Evidence map›Paper›PMID 37153628›Full record

ArticleFrontiers in immunology2023

Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity.

Modi Safra, Zvi Tamari, Pazit Polak, Shachaf Shiber, Moshe Matan, Hani Karameh, Yigal Helviz, Adva Levy-Barda, Vered Yahalom, Avi Peretz and 5 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.8field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 13 citations in OpenAlex.

  1. AI-Driven BCR Modeling for Precision Immunology.International journal of molecular sciences · 2026
    Review
  2. Article
  3. Article
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  5. Explore antibody repertoire in the era of AI.Acta biochimica et biophysica Sinica · 2025
    Article
  6. Review
  7. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 5 institutions in 1 country.

Modi SafraBio-engineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel.
Zvi TamariBio-engineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel.
Pazit PolakBio-engineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel.
Shachaf ShiberEmergency Department, Rabin Medical Center-Belinson Campus, Petah Tikva, Israel.
Moshe MatanClinical Microbiology Laboratory, Baruch Padeh Medical Center, Poriya, Israel.
Hani KaramehJesselson Integrated Heart Center, Shaare Zedek Medical Center, Hebrew University School of Medicine, Jerusalem, Israel.
Yigal HelvizIntensive Care Unit, Shaare Zedek Medical Center, Hebrew University School of Medicine, Jerusalem, Israel.
Adva Levy-BardaBiobank, Department of Pathology, Rabin Medical Center-Belinson Campus, Petah Tikva, Israel.
Vered YahalomBlood Services and Apheresis Institute, Rabin Medical Center, Petah Tikva, Israel.
Avi PeretzClinical Microbiology Laboratory, Baruch Padeh Medical Center, Poriya, Israel.
Eli Ben-ChetritInfectious Diseases Unit, Shaare Zedek Medical Center, Hebrew University School of Medicine, Jerusalem, Israel.
Baruch BrennerSackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Tamir TullerDepartment of Biomedical Engineering and The Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
Meital Gal-TanamyThe Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel.
Gur YaariBio-engineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel.
Bar-Ilan University · ILShaare Zedek Medical Center · ILTel Aviv University · ILRabin Medical Center · ILPoriya Medical Center · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The success of the human body in fighting SARS-CoV2 infection relies on lymphocytes and their antigen receptors. Identifying and characterizing clinically relevant receptors is of utmost importance. Methods: We report here the application of a machine learning approach, utilizing B cell receptor repertoire sequencing data from severely and mildly infected individuals with SARS-CoV2 compared with uninfected controls. Results: In contrast to previous studies, our approach successfully stratifies non-infected from infected individuals, as well as disease level of severity. The features that drive this classification are based on somatic hypermutation patterns, and point to alterations in the somatic hypermutation process in COVID-19 patients. Discussion: These features may be used to build and adapt therapeutic strategies to COVID-19, in particular to quantitatively assess potential diagnostic and therapeutic antibodies. These results constitute a proof of concept for future epidemiological challenges.

Indexed as

B-LymphocytesCOVID-19HumansPatient AcuityReceptors, Antigen, B-CellRNA, ViralSARS-CoV-2Receptors, Antigen, B-CellRNA, ViralAIRR-seqB cellBCRCOVID-19machine learningsomatic hypermutation

Identifiers

PMID37153628
PMCPMC10154551
OpenAlexW4366427880

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.