Control of growth and differentiation of the mammary gland by growth factors

Mammary gland development is a complex process regulated by various steroid and polypeptide hormones. Although the systemic importance of hormones, such as estrogen, progesterone, prolactin, and growth hormone, in the growth of the mammary gland has been well documented, these hormones are virtually incapable of stimulating mammary cell growth in vitro. Thus, the growth stimuli for mammary cells requires clarification. In recent years, a number of growth factors have been discovered; this has prompted interest in examining possible involvement of these biologically active agents in the process of mammary gland development. Results presented herein indicate that several growth factors including epidermal growth factor, transforming growth factor alpha and beta, basic fibroblast growth factor, insulin-like growth factor-I, and mesenchyme-derived growth factor participate in the regulation of mammary cell growth and differentiation.     

Quantifying the hurdle concept by modelling the bacterial growth/no growth interface

The hurdle concept described eloquently over many years by Professor Leistner and his colleagues draws attention to the interaction of factors that affect microbial behaviour in foods. Under some circumstances these effects are additive. Under others the implication is that synergistic interactions lead to a combined effect of greater magnitude than the sum of constraints applied individually. Predictive modelling studies on the combined effects of temperature and water activity and temperature and pH suggest that the effect of these combinations on growth rate is independent. Where the effect of the two factors is interactive rather than independent is at the point where growth ceases--the growth/no growth interface. An interesting and consistent observation is that a very sharp cut off occurs between conditions permitting growth and those preventing growth, allowing those combinations of factors to be defined precisely and modelled. Growth/no growth interface models quantify the effects of various hurdles on the probability of growth and define combinations at which the growth rate is zero or the lag time infinite. Increasing the stringency of one or more hurdles at the interface by only a small amount will significantly decrease the probability of an organism growing. Understanding physiological processes occurring near the growth/no growth interface and changes induced by moving from one side of the interface to the other may well provide insights that can be exploited in a new generation of food preservation techniques with minimal impact on product quality.     

The boundary for growth of Zygosaccharomyces bailii in acidified products described by models for time to growth and probability of growth

Models to predict days to growth and probability of growth of Zygosaccharomyces bailii in high-acid foods were developed, and the equations are presented here. The models were constructed from measurements of growth of Z. bailii using automated turbidimetry over a 29-day period at various pH, NaCl, fructose, and acetic acid levels. Statistical analyses were carried out using Statistical Analysis Systems LIFEREG procedures, and the data were fitted to log-logistic models. Model 1 predicts days to growth based on two factors, combined molar concentration of salt plus sugar and undissociated acetic acid. This model allows a growth/no-growth boundary to be visualized. The boundary is comparable with that established by G. Tuynenburg Muys (Process Biochem. 6:25-28, 1971), which still forms the basis of industry assumptions about the stability of acidic foods. Model 2 predicts days to growth based on the four independent factors of salt, sugar, acetic acid, and pH levels and is, therefore, much more useful for product development. Validation data derived from challenge studies in retail products from the U.S. market are presented for Model 2, showing that the model gives reliable, fail-safe predictions and is suitable for use in predicting growth responses of Z. bailii in high-acid foods. Model 3 predicts probability of growth of Z. bailii in 29 days. This model is most useful for spoilage risk assessment. All three models showed good agreement between predictions and observed values for the underlying data.     

Serum growth hormone concentrations after growth hormone or thyrotropin releasing hormone in cows.

A single subcutaneous injection of 10, 50, or 100 mg bovine growth hormone into lactating Holstein cows increased concentrations of growth hormone, insulin, and glucose in serum above preinjection baselines for at least 16 h. Growth hormone concentrations in serum after injection of growth hormone or thyrotropin-releasing hormone were greater in cows during early (2 to 4 mo) as compared with cows during late (8 to 12 mo) lactation. Stage of lactation did not affect rate of metabolic clearance of growth hormone in eight cows, but larger cows cleared growth hormone more rapidly than smaller cows. Data of these experiments may help to explain how injections of growth hormone every 3 days stimulate milk yields.     

