Biology subjects

Sevilla-Salcedo, C.

Publications and source records attributed to Sevilla-Salcedo, C..

5 recordsLinked to original sources

bioRxiv2025

MARISMa: a routine MALDI-TOF MS database from 2018 to 2024

Clinical microbiology laboratories play a crucial role in identifying pathogens, guiding antibiotic treatment, and managing antimicrobial resistance (AMR). Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) has become essential for rapid, accurate, and cost-effective microbial identification. Recent advances in integrating MALDI-TOF MS with Artificial Intelligence (AI) show promise in improving microbial detection and prediction of AMR. However, progress is limited by the lack of comprehensive and openly accessible datasets that restrict the validation, reproducibility, and applicability of the model. To address this gap, we introduce a publicly available MALDI-TOF MS dataset comprising 202,700 unique spectra from isolates collected between 2018 and 2024 at the Hospital General Universitario Gregorio Maranon, Spain. This dataset includes 186,213 bacteria, 16,163 fungal, and 371 mycobacterial samples, of which 29,679 contain AMR annotations. This resource is openly and freely shared, rigorously curated, and designed to support a wide range of machine learning. By ensuring unrestricted access to high-quality, standardized data, this dataset aims to promote transparency, reproducibility, comparative benchmarking, and collaborative progress in AI-driven clinical microbiology.

microbiology↗

bioRxiv2025

Applied Machine Learning for human bacteriaMALDI-TOF Mass Spectrometry: a systematicreview

Bacterial identification, antimicrobial resistance prediction, and strain typification are critical tasks in clinical microbiology, essential for guiding patient treatment and controlling the spread of infectious diseases. While Machine Learning (ML) has shown immense promise in enhancing Matrix-Assisted Laser Desorption/Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) applications for these tasks, there is currently no comprehensive review that fully addresses this from a technical ML perspective. To address this gap, we systematically reviewed 115 studies published between 2004 and 2025, focusing on key ML aspects such as data size and balance, pre-processing pipelines, model selection and evaluation, open-source data, and code availability. Our analysis highlights the predominant use of classical ML models like Random Forest and Support Vector Machines, alongside emerging interest in Deep Learning approaches for handling complex, high-dimensional data. Despite significant progress, challenges such as inconsistent pre-processing workflows, reliance on black-box models, limited external validation, and insufficient open-source resources persist, hindering transparency, reproducibility, and broader adoption. This review offers actionable insights to enhance ML-driven bacterial diagnostics, advocating for standardized methodologies, greater transparency, and improved data accessibility. In addition, we provide guidelines on how to approach ML for MALDI-TOF MS analysis, helping researchers navigate key decisions in model development and evaluation.

bioengineering↗

bioRxiv2024

Automatic surveillance of Escherichia coli bacteriological strains within clinical settings

Healthcare-associated infections (HAIs) are a significant concern within hospital environments, with the World Health Organization (WHO) identifying them as a major source of bacteriological infections. HAIs affect millions of patients annually, leading to substantial morbidity and mortality. However, a significant proportion of HAIs are preventable through early detection and appropriate intervention and isolation. Traditional methods for identifying bacterial species and strains, such as antigen tests, are often time-consuming and hamper the realtime tracking of outbreaks. The matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) technique has emerged as a more rapid and precise alternative, though it remains limited by manual identification processes and database constraints. Recent advancements have demonstrated the potential of combining MALDI-TOF MS data with machine learning (ML) to enhance in silico identification speed. In this paper we propose the first method for unsupervised Escherichia coli novel strain detection through the application of a efficient PIKE tailored to strain identification, spectral clustering techniques and a one-class support vector machine (OCSVM) novelty detection model, presenting promising results.

microbiology↗

bioRxiv2024

Overcoming Challenges of Reproducibility and Variability for the Clostridioides difficile typification

Machine learning (ML) approaches applied to Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) spectra have shown promise for the typing of Clostridioides difficile, yet their deployment in routine clinical settings remains challenging due to strong sensitivity to acquisition variability. Differences in culture media, incubation time, protein extraction protocols, and instrumentation across hospitals often lead to substantial performance degradation when models are evaluated under heterogeneous or previously unseen conditions. In this work, we systematically analyze the impact of methodological and technical variability on ML-based C. difficile typing and investigate whether data augmentation (DA) strategies can mitigate these effects. Using a dedicated dataset of 60 isolates acquired under diverse conditions, we show that DA substantially improves robustness to variability when training on spectra from selective C. difficile agar media. Importantly, models trained with DA achieve performance levels approaching those obtained using enriched Schaedler agar media, while relying exclusively on standard 24-hour incubation. Evaluation on an independent cohort of 28 newly acquired isolates confirms that DA significantly reduces performance degradation under real-world domain shift. To facilitate adoption and reproducibility, we release MAL-DIDA, an open-source Python library for DA of MALDI-TOF MS spectra.

microbiology↗

bioRxiv2021

Development and Validation of a MALDI-TOF-Based Model to Predict Extended-Spectrum Beta-Lactamase and/or Carbapenemase-Producing in Klebsiella pneumoniae Clinical Isolates

Matrix-Assisted Laser Desorption Ionization Time-Of-Flight (MALDI-TOF) Mass Spectrometry (MS) is a reference method for microbial identification and it can be used to predict Antibiotic Resistance (AR) when combined with artificial intelligence methods. However, current solutions need time-costly preprocessing steps, are difficult to reproduce due to hyperparameter tuning, are hardly interpretable, and do not pay attention to epidemiological differences inherent to data coming from different centres, which can be critical. We propose using a multi-view heterogeneous Bayesian model (KSSHIBA) for the prediction of AR using MALDI-TOF MS data together with their epidemiological differences. KSSHIBA is the first model that removes the ad-hoc preprocessing steps that work with raw MALDI-TOF data. In addition, due to its Bayesian probabilistic nature, it does not require hyperparameter tuning, provides interpretable results, and allows exploiting local epidemiological differences between data sources. To test the proposal, we used data from 402 Klebsiella pneumoniae isolates coming from two different domains and 20 different hospitals located in Spain and Portugal. KSSHIBA outperforms current state-of-the-art approaches in antibiotic susceptibility prediction, obtaining a 0.78 AUC score in Wild Type classification and a 0.90 AUC score in Extended-Spectrum Beta-Lactamases (ESBL)+Carbapenemases (CP)-producers. The proposal consistently removes the need for ad-hoc preprocessing by working with raw MALDI-TOF data, which, in turn, reduces the time needed to obtain the results of the resistance mechanism in microbiological laboratories. The proposed model implementation as well as both data domains are publicly available.

microbiology↗