Virological Response to Lamivudine and Tenofovir Treatment in a Mono-infected Chronic Hepatitis B Patient with Potential Tenofovir Resistance: A Case Report
Bibliographic record
Abstract
Few cases of tenofovir resistance have been reported, and the appropriate treatment for such cases remains unclear. We aimed to share a case of a chronic hepatitis B mono-infected patient with potential tenofovir resistance who required combined lamivudine and tenofovir therapy to achieve adequate viral suppression. The patient's viral load (plasma) was monitored using the cobas® hepatitis B virus Test on the cobas® 6800 system. Hepatitis B antiviral drug resistance (AVDR) mutations were assessed by amplicon-based sequencing. Plasma was extracted using the MagNa Pure 24 system, and polymerase chain reaction targeting the polymerase gene (860bp) was performed. Sequencing was conducted on GridION R10.4.1 flow cells, and the resulting FASTQ files were analyzed using DeepChek®-HBV Software. We describe a female patient in her 60s with chronic hepatitis B who was e-antigen positive. She met treatment criteria in May 2020, when her alanine transaminase levels were 1.5 times above the upper limit of normal. She was initially started on entecavir but had to switch to tenofovir alafenamide in June 2020 due to a rash. Despite three years of tenofovir therapy, her viral load remained unsuppressed. AVDR testing identified two suspected tenofovir resistance mutations (V191I and A317S). Since no mutations associated with lamivudine resistance were detected, the patient was treated with a combination of lamivudine and tenofovir, achieving viral suppression after four months. Although rare, tenofovir resistance should be considered in patients with persistent viremia despite long-term therapy. AVDR sequencing facilitated the detection of potential tenofovir resistance and guided treatment decisions, leading to successful viral suppression in this case.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".