Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome

• URL: http://arxiv.org/abs/2502.18305v1
• Date: Tue, 25 Feb 2025 15:46:41 GMT
• Title: Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome
• Authors: Adolfo Ruiz-Sanmartín, Vicent Ribas, David Suñol, Luis Chiscano-Camón, Laura Martín, Iván Bajaña, Juliana Bastida, Nieves Larrosa, Juan José González, M Dolores Carrasco, Núria Canela, Ricard Ferrer, Juan Carlos Ruiz-Rodrígue,
• Abstract summary: The aim of this study is to identify potential protein biomarkers of differential expression between sepsis and non-infectious systemic inflammatory response syndrome (NISIRS)<n>A mass spectrometry-based approach was used to analyze the plasma proteins in the enrolled subjects.<n>The protein-protein interaction network was analyzed with String software.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Background: The search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. The objective of this study is to identify potential protein biomarkers of differential expression between sepsis and non-infectious systemic inflammatory response syndrome (NISIRS). Methods: Prospective observational study of a cohort of septic patients activated by the Sepsis Code and patients admitted with NISIRS, during the period 2016-2017. A mass spectrometry-based approach was used to analyze the plasma proteins in the enrolled subjects. Subsequently, using recursive feature elimination (RFE) classification and cross-validation with a vector classifier, an association of these proteins in patients with sepsis compared to patients with NISIRS. The protein-protein interaction network was analyzed with String software. Results: A total of 277 patients (141 with sepsis and 136 with NISIRS) were included. After performing RFE, 25 proteins in the study patient cohort showed statistical significance, with an accuracy of 0.960, specificity of 0.920, sensitivity of 0.973, and an AUC of 0.985. Of these, 14 proteins (vWF, PPBP, C5, C1RL, FCN3, SAA2, ORM1, ITIH3, GSN, C1QA, CA1, CFB, C3, LBP) have a greater relationship with sepsis while 11 proteins (FN1, IGFALS, SERPINA4, APOE, APOH, C6, SERPINA3, AHSG, LUM, ITIH2, SAA1) are more expressed in NISIRS.

