Effect of temperature in domestic refrigerators on fresh-cut Iceberg salad quality and waste

The evolution of different quality parameters (firmness, weight loss, colour changes, microbial counts, consumer rejection) of packed fresh-cut Iceberg salad was assessed at 4, 8 and 12°C to simulate domestic refrigerators running at different conditions. The increase in storage temperature did not affect salad firmness and weight loss but increased colour changes, microbial growth and consumer rejection. A survey among Italian consumers was also carried out and demonstrated that fresh-cut salad was mainly consumed within the first 5days after purchasing. Consumer rejection data were combined with data relevant to the distribution of salad consumption over the days following product purchase, to estimate salad wasting risk. When salad was stored at 4 and 8°C, estimated wasted packages within the expiration date (7days) were <1%. By contrast, 13% of the packages was estimated to be wasted within 7days of storage at 12°C. Quantification of wasting risk is a necessary information to identify efficient and sustainable interventions to tackle food waste.

Mitochondrial DNA in Day 3 embryo culture medium is a novel, non-invasive biomarker of blastocyst potential and implantation outcome

In assisted reproduction technology, embryo competence is routinely evaluated on morphological criteria. Over the last decade, efforts in improving non-invasive embryo assessment have looked into the secretome of embryos. Human embryos release genomic DNA (gDNA) and mitochondrial DNA (mtDNA) into the culture medium, and the mtDNA/gDNA ratio is significantly correlated with embryo fragmentation. Here, we investigate whether mtDNA/gDNA ratio in embryo spent medium is correlated with blastulation potential and implantation. The mtDNA/gDNA ratio was assessed in 699 Day 3 culture media by quantitative polymerase chain reaction (qPCR) to investigate its correlation with embryo morphology, blastocyst development and implantation. A logistic regression model evaluated whether mtDNA/gDNA ratio in the secretome may improve the prediction of blastulation. We found that embryos that successfully developed into blastocysts exhibited a significantly higher mtDNA/gDNA ratio in the culture medium compared with those that arrest (P = 0.0251), and mtDNA/gDNA, combined with morphological grading, has the potential to predict blastulation better than morphology alone (P = 0.02). Moreover, mtDNA/gDNA ratio was higher in the media from good-quality embryos that reached the full blastocyst stage on Day 5 compared with those that developed more slowly (P < 0.0001). With respect to blastocyst morphology, higher trophectoderm quality was associated with a higher mtDNA/gDNA ratio in the culture medium. Finally, a high mtDNA/gDNA ratio in spent medium was associated with successful implantation outcome (P = 0.0452) of good-quality embryos. In summary, the mtDNA/gDNA ratio in the Day 3 embryo secretome, in combination with morphological grading, may be a novel, non-invasive, early biomarker to improve identification of viable embryos with high developmental potential.

Covariate selection in multivariate spatial analysis of ovine parasitic infection

Gastrointestinal (GI) strongyle and fluke infections remain one of the main constraints on health and productivity in sheep dairy production. A cross-sectional survey was conducted in 2004-2005 on ovine farms in the Campania region of southern Italy in order to evaluate the prevalence of Haemonchus contortus, Fasciola hepatica, Dicrocoelium dendriticum and Calicophoron daubneyi from among other parasitic infections. In the present work, we focused on the role of the ecological characteristics of the pasture environment while accounting for the underlying long range geographical risk pattern. Bayesian multivariate spatial statistical analysis was used. A systematic grid (10 km×10 km) sampling approach was used. Laboratory procedures were based on the FLOTAC technique to detect and count eggs of helminths. A Geographical Information System (GIS) was constructed by using environmental data layers. Data on each of these layers were then extracted for pasturing areas that were previously digitalized aerial images of the ovine farms. Bayesian multivariate statistical analyses, including improper multivariate conditional autoregressive models, were used to select covariates on a multivariate spatially structured risk surface. Out of the 121 tested farms, 109 were positive for H. contortus, 81 for D. dendriticum, 17 for C. daubneyi and 15 for F. hepatica. The statistical analysis highlighted a north-south long range spatially structured pattern. This geographical pattern is treated here as a confounder, because the main interest was in the causal role of ecological covariates at the level of each pasturing area. A high percentage of pasture and impermeable soil were strong predictors of F. hepatica risk and a high percentage of wood was a strong predictor of C. daubneyi. A high percentage of wood, rocks and arable soil with sparse trees explained the spatial distribution of D. dendriticum. Sparse vegetation, river, mixed soil and permeable soil explained the spatial distribution of the H. contortus. Bayesian multivariate spatial analysis of parasitic infections with covariates from remote sensing at a very small geographical level allowed us to identify relevant risk predictors. All the covariates selected are consistent with the life cycles of the helminths investigated. This research showed the utility of appropriate GIS-driven surveillance systems. Moreover, spatial features can be used to tailor sampling design where the sampling fraction can be a function of remote sensing covariables.