Muscle growth and exercise

This paper first reviews muscle growth and then considers the influence of exercise in growth. Knowledge about how muscle cells grow and some factors that may influence the growth pattern are discussed first since these effects must be considered before the influence of exercise becomes clear. Growth of muscle can occur in three ways: (1) by an increase in muscle cell numbers, (2) by an increase in muscle fiber diameter, and (3) by an increase in fiber length. All three of these mechanisms are involved in muscle growth. However, growth in cell numbers is limited to the prenatal and immediately postnatal period, with the animals and man being born with or soon reaching their full complement of muscle cells. Thus, growth occurs by either hypertrophy of the existing muscle fibers by adding additional myofibrils to increase the muscle mass or by adding new sarcomeres to the ends of the existing muscle fibers to increase their length. Both of these mechanisms occur during the growth process. Growth in the girth of the muscle fibers appears to take place by splitting of the myofibrils. This may be stimulated by development of stress creating an unequal pressure with splitting at the Z-band and development of additional SR and T-tubule systems. This adds to the diameter or girth of myofibers without any hyperplasia. The growth in length occurs at either end of the fibers and results in addition of new sarcomeres. In both cases, new myofibrillar protein must be synthesized and deposited in the muscle cells. It is suggested that adaptation by adding or removing sarcomeres is physiologically determined by the degree of force a muscle can generate that is in turn dependent on the degree of overlap of the thick and thin filaments. Thus, the amount of tension would control the number of in-series sarcomeres in a single muscle fiber. Nutrition is also known to play an important role in muscle and was discussed from the standpoint of the effects of nutritional adequacy and restriction. Although a nutritionally balanced and calorically adequate diet is required to achieve optimum muscle growth, it may be less efficient in terms of protein deposition than a moderately restricted diet. Muscle and bone deposition, however, can be limited on severely restricting the dietary intake. Although fat deposition is the first tissue to suffer on a severely restricted diet, muscle and bone follow next with the nervous system, brain and eyes being the last systems to be affected.(ABSTRACT TRUNCATED AT 400 WORDS)     

A simplified approach for modelling the bacterial growth/no growth boundary

A simplified growth/no growth (G/NG) model, conceptually derived from the Gamma model and making direct and explicit use of growth limits of bacteria through a normalization constant (η), was proposed. The η value, which quantifies the product of the cardinal optimal distances for growth probability, is a species-independent constant. This is of importance when experimental data is missing or insufficient. The simplified G/NG model was developed including the effect of temperature, pH and water activity, and was expanded incorporating the preservative effects. As a practical application, the model was investigated for its ability to describe published data. The successful validation of the simplified G/NG model is discussed in regard to its potential applicability as a first estimate method for the development of safe food products.     

Genetic parameters of growth in dairy cattle and associations between growth and health traits

Body weight (BW) observations on dairy cattle taken on average 35 times between birth and 1,000 d of life were used to estimate daily heritabilities and predict daily breeding values for both pregnancy-adjusted BW (PABW) and growth rate. Daily heritabilities for PABW were moderate to high, ranging from 0.41 (±0.027) to 0.82 (±0.041). Daily heritabilities for growth rate were high (>0.68 ± 0.034). The genetic association between various health events, including mastitis and lameness, and weight and growth was investigated by regressing the incidence of health events on breeding values for weight at birth, weaning, calving, and growth rate at 56 d after calving, growth rate at 110 d after calving, and maximum growth rate. Growth at weaning was the only BW measure to significantly affect mastitis (r_g = 0.24), indicating that cows growing faster at weaning are more prone to mastitis. Increased weight (r_g = 0.65) and growth rate at weaning (r_g = 0.38) and increased maximum growth rate (r_g = 0.71) all contributed to increased feet disorders. The only significant negative genetic association was obtained between reproduction and weight at calving (r_g = −0.61).     

Selection of microalgal growth model for describing specific growth rate-light response using extended information criterion

The effects of light intensity and carbon dioxide (CO2) concentration on the growth of the highly CO2-tolerant green alga Chlorococcum littorale were studied in batch cultures. Four mathematical representations were compared for the specific growth rate-light response curve: a rectangular hyperbolic function, Steele's exponential function, a Poisson function and a hyperbolic tangent function. The hyperbolic tangent function, which is commonly used for representing the photosynthesis-light relationship, gave the best fit as evaluated by the extended information criterion (EIC). EIC proved to be applicable as a criterion to this kind of nonlinear model selection problem. Carbon dioxide, the sole carbon source for photoautotrophic growth of this alga, inhibited the growth rate at concentrations higher than pCO2 of 0.02. A substrate inhibition model was successfully used to simulate the relationship between the specific growth rate and CO2 response.     

