The epidemiological patterns of respiratory viruses are complex and highly diverse. Infections caused particularly by SARS-CoV-2 and influenza viruses are frequently followed by secondary mucormycosis, posing significant challenges to clinical management and public health. To address the challenges posed by secondary mucormycosis following respiratory viral infections, the Fungal Prevention and Control Team of the Center for Infectious Diseases at West China Hospital, Sichuan University, convened experts from relevant disciplines across the world to develop the "Expert Consensus on the Prevention of Secondary Mucormycosis Following Respiratory Viral Infections". This consensus aims to provide systematic guidance and optimize prevention strategies. It identifies high-risk populations for mucormycosis after respiratory viral infections and offers 12 preventive recommendations focusing on four areas: education and training, personal protection, early diagnosis and treatment, and infection control. The document also highlights key measures that remain controversial or insufficiently supported by evidence and outlines directions for future research to promote greater awareness and research of mucormycosis.

Antifungal tolerance can promote the emergence of resistance yet often incurs fitness costs for fungal pathogens. How tolerant populations compensate for these deficits and how they may be therapeutically targeted remain poorly understood. Here, we investigate four sequential Candida parapsilosis isolates recovered from a patient with persistent candidemia and failure of micafungin therapy. The infection was ultimately cleared with liposomal amphotericin B (LAMB). Whole-genome sequencing (WGS) confirmed clonal relatedness and the absence of known resistance mutations. Later isolates displayed marked cell wall remodeling (CWR), characterized by increased mannan and reduced β-glucan content, as revealed by microscopy and solid-state nuclear magnetic resonance. These isolates formed thicker biofilms and displayed enhanced echinocandin tolerance but paradoxically showed increased susceptibility to amphotericin B (AMB) in vitro and during systemic infection in mice. Despite a complex mutational landscape, transcriptomic profiling across planktonic and biofilm growth showed minimal divergence from the earliest isolate. Functionally, evolved isolates suppressed M1 macrophage polarization, dampened proinflammatory cytokine production, survived better during neutrophil interactions, and transiently increased fungal burden in vivo. These findings show that host-driven CWR could promote echinocandin tolerance while simultaneously sensitizing C. parapsilosis to AMB. Our results suggest that alternating echinocandin and LAMB therapy may effectively eliminate echinocandin-tolerant fungal populations.IMPORTANCEAntifungal tolerance is increasingly recognized as a precursor to resistance, yet its clinical and biological consequences remain poorly defined. By analyzing sequential Candida parapsilosis isolates from a case of persistent candidemia, we show that cell wall remodeling is associated with echinocandin tolerance, alters host immune interactions, and increases susceptibility to amphotericin B (AMB). These findings reveal how tolerance-associated adaptations shape pathogen fitness during infection and highlight the therapeutic potential of alternating echinocandin and AMB therapy. This work advances our understanding of antifungal tolerance and suggests that exploiting opposing drug susceptibilities may improve treatment outcomes for challenging-to-treat Candida infections.

Fungi are ubiquitous in the biosphere's natural environments, inhabiting humans, animals, and plants, both externally and internally. This widespread presence enables them to occupy diverse ecological niches and enhances their remarkable environmental adaptability, allowing them to thrive on Earth. The mycobiome, a dynamic fungal ecological community inhabiting a well-defined habitat, exhibits distinct physicochemical properties. Beyond its role in maintaining Earth's ecosystem homeostasis, the mycobiome significantly influences the health of humans, animals, and plants under dysbiosis conditions. Although fungal interactions with these hosts have been extensively studied, comprehensive reviews on fungal impacts across the One Health framework remain scarce. In this review, we summarize the recent progress in understanding the role of fungi in human, animal, and plant health, with a focus on characterizing mycobiome-ecosystem interactions through the lens of One Health. By synthesizing these insights, we aim to elucidate the mechanistic underpinnings and translational potential of fungi in combating human diseases. We further highlight how targeting the mycobiome could inform the development of novel preventive and therapeutic strategies across these interconnected health domains.

