Search

Objectives We sought to evaluate the impact of an emergency psychiatric assessment, treatment, and healing (EmPATH) unit on emergency department (ED) revenue, psychiatric boarding time, and length of stay (LOS). Methods We conducted a before-and-after economic evaluation of a single academic midwestern ED (60,000 annual visits) for all adult (≥18 years) patients before (December 2017-May 2018) and after (December 2018-May 2019) opening an EmPATH unit. These are outpatient hospital-based programs that provide emergent treatment and stabilization for mental health emergencies from ED patients. The Holt-Winters method was used to forecast pre-EmPATH expected ED levels of patients leaving without being seen, leaving against medical advice, eloping, or being transferred using 3 years of ED visits. ED revenues were calculated by finding the difference of pre-EmPATH expected and post-EmPATH observed values and multiplying by the revenue per visit. ED boarding time and LOS were obtained from the hospital's electronic medical record. Results There were 23,231 and 23,336 ED visits evaluated during the pre- and post-EmPATH unit periods. The ED generated an estimated additional $404,954 in the 6 months and $861,065 annually after the implementation of the EmPATH unit. The median (interquartile range [IQR]) psychiatric boarding time decreased from 212 (119-536) minutes to 152 (86-307) minutes (mean difference = 189 minutes, 95% confidence interval [CI] = 150 to 228 minutes) and median (IQR) LOS decreased from 351 (204-631) minutes to 334 (212-517) minutes (mean difference = 114 minutes, 95% CI = 87 to 143 minutes). Conclusion The EmPATH unit had a positive impact on ED revenue and decreased ED boarding time and LOS for psychiatric patients.

Ischemic stroke results from arterial occlusion and can cause irreversible brain injury. A non-human primate (NHP) model of ischemic stroke was previously developed to investigate its pathophysiology and for efficacy testing of therapeutic candidates; however, fine motor impairment remains to be well-characterized. We evaluated hand motor function in a cynomolgus monkey model of ischemic stroke. Endovascular transient middle cerebral artery occlusion (MCAO) with an angiographic microcatheter induced cerebral infarction. In vivo magnetic resonance imaging mapped and measured the ischemia-induced infarct lesion. In vivo diffusion tensor imaging (DTI) of the stroke lesion to assess the neuroplastic changes and fiber tractography demonstrated three-dimensional patterns in the corticospinal tract 12 weeks after MCAO. The hand dexterity task (HDT) was used to evaluate fine motor movement of upper extremity digits. The HDT was modified for a home cage-based training system, instead of conventional chair restraint training. The lesion was localized in the middle cerebral artery territory, including the sensorimotor cortex. Maximum infarct volume was exhibited over the first week after MCAO, which progressively inhibited ischemic core expansion, manifested by enhanced functional recovery of the affected hand over 12 weeks after MCAO. The total performance time decreased with increasing success rate for both hands on the HDT. Compensatory strategies and retrieval failure improved in the chronic phase after stroke. Our findings demonstrate the recovery of fine motor skill after stroke, and outline the behavioral characteristics and features of functional disorder of NHP stroke model, providing a basis for assessing hand motor function after stroke.

In this study, chemical characteristics of PM2.5 collected between October 1 and November 4, 2019 at Chungcheong region air quality research center in Seosan, were studied to understand reasons leading to PM2.5 increases. 24-hr integrated PM2.5 samples were analysed for organic and elemental carbon (OC and EC), water-soluble OC (WSOC), and eight water-soluble inorganic ions. Over the study period, increase in PM2.5 concentration was attributed to enhancement of organic mass (OM=1.8×OC) and NO₃- concentrations. Relationships of OC and WSOC with EC, K+, Cl-, and secondary NO₃- showed good-to-strong correlations, suggesting primary emissions (e.g., traffic and biomass burning sources) and secondary organic aerosol (OA) formation contributed to WSOC and OC concentrations. Moreover, high WSOC/OC (mean: 0.54) and OC/EC (mean: 9.0) ratios support the existence of various OA sources at the site. Four PM2.5 pollution episodes (October, 10 (event I), 17 (event II), 20~22 (event III), and November 02 (event IV)), which exceeded 24-hr PM2.5 Korean standard of 35 μg/m³, occurred and were related to concentration increases in OM and secondary ionic species. Based on synoptic weather patterns, PM2.5 forecast results, and air mass transport pathways, it was concluded that events I, II, and IV were significantly influenced by locally produced pollutions, while event III was impacted by both long-range transport of air pollutants and local emissions, with the highest concentration of SO₄2- (7.3 μg/m³). Among the four events, highest OM and NO₃- concentrations were found in the events IV and III, respectively. Results from this study suggest that strategies to reduce nitrogen oxides and organic aerosols in the study region are formulated to control PM2.5 levels.

