Towards human-AI collaboration in radiology: a multidimensional evaluation of the acceptability of AI for chest radiograph analysis in supporting pulmonary tuberculosis diagnosis.

Objective: Artificial intelligence (AI) technology promises to be a powerful tool in addressing the global health challenges posed by tuberculosis (TB). However, evidence for its real-world impact is lacking, which may hinder safe, responsible adoption. This case study addresses this gap by assessing the technical performance, usability and workflow aspects, and health impact of implementing a commercial AI system (qXR by Qure.

Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review.

Background: Artificial intelligence (AI) technology has the potential to transform medical practice within the medical imaging industry and materially improve productivity and patient outcomes. However, low acceptability of AI as a digital healthcare intervention among medical professionals threatens to undermine user uptake levels, hinder meaningful and optimal value-added engagement, and ultimately prevent these promising benefits from being realised. Understanding the factors underpinning AI acceptability will be vital for medical institutions to pinpoint areas of deficiency and improvement within their AI implementation strategies.

Benchmarking the diagnostic test accuracy of certified AI products for screening pulmonary tuberculosis in digital chest radiographs: Preliminary evidence from a rapid review and meta-analysis.

Background And Objective: The global market for AI systems used in lung tuberculosis (TB) detection has expanded significantly in recent years. Verifying their performance across diverse settings is crucial before medical organisations can invest in them and pursue safe, wide-scale deployment. The goal of this research was to synthesise the clinical evidence for the diagnostic accuracy of certified AI products designed for screening TB in chest X-rays (CXRs) compared to a microbiological reference standard.

Distinguishing academic science writing from humans or ChatGPT with over 99% accuracy using off-the-shelf machine learning tools.

ChatGPT has enabled access to artificial intelligence (AI)-generated writing for the masses, initiating a culture shift in the way people work, learn, and write. The need to discriminate human writing from AI is now both critical and urgent. Addressing this need, we report a method for discriminating text generated by ChatGPT from (human) academic scientists, relying on prevalent and accessible supervised classification methods.

Advances, obstacles, and opportunities for machine learning in proteomics.

The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning.

Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier.

Mass spectrometry data sets from omics studies are an optimal information source for discriminating patients with disease and identifying biomarkers. Thousands of proteins or endogenous metabolites can be queried in each analysis, spanning several orders of magnitude in abundance. Machine learning tools that effectively leverage these data to accurately identify disease states are in high demand.

The local-balanced model for improved machine learning outcomes on mass spectrometry data sets and other instrumental data.

One unifying challenge when classifying biological samples with mass spectrometry data is overcoming the obstacle of sample-to-sample variability so that differences between groups, such as between a healthy set and a disease set, can be identified. Similarly, when the same sample is re-analyzed under identical conditions, instrument signals can fluctuate by more than 10%. This signal inconsistency imposes difficulties in identifying subtle differences across a set of samples, and it weakens the mass spectrometrist's ability to effectively leverage data in domains as diverse as proteomics, metabolomics, glycomics, and imaging.

How to Apply Supervised Machine Learning Tools to MS Imaging Files: Case Study with Cancer Spheroids Undergoing Treatment with the Monoclonal Antibody Cetuximab.

As the field of mass spectrometry imaging continues to grow, so too do its needs for optimal methods of data analysis. One general need in image analysis is the ability to classify the underlying regions within an image, as healthy or diseased, for example. Classification, as a general problem, is often best accomplished by supervised machine learning strategies; unfortunately, conducting supervised machine learning on MS imaging files is not typically done by mass spectrometrists because a high degree of specialized knowledge is needed.

Adaption of the Aristotle Classifier for Accurately Identifying Highly Similar Bacteria Analyzed by MALDI-TOF MS.

MALDI-TOF MS has shown great utility for rapidly identifying microbial species. It can be used to successfully type bacteria and fungi from a variety of sources more rapidly and cost-effectively than traditional methods. One area where improvements are necessary is in the typing of highly similar samples, such as those samples from the same genus but different species or samples from within a single species but from different strains.

The Aristotle Classifier: Using the Whole Glycomic Profile To Indicate a Disease State.

"The totality is not, as it were, a mere heap, but the whole is something besides the parts."-Aristotle. We built a classifier that uses the totality of the glycomic profile, not restricted to a few glycoforms, to differentiate samples from two different sources.

DecoyDeveloper: An On-Demand, De Novo Decoy Glycopeptide Generator.

Glycopeptide analysis is a growing field that is struggling to adopt effective, automated tools. Many creative workflows and software apps have emerged recently that offer promising capabilities for assigning glycopeptides to MS data in an automated fashion. The effectiveness of these tools is best measured and improved by determining how often they would select a glycopeptide decoy as a spectral match, instead of its correct assignment; yet generating the appropriate number and type of glycopeptide decoys can be challenging.

Dissecting the Dissociation Patterns of Fucosylated Glycopeptides Undergoing CID: A Case Study in Improving Automated Glycopeptide Analysis Scoring Algorithms.

The need to investigate the fragmentation of fucosylated glycopeptides is driven by recent work showing that at least one, and perhaps many, glycopeptide analysis scoring algorithms are less effective at identifying fucosylated glycopeptides than non-fucosylated glycopeptides. Herein, we study the CID fragmentation characteristics of fucosylated glycopeptides and the scoring rules of the glycopeptide analysis software, GlycoPep Grader, in an effort to improve automated assignments of these important glycopeptides. We identified some prominent product ions from a common fragmentation pathway of fucosylated glycopeptides that were not accounted for in the scoring rules.

