Vector control programmes are a strategic priority in the fight against malaria. However, vector control interventions require rigorous monitoring. Entomological tools for characterizing malaria transmission drivers are limited and are difficult to establish in the field. To predict Anopheles drivers of malaria transmission, such as mosquito age, blood feeding and Plasmodium infection, we evaluated artificial neural networks (ANNs) coupled to matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry (MS) and analysed the impact on the proteome of laboratory-reared Anopheles stephensi mosquitoes. ANNs were sensitive to Anopheles proteome changes and specifically recognized spectral patterns associated with mosquito age (0–10 days, 11–20 days and 21–28 days), blood feeding and P. berghei infection, with best prediction accuracies of 73%, 89% and 78%, respectively. This study illustrates that MALDI-TOF MS coupled to ANNs can be used to predict entomological drivers of malaria transmission, providing potential new tools for vector control. Future studies must assess the field validity of this new approach in wild-caught adult Anopheles. A similar approach could be envisaged for the identification of blood meal source and the detection of insecticide resistance in Anopheles and to other arthropods and pathogens.

The spread of fungal clones is hard to detect in the daily routines in clinical laboratories, and there is a need for new tools that can facilitate clone detection within a set of strains. Currently, Matrix Assisted Laser Desorption-Ionization Time-of-Flight Mass Spectrometry is extensively used to identify microbial isolates at the species level. Since most of clinical laboratories are equipped with this technology, there is a question of whether this equipment can sort a particular clone from a population of various isolates of the same species. We performed an experiment in which 19 clonal isolates of Aspergillus flavus initially collected on contaminated surgical masks were included in a set of 55 A. flavus isolates of various origins. A simple convolutional neural network (CNN) was trained to detect the isolates belonging to the clone. In this experiment, the training and testing sets were totally independent, and different MALDI-TOF devices (Microflex) were used for the training and testing phases. The CNN was used to correctly sort a large portion of the isolates, with excellent (> 93%) accuracy for two of the three devices used and with less accuracy for the third device (69%), which was older and needed to have the laser replaced.