Transcriptome sequencing of medical herb Salvia Rosmarinus (Rosemary) revealed the phenylpropanoid biosynthesis pathway genes and their phylogenetic relationships.

BACKGROUND: The Salvia rosmarinus spenn. (rosemary) is considered an economically important ornamental and medicinal plant and is widely utilized in culinary and for treating several diseases. However, the procedure behind synthesizing secondary metabolites-based bioactive compounds at the molecular level in S. rosmarinus is not explored completely. METHODS AND RESULTS: We performed transcriptomic sequencing of the pooled sample from leaf and stem tissues on the Illumina HiSeqTM X10 platform. The transcriptomics analysis led to the generation of 29,523,608 raw reads, followed by data pre-processing which generated 23,208,592 clean reads, and de novo assembly of S. rosmarinus obtained 166,849 unigenes. Among them, nearly 75.1% of unigenes i.e., 28,757 were interpreted against a non-redundant protein database. The gene ontology-based annotation classified them into 3 main categories and 55 sub-categories, and clusters of orthologous genes annotation categorized them into 23 functional categories. The Kyoto Encyclopedia of Genes and Genomes database-based pathway analysis confirmed the involvement of 13,402 unigenes in 183 biochemical pathways, among these unigenes, 1,186 are involved in the 17 secondary metabolite production pathways. Several key enzymes involved in producing aromatic amino acids and phenylpropanoids were identified from the transcriptome database. Among the identified 48 families of transcription factors from coding unigenes, bHLH, MYB, WRKYs, NAC, C2H2, C3H, and ERF are involved in flavonoids and other secondary metabolites biosynthesis. CONCLUSION: The phylogenetic analysis revealed the evolutionary relationship between the phenylpropanoid pathway genes of rosemary with other members of Lamiaceae. Our work reveals a new molecular mechanism behind the biosynthesis of phenylpropanoids and their regulation in rosemary plants.

Sustainable pathways for solar desalination using nanofluids: A critical review.

Water is a fundamental requirement for the survival of human beings. Although water is abundantly available across the globe, access to freshwater still remains a major concern. Most of the water available is saline or brackish, which is not fit for human consumption. Desalination is the optimum solution for production of potable water from saline water. A major shortcoming of conventional desalination technologies is their dependence on fossil fuel that results in environmental degradation, global warming, etc. Therefore, sustainable desalination technology has evolved as a need of hour. Among all renewable energy resources, solar energy is abundantly available and can be potentially harvested. Therefore, solar energy can be used to drive sustainable desalination technologies. A solar still converts saline water into freshwater in a single step using solar energy. But the major drawbacks of solar still are relatively lower efficiency and lower yield. Nanofluids are widely used to overcome these limitations due to their extraordinary and unique properties. This paper critically reviews the recent research performed on the application of nanofluids in solar desalination systems. Methods of nanofluid preparation, their types and properties are also discussed in detail. Application of nanofluids in solar desalination systems is discussed with special attention on performance enhancement of solar stills. Combinations of nanofluids with various other performance enhancement techniques are also considered. The effectiveness of nanofluids in solar stills is found to be dependent majorly on the nature and concentration of the nanofluid used.

Applications of some advanced sequencing, analytical, and computational approaches in medicinal plant research: a review.

The potential active chemicals found in medicinal plants, which have long been employed as natural medicines, are abundant. Exploring the genes responsible for producing these compounds has given new insights into medicinal plant research. Previously, the authentication of medicinal plants was done via DNA marker sequencing. With the advancement of sequencing technology, several new techniques like next-generation sequencing, single molecule sequencing, and fourth-generation sequencing have emerged. These techniques enshrined the role of molecular approaches for medicinal plants because all the genes involved in the biosynthesis of medicinal compound(s) could be identified through RNA-seq analysis. In several research insights, transcriptome data have also been used for the identification of biosynthesis pathways. miRNAs in several medicinal plants and their role in the biosynthesis pathway as well as regulation of the disease-causing genes were also identified. In several research articles, an in silico study was also found to be effective in identifying the inhibitory effect of medicinal plant-based compounds against virus' gene(s). The use of advanced analytical methods like spectroscopy and chromatography in metabolite proofing of secondary metabolites has also been reported in several recent research findings. Furthermore, advancement in molecular and analytic methods will give new insight into studying the traditionally important medicinal plants that are still unexplored.

