Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1077456 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:We used a mouse model of alveolar echinococcosis to assess gene expression profiles in the liver after establishment of a chronic disease status as a result of a primary peroral infection with eggs of the fox tapeworm Echinococcus multilocularis.
2010-04-01 | E-MEXP-2615 | biostudies-arrayexpress
Project description:Hepatic echinococcosis (HE) caused by Echinococcus spp. transmitted from carnivores is known as a severe zoonotic disease that formed into hepatic cystic echinococcosis (HCE) and hepatic alveolar echinococcosis (HAE). It continues to be a serious public health issue around the world. HAE is uncommon infection which is characterized by poor prognosis but it is the most life-threatening compare to HCE. Many cases of HAE are recognized at forward stages because clinical symptoms may remain silent in about 10 years of incubation period. Several pathological cases could be observed including septicemia, abscess, recurrent cholangitis, and portal hypertensive gastrointestinal bleeding in untreated people. It is provided that correlated factors includes CD44, Soluble ST2 (sST2), Plasma IL-23, IL-27, and IL-5 for metastasis and prognosis for patients with HAE were identified. However, data on metastatic and prognostic marker are still so poor. Recent studies have revealed that circRNAs are enriched and stable in exosomes. Exosomal circRNAs may be localized in platelet-derived extracellular vesicles, hepatic cells, and pancreatic cancer cells. Studies have suggested that it is possible that cells may transfer circRNAs by excreting them in exosomes. Whereas there are a lot of studies on cancer and other human diseases related exosomal circRNA, no studies on hepatic alveolar echinococcosis-related exosomal circRNAs in humans. Current study provides that exosomal circular RNAs from healthy humans and humans with hepatic alveolar echinococcal disease were detected and characterized using the RNA sequencing.
2022-09-01 | GSE183607 | GEO
Project description:Circulating small RNA profiling of patients with alveolar and cystic echinococcosis
| PRJNA970814 | ENA
Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.
2024-01-08 | GSE252118 | GEO