Search Results (485)

Wildfires can significantly alter soil physicochemical conditions and microbial communities in forest ecosystems. This study aimed to characterize the culturable soil fungal community and evaluate biological activity in Banco Totumo Bijibana, a protected dry tropical forest in Atlántico, Colombia, affected by a wildfire in 2014. Twenty soil samples were collected for microbiological (10 cm depth) and physicochemical (30 cm) analysis. Basal respiration was measured using Stotzky’s method, nitrogen mineralization via Rawls’ method, and fungal diversity through culture-based identification and colony-forming unit (CFU) counts. Diversity was assessed using Simpson, Shannon–Weaver, and ACE indices. The soils presented low organic matter (0.70%) and nitrogen content (0.035%), with reduced biological activity as indicated by basal respiration (0.12 kg C ha⁻¹ d⁻¹) and mineralized nitrogen (5.61 kg ha⁻¹). Four fungal morphotypes, likely from the genus Aspergillus, were identified. Simpson index indicated moderate dominance, while Shannon–Weaver values reflected low diversity. Correlation analysis showed Aspergillus-3 was positively associated with moisture, whereas Aspergillus-4 correlated negatively with pH and sand content. The species accumulation curve reached an asymptote, suggesting an adequate sampling effort. Although no control site was included, the findings provide a baseline characterization of post-fire soil microbial structure and function in a dry tropical ecosystem. Full article

The demand for wild animal meat, popularly called “bushmeat”, serves as a driving force behind the emergence of infectious diseases, potentially transmitting a variety of pathogenic bacteria to humans through handling and consumption. This study investigated the microbial load and bacterial diversity in bushmeat sourced from a prominent bushmeat market in Kumasi, Ghana. Carcasses of 61 wild animals, including rodents (44), antelopes (14), and African civets (3), were sampled for microbiological analysis. These samples encompassed meat, intestines, and anal and oral swabs. The total aerobic bacteria plate count (TPC), Enterobacteriaceae count (EBC), and fungal counts were determined. Bacterial identification was conducted using MALDI-TOF biotyping. Fungal counts were the highest across all animal groups, with African civets having 11.8 ± 0.3 log₁₀ CFU/g and 11.9 ± 0.2 log₁₀ CFU/g in intestinal and meat samples, respectively. The highest total plate count (TPC) was observed in rodents, both in their intestines (10.9 ± 1.0 log₁₀ CFU/g) and meat (10.9 ± 1.9 log₁₀ CFU/g). In contrast, antelopes exhibited the lowest counts across all categories, particularly in EBC from intestinal samples (6.1 ± 1.5 log₁₀ CFU/g) and meat samples (5.6 ± 1.2 log₁₀ CFU/g). A comprehensive analysis yielded 524 bacterial isolates belonging to 20 genera, with Escherichia coli (18.1%) and Klebsiella spp. (15.5%) representing the most prevalent species. Notably, the detection of substantial microbial contamination in bushmeat underscores the imperative for a holistic One Health approach to enhance product quality and mitigate risks associated with its handling and consumption. Full article

Tree diseases affecting Camellia japonica have emerged as a significant threat to the health and longevity of this ornamental tree, particularly in countries where this tree species is widely distributed and cultivated. Among these, Pestalotiopsis spp. have been frequently reported and are considered one of the most impactful fungal pathogens, causing leaf blight or leaf spot, in multiple countries. Understanding the etiology and distribution of these diseases is essential for effective management and conservation of C. japonica populations. The traditional methods based on pathogen isolation and pure culture cultivation for diagnosis of tree diseases are labor intensive and time-consuming. In addition, the frequent coexistence of the major pathogens with other endophytes within a single C. japonica tree, coupled with inconsistent symptom expression and the occurrence of pathogens in asymptomatic hosts, further complicates disease diagnosis. These challenges highlight the urgent need to develop more rapid, accurate, and efficient diagnostic or monitoring tools to improve disease monitoring and management on trees, including C. japonica. To address these challenges, we applied a metagenomic approach to screen fungal communities within C. japonica trees. This method enabled comprehensive detection and characterization of fungal taxa present in symptomatic and asymptomatic tissues. By analyzing the correlation between fungal dominance and symptom expression, we identified key pathogenic taxa associated with disease manifestation. To validate the metagenomic approach, we employed a combined strategy integrating metagenomic screening and traditional fungal isolation to monitor foliar diseases in C. japonica. The correlation between dominant taxa and symptom expression was confirmed. Simultaneously, traditional isolation enabled the identification of a novel species, Pestalotiopsis, as the causal agent of leaf spot disease on C. japonica. In addition to confirming previously known pathogens, our study led to the discovery and preliminary characterization of a novel fungal taxon with pathogenic potential. Our findings provide critical insights into the fungal community of C. japonica and lay the groundwork for developing improved, rapid diagnostic tools for effective disease monitoring and management of tree diseases. Full article

