Correction: Rehman et al. Fabrication, In Vitro and In Vivo Assessment of Eucalyptol-Loaded Nanoemulgel as a Novel Paradigm for Wound Healing. 2022, 1971.

In the original publication, there was a mistake in Figure 4 as published [...

Multiple image encryption algorithm using channel randomization and multiple chaotic maps.

Developing robust and secure image encryption methods for transmitting multiple images in batches over unprotected networks has become imperative. This necessity arises from the limitations of single-image encryption techniques in managing the escalating volume of extensive data. This paper introduces a novel three-layer multiple-image encryption (MIE) technique to encrypt batch images based on three 2D-chaotic maps.

A new superfluity deep learning model for detecting knee osteoporosis and osteopenia in X-ray images.

This study proposes a new deep-learning approach incorporating a superfluity mechanism to categorize knee X-ray images into osteoporosis, osteopenia, and normal classes. The superfluity mechanism suggests the use of two distinct types of blocks. The rationale is that, unlike a conventional serially stacked layer, the superfluity concept involves concatenating multiple layers, enabling features to flow into two branches rather than a single branch.

OncomiR-181a promotes carcinogenesis by repressing the extracellular matrix proteoglycan decorin in hepatocellular carcinoma.

Background: Proteoglycans are important tumor microenvironment extracellular matrix components. The regulation of key proteoglycans, such as decorin (DCN), by miRNAs has drawn attention since they have surfaced as novel therapeutic targets in cancer. Accordingly, this study aimed at identifying the impact of miR-181a in liver cancer and its regulatory role on the extracellular matrix proteoglycan, DCN, and hence on downstream oncogenes and tumor suppressor genes.