Fungal fluorescence microscopy using Calcofluor White is a rapid and sensitive diagnostic tool for fungal infections. However, manual microscopic examination is limited by high subjectivity, observer fatigue, poor reproducibility, and a heavy reliance on professional expertise—particularly in high-throughput clinical screening. To develop and validate a DeepLabv3+-based deep learning model for the automated identification, precise segmentation, and quantification of fungal hyphae and spores in clinical fluorescence microscopy images. A total of 60,484 fungal fluorescence images were collected from 1863 clinical specimens across seven medical centers. Of these, 38,494 images were used for model training, while 2561 independent images were selected for clinical validation. Blinded manual microscopy by expert technicians served as the gold standard. The model’s diagnostic performance, stability, and agreement were evaluated. The model demonstrated robust diagnostic performance, with an accuracy of 90.32

Abstract The “microbiome age,” a computationally derived systemic biosignature, is emerging as a pivotal framework for deciphering host aging trajectories and multi‐organ health status. Beyond merely cataloging taxonomic shifts within specific niches, this concept integrates the cumulative biological effects of microbial community structure, functional homeostasis, ecological interactions, and host regulatory dynamics over time. Accumulating evidence indicates that a deviation between chronological and microbiome age‐termed the “microbiome age gap”‐closely correlates with systemic declines in immunomodulation, metabolic robustness, barrier integrity, and neuroendocrine regulation, positioning it as a potent predictor of healthspan and disease risk. A defining feature of microbial aging is the convergence of divergent ecological niches toward a state of dysbiosis. Despite distinct compositional profiles across the gut, oral cavity, skin, and urogenital tract, these ecosystems commonly exhibit diminished stability, functional remodeling, and altered host–microbe interaction modes with advancing age. Concurrently, site‐specific signatures persist, linking microbial shifts to distinct physiological dimensions such as metabolic‐inflammatory axes, barrier function, and local hormonal environments. In this review, we systematically delineate the theoretical underpinnings and computational strategies for modeling microbiome age. We synthesize current evidence regarding age‐related microbial trajectories across diverse body habitats and propose an integrative framework: microbiome age serves dually as a holistic indicator of systemic aging and a sensitive window into localized organ vulnerability. This perspective not only advances our understanding of host‐microbe interactions in aging but also opens new avenues for precision stratification, personalized intervention, and healthspan management.

Objective:To efficiently and accurately identify chromogenic images of Candida and thereby achieve rapid diagnosis,this study adopts an artificial intelligence(AI)model based on the Deeplabv3+algo-rithm.Methods:A total of 167 strains of clinically common Candida were selected,and 1,020 images were col-lected after chromogenic culture for the construction and performance verification of the Deeplabv3+model.Results:The test results showed that the AI achieved an identification accuracy of 91.00%for Candida albicans,94.00%for Candida tropicalis,and 86.00%for Candida glabrata,with an average identification accuracy of 90.33%across the three species.To further verify the performance of the AI technology,three experienced clini-cal laboratory technicians were selected for simultaneous visual identification testing,and their average identifica-tion accuracy was 89.33%.Statistical analysis indicated no significant difference between the AI's identification accuracy and that of human eyes(χ²=0.14,P＞0.05).In terms of recognition speed,the average speed of AI recog-nition was 1.88±0.04 seconds per image,while that of human eye image recognition was 1.93±0.33 seconds per image.No statistically significant difference was observed between the two groups(U=0.45,P＞0.05).However,AI image recognition exhibited greater stability than human eye recognition.Additionally,it enabled batch data processing without being affected by multiple interfering factors.Conclusion:The AI model constructed based on the Deeplabv3+algorithm in this study exhibits efficient and accurate identification capabilities for chromo-genic medium images of Candida,demonstrating promising prospects for promotion.