A key aspect driving advancements in machine learning applications in medicine is the availability of publicly accessible datasets. Evidently, there are studies conducted in the past with promising results, but they are not reproducible due to the fact that the data used are closed or proprietary or the authors were not able to publish them. The current study aims to narrow this gap for researchers who focus on image recognition tasks in microbiology, specifically in fungal identification and classification. An open database named OpenFungi is made available in this work; it contains high-quality images of macroscopic and microscopic fungal genera. The fungal cultures were grown from food products such as green leaf spices and cereals. The quality of the dataset is demonstrated by solving a classification problem with a simple convolutional neural network. A thorough experimental analysis was conducted, where six performance metrics were measured in three distinct validation scenarios. The results obtained demonstrate that in the fungal species classification task, the model achieved an overall accuracy of 99.79%, a true-positive rate of 99.55%, a true-negative rate of 99.96%, and an F1 score of 99.63% on the macroscopic dataset. On the microscopic dataset, the model reached a 97.82% accuracy, a 94.89% true-positive rate, a 99.19% true-negative rate, and a 95.20% F1 score. The results also reveal that the model maintains promising performance even when trained on smaller datasets, highlighting its robustness and generalization capabilities. Full article

Stropharia rugosoannulata, an ecologically valuable and economically important edible mushroom, faces challenges in strain-level identification and breeding due to limited genomic resources and the lack of high-resolution molecular markers. In this study, we generated high-quality genomic data for 105 S. rugosoannulata strains and identified over 2.7 million SNPs, unveiling substantial genetic diversity within the species. Using core gene-associated multiple nucleotide polymorphism (cgMNP) markers, we developed an efficient and transferable framework for strain discrimination. The analysis revealed pronounced genetic differentiation among cultivars, clustering them into two distinct phylogenetic groups. Nucleotide diversity (π) across 83 core genes varied significantly, highlighting both highly conserved loci under purifying selection and highly variable loci potentially associated with adaptive evolution. Phylogenetic analysis of the most variable gene, Phosphatidate cytidylyltransferase mitochondrial, identified 865 SNPs, enabling precise differentiation of all 85 cultivars. Our findings underscore the utility of cgMNP markers in addressing challenges posed by horizontal gene transfer and phylogenetic noise, demonstrating their robustness in cross-species applications. By providing insights into genetic diversity, evolutionary dynamics, and marker utility, this study establishes a foundation for advancing breeding programs, conservation strategies, and functional genomics in S. rugosoannulata. Furthermore, the adaptability of cgMNP markers offers a universal tool for high-resolution strain identification across diverse fungal taxa, contributing to broader fungal phylogenomics and applied mycology. Full article

Dual mycorrhizal symbiosis, i.e., the association with both arbuscular and ectomycorrhizal fungal symbionts, is an ambiguous phenomenon concurrently considered as common among various genetic lineages of trees and a result of bias in data analyses. Recent studies have shown that the ability to form dual mycorrhizal associations is a distinguishing factor for the continental-scale invasion of alien tree species. However, the phylogenetic mechanisms that drive it remain unclear. In this study, all the evidence on root-associated symbionts of Juglandaceae from South and North America, Asia, and Europe was combined and re-analysed following current knowledge and modern molecular-based identification methods. The Juglandaceae family was revealed to represent a specific pattern of symbiotic interactions that are rare among deciduous trees and absent among conifers. Closely related phylogenetic lineages of trees usually share the same type of symbiosis, but Juglandaceae contains several possible ones concurrently. The hyperdiversity of root symbionts of Juglandaceae, unlike other tree families, was concurrently found in Central and North America, Asia, and Europe, indicating its phylogenetic determinants, which endured geographical isolation. However, for many Juglandaceae, including the invasive Juglans and Pterocarya species, this was never studied or was studied only with outdated methods. Further molecular research on root symbionts of Juglandaceae, providing long sequences and high taxonomic resolutions, is required to explain their ecological roles. Full article

