Automated medication verification system (AMVS): System based on edge detection and CNN classification drug on embedded systems.

A novel automated medication verification system (AMVS) aims to address the limitation of manual medication verification among healthcare professionals with a high workload, thereby reducing medication errors in hospitals. Specifically, the manual medication verification process is time-consuming and prone to errors, especially in healthcare settings with high workloads. The proposed system strategy is to streamline and automate this process, enhancing efficiency and reducing medication errors.

HArmonized single-cell RNA-seq Cell type Assisted Deconvolution (HASCAD).

Background: Cell composition deconvolution (CCD) is a type of bioinformatic task to estimate the cell fractions from bulk gene expression profiles, such as RNA-seq. Many CCD models were developed to perform linear regression analysis using reference gene expression signatures of distinct cell types. Reference gene expression signatures could be generated from cell-specific gene expression profiles, such as scRNA-seq.

Deconvolution of bulk gene expression profiles reveals the association between immune cell polarization and the prognosis of hepatocellular carcinoma patients.

Background: Many studies have utilized computational methods, including cell composition deconvolution (CCD), to correlate immune cell polarizations with the survival of cancer patients, including those with hepatocellular carcinoma (HCC). However, currently available cell deconvolution estimated (CDE) tools do not cover the wide range of immune cell changes that are known to influence tumor progression.

Results: A new CCD tool, HCCImm, was designed to estimate the abundance of tumor cells and 16 immune cell types in the bulk gene expression profiles of HCC samples.

TSSUNet-MB - ab initio identification of σ promoter transcription start sites in Escherichia coli using deep multitask learning.

Motivation: Computational promoter prediction (CPP) tools designed to classify prokaryotic promoter regions usually assume that a transcription start site (TSS) is located at a predefined position within each promoter region. Such CPP tools are sensitive to any positional shifting of the TSS in a windowed region, and they are unsuitable for determining the boundaries of prokaryotic promoters.

Results: TSSUNet-MB is a deep learning model developed to identify the TSSs of σ promoters.

Improved cell composition deconvolution method of bulk gene expression profiles to quantify subsets of immune cells.

Background: To facilitate the investigation of the pathogenic roles played by various immune cells in complex tissues such as tumors, a few computational methods for deconvoluting bulk gene expression profiles to predict cell composition have been created. However, available methods were usually developed along with a set of reference gene expression profiles consisting of imbalanced replicates across different cell types. Therefore, the objective of this study was to create a new deconvolution method equipped with a new set of reference gene expression profiles that incorporate more microarray replicates of the immune cells that have been frequently implicated in the poor prognosis of cancers, such as T helper cells, regulatory T cells and macrophage M1/M2 cells.

Nesfatin-1/Nucleobindin-2 enhances cell migration, invasion, and epithelial-mesenchymal transition via LKB1/AMPK/TORC1/ZEB1 pathways in colon cancer.

Recent studies indicate that a high level of nesfatin-1/Nucleobindin-2 (NUCB-2) is associated with poor outcome and promotes cell migration in breast cancer and prostate cancer. However, the role of NUCB2 is not well known in colon cancer. In this study, NUCB-2 level in colon cancer tissue was higher than that in non-tumor tissue.

Chemokine (C-C Motif) Ligand 5 is Involved in Tumor-Associated Dendritic Cell-Mediated Colon Cancer Progression Through Non-Coding RNA MALAT-1.

Tumor micro-environment is a critical factor in the development of cancer. The aim of this study was to investigate the inflammatory cytokines secreted by tumor-associated dendritic cells (TADCs) that contribute to enhanced migration, invasion, and epithelial-to-mesenchymal transition (EMT) in colon cancer. The administration of recombinant human chemokine (C-C motif) ligand 5 (CCL5), which is largely expressed by colon cancer surrounding TADCs, mimicked the stimulation of TADC-conditioned medium on migration, invasion, and EMT in colon cancer cells.