Internet of Things for Smart Community Solutions.

The term IoT (Internet of Things) constitutes the quickly developing advanced gadgets with highest computing power with in a constrained VLSI design space [...].

A novel monopartite begomovirus and satellites associated with yellow mosaic disease of Sida spp. in India.

Begomoviruses (family Geminiviridae) cause severe diseases in many economically important crops and non-cultivated plants in the warmer regions of the world. Non-cultivated weeds have been reported to act as natural virus reservoirs. In January 2016, Sida plants with yellow mosaic symptoms were found at the edge of an agricultural field in Gujarat, India. Sequence analysis of the viral genomic components cloned from a diseased Sida plant indicated the presence of a distinct monopartite begomovirus (proposed as sida yellow mosaic Gujarat virus) along with a betasatellite (ludwigia leaf distortion betasatellite) and an alphasatellite (malvastrum yellow mosaic alphasatellite). Our results emphasize that this weed may harbor a begomovirus-alphasatellite-betasatellite complex. This host serves as a potential source of virus inoculum, which can be transmitted by whiteflies to other cultivated crops.

SafeDrive: Hybrid Recommendation System Architecture for Early Safety Predication Using Internet of Vehicles.

The Internet of vehicles (IoV) is a rapidly emerging technological evolution of Intelligent Transportation System (ITS). This paper proposes SafeDrive, a dynamic driver profile (DDP) using a hybrid recommendation system. DDP is a set of functional modules, to analyses individual driver's behaviors, using prior violation and accident records, to identify driving risk patterns. In this paper, we have considered three synthetic data-sets for 1500 drivers based on their profile information, risk parameters information, and risk likelihood. In addition, we have also considered the driver's historical violation/accident data-set records based on four risk-score levels such as high-risk, medium-risk, low-risk, and no-risk to predict current and future driver risk scores. Several error calculation methods have been applied in this study to analyze our proposed hybrid recommendation systems' performance to classify the driver's data with higher accuracy based on various criteria. The evaluated results help to improve the driving behavior and broadcast early warning alarm to the other vehicles in IoV environment for the overall road safety. Moreover, the propoed model helps to provide a safe and predicted environment for vehicles, pedestrians, and road objects, with the help of regular monitoring of vehicle motion, driver behavior, and road conditions. It also enables accurate prediction of accidents beforehand, and also minimizes the complexity of on-road vehicles and latency due to fog/cloud computing servers.

DeepCAT: Deep Computer-Aided Triage of Screening Mammography.

Although much deep learning research has focused on mammographic detection of breast cancer, relatively little attention has been paid to mammography triage for radiologist review. The purpose of this study was to develop and test DeepCAT, a deep learning system for mammography triage based on suspicion of cancer. Specifically, we evaluate DeepCAT's ability to provide two augmentations to radiologists: (1) discarding images unlikely to have cancer from radiologist review and (2) prioritization of images likely to contain cancer. We used 1878 2D-mammographic images (CC & MLO) from the Digital Database for Screening Mammography to develop DeepCAT, a deep learning triage system composed of 2 components: (1) mammogram classifier cascade and (2) mass detector, which are combined to generate an overall priority score. This priority score is used to order images for radiologist review. Of 595 testing images, DeepCAT recommended low priority for 315 images (53%), of which none contained a malignant mass. In evaluation of prioritizing images according to likelihood of containing cancer, DeepCAT's study ordering required an average of 26 adjacent swaps to obtain perfect review order. Our results suggest that DeepCAT could substantially increase efficiency for breast imagers and effectively triage review of mammograms with malignant masses.

Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms.

Smart wristbands and watches have become an important accessory to fitness, but their application to healthcare is still in a fledgling state. Their long-term wear facilitates extensive data collection and evolving sensitivity of smart wristbands allows them to read various body vitals. In this paper, we hypothesized the use of heart rate variability (HRV) measurements to drive an algorithm that can pre-empt the onset or worsening of an affliction. Due to its significance during the time of the study, SARS-Cov-2 was taken as the case study, and a hidden Markov model (HMM) was trained over its observed symptoms. The data used for the analysis was the outcome of a study hosted by Welltory. It involved the collection of SAR-Cov-2 symptoms and reading of body vitals using Apple Watch, Fitbit, and Garmin smart bands. The internal states of the HMM were made up of the absence and presence of a consistent decline in standard deviation of NN intervals (SSDN), the root mean square of the successive differences (rMSSD) in R-R intervals, and low frequency (LF), high frequency (HF), and very low frequency (VLF) components of the HRV measurements. The emission probabilities of the trained HMM instance confirmed that the onset or worsening of the symptoms had a higher probability if the HRV components displayed a consistent decline state. The results were further confirmed through the generation of probable hidden states sequences using the Viterbi algorithm. The ability to pre-empt the exigent state of an affliction would not only lower the chances of complications and mortality but may also help in curbing its spread through intelligence-backed decisions.

Selfishness in Vehicular Delay-Tolerant Networks: A Review.

Various operational communication models are using Delay-Tolerant Network as a communication tool in recent times. In such a communication paradigm, sometimes there are disconnections and interferences as well as high delays like vehicle Ad hoc networks (VANETs). A new research mechanism, namely, the vehicle Delay-tolerant network (VDTN), is introduced due to several similar characteristics. The store-carry-forward mechanism in VDTNs is beneficial in forwarding the messages to the destination without end-to-end connectivity. To accomplish this task, the cooperation of nodes is needed to forward messages to the destination. However, we cannot be sure that all the nodes in the network will cooperate and contribute their computing resources for message forwarding without any reward. Furthermore, there are some selfish nodes in the network which may not cooperate to forward the messages, and are inclined to increase their own resources. This is one of the major challenges in VDTNs and incentive mechanisms are used as a major solution. This paper presents a detailed study of the recently proposed incentive schemes for VDTNs. This paper also gives some open challenges and future directions for interested researchers in the future.

Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks.

Internet of things (IoT) is a very important research area, having many applications such as smart cities, intelligent transportation system, tracing, and smart homes. The underlying technology for IoT are wireless sensor networks (WSN). The selection of cluster head (CH) is significant as a part of the WSN's optimization in the context of energy consumption. In WSNs, the nodes operate on a very limited energy source, therefore, the routing protocols designed must meet the optimal utilization of energy consumption in such networks. Evolutionary games can be designed to meet this aspect by providing an adequately efficient CH selection mechanism. In such types of mechanisms, the network nodes are considered intelligent and independent to select their own strategies. However, the existing mechanisms do not consider a combination of many possible parameters associated with the smart nodes in WSNs, such as remaining energy, selfishness, hop-level, density, and degree of connectivity. In our work, we designed an evolutionary game-based approach for CH selection, combined with some vital parameters associated with sensor nodes and the entire networks. The nodes are assumed to be smart, therefore, the aspect of being selfish is also addressed in this work. The simulation results indicate that our work performs much better than typical evolutionary game-based approaches.

Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems.

