Novel laser-fabricated plasmonic chip combines 185 nm Raman resolution and deep learning to identify and sort cancer cells with 96.2% accuracy.
Accurate and efficient isolation and recovery of interest cells allow detailed analyses of cellular function, growth and culture, and gene-expression profiling of cells within heterogeneous populations. Researchers have developed a novel microfluidic surface-enhanced Raman scattering (SERS) chip capable of performing deep-learning-assisted, label-free single-cell identification and sorting. The results could be used to guide future research on the design and development of non-invasive, efficient cell-sorting techniques with potential applications in clinical diagnostics and precision medicine.
Cell sorting is a basic yet vital procedure in precision biomedicine. Cell sorting separates mixed cells into pure subgroups based on their unique features. Biological tissues and blood contain diverse cell types, and mixed samples blur true cell signals in gene or protein tests. By sorting, researchers can get uniform cells to study cell functions, stem cell development, and disease mechanisms clearly. It also supports drug screening and cell product quality control. Without these techniques, accurate single-cell analysis and modern cell-based treatments would be impossible. The analysis of rare cells, such as cancer stem cells and circulating tumor cells, to assess their functional responses is rapidly gaining prominence given its relevance for its potential to address issues in the development of new drugs and clinical diagnostics.
Bacteria use strategies similar to artificial neural networks to use the past to adapt to the future.
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Biology – the study of living things – can admittedly be a little reductive at times.
It’s easy to miss important connections depending on how you look at an organism. Microscopes show us the fine details of a body, dissection has helped us identify different organs, and X-rays can show us the bigger picture of internal systems.
But until now, it’s been tricky to see how living cells communicate across those systems, which traditionally have been studied in isolation, even though our bodies ultimately work as a whole.
Mesenchymal stem/stromal cell (MSC)-based therapy holds promise for reversal of bone loss in osteoporosis. This phase 1 study establishes the safety and feasibility of intravenous administration of glycocalyx-engineered human MSCs and provides evidence that MSCs derived from the bone marrow of older individuals with osteoporosis retain clinically meaningful osteoregenerative capacity.
Early-life exposure to both modifiable and nonmodifiable risk factors may induce chronic low-grade inflammation (CLGI), a silent threat potentially driving the rising incidence of early-onset colorectal cancer (EOCRC). Notably, CLGI could promote EOCRC by altering the proinflammatory and immune microenvironment that supports tumor growth and cancer development. In this review, we examine how risk factors disrupt immune homeostasis and contribute to CLGI in young individuals, thereby promoting the development of EOCRC. Additionally, identifying CLGI-specific biomarkers could aid in early detection and diagnosis, while targeting specific molecular pathways may enable personalized therapeutic interventions for EOCRC by reducing the chronic inflammatory threat.
