PREVALENCE OF BYOGENES AMMINS IN SEASONED D.O.P. CHEESE TOSCANO PECORINO

Pecorino cheese is one of the “D.O.P.” products made in Tuscany. There are two types of pecorino cheese one of which undergoes a curing time period which is not less than four months. Considering this curing time period, processes are conceivable that could lead to the formation of amines such as free amino acids. The biogenic amines have unquestionable effects on health in particular histamine and tyramine, they are also important indicators of hygienic quality of the profile of the movement shortly after the curing time period and included in the shelf life period which the product is commercialized

Biological monitoring of workers exposed to N, N-dimethylfomamide. I. Methods of analysis

Some methods for analysing N,N-dimethylformamide and its metabolites [hydroxymethyl-N-methylformamide, hydroxymethylformamide and N-acetyl-S-(N-methylcarbamoyl)cysteine] in the urine of exposed workers are described. Unchanged dimethylformamide was measured after pretreatment of urine (2 ml) with silica gel cartridges and elution with methanol. The gas chromatographic analysis using a nitrogen phosphor detector made it possible to detect N,N-dimethylformamide in urine even when workers were exposed to low concentrations of the solvent (about 1 mg/m3). N-Hydroxymethyl-N-methylformamide and N-hydroxymethylformamide were analysed as N-methylformamide and formamide respectively after direct injection of urine into the gas chromatograph. The injection port temperature played an important role in the gas chromatographic determination of these products. Reliable results were obtained when direct or split injections were performed at 250 degrees C. The splitless injection gave the same reliable results at 150 degrees C. In urine samples from occupationally non-exposed persons, N-methylformamide could not be detected. In contrast, formamide (or its precursor, hydroxymethylformamide) was present in every urine sample. Our results in respect of 19 urine samples analysed with the injection port of the gas chromatograph at 250 degrees C gave a mean of 8.6 mg/l of formamide. N-Acetyl-S-(N-methylcarbamoyl)cysteine was determined using a modified method for analysing organic acid in urine samples. The metabolite was extracted with ethyl ether in an acid environment, treated with a silylating reagent and measured by gas chromatography/mass spectrometry

Valori di riferimento di solventi e metaboliti in campioni biologici

Abstract This article presents a review of reference values for organic solvent biomarkers. Some of these results were obtained in the research activities of the Italian Society Reference Values (SIVR). Most experiences show data obtained from control groups during occupational exposures assessment investigations. We considered only data related to the following biomarkers: immodified solvents in blood and urine, their main urinary metabolites. The reference values of the following solvents are reported: benzene, toluene, xylene, n-hexane, cyclohexane, trichloroethylene, perchloroethylene, methanol, acetone, N,N-dimethylformamide, carbon disulphide. In the text also the influence of some confounding factors is discussed

[Occupational exposure to nickel in Tuscany: analysis of a biological monitoring register for the period 1991-1998].

OBJECTIVE: The study assesses the time trend in exposure to nickel among factory workers in Florence, via data on biological monitoring. A data-base of nickel in urinary samples (Ni-U mg/l) was created for the period 1991 to 1998. METHODS: The data-base contained 2.138 samples, measured by atomic absorption (GF-AAS),from 893 workers. Subjects came from 157 factories in various manufacturing sectors, especially electroplating, mechanical workshops, jewellery. RESULTS: Ni-U levels were correlated with manufacturing sector. The highest levels were found among workers from electroplating industries, where exposure was mainly due to water-soluble nickel compounds. The eight-year time trend showed a statistically significant decrease in Ni-U values, with a sharper drop during the last two-year period. Age, sex and number of samples per subject were not statistically related to this trend. CONCLUSION: The observed Ni-U decrease could be related to the efficacy of new legislation introduced in Italy during the study period (Law 626/94 and subsequent laws), but also to the intense labour inspection activities that officials of National Health Service performed, which were rightly focused on nickel exposure in different manufacturing sectors. This study confirms the usefulness for occupational risk evaluation of a biological monitoring data-base of routinely collected data

Classification of residents in nursing homes in Tuscany (Italy) using resource utilization groups version III (RUG-III)

Background and aims: Samples of nursing homes in Tuscany (Italy) classify their residents and determine their case-mix according to the Resource Utilization Groups System, Version III (RUG-III). Methods: A large sample of nursing homes was selected, based on willingness to participate, representation of both public and private institutions, and wide geographic representation. Two registered nurses assessed all residents using the RUG questionnaire. The information collected was then used to group residents into 44 RUGs, and facility-specific case-mix indices were calculated using the RUG-specific weights previously validated in Italy. Results: A total of 3981 residents from 93 nursing homes were assessed. Most residents were over 75 years old (87.4%) and women (68.6%). A large percentage was classified into RUGs within the following primary categories: reduced physical function (33.6%), impaired cognition (17.6%) and clinically complex (17.6%). The resulting nursing home case-mix indices ranged from 0.627 to 1.108 (mean 0.807 +/- 0.110). No significant association was found between type of facility, level of fees, or extent of staff in the nursing homes and their case-mix indices. Conclusions: RUG-III can provide information on types of nursing home residents and their care needs. This is useful for monitoring and evaluating long-term care services for the elderly, and allows for more effective planning and allocation of staffing and financial resources