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n the notion of performance monitoring event-related potentials as transdiagnostic neural risk markers and highlight the relevance of stress as a catalyst for symptom development.

Clinical reports from across the world have documented psychosis in the context of COVID-19 infection; however, there has yet to be a large-scale epidemiological study to confirm this association.

We analyzed data from the Healthy Minds Study (

= 15,935; conducted between September and December 2020), which was administered online to students attending one of 28 colleges in the United States. Using multivariable logistic regression, we examined the associations between COVID-19 infection/severity and psychotic experiences over the past 12 months, adjusting for age, gender, race/ethnicity, and international student status as well as anxiety and depression.

More than one fifth of the analytic sample reported COVID-19 infection, and about one in six students with COVID-19 infection reported psychotic experiences over the past 12 months. In weighted multivariable logistic regression models, COVID-19 infection was associated with significantly greater odds of having psychotic experiences (adjusted odds ratie students should be replicated outside of the college context to determine whether psychosis is a neuropsychiatric symptom during and after COVID-19 infection.

People with mental disorders and intellectual disabilities experience excess mortality compared with the general population. The impact of COVID-19 on exacerbating this, and in widening ethnic inequalities, is unclear.

Prospective data (N=167,122) from a large mental healthcare provider in London, UK, with deaths from 2019 to 2020, used to assess age- and gender-standardised mortality ratios (SMRs) across nine psychiatric conditions (schizophrenia-spectrum disorders, affective disorders, somatoform/ neurotic disorders, personality disorders, learning disabilities, eating disorders, substance use disorders, pervasive developmental disorders, dementia) and by ethnicity.

Prior to the World Health Organization (WHO) declaring COVID-19 a public health emergency on 30th January 2020, all-cause SMRs across all psychiatric cohorts were more than double the general population. By the second quarter of 2020, when the UK experienced substantial peaks in COVID-19 deaths, all-cause SMRs increased further, with COVID-19 SMRs elevated across all conditions (notably learning disabilities SMR 9.24 (95% CI 5.98-13.64), pervasive developmental disorders 5.01 (95% CI 2.40-9.20), eating disorders 4.81 (95% CI 1.56-11.22), schizophrenia-spectrum disorders 3.26 (95% CI 2.55-4.10), dementia 3.82 (95% CI 3.42, 4.25) personality disorders 4.58 (95% CI 3.09-6.53)). Deaths from other causes remained at least double the population average over the whole year. Increased SMRs were similar across ethnic groups.

People with mental disorders and intellectual disabilities were at a greater risk of deaths relative to the general population before, during and after the first peak of COVID-19 deaths, with similar risks by ethnicity. Mortality from non-COVID-19/ other causes was elevated before/ during the pandemic, with higher COVID-19 mortality during the pandemic.

ESRC (JD, CM), NIHR (JD, RS, MH), Health Foundation (JD), GSK, Janssen, Takeda (RS).

