Over the past decades, consistent studies have shown that race/ethnicity have a great impact on cancer incidence, survival, drug response, molecular pathways and epigenetics. Despite the influence of race/ethnicity in cancer outcomes and its impact in health care quality, a comprehensive understanding of racial/ethnic inclusion in oncological research has never been addressed. We therefore explored the racial/ethnic composition of samples/individuals included in fundamental (patient-derived oncological models, biobanks and genomics) and applied cancer research studies (clinical trials). Regarding patient-derived oncological models (n = 794), 48.3% have no records on their donor’s race/ethnicity, the rest were isolated from White (37.5%), Asian (10%), African American (3.8%) and Hispanic (0.4%) donors. Biobanks (n = 8,293) hold specimens from unknown (24.56%), White (59.03%), African American (11.05%), Asian (4.12%) and other individuals (1.24%). Genomic projects (n = 6,765,447) include samples from unknown (0.6%), White (91.1%), Asian (5.6%), African American (1.7%), Hispanic (0.5%) and other populations (0.5%). Concerning clinical trials (n = 89,212), no racial/ethnic registries were found in 66.95% of participants, and records were mainly obtained from Whites (25.94%), Asians (4.97%), African Americans (1.08%), Hispanics (0.16%) and other minorities (0.9%). Thus, two tendencies were observed across oncological studies: lack of racial/ethnic information and overrepresentation of Caucasian/White samples/individuals. These results clearly indicate a need to diversify oncological studies to other populations along with novel strategies to enhanced race/ethnicity data recording and reporting.
Electronic laboratory notebooks (ELNs) will probably replace paper laboratory notebooks (PLNs) in academic research due to their advantages in data recording, sharing and security. Despite several reports describing technical characteristics of ELNs and their advantages over PLNs, no study has directly tested ELN performance among researchers. In addition, the usage of tablet-based devices or wearable technology as ELN complements has never been explored in the field. To implement an ELN in our biomedical research institute, here we first present a technical comparison of six ELNs using 42 parameters. Based on this, we chose two ELNs, which were tested by 28 scientists for a 3-month period and by 80 students via hands-on practical exercises. Second, we provide two survey-based studies aimed to compare these two ELNs (PerkinElmer Elements and Microsoft OneNote) and to analyze the use of tablet-based devices. We finally explore the advantages of using wearable technology as ELNs tools. Among the ELNs tested, we found that OneNote presents almost all parameters evaluated (39/42) and both surveyed groups preferred OneNote as an ELN solution. In addition, 80% of the surveyed scientists reported that tablet-based devices improved the use of ELNs in different respects. We also describe the advantages of using OneNote application for Apple Watch as an ELN wearable complement. This work defines essential features of ELNs that could be used to improve ELN implementation and software development.
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