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.

Candida auris is a fungal pathogen with frequent development of multidrug-resistance or pan-drug resistance. Currently, the treatment options for Candida auris are limited. Therefore, there is an urgent need for alternative therapeutic strategies. Antimicrobial photodynamic therapy (aPDT), which generates reactive oxygen species (ROS) through light-activated photosensitizers, has shown promise against C. auris; however, its molecular mechanism remains unclear. To investigate COP1T-HA-mediated PDT-induced genomic alterations, we constructed a 3D genome map of Candida species, which uncovered the reorganization of chromatin architecture in response to PDT treatment. Our data showed that low-dose PDT causes subtle local adjustments in chromatin topology, whereas high-dose PDT leads to more pronounced changes in A/B compartmentalization, topologically associating domain (TAD) organization, and chromatin looping associated with key genes related to mitochondrial energy metabolism. Confocal imaging confirmed that high-dose COP1T-HA-mediated PDT induces localized ROS accumulation near the nucleus and a temporally ordered cellular stress response. Furthermore, functional validation through QCR10, NDUFA5, and MP knockouts confirmed the essential roles of these genes in mitochondrial integrity, ATP synthesis, ROS homeostasis, and biofilm formation. Mutants showed altered mitochondrial membrane potential, intracellular pH imbalance, and enhanced glycolytic compensation, highlighting the impact of electron transport disruption on energy metabolism. This study provides the first comprehensive insight into COP1T-HA-mediated PDT-induced chromatin reorganization in C. auris and establishes a direct connection between 3D genome remodeling and fungal energy metabolism, offering a foundation for chromatin-targeted antifungal strategies.

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.

Background: Candidiasis in cancer patients remains largely unexplored in China. This study examines risk factors and antifungal susceptibility patterns of Candida in cancer patients from Jiangxi, China.Methods: Clinical and demographic data on Candida in cancer patients (2018–2024) were retrospectively collected at Jiangxi Cancer Hospital, Nanchang, China. Candida distribution across cancers and antifungal susceptibility patterns were analyzed. Risk factors were identified via logistic regression, and antifungal consumption was correlated with Candida distribution. Survival probabilities were compared between patients with C. albicans and those with non-albicans Candida (NAC) infections.Results: Among 2,761 Candida isolates, 1,703 (61.68%) were C. albicans and 1,058 (38.31%) were NAC, with a year-wise trend showing a decline in C. albicans and a rise in NAC. C. albicans was significantly higher in lung (40.57%) and nasopharyngeal (11.33%) cancers, while NAC were more common in gastric (7.56%), colon (8.69%), and urogenital (14.65%) cancers. NAC risk factors included inappropriate empirical therapy (OR 13.8, P < 0.001), hypoproteinemia (OR 1.35), anemia (OR 1.28), urinary tract infection (OR 1.71), and indwelling catheters (OR 1.27) (all P < 0.05). Radiotherapy, targeted therapy, glucocorticoids, chest tube insertion, and parenteral nutrition were associated with C. albicans (P ≤ 0.01). Amphotericin B (>99%) and echinocandins (>96%) showed the highest efficacy. C. tropicalis displayed notable azole resistance (40.9–74.45%). Caspofungin use negatively correlated with C. albicans (r = −0.84, P = 0.02) and positively with C. tropicalis (r = 0.78, P = 0.04) and N. glabrata (r = 0.85, p = 0.02). NAC infections showed 1.5-fold higher mortality rate than C. albicans (95% CI: 1.1–2.0; P = 0.0075).Conclusion: These findings may aid healthcare officials in improving Candida management in the region and similar settings.

Talaromycosis has long been considered to be exclusively associated with human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS). In recent years, with effective control measures for HIV, the number of talaromycosis patients without HIV infection has been increasing annually. All of these patients have various immunosuppressive factors, including tumors. However, we find that talaromycosis among HIV-negative lung cancer patients remains a rarity and is without comprehensive reviews, contributing to significant gaps in clinical knowledge. We report a case of lung squamous cell carcinoma combined with Talaromyces marneffei (T.marneffei) infection in an HIV-negative patient. The patient, a male with a history of long-term smoking, presented with recurrent fever and cough. Chest computed tomography (CT) scans revealed pleural effusion and nodules. The patient was diagnosed with lung squamous cell carcinoma and talaromycosis through sputum cytology and blood/cerebrospinal fluid metagenomics next-generation sequencing (mNGS). The patient underwent only antifungal therapy and succumbed to respiratory failure, liver and kidney failure, and sepsis in January 2024, before receiving any anti-tumor therapy. The mortality rate of talaromycosis combined with lung cancer is extremely high. Therefore, regardless of whether patients have a history of travel to endemic areas of T. marneffei infection, it is crucial to test for HIV and anti-IFN-γ autoantibodies (AIGA) in patients suspected of having a pulmonary fungal infection, as well as conducting multiple cultures of specimens from different sites and utilizing mNGS to enhance diagnostic accuracy. Additionally, it is essential to perform biopsies in various methods from multiple sites to ascertain the presence of lung cancer. With effective control of T. marneffei infection and timely diagnosis and treatment of lung cancer, there can be a significant improvement in patient survival rates.