ESRC (JD, CM), NIHR (JD, RS, MH), Health Foundation (JD), GSK, Janssen, Takeda (RS).The rapid spread of COVID-19 resulted in various public lockdowns across the globe. Previous studies showed that resultant travel restrictions improved air quality. The novel results presented here focus on source-specific changes and compare air quality for multiple years controlled for precipitation. This study sought to analyze air pollution changes in Pittsburgh, a city where an industrial past and present has led to elevated levels of particulate matter with representative diameter of ≤ 2.5μm (PM2.5). Data from the Allegheny County Health Department, from monitors located near a variety of site types, were analyzed with generalized linear models that used a gamma distribution with a log link to determine the magnitude and significance of changes in air pollution during the COVID-19 lockdown. The hypothesis was that nitrogen dioxide (NO2), which is primarily linked to vehicular traffic, would decrease significantly while potential decreases in particulate matter (PM2.5 and PM10) would be less apparent. Results of the regression models showed that NO2 was significantly reduced during lockdown at both monitoring sites and that PM10 was also significantly reduced at the majority of monitoring sites. However, decreases in PM2.5 pollution were only observed at half of the monitoring locations, and the location which observed the greatest decreases is located adjacent to an industrial source. Decreases in PM2.5 at this monitoring site were likely a result of reduced industrial processes both dependent and independent of the COVID-19 lockdown. This study suggests that industrial sources are a larger contributor of particulate matter than vehicular transportation in the city of Pittsburgh and that future air pollution reduction efforts should focus attention on emission reduction at these industrial facilities.Global development has been heavily reliant on the overexploitation of natural resources since the Industrial Revolution. With the extensive use of fossil fuels, deforestation, and other forms of land-use change, anthropogenic activities have contributed to the ever-increasing concentrations of greenhouse gases (GHGs) in the atmosphere, causing global climate change. In response to the worsening global climate change, achieving carbon neutrality by 2050 is the most pressing task on the planet. To this end, it is of utmost importance and a significant challenge to reform the current production systems to reduce GHG emissions and promote the capture of CO2 from the atmosphere. Herein, we review innovative technologies that offer solutions achieving carbon (C) neutrality and sustainable development, including those for renewable energy production, food system transformation, waste valorization, C sink conservation, and C-negative manufacturing. The wealth of knowledge disseminated in this review could inspire the global community and drive the further development of innovative technologies to mitigate climate change and sustainably support human activities.Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.Atherosclerosis is the predominant cause of coronary artery disease. S(-)-Propranolol supplier The last several decades have witnessed significant advances in lipid-lowering therapies, which comprise a central component of atherosclerotic cardiovascular disease prevention. In addition to cardiovascular risk reduction with dyslipidemia management, some lipid-based therapies show promise at the level of the atherosclerotic plaque itself through mechanisms governing lipid accumulation, plaque stability, local inflammation, endothelial dysfunction, and thrombogenicity. The capacity of lipid-lowering therapies to modify atherosclerotic plaque burden, size, composition, and vulnerability should correlate with their ability to reduce disease progression. This review discusses plaque characteristics, diagnostic modalities to evaluate these characteristics, and how they are altered by current and emerging lipid-lowering therapies, all in human coronary artery disease.

Shared decision-making (SDM), one of the pillars of patient centered care is strongly encouraged and has been incorporated into the management of atherosclerotic cardiovascular disease (ASCVD) but the expansion of its use has been limited.

To determine the association of SDM on patient-reported health status, measures of quality of care, healthcare resource utilization, and healthcare spending among US adults with ASCVD.

This is a retrospective cohort study in an ambulatory setting, utilizing the Medical Expenditure Panel Survey (MEPS) 2006-2015. Analysis completed in December 2020. Participants included were adults 18 years and over with a diagnosis of ASCVD. We used the average weighted response to self-administered questionnaire evaluating shared-decision-making process as the exposure variable in the regression model. Outcome measures included inpatient hospitalizations, Emergency Department (ED) visits, statin and aspirin use, self-perception of health, and healthcare expenditure.

When compared with individuals reporting poor SDM, those with optimal SDM were more likely to report statin and aspirin use [statin use, Odds Ratio (OR) 1.26 (95% CI, 1.09-1.46)], [aspirin use, 1.25 (1.07-1.45)], more likely to have a positive perception of their health and healthcare related quality of life, and were less likely to visit the ED [OR for ≥ 2 ED visits 0.81 (0.67-0.99)]. There was no difference between groups in annual total or out of pocket healthcare expenditure.

This study suggests that effective SDM is associated with better utilization of healthcare resources and patient reported health outcomes. We hope these results could provide useful evidence for expanding the use of SDM in patient-centered care among individuals with ASCVD.

This study suggests that effective SDM is associated with better utilization of healthcare resources and patient reported health outcomes. We hope these results could provide useful evidence for expanding the use of SDM in patient-centered care among individuals with ASCVD.

In cases of lung tumors that occur after treatment for malignancies in other organs, the tumor may represent either a primary lung cancer or a solitary pulmonary metastasis from the other tumor. Because some lung tumors are difficult to differentiate on the basis of imaging and pathologic findings, treatment selection is often difficult. In this study, we attempted to make a genomic diagnosis of primary and metastatic lung tumors by analyzing tumor samples using next-generation sequencing and evaluated the efficacy and validity of the genomic diagnosis.

A total of 24 patients with a solitary lung nodule and a history of other malignancies were enrolled in this study. Tumor cells were selected from tissue samples using laser capture microdissection. DNA was extracted from those cells and subjected to targeted deep sequencing of 53 genes.

The driver mutation profiles of the primary lung tumors were discordant from those of the primary tumors in other sites, whereas the mutation profiles of pulmonary metastases and previous malignancies were concordant.