Candida parapsilosis is a major human fungal pathogen, with recent global outbreaks driven by fluconazole-resistant (FLCR-Cp) isolates that are difficult to eradicate and associated with poor clinical outcomes. However, the microbial traits enabling persistence of these outbreak lineages remain poorly defined. Here, we show that FLCR-Cp isolates responsible for prolonged, multi-country outbreaks consistently exhibit a striking low-biofilm-producing (LBP) phenotype. Contrary to the prevailing view that robust biofilm formation promotes persistence, LBP strains displayed enhanced stress tolerance, increased cell wall masking, and reduced immune recognition. These traits conferred resistance to neutrophil and macrophage killing and enhanced survival in immune cell-rich organs during systemic infection. Genome-wide transcriptomic profiling revealed extensive metabolic and regulatory rewiring in LBP strains. Whole-genome sequencing (WGS) of a global isolate collection further demonstrated that the LBP phenotype has emerged independently multiple times, supporting convergent evolution under host selection. Functional genomic analyses suggest that biofilm attenuation arises through multigenic changes, and disruption of key biofilm-associated transcriptional regulators enhanced fitness during immune interactions. Together, our findings overturn the assumption that robust biofilm formation drives outbreak persistence and instead identify biofilm attenuation as an adaptive tradeoff that promotes immune evasion and long-term survival. These results redefine our understanding of C. parapsilosis adaptation during healthcare-associated outbreaks and shift attention toward host-driven evolutionary processes than environmental persistence alone.

Echinocandins are frontline antifungal drugs, and the emergence of echinocandin-resistant (ECR) species, such as Nakaseomyces glabratus, complicates patient outcomes. Intriguingly, under laboratory conditions, we previously showed that echinocandin alternation with metabolic-independent antifungals, such as amphotericin B (AMB), more effectively kills and minimizes the ECR in N. glabratus. Building upon our previous observations, we examined the efficacy of echinocandin alternation to amphotericin B (EAMB) over echinocandin monotherapy using a systemic candidiasis mouse model to assess if EAMB warrants investigation with potential for clinical evaluation. Interestingly, we show that regardless of the mice's immune status (immunocompromised and immunocompetent) and the N. glabratus isolates [high and low echinocandin tolerance (ECT)] tested, EAMB more rapidly cleared the infection, and minimized ECR in all organs tested compared to caspofungin monotherapy. Pharmacokinetic data suggested that the superiority of EAMB is due to concentration-independent killing activity of liposomal AMB. Although biomarkers suggested higher kidney and liver damage in the EAMB group, histological analysis showed similar damage among both groups. Collectively, using comprehensive ex vivo and in vitro/in vivo experimental conditions, we introduce a novel antifungal therapeutic regimen, which effectively minimizes the ECT and ECR rate in N. glabratus and lays the foundation for in-human studies and clinical trials.

Table S1: Extra-tumoral fungi in association with various cancer types. Figure S1: Interactions of Candida species with the immune system and their roles in cancer development (a): C. albicans triggers Th17/IL-17 responses, recruiting M2-like TAMs and promoting oral cancer. (b): Candida species translocate to the liver, inducing Th17/Th1 responses, leading to inflammation and liver cancer. (c): Candida spp upregulates MDSCs in the gut, driving immune evasion in CRC. C. albicans interacts with Dectin-3 on macrophages, inducing IL-22 production and contributing to CRC. C. tropicalis interacts with CARD9-expressing macrophages, which recruit MDSCs, influencing CRC. Abbreviations: SCC, squamous cell carcinoma; IL, interleukin; CCL2, C C motif chemokine ligand 2; CD163, Cluster of differentiation 163; AGR-1, arginase 1; GAL9, galentin 9; PD-L1, Programmed death-ligand 1; CD4, Cluster of differentiation 4; MDSC, myeloid-derived suppressor cells; PD1, programmed cell death protein 1; Th, T helper; CLRs, C type lectins; ILC2, innate lymphocyte 2; GM-CSF, granulocyte-macrophage colony-stimulating factor; CTL, Cytotoxic T-lymphocyte. Figure S2: Fungal interactions with the immune system contribute to cancer development in various organs (a): Alternaria species translocate to the pancreas, driving immune suppression and pancreatic cancer. (b): Malassezia species translocate to the pancreas, driving pancreatic cancer development through C3a/C3aR signaling. (c): Aspergillus sydowii activates CARD9 macrophages and recruits MDSCs, driving immune suppression and lung cancer. (d): Highly autoreactive T cells in the esophagus lead to Cladosporium infection and activate EGFR signaling, contributing to esophageal cancer. Abbreviations: Th, T helper; IL, interleukin; CD, Cluster of differentiation; MDSC, myeloid-derived suppressor cells; PD1, programmed cell death protein 1; C3aR, C3a receptor; ILC2, innate lymphocyte 2; EGFR, Epidermal growth factor receptor. Table S2: Overview of clinical strategies targeting specific fungi for the treatment and prevention of cancer.

