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In the present research, oleuropein (OLE) contents from two Saudi Arabian wild olive trees (Olea europaea L.) leaves (O1 and O2), were collected from two nearby geographical sites differing in altitudes, and were determined via UHPLC-MS analysis. Moreover, total bioactive contents, antioxidant, and cytotoxicity (against MCF-7 and MDA-MB-231 cells) potential were also evaluated. The sample (O2) was found to contain significantly (p < 0.05) higher OLE content (4.13 ± 1.0 mg/g DW) compared with the sample (O1) having OLE content (3.63 ± 1.1 mg/g DW). A similar trend was observed regarding total bioactive contents and antioxidant potential. However, both samples exhibited low cytotoxicity against tested cell lines. Furthermore, with hierarchical cluster analysis that compared the results of our samples (O1 and O2) to other samples reported in the literature, it was found that the variance in OLE content and biological activities from Al Baha region leaves had a resemblance to other reported superior cultivars.

Strobilanthes glutinosus (family: Acanthaceae) is one of the important medicinal plants and the current research work was carried out to assess the phytochemical, biological, and in-silico evaluations of different solvent extracts of S. glutinosus. The phytochemical composition was assessed by determining qualitative phytochemical testing, total bioactive contents, and GC-MS analysis. For biological evaluation, antioxidant (DPPH and FRAP), antibacterial, and enzyme inhibition (urease) potential were determined. The methanol extract was found to contain the highest total phenolic (247.85 mg GAE/g extract), and flavonoid content (71.91 mg QAE/g extract), which can be correlated to the observed higher antioxidant potential of this extract. On the contrary, the n-hexane extract presented maximum anti-urease activity with IC50 value of 0.244 mg/ml, whereas, in the case of antibacterial assays, a dose-dependent activity was observed. Similarly, the GC-MS investigation of methanol extract and n-hexane fraction revealed the tentative identification of several important classes of secondary metabolites. Furthermore, the main compounds as identified by GC-MS were further studied for in-silico molecular docking studies in order to highlight any interactions between the compounds identified and the urease enzyme. Overall, the present investigation highlighted the studied plant extracts to be considered further for the isolation of bioactive compounds for the pharmaceutical and food industry.

The study aimed to assess the phytochemical composition and enzyme inhibitory potential of methanol, n-hexane, chloroform, and n-butanol extracts/fractions of Indigofera argentea Berm. f. (whole plant), a medicinal plant from Pakistan. Chemical profiling of I. argentea crude methanol extract was done via ultra-high performance liquid chromatography mass spectrometry (UHPLC-MS) in both the positive and negative ionization modes. Standardin vitro bioassays were used to evaluate the enzyme inhibition potential against carbonic anhydrase, xanthine oxidase, and α-glucosidase. Additionally, nine dominant compounds as tentatively identified by UHPLC-MS profiling were docked against the tested enzymes to provide additional insights into the binding affinities against the target enzymes. UHPLC-MS chemical profiling showed the tentative presence of seventy-six secondary metabolites. The identified compounds include flavonoids and lithocholic acid derivatives of steroids in major amounts along with terpenes. Lithocholic acid derivatives were first reported from this plant and consider being potential candidates for treating diabetes. All extracts showed promising results against α-glucosidase with percentage inhibition of 82.8–21.2%, while for the xanthine oxidase the percentage inhibition values varying from 67.8–47.3%. Furthermore, moderate inhibition was observed against carbonic anhydrase. The methanolic extracts showed good inhibition against B. subtilis, K. pneumonia, P. vulgaris and P. aeruginosa as compared to Ciprofloxacin. Docking studies showed that atalanine showed higher inhibition affinity against the three studied enzymes as compared to morphine. Indigofera argentea showed promising enzyme inhibitory activities and can be considered as a potential source of bioactive secondary metabolites. Isolation of the bioactive compounds and further mechanistic studies could provide further insights into the biopharmaceutical potential of this plant as a nutraceutical and/or dietary supplement.

