Integrating artificial intelligence and wing geometric morphometry to automate mosquito classification.

Mosquitoes (Diptera: Culicidae) comprise over 3500 global species, primarily in tropical regions, where the females act as disease vectors. Thus, identifying medically significant species is vital. In this context, Wing Geometric Morphometry (WGM) emerges as a precise and accessible method, excelling in species differentiation through mathematical approaches. Computational technologies and Artificial Intelligence (AI) promise to overcome WGM challenges, supporting mosquito identification. AI explores computers' thinking capacity, originating in the 1950s. Machine Learning (ML) arose in the 1980s as a subfield of AI, and deep Learning (DL) characterizes ML's subcategory, featuring hierarchical data processing layers. DL relies on data volume and layer adjustments. Over the past decade, AI demonstrated potential in mosquito identification. Various studies employed optical sensors, and Convolutional Neural Networks (CNNs) for mosquito identification, achieving average accuracy rates between 84 % and 93 %. Furthermore, larval Aedes identification reached accuracy rates of 92 % to 94 % using CNNs. DL models such as ResNet50 and VGG16 achieved up to 95 % accuracy in mosquito identification. Applying CNNs to georeference mosquito photos showed promising results. AI algorithms automated landmark detection in various insects' wings with repeatability rates exceeding 90 %. Companies have developed wing landmark detection algorithms, marking significant advancements in the field. In this review, we discuss how AI and WGM are being combined to identify mosquito species, offering benefits in monitoring and controlling mosquito populations.

Alkaloids extracted from Pterogyne nitens induce apoptosis in malignant breast cell line.

In the present study, two alkaloids isolated from Pterogyne nitens, a plant native to Brazil, have been shown to induce apoptosis in human breast cancer cells. These compounds, pterogynine (PGN) and pterogynidine (PGD), were tested for their effect on a human infiltrating ductal carcinoma cell line (ZR-7531). The cell line was treated with each alkaloid at several concentrations. Time-dependence (with or without recuperation time) and concentration-dependence (in the range 0.25-10 mM) were investigated in cytotoxicity and apoptosis assays. The annexin assay indicated an apparently higher percentage of death by necrosis of malignant cells after 24 h exposure to both P. nitens extracts than the Hoechst assay. Thus, our results in the two tests demonstrated that the Hoechst assay can discriminate between late apoptotic cells and necrosis, whereas the flow cytometry-based annexin V assay cannot. We concluded that PGN and PGD have effective antineoplastic activity against human breast cancer cells in vitro, by inducing programmed cell death.