Development of a multi-classification neural network model to determine the microbial growth/no growth interface

Boundary models have been recognized as useful tools to predict the ability of microorganisms to grow at limiting conditions. However, at these conditions, microbial behaviour can vary, being difficult to distinguish between growth or no growth. In this paper, the data from the study of Valero et al. [Valero, A., Pérez-Rodríguez, F., Carrasco, E., Fuentes-Alventosa, J.M., García-Gimeno, R.M., Zurera, G., 2009. Modelling the growth boundaries of Staphylococcus aureus: Effect of temperature, pH and water activity. International Journal of Food Microbiology 133 (1-2), 186-194] belonging to growth/no growth conditions of Staphylococcus aureus against temperature, pH and a_w were divided into three categorical classes: growth (G), growth transition (GT) and no growth (NG). Subsequently, they were modelled by using a Radial Basis Function Neural Network (RBFNN) in order to create a multi-classification model that was able to predict the probability of belonging at one of the three mentioned classes. The model was developed through an over sampling procedure using a memetic algorithm (MA) in order to balance in part the size of the classes and to improve the accuracy of the classifier. The multi-classification model, named Smote Memetic Radial Basis Function (SMRBF) provided a quite good adjustment to data observed, being able to correctly classify the 86.30% of training data and the 82.26% of generalization data for the three observed classes in the best model. Besides, the high number of replicates per condition tested (n = 30) produced a smooth transition between growth and no growth. At the most stringent conditions, the probability of belonging to class GT was higher, thus justifying the inclusion of the class in the new model. The SMRBF model presented in this study can be used to better define microbial growth/no growth interface and the variability associated to these conditions so as to apply this knowledge to a food safety in a decision-making process.     

Estimating the maximum growth rate from microbial growth curves: definition is everything

The maximum growth rate (μ_max) is an important parameter in modelling microbial growth under batch conditions. However, there are two definitions of this growth parameter in current use and some of the comparisons of data made in the literature fail to acknowledge this important fact.We compared values of μ_max obtained by applying the Gompertz, logistic and Baranyi–Roberts models to experimental data on the growth of Listeria monocytogenes and Listeria innocua using both absorbance and viable counts measurements of cell concentration. All three models fitted the experimental data well, however, the values of μ_max obtained using the Gompertz and logistic models were similar to each other but substantially different from those predicted by the Baranyi–Roberts model. The latter growth model was used to derive a second estimate of μ_max based on the slope at the inflection point of the growth curve function; this value was in closer agreement with those obtained using the Gompertz or logistic models. Conditions were identified when values of μ_max based on different definitions would converge towards one another.     

人造金刚石晶体的生长速率

文章讨论了影响人造金刚石生长速率的因素,指出生长速率低是生产大颗粒人造金刚石单晶的瓶颈。作者预测：在保证金刚石晶体质量的同时,提高其生长速率的可能性是存在的。     

蜡样芽胞杆菌生长/非生长界面模型的建立和评价

蜡样芽胞杆菌是软烤虾仁产品的主要变质菌,它是一种条件致病菌,通过产生腹泻毒素和呕吐毒素导致食物中毒。该研究旨在建立一种概率模型来预测出蜡样芽胞杆菌的生长/非生长情况或者生长概率。用lo-gistic回归模型建立不同温度、水分活度和pH环境因子作用下蜡样芽胞杆菌的生长/非生长界面模型。实验结果表明蜡样芽胞杆菌在脑心浸液肉汤培养基中生长的最低温度为9.99℃,最低水分活度为0.931,最小pH值为4.5。在此基础上建立的蜡样芽胞杆菌生长/非生长界面模型的χ2=49.73,P<0.000 1。用logistic回归模型建立的生长/非生长模型拟合效果达到极显著水平。模型的预测值同时很好地量化了环境因子对蜡样芽胞杆菌的协同作用,为软烤虾仁产品中蜡样芽胞杆菌的生长/非生长界面模型的建立提供了参考。     

Exploring the performance of logistic regression model types on growth/no growth data of Listeria monocytogenes

