Osteoporosis and stroke: a bidirectional mendelian randomization study.

Introduction: Numerous observational studies have identified a link between osteoporosis and stroke. However, the causal genetic relationship between these conditions remains unclear. This study employs a two-sample bidirectional Mendelian randomization (MR) approach to ascertain the causal relationship between osteoporosis and stroke.

Population and Co-Occurrence Characteristics of Diagnoses and Comorbidities in Coronary Artery Disease Patients: A Case Study from a Hospital in Guangxi, China.

Coronary artery disease (CAD) remains a major global health concern, significantly contributing to morbidity and mortality. This study aimed to investigate the co-occurrence patterns of diagnoses and comorbidities in CAD patients using a network-based approach. A retrospective analysis was conducted on 195 hospitalized CAD patients from a single hospital in Guangxi, China, with data collected on age, sex, and comorbidities.

Optimal adaptive nonpharmaceutical interventions to mitigate the outbreak of respiratory infections following the COVID-19 pandemic: a deep reinforcement learning study in Hong Kong, China.

Background: Long-lasting nonpharmaceutical interventions (NPIs) suppressed the infection of COVID-19 but came at a substantial economic cost and the elevated risk of the outbreak of respiratory infectious diseases (RIDs) following the pandemic. Policymakers need data-driven evidence to guide the relaxation with adaptive NPIs that consider the risk of both COVID-19 and other RIDs outbreaks, as well as the available healthcare resources.

Methods: Combining the COVID-19 data of the sixth wave in Hong Kong between May 31, 2022 and August 28, 2022, 6-year epidemic data of other RIDs (2014-2019), and the healthcare resources data, we constructed compartment models to predict the epidemic curves of RIDs after the COVID-19-targeted NPIs.

The Hub Genes Related to Osteoporosis Were Identified by Bioinformatics Analysis.

Osteoporosis (OP) is commonly encountered, which is a kind of systemic injury of bone mass and microstructure, leading to brittle fractures. With the aging of the population, this disease will pose a more serious impact on medical, social, and economic aspects, especially postmenopausal osteoporosis (PMOP). This study is aimed at figuring out potential therapeutic targets and new biomarkers in OP via bioinformatics tools.

Future trajectory of respiratory infections following the COVID-19 pandemic in Hong Kong.

The accumulation of susceptible populations for respiratory infectious diseases (RIDs) when COVID-19-targeted non-pharmaceutical interventions (NPIs) were in place might pose a greater risk of future RID outbreaks. We examined the timing and magnitude of RID resurgence after lifting COVID-19-targeted NPIs and assessed the burdens on the health system. We proposed the Threshold-based Control Method (TCM) to identify data-driven solutions to maintain the resilience of the health system by re-introducing NPIs when the number of severe infections reaches a threshold.

Role of Asymptomatic COVID-19 Cases in Viral Transmission: Findings From a Hierarchical Community Contact Network Model.

As part of ongoing efforts to contain the coronavirus disease (COVID-19) pandemic, understanding the role of asymptomatic patients in the transmission system is essential for infection control. However, the optimal approach to risk assessment and management of asymptomatic cases remains unclear. This study proposed a Susceptible, Exposed, Infectious, No symptoms, Hospitalized and reported, Recovered, Death (SEINRHD) epidemic propagation model.

Impacts of Export Restrictions on the Global Personal Protective Equipment Trade Network During COVID-19.

The COVID-19 pandemic has caused a dramatic surge in demand for personal protective equipment (PPE) worldwide. Many countries have imposed export restrictions on PPE to ensure the sufficient domestic supply. The surging demand and export restrictions cause shortage contagions on the global PPE trade network.

Human umbilical cord mesenchymal stromal cells promotes the proliferation and osteogenic differentiation of autologous bone marrow stem cells by secreting exosomes.

Fractures are frequently encountered diseases troubling the senior population, and the research on fracture repair and the exploration of effective treatment methods are of great significance. This study aimed to clarify the effect of human umbilical cord mesenchymal stromal cell-derived extracellular vesicles (hUMSC-EVs) on the proliferation and osteogenic differentiation of autologous bone marrow stem cells (ABMSCs). The two kinds of cells were co-cultured firstly, 5-Ethynyl-2'- deoxyuridine (EDU) staining and alizarin red staining were used to detect the proliferation and osteogenic differentiation of ABMSCs.

A Pan-Cancer Analysis on the Systematic Correlation of MutS Homolog 2 (MSH2) to a Malignant Tumor.

MutS homolog 2 (MSH2) is a crucial participant in human DNA repair, and lots of the studies functionally associated with it were begun with hereditary nonpolyposis colorectal cancer (HNPCC). MSH2 has also been reported to take part in the progresses of various tumors' formation. With the help of GTEx, CCLE, and TCGA pan-cancer databases, the analysis of MSH2 gene distribution in both tumor tissues and normal control tissues was carried out.

Equitable access to COVID-19 vaccines makes a life-saving difference to all countries.

