A universal CRISPR/Cas12a-powered intelligent point-of-care testing platform for multiple small molecules in the healthcare, environment, and food.

Growing studies focusing on nuclear acid detection via the emerging CRISPR technique demonstrate its promising application. However, limited works solve the identification of non-nucleic acid targets, especially multiple small molecules. To address challenges for point-of-care testing (POCT) in complex matrices for healthcare, environment, and food safety, we developed CRISPR Cas12a-powered highly sensitive, high throughput, intelligent POCT (iPOCT) for multiple small molecules based on a smartphone-controlled reader. As a proof of concept, aflatoxin B1 (AFB1), benzo[a]pyrene (BaP), and capsaicin (CAP) were chosen as multiple targets. First, three antigens were preloaded in independent microwells. Then, the antibody/antigen-induced fluorescent signals were consecutively transferred from the biotin-streptavidin to CRISPR/Cas12a system. Third, the fluorescent signals were recorded by a smartphone-controlled handheld dark-box readout. Under optimization, detection limits in AFB1, BaP, and CAP were 0.00257, 4.971, and 794.6 fg/mL with wide linear ranges up to four orders of magnitude. Using urine, water, soybean oil, wheat, and peanuts as the complex matrix, we recorded high selectivity, considerable recovery, repeatability, and high consistency comparison to HPLC-MS/MS methods. This work promises a practical intelligent POCT platform for multiple targets in lipid-soluble and water-soluble matrices and could be extensively applied for healthcare, environment, and food safety.

Factors Influencing the Level of Depression and Anxiety of Community-Dwelling Patients with Schizophrenia in China during the COVID-19 Pandemic

The coronavirus disease 2019 (COVID-19) poses a huge challenge to global public health. People with schizophrenia living in communities urgently need effective interventions to help them adjust to life and work, but they have not received enough attention. This study aims to assess the prevalence of anxiety and depression symptoms in community-dwelling patients with schizophrenia in China during the epidemic and to explore the possible influencing factors. Methods: Using a cross-sectional survey, we collected 15,165 questionnaires. Assessments included demographic information, concern about COVID-19-related information, sleep status, anxiety and depressive symptoms, and accompanying illnesses. The 7-item Generalized Anxiety Disorder (GAD-7) and the 9-item Patient Health Questionnaire (PHQ-9) were used to evaluate depression and anxiety levels. Group comparison was conducted by t-test, ANOVA, or chi-square test wherever suitable, with Bonferroni pairwise correction. Multivariate logistic regression was performed to identify predictors for anxiety and depression. Results: 16.9% of patients had at least moderate anxiety, and 34.9% had at least moderate depression. T-test showed that females scored higher on GAD-7 and PHQ-9 than males, and patients without accompanying long-standing diseases, who were not concerned about COVID-19, had lower GAD-7 and PHQ-9 scores. ANOVA showed that participants aged from 30 to 39, with higher education scored higher on GAD-7, and patients with better sleep, and having less concern about COVID-19, had lower GAD-7 and PHQ-9 scores. Regression analysis indicated that participant ages of 30–39 and 40–49 positively predicted anxiety, whereas patient ages of 30–39 years positively predicted depression. Patients with poor sleep, accompanying diseases, and concern about the COVID-19 pandemic were more likely to experience anxiety and depression. Conclusion: During the pandemic, Chinese community-dwelling patients with schizophrenia had high rates of anxiety and depression. These patients deserve clinical attention and psychological intervention, especially those with risk factors.

Factors Influencing the Level of Depression and Anxiety of Community-Dwelling Patients with Schizophrenia in China during the COVID-19 Pandemic.

