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Traumatic brain injury (TBI) has a significant burden of disease worldwide and outcomes vary widely. Current prognostic tools fail to fully account for this variability despite incorporating clinical, radiographic, and biochemical data. This variance could possibly be explained by genotypic differences in the patient population. In this review, we explore single nucleotide polymorphism (SNP) TBI outcome association studies.

In recent years, SNP association studies in TBI have focused on global, neurocognitive/neuropsychiatric, and physiologic outcomes. While the APOE gene has been the most extensively studied, other genes associated with neural repair, cell death, the blood-brain barrier, cerebral edema, neurotransmitters, mitochondria, and inflammatory cytokines have all been examined for their association with various outcomes following TBI. The results have been mixed across studies and even within genes. SNP association studies provide insight into mechanisms by which outcomes may vary following TBI. Their individual clinical utility, however, is often limited by small sample sizes and poor reproducibility. In the future, they may serve as hypothesis generating for future therapeutic targets.

In recent years, SNP association studies in TBI have focused on global, neurocognitive/neuropsychiatric, and physiologic outcomes. While the APOE gene has been the most extensively studied, other genes associated with neural repair, cell death, the blood-brain barrier, cerebral edema, neurotransmitters, mitochondria, and inflammatory cytokines have all been examined for their association with various outcomes following TBI. The results have been mixed across studies and even within genes. SNP association studies provide insight into mechanisms by which outcomes may vary following TBI. Their individual clinical utility, however, is often limited by small sample sizes and poor reproducibility. In the future, they may serve as hypothesis generating for future therapeutic targets.

Early diagnosis and prognosis of patients with community-acquired pneumonia (CAP) are still difficult clinical challenges. This study aimed to investigate the role of lysophosphatidylethanolamine acyltransferase (LPEAT) in CAP and to evaluate the effectiveness of this enzyme as an indicator of disease severity and risk of death in CAP.

This retrospective, multi-center study was conducted in 2017. Bleomycin cell line A total of 267 patients with CAP were included. Of these 267 patients, 175 patients had non-severe CAP (non-SCAP) and 92 patients had severe CAP (SCAP). In addition, we recruited 15 healthy volunteers and 42 hospitalized disease controls in our study. The demographic and clinical characteristics were recorded for all participants. Admission levels of LPEAT were determined by quantitative enzyme-linked immunosorbent assay.

Admission levels of LPEAT in patients with SCAP were significantly higher, particularly in non-survivors and were not affected by the causative etiology. Furthermore, when the patients were stratified according to PSI and CURB-65 scores, the patients with high severity scores had higher LPEAT levels upon admission than patients with low severity scores. LPEAT also performed well in predicting SCAP in patients with CAP. Moreover, LPEAT could predict the 30-day mortality rate of patients with CAP, and combining LPEAT with the clinical severity score further improved the accuracy of mortality prediction.

Elevated LPEAT levels can reliably predict the severity of illness in patients with CAP at the time of admission. Adding LPEAT to clinical scoring methods could improve prognostic accuracy. Trial registration ClinicalTrials.gov, NCT03093220. Registered on March 28th, 2017.

Elevated LPEAT levels can reliably predict the severity of illness in patients with CAP at the time of admission. Adding LPEAT to clinical scoring methods could improve prognostic accuracy. Trial registration ClinicalTrials.gov, NCT03093220. Registered on March 28th, 2017.Although vaccines are available for many infectious diseases, there are still unresolved infectious diseases that threaten global public health. In particular, the rapid spread of unpredictable, highly contagious viruses has recorded numerous infection cases and deaths, and has changed our lives socially or economically through social distancing and wearing masks. The pandemics of unpredictable, highly contagious viruses increase the ever-high social need for rapid vaccine development. Nanotechnologies may hold promise and expedite the development of vaccines against newly emerging infectious viruses. As potential nanoplatforms for delivering antigens to immune cells, delivery systems based on lipids, polymers, proteins, and inorganic nanomaterials have been studied. These nanoplatforms have been tested as a means to deliver vaccines not as a whole, but in the form of protein subunits or as DNA or mRNA sequences encoding the antigen proteins of viruses. This review covers the current status of nanomaterial-based delivery systems for viral antigens, with highlights on nanovaccines against recently emerging infectious viruses, such as severe acute respiratory syndrome coronavirus-2, Middle East respiratory syndrome coronavirus, and Zika virus.Recalcitrant respiratory tract infections caused by bacteria have emerged as one of the greatest health challenges worldwide. Aerosolized antimicrobial therapy is becoming increasingly attractive to combat such infections, as it allows targeted delivery of high drug concentrations to the infected organ while limiting systemic exposure. However, successful aerosolized antimicrobial therapy is still challenged by the diverse biological barriers in infected lungs. Nanoparticle-mediated pulmonary drug delivery is gaining increasing attention as a means to overcome the biological barriers and accomplish site-specific drug delivery by controlling release of the loaded drug(s) at the target site. With the aim to summarize emerging efforts in combating respiratory tract infections by using nanoparticle-mediated pulmonary delivery strategies, this review provides a brief introduction to the bacterial infection-related pulmonary diseases and the biological barriers for effective treatment of recalcitrant respiratory tract infections. This is followed by a summary of recent advances in design of inhalable nanoparticle-based drug delivery systems that overcome the biological barriers and increase drug bioavailability. Finally, challenges for the translation from exploratory laboratory research to clinical application are also discussed and potential solutions proposed.Pictures with affective content have been widely used in the scientific study of emotions, from two main perspectives on the one hand, dimensional theories claiming that affective experiences can be described according to a few fundamental dimensions such as valence and arousal, and on the other hand, discrete-category theories proposing the presence of a number of basic and universal emotions. Although it has been demonstrated that these two approaches are not mutually exclusive, the existing standardized affective picture databases have been created from the dimensional perspective, which has led to important gaps for research focused on discrete emotions. The present work introduces MATTER, a new database comprising 540 pictures depicting disgusting, fearful, neutral, erotic, mirthful and incongruent content, which provides normative values (total N = 368, mean = 120.47 ratings/picture) in valence and arousal dimensions, as well as in discrete affective (disgust, fear, erotica and mirth) and cognitive (incongruence and interest) features. A tentative classification into discrete categories is presented, and the physical properties of each picture are reported. Our findings suggest that MATTER constitutes a modern and suitable set of affective images including, for the first time, both mirth- and incongruence-related pictures. Additionally, it will enable the examination of affective and cognitive processes in fear/disgust and humor/incongruence fields.Many studies of speech perception assess the intelligibility of spoken sentence stimuli by means of transcription tasks ('type out what you hear'). The intelligibility of a given stimulus is then often expressed in terms of percentage of words correctly reported from the target sentence. Yet scoring the participants' raw responses for words correctly identified from the target sentence is a time-consuming task, and hence resource-intensive. Moreover, there is no consensus among speech scientists about what specific protocol to use for the human scoring, limiting the reliability of human scores. The present paper evaluates various forms of fuzzy string matching between participants' responses and target sentences, as automated metrics of listener transcript accuracy. We demonstrate that one particular metric, the token sort ratio, is a consistent, highly efficient, and accurate metric for automated assessment of listener transcripts, as evidenced by high correlations with human-generated scores (best correlation r = 0.940) and a strong relationship to acoustic markers of speech intelligibility. Thus, fuzzy string matching provides a practical tool for assessment of listener transcript accuracy in large-scale speech intelligibility studies. See https//tokensortratio.netlify.app for an online implementation.To understand when, how, and why people cheat, the ability to detect cheating in a laboratory setting is crucial. However, commonly used paradigms are confronted with a conflict between allowing participants to believe they can cheat unnoticed and allowing experimenters to detect cheating. This project aimed to develop and establish a new nonverbal task to resolve this conflict. Study 1 and Study 2 developed a new unsolvable paradigm called the Difference Spotting Task. In Study 1, participants were incentivized to indicate whether they found any difference between a pair of pictures without being asked to point the difference(s) out, so they could overreport their performance to earn extra money. Unbeknownst to them, the pairs of pictures from half of the items were identical so that the task could not be solved without cheating. This paradigm allowed experimenters to detect cheating for each unsolvable item. Study 3 examined the validity of the Difference Spotting Task and demonstrated it as a valid tool to assess cheating. The Difference Spotting Task is nonverbal and thus applicable to populations across age, educational level, and culture. In this unsolvable task, participants feel safe in cheating, and experimenters can detect cheating at the item level. The task holds the potential to gain acceptance by many researchers and facilitate the investigation of the underlying processes of cheating behavior.Poor response to treatment is a defining characteristic of reading disorder. In the present systematic review and meta-analysis, we found that the overall average effect size for treatment efficacy was modest, with a mean standardized difference of 0.38. Small true effects, combined with the difficulty to recruit large samples, seriously challenge researchers planning to test treatment efficacy in dyslexia and potentially in other learning disorders. Nonetheless, most published studies claim effectiveness, generally based on liberal use of multiple testing. This inflates the risk that most statistically significant results are associated with overestimated effect sizes. To enhance power, we propose the strategic use of repeated measurements with mixed-effects modelling. This novel approach would enable us to estimate both individual parameters and population-level effects more reliably. We suggest assessing a reading outcome not once, but three times, at pre-treatment and three times at post-treatment. Such design would require only modest additional efforts compared to current practices.