Several model types have already been developed to describe the boundary between growth and no growth conditions. In this article two types were thoroughly studied and compared, namely (i) the ordinary (linear) logistic regression model, i.e., with a polynomial on the right-hand side of the model equation (type I) and (ii) the (nonlinear) logistic regression model derived from a square root-type kinetic model (type II). The examination was carried out on the basis of the data described in Vermeulen et al. [Vermeulen, A., Gysemans, K.P.M., Bernaerts, K., Geeraerd, A.H., Van Impe, J.F., Debevere, J., Devlieghere, F., 2006-this issue. Influence of pH, water activity and acetic acid concentration on Listeria monocytogenes at 7 degrees C: data collection for the development of a growth/no growth model. International Journal of Food Microbiology. .]. These data sets consist of growth/no growth data for Listeria monocytogenes as a function of water activity (0.960-0.990), pH (5.0-6.0) and acetic acid percentage (0-0.8% (w/w)), both for a monoculture and a mixed strain culture. Numerous replicates, namely twenty, were performed at closely spaced conditions. In this way detailed information was obtained about the position of the interface and the transition zone between growth and no growth. The main questions investigated were (i) which model type performs best on the monoculture and the mixed strain data, (ii) are there differences between the growth/no growth interfaces of monocultures and mixed strain cultures, (iii) which parameter estimation approach works best for the type II models, and (iv) how sensitive is the performance of these models to the values of their nonlinear-appearing parameters. The results showed that both type I and II models performed well on the monoculture data with respect to goodness-of-fit and predictive power. The type I models were, however, more sensitive to anomalous data points. The situation was different for the mixed strain culture. In that case, the type II models could not describe the curvature in the growth/no growth interface which was reversed to the typical curvatures found for monocultures. This unusual curvature may originate from the fact that (i) an interface of a mixed strain culture can result from the superposition of the interfaces of the individual strains, or that (ii) only a narrow range of the growth/no growth interface was studied (the local trend can be different from the trend over a wider range). It was also observed that the best type II models were obtained with the flexible nonlinear logistic regression, although reasonably good models were obtained with the less flexible linear logistic regression with the nonlinear-appearing parameters fixed at experimentally determined values. Finally, it was found that for some of the nonlinear-appearing parameters, deviations from their experimentally determined values did not influence the model fit. This was probably caused by the fact that only a limited part of the growth/no growth interface was studied.     

Modelling of growth, growth/no-growth interface and nonthermal inactivation areas of Listeria in foods

Growth, growth boundary and inactivation models have been extensively developed in predictive microbiology and are commonly applied in food research nowadays. Few studies though report the development of models which encompass all three areas together. A tiered modelling approach, based on the Gamma hypothesis, is proposed here to predict the behaviour of Listeria.Datasets of Listeria spp. behaviour in laboratory media, meat, dairy, seafood products and vegetables were collected from literature, unpublished sources and from the databases ComBase and Sym'Previus. The explanatory factors were temperature, pH, water activity, lactic and sorbic acids. For the growth part, 697 growth kinetic datasets were fitted. The estimated growth rates and 2021 additional growth primary datasets were used to fit the secondary growth models. In a second step, the fitted model was used to predict the growth/no-growth boundary. For the inactivation modelling phase, 535 inactivation curves were used.Gamma models with and without interactions between the explanatory factors were used for the growth and boundary models. The correct prediction percentage (predicted growth when growth is observed + predicted inactivation when inactivation is observed) varied from 62% to 81% for the models without interactions, and from 85% to 87% for the models with interactions. The median error for the predicted population size was less than 0.34 log₁₀(CFU/mL) for all models. The kinetics of inactivation were fitted with modified Weibull primary models and the estimated bacterial resistance was then modelled as a function of the explanatory factors. The error for the predicted microbial population size was less than 0.71 log₁₀(CFU/mL) with a median value of less than 0.21 for all foods.The model enables the quantification of the increase or decrease in the bacterial population for a given formulation or storage condition. It might also be used to optimise a food formulation or storage condition in the case of a targeted increase or decrease of the bacterial population.     

