Background. Dermatology is under-represented in UK undergraduate curricula, and with a diagnostic and educational toolkit that is heavily centred on face-to-face (F2F) clinical examination, dermatology education has been disproportionately affected by the COVID-19 pandemic. Online channel-based messaging apps such as Slack offer an opportunity to engage students in remote, multimodal collaborative learning by reproducing a classroom environment in the virtual space. Aim. To determine the feasibility, acceptability and proof of concept for an online Slack community in undergraduate dermatology education. Methods. Undergraduate medical students participated in an online classroom for a 6-week programme encompassing case-based discussions, seminars and journal clubs. The platform was facilitated by junior doctors (n = 10) and patient educators (n = 6). Students and faculty completed a post-course evaluation. Students additionally completed a pre-and post-intervention dermatology quiz. Mixed methods analyses included quantitative analyses to explore data trends and qualitative phenomenographic analyses to assimilate key underlying themes. Results. Students (n = 65) were enrolled to join the platform. The evaluation was completed by students (n = 52) from UK universities (n = 27). The majority of students (n = 27) interacted with the platform as passive observers (≤ 5 active interactions with the channel), with a small group (n = 4) of 'super-users' (≥ 100 active interactions). The overall quality of the course was described as 'excellent' by 96% of participants and 100% of faculty. Conclusion.A community-based online classroom can act as an enjoyable, acceptable and collaborative means of delivering dermatology education to undergraduate medical students. Its ease of use and supportive nature may also facilitate patient involvement. Such advances may provide vital safeguards against the reduction in F2F learning that has accompanied the COVID-19 pandemic.

Haematopoietic stem cell transplantation is a well-established treatment option for both hematological malignancies and nonmalignant conditions such as aplastic anemia and haemoglobinopathies. For those patients lacking a suitable matched sibling or matched unrelated donor, haploidentical donors are an alternative expedient donor pool. Historically, haploidentical transplantation led to high rates of graft rejection and GVHD. Strategies to circumvent these issues include T cell depletion and management of complications thereof or T replete transplants with GVHD prophylaxis. This review is an overview of these strategies and contemporaneous outcomes for hematological malignancies in adult haploidentical stem cell transplant recipients.

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn without being explicitly programmed, through a combination of statistics and computer science. It encompasses a variety of techniques used to analyse and interpret extremely large amounts of data, which can then be applied to create predictive models. Such applications of this technology are now ubiquitous in our day-to-day lives: predictive text, spam filtering, and recommendation systems in social media, streaming video and e-commerce to name a few examples. It is only more recently that ML has started to be implemented against the vast amount of data generated in healthcare. The emerging role of AI in refining healthcare delivery was recently highlighted in the ‘National Health Service Long Term Plan 2019’. In paediatrics, workforce challenges, rising healthcare attendance and increased patient complexity and comorbidity mean that demands on paediatric services are also growing. As healthcare moves into this digital age, this review considers the potential impact ML can have across all aspects of paediatric care from improving workforce efficiency and aiding clinical decision-making to precision medicine and drug development.

London, United Kingdom BACKGROUND: Smartphone applications (apps) have become integrated with many aspects of modern life, with >100,000 apps currently available for use in healthcare. Apps are unregulated and potentially place patients at risk if the information they contain is inaccurate. Additionally, any app that transforms clinical data (e.g. calculating a mean blood pressure (BP)) is a Class 1 medical device and should be registered with the Medicines and Healthcare Regulatory Agency in the UK. Here we present a systematic review of blood pressure (BP) monitoring apps available in the UK. METHODS: The Apple App Store (UK) was searched on 15th March 2014 using the terms "hypertension" OR "blood pressure". Inclusion criteria were iPhone compatibility, free to use, English language, targeted for patient use, ability to record multiple BP readings and app functionality independent of other medical devices. Data were extracted from each app according to a predefined protocol: conformity to guidelines, presence of data transformation, limits to data input and data protection policy. RESULTS: Following the removal of duplicates, 608 apps were identified of which 96 met the inclusion criteria. Five apps provided advice on how to measure BP, of which four claimed conformity to national and/or international guidelines. A further 11 apps providing advice unrelated to BP measurement technique also claimed conformity to guidelines (15 apps in total; 16%). No apps stated approval by recognised societies.Data transformation was performed by 24 apps (25%), none of which displayed regulatory approval. Three apps did recommend using a specific approved sphygmomanometer, with one providing formulae used for subsequent data transformation.Data input was subject to checks in 60% (58 apps) to ensure that diastolic values were lower than systolic. 25% (24 apps) required a passcode to access stored data, but it was unclear where data was stored (e.g. locally on device or on a server) in 89% of apps. CONCLUSION: There are many apps freely available to patients that enable them to record BP measurements. These unregulated apps conform poorly to recognised guidelines and have the potential to harm if their advice or data transformations are inaccurate.