EBioMedicine最新文献

{"title":"A strategy to re-sensitise drug-resistant Gram-positive bacteria to oxazolidinone-class antibiotics.","authors":"Qi Zhang, Yang Yang, Ying Yang, Jin Shang, Shan Su, Peng Gao, Xiao-Xiao Li, Zhao Liu, Richard Yi-Tsun Kao, Ben Chi-Bun Ko, Benjamin Thompson, Qian Zhao","doi":"10.1016/j.ebiom.2025.105914","DOIUrl":"10.1016/j.ebiom.2025.105914","url":null,"abstract":"<p><strong>Background: </strong>Multidrug-resistant bacterial infections have high mortality rates and few treatment options. Synergistic combinations may improve clinical outcome but traditional strategies often damage healthy microbiome. Oxazolidinone-class antibiotics are typical last-resort drugs for treating drug-resistant bacterial infections but are becoming less effective due to resistance development.</p><p><strong>Methods: </strong>After high-throughput screening, synergy was further assessed by in vitro indices (like fractional inhibitory concentration index, biofilm formation and resistance development) and in vivo symptoms in animals with skin and ocular bacterial infections (and ocular microbiome extraction analysis). Proteomics, chemical synthesis, multi-microscopy techniques and antibiotic real-time/kinetic accumulation were employed to explore mechanisms and expand translational applications.</p><p><strong>Findings: </strong>Combining phosphorylated oxazolidinone-class antibiotics with positively charged compounds (lysozyme as native representative) resulted in broad-spectrum drug re-sensitisation. In representative combination, urea cycle was disrupted to alkalinise cytoplasm, which subsequently activated alkaline phosphatase to promote conversion of phosphorylated prodrug to active form. By introducing concept of restored healthy microbiome as the evaluated index in antibiotic therapy, we confirmed excellent translational and microbiome-friendly potential of this strategy in clinical settings because it not only inhibited biofilm formation and development of drug-resistant mutations in vitro, but also alleviated symptoms in infected animals including the restoration of healthy microbiome.</p><p><strong>Interpretation: </strong>As both agents have excellent safety profiles, such clinical investigation may immediately be contemplated in humans. Translationally, scientists benefit from strategy by simultaneously achieving greater efficacy (>500-fold re-sensitisation) and higher safety (prodrug-based and microbiome-friendly strategy especially when active form may be toxic).</p><p><strong>Funding: </strong>Collaborative Research Funds from Research Grants Council (C5033-19E).</p>","PeriodicalId":11494,"journal":{"name":"EBioMedicine","volume":"119 ","pages":"105914"},"PeriodicalIF":10.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tumour extracellular vesicle surface Protein-mRNA integration assay for early detection of epithelial ovarian cancer.","authors":"Ying-Tzu Yen, Chen Zhao, Tian Gao, Jacqueline Ziqian Yang, Ning Kang, Jiahui Pu, Lei Qiu, Qixin Hu, Hyoyong Kim, Anmin Wang, Junseok Lee, Ryan Y Zhang, Na Liu, Yue Ma, You-Ren Ji, Yong Ju, Lynn L Zheng, James Lee-South, Vivian X Zuo, Audrey Qian, Aaron Kwan, Yating Zhang, Shenghua Zhang, Zhili Wang, Jing Ren, Huaichao Liu, Zihan Wang, Yang Yue, Jina Kim, Jennifer Sun, Gabriella A DiBernardo, Laura B James-Allan, Ying Chen, Weipei Zhu, Guoyun Wang, Renjun Pei, Sanaz Memarzadeh, Sungyong You, Bobbie J Rimel, Kate Lawrenson, Beth Y Karlan, Myung Shin Sim, Shaohua Lu, Jipeng Wan, Na Sun, Hsian-Rong Tseng, Yazhen Zhu","doi":"10.1016/j.ebiom.2025.105884","DOIUrl":"10.1016/j.ebiom.2025.105884","url":null,"abstract":"<p><strong>Background: </strong>Early detection of epithelial ovarian cancer (EOC) is crucial for improving clinical outcomes. However, the sensitivity of primary serological marker cancer antigen 125 (CA125) is suboptimal for detecting early-stage EOC. Tumour-derived extracellular vesicles (EVs) are promising biomarkers for early cancer detection.</p><p><strong>Methods: </strong>We developed an EOC EV Surface Protein-mRNA Integration (SPRI) Assay for early detection of EOC. This assay quantifies reference mRNAs within subpopulations of EOC EVs enriched by EV Click Beads targeting three EOC EV surface protein markers. Three EOC EV surface protein markers (i.e., FRα, MSLN, and TROP2) were selected through a bioinformatic framework using multi-omics data and underwent rigorous validation using EOC cell lines and EOC tissue microarrays. We then explored the translational potential of the EOC EV SPRI Assay through a phase II case-control study. The EOC EV SPRI Score was established using a logistic regression model in a training cohort (n = 118) and then validated in an independent validation cohort (n = 118).