Background Biphasic reaction rates and potential associated risk factors are not well understood. Objective To evaluate biphasic reaction rates and associated risk factors. Methods We prospectively enrolled patients with anaphylaxis at 2 Midwestern academic emergency departments (EDs). We gathered data using patient and ED provider surveys and a structured health record review. Biphasic reaction rates and clinically significant biphasic reaction rates, defined as recurrent reactions that met anaphylaxis diagnostic criteria or were treated with epinephrine, were calculated. Characteristics associated with biphasic reactions were assessed with logistic regression and reported with odds ratios (ORs) and 95% CIs. Results Of 430 ED anaphylaxis visits, 31 (7.2%) patients had biphasic reactions; 22 (5.1%) had clinically significant biphasic reactions. The median time from anaphylaxis onset to first epinephrine dose was longer for patients with biphasic (78 minutes) than uniphasic courses (45 minutes) (P = .005). A biphasic course was associated with an ED setting of first epinephrine dose (OR, 3.72; 95% CI, 1.36-10.14) and a delay of more than 30 minutes from symptom onset to first epinephrine dose (OR, 3.39; 95% CI, 1.13-10.18), and was inversely associated with arrival by ambulance (OR, 0.18; 95% CI, 0.05-0.61). A clinically significant biphasic reaction was associated with an ED setting of first epinephrine dose (OR, 3.32; 95% CI, 1.08-10.25) and inversely associated with arrival by ambulance (OR, 0.08; 95% CI, 0.01-0.61). Conclusions Biphasic reactions and clinically significant biphasic reactions occurred in 7.2% and 5.1% of ED anaphylaxis patients, respectively. Delayed epinephrine administration was associated with biphasic reactions.

Depression is clinically characterized by obvious changes in decision making that cause distress and impairment. Though several studies suggest impairments in depressed individuals in single tasks, there has been no systematic investigation of decision making in depression across tasks. We compare participants diagnosed with Major Depressive Disorder (MDD) (n = 64) to healthy controls (n = 64) using a comprehensive battery of nine value-based decision-making tasks which yield ten distinct measures. MDD participants performed worse on punishment (d = −0.54) and reward learning tasks (d = 0.38), expressed more pessimistic predictions regarding winning money in the study (d = −0.47) and were less willing to wait in a persistence task (d = −0.39). Performance on learning, expectation, and persistence tasks each loaded on unique dimensions in a factor analysis and punishment learning and future expectations each accounted for unique variance in predicting depressed status. Decision-making performance alone could predict depressed status out-of-sample with 72% accuracy. The findings are limited to MDD patients ranging between moderate to severe depression and the effects of medication could not be accounted for due to the cross sectional nature of the study design. These results confirm hints from single task studies that depression has the strongest effects on reinforcement learning and expectations about the future. Our results highlight the decision processes that are impacted in major depression, and whose further study could lead to a more detailed computational understanding of distinct facets of this heterogeneous disorder.

Background Delirium is an acute disorder of attention and cognition that is common, serious, costly, under-recognized, and potentially fatal. Delirium is particularly problematic in the emergency department (ED) care of medically complex older adults, who are being seen in greater numbers. Objective This evidence-based narrative review focuses on the key components of delirium screening, prevention, and treatment. Discussion The recognition of delirium requires a systematic approach rather than a clinical gestalt alone. Several delirium assessment tools with high sensitivity and specificity, such as delirium triage screen and brief Confusion Assessment Method, can be used in the ED. The prevention of delirium requires environmental modification and unique geriatric care strategies tailored to the ED. The key approaches to treatment include the removal of the precipitating etiology, re-orientation, hydration, and early mobilization. Treatment of delirium requires a multifaceted and comprehensive care plan, as there is limited evidence for significant benefit with pharmacological agents. Conclusion Older ED patients are at high risk for current or subsequent development of delirium, and a focused screening, prevention, and intervention for those who are at risk for delirium and its associated complications are the important next steps.

In Fractal Manufacturing System (FrMS), goal-orientation is solved by its distinguished technology, which is the goal-formation process (GFP). GFP consists of three procedures, which are goal-generation process (GGP), goal-harmonizing process (GHP), and goal-balancing process (GBP). However, in order to operate the goal-orientation technology, it requires the development of the defined goal model mechanism which can show the relationship between goals. FrMS requires a development of goal model in order to define mutual relationships between multiple objectives or goals such as performance, productivity, quality, flexibility, and so on. Therefore, this paper proposes the development of goal model for FrMS by analyzing former objectives or goals and obtain a new goal based on manufacturing dataset from mold industry. In order to develop the FrMS goal model, hierarchy in the reference goal model is identified and logics is developed for creation of bottom-leveled tasks in the reference model. After analyzing how their mutual relationships could affect the generation of goals and sub-goals, the initial goal model can be established and customized subjectively regarding to each company or factory’s preference. In order to establish initial goal model, support vector machine is used. The proposed goal model mechanism is developed to complete and support the goal-orientation technology by adjusting company’s goal.

Researchers often seek to decode mental states from brain activity measured with functional MRI. Rigorous decoding requires the use of formal neural prediction models, which are likely to be the most accurate if they use the whole brain. However, the computational burden and lack of interpretability of off-the-shelf statistical methods can make whole-brain decoding challenging. Here, we propose a method to build whole-brain neural decoders that are both interpretable and computationally efficient. We extend the partial least squares algorithm to build a regularized model with variable selection that offers a unique "fit once, tune later" approach: users need to fit the model only once and can choose the best tuning parameters post hoc. We show in real data that our method scales well with increasing data size and yields interpretable predictors. The algorithm is publicly available in multiple languages in the hope that interpretable whole-brain predictors can be implemented more widely in neuroimaging research.

Background: We conducted a secondary analysis of changes in the Kansas City Cardiomyopathy Questionnaire (KCCQ)-12 over 30 days in a randomized trial of self-care coaching versus structured usual care in patients with acute heart failure who were discharged from the emergency department. Methods: Patients in 15 emergency departments completed the KCCQ-12 at emergency department discharge and at 30 days. We compared change in KCCQ-12 scores between the intervention and usual care arms, adjusted for enrollment KCCQ-12 and demographic characteristics. We used linear regression to describe changes in KCCQ-12 summary scores and logistic regression to characterize clinically meaningful KCCQ-12 subdomain changes at 30 days. Results: There were 350 patients with both enrollment and 30-day KCCQ summary scores available; 166 allocated to usual care and 184 to the intervention arm. Median age was 64 years (interquartile range, 55–70), 37% were female participants, 63% were Black, median KCCQ-12 summary score at enrollment was 47 (interquartile range, 33–64). Self-care coaching resulted in significantly greater improvement in health status compared with structured usual care (5.4-point greater improvement, 95% CI, 1.12–9.68; P =0.01). Improvements in health status in the intervention arm were driven by improvements within the symptom frequency (adjusted odds ratio, 1.62 [95% CI, 1.01–2.59]) and quality of life (adjusted odds ratio, 2.39 [95% CI, 1.46–3.90]) subdomains. Conclusions: In this secondary analysis, patients with acute heart failure who received a tailored, self-care intervention after emergency department discharge had clinically significant improvements in health status at 30 days compared with structured usual care largely due to improvements within the symptom frequency and quality of life subdomains of the KCCQ-12. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02519283.

The evaluation of patients who have experienced a fall has been an integral part of geriatric emergency care. All physicians who engage in the care of the geriatric population in acute settings need to familiarize themselves with the current literature on this topic. However, it can be challenging to navigate the large body of literature on this topic. The purpose of this article is to identify and summarize the key studies that can be helpful for faculty interested in an evidence-based fall evaluation. The authors compiled a list of key papers on emergency department (ED) based upon a structured literature search supplemented with suggestions by key informants and an open call on social media; 32 studies on ED evaluation were identified. Our authorship group then engaged in a modified Delphi technique to develop consensus on the most important studies about fall evaluation for emergency physicians. This process eventually resulted in the selection of the top five articles on fall evaluation. Additionally, we summarize these studies with regard to their relevance to emergency medicine (EM) trainees and junior faculty. Evaluation of older patients with a history of falls is a challenging but crucial component of EM training. We believe our review will be educational for junior and senior EM faculty to better understand these patients' care and to design an evidence-based practice.

As machine learning (ML) and precision medicine become more readily available and used in practice, emergency physicians must understand the potential advantages and limitations of the technology. This narrative review focuses on the key components of machine learning, artificial intelligence, and precision medicine in emergency medicine (EM). Based on the content expertise, we identified articles from EM literature. The authors provided a narrative summary of each piece of literature. Next, the authors provided an introduction of the concepts of ML, artificial intelligence as an extension of ML, and precision medicine. This was followed by concrete examples of their applications in practice and research. Subsequently, we shared our thoughts on how to consume the existing research in these subjects and conduct high-quality research for academic emergency medicine. We foresee that the EM community will continue to adapt machine learning, artificial intelligence, and precision medicine in research and practice. We described several key components using our expertise.

OBJECTIVE: Delirium is a common and serious brain dysfunction. The objective of our study was to test the hypothesis that opioids and benzodiazepines exposure in the emergency department (ED) is associated with delirium. METHODS: This was a retrospective cohort study, including patients aged 65 years and older who were hospitalized from ED at an academic medical center from 2014 to 2017. Medication administration records were used to identify opioids and benzodiazepines given during the ED stay. Nurses used the Delirium Observation Screening Scale (DOSS) twice daily to assess delirium during hospitalization. The outcome was a positive DOSS within 1 day of ED encounter. We used logistic regression to predict the outcome of positive delirium screening by opioids and benzodiazepines. RESULTS: A total of 7927 ED encounters that resulted in hospitalization were included in the analysis. We identified 2008 visits (25.3%) with a positive delirium screen. A total of 3304 (41.7%) received opioids, and 1801 (22.7%) received benzodiazepines. In this cohort, opioids were not associated with an increased odds of delirium (OR 1.00, 95% CI 0.87–1.15). Benzodiazepines were associated with increased odds of delirium (OR 1.37, 95% CI 1.13–1.65), as were benzodiazepines combined with opioids (OR 1.61, 95% CI 1.33–1.97). CONCLUSION: In this study, the use of benzodiazepines was associated with a risk of delirium. The use of opioids did not increase the risk of delirium. Our findings imply that judicious pain management with opioids in the ED might not increase the risk of delirium.

