Organic diets and physical activity: Research experience using a rat model

Total energy expenditure or heat production is comprised of basal metabolic rate, thermic effect of food, and physical activity. Physical activity is the most variable and easily altered component of total heat production. Physical activity is influenced by a number of biological parameters i.e. diet, genetics, age, and gender. The diet components of the macro as well as micronutrients and other components (secondary metabolites) associated with a diet could contribute to the well being of the animal and cause variation in physical activity. In order to investigate physical activity as a parameter to differentiate diets based on conventional or organic grown carrots the total heat production and physical activity was measured in a rat model. The experimental diets were formulated to meet the NRC requirements for rats by mixing 40% of freeze dried carrots with an Altromin chow diet. The carrots were from a 2-year field study. The carrots were grown by three different cultivation strategies: one conventional (C) and two organic systems (OA, organic using animal manure; and OB, organic using cover crops). The diets were given to weaned female GKMol rats, in groups of five rats per diet and given their assigned diet, for approx. 2.5 months. Throughout the experimental period the rats were monitored and weighed each week. The rat’s heat production and physical activity was measured with two open-air circuit respiration chambers, and measurement was done on a group of 5 rats. In the chamber the rats were placed in individual cages. The activity was measured using both passive infrared detectors and with video recording. There was not surprisingly lower physical activity level of rats during the day. Being night-active animals, rats are usually resting during the day-time

The R Package geepack for Generalized Estimating Equations

This paper describes the core features of the R package geepack, which implements the generalized estimating equations (GEE) approach for fitting marginal generalized linear models to clustered data. Clustered data arise in many applications such as longitudinal data and repeated measures. The GEE approach focuses on models for the mean of the correlated observations within clusters without fully specifying the joint distribution of the observations. It has been widely used in statistical practice. This paper illustrates the application of the GEE approach with geepack through an example of clustered binary data.

Preference of organic grown carrorts in a rat model

Food preference tests represent an approach in food quality research, taking advantage of the instinctive feeding behavior of animals by allowing them to choose between food samples. A great number of investigations using laboratory rats concerning essential and/or dangerous contents are based on this method and have shown its effectiveness. The selection of food is influenced to some degree by smell and taste, but mostly by wholesomeness and need. In the present study a preference test was used in order to test eventual selective differences among rats with regard to same composition, but cultivated in three different systems. The experimental diets were formulated to meet the NRC requirements for rats by mixing 40% of freeze dried carrots with an Altromin chow diet. The carrots were from a 2-year field study. The carrots were grown by three different cultivation strategies: one conventional (C) and two organic systems (OA, organic using animal manure; and OB, organic using cover crops). Wistar female rats (N=30, 15 per year) weighing 230 g, were kept individually in cages, and were arranged in a block design with three blocks of 5 rats. For 5 days, each rat had free access to each of the three kinds of diets (C, OA, OB), and consumption of feed from each of the diet-troughs was measured every day. Thereafter, the rats were offered an ordinary rat chow until the preference test was repeated 2 weeks later. Preferences expressed by daily food-intake were analyzed statistically taking into account the correlations of the choices of a rat per experimental day, and over the course of the experiment. The overall conclusion of the study is that rats show individual preference for the test diets, and that no clear difference among the dietary treatments could be obtained

USE OF A RAT MODEL TO ELUDICATE IMPACT OF ORGANIC FOOD ON HEALTH

Ingredients (potatoes, carrots, peas, green kale, apples, and rapeseed oil) were grown according to three different cultivation systems (“Organic”, low input of fertilizer without pesticides; “Minimally fertil-ised”, low input of fertilizer and high input of pesti-cides, “Conventional”, high input of fertilizer and high input of pesticides). Three iso-energetic and iso-nitrogeneous diets were composed of equal propor-tions of the ingredients originating from each of the cultivation systems, and the diets were investigated with respect to several physiological responses and biomarkers of health using a rat model. In addition, the diets were tested in a food preference test using the same type of rats, but another generation. In both experiments, the diets were optimized according to the nutritional requirements of reproducing rats, except for a high content of fat. Most of the measured variables (biomarkers of health) showed no differ-ences between the experimental diets, however, some differences between dietary treatments were obtained, which were in favour of the “organic” diet contrasted with the “conventional” diet. The prefer-ence test showed a significant interaction between diet choice and mother’s diet. However, the results obtained from the present study cannot be extrapo-lated to all organic and conventional cropping sys-tems, mainly because crops were grown only in one replication. Thus, it is of outmost importance that future investigations on the effect of organic food in relation to human health and well-being should be based on well-defined and controlled food produce system with replication

