Sa-TTCA: An SVM-based approach for tumor T-cell antigen classification using features extracted from biological sequencing and natural language processing.

Accurately predicting tumor T-cell antigen (TTCA) sequences is a crucial task in the development of cancer vaccines and immunotherapies. TTCAs derived from tumor cells, are presented to immune cells (T cells) through major histocompatibility complex (MHC), via the recognition of specific portions of their structure known as epitopes. More specifically, MHC class I introduces TTCAs to T-cell receptors (TCR) which are located on the surface of CD8+ T cells.

Hyper-methylation of ABCG1 as an epigenetics biomarker in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the most prevalent histological type of lung cancer and the leading cause of death globally. Patients with NSCLC have a poor prognosis for various factors, and a late diagnosis is one of them. The DNA methylation of CpG island sequences found in the promoter regions of tumor suppressor genes has recently received attention as a potential biomarker of human cancer.

Omics-based deep learning approaches for lung cancer decision-making and therapeutics development.

Lung cancer has been the most common and the leading cause of cancer deaths globally. Besides clinicopathological observations and traditional molecular tests, the advent of robust and scalable techniques for nucleic acid analysis has revolutionized biological research and medicinal practice in lung cancer treatment. In response to the demands for minimally invasive procedures and technology development over the past decade, many types of multi-omics data at various genome levels have been generated.

ALDH2 as a potential stem cell-related biomarker in lung adenocarcinoma: Comprehensive multi-omics analysis.

Lung adenocarcinoma (LUAD) is the most prevalent lung cancer and one of the leading causes of death. Previous research found a link between LUAD and Aldehyde Dehydrogenase 2 (ALDH2), a member of aldehyde dehydrogenase gene (ALDH) superfamily. In this study, we identified additional useful prognostic markers for early LUAD identification and targeting LUAD therapy by analyzing the expression level, epigenetic mechanism, and signaling activities of ALDH2 in LUAD patients.

An interpretable deep learning model for classifying adaptor protein complexes from sequence information.

Adaptor proteins (APs) are a family of proteins that aids in intracellular membrane trafficking, and their impairments or defects are closely related to various disorders. Traditional methods to identify and classify APs require time and complex techniques, which were then advanced by machine learning and computational approaches to facilitate the APs recognition task. However, most studies focused on recognizing separate ones in the APs family or the APs in general with non-APs, lacking one comprehensive strategy to distinguish the complexes of AP subtypes.

A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas.

Glioma is a Center Nervous System (CNS) neoplasm that arises from the glial cells. In a new scheme category of the World Health Organization 2016, lower-grade gliomas (LGGs) are grade II and III gliomas. Following the discovery of suppression of negative immune regulation, immunotherapy is a promising effective treatment method for lower-grade glioma patients.

Diagnostic stewardship aiming at expectorated or induced sputum promotes microbial diagnosis in community-acquired pneumonia.

Purpose: Studies on aetiology of community-acquired pneumonia (CAP) vary in terms of microbial sampling methods, anatomical locations, and laboratory analyses, since no gold standard exists. In this large, multicentre, retrospective, regional study from Norway, our primary objective was to report the results of a strategic diagnostic stewardship intervention, targeting diagnostic yield from lower respiratory tract sampling. The secondary objective was to report hospitalized CAP aetiology and the diagnostic yield of various anatomical sampling locations.

Evaluation of sperm DNA fragmentation index, Zinc concentration and seminal parameters from infertile men with varicocele.

The aim of this study was to investigate the effect of varicocele on DNA fragmentation index (DFI), zinc concentration and seminal parameters in infertile patients. In this prospective study, 179 men with at least 1-year history of infertility and varicocele were examined for semen quality at Hanoi Medical University Hospital (HMUH), Hanoi, Vietnam. In addition, an inverse correlation between zinc concentration and the degree of sperm DNA fragmentation in patients with clinical varicocele was found.

Effect of surfactant type and redox polymer type on single-walled carbon nanotube modified electrodes.

Electrodes modified with single-walled carbon nanotubes (SWNTs) offer a number of attractive properties for developing novel electrochemical sensors. A common method to immobilize SWNTs onto the electrode surface is by placing a droplet of a SWNT suspension onto the electrode surface and allowing the solvent to evaporate. In order to maximize the properties of individual SWNTs, surfactants are normally present in these suspensions to provide stable and homogeneous SWNT dispersions.

Incorporation of single-walled carbon nanotubes into ferrocene-modified linear polyethylenimine redox polymer films.

In this study, we describe the effects of incorporating single-walled carbon nanotubes (SWNTs) into redox polymer-enzyme hydrogels. The hydrogels were constructed by combining the enzyme glucose oxidase with a redox polymer (Fc-C(6)-LPEI) in which ferrocene was attached to linear poly(ethylenimine) by a six-carbon spacer. Incorporation of SWNTs into these films changed their morphology and resulted in a significant increase in the enzymatic response at saturating glucose concentrations (3 mA/cm(2)) as compared to films without SWNTs (0.

High-sensitivity amperometric biosensors based on ferrocene-modified linear poly(ethylenimine).

Amperometric biosensors for glucose and hydrogen peroxide have been built by immobilizing glucose oxidase (GOX) and horseradish peroxidase (HRP) in cross-linked films of ferrocene-modified linear poly(ethylenimine). At pH 7, the glucose sensors generated limiting catalytic current densities of 1.2 mA/cm2.