Results 61 to 70 of about 534,772 (185)

Detecting homologous recombination deficiency for breast cancer through integrative analysis of genomic data

Molecular Oncology, EarlyView.
This study develops a semi‐supervised classifier integrating multi‐genomic data (1404 training/5893 validation samples) to improve homologous recombination deficiency (HRD) detection in breast cancer. Our method demonstrates prognostic value and predicts chemotherapy/PARP inhibitor sensitivity in HRD+ tumours.
Rong Zhu, Katherine Eason, Suet‐Feung Chin, Paul A. W. Edwards, Raquel Manzano Garcia, Richard Moulange, Jia Wern Pan, Soo Hwang Teo, Sach Mukherjee, Maurizio Callari, Carlos Caldas, Stephen‐John Sammut, Oscar M. Rueda   +12 more
wiley   +1 more source

Specialty preferences among Nigerian medical students: a cross sectional study

BMC Medical Education
Introduction Specialty preferences among medical students significantly influence healthcare workforce distribution and equity. Nigeria, with its low physician-to-patient ratio, faces critical challenges in addressing healthcare demands.
Tobi Olajide, Chioma Ezebialu, Christabel Uche-Orji, Ridwanullah Abdullateef, Chisom Okoye, Esther Olaoye, Jolly Thomas, Joy Adeniyi, Angel Ogiemudia, Simon Aina, Oluwatimilehin Oladapo, Emmanuel Ibitunde, Olumide Bankole, Chukwuebuka Asogwa, Mafusat Ojewumi, Precious Wagwula, Ayomide Fatola, Oluwadabira Adewara, Kenneth Christian, Oghenemaro Jerry-Ogeme, Lawrence Rejoice, Chisom Obah, Nouh Sanusi, Joshua Alabi, Elizabeth Ayegboyin, Atoyebi Olaadura, Isaac Adejumo, Promise Enyesiobi, Peace Alemede, David Uzoechi, Abdulaziz Ahmad, Chimdalu Ezeani, Brian Uche-Orji, Oluwatobi Joshua, Ihunanyachi Agha-Okoro, Godson Omumuawuike, Jessica Gambo, Peace Gbenle, Evan Adetoye, Olamide Odusola, Muhammed Ya’u, Muhammad Karaye, Rufus Akinyemi   +42 more
doaj   +1 more source

Data‐driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes

Molecular Oncology, EarlyView.
This study investigates gene expression differences between two major pediatric acute lymphoblastic leukemia (ALL) subtypes, B‐cell precursor ALL, and T‐cell ALL, using a data‐driven approach consisting of biostatistics and machine learning methods. Following analysis of a discovery dataset, we find a set of 14 expression markers differentiating the ...
Mona Nourbakhsh, Nikola Tom, Anna Schrøder Lassen, Helene Brasch Lind Petersen, Ulrik Kristoffer Stoltze, Karin Wadt, Kjeld Schmiegelow, Matteo Tiberti, Elena Papaleo   +8 more
wiley   +1 more source

Characterization of the student component in the door-to-door active screening related to COVID-19

Revista Electrónica Dr. Zoilo E. Marinello Vidaurreta, 2020
Background: the student component in the door-to-door active screening related to COVID-19 is essential for the early detection of positive cases and risk groups.Objective: to characterize the student component in the door-to-door active screening ...
Vladimir Molina-Raad
doaj  

Adequate Knowledge and Low Vaccination Rates of Hepatitis B Virus Infection Among Students, Medical, and Paramedical Persons in a Tertiary Care Teaching Hospital. [PDF]

Cureus, 2020
Kandi V, Katoch A, Miniskar H, Jaripiti S, Rv SS, Burugu HR, Reddy AV, Bhasin A.   +7 more
europepmc   +1 more source

Comprehensive omics‐based classification system in adult patients with B‐cell acute lymphoblastic leukemia

Molecular Oncology, EarlyView.
The COMBAT classification system, developed through multi‐omics integration, stratifies adult patients with B‐cell acute lymphoblastic leukemia(B‐ALL) into three molecular subtypes with distinct surface antigen patterns, immune landscape, methylation patterns, biological pathways and prognosis.
Yang Song, Ting Liu, Qishan Hao, Qiuyun Fang, Xiaoyuan Gong, Yan Li, Zheng Tian, Hui Wei, Min Wang, Jianxiang Wang, Tao Cheng, Yingchang Mi   +11 more
wiley   +1 more source

Unraveling LINE‐1 retrotransposition in head and neck squamous cell carcinoma

Molecular Oncology, EarlyView.
The novel RetroTest method allows the detection of L1 activation in clinical samples with low DNA input, providing global L1 activity and the identification of the L1 source element. We applied RetroTest to a real‐world cohort of HNSCC patients where we reported an early L1 activation, with more than 60% of T1 patients showing L1 activity.
Jenifer Brea‐Iglesias, Ana Oitabén, Sonia Zumalave, Bernardo Rodriguez‐Martin, María Gallardo‐Gómez, Martín Santamarina, Ana Pequeño‐Valtierra, Laura Juaneda‐Magdalena, Ramón García‐Escudero, José Luis López‐Cedrún, Máximo Fraga, José M. C. Tubio, Mónica Martínez‐Fernández   +12 more
wiley   +1 more source

Tumor clusters with divergent inflammation and human retroelement expression determine the clinical outcome of patients with serous ovarian cancer

Molecular Oncology, EarlyView.
Analysis of treatment‐naïve high‐grade serous ovarian carcinoma (HGSOC) and control tissues for ERVs, LINE‐1 (L1), inflammation, and immune checkpoints identified five clusters with diverse patient recurrence‐free survivals. An inflammation score was calculated and correlated with retroelement expression, where one novel cluster (Triple‐I) with high ...
Laura Glossner, Markus Eckstein, Christoph Mark, Matthias W. Beckmann, Arndt Hartmann, Pamela L. Strissel, Reiner Strick   +6 more
wiley   +1 more source

Awareness and attitude towards fertility and parenthood: a comparative study among medical students in Saudi Arabia

Khyber Medical University Journal
OBJECTIVE:  To compare the awareness, attitudes towards fertility, and intentions for parenthood between preclinical and clinical medical students. METHODS:  An online cross-sectional study was conducted among preclinical and clinical medical students ...
Fehmida Tehsin, Sayed Ibrahim Ali, Ghadah Al Qarni   +2 more
doaj   +1 more source

Machine learning for identifying liver and pancreas cancers through comprehensive serum glycopeptide spectra analysis: a case‐control study

Molecular Oncology, EarlyView.
This study presents a novel AI‐based diagnostic approach—comprehensive serum glycopeptide spectra analysis (CSGSA)—that integrates tumor markers and enriched glycopeptides from serum. Using a neural network model, this method accurately distinguishes liver and pancreatic cancers from healthy individuals.
Motoyuki Kohjima, Yuko Takami, Ken Kawabe, Kazuhiro Tanabe, Chihiro Hayashi, Mikio Mikami, Tetsuya Kusumoto   +6 more
wiley   +1 more source