中国食品工业发展迅猛——“新一代调味品现状与发展国际研讨会”发言摘要

<正> 食品工业与农业和其他工业联系十分密切,与人民生活更是密不可分。把食品工业说成是朝阳工业,并不过分。近年来,我国食品工业得到较大的发展,市场上的加工食品品种繁多,供应充分,这是有目共睹的。     

Effect of synthetic human pancreatic growth hormone-releasing factors on plasma growth hormone concentrations in lactating cows

Two fragments of human pancreatic growth hormone-releasing factor, hpGRF-(1-24)-NH2 and hpGRF(1-29)-NH2, were administered to lactating dairy cows in separate experiments. Each experiment involved a 4 X 4 Latin square with treatments being intravenous administration of saline (controls) or three doses of releasing factor (500, 1000, and 2000 micrograms). Administration of the 24-amino acid fragment increased plasma growth hormone concentration from baseline 3 ng/ml to peak 14 ng/ml within 10 min. Growth hormone concentrations returned to baseline by 60 min post treatment. The 29-amino acid fragment resulted in greater responses in both peak concentration (30 ng/ml) and persistency (greater than 240 min). Relationships between dose of releasing factor and response in growth hormone release (as measured by area under the response curve) were not consistent in most cows. Responses of concentrations of growth hormone in blood plasma that result from intravenous injection of up to 2000 micrograms of growth hormone-releasing factor (1-29) would not be sufficient to increase milk yield of cows.     

Effect of growth phase on the subsequent growth kinetics of psychrotrophic bacteria of raw milk origin

Psychrotrophs isolated at days 1, 3 and 5 from refrigerated raw milk were monitored for growth kinetics using conductance when they had been inoculated during their lag, log and stationary growth phases. The results indicated that there was a more rapid change in the microflora during the initial stages of storage when the lag phases of the 1, 3 and 5 day isolates were analysed. There was also evidence of the 'suicide response'. In addition, the fact that the stationary phase cells showed the greatest exponential growth rate may be a contributory factor to their selective advantage.     

Factors affecting growth factor activity in goat milk

Growth factors that are present in goat milk may be responsible for its beneficial effects on the digestive system as described in ancient Chinese medical texts. To develop a nutraceutical product rich in growth factors for promoting gastrointestinal health, it is essential to collect milk with consistently high growth factor activity. Therefore, we investigated the factors affecting growth factor activity in goat milk. Among the 5 breeds of dairy goats tested, milk from Nubian goats had the highest growth factor activity. Tight-junction leakage induced by a 24-h milking interval did not increase growth factor activity in the milk. Milk collected from pregnant does had a significantly higher growth factor activity than milk collected postpartum. Growth factor activity decreased during the first 8 wk of lactation, fluctuated thereafter, and then increased dramatically after natural mating. During wk 1 to 8, growth factor activity was inversely correlated with milk yield and week of lactation. No correlation was observed during wk 9 to 29. After natural mating of the goats, the growth factor activity in the milk correlated significantly with somatic cell count and conductivity (a measure of membrane permeability), and correlated inversely with milk yield. Based on the above data, goat milk with higher growth factor activity could be selectively collected from Nubian pregnant does.     

The effects of accelerated growth rates and estrogen implants in prepubertal Holstein heifers on growth, feed efficiency, and blood parameters.

Sixty-eight Holstein heifers were used to determine the effects of accelerated growth rates by increased nutrient intake and estrogen implants on feed efficiency, structural growth, and blood parameters in heifers between 19 and 39 wk of age. At the beginning of the treatment period, the heifers were assigned to one of four treatment groups by using a randomized complete block design in a 2 x 2 factorial arrangement. The treatments were standard growth rate (700 g/d), accelerated growth rate (1000 g/d), standard growth rate with an estradiol implant, and accelerated growth rate with an estradiol implant. All heifers received the same diet, but dry matter intake was adjusted weekly to achieve the target rate of gain. Accelerating heifer growth rates from 705 to 1007 g/d improved feed efficiency 5.1%, increased the rate of withers height, heart girth, and hip width growth 12, 27, and 27%, respectively, and body condition scores 0.25 points. Estradiol implants improved feed efficiency 2.4% and decreased the rate of withers height 6% and heart girth growth 3.5%. Increased nutrient intake and average daily gain depressed mean plasma growth hormone and urea nitrogen content 17 and 7%, respectively, while elevating insulin-like growth factor-1 levels by 10%. Estradiol implants increased mean plasma growth hormone content by 29% and insulin-like growth factor-1 levels by 17%, but decreased urea nitrogen content by 11%. Feeding prepubertal heifers for accelerated growth rates increased structural growth with a small increase in body condition, whereas estradiol implants improved feed efficiency and decreased the growth rate of withers height and heart girth without affecting the rate of hip width growth.