Development of Compendium for Esophageal Squamous Cell Carcinoma.

Esophageal cancer (EC) ranks as the 8th most aggressive malignancy, and its treatment remains challenging due to the lack of biomarkers facilitating early detection. EC manifests in two major histological forms - adenocarcinoma (EAD) and squamous cell carcinoma (ESCC) - both exhibiting variations in incidence across geographically distinct populations. High-throughput technologies are transforming the understanding of diseases, including cancer. A significant challenge for the scientific community is dealing with scattered data in the literature. To address this, a simple pipeline is proposed for the analysis of publicly available microarray datasets and the collection of differentially regulated molecules between cancer and normal conditions. The pipeline can serve as a standard approach for differential gene expression analysis, identifying genes differentially expressed between cancer and normal tissues or among different cancer subtypes. The pipeline involves several steps, including Data preprocessing (involving quality control and normalization of raw gene expression data to remove technical variations between samples), Differential expression analysis (identifying genes differentially expressed between two or more groups of samples using statistical tests such as t-tests, ANOVA, or linear models), Functional analysis (using bioinformatics tools to identify enriched biological pathways and functions in differentially expressed genes), and Validation (involving validation using independent datasets or experimental methods such as qPCR or immunohistochemistry). Using this pipeline, a collection of differentially expressed molecules (DEMs) can be generated for any type of cancer, including esophageal cancer. This compendium can be utilized to identify potential biomarkers and drug targets for cancer and enhance understanding of the molecular mechanisms underlying the disease. Additionally, population-specific screening of esophageal cancer using this pipeline will help identify specific drug targets for distinct populations, leading to personalized treatments for the disease.

The Influence of COVID-19 in Endocrine Research: Critical Overview, Methodological Implications and a Guideline for Future Designs.

The COVID-19 pandemic has changed many aspects of people's lives, including not only individual social behavior, healthcare procedures, and altered physiological and pathophysiological responses. As a result, some medical studies may be influenced by one or more hidden factors brought about by the COVID-19 pandemic. Using the literature review method, we are briefly discussing the studies that are confounded by COVID-19 and facemask-induced partiality and how these factors can be further complicated with other confounding variables. Facemask wearing has been reported to produce partiality in studies of ophthalmology (particularly dry eye and related ocular diseases), sleep studies, cognitive studies (such as emotion-recognition accuracy research, etc.), and gender-influenced studies, to mention a few. There is a possibility that some other COVID-19 related influences remain unrecognized in medical research. To account for heterogeneity, current and future studies need to consider the severity of the initial illness (such as diabetes, other endocrine disorders), and COVID-19 infection, the timing of analysis, or the presence of a control group. Face mask-induced influences may confound the results of diabetes studies in many ways.

Fe3C-filled carbon nanotubes: permanent cylindrical nanomagnets possessing exotic magnetic properties.

The present study aims to deduce the confinement effect on the magnetic properties of iron carbide (Fe3C) nanorods filled inside carbon nanotubes (CNTs), and to document any structural phase transitions that can be induced by compressive/tensile stress generated within the nanorod. Enhancement in the magnetic properties of the nanorods is attributed to tensile stress as well as to compression, present in the radial direction and along the nanotube axis, respectively. Finally, the growth of permanent cylindrical nanomagnets has been optimized by applying a field gradient. Besides presenting the growth model of in situ filling, we have also proposed the mechanism of magnetization of the nanotubes. Magnetization along the tube axis has been probed by confirming the pole formation. Fe3C has been selected because of its ease of formation, low TC and incompressibility.