</p><p><strong>Findings: </strong>EOC EV SPRI Score demonstrated superior performance for distinguishing EOC from benign ovarian masses and healthy donors with an area under the receiver operating characteristic (AUROC) of 0.99 (95% CI: 0.97-1.00) in the training cohort and 0.93 (95% CI: 0.88-0.97) in the validation cohort. It outperformed matched serum CA125, and the performance remained excellent in earlier stages of EOC (Stage I/II, AUROC = 0.93, 95% CI: 0.88-0.98) and the subgroup of high-grade serous carcinoma (AUROC = 0.97, 95% CI: 0.87-0.97).</p><p><strong>Interpretation: </strong>The EOC EV SPRI assay demonstrated significant potential for early detection of EOC and improving long-term patient outcomes.</p><p><strong>Funding: </strong>This work is supported by National Institutes of Health (R01CA277530, R01CA255727, R01CA253651, R01CA253651-04S1, R21CA280444, R01CA246304, U01EB026421, R44CA288163, U01CA271887, and U01CA230705), DOD (HT9425-23-1-0361) and OCRA (CRDG-2023-3-1000) for the U.S.</p><p><strong>Study: </strong>Additionally, we acknowledge the support of the Science and Technology Foundation of Suzhou (SZS2023006, SSD2023004) and the Youth Innovation Promotion Association CAS (2023335) for the work conducted at SINANO.</p>","PeriodicalId":11494,"journal":{"name":"EBioMedicine","volume":"119 ","pages":"105884"},"PeriodicalIF":10.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12359246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144845031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to \"Systematic review and meta-analysis of enteric virus shedding in human excretions\" [eBioMedicine, Volume 119, September 2025, 105878].","authors":"Gang Zheng, Elana M G Chan, Alexandria B Boehm","doi":"10.1016/j.ebiom.2025.105906","DOIUrl":"10.1016/j.ebiom.2025.105906","url":null,"abstract":"","PeriodicalId":11494,"journal":{"name":"EBioMedicine","volume":"119 ","pages":"105906"},"PeriodicalIF":10.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12419097/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144946935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative assessment of line probe assays and targeted next-generation sequencing in drug-resistant tuberculosis diagnosis.","authors":"Giovanna Carpi, Marva Seifert, Andres De la Rossa, Swapna Uplekar, Camilla Rodrigues, Nestani Tukvadze, Shaheed V Omar, Anita Suresh, Timothy C Rodwell, Rebecca E Colman","doi":"10.1016/j.ebiom.2025.105875","DOIUrl":"10.1016/j.ebiom.2025.105875","url":null,"abstract":"<p><strong>Background: </strong>Rapid and accurate detection of drug-resistant tuberculosis (DR-TB) is crucial for ensuring effective treatment, halting transmission and preventing the amplification of resistance. Comparative evaluations of molecular diagnostic assays in high-burden settings are essential for informing clinical decision-making for DR-TB treatment.</p><p><strong>Methods: </strong>The Seq&Treat clinical study previously evaluated the performance of two targeted next-generation sequencing (tNGS) workflows, GenoScreen Deeplex Myc-TB and Oxford Nanopore Technologies Tuberculosis Drug Resistance Test, on direct sediment samples from persons at risk for DR-TB. Hain Line Probe Assay (LPAs-MTBDRplus and MTBDRsl) were run as a comparator test using an aliquot of the same sediment samples. Diagnostic performance of the LPAs and previously established tNGS performance were compared, including sensitivity and specificity, for rifampicin, isoniazid, fluoroquinolones (moxifloxacin, levofloxacin), and amikacin, using a composite reference standard of phenotypic drug susceptibility testing and whole-genome sequencing.</p><p><strong>Findings: </strong>Among 720 clinical samples tested, MTBDRplus LPA sensitivity for rifampicin and isoniazid was 92.3% (95% CI 88.9-94.8) and 91.9% (88.4-94.4), each significantly lower than ≥95% achieved by both tNGS workflows (p < 0.01). For fluoroquinolones (moxifloxacin and levofloxacin), the MTBDRsl LPA and ONT had similar sensitivities (94.3% and 92.7%, and 94.8% and 93.9%, respectively), while GenoScreen outperformed both (97.3% and 96.6%). GenoScreen also demonstrated the highest sensitivity for amikacin resistance (94.6%) compared to LPAs (88.7%) and ONT (88.3%). Complete assay failure rates were low for LPAs (4.9%) and ONT (5.0%) and moderately higher for GenoScreen (8.6%), with differences in single-target failures across all assays.