Following the publication of the above article, the authors contacted the Editorial Office to explain that a couple of errors concerning data handling/labelling had been made, firstly during the preparation of the representative images in Fig. 3B, resulting in the wrong image being selected for the data panel showing the ACHN cells treated with 'Inhibitor NC' at 0 h experiment, and secondly in Fig. 5A, resulting in the wrong image being selected for the data panel showing the ACHN cells treated with 'Inhibitor NC' experiment. The authors requested that a corrigendum be published to take account of the errors that were made during the preparation of this figure. Subsequently, an independent investigation of the published data was undertaken by the Editorial Office, which revealed that the 'Inhibitor' data panel in Fig. 6A and the 'Mimic NC' data panel in Fig. 6B were also overlapping, such that these data were likely to have been derived from the same original source, even though these data panels were intended to have shown the results from differently performed experiments. The Editor of has considered the authors' request to publish a corrigendum, but given the number of overlapping data panels that have been identified and the number of figures that would be in need of correction, the Editor has decided to decline the authors' request to publish a corrigendum on account of an overall lack of confidence in the presented data, and instead has determined that the paper should be retracted. Upon receiving this news from the Editor, the authors accepted the Editor's decision. The Editor apologizes to the readership of the Journal for any inconvenience caused. [Molecular Medicine Reports 17: 2051‑2060, 2018; DOI: 10.3892/mmr.2017.8052].
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http://dx.doi.org/10.3892/mmr.2024.13210 | DOI Listing |
Sci Rep
December 2024
Cereal Disease Laboratory, Agricultural Research Service, US Department of Agriculture, St. Paul, MN, 55108, USA.
Fusarium graminearum is a primary cause of Fusarium head blight (FHB) on wheat and barley. The fungus produces trichothecene mycotoxins that render grain unsuitable for food, feed, or malt. Isolates of F.
View Article and Find Full Text PDFNat Commun
December 2024
Center for Health and Data Science (CHDS), the Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Limited whole genome sequencing (WGS) studies in Asian populations result in a lack of representative reference panels, thus hindering the discovery of ancestry-specific variants. Here, we present the South and East Asian reference Database (SEAD) panel ( https://imputationserver.westlake.
View Article and Find Full Text PDFJ Antimicrob Chemother
December 2024
Division of Mycobacterial and Respiratory Infections, Department of Medicine, National Jewish Health, Denver, CO, USA.
Background: Mycobacterium abscessus is a highly drug-resistant non-tuberculous mycobacterium (NTM) for which treatment is limited by the lack of active oral antimycobacterials and frequent adverse reactions. Epetraborole is a novel oral, boron-containing antimicrobial that inhibits bacterial leucyl-tRNA synthetase, an essential enzyme in protein synthesis, and has been shown to have anti-M. abscessus activity in preclinical studies.
View Article and Find Full Text PDFJ Antimicrob Chemother
December 2024
Food and Veterinary Institute Oldenburg, Lower Saxony State Office for Consumer Protection and Food Safety (LAVES), Oldenburg, Germany.
Antimicrobial susceptibility testing (AST) in the veterinary sector by broth microdilution is mainly based on commercially available microtitre plates with specific panels. A critical review of commercially available microtitre panels identified AST panels that fulfil the requirements for obtaining reliable AST results by covering the necessary antimicrobial concentrations for both clinical breakpoints as well as quality control (QC) ranges for approved QC strains. However, there are AST panels in which these prerequisites are only in part fulfilled, and some AST panels that do not fulfil the aforementioned criteria at all.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Medicine, Institute of Biomedicine, University of Eastern Finland, Yliopistonranta 1, PO Box 1627, 70211 Kuopio, Finland.
The selection of biomarker panels in omics data, challenged by numerous molecular features and limited samples, often requires the use of machine learning methods paired with wrapper feature selection techniques, like genetic algorithms. They test various feature sets-potential biomarker solutions-to fine-tune a machine learning model's performance for supervised tasks, such as classifying cancer subtypes. This optimization process is undertaken using validation sets to evaluate and identify the most effective feature combinations.
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