An update on existing therapeutic options and status of novel anti-metastatic agents in breast cancer: Elucidating the molecular mechanisms underlying the pleiotropic action of Withania somnifera (Indian ginseng) in breast cancer attenuation.

Major significant advancements in pharmacology and drug technology have been made to heighten the impact of cancer therapies, improving the life expectancy of subjects diagnosed with malignancy. Statistically, 99% of breast cancers occur in women while 0.5-1% occur in men, the female gender being the strongest breast cancer risk factor. Despite several breakthroughs, breast cancer continues to have a worldwide impact and is one of the leading causes of mortality. Additionally, resistance to therapy is a crucial factor enabling cancer cell persistence and resurgence. As a result, the search and discovery of novel modulatory agents and effective therapies capable of controlling tumor progression and cancer cell proliferation is critical. Withania somnifera (L.) Dunal (WS), commonly known as Indian ginseng, has long been used traditionally for the treatment of several ailments in the Indian context. Recently, WS and its phytoconstituents have shown promising anti-breast cancer properties and, as such, can be employed as prophylactic as well as therapeutic adjuncts to the main line of breast cancer treatment. The present review is an attempt to explore and provide experimental evidences in support of the prophylactic and therapeutic potential of WS in breast cancer, along with a deeper insight into the multiple molecular mechanisms and novel targets through which it acts against breast and other hormonally-induced cancers viz. ovarian, uterine and cervical. This exploration might prove crucial in providing better understanding of breast cancer progression and metastasis and its use as an adjunct in improving disease prognosis and therapeutic outcome.

MCC950 reduces autophagy and improves cognitive function by inhibiting NLRP3-dependent neuroinflammation in a rat model of Alzheimer's disease.

Alzheimer's disease (AD) is the seventh most common cause of mortality and one of the major causes of disability and vulnerability in the elderly. AD is characterized by gradual cognitive deterioration, the buildup of misfolded amyloid beta (Aß) peptide, and the generation of neurofibrillary tangles. Despite enormous scientific progress, there is no effective cure for AD. Thus, exploring new treatment options to stop AD or at least slow down its progress is important. In this study, we investigated the potential therapeutic effects of MCC950 on NLRP3-mediated inflammasome-driven inflammation and autophagy in AD. Rats treated with streptozotocin (STZ) exhibited simultaneous activation of the NLRP3 inflammasome and autophagy, as confirmed by Western blot, immunofluorescence, and co-immunoprecipitation analyses. MCC950, a specific NLRP3 inhibitor, was intraperitoneally administered (50 mg/kg body weight) to rats with AD-like symptoms induced by intracerebroventricular STZ injections (3 mg/kg body weight). MCC950 effectively suppressed STZ-induced cognitive impairment and anxiety by inhibiting NLRP3-dependent neuroinflammation. Moreover, our findings indicate that MCC950 exerts neuroprotective effects by attenuating autophagy in neuronal cells. The inhibiting effects of MCC950 on inflammasome activation and autophagy were reproduced in vitro, provding further mechansistic insights into MCC950 therapeutic action. Our findings suggest that MCC950 impedes the progression of AD and may also improve cognitive function through the mitigation of autophagy and NLRP3 inflammasome inhibition.

Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete.

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (), the percentage of plasticizer (), the initial curing temperature (), the age of the specimen (), the curing duration (), the fine aggregate to total aggregate ratio (), the percentage of total aggregate by volume (), the percent SiO2 solids to water ratio () in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (), the activator or alkali to FA ratio (), the sodium oxide (Na2O) to water ratio () for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (). A GEP empirical equation is proposed to estimate the of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.

Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence.

The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted Nst by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for Nlg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for Nst, and 0.1000, 0.2700, and 0.1500 for Nlg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program.