</p><p><strong>Interpretation: </strong>LPAs demonstrated lower sensitivity and more limited drug resistance detection compared to tNGS workflows, underscoring the advantages of tNGS for improving DR-TB diagnostic algorithms. These findings provide critical evidence to guide updates in DR-TB diagnostic programs.</p><p><strong>Funding: </strong>Support for the Seq&Treat project was provided through funding from Unitaid (2019-32-FIND MDR).</p>","PeriodicalId":11494,"journal":{"name":"EBioMedicine","volume":"119 ","pages":"105875"},"PeriodicalIF":10.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12344966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144788550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic review and meta-analysis of enteric virus shedding in human excretions.","authors":"Gang Zheng, Elana M G Chan, Alexandria B Boehm","doi":"10.1016/j.ebiom.2025.105878","DOIUrl":"10.1016/j.ebiom.2025.105878","url":null,"abstract":"<p><strong>Background: </strong>Wastewater-based epidemiology can inform the understanding of infectious disease occurrence in communities. Quantitative information on shedding of pathogen biomarkers in excretions that enter wastewater is needed to link measurements of pathogen biomarkers to rates of disease occurrence.</p><p><strong>Methods: </strong>We compile, summarise, and compare data on shedding of human norovirus, rotavirus, hepatitis A virus, and adenovirus group F in stool, vomit, urine, saliva, mucus, and sputum using a systematic review and meta-analysis approach.</p><p><strong>Findings: </strong>We provide summaries of measured concentrations of the viruses across excretions where data exist. We provide longitudinal shedding profiles in terms of concentrations and positivity rates. Duration of shedding and day of peak shedding are also provided.</p><p><strong>Interpretation: </strong>There are limited data available for excretions other than stool, and limited data available for adenovirus group F. The aggregated data provided herein can serve as model inputs to translate wastewater enteric virus biomarker concentrations to disease occurrence rates. The study highlights data gaps and research needs.</p><p><strong>Funding: </strong>This study was funded by a gift from the Sergey Brin Family Foundation to ABB.</p>","PeriodicalId":11494,"journal":{"name":"EBioMedicine","volume":"119 ","pages":"105878"},"PeriodicalIF":10.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144793788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of interleukin-6 signalling in pleural infection: observational and genetic analyses.","authors":"Amerikos Argyriou, Alex Robbins, Rachel Scott, Jodie Chalmers, Harrison I W Wright, Robin N Beaumont, Karen T Elvers, Michael N Weedon, Nick A Maskell, David T Arnold, Fergus W Hamilton","doi":"10.1016/j.ebiom.2025.105887","DOIUrl":"10.1016/j.ebiom.2025.105887","url":null,"abstract":"<p><strong>Background: </strong>Pleural infection is associated with marked local and systemic inflammation leading to significant morbidity. It may be possible to therapeutically augment this response and interleukin-6 is a key signalling cascade in inflammatory pathologies.</p><p><strong>Methods: </strong>We performed a prospective observational study recruiting patients with pleural effusions secondary to infection and measured interleukin-6 in matched pleural fluid and serum (n = 76). We subsequently performed a large-scale, two sample Mendelian Randomisation study (1601 cases and 830,709 controls), using genetic variation at IL6R to proxy the effect of interleukin-6 inhibition on pleural infection and overcome confounding inherent in observational analyses.</p><p><strong>Findings: </strong>Pleural interleukin-6 levels in infection were 5000-fold higher than matched serum levels (median 72,752 pg/ml vs. 15 pg/ml). Pleural interleukin-6 predicted systemic inflammation (neutrophil count, C- reactive protein), correlated with clinical markers of disease severity (effusion size, pH, glucose), and was strongly associated with length of hospital stay. In Mendelian randomisation analyses, interleukin-6 inhibition was predicted to have a large protective effect on the incidence of infection (OR 0.23; 95% CI 0.14-0.39 per standard deviation decrease in C- reactive protein). The effect size was larger than that seen in COVID-19 and coronary artery disease, where interleukin-6 inhibition has been successful in trials.</p><p><strong>Interpretation: </strong>Multiple lines of evidence suggest pleural interleukin-6 drives pathology in pleural infection. Targeting interleukin-6 may hold promise and should be considered in randomised trials.