Basic fibroblast growth factor-mediated overexpression of vascular endothelial growth factor in 1F6 human melanoma cells is regulated by activation of PI-3K and p38 MAPK

Abstract. Background: 1F6 human melanoma xenografts overexpressing either the 18 kD (18kD) form or all (ALL) forms of human basic fibroblast growth factor (bFGF) demonstrate an abundant number of microvessels and accelerated growth. We now examined whether bFGF mediates vascular endothelial growth factor (VEGF) expression. Methods: Quantitative RT-PCR was used to determine bFGF and VEGF mRNA, VEGF protein secretion was measured by ELISA and VEGF promoter activation was assessed by a dual luciferase activity assay. Western blot was carried out to detect phosphorylation of bFGF-regulated target proteins. Results: In 1F6-18kD and 1F6-ALL clones VEGF mRNA was increased 4-to 5-fold and VEGF protein secretion was highly stimulated due to activation of the VEGF promotor. PI-3K, p38 MAPK and ERK1/2 MAPK pathways were activated, while inhibition of PI-3K or p38 resulted in, respectively, 55% and up to 70% reduction of VEGF mRNA overexpression. A concurrent 60% decrease in VEGF protein secretion was mostly apparent upon inhibition of PI-3K. Inhibition of ERK1/2 hardly affected VEGF mRNA or protein secretion. Two unselected human melanoma cell lines with high metastatic potential contained high bFGF and VEGF, while three non-or sporadically metastatic cell lines displayed low bFGF and VEGF. Conclusion: These data indicate that stimulation of VEGF protein secretion in response to bFGF overexpression may contribute to increased vascularization and enhanced aggressiveness in melanoma

Statistical Challenges When Analyzing SARS-CoV-2 RNA Measurements Below the Assay Limit of Quantification in COVID-19 Clinical Trials

Most clinical trials evaluating coronavirus disease 2019 (COVID-19) therapeutics include assessments of antiviral activity. In recently completed outpatient trials, changes in nasal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA levels from baseline were commonly assessed using analysis of covariance (ANCOVA) or mixed models for repeated measures (MMRM) with single imputation for results below assay lower limits of quantification (LLoQ). Analyzing changes in viral RNA levels with singly imputed values can lead to biased estimates of treatment effects. In this article, using an illustrative example from the ACTIV-2 trial, we highlight potential pitfalls of imputation when using ANCOVA or MMRM methods, and illustrate how these methods can be used when considering value

A Review of Dietary Prevention of Human Papillomavirus-Related Infection of the Cervix and Cervical Intraepithelial Neoplasia

The natural history of cervical cancer suggests that prevention can be achieved by modification of the host's immune system through a nutrient-mediated program. This study reviews the preventive role of dietary intake on cervical intraepithelial neoplasia (CIN) induced by human papillomavirus (HPV). Electronic databases were searched using relevant keywords such as, but not limited to, human papillomavirus infection, cervical intraepithelial neoplasia, lifestyle factors, nutrients intake, and diet. High consumption of fruit and vegetables appears to be protective against CIN. The findings also highlight the possibility of consuming high levels of specific nutrients, vitamins, and minerals, and retaining sufficient level of these elements in the body, especially those with high antioxidants and antiviral properties, to prevent progression of transient and persistent HPV infections to high-grade CIN 2 and 3 (including in situ cervical cancer). The protective effect is not significant for high-risk HPV persistent infections and invasive cervical cancer. Although it appears that intake of specific nutrients, vitamins, and minerals may be good in CIN prevention, there is lack of evidence from controlled trial to confirm this. Health professionals shall focus on implementation of a balanced-diet prevention strategy at an early stage for cervical cancer prevention

Healthcare consumption of patients with left ventricular assist device: real-world data

