Biology subjects

Casals-Pascual, C.

Publications and source records attributed to Casals-Pascual, C..

9 recordsLinked to original sources

bioRxiv2025

Rapid quantification of short-chain fatty acids by Fourier-transform infrared spectroscopy for microbiota quality assessment

Short-chain fatty acids (SCFAs) are bacterial metabolites with crucial roles in host homeostasis and immune system modulation. Given their benefits, they have been proposed as markers of a healthy microbiota. Their quantification, however, is time-consuming, expensive and requires specialized personal and equipment. Thus, their use for quality assessment of stool samples in clinical contexts is limited. In this study we explored the use of Fourier-transform infrared (FT-IR) spectroscopy as an alternative method for metabolomic quality assessment of stool samples. FT-IR identifies the chemical bonds present in SCFAs and allows clear discrimination between healthy and dysbiotic stool samples (caused by Clostridioides difficile infection) through Principal Component Analysis (PCA). Overall, FT-IR provides a rapid, cost-effective and simple method for stool samples quality assessment. Combined with the widespread availability of this technology in most hospitals, these advantages highlight its potential application for routine implementation in clinical laboratories.

microbiology↗

bioRxiv2022

Scalable power analysis and effect size exploration of microbiome community differences with Evident

Differentiating microbial communities among samples is a major objective in biomedicine. Quantifying the effect size of these differences allows researchers to understand the factors most associated with communities and to optimize the design and clinical resources required to address particular research questions. Here, we present Evident, a package for effect size calculations and power analysis on microbiome data and show that Evident scales to large datasets with numerous metadata covariates.

bioinformatics↗

bioRxiv2021

Kawasaki Disease patient stratification and pathway analysis based on host transcriptomic and proteomic profiles

The aetiology of Kawasaki Disease (KD), an acute inflammatory disorder of childhood, remains unknown despite various triggers of KD having been proposed. Host omic profiles offer insights into the host response to infection and inflammation, with the interrogation of multiple omic levels in parallel providing a more comprehensive picture. We used differential abundance analysis, pathway analysis, clustering and classification techniques to explore whether the host response in KD is more similar to the response to bacterial or viral infection at the transcriptomic and proteomic levels through comparison of omic profiles from children with KD to those with bacterial and viral infections. Pathways activated in patients with KD included those involved in anti-viral and anti-bacterial responses. Unsupervised clustering showed that the majority of KD patients clustered with bacterial patients on both omic levels, whilst application of diagnostic signatures specific for bacterial and viral infections revealed that many transcriptomic KD samples had low probabilities of having bacterial or viral infections, suggesting that KD may be triggered by a different process not typical of either common bacterial or viral infections. Clustering based on the transcriptomic and proteomic responses during KD revealed three clusters of KD patients on both omic levels, suggesting heterogeneity within the inflammatory response during KD. The observed heterogeneity may reflect differences in the host response to a common trigger, or variation dependent on different triggers of the condition.

bioinformatics↗

bioRxiv2020

Assessment of the use and quick preparation of saliva for rapid microbiological diagnosis of COVID-19.

The objective of this study was to assess the performance of direct real time RT-PCR detection of SARS-CoV-2 in heated saliva samples, avoiding the RNA isolation step. Oropharyngeal and nasopharyngeal swabs together with saliva samples were obtained from 51 patients clinically diagnosed as potentially having COVID-19. Two different methods were compared: 1. RNA was extracted from 500 l of sample using a MagNA Pure Compact Instrument with an elution volume of 50l and 2. 700{micro}L of saliva were heat-inactivated at 96{degrees}C for 15 minutes, and directly subjected to RT-PCR. One step real time RT-PCR was performed using 5 l of extracted RNA or directly from 5 l of heated sample. RT-PCR was performed targeting the SARS-CoV-2 envelope (E) gene region. Diagnostic performance was assessed using the results of the RT-PCR from nasopharyngeal and oropharyngeal swabs as the gold standard. The overall sensitivity, specificity, positive and negative predictive values were 81.08%, 92.86%, 96.77% and 65.00%, respectively when RNA extraction was included in the protocol with saliva, whereas sensitivity, specificity, positive and negative predictive values were 83.78%, 92.86%, 68.42% and 96.88%, respectively, for the heat-inactivation protocol. However, when the analysis was performed exclusively on saliva samples with a limited time from the onset of symptoms (<9 days, N=28), these values were 90%, 87.5%, 44% and 98.75% for the heat-inactivation protocol. The study showed that RT-PCR can be performed using saliva in an RNA extraction free protocol, showing good sensitivity and specificity.

