Probabilistic machine learning for breast cancer classification.

A probabilistic neural network has been implemented to predict the malignancy of breast cancer cells, based on a data set, the features of which are used for the formulation and training of a model for a binary classification problem. The focus is placed on considerations when building the model, in order to achieve not only accuracy but also a safe quantification of the expected uncertainty of the calculated network parameters and the medical prognosis. The source code is included to make the results reproducible, also in accordance with the latest trending in machine learning research, named Papers with Code.

Doxorubicin selects for fluconazole-resistant petite mutants in Candida glabrata isolates.

Candida infections are a permanent threat to immunocompromised individuals such as cancer patients, and Candida glabrata has emerged as a major problem in recent years. Resistance may develop during lengthy antifungal therapies and is often mediated by upregulation of fungal drug efflux pumps. During chemotherapy the yeast cell is also exposed to cytotoxic agents that may affect its drug susceptibility.

Influence of doxorubicin on fluconazole susceptibility and efflux pump gene expression of Candida dubliniensis.

The effect of doxorubicin (DOX) on the fluconazole (FLU) susceptibility of C. dubliniensis was investigated. Isolates were exposed to DOX and FLU in a chequerboard assay and resistance gene expressions were analysed after DOX exposure.

Difference in virulence between fluconazole-susceptible and fluconazole-resistant Candida albicans in a mouse model.

Two Candida albicans isolates were collected from a HIV-positive patient with recurrent oropharyngeal candidosis (OPC). One isolate was taken during the first episode of oral candidosis [fluconazole susceptible (FLU-S), minimal inhibitory concentration (MIC) = 0.25 mg l(-1) ] and the second after the patient developed refractory OPC and resistance to fluconazole (FLU-R, MIC = 64 mg l(-1)).