Ajuga bracteosa root extracts may provide a basis for development of antioxidant, antimicrobial and antidiabetic drugs.

Earthworms (Crassiclitellata) being ecosystem engineers significantly affect the physical, chemical, and biological properties of the soil by recycling organic material, increasing nutrient availability, and improving soil structure. The efficiency of earthworms in ecology varies along with species. Therefore, the role of taxonomy in earthworm study is significant. The taxonomy of earthworms cannot reliably be established through morphological characteristics because the small and simple body plan of the earthworm does not have anatomical complex and highly specialized structures. Recently, molecular techniques have been adopted to accurately classify the earthworm species but these techniques are time-consuming and costly. To combat this issue, in this study, we propose a machine learning-based earthworm species identification model that uses digital images of earthworms. We performed a stringent performance evaluation not only through 10-fold cross-validation and on an external validation dataset but also in real settings by involving an experienced taxonomist. In all the evaluation settings, our proposed model has given state-of-the-art performance and justified its use to aid earthworm taxonomy studies. We made this model openly accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/ESIDE.

Earthworms are highly productive invertebrates and play a vital role in organic farming and improving soil structure and function. The gastrointestinal tract of earthworms possessed agricultural important bacteria. So, the current research aimed was to examine, screen, and identify the plant growth promoting bacteria existing in the digestive tract of Eisenia fetida called plant growth promoting vermi-bacteria. The plant growth promoting traits such as siderophore, phytohormone, and hydrolytic enzymes production, and phosphate solubiliation were assessed. Eleven vermi-bacteria i.e. Bacillus mycoides, B. aryabhattai, B. megaterium, Staphylococcus hominis, B. subtilis, B. spizizenii, B. licheniformis, B. mojavensis, B. toyonensis, B. anthracis, B. cereus, B. thuringiensis, and B. paranthracis were isolated and identified based on microscopic studies, biochemical tests, ribotyping, and agricultural traits. All vermi-bacteria are Gram-positive rods except Staphylococcus hominis and produce different compounds such as siderophore, indole acetic acid, catalase, oxidase, proteases, amylases, and lipases. All vermi-bacteria also act as phosphate solubilizers. Therefore, all isolated vermi-bacteria could be used as potential microbial biofertilizers to enhance crops production in Pakistan.

The results of antioxidant assays with Gymnema sylvestre leaf extracts revealed that methanol, acetone, and aqueous extracts possessed maximum DPPH (83, 83, and 75%) and ABTS scavenging potential (85, 71, and 82%). Chloroform possessed maximum total flavonoid (24.6 mg/100 ml) and total iron contents (435.3 ± 0.0125 mg FeSO4 E/G). The extracts of n-hexane, ethyl acetate, and chloroform of Gymnema sylvestre leaves displayed an IC50 value of 170.2, 44.4, 131.6 μg/ml, respectively in α-glucosidase inhibition assay. Methanol and aqueous extracts showed maximum inhibition of E. ammni (17 ± 2.0 and 19.6 ± 0.57 mm), E. coli (15.3 ± 0.57 and 17.6 ± 0.57 mm), and S. aureus (16.6 ± 1.52 and 19.3 ± 1.15 mm). It may be concluded that all potential effects of G. sylvestre leaf extracts were due to the presence of phytochemical constituents such as flavonoids, steroids, quinones, phenols, triterpenoids, tannins, alkaloids, and saponins. Present findings suggest that these extracts present a satisfactory source for the preparation of antioxidant and antidiabetic medicines.

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