Statistical methods for geographical surveillance in veterinary epidemiology

Spatial clustering and cluster detection are statistical analysis developed to address relevant scientific hypothesis. The difficulty stays in the large number of alternative hypothesis due to the different mechanisms that could generate the anomalous cases aggregation. We review methods for marked point data (case/control) aimed to describe spatial intensity of disease risk, to test for randomness and to locate significant excesses. Bayesian Gaussian Spatial Exponential models are used to illustrate probabilistic aspects and the link with simpler non parametric tools are shown. We develop an informal guideline to the analysis and used data on faecal contamination and dog parasitic diseases in the city of Naples, Italy. Kernel density estimation resulted very sensitive to bandwidth choice and overemphasized localized excess, Ripley'K function and Cuzick-Edwards test were very consistent each other while the SatScan failed to detect excesses. The spatial range was around 600 meters and justifies several small clusters. Bayesian models were very powerful in reconstructing the phenomenon and allow inference on model parameters in good agreement with the non parametric analysis.

Statistical modelling of the spatial distribution of prevalence of Calicophoron daubneyi infection in sheep from central Italy

Statistical modelling for Disease Mapping and Ecological Analysis is of particular importance in veterinary parasitology because environmental characteristics can affect parasite distribution. However, the main difficulties relate to the concentration of animal populations within farms, which contrasts to the study of wild animal populations. In the present paper we report the results of a cross-sectional coprological survey designed to study the presence and distribution of the rumen fluke Calicophoron daubneyi--which causes paramphistomosis, a snail borne disease--in pastured sheep living in the Latina province of central Italy. We show how techniques derived from human epidemiology can be used to study the spatial distribution of parasite infection in animals. We proposed a hierarchical Bayesian model with random terms for unstructured variability (heterogeneity) to account for local farm characteristics and spatially structure terms (clustering) to cope with medium-large scale environmental characteristics.

[Statistical models for spatial analysis in parasitology]

The simplest way to study the spatial pattern of a disease is the geographical representation of its cases (or some indicators of them) over a map. Maps based on raw data are generally "wrong" since they do not take into consideration for sampling errors. Indeed, the observed differences between areas (or points in the map) are not directly interpretable, as they derive from the composition of true, structural differences and of the noise deriving from the sampling process. This problem is well known in human epidemiology, and several solutions have been proposed to filter the signal from the noise. These statistical methods are usually referred to as Disease Mapping. In geographical analysis a first goal is to evaluate the statistical significance of the heterogeneity between areas (or points). If the test indicates rejection of the hypothesis of homogeneity the following task is to study the spatial pattern of the disease. The spatial variability of risk is usually decomposed into two terms: a spatially structured (clustering) and a non spatially structured (heterogeneity) one. The heterogeneity term reflects spatial variability due to intrinsic characteristics of the sampling units (e.g. igienic conditions of farms), while the clustering term models the association due to proximity between sampling units, that usually depends on ecological conditions that vary over the study area and that affect in similar way breedings that are close to each other. Hierarchical bayesian models are the main tool to make inference over the clustering and heterogeneity components. The results are based on the marginal posterior distributions of the parameters of the model, that are approximated by Monte Carlo Markov Chain methods. Different models can be defined depending on the terms that are considered, namely a model with only the clustering term, a model with only the heterogeneity term and a model where both are included. Model selection criteria based on a compromise between degree of complexity and goodness of fit are then needed to discriminate among them, because each specification has a different biological meaning. Our aim is to demonstrate that these techniques can be used to study the geographical distribution of a parasite infection. Our analyses are based on data collected in 142 farms of the province of Latina. In each breeding a fixed number of sheeps has been sampled (20) and checked for the presence of C. daubneyi. We have specified a Binomial model for the proportion of infected animals in each breeding. The heterogeneity component is modelled in a standard way, while we have used different prior specifications for the clustering term to show how they affect the results. When we use the usual specification also for clustering, the two models show a completely different spatial pattern of infection, probably because the intrinsic spatial structure of the clustering term tend to bias our inferences. The selection criterion indicates in this case the heterogeneity model as the "best" one. However, if we modify the prior so that a lower degree of spatial interaction is assumed, the clustering model is less complex and its goodness of fit better and it should be preferred.