</p><p><strong>Funding: </strong>This study has been funded by the National Institutes of Health and Care Research Bristol Biomedical Research Centre.</p>","PeriodicalId":11494,"journal":{"name":"EBioMedicine","volume":"119 ","pages":"105887"},"PeriodicalIF":10.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12391500/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144872001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated quantification of lung pathology on micro-CT in diverse disease models using deep learning.","authors":"Flore Belmans, Laura Seldeslachts, Eliane Vanhoffelen, Birger Tielemans, Wim Vos, Frederik Maes, Greetje Vande Velde","doi":"10.1016/j.ebiom.2025.105904","DOIUrl":"10.1016/j.ebiom.2025.105904","url":null,"abstract":"<p><strong>Background: </strong>Micro-CT significantly enhances the efficiency, predictive power and translatability of animal studies to human clinical trials for respiratory diseases. However, the analysis of large micro-CT datasets remains a bottleneck.</p><p><strong>Methods: </strong>We developed a generic deep learning (DL)-based lung segmentation model using longitudinal micro-CT images from studies of Down syndrome, viral and fungal infections, and exacerbation with variable lung pathology and degree of disease burden. 2D models were trained with cross-validation on axial, coronal and sagittal slices. Predictions from these single-orientation models were combined to create a 2.5D model using majority voting or probability averaging. The generalisability of these models to other studies (COVID-19, lung inflammation and fibrosis), scanner configurations and rodent species (rats, hamsters, degus) was tested, including a publicly available database.</p><p><strong>Findings: </strong>On the internal validation data, the highest mean Dice Similarity Coefficient (DSC) was found for the 2.5D probability averaging model (0.953 ± 0.023), further improving the output of the 2D models by removing erroneous voxels outside the lung region. The models demonstrated good generalisability with average DSC values ranging from 0.89 to 0.94 across different lung pathologies and scanner configurations. The biomarkers extracted from manual and automated segmentations are well in agreement and proved that our proposed solution effectively monitors longitudinal lung pathology development and response to treatment in real-world preclinical studies.</p><p><strong>Interpretation: </strong>Our DL-based pipeline for lung pathology quantification offers efficient analysis of large micro-CT datasets, is widely applicable across rodent disease models and acquisition protocols and enables real-time insights into therapy efficacy.</p><p><strong>Funding: </strong>This research was supported by the Service Public de Wallonie (AEROVID grant to FB, WV) and The Flemish Research Foundation (FWO, doctoral mandate 1SF2224N to EV and 1186121N/1186123N to LS, infrastructure grant I006524N to GVV).</p>","PeriodicalId":11494,"journal":{"name":"EBioMedicine","volume":"119 ","pages":"105904"},"PeriodicalIF":10.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12419106/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144946924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early T cell responses correlate with SARS-CoV-2 viral clearance.","authors":"Marios Koutsakos, Jennifer A Juno","doi":"10.1016/j.ebiom.2025.105880","DOIUrl":"10.1016/j.ebiom.2025.105880","url":null,"abstract":"","PeriodicalId":11494,"journal":{"name":"EBioMedicine","volume":"119 ","pages":"105880"},"PeriodicalIF":10.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12344955/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144793784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sex-stratified metabolic signatures of adiposity indices and their associations with clinical biomarkers in the UK Biobank.","authors":"Christos K Papagiannopoulos, Georgios Markozannes, Christos V Chalitsios, Sofia Christakoudi, Marc J Gunter, Laure Dossus, Richard M Martin, Ioanna Tzoulaki, Christopher Papandreou, Konstantinos K Tsilidis","doi":"10.1016/j.ebiom.2025.105868","DOIUrl":"10.1016/j.ebiom.2025.105868","url":null,"abstract":"<p><strong>Background: </strong>Excessive adiposity increases disease risk, however, the metabolic processes underlying these associations remain incompletely understood.</p><p><strong>Methods: </strong>We compared metabolic signatures (MSs) of adiposity indices (non-allometric: body fat %, waist circumference, hip circumference, waist-to-hip ratio, body mass index; allometric: a body shape index [ABSI], hip index [HI], waist-to-HI ratio) by sex and examined their cross-sectional associations with 29 clinical biomarkers in 151,526 UK Biobank participants. MSs performance was validated in an independent cohort.