Bibliographic record
Abstract
Unfortunately for the estimated 250 million sufferers of chronic hepatitis-B viral (HBV) infection worldwide, the liver terrain is typically ignored. An immuno-tolerant environment attractive for pathogens, the essential metabolic roles and structural features of the liver are aligned with distinctive gradients of oxygen and nutrients established along blood flows through fundamental hepatic processing units known as sinusoids. Capillaries surrounded by banks of hepatocytes, sinusoids express spatial configurations and concentrations of not only metabolic roles but also immune cell localisations, blood filtering and transporter specialisations: the liver terrain. HBV targets proteins regulating gluconeogenesis, a crucial liver function of blood glucose management, highly active at blood entry points-the periportal sites of sinusoids. Meanwhile, at these same sites, specialised liver macrophages, Kupffer cells (KC), aggregate and perform critical pathogen capture, detection and signaling for modulating immune responses. In tandem with KC, liver sinusoidal endothelial cells (LSECs) complement KC blood filtration and capture of pathogens as well as determine KC aggregation at the periportal sites. Failure of these systems to establish critical spatial configurations could ironically facilitate HBV invasion and entrenchment. Investigating the impacts of spatial and structural variations on the HBV infection dynamic is experimentally challenging at best. Alternatively, mathematical modeling methods provide exquisite control over said variations, permitting teasing out the subtle and competing dynamics at play within the liver terrain. Coordinating with experimental observations, multi-scale modeling methods hold promise to illuminate HBV reliance on features of the liver terrain, and potentially how it may be defeated.
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".