Mushrooms are a well known source of many bioactive and nutritional compounds with immense applicability in both the pharmaceutical and food industries. They are widely used to cure various kinds of ailments in traditional medicines. They have a low amount of fats and cholesterol and possess a high number of proteins. Immunomodulators have the ability which can improve immunity and act as defensive agents against pathogens. One such class of immunomodulators is fungal immunomodulatory proteins (FIPs). FIPs have potential roles in the treatment of cancer, and immunostimulatory effects and show anti-tumor activities. In the current study, 19 FIPs from edible mushrooms have been used for comparison and analysis of the conserved motifs. Phylogenetic analysis was also carried out using the FIPs. The conserved motif analysis revealed that some of the motifs strongly supported their identity as FIPs while some are novel. The fungal immunomodulatory proteins are important and have many properties which can be used for treating ailments and diseases and this preliminary study can be used for the identification and functional characterization of the proposed novel motifs and in unraveling the potential roles of FIPs for developing newer drugs.
Mosquitoes are infectious vectors for a wide range of pathogens and parasites thereby transmitting several diseases including malaria, dengue, Zika, Japanese encephalitis and chikungunya which pose a major public health concern. Mostly synthetic insecticides are usually applied as a primary control strategy to manage vector-borne diseases. However excessive and non-judicious usage of such chemically derived insecticides has led to serious environmental and health issues owing to their biomagnification ability and increased toxicity towards non-target organisms. In this context, many such bioactive compounds originating from entomopathogenic microbes serve as an alternative strategy and environmentally benign tool for vector control. In the present paper, the entomopathogenic fungus, Lecanicillium lecanii (LL) was processed to make the granules. Developed 4% LL granules have been characterized using the technique of Fourier transform infrared spectroscopy (FTIR) and scanning electron microscope (SEM). The developed formulation was also subjected to an accelerated temperature study at 40 °C and was found to be stable for 3 months. Further, GCMS of the L. lecanii was also performed to screen the potential biomolecules present. The developed formulation was found to be lethal against Anopheles culicifacies with an LC50 value of 11.836 µg/mL. The findings from SEM and histopathology also substantiated the mortality effects. Further, the SEM EDX (energy dispersive X-ray) studies revealed that the treated larvae have lower nitrogen content which is correlated to a lower level of chitin whereas the control ones has higher chitin content and healthy membranes. The developed LL granule formulation exhibited high toxicity against Anopheles mosquitoes. The granule formulations can be used as an effective biocontrol strategy against malaria-causing mosquitoes.
Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs.
Ascariasis and intestinal parasitic nematodes are the leading cause of mass mortality infecting many people across the globe. In light of the various deleterious side effects of modern chemical-based allopathic drugs, our preferences have currently shifted towards the use of traditional plant-based drugs or botanicals for treating diseases. The defensive propensities in the botanicals against parasites have probably evolved during their co-habitation with parasites, humans and plants in nature and hence their combative interference in one another's defensive mechanisms has occurred naturally ultimately being very effective in treating diseases. This article broadly outlines the utility of plant-based compounds or botanicals prepared from various medicinal herbs that have the potential to be developed as effective therapies against the important parasites causing ascariasis and intestinal hookworm infections leading to ascariasis & infections and thereby human mortality, wherein allopathic treatments are less effective and causes enormous side-effects.
Cardiovascular diseases (CVDs) are one of the major reasons for deaths globally. The renin-angiotensin-aldosterone system (RAAS) regulates body hypertension and fluid balance which causes CVD. Angiotensin-converting enzyme I (ACE I) is the central Zn-metallopeptidase component of the RAAS playing a crucial role in maintaining homeostasis of the cardiovascular system. The available drugs to treat CVD have many side effects, and thus, there is a need to explore phytocompounds and peptides to be utilized as alternative therapies. Soybean is a unique legume cum oilseed crop with an enriched source of proteins. Soybean extracts serve as a primary ingredient in many drug formulations against diabetes, obesity, and spinal cord-related disorders. Soy proteins and their products act against ACE I which may provide a new scope for the identification of potential scaffolds that can help in the design of safer and natural cardiovascular therapies. In this study, the molecular basis for selective inhibition of 34 soy phytomolecules (especially of beta-sitosterol, soyasaponin I, soyasaponin II, soyasaponin II methyl ester, dehydrosoyasaponin I, and phytic acid) was evaluated using in silico molecular docking approaches and dynamic simulations. Our results indicate that amongst the compounds, beta-sitosterol exhibited a potential inhibitory action against ACE I.