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Diagnostics Digital and computational pathology, Genetics and epigenetics, Omics, Precision medicine

Laboratory Learnings

Looking at Lipids
At present, diagnosis and stratification of asthma patients is performed using blood tests, X-rays, lung function tests, and examination of allergies – hardly simple. But new findings show that urinary eicosanoids can accurately distinguish type 2 asthma, opening the door for direct, noninvasive phenotyping of the condition (1).

We the Jury
Artificial intelligence (AI)’s role in digital pathology is on the rise – and now, researchers have found that jurors are less likely to find a physician liable for malpractice if they have accepted AI’s precision medicine recommendations rather than rejected them (2).

Miscreant Monocytes
Despite their role in inflammation, monocytes and monocyte-derived cells are poorly understood in sarcoidosis. Now, research has found higher levels of these mononuclear phagocytes in the blood and bronchoalveolar lavage fluid of pulmonary sarcoidosis patients (3) – indicating their potential role as predictors of disease outcome.

Complementary Proteomics
Researchers have used a nanoparticle-based protein enrichment technology to identify a highly sensitive and specific protein signature of early-stage breast cancer (4). The assay could be used in conjunction with mammography to improve screening for the disease and reduce false positives.

New Day, New Disease
A new genetic disorder has been discovered: linkage-specific deubiquitylation deficiency-induced embryonic defects syndrome, or LINKED syndrome for short (5). The multiple-congenital-anomaly disorder is characterized by developmental delays and brain, heart, and facial malformations caused by a mutation of OTUD5 that disrupts key molecular stages during embryonic development.

Computational Cancers
Researchers at St. Jude Children’s Research Hospital have developed a new computational framework – MethylationToActivity – to infer gene promoter activities from DNA methylation signatures in adult and pediatric cancers (6). The deep learning tool may improve precision treatment decisions by factoring in genetic variants and epigenetic deregulation.

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  1. J Kolmert et al., Am J Respir Crit Care Med, 203, 37 (2021). PMID: 32667261.
  2. K Tobia et al., J Nucl Med, 62, 17 (2021). PMID: 32978285.
  3. R Lepzien et al., Eur Respir J, [Online ahead of print] (2021). PMID: 33446605.
  4. C Fredolini et al., Breast Cancer Res, 22, 135 (2020). PMID: 33267867.
  5. DB Beck et al., Sci Adv, 7, eabe2116 (2021). PMID: 33523931.
  6. J Williams et al., Genome Biol, 22, 24 (2021). PMID: 33461601.

About the Author

Olivia Gaskill

During my undergraduate degree in psychology and Master’s in neuroimaging for clinical and cognitive neuroscience, I realized the tasks my classmates found tedious – writing essays, editing, proofreading – were the ones that gave me the greatest satisfaction. I quickly gathered that rambling on about science in the bar wasn’t exactly riveting for my non-scientist friends, so my thoughts turned to a career in science writing. At Texere, I get to craft science into stories, interact with international experts, and engage with readers who love science just as much as I do.

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