Introduction: Biofilm production is an important yet currently overlooked aspect of diagnostic microbiology that has implications for antimicrobial stewardship. In this study, we aimed to validate and identify additional applications of the BioFilm Ring Test® (BRT) for Pseudomonas aeruginosa (PA) isolates from patients with bronchiectasis (BE).Materials and methods: Sputa were collected from BE patients who had at least one PA positive culture in the previous year. We processed the sputa to isolate both mucoid and non-mucoid PA, and determined their susceptibility pattern, mucA gene status, and presence of ciprofloxacin mutations in QRDR genes. The Biofilm production index (BPI) was obtained at 5 and 24 hours. Biofilms were imaged using Gram staining.Results: We collected 69 PA isolates, including 33 mucoid and 36 non-mucoid. A BPI value below 14.75 at 5 hours predicted the mucoid PA phenotype with 64% sensitivity and 72% specificity.Conclusion: Overall, our findings suggest that the fitness-cost associated with the mucoid phenotype or ciprofloxacin resistance is shown through a time-dependent BPI profile. The BRT has the potential to reveal biofilm features with clinical implications.

Alterations in pulmonary and gut microbiome are associated with an impairment in clinical outcomes in critically ill patients, including those with acute respiratory distress syndrome (ARDS) [1–6]. Although experimental studies have suggested gut-to-lung bacterial translocation [6], there is limited knowledge of simultaneous microbiome changes across different body compartments in patients with ARDS. Additionally, the biological consequences of these alterations at different body sites have not been sufficiently explored. Therefore, the aim of this study is to study the gut, lung and blood microbiome composition in patients with ARDS, and analyse its relationship to the systemic biological response.

Background: Upper tract urothelial carcinoma (UTUC) is a rare and aggressive malignancy with limited prognostic tools to predict disease progression. Due to its low incidence, the molecular pathogenesis of UTUC remains poorly understood, and few studies have explored transcriptomic profiling in this setting. Identifying gene expression biomarkers associated with progression may help improve risk stratification and guide postoperative management. Methods: In this study, we applied a machine learning approach to gene expression data from radical nephroureterectomy (RNU) specimens of 17 consecutive patients with pT2 or pT3 UTUC treated at our institution. RNA was extracted from formalin-fixed paraffin-embedded tissues and sequenced using the Ion AmpliSeq™ Transcriptome Human Gene Expression Kit on an Illumina HiSeq 2500 platform. Differential gene expression was assessed using DESeq2, and results were visualized with volcano plots. Predictive power was evaluated through logistic regression and receiver operating characteristic (ROC) analysis. Gene Ontology enrichment analysis was used to explore biological pathways. Results: A total of 76 genes were differentially expressed between progressive and non-progressive patients. A random forest classifier identified ten key genes with prognostic potential. Validation with logistic regression yielded an area under the ROC curve (AUC) of 0.88, indicating high discriminative ability. These genes were associated with immune regulation, cell cycle control, and tumor progression. Conclusions: This pilot study demonstrates the potential of integrating machine learning with transcriptomic analysis to identify prognostic biomarkers in UTUC. Further validation in larger, independent cohorts is needed to confirm these findings and support their clinical application.

COVID-19 vaccination strategies are already available almost worldwide. However, it is also crucial to develop new therapeutic approaches, especially for vulnerable populations that may not fully respond to vaccination, such as the immunocompromised. In this project, we predicted 25 B-cell epitopes in silico in the SARS-CoV-2 Spike (S) protein and screened these against serum and plasma samples from 509 COVID-19 convalescent patients. The aim was to identify those epitopes with the highest IgG reactivity to produce monoclonal antibodies against them for COVID-19 treatment. We implemented Brewpitopes, a computational pipeline based on B-cell epitope prediction tools, such as BepiPred v2.0 and Discotope v2.0, and a series of antibody-epitope accessibility filters. We mapped the SARS-CoV-2 S protein epitopes most likely to be recognised by human neutralizing antibodies. Linear and structural epitope predictions were included and were further refined considering accessibility factors influencing their binding to antibodies like glycosylation status, localization in the viral membrane and accessibility on the 3D-surface of S. Blood samples were collected from 509 COVID-19 patients prospectively recruited 35 days after symptoms initiation, positive RT-qPCR or hospital/ICU discharge. Presence of IgG against SARS-CoV-2 was confirmed by lateral flow immunoassays. Epitopes immunogenicity was tested through the analysis of IgG levels and seropositivity in the convalescent serum and plasma samples and 126 pre-pandemic negative controls by Luminex to identify those with the highest reactivity. The seropositivity cut-offs for each epitope were calculated using a set of 126 pre-pandemic samples as negative controls (NC). Twenty-five SARS-CoV-2 S epitopes were predicted in silico as potentially the most immunogenic. These were synthesized and tested in a multiplex immunoassay against sera/plasmas from convalescent COVID-19 patients (5.7% asymptomatic, 35.6% mild, 13.8% moderate, 23% severe and 22% unknown because of anonymous donation). Among the 25 epitopes tested, 3 exhibited significantly higher IgG reactivity compared to the rest. The proportion of seropositive patients towards these 3 epitopes, based on median fluorescence intensity (MFI or Log₁₀ MFI) above that from NC, ranged between 11 and 48%. Two out of the three most immunogenic epitopes were scaled up, resulting in the generation of two monoclonal antibodies (mAbs). These two mAbs exhibited comparable levels of S protein affinity to commercialized mAbs. Our data shows that the candidate S epitopes predicted in silico are recognised by IgG present in convalescent serum and plasma. This evidence suggests that our computational and experimental pipeline is able to yield immunogenic epitopes against SARS-CoV-2 S. These epitopes are suitable for the development of novel antibodies for preventive or therapeutic approaches against COVID-19.

