Maximising the Opportunities of Artificial Intelligence for People Living With Cancer

Hopes and Hypes for Artificial Intelligence in Colorectal Cancer Screening

Learning from toxicity patterns in phase I trials during the era of mechanism targeted agents

Continual learning in medical devices: FDA's action plan and beyond

Report of the Topical Calcineurin Inhibitor Task Force of the American College of Allergy, Asthma and Immunology and the American Academy of Allergy, Asthma and Immunology

Please mind the gap—about equity and access to care in oncology

SPN Position Statement: Transition of Pediatric Patients Into Adult Care

Artificial intelligence (AI) promises to change health care, with some studies showing proof of concept of a provider-level performance in various medical specialties. However, there are many barriers to implementing AI, including patient acceptance and understanding of AI. Patients' attitudes toward AI are not well understood. We systematically reviewed the literature on patient and general public attitudes toward clinical AI (either hypothetical or realised), including quantitative, qualitative, and mixed methods original research articles. We searched biomedical and computational databases from Jan 1, 2000, to Sept 28, 2020, and screened 2590 articles, 23 of which met our inclusion criteria. Studies were heterogeneous regarding the study population, study design, and the field and type of AI under study. Six (26%) studies assessed currently available or soon-to-be available AI tools, whereas 17 (74%) assessed hypothetical or broadly defined AI. The quality of the methods of these studies was mixed, with a frequent issue of selection bias. Overall, patients and the general public conveyed positive attitudes toward AI but had many reservations and preferred human supervision. We summarise our findings in six themes: AI concept, AI acceptability, AI relationship with humans, AI development and implementation, AI strengths and benefits, and AI weaknesses and risks. We suggest guidance for future studies, with the goal of supporting the safe, equitable, and patient-centred implementation of clinical AI.

Quality Measures for Colonoscopy: A Critical Evaluation

Erratum

Strengths and Weaknesses of an Artificial Intelligence Polyp Detection Program as Assessed by a High-Detecting Endoscopist

Drug-Induced Liver Injury, Dosage, and Drug Disposition: Is Idiosyncrasy Really Unpredictable?

Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism.

Familial hypercholesterolaemia (FH) is a dominant and highly penetrant monogenic disorder present from birth that markedly elevates plasma low-density lipoprotein (LDL)-cholesterol concentration and, if untreated, leads to premature atherosclerosis and coronary artery disease (CAD). There are approximately 100,000 people with FH in Australia. However, an overwhelming majority of those affected remain undetected and inadequately treated, consistent with FH being a leading challenge for public health genomics. To further address the unmet need, we provide an updated guidance, presented as a series of systematically collated recommendations, on the care of patients and families with FH. These recommendations have been informed by an exponential growth in published works and new evidence over the last 5 years and are compatible with a contemporary global call to action on FH. Recommendations are given on the detection, diagnosis, assessment and management of FH in adults and children. Recommendations are also made on genetic testing and risk notification of biological relatives who should undergo cascade testing for FH. Guidance on management is based on the concepts of risk re-stratification, adherence to heart healthy lifestyles, treatment of non-cholesterol risk factors, and safe and appropriate use of LDL-cholesterol lowering therapies, including statins, ezetimibe, proprotein convertase subtilisin/kexin type 9 inhibitors and lipoprotein apheresis. Broad recommendations are also provided for the organisation and development of health care services. Recommendations on best practice need to be underpinned by good clinical judgment and shared decision making with patients and families. Models of care for FH need to be adapted to local and regional health care needs and available resources. A comprehensive and realistic implementation strategy, informed by further research, including assessments of cost-benefit, will be required to ensure that this new guidance benefits all Australian families with or at risk of FH.

Cells to Surgery Quiz: April 2019

During apoptosis, Smac (second mitochondria-derived activator of caspases)/DIABLO, an IAP (inhibitor of apoptosis protein)-binding protein, is released from mitochondria and potentiates apoptosis by relieving IAP inhibition of caspases. We demonstrate that exposure of MCF-7 cells to the death-inducing ligand, TRAIL (tumor necrosis factor-related apoptosis-inducing ligand), results in rapid Smac release from mitochondria, which occurs before or in parallel with loss of cytochrome c. Smac release is inhibited by Bcl-2/Bcl-xL or by a pan-caspase inhibitor demonstrating that this event is caspase-dependent and modulated by Bcl-2 family members. Following release, Smac is rapidly degraded by the proteasome, an effect suppressed by co-treatment with a proteasome inhibitor. As the RING finger domain of XIAP possesses ubiquitin-protein ligase activity and XIAP binds tightly to mature Smac, an in vitro ubiquitination assay was performed which revealed that XIAP functions as a ubiquitin-protein ligase (E3) in the ubiquitination of Smac. Both the association of XIAP with Smac and the RING finger domain of XIAP are essential for ubiquitination, suggesting that the ubiquitin-protein ligase activity of XIAP may promote the rapid degradation of mitochondrial-released Smac. Thus, in addition to its well characterized role in inhibiting caspase activity, XIAP may also protect cells from inadvertent mitochondrial damage by targeting pro-apoptotic molecules for proteasomal degradation.