The air we breathe has long been a concern for public health, but a recent study by Emory University researchers sheds light on a specific and alarming link between air pollution and pregnancy risks. Published in Environmental Science & Technology, the research reveals that exposure to tiny particles in air pollution during pregnancy can disrupt maternal metabolism, leading to increased risk of various negative birth outcomes.

The study analyzed blood samples from 330 pregnant women in the Atlanta metropolitan area, providing a detailed insight into how ambient fine particulate matter (PM2.5) affects the metabolism of pregnant women and contributes to increased risks of preterm and early term births. This pioneering work marks the first time researchers have been able to investigate the specific fine particles responsible for these adverse outcomes.

“The link between air pollution and premature birth has been well established, but for the first time we were able to look at the detailed pathway and specific fine particles to identify how they are reflected in the increased risk of adverse birth outcomes,” says Donghai Liang, PhD, study lead author and associate professor of environmental health. “This is important because if we can figure out the ‘why’ and ‘how,’ then we can know better how to address it.”

Previous research has shown that pregnant women and fetuses are more vulnerable than other populations to exposure to PM2.5, which is emitted from combustion sources such as vehicle exhaust, industrial processes, and wildfires. This increased vulnerability is linked to a higher likelihood of preterm births, the leading cause of death globally among children under the age of five.

Preterm birth is associated with complications such as cerebral palsy, respiratory distress syndrome, and long-term noncommunicable disease risks. Early term births (37-39 weeks of gestation) are also linked to increased neonatal morbidity and developmental challenges. Approximately 10% of preterm births worldwide are attributable to PM2.5 exposure.

As an air pollution scientist, Liang emphasizes the importance of addressing this issue beyond simply asking people to move away from highly polluted areas. “From a clinical intervention standpoint, it’s critical to gain a better understanding on these pathways and molecules affected by pollution,” he says. “In the future, we may be able to target some of these molecules to develop effective strategies or clinical interventions that could help reduce these adverse health effects.”

This groundbreaking study highlights the urgent need for policymakers and healthcare providers to take action against air pollution, particularly in areas with high levels of PM2.5 exposure. By understanding the molecular link between air pollution and pregnancy risks, we can work towards developing targeted solutions to mitigate these negative outcomes and protect the health of future generations.

SAVANA is a groundbreaking algorithm that uses machine learning to accurately identify structural variants and copy number aberrations in cancer genomes. This innovative tool has been developed to overcome the limitations of existing analysis tools, which often fall short when analyzing long-read sequencing data. The complex structure of cancer genomes means that standard analysis tools can lead to false-positive results and unreliable interpretations of the data.

Researchers at EMBL’s European Bioinformatics Institute (EMBL-EBI) and the R&D laboratory of Genomics England have developed SAVANA in collaboration with clinical partners at University College London (UCL), the Royal National Orthopaedic Hospital (RNOH), Instituto de Medicina Molecular João Lobo Antunes, and Boston Children’s Hospital. The algorithm was tested across 99 human tumour samples and has shown remarkable accuracy in distinguishing between true cancer-related genomic alterations and sequencing artefacts.

“SAVANA changes the game,” said Isidro Cortes-Ciriano, Group Leader at EMBL-EBI. “By training the algorithm directly on long-read sequencing data from cancer samples, we created a new method that can tell the difference between true cancer-related genomic alterations and sequencing artefacts, thereby enabling us to elucidate the mutational processes underlying cancer using long-read sequencing with unprecedented resolution.”

The team’s focus was clear: create a tool sophisticated enough to characterise complex cancer genomes but practical enough for clinical use. SAVANA can accurately distinguish somatic structural variants, copy number aberrations, tumour purity, and ploidy – all key to understanding tumour biology and guiding clinical treatment decisions.

Its rapid analysis and robust error correction make SAVANA well suited for clinical use. The method was recently applied to study osteosarcoma, a rare and aggressive bone cancer that mostly affects young people, where it helped researchers uncover new genomic rearrangements, providing novel insights into how osteosarcoma evolves and progresses.