Essential oils (EOs) are widely recognized for their antifungal properties, but their efficacy against specific phytopathogenic fungi associated with banana (Musa paradisiaca) rot remains underexplored. This study aimed to evaluate the antifungal potential of EOs from Origanum vulgare, Salvia rosmarinus, Syzygium aromaticum, Thymus vulgaris, Cinnamomum verum, and Ocimum basilicum against five fungal species isolated from infected banana peels. Fungal isolates were obtained using PDA medium supplemented with chloramphenicol and were purified by weekly subculturing. Morphological and microscopic characterization was complemented by molecular identification based on ITS sequencing and phylogenetic reconstruction using Neighbor-Joining and UPGMA methods in MEGA v11. In vitro and ex vivo antifungal assays were performed at EO concentrations ranging from 200 to 1000 ppm. Thyme oil exhibited the strongest inhibitory effect, with complete growth suppression at 1000 ppm. Cinnamon and oregano also demonstrated effective inhibition at 600 ppm, while clove, rosemary, and basil were markedly less effective. Statistical analysis confirmed significant effects of EO type and concentration on fungal growth (p < 0.001). Molecular results showed strong phylogenetic support for isolate identification, with bootstrap values above 93% in most clades. These findings support the selective use of specific EOs as sustainable alternatives to synthetic fungicides in the postharvest management of banana diseases and provide a molecularly supported basis for their targeted application in integrated control strategies. Full article

Puffballs, a group of macrofungi belonging to the Basidiomycota, pose taxonomic challenges due to their convergent morphological features, including spherical basidiocarps and similar peridial structures, which often hinder accurate species-level identification. This study proposes a deep learning-based classification framework for eight ecologically and taxonomically important puffball species: Apioperdon pyriforme, Bovista plumbea, Bovistella utriformis, Lycoperdon echinatum, L. excipuliforme, L. molle, L. perlatum, and Mycenastrum corium. A balanced dataset of 1600 images (200 per species) was used, divided into 70% training, 15% validation, and 15% testing. To enhance generalizability, images were augmented to simulate natural variability in orientation, lighting, and background. In this study, five different deep learning models (ConvNeXt-Base, Swin Transformer, ViT, MaxViT, EfficientNet-B3) were comparatively evaluated on a balanced dataset of eight puffball species. Among these, the ConvNeXt-Base model achieved the highest performance, with 95.41% accuracy, and proved especially effective in distinguishing morphologically similar species such as Mycenastrum corium and Lycoperdon excipuliforme. The findings demonstrate that deep learning models can serve as powerful tools for the accurate classification of visually similar fungal species. This technological approach shows promise for developing automated mushroom identification systems that support citizen science, amateur naturalists, and conservation professionals. Full article

Root rot, a globally devastating disease of common bean (Phaseolus vulgaris L.), remains a major constraint on bean production across China. Despite its agricultural impact, the pathogen complex associated with this disease has been poorly characterized in most provinces. To address this critical knowledge gap, we conducted nationwide surveys during 2016–2018, systematically sampling 1–10 symptomatic plants from each of 121 (2016) and 170 (2018) field sites across 17 provinces in China’s major vegetable production regions. Isolates obtained from symptomatic root tissues underwent morphological screening, followed by molecular identification using partial sequences of EF1-α for Fusarium species and ITS regions for other genera. Pathogenicity of representative isolates was subsequently confirmed through controlled greenhouse assays. This integrated approach revealed fourteen fungal and oomycete genera, with Fusarium (predominantly F. oxysporum and F. solani) and Rhizoctonia (R. solani) emerging as the most prevalent pathogens. Notably, pathogen composition exhibited significant regional variation and underwent temporal shifts across developmental stages. Additionally, F. oxysporum, F. solani, and R. solani demonstrated significant interspecies associations with frequent co-occurrence in bean root rot systems. Collectively, this first comprehensive characterization of China’s common bean root rot complex not only clarifies spatial–temporal pathogen dynamics but also provides actionable insights for developing region- and growth stage-specific management strategies, particularly through targeted control of dominant pathogens during key infection windows. Full article