The current research investigated the chemical profiling and biological activities of Emex australis Steinh. aerial and fruit parts (methanol and dichloromethane-DCM extracts). Chemical composition was established by determining the amounts of phenolic and flavonoid contents, HPLC-PDA polyphenolic quantification, and UHPLC-MS secondary metabolites profiling to understand the observed biological activities. Antioxidant evaluation was performed utilizing six different assays (DPPH, ABTS, FRAP, CUPRAC, phosphomolybdenum, and metal chelation). Moreover, enzyme inhibition abilities against cholinesterases, α-amylase, α-glucosidase, and tyrosinase were also determined. The fruit methanol extract contained the highest phenolic (157.96 mg GAE/ g extract) and flavonoid contents (41.43 mg QE/g extract), which could justify the observed significant antioxidant (except metal chelating), α-amylase, α-glucosidase, and tyrosinase inhibitory potentials. The other extracts were active against cholinesterases and tyrosinase. The HPLC-PDA polyphenolic analysis quantified a number of 11 phenolic secondary metabolites. The fruit-MeOH extract was found to quantify the maximum phenolics with catechin (4.27 μg/g extract) and gallic acid (4.84 μg/g extract) in higher amounts. Similarly, rutin present in maximum quantity in the aerial-MeOH extract (2.93 μg/g extract). Furthermore, the UHPLC-MS study of aerial and fruit methanol extracts reveals the existence of 22 secondary metabolites, most of which were flavonoid derivatives. Besides that, Pearson coefficient analysis (PCA) was also carried to determine a possible correlation between bioactive contents and observed biological assays. Thus, it is concluded that E. australis extracts contain important classes of secondary metabolites and also exhibited considerable antioxidant and inhibition potential against clinically relevant enzymes.

The plants of the Erythrina genus possess tremendous pharmacological properties and thus are utilized in traditional medicines owing to their bioactivities. In the present study, methanolic extract (EsM) of flowers of Erythrina suberosa Roxb. and its fractions (n-hexane (EsH), chloroform (EsC) and aqueous (EsW) parts) were tested for preliminary phytochemical analysis, antioxidant and urease inhibition activities. The EsM extract was found to be most active for antioxidant activity, whereas the EsH extract showed the highest anti-urease potential with an inhibition value of 62.9 % (at a concentration of 100 µg/ml), while the other extracts were least active. The EsH extract being most active against urease enzyme, was further subjected to gas chromatography-mass spectrometry (GC-MS) analysis, which revealed the tentative presence of 18 secondary metabolites of various classes. Furthermore, the in-silico studies of the major compounds as detected in EsH extract by GC-MS analysis were performed to correlate the potential interaction of compounds with the active site of the enzyme. The selected compounds were docked against the urease enzyme, and their affinities were compared. Binding affinity data and interaction patterns of all the studied phytochemicals indicated that these compounds could inhibit urease enzyme synergistically and can be further studied to prevent ulcer and related problems. It is, therefore concluded that E. suberosa flowers possess interesting antioxidant and anti-urease potential and thus could further be adopted for industrial and biopharmaceutical applications.

In the current study, we investigated the phytochemical and neuropharmacological potential of Indigofera sessiliflora, an indigenous least characterized plant widely distributed in deserted areas of Pakistan. The crude extract of the whole plant Indigofera sessiliflora (IS.CR) was preliminary tested in-vitro for the existence of polyphenol content, antioxidant and anticholinesterase potential followed by detailed chemical characterization through UHPLC-MS. Rats administered with different doses of IS.CR (100-300 mg/kg) for the duration of 4-weeks were behaviorally tested for anxiety and cognition followed by biochemical evaluation of dissected brain. The in-silico studies were employed to predict the blood-brain barrier crossing tendencies of secondary metabolites with the elucidation of the target binding site. The in-vitro assays revealed ample phenols and flavonoids content in IS.CR with adequate anti-oxidant and anticholinesterase potential. The dose-dependent anxiolytic potential of IS.CR was demonstrated in open field (OFT), light/dark (L/D) and elevated plus maze (EPM) tests as animals spent more time in open, illuminated and elevated zones (P < 0.05). In the behavioral tests for learning/memory, the IS.CR reversed the scopolamine-induced cognitive deficits, as animals showed better (P < 0.05) spontaneous alternation and discrimination index in y-maze and novel object recognition (NOR) tests. Similarly, as compared to amnesic rats, the step-through latencies were increased (P < 0.05) and escape latencies were decreased (P < 0.05) in passive avoidance (PAT) and Morris water maze (MWM) tests, respectively. Biochemical analysis of rat brains showed significant reduction in malondialdehyde and acetylcholinesterase levels, alongwith preservation of glutathione peroxidase and superoxide dismutase activity. The docking studies further portrayed a possible interaction of detected phytoconstituents with acetylcholinesterase target. The results of the study show valuable therapeutic potential of phytoconstituents present in IS.CR to correct the neurological disarrays which might be through antioxidant activity or via modulation of GABAergic and cholinergic systems by artocommunol, 1,9-dideoxyforskolin and 6E,9E-octadecadienoic acid.

