The Genome of the Zoonotic Malaria Parasite Plasmodium simium Reveals Adaptions to Host-switching

Oral Session

The use of a risk assessment and decision support tool (CRISP) compared with usual care in general practice to increase risk-stratified colorectal cancer screening: study protocol for a randomised controlled trial.

BACKGROUND: Australia and New Zealand have the highest incidence rates of colorectal cancer worldwide. In Australia there is significant unwarranted variation in colorectal cancer screening due to low uptake of the immunochemical faecal occult blood test, poor identification of individuals at increased risk of colorectal cancer, and over-referral of individuals at average risk for colonoscopy. Our pre-trial research has developed a novel Colorectal cancer RISk Prediction (CRISP) tool, which could be used to implement precision screening in primary care. This paper describes the protocol for a phase II multi-site individually randomised controlled trial of the CRISP tool in primary care. METHODS: This trial aims to test whether a standardised consultation using the CRISP tool in general practice (the CRISP intervention) increases risk-appropriate colorectal cancer screening compared to control participants who receive standardised information on cancer prevention. Patients between 50 and 74 years old, attending an appointment with their general practitioner for any reason, will be invited into the trial. A total of 732 participants will be randomised to intervention or control arms using a computer-generated allocation sequence stratified by general practice. The primary outcome (risk-appropriate screening at 12 months) will be measured using baseline data for colorectal cancer risk and objective health service data to measure screening behaviour. Secondary outcomes will include participant cancer risk perception, anxiety, cancer worry, screening intentions and health service utilisation measured at 1, 6 and 12 months post randomisation. DISCUSSION: This trial tests a systematic approach to implementing risk-stratified colorectal cancer screening in primary care, based on an individual's absolute risk, using a state-of-the-art risk assessment tool. Trial results will be reported in 2020. TRIAL REGISTRATION: Australian and New Zealand Clinical Trial Registry, ACTRN12616001573448p . Registered on 14 November 2016

The genome of the zoonotic malaria parasite Plasmodium simium reveals adaptations to host switching

BACKGROUND: Plasmodium simium, a malaria parasite of non-human primates (NHP), was recently shown to cause zoonotic infections in humans in Brazil. We sequenced the P. simium genome to investigate its evolutionary history and to identify any genetic adaptions that may underlie the ability of this parasite to switch between host species. RESULTS: Phylogenetic analyses based on whole genome sequences of P. simium from humans and NHPs reveals that P. simium is monophyletic within the broader diversity of South American Plasmodium vivax, suggesting P. simium first infected NHPs as a result of a host switch of P. vivax from humans. The P. simium isolates show the closest relationship to Mexican P. vivax isolates. Analysis of erythrocyte invasion genes reveals differences between P. vivax and P. simium, including large deletions in the Duffy-binding protein 1 (DBP1) and reticulocyte-binding protein 2a genes of P. simium. Analysis of P. simium isolated from NHPs and humans revealed a deletion of 38 amino acids in DBP1 present in all human-derived isolates, whereas NHP isolates were multi-allelic. CONCLUSIONS: Analysis of the P. simium genome confirmed a close phylogenetic relationship between P. simium and P. vivax, and suggests a very recent American origin for P. simium. The presence of the DBP1 deletion in all human-derived isolates tested suggests that this deletion, in combination with other genetic changes in P. simium, may facilitate the invasion of human red blood cells and may explain, at least in part, the basis of the recent zoonotic infections

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

More than health : the role and value of meta-health effects in health care decisions