microbiology↗

bioRxiv2020

Diagnostic Cerebrospinal Fluid Biomarker Discovery and Validation in Patients with Central Nervous System Infections

BackgroundCentral nervous system (CNS) infections are common causes of morbidity and mortality worldwide. Rapid, accurate identification of the likely cause is essential for clinical management and the early initiation of antimicrobial therapy, which potentially improves clinical outcome. MethodsWe applied liquid chromatography tandem mass-spectrometry on 45 cerebrospinal fluid (CSF) samples from a cohort of adults with/without CNS infections to discover potential diagnostic protein biomarkers. We then validated the diagnostic performance of a selected biomarker candidate in an independent cohort of 364 consecutively treated adults with CNS infections admitted to a referral hospital in southern Vietnam. ResultsIn the discovery cohort, we identified lipocalin 2 (LCN2) as a potential biomarker of bacterial meningitis. The analysis of the validation cohort showed that LCN2 could discriminate bacterial meningitis from other CNS infections, including tuberculous meningitis, cryptococcal meningitis and viral/antibody-mediated encephalitis (sensitivity: 0.88 (95% confident interval (CI): 0.77-0.94), specificity: 0.91 (95%CI: 0.88-0.94) and diagnostic odd ratio: 73.8 (95%CI: 31.8-171.4)). LCN2 outperformed other CSF markers (leukocytes, glucose, protein and lactate) commonly used in routine care worldwide. The combination of LCN2 and these four routine CSF markers resulted in the highest diagnostic performance for bacterial meningitis (area under receiver-operating-characteristic-curve 0.96; 95%CI: 0.93-0.99). ConclusionsOur results suggest that LCN2 is a sensitive and specific biomarker for discriminating bacterial meningitis from a broad spectrum of CNS infections. A prospective study is needed to further assess the diagnostic utility of LCN2 in the diagnosis and management of CNS infections.

microbiology↗

bioRxiv2019

Assessment of a loop-mediated isothermal amplification (LAMP) assay for the rapid detection of pathogenic bacteria from respiratory samples in patients with hospital-acquired pneumonia

IntroductionHospital-acquired pneumonia (HAP) is the one that presents clinically two or more days after admission into the hospital. Rapid identification of the causative agent of HAP will allow an earlier administration of a more appropriate antibiotic therapy and could lead to an improved outcome of patients with HAP.\n\nMethodsFirst of all, a rapid procedure (< 30 min) to extract the DNA from bronchoalveolar lavage (BAL), endotracheal aspirate (EA) or bronchoaspirate (BAS) was set up. A loop-mediated isothermal amplification reaction (LAMP) specific for Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Stenotrophomonas maltophilia and Acinetobacter baumannii was carried out with the extracted solution. The reaction was performed at 65{o}C for 30-40 min. LAMP was compared with bacterial culture method.\n\nResultsOverall, 58 positive BAL and 83 EA/BAS samples were tested. The limits of detection varied according to the microorganism detected and to the respiratory sample analyzed. Validation of the LAMP assay with BAL samples showed that the assay was 100% specific and 86.3% sensitive (positive predictive value of 100% and a negative predictive value of 50%). Meanwhile for BAS/EA samples, the assay rendered the following statistical parameters: 100% specificity, 94.6% sensitivity, 100% positive predictive value and 69.2% negative predictive value. These scores were obtained including minor errors as correct. The turnaround time including preparation of the sample and LAMP was circa 1 hour.\n\nConclusionsLAMP method may be used to detect the most frequent bacteria causing HAP. It is a simple, cheap, sensitive, specific and rapid assay.

microbiology↗

bioRxiv2019

Lipocalin-2 is a Sensitive and Specific Marker of Bacterial Infection in Children