Background: The different exacerbation patterns exhibited by patients colonized with Pseudomonas aeruginosa (PA) are not fully understood, and may be influenced by the stage of chronic colonization. We aimed to determine the implications of intermittent versus chronic PA colonization in biological and clinical outcomes while attempting to improve PA diagnosis. Methods: Longitudinal study of patients with BE colonized by PA, including sequential measurements of: sputum microbiology and 16SrRNA gene sequencing by llumina Miseq platform; PA biofilms by Gram-staining and FISH+CLSM; serum AntiPAIgG through ELISA; mucus rheology and the Quality-of-Life Bronchiectasis questionnaire (QoL-B); lung function and the number of mild and hospitalized exacerbations during long-term follow-up. Microbiology of sputum included comparison between standard of care (SOC) and methods recommended by ESCMID for biofilms. Findings: We collected 235 sputa and 262 serum samples from 80 patients, 61 with chronic and 19 with intermittent PA colonization. ESCMID methods achieved better PA detection than SOC. Chronic colonization was associated with higher PA load, mucoid PA and biofilms, but lower quality of life and fewer hospitalized exacerbations than intermittent colonization. Our data suggest a role for high AntiPAIgG in reducing severe exacerbations. Mucoid PA was an independent predictor of chronic PA associated with increased mucus viscoelasticity and with a pathobiome characterized by damaged respiratory biodiversity indicative of late chronic colonization. Interpretation: Since PA biofilms constitute the mechanism by which chronic PA colonization occurs, diagnosis of biofilm infections is a priority to prevent the mucoid pathobiome and to improve patient’s QoL. Funding Information: ISCIII-FEDER (Code: PI18/00145) grant awarded to AT and LFB; Intramural Ciber project 2018 (ES18PI01X1-2021) awarded to AT and LFB; ISCIII-FOS (FI19/00090) grant awarded to RLA, CB 06/06/0028/CIBER de enfermedades respiratorias (Ciberes), Ciberes is an initiative of ISCIII, 2.603/IDIBAPS, ICREA Academy award to AT, SGR/Generalitat de Catalunya, SEPAR grants 2016 (Code:208) and 2019 (Code: 628) awarded to LFB. Funders did not play any role in project design, data collection, data analysis, interpretation, or writing of the paper. Declaration of Interests: A. Torres has received grants from MedImmune, Cubist, Bayer, Theravance and Polyphor, and personal fees as an advisory board member from Bayer, Roche, The Medicines CO and Curetis. He has received bureau fees for keynote speaker presentations from GSK, Pfizer, Astra Zeneca and Biotest Advisory Board, but these were not associated with the study described in this paper. All others have nothing to declare. Ethics Approval Statement: The study was carried out in compliance with the Declaration of Helsinki (current version: Fortaleza, Brazil, October 2013) and with the requirements of the 2007 Spanish Biomedical Research Act. Written informed consent was obtained from all patients and the study was approved by the Internal Review Board of the Hospital Clínic of Barcelona (registry number HCB/0236).

Background: Exacerbations are key determinants of disease progression and impaired health-related quality of life (HRQoL) in non-cystic fibrosis bronchiectasis (BE). Despite the recognised association between Pseudomonas aeruginosa (PA) and worse outcomes, factors driving exacerbation burden remain poorly characterised. In patients with PA-colonised BE, we aimed to assess whether microbiological factors, including colonisation status and PA phenotype, together with HRQoL domains and respiratory symptoms, were associated with time to first exacerbation, exacerbation severity, and post-exacerbation changes in HRQoL. Materials and Methods: This prospective longitudinal study followed PA-colonised BE adults for 12 months with quarterly stable-state assessments focussing on time and severity of first exacerbation. HRQoL was measured by the Quality of Life Questionnaire-Bronchiectasis (QOL-B). Multivariable Cox, Firth penalised logistic, and ANCOVA models were fitted in R. Results: A total of 68 patients were included. During follow-up, 52 patients (76.5%) experienced ≥1 exacerbation, including 14 (26.9%) whose first event required hospitalisation. Neither colonisation status nor PA phenotype were associated with exacerbation risk or hospitalisation. Better daytime cough (HR 0.63, 95%CI 0.41–0.97; p = 0.036) and dyspnoea while talking (HR 0.47, 95%CI 0.26–0.87; p = 0.016) were independently associated with lower exacerbation risk. Lower exertional dyspnoea burden (OR 0.29, 95%CI 0.10–0.67; p = 0.003) and lower sputum quantity (OR 0.46, 95%CI 0.19–0.93; p = 0.030) were associated with lower odds of hospitalisation, together with poorer Vitality, Social Functioning, and Health Perceptions. After exacerbation, Social Functioning declined significantly (Δ −7.50 points, 95%CI −14.87 to −0.14; p = 0.046) and mucoid PA was independently associated with worse Emotional Functioning (β −15.64, 95%CI −27.89 to −3.39; p = 0.013). Conclusions: In PA-colonised BE, selected respiratory symptoms and HRQoL domains were associated with exacerbation burden, whereas microbiological characteristics showed no clear associations with exacerbation risk or hospitalisation, supporting the incorporation of patient-reported outcomes into longitudinal assessment.