Neil 1 deficiency facilitates chemoresistance through upregulation of RAD18 expression in ovarian cancer stem cells.

Over the past decades, cancer stem cells (CSCs) have emerged as a critical subset of tumor cells associated with tumor recurrence and resistance to chemotherapy. Understanding the mechanisms underlying CSC-mediated chemoresistance is imperative for improving cancer therapy outcomes. This study delves into the regulatory role of NEIL1, a DNA glycosylase, in chemoresistance in ovarian CSCs. We first observed a decreased expression of NEIL1 in ovarian CSCs, suggesting its potential involvement in CSC regulation. Using pan-cancer analysis, we confirmed the diminished NEIL1 expression in ovarian tumors compared to normal tissues. Furthermore, NEIL1 downregulation correlated with an increase in stemness markers and enrichment of CSCs, highlighting its role in modulating CSC phenotype. Further mechanistic investigation revealed an inverse correlation between NEIL1 and RAD18 expression in ovarian CSCs. NEIL1 depletion led to heightened RAD18 expression, promoting chemoresistance possibly via enhancing Translesion DNA Synthesis (TLS)-mediated DNA lesion bypass. Moreover, dowregulation of NEIL1 results in reduced DNA damage accumulation and suppressed apoptosis in ovarian cancer. Overall, our findings unveil a novel mechanism involving NEIL1 and RAD18 in regulating chemoresistance in ovarian CSCs. Targeting this NEIL1-RAD18 axis may offer promising therapeutic strategies for combating chemoresistance and improving ovarian cancer treatment outcomes.

Mitochondria act as a key regulatory factor in cancer progression: Current concepts on mutations, mitochondrial dynamics, and therapeutic approach.

The diversified impacts of mitochondrial function vs. dysfunction have been observed in almost all disease conditions including cancers. Mitochondria play crucial roles in cellular homeostasis and integrity, however, mitochondrial dysfunctions influenced by alterations in the mtDNA can disrupt cellular balance. Many external stimuli or cellular defects that cause cellular integrity abnormalities, also impact mitochondrial functions. Imbalances in mitochondrial activity can initiate and lead to accumulations of genetic mutations and can promote the processes of tumorigenesis, progression, and survival. This comprehensive review summarizes epigenetic and genetic alterations that affect the functionality of the mitochondria, with considerations of cellular metabolism, and as influenced by ethnicity. We have also reviewed recent insights regarding mitochondrial dynamics, miRNAs, exosomes that play pivotal roles in cancer promotion, and the impact of mitochondrial dynamics on immune cell mechanisms. The review also summarizes recent therapeutic approaches targeting mitochondria in anti-cancer treatment strategies.

Identification and mechanistic exploration of structural and conformational dynamics of NF-kB inhibitors: rationale insights from in silico and in vitro studies.

Increased expression of target genes that code for proinflammatory chemical mediators results from a series of intracellular cascades triggered by activation of dysregulated NF-κB signaling pathway. Dysfunctional NF-kB signaling amplifies and perpetuates autoimmune responses in inflammatory diseases, including psoriasis. This study aimed to identify therapeutically relevant NF-kB inhibitors and elucidate the mechanistic aspects behind NF-kB inhibition. After virtual screening and molecular docking, five hit NF-kB inhibitors opted, and their therapeutic efficacy was examined using cell-based assays in TNF-α stimulated human keratinocyte cells. To investigate the conformational changes of target protein and inhibitor-protein interaction mechanisms, molecular dynamics (MD) simulations, binding free energy calculations together with principal component (PC) analysis, dynamics cross-correlation matrix analysis (DCCM), free energy landscape (FEL) analysis and quantum mechanical calculations were carried out. Among identified NF-kB inhibitors, myricetin and hesperidin significantly scavenged intracellular ROS and inhibited NF-kB activation. Analysis of the MD simulation trajectories of ligand-protein complexes revealed that myricetin and hesperidin formed energetically stabilized complexes with the target protein and were able to lock NF-kB in a closed conformation. Myricetin and hesperidin binding to the target protein significantly impacted conformational changes and internal dynamics of amino acid residues in protein domains. Tyr57, Glu60, Lys144 and Asp239 residues majorly contributed to locking the NF-kB in a closed conformation. The combinatorial approach employing in silico tools integrated with cell-based approaches substantiated the binding mechanism and NF-kB active site inhibition by the lead molecule myricetin, which can be explored as a viable antipsoriatic drug candidate associated with dysregulated NF-kB.Communicated by Ramaswamy H. Sarma.

Crop establishment and diversification strategies for intensification of rice-based cropping systems in rice-fallow areas in Odisha.

Context or problem: In the Indian state of Odisha, rice-based system productivity is poor due to: (i) low rice yield in the monsoon (wet) season (2-4 t ha-1 compared to 6-8 t ha-1 in Punjab or Haryana); and (ii) limited cropping during the post-monsoon (dry) season (59% of the wet season rice area is left fallow in the dry season). Objective: Our study identifies strategies for increasing rice-based system productivity through: (i) alternative crop establishment methods in the wet season (Dry-Direct Seeded Rice or DSR, and mechanical puddled transplanted rice or PTR-M) to traditional methods such as broadcasting followed by post-emergence tillage (locally known as beushening) and manual random puddled transplanted rice (PTR-R); (ii) to identify rice-fallow areas suitable for pulse and oilseed cultivation in the dry season; and (iii) to evaluate the performance of short-duration pulses (green gram, Vigna radiata; black gram, Vigna mungo), and oilseeds (Brassica rapa var. toria, Helianthus annuus) in rice-fallow areas in the dry season. Methods: On-farm experiments were conducted between 2017 and 2019 in three districts of Odisha (Bhadrak, Cuttack and Mayurbhanj) to evaluate DSR compared to beushening and PTR-R; and PTR-M compared to PTR-R and manual line puddled transplanted rice (PTR-L) in the wet season. The data from Landsat-8 Operational Land Imager (OLI) and Sentinel-1satellite sensors was used to identify rice-fallow areas, and the daily SMAP (Soil Moisture Active Passive) L-band soil moisture was used for mapping suitable rice-fallow areas for growing pulses and oilseeds. Short duration crops were evaluated in suitable rice-fallow areas. Results: In the wet season, DSR (range -4 to + 53%) had a significant effect on rice yield over beushening. Similarly, PTR-M consistently increased rice yield by 16-26% over PTR-R, and by 5-23% over PTR-L. In the dry season, pulse crops (green gram and black gram) performed well compared to Indian mustard under rainfed cultivation. However, under irrigated conditions, dry-season rice yield was more productive than the rice equivalent yield of green gram, black gram and sunflower. We found that 1.03 M ha (i.e., ∼50%) of total rice-fallow areas of 2.1 M ha were suitable for growing short duration green gram and black gram in the dry season. Conclusions: We conclude that system productivity and cropping intensity can be increased by adoption of DSR and PTR-M in the wet season, and growing of green gram and black gram in the dry season. Implications: Odisha state can potentially produce an additional 0.67 million tonnes pulses if suitable rice-fallow areas are brought under green gram and black gram cultivation in the dry the season.

Impact of climate extreme events and their causality on maize yield in South Africa.

Extreme climate events can have a significant negative impact on maize productivity, resulting in food scarcity and socioeconomic losses. Thus, quantifying their effect is needed for developing future adaptation and mitigation strategies, especially for countries relying on maize as a staple crop, such as South Africa. While several studies have analyzed the impact of climate extremes on maize yields in South Africa, little is known on the quantitative contribution of combined extreme events to maize yield variability and the causality link of extreme events. This study uses existing stress indices to investigate temporal and spatial patterns of heatwaves, drought, and extreme precipitation during maize growing season between 1986/87 and 2015/16 for South Africa provinces and at national level and quantifies their contribution to yield variability. A causal discovery algorithm was applied to investigate the causal relationship among extreme events. At the province and national levels, heatwaves and extreme precipitation showed no significant trend. However, drought severity increased in several provinces. The modified Combined Stress Index (CSIm) model showed that the maize yield nationwide was associated with drought events (explaining 25% of maize yield variability). Heatwaves has significant influence on maize yield variability (35%) in Free State. In North West province, the maize yield variability (46%) was sensitive to the combination of drought and extreme precipitation. The causal analysis suggests that the occurrence of heatwaves intensified drought, while a causal link between heatwaves and extreme precipitation was not detected. The presented findings provide a deeper insight into the sensitivity of yield data to climate extremes and serve as a basis for future studies on maize yield anomalies.

Visfatin-induced upregulation of lipogenesis via EGFR/AKT/GSK3ß pathway promotes breast cancer cell growth.

Breast cancer (BC) incidence and associated mortality have increased in tandem with the growth in obesity among the females worldwide. An adipokine, visfatin, has been shown to potentially impact glucose, lipid, and protein metabolism, and promote cancer growth however, the mechanism underlying the effect of visfatin on lipid metabolism dysregulation contributing to BC cell survival, proliferation, and metastasis has not been elucidated. Herein, we have investigated the role of visfatin on the induction of Sterol regulatory element binding protein (SREBP-1) and its upstream and downstream mediators in MCF-7 breast cancer cells. The survival and proliferation was investigated using MTT and Trypan blue assays, cytosolic lipid accumulation was observed using Nile red staining, mRNA and protein expressions were examined using RT-qPCR and western blotting, respectively, and cell cycle analysis was performed using fluorescence-activated cell sorting. Our results indicate that visfatin increased the survival and proliferation of MCF-7 cells in a time- and dose-dependent manner and augmented lipid buildup via activation of SREBP-1 and its associated downstream lipid synthesizing enzymes, at both mRNA and protein levels in MCF-7 cells. Inhibiting SREBP-1 using fatostatin or silencing with siRNA abrogated excessive lipid deposition by suppressing the expression of genes related to lipid synthesis pathway. Further, in-silico study showed high affinity binding of visfatin with epidermal growth factor receptor (EGFR), which was confirmed in an in-vitro study where visfatin increased the phosphorylation of EGFR at tyrosine residue and activated its downstream proteins via phosphorylation of AKT and GSK3ß in MCF-7 cells. Inhibition of GSK3ß by phosphorylation led to increased activity of SREBP-1 and associated downstream proteins. In summary, SREBP-1 may be a critical player in visfatin-induced lipid synthesis and accumulation in BC cells via activation of EGFR/AKT/GSK3ß pathway leading to increased cell survival and proliferation of BC cells.

Exploration of Antibiofilm and In Vivo Wound Healing Activity of p-Cymene-Loaded Gellan/PVA Nanofibers.

Wound dressings with outstanding biocompatibility, antimicrobial, and tissue regeneration activities are essential to manage emerging recalcitrant antifungal infections to speed up healing. In this study, we have engineered p-cymene-loaded gellan/PVA nanofibers using electrospinning. Morphological and physicochemical properties of the nanofibers were characterized using a multitude of techniques to validate the successful integration of p-cymene (p-cym). The fabricated nanomaterials exhibited strong antibiofilm activity against Candida albicans and Candida glabrata compared to pure p-cymene. In vitro biocompatibility assay demonstrated that nanofibers did not possess any cytotoxicity to the NIH3T3 cell lines. In vivo, full-thickness excision wound healing study showed that the nanofibers were able to heal skin lesions faster than the conventional clotrimazole gel in 24 days without forming any scar. These findings unraveled p-cymene-loaded gellan gum (GA)/poly(vinyl alcohol) (PVA) nanofibers as an effective biomaterial for cutaneous tissue regeneration.

Evaluation of machine learning approaches for prediction of pigeon pea yield based on weather parameters in India.

Pigeon pea is the second most important grain legume in India, primarily grown under rainfed conditions. Any changes in agro-climatic conditions will have a profound influence on the productivity of pigeon pea (Cajanus cajan) yield and, as a result, the total pulse production of the country. In this context, weather-based crop yield prediction will enable farmers, decision-makers, and administrators in dealing with hardships. The current study examines the application of the stepwise linear regression method, supervised machine learning algorithms (support vector machines (SVM) and random forest (RF)), shrinkage regression approaches (least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)), and artificial neural network (ANN) model for pigeon pea yield prediction using long-term weather data. Among the approaches, ANN resulted in a higher coefficient of determination (R2 = 0.88-0.99), model efficiency (0.88-1.00) with subsequent lower normalised root mean square error (nRMSE) during calibration (1.13-12.55%), and validation (0.33-21.20%) over others. The temperature alone or its interaction with other weather parameters was identified as the most influencing variables in the study area. The Pearson correlation coefficients were also determined for the observed and predicted yield. Those values also showed ANN as the best model with correlation values ranging from 0.939 to 0.999 followed by RF (0.955-0.982) and LASSO (0.880-0.982). However, all the approaches adopted in the study were outperformed the statistical method, i.e. stepwise linear regression with lower error values and higher model efficiency. Thus, these approaches can be effectively used for precise yield prediction of pigeon pea over different districts of Karnataka in India.

The effects of multiwalled carbon nanotubes and Bacillus subtilis treatments on the salt tolerance of maize seedlings.

Nanomaterials, including multiwalled carbon nanotubes (MWCNTs), have been recently applied in agriculture to improve stress resistance, leading to contradictory findings for antioxidant responses and mineral nutrient uptake. A pot experiment involving maize in low-salinity sandy loam soils was conducted with the application of different concentrations (0, 20, 50 mg/L) of MWCNTs and the growth-promoting rhizobacterium Bacillus subtilis (B. subtilis). The dose-dependent effects of MWCNTs were confirmed: 20 mg/L MWCNTs significantly promoted the accumulation of osmolytes in maize, particularly K+ in the leaves and roots, increased the leaf indoleacetic acid content, decreased the leaf abscisic acid content; but the above-mentioned promoting effects decreased significantly in 50 mg/L MWCNTs-treated plants. We observed a synergistic effect of the combined application of MWCNTs and B. subtilis on plant salt tolerance. The increased lipid peroxidation and antioxidant-like proline, peroxidase (POD), and catalase (CAT) activities suggested that MWCNTs induced oxidative stress in maize growing in low-salinity soils. B. subtilis reduced the oxidative stress caused by MWCNTs, as indicated by a lower content of malondialdehyde (MDA). The MWCNTs significantly increased the leaf Na+ content and leaf Na+/K+ ratio; however, when applied in combination with B. subtilis, the leaf Na+/K+ ratio decreased sharply to 69% and 44%, respectively, compared to those of the control (CK) group, the contents of which were partially regulated by abscisic acid and nitrate, according to the results of the structural equation model (SEM). Overall, the increased osmolytes and well-regulated Na+/K+ balance and transport in plants after the combined application of MWCNTs and B. subtilis reveal great potential for their use in combating abiotic stress.

Spatial and temporal patterns of agrometeorological indicators in maize producing provinces of South Africa.

Climate change impacts on maize production in South Africa, i.e., interannual yield variabilities, are still not well understood. This study is based on a recently released reanalysis of climate observations (AgERA5), i.e., temperature, precipitation, solar radiation, and wind speed data. The study assesses climate change effects by quantifying the trend of agrometeorological indicators, their correlation with maize yield, and analyzing their spatiotemporal patterns using Empirical Orthogonal Function. Thereby, the main agrometeorological factors that affected yield variability for the last 31 years (1990/91-2020/21 growing season) in major maize production provinces, namely Free State, KwaZulu-Natal, Mpumalanga, and North West are identified. Results show that there was a significant positive trend in temperature that averages 0.03-0.04 °C per year and 0.02-0.04 °C per growing season. There was a decreasing trend in precipitation in Free State with 0.01 mm per year. Solar radiation did not show a significant trend. Wind speed in Free State increased at a rate of 0.01 ms-1 per growing season. Yield variabilities in Free State, Mpumalanga, and North West show a significant positive correlation (r > 0.43) with agrometeorological variables. Yield in KwaZulu-Natal is not influenced by climate factors. The leading mode (50-80% of total variance) of each agrometeorological variable indicates spatially homogenous pattern across the regions. The dipole patterns of the second and the third mode suggest the variabilities of agrometeorological indicators are linked to South Indian high pressure and the warm Agulhas current. The corresponding principal components were mainly associated with strong climate anomalies which are identified as El Niño and La Niña events.

Simulating Root Growth as a Function of Soil Strength and Yield With a Field-Scale Crop Model Coupled With a 3D Architectural Root Model.

Accurate prediction of root growth and related resource uptake is crucial to accurately simulate crop growth especially under unfavorable environmental conditions. We coupled a 1D field-scale crop-soil model running in the SIMPLACE modeling framework with the 3D architectural root model CRootbox on a daily time step and implemented a stress function to simulate root elongation as a function of soil bulk density and matric potential. The model was tested with field data collected during two growing seasons of spring barley and winter wheat on Haplic Luvisol. In that experiment, mechanical strip-wise subsoil loosening (30-60 cm) (DL treatment) was tested, and effects on root and shoot growth at the melioration strip as well as in a control treatment were evaluated. At most soil depths, strip-wise deep loosening significantly enhanced observed root length densities (RLDs) of both crops as compared to the control. However, the enhanced root growth had a beneficial effect on crop productivity only in the very dry season in 2018 for spring barley where the observed grain yield at the strip was 18% higher as compared to the control. To understand the underlying processes that led to these yield effects, we simulated spring barley and winter wheat root and shoot growth using the described field data and the model. For comparison, we simulated the scenarios with the simpler 1D conceptual root model. The coupled model showed the ability to simulate the main effects of strip-wise subsoil loosening on root and shoot growth. It was able to simulate the adaptive plasticity of roots to local soil conditions (more and thinner roots in case of dry and loose soil). Additional scenario runs with varying weather conditions were simulated to evaluate the impact of deep loosening on yield under different conditions. The scenarios revealed that higher spring barley yields in DL than in the control occurred in about 50% of the growing seasons. This effect was more pronounced for spring barley than for winter wheat. Different virtual root phenotypes were tested to assess the potential of the coupled model to simulate the effect of varying root traits under different conditions.

Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment.

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

Interleukin-1ß activated c-FOS transcription factor binds preferentially to a specific allele of the matrix metalloproteinase-13 promoter and increases susceptibility to endometriosis.

Endometriosis is a benign gynecological condition characterized by increased growth, inflammation, invasion, and angiogenesis, partly regulated by a class of enzymes called matrix metalloproteinases (MMPs). The importance of a few MMPs, e.g., MMP-9, -3, and -7 has been studied in endometriosis progression. Although MMP-13 plays an essential role in bone regeneration and cancer, no report has been found on the part of MMP-13 and endometriosis progression. We found the upregulation of MMP-13 expression and activity in patients having endometriosis in the eastern Indian population. In addition, the -77A/G polymorphism of the MMP13 promoter (rs: 2252070) is associated with regulating transcription and subsequent susceptibility to disease. In eastern Indian case-control groups, the effect of the -77A/G single-nucleotide polymorphism on MMP13 promoter activity and its relationship with endometriosis susceptibility was studied. The AG genotype was shown to be more predisposed to endometriosis risk than the GG genotype (p: 0.02; odds ratio [OR]: 1.65, 95% confidence interval [CI]: 1.10-2.49), also AG genotype was more frequent in late-stage patients compared to early-stage (p: 0.03, OR: 2.0, 95% CI: 1.09-3.66). Furthermore, the MMP13 gene levels were greater in AA compared to GG individuals. Additionally, MMP13 promoter-reporter experiments in cultured endometrial epithelial cells and in silico analyses both demonstrated increased transcriptional activity near the G to A transition under basal/IL-1ß -induced/c-FOS overexpressed condition. Overall, c-FOS tighter binding to the A allele-carrying promoter enhances MMP13 transcription, which is further amplified by IL-1ß due to increased c-FOS phosphorylation, promoting MMP-13 production and endometriosis risk.

Chemokines driven ovarian cancer progression, metastasis and chemoresistance: Potential pharmacological targets for cancer therapy.

Ovarian cancer is a leading cause of death among women globally often characterized by poor prognosis and aggressive tumor growth. The therapeutic outcomes of ovarian cancer patients are majorly limited by the development of acquired chemo/radioresistance and the lack of targeted therapies. The tumor microenvironment (TME) comprises a diverse population of cells including adipocytes, fibroblasts, tumor cells, and immune cells which play an imperative role in promoting tumor growth, invasion, and malignant phenotypes of cancer cells. The cells present in TME secrete various inflammatory mediators including chemokines and cytokines, which regulate the tumor progression and metastasis. This review article highlights new insights about the general mechanisms associated with chemokines-mediated cell proliferation, inflammation, tumor initiation, progression, metastasis, chemoresistance, and immune evasion in ovarian cancer. We also discuss the microRNAs (miRNAs) regulating the oncogenic potential of chemokines. Overall, this is a comparatively less explored area that could provide important insights into ovarian cancer development and a promising avenue for targeted therapy of ovarian cancer.

Winter wheat yield prediction using convolutional neural networks from environmental and phenological data.

Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an extensive dataset of weather, soil, and crop phenology variables in 271 counties across Germany from 1999 to 2019. We proposed a Convolutional Neural Network (CNN) model, which uses a 1-dimensional convolution operation to capture the time dependencies of environmental variables. We used eight supervised machine learning models as baselines and evaluated their predictive performance using RMSE, MAE, and correlation coefficient metrics to benchmark the yield prediction results. Our findings suggested that nonlinear models such as the proposed CNN, Deep Neural Network (DNN), and XGBoost were more effective in understanding the relationship between the crop yield and input data compared to the linear models. Our proposed CNN model outperformed all other baseline models used for winter wheat yield prediction (7 to 14% lower RMSE, 3 to 15% lower MAE, and 4 to 50% higher correlation coefficient than the best performing baseline across test data). We aggregated soil moisture and meteorological features at the weekly resolution to address the seasonality of the data. We also moved beyond prediction and interpreted the outputs of our proposed CNN model using SHAP and force plots which provided key insights in explaining the yield prediction results (importance of variables by time). We found DUL, wind speed at week ten, and radiation amount at week seven as the most critical features in winter wheat yield prediction.

Jeopardy of COVID-19: Rechecking the Perks of Phytotherapeutic Interventions.

The novel coronavirus disease (COVID-19), the reason for worldwide pandemic, has already masked around 220 countries globally. This disease is induced by Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2). Arising environmental stress, increase in the oxidative stress level, weak immunity and lack of nutrition deteriorates the clinical status of the infected patients. Though several researches are at its peak for understanding and bringing forward effective therapeutics, yet there is no promising solution treating this disease directly. Medicinal plants and their active metabolites have always been promising in treating many clinical complications since time immemorial. Mother nature provides vivid chemical structures, which act multi-dimensionally all alone or synergistically in mitigating several diseases. Their unique antioxidant and anti-inflammatory activity with least side effects have made them more effective candidate for pharmacological studies. These medicinal plants inhibit attachment, encapsulation and replication of COVID-19 viruses by targeting various signaling molecules such as angiotensin converting enzyme-2, transmembrane serine protease 2, spike glycoprotein, main protease etc. This property is re-examined and its potency is now used to improve the existing global health crisis. This review is an attempt to focus various antiviral activities of various noteworthy medicinal plants. Moreover, its implications as prophylactic or preventive in various secondary complications including neurological, cardiovascular, acute kidney disease, liver disease are also pinpointed in the present review. This knowledge will help emphasis on the therapeutic developments for this novel coronavirus where it can be used as alone or in combination with the repositioned drugs to combat COVID-19.

Eucalyptol/ ß-cyclodextrin inclusion complex loaded gellan/PVA nanofibers as antifungal drug delivery system.

Fungal infections pose a serious threat to humankind due to the toxicity of conventional antifungal therapy and continuous emerging incidence of multidrug resistance. Essential oils fascinated researchers because of their broad antimicrobial activity and minimal cytotoxicity. However, hydrophobic, volatile and low water solubility of essential oils hinder their applications in pharmaceutical industries. Therefore, in this study we have loaded eucalyptol/ ß-cyclodextrin inclusion complex to gellan/polyvinyl alcohol nanofibers (EPNF) to eradicate Candida albicans and Candida glabrata biofilms. The electrospun nanofibers characterized by various physicochemical techniques and it was observed that EPNF possess highly hydrophilic surface property that facilitate rapid drug release. EPNF inhibited approximately 70% biofilm of C. albicans and C. glabrata. Time kill results depicted that eucalyptol (EPTL) encapsulation in the nanofibers prolonged its antifungal activity than the pure EPTL. Electron microscopy studies revealed that EPNF disrupted the cell surface of Candida. Collectively the current study suggested nanofiber encapsulation enhanced antibiofilm activity of eucalyptol and these nanoscale systems can serve as an alternative therapeutic strategy to treat fungal infections. Further, the developed nanofibrous materials can be applied as cost effective coating agent for biomedical implants.

Repurposing nonnucleoside antivirals against SARS-CoV2 NSP12 (RNA dependent RNA polymerase): In silico-molecular insight.

The pandemic of SARS-CoV-2 has necessitated expedited research efforts towards finding potential antiviral targets and drug development measures. While new drug discovery is time consuming, drug repurposing has been a promising area for elaborate virtual screening and identification of existing FDA approved drugs that could possibly be used for targeting against functions of various proteins of SARS-CoV-2 virus. RNA dependent RNA polymerase (RdRp) is an important enzyme for the virus that mediates replication of the viral RNA. Inhibition of RdRp could inhibit viral RNA replication and thus new virus particle production. Here, we screened non-nucleoside antivirals and found three out of them to be strongest in binding to RdRp out of which two retained binding even using molecular dynamic simulations. We propose these two drugs as potential RdRp inhibitors which need further in-depth testing.