Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Flow cytometrically measured CD8 T cells and tubular epithelial cells as urine biomarkers for kidney graft rejection

Eine Abstoßung ist eine gefürchtete Komplikation nach Nierentransplantation. Die klinischen Symptome sind unspezifisch, wie auch Serum-Kreatinin als wichtigster Laborparameter zur Verlaufskontrolle. Die Diagnose gelingt bis heute nur histologisch nach Biopsie. CD8 T-Zellen und Tubulusepithelzellen (TEC) sind an den pathologischen Prozessen der Nierentransplantat-Abstoßung beteiligt. Ihre Zellpopulationen sind im Urin messbar und Biomarker-Kandidaten für das Monitoring von transplantierten Patienten. Methodik: CD8 T-Zellen (DAPI– CD3+ CD8+), proximale TEC (CD10+ Cytokeratin intrazellulär+) und distale TEC (EPCAM+ Cytokeratin intrazellulär+) wurden mittels Durchflusszytometrie im Urin von Patienten nach Nierentransplantation bestimmt. In einer Längsschnittkohorte (n = 31) wurden Patienten mit „Guter Transplantatunktion“ (engl. Good Graft Function) und „Verzögerter Transplantatfunktion“ (engl. Delayed Graft Function“) bis zu 110 Tage nach Transplantation untersucht. In einer Querschnittskohorte (n = 63) wurden Patienten mit Verdacht auf Abstoßung biopsiert und anhand der Histologie in die Gruppen T-Zell-vermittelte Rejektion (TCMR), Antikörper-vermittelte Rejektion (ABMR) und keine Rejektion (No RX) eingeteilt. Die Zellzahlen wurden mit Banff-Scores korreliert. Des Weiteren wurde eine klinisch unauffällige Kontrollgruppe analysiert. Ergebnisse: Patienten mit Delayed Graft Function hatten an Tag 6-10 postoperativ mehr CD8 T-Zellen im Urin als die Vergleichsgruppe (p < 0,5). Interstitielle Entzündung und Tubulitis im Transplantat korrelierten mit CD8 T-Zellen im Urin (interstitielle Entzündung: Spearman r = 0,73; p < 0,001; Tubulitis: Spearman r = 0,48; p < 0,05). Patienten mit TCMR hatten mehr CD8 T-Zellen als Patienten mit ABMR (p < 0,05). Bei No RX waren die TEC höher als bei TCMR (p < 0,01), ABMR (CD10+ TEC: p < 0,001; EPCAM+ TEC: p < 0,01) und der Kontrollgruppe (CD10+ TEC: p < 0,0001; EPCAM TEC: p < 0,001). Der beste Biomarker zur Detektion von Abstoßung waren CD10+ TEC mit einer Spezifität von 100% und einer Sensitivität von 72% (Cut-Off: <642 Zellen/100 ml; AUC = 0,91). Schlussfolgerung: CD8 T-Zellen und TEC sind potenzielle Urin-Biomarker zur Verlaufskontrolle nach Nierentransplantation. Sie zeigten gute Ergebnisse zur Detektion von Delayed Graft Function und Transplantatabstoßung. Möglicherweise erzielen die Zellen in Kombination mit anderen Biomarkern noch bessere Ergebnisse. Zur Validierung dieser explorativen Studie sind größere Patientenzahlen erforderlich.Rejection is a feared complication after kidney transplantation. The clinical symptoms are non-specific, as is serum creatinine as the most important laboratory parameter for monitoring. To date, the diagnosis is only possible histologically after a biopsy. CD8 T cells and tubular epithelial cells (TEC) are involved in the pathological processes of renal graft rejection. The cell populations can be measured in urine and are biomarker candidates for monitoring transplanted patients. Methods: The presence of CD8 T cells (DAPI– CD3+ CD8+), proximal TEC (CD10+ Cytokeratin intracellular+) and distal TEC (EPCAM+ Cytokeratin intrazellular+) in the urine of patients after kidney transplantation was determined using flow cytometry. The technique of flow cytometry was used for the measurement. In a longitudinal cohort (n = 31), patients with “good graft function“ and “delayed graft function“ were examined up to 110 days after transplantation. In a cross-sectional cohort (n = 63) patients suspected of rejection were biopsied and divided into the groups T cell-mediated rejection (TCMR), Antibody-mediated rejection (ABMR) and No Rejection (No RX). The cell numbers were correlated with Banff-Scores. A clinically normal control group was also analyzed. Results: Patients with delayed graft function had more CD8 T cells in the urine than the comparison group on day 6-10 postoperatively (p < 0,05). Interstitial inflammation and tubulitis in the graft correlated with CD8 T cells in the urine (interstitial inflammation: Spearman r = 0,73; p < 0,001; tubulitis: Spearman r = 0,48; p < 0,05). Patients with TCMR had more CD8 T cells than patients with ABMR (p < 0,05). No RX had more TEC than TCMR (p < 0,01), ABMR (CD10+ TEC: p < 0,001; EPCAM+ TEC: p < 0,01) and the control group (CD10+ TEC: p < 0,0001; EPCAM TEC: p < 0,001). The best biomarker for the detection of rejection were CD10+ TEC with a specificity of 100% and a sensitivity of 72% (cut-off: <642 Zellen/100 ml; AUC 0,91). Conclusion: CD8 T cells and TEC are potential urine biomarkers for follow-up after kidney transplantation. They showed good results for the detection of delayed graft function and graft rejection. In combination with other biomarkers, the cells may achieve even better results. Larger numbers of patients are required to validate this explorative study

