Resting-state functional connectome predicts individual differences in depression during COVID-19 pandemic.

Stressful life events are significant risk factors for depression, and increases in depressive symptoms have been observed during the COVID-19 pandemic. The aim of this study is to explore the neural makers for individuals' depression during COVID-19, using connectome-based predictive modeling (CPM). Then we tested whether these neural markers could be used to identify groups at high/low risk for depression with a longitudinal dataset. The results suggested that the high-risk group demonstrated a higher level and increment of depression during the pandemic, as compared to the low-risk group. Furthermore, a support vector machine (SVM) algorithm was used to discriminate major depression disorder patients and healthy controls, using neural features defined by CPM. The results confirmed the CPM's ability for capturing the depression-related patterns with individuals' resting-state functional connectivity signature. The exploration for the anatomy of these functional connectivity features emphasized the role of an emotion-regulation circuit and an interoception circuit in the neuropathology of depression. In summary, the present study augments current understanding of potential pathological mechanisms underlying depression during an acute and unpredictable life-threatening event and suggests that resting-state functional connectivity may provide potential effective neural markers for identifying susceptible populations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Additional Value of Non-contrast Chest CT in the Prediction of Adverse Cardiovascular Events in Patients With Novel Coronavirus Disease 2019 (COVID-19).

Background: Coronavirus disease 2019 (COVID-19) has outbroken in China and subsequently spread worldwide since the end of 2019. Chest computed tomography (CT) plays an important role in the diagnosis of lung diseases, but its value in the diagnosis of cardiac injury remains unknown. Methods: We enrolled 241 consecutive hospitalized patients (aged 61 ± 16 years, 115 males) with laboratory-confirmed COVID-19 at Renmin Hospital of Wuhan University from January 11 to March 2, 2020. They were divided into two groups according to whether major adverse cardiovascular events (MACEs) occurred during the follow-up. The anteroposterior diameter of the left atrium (LAD), the length of the left ventricle (LV), and cardiothoracic ratio (CTR) were measured. The values of myocardial CT were also recorded. Results: Of 241 patients, 115 patients (47.7%) had adverse cardiovascular events. Compared with no MACEs, patients with MACEs were more likely to have bilateral lesions (95.7% vs. 86.5%, p = 0.01). In multivariable analysis, bronchial wall thickening would increase the odds of MACEs by 13.42 (p = 0.01). LAD + LV and CTR was the best predictor for MACEs (area under the curve = 0.88, p < 0.001) with a sensitivity of 82.6% and a specificity of 80.2%. Plasma high-sensitivity troponin I levels in patients with cardiac injury showed a moderate negative correlation with minimum CT value (R 2 = -0.636, p < 0.001). Conclusions: Non-contrast chest CT can be a useful modality for detection cardiac injury and provide additional value to predict MACEs in COVID-19 patients.

The prognostic value of myocardial injury in COVID-19 patients and associated characteristics.

BACKGROUND: Since December 2019, coronavirus disease 2019 (COVID-19) has emerged as an international pandemic. COVID-19 patients with myocardial injury might need special attention. However, an understanding on this aspect remains unclear. This study aimed to illustrate clinical characteristics and the prognostic value of myocardial injury to COVID-19 patients. METHODS: This retrospective, single-center study finally included 304 hospitalized COVID-19 cases confirmed by real-time reverse-transcriptase polymerase chain reaction from January 11 to March 25, 2020. Myocardial injury was determined by serum high-sensitivity troponin I (Hs-TnI). The primary endpoint was COVID-19-associated mortality. RESULTS: Of 304 COVID-19 patients (median age, 65 years; 52.6% males), 88 patients (27.3%) died (61 patients with myocardial injury, 27 patients without myocardial injury on admission). COVID-19 patients with myocardial injury had more comorbidities (hypertension, chronic obstructive pulmonary disease, cardiovascular disease, and cerebrovascular disease); lower lymphocyte counts, higher C-reactive protein (CRP; median, 84.9 vs. 28.5 mg/L; p < .001), procalcitonin levels (median, 0.29 vs. 0.06 ng/ml; p < .001), inflammatory and immune response markers; more frequent need for noninvasive ventilation, invasive mechanical ventilation; and was associated with higher mortality incidence (hazard ratio [HR] = 7.02; 95% confidence interval [CI], 4.45-11.08; p < .001) than those without myocardial injury. Myocardial injury (HR = 4.55; 95% CI, 2.49-8.31; p < .001), senior age, CRP levels, and novel coronavirus pneumonia types on admission were independent predictors to mortality in COVID-19 patients. CONCLUSIONS: COVID-19 patients with myocardial injury on admission is associated with more severe clinical presentation and biomarkers. Myocardial injury and higher Hs-TnI are both strongest independent predictors to COVID-19-related mortality after adjusting confounding factors.