Invasive pulmonary fungal diseases (IPFD) continue to posing an increasing clinical and public health burden on immunocompromised populations. In many Asian settings, the diagnosis of IPFD faces substantial challenges driven by the high prevalence of comorbidities, including poorly controlled diabetes, tuberculosis (TB), and human immunodeficiency virus (HIV) infection, considerable heterogeneity in local pathogens, and disparities in access to diagnostic resources. To address these unmet needs, experts from 18 countries collaboratively developed this consensus, which provides a diagnostic algorithm tailored to high-burden Asian settings based on existing international and regional guidelines. When clinical manifestations are atypical or diagnostic clues are limited, the algorithm prioritizes evaluation for common and regionally prevalent IPFD, followed by stepwise expansion to other potential fungal pathogens. In addition, this consensus outlines the regional accessibility of different diagnostic modalities across Asia. This consensus focuses exclusively on optimization of the diagnostic algorithm and does not provide specific recommendations regarding antifungal therapy. Notably, improved diagnosis of IPFD through this algorithm may contribute to better patient outcomes and strengthened public health strategies in high-burden regions.

A biomarker is an important indicator of a normal physiological or pathological process, or a pharmacological response to a therapeutic intervention. This retrospective study aimed to measure blood biomarkers in wound patients, identify the microorganisms responsible for wound infections and determine their drug susceptibility patterns at a tertiary care hospital in China. The study was conducted between 2022 and 2024, including 279 patients. A total of 33 microbial species were isolated using culture techniques, identified, and analyzed for their antibiotic susceptibility. The organisms were predominantly gram-positive (50.8%), with Staphylococcus aureus (80.2%) being the most prevalent species. Among the gram-negative bacteria (41.2%), Pseudomonas aeruginosa (22.6%) was the most predominant species. Biomarkers such as white blood cells, neutrophils, lymphocytes, and erythrocyte sedimentation rate (ESR) values were higher than normal in most of microbial species associated with wound infections. The WBC value in gram-positive infections and the neutrophil and ESR values in fungal infections were statistically significantly higher than the normal range (p = 0.0002, p = 0.002, and p = 0.003, respectively). Albumin levels were high value in P. aeruginosa and K. pneumoniae (0.48 and 0.56 respectively), while lymphocytes levels were the lowest value (-0.62) in S. aureus. Resistance to at least one antibiotic was identified in 82.4% of the isolates. The prevalence of multidrug-resistant microbes in different wound infections is a significant concern in China. A health awareness campaign, coupled with improved hygiene measures, should be implemented to prevent the spread of microorganisms responsible for wound infections within the community.

Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes to address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, and epidemiological data for predicting AMR outbreaks. Although AI/ML are relatively new fields, numerous case studies offer substantial evidence of their successful application in predicting AMR outbreaks with greater accuracy. These models can provide insights into the discovery of novel antimicrobials, the repurposing of existing drugs, and combination therapy through the analysis of their molecular structures. In addition, AI-based clinical decision support systems in real-time guide healthcare professionals to improve prescribing of antibiotics. The review also outlines how can AI improve AMR surveillance, analyze resistance trends, and enable early outbreak identification. Challenges, such as ethical considerations, data privacy, and model biases exist, however, the continuous development of novel methodologies enables AI/ML to play a significant role in combating AMR.

