Time in treatment: examining mental illness trajectories across inpatient psychiatric treatment

Early discharge or reduced length of stay for inpatient psychiatric patients is related to increased readmission rates and worse clinical outcomes including increased risk for suicide. Trajectories of mental illness outcomes have been identified as an important method for predicting the optimal length of stay but the distinguishing factors that separate trajectories remain unclear. We sought to identify the distinct classes of patients who demonstrated similar trajectories of mental illness over the course of inpatient treatment, and we explore the patient characteristics associated with these mental illness trajectories. We used data (N = 3406) from an inpatient psychiatric hospital with intermediate lengths of stay. Using growth mixture modeling, latent mental illness scores were derived from six mental illness indicators: psychological flexibility, emotion regulation problems, anxiety, depression, suicidal ideation, and disability. The patients were grouped into three distinct trajectory classes: (1) High-Risk, Rapid Improvement (HR-RI); (2) Low-Risk, Gradual Improvement (LR-GI); and (3) High-Risk, Gradual Improvement (HR-GI). The HR-GI was significantly younger than the other two classes. The HR-GI had significantly more female patients than males, while the LR-GI had more male patients than females. Our findings indicated that younger females had more severe mental illness at admission and only gradual improvement during the inpatient treatment period, and they remained in treatment for longer lengths of stay, than older males

Climate change responses among the Maasai Community in Kenya

© 2017, Springer Science+Business Media B.V. The impacts of climate change to the dryland areas of East Africa are especially strong, especially if it is considered that these areas have weak institutions and governance systems. Climate change has also affected many rural communities in a severe way, reducing crop yields and sometimes causing crop failure. In Kenya and Tanzania, where drylands cover over around 80 and 50% of their respective land areas, rural populations have been especially affected. Among them is the tribal group of the Maasai, legendary nomad warriors, who have been suffering from persistent droughts and the negative impacts on their cattle herds. This paper describes how climate change affects the Maasai communities in Kenya and the changes seen in their habits and diet, in order to adapt to a changing climate

Potential therapeutic applications of microbial surface-activecompounds

Numerous investigations of microbial surface-active compounds or biosurfactants over the past two decades have led to the discovery of many interesting physicochemical and biological properties including antimicrobial, anti-biofilm and therapeutic among many other pharmaceutical and medical applications. Microbial control and inhibition strategies involving the use of antibiotics are becoming continually challenged due to the emergence of resistant strains mostly embedded within biofilm formations that are difficult to eradicate. Different aspects of antimicrobial and anti-biofilm control are becoming issues of increasing importance in clinical, hygiene, therapeutic and other applications. Biosurfactants research has resulted in increasing interest into their ability to inhibit microbial activity and disperse microbial biofilms in addition to being mostly nontoxic and stable at extremes conditions. Some biosurfactants are now in use in clinical, food and environmental fields, whilst others remain under investigation and development. The dispersal properties of biosurfactants have been shown to rival that of conventional inhibitory agents against bacterial, fungal and yeast biofilms as well as viral membrane structures. This presents them as potential candidates for future uses in new generations of antimicrobial agents or as adjuvants to other antibiotics and use as preservatives for microbial suppression and eradication strategies

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified