Plos One
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Prognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to data from admission or the first 24 hours. Most ICU admissions last longer than this, decisions are made throughout an admission, and some admissions are explicitly intended as time-limited prognostic trials. Despite this, temporal changes in prognostic ability during ICU admission has received little attention to date. Current predictive models, in the form of prognostic clinical tools, are typically derived from linear models and do not explicitly handle incremental information from trends. Machine learning (ML) allows predictive models to be developed which use non-linear predictors and complex interactions between variables, thus allowing incorporation of trends in measured variables over time; this has made it possible to investigate prognosis throughout an admission. ⋯ This has implications for decision making in intensive care and provides a justification for time-limited trials of ICU therapy; the assessment of prognosis over more than one day may be a valuable strategy as new information on the second day helps to differentiate outcomes. New ML models based on trend data beyond the first day could greatly improve upon current risk stratification tools.
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Immune checkpoint regulators, cytotoxic T lymphocyte antigen 4 (CTLA-4) and the programmed cell death protein-1/programmed death-ligand 1 (PD-1/PD-L1) have emerged as promising new targets for cancer therapeutics. While tumor expression of PD-L1 has been shown to have objective responses to anti-PD-L1 immunotherapies, the clinical implications of CTLA-4 expression in tumor cells or immune cells in the tumor microenvironment is still controversial. We investigated the expression of CTLA-4 and PD-L1 in human breast tumors and provided a scoring system for the systematic evaluation of CTLA-4 staining. ⋯ This study shows a significant overexpression of CTLA-4 in >50% of breast carcinomas with no such overexpression of CTLA-4 in benign breast tissues. PDL-1 staining is seen in only a small number of invasive ductal carcinomas (4.1%). These findings suggest the need for further investigation of anti-CTLA-4 and anti-PD-L1 immunotherapies and their efficacy in the treatment of breast carcinomas with overexpression of these immune modulators. In addition, the proposed scoring system will facilitate a more systematic correlation between tumor reactivity and clinical outcome which can be applied to all intracytoplasmic tumor markers.
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Zika virus (ZIKV) was first isolated in Uganda in 1947. In Brazil, the first reported case of ZIKV infection was in May 2015. Additionally, dengue (DENV) is endemic and there has been a recent outbreak of chikungunya (CHIKV). Since the clinical manifestations of different arboviral infections (AI) can be similar, definitive diagnosis requires laboratory testing. ⋯ Among HIV-infected pregnant women with laboratory evidence of a recent AI, 72% were ZIKV-infected. In this cohort, CNS malformations occurred among infants born to both symptomatic and asymptomatic pregnant women with Zika infection.
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Dot-probe studies consistently show that high trait anxious individuals have an attentional bias towards threatening faces. However, little is known about the influence of perceptual confounds of specific emotional expressions on this effect. Teeth-exposure was recently recognized as an important factor for the occurrence of attentional bias towards angry faces in a closely related paradigm (the face-in-the-crowd paradigm). ⋯ For angry faces with non-exposed teeth, we found the expected positive correlation (r = .441) of trait anxiety with the attentional bias score (reaction times for probes replacing the neutral face minus reaction times for probes replacing the angry face). However, we found no influence of trait anxiety on attentional bias towards angry faces with exposed teeth. These results suggest that natural low-level stimulus confounds of emotional faces like exposed teeth can affect the manifestation of anxiety-related attentional biases towards angry faces in the dot-probe task.
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Trauma is predicted to become the third leading cause of death in India by 2020, which indicate the need for urgent action. Trauma scores such as the international classification of diseases injury severity score (ICISS) have been used with great success in trauma research and in quality programmes to improve trauma care. To this date no valid trauma score has been developed for the Indian population. ⋯ We attempted to derive and validate a version of the ICISS using SRRs calculated from an Indian population. However, the developed ICISS-scores overestimate mortality and implementing these scores in clinical or policy contexts is not recommended. This study, as well as previous reports, suggest that other scoring systems might be better suited for India and other Low- and middle-income countries until more data are available.