The coronavirus disease 2019 (COVID-19) poses a huge challenge to global public health. People with schizophrenia living in communities urgently need effective interventions to help them adjust to life and work, but they have not received enough attention. This study aims to assess the prevalence of anxiety and depression symptoms in community-dwelling patients with schizophrenia in China during the epidemic and to explore the possible influencing factors. METHODS: Using a cross-sectional survey, we collected 15,165 questionnaires. Assessments included demographic information, concern about COVID-19-related information, sleep status, anxiety and depressive symptoms, and accompanying illnesses. The 7-item Generalized Anxiety Disorder (GAD-7) and the 9-item Patient Health Questionnaire (PHQ-9) were used to evaluate depression and anxiety levels. Group comparison was conducted by t-test, ANOVA, or chi-square test wherever suitable, with Bonferroni pairwise correction. Multivariate logistic regression was performed to identify predictors for anxiety and depression. RESULTS: 16.9% of patients had at least moderate anxiety, and 34.9% had at least moderate depression. T-test showed that females scored higher on GAD-7 and PHQ-9 than males, and patients without accompanying long-standing diseases, who were not concerned about COVID-19, had lower GAD-7 and PHQ-9 scores. ANOVA showed that participants aged from 30 to 39, with higher education scored higher on GAD-7, and patients with better sleep, and having less concern about COVID-19, had lower GAD-7 and PHQ-9 scores. Regression analysis indicated that participant ages of 30-39 and 40-49 positively predicted anxiety, whereas patient ages of 30-39 years positively predicted depression. Patients with poor sleep, accompanying diseases, and concern about the COVID-19 pandemic were more likely to experience anxiety and depression. CONCLUSION: During the pandemic, Chinese community-dwelling patients with schizophrenia had high rates of anxiety and depression. These patients deserve clinical attention and psychological intervention, especially those with risk factors.

Predicting ozone formation in petrochemical industrialized Lanzhou city by interpretable ensemble machine learning.

Ground-level ozone (O3) formation depends on meteorology, precursor emissions, and atmospheric chemistry. Understanding the key drivers behind the O3 formation and developing an accurate and efficient method for timely assessing the O3-VOCs-NOx relationships applicable in different O3 pollution events are essential. Here, we developed a novel machine learning ensemble model coupled with a Shapley additive explanation algorithm to predict the O3 formation regime and derive O3 formation sensitivity curves. The algorithm was tested for O3 events during the COVID-19 lockdown, a sandstorm event, and a heavy O3 pollution episode (maximum hourly O3 concentration >200 µg/m3) from 2019 to 2021. We show that increasing O3 concentrations during the COVID-19 lockdown and the heavy O3 pollution event were mainly caused by the photochemistry subject to local air quality and meteorological conditions. Influenced by the sandstorm weather, low O3 levels were mainly attributable to weak sunlight and low precursor levels. O3 formation sensitivity curves demonstrate that O3 formation in the study area was in a VOCs-sensitive regime. The VOCs-specific O3 sensitivity curves can also help make hybrid and timely strategies for O3 abatement. The results demonstrate that machine learning driven by observational data has the potential to be a very useful tool in predicting and interpreting O3 formation.

3- to 24-month Follow-up on COVID-19 with Pulmonary Tuberculosis Survivors after Discharge: Results from a Prospective, Multicenter Study.

Objective: Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are major public health and social issues worldwide. The long-term follow-up of COVID-19 with pulmonary TB (PTB) survivors after discharge is unclear. This study aimed to comprehensively describe clinical outcomes, including sequela and recurrence at 3, 12, and 24 months after discharge, among COVID-19 with PTB survivors. Methods: From January 22, 2020 to May 6, 2022, with a follow-up by August 26, 2022, a prospective, multicenter follow-up study was conducted on COVID-19 with PTB survivors after discharge in 13 hospitals from four provinces in China. Clinical outcomes, including sequela, recurrence of COVID-19, and PTB survivors, were collected via telephone and face-to-face interviews at 3, 12, and 24 months after discharge. Results: Thirty-two COVID-19 with PTB survivors were included. The median age was 52 (45, 59) years, and 23 (71.9%) were men. Among them, nearly two-thirds (62.5%) of the survivors were moderate, three (9.4%) were severe, and more than half (59.4%) had at least one comorbidity (PTB excluded). The proportion of COVID-19 survivors with at least one sequela symptom decreased from 40.6% at 3 months to 15.8% at 24 months, with anxiety having a higher proportion over a follow-up. Cough and amnesia recovered at the 12-month follow-up, while anxiety, fatigue, and trouble sleeping remained after 24 months. Additionally, one (3.1%) case presented two recurrences of PTB and no re-positive COVID-19 during the follow-up period. Conclusion: The proportion of long symptoms in COVID-19 with PTB survivors decreased over time, while nearly one in six still experience persistent symptoms with a higher proportion of anxiety. The recurrence of PTB and the psychological support of COVID-19 with PTB after discharge require more attention.

Optimization of potential non-covalent inhibitors for the SARS-CoV-2 main protease inspected by a descriptor of the subpocket occupancy.

