Agharkar Research Institute

Endophytic fungi profoundly influence plant physiology and chemical ecology, yet their integrated functional roles remain underexplored, especially in ferns. Here, we reveal that Tectaria coadunata (J.Sm.) C.Chr. harbours a taxonomically rich and metabolically active endophytic mycobiome comprising ten genera with distinct ecological and evolutionary lineages. Morphological diagnostics combined with multilocus phylogenetics resolved all isolates with high confidence, delineating well-supported clades across Aspergillus, Calonectria, Diaporthe, Fusarium, Macrophomina, Neurospora, Nigrospora, Penicillium, Rhizoctonia, and Xylaria. LC-Q-TOF-MS/MS profiling uncovered extensive metabolic cross-communication between the host plant and endophytes. Nine phytochemicals previously attributed to T. coadunata were also present in fungal endophytes, while eleven additional metabolites, including potent polyketides, mellein, and mycotoxins were exclusively endophytic. Multivariate and network analyses revealed distinct chemical profiles among different fungal isolates and coordinated biosynthetic modules, underscoring functional heterogeneity of the community. All isolates exhibited antimicrobial activity, but Xylaria grammica had the least minimum inhibitory concentration of 15.63 µg/mL against Bacillus subtilis and methicillin-resistant Staphylococcus aureus. This pronounced activity, coupled with its broad-spectrum inhibition, positions X. grammica as a promising reservoir of pharmaceutically relevant molecules. Collectively, our findings establish T. coadunata as a reservoir of phylogenetically diverse, chemically productive, and bioactive endophytes. These combined taxonomic, metabolomic, and functional insights into endophytes from T. coadunata open new avenues for potential applications across various fields.

The genus Haplanthodes (family: Acanthaceae) is endemic to India and comprises five taxa. Due to their morphological similarities, they are often difficult to distinguish from one another. To clarify interspecific relationships and identify key diagnostic characteristics, a comparative study was conducted examining the organoleptic, macroscopic, and anatomical features of the stems, roots, cladodes, and leaves of all five taxa. The findings suggest that both external and internal morphological features provide valuable insights into the macro and microstructural classification of Haplanthodes. The genus has a unique morphological feature that sets it apart from other genera in the Acanthaceae family, such as Andrographis and Haplanthus. At the species level, H. verticillata, cladodes are stiff, spinose, and verticillate; whereas in H. tentaculata, they are of similar size but smooth and non-spinose; in H. plumosa, the lower half of the cladodes are densely hairy, giving a plume-like appearance; and in H. neilgherryensis and H. neilgherryensis var. toranganensis, they are thin, soft, and filiform. These features are particularly useful for species identification in the absence of reproductive structures. Anatomical differences in the leaf midrib also support species-level distinctions: H. verticillata exhibits a plano-convex, squarish outline with conjoint, closed, collateral vascular bundles; H. plumosa, H. neilgherryensis, H. tentaculata, and H. neilgherryensis var. toranganensis show a convex, triangular to wavy-squarish outline with similar vascular bundle configurations. These foundational anatomical and morphological data contribute to a more robust framework for species delimitation within the genus and provide a basis for future studies within the tribe Acantheae and the broader Acanthaceae family.

The review provides an in-depth analysis of various factors that affect the long-term success of implants and scrutinizes all available techniques for dental implant modifications, along with their advantages and limitations. Along with established and proposed strategies, newer trends such as responsive coatings, ‘omics’ and AI-based possibilities for translating research into clinical settings are discussed. The available scientific literature on dental implants, causes for their failures, and possible surface modification techniques was collected and analyzed. Strategies to prevent implant failures are presented as a comprehensive, structured review. A literature review of scientific research papers published over the last decade clearly indicates that surface modification of dental implants is critical for ensuring long-term success. Strategies aimed at surface changes consider the intrinsic antibacterial activity, surface texture, and geometry of the implant material. In both healthy and compromised patients, bio-functionalized surfaces can improve osseointegration and reduce peri-implantitis, boosting the success of dental implants. Dental implants, while promising, face hurdles that hinder their long-term success. Modifying implants through physical, chemical, or mechanical methods could potentially address these challenges. These techniques would require clinical validation before being fully integrated into clinical practice. Moreover, crucial factors such as immune response and in vivo testing are often overlooked.

A genetic algorithm-optimized deep neural network was developed using proximal sensing data to accurately predict wheat yield at field scale, outperforming traditional machine learning models under diverse conditions. Hand held or vehicle-mounted active proximal sensing technologies offer a rapid, non-destructive method for real-time crop monitoring through spectral vegetation indices. This study integrates such proximal sensing data into a deep learning framework for field-scale wheat yield prediction. Specifically, wheat yield is predicted using normalized difference vegetation index (NDVI), canopy temperature (CT), and plant height (PH) through a deep neural network (DNN) optimized using a genetic algorithm (GA). The model is trained on data from 3,350 diverse wheat germplasm grown under irrigated and rainfed conditions at two locations during the 2020–2021 winter season. Comparative analysis demonstrates that the GA-optimized DNN outperforms traditional machine learning models such as Random Forest Regression (RFR), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Regression (SVR). Among individual feature groups, NDVI measured at five wheat growth stages showing strong predictive capability, with R2 values ≥ 60

Lichen is a unique symbiotic organism that consists of fungi and photosynthetic algae and or cyanobacteria. They are known for producing a large repository of secondary metabolites, among which depsides and depsidones gain pharmacological interest. This review meticulously examines the anticancer efficacy of lichen-derived depsides and depsidones, with a focus on their chemical composition, biosynthetic pathways, and molecular mechanisms that underpin their antitumor activities across various cancer cell lines. These compounds have shown notable bioactivities, including cytotoxicity, apoptosis, and suppression of critical oncogenic cascades such as cellular proliferation, metastasis, and angiogenesis. In some studies, they have shown their selectivity for malignant cells while having minimal cytotoxicity towards healthy cells. This review also addresses the challenges for isolation and large-scale production of these metabolites and also explores the aspect of chemical synthesis or designing of synthetic analogues to increase stability, potency, and pharmacokinetic profile. In conclusion, this review emphasizes the potential application of depsides and depsidones as natural anticancer drugs, as studies strongly recommend conducting further analysis using laboratory models.