A bibliometric analysis of the knowledge related to mental health during and post COVID-19 pandemic

ObjectiveCOVID-19 led to a horrific global pandemic, with strict lockdowns and prolonged indoor stays increasing the risk of mental health problems, affecting people of different ages, genders, regions, and types of work to varying degrees. This study provides a bibliometric summary of the knowledge map related to mental health during and post COVID-19 pandemic.MethodsPublications related to mental health during and post COVID-19 pandemic were searched in the Web of Science Core Collection (WoSCC) database through March 19, 2024. After screening the search results, the literature included in the final was first quantitatively analyzed using GraphPad Prism software and then visualized using VOSviewer, CiteSpace, and R (the bibliometrix package).ResultsThe 7,047 publications from 110 countries were included, with the highest number of publications from China and the United States, and the number of publications related to mental health during and post the COVID-19 pandemic increased annually until 2023, after which it began to decline. The major institutions were University of Toronto, University of London, Harvard University, King’s College London, University College London, University of California System, University of Melbourne, Institut National De La Sante Et De La Recherche Medicale (Inserm), Mcgill University, and University of Ottawa; Frontiers in Psychiatry had the highest number of publications, and the Journal of Affective Disorders had the highest number of co-citations; 36,486 authors included, with Xiang, Yu-Tao, Cheung, Teris, Chung, Seockhoon published the most papers, and World Health Organization, Kroenke K, and Wang CY were the most co-cited; epidemiologically relevant studies on mental health related to COVID-19, and the importance of mental health during normalized epidemic prevention and control are the main directions of this research area, especially focusing on children’s mental health; “pandemic,” “sars-cov-2,” “epidemic,” “depression,” “coronavirus anxiety,” “anxiety,” “longitudinal,” “child,” “coronavirus anxiety,” “longitudinal,” “child,” and “coronavirus” are the top keywords in recent years.ConclusionThis comprehensive bibliometric study summarizes research trends and advances in mental health during and after the COVID-19 Pandemic. It serves as a reference for mental health research scholars during and after the COVID-19 pandemic, clarifying recent research preoccupations and topical directions

Multiple influence of immune cells in the bone metastatic cancer microenvironment on tumors

Bone is a common organ for solid tumor metastasis. Malignant bone tumor becomes insensitive to systemic therapy after colonization, followed by poor prognosis and high relapse rate. Immune and bone cells in situ constitute a unique immune microenvironment, which plays a crucial role in the context of bone metastasis. This review firstly focuses on lymphatic cells in bone metastatic cancer, including their function in tumor dissemination, invasion, growth and possible cytotoxicity-induced eradication. Subsequently, we examine myeloid cells, namely macrophages, myeloid-derived suppressor cells, dendritic cells, and megakaryocytes, evaluating their interaction with cytotoxic T lymphocytes and contribution to bone metastasis. As important components of skeletal tissue, osteoclasts and osteoblasts derived from bone marrow stromal cells, engaging in ‘vicious cycle’ accelerate osteolytic bone metastasis. We also explain the concept tumor dormancy and investigate underlying role of immune microenvironment on it. Additionally, a thorough review of emerging treatments for bone metastatic malignancy in clinical research, especially immunotherapy, is presented, indicating current challenges and opportunities in research and development of bone metastasis therapies

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BACKGROUND: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. METHODS: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING: Bill & Melinda Gates Foundation

Fatigue Life Prediction for Semi-Closed Noise Barrier of High-Speed Railway under Wind Load

The fatigue state of the semi-closed noise barrier directly affects driving safety, and replacement after damage leads to train delays and increased operating costs. It is more eco-friendly and sustainable to predict the fatigue life of noise barriers to reinforce the structure in time. However, previous life prediction methods provide a limited reference in the design stage. In this study, a novel fatigue life prediction method for noise barriers was proposed. The computational fluid dynamics and finite element model of the semi-closed noise barrier were established and subjected to simulated natural wind and train aerodynamic impulse wind loads to calculate the stress time-history on the noise barrier. Based on the rain flow counting method and Miner linear cumulative fatigue damage theory, the fatigue life of noise barriers in three Chinese cities was predicted. The results show that the fatigue life of the noise barrier is closely related to the wind conditions and train operation modes. Targeted reinforcement for noise barriers in different fatigue states can save materials and reduce maintenance workload. Moreover, the influence of wind load on the noise barrier was summarized, and engineering suggestions on prolonging the fatigue life of noise barriers were put forward

A rational delineation method for active land blocks on the southeastern margin of the Tibetan Plateau based on high-precision GNSS horizontal velocity fields