Related papers

• Graph AI generates neurological hypotheses validated in molecular, organoid, and clinical systems [40.641407952995856]
  We present PROTON, a graph transformer that generates hypotheses across molecular, organoid, and clinical systems.<n>In Parkinson's disease, PROTON linked genetic risk loci to genes essential for dopaminergic neuron survival and predicted pesticides toxic to patient-derived neurons.<n>In bipolar disorder, PROTON predicted calcitriol as a candidate drug that reversed proteomic alterations observed in cortical organoids derived from BD patients.
  arXiv  Detail & Related papers  (2025-12-13T06:55:20Z)
• Integrated Transcriptomic-proteomic Biomarker Identification for Radiation Response Prediction in Non-small Cell Lung Cancer Cell Lines [7.496897814762568]
  First proteotranscriptomic framework for SF2 prediction in non-small cell lung cancer (NSCLC)<n> integrated transcriptome-proteome framework for identifying concurrent biomarkers predictive of radiation response.<n>Independent pipelines identified 20 prioritized gene signatures from transcriptomic, proteomic, and combined datasets.
  arXiv  Detail & Related papers  (2025-11-27T20:01:51Z)
• PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs [80.08310253195144]
  PRING is the first benchmark that evaluates protein-protein interaction prediction from a graph-level perspective.<n> PRING curates a high-quality, multi-species PPI network dataset comprising 21,484 proteins and 186,818 interactions.
  arXiv  Detail & Related papers  (2025-07-07T15:21:05Z)
• Phenome-wide causal proteomics enhance systemic lupus erythematosus flare prediction: A study in Asian populations [8.003585790309332]
  Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by unpredictable flares. This study aimed to develop a novel lupus-based risk prediction model specifically for Asian SLE populations.
  arXiv  Detail & Related papers  (2024-11-18T01:50:36Z)
• Assessing Concordance between RNA-Seq and NanoString Technologies in Ebola-Infected Nonhuman Primates Using Machine Learning [0.0]
  We compare RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates infected with Ebola virus (EBOV) A machine learning approach, using the Supervised Magnitude-Altitude Scoring (SMAS) method trained on NanoString data, identified OAS1 as a key marker for distinguishing RT-qPCR positive from negative samples. OAS1 also achieved 100% accuracy in differentiating infected from uninfected samples using logistic regression, demonstrating its robustness across platforms.
  arXiv  Detail & Related papers  (2024-10-30T20:21:20Z)
• Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection Layers [76.95505296417866]
  Self-supervised training of language models (LMs) has seen great success for protein sequences in learning meaningful representations and for generative drug design. Most protein LMs are based on the Transformer architecture trained on individual proteins with short context lengths. In this work, we propose LC-PLM based on an alternative protein LM architecture, BiMamba-S, built upon selective structured state-space models.
  arXiv  Detail & Related papers  (2024-10-29T16:43:28Z)
• Integrating Protein Sequence and Expression Level to Analysis Molecular Characterization of Breast Cancer Subtypes [0.0]
  This study aims to integrate protein sequence data with expression levels to improve the molecular characterization of breast cancer subtypes. Using ProtGPT2, a language model designed for protein sequences, we generated embeddings that capture the functional and structural properties of proteins sequence. These embeddings were integrated with protein expression level to form enriched biological representations, which were analyzed using machine learning methods.
  arXiv  Detail & Related papers  (2024-10-02T17:05:48Z)
• New spectral imaging biomarkers for sepsis and mortality in intensive care [0.23592579902069336]
  The driving hypothesis of this study was that hyperspectral imaging (HSI) could provide novel biomarkers for sepsis diagnosis and treatment management. We conducted a comprehensive study involving HSI data of the palm and fingers from more than 480 patients on the day of their intensive care unit admission.
  arXiv  Detail & Related papers  (2024-08-19T10:24:57Z)
• Development of Automated Neural Network Prediction for Echocardiographic Left ventricular Ejection Fraction [36.58987036154144]
  This paper proposes a new pipeline method based on deep neural networks and ensemble learning to quantify left ventricular ejection fraction (LVEF) The method was developed and validated in an open-source dataset containing 10,030 echocardiograms. This study demonstrates that an automated neural network-based calculation of LVEF is comparable to expert clinicians performing frame-by-frame manual evaluation of cardiac systolic function.
  arXiv  Detail & Related papers  (2024-03-18T18:09:22Z)
• PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction [63.50967073653953]
  Compound-Protein Interaction prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery. Existing deep learning-based methods utilize only the single modality of protein sequences or structures. We propose a novel multi-scale Protein Sequence-structure Contrasting framework for CPI prediction.
  arXiv  Detail & Related papers  (2024-02-13T03:51:10Z)
• Effective Protein-Protein Interaction Exploration with PPIretrieval [46.07027715907749]
  We propose PPIretrieval, the first deep learning-based model for protein-protein interaction exploration. PPIretrieval searches for potential PPIs in an embedding space, capturing rich geometric and chemical information of protein surfaces.
  arXiv  Detail & Related papers  (2024-02-06T03:57:06Z)
• Clinical Characteristics and Laboratory Biomarkers in ICU-admitted Septic Patients with and without Bacteremia [7.152311859951986]
  This study evaluated the prediction power of laboratory biomarkers to optimize the predictive model for bacteremia. A total of 218 patients with 48 cases of true bacteremia were analyzed in this research.
  arXiv  Detail & Related papers  (2023-11-14T06:44:26Z)
• Neural Network-Based Histologic Remission Prediction In Ulcerative Colitis [38.150634108667774]
  Histologic remission is a new therapeutic target in ulcerative colitis (UC) Endocytoscopy (EC) is a novel ultra-high magnification endoscopic technique. We propose a neural network model that can assess histological disease activity in EC images.
  arXiv  Detail & Related papers  (2023-08-28T15:54:14Z)
• Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography [53.38589633687604]
  We propose a deep neural network that classifies the tissues from the phase and intensity data of complex OCT signals acquired at the needle tip. We show that with 10% of the training set, our proposed pretraining strategy helps the model achieve an F1 score of 0.84 whereas the model achieves an F1 score of 0.60 without it.
  arXiv  Detail & Related papers  (2023-04-26T14:11:04Z)
• Deep-Learning Tool for Early Identifying Non-Traumatic Intracranial Hemorrhage Etiology based on CT Scan [40.51754649947294]
  The deep learning model was developed with 1868 eligible NCCT scans with non-traumatic ICH collected between January 2011 and April 2018. The model's diagnostic performance was compared with clinicians's performance. The clinicians achieve significant improvements in the sensitivity, specificity, and accuracy of diagnoses of certain hemorrhage etiologies with proposed system augmentation.
  arXiv  Detail & Related papers  (2023-02-02T08:45:17Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• Detection of sepsis during emergency department triage using machine learning [13.553957919946638]
  Sepsis is a life-threatening condition with organ dysfunction and is a leading cause of death and critical illness worldwide. A machine learning model (KATE Sepsis) was developed using patient encounters with triage data from 16participating hospitals. The KATE Sepsis model trained to detect sepsis demonstrates 77.67% (75.78% -79.42%) sensitivity in detecting severe sepsis and 86.95% (84.2% - 88.81%) sensitivity in detecting septic shock.
  arXiv  Detail & Related papers  (2022-04-15T21:57:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.