Despite broad agreement on the negative consequences of vaccine inequity, the distribution of COVID-19 vaccines is imbalanced. Access to vaccines in high-income countries (HICs) is far greater than in low- and middle-income countries (LMICs). As a result, there continue to be high rates of COVID-19 infections and deaths in LMICs.

Intervertebral bridging ossification after kyphoplasty in a Parkinson's patient with Kummell's disease: A case report.

Background: The short-term therapeutic efficacy of kyphoplasty on Kummell's disease is obvious. However, postoperative refracture and adjacent vertebral fracture occur occasionally and are difficult to treat. Parkinson's disease (PD) is a pathological disorder associated with heterotopic ossification.

Reconstruction of the Transmission Chain of COVID-19 Outbreak in Beijing's Xinfadi Market, China.

Objectives: The aim of the study was to reconstruct the complete transmission chain of the COVID-19 outbreak in Beijing's Xinfadi Market using data from epidemiological investigations, which contributes to reflecting transmission dynamics and transmission risk factors.

Methods: We set up a transmission model, and the model parameters are estimated from the survey data via Markov chain Monte Carlo sampling. Bayesian data augmentation approaches are used to account for uncertainty in the source of infection, unobserved onset, and infection dates.

The Effect of miRNA-Modified Exosomes in Animal Models of Spinal Cord Injury: A meta-Analysis.

Spinal cord injury (SCI) is currently not completely curable. Exosomes have been widely used in preclinical studies of spinal cord injury. Here, in this meta-analysis, we focused on evaluating the overall efficacy of therapies based on miRNA-modified exosomes on functional recovery in animal models of SCI.

Mobility in China, 2020: a tale of four phases.

2020 was an unprecedented year, with rapid and drastic changes in human mobility due to the COVID-19 pandemic. To understand the variation in commuting patterns among the Chinese population across stable and unstable periods, we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020, ranging from the Lunar New Year travel season (), to the exceptional calm of COVID-19 lockdown, and then to the recovery period. We observed that cross-city movements, which increased substantially in and then dropped sharply during the lockdown, are primarily dependent on travel distance and the socio-economic development of cities.

Data science approaches to confronting the COVID-19 pandemic: a narrative review.

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale 'big data' generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development.

Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong.

Nonpharmaceutical interventions (NPIs) for contact suppression have been widely used worldwide, which impose harmful burdens on the well-being of populations and the local economy. The evaluation of alternative NPIs is needed to confront the pandemic with less disruption. By harnessing human mobility data, we develop an agent-based model that can evaluate the efficacies of NPIs with individualized mobility simulations.

Demand-driven spreading patterns of African swine fever in China.

African swine fever (ASF) is a highly contagious hemorrhagic viral disease of domestic and wild pigs. ASF has led to major economic losses and adverse impacts on livelihoods of stakeholders involved in the pork food system in many European and Asian countries. While the epidemiology of ASF virus (ASFV) is fairly well understood, there is neither any effective treatment nor vaccine.

Control strategies and their effects on the COVID-19 pandemic in 2020 in representative countries.

COVID-19 is the most severe pandemic globally since the 1918 influenza pandemic. Effectively responding to this once-in-a-century global pandemic is a worldwide challenge that the international community needs to jointly face and solve. This study reviews and discusses the key measures taken by major countries in 2020 to fight against COVID-19, such as lockdowns, social distancing, wearing masks, hand hygiene, using Fangcang shelter hospitals, large-scale nucleic acid testing, close-contacts tracking, and pandemic information monitoring, as well as their prevention and control effects.

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong.

Recent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables.

The impact of non-pharmaceutical interventions on the prevention and control of COVID-19 in New York City.

The emergence of coronavirus disease 2019 (COVID-19) has infected more than 62 million people worldwide. Control responses varied across countries with different outcomes in terms of epidemic size and social disruption. This study presents an age-specific susceptible-exposed-infected-recovery-death model that considers the unique characteristics of COVID-19 to examine the effectiveness of various non-pharmaceutical interventions (NPIs) in New York City (NYC).

A Dissemination Model Based on Psychological Theories in Complex Social Networks.

Information spread on social media has been extensively studied through both model-driven theoretical research and data-driven case studies. Recent empirical studies have analyzed the differences and complexity of information dissemination, but theoretical explanations of its characteristics from a modeling perspective are underresearched. To capture the complex patterns of the information dissemination mechanism, we propose a resistant linear threshold (RLT) dissemination model based on psychological theories and empirical findings.

The collective wisdom in the COVID-19 research: Comparison and synthesis of epidemiological parameter estimates in preprints and peer-reviewed articles.

Objectives: We aimed to explore the collective wisdom of preprints related to COVID-19 by comparing and synthesizing them with results of peer-reviewed publications.

Methods: PubMed, Google Scholar, medRxiv, bioRxiv, arXiv, and SSRN were searched for papers regarding the estimation of four epidemiological parameters of COVID-19: the basic reproduction number, incubation period, infectious period, and case-fatality-rate. Distributions of parameters and timeliness of preprints and peer-reviewed papers were compared.