Different parts of a plant (seeds, fruits, flower, leaves, stem, and roots) contain numerous biologically active compounds called “phytoconstituents” that consist of phenolics, minerals, amino acids, and vitamins. The conventional techniques applied to extract these phytoconstituents have several drawbacks including poor performance, low yields, more solvent use, long processing time, and thermally degrading by-products. In contrast, modern and advanced extraction nonthermal technologies such as pulsed electric field (PEF) assist in easier and efficient identification, characterization, and analysis of bioactive ingredients. Other advantages of PEF include cost-efficacy, less time, and solvent consumption with improved yields. This review covers the applications of PEF to obtain bioactive components, essential oils, proteins, pectin, and other important materials from various parts of the plant. Numerous studies compiled in the current evaluation concluded PEF as the best solution to extract phytoconstituents used in the food and pharmaceutical industries. PEF-assisted extraction leads to a higher yield, utilizes less solvents and energy, and it saves a lot of time compared to traditional extraction methods. PEF extraction design should be safe and efficient enough to prevent the degradation of phytoconstituents and oils.

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Nanoparticles (NPs) that are ∼100 nm in diameter can potentially cause toxicity in the central nervous system (CNS). Although NPs exhibit positive aspects, these molecules primarily exert negative or harmful effects. Thus, the beneficial and harmful effects should be compared. The prevalence of neurodegenerative diseases, such as Alzheimer disease, Parkinson disease, and some brain tumors, has increased. However, the major cause of these diseases remains unknown. NPs have been considered as one of the major potential causes of these diseases, penetrating the human body via different pathways. This review summarizes various pathways for NP-induced neurotoxicity, suggesting the development of strategies for nanoneuroprotection using in silico and biological methods. Studies of oxidative stress associated with gene expression analyses provide efficient information for understanding neuroinflammation and neurodegeneration associated with NPs. The brain is a sensitive and fragile organ, and evolution has developed mechanisms to protect it from injury; however, this protection also hinders the methods used for therapeutic purposes. Thus, brain and CNS-related diseases that are the cause of disability and disorder are the most difficult to treat. There are many obstacles to drug delivery in the CNS, such as the blood brain barrier and blood tumor barrier. Considering these barriers, we have reviewed the strategies available to map NPs using biological techniques. The surface adsorption energy of NPs is the basic force driving NP gathering, protein corona formation, and many other interactions of NPs within biological systems. These interactions can be described using an approach named the biological surface adsorption index. A quantitative structural activity relationship study helps to understand different protein-protein or protein-ligand interactions. Moreover, equilibrium between cerebrovascular permeability is required when a drug is transferred via the circulatory system for the therapy of neurodegenerative diseases. Various drug delivery approaches, such as chemical drug delivery and carrier-mediated drug delivery, have been established to avoid different barriers inhibiting CNS penetration by therapeutic substances. Developing an improved understanding of drug receptors and the sites of drug action, together with advances in medicinal chemistry, will make it possible to design drugs with greatly enhanced activity and selectivity; this may result in a significant increase in the therapeutic index.

Lipopeptides have a widespread role in different pathways of Bacillus subtilis; they can act as antagonists, spreader and immunostimulators. Plipastatin, an antifungal antibiotic, is one of the most important lipopeptide nonribosomly produced by Bacillus subtilis. Plipastatin has strong fungitoxic activity and involve in inhibition of phospholipase A2 and biofilm formation. For better understanding of the molecule and pathway by which lipopeptide plipastatin is synthesized, we present a computationally predicted structure of plipastatin using homology modeling. Primary and secondary structure analysis suggested that ppsD is a hydrophilic protein containing a significant proportion of alpha helices, and subcellular localization predictions suggested it is a cytoplasmic protein. The tertiary structure of protein (plipastatin synthase subunit D) was predicted by homology modeling. The results suggest a flexible structure which is also an important characteristic of active enzymes enabling them to bind various cofactors and substrates for proper functioning. Validation of 3D structure was done using Ramachandran plot ProsA-web and QMEAN score.This predicted information will help in better understanding of mechanisms underlying plipastatin synthase subunit D synthesis. Plipastatin can be used as an inhibitor of various fungal diseases in plants.

Purpose Pharmaceutical industry is tightly regulated owing to health concerns. Over the years, the use of computational intelligence (CI) tools has increased in pharmaceutical research and development, manufacturing, and quality control. Quality characteristics of tablets like tensile strength are important indicators of expected tablet performance. Predictive, yet transparent, CI models which can be analysed for insights into the formulation and development process. Methods This work uses data from a galenical tableting study and computational intelligence methods like decision trees, random forests, fuzzy systems, artificial neural networks, and symbolic regression to establish models for the outcome of tensile strength. Data was divided in training and test fold according to ten fold cross validation scheme and RMSE was used as an evaluation metric. Tree based ensembles and symbolic regression methods are presented as transparent models with extracted rules and mathematical formula, respectively, explaining the CI models in greater detail. Results CI models for tensile strength of tablets based on the formulation design and process parameters have been established. Best models exhibit normalized RMSE of 7 %. Rules from fuzzy systems and random forests are shown to increase transparency of CI models. A mathematical formula generated by symbolic regression is presented as a transparent model. Conclusions CI models explain the variation of tensile strength according to formulation and manufacturing process characteristics. CI models can be further analyzed to extract actionable knowledge making the artificial learning process more transparent and acceptable for use in pharmaceutical quality and safety domains.

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The growth of functional components containing agricultural foods is enhancing because these components aid the human body against different chronic diseases. Currently, chia seeds basically belong to the mint family and are edible seeds of Salvia hispanica. These seeds are composed of different functional components including fiber, polyphenols, antioxidants, omega‐3 fatty acid vitamins, minerals, and peptides. Besides, these seeds are also a good source of vegetable protein, unsaturated fat, carbohydrates, and ash. Chia seed components are helpful in cardiovascular disease (CVD) by reducing blood pressure, platelet aggregation, cholesterol, and oxidation. In GI‐tract‐related diseases like diabetes and constipation, chia fiber reduces the blood glucose level and provides bulk to stool. However, antioxidants and polyphenols are protected beta cells of the pancreas from inflammation. These components are protected from the cell damage of the different body parts, which can provide help in different types of cancer including breast, colorectal, liver, and pancreatic. Conclusively, some pervious studies approved that chia seed components are played important role in chronic diseases.