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Record W4393988117 · doi:10.7717/peerj.17170

The role of CXCL10 as a biomarker for immunological response among patients with leprosy: a systematic literature review

2024· review· en· W4393988117 on OpenAlexaboutno aff
Flora Ramona Sigit Prakoeswa, Nabila Haningtyas, Listiana Masyita Dewi, Ellen Josephine Handoko, Moch. Tabriz Azenta, Muhana Fawwazy Ilyas

Bibliographic record

VenuePeerJ · 2024
Typereview
Languageen
FieldMedicine
TopicLeprosy Research and Treatment
Canadian institutionsnot available
FundersUniversitas Muhammadiyah Surakarta
KeywordsSystematic reviewMedicineMeta-analysisLeprosyScopusMEDLINEInternal medicineImmunologyBiology

Abstract

fetched live from OpenAlex

Introduction: Involvement of a chemokine known as C-X-C motif chemokine ligand 10 or CXCL10 in the immunopathology of leprosy has emerged as a possible immunological marker for leprosy diagnosis and needed to be investigate further. The purpose of this systematic review is to assess CXCL10's potential utility as a leprosy diagnostic tool and evaluation of therapy. Methods: This systematic review is based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. A thorough search was carried out to find relevant studies only in English and limited in humans published up until September 2023 using PubMed, Scopus, Science Direct, and Wiley Online Library database with keywords based on medical subject headings (MeSH) and no exclusion criteria. The Newcastle-Ottawa Scale (NOS) was utilized for quality assessment, while the Risk of Bias Assessment tool for Non-randomized Studies (RoBANS) was utilized for assessing the risk of bias. Additionally, a narrative synthesis was conducted to provide a comprehensive review of the results. Results: We collected a total of 115 studies using defined keywords and 82 studies were eliminated after titles and abstracts were screened. We assessed the eligibility of the remaining 26 reports in full text and excluded four studies due to inappropriate study design and two studies with incomplete outcome data. There were twenty included studies in total with total of 2.525 samples. The included studies received NOS quality evaluation scores ranging from 6 to 8. The majority of items in the risk bias assessment, using RoBANS, across all included studies yielded low scores. However, certain items related to the selection of participants and confounding variables showed variations. Most of studies indicate that CXCL10 may be a helpful immunological marker for leprosy diagnosis, particularly in leprosy reactions as stated in seven studies. The results are better when paired with other immunological markers. Its effectiveness in field-friendly diagnostic tools makes it one of the potential biomarkers used in diagnosing leprosy patients. Additionally, CXCL10 may be utilized to assess the efficacy of multidrug therapy (MDT) in leprosy patients as stated in three studies. Conclusion: The results presented in this systematic review supports the importance of CXCL10 in leprosy diagnosis, particularly in leprosy responses and in tracking the efficacy of MDT therapy. Using CXCL10 in clinical settings might help with leprosy early diagnosis. Yet the findings are heterogenous, thus more investigation is required to determine the roles of CXCL10 in leprosy while taking into account for additional confounding variables.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.052
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.010
Bibliometrics0.0100.011
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.364
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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