Deep learning (DL) is an effective approach to identifying plant diseases. Among several DL-based techniques, transfer learning (TL) produces significant results in terms of improved accuracy. However, the usefulness of TL has not yet been explored using weights optimized from agricultural datasets. Furthermore, the detection of plant diseases in different organs of various vegetables has not yet been performed using a trained/optimized DL model. Moreover, the presence/detection of multiple diseases in vegetable organs has not yet been investigated. To address these research gaps, a new dataset named NZDLPlantDisease-v2 has been collected for New Zealand vegetables. The dataset includes 28 healthy and defective organs of beans, broccoli, cabbage, cauliflower, kumara, peas, potato, and tomato. This paper presents a transfer learning method that optimizes weights obtained through agricultural datasets for better outcomes in plant disease identification. First, several DL architectures are compared to obtain the best-suited model, and then, data augmentation techniques are applied. The Faster Region-based Convolutional Neural Network (RCNN) Inception ResNet-v2 attained the highest mean average precision (mAP) compared to the other DL models including different versions of Faster RCNN, Single-Shot Multibox Detector (SSD), Region-based Fully Convolutional Networks (RFCN), RetinaNet, and EfficientDet. Next, weight optimization is performed on datasets including PlantVillage, NZDLPlantDisease-v1, and DeepWeeds using image resizers, interpolators, initializers, batch normalization, and DL optimizers. Updated/optimized weights are then used to retrain the Faster RCNN Inception ResNet-v2 model on the proposed dataset. Finally, the results are compared with the model trained/optimized using a large dataset, such as Common Objects in Context (COCO). The final mAP improves by 9.25% and is found to be 91.33%. Moreover, the robustness of the methodology is demonstrated by testing the final model on an external dataset and using the stratified k-fold cross-validation method.

Recently, agriculture has gained much attention regarding automation by artificial intelligence techniques and robotic systems. Particularly, with the advancements in machine learning (ML) concepts, significant improvements have been observed in agricultural tasks. The ability of automatic feature extraction creates an adaptive nature in deep learning (DL), specifically convolutional neural networks to achieve human-level accuracy in various agricultural applications, prominent among which are plant disease detection and classification, weed/crop discrimination, fruit counting, land cover classification, and crop/plant recognition. This review presents the performance of recent uses in agricultural robots by the implementation of ML and DL algorithms/architectures during the last decade. Performance plots are drawn to study the effectiveness of deep learning over traditional machine learning models for certain agricultural operations. The analysis of prominent studies highlighted that the DL-based models, like RCNN (Region-based Convolutional Neural Network), achieve a higher plant disease/pest detection rate (82.51%) than the well-known ML algorithms, including Multi-Layer Perceptron (64.9%) and K-nearest Neighbour (63.76%). The famous DL architecture named ResNet-18 attained more accurate Area Under the Curve (94.84%), and outperformed ML-based techniques, including Random Forest (RF) (70.16%) and Support Vector Machine (SVM) (60.6%), for crop/weed discrimination. Another DL model called FCN (Fully Convolutional Networks) recorded higher accuracy (83.9%) than SVM (67.6%) and RF (65.6%) algorithms for the classification of agricultural land covers. Finally, some important research gaps from the previous studies and innovative future directions are also noted to help propel automation in agriculture up to the next level.

