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Record W1986817024 · doi:10.1371/journal.pone.0031139

High Diversity of Cryptosporidium Subgenotypes Identified in Malaysian HIV/AIDS Individuals Targeting gp60 Gene

2012· article· en· W1986817024 on OpenAlexaff
Asma Iqbal, Yvonne Ai Lian Lim, Johari Surin, Benedict Lim Heng Sim

Bibliographic record

VenuePLoS ONE · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsHealth Canada
FundersUniversiti Malaya
KeywordsCryptosporidiumCryptosporidium parvumBiologyGenotypeGenetic diversityVirologyMicrobiologyFecesGeneGeneticsPopulationMedicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Currently, there is a lack of vital information in the genetic makeup of Cryptosporidium especially in developing countries. The present study aimed at determining the genotypes and subgenotypes of Cryptosporidium in hospitalized Malaysian human immunodeficiency virus (HIV) positive patients. METHODOLOGY/PRINCIPAL FINDINGS: In this study, 346 faecal samples collected from Malaysian HIV positive patients were genetically analysed via PCR targeting the 60 kDa glycoprotein (gp60) gene. Eighteen (5.2% of 346) isolates were determined as Cryptosporidium positive with 72.2% (of 18) identified as Cryptosporidium parvum whilst 27.7% as Cryptosporidium hominis. Further gp60 analysis revealed C. parvum belonging to subgenotypes IIaA13G1R1 (2 isolates), IIaA13G2R1 (2 isolates), IIaA14G2R1 (3 isolates), IIaA15G2R1 (5 isolates) and IIdA15G1R1 (1 isolate). C. hominis was represented by subgenotypes IaA14R1 (2 isolates), IaA18R1 (1 isolate) and IbA10G2R2 (2 isolates). CONCLUSIONS/SIGNIFICANCE: These findings highlighted the presence of high diversity of Cryptosporidium subgenotypes among Malaysian HIV infected individuals. The predominance of the C. parvum subgenotypes signified the possibility of zoonotic as well as anthroponotic transmissions of cryptosporidiosis in HIV infected individuals.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.025
GPT teacher head0.218
Teacher spread0.193 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations86
Published2012
Admission routes1
Has abstractyes

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