Uplink and Downlink channel estimation in massive Multiple Input Multiple Output (MIMO) systems is an intricate issue because of the increasing channel matrix dimensions. The channel feedback overhead using traditional codebook schemes is very large, which consumes more bandwidth and decreases the overall system efficiency. The purpose of this paper is to decrease the channel estimation overhead by taking the advantage of sparse attributes and also to optimize the Energy Efficiency (EE) of the system. To cope with this issue, we propose a novel approach by using Compressed-Sensing (CS), Block Iterative-Support-Detection (Block-ISD), Angle-of-Departure (AoD) and Structured Compressive Sampling Matching Pursuit (S-CoSaMP) algorithms to reduce the channel estimation overhead and compare them with the traditional algorithms. The CS uses temporal-correlation of time-varying channels to produce Differential-Channel Impulse Response (DCIR) among two CIRs that are adjacent in time-slots. DCIR has greater sparsity than the conventional CIRs as it can be easily compressed. The Block-ISD uses spatial-correlation of the channels to obtain the block-sparsity which results in lower pilot-overhead. AoD quantizes the channels whose path-AoDs variation is slower than path-gains and such information is utilized for reducing the overhead. S-CoSaMP deploys structured-sparsity to obtain reliable Channel-State-Information (CSI). MATLAB simulation results show that the proposed CS based algorithms reduce the feedback and pilot-overhead by a significant percentage and also improve the system capacity as compared with the traditional algorithms. Moreover, the EE level increases with increasing Base Station (BS) density, UE density and lowering hardware impairments level.

Development, optimization, and characterization of a novel tea tree oil nanogel using response surface methodology.

PURPOSE: To develop and optimize nanoemulsion (NE)-based emulgel (EG) formulation as a potential vehicle for topical delivery of tea tree oil (TTO). METHODOLOGY: Central composite design was adopted for optimizing the processing conditions for NE preparation by high energy emulsification method viz. surfactant concentration, co-surfactant concentration, and stirring speed. The optimized NE was developed into emulgel (EG) using pH sensitive polymer Carbopol 940 and triethanolamine as alkalizer. The prepared EG was evaluated for its pH, viscosity, and texture parameters, ex vivo permeation at 37 °C and stability. Antimicrobial evaluation of EG in comparison to conventional gel and pure TTO was also carried out against selected microbial strains. RESULTS AND DISCUSSION: Optimized NE had particle size and zeta potential of 16.23 ± 0.411 nm and 36.11 ± 1.234 mV, respectively. TEM analysis revealed the spherical shape of droplets. The pH of EG (5.57 ± 0.05 ) was found to be in accordance with the range of human skin pH. EG also illustrated efficient permeation (79.58 µL/cm(2)) and flux value (JSS) of 7.96 µL cm(2)/h through skin in 10 h. Viscosity and texture parameters, firmness (9.3 ± 0.08 g), spreadability (2.26 ± 0.06 mJ), extrudability (61.6 ± 0.05 mJ), and adhesiveness (8.66 ± 0.08 g) depict its suitability for topical application. Antimicrobial evaluation of EG with same amount of TTO as conventional gel revealed broader zones of growth inhibitions against all the selected microbial strains. Moreover, EG was also found to be nonirritant (PII 0.0833). These parameters were consistent over 90 d. CONCLUSION: TTO EG turned out to be a promising vehicle for the topical delivery of TTO with enhanced therapeutic efficacy.

PsAP2 an AP2/ERF family transcription factor from Papaver somniferum enhances abiotic and biotic stress tolerance in transgenic tobacco.