BACKGROUND:Aspergillus infections pose significant challenges in clinical management due to rising resistance rates and limited diagnostic accuracy. Superficial infections, particularly in immunocompetent individuals, are often understudied, despite their prevalence in specific populations. OBJECTIVES:This study aimed to characterise the distribution and antifungal susceptibility patterns of Aspergillus isolates from a tertiary hospital in Shandong, China, and evaluate the performance of matrix-assisted laser desorption time-of-flight (MALDI-TOF) mass spectrometry versus multi-gene sequencing for species identification. PATIENTS/METHODS:A total of 120 Aspergillus isolates were collected from patients with localised aspergillosis (nails, external auditory canal, cornea, sub-throat secretions) between 2020 and 2021. Species identification was performed using MALDI-TOF and multi-gene sequencing (ITS, BenA, CaM, RPB2). Antifungal susceptibility testing was conducted for micafungin, azoles (itraconazole, voriconazole, posaconazole, fluconazole), and amphotericin B following standard protocols. RESULTS:Species Identification: MALDI-TOF identified 52.5% of isolates to the species level, whereas multi-gene sequencing achieved 100% accuracy. Aspergillus terreus was the most prevalent species (38.3%). Antifungal Susceptibility: Micafungin showed the highest resistance rate (40%), followed by amphotericin B (reduced susceptibility in 31.7%). Azoles demonstrated low resistance (3.3%-6.7%) except for fluconazole (21.7%). Clinical Correlates: Superficial infections were most common in middle-aged/elderly patients (68.3%), frequently linked to external trauma (41.7%) or environmental exposure (35.8%). CONCLUSIONS:Multi-gene sequencing outperformed MALDI-TOF for Aspergillus identification. A. terreus dominance and micafungin resistance highlight regional epidemiological trends. Natamycin and nystatin remain cost-effective first-line topical options. Enhanced surveillance in trauma-prone and environmentally exposed populations is warranted.

Artificial intelligence holds great promise for the design of antimicrobial peptides (AMPs); however, current models face limitations in generating AMPs with sufficient novelty and diversity, and they are rarely applied to the generation of antifungal peptides. Here, we develop an alternative pipeline grounded in a diffusion model and molecular dynamics for the de novo design of AMPs. The peptides generated by our pipeline have lower similarity and identity than those of other reported methodologies. Among the 40 peptides synthesized for an experimental validation, 25 exhibit either antibacterial or antifungal activity. AMP-29 shows selective antifungal activity against Candida glabrata and in vivo antifungal efficacy in a murine skin infection model. AMP-24 exhibits potent in vitro activity against Gram-negative bacteria and in vivo efficacy against both skin and lung Acinetobacter baumannii infection models. The proposed approach offers a pipeline for designing diverse AMPs to counteract the threat of antibiotic resistance.

Cryptococcus neoformans is a significant human fungal pathogen, particularly concerning for immunocompromised individuals. Key kinases, Hsl101 and Urk1, are critical for crossing the blood-brain barrier, although the specific mechanisms by which they do so remain undefined. In this study, we systematically investigate the impact of HSL101 or URK1 deletion on the proteomic and metabolomic profiles of C. neoformans. By generating a deletion mutant for HSL101, we observed profound alterations in the expression levels of 366 proteins and 421 metabolites, highlighting significant disruptions in key biological processes, such as oxidative phosphorylation and ATP synthesis, which are vital for energy production in the cell. Similarly, the deletion of URK1 resulted in significant changes in the expression of 236 proteins and 366 metabolites, particularly affecting pathways related to steroid biosynthesis and starch and sucrose metabolism, which are critical for cellular function and adaptation. These findings shed light on the regulatory roles of Hsl101 and Urk1 in the metabolic pathways of C. neoformans.

