Effect of the addition of zeolites and silicate compounds on the composition of the smoke generated in the decomposition of Heet tobacco under inert and oxidative atmospheres

Five potential additives, three zeolites and two silicates, have been studied for reducing the amount of compounds generated when smoking Heet tobacco. Pyrolysis-gas chromatography-mass spectrometry experiments (Py-GC/MS) show that this type of tobacco generates large amounts of nicotine and glycerol and compounds such as phenol, acetaldehyde, acetone and formaldehyde that are classified as harmful components and potentially harmful (HPHC). USY and SBA-15 are the additives that present major reductions in inert and oxidative atmosphere, being these reductions greater for SBA-15. These results have been confirmed by smoking experiments that show significant reductions for SBA-15, especially in the total particle matter (TPM) where a reduction of more than 40% is obtained for most of the compounds, being for several compounds even higher than 60%. These additives may further reduce the toxicity of Heet tobacco.Financial support for this investigation has been provided by the “Conselleria de Educacion, Investigacion, Cultura y Deportes” (IDIFEDER 2018/009 and PROMETEO2020/093)

Effect of Reaction Time and Hydrothermal Treatment Time on the Textural Properties of SBA-15 Synthesized Using Sodium Silicate as a Silica Source and Its Efficiency for Reducing Tobacco Smoke Toxicity

The synthesis of SBA-15 has been optimized using sodium silicate, an inexpensive precursor of SBA-15. In this work, the influence of synthesis times of the precipitation and the hydrothermal treatment steps, on the textural properties developed as well as for reducing the toxic compounds generated in tobacco smoking, has been studied. The hydrothermal treatment has been proved to be necessary to obtain materials with adequate performance in this particular application. Twenty-four hours of hydrothermal treatment provide materials with the best properties. Although the reaction stage usually involves the mixing of reagents during 24 h, 40 min is enough to obtain a material with stick-like morphology and typical textural properties. Moreover, between 1 and 2 h of reaction time, the material proved to have the best performance for the purpose of reducing the toxicity of the products generated during the tobacco smoking process. These results are of great significance for an eventual scaling up to industrial scale of the SBA-15 manufacturing process. Results of a pilot plant experiment in a batch of 4 kg of SBA-15 are reported.This research was funded by the Spanish “Secretaría de Estado de Investigación” of the Ministerio de Ciencia e Innovación (CTQ2015-70726-P), Generalitat Valenciana (PROMETEO/2016/056, PROMETEO 2020/093 and IDIFEDER 2018/009)

Impact of late presentation of HIV infection on short-, mid- and long-term mortality and causes of death in a multicenter national cohort : 2004-2013

To analyze the impact of late presentation (LP) on overall mortality and causes of death and describe LP trends and risk factors (2004-2013). Cox models and logistic regression were used to analyze data from a nation-wide cohort in Spain. LP is defined as being diagnosed when CD4 < 350 cells/ml or AIDS. Of 7165 new HIV diagnoses, 46.9% (CI:45.7-48.0) were LP, 240 patients died.First-year mortality was the highest (aHR = 10.3[CI:5.5-19.3]); between 1 and 4 years post-diagnosis, aHR = 1.9(1.2-3.0); an

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts