Standard emission levels of total particulate matter (TPM) and nicotine in bidi and cigarette smoke were compared with exposure levels based on smoking behaviour of smokers in Bombay. Bombay cigarette smokers are getting much higher amount of carcinogenic dry TPM (28 to 79%) and nicotine (31 to 104%), compared to standard machine estimates. In the case of regular bidi (60 mm) harmful ingredients like dry TPM decreased from 11 to 15 percent and nicotine increased from 11 to 22 percent, whereas long bidi (80 mm) delivered higher amount of dry TPM (14 to 22%) and nicotine (33 to 37%) in smokers in Bombay compared to standard machine estimates.

Download full-text PDF

Source

Publication Analysis

Top Keywords

standard machine
12
machine estimates
12
smokers bombay
12
dry tpm
12
total particulate
8
particulate matter
8
exposure levels
8
higher amount
8
compared standard
8
nicotine
5

Similar Publications

Study Question: How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?

Summary Answer: AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.

What Is Known Already: Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.

View Article and Find Full Text PDF

Artificial intelligence in respiratory care.

Front Digit Health

December 2024

Faculty of Engineering and Computing, Liwa College, Abu Dhabi, United Arab Emirates.

The evolution of artificial intelligence (AI) has revolutionised numerous aspects of our daily lives, with profound implications across various sectors, including healthcare. Although the concept of AI in healthcare was introduced in the early 1970s, the integration of this technology in healthcare is still in the evolution phase. Despite barriers, the current decade is witnessing an increased utility of AI into diverse specialities of the medical field to enhance precision medicine, predict diagnosis, therapeutic results, and prognosis; this includes respiratory medicine, critical care, and in their allied specialties.

View Article and Find Full Text PDF

Background: Predicting dementia early has major implications for clinical management and patient outcomes. Yet, we still lack sensitive tools for stratifying patients early, resulting in patients being undiagnosed or wrongly diagnosed. Despite rapid expansion in machine learning models for dementia prediction, limited model interpretability and generalizability impede translation to the clinic.

View Article and Find Full Text PDF

Background: Recent studies suggest a connection between immunoglobulin light chains (IgLCs) and coronary heart disease (CHD). However, current diagnostic methods using peripheral blood IgLCs levels or subtype ratios show limited accuracy for CHD, lacking comprehensive assessment and posing challenges in early detection and precise disease severity evaluation. We aim to develop and validate a Coronary Health Index (CHI) incorporating total IgLCs levels and their distribution.

View Article and Find Full Text PDF

Background: Machine learning (ML) is increasingly used to predict clinical deterioration in intensive care unit (ICU) patients through scoring systems. Although promising, such algorithms often overfit their training cohort and perform worse at new hospitals. Thus, external validation is a critical - but frequently overlooked - step to establish the reliability of predicted risk scores to translate them into clinical practice.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!