Withania somnifera seed oil exhibits antibiofilm properties against drug-resistant Candida auris clinical isolate through modulation in cell permeability.

AIM: Candida auris, fast evolving drug-resistant fungus, poses an imminent global health threat. Alternative drug-resistance nonevoking treatment options are necessary. This study explored the antifungal and antibiofilm efficacies of Withania somnifera seed oil extracted using super critical CO2 (WSSO) against clinically isolated Fluconazole-resistant C. auris and its putative mode-of-action. METHODS AND RESULTS: Effects of WSSO on C. auris were tested by broth microdilution method, with observed IC50 at 5.96 mg ml-1. Time-kill assay revealed that WSSO is fungistatic. Mechanistically, ergosterol binding and sorbitol protection assays showed that C. auris cell membrane and cell wall are the targets for WSSO. Lactophenol: Cotton-Blue: Trypan-Blue staining confirmed loss of intracellular contents by WSSO treatment. Candida auris biofilm formation was disrupted by WSSO (BIC50: 8.52 mg ml-1). Additionally, WSSO exhibited dose and time-dependent mature biofilm eradication property with 50% efficacies at 23.27, 19.28, 18.18, and 7.22 mg ml-1 over 24, 48, 72, and 96 h, respectively. Biofilm eradication by WSSO was further substantiated through scanning electron microscopy. Standard-of-Care Amphotericin B, at its break-point concentration, (2 µg ml-1) was found to be inefficient as an antibiofilm agent. CONCLUSIONS: WSSO is a potent antifungal agent effective against planktonic C. auris and its biofilm.

A review on fake news detection 3T's: typology, time of detection, taxonomies.

Fake news has become an industry on its own, where users paid to write fake news and create clickbait content to allure the audience. Apparently, the detection of fake news is a crucial problem and several studies have proposed machine-learning-based techniques to combat fake news. Existing surveys present the review of proposed solutions, while this survey presents several aspects that are required to be considered before designing an effective solution. To this aim, we provide a comprehensive overview of false news detection. The survey presents (1) a clarity to problem definition by explaining different types of false information (like fake news, rumor, clickbait, satire, and hoax) with real-life examples, (2) a list of actors involved in spreading false information, (3) actions taken by service providers, (4) a list of publicly available datasets for fake news in three different formats, i.e., texts, images, and videos, (5) a novel three-phase detection model based on the time of detection, (6) four different taxonomies to classify research based on new-fangled viewpoints in order to provide a succinct roadmap for future, and (7) key bibliometric indicators. In a nutshell, the survey focuses on three key aspects represented as the three T's: Typology of false information, Time of detection, and Taxonomies to classify research. Finally, by reviewing and summarizing several studies on fake news, we outline some potential research directions.

Disinformation detection on social media: An integrated approach.

The emergence of social media platforms has amplified the dissemination of false information in various forms. Social media gives rise to virtual societies by providing freedom of expression to users in a democracy. Due to the presence of echo chambers on social media, social science studies play a vital role in the spread of false news. To this aim, we provide a comprehensive framework that is adapted from several scholarly studies. The framework is capable of detecting information into various types, namely real, disinformation and satire based on authenticity as well as intention. The process highlights the use of interdisciplinary approaches derived from fundamental theories of social science and integrating them with modern computational tools and techniques. Few of these theories claim that malicious users suggest writing fabricated content in a different style to attract the audience. Style-based methods evaluate the intention i.e., the content is written with an intent to mislead the audience or not. However, the writing style can be deceptive. Thus, it is important to involve user-oriented social information to improve model strength. Therefore, the paper used an integrated approach by combining style based and propagation-based features with a total of thirty-one features. The extracted features are divided into ten categories: relative frequency, quantity, complexity, uncertainty, sentiment, subjectivity, diversity, informality, additional, and popularity. The features have been iteratively utilized by supervised classifiers and then selected the best-correlated ones using the ANOVA test. Our experimental results have shown that the selected features are able to distinguish real from disinformation and satirical news. It has been observed that the Ensemble machine learning model outperformed other models over the developed multi-labelled corpus.

Anu taila, an herbal nasal drop, suppresses mucormycosis by regulating host TNF-α response and fungal ergosterol biosynthesis.

AIM: The intractable, mucormycosis, caused by Mucorales primarily targets immunocompromised individuals. The first-line therapy, intravenous liposomal amphotericin B and surgical debridement of necrotic tissue, is contraindicative in individuals with compromised kidneys. This invokes a pressing need to identify safer treatment options. METHODS AND RESULTS: The antifungal effect of the classical nasal drop, Anu taila, against Mucor spp. was investigated through microbiological, cytological, analytical chemical (HPLC and GS-MS/MS) and scanning electron microscopic (SEM) approaches. Anu taila-pretreated spores germinated late, resulting in reduced infectivity, observed as milder monocytic immune response. Conversely, Anu taila-pretreated human THP-1 cells exhibited an improved immune response against Mucor spores, through TNF-α. Repeated Anu taila application rapidly abolished fungal microarchitectures than amphotericin B, evident from swift replacement of hyphae, sporangiophores and sporangia with fused biomass, in the SEM images. HPLC analysis showed that Anu taila treatment significantly reduced overall ergosterol content in Mucor biomass. Anu taila also downregulated sterol-C5-desaturase-coding ERG3 gene, crucial for ergosterol biosynthesis and resultant structural integrity, in Mucor spp. CONCLUSION: Taken together, Anu taila was found effective against Mucor spp., with both prophylactic and curative implications, which is attributable to the phytochemical composition of this classical nasal drop. SIGNIFICANCE AND IMPACT STATEMENT: The potential remedial effects of a classical nasal drop against an obdurate and challenging fungal infection are identified.

Vishaghn Dhoop, Nano-Scale Particles with Detoxifying Medicinal Fume, Exhibits Robust Anti-Microbial Activities: Implications of Disinfection Potentials of a Traditional Ayurvedic Air Sterilization Technique.

The rapidly increasing global burden of healthcare associated infections (HAI) is resulting in proportionate increase in chemical disinfection in healthcare settings, adding an extra burden of environmental toxicity. Therefore, alternative disinfection techniques with less or no adverse side-effects need to be explored. In this regard, ayurvedic 'dhoopan' technique involving slow combustion of medicinal herbs, minerals and animal products hold great promise. In this study, dhoopan of a traditionally defined ayurvedic medicinal mix, 'Vishaghn Dhoop' (VD) has been assessed for its anti-microbial potentials against both Gram-positive and negative pathogenic bacteria, Mycobacterium and pathogenic fungus, Candida albicans. Fume generated from slow combustion of VD was subjected to physico-chemical characterization and was assessed for anti-microbial effects. VD fume contained particles of 354 ± 84 nm size, laden with anti-microbial metabolites. On agar plates, VD fumigation reduced bacterial growth by 13 - 38%. Liquid culture aeration with VD fume inhibited bacterial growth by 50 - 85%, and fungal growth by 80%. In real life settings (in vivo), un-sanitized rooms fumigated with VD fumes for 30 min reduced the environmental microbial loads by 10 folds. In addition, the safety of VD fumigation was evaluated through in vitro cytotoxicity assay on human lung epithelial (A549) cells. Cells exposed to media-collected VD fumes for 24 h exhibited normal cyto-safety profile. Collectively, these observations provide scientific evidence in support of a traditional technique of disinfection, which can be fine-tuned to have implications in clinical, healthcare and food industry where, disinfection is a prime requirement.

Unravelling the capability of Pyrenophora phaeocomes S-1 for the production of ligno-hemicellulolytic enzyme cocktail and simultaneous bio-delignification of rice straw for enhanced enzymatic saccharification.

A natural variant of Pyrenophora phaeocomes isolated from natural biodiversity was able to grow on various agricultural residues by co-producing laccase, xylanase and mannanase. Solid state fermentation of rice straw induced the highest productivities corresponding to 10,859.51±46.74, 22.01±1.00 and 10.45±0.128IUgds-1 for laccase, xylanase and mannanase respectively after 4days. Besides producing the ligno-hemicellulolytic enzyme cocktail, 40days cultivation of P. phaeocomes S-1 on rice straw brought about the 63 and 51% degradation of lignin and hemicellulose. These components were further removed with mild alkali extraction revealing the overall losses amounting to 78 and 60% respectively for lignin, and hemicellulose. The biologically pretreated straw upon enzymatic hydrolysis revealed 50% saccharification efficiency releasing 470mgg-1 sugars. Application of this knowledge will lead to efficient management of waste rice straw with low cost production of industrially important enzymes cocktail and its biological delignification for effective enzymatic hydrolysis to free sugars.