Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921984 | PMC |
| http://dx.doi.org/10.4103/EUS-D-21-00133 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Randomized Controlled Trial of Impact of Mobile Health Technologies on Human Papillomavirus Vaccination Uptake in Mothers of Vaccine-Eligible Girls in Lagos, Nigeria (mHealth-HPVac).
JCO Glob Oncol
May 2025
Department of Obstetrics and Gynaecology, Stanford University School of Medicine, Stanford, CA.
Purpose: Expanding high-risk human papillomavirus (HPV) vaccine coverage in resource-constrained settings is critical to bridging the cervical cancer gap and achieving the global action plan for elimination. Mobile health (mHealth) technology via short message services (SMS) has the potential to improve HPV vaccination uptake. The mHealth-HPVac study evaluated the effectiveness of mHealth interventions in increasing HPV vaccine uptake among mothers of unvaccinated girls aged 9-14 years in Lagos, Nigeria.
View Article and Find Full Text PDFEfficacy and mechanism of high-purity HAMCC combined with CGF in promoting the repair of radiation-induced skin and soft tissue damage.
PLoS One
September 2025
Department of Emergency, The People's Hospital of Guangxi Zhuang Autonomous Region and Research Center of Medical Sciences, Guangxi Academy of Medical Sciences, Nanning, Guangxi, China.
Radiotherapy, a prevalent and effective treatment for various malignancies, often causes collateral damage to normal skin and soft tissues in the irradiated area. To address this, we developed a novel approach combining SVFG-modified adipose-derived high-activity matrix cell clusters (HAMCC) with concentrated growth factors (CGF) to enhance regeneration and repair of radiation-induced skin and soft tissue injuries. Our study included cellular assays, wound healing evaluations, and histological analyses.
View Article and Find Full Text PDFPredicting mortality dynamics in cancer patients: A machine learning approach to pre-death events.
PLoS One
September 2025
Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Capturing the dynamic changes in patients' internal states as they approach death due to fatal diseases remains a major challenge in understanding individual pathologies and improving end-of-life care. However, existing methods primarily focus on specific test values or organ dysfunction markers, failing to provide a comprehensive view of the evolving internal state preceding death. To address this, we analyzed electronic health record (EHR) data from a single institution, including 8,976 cancer patients and 77 laboratory parameters, by constructing continuous mortality prediction models based on gradient-boosting decision trees and leveraging them for temporal analyses.
View Article and Find Full Text PDFMulti-layer matrix factorization for cancer subtyping using full and partial multi-omics dataset.
Brief Bioinform
August 2025
School of Computer Science, Xi'an Polytechnic University, 710048, Xi'an, China.
Cancer, with its inherent heterogeneity, is commonly categorized into distinct subtypes based on unique traits, cellular origins, and molecular markers specific to each type. However, current studies primarily rely on complete multi-omics datasets for predicting cancer subtypes, often overlooking predictive performance in cases where some omics data may be missing and neglecting implicit relationships across multiple layers of omics data integration. This paper introduces Multi-Layer Matrix Factorization (MLMF), a novel approach for cancer subtyping that employs multi-omics data clustering.
View Article and Find Full Text PDFProteomic profiling and scRNA sequencing identify signatures associated with infection and risk of developing gastric cancer.
Cancer Biol Med
September 2025
State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Peking University Cancer Hospital & Institute, Beijing 100142, China.
Objective: The key molecular events signifying the -induced gastric carcinogenesis process are largely unknown.
Methods: Bulk tissue-proteomics profiling were leveraged across multi-stage gastric lesions from Linqu ( = 166) and Beijing sets ( = 99) and single-cell transcriptomic profiling ( = 18) to decipher key molecular signatures of -related gastric lesion progression and gastric cancer (GC) development. The association of key proteins association with gastric lesion progression and GC development were prospectively studied building on follow-up of the Linqu set and UK Biobank ( = 48,529).