Cancer is the leading cause of morbidity and mortality around the globe. For certain types of cancer, chemotherapy drugs have been extensively used for treatment. However, severe side effects and the development of resistance are the drawbacks of these agents. Therefore, development of new agents with no or minimal side effects is of utmost importance. In this regard, natural compounds are well recognized as drugs in several human ailments, including cancer. One class of fungi, “mushrooms,” contains numerous compounds that exhibit interesting biological activities, including antitumor activity. Many researchers, including our own group, are focusing on the anticancer potential of different mushrooms and the underlying molecular mechanism behind their action. The aim of this review is to discuss PI3K/AKT, Wnt-CTNNB1, and NF-κB signaling pathways, the occurrence of genetic alterations in them, the association of these aberrations with different human cancers and how different nodes of these pathways are targeted by various substances of mushroom origin. We have given evidence to propose the therapeutic attributes and possible mode of molecular actions of various mushroom-originated compounds. However, anticancer effects were typically demonstrated in in vitro and in vivo models and very limited number of studies have been conducted in the human population. It is our belief that this review will help the research community in designing concrete preclinical and clinical studies to test the anticancer potential of mushroom-originated compounds on different cancers harboring particular genetic alteration(s).

Present study aimed to elucidate the anticancer effect and the possible molecular mechanism underlying the action of Latcripin 1 (LP1), from the mushroom Lentinula edodes strain C91-3 against gastric cancer cell lines SGC-7901 and BGC-823. Cell viability was measured by Cell Counting Kit-8 (CCK-8); morphological changes were observed by phase contrast microscope; autophagy was determined by transmission electron microscope and fluorescence microscope. Apoptosis and cell cycle were assessed by flow cytometer; wound-healing, transwell migration and invasion assays were performed to investigate the effect of LP1 on gastric cancer cell’s migration and invasion. Herein, we found that LP1 resulted in the induction of autophagy by the formation of autophagosomes and conversion of light chain 3 (LC3I into LC3II. LP1 up-regulated the expression level of autophagy-related gene (Atg7, Atg5, Atg12, Atg14) and Beclin1; increased and decreased the expression level of pro-apoptotic (Bax) and anti-apoptotic (Bcl-2) proteins respectively, along with the activation of Caspase-3. At lower-doses, LP1 have shown to arrest cells in the S phase of the cell cycle and decreased the expression level of matrix metalloproteinase MMP-2 and MMP-9. In addition, it has also been shown to regulate the phosphorylation of one of the most hampered gastric cancer pathway, that is, protein kinase B/mammalian target of rapamycin (Akt/mTOR) channel and resulted in cell death. These findings suggested LP1 as a potential natural anti-cancer agent, for exploring the gastric cancer therapies and as a contender for further in vitro and in vivo investigations.

The formation of inclusion bodies (IBs) is considered as an Achilles heel of heterologous protein expression in bacterial hosts. Wide array of techniques has been developed to recover biochemically challenging proteins from IBs. However, acquiring the active state even from the same protein family was found to be an independent of single established method. Here, we present a new strategy for the recovery of wide sub-classes of recombinant protein from harsh IBs. We found that numerous methods and their combinations for reducing IB formation and producing soluble proteins were not effective, if the inclusion bodies were harsh in nature. On the other hand, different practices with mild solubilization buffers were able to solubilize IBs completely, yet the recovery of active protein requires large screening of refolding buffers. With the integration of previously reported mild solubilization techniques, we proposed an improved method, which comprised low sarkosyl concentration, ranging from 0.05 to 0.1% coupled with slow freezing (- 1 °C/min) and fast thaw (room temperature), resulting in greater solubility and the integrity of solubilized protein. Dilution method was employed with single buffer to restore activity for every sub-class of recombinant protein. Results showed that the recovered protein's activity was significantly higher compared with traditional solubilization/refolding approach. Solubilization of IBs by the described method was proved milder in nature, which restored native-like conformation of proteins within IBs.

Abstract:14 Lp16-PSP from Lentinula edodes strain C91-3 has been reported previously in our laboratory to have selective

Echocardiography is one of the imaging systems most often utilized for assessing heart anatomy and function. Left ventricle ejection fraction (LVEF) is an important clinical variable assessed from echocardiography via the measurement of left ventricle (LV) parameters. Significant inter-observer and intra-observer variability is seen when LVEF is quantified by cardiologists using huge echocardiography data. Machine learning algorithms have the capability to analyze such extensive datasets and identify intricate patterns of structure and function of the heart that highly skilled observers might overlook, hence paving the way for computer-assisted diagnostics in this field. In this study, LV segmentation is performed on echocardiogram data followed by feature extraction from the left ventricle based on clinical methods. The extracted features are then subjected to analysis using both neural networks and traditional machine learning algorithms to estimate the LVEF. The results indicate that employing machine learning techniques on the extracted features from the left ventricle leads to higher accuracy than the utilization of Simpson’s method for estimating the LVEF. The evaluations are performed on a publicly available echocardiogram dataset, EchoNet-Dynamic. The best results are obtained when DeepLab, a convolutional neural network architecture, is used for LV segmentation along with Long Short-Term Memory Networks (LSTM) for the regression of LVEF, obtaining a dice similarity coefficient of 0.92 and a mean absolute error of 5.736%.