On the Influence of the Risk of Virus Infections by Heat Sanitization of the PHD Hemodialysis Equipment
Bibliographic record
Abstract
Virus infections, particularly the hepatitis viruses and HIV are constant threats to dialysis patients. There are no studies of the influence of heat sanitization of dialysis equipment. When multiple patients use a dialysis machine, the possibility of human blood-borne virus transmission exists. To assure the safety of the PHD we studied inactivation of several logs of viral infectivity, by the PHD disinfection process. Methods: Heated RO water was spiked with model viruses at 75°C ± 1°C and 82°C ± 2°C for 0, 20 and 45 minutes. As model viruses we used bovine viral diarrhea virus (BVDV)Ñ model for hepatitis C, hepatitis A virus (HAV), and porcine parvovirus (PPV). PPV is very robust and resistant to heat sanitaization procedures and served as the model for Hep-B and HIV that cannot be used in vitro studies. Results: 75C treatment instantaneously reduced the BVDVviral titer to non-detectable levels. HAV, one of the most robust viruses evaluated in viral clearance studies, was also reduced to non-detectable levels within 20 minutes. The PPV titer was reduced by approximately 5 log10 within 45 minutes. At 82°C ± 2°C, PPV was reduced to non-detectable levels within 20 minutes. Given that parvoviruses are the most robust viruses evaluated to date in viral clearance studies, the true potential of the treatment step to inactivate viruses is much greater than can be demonstrated. Conclusions: The heat treatment used by the PHD is very “robust”. Given that this study was performed at reduced temperature and time and that the typical PHD System disinfection temperature is 85°C ± 5°C for 1 hour, the convincing log10 reduction values for PPV not only demonstrate the safety of the PHD heat sanitization presently, but also provide a strong indication that the treatment will also inactivate other currently unknown viruses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".