University of Technology Sydney. Faculty of Business.One of the most visible functions of government is to make decisions about funding health care treatments. This thesis investigates the role and value of meta-health effects in such decisions. Meta-health effects are effects other than health that result from the consumption of health care, and have value in their own right regardless of health status. The research in this thesis is facilitated via four inter-related case studies. The first examines the available information on decisions made by the Pharmaceutical Benefits Advisory Committee (PBAC) in Australia for evidence of the use of meta-health effects in drug reimbursement decisions. This is supplemented in that same case study by a systematic review of the methods used to value meta-health effects for use in economic evaluations. Three empirical case studies are subsequently presented which focus on the role of meta-health effects in individuals’ decisions regarding health care as a means of informing what might be considered in public decision making. All three case studies use survey-based methods: a general community survey on experiences and attitudes on general practitioner use, and two discrete choice experiment (DCE) surveys (one on ongoing therapy for rheumatoid arthritis, the other for the management of breast cancer recurrence risk). Together these three case studies explore how differences in the decision-making context, and methods of elicitation (such as attitudes or preferences) influence the role and value of meta-health effects. Within the DCEs those values are explored using willingness to pay, investigating how they are affected by framing. The results show that meta-health effects do influence choice. The review of PBAC decisions and the systematic review show that gains in convenience (e.g. gains in mode of administration) are investigated most often, but that differences in study methods influence the values derived. An important finding of the results of the empirical case studies is that meta-health effects do influence individual choices and the extent of that influence declines the greater the health implications of that decision. Similarly, they find that the amount and type of information presented influences the values derived in studies eliciting values for meta-health effects. This is not only a contribution to the literature, but highlights the importance to government decision makers of understanding how values for meta-health effects have been derived; careful attention needs to be paid to the manner in which such values have been derived lest they misrepresent the resulting value to society

Factors Impacting Treatment Choice in the First-Line Treatment of Colorectal Cancer

<p><b>Article full text</b></p> <p><br></p> <p>The full text of this article can be found here<b>. </b><a href="https://link.springer.com/article/10.1007/s40487-016-0020-4?view=classic">https://link.springer.com/article/10.1007/s40487-016-0020-4?view=classic</a></p><p></p> <p><br></p> <p><b>Provide enhanced content for this article</b></p> <p><br></p> <p>If you are an author of this publication and would like to provide additional enhanced content for your article then please contact <a href="http://www.medengine.com/Redeem/”mailto:[email protected]”"><b>[email protected]</b></a>.</p> <p><br></p> <p>The journal offers a range of additional features designed to increase visibility and readership. All features will be thoroughly peer reviewed to ensure the content is of the highest scientific standard and all features are marked as ‘peer reviewed’ to ensure readers are aware that the content has been reviewed to the same level as the articles they are being presented alongside. Moreover, all sponsorship and disclosure information is included to provide complete transparency and adherence to good publication practices. This ensures that however the content is reached the reader has a full understanding of its origin. No fees are charged for hosting additional open access content.</p> <p><br></p> <p>Other enhanced features include, but are not limited to:</p> <p><br></p> <p>• Slide decks</p> <p>• Videos and animations</p> <p>• Audio abstracts</p> <p>• Audio slides</p

The use of a risk assessment and decision support tool (CRISP) compared with usual care in general practice to increase risk-stratified colorectal cancer screening: study protocol for a randomised controlled trial

Abstract Background Australia and New Zealand have the highest incidence rates of colorectal cancer worldwide. In Australia there is significant unwarranted variation in colorectal cancer screening due to low uptake of the immunochemical faecal occult blood test, poor identification of individuals at increased risk of colorectal cancer, and over-referral of individuals at average risk for colonoscopy. Our pre-trial research has developed a novel Colorectal cancer RISk Prediction (CRISP) tool, which could be used to implement precision screening in primary care. This paper describes the protocol for a phase II multi-site individually randomised controlled trial of the CRISP tool in primary care. Methods This trial aims to test whether a standardised consultation using the CRISP tool in general practice (the CRISP intervention) increases risk-appropriate colorectal cancer screening compared to control participants who receive standardised information on cancer prevention. Patients between 50 and 74 years old, attending an appointment with their general practitioner for any reason, will be invited into the trial. A total of 732 participants will be randomised to intervention or control arms using a computer-generated allocation sequence stratified by general practice. The primary outcome (risk-appropriate screening at 12 months) will be measured using baseline data for colorectal cancer risk and objective health service data to measure screening behaviour. Secondary outcomes will include participant cancer risk perception, anxiety, cancer worry, screening intentions and health service utilisation measured at 1, 6 and 12 months post randomisation. Discussion This trial tests a systematic approach to implementing risk-stratified colorectal cancer screening in primary care, based on an individual’s absolute risk, using a state-of-the-art risk assessment tool. Trial results will be reported in 2020. Trial registration Australian and New Zealand Clinical Trial Registry, ACTRN12616001573448p . Registered on 14 November 2016