As one of the most geologically active regions on Earth, accurate delineation of active land blocks and analysis of crustal deformation characteristics in the southeastern margin of the Tibetan Plateau are crucial for understanding regional tectonic movements and assessing seismic hazards. Current studies in this region are limited and predominantly rely on single clustering methods. In this study, we proposed a general pipeline for active block delineation, which aggregated three clustering methods (K-means clustering, hierarchical clustering (HC), and density peak clustering (DPC)) to cluster the GNSS horizontal velocity field sites in the southeast margin of the Tibetan Plateau, and assessed the outcomes using the Davies–Bouldin Index (DBI) and Silhouette Coefficient to determine the optimal result. Subsequently, we calculated strain field parameters using GNSS horizontal velocity field data and analyzed seismic hazard combining historical earthquake data. By considering the stress and activity of fault zones, we refined the boundaries of active blocks and verified the reasonableness of the division. Finally, we divided the southeastern margin of the Tibetan Plateau into six land blocks: Bayan Kola, Qiangtang, South China, Southwest Yunnan, North Chuandian Subblock and South Chuandian Subblock. Analysis of strain field parameters revealed that the region is predominantly dilatational, transitioning from compressive to extensional states from west to east. The active zones of plate motion are mainly concentrated around specific fault zones around the Sichuan–Yunnan rhombic massif. Furthermore, we validated the generalization of the proposed pipeline in the other region, namely the Kazakhstan-Tienshan region. The clustering results were also consistent with the geologically determined block delineation model

Identification of the risk factors for insomnia in nurses with long COVID-19

Abstract Purpose To investigate the prevalence of insomnia among nurses with long COVID-19, analyze the potential risk factors and establish a nomogram model. Methods Nurses in Ningbo, China, were recruited for this study. General demographic information and insomnia, burnout, and stress assessment scores were collected through a face-to face questionnaire survey administered at a single center from March to May 2023. We used LASSO regression to identify potential factors contributing to insomnia. Then, a nomogram was plotted based on the model chosen to visualize the results and evaluated by receiver operating characteristic curves and calibration curves. Results A total of 437 nurses were recruited. 54% of the nurses had insomnia according to the Insomnia Severity Index (ISI) score. Eleven variables, including family structure, years of work experience, relaxation time, respiratory system sequelae, nervous system sequelae, others sequelae, attitudes toward COVID-19, sleep duration before infection, previous sleep problems, stress, and job burnout, were independently associated with insomnia. The R-squared value was 0.464, and the area under the curve was 0.866. The derived nomogram showed that neurological sequelae, stress, job burnout, sleep duration before infection, and previous sleep problems contributed the most to insomnia. The calibration curves showed significant agreement between the nomogram models and actual observations. Conclusion This study focused on insomnia among nurses with long COVID-19 and identified eleven risk factors related to nurses’ insomnia. A nomogram model was established to illustrate and visualize these factors, which will be instrumental in future research for identifying nurses with insomnia amid pandemic normalization and may increase awareness of the health status of healthcare workers with long COVID-19

Similarities and differences in dynamic properties of brain networks between internet gaming disorder and tobacco use disorder

Background: Internet gaming disorder (IGD) and tobacco use disorder (TUD) are two major addiction disorders that result in substantial financial loss. Identifying the similarities and differences between these two disorders is important to understand substance addiction and behavioral addiction. The current study was designed to compare these two disorders utilizing dynamic analysis. Method: Resting-state data were collected from 35 individuals with IGD, 35 individuals with TUD and 35 healthy controls (HCs). Dynamic coactivation pattern analysis was employed to decipher their dynamic patterns. Results: IGD participants showed decreased coactivation patterns within the default mode network (DMN) and between the DMN and the salience network (SN). The SN showed reduced coactivation patterns with the executive control network (ECN) and DMN, and the ECN showed decreased coactivation patterns with the DMN. In the TUD group, the DMN exhibited decreased coactivation patterns with the SN, the SN exhibited reduced coactivation patterns with the DMN and ECN, and the ECN showed decreased coactivation patterns with the DMN and within the ECN. Furthermore, the triple network model was fitted to the dynamic properties of the two addiction disorders. Decoding analysis results indicated that addiction-related memory and memory retrieval displayed similar dysfunctions in both addictions. Conclusion: The dynamic characteristics of IGD and TUD suggest that there are similarities in the dynamic features between the SN and DMN and differences in the dynamic features between the DMN and ECN. Our results revealed that the two addiction disorders have dissociable brain mechanisms, indicating that future studies should consider these two addiction disorders as having two separate mechanisms to achieve precise treatment for their individualized targets