The phenomenon of drug resistance in micro-organisms necessitates the search for new compounds capable of combating them. Fungi emerge as a promising source of such compounds as they produce a wide range of secondary metabolites with bacteriostatic or fungistatic activity. These compounds can serve as alternatives for commonly used antibiotics. Furthermore, fungi also accumulate compounds with antiviral activity. This review focuses on filamentous fungi and macrofungi as sources of antimicrobial compounds. The article describes both individual isolated compounds and extracts that exhibit antibacterial, antifungal, and antiviral activity. These compounds are produced by the fruiting bodies and mycelium, as well as the biomass of mycelial cultures. Additionally, this review characterizes the chemical compounds extracted from mushrooms used in the realm of cosmetology; specifically, their antimicrobial activity.

Listeriosis is one of the most serious foodborne diseases under surveillance, with an overall mortality rate in the EU currently being high at 18.1%. Therefore, this study aims to investigate Listeria monocytogenes strains isolated from clinical and food samples for susceptibility to antimicrobials, presence of virulence factors, and genetic diversity. Species were identified using the MALDI-TOF, resistance to 11 antibiotics was determined according to EUCAST guidelines, and multiplex PCR was used for serotyping and detecting virulence genes. Strains were genotyped using the PFGE method. Clinical strains showed full sensitivity to all tested antibiotics. In total, 33.3% of strains from food products were found to be resistant to ciprofloxacin and 4.2% to tetracycline. Most of the tested isolates (79.2%) belonged to serotype 1/2a-3a, and the rest (20.8%) belonged to serotype 4ab-4b,4d-4e. Five virulence genes (prfA, hlyA, plcB, inlA, and lmo2672) were detected in all strains studied. The llsX gene was the least common, in 37.5% of clinical strains and 18.75% of strains isolated from food products. Among the analyzed strains, 13 strains displayed unique PFGE profiles. The other 11 strains belong to 3 clusters of pulsotypes: cluster 1 (2 strains), cluster 2 (6 strains), and cluster 3 (2 strains). The percentage of hospitalizations and deaths of Polish patients with listeriosis indicates the seriousness of this disease, especially in an aging society, while the molecular testing of clinical strains has been rarely performed, which makes it difficult to determine the source of infection.

Candida fungi are among the most common human fungal pathogens and invasive candidiasis is one of the predominant invasive mycoses that mainly affects hospitalized patients with suppression of the immunological system and breaches in skin or mucosal barriers. Rapid diagnosis and implementation of appropriate antifungal treatment are key to achieving recovery. In recent years, attention has been drawn to the increasing significance of Candida species other than C. albicans, in particular C. auris and fluconazole-resistant C. parapsilosis. The aim of this work was to present the species spectrum and drug susceptibility of 570 Candida strains isolated from candidemia cases diagnosed in patients hospitalized in southern Poland in the period 2017–2022. The results of Candida-positive blood cultures obtained from five hospitals were analyzed. C. albicans was the most common species, accounting for 42.6% of all strains, followed by Nakaseomyces glabratus (formerly C. glabrata) and C. parapsilosis complex—22.1% and 18.8%, respectively. No C. auris was found. Fluconazole resistance was found in 4.9% of C. albicans strains, 34.7% of N. glabratus strains, and 8.7% of C. parapsilosis complex strains.

Sepsis is a life-threatening condition which requires rapid diagnosis and treatment. Traditional microbiological methods are time-consuming and expensive. In response to these challenges, deep learning algorithms were developed to identify 14 bacteria species and 3 yeast-like fungi from microscopic images of Gram-stained smears of positive blood samples from sepsis patients. A total of 16,637 Gram-stained microscopic images were used in the study. The analysis used the Cellpose 3 model for segmentation and Attention-based Deep Multiple Instance Learning for classification. Our model achieved an accuracy of 77.15% for bacteria and 71.39% for fungi, with ROC AUC of 0.97 and 0.88, respectively. The highest values, reaching up to 96.2%, were obtained for Cutibacterium acnes, Enterococcus faecium, Stenotrophomonas maltophilia and Nakaseomyces glabratus. Classification difficulties were observed in closely related species, such as Staphylococcus hominis and Staphylococcus haemolyticus, due to morphological similarity, and within Candida albicans due to high morphotic diversity. The study confirms the potential of our model for microbial classification, but it also indicates the need for further optimisation and expansion of the training data set. In the future, this technology could support microbial diagnosis, reducing diagnostic time and improving the effectiveness of sepsis treatment due to its simplicity and accessibility. Part of the results presented in this publication was covered by a patent application at the European Patent Office EP24461637.1 "A computer implemented method for identifying a microorganism in a blood and a data processing system therefor".