Aim Angioedema is a nonpitting edema that can lead to death secondary to airway obstruction. Previously, a staging system based on localization of the angioedema was proposed for risk stratification of likelihood of need for admission or airway intervention. This study aims to evaluate a staging system based on angioedema localization as a method of predicting need for admission or airway intervention. Methods This was a retrospective chart review of angioedema cases that presented to an academic emergency department (ED) from August 1, 2006, to January 31, 2018. Data were collected on location of swelling, treatment setting, and medical and procedural interventions. Cases were categorized by modified Ishoo criteria, defined as follows: 1, lips, face, periorbital, extremities, total body/diffuse swelling; 2, soft palate, posterior pharynx; 3, tongue; 4, larynx. Predictive probability of disposition by stage was then compared. Results A total of 320 patients were included in this study (median age, 44 years; 54.4% female). Stage 4 was more likely to require intensive care unit care without (probability 17%) and with (67%) airway intervention compared with stage 1 without (2.5%) and with (0.1%) airway intervention. Conversely, stage 1 was more likely to be treated in ED and discharged (85%) compared with stage 4 (0%). Stage 4 was also more likely to require airway intervention (67%) compared with other stages (1, 0.1%; 2, 8.6%; 3, 16%). Conclusion Higher‐stage patients were more likely to require higher levels of care and airway intervention. Thus, the staging system appears to be a valid method of predicting risk among ED angioedema patients., We have evaluated the Ishoo criteria for angioedema in the emergency department and reported that they are valid predictors of disposition and need for airway intervention. By this, higher‐stage patients were more likely to require higher levels of care and airway intervention, suggesting that these staging criteria could be of benefit to the triage and management of patients presenting with angioedema.

We have previously developed a bispectral electroencephalography (BSEEG) device, which was shown to be effective in detecting delirium and predicting patient outcomes. In this study we aimed to apply the BSEEG approach for a sepsis. This was a retrospective cohort study conducted at a single center. Sepsis-positive cases were identified based on retrospective chart review. EEG raw data and calculated BSEEG scores were obtained in the previous studies. The relationship between BSEEG scores and sepsis was analyzed, as well as the relationship among sepsis, BSEEG score, and mortality. Data were analyzed from 628 patients. The BSEEG score from the first encounter (1st BSEEG) showed a significant difference between patients with and without sepsis (p = 0.0062), although AUC was very small indicating that it is not suitable for detection purpose. Sepsis patients with high BSEEG scores showed the highest mortality, and non-sepsis patients with low BSEEG scores showed the lowest mortality. Mortality of non-sepsis patients with high BSEEG scores was as bad as that of sepsis patients with low BSEEG scores. Even adjusting for age, gender, comorbidity, and sepsis status, BSEEG remained a significant predictor of mortality (p = 0.008). These data are demonstrating its usefulness as a potential tool for identification of patients at high risk and management of sepsis.

Nonhuman primate models are valuable in biomedical research. However, reference data for clinical pathology parameters in cynomolgus and rhesus monkeys are limited. In the present study, we established hematologic and biochemical reference intervals for healthy cynomolgus and rhesus monkeys anesthetized with ketamine hydrochloride. A total of 142 cynomolgus monkeys (28 males and 114 females) and 42 rhesus monkeys (22 males and 20 females) were selected and analyzed in order to examine reference intervals of 20 hematological and 16 biochemical parameters. The effects of sex were also investigated. Reference intervals for hematological and biochemical parameters were separately established by species (cynomolgus and rhesus) and sex (male and female). No sex-related differences were determined in erythrocyte-related parameters for cynomolgus and rhesus monkey housed in indoor laboratory conditions. Alkaline phosphatase and gamma glutamyltransferase were significantly lower in females than males in both cynomolgus and rhesus monkeys aged 48–96 months. The reference values for hematological and biochemical parameters established herein might provide valuable information for researchers using cynomolgus and rhesus monkeys in experimental conditions for biomedical studies.

Health informatics is a vital technology that holds great promise in the healthcare setting. We describe two prominent health informatics tools relevant to emergency care, as well as the historical background and the current state of informatics. We also identify recent research findings and practice changes. The recent advances in machine learning and natural language processing (NLP) are a prominent development in health informatics overall and relevant in emergency medicine (EM). A basic comprehension of machine-learning algorithms is the key to understand the recent usage of artificial intelligence in healthcare. We are using NLP more in clinical use for documentation. NLP has started to be used in research to identify clinically important diseases and conditions. Health informatics has the potential to benefit both healthcare providers and patients. We cover two powerful tools from health informatics for EM clinicians and researchers by describing the previous successes and challenges and conclude with their implications to emergency care.

In this study, the 24-h PM2.5 samples were collected from an urban site in Korea during summer and winter. The collected samples were analyzed for organic carbon (OC), elemental carbon (EC), water-soluble OC, humic-like substances (HULIS), and ionic species. Furthermore, in-situ measurements of PM2.5 aerosol absorption coefficients were made by using a dual-spot multi-wavelength Aethalometer. Primary traffic emissions and secondary formation were the major contributors to the HULIS concentration during summer. Furthermore, biomass burning (BB) contributed to HULIS concentration to a certain extent. During winter, the HULIS with higher OC/EC ratio (6.4) were strongly associated with BB and secondary formation, while the HULIS with lower OC/EC ratio (2.9) was mainly attributed to secondary formation and, to a certain extent, primary combustion sources. Light absorption by brown carbon (BrC) was significantly enhanced in the near-ultraviolet and visible spectral regions and exhibited strong spectral dependence. Ambient BrC absorption was estimated to account for 2.1–16.6% and 5.9–29.3% of the total aerosol absorption during summer and winter, respectively. The average values of absorption Angstrom exponent (AAE) in the wavelength range of 370–660 nm for light-absorbing BrC aerosols were estimated to be 4.0 and 4.4 during summer and winter, respectively. This study demonstrates that the AAE values for BrC can be derived from multi-wavelength Aethalometer data without filter sampling and detailed laboratory analysis.