BACKGROUND: A left ventricular assist device (LVAD) is a life-saving but intensive therapy for patients with end-stage heart failure. We evaluated the healthcare consumption in a cohort of LVAD patients in our centre over 6 years. METHODS: All patients with a primary LVAD implantation at the University Medical Centre Utrecht in Utrecht, the Netherlands from 2016 through 2021 were included in this analysis. Subsequent hospital stay, outpatient clinic visits, emergency department visits and readmissions were recorded. RESULTS: During the investigated period, 226 LVADs were implanted, ranging from 32 in 2016 to 45 in 2020. Most LVADs were implanted in patients aged 40-60 years, while they were supported by or sliding on inotropes (Interagency Registry for Mechanically Assisted Circulatory Support class 2 or 3). Around the time of LVAD implantation, the median total hospital stay was 41 days. As the size of the LVAD cohort increased over time, the total annual number of outpatient clinic visits also increased, from 124 in 2016 to 812 in 2021 (p = 0.003). The numbers of emergency department visits and readmissions significantly increased in the 6‑year period as well, with a total number of 553 emergency department visits and 614 readmissions. Over the years, the annual number of outpatient clinic visits decreased by 1 per patient-year follow-up, while the annual numbers of emergency department visits and readmissions per patient-year remained stable. CONCLUSION: The number of patients supported by an LVAD has grown steadily over the last years, requiring a more specialised healthcare in this particular population

Fecal Tests: From Blood to Molecular Markers

Detection of molecular markers for colorectal neoplasia in feces has the potential to improve performance of simple noninvasive screening tests for colorectal cancer. Most research has explored the value of DNA-based, RNA-based, and protein-based markers. In all cases there has been a trend to move from a single marker to a panel of markers to improve sensitivity. Unfortunately, no type of molecular marker has proved specific for neoplasia. DNA tests have been improved by combining mutation detection with assessment of DNA integrity plus epigenetic markers of neoplasia. RNA-based approaches are just beginning to explore the full power of transcriptomics. So far, no protein-based fecal test has proved better than fecal immunochemical tests for hemoglobin. Finally, no marker or panel of markers has yet been developed to the point where it has been evaluated in large unbiased population studies to assess performance across all stages of neoplasia and in all practical environments

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Consensus molecular subtype classification of colorectal adenomas

Consensus molecular subtyping is an RNA expression-based classification system for colorectal cancer (CRC). Genomic alterations accumulate during CRC pathogenesis, including the premalignant adenoma stage, leading to changes in RNA expression. Only a minority of adenomas progress to malignancies, a transition that is associated with specific DNA copy number aberrations or microsatellite instability (MSI). We aimed to investigate whether colorectal adenomas can already be stratified into consensus molecular subtype (CMS) classes, and whether specific CMS classes are related to the presence of specific DNA copy number aberrations associated with progression to malignancy. RNA sequencing was performed on 62 adenomas and 59 CRCs. MSI status was determined with polymerase chain reaction-based methodology. DNA copy number was assessed by low-coverage DNA sequencing (n = 30) or array-comparative genomic hybridisation (n = 32). Adenomas were classified into CMS classes together with CRCs from the study cohort and from The Cancer Genome Atlas (n = 556), by use of the established CMS classifier. As a result, 54 of 62 (87%) adenomas were classified according to the CMS. The CMS3 ‘metabolic subtype’, which was least common among CRCs, was most prevalent among adenomas (n = 45; 73%). One of the two adenomas showing MSI was classified as CMS1 (2%), the ‘MSI immune’ subtype. Eight adenomas (13%) were classified as the ‘canonical’ CMS2. No adenomas were classified as the ‘mesenchymal’ CMS4, consistent with the fact that adenomas lack invasion-associated stroma. The distribution of the CMS classes among adenomas was confirmed in an independent series. CMS3 was enriched with adenomas at low risk of progressing to CRC, whereas relatively more high-risk adenomas were observed in CMS2. We conclude that adenomas can be stratified into the CMS classes. Considering that CMS1 and CMS2 expression signatures may mark adenomas at increased risk of progression, the distribution of the CMS classes among adenomas is consistent with the proportion of adenomas expected to progress to CRC