cells that remain inactive, circulating between the blood and the lymph nodes, but able to rapidly snap into action if the body is exposed again to the pathogen.

Brain tumor map finds immune cell states that may predict meningioma recurrence

One of the most detailed maps to date of meningioma—the most common brain tumor in adults—reveals how the tumor’s surrounding environment helps drive disease behavior and patient outcomes, according to new research from Mayo Clinic.

The study, published in Nature Genetics and conducted in collaboration with scientists at Princess Margaret Cancer Center in Toronto, combines several advanced laboratory techniques to examine tumors at an unprecedented level of detail, offering clues to why some meningiomas grow slowly while others recur or become more aggressive. The findings could lead to more precise ways to predict risk and guide treatment decisions.

Growing evidence suggests that traditional grading systems for meningioma do not fully capture the behavior of these complex tumors, prompting the development of molecular classification tools that more accurately predict which tumors are more likely to recur after surgery.

Nanoparticle Motion Measured Beyond Quantum Limit

Researchers boosted the sensitivity for measurements of the motion of a levitated nanoparticle, with potential uses in dark matter searches.

Researchers have a bold plan to detect unknown fundamental particles: Levitate a nanoscale object in a vacuum and watch for a microscopic recoil caused by a collision with an exotic particle. Precision measurements of macroscopic objects have been a challenge, but now a research team has demonstrated a significant sensitivity improvement with a levitated object some 6 orders of magnitude larger than in previous experiments [1]. The team hopes the method will find use in experimental searches in the next few years.

Searching for particles not accounted for by the standard model of particle physics requires experiments with unprecedented sensitivity. One method is to use laser light to levitate a small object in a vacuum, isolating it from surrounding noise. Researchers can monitor its motion and potentially detect minuscule recoils caused by rare collisions with exotic particles, such as those of dark matter.

Faster biological aging consistently linked to poverty and discrimination

The study, published in Nature Human Behaviour, demonstrates that social inequality, such as poverty and racism, is related to biological aging measured in the epigenome, also known as “epigenetic clocks.” Epigenetic clocks analyze patterns of chemical marks on DNA to estimate a person’s biological age or the rate at which their body is aging. These tools are increasingly used by scientists to study how environmental exposures, lifestyle and social conditions affect health across the life course.

Previous individual studies have shown that epigenetic clocks are sensitive to socioeconomic and racial or ethnic disparities. However, because multiple types of epigenetic clocks exist, it has remained unclear which measures best capture the effects of social determinants of health, at which stages of life socioeconomic exposures most affect epigenetic aging, and whether associations differ by sex or by technical factors such as the tissue in which epigenetic data are collected. This study integrates findings across many independent studies, offering a comprehensive test of whether these associations are consistent and robust.

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