Among emergency department (ED) mental health and substance abuse (MHSA) patients, we sought to compare mortality and healthcare utilization by ED discharge disposition and inpatient bed request status.A retrospective cohort study of 492 patients was conducted at a single University ED. We reviewed three groups of MHSA patients including ED patients that were admitted, ED patients with a bed request that were discharged from the ED, and ED patients with no bed request that were discharged from the ED. We identified main outcomes as ED return visit, re-hospitalization and mortality within 12months based on chart review and reference from the National Death Index.The average age of patients presenting was 30.5 (SD16.4) years and 251 (51.0%) were female patients. Of these patients, 216 (43.9%) presented with mood disorder and 93 (18.9%) with self-harm. The most common reason for discharge from the ED after an admission request was placed was from stabilization of the patient (n=138). An ED revisit within 12months was significantly higher among patients discharged who had a bed request in place prior to departure (54.0%, p0.001), than those discharged from the ED (40.9%) or admitted to inpatient care (30.5%). The rate of suicide attempt and death did not show statistical significance (p=0.55 and p=0.88).MHSA patients who were discharged from ED after bed requests were placed were at greater risk for return visits to the ED. This implicates that these patients require outpatient planning to prevent further avoidable healthcare utilization.

Aim Delirium is common and dangerous among elderly inpatients; yet, it is underdiagnosed and thus undertreated. This study aimed to test the diagnostic characteristics of a noninvasive point-of-care device with two-channel (bispectral) electroencephalography (EEG) for the screening of delirium in the hospital. Methods Patients admitted to the University of Iowa Hospitals and Clinics were assessed for the presence of delirium with a clinical assessment, the Confusion Assessment Method for Intensive Care Unit and Delirium Rating Scale. Subsequently, we obtained a 10-min bispectral EEG (BSEEG) recording from a hand-held electroencephalogram device during hospitalization. We performed power spectral density analysis to differentiate between those patients with and without delirium. Results Initially 45 subjects were used as a test dataset to establish a cut-off. The BSEEG index was determined to be a significant indicator of delirium, with sensitivity 80% and specificity 87.7%. An additional independent validation dataset with 24 patients confirmed the validity of the approach, with a sensitivity of 83.3% and specificity of 83.3%. Conclusion In this pilot study, the BSEEG method was able to distinguish delirious patients from non-delirious patients. Our data showed the feasibility of this technology for mass screening of delirium in the hospital.

Individual differences in impatience—how much we discount future compared to immediate rewards—are associated with general life outcomes and related to substance use, psychiatric diseases, and obesity. Here, we use machine-learning on fMRI activity during an intertemporal choice task to develop a brain marker of individual differences in delay discounting. Study 1 (N=110) was used as a training and cross-validation set, resulting in significant prediction accuracy (r = 0.49) and suggesting an interplay between brain regions associated with affect, value, and cognitive control. The validity of the brain marker was replicated in an independent data set (Study 2, N=145, r = 0.45). In both studies, responses of the marker significantly differed between overweight and lean individuals. This pattern is a first step towards a generalizable neuromarker of delay discounting and a potentially transdiagnostic phenotype, which can be used as a brain-based target measure in future studies.

IntroductionDelirium is a cerebral dysfunction seen commonly in the acute care setting. Delirium is associated with increased mortality and morbidity and is frequently missed in the emergency department (ED) by clinical gestalt alone. Identifying those at risk of delirium may help prioritize screening and interventions.ObjectiveOur objective was to identify clinically valuable predictive models for prevalent delirium within the first 24 hours of hospitalization based on the available data by assessing the performance of logistic regression and a variety of machine learning models.MethodsThis was a retrospective cohort study to develop and validate a predictive risk model to detect delirium using patient data obtained around an ED encounter. Data from electronic health records for patients hospitalized from the ED between January 1, 2014, and December 31, 2019, were extracted. Eligible patients were aged 65 or older, admitted to an inpatient unit from the emergency department, and had at least one DOSS assessment or CAM-ICU recorded while hospitalized. The outcome measure of this study was delirium within one day of hospitalization determined by a positive DOSS or CAM assessment. We developed the model with and without the Barthel index for activity of daily living, since this was measured after hospital admission.ResultsThe area under the ROC curves for delirium ranged from .69 to .77 without the Barthel index. Random forest and gradient-boosted machine showed the highest AUC of .77. At the 90% sensitivity threshold, gradient-boosted machine, random forest, and logistic regression achieved a specificity of 35%. After the Barthel index was included, random forest, gradient-boosted machine, and logistic regression models demonstrated the best predictive ability with respective AUCs of .85 to .86.ConclusionThis study demonstrated the use of machine learning algorithms to identify the combination of variables that are predictive of delirium within 24 hours of hospitalization from the ED.

Why do people discount future rewards? Multiple theories in psychology argue that future events are imagined less concretely than immediate events, thereby diminishing their perceived value. Here we provide neuroscientific evidence for this proposal. First, we construct a neural signature of the concreteness of prospective thought, using an fMRI dataset where the concreteness of imagined future events is orthogonal to their valence by design. Then, we apply this neural signature in two additional fMRI datasets, each using a different delay discounting task, to show that neural measures of concreteness decline as rewards are delayed farther into the future.Significance StatementPeople tend to devalue, or discount, outcomes in the future relative to those that are more immediate. This tendency is evident in people’s difficulty in making healthy food choices or saving money for retirement. Several psychological theories propose that discounting occurs because delayed outcomes are perceived less concretely that more immediate ones. Here we build a brain decoder for the concreteness of future thought and use this unobtrusive measure to show that outcomes are processed less concretely as they occur farther into the future.

Recent neuroimaging research has shown that it is possible to decode mental states and predict future consumer behavior from brain activity data (a time-series of images). However, the unique characteristics (and high dimensionality) of neuroimaging data, coupled with a need for neuroscientifically interpretable models, has largely discouraged the use of the entire brain’s data as predictors. Instead, most neuroscientific research uses “regionalized” (partial-brain) data to reduce the computational burden and to improve interpretability (i.e., localizability of signal), at the cost of losing potential information. Here we propose a novel approach that can build whole-brain neural decoders (using the entire data set and capitalizing on the full correlational structure) that are both interpretable and computationally efficient. We exploit analytical properties of the partial least squares algorithm to build a regularized regression model with variable selection that boasts (in contrast to most statistical methods) a unique ‘fit-once-tune-later’ approach where users need to fit the model only once and can choose the best tuning parameters post-hoc. We demonstrate its efficacy in a large neuroimaging dataset against off-the-shelf prediction methods and show that our new method scales exceptionally with increasing data size, yields more interpretable results, and uses less computational memory, while retaining high predictive power.

BackgroundThroughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available.ObjectiveThis systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19.MethodsSearches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and July 2020 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized.ResultsA primary review found 292 articles relevant based on title and abstract. After further review, 246 were excluded based on the defined inclusion and exclusion criteria. Forty-six articles were included in the qualitative analysis. Inter observer agreement on inclusion was 0.86 (95% confidence interval: 0.79 - 0.93). When the PROBAST tool was applied, 44 of the 46 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Two studied reported prediction models, 4C Mortality Score from hospital data and QCOVID from general public data from UK, and were rated as low risk of bias and low concerns for applicability.ConclusionSeveral prognostic models are reported in the literature, but many of them had concerning risks of biases and applicability. For most of the studies, caution is needed before use, as many of them will require external validation before dissemination. However, two articles were found to have low risk of bias and low applicability can be useful tools.

Deficits in motivation and pleasure are common across many psychiatric disorders, and manifest as symptoms of amotivation and anhedonia, which are prominent features of both mood and psychotic disorders. Here we provide evidence for a shared transdiagnostic mechanism underlying impairments in motivation and pleasure across major depression, bipolar disorder, and schizophrenia. We found that value signals in the ventromedial prefrontal cortex (vmPFC) during decision-making were dampened in individuals with greater motivational and hedonic deficits, regardless of the primary diagnosis. This relationship remained significant while controlling for diagnosis-specific symptoms of mood and psychosis, such as depression as well as positive and negative symptoms. Our results demonstrate that dysfunction in the vmPFC during value-based decision-making is specifically linked to motivational and hedonic impairments across various psychiatric conditions. These findings provide a quantitative neural target for the potential development of novel treatments for amotivation and anhedonia.

Particulate matter (PM) is a general atmospheric pollutant released into the air by an anthropogenic and naturally derived mixture of substances. Current studies indicate that fine dust can result in different health defects, including endothelial dysfunction, asthma, lung cancer, cardiovascular diseases, uterine leiomyoma, deterioration in sperm quality, and overall birth impairment. However, the most prominent effects of PM10(diameter < 10 μM) exposure on the female reproductive system, especially with respect to oocyte maturation, remain unclear. In the present study, maturing mouse oocytes were treated with PM10and the phenotypes of the resulting toxic effects were investigated. Exposure to PM10led to impairment of maturation capacity by inducing cell cycle arrest and blocking normal polar body extrusion duringin vitromaturation and activation of fertilization of mouse oocytes. Additionally, defects in tubulin formation and DNA alignment were observed in PM10-treated oocytes during metaphase I to anaphase/telophase I transition. Moreover, PM10induced reactive oxygen species generation, mitochondrial dysfunction, DNA damage, and early apoptosis. Taken together, these results indicate that PM10exposure leads to a decline in oocyte quality and affects the subsequent embryonic development potential of mammalian oocytes.