“The capability to accurately detect structural variants is transformative for clinical diagnostics,” said Adrienne Flanagan, Professor at UCL, Consultant Histopathologist at RNOH. “SAVANA could help us confidently identify genomic alterations relevant for diagnosis and prognosis. Ultimately, this means we would be better placed to deliver personalised treatments for cancer patients.”

The UK is investing significantly in genomic sequencing technologies as part of the NHS Genomic Medicine Service. This initiative aims to improve diagnostic accuracy and support personalised cancer treatments. However, investments in clinical genomics will only achieve their intended impact if genomic data are interpreted accurately.

“Using SAVANA will ensure clinicians receive accurate and reliable genomic data, enabling them to confidently integrate advanced genomic sequencing methods such as long-read sequencing into routine patient care,” said Greg Elgar, Director of Sequencing R&D at Genomics England.

SAVANA is being deployed as part of nationwide initiatives, such as the UK Stratified Medicine Paediatrics project funded by Cancer Research UK and Children With Cancer UK, and co-led by Cortes-Ciriano. This project aims to develop more efficacious and less toxic treatments for childhood cancers using advanced sequencing technologies to better understand tumour biology and monitor disease recurrence.

Additionally, SAVANA is being used in Societal, Ancestry, Molecular and Biological Analyses of Inequalities (SAMBAI), a Cancer Grand Challenges funded project aimed at addressing cancer disparities in recent African heritage populations.

The article you provided is a fascinating study on a groundbreaking method for extracting and identifying proteins from ancient human soft tissues. Here’s a rewritten version, maintaining the core ideas but improving clarity, structure, and style:

Unlocking the Secrets of Ancient Human Remains: A New Method for Accessing Proteins in Soft Tissues

A team of researchers at the University of Oxford has developed a revolutionary method that could soon unlock the vast repository of biological information held in the proteins of ancient human soft tissues. This discovery, published in PLOS ONE, opens up a new era for palaeobiological discovery and promises to vastly expand our understanding of ancient diet, disease, environment, and evolutionary relationships.

Up until now, studies on ancient proteins have been confined largely to mineralized tissues such as bones and teeth. However, the internal organs – which are a far richer source of biological information – have remained inaccessible due to the lack of an established protocol for their analysis. This new method changes that.

A key hurdle was finding an effective way to disrupt cell membranes to liberate proteins. The team discovered that urea successfully broke open cells and released proteins within. After extraction, the proteins were then separated using liquid chromatography and identified using mass spectrometry. By coupling this step with high-field asymmetric-waveform ion mobility spectrometry (which separates ions based on how they move in an electric field), the researchers found that they could increase the number of proteins identified by up to 40%.

This technique makes it possible to recover proteins from samples that are hard to analyze, including degraded or very complex mixtures. The team was able to identify over 1,200 ancient proteins from just 2.5 mg of sample – a feat that has never been achieved before.

Using the combined method, the researchers identified a diverse array of proteins that govern healthy brain function, reflecting the molecular complexity of the human nervous system. They also identified potential biomarkers for neurological diseases such as Alzheimer’s and multiple sclerosis. This new technique opens a window on human history we haven’t looked through before.

The vast majority of human diseases – including psychiatric illness and mental health disorders – leave no marks on the bone, making them essentially invisible in the archaeological record. This discovery promises to transform our understanding of ancient human health and disease.

Senior author Professor Roman Fischer, Centre for Medicines Discovery at the University of Oxford, added: “By enabling the retrieval of protein biomarkers from ancient soft tissues, this workflow allows us to investigate pathology beyond the skeleton, transforming our ability to understand the health of past populations.”

This method has already attracted interest for its applicability to a wide range of archaeological materials and environments – from mummified remains to bog bodies, and from antibodies to peptide hormones. As Dr Christiana Scheib, Department of Zoology at the University of Cambridge, noted: “Ancient soft tissues are so rarely preserved, yet could hold such powerful information regarding evolutionary history.”