Rats show individual preference for short-term choice of three human diets

A preference test was conducted to investigate whether rats could distinguish among three iso-energetic and iso-nitrogeneous human diets prepared with ingredients cultivated by each of three different farming strategies: LIminusP: low input of feritlizer without pesticides, LIplusP: lowinput of fertilicer and high input of pesticides and LHplusP: high input of fertilizer and high input of pesticides. The experimental diets were formulated to meet the NRC requirements for growing rats by mixing potatoes, carrots, peas, green kales, apples, and rapeseed oil. For five days, rats (N=27) had free access to each of the three diets, and consumption of each of the diets was recorded daily. Thereafter, rats were offered a standard laboratory chow until the test was repeated. The results indicated that the majority of the rats showed individual preference for the diets and behaved similarly on different experimental days (ρ = 0.63 in repetition 1 and ρ = 0.73 in repetition 2) and in the two repetitions (τ = 0.79). However, no clear difference among the dietary treatments could be obtained

OrgTrace – No Difference in Levels of Bioactive Compounds found in Crops from Selected Organic and Conventional Cultivation Systems

The objective of the present study was to compare the content of selected bioactive compounds in organically and conventionally grown crops, and to evaluate if the ability of the crops to synthesize selected secondary metabolites was systematically affected by growth systems across different growth years as well as soil types. The results showed that contents of neither polyacetylenes and carotenoids in carrots, flavonoids in onions, nor phenolic acids in carrots and potatoes were significantly influenced by growth system. Thus it could not be concluded that the organically grown crops had higher contents of bioactive compounds than the conventionally grown. This indicates that giving preference to organic products because they contain more bioactive components is doubtfull. However, there are many other reasons for the consumer to choose organic food products, including: no pesticide residues in foods, animal welfare, and environmental protection

Organic food and health - status and perspectives

In a recent study it was investigated, through a well-controlled animal feeding experiment, whether conventional and organic food products showed differences in animal physiology of a type and magnitude that could indicate that organic products would affect humans differently. The primary, but still tentative conclusion from this study was that the most significant effects on rats was observed on health aspects that have rarely been assessed in prior studies: Immune status, sleep/activity pattern, accumulation of adipose tissue, liver function, and vitamin E status, while a large numbers of markers of “traditional” nutritional value showed no differences. Although the results of the present study could not directly be applied to organic and conventional production systems the observed differences were all in favour of the organic treatment, and thus pointed in the direction of potential health benefits when eating organically grown rather than conventionally grown food. However, this study like other studies related to the issue suffers from the fact that only one replication per food produce was used in the animal studies. Therefore the size of the effects could not be evaluated with respect to replication variation, which could have been determined by establishment of field trials. In addition, it was not possible to correlate the responses of the animals to the analysed diet composition due to the limited number of replicates, whereby the explanations of the effects were limited. Thus, it is of outmost concern that future investigations on the effect of organic food in relation to human health and well-being should be based on well-defined and controlled food produce system with replications

Which Tumor Suppressor Gene Plays an Important Role in Oncogenesis of Oral Cancer?

