These results are consistent with deficits in hippocampal function and structure in abuse-related PTSD.

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There is growing interest in the potential of artificial intelligence to support decision-making in health and social care settings. There is, however, currently limited evidence of the effectiveness of these systems. The aim of this study was to investigate the effectiveness of artificial intelligence-based computerised decision support systems in health and social care settings. We conducted a systematic literature review to identify relevant randomised controlled trials conducted between 2013 and 2018. We searched the following databases: MEDLINE, EMBASE, CINAHL, PsycINFO, Web of Science, Cochrane Library, ASSIA, Emerald, Health Business Fulltext Elite, ProQuest Public Health, Social Care Online, and grey literature sources. Search terms were conceptualised into three groups: artificial intelligence-related terms, computerised decision support -related terms, and terms relating to health and social care. Terms within groups were combined using the Boolean operator OR, and groups were combined using the Boolean operator AND. Two reviewers independently screened studies against the eligibility criteria and two independent reviewers extracted data on eligible studies onto a customised sheet. We assessed the quality of studies through the Critical Appraisal Skills Programme checklist for randomised controlled trials. We then conducted a narrative synthesis. We identified 68 hits of which five studies satisfied the inclusion criteria. These studies varied substantially in relation to quality, settings, outcomes, and technologies. None of the studies was conducted in social care settings, and three randomised controlled trials showed no difference in patient outcomes. Of these, one investigated the use of Bayesian triage algorithms on forced expiratory volume in 1 second (FEV1) and health-related quality of life in lung transplant patients. Another investigated the effect of image pattern recognition on neonatal development outcomes in pregnant women, and another investigated the effect of the Kalman filter technique for warfarin dosing suggestions on time in therapeutic range. The remaining two randomised controlled trials, investigating computer vision and neural networks on medication adherence and the impact of learning algorithms on assessment time of patients with gestational diabetes, showed statistically significant and clinically important differences to the control groups receiving standard care. However, these studies tended to be of low quality lacking detailed descriptions of methods and only one study used a double-blind design. Although the evidence of effectiveness of data-driven artificial intelligence to support decision-making in health and social care settings is limited, this work provides important insights on how a meaningful evidence base in this emerging field needs to be developed going forward. It is unlikely that any single overall message surrounding effectiveness will emerge - rather effectiveness of interventions is likely to be context-specific and calls for inclusion of a range of study designs to investigate mechanisms of action.

More than 250 million South and South East Asians use SLT in some form. As cigarettes prices climb up all over the world, more people could potentially take up SLT, particularly in the absence of epidemiological evidence regarding the harmful effects of these products, and SLT being advocated as a means of tobacco harm reduction. Our findings are thus relevant and timely in highlighting the harmful effects of SLT use, with a potential of influencing tobacco control policies in South Asia and beyond.

In the wake of smokeless tobacco (SLT) being advocated as a mean of tobacco harm reduction, it is pertinent to establish individual health risks associated with each SLT product. This case-control study was aimed at assessing the risk of oral cancer associated with a smokeless tobacco product (Naswar). The study was conducted from September 2014 till May 2015 in Khyber Pakhtunkhwa, Pakistan. Exposure and covariate information was collected through a structured questionnaire. Conditional logistic regression was used to calculate odds ratios (OR) along with their 95% confidence intervals (CI). 84 oral cancer cases (62% males) and 174 age- and sex-matched controls were recruited. Ever users of Naswar had more than a 20-fold higher risk of oral cancer compared to never-users (OR 21.2, 95% CI 8.4–53.8). Females had a higher risk of oral cancer with the use of Naswar (OR 29.0, 95% CI 5.4–153.9) as compared to males (OR 21.0, 95% CI 6.1–72.1). Based on this result, 68% (men) and 38% (women) of the oral cancer burden in Pakistan is attributable to Naswar. The risk estimates observed in this study are comparable to risk estimates reported by previous studies on other forms of SLT use and the risk of oral cancer in Pakistan. The exposure-response relationship also supports a strong role of Naswar in the etiology of oral cancer in Pakistan. Although still requiring further validation through independent studies, these findings may be used for smokeless tobacco control in countries where Naswar use is common.

Due to extensive utilization of fossil fuels, the energy demand of the growing population of the world is increasing day by day. The utilization of these fossil fuels also results in environmental pollution and energy crisis around the globe. As such, scientists and academics are trying to develop green materials which tend to reduce the impact of human on environment. Starch is the most abundant organic compound found in nature after cellulose. Thus, starch is an attractive natural green material candidate for researchers to replace the petro‐chemical‐based ingredients in wood composite products. There is an increasing demand for the wood adhesives in the recent years due to increasing applications of wood products. The wood adhesive industry remains dependent on the petroleum resources due to attributes such as high performance and low cost. However, these adhesives can create serious risks to human health and spoil the indoor air quality by emitting these carcinogenic compounds into the environment. Therefore, there is a growing interest to replace the petro‐chemical based ingredients in the adhesive formulation with green renewable material. This review focuses on research conducted in the recent past to develop adhesive for wood application from green renewable material such as starch.