Non-parametric maximum likelihood estimators for disease mapping

A Non-Parametric Maximum Likelihood approach to the estimation of relative risks in the context of disease mapping is discussed and a NPML approximation to conditional autoregressive models is proposed. NPML estimates have been compared to other proposed solutions (Maximum Likelihood via Monte Carlo Scoring, Hierarchical Bayesian models) using real examples. Overall, the NPML autoregressive estimates (with weighted term) were closer to the Bayesian estimates. The exchangeable NPML model ranked immediately after, even if it implied a greater shrinkage, while the truncated auto-Poisson showed inadequate for disease mapping. The coefficients of the autoregressive term for the different mixtures have clear interpretations: in the breast cancer example, the larger cities in the region showed high rates and very low correlation with the neighbouring areas, while the less populated rural areas with low rates were strongly positively correlated each other. This pattern is expected since breast cancer is strongly correlated with parity and age at first birth, and the female population of the rural areas experienced a decline in fertility much later than those living in the larger cities. The leukemia example highlighted the failure of the Poisson-Gamma model and other general overdispersion tests to detect high risk areas under specific conditions. The NPML approach in Aitkin is very general, simple and flexible. However the user should be warned against the possibility of local maxima and the difficulty in detecting the optimal number of components. Special software (such as CAMAN or DismapWin) had been developed and should be recommended mainly to not experienced users.

Mitochondrial DNA restriction enzyme analysis and evaluation of the enological characteristics of Saccharomyces cerevisiae strains isolated from grapes of the wine-producing area of Collio (Italy)

A total of 70 strains of Saccharomyces cerevisiae were isolated from different grapes from the Collio Region. Chemical parameters and mitochondrial DNA (mtDNA) restriction patterns were determined. Higher alcohols were the main parameter useful for differentiating between strains, whereas the mtDNA analysis demonstrated a high genetic variability between strains. A weak correlation was observed when the dendrograms obtained from the chemical and genetic results were compared.

[Trends in mortality from primary pleural tumor and incidence of pleural mesothelioma in Italy: a particularly serious situation]

We present: a) an analysis of the past mortality from Primary Pleural Tumors (PPT) occurred in Italy between 1968 and 1992 by an age-cohort-period model, using a Poisson regression model, estimating the risk of dying by birth cohort, the Lifetime Cumulative risk (25-84 years) by birth cohort, the risk by calendar period and testing the full model (age-cohort-period effects); b) a summary of the incidence of mesothelioma as recorded in Italy by Cancer Registries and Mesothelioma Registries. The highest Lifetime Cumulative Risk of dying from TTP is recorded for the birth cohort 1946-'50 (6.2 per thousand among males, 1.64 among females). Whereas the risk by birth cohort becomes flat among females born after 1936, among males the risk is increasing up to the youngest birth cohorts. By calendar period, the highest risk of dying is observed in the last period (1991-'92). The inclusion in the full model of the calendar period term increases significantly the goodness-of-fit of the model among females, but not among males. The highest incidence of mesothelioma in both genders registered by 150 Cancer Registries all over the world is currently recorded among the population of Genoa and Trieste, where large ship-building plants are located. Even higher incidence mesothelioma rates have been recently recorded in other areas of Italy. The trend in PPT mortality in Italy could have been influenced, but not explained, by the increased awareness over time of the disease, but it fits well with the pattern occurring in most industrialized countries of western Europe, with the unprotected industrial use of asbestos which occurred in Italy, and also with the gender characteristics of the work-force employed in asbestos-exposing activities. A ban of asbestos use has been introduced in Italy in 1992. However, considering that asbestos seems to act as an initiator for mesothelioma, the trend in male mortality for PPT will not peak until two or three decades.

Anthropometric features in infants of mothers with gestational diabetes: relationship with treatment modalities

We compared the effects of two treatment strategies (diet alone versus a combination of insulin and diet) on neonatal anthropometric measurements and the outcome of a full-term white infant sample born to mothers with gestational diabetes mellitus (GDM). Study subjects included 217 consecutive nonrandomized women with GDM with term singleton pregnancies. Insulin therapy was administered on the basis of anamnestic and maternal-fetal criteria. One hundred and twenty-one patients (group 1) received a combination of insulin and diet, and 96 (group 2) underwent diet alone. All 1,052 white patients with term singleton pregnancies without GDM, screened at the same time as the study group, formed the control group. The incidence of large-for-gestational-age (LGA) infants was significantly higher in group 2 (18.8%) compared with group 1 (9.9%) and the control group (8.3%). Male and female infants born to diabetic mothers of group 2 had significantly greater mean birth weights, ponderal indices, thoracic circumferences, weight/length ratios and significantly smaller mean cranial/thoracic circumference ratios than male and female infants in group 1 and the control group (p < 0.05, Scheffe test). Treatment of GDM mothers with insulin and diet has been shown to be able not only to normalize the incidence of LGA infants but also to influence the anthropometric characteristics of the infants born to these mothers to such an extent that they showed no significant differences compared to infants born to non-diabetic mothers.

Risk factors for fetal macrosomia: the importance of a positive oral glucose challenge test

OBJECTIVE

The aim of this study was to investigate whether minor abnormalities of glucose metabolism without gestational diabetes are a risk factor for fetal overgrowth.

DESIGN

A sample of 1883 unselected white mother-infant pairs were screened for gestational diabetes using a 50 g 1-h oral glucose challenge test (GCT) in two periods of pregnancy: early (16-20 weeks) and late (26-30 weeks).

METHODS

The effects of risk factors (glucose metabolism, previous history of mothers, obesity, multiparity and age of mothers) were estimated using a multinomial logit model.

RESULTS

The level of risk was related to gestational age at the appearance of an abnormal GCT. Patients with an abnormal GCT in the early and late periods of pregnancy (Group 1) had a risk of delivering a large for gestational age (LGA) infant seven times higher than the control group (normal GCT in both periods), and patients with a normal GCT in the early period and an abnormal GCT in the late period (Group 2) showed a risk three times higher than the control group. Among the historical risk factors for LGA infants, such as maternal obesity, multiparity, previous gestational diabetes and previous delivery of an infant weighing 4000 g or more, only the latter was associated with fetal overgrowth with a risk level 4.7 higher than the control group. Group 1 patients had a significantly higher incidence of pregnancy-induced hypertension and preterm birth. There were no differences in the frequency of 5-min Apgar score < 7 and metabolic complications among the infants of all groups. We found a significantly higher rate of shoulder dystocia in Group 1 infants than in infants in the other groups.

CONCLUSIONS

Our results suggest that a positive GCT at 26-30 weeks is the most important risk factor for fetal overgrowth. This result was strongly enforced in patients who had also shown a positive early GCT at 16-20 weeks.

Anthropometric characteristics of full-term infants: effects of varying degrees of "normal" glucose metabolism

Aim of this study was to examine the maternal-neonatal outcome and the neonatal anthropometric characteristics of a full-term mother-infant pairs group with a positive oral glucose challenge test (GCT) without gestational diabetes mellitus (GDM). Our study involved 1615 white women with singleton pregnancies who underwent universal screening for GDM in two periods of pregnancy. This population was divided into three groups according to GCT results: 1) 172 patients with abnormal GCT in both periods; 2) 391 patient with normal GCT in the early period and abnormal GCT in the late period; 3) 1052 patients with normal GCT in both periods (control group). The incidence of LGA (large for gestational age) infants was higher in Group (40.7%) and Group 2 (22.0%) respect to control group (8.3%) (p < 0.00001 and p < 0.0001 respectively) and was significantly different in the two groups (p < 0.0008). Comparison among the three groups of LGA infants showed the following results: male and female newborns of Group I were heavier than those of Group 2 and of the control group, while males and females of the control group had significantly greater length and cranial circumference means. A significant decrease in ponderal index, choracic circumference, weight/length ratio means could be seen as well as a significative increase in cranial/thoracic circumference ratio means from Group I to the control group. These data confirm the involvement of fetal development in terms of weight and anthropometric characteristics in the presence of alterations in maternal glucose metabolism which are not currently classified as gestational diabetes.

