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Subsequently to the publication of the above article, an interested reader drew to the authors' attention that a possible error had been identified in the selection of images in Figs. 1 and/or 7. After having consulted their original data, the authors realized that an erroneous image appeared on p. 593, in Fig. 7F [the 'Hep‑G2 / IL‑8 (5 ng/ml)' data panel], where part of this figure panel was overlapping with an image on p. 589 in Fig. 1C (the 'Hep‑G2 Co‑cultured' data panel). After a thorough review and verification of the data by all the authors, they have confirmed that the original data presented in the paper were accurate, and the error was solely due to the selection of an incorrect image during figure arrangement. The authors confirm that this mistake in image selection did not affect the overall conclusions reported in the article. A corrected version of Fig. 7, including the correct data for the 'Hep‑G2 / IL‑8 (5 ng/ml)' panel in Fig. 7F, is shown on the next page. The authors are grateful to the Editor of for granting them the opportunity to publish this Corrigendum. All the authors agree to the publication of this Corrigendum, and apologize to the readership for any inconvenience caused. [International Journal of Oncology 46: 587‑596, 2015; DOI: 10.3892/ijo.2014.2761].
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http://dx.doi.org/10.3892/ijo.2024.5672 | DOI Listing |
ACS Sens
September 2025
Institute of Applied Mechanics, National Taiwan University, Taipei 106, Taiwan.
In recent AI-driven disease diagnosis, the success of models has depended mainly on extensive data sets and advanced algorithms. However, creating traditional data sets for rare or emerging diseases presents significant challenges. To address this issue, this study introduces a direct-self-attention Wasserstein generative adversarial network (DSAWGAN) designed to improve diagnostic capabilities in infectious diseases with limited data availability.
View Article and Find Full Text PDFPsychon Bull Rev
September 2025
Department of Experimental Psychology, University of Oxford, Oxford, UK.
Individuals who are superior at face recognition are described as 'super recognisers' (SRs). On standard face recognition tasks SRs outperform individuals who have typical face recognition ability. However, high accuracy on face recognition tasks may be driven by superior ability in one or more of several component processes including face perception, face matching, and face memory.
View Article and Find Full Text PDFPatient
September 2025
Patient Services, Anthony Nolan, 2 Heathgate Place, London, NW3 2NU, UK.
Background: There is increasing interest in using patient-reported outcome measures (PROMs) to assess quality of life (QoL) following hematopoietic cell transplant (HCT). However, there is limited consensus on how such data should be collected within HCT services. This survey study investigated health professionals (HCPs) views towards QoL data collection and factors affecting the use of PROMs within HCT centres in the UK.
View Article and Find Full Text PDFNeotrop Entomol
September 2025
Dept of Entomology, Federal Univ of Viçosa, Viçosa, MG, Brazil.
The fruit fly Anastrepha fraterculus (Wiedemann) (Diptera: Tephritidae) is one of the main pests in apple orchards. Artificial neural networks (ANNs) are tools with good ability to predict phenomena such as the seasonal dynamics of pest populations. Thus, the objective of this work was to determine a prediction model for the seasonal dynamics of A.
View Article and Find Full Text PDFJ Safety Res
September 2025
MAIC/UniSC Road Safety Research Collaboration, University of the Sunshine Coast, 90 Sippy Downs Dr, Sippy Downs, Queensland 4556, Australia.
Introduction: Drink driving is a dangerous behavior associated with significant road trauma. The ability to estimate one's alcohol plays an important role in the decision to drink and drive. This systematic review aimed to synthesize the evidence regarding what factors are associated with the accuracy of self-estimated blood and breath alcohol concentrations (BAC/BrAC) and discuss relevant implications for drink driving.
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