口腔領域癌の遺伝子学的解析はあまり行われていないため, 本稿では当科における癌抑制遺伝子異常の検索結果を用いて, 口腔癌においてどの遺伝子が重要な役割を果たしているのかを検討した。試料は, 口腔扁平上皮癌組織と, 同一症例の正常組織を用いた。RB遺伝子とWT遺伝子についてはサザンブロッティング法により, また, APC, P53, VHL, p15, p16, p18遺伝子についてはPCR-SSCP法および塩基配列決定法を用いて検索した。さらに, APC遺伝子についてはエクソン11のポリモルフィズムを利用したPCR-LOH分析をあわせて行った。以下に示す結果を得た。(1)APC遺伝子の点突然変異は8.3%, APC-LOHは informative 症例中の72.7%に認められた。(2)p16遺伝子とp18遺伝子の点突然変異はそれぞれ4.0%, 3.8%に認められた。p15遺伝子異常は認められなかった。(3)p53心遺伝子異常は7.7%に認められた。(4)WT遺伝子の部分欠失を6.5%に認めた。(5)RB遺伝子とVHL遺伝子の異常は認められなかった。以上の結果から, 種々の癌抑制遺伝子のうち, 特にAPC遺伝子が口腔扁平上皮癌発生に重要な役割を果たしていることが示唆された。Since there are few molecular biological studies on oral cancer, we evaluated genes which would play an important role in the oncogenesis of oral cancer based on our previous studies. To elucidate whether or not abnormalities of RB and WT genes could contribute to the development of oral squamous-cell carcinoma (SCC), genomic DNAs obtained from tumors and corresponding normal tissues were examined with Southern blotting. On the other hand, PCR-SSCP (polymerase chain reaction single-strand conformation polymorphism) and sequence analyses were performed to detect possible mutations of APC, VHL, p15, p16, p18 and p53. We also examined the tissue samples to detect loss of heterozygosity (LOH) at exon 11 of APC gene by PCR-LOH assay. We obtained the following results: (1) Point mutations of APC gene in tumor tissues were identified in 8.3(nil)atients with oral SCC, and PCR-LOH assay revealed LOH at exon 11 of APC gene in 72.71024720030f informative cases. (2) Point mutations of p16 and p18 genes were detected in 4.0-0x1.db55b085084p-148nd 3.80f oral SCC patients, respectively. However, no mutation of p15 gene was found. (3) Abnormalities of p53 gene were detected in 7.726725462457f all cases. (4) Southern blotting revealed partial deletions and rearrangements of WT gene in 6.50f the cases. (5) No abnormality of VHL and RB genes was detected in any of the cases. These results suggest that APC gene is most significantly associated with the development of human oral SCC among the known tumor suppressor genes

How to assess intra- and inter-observer agreement with quantitative PET using variance component analysis:a proposal for standardisation

BACKGROUND: Quantitative measurement procedures need to be accurate and precise to justify their clinical use. Precision reflects deviation of groups of measurement from another, often expressed as proportions of agreement, standard errors of measurement, coefficients of variation, or the Bland-Altman plot. We suggest variance component analysis (VCA) to estimate the influence of errors due to single elements of a PET scan (scanner, time point, observer, etc.) to express the composite uncertainty of repeated measurements and obtain relevant repeatability coefficients (RCs) which have a unique relation to Bland-Altman plots. Here, we present this approach for assessment of intra- and inter-observer variation with PET/CT exemplified with data from two clinical studies. METHODS: In study 1, 30 patients were scanned pre-operatively for the assessment of ovarian cancer, and their scans were assessed twice by the same observer to study intra-observer agreement. In study 2, 14 patients with glioma were scanned up to five times. Resulting 49 scans were assessed by three observers to examine inter-observer agreement. Outcome variables were SUVmax in study 1 and cerebral total hemispheric glycolysis (THG) in study 2. RESULTS: In study 1, we found a RC of 2.46 equalling half the width of the Bland-Altman limits of agreement. In study 2, the RC for identical conditions (same scanner, patient, time point, and observer) was 2392; allowing for different scanners increased the RC to 2543. Inter-observer differences were negligible compared to differences owing to other factors; between observer 1 and 2: −10 (95 % CI: −352 to 332) and between observer 1 vs 3: 28 (95 % CI: −313 to 370). CONCLUSIONS: VCA is an appealing approach for weighing different sources of variation against each other, summarised as RCs. The involved linear mixed effects models require carefully considered sample sizes to account for the challenge of sufficiently accurately estimating variance components. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12880-016-0159-3) contains supplementary material, which is available to authorized users