Fungal Infection

Deep convolutional neural network: a novel approach for the detection of Aspergillus fungi via stereomicroscopy.

Fungi of the genus Aspergillus are ubiquitously distributed in nature, and some cause invasive aspergillosis (IA) infections in immunosuppressed individuals and contamination in agricultural products. Because microscopic observation and molecular detection of Aspergillus species represent the most operator-dependent and time-intensive activities, automated and cost-effective approaches are needed....

A novel method for clinical risk prediction with low-quality data.

In real-world data, predictive models for clinical risks (such as adverse drug reactions, hospital readmission, and chronic disease onset) are constantly struggling with low-quality issues, namely redundant and highly correlated features, extreme category imbalances, and most importantly, a large number of missing values. In most existing work, each patient is represented as a value vector with th...

Computational approach for identification, characterization, three-dimensional structure modelling and machine learning-based thermostability prediction of xylanases from the genome of Aspergillus fumigatus.

Identification of thermostable and alkaline xylanases from different fungal and bacterial species have gained an interest for the researchers because ...

Deep Learning for Imaging and Detection of Microorganisms.

Despite tremendous recent interest, the application of deep learning in microbiology has still not reached its full potential. To tackle the challenge...

Branching principles of animal and plant networks identified by combining extensive data, machine learning and modelling.

Branching in vascular networks and in overall organismic form is one of the most common and ancient features of multicellular plants, fungi and animal...

The design and application of an automated microscope developed based on deep learning for fungal detection in dermatology.

BACKGROUND: Light microscopy to study the infection of fungi in skin specimens is time-consuming and requires automation.

Machine learning approach for predicting Fusarium culmorum and F. proliferatum growth and mycotoxin production in treatments with ethylene-vinyl alcohol copolymer films containing pure components of essential oils.

Fusarium culmorum and F. proliferatum can grow and produce, respectively, zearalenone (ZEA) and fumonisins (FUM) in different points of the food chain...

Effect of cross-cultural differences on thickness, firmness and sweetness sensitivity.

Sensitivity of the somatosensory system may be influenced by multiple physiological parameters. Variations in oral physiology can arise from cross-cul...

Piperidine based 1,2,3-triazolylacetamide derivatives induce cell cycle arrest and apoptotic cell death in .

The fungal pathogen , is a serious threat to public health and is associated with bloodstream infections causing high mortality particularly in patie...

Comparison of manual and machine learning image processing approaches to determine fungiform papillae on the tongue.

Human taste perception is associated with the papillae on the tongue as they contain a large proportion of chemoreceptors for basic tastes and other c...

Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning.

Microbial natural products (NPs) are an important source of drugs, however, their structural diversity remains poorly understood. Here we used our rec...

Ontology and values anchor indigenous and grey nomenclatures: a case study in lichen naming practices among the Samí, Sherpa, Scots, and Okanagan.

Ethnobotanical research provides ample justification for comparing diverse biological nomenclatures and exploring ways that retain alternative naming ...

PCR-detectable DNA exists a short period in the blood of systemic candidiasis murine model.

Invasive candidiasis is a major challenge to clinical medicine today. However, traditional fungal diagnostic techniques and empirical treatments have ...

A deep learning approach in diagnosing fungal keratitis based on corneal photographs.

Fungal keratitis (FK) is the most devastating and vision-threatening microbial keratitis, but clinical diagnosis a great challenge. This study aimed t...

A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes.

Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds ...

Text Processing for Detection of Fungal Ocular Involvement in Critical Care Patients: Cross-Sectional Study.

BACKGROUND: Fungal ocular involvement can develop in patients with fungal bloodstream infections and can be vision-threatening. Ocular involvement has...

Raman spectroscopy of potential bio-hazards commonly found in bio-aerosols.

Pathogenic bio-aerosols are a threat to public health today, and thus quick detection and identification is of paramount importance. In this study, Ra...

Antifungal activity of selected volatile essential oils against sp.

Phytopathogenic fungi have been responsible for considerable economic losses in vineyards, and therefore, more attention should be paid to the develop...

Deep learning approach to describe and classify fungi microscopic images.

Preliminary diagnosis of fungal infections can rely on microscopic examination. However, in many cases, it does not allow unambiguous identification o...

Talaromydien a and talaroisocoumarin A, new metabolites from the marine-sourced fungus sp. ZZ1616.

New talaromydien A and talaroisocoumarin A (), together with nine known compounds (-), were isolated from a culture of the marine-derived sp. ZZ1616...