RESUMO
The synergy between the genetic potential and the nutrient intake determines the growth performance of meat-type chicken and nutrigenomics approach helps us understand the response of candidate genes of growth in chicken to dietary manipulations. The current study aimed to assess the growth performance and expression of hepatic growth related genes in the naked neck broiler chicken in response to different dietary energy and protein levels with a hypothesis that high plane of nutrition enhances both of these positively. The results revealed that birds have shown significantly better growth performance under high protein (HP) and high energy (HE) dietary regime. The expression profiles of the genes studied revealed upregulation of IGF-1, IGF-2, and GH under dietary HP and HE regime relative to other protein and energy levels with greater upregulation at 3rd week than the 1st and 5th week of age of birds. The IGFR and GHR mRNA expression was significantly higher under HP and HE dietary regimen with an increasing and decreasing trend from 1st to 5th week of age, respectively. A consistent and significant downregulation of IGFBP-2 was observed under HP and HE regime throughout the feeding trial. The myostatin expression was higher at 3rd week of age followed by 1st week expression. The HP and HE as well as LP (Low protein) and HE diet resulted in significant upregulation of myostatin gene expression in liver. In support to the set hypothesis of this study the high protein and high energy diet resulted in better growth performance of broiler chickens with corresponding upregulation of IGF-1, IGF-2, IGFR, GH, GHR, and Myostatin gene expression and downregulation of IGFBP-2 in liver.
Assuntos
Galinhas/crescimento & desenvolvimento , Galinhas/genética , Dieta , Proteínas Alimentares/administração & dosagem , Metabolismo Energético/efeitos dos fármacos , Ração Animal , Fenômenos Fisiológicos da Nutrição Animal/genética , Animais , Galinhas/metabolismo , Proteínas Alimentares/farmacologia , Ingestão de Energia/efeitos dos fármacos , Metabolismo Energético/genética , Feminino , Expressão Gênica/efeitos dos fármacos , Hormônio do Crescimento/genética , Hormônio do Crescimento/metabolismo , Proteína 2 de Ligação a Fator de Crescimento Semelhante à Insulina/genética , Proteína 2 de Ligação a Fator de Crescimento Semelhante à Insulina/metabolismo , Fator de Crescimento Insulin-Like I/genética , Fator de Crescimento Insulin-Like I/metabolismo , Fator de Crescimento Insulin-Like II/genética , Fator de Crescimento Insulin-Like II/metabolismo , Masculino , Distribuição Aleatória , Receptor IGF Tipo 1/genética , Receptor IGF Tipo 1/metabolismoRESUMO
Healthcare technology is evolving from a conventional hub-based system to a personalized healthcare system accelerated by rapid advancements in smart fitness trackers. Modern fitness trackers are mostly lightweight wearables and can monitor the user's health round the clock, supporting ubiquitous connectivity and real-time tracking. However, prolonged skin contact with wearable trackers can cause discomfort. They are susceptible to false results and breach of privacy due to the exchange of user's personal data over the internet. We propose tinyRadar, a novel on-edge millimeter wave (mmWave) radar-based fitness tracker that solves the issues of discomfortness, and privacy risk in a small form factor, making it an ideal choice for a smart home setting. This work uses the Texas Instruments IWR1843 mmWave radar board to recognize the exercise type and measure its repetition counts, using signal processing and Convolutional Neural Network (CNN) implemented on board. The radar board is interfaced with ESP32 to transfer the results to the user's smartphone over Bluetooth Low Energy (BLE). Our dataset comprises eight exercises collected from fourteen human subjects. Data from ten subjects were used to train an 8-bit quantized CNN model. tinyRadar provides real-time repetition counts with 96% average accuracy and has an overall subject-independent classification accuracy of 97% when evaluated on the rest of the four subjects. CNN has a memory utilization of 11.36 KB, which includes only 1.46 KB for the model parameters (weights and biases) and the remaining for output activations.