This study shows that coupling to designed plasmonic nanoparticles can modulate the electrophysiological function of proteins in living mammalian cells. Nanostar-shaped particles, that are robust to biological noise, are designed to enable near-field-coupling to plasma membrane-localized mutated Archaerhodopsin proteins in live cells. The coupled rhodopsins exhibit enhanced fluorescence and an increased response speed to membrane voltage. Incorporating this plasmonic enhancement into a Markov chain photocycle model of the Archaerhodopsin mutant QuasAr6a, shows an increased fluorescence emission rate and manipulation of the protein dynamics through a combination of photocycle transition rate enhancements. The results show an improvement in fluorescence and voltage-response dynamics of the functional QuasAr6a Archaerhodopsin mutant, beyond what has been achievable through genetic engineering. This opens up possibilities for engineering the biological functionality of proteins through plasmonics: manipulating protein photocycles could improve light sensitivity, change optogenetic applications, and lead to fluorescent biosensors with enhanced dynamics.

Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive and error-prone. This study introduces an automated system for analyzing Romanowsky-stained thick blood smears, focusing on image quality evaluation, leukocyte detection, and malaria parasite classification. Using a dataset of 1000 clinically diagnosed images, we applied feature extraction techniques, including histogram bins and texture analysis with the gray level co-occurrence matrix (GLCM), alongside support vector machines (SVMs), for image quality assessment. Leukocyte detection employed optimal thresholding segmentation utility (OTSU) thresholding, binary masking, and erosion, followed by the connected components algorithm. Parasite detection used high-intensity region selection and adaptive bounding boxes, followed by a custom convolutional neural network (CNN) for candidate identification. A second CNN classified parasites into trophozoites, schizonts, and gametocytes. The system achieved an F1-score of 95% for image quality evaluation, 88.92% for leukocyte detection, and 82.10% for parasite detection. The F1-score—a metric balancing precision (correctly identified positives) and recall (correctly detected instances out of actual positives)—is especially valuable for assessing models on imbalanced datasets. In parasite stage classification, CNN achieved F1-scores of 85% for trophozoites, 88% for schizonts, and 83% for gametocytes. This study introduces a robust and scalable automated system that addresses critical challenges in malaria diagnosis by integrating advanced image quality assessment and deep learning techniques for parasite detection and classification. This system’s adaptability to low-resource settings underscores its potential to improve malaria diagnostics globally.