Browse Articles

Unravelling nonclassical beam damage mechanisms in metal-organic frameworks by low-dose electron microscopy

Tumor cell-based liquid biopsy using high-throughput microfluidic enrichment of entire leukapheresis product

AbstractCirculating Tumor Cells (CTCs) in blood encompass DNA, RNA, and protein biomarkers, but clinical utility is limited by their rarity. To enable tumor epitope-agnostic interrogation of large blood volumes, we developed a high-throughput microfluidic device, depleting hematopoietic cells through high-flow channels and force-amplifying magnetic lenses. Here, we apply this technology to analyze patient-derived leukapheresis products, interrogating a mean blood volume of 5.83 liters from seven patients with metastatic cancer. High CTC yields (mean 10,057 CTCs per patient; range 100 to 58,125) reveal considerable intra-patient heterogeneity. CTC size varies within patients, with 67% overlapping in diameter with WBCs. Paired single-cell DNA and RNA sequencing identifies subclonal patterns of aneuploidy and distinct signaling pathways within CTCs. In prostate cancers, a subpopulation of small aneuploid cells lacking epithelial markers is enriched for neuroendocrine signatures. Pooling of CNV-confirmed CTCs enables whole exome sequencing with high mutant allele fractions. High-throughput CTC enrichment thus enables cell-based liquid biopsy for comprehensive monitoring of cancer.

Adjuvant Pembrolizumab versus Observation in Muscle-Invasive Urothelial Carcinoma

Small target detection in UAV view based on improved YOLOv8 algorithm

High Arctic lakes reveal accelerating ecological shifts linked to twenty-first century warming

Rényi relative entropy based monogamy of entanglement in tripartite systems

Efficient direct formic acid electrocatalysis enabled by rare earth-doped platinum-tellurium heterostructures

Ir-O-Mn embedded in porous nanosheets enhances charge transfer in low-iridium PEM electrolyzers

Vutrisiran for ATTR Amyloidosis with Cardiomyopathy

A novel energy pattern factor-based optimized approach for assessing Weibull parameters for wind power applications

Study on the spatial and temporal evolution of ecosystem service value based on land use change in Xi’an City

Sympathetic nervous system inhibition enhances cardiac metabolism and improves hemodynamics and glucose-insulin dynamics in obese and lean rat models

Structural characterization of two γδ TCR/CD3 complexes

NEJM at ESMO — Adjuvant Pembrolizumab versus Observation in Muscle-Invasive Urothelial Carcinoma

Critical depth prediction based on in-situ stress and gas content model of deep coalbed methane in Liupanshui Coalfield in China

Reflective metasurface for 5G & beyond Wireless communications

Conformational landscape of soluble α-klotho revealed by cryogenic electron microscopy

Electrolyte design for reversible zinc metal chemistry

Long-Acting HIV Medicines and the Pandemic Inequality Cycle — Rethinking Access

Differential diagnosis of iron deficiency anemia from aplastic anemia using machine learning and explainable Artificial Intelligence utilizing blood attributes

AbstractAs per world health organization, Anemia is a most prevalent blood disorder all over the world. Reduced number of Red Blood Cells or decrease in the number of healthy red blood cells is considered as Anemia. This condition also leads to the decrease in the oxygen carrying capacity of the blood. The main goal of this research is to develop a dependable method for diagnosing Aplastic Anemia and Iron Deficiency Anemia by examining the blood test attributes. As of today, there are no studies which use Interpretable Artificial Intelligence to perform the above differential diagnosis. The dataset used in this study is collected from Kasturba Medical College, Manipal. The dataset consisted of various blood test attributes such as Red Blood cell count, Hemoglobin level, Mean Corpuscular Volume, etc. One of the trending topics in Machine Learning is Explainable Artificial Intelligence. They are known to demystify the machine learning outputs to all its stakeholders. Hence, Five XAI tools including SHAP, LIME, Eli5, Qlattice and Anchor are used to understand the model’s predictions. The importance characteristics according to XAI models are PLT, PCT, MCV, PDW, HGB, ABS LYMP, WBC, MCH, and MCHC. are employed to train and test the data. The goal of using data analytic techniques is to give medical professionals a useful tool that improves decision-making, enhances resource management, and eventually raises the standard of patient care. By considering the unique qualities of each patient, medical professionals who must rely on AI-assisted diagnosis and treatment suggestions, XAI offers arguments to strengthen their faith in the model outcomes.