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Record W3186768198 · doi:10.2478/cttr-2021-0008

Robustness of HPHC Reduction in THS 2.2 Aerosol Relative to 3R4F Reference Cigarette Smoke under Extreme Climatic Conditions

2021· article· en· W3186768198 on OpenAlexaboutno aff
Laurent Poget, Catherine Goujon, Samuel Kleinhans, Serge Maeder, Jean‐Pierre Schaller

Bibliographic record

VenueContributions to Tobacco & Nicotine Research · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsRelative humidityAerosolEnvironmental scienceSmokeHumidityHumid subtropical climateAtmospheric sciencesQuitlineMeteorologyMedicineGeography

Abstract

fetched live from OpenAlex

Summary In order to assess robustness for the reduction of harmful and potentially harmful constituent (HPHC) levels generated by the Tobacco Heating System 2.2 (THS 2.2), a heated tobacco product, we compared the aerosol of this product with mainstream smoke from the 3R4F reference cigarette under different conditions of temperature and humidity. The desired climatic conditions were achieved by using an air-conditioning system coupled with the smoking-machine housing. Two extreme climatic conditions were selected, representing a “Hot and Dry” climate (30 °C and 35% relative humidity RH) and a “Hot and Very Humid” climate (30 °C and 75% RH). In addition, aerosol and smoke were generated using the standard conditions recognized for smoking-machine analyses of tobacco products (22 °C and 60% RH), which were close to the climatic conditions defined for “Subtropical and Mediterranean” environments (25 °C and 60% RH). The experimental conditions were chosen to simulate the use of THS 2.2 and cigarettes under extreme conditions of temperature and humidity. HeatSticks and cigarettes taken from freshly opened packs were subjected to short-term conditioning from two to a few more days under the same experimental conditions. We analyzed 54 HPHCs in THS 2.2 aerosol and 3R4F cigarette smoke, generated in accordance with the Health Canada Intense (HCI) standard, using modified temperature and humidity conditions for sample conditioning and machine-smoking experiments. We used a volume-adjusted approach for comparing HPHC reductions across the different climatic conditions investigated. Although a single puffing regimen was used, the total puff volume recorded for the 3R4F cigarette smoke varied due to the influence of temperature and humidity on combustion rate, which justified the use of a volume-adjusted approach. Volume-adjusted yields were derived from HPHC yields expressed in mass-per-tobacco stick normalized per total puff volume. The results indicated that, regardless of the considered climatic conditions, the HPHC levels investigated in THS 2.2 aerosol were reduced by at least 90%, on average, when compared with the concentrations in 3R4F cigarette mainstream smoke. This confirmed the robustness in performance for THS 2.2 to deliver reduced levels of HPHCs under the extreme climatic conditions investigated in this study. In order to further characterize the robustness of these reductions, the lowest reduction performance achieved for individual HPHCs across all climatic conditions was used to define the threshold for a robust reduction. The majority of the 54 HPHCs investigated in THS 2.2 aerosol showed more than 90% reduction. Calculations derived from nicotine-adjusted yields also confirmed robust reductions for all investigated HPHCs. The small differences in absolute reduction between the volume- and nicotine-adjusted approaches were predominantly attributed to a combination of the differences in both nominal nicotine deliveries and total puff volumes between THS 2.2 and 3R4F cigarettes; however, this did not influence the determination of robustness. Our findings confirm the value of this approach for assessing the robustness of a product’s performance under different climatic conditions.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.086
GPT teacher head0.403
Teacher spread0.317 · 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 designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

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