Comprehensive reference genomes are needed for the classification and functional characterization of the human skin microbiota. Here, we established human skin microbiome genome (HSMG) and protein (HSMP) catalogs by integrating 739 newly sequenced and 2,520 published samples, along with two published microbial genome catalogs. The HSMG includes 3547 prokaryotic species, of which 1556 (43.87%) are unidentified, and the HSMP contains 39,283,339 nonredundant proteins, with 64.8% of which are poorly characterized. Using the HSMG as a reference, we identified distinct features and biogeographical traits of the skin microbiome in plateau adults, revealing significant differences between sebaceous and dry skin, with 1784 of 3547 inferred prokaryotes showing considerable variation. Additionally, host characteristics, skincare, and daylight habits were found to shape the skin microbiome. This work expands our understanding of the diversity of uncultured skin bacteria and provides a comprehensive characterization of the human skin microbiome in plateau environments.

Accurate, timely diagnosis of varicella–zoster virus (VZV) is important for treatment and infection control. While loop-mediated isothermal amplification (LAMP) is operationally simple, nonspecific priming can degrade performance. We assessed a polyethylene glycol–modified LAMP (PEG-LAMP) that tunes the reaction microenvironment via macromolecular crowding. PEG (1 μL per reaction) was titrated across concentrations; 100 mM was selected as the optimized condition because negatives remained at baseline while target amplification kinetics were maintained. PEG-LAMP preserved a log–linear relation between threshold time and input and improved the detection limit from 103 to 102 copies/μL compared with conventional LAMP. Precision at a fixed input exhibited low variability, and specificity was supported by flat traces in non-target reactions. In a 30-sample panel (15 spiked positives, 15 negatives) tested in parallel by PCR, conventional LAMP, and PEG-LAMP, PEG-LAMP was fully concordant with PCR and yielded shorter time-to-threshold for positives, whereas conventional LAMP produced one false negative and four false positives. Taken together, the results demonstrate that microenvironmental tuning with PEG provides a low-complexity means to suppress nonspecific LAMP while preserving on-target amplification, yielding a lower detection limit and faster time-to-result with PCR-level qualitative agreement in clinical VZV diagnosis.

IntroductionThe ability to acquire iron and maintain iron homeostasis is crucial for the virulence of the human pathogenic fungus Cryptococcus neoformans. This study investigates the role of Bud32, a core virulence kinase and component of the KEOPS complex, within the iron regulatory network of C. neoformans.MethodsWe used gene deletion techniques to study the phenotypic effects of BUD32 gene knockout and conducted proteomic and metabolomic analyses to assess changes in protein expression and metabolite levels in the mutant. Additionally, we performed in vivo phosphoproteomics analysis to evaluate Bud32 impact on iron regulatory proteins.ResultsOur findings revealed that deletion of BUD32 gene significantly impaired growth in iron-limiting environments, leading to notable alterations in the expression of iron transport and iron-sulfur cluster (ISC)-containing proteins. Specifically, Bud32 was shown to modulate ISC assembly and influence the activity of key iron-sulfur binding proteins, including Grx4, Cir1, and HapX. Metabolic profiling indicated changes in 696 metabolites, with reductions in biliverdin levels. Additionally, BUD32 gene deletion resulted in widespread changes in the phosphorylation status of numerous proteins, including the iron regulators Cir1 and Rim101.ConclusionThese findings provide evidence for the involvement of the kinase Bud32 in regulating iron homeostasis in C. neoformans, thereby contributing to our understanding of its virulence mechanisms.

Abstract Fungal dysbiosis is increasingly recognized as a key factor in cancer, influencing tumor initiation, progression, and treatment outcomes. This review explores the role of fungi in carcinogenesis, with a focus on mechanisms such as immunomodulation, inflammation induction, tumor microenvironment remodeling, and interkingdom interactions. Fungal metabolites are involved in oncogenesis, and antifungals can interact with anticancer drugs, including eliciting potential adverse effects and influencing immune responses. Furthermore, mycobiota profiles have potential as diagnostic and prognostic biomarkers, emphasizing their clinical relevance. The interplay between fungi and cancer therapies can affect drug resistance, therapeutic efficacy, and risk of invasive fungal infections associated with targeted therapies. Finally, emerging strategies for modulating mycobiota in cancer care are promising approaches to improve patient outcomes.