The main protease is regarded as an essential drug target for treating Coronavirus Disease 2019. In the present study, 13 marketed drugs were investigated to explore the possible binding mechanism, utilizing molecular docking, molecular dynamics simulation, and MM-PB(GB)SA binding energy calculations. Our results suggest that fusidic acid, polydatin, SEN-1269, AZD6482, and UNC-2327 have high binding affinities of more than 23 kcal mol-1. A descriptor was defined for the energetic occupancy of the subpocket, and it was found that S4 had a low occupancy of less than 10% on average. The molecular optimization of ADZ6482 via reinforcement learning algorithms was carried out to screen out three lead compounds, in which slight structural changes give more considerable binding energies and an occupancy of the S4 subpocket of up to 43%. The energetic occupancy could be a useful descriptor for evaluating the local binding affinity for drug design.

Industry herding in crypto assets

The aim of this paper is to investigate if herd behaviour is present in crypto assets at industry level. Using price information extracted from coinmarketcap.com between 29 April 2013 and 9 May 2022, we find evidence of herding and reverse herding in the crypto assets market. Concentrated periods of herding and reverse herding are particularly evident in the January 2020–April 2022 Covid period. At industry level, herding is more profound in large sectors with higher volatility. In smaller sectors where ventures are backed by ‘real assets’, very short periods of herding with marginal significance are detected. Reverse herding is present in all industries except Real Estate between June 2021 and May 2022, implying that strategies such as excessive ‘flight to quality’ or/and token picking are at play during the recent crypto crash. We also detect varying asymmetric herding at industry level. This paper further examines the factors that drive such industry herding and reverse herding in the crypto assets market, and our results show that industry concentration and investor sentiments contribute to the probability of herding/reverse herding. Our study provides further insights to the forces that drive the dispersion in crypto assets prices and contribute to the behavioural studies of the crypto market.

Histones released by NETosis enhance the infectivity of SARS-CoV-2 by bridging the spike protein subunit 2 and sialic acid on host cells.

Neutrophil extracellular traps (NETs) can capture and kill viruses, such as influenza viruses, human immunodeficiency virus (HIV), and respiratory syncytial virus (RSV), thus contributing to host defense. Contrary to our expectation, we show here that the histones released by NETosis enhance the infectivity of SARS-CoV-2, as found by using live SARS-CoV-2 and two pseudovirus systems as well as a mouse model. The histone H3 or H4 selectively binds to subunit 2 of the spike (S) protein, as shown by a biochemical binding assay, surface plasmon resonance and binding energy calculation as well as the construction of a mutant S protein by replacing four acidic amino acids. Sialic acid on the host cell surface is the key molecule to which histones bridge subunit 2 of the S protein. Moreover, histones enhance cell-cell fusion. Finally, treatment with an inhibitor of NETosis, histone H3 or H4, or sialic acid notably affected the levels of sgRNA copies and the number of apoptotic cells in a mouse model. These findings suggest that SARS-CoV-2 could hijack histones from neutrophil NETosis to promote its host cell attachment and entry process and may be important in exploring pathogenesis and possible strategies to develop new effective therapies for COVID-19.

Three doses of prototypic SARS-CoV-2 inactivated vaccine induce cross-protection against its variants of concern.

Variants are globally emerging very quickly following pandemic prototypic SARS-CoV-2. To evaluate the cross-protection of prototypic SARS-CoV-2 vaccine against its variants, we vaccinated rhesus monkeys with three doses of prototypic SARS-CoV-2 inactivated vaccine, followed by challenging with emerging SARS-CoV-2 variants of concern (VOCs). These vaccinated animals produced neutralizing antibodies against Alpha, Beta, Delta, and Omicron variants, although there were certain declinations of geometric mean titer (GMT) as compared with prototypic SARS-CoV-2. Of note, in vivo this prototypic vaccine not only reduced the viral loads in nasal, throat and anal swabs, pulmonary tissues, but also improved the pathological changes in the lung infected by variants of Alpha, Beta, and Delta. In summary, the prototypic SARS-CoV-2 inactivated vaccine in this study protected against VOCs to certain extension, which is of great significance for prevention and control of COVID-19.