The AP2/ERFs are one of the most important family of transcription factors which regulate multiple responses like stress, metabolism and development in plants. We isolated PsAP2 a novel AP2/ERF from Papaver somniferum which was highly upregulated in response to wounding followed by ethylene, methyl jasmonate and ABA treatment. PsAP2 showed specific binding with both DRE and GCC box elements and it was able to transactivate the reporter genes in yeast. PsAP2 overexpressing transgenic tobacco plants exhibited enhanced tolerance towards both abiotic and biotic stresses . Real time transcript expression analysis showed constitutive upregulation of tobacco Alternative oxidase1a and Myo-inositol-1-phosphate synthase in PsAP2 overexpressing tobacco plants. Further, PsAP2 showed interaction with NtAOX1a promoter in vitro, it also specifically activated the NtAOX1a promoter in yeast and tobacco BY2 cells. The silencing of PsAP2 using VIGS lead to significant reduction in the AOX1 level in P. somniferum. Taken together PsAP2 can directly bind and transcriptionally activate NtAOX1a and its overexpression in tobacco imparted increased tolerance towards both abiotic and biotic stress.

Continuous synthesis of polymer-coated drug particles by porous hollow fiber membrane-based antisolvent crystallization.

Using porous hollow fiber membranes, this study illustrates a novel technique to continuously synthesize polymer-coated drug crystals by antisolvent crystallization. The synthesized polymer-coated drug crystals involve crystals of the drug Griseofulvin (GF) coated by a thin layer of the polymer Eudragit RL100. The process feed, an acetone solution of the drug GF containing the dissolved polymer, was passed through the shell side of a membrane module containing many porous hollow fibers of Nylon-6. Through the lumen of the hollow fibers, the antisolvent water was passed at a higher pressure to inject water jets through every pore in the fiber wall into the shell-side acetone feed solution, creating an extremely high level of supersaturation and immediate crystallization. It appears that the GF crystals are formed first and serve as nuclei for the precipitation of the polymer Eudragit, which forms a thin coating around the GF crystals. The polymer-coated drug crystals were collected by a filtration device at the shell-side outlet of the membrane module, and the surface morphology, particle size distribution, and the polymer coating thickness were then characterized by scanning electron microscopy (SEM), scanning transmission electron microscopy (STEM), laser diffraction spectroscopy (LDS), and thermogravimetric analysis (TGA). To study the properties of the coated drug crystals, X-ray diffraction (XRD), Raman spectroscopy, and dissolution tests were implemented. These results indicate that a polymer-coated, free-flowing product was successfully developed under appropriate conditions in this novel porous hollow fiber antisolvent crystallization (PHFAC) method. The coated drug particles can be potentially used for controlled release. The molecular and the crystal structures of GF were not affected by the PHFAC method, which may be easily scaled up.

Continuous polymer nanocoating on silica nanoparticles.

Continuous polymer coating of nanoparticles is of interest in many industries such as pharmaceuticals, cosmetics, food, and electronics. Here we introduce a polymer coating/precipitation technique to achieve a uniform and controllable nanosize polymer coating on nanoparticles in a continuous manner. The utility of this technique is demonstrated by coating Aerosil silica nanoparticles (SNPs) of diameter 12 nm with the polymer Eudragit RL 100. Both hydrophilic and hydrophobic SNPs were successfully coated. After determining the cloud point of an acetone solution of the polymer containing a controlled amount of the nonsolvent water, the solid hollow fiber cooling crystallization (SHFCC) technique was employed to continuously coat SNPs with the polymer. A suspension of the SNPs in an acetone-water solution of the polymer containing a surfactant was pumped through the lumen of solid polypropylene hollow fibers in a SHFCC device; cold liquid was circulated on the shell side. Because of rapid cooling-induced supersaturation and heterogeneous nucleation, precipitated polymers will coat the nanoparticles. The thickness and morphology of the nanocoating and the particle size distribution of the coated SNPs were analyzed by scanning transmission electron microscopy (STEM) with electron energy loss spectroscopy (EELS), thermogravimetric analysis (TGA), and dynamic light scattering (DLS). Results indicate that uniformly polymer-coated SNPs can be obtained from the SHFCC device after suitable post-treatments. The technique is also easily scalable by increasing the number of hollow fibers in the SHFCC device.

Further evidence that a terminal drought tolerance QTL of pearl millet is associated with reduced salt uptake.

