In June 2015, invitations were sent by email to 151 APAME journals to participate in an online survey with an objective of gaining insight into the common publication misconduct encountered by APAME editors. The survey, conducted through SurveyMonkey over a 20-day-period, comprised 10 questions with expansions to allow anecdotes limited to 400 characters, estimated to take less than 10 minutes to complete. Only one invitation was issued per journal, targeting (in order of priority) editors, editorial board members and editorial staff, and limited by email availability. 54 (36%) journals responded. 98% of respondents held Editor or Editorial Board positions. All respondent journals have editorial policies on publication ethics and 96% provide instructions related to ethics. 45% use anti-plagiarism software to screen manuscripts, the most popular being iThenticate, CrossCheck and Turnitin. Up to 50% of journals had encountered studies without IRB approval. Author misconduct encountered were (in rank order): plagiarism (75%), duplicate publication (58%), unjustified authorship (39%), authorship disputes (33%), data falsification (29%), data/image manipulation (27%), conflict of interest (25%), copyright violation (17%) and breach of confidentiality (10%). Reviewer misconduct encountered were: conflict of interest (19%), plagiarism (17%), obstructive behavior (17%), abusive language (13%) and breach of confidentiality (13%). Notwithstanding the limitations of the survey and the response rate, a few insights have been gained: (1) the need for strengthening the ethical culture of researchers/authors and reviewers, (2) anti-plagiarism software can improve plagiarism detection by about 15%, and (3) the need for technical support to detect plagiarism, duplicate publication and image manipulation.
BACKGROUND: The interesting preponderance of Chinese with colorectal carcinoma (CRC) amongst the three major ethnic groups in Malaysia prompted a study to determine DNA mismatch repair (MMR) status in our CRC and attempt correlation with patient age, gender and ethnicity as well as location, grade, histological type and stage of tumour. Histologically re-confirmed CRC, diagnosed between 1st January 2005 and 31st December 2007 at the Department of Pathology, University of Malaya Medical Centre, were immunohistochemically stained with monoclonal antibodies to MMR proteins, MLH1, MSH2, MSH6 and PMS2 on the Ventana Benchmark XT autostainer. Of the 142 CRC cases entered into the study, there were 82 males and 60 females (M:F=1.4:1). Ethnically, 81 (57.0%) were Chinese, 32 (22.5%) Malays and 29 (20.4%) Indians. The patient ages ranged between 15-87 years (mean=62.4 years) with 21 cases <50-years and 121 ≥50-years of age. 14 (9.9%) CRC showed deficient MMR (dMMR). Concurrent loss of MLH1 and PMS2 occurred in 10, MSH2 and MSH6 in 2 with isolated loss of MSH6 in 1 and PMS2 in 1. dMMR was noted less frequently amongst the Chinese (6.2%) in comparison with their combined Malay and Indian counterparts (14.8%), and was associated with right sided and poorly differentiated tumours (p<0.05). 3 of the 5 (60.0%) dMMR CRC cases amongst the Chinese and 1 of 9 cases (11.1%) amongst the combined Malay and Indian group were <50-years of age. No significant association of dMMR was noted with patient age and gender, tumour stage or mucinous type.
In medical image segmentation, manual segmentation is considered both labor- and time-intensive while automated segmentation often fails to segment anatomically intricate structure accordingly. Interactive segmentation can tackle shortcomings reported by previous segmentation approaches through user intervention. To better reflect user intention, development of suitable editing functions is critical. In this paper, we propose an interactive knee cartilage extraction software that covers three important features: intuitiveness, speed, and convenience. The segmentation is performed using multi-label random walks algorithm. Our segmentation software is simple to use, intuitive to normal and osteoarthritic image segmentation and efficient using only two third of manual segmentation's time. Future works will extend this software to three dimensional segmentation and quantitative analysis.
Well-defined image can assist user to identify region of interest during segmentation. However, complex medical image is usually characterized by poor tissue contrast and low background luminance. The contrast improvement can lift image visual quality, but the fundamental contrast enhancement methods often overlook the sudden jump problem. In this work, the proposed bihistogram Bezier curve contrast enhancement introduces the concept of "adequate contrast enhancement" to overcome sudden jump problem in knee magnetic resonance image. Since every image produces its own intensity distribution, the adequate contrast enhancement checks on the image's maximum intensity distortion and uses intensity discrepancy reduction to generate Bezier transform curve. The proposed method improves tissue contrast and preserves pertinent knee features without compromising natural image appearance. Besides, statistical results from Fisher's Least Significant Difference test and the Duncan test have consistently indicated that the proposed method outperforms fundamental contrast enhancement methods to exalt image visual quality. As the study is limited to relatively small image database, future works will include a larger dataset with osteoarthritic images to assess the clinical effectiveness of the proposed method to facilitate the image inspection.