Breast cancer is the most common malignancy in women all over the world. Genetic background of women contributes to her risk of having breast cancer. Certain inherited DNA mutations can dramatically increase the risk of developing certain cancers and are responsible for many of the cancers that run in some families. Regarding the widespread multigene panels, whole exome sequencing is capable of providing the evaluation of genetic function mutations for development novel strategy in clinical trials. Targeting the mutant proteins involved in breast cancer can be an effective therapeutic approach for developing novel drugs. This systematic review discusses gene mutations linked to breast cancer, focusing on signaling pathways that are being targeted with investigational therapeutic strategies, where clinical trials could be potentially initiated in the future are being highlighted.

Accurate knowledge of true digestible amino acid (TDAA) contents of feedstuffs is necessary to accurately formulate poultry diets for profitable production. Several experimental approaches that are highly expensive and time consuming have been used to determine available amino acids. Prediction of the nutritive value of a feed ingredient from its chemical composition via regression methodology has been attempted for many years. The artificial neural network (ANN) model is a powerful method that may describe the relationship between digestible amino acid contents and chemical composition. Therefore, multiple linear regressions (MLR) and ANN models were developed for predicting the TDAA contents of sorghum grain based on chemical composition. A precision-fed assay trial using cecectomized roosters was performed to determine the TDAA contents in 48 sorghum samples from 12 sorghum varieties differing in chemical composition. The input variables for both MLR and ANN models were CP, ash, crude fiber, ether extract, and total phenols whereas the output variable was each individual TDAA for every sample. The results of this study revealed that it is possible to satisfactorily estimate the TDAA of sorghum grain through its chemical composition. The chemical composition of sorghum grain seems to highly influence the TDAA contents when considering components such as CP, crude fiber, ether extract, ash and total phenols. It is also possible to estimate the TDAA contents through multiple regression equations with reasonable accuracy depending on composition. However, a more satisfactory prediction may be achieved via ANN for all amino acids. The R(2) values for the ANN model corresponding to testing and training parameters showed a higher accuracy of prediction than equations established by the MLR method. In addition, the current data confirmed that chemical composition, often considered in total amino acid prediction, could be also a useful predictor of true digestible values of selected amino acids for poultry.

Sorghum grain is an important ingredient in poultry diets. The TMEn content of sorghum grain is a measure of its quality. As for the other feed ingredients, the biological procedure used to determine the TMEn value of sorghum grain is costly and time consuming. Therefore, it is necessary to find an alternative method to accurately estimate the TMEn content. In this study, 2 methods of regression and artificial neural network (ANN) were developed to describe the TMEn value of sorghum grain based on chemical composition of ash, crude fiber, CP, ether extract, and total phenols. A total of 144 sorghum samples were used to determine chemical composition and TMEn content using chemical analyses and bioassay technique, respectively. The values were consequently subjected to regression and ANN analysis. The fitness of the models was tested using R(2) values, MS error, and bias. The developed regression and ANN models could accurately predict the TMEn of sorghum samples from their chemical composition. The goodness of fit in terms of R(2) values corresponding to testing and training of the ANN model showed a higher accuracy of prediction than the equation established by regression method. In terms of MS error, the ANN model showed lower residuals distribution than the regression model. The results suggest that the ANN model may be used to accurately estimate the TMEn value of sorghum grain from its corresponding chemical composition.

One of the techniques utilised in the management of cancer in all stages is multiple biomedical imaging. Imaging as an important part of cancer clinical protocols can provide a variety of information about morphology, structure, metabolism and functions. Application of imaging technics together with other investigative apparatus including in fluids analysis and vitro tissue would help clinical decision-making. Mixed imaging techniques can provide supplementary information used to improve staging and therapy planning. Imaging aimed to find minimally invasive therapy to make better results and reduce side effects. Probably, the most important factor in reducing mortality of certain cancers is an early diagnosis of cancer via screening based on imaging. The most common cancer in women is breast cancer. It is considered as the second major cause of cancer deaths in females, and therefore it remained as an important medical and socio-economic issue. Medical imaging has always formed part of breast cancer care and has used in all phases of cancer management from detection and staging to therapy monitoring and post-therapeutic follow-up. An essential action to be performed in the preoperative staging of breast cancer based on breast imaging. The general term of breast imaging refers to breast sonography, mammography, and magnetic resonance tomography (MRT) of the breast (magnetic resonance mammography, MRM). Further development in technology will lead to increase imaging speed to meet physiological processes requirements. One of the issues in the diagnosis of breast cancer is sensitivity limitation. To overcome this limitation, complementary imaging examinations are utilised that traditionally includes screening ultrasound, and combined mammography and ultrasound. Development in targeted imaging and therapeutic agents calls for close cooperation among academic environment and industries such as biotechnological, IT and pharmaceutical industries.

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SUMMARYAccurate information on metabolizable energy and true digestible amino acid (TDAA) content of sorghum grain is important in order to formulate sorghum-based poultry diets accurately. Estimates of ingredient nutritional values using bioassay methods require live birds and special facilities, which are time-consuming and costly. Accordingly, prediction by mathematical models would be of some considerable benefit. Sixty-eight samples of sorghum grain, representing 32 different varieties, were used to test the correlation between TDAA and nitrogen-corrected true metabolizable energy (TMEn) with total essential amino acids. Two methods of multiple linear regressions (MLR) and artificial neural network (ANN) models were used to find the relationship between total amino acids (model inputs) with TDAA and TMEn contents (model outputs) in sorghum grain. The fitness of the models was tested usingR2, mean square (MS) error and bias. There is a strong relationship between total amino acid concentration with both TDAA and TMEn content in sorghum grain. The TDAA and TMEn values were more accurately estimated by ANN model compared to values obtained from the MLR model. TheR2values corresponding to testing and training of the ANN model showed a higher accuracy of prediction than the equation constructed by MLR method. Based on the experimental evidence, it is concluded that the TDAA and TMEn values in sorghum grain can be predicted from total essential amino acids using ANN models. Consequently, this method provides an opportunity to reduce the risk of formulating an unbalanced TDAA diet for poultry.