Earlier, we established that a major drought tolerance QTL on linkage group 2 of pearl millet is also associated with reduced salt uptake and enhanced growth under salt stress. Present study was undertaken to re-assess the performance of drought tolerant (PRLT 2/89-33) and drought sensitive (H 77/833-2) parents along with two QTL-NILs (ICMR 01029 and ICMR 01040), under salinity stress specifically imposed during post-flowering growth stages when plants had developed their ion sinks in full. Time course changes in ionic accumulation and their compartmentalization in different plant parts was studied, specifically to monitor and capture changes conferred by the two alleles at this QTL, at small intervals. Amongst different plant parts, higher accumulation of toxic ion Na+ was recorded in roots. Further, the Na+ concentration in roots of the testcross hybrid of the drought-sensitive parent (H 77/833-2) reached its maximum at ECiw 15 dS m-1 within 24 h after salinity imposition, whereas it continued to increase with time in the testcross hybrids of the drought tolerant parent PRLT 2/89-33 as well as those of its QTL-NILs (ICMR 01029 and ICMR 01004) and reached at its maximum at 120 h stage. Comparison of differential distribution of toxic ions in individual leaves revealed that Na+ ions were not uniformly distributed in the leaves of the drought-tolerant parent and drought-tolerant QTL-NILs; but accumulated preferentially in the older leaves, whereas the hybrid of the drought-sensitive parent showed significantly higher Na+ concentration in all main stem leaves irrespective of their age. Dynamics of chlorophyll and proline concentration variation studied under salt stress at late flowering stages revealed a greater reduction, almost twice, in both leaf chlorophyll and proline concentrations in younger leaves in the hybrids of the sensitive parent as compared to the tolerant parent and QTL NILs. Imposition of salinity stress even at flowering stage affected the yield performance in pearl millet, wherein higher yield was recorded in drought tolerant parent and the two QTL-NILs compared to drought sensitive parent.

Quality of osmotically pre-treated and vacuum dried pineapple cubes on storage as influenced by type of solutes and packaging materials.

The quality and stability of osmotically pre-treated and subsequently vacuum dried pineapple cubes using three different solutes and packed in three different types of packaging materials on storage was evaluated. The experiment was laid out in completely randomized block design with two factors and three replications for each treatment. Treatment combinations were considered as one factor and storage interval as another factor. Pineapple cubes stored in glass bottle showed very little percentage variation in moisture content due to its high moisture barrier properties. In all treatment combination, acidity values were invariably found to increase as the storage progressed. For all three different osmotic treatments, HDPE pouch packet always showed highest acidity followed by PVDC pouch. Again among three solutes under consideration, invert sugar recorded a rapid increase in acidity than other solutes. In pineapple cubes osmotically treated with sucrose solution, the rates of decrease of total sugar content were lower than that of invert sugar and sorbitol treated pineapple cubes. The percentage decrease of total sugar content was highest when the osmotically dehydrated pineapple cubes were packed in HDPE pouch and it was least in glass bottles. There was a gradual decrease in ascorbic acid content with the extension of storage period and this decrease was statistically significant at all storage intervals up to six-month. Lowest value of ascorbic acid content (15.210 mg per 100 g initial solid) was recorded in invert sugar treated pineapple cube packed in HDPE pouch after 6 months of storage.

Computational study of the performance of a solar dryer for improvement in the shelf life of the food materials.

In this study, the thermal and drying characteristics of a thin layer food sample were investigated. An indirect type, simple, efficient, and economically feasible solar dryer was fabricated and used for food preservation. However, a dynamic model of a fabricated solar dryer was also presented to gain a better insight into the drying and thermal actions. This model consists of thermal modeling of the drying chamber, solar collector, and solar-dried food sample. The law of conservation of energy was applied to evaluate the temperature at different sections of the solar dryer with respect to drying time. All listed model equations were solved in the MATLAB environment. This study helps to examine the influence of solar radiation on the collector plate temperature, drying chamber temperature, food sample temperature, and performance parameters such as thermal efficiency with respect to drying time. Model data was found in good agreement with experimental data within a 4% error. It is concluded that the drying of food material is affected by air temperature, the collector temperature, mode of heat transfer, and material characteristics such as dimension and mass of the food sample.

