Growth response of broiler chickens to inclusion of hydrolyzed porcine mucosa (Palbio) in diets varying in total lysine content.

Growth response of broiler chickens to inclusion of hydrolyzed porcine mucosa (Palbio) in diets varying in total lysine content

Growth response of broilers to lysine levels and hydrolyzed porcine digestive mucosa (Palbio) inclusion in diet from 1 to 21 d of age

Palbio (PAL, Palbio 50 RD, Bioibérica, Spain) is a protein concentrate based on hydrolyzed porcine digestive mucosa dried under a fluid bed system over a soybean carrier, currently used in piglet feeds. The digestibility of PAL is very high and the product may be an excellent source of protein for young chicks. An experiment was conducted with 1,280 straight-run one-d-old Ross 308 chicks to evaluate the growth response of broilers to dietary inclusion of PAL.

Efecto de la incusion de hidrolizado de mucosa digestiva porcina (palbio 50 rd®) y del nivel de lisina del pienso sobre los parametros productivos en pollos de 1 a 21 dias de edad

El hidrolizado de mucosa digestiva de porcino (Palbio 50 RD ® , Bioiéerica, S.A., PAL) se utiliza con resultados óptimos en la alimentación de lechones recién destetados (Lindeman el al. 2000; Corassa et al. 2007). En un trabajo reciente, Mohiti-Asli et al. (2011) observaron que la inclusión de PAL mejoraba los resultados productivos en pollos de engorde a cualquier edad. En este trabajo se demostró que los niveles más recomendables de utilización de PAL teniendo en cuenta razones productivas y económicas, era el 2,5%. En esta investigación se estudio el efecto de la inclusión de 2,5% de PAL en piensos para pollos con niveles crecientes de lisina total (LYS, 1,1 a 1,4%). El objetivo fue estudiar si los efectos beneficiosos del PAL sobre la productividad de los pollos eran independientes o no del nivel de LYS del pienso.

Efecto de la inclusión de un hidrolizado de mucosa digestiva porcina (palbio 50 rd®) en piensos de broilers con niveles diferentes de lisina

La alimentación del pollo broiler durante la primera semana de vida es de creciente importancia debido a que la edad de sacrificio ha disminuido de forma constante en los últimos años. Además, consumos elevados durante la primera semana de vida mejoran el desarrollo del aparato digestivo de las aves, favoreciendo el crecimiento de las vellosidades intestinales y la eficiencia alimenticia (Lilburn, 1998, Noy et al., 2005). En los últimos años, el mercado dispone de nuevos productos de origen animal obtenidos durante el proceso de obtención de la heparina para uso farmacéutico. Uno de estos productos comerciales (Palbio 50 RD, Bioibérica S.A., Palafolls, Barcelona) está formado por la proteína hidrolizada de la mucosa digestiva de porcino limpia de contenidos intestinales, secada mediante un procedimiento especial que incluye la utilización de harina de soja como excipiente. Recientes estudios han demostrado de forma fehaciente el interés de utilizar este ingrediente en piensos de lechones de primera edad (Lindeman, et al., 2000; Corassa et al., 2007) pero los datos existentes en aves son más limitados. El objetivo de esta investigación fue evaluar los efectos de niveles crecientes de este hidrolizado proteico (PAL) sobre la productividad de pollos que recibían piensos con dos niveles diferentes de lisina total (Lys)

Identification of a biomarker panel for colorectal cancer diagnosis

Background Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries. Methods A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60-mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables. Results After an exhaustive process of pre-processing to ensure data quality--lost values imputation, probes quality, data smoothing and intraclass variability filtering--the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples. Conclusions We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955)

Identification of a biomarker panel for colorectal cancer diagnosis

Background:Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries. Methods: A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60-mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables. Results: After an exhaustive process of pre-processing to ensure data quality--lost values imputation, probes quality, data smoothing and intraclass variability filtering--the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples. Conclusions: We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955).

On-line breath analysis of volatile organic compounds as a method for colorectal cancer detection

Background: Analysis of exhaled volatile organic compounds (VOCs) in breath is an emerging approach for cancer diagnosis, but little is known about its potential use as a biomarker for colorectal cancer (CRC). We investigated whether a combination of VOCs could distinct CRC patients from healthy volunteers. Methods: In a pilot study, we prospectively analyzed breath exhalations of 38 CRC patient and 43 healthy controls all scheduled for colonoscopy, older than 50 in the average-risk category. The samples were ionized and analyzed using a Secondary ElectroSpray Ionization (SESI) coupled with a Time-of-Flight Mass Spectrometer (SESI-MS). After a minimum of 2 hours fasting, volunteers deeply exhaled into the system. Each test requires three soft exhalations and takes less than ten minutes. No breath condensate or collection are required and VOCs masses are detected in real time, also allowing for a spirometric profile to be analyzed along with the VOCs. A new sampling system precludes ambient air from entering the system, so background contamination is reduced by an overall factor of ten. Potential confounding variables from the patient or the environment that could interfere with results were analyzed. Results: 255 VOCs, with masses ranging from 30 to 431 Dalton have been identified in the exhaled breath. Using a classification technique based on the ROC curve for each VOC, a set of 9 biomarkers discriminating the presence of CRC from healthy volunteers was obtained, showing an average recognition rate of 81.94%, a sensitivity of 87.04% and specificity of 76.85%. Conclusions: A combination of cualitative and cuantitative analysis of VOCs in the exhaled breath could be a powerful diagnostic tool for average-risk CRC population. These results should be taken with precaution, as many endogenous or exogenous contaminants could interfere as confounding variables. On-line analysis with SESI-MS is less time-consuming and doesn’t need sample preparation. We are recruiting in a new pilot study including breath cleaning procedures and spirometric analysis incorporated into the postprocessing algorithms, to better control for confounding variables.