Machine Learning-Based Quantification of Lateral Flow Assay Using Smartphone-Captured Images.

Lateral flow assay has been extensively used for at-home testing and point-of-care diagnostics in rural areas. Despite its advantages as convenient and low-cost testing, it suffers from poor quantification capacity where only yes/no or positive/negative diagnostics are achieved. In this study, machine learning and deep learning models were developed to quantify the analyte load from smartphone-captured images of the lateral flow assay test.

Water quality performance of vegetated compost blankets for highway stormwater management: Particulate matter and trace metals.

Urban stormwater pollution poses serious risks to human and environmental health, including trace metals toxicity. To improve the performance of existing highway Vegetated Filter Strips (VFS), which have limited performance for volume reduction and pollutant removal, amendment with a Vegetated Compost Blanket (VCB), a layer of seeded compost, has been proposed. A novel VCB/VFS system was assessed as a Stormwater Control Measure (SCM) via particulate matter and trace metals removal performance.

Defective homologous recombination and genomic instability predict increased responsiveness to carbon ion radiotherapy in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is notably resistant to conventional chemotherapy and radiation treatment. However, clinical trials indicate that carbon ion radiotherapy (CIRT) with concurrent gemcitabine is effective for unresectable locally advanced PDAC. This study aimed to identify patient characteristics predictive of CIRT response.

Defining the Speciation, Coordination Chemistry, and Lewis Acid Catalysis of Electronically Diverse Zinc Benzoates.

Zinc benzoates may provide an element of tunability that is not available to their ubiquitous acetate analogues. Unfortunately, the synthesis, speciation, and coordination chemistry of zinc benzoates are less developed than the acetates. In this study, we systematically investigate zinc benzoates to understand their propensity to favor solvate (Zn(OCAr)(L)) or cluster (ZnO(OCAr)) formation as well as their utility as metal complex precursors.

SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden.

Background: Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and immunofluorescence markers are required. However, emerging scRNA-seq datasets have enabled an increased understanding of senescent cell heterogeneity.