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Record W2342633252 · doi:10.15562/gnc.32

NS1: An early Diagnostic Tool for Dengue Virus Fever

2016· article· en· W2342633252 on OpenAlexvenueno aff
Affifa Kiran Chaudhary, Mehwish Anwer, Mahmood Qazi

Bibliographic record

VenueJournal of Genes and Cells · 2016
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsnot available
Fundersnot available
KeywordsDengue feverDengue virusVirologyMedicine

Abstract

fetched live from OpenAlex

Dengue virus is a highly prevailing unremitting pathological menace in the developing countries like Pakistan. The variation in the type and frequency of dengue infection demands a continuous surveillance on its spread and diagnosis, in order to develop appropriate management and therapeutic strategies. Dengue Virus is highly complex disease with various manifestations. It is hard to characterize the dengue viral infection with ordinary laboratory tests. However, NS1 could serve a good diagnostic tool in the first few days of fever. In this research, 88 patients from Lahore regions of Punjab were analysed for the presence of dengue specific NS1 antigen from 1 to 30 days of dengue fever. ELISA based kit was employed. The obtained data was analysed expressed percentage frequency of NS1 positive and negative along with IgM positive and negative in all dengue fever patients. Importantly it was observed that 36.3 % were infected by dengue. NS1 antigen was efficiently quantified at earlier days of infection. IgM was negative in all the subjects who were positive for NS1 on the second day of fever. However, this association weakened with the progression of fever. It was analysed that thrombocytopenia and leuckocytopenia are not linked with the NS1 positivity and these states were random. It can be inferred that dengue diagnosis is very complicated and the antigenic profile dramatically changes with the progression of fever in terms of days. Clinicians should employ a diagnostic method based on a comprehensive analysis of subjects in order to minimize

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 score0.613
Threshold uncertainty score0.196

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)0.0000.000

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 head0.010
GPT teacher head0.255
Teacher spread0.245 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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