AIMC Topic: Mycoses

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

Journal of medical Internet research Aug 14, 2020
BACKGROUND: Fungal ocular involvement can develop in patients with fungal bloodstream infections and can be vision-threatening. Ocular involvement has become less common in the current era of improved antifungal therapies. Retrospectively determining...
Retrospective Studies Female Risk Factors Humans Natural Language Processing Mycoses Critical Care Cross-Sectional Studies Male Middle Aged Eye Endophthalmitis
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Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases Feb 12, 2020
BACKGROUND: Machine learning (ML) allows the analysis of complex and large data sets and has the potential to improve health care. The clinical microbiology laboratory, at the interface of clinical practice and diagnostics, is of special interest for...
Information Technology Parasitic Diseases Data Analysis Microbial Sensitivity Tests Bacterial Infections Mycoses Virus Diseases Clinical Laboratory Services Humans Machine Learning
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Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network.

Computer methods and programs in biomedicine Aug 9, 2019
BACKGROUND AND OBJECTIVES: Fungal keratitis is caused by inflammation of the cornea that results from infection by fungal organisms. The lack of an early effective diagnosis often results in serious complications even blindness. Confocal microscopy i...
False Positive Reactions Microscopy, Confocal Mycoses Pattern Recognition, Automated Reproducibility of Results Neural Networks, Computer Cornea Humans Algorithms Keratitis Diagnosis, Computer-Assisted Databases, Factual Deep Learning Image Processing, Computer-Assisted
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AI in fungal drug development: opportunities, challenges, and future outlook.

Frontiers in cellular and infection microbiology Jan 1, 2025
The application of artificial intelligence (AI) in fungal drug development offers innovative strategies to address the escalating threat of fungal infections and the challenge of antifungal resistance. This review evaluates the current landscape of f...
Humans Artificial Intelligence Mycoses Fungi Drug Discovery Drug Resistance, Fungal Drug Development Antifungal Agents
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From patterns to prediction: machine learning and antifungal resistance biomarker discovery.

Canadian journal of microbiology Jan 1, 2025
Fungal pathogens significantly impact human health, agriculture, and ecosystems, with infections leading to high morbidity and mortality, especially among immunocompromised individuals. The increasing prevalence of antifungal resistance (AFR) exacerb...
Drug Resistance, Fungal Antifungal Agents Mycoses Fungi Humans Machine Learning Biomarkers
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DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning.

Protein science : a publication of the Protein Society Oct 1, 2023
Fungal infections have become a significant global health issue, affecting millions worldwide. Antifungal peptides (AFPs) have emerged as a promising alternative to conventional antifungal drugs due to their low toxicity and low propensity for induci...
Antifungal Agents Mycoses alpha-Fetoproteins Algorithms Peptides Deep Learning Humans
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[Analysis of Cases with Elevated Blood (1->3)-β-D-glucan in Relation to an Infection Marker, Neutrophil CD64 Expression].

Kansenshogaku zasshi. The Journal of the Japanese Association for Infectious Diseases Nov 1, 2015
(1->3)-β-D-glucan (BDG) is a constituent of the fungal cell wall and its blood level is known as a marker of fungal infection including pneumocystis pneumonia (PCP). Meanwhile, peripheral blood neutrophil CD64 expression (CD64) is upregulated in vari...
Pneumonia, Pneumocystis Male Female Humans beta-Glucans Aged Middle Aged Biomarkers Mycoses Adult Neutrophils Receptors, IgG
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Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.

Asian Pacific journal of cancer prevention : APJCP Jan 1, 2015
BACKGROUND: The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model anal...
Carcinoma, Non-Small-Cell Lung Prognosis Follow-Up Studies Retrospective Studies Aged, 80 and over Female Humans Neoplasm Staging Adult Neural Networks, Computer Mycoses Lung Neoplasms Male Middle Aged Carcinoma, Large Cell Fungi Carcinoma, Squamous Cell Logistic Models Incidence Small Cell Lung Carcinoma Risk Factors Adenocarcinoma Cross Infection ROC Curve Aged
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