Air pollution and lung cancer in Trieste, Italy: spatial analysis of risk as a function of distance from sources

To investigate the relationship between four sources of environmental pollution (shipyard, iron foundry, incinerator, and city center) and lung cancer risk, we conducted a case-control study of decreased men in Trieste, Italy. We identified 755 cases of lung cancer and 755 controls through the local autopsy registry. Information on smoking habits, occupational history, and place of residence were obtained from the subject's next of kin. The case-control design was used to properly account for subject-specific confounders, which represent a major problem in geographical analysis. Spatial models were used to evaluate the effect of sources of pollution on lung cancer after adjustment for age, smoking habits, likelihood of exposure to occupational carcinogens, and levels of air particulate. The models are based on distance from the sources and enable estimation of the risk gradient and directional effects separately for each source. The risk of lung cancer was highly related to the city center (p = 0.0243), with an excess relative risk at zero distance of 2.2 and a smooth decrease moving away from the source (-0.015), and related to the incinerator (p = 0.0098), with an excess relative risk of 6.7 in the source and a very steep decrease (-0.176). These results are consistent with findings of previous analyses and provide further evidence that air pollution is a moderate risk factor of lung cancer.

Exploratory tools for disease mapping in geographical epidemiology

In the present paper a new shrinkage estimator of relative risk, useful in disease mapping, is compared with the empirical and full Bayes estimators, using death certificate data for lung cancer (males and females) and breast cancer (females) 1982-1988 in the Emilia-Romagna region (Italy). The estimates are obtained averaging the relative risks obtained from recursive partitioning of the set of observed values. The number of partitions is fixed in advance and behaves as a smoothing parameter, the lower the number the higher the degree of shrinkage toward the overall mean of the observed values.

[Case-control studies to assess relative risk in proximity of putative sources of environmental pollution]

This paper discusses the analysis of the disease risk as function of distance from prespecified exposure sources. Instead of the usual ecological framework, the proposed model is based on individual data and includes the major individual risks factors as covariates. The risk due to environmental exposure is modelled using excess relative risk as effect measure, while the effect of individual risk factors is modelled using relative risk. A case-control design is proposed to fit this model and to obtain effect estimates.

[Comparison of epidemiologic methods in a case-control study of lung cancer and air pollution in Trieste, Italy]

To investigate the relationship between air pollution and histologic type of lung cancer we conducted a case-control study among deceased men in Trieste, Italy. 755 cases of lung cancer and 755 controls were identified through the local autopsy registry. Information on smoking habits, occupation and place of residence was obtained from the subject's next-of-kin. Air pollution at the residence of each subject was estimated from the average value of total particulate at the nearest monitoring station. Spatial models and logistic regression were used to evaluate the effect of residence and air pollution on LC after adjustment for age, smoking habits, likelihood of exposure to occupational carcinogens and social group. The risk of lung cancer increased with increasing level of air pollution for all types of lung cancer combined (P = 0.022), for small cell carcinoma (P = 0.016) and for large cell carcinoma (P = 0.049). Compared with inhabitants of the residential area, residents of the rural area had a relative risk (RR) of 0.6 (95% confidence interval (CI): 0.4-1.0). The RR was 1.5 (1.0-2.2) for residents of the center of the city and 1.4 (1.0-2.1) for residents of the industrial area. In the center of the city the excess risk was almost completely restricted to small cell carcinoma (RR = 2.0) and to large cell carcinoma (RR = 2.6). In the industrial area the risk was increased especially for adenocarcinoma (RR = 2.1). These results provide evidence that air pollution is a moderate risk factor for certain histologic types of lung cancer.

[The assessment of disease risk in the proximity of a source of environmental pollution: a review]

An increasingly common public health problem is the perception that disease incidence has increased or a cluster of disease has occurred in a community. In most cases, a local factor (e.g. the location of a nuclear installation) is hypothesized to be associated with the disease. The paper presents a review of the main statistical methods used to assess disease risk near a putative source of environmental pollution. Both techniques based on counts in area units (e.g. administrative units) and case-control studies are considered.