Breath Fingerprint of Colorectal Cancer Patients Based on the Gas Chromatography-Mass Spectrometry Analysis.

The human body emits a multitude of volatile organic compounds (VOCs) via tissues and various bodily fluids or exhaled breath. These compounds collectively create a distinctive chemical profile, which can potentially be employed to identify changes in human metabolism associated with colorectal cancer (CRC) and, consequently, facilitate the diagnosis of this disease. The main goal of this study was to investigate and characterize the VOCs' chemical patterns associated with the breath of CRC patients and controls and identify potential expiratory markers of this disease.

Age-Based Comparative Analysis of Colorectal Cancer Colonoscopy Screening Findings.

: Colorectal cancer (CRC) incidence is rapidly emerging among individuals <50 years, termed as early-onset colorectal cancer (EOCRC). This study aimed to probe variations in tumorigenic pathology and relevant manifestations (polyp and adenoma incidence) between suspected cases of EOCRC and late-onset CRC (LOCRC; ≥50 years of age). : Between September 2022 and February 2023, colonoscopy-based screening data from 1653 patients were included in this study.

The Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis.

Colorectal cancer (CRC) is the third most common malignancy and the second most common cause of cancer-related deaths worldwide. While CRC screening is already part of organized programs in many countries, there remains a need for improved screening tools. In recent years, a potential approach for cancer diagnosis has emerged via the analysis of volatile organic compounds (VOCs) using sensor technologies.

The Role of an Artificial Intelligence Method of Improving the Diagnosis of Neoplasms by Colonoscopy.

Background: Colorectal cancer (CRC) is the third most common cancer worldwide. Colonoscopy is the gold standard examination that reduces the morbidity and mortality of CRC. Artificial intelligence (AI) could be useful in reducing the errors of the specialist and in drawing attention to the suspicious area.

Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection.

Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters).

Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants.

Pepsinogen testing for evaluation of the success of Helicobacter pylori eradication at 4 weeks after completion of therapy.

Background And Objective: Pepsinogen levels in plasma are increased by inflammation in the gastric mucosa, including inflammation resulting from Helicobacter pylori infection. A decrease in pepsinogen II level has been suggested as a reliable marker to confirm the successful eradication of infection. The aim of our study was to evaluate the potential role of pepsinogens I and II, gastrin-17 and H.

Prevalence of Helicobacter pylori infection and atrophic gastritis in Latvia.

Objectives: Helicobacter pylori infection and atrophic gastritis are related to an increased risk for gastric cancer. There is a decrease in global H. pylori prevalence.