Weeds compete with crops for resources, posing multiple negative impacts for agricultural production systems and triggering degradation of ecosystem services (e.g., alterations in the soil microbial community structure). Under the guidance of green plant protection, the development of efficient biocontrol strains with environmentally friendly characteristics has become a crucial research direction for sustainable agriculture. This study aimed to develop a fungal bioherbicide by isolating and purifying a pathogenic fungal strain (JZ-5) from infected redroot pigweed (Amaranthus retroflexus L.). The strain exhibited pathogenicity rates ranging from 23.46% to 86.25% against six weed species, with the most pronounced control efficacy observed against henbit deadnettle (Lamium amplexicaule L.), achieving a pathogenicity rate of 86.25%. Through comprehensive characterization of cultural features, morphological observations, and molecular biological identification, the strain was taxonomically classified as Fusarium oxysporum. Scanning electron microscopy revealed that seven days post-inoculation, F. oxysporum JZ-5 formed dense mycelial networks on the leaf surfaces of cluster mallow (Malva verticillata L.), causing severe tissue damage. Safety assessments demonstrated that the spore suspension (10⁴ spores/mL) had no adverse effects on three crops: hulless barley (Hordeum vulgare var. coeleste L.), wheat (Triticum aestivum L.), and potato (Solanum tuberosum L.). These findings suggest that F. oxysporum strain JZ-5 warrants further investigation as a potential bioherbicide for controlling three problematic weed species—Chenopodium album L. (common lambsquarters), Elsholtzia densa Benth. (dense-flowered elsholtzia), and Lamium amplexicaule L. (henbit deadnettle)—in cultivated fields of hulless barley (Hordeum vulgare var. coeleste L.), wheat (Triticum aestivum L.), and potato (Solanum tuberosum L.). This discovery provides valuable fungal resources for ecologically sustainable weed management strategies, contributing significantly to the advancement of sustainable agricultural practices. Full article

Candida auris (C. auris) is an emerging multidrug-resistant fungal pathogen recognized by the World Health Organization (WHO) as a critical global health threat. Its rapid transmission, high mortality rate, and frequent misidentification in clinical laboratories present significant challenges for diagnosis and infection control. This review provides a comprehensive overview of current and emerging diagnostic methods for C. auris detection, including culture-based techniques, biochemical assays, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), and molecular diagnostics such as PCR and loop-mediated isothermal amplification (LAMP). We evaluate each method’s sensitivity, specificity, turnaround time, and feasibility in clinical and surveillance settings. While culture remains the diagnostic gold standard, it is limited by slow turnaround and phenotypic overlap with related species. Updated biochemical platforms and MALDI-TOF MS with expanded databases have improved identification accuracy. Molecular assays offer rapid, culture-independent detection. Antifungal susceptibility testing (AFST), primarily using broth microdilution, is essential for guiding treatment, although standardized breakpoints remain lacking. This review proposes an integrated diagnostic workflow and discusses key innovations and gaps in current practice. Our findings aim to support clinicians, microbiologists, and public health professionals in improving early detection, containment, and management of C. auris infections. Full article

Invasive fungal infections (IFIs), primarily caused by Candida species, represent a significant global public health concern due to their high mortality rates and growing antifungal resistance. In Honduras, data on their epidemiology remains scarce. This study aimed to characterize Candida species associated with candidemia and assess key virulence factors. A total of 80 clinical isolates were collected from four hospitals in Honduras’s major cities, Tegucigalpa and San Pedro Sula. Identification was performed using both phenotypic and molecular methods. Hemolytic activity, phospholipase and protease production, and biofilm formation were evaluated. C. albicans and C. tropicalis were the most prevalent species (30% each), followed by C. parapsilosis (27.5%). Phenotypic methods misidentified 13.8% of the isolates. Most strains (96.3%) exhibited strong hemolytic activity. C. albicans showed the highest phospholipase activity, while C. tropicalis was the most robust film producer. These findings highlight an evolving epidemiological landscape characterized by an increasing prevalence of non-albicans Candida species, often less susceptible to antifungal agents, and diverse virulence profiles such as strong biofilm formation. This underscores the clinical need for accurate species-level identification through molecular diagnostics and ongoing surveillance to guide targeted antifungal therapy and enable early, locally adapted interventions. Full article

