Periodontal disease (PD) is complex polymicrobial disease which destroys tooth-supporting tissue. Although various synthetic inhibitors of periodontitis-triggering pathogens have been recognized, their undesirable side effects limit their application. Hence, the present study intended to perform the synthesis, characterization, antimicrobial evaluation, and cytotoxicity analysis of novel benzamidine analogues (NBA). This study involved the synthesis of novel imino bases of benzamidine (4a–c), by reacting different aromatic aldehydes with 2-(4-carbamimidoylphenoxy) acetohydrazide (3), which was synthesized by the hydrazination of ethyl 2-(4-carbamimidoylphenoxy) acetate (2), the derivative of 4-hydroxybenzene carboximidamide (1). This was followed by characterization using FTIR, 1H, 13C NMR and mass spectrometry. All synthesized compounds were further tested for antimicrobial potential against PD-triggering pathogens by the micro broth dilution method. The cytotoxicity analysis of the NBA against HEK 293 cells was conducted using an MTT assay. The present study resulted in a successful synthesis of NBA and elucidated their structures. The synthesized NBA exhibited significant antimicrobial activity values between 31.25 and 125 µg/mL against tested pathogens. All NBA exhibited weak cytotoxicity against HEK 293 cells at 7.81 µg, equally to chlorhexidine at 0.2%. The significant antimicrobial activity of NBA against PD-triggering pathogens supports their potential application in periodontitis treatment.

In general, diabetic retinopathy (DR) is a common ocular disease that causes damage to the retina due to blood leakage from the vessels. Earlier detection of DR becomes a complicated task and it is necessary to prevent complete blindness. Various physical examinations are employed in DR detection but manual diagnosis results in misclassification results. Therefore, this article proposes a novel technique to predict and classify the DR disease effectively. The significant objective of the proposed approach involves the effective classification of fundus retinal images into two namely, normal (absence of DR) and abnormal (presence of DR). The proposed DR detection utilizes three vital phases namely, the data preprocessing, image augmentation, feature extraction, and classification. Initially, the image preprocessing is done to remove unwanted noises and to enhance images. Then, the preprocessed image is augmented to enhance the size and quality of the training images. This article proposes a novel modified Gaussian convolutional deep belief network based dwarf mongoose optimization algorithm for effective extraction and classification of retinal images. In this article, an ODIR-2019 dataset is employed in detecting and classifying DR disease.Finally, the experimentation is carried out and the proposed approach achieved 97% of accuracy. This implies that our proposed approach effectively classifies the fundus retinal images.

Gingipains (RgpA, RgpB, and Kgp) are major virulence factors of the periodontitis-causing bacterium Porphyromonas gingivalis. Isolation of gingipains from the crude protein sample of P. gingivalis is critical for studying the underlying invasion mechanism that contributes to periodontitis, Alzheimer’s disease, and cardiovascular disease (CVD). Chromatographic processes and molecular cloning are two standard techniques often used for gingipains isolation, which are time-consuming and costly. In this study, considerably easier methods based on passive-mediated diffusion gel elution and gelatin zymogram were used to isolate and characterize gingipains. Importantly, proteins eluted from Native-PAGE showed enzymatic activity for both Rgp and Kgp. In gelatin zymography, the proteins with a molecular size of ~50 kDa and above 245 kDa were suggested as arginine-specific gingipains. The passive diffusion-mediated gel elution method is a simpler technique to isolate gingipains from crude protein samples of P. gingivalis. By using covalent and highly specific gingipain inhibitors, gelatin zymography enabled an individual characterization of gingipain activity and inhibition. Finally, this protocol can be easily extended by adding the isoelectric focusing to further improve the protein separation and characterization.

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