Anagallis arvensis L. commonly known as ‘Scarlet Pimpernel’ has been used in folklore as natural remedy for treating common ailments. The present research is aimed to explore the phytochemical comp...

The accurate identification of weeds is an essential step for a site-specific weed management system. In recent years, deep learning (DL) has got rapid advancements to perform complex agricultural tasks. The previous studies emphasized the evaluation of advanced training techniques or modifying the well-known DL models to improve the overall accuracy. In contrast, this research attempted to improve the mean average precision (mAP) for the detection and classification of eight classes of weeds by proposing a novel DL-based methodology. First, a comprehensive analysis of single-stage and two-stage neural networks including Single-shot MultiBox Detector (SSD), You look only Once (YOLO-v4), EfficientDet, CenterNet, RetinaNet, Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Network (RFCN), has been performed. Next, the effects of image resizing techniques along with four image interpolation methods have been studied. It led to the final stage of the research through optimization of the weights of the best-acquired model by initialization techniques, batch normalization, and DL optimization algorithms. The effectiveness of the proposed work is proven due to a high mAP of 93.44% and validated by the stratified k-fold cross-validation technique. It was 5.8% improved as compared to the results obtained by the default settings of the best-suited DL architecture (Faster RCNN ResNet-101). The presented pipeline would be a baseline study for the research community to explore several tasks such as real-time detection and reducing the computation/training time. All the relevant data including the annotated dataset, configuration files, and inference graph of the final model are provided with this article. Furthermore, the selection of the DeepWeeds dataset shows the robustness/practicality of the study because it contains images collected in a real/complex agricultural environment. Therefore, this research would be a considerable step toward an efficient and automatic weed control system.

Natural Products (NP), specifically from medicinal plants or herbs, have been extensively utilized to analyze the fundamental mechanisms of ultimate natural sciences as well as therapeutics. Isolation of secondary metabolites from these sources and their respective biological properties, along with their lower toxicities and cost-effectiveness, make them a significant research focus for drug discovery. In recent times, there has been a considerable focus on isolating new chemical entities from natural flora to meet the immense demand for kinase modulators, and also to overcome major unmet medical challenges in relation to signal transduction pathways. The signal transduction systems are amongst the foremost pathways involved in the maintenance of life and protein kinases play an imperative part in these signaling pathways. It is important to find a kinase inhibitor, as it can be used not only to study cell biology but can also be used as a drug candidate for cancer and metabolic disorders. A number of plant extracts and their isolated secondary metabolites such as flavonoids, phenolics, terpenoids, and alkaloids have exhibited activities against various kinases. In the current review, we have presented a brief overview of some important classes of plant secondary metabolites as kinase modulators. Moreover, a number of phytocompounds with kinase inhibition potential, isolated from different plant species, are also discussed.

The biological, chemical, and in silico properties of methanol and dichloromethane (DCM) extracts of Alhagi maurorum roots with respect to the antioxidant, enzyme inhibition, and phytochemical comp...

In this paper, sensitivity analyses (SA) are performed in order to find the effect of variations of master/slave site dynamics parameters on the perceived impedance in haptic teleoperation in the absence/presence of communication time delays. These analyses are useful in finding the dominant parameter perceived by the human operator during the teleoperation. In this regard, the comprehensive sensitivity analyses are performed for the prominent force reflecting teleoperation architectures such as (i) two-channel (2C) teleoperation architectures mainly (a) position error-based architecture (PE) and (b) direct force reflection architecture (PF), (ii) four-channel architecture (4C) and, (iii) three-channel (3C) architecture (a) OFC architecture and (b) EFC architecture. Several important results and conclusions are presented for sensitivity of perceived impedance in frequency domain using MATLAB/Simulink.

This study sets out to probe into total bioactive contents, UHPLC-MS secondary metabolites profiling, antioxidant (DPPH, ABTS, FRAP, CUPRAC, phosphomolybdenum and metal chelating) and enzyme inhibi...