IntroductionBacterial infection is the leading cause of death in children globally. Clinical algorithms to identify children who are likely to benefit from antimicrobial treatment remain suboptimal. Biomarkers that accurately identify serious bacterial infection (SBI) could improve diagnosis and clinical management. Lipocalin 2 (LCN2) and neutrophil collagenase (MMP-8) are neutrophil-derived biomarkers associated with bacterial infection.\n\nMethodsWe evaluated LCN2 and MMP-8 as candidate biomarkers in 40 healthy controls and 151 febrile children categorised confirmed SBI, probable SBI, or viral infection. The diagnostic performance of LCN2 and MMP-8 to predict SBI was estimated by the area under the receiver operating characteristic curve (AUROC) and compared to the performance of C-reactive protein (CRP).\n\nResultsPlasma LCN2 and MMP-8 concentration were predictive of SBI. The AUROC (95% CI) for LCN2, MMP8 and CRP to predict SBI was 0.88 (0.82-0.94); 0.80 (0.72-0.87) and 0.89 (0.84-0.94), respectively. The diagnostic performance of LCN2 in combination with CRP was significantly superior to either marker alone: AUROC 0.92 (95% CI: 0.88-0.96).\n\nConclusionLCN2 is a sensitive and specific predictor of SBI in children which could be used to improve clinical management and antimicrobial stewardship. LCN2 should be further evaluated in prospective clinical studies.

microbiology↗

bioRxiv2019

Evaluation of the Accelerate Pheno(TM)system to identify bacteria and determine antimicrobial susceptibility in positive blood cultures

IntroductionNew platforms have recently been developed to reduce response time of identification and antimicrobial susceptibility of bacterial isolates in positive blood cultures from patients with bloodstream infections. The Accelerate Pheno system (Accelerate Diagnostics, Inc.) provides information on pathogen identification and antibiotic susceptibility in approximately 1.5 and 7 hours, respectively.\n\nMethodsIn this study we compared the Accelerate Pheno system with the standard procedure used in our laboratory. A total of 41 blood cultures were prospectively analysed with the Accelerate Pheno system and our standard methods, which include identification by MALDI-TOF mass spectrometry and antibiotic susceptibility testing (AST) by BD Phoenix system and E-test.\n\nResultsThe correlation between the two methods using Cohens kappa coefficient was 0.82; mean (sd) time of identification for MALDI-TOF MS was 0.7 (0.22) hours and 1.43 (0.14) hours for the Accelerate Pheno system. The mean (sd) time of AST with the BD Phoenix system was 15.85 (2.57) hours and with the Accelerate Pheno system 6.7 (0.12) hours. AST results showed an overall essential agreement of 92% for the minimal inhibitory concentrations (MIC) and an overall category agreement of 96%. Among Gram positive isolates, essential and category agreements of 100% were observed. In Gram negative isolates 10 discrepancies were detected, which were classified as 7 major and 3 minor errors. Discrepancies in the Accelerate Pheno system were observed particularly for P. aeruginosa.\n\nConclusionThe Accelerate Pheno system can improve turn-around time in the management of patients with bloodstream infections.

microbiology↗

bioRxiv2018

Precision identification and prediction of high mortality phenotypes and disease progression pathways in severe malaria without requiring longitudinal data

The parasite Plasmodium falciparum is the main cause of severe malaria (SM). Despite treatment with antimalarial drugs, more than 450,000 SM deaths are reported every year, mainly in African children. The diversity of clinical presentations associated with SM indicate important differences in disease pathogenesis that often require specific treatment, and this clinical heterogeneity of SM is largely unresolved. In this study, we apply new machine learning and inference tools for large-scale data analysis to dissect the heterogeneity in patterns of clinical features associated with SM in 2,695 Gambian children admitted to hospital with Plasmodium falciparum malaria. This quantitative analysis, including the powerful HyperTraPS algorithm for inference of progressive processes, reveals pathways of SM symptom progression and features predicting the severity of individual patient outcomes. Notably, our approach allows the identification and dissection of disease progression pathways without the need for longitudinal observations. Learning these pathways and features from this rich dataset allows us to construct several quantitative measures of the mortality risk associated with a patient presenting with a given set of symptoms. By independently surveying expert practitioners, we show that this data-driven approach agrees with and expands the current state of knowledge on malaria progression, while simultaneously providing a data-supported framework for predicting clinical risk.

pathology↗