</p><p><strong>Findings: </strong>In females, MSs mainly consisted of lipoprotein particle concentrations, apolipoproteins, fatty acids and inflammation-linked glycoprotein acetyls, whereas in males lipoproteins rich in cholesteryl esters and aromatic/branched-chain amino acids predominated. The highest percentages of common metabolites were observed between non-allometric adiposity indices (median: 42.4%; range: 9%-56%). MSs were independently associated with over 25 biomarkers with differences observed by sex and adiposity index, and these associations were stronger compared to the respective phenotypic associations.</p><p><strong>Interpretation: </strong>MS_ABSI was found to be more atherogenic, whereas MS_HI was more favourable for health. This study highlights i) that different regions of adipose tissue undergo distinct metabolic processes overall and by sex, each having unique impact on health, and ii) the importance of considering metabolic factors beyond simple adiposity indices in assessing health risk.</p><p><strong>Funding: </strong>This work was supported by Cancer Research UK (grant number C18281/A29019).</p>","PeriodicalId":11494,"journal":{"name":"EBioMedicine","volume":"119 ","pages":"105868"},"PeriodicalIF":10.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144793787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultra-sensitive urinary lipoarabinomannan (LAM) immunoassay for tuberculosis detection: a performance evaluation.","authors":"Qisheng Jiang, Carolyn Duncan, Harisha Ramachandraiah, Ige A George, Sumanth Gandra, Marcos Perez, Lorraine Lillis, David S Boyle, Scott Crick, Morten Ruhwald, Srikanth Singamaneni","doi":"10.1016/j.ebiom.2025.105885","DOIUrl":"10.1016/j.ebiom.2025.105885","url":null,"abstract":"<p><strong>Background: </strong>The development of rapid non-sputum tests remains a global priority to accelerate Tuberculosis (TB) diagnosis and treatment initiation. The only WHO-recommended rapid diagnostic test (RDT), the Alere Determine TB Lipoarabinomannan Ag (AlereLAM) has suboptimal sensitivity. A laboratory-based electrochemiluminescence LAM assay (EclLAM) is the current sensitivity benchmark for RDT development and the gold standard for urinary LAM detection. We assessed the diagnostic accuracy of an ultra-sensitive, Plasmonic Fluor-linked Immunosorbent LAM assay (PFLISA-LAM) compared to Sputum Xpert MTB/RIF, sputum culture and urine EclLAM.</p><p><strong>Methods: </strong>We developed and evaluated the assay performance of PFLISA-LAM. Two sub-studies were conducted using banked urine samples: 1. Preclinical study using 337 well-characterised urine samples for cutoff determination and initial evaluation of the performance of PFLISA-LAM compared to sputum Xpert MTB/RIF and culture. 2. A Diagnostic accuracy assessment study using 77 blinded samples to evaluate the performance of PFLISA-LAM compared to EclLAM versus microbiological reference standard (MRS, Xpert positive and/or culture positive).</p><p><strong>Findings: </strong>PFLISA-LAM has a limit of detection (LOD) of 0.84 ± 0.9 pg/mL when detecting purified LAM spiked in urine. In the preclinical study, the optimal assay cutoff was determined to be 1.7 pg/mL. The sensitivities of PFLISA-LAM and sputum Xpert MTB/RIF compared to culture were 51% (95% confidence interval [CI]: 43%-59%) and 62% (95% CI: 53%-70%). The specificities of PFLISA-LAM and Xpert MTB/RIF were 99% (95% CI: 96%-100%) and 100% (95% CI: 100%-100%). Combining PFLISA-LAM and Xpert MTB/RIF test data, an improved sensitivity of 76% (95% CI: 69%-83%) can be achieved. In the diagnostic study, the sensitivities of EclLAM and PFLISA-LAM assays were 42% (95% CI: 27%-59%) and 73% (95% CI: 56%-85%). The specificities of EclLAM and PFLISA-LAM were 95% (95% CI: 85%-99%) and 98% (95% CI: 88%-100%).</p><p><strong>Interpretation: </strong>With better analytical and diagnostic sensitivity compared to EclLAM, PFLISA-LAM can better detect urinary LAM in TB-positive cases. PFLISA-LAM assay also demonstrated the capability to increase the diagnostic value in detecting urinary LAM, complementing molecular tests, achieving improved diagnostic outcome.</p><p><strong>Funding: </strong>We report no external financial support for conducting the study.</p>","PeriodicalId":11494,"journal":{"name":"EBioMedicine","volume":"119 ","pages":"105885"},"PeriodicalIF":10.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12362013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144845032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}