In this study, different parts (aerial, stem and root) of Salvadora oleoides Decne were investigated in order to explore their phytochemical composition and biological potential. The bioactive contents were evaluated by conventional spectrophotometric methods. Additionally, the secondary metabolite compounds were identified by UHPLC-MS analysis. Biological potential was evaluated by determining antioxidant (DPPH, FRAP, and Phosphomolybdenum) and enzyme inhibitory (butrylcholinesterase and lipoxygenase) effects. Higher total bioactive contents were found in methanolic extracts which tend to correlate with higher radical scavenging and reducing potential of these extracts. LC/MS spectrum revealed the presence of 16 different secondary metabolites belonging to terpene, glucoside and sesquiterpenoid dervivatives. Glucocleomin and emotin A were the main compounds present in all three parts. The strongest butrylcholinesterase and lipoxygenase inhibitory activity was observed for root and stem DCM extracts. Demonstrated biological potential of S. oleoides plant can trace a new road map for developing newly designed bioactive pharmaceuticals.

In this study, phytochemical composition, antioxidant, enzyme inhibition and cytotoxic activities of methanol and dichloromethane (DCM) extracts of Bougainvillea glabra (B. glabra) flowers were investigated. Methanol extract was found to have higher total bioactive contents and UHPLC-MS analysis of methanol extract revealed the presence of well-known phenolic and flavonoid compounds. Antioxidant activities were performed by radical scavenging (DPPH and ABTS), reducing power (FRAP and CUPRAC), phosphomolybdenum (TAC) and metal chelating assays. From our result, we observed that methanol extract had many antioxidant compounds. The DCM extract exhibited higher cholinesterases and α-glucosidase enzyme inhibition, while methanol extract showed significant urease inhibition. Both extracts exhibited strong to moderate cytotoxicity against MCF-7, MDA-MB-231, CaSki, DU-145 and SW-480 cancer cells with IC50 values ranging from 88.49 to 304.7 µg/mL. The findings showed the B. glabra to possess considerable antioxidant, enzyme inhibition and cytotoxic potentials and therefore has potential to discover novel bioactive molecules.

We investigated into the effects of methanol and dichloromethane extracts from aerial and roots of Filago germanica (L.) Huds (Astearaceae) on key enzymes (cholinesterases, α-glucosidase and urease), antioxidant capabilities, cytotoxic potential and secondary metabolomics profile. Total phenolic and flavonoids were determined by spectrophotometric technique and secondary metabolites composition by UHPLC-MS. Antioxidant activities were assessed employing free radical scavenging, ferric reducing power and phosphomolybdenum assays. The cell-toxicity was evaluated by MTT assay against breast (MCF-7, MDA-MB-231), cervix (CaSki) and prostate (DU-145) cancers. Overall, methanol extracts were found to have higher total bioactive contents and antioxidant potential. UHPLC-MS analysis revealed significant variation in the secondary metabolites in the methanol extracts. The most common derivatives belong to seven groups i.e. alkaloids, benzoic acids, flavones, flavonols, flavan-3-ols, terpenoids and saponins. The major polyphenolic compounds were found to be kampferol, robinin, luteolin, ferulic acid, benzoic acid and salicylic acid. All the extracts showed moderate cholinesterases inhibition, whereas methanol extracts exhibited highest urease inhibition and all extracts presented a relatively high inhibition against α-glucosidase. Similarly, all extracts showed strong to moderate cytotoxicity with IC50 values ranging from 53.02 to 382.7 μg/mL. Overall, results have suggested F. germanica to be a lead source for novel natural products.

Sorghum halepense L (Poaceae), ordinarily it is known as Johnson grass and locally as baru. This study was designed to find the vascular mechanisms underlying the hypotensive activity of S. halepense. In this study, effect of S. halepense seed extract/fractions on various blood pressure parameters were evaluated in normal and fructose induced hypertensive rats by invasive technique. Possible underlying hypotensive mechanism of active fraction was determined by using various pharmacological inhibitors. S. halepense extract/fractions vasorelaxant effect were also evaluated on rat aorta rings in organ bath and various intracellular signaling pathway inhibitors were used for determination of underlying mechanisms. S. halepense extract/fractions produced blood pressure lowering effect with most significant effect by its aqueous soluble fraction at dose of 10mg/kg. This effect was attenuated by pretreatment of atropine. Aqueous soluble fraction produced endothelium dependent vasorelaxation in rat aortic rings that was inhibited by pretreatment of atropine after phenylephrine induced contraction. The vasorelaxant effect of aqueous soluble fraction was attenuated by potassium channel blockers and also produced inhibitory effect on calcium entry through calcium channels. It also suppressed phenylephrine induced contraction like verapamil. By HPLC analysis found vanillic acid and naringinin in it. In conclusion, aqueous soluble fraction of S.halepense possess phytoconstituents which may be responsible for hypotensive and vasorelaxant effect of Sorghum halepense.

