An adamantyl-caffeoyl-anilide exhibits broad-spectrum antibacterial activity by inhibiting FtsZ assembly and Z-ring formation.

Several harmful bacteria have evolved resistance to conventional antibiotics due to their extensive usage. FtsZ, a principal bacterial cell division protein, is considered as an important drug target to combat resistance. We identified a caffeoyl anilide derivative, (E)-N-(4-(3-(3,4-dihydroxyphenyl)acryloyl)phenyl)-1-adamantylamide (compound 11) as a new antimicrobial agent targeting FtsZ. Compound 11 caused cell elongation in Mycobacterium smegmatis, Bacillus subtilis, and Escherichia coli cells, indicating that it inhibits cell partitioning. Compound 11 inhibited the assembly of Mycobacterium smegmatis FtsZ (MsFtsZ), forming short and thin filaments in vitro. Interestingly, the compound increased the rate of GTP hydrolysis of MsFtsZ. Compound 11 also impeded the assembly of Mycobacterium tuberculosis FtsZ. Fluorescence and absorption spectroscopic analysis suggested that compound 11 binds to MsFtsZ and produces conformational changes in FtsZ. The docking analysis indicated that the compound binds at the interdomain cleft of MsFtsZ. Further, it caused delocalization of the Z-ring in Mycobacterium smegmatis and Bacillus subtilis without affecting DNA segregation. Notably, compound 11 did not inhibit tubulin polymerization, the eukaryotic homolog of FtsZ, suggesting its specificity on bacteria. The evidence indicated that compound 11 exerts its antibacterial effect by impeding FtsZ assembly and has the potential to be developed as a broad-spectrum antimicrobial agent.

A vision transformer based approach for analysis of plasmodium vivax life cycle for malaria prediction using thin blood smear microscopic images.

BACKGROUND AND OBJECTIVES: Microscopic images are an important part for haematologists in diagnosing various diseases in the blood cell. Changes in blood cells are caused by malaria disease, and early diagnosis can prevent the disease from entering its severe stage. METHODS: In this paper, an automated non-invasive and efficient deep learning-based framework is developed for multi-class plasmodium vivax life cycle classification and malaria diagnosis. A multi-class microscopic blood cell of different plasmodium vivax life cycle stage dataset is analysed, and a diagnostic framework is designed. Several stages of the disease are examined and augmented through various techniques to make the framework robust in real-time. Generative adversarial network is specially designed to generate extended training samples of various life cycle stages to increase robustness of the resulting model. A special transformer-based neural network vision transformer is designed to improve generalisation capabilities. Microscopic images are classified into multi classes of plasmodium vivax life cycle stage, where the keystone transformer layers extract relevant disease features from microscopic colour images, and the extracted relevant features are used to make predictive diagnostic decisions. RESULTS: The capabilities of the vision transformer are computed and analysed by statistical parameters, and the performance of the vision transformer model is compared with baseline architectures, where it was evident that the performance of the vision transformer was significantly better, reaching 90.03% accuracy. CONCLUSIONS: A comprehensive comparison of the proposed framework to the state-of-the-art methods proves its efficiency in the classification of plasmodium vivax life cycle for malaria disease identification through thin blood smear microscopic images.