Functional Connectome Prediction of Anxiety Related to the COVID-19 Pandemic.

OBJECTIVE: Increased anxiety in response to the COVID-19 pandemic has been widely noted. The purpose of this study was to test whether the prepandemic functional connectome predicted individual anxiety induced by the pandemic. METHODS: Anxiety scores from healthy undergraduate students were collected during the severe and remission periods of the pandemic (first survey, February 22-28, 2020, N=589; second survey, April 24 to May 1, 2020, N=486). Brain imaging data and baseline (daily) anxiety ratings were acquired before the pandemic. The predictive performance of the functional connectome on individual anxiety was examined using machine learning and was validated in two external undergraduate student samples (N=149 and N=474). The clinical relevance of the findings was further explored by applying the connectome-based neuromarkers of pandemic-related anxiety to distinguish between individuals with specific mental disorders and matched healthy control subjects (generalized anxiety disorder, N=43; major depression, N=536; schizophrenia, N=72). RESULTS: Anxiety scores increased from the prepandemic baseline to the severe stage of the pandemic and remained high in the remission stage. The prepandemic functional connectome predicted pandemic-related anxiety and generalized to the external sample but showed poor performance for predicting daily anxiety. The connectome-based neuromarkers of pandemic-related anxiety further distinguished between participants with generalized anxiety and healthy control subjects but were not useful for diagnostic classification in major depression and schizophrenia. CONCLUSIONS: These findings demonstrate the feasibility of using the functional connectome to predict individual anxiety induced by major stressful events (e.g., the current global health crisis), which advances our understanding of the neurobiological basis of anxiety susceptibility and may have implications for developing targeted psychological and clinical interventions that promote the reduction of stress and anxiety.

Is SARS-CoV-2 vaccination safe and effective for elderly individuals with neurodegenerative diseases?

INTRODUCTION: Coronavirus Disease 2019 (COVID-19) poses a substantial threat to the lives of the elderly, especially those with neurodegenerative diseases, and vaccination against viral infections is recognized as an effective measure to reduce mortality. However, elderly patients with neurodegenerative diseases often suffer from abnormal immune function and take multiple medications, which may complicate the role of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines. Currently, there is no expert consensus on whether SARS-CoV-2 vaccines are suitable for patients with neurodegenerative diseases. AREAS COVERED: We searched Pubmed to conduct a systematic review of published studies, case reports, reviews, meta-analyses, and expert guidelines on the impact of SARS-CoV-2 on neurodegenerative diseases and the latest developments in COVID-19 vaccines. We also summarized the interaction between vaccines and age-related neurodegenerative diseases. The compatibility of future SARS-CoV-2 vaccines with neurodegenerative diseases is discussed. EXPERT OPINION: Vaccines enable the body to produce immunity by activating the body's immune response. The pathogenesis and treatment of neurodegenerative diseases is complex, and these diseases often involve abnormal immune function, which can substantially affect the safety and effectiveness of vaccines. In short, this article provides recommendations for the use of vaccine candidates in patients with neurodegenerative diseases.

The functional connectome predicts feeling of stress on regular days and during the COVID-19 pandemic.

Although many studies have explored the neural mechanism of the feeling of stress, to date, no effort has been made to establish a model capable of predicting the feeling of stress at the individual level using the resting-state functional connectome. Although individuals may be confronted with multidimensional stressors during the coronavirus disease 2019 (COVID-19) pandemic, their appraisal of the impact and severity of these events might vary. In this study, connectome-based predictive modeling (CPM) with leave-one-out cross-validation was conducted to predict individual perceived stress (PS) from whole-brain functional connectivity data from 817 participants. The results showed that the feeling of stress could be predicted by the interaction between the default model network and salience network, which are involved in emotion regulation and salience attribution, respectively. Key nodes that contributed to the prediction model comprised regions mainly located in the limbic systems and temporal lobe. Critically, the CPM model of PS based on regular days can be generalized to predict individual PS levels during the COVID-19 pandemic, which is a multidimensional, uncontrollable stressful situation. The stability of the results was demonstrated by two independent datasets. The present work not only expands existing knowledge regarding the neural mechanism of PS but also may help identify high-risk individuals in healthy populations.