Candidiasis is a major fungal infection worldwide, with invasive forms linked to high morbidity and mortality. The emergence of azole resistance in Candida parapsilosis causing candidemia led us to examine the epidemiology and antifungal susceptibility of Candida species at the University Hospital of Liège between January 2017 and December 2023. A total of 916 isolates from blood or sterile body fluids, tissues, and abscesses were analyzed. Species identification was performed using MALDI-TOF MS and antifungal susceptibility testing via Sensititre YO10 AST was interpreted according to the CLSI guidelines. Candida albicans remained the predominant species (56%), followed by Nakaseomyces glabratus (19%), Candida parapsilosis (8%), and Candida tropicalis (7%). No significant shift toward non-albicans Candida species (NAC) was observed even during the COVID-19 pandemic, supporting the use of narrow-spectrum empirical therapy in selected patients. Fluconazole susceptibility was high in C. albicans (98.8%), whereas N. glabratus and C. tropicalis showed high resistance rates with 10.1% and 16.9%, respectively. C. parapsilosis showed stable fluconazole susceptibility across the study period. Echinocandins demonstrated excellent activity (95.6–100%), and amphotericin B was effective against nearly all isolates. This seven-year surveillance at the University Hospital of Liège confirms that while C. albicans remains the predominant and highly susceptible species, rising azole resistance in non-albicans Candida—particularly N. glabratus and C. tropicalis—highlights the critical need for ongoing local epidemiological monitoring to guide effective and targeted antifungal therapy. Full article

This study presents a novel approach for classifying Discomycetes species using deep learning and explainable artificial intelligence (XAI) techniques. The EfficientNet-B0 model achieved the highest performance, reaching 97% accuracy, a 97% F1-score, and a 99% AUC, making it the most effective model. MobileNetV3-L followed closely, with 96% accuracy, a 96% F1-score, and a 99% AUC, while ShuffleNet also showed strong results, reaching 95% accuracy and a 95% F1-score. In contrast, the EfficientNet-B4 model exhibited lower performance, achieving 89% accuracy, an 89% F1-score, and a 93% AUC. These results highlight the superior feature extraction and classification capabilities of EfficientNet-B0 and MobileNetV3-L for biological data. Explainable AI (XAI) techniques, including Grad-CAM and Score-CAM, enhanced the interpretability and transparency of model decisions. These methods offered insights into the internal decision-making processes of deep learning models, ensuring reliable classification results. This approach improves traditional taxonomy by advancing data processing and supporting accurate species differentiation. In the future, using larger datasets and more advanced AI models is recommended for biodiversity monitoring, ecosystem modeling, medical imaging, and bioinformatics. Beyond high classification performance, this study offers an ecologically meaningful approach by supporting biodiversity conservation and the accurate identification of fungal species. These findings contribute to developing more precise and reliable biological classification systems, setting new standards for AI-driven research in biological sciences. Full article

Wheat is an important agricultural crop grown under various conditions on five continents. The ability to promptly detect and defeat fungal diseases has a significant impact on the volume of the obtained harvest. One of the most significant threats to human and domestic animal health is metabolites produced by Fusarium genus fungi. In this regard, this work is devoted to the possibility of the rapid differentiation between healthy grains and grains simultaneously infected with several species of Fusarium genus fungi (Fusarium graminearum Schwabe FG-30, Fusarium poae Kr-20-14, Fusarium roseum (sambucinum) St-20-3) for practical reasons. An approach based on obtaining hyperspectral data with their subsequent processing using the principal component analysis (PCA) method and determining statistically important spectral regions sensitive for grain infection at different stages (5 and 40 days) was proposed. The effects of the grain orientation and data dimensionality on the classification result were studied. For further practical application in devices for the rapid identification of wheat grains infected with Fusarium, a method based on the use of reflection at wavelengths of 400, 451, 708, 783, 801, and 863 nm, together with normalization [0, 1] and the subsequent projection of spectral data onto the first three principal components (PCs), was proposed, regardless of the grain orientation. Full article