Kidney transplant monitoring by urinary flow cytometry: Biomarker combination of T cells, renal tubular epithelial cells, and podocalyxin-positive cells detects rejection

Creatinine and proteinuria are used to monitor kidney transplant patients. However, renal biopsies are needed to diagnose renal graft rejection. Here, we assessed whether the quantification of different urinary cells would allow non-invasive detection of rejection. Urinary cell numbers of CD4+ and CD8+ T cells, monocytes/macrophages, tubular epithelial cells (TEC), and podocalyxin(PDX)-positive cells were determined using flow cytometry and were compared to biopsy results. Urine samples of 63 renal transplant patients were analyzed. Patients with transplant rejection had higher amounts of urinary T cells than controls; however, patients who showed worsening graft function without rejection had similar numbers of T cells. T cells correlated with histological findings (interstitial inflammation p = 0.0005, r = 0.70; tubulitis p = 0.006, r = 0.58). Combining the amount of urinary T cells and TEC, or T cells and PDX+ cells, yielded a significant segregation of patients with rejection from patients without rejection (all p < 0.01, area under the curve 0.89–0.91). Urinary cell populations analyzed by flow cytometry have the potential to introduce new monitoring methods for kidney transplant patients. The combination of urinary T cells, TEC, and PDX-positive cells may allow non-invasive detection of transplant rejection

Identification and characterization of antigen-specific CD4+ T cells targeting renally expressed antigens in human lupus nephritis with two independent methods

In the search for anti-renal autoreactivity in human lupus nephritis, we stimulated blood-derived CD4+ T cells from patients with systemic lupus erythematosus with various kidney lysates. Although only minor responses were detectable, these experiments led to the development of a search algorithm that combined autoantibody association with human lupus nephritis and target gene expression in inflamed kidneys. Applying this algorithm, five potential T cell antigens were identified. Blood-derived CD4+ T cells were then stimulated with these antigens. The cells were magnetically enriched prior to measurement with flow cytometry to facilitate the detection of very rare autoantigen-specific cells. The detected responses were dominated by IFN-γ-producing CD4+ T cells. Additionally, IL-10-producing CD4+ T cells were found. In a next step, T cell reactivity to each single antigen was independently evaluated with T cell libraries and [3H]-thymidine incorporation assays. Here, Vimentin and Annexin A2 were identified as the main T cell targets. Finally, Vimentin reactive T cells were also found in the urine of three patients with active disease. Overall, our experiments show that antigen-specific CD4+ T cells targeting renally expressed antigens arise in human lupus nephritis and correlate with disease activity and are mainly of the Th1 subset

Spatially resolved qualified sewage spot sampling to track SARS-CoV-2 dynamics in Munich - One year of experience

Rubio-Acero R, Beyerl J, Muenchhoff M, et al. Spatially resolved qualified sewage spot sampling to track SARS-CoV-2 dynamics in Munich - One year of experience. Science of The Total Environment. 2021;797: 149031

The representative COVID-19 cohort Munich (KoCo19): from the beginning of the pandemic to the Delta virus variant

Le Gleut R, Plank M, Pütz P, et al. The representative COVID-19 cohort Munich (KoCo19): from the beginning of the pandemic to the Delta virus variant. BMC Infectious Diseases. 2023;23(1): 466.**Background** Population-based serological studies allow to estimate prevalence of SARS-CoV-2 infections despite a substantial number of mild or asymptomatic disease courses. This became even more relevant for decision making after vaccination started. The KoCo19 cohort tracks the pandemic progress in the Munich general population for over two years, setting it apart in Europe. **Methods** Recruitment occurred during the initial pandemic wave, including 5313 participants above 13 years from private households in Munich. Four follow-ups were held at crucial times of the pandemic, with response rates of at least 70%. Participants filled questionnaires on socio-demographics and potential risk factors of infection. From Follow-up 2, information on SARS-CoV-2 vaccination was added. SARS-CoV-2 antibody status was measured using the Roche Elecsys® Anti-SARS-CoV-2 anti-N assay (indicating previous infection) and the Roche Elecsys® Anti-SARS-CoV-2 anti-S assay (indicating previous infection and/or vaccination). This allowed us to distinguish between sources of acquired antibodies. **Results** The SARS-CoV-2 estimated cumulative sero-prevalence increased from 1.6% (1.1-2.1%) in May 2020 to 14.5% (12.7-16.2%) in November 2021. Underreporting with respect to official numbers fluctuated with testing policies and capacities, becoming a factor of more than two during the second half of 2021. Simultaneously, the vaccination campaign against the SARS-CoV-2 virus increased the percentage of the Munich population having antibodies, with 86.8% (85.5-87.9%) having developed anti-S and/or anti-N in November 2021. Incidence rates for infections after (BTI) and without previous vaccination (INS) differed (ratio INS/BTI of 2.1, 0.7-3.6). However, the prevalence of infections was higher in the non-vaccinated population than in the vaccinated one. Considering the whole follow-up time, being born outside Germany, working in a high-risk job and living area per inhabitant were identified as risk factors for infection, while other socio-demographic and health-related variables were not. Although we obtained significant within-household clustering of SARS-CoV-2 cases, no further geospatial clustering was found. **Conclusions** Vaccination increased the coverage of the Munich population presenting SARS-CoV-2 antibodies, but breakthrough infections contribute to community spread. As underreporting stays relevant over time, infections can go undetected, so non-pharmaceutical measures are crucial, particularly for highly contagious strains like Omicron

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press