A few leading semiconductors recently released high performance products adopting 3-dimensional (3D) packaging technology moved to the forefront in the electronic packaging industry in order to meet the requirements of device performance and form factor driven by consumer electronics trends. Even after 3D commercial products were produced into electronic markets, the leading companies still struggle to demonstrate a competitive 3D packaging assembly process compared to traditional packaging assembly process. In the 3D packaging process, bonding technology among technical challenges is most problematic. The in situ bonding technology referred to as Thermal Compression Bonding (TCB), typically controls force, temperature, and displacement simultaneously, which are applied to packages when to reflow microbump solder interconnect of 3D TSV die. Using the in situ bonding technology, a bonding cycle can be completed in several seconds, which means that bonding equipment and process materials sustain heavy process stress due to rapidly changing thermal conditions. Thus, this chapter reviews the newly developed TCB technology and related assembly materials to provide engineering sciences, fundamentals of the bonding technology, and process materials for advanced application. First, this chapter compares the new technology with traditional assembly process regarding process, material, and equipment. Next, the subsection presents traditional analytical methods and practical fundamental investigation methods to characterize material formulation. Then, a comprehensive analysis is presented to provide the pros and cons of major assembly building blocks. Consequently, this chapter would help to understand how to design assembly building blocks that are adequate to the package configuration. In addition, this chapter introduces a hybrid bonding technology and alternative interconnect technology to overcome throughput challenge induced by TCB bonding mechanism used for 3D stacking.

Advanced operation scenarios such as high poloidal beta (β P) or high q min are promising concepts to achieve the steady-state high-performance fusion plasmas. However, those scenarios are prone to substantial Alfvénic activity, causing fast-ion transport and losses. Recent experiments with the advanced operation scenario on KSTAR tokamak have shown that the electron cyclotron current drive (ECCD) is able to mitigate and suppress the beam-ion driven toroidal Alfvén eigenmodes (TAEs) for over several tens of global energy confinement time. Co-current directional intermediate off-axis ECCD lowers the central safety factor slightly and tilts the central q-profile shape so that the continuum damping in the core region increases. Besides, the rise of central plasma pressure and increased thermal-ion Landau damping contribute to TAE stabilization. While the TAEs are suppressed, neutron emission rate and total stored energy increase by approximately 45% and 25%, respectively. Fast-ion transport estimated by TRANSP calculations approaches the classical level during the TAE suppression period. Substantial reduction in fast-ion loss and neutron deficit is also observed. Enhancement of fast-ion confinement by suppressing the TAEs leads to an increase of non-inductive current fraction and will benefit the sustainment of the long-pulse high-performance discharges.

Main text The CCQM-K10.2018 comparison aimed to evaluate the capabilities of national metrology institutes (NMIs) to prepare and/or value assign primary reference mixtures containing benzene, toluene, ethylbenzene, p-xylene, m-xylene and o-xylene (subsequently referred to as BTEX) in nitrogen. This key comparison was separated into two parallel studies. The first study, deemed the "gravimetric study", evaluated the level of compatibility of BTEX standards provided by NMIs. Seven participating laboratories each submitted one standard at nominal 5 nmol mol-1 BTEX in nitrogen. Comparison measurements were performed by the National Institute of Standards and Technology (NIST) from July to October 2018. The second study, deemed the "comparative study", compared the capabilities of NMIs to measure the amount fractions of BTEX in a mixture provided by NIST. NIST prepared one standard at nominal 5 nmol mol-1 BTEX in nitrogen, which was measured consecutively by three participating laboratories. The participants' measurements took place from August 2018 to March 2019. The comparison protocol adopted for CCQM-K10.2018 was more challenging than for the original comparison, CCQM-K10 in 2001, and the results provide an extended evaluation of the level of compatibility of BTEX in nitrogen standards. To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/. The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Main text CCQM-K118 was an international key comparison on natural gas composition with two types of gases, i.e., a low calorific hydrogen-enriched natural gas and a high calorific LNG type of gas. There were 14 participating laboratories. The traveling standards (i.e., 14 mixtures each) were obtained from an external source and checked for homogeneity and stability before and after the participants' measurements at the two coordinating laboratories. The data evaluation was performed using a consensus value and a laboratory effect model. The results of the participants were benchmarked against a key comparison reference value computed from the largest consistent subset (LCS) of the submitted results, adjusted for the differences between the travelling standards. For the first time in a key comparison in gas analysis, the model included a term to account for excess variability in the LCS. Most of the participants reported one or a few (slightly) discrepant results. Partly this is due to the heterogeneity and heteroscedasticity of the datasets. In all, the results in this key comparison demonstrate the good comparability of the national measurement standards for natural gas composition maintained by the participating NMIs. Main text. To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database kcdb.bipm.org/. The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (MRA). To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/. The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Symptoms of Parkinson's disease (PD) caused by loss of dopaminergic neurons are accompanied by movement disorders, including tremors, rigidity, bradykinesia, and akinesia. Non-human primate (NHP) models with PD play an essential role in the analysis of PD pathophysiology and behavior symptoms. As impairments of hand dexterity function can affect activities of daily living in patients with PD, research on hand dexterity function in NHP models with chronic PD is essential. Traditional rating scales previously used in the evaluation of animal spontaneous behavior were insufficient due to factors related to subjectivity and passivity. Thus, experimentally designed applications for an appropriate apparatus are necessary. In this study, we aimed to longitudinally assess hand dexterity function using hand dexterity task (HDT) in NHP-PD models. To validate this assessment, we analyzed the alteration in Parkinsonian tremor signs and the functionality of presynaptic dopaminergic neuron using positron emission tomography imaging of dopamine transporters in these models. In addition, a significant inverse correlation between HDT and DAT level was identified, but no local bias was found. The correlation with intention tremor signs was lower than the resting tremor. In conclusion, the evaluation of HDT may reflect behavioral symptoms of NHP-PD models. Furthermore, HDT was effectively used to experimentally distinguish intention tremors from other tremors.

