Evidence map›Paper›PMID 42051967›Full record

ReviewFrontiers in transplantation2026

Building pragmatic transplant immunobiology in Sri Lanka: a concise review with an LMIC lens.

Anura Priyantha Hewageegana

Abstract readReview
In one paragraph

Review in Frontiers in transplantation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Anura Priyantha HewageeganaDivision of Nephrology, National Hospital of Sri Lanka, Colombo, Sri Lanka.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sri Lanka's kidney transplantation programme has matured within a resource-constrained public health system and has achieved short-term outcomes that appear comparable to published ranges from similar middle-income programmes, despite major limitations in real-time transplant immunobiology support. However, increasing recipient sensitisation, prolonged dialysis vintage, and a growing deceased-donor programme demand more reliable immunological risk assessment to improve equity and graft outcomes. This pragmatic narrative review summarises the minimum immunobiology package that can support safer kidney transplantation in low- and middle-income settings, using Sri Lanka as an example. Key concepts are explained in simple operational terms, including sensitisation and antibody screening (PRA, including the Zora assay), single-antigen bead testing and donor-specific antibody interpretation, practical use and limitations of virtual crossmatch when donor HLA data are incomplete, and the continuing role of CDC crossmatch where flow-cytometry crossmatch is unavailable. The review also highlights why unexpected early rejection may still occur even when HLA-based testing appears reassuring, and why over-reliance on any single assay can be misleading. Finally, a staged roadmap is proposed-prioritising feasible upgrades, quality assurance, workforce development, and national coordination-to progressively strengthen transplant immunobiology while preserving affordability, fairness, and sustainability. This is a pragmatic narrative review informed by consensus guidance and key peer-reviewed literature, synthesised to prioritise actionable, scalable immunobiology components for low- and middle-income settings.

Indexed as

CDC crossmatchdonor-specific antibodyHLAkidney transplantationsingle-antigen beadSri Lankatransplant immunobiologyvirtual crossmatch

Identifiers

PMID42051967
PMCPMC13111550

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.