Fungal Infection

Machine learning reveals biocontrol agents shaping disease outcome in natural Arabidopsis populations.

Plants recruit antagonistic microbes to defend against phytopathogens, offering a route to rational biocontrol beyond empirical screening. Here, using six generations of leaf-microbiome data from natural Arabidopsis populations infected by the oomycete Albugo laibachii, we show that microbial diversity is driven by infection, site, and host genotype, and that infected plants form modular networks ...

Rapid identification of feline sporotrichosis by ATR-FTIR spectroscopy coupled with machine learning.

Feline sporotrichosis is a zoonotic subcutaneous mycosis of major relevance in endemic regions of Latin America, particularly in Brazil. Current diagnostic methods are limited by turnaround time, specificity, and cost. In this context, spectroscopy combined with machine learning has emerged as a promising alternative. This study aimed to develop and validate predictive models for the rapid and min...

Predicting the absence of World Health Organization fungal priority pathogens in agricultural soils using machine learning and ITS metabarcoding.

Environmental fungal pathogens relevant to human and animal health pose significant risks, particularly in regions with intensive farming and climate ...

An Interpretable Machine Learning Model for Predicting the Presence of Talaromycosis in HIV Patients Lacking Skin Lesions.

INTRODUCTION: The existing predictive models for talaromycosis in people living with HIV without skin lesions are limited by established risk factors ...

Mechanistic Insights into Effector-Mediated Modulation of Rice Mitochondrial Proteins by Magnaporthe oryzae.

Mitochondria are crucial for the plant and animal immunity. The effector protein MoCDIP4 reduces rice immunity by targeting the mitochondria-associate...

Microbiome-based control of postharvest mycotoxin-producing fungi in cereals.

Mycotoxin contamination caused by fungal pathogens remains a persistent threat to global food security, while the long-standing reliance on synthetic ...

Predicting the seed microbiome using phylogeny-driven machine learning.

BACKGROUND: The composition of the seed-associated bacterial microbiome can reflect host evolutionary relationships, a pattern consistent with phylosy...

A low-cost "plant-scanner" platform for automated detection of Ustilago maydis infection in maize using deep learning.

Ustilago maydis is a biotrophic fungus that causes smut disease in maize, leading to tumor formation on aerial parts of the plant. While U. maydis has...

Machine learning-enabled label-free SERS for microbial sensing: Toward robust, generalizable, and deployable workflows.

Label-free surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) provides a rapid, reagent-light approach to microbial detect...

Novel Imaging Approaches for Visualising Root-Mycorrhizal Fungal Interactions.

Mycorrhizal fungi form essential symbiotic relationships with plant roots, facilitating nutrient exchange and promoting plant health. Understanding th...

Automated identification of clinically important Candida yeast species for microscopic images using self-supervised learning.

Opportunistic fungal infections are an escalating global challenge, impacting over a billion people worldwide, particularly immunocompromised individu...

Rapid species authentication and protein prediction of porcini mushrooms using FTIR-2DCOS coupled with deep learning.

Edible fungi are highly valued worldwide for their unique culinary and therapeutic properties. Nevertheless, the pronounced morphological similarities...

From manual scoring to machine learning: recent developments in image-based arbuscular mycorrhizal fungi root quantification.

Selecting an appropriate method to quantify arbuscular mycorrhizal fungi (AMF) colonization in plants can be challenging, particularly for non-experts...

Optimizing lung cancer diagnosis using improved fungal growth optimizer-based medical image segmentation.

Medical image segmentation is one of the most important processes in computer-aided diagnosis. It plays a critical role in detecting and analyzing dis...

A review of bloodstream infections-pathogens, pathogenesis, diagnostic strategies, treatment methods-challenges and future aspects.

PURPOSE: Bloodstream infections (BSIs) remain a major cause of morbidity and mortality worldwide and continue to represent a substantial challenge to ...

Plant-derived compounds and artificial intelligence: Outsmarting microbial pathogens and antimicrobial resistance.

BACKGROUND: From the discovery of the first antimicrobial drug to nowadays, the development of antimicrobial resistance (AMR) - a silent pandemic amon...

Deep learning analysis of 12-lead electrocardiograms for bloodstream infection prediction: a multi-center validation study.

BACKGROUND: Prompt diagnosis of bloodstream infections (BSIs) is critical for antimicrobial stewardship but hindered by blood culture delays of 48 h o...

Enhancing the severity classification of tomato plant epidemic pathogens using adaptive segmentation of mask RCNN and multiscale recurrent MobileNet.

Tomatoes are the most significant and widely consumed crops globally. Leaf diseases cause an important threat to crop production and quality. Further,...

Integrative network toxicology, multiomics, and machine learning elucidate zearalenone-induced developmental toxicity mechanisms in zebrafish (Danio rerio).

Zearalenone (ZEN) is a naturally occurring environmental contaminant that is produced by fungi. Developing animals are particularly sensitive to ZEN, ...

IL4Rα blockade inhibits proliferation of malignant lymphocytes and the immunosuppressive tumor microenvironment of mycosis fungoides.

BACKGROUND: Limited therapeutic options are available for patients with advanced-stage mycosis fungoides (MF), and the 5-year survival rate is 25%. Du...