Exploring the therapeutic potential of rosemary compounds against Alzheimer's disease through GC-MS and molecular docking analysis.

Alzheimer's disease (AD) is an age-related neurodegenerative disorder that is the leading cause of dementia in elderly individuals. Currently, there is no permanent treatment option available for this disorder, and the existing drug regimens are associated with limited effectiveness and side effects. To evaluate the neuroprotective effect of rosemary compounds, an extensive study was started with gas chromatography-mass spectrometry (GC-MS) analysis. GC-MS was performed to study the composition of rosemary essential oil and a total of 120 volatile compounds were identified. The 36 compounds from GC-MS data of rosemary essential oil having > 1% concentration in the oil were selected along with 3 already reported well-known non-volatile compounds of rosemary. se39 bioactive natural compounds of rosemary were docked against ACE, BACE1, GSK3, and TACE proteins, which are involved in AD progression. The top 3 compounds against each target protein were selected based on their binding energies and a total of 6 compounds were found as best candidates to target the AD; α Amyrin, Rosmanol, Androsta-1,4-dien-3-one,16,17-dihydroxy-(16.beta.,17.beta), Benzenesulfonamide,4-methyl-N-(5-nitro-2-pyridinyl), Methyl abietate, and Rosmarinic acid were the best compounds. The binding energy of α-Amyrin, Rosmanol, and Androsta-1,4-dien-3-one,16,17-dihydroxy-(16.beta.,17.beta) to ACE target is -10 kcal/mol, -9.3 kcal/mol, and - 9.3 kcal/mol, respectively. The best binding affinity was shown by complexes formed between GSK3-α-Amyrin (-9.1 kcal/mol), BACE1- α-Amyrin (-9.9 kcal/mol), and TACE- Benzenesulfonamide,4-methyl-N-(5-nitro-2-pyridinyl) (-9.1 kcal/mol). The comparative analysis between known inhibitors/ drugs of target proteins and the rosemary compound that shows the highest binding affinity against each protein also revealed the higher potential of rosemary natural compounds in terms of binding energy. The drug-likeliness properties like Lipinski's rule of five and the ADME/T analysis of top-selected compounds were screened through PkCSM and Deep-PK tools. The findings from this study suggested that rosemary compounds have the potential as a therapeutic lead for treating AD. This kind of experimental confirmation can lead to novel drug candidates against the pharmacological targets of AD. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-024-00238-9.

Natural wax recovery from Musa acuminata biomass using organic solvents.

The main aim of this study is to experimentally investigate the yield of extraction and the presence of wax in the extracted yield from Musaacuminata (banana) biomass based on various functional groups that are present in natural wax. Extraction of natural wax from Musaacuminata (banana) biomass has been done by using the Soxhlet apparatus method in the presence of both polar (ethyl acetate and ethanol) and non-polar (toluene and hexane) solvents. The extracted yield has been found as 3.58% from hexane, 5.16% from toluene, 7.03% from ethyl acetate, and 10.26% from ethanol. The wax was also found in the extracted yield only in the case of nonpolar solvents (toluene and hexane). The novelty of this work is that Musaacuminata (banana) waste biomass has been utilized to recover the natural wax using nonpolar solvents and also compared with that of polar solvents to check the scope of wax extraction using polar solvents. Also, statistical analysis has been performed of the extracted yield using both solvents. Thin Layer Chromatography (TLC) and Fourier Transform Infrared Spectroscopy (FTIR) methods have been used to determine the various hydrocarbon chains present in the extracted yield which is similar to that of natural wax.