This study investigated the clinical characteristics of alcohol use disorder (AUD) patients using a typology based on the MMPI-2 to compare patients with and without comorbid disorders. A total of 244 patients with AUD at an alcohol specialty hospital were categorized into the two groups. The noncomorbid group (

International audience; Here we demonstrate an unconventional fabrication of highly transparent supercapacitors and electrodes using random networks of nanostructured metallic glass nanotroughs for their integrations as wirelessly rechargeable and invisible, skin heat patches. Transparent supercapacitors with fine conductive patterns were printed using an electrohydrodynamic jet-printing. Also, transparent and stretchable electrodes, for wireless antennas, heaters and interconnects, were formed using random network based on nanostructured CuZr nanotroughs and Ag nanowires with superb optoelectronic properties (sheet resistance of 3.0 Ω/sq at transmittance of 91.1%). Their full integrations, as an invisible heat patch on skin, enabled the wireless recharge of supercapacitors and the functions of heaters for thermal therapy of skin tissue. The demonstration of this transparent thermotherapy patch to control the blood perfusion level and hydration rate of skin suggests a promising strategy toward next-generation wearable electronics.

Background/objectives Detecting delirium is important to identify patients with a high risk of poor outcomes. Although many different kinds of screening instruments for delirium exist, there is no solid consensus about which methods are the most effective. In addition, it is important to find the most useful tools in predicting outcomes such as mortality. Design Retrospective cohort study. Setting University of Iowa Hospitals and Clinics. Participants A total of 1,125 adult inpatients (mean age = 67.7; median age = 69). Measurements Post hoc analyses were performed based on existing data from the Confusion Assessment Method for Intensive Care Unit (CAM-ICU), Delirium Rating Scale-Revised-98 (DRS), and the Delirium Observation Screening Scale (DOSS). Correlation among these scales and relationships between 365-day mortality and each scale were evaluated. Results A positive result on the CAM-ICU ("CAM-ICU positive") was associated with higher DRS and DOSS scores. A DRS score = 9/10 was the best cutoff to detect CAM-ICU positive, and DOSS = 2/3 was the best cutoff to detect CAM-ICU positive. CAM-ICU positive was associated with high 365-day mortality. DRS score = 9/10 and DOSS score = 0/1 were found to differentiate mortality risk the most significantly. Higher DRS and DOSS scores significantly coincided with a decrease in a patient's survival rate at 365 days. Conclusion The best DRS and DOSS cutoff scores to differentiate 365-day mortality risk were lower than those commonly used to detect delirium in the literature. New cutoff scores for the DRS and DOSS might be useful in differentiating risk of mortality among hospital patients.

We previously reported the association between DNA methylation (DNAm) of pro-inflammatory cytokine genes and age. In addition, neurotrophic factors are known to be associated with age and neurocognitive disorders. Therefore, we hypothesized that DNAm of neurotrophic genes change with age, especially in delirium patients. DNAm were analyzed using the Illumina HumanMethylation450 or HumanMethylationEPIC BeadChip Kit in 3 independent cohorts: blood from 383 Grady Trauma Project subjects, brain from 21 neurosurgery patients, and blood from 87 inpatients with and without delirium. Both blood and brain samples showed that most of the DNAm of neurotrophic genes were positively correlated with age. Furthermore, DNAm of neurotrophic genes were more positively correlated with age in delirium cases than in non-delirium controls. These findings support our hypothesis that the neurotrophic genes may be epigenetically modulated with age, and this process may be contributing to the pathophysiology of delirium.

Recent work has shown that the brain’s default mode network (DMN) is active when people imagine the future. Here we test whether future imagination can be decomposed into two dissociable psychological processes linked to different subcomponents of the DMN. While measuring brain activity with fMRI as subjects imagine future events, we manipulate the vividness of these events to modulate the demands for scene construction, and we manipulate the valence of these events to modulate the demands for evaluation. We found that one subcomponent of the DMN, the ventral DMN or medial temporal lobe subsystem, responds to the vividness but not the valence of imagined events. In contrast, another subcomponent, the dorsal or core DMN, responds to the valence but not the vividness of imagined events. This separate modifiability of different subcomponents of the DMN by vividness and valence provides strong evidence for a neurocognitive dissociation between (1) the construction of novel, imagined scenes from individual components from memory and (2) the evaluation of these constructed events as desirable or undesirable.Significance StatementPrevious work has suggested that imagination may depend on separate neural networks involved in the construction and evaluation of imagined future events. This study provides strong neural evidence for this dissociation by demonstrating that two components of the brain’s default mode network (DMN) uniquely and specifically respond to different aspects of imagination. The vividness of imagined events modulates the ventral DMN, but not the dorsal DMN, while the valence of imagined events modulates the dorsal DMN, but not the ventral DMN. This supports the dissociable engagement of these sub-networks in constructing and evaluating imagined future events.

Botvinik-Nezer, Rotem et al., Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed., European Union’s Horizon 2020 research and innovation programme (https://ec.europa.eu/programmes/horizon2020/en) under the Marie Skłodowska-Curie grant agreement no. 665501. A. Eed was supported by a predoctoral fellowship La Caixa-Severo Ochoa from Obra Social La Caixa. S.B.E. was supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 785907 (HBP SGA2).