This study is to evaluate the dose calculation accuracy using Varian's cone-beam CT (CBCT) for pelvic adaptive radiotherapy. We first calibrated the Hounsfield Unit (HU) to electron density (ED) for CBCT using a mini CT QC phantom embedded into an IMRT QA phantom. We then used a Catphan 500 with an annulus around it to check the calibration. The combined CT QC and IMRT phantom provided correct HU calibration, but not Catphan with an annulus. For the latter, not only was the Teflon an incorrect substitute for bone, but the inserts were also too small to provide correct HUs for air and bone. For the former, three different scan ranges (6 cm, 12 cm and 20.8 cm) were used to investigate the HU dependence on the amount of scatter. To evaluate the dose calculation accuracy, CBCT and plan-CT for a pelvic phantom were acquired and registered. The single field plan, 3D conformal and IMRT plans were created on both CT sets. Without inhomogeneity correction, the two CT generated nearly the same plan. With inhomogeneity correction, the dosimetric difference between the two CT was mainly from the HU calibration difference. The dosimetric difference for 6 MV was found to be the largest for the single lateral field plan (maximum 6.7%), less for the 3D conformal plan (maximum 3.3%) and the least for the IMRT plan (maximum 2.5%). Differences for 18 MV were generally 1-2% less. For a single lateral field, calibration with 20.8 cm achieved the minimum dosimetric difference. For 3D and IMRT plans, calibration with a 12 cm range resulted in better accuracy. Because Catphan is the standard QA phantom for the on-board imager (OBI) device, we specifically recommend not using it for the HU calibration of CBCT.

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Panax ginseng has long been used in Asia as a herbal medicine for the prevention and treatment of various diseases, including cancer. The current study evaluated the cytotoxic potency against a variety of cancer cells by using ginseng ethanol extracts (RSE), protopanaxadiol (PPD)-type, protopanaxatriol (PPT)-type ginsenosides fractions, and their hydrolysates, which were prepared by stepwise hydrolysis of the sugar moieties of the ginsenosides. The results showed that the cytotoxic potency of the hydrolysates of RSE and total PPD-type or PPT-type ginsenoside fractions was much stronger than the original RSE and ginsenosides; especially the hydrolysate of PPD-type ginsenoside fractions. Subsequently, two derivatives of protopanaxadiol (1), compounds 2 and 3, were synthesized via hydrogenation and dehydration reactions of compound 1. Using those two derivatives and the original ginsenosides, a comparative study on various cancer cell lines was conducted; the results demonstrated that the cytotoxic potency was generally in the descending order of compound 3 > 20(S)-dihydroprotopanaxadiol (2) > PPD (1) > 20(S)-Rh2 > 20(R)-Rh2 ≈ 20(R)-Rg3 ≈ 20(S)-Rg3. The results clearly indicate the structure-related activities in which the compound with less polar chemical structures possesses higher cytotoxic activity towards cancer cells.

Background Natural language processing (NLP) has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses and quantifies recent literature in NLP applied to radiology reports. Methods We conduct an automated literature search yielding 4836 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics. Results We present a comprehensive analysis of the 164 publications retrieved with publications in 2019 almost triple those in 2015. Each publication is categorised into one of 6 clinical application categories. Deep learning use increases in the period but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results. Conclusions Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process and we show that research in this field continues to grow. Reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers in the field providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication.

Purpose: Circular RNA (circRNA) is a key regulatory factor in the development and progression of human tumors. However, the working mechanism and clinical significance of most circRNAs remain unknown in human cancers, including multiple myeloma (MM).Patients and Methods: This study employs high-throughput circRNA microarray with bioinformatics to identify differentially expressed circRNAs in patients with MM. The hsa_circ_0007841 expressions were observed in the MM tissues of 86 patients. Drug-resistant cell lines and pathological features were also detected. In addition, the relationship between hsa_circ_0007841 expressions in the MM tissues and the pathological features of patients with MM were evaluated and role of hsa_circ_0007841 as a potential biomarker and therapeutic target was assessed.Results: The results show that in the MM cell lines and drug-resistant cell lines, hsa_circ_0007841 expression was significantly upregulated, which was closely associated with disease prognosis. Specifically, hsa_circ_0007841 upregulation was correlated with chromosomal aberrations such as gain 1q21, t (4:14) and mutations in ATR and IRF4 genes. This finding was corroborated in large samples. Finally, bioinformatics analysis showed that eight differentially expressed miRNAs and 10 candidate mRNAs interacted with hsa_circ_0007841, shedding some new light on the basic functional research.Conclusion: This study may be the first to report that hsa_circ_0007841 is significantly upregulated in MM. It also suggests that hsa_circ_0007841 may be a novel biomarker for MM and its involvement in the progression of MM.

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This study investigates motivations for self‐archiving research items on academic social networking sites (ASNSs). A model of these motivations was developed based on two existing motivation models: motivation for self‐archiving in academia and motivations for information sharing in social media. The proposed model is composed of 18 factors drawn from personal, social, professional, and external contexts, including enjoyment, personal/professional gain, reputation, learning, self‐efficacy, altruism, reciprocity, trust, community interest, social engagement, publicity, accessibility, self‐archiving culture, influence of external actors, credibility, system stability, copyright concerns, additional time, and effort. Two hundred and twenty‐six ResearchGate users participated in the survey. Accessibility was the most highly rated factor, followed by altruism, reciprocity, trust, self‐efficacy, reputation, publicity, and others. Personal, social, and professional factors were also highly rated, while external factors were rated relatively low. Motivations were correlated with one another, demonstrating that RG motivations for self‐archiving could increase or decrease based on several factors in combination with motivations from the personal, social, professional, and external contexts. We believe the findings from this study can increase our understanding of users' motivations in sharing their research and provide useful implications for the development and improvement of ASNS services, thereby attracting more active users.