Evidence map›Paper›PMID 40390870›Full record

ReviewHemaSphere2025

Metabolism in hematology: Technological advances open new perspectives on disease biology and treatment.

Eileen Haring, Joerg M Buescher, Petya Apostolova

Abstract readReview
In one paragraph

Review in HemaSphere, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Eileen HaringDepartment of Biomedicine University Hospital Basel, University of Basel Basel Switzerland.
Joerg M BuescherMax Planck Institute of Immunobiology and Epigenetics Freiburg Germany.
Petya ApostolovaDepartment of Biomedicine University Hospital Basel, University of Basel Basel Switzerland.ORCID 0000-0002-3856-5109

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The term metabolism refers to the multi-faceted biochemical reactions within a cell or an organism that occur to maintain energy homeostasis, cell growth, and oxidative balance. Cells possess a high metabolic plasticity, allowing them to adapt to the dynamic requirements of their functional state and environment. Deregulated cellular metabolism is a hallmark of many diseases, including benign and malignant hematological conditions. In the last decade, multiple technological innovations in the metabolism field have made in-depth metabolic analysis broadly applicable. Such studies are shedding new light on normal and malignant hematopoiesis and open avenues to a better understanding of the biology of hematological diseases. In this review, we will first give a brief overview of central metabolic processes. Furthermore, we discuss the most commonly used methods to study metabolism. We begin by elaborating on the use of next-generation sequencing to detect metabolism-related genomic mutations and study transcriptional signatures. Furthermore, we discuss methods for measuring protein expression, such as mass spectrometry (MS), flow cytometry, and cytometry time-of-flight. Next, we describe the use of nuclear magnetic resonance spectroscopy, MS, and flow cytometry for metabolite quantification. Finally, we highlight functional assays to probe metabolic pathways in real-time. We illustrate how these technologies and their combination have advanced our understanding of the role of metabolism. Our goal is to provide hematologists with a comprehensive guide to modern techniques in metabolism research, their benefits and disadvantages, and how they guide our understanding of disease and potentially future personalized therapy decisions.

Identifiers

PMID40390870
PMCPMC12086526

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.