Deep Learning to Predict the Future Growth of Geographic Atrophy from Fundus Autofluorescence.

Purpose: The region of growth (ROG) of geographic atrophy (GA) throughout the macular area has an impact on visual outcomes. Here, we developed multiple deep learning models to predict the 1-year ROG of GA lesions using fundus autofluorescence (FAF) images.

Design: In this retrospective analysis, 3 types of models were developed using FAF images collected 6 months after baseline to predict the GA lesion area (segmented lesion mask) at 1.

Pursuing Clinical Predictors and Biomarkers for Progression in ILD: Analysis of the Pulmonary Fibrosis Foundation (PFF) Registry.

Introduction: Pulmonary fibrosis is a characteristic of various interstitial lung diseases (ILDs) with differing etiologies. Clinical trials in progressive pulmonary fibrosis (PPF) enroll patients based on previously described clinical criteria for past progression, which include a clinical practice guideline for PPF classification and inclusion criteria from the INBUILD trial. In this study, we compared the ability of past FVC (forced vital capacity) progression and baseline biomarker levels to predict future progression in a cohort of patients from the PFF Patient Registry.

Zinpentraxin Alfa for Idiopathic Pulmonary Fibrosis: The Randomized Phase III STARSCAPE Trial.

A phase II trial reported clinical benefit over 28 weeks in patients with idiopathic pulmonary fibrosis (IPF) who received zinpentraxin alfa. To investigate the efficacy and safety of zinpentraxin alfa in patients with IPF in a phase III trial. This 52-week phase III, double-blind, placebo-controlled, pivotal trial was conducted at 275 sites in 29 countries.

A review of the challenges, learnings and future directions of home handheld spirometry in interstitial lung disease.

Background: Patients with interstitial lung disease (ILD) require regular physician visits and referral to specialist ILD clinics. Difficulties or delays in accessing care can limit opportunities to monitor disease trajectory and response to treatment, and the COVID-19 pandemic has added to these challenges. Therefore, home monitoring technologies, such as home handheld spirometry, have gained increased attention as they may help to improve access to care for patients with ILD.

Untargeted metabolomics of newborn dried blood spots reveals sex-specific associations with pediatric acute myeloid leukemia.

The etiology of pediatric acute myeloid leukemia (AML) is largely unknown, but evidence for mutations in utero and long latency periods suggests that environmental factors play a role. Therefore, we used untargeted metabolomics of archived newborn dried blood spots (DBS) to investigate neonatal exposures as potential causal risk factors for AML. Untargeted metabolomics profiling was performed on DBS punches from 48 pediatric patients with AML and 46 healthy controls as part of the California Childhood Leukemia Study (CCLS).

Untargeted adductomics of newborn dried blood spots identifies modifications to human serum albumin associated with childhood leukemia.

The developing fetus is exposed to chemicals, which are metabolized to electrophiles that form adducts with nucleophilic Cys34 of human serum albumin (HSA). By measuring these adducts in neonatal blood spots (NBS), we obtain information regarding fetal exposures during the last month of gestation. To discover potential risk factors for childhood leukemia resulting from in utero exposures, we used untargeted adductomics to measure HSA-Cys34 adducts in 782 archived NBS, collected from incident cases of childhood acute lymphoblastic leukemia (ALL) or acute myeloid leukemia (AML) and matched population-based controls.

Cys34 Adductomics Links Colorectal Cancer with the Gut Microbiota and Redox Biology.

Chronic inflammation is an established risk factor for colorectal cancer. To study reactive products of gut inflammation and redox signaling on colorectal cancer development, we used untargeted adductomics to detect adduct features in prediagnostic serum from the EPIC Italy cohort. We focused on modifications to Cys34 in human serum albumin, which is responsible for scavenging small reactive electrophiles that might initiate cancers.

Filtering procedures for untargeted LC-MS metabolomics data.

Background: Untargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms.

Metabolomics of neonatal blood spots reveal distinct phenotypes of pediatric acute lymphoblastic leukemia and potential effects of early-life nutrition.

Early-life exposures are believed to influence the incidence of pediatric acute lymphoblastic leukemia (ALL). Archived neonatal blood spots (NBS), collected within the first days of life, offer a means to investigate small molecules that reflect early-life exposures. Using untargeted metabolomics, we compared abundances of small-molecule features in extracts of NBS punches from 332 children that later developed ALL and 324 healthy controls.

Untargeted adductomics of Cys34 modifications to human serum albumin in newborn dried blood spots.

Metabolism of chemicals from the diet, exposures to xenobiotics, the microbiome, and lifestyle factors (e.g., smoking, alcohol intake) produce electrophiles that react with nucleophilic sites in circulating proteins, notably Cys34 of human serum albumin (HSA).

Untargeted lipidomic features associated with colorectal cancer in a prospective cohort.

Background: Epidemiologists are beginning to employ metabolomics and lipidomics with archived blood from incident cases and controls to discover causes of cancer. Although several such studies have focused on colorectal cancer (CRC), they all followed targeted or semi-targeted designs that limited their ability to find discriminating molecules and pathways related to the causes of CRC.

Methods: Using an untargeted design, we measured lipophilic metabolites in prediagnostic serum from 66 CRC patients and 66 matched controls from the European Prospective Investigation into Cancer and Nutrition (Turin, Italy).

Identification of gene expression predictors of occupational benzene exposure.

Background: Previously, using microarrays and mRNA-Sequencing (mRNA-Seq) we found that occupational exposure to a range of benzene levels perturbed gene expression in peripheral blood mononuclear cells.

Objectives: In the current study, we sought to identify gene expression biomarkers predictive of benzene exposure below 1 part per million (ppm), the occupational standard in the U.S.

Electrophoretic cytopathology resolves ERBB2 forms with single-cell resolution.

In addition to canonical oncoproteins, truncated isoforms and proteolysis products are implicated in both drug resistance and disease progression. In HER2-positive breast tumors, expression of truncated HER2 isoforms resulting from alternative translation and/or carboxy-terminal fragments (CTFs) resulting from proteolysis (collectively, t-erbB2) have been associated with shortened progression-free survival of patients. Thus, to advance clinical pathology and inform treatment decisions, we developed a high-selectivity cytopathology assay capable of distinguishing t-erbB2 from full-length HER2 expression without the need for isoform-specific antibodies.

An untargeted metabolomics method for archived newborn dried blood spots in epidemiologic studies.

Introduction: For pediatric diseases like childhood leukemia, a short latency period points to in-utero exposures as potentially important risk factors. Untargeted metabolomics of small molecules in archived newborn dried blood spots (DBS) offers an avenue for discovering early-life exposures that contribute to disease risks.

Objectives: The purpose of this study was to develop a quantitative method for untargeted analysis of archived newborn DBS for use in an epidemiological study (California Childhood Leukemia Study, CCLS).

SIDEseq: A Cell Similarity Measure Defined by Shared Identified Differentially Expressed Genes for Single-Cell RNA sequencing Data.

One goal of single-cell RNA sequencing (scRNA seq) is to expose possible heterogeneity within cell populations due to meaningful, biological variation. Examining cell-to-cell heterogeneity, and further, identifying subpopulations of cells based on scRNA seq data has been of common interest in life science research. A key component to successfully identifying cell subpopulations (or clustering cells) is the (dis)similarity measure used to group the cells.