Cutting down: insights from qualitative studies of smoking in pregnancy

The adverse effects of smoking in pregnancy are minimised if the mother quits completely in early pregnancy. Smokers are therefore advised to quit abruptly; cutting down is not recommended either as a method of, or alternative to, quitting. However, most pregnant smokers do not quit and cutting down is widely reported. Evidence comes primarily from quantitative studies; qualitative research has contributed little to understandings of cigarette consumption in pregnancy. In consequence, little is known about the place and meaning of cutting down for pregnant smokers. The paper investigates this important dimension of maternal smoking. It explores perceptions and experiences of cutting down among pregnant smokers by examining data from a systematic review of qualitative studies of smoking in pregnancy. The studies were located in high‐income countries and published between 1970 and 2012. Twenty‐six studies, reported in 29 papers, were included, representing over 640 women. Meta‐ethnography guided the analysis and synthesis. Data (participants' accounts and authors' interpretations) were extracted and coded; codes were progressively combined to identify overarching themes (‘lines of argument’). Running through the lines of argument was evidence on cutting down; the paper presents and analyses this evidence. The analysis indicates that cutting down figured centrally as both a method of quitting and, for persistent smokers, a method of harm reduction. While pregnant women were aware that official advice was to quit abruptly, cutting down was seen as a positive behaviour change in often‐difficult domestic circumstances, and one that health professionals condoned. Our findings suggest that cutting down in pregnancy, as an aid and an alternative to quitting, requires greater recognition if healthcare and tobacco control policies are to be sensitive to the perspectives and circumstances of pregnant smokers.     

A small element from the Mason-Pfizer monkey virus genome makes human immunodeficiency virus type 1 expression and replication Rev-independent.

Replication of human immunodeficiency virus type 1(HIV-1) is dependent on the viral Rev protein. This protein acts in concert withthe cis-acting rev-responsive element present in intron-containing RNAs tofacilitate nuclear export of these RNAs. Here we show that a cis-acting219-nucleotide sequence from an unrelated "simple" retrovirus,Mason-Pfizer monkey virus (MPMV), enables Rev-independent HIV-1 replication.This sequence is present in an untranslated region near the 3' end of theMPMV genome. The MPMV element is also able to efficiently substitute for Rev inexpression of Gag/Pol and Env proteins from subgenomic constructs. Wehypothesize that the MPMV element functions by interacting with a cellularfactor that plays a role in mRNA transport analogous to that of the Rev protein.It might be possible to exploit this element in the development of an HIVvaccine.     

Review: Power of big data to improve patient care in gastroenterology

Big data is defined as being large, varied or frequently updated, and usually generated from real-world interaction. With the unprecedented availability of big data, comes an obligation to maximise its potential for healthcare improvements in treatment effectiveness, disease prevention and healthcare delivery. We review the opportunities and challenges that big data brings to gastroenterology. We review its sources for healthcare improvement in gastroenterology, including electronic medical records, patient registries and patient-generated data. Big data can complement traditional research methods in hypothesis generation, supporting studies and disseminating findings; and in some cases holds distinct advantages where traditional trials are unfeasible. There is great potential power in patient-level linkage of datasets to help quantify inequalities, identify best practice and improve patient outcomes. We exemplify this with the UK colorectal cancer repository and the potential of linkage using the National Endoscopy Database, the inflammatory bowel disease registry and the National Health Service bowel cancer screening programme. Artificial intelligence and machine learning are increasingly being used to improve diagnostics in gastroenterology, with image analysis entering clinical practice, and the potential of machine learning to improve outcome prediction and diagnostics in other clinical areas. Big data brings issues with large sample sizes, real-world biases, data curation, keeping clinical context at analysis and General Data Protection Regulation compliance. There is a tension between our obligation to use data for the common good and protecting individual patient’s data. We emphasise the importance of engaging with our patients to enable them to understand their data usage as fully as they wish.