Indoor air quality index for preoccupancy assessment

The purpose of this study is to document the potential impacts on indoor air quality associated with different types of building materials (wall and floor finishes) through the development of an Indoor Air Quality index. The study first identifies pollutant sources and their corresponding health impacts due to short-term and long-term exposures. The study also quantifies levels of certain pollutants within a steady-state controlled environment, comparing the results of this study with previous studies conducted in different regions. It also proposes an IAQ index as an assessment tool which can be utilized preoccupancy. The field studies were conducted in residential buildings during January and February in Cairo to monitor volatile organic compounds (VOCs), formaldehyde (HCHO), ammonia (NH₃), radon gas, and particulate matter (PM). The indoor air was monitored in nine locations: four during the construction process and five following completion of construction. For this investigation, three rooms under construction within a Cairene building site were utilized to test the finishing materials. Chemical analysis and direct reading devices were used for air sampling and monitoring. The results revealed that the concentration of some pollutants decreased within the first year of construction, while others remained above target limits. The results of this study offer recommendations for engineers regarding the selection of appropriate materials through the implementation of source control strategies and an IAQ index which can be used as an assessment tool to ensure that the Indoor Air Quality meets recommended standards. Based on the conclusions and limitations of this study, recommendations for future work are documented such as the screening of materials and monitoring of Indoor Air Quality.     

Management of smoking patients by specialist periodontists and hygienists in the United Kingdom

OBJECTIVE: To assess the management of patients who are current smokers in dental practice. MATERIALS AND METHODS: A postal questionnaire to the 243 periodontists on the UK specialist register and 239 randomly selected hygienists on the General Dental Council register. RESULTS: Eighty per cent of both groups responded. There were significant demographic differences between the two groups of respondents: 88% of periodontists were in specialist practice and/or hospital or university departments, while 89% of hygienists were in general dental practice. Periodontists routinely asked their patients about smoking 99% of the time, hygienists 89%. More than 5 min. was spent advising a smoker patient by 35% of periodontists and 19% of hygienists. A proportion of both groups claimed to have had some recognized training in quit smoking (44% and 39%). When asked about the oral conditions of their smoking patients, 26% of periodontists stated that these patients did not have poorer oral hygiene, while 67% of hygienists thought that they did. While the majority of both groups discussed the oral effects of smoking, less than two-thirds discussed methods of giving up. CONCLUSION: Both periodontists and hygienists are generally aware of the problems of treating smokers, and accordingly ask and advise their patients. However, lack of time and poor patient response may inhibit active involvement in assisting patients to quit smoking.     

A review on current advances in machine learning based diabetes prediction

Diabetes is a metabolic disorder comprising of high glucose level in blood over a prolonged period in the body as it is not capable of using it properly. The severe complications associated with diabetes include diabetic ketoacidosis, nonketotic hypersmolar coma, cardiovascular disease, stroke, chronic renal failure, retinal damage and foot ulcers. There is a huge increase in the number of patients with diabetes globally and it is considered a major health problem worldwide. Early diagnosis of diabetes is helpful for treatment and reduces the chance of severe complications associated with it. Machine learning algorithms (such as ANN, SVM, Naive Bayes, PLS-DA and deep learning) and data mining techniques are used for detecting interesting patterns for diagnosing and treatment of disease. Current computational methods for diabetes diagnosis have some limitations and are not tested on different datasets or peoples from different countries which limits the practical use of prediction methods. This paper is an effort to summarize the majority of the literature concerned with machine learning and data mining techniques applied for the prediction of diabetes and associated challenges. This report would be helpful for better prediction of disease and improve in understanding the pattern of diabetes. Consequently, the report would be helpful for treatment and reduce risk of other complications of diabetes.     

Hypothalamic expression of anorexigenic and orexigenic hormone receptors in obese females Neotomodon alstoni: Effect of fasting

Obesity is a world problem that requires a better understanding of its physiological and genetic basis, as well as the mechanisms by which the hypothalamus controls feeding behavior. The volcano mouse Neotomodon alstoni develops obesity in captivity when fed with regular chow diet, providing a novel model for the study of obesity. Females develop obesity more often than males; therefore, in this study, we analysed in females, in proestrous lean and obese, the differences in hypothalamus expression of receptors for leptin, ghrelin (growth hormone secretagogue receptor GHS-R), and VPAC, and correlates for plasma levels of total ghrelin. The main comparisons are between mice fed ad libitum and mice after 24 hours of fasting. Mice above 65 g body weight were considered obese, based on behavioral and physiological parameters such as food intake, plasma free fatty acids, and glucose tolerance. Hypothalamic tissue from obese and lean mice was analysed by western blot. Our results indicate that after ad libitum food access, obese mice show no significant differences in hypothalamic leptin receptors, but a significant increase of 60% in the GHS-R, and a nearly 62% decrease in VPAC2 was noted. After a 24-hour fast, plasma ghrelin increased nearly two fold in both lean and obese mice; increases of hypothalamic leptin receptors and GHS-R were also noted, while VPAC2 did not change significantly; levels of plasma free fatty acids were 50% less after fasting in obese than in lean animals. Our results indicate that in obese N. alstoni mice, the levels of orexigenic receptors in the hypothalamus correlate with overfeeding, and the fact that lean and obese females respond in different ways to a metabolic demand such as a 24-hour fast.     

Angiotensin-converting enzyme gene polymorphisms and hypertension in occupational noise exposure in Egypt

Background:

The gene–environment interaction in the pathogenesis of hypertension has not been extensively studied in occupational noise.

Objectives:

The aim of this study was to determine the relationship between noise and hypertension in Egyptian workers, the interaction of angiotensin-converting enzyme (ACE) gene polymorphisms as modifiers, and the possible relationship between noise hearing impairment and hypertension.

Methods:

Study subjects were divided into two groups depending on noise exposure level. The control group (n = 161) was exposed to noise intensity <85 dB and the exposed group (n = 217) was exposed to noise intensity ≧85 dB. A polymerase chain reaction was used to differentiate the various genotypes of ACE insertion/deletion (I/D) and ACE G2350A.

Results:

Noise significantly increased the likelihood of hypertension. Carriers of the genotypes AG, GG, and DD were vulnerable to hypertension on noise exposure. No association between hypertension and hearing impairment or noise-induced hearing loss (NIHL) was found.

Conclusion:

Our results support the association between ACE gene polymorphisms and occurrence of hypertension in noise-exposed workers.