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. The exosomes of hUMSCs were subsequently extracted to process ABMSCs to further test the effect on the cells. The EDU positive rate of ABMSCs and Collagen II expression were elevated, whereas the TdT-mediated dUTP nick end labeling (TUNEL) positive rate and Matrix Metallopeptidase 13 (MMP13) were markedly decreased after the co-culture of hUMSCs and ABMSCs using Transwell chamber assays. The results indicated that hUMSCs could increase the proliferation of ABMSCs, reduce apoptosis, and promote matrix metabolism. The hUMSCs exosomes were separated and added to ABMSCs. As the exosomes content increased, the proliferation of ABMSCs increased simultaneously, and ABMSCs apoptosis decreased. Meanwhile, ABMSCs that migrated to the submembrane increased compared with untreated ABMSCs. Western blot, qPCR and immunofluorescence results revealed that increased exosomes contents promoted the expression of ABMSCs anabolic-related indicators gradually, while decreased the expression of catabolism-related indicators gradually. The previously described results indicated that hUMSCs promoted the proliferation and osteogenic differentiation of ABMSCs by secreting exosomes.

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 population 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. Based on the model, we propose data-driven targeted interventions to mitigate the COVID-19 pandemic in Hong Kong without city-wide NPIs. We develop a data-driven agent-based model for 7.55 million Hong Kong residents to evaluate the efficacies of various NPIs in the first 80 days of the initial outbreak. The entire territory of Hong Kong is split into 4,905 500m x 500m grids. The model can simulate detailed agent interactions based on the demographics data, public facilities and functional buildings, transportation systems, and travel patterns. The general daily human mobility patterns are adopted from Googles Community Mobility Report. The scenario without any NPIs is set as the baseline. By simulating the epidemic progression and human movement at the individual level, we proposed model-driven targeted interventions, which focus on the surgical testing and quarantine of only a small portion of regions instead of enforcing NPIs in the whole city. The efficacious of common NPIs and the proposed targeted interventions are evaluated by extensive 100 simulations. The proposed model can inform targeted interventions, which are able to effectively contain the COVID-19 outbreak with much lower disruption of the city. It represents a promising approach to sustainable NPIs to help us revive the economy of the city and the world.

Renin-angiotensin system blockers and mortality in COVID-19: a territory-wide study from Hong Kong

AimsRenin-angiotensin system blockers such as angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) may increase the risk of adverse outcomes in COVID-19. In this study, the relationships between ACEI/ARB use and COVID-19 related mortality were examined. MethodsConsecutive patients diagnosed with COVID-19 by RT-PCR at the Hong Kong Hospital Authority between 1st January and 28th July 2020 were included. ResultsThis study included 2774 patients. The mortality rate of the COVID-19 positive group was 1.5% (n=42). Those who died had a higher median age (82.3[76.5-89.5] vs. 42.9[28.2-59.5] years old; P<0.0001), more likely to have baseline comorbidities of cardiovascular disease, diabetes mellitus, hypertension, and chronic kidney disease (P<0.0001). They were more frequently prescribed ACEI/ARBs at baseline, and steroids, lopinavir/ritonavir, ribavirin and hydroxychloroquine during admission (P<0.0001). They also had a higher white cell count, higher neutrophil count, lower platelet count, prolonged prothrombin time and activated partial thromboplastin time, higher D-dimer, troponin, lactate dehydrogenase, creatinine, alanine transaminase, aspartate transaminase and alkaline phosphatase (P<0.0001). Multivariate Cox regression showed that age, cardiovascular disease, renal disease, diabetes mellitus, the use of ACEIs/ARBs and diuretics, and various laboratory tests remained significant predictors of mortality. ConclusionsWe report that an association between ACEIs/ARBs with COVID-19 related mortality even after adjusting for cardiovascular and other comorbidities, as well as medication use. Patients with greater comorbidity burden and laboratory markers reflecting deranged clotting, renal and liver function, and increased tissue inflammation, and ACEI/ARB use have a higher mortality risk. Key PointsO_LIWe report that an association between ACEIs/ARBs with COVID-19 related mortality even after adjusting for cardiovascular and other comorbidities, as well as medication use. C_LIO_LIPatients with greater comorbidity burden and laboratory markers reflecting deranged clotting, renal and liver function, and increased tissue inflammation, and ACEI/ARB use have a higher mortality risk. C_LI

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 37 million people worldwide. The control responses varied across countries with different outcomes in terms of epidemic size and social disruption. In this study, we presented 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). Numerical experiments from our model show that the control policies implemented in NYC reduced the number of infections by 72% (IQR 53-95), and the number of deceased cases by 76% (IQR 58-96) by the end of 2020, respectively. Among all the NPIs, social distancing for the entire population and the protection for the elderly in the public facilities is the most effective control measure in reducing severe infections and deceased cases. School closure policy may not work as effectively as one might expect in terms of reducