The emerging and reemerging forms of fungal infections encountered in the course of allogeneic bone marrow transplantations, cancer therapy, and organ transplants have necessitated the discovery of antifungal compounds with enhanced efficacy and better compatibility. A very limited number of antifungal compounds are in practice against the various forms of topical and systemic fungal infections. The trends of new antifungals being introduced into the market have remained insignificant while resistance towards the introduced drug has apparently increased, specifically in patients undergoing long-term treatment. Considering the immense potential of natural microbial products for the isolation and screening of novel antibiotics for different pharmaceutical applications as an alternative source has remained largely unexplored. Endophytes are one such microbial community that resides inside all plants without showing any symptoms with the promise of producing diverse bioactive molecules and novel metabolites which have application in medicine, agriculture, and industrial set ups. This review substantially covers the antifungal compounds, including volatile organic compounds, isolated from fungal endophytes of medicinal plants during 2013–2018. Some of the methods for the activation of silent biosynthetic genes are also covered. As such, the compounds described here possess diverse configurations which can be a step towards the development of new antifungal agents directly or precursor molecules after the required modification.

As a result of the increasing environmental and health-related problems caused by the synthetic agrochemicals currently used, suitable and non-hazardous innovative alternatives are being sought. Antagonism and allelopathy, both in nature and in agro-ecosystems, have attracted these researchers' attention, with the main goal of using these phenomena in the biological control of weeds. This article presents a review on the use and efficacy of microbial secondary metabolites which have potential as natural herbicides, either directly or as templates for bio-rational eco-friendly agrochemicals (allelochemicals). Their merits as alternatives to synthetic chemicals and biological control agents have been highlighted for an holistic approach in integrated pest/weed management.

Nowadays polylactic acid (PLA) is widely used in orthopedics surgeries as implants material due to well mechanical characterization and biomedical properties. But the PLA implants suffer from slow degradation rate when it is used in real-life scenario. In the present research work, the PLA specimens using additive manufacturing technique are fabricated and further assessed for mechanical characterization and its degradation behavior with different parameters. The change in weight of scaffolds was measured using digital weight measure, and pH value was measured using pH meter. Morphology and elemental composition of PLA scaffolds were characterized by SEM and EDS, respectively, while compressive strength is measured by the universal testing machine. Apatite formation and biocompatible nature of fabricated scaffolds were analyzed by in vitro simulated body fluid study. The outcomes of characterization exposed that scaffold with 60% infill percentage had maximum porosity, which is beneficial for the apatite formation and osseointegration. The average change in compressive strength was measured as 49.79 MPa after 14 days and 46.11 MPa after 28 days, whereas the average change in pH value was measured as 5.67 and 5.27 after 14 and 28 days of incubation period, respectively. The degradation rate of specimen 3 was 27.92% less than that of specimen 1, 35.69% less than that of specimen 5, and 87.98% more than that of specimen 9. This study concludes the positive effect of process parameters on degradation rate and biocompatible behavior of PLA implants.

PurposeCurrently cardiovascular diseases (CVDs) are the main cause of death worldwide. Disease risk estimates can be used as prognostic information and support for treating CVDs. The commonly used Framingham risk score (FRS) for CVD prediction is outdated for the modern population, so FRS may not be accurate enough. In this paper, a novel CVD prediction system based on machine learning is proposed.MethodsThis study has been conducted with the data of 689 patients showing symptoms of CVD. Furthermore, the dataset of 5,209 CVD patients of the famous Framingham study has been used for validation purposes. Each patient’s parameters have been analyzed by physicians in order to make a diagnosis. The proposed system uses the quantum neural network for machine learning. This system learns and recognizes the pattern of CVD. The proposed system has been experimentally evaluated and compared with FRS.ResultsDuring testing, patients’ data in combination with the doctors’ diagnosis (predictions) are used for evaluation and validation. The proposed system achieved 98.57% accuracy in predicting the CVD risk. The CVD risk predictions by the proposed system, using the dataset of the Framingham study, confirmed the potential risk of death, deaths which actually occurred and had been recorded as due to myocardial infarction and coronary heart disease in the dataset of the Framingham study. The accuracy of the proposed system is significantly higher than FRS and other existing approaches.ConclusionThe proposed system will serve as an excellent tool for a medical practitioner in predicting the risk of CVD. This system will be serving as an aid to medical practitioners for planning better medication and treatment strategies. An early diagnosis may be effectively made by using this system. An overall accuracy of 98.57% has been achieved in predicting the risk level. The accuracy is considerably higher compared to the other existing approaches. Thus, this system must be used instead of the well-known FRS.

Resveratrol is extensively being used as a therapeutic moiety, as well as a pharmacophore for development of new drugs due to its multifarious beneficial effects. The objective of the present study was to isolate and screen the resveratrol-producing endophytic fungi from different varieties of Vitis vinifera. A total of 53 endophytic fungi belonging to different fungal genera were isolated from the stem and leaf tissues of Vitis vinifera (merlot, wild, pinot noir, Shiraz, muscat) from different grape-producing locations of India. Only 29 endophytic fungal isolates exhibited a positive test for phenolics by phytochemical methods. The resveratrol obtained after ethyl acetate extraction was confirmed using standard molecule on thin layer chromatography (TLC) with a retention factor (R) of 0.69. The purified and standard resveratrol were visualized under UV light as a violet-colored spot. In HPLC analysis of the ethyl acetate extract of culture broth of 11 endophytic isolates, the highest resveratrol content was found in #12VVLPM (89.1 μg/ml) followed by #18VVLPM (37.3 μg/ml) and 193VVSTPM (25.2 μg/ml) exhibiting a retention time of 3.36 min which corresponded to the standard resveratrol. The resveratrol-producing isolates belong to seven genera viz. Aspergillus, Botryosphaeria, Penicillium, Fusarium, Alternaria, Arcopilus, and Lasiodiplodia, and using morphological and molecular methods, #12VVLPM was identified as Arcopilus aureus.