Two New Tools for Glycopeptide Analysis Researchers: A Glycopeptide Decoy Generator and a Large Data Set of Assigned CID Spectra of Glycopeptides.

The glycopeptide analysis field is tightly constrained by a lack of effective tools that translate mass spectrometry data into meaningful chemical information, and perhaps the most challenging aspect of building effective glycopeptide analysis software is designing an accurate scoring algorithm for MS/MS data. We provide the glycoproteomics community with two tools to address this challenge. The first tool, a curated set of 100 expert-assigned CID spectra of glycopeptides, contains a diverse set of spectra from a variety of glycan types; the second tool, Glycopeptide Decoy Generator, is a new software application that generates glycopeptide decoys de novo.

Glycosylation Benchmark Profile for HIV-1 Envelope Glycoprotein Production Based on Eleven Env Trimers.

HIV-1 envelope glycoprotein (Env) glycosylation is important because individual glycans are components of multiple broadly neutralizing antibody epitopes, while shielding other sites that might otherwise be immunogenic. The glycosylation on Env is influenced by a variety of factors, including the genotype of the protein, the cell line used for its expression, and the details of the construct design. Here, we used a mass spectrometry (MS)-based approach to map the complete glycosylation profile at every site in multiple HIV-1 Env trimers, accomplishing two goals.

Comparative Analysis of the Glycosylation Profiles of Membrane-Anchored HIV-1 Envelope Glycoprotein Trimers and Soluble gp140.

Unlabelled: The human immunodeficiency virus type 1 (HIV-1) envelope glycoprotein (Env) trimer, which consists of the gp120 and gp41 subunits, is the focus of multiple strategies for vaccine development. Extensive Env glycosylation provides HIV-1 with protection from the immune system, yet the glycans are also essential components of binding epitopes for numerous broadly neutralizing antibodies. Recent studies have shown that when Env is isolated from virions, its glycosylation profile differs significantly from that of soluble forms of Env (gp120 or gp140) predominantly used in vaccine discovery research.

Glycosylation and disulfide bond analysis of transiently and stably expressed clade C HIV-1 gp140 trimers in 293T cells identifies disulfide heterogeneity present in both proteins and differences in O-linked glycosylation.

The HIV-1 envelope protein (Env) mediates viral entry into host cells to initiate infection and is the sole target of antibody-based vaccine development. Significant efforts have been made toward the design, engineering, and expression of various soluble forms of HIV Env immunogen, yet a highly effective immunogen remains elusive. One of the key challenges in the development of an effective HIV vaccine is the presence of the complex set of post-translational modifications (PTMs) on Env, namely, glycosylation and disulfide bonds, that affect protein folding, epitope accessibility, and immunogenecity.

GlycoPep Detector: a tool for assigning mass spectrometry data of N-linked glycopeptides on the basis of their electron transfer dissociation spectra.

Electron transfer dissociation (ETD) is commonly used in fragmenting N-linked glycopeptides in their mass spectral analyses to complement collision-induced dissociation (CID) experiments. The glycan remains intact through ETD, while the peptide backbone is cleaved, providing the sequence of amino acids for a glycopeptide. Nonetheless, data analysis is a major bottleneck to high-throughput glycopeptide identification based on ETD data, due to the complexity and diversity of ETD mass spectra compared to CID counterparts.

Characterization of host-cell line specific glycosylation profiles of early transmitted/founder HIV-1 gp120 envelope proteins.

Glycosylation plays an essential role in regulating protein function by modulating biological, structural, and therapeutic properties. However, due to its inherent heterogeneity and diversity, the comprehensive analysis of protein glycosylation remains a challenge. As part of our continuing effort in the analysis of glycosylation profiles of recombinant HIV-1 envelope-based immunogens, we evaluated and compared the host-cell specific glycosylation pattern of recombinant HIV-1 surface glycoprotein, gp120, derived from clade C transmitted/founder virus 1086.

GlycoPep grader: a web-based utility for assigning the composition of N-linked glycopeptides.

GlycoPep grader (GPG) is a freely available software tool designed to accelerate the process of accurately determining glycopeptide composition from tandem mass spectrometric data. GPG relies on the identification of unique dissociation patterns shown for high mannose, hybrid, and complex N-linked glycoprotein types, including patterns specific to those structures containing fucose or sialic acid residues. The novel GPG scoring algorithm scores potential candidate compositions of the same nominal mass against MS/MS data through evaluation of the Y(1) ion and other peptide-containing product ions, across multiple charge states, when applicable.

Characterization of glycosylation profiles of HIV-1 transmitted/founder envelopes by mass spectrometry.

The analysis of HIV-1 envelope carbohydrates is critical to understanding their roles in HIV-1 transmission as well as in binding of envelope to HIV-1 antibodies. However, direct analysis of protein glycosylation by glycopeptide-based mass mapping approaches involves structural simplification of proteins with the use of a protease followed by an isolation and/or enrichment step before mass analysis. The successful completion of glycosylation analysis is still a major analytical challenge due to the complexity of samples, wide dynamic range of glycopeptide concentrations, and glycosylation heterogeneity.