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SEN virus infection in patients with hepatocellular carcinoma

2005· article· en· W2133941144 on OpenAlexaffabout
Seiya Momosaki, Takeji Umemura, Charles H. Scudamore, Masamichi Kojiro, H. J. Alter, Edward Tabor

Bibliographic record

VenueJournal of Viral Hepatitis · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHepatocellular carcinomaHBsAgMedicineHepatitis B virusVirologyAntibodyVirusHepatitis C virusInternal medicineGastroenterologyImmunology

Abstract

fetched live from OpenAlex

Although most cases of hepatocellular carcinoma (HCC) are associated with either the hepatitis B or C viruses (HBV, HCV), about 10-20% of HCCs occur in patients with chronic hepatitis that is aetiologically undefined. The aim of the present study was to determine the prevalence of the transfusion-transmitted SEN virus (SEN-V) in patients with HCC, including those patients who do not otherwise appear to be infected with HBV or HCV. Fragments of SEN-V subtypes D and H were amplified separately by PCR from the sera of 50 patients with HCC (31 from Canada and 19 from Japan) as well as from HCC and adjacent nontumourous liver tissues from eight of the Canadian patients. SEN-V DNA was found in the serum of 10 of 31 (32%) Canadian patients and eight of 19 (42%) Japanese patients [overall, 18 of 50 (36%) HCC patients]. SEN-V DNA was detected in the serum of 10 of 23 (43%) HCC patients with antibody to HCV (anti-HCV), six of 11 (55%) with hepatitis B surface antigen (HBsAg), and two of 16 (12%) without detectable anti-HCV or HBsAg. Twenty-three HCC patients in this study had 'silent HBV,' characterized by the detection of HBV DNA in the absence of HBsAg; eight of these (35%) also had SEN-V infections. SEN-V DNA was detected in HCC patients most typically in those with coexistent HBV or HCV infection. SEN-V was found in only one of seven HCC patients without HBV (without HBsAg or HBV DNA) or HCV and thus does not appear to be an important cause of 'cryptogenic' HCC.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.788

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 head0.011
GPT teacher head0.201
Teacher spread0.191 · 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

Citations8
Published2005
Admission routes2
Has abstractyes

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