Aspergillus Genus and Its Various Human Superficial and Cutaneous Features

Merad, Yassine; Derrar, Hichem; Belmokhtar, Zoubir; Belkacemi, Malika

doi:10.3390/pathogens10060643

Cited by 26 publications

(17 citation statements)

References 107 publications

(163 reference statements)

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“…However, differences in the pathogenic fungi species directly affects the positive rate of direct microscopic examination results (Ali et al, 2018). By culturing isolated specimens, further morphological and molecular biological identifications can be carried out, and the pathogenic species can be identified (Merad et al, 2021). However, conventional identification methods are timeconsuming and prone to cross-contamination of specimens, which often leads to failure of clinicians to select effective antifungal drugs at a timely and early stage, affecting the prognosis (Hagiwara et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Efficient and accurate diagnosis of otomycosis using an ensemble deep-learning model

Mao

et al. 2022

Front. Mol. Biosci.

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Otomycosis accounts for over 15% of cases of external otitis worldwide. It is common in humid regions and Chinese cultures with ear-cleaning custom. Aspergillus and Candida are the major pathogens causing long-term infection. Early endoscopic and microbiological examinations, performed by otologists and microbiologists, respectively, are important for the appropriate medical treatment of otomycosis. The deep-learning model is a novel automatic diagnostic program that provides quick and accurate diagnoses using a large database of images acquired in clinical settings. The aim of the present study was to introduce a machine-learning model to accurately and quickly diagnose otomycosis caused by Aspergillus and Candida. We propose a computer-aided decision-making system based on a deep-learning model comprising two subsystems: Java web application and image classification. The web application subsystem provides a user-friendly webpage to collect consulted images and display the calculation results. The image classification subsystem mainly trained neural network models for end-to-end data inference. The end user uploads a few images obtained with the ear endoscope, and the system returns the classification results to the user in the form of category probability values. To accurately diagnose otomycosis, we used otoendoscopic images and fungal culture secretion. Fungal fluorescence, culture, and DNA sequencing were performed to confirm the pathogens Aspergillus or Candida spp. In addition, impacted cerumen, external otitis, and normal external auditory canal endoscopic images were retained for reference. We merged these four types of images into an otoendoscopic image gallery. To achieve better accuracy and generalization abilities after model-training, we selected 2,182 of approximately 4,000 ear endoscopic images as training samples and 475 as validation samples. After selecting the deep neural network models, we tested the ResNet, SENet, and EfficientNet neural network models with different numbers of layers. Considering the accuracy and operation speed, we finally chose the EfficientNetB6 model, and the probability values of the four categories of otomycosis, impacted cerumen, external otitis, and normal cases were outputted. After multiple model training iterations, the average accuracy of the overall validation sample reached 92.42%. The results suggest that the system could be used as a reference for general practitioners to obtain more accurate diagnoses of otomycosis.

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Section: Discussionmentioning

confidence: 99%

Efficient and accurate diagnosis of otomycosis using an ensemble deep-learning model

Mao

et al. 2022

Front. Mol. Biosci.

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“…Aspergillus is a filamentous fungus that is widespread. Inhalation of fungal spores is the most common way fungi enter the body, with the sinus and lung being the most common sites of invasion 29 . Aspergillus invasion of the lungs will lead to IPA, CPA and ABPA.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic value of bronchoalveolar lavage fluid galactomannan assay for invasive pulmonary aspergillosis in adults: A meta‐analysis

Li¹,

Sun²,

Liu³

et al. 2022

Clinical Pharmacy Therapeu

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What is known and objective: To analyse the diagnostic accuracy of bronchoalveolar lavage fluid galactomannan (BALF-GM) assay for invasive pulmonary aspergillosis (IPA) in adults to determine the optimal diagnostic cut-off by meta-analysis.Methods: PubMed, Embase, Web of Science, Cochrane Library, China national knowledge infrastructure (CNKI), and China Wanfang databases were searched to collect relevant studies on the diagnostic value of BALF-GM for IPA from inception to March 2022. The summary receiver operating characteristic (SROC) curve was drawn to determine the optimal diagnostic cut-off.Results and discussion: Nineteen articles (56 data sets) were included. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were 0.79 (95% CI: 0.72-0.84), 0.92 (95% CI: 0.88-0.94), 9.25 (95% CI: 6.84-12.52), 0.23 (95% CI: 0.18-0.30), 39.44 (95% CI: 29.55-52.65), and 0.92 (95% CI: 0.90-0.94), respectively. The area under the curves (AUCs) were 0.92, 0.86, 0.93, 0.89, 0.88, and 0.94 when the cut-off values were 0.5, 0.8, 1.0, 1.5, 2.0, and 3.0, respectively. Sixteen studies were included in the combined analysis when the cut-off value was 0.5. The results showed that the pooled sensitivity, specificity, PLR, NLR and DOR of BALF-GM (cut-off 0.5) for the diagnosis of IPA were 0.89 (95%

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“…The infection caused by Aspergillus in human is extremely alarming. Particularly, Aspergillus niger causes various diseases to humans which includes pulmonary diseases, fungal ear infections, temporary hearing loss, and skin diseases [31]. The Candida spp.…”

Section: Antimicrobial Resistance (Amr)mentioning

confidence: 99%

Antimicrobial and anticancer potential of soil bacterial metabolites - a comprehensive and updated review

Kumar¹,

Kumaresan²

2023

J App Biol Biotech

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The majority of natural products currently used in the medical field are derived from microbial or plant sources. The bioactive compounds derived from natural sources exhibit tremendous structural and chemical diversity. According to previous research, only a small percentage of the world's plant and microbial diversity has been examined for bioactivities. The compounds originating from secondary metabolites of microorganisms are more useful for the development of novel drugs due to their biological friendliness and drug-likeness than any other compounds. Thus, recent research suggests that microorganisms obtained from diverse habitats and natural resources offer various bioactive secondary metabolites with incredibly wider chemical entities, hopefully, an alternative remedy for many diseases. Soil bacteria are capable of producing a variety of natural bioactive compounds for the treatment of various diseases. The three genera Bacillus spp., Streptomyces spp., and Pseudomonas spp. have been the prime focus to produce different types of antibiotics. However, to date, there are no reviews that evaluated the antimicrobial and anticancer properties of soil bacterial metabolites. Hence, the current review aimed to assess the antimicrobial and anticancer potential of soil bacterial metabolites.

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Aspergillus Genus and Its Various Human Superficial and Cutaneous Features

Cited by 26 publications

References 107 publications

Efficient and accurate diagnosis of otomycosis using an ensemble deep-learning model

Efficient and accurate diagnosis of otomycosis using an ensemble deep-learning model

Diagnostic value of bronchoalveolar lavage fluid galactomannan assay for invasive pulmonary aspergillosis in adults: A meta‐analysis

Antimicrobial and anticancer potential of soil bacterial metabolites - a comprehensive and updated review

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