Practice and experience of regional medical center entrance linkage and closed-loop management under the wartime situation of the COVID-19 in China

On 28 July 2021, the first indigenous case of novel coronavirus pneumonia (COVID-19) emerged in Yangzhou, marking the beginning of a public health crisis caused by the new coronavirus pneumonia. It is a significant challenge for hospitals to carry out prevention and control measures to ensure the safety of medical professionals and patients when facing the changes in an epidemic situation. Subei People’s Hospital, as one of the first group of “Grade III-class A” hospitals in Jiangsu Province and the Yangzhou Regional Medical Centre, responded quickly and scientifically to prevent and control the disease. A closed-loop management system was implemented at the hospital entrance (consisting of the outpatient clinic, emergency clinic, fever clinic, and buffer ward) and an epidemic prevention and control group was established with the assistance of multiple departments. This group optimized the pre-screening and triage system, standardized the fever clinic consultation process, and improved the construction of an information-based prevention and control network so that patients were detected, diagnosed, isolated, and treated early. The emergency management capability was improved to achieve zero missed consultations of patients attending for COVID-19 and to effectively maintain medical order during this critical period. This current report systematically summarizes the operational practices and the effectiveness achieved by implementation of the entrance closed-loop management in the hospital and analyzed the key operational issues for future reference by medical institutions and management departments.

Machine learning and algorithmic fairness in public and population health

Until now, much of the work on machine learning and health has focused on processes inside the hospital or clinic. However, this represents only a narrow set of tasks and challenges related to health;there is greater potential for impact by leveraging machine learning in health tasks more broadly. In this Perspective we aim to highlight potential opportunities and challenges for machine learning within a holistic view of health and its influences. To do so, we build on research in population and public health that focuses on the mechanisms between different cultural, social and environmental factors and their effect on the health of individuals and communities. We present a brief introduction to research in these fields, data sources and types of tasks, and use these to identify settings where machine learning is relevant and can contribute to new knowledge. Given the key foci of health equity and disparities within public and population health, we juxtapose these topics with the machine learning subfield of algorithmic fairness to highlight specific opportunities where machine learning, public and population health may synergize to achieve health equity.Algorithmic solutions to improve treatment are starting to transform health care. Mhasawade and colleagues discuss in this Perspective how machine learning applications in population and public health can extend beyond clinical practice. While working with general health data comes with its own challenges, most notably ensuring algorithmic fairness in the face of existing health disparities, the area provides new kinds of data and questions for the machine learning community.

Can situations awaken emotions? The compilation and evaluation of the Emotional Situation Sentence System (ESSS).

We aimed to establish and evaluate a standardized emotional situation sentence system (ESSS) relevant to the lives of college students to supplement prior literature and adapt to the needs of emotional research. Two studies were designed for this research; study 1 examined the effect of words in the ESSS and study 2 involved the use of pictures. For Study 1, 778 items were selected by 607 college students and 15 experts. We then tested the scale with 80 undergraduate participants. The ESSS sentences were rated on their degree of valence, arousal, and dominance using a 9-point scale. Cronbach's α (greater than 0.986) of the overall score as well as each sub-score in the three components confirmed the scale's reliability. As seen on a scatter plot, the results suggest that negative emotions (fear, disgust, anger, sadness, anxiety) are convergent and different from the distribution of positive (happiness) and neutral emotions. Study 2 included 30 participants to compare the difference in valence and arousal between the ESSS and emotional pictures. The results indicate that the ESSS is a standardized, situational, and ecological emotional contextual text system, well-suited to invoke emotion in college students. The ESSS has significantly better arousal and potency than pictures; moreover, it can be applied to experimental studies of anxiety-related emotions. However, emotion pictures have shorter response times, and wider application ranges, and they can include more cross-cultural characteristics compared to words.

Unsupervised PM2.5 anomalies in China induced by the COVID-19 epidemic.

To stop the spread of COVID-19 (2019 novel coronavirus), China placed lockdown on social activities across China since mid-January 2020. The government actions significantly affected emissions of atmospheric pollutants and unintentionally created a nationwide emission reduction scenario. In order to assess the impacts of COVID-19 on fine particular matter (PM2.5) levels, we developed a "conditional variational autoencoder" (CVAE) algorithm based on the deep learning to discern unsupervised PM2.5 anomalies in Chines cities during the COVID-19 epidemic. We show that the timeline of changes in number of cities with unsupervised PM2.5 anomalies is consistent with the timeline of WHO's response to COVID-19. Using unsupervised PM2.5 anomaly as a time node, we examine changes in PM2.5 before and after the time node to assess the response of PM2.5 to the COVID-19 lockdown. The rate of decrease of PM2.5 around the time node in northern China is 3.5 times faster than southern China, and decreasing PM2.5 levels in southern China is 3.5 times of that in northern China. Results were also compared with anomalous PM2.5 occurring in Chinese's Spring Festival from 2017 to 2019, PM2.5 anomalies during around Chinese New Year in 2020 differ significantly from 2017 to 2019. We demonstrate that this method could be used to detect the response of air quality to sudden changes in social activities.