Prevention and control of COVID-19 in nursing homes, orphanages, and prisons.

As the number of Coronavirus Disease (2019) (COVID-19) cases increase globally, countries are taking more aggressive preventive measures against this pandemic. Transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) include droplet and contact transmissions. There are also evidence of transmission through aerosol generating procedures (AGP) in specific circumstances and settings. Institutionalized populations without mobility and living in close proximity with unavoidable contact are especially vulnerable to higher risks of COVID-19 infection, such as the elderly in nursing homes, children in orphanages, and inmates in prisons. In these places, higher prevention and control measures are needed. In this study, we proposed prevention and control strategies for these facilities and provided practical guidance for general measures, health management, personal protection measures, and prevention measures in nursing homes, orphanages, and prisons, respectively.

Disinfection technology of hospital wastes and wastewater: Suggestions for disinfection strategy during coronavirus Disease 2019 (COVID-19) pandemic in China.

Hospitals are important sources of pollutants resulted from diagnostic, laboratory and research activities as well as medicine excretion by patients, which include active component of drugs and metabolite, chemicals, residues of pharmaceuticals, radioactive markers, iodinated contrast media, etc. The discharge of hospital wastes and wastewater, especially those without appropriate treatment would expose the public in danger of infection. In particular, under the Coronavirus Disease 2019 (COVID-19) pandemic context in China, it is of great significance to reduce the health risks to the public and environment. In this study, technologies of different types of hospital wastes and wastewater disinfection have been summarized. Liquid chlorine, sodium hypochlorite, chlorine dioxide, ozone, and ultraviolet irradiation disinfection are commonly used for hospital wastewater disinfection. While incineration, chemical disinfection, and physical disinfection are commonly used for hospital wastes disinfection. In addition, considering the characteristics of various hospital wastes, the classification and selection of corresponding disinfection technologies are discussed. On this basis, this study provides scientific suggestions for management, technology selection, and operation of hospital wastes and wastewater disinfection in China, which is of great significance for development of national disinfection strategy for hospital wastes and wastewater during COVID-19 pandemic.

The role of imaging in 2019 novel coronavirus pneumonia (COVID-19).

Almost the entire world, not only China, is currently experiencing the outbreak of a novel coronavirus that causes respiratory disease, severe pneumonia, and even death. The outbreak began in Wuhan, China, in December of 2019 and is currently still ongoing. This novel coronavirus is highly contagious and has resulted in a continuously increasing number of infections and deaths that have already surpassed the SARS-CoV outbreak that occurred in China between 2002 and 2003. It is now officially a pandemic, announced by WHO on the 11th of March. Currently, the 2019 novel coronavirus (SARS-CoV-2) can be identified by virus isolation or viral nucleic acid detection; however, false negatives associated with the nucleic acid detection provide a clinical challenge and thus make the imaging examination crucial. Imaging exams have been a main clinical diagnostic criteria for the 2019 novel coronavirus disease (COVID-19) in China. Imaging features of multiple patchy areas of ground glass opacity and consolidation predominately in the periphery of the lungs are characteristic manifestations on chest CT and extremely helpful in the early detection and diagnosis of this disease, which aids prompt diagnosis and the eventual control of this emerging global health emergency. Key Points • In December 2019, China, an outbreak of pneumonia caused by a novel, highly contagious coronavirus raised grave concerns and posed a huge threat to global public health. • Among the infected patients, characteristic findings on CT imaging include multiple, patchy, ground-glass opacity, crazy-paving pattern, and consolidation shadows, mainly distributed in the peripheral and subpleural areas of both lungs, which are very helpful for the frontline clinicians. • Imaging examination has become the indispensable means not only in the early detection and diagnosis but also in monitoring the clinical course, evaluating the disease severity, and may be presented as an important warning signal preceding the negative RT-PCR test results.