Subsequently to the publication of this paper, an interested reader drew to the authors' attention that, in Fig. 3 on p. 4382, the 'Invasion' assay data for the negative control (NC) experiments for the T24 and EJ cell lines appeared to contain an overlap of data, such that they may have been derived from the same original source even though the data were purportedly intended to show the results from differently peformed experiments. The authors have re‑examined their original data, and realize that this figure was inadvertently assembled incorrectly. The revised version of Fig. 3, showing alternative data from one of the repeated experiments, is shown below. Note that this error did not significantly affect either the results or the conclusions reported in this paper, and all the authors agree to this corrigendum. Furthermore, the authors thank the Editor of for allowing them the opportunity to publish this corrigendum, and apologize to the readership for any inconvenience caused. [Molecular Medicine Reports 13: 4379-4385, 2016; DOI: 10.3892/mmr.2016.5055].
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364133 | PMC |
| http://dx.doi.org/10.3892/mmr.2022.12799 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Pivotal trial characteristics and types of endpoints used to support Food and Drug Administration rare disease drug approvals between 2013 and 2022.
Clin Trials
January 2025
Rare Diseases Team, Office of New Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Background/aims: Rare disease drug development faces unique challenges, such as genotypic and phenotypic heterogeneity within small patient populations and a lack of established outcome measures for conditions without previously successful drug development programs. These challenges complicate the process of selecting the appropriate trial endpoints and conducting clinical trials in rare diseases. In this descriptive study, we examined novel drug approvals for non-oncologic rare diseases by the U.
View Article and Find Full Text PDFIn the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance.
View Article and Find Full Text PDFPlatelet indices in preeclampsia: comparative analysis with normotensive pregnant women.
Expert Rev Hematol
January 2025
Nishtar Medical University and Hospital, Multan, Pakistan.
Background: To compare plateletcount (PC), mean platelet volume (MPV), and platelet distribution width (PDW)between women with preeclampsia (PE) and normotensive pregnant women, andevaluate their effectiveness as predictors of PE.
Research Design Andmethods: This cross-sectionalstudy at Nishtar Hospital, Multan, included 141 women: 74 normotensive and 67preeclamptic. Data was collected using an automated hematology analyzer andanalyzed with SPSS version 26 and ROC curves.
Background/aims: Certain sociodemographic groups are routinely underrepresented in clinical trials, limiting generalisability. Here, we describe the extent to which enriched enrolment approaches yielded a diverse trial population enriched for older age in a randomised controlled trial of a blood-based multi-cancer early detection test (NCT05611632).
Methods: Participants aged 50-77 years were recruited from eight Cancer Alliance regions in England.
Unveiling the role of miRNAs in Diminished Ovarian Reserve: an in silico network approach.
Syst Biol Reprod Med
December 2025
Department of Biosciences and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy.
MicroRNAs (miRNAs) have acquired an increased recognition to unravel the complex molecular mechanisms underlying Diminished Ovarian Reserve (DOR), one of the main responsible for infertility. To investigate the impact of miRNA profiles in granulosa cells and follicular fluid, crucial players in follicle development, this study employed a computational network theory approach to reconstruct potential pathways regulated by miRNAs in granulosa cells and follicular fluid of women suffering from DOR. Available data from published research were collected to create the FGC_MiRNome_MC, a representation of miRNA target genes and their interactions.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!