The olfactory route is a potential way for SARS-CoV-2 to invade the central nervous system of rhesus monkeys.

Neurological manifestations are frequently reported in the COVID-19 patients. Neuromechanism of SARS-CoV-2 remains to be elucidated. In this study, we explored the mechanisms of SARS-CoV-2 neurotropism via our established non-human primate model of COVID-19. In rhesus monkey, SARS-CoV-2 invades the CNS primarily via the olfactory bulb. Thereafter, viruses rapidly spread to functional areas of the central nervous system, such as hippocampus, thalamus, and medulla oblongata. The infection of SARS-CoV-2 induces the inflammation possibly by targeting neurons, microglia, and astrocytes in the CNS. Consistently, SARS-CoV-2 infects neuro-derived SK-N-SH, glial-derived U251, and brain microvascular endothelial cells in vitro. To our knowledge, this is the first experimental evidence of SARS-CoV-2 neuroinvasion in the NHP model, which provides important insights into the CNS-related pathogenesis of SARS-CoV-2.

Telemedicine and healthcare disparities: a cohort study in a large healthcare system in New York City during COVID-19.

OBJECTIVE: Through the coronavirus disease 2019 (COVID-19) pandemic, telemedicine became a necessary entry point into the process of diagnosis, triage, and treatment. Racial and ethnic disparities in healthcare have been well documented in COVID-19 with respect to risk of infection and in-hospital outcomes once admitted, and here we assess disparities in those who access healthcare via telemedicine for COVID-19. MATERIALS AND METHODS: Electronic health record data of patients at New York University Langone Health between March 19th and April 30, 2020 were used to conduct descriptive and multilevel regression analyses with respect to visit type (telemedicine or in-person), suspected COVID diagnosis, and COVID test results. RESULTS: Controlling for individual and community-level attributes, Black patients had 0.6 times the adjusted odds (95% CI: 0.58-0.63) of accessing care through telemedicine compared to white patients, though they are increasingly accessing telemedicine for urgent care, driven by a younger and female population. COVID diagnoses were significantly more likely for Black versus white telemedicine patients. DISCUSSION: There are disparities for Black patients accessing telemedicine, however increased uptake by young, female Black patients. Mean income and decreased mean household size of a zip code were also significantly related to telemedicine use. CONCLUSION: Telemedicine access disparities reflect those in in-person healthcare access. Roots of disparate use are complex and reflect individual, community, and structural factors, including their intersection-many of which are due to systemic racism. Evidence regarding disparities that manifest through telemedicine can be used to inform tool design and systemic efforts to promote digital health equity.

The Gastrointestinal Tract Is an Alternative Route for SARS-CoV-2 Infection in a Nonhuman Primate Model.

BACKGROUND & AIMS: Gastrointestinal (GI) manifestations have been increasingly reported in patients with coronavirus disease 2019 (COVID-19). However, the roles of the GI tract in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are not fully understood. We investigated how the GI tract is involved in SARS-CoV-2 infection to elucidate the pathogenesis of COVID-19. METHODS: Our previously established nonhuman primate (NHP) model of COVID-19 was modified in this study to test our hypothesis. Rhesus monkeys were infected with an intragastric or intranasal challenge with SARS-CoV-2. Clinical signs were recorded after infection. Viral genomic RNA was quantified by quantitative reverse transcription polymerase chain reaction. Host responses to SARS-CoV-2 infection were evaluated by examining inflammatory cytokines, macrophages, histopathology, and mucin barrier integrity. RESULTS: Intranasal inoculation with SARS-CoV-2 led to infections and pathologic changes not only in respiratory tissues but also in digestive tissues. Expectedly, intragastric inoculation with SARS-CoV-2 resulted in the productive infection of digestive tissues and inflammation in both the lung and digestive tissues. Inflammatory cytokines were induced by both types of inoculation with SARS-CoV-2, consistent with the increased expression of CD68. Immunohistochemistry and Alcian blue/periodic acid-Schiff staining showed decreased Ki67, increased cleaved caspase 3, and decreased numbers of mucin-containing goblet cells, suggesting that the inflammation induced by these 2 types of inoculation with SARS-CoV-2 impaired the GI barrier and caused severe infections. CONCLUSIONS: Both intranasal and intragastric inoculation with SARS-CoV-2 caused pneumonia and GI dysfunction in our rhesus monkey model. Inflammatory cytokines are possible connections for the pathogenesis of SARS-CoV-2 between the respiratory and digestive systems.