The aim of this study was the evaluation of diuretic potential of Delphinium brunonianum. Acute diuretic effect in rats was evaluated 8 h after administration of various doses of crude extract, fractions and hydrochlorthiazide. While, prolonged effect of butanolic fraction was assessed after 7days of oral administration in rats. Thereafter, involvement of different pathways in diuretic activity was also appraised. Furthermore, polyphenolic contents in butanolic fraction were assessed using HPLC/UV-VIS technique. All doses of extract and fractions induced a prominent increase in urine and Na

To apply weed control treatments effectively, the weeds must be accurately detected. Deep learning (DL) has been quite successful in performing the weed identification task. However, various aspects of the DL have not been explored in previous studies. This research aimed to achieve a high average precision (AP) of eight classes of weeds and a negative (non-weed) class, using the DeepWeeds dataset. In this regard, a DL-based two-step methodology has been proposed. This article is the second stage of the research, while the first stage has already been published. The former phase presented a weed detection pipeline and consisted of the evaluation of various neural networks, image resizers, and weight optimization techniques. Although a significant improvement in the mean average precision (mAP) was attained. However, the Chinee apple weed did not reach a high average precision. This result provided a solid ground for the next stage of the study. Hence, this paper presents an in-depth analysis of the Faster Region-based Convolutional Neural Network (RCNN) with ResNet-101, the best-obtained model in the past step. The architectural details of the Faster RCNN model have been thoroughly studied to investigate each class of weeds. It was empirically found that the generation of anchor boxes affects the training and testing performance of the Faster RCNN model. An enhancement to the anchor box scales and aspect ratios has been attempted by various combinations. The final results, with the addition of 64 × 64 scale size, and aspect ratio of 1:3 and 3:1, produced the best classification and localization of all classes of weeds and a negative class. An enhancement of 24.95% AP was obtained in Chinee apple weed. Furthermore, the mAP was improved by 2.58%. The robustness of the approach has been shown by the stratified k-fold cross-validation technique and testing on an external dataset.

Heliotropium is one of the most important plant genera to have conventional folklore importance, and hence is a potential source of bioactive compounds. Thus, the present study was designed to explore the therapeutic potential of Heliotropium crispum Desf., a relatively under-explored medicinal plant species. Methanolic extracts prepared from a whole plant of H. crispum were studied for phytochemical composition and possible in vitro and in silico biological properties. Antioxidant potential was assessed via six different assays, and enzyme inhibition potential against key clinical enzymes involved in neurodegenerative diseases (acetylcholinesterase (AChE) and butyrylcholinesterase (BChE)), diabetes (α-amylase and α-glucosidase), and skin problems (tyrosinase) was assayed. Phytochemical composition was established via determination of the total bioactive contents and reverse phase ultra-high performance liquid chromatography mass spectrometry (RP-UHPLC-MS) analysis. Chemical profiling revealed the tentative presence of 50 secondary metabolites. The plant extract exhibited significant inhibition against AChE and BChE enzymes, with values of 3.80 and 3.44 mg GALAE/g extract, respectively. Further, the extract displayed considerable free radical scavenging activity against DPPH and ABTS radicals, with potential values of 43.19 and 41.80 mg TE/g extract, respectively. In addition, the selected compounds were then docked against the tested enzymes, which have shown high inhibition affinity. To conclude, H. crispum was found to harbor bioactive compounds and showed potent biological activities which could be further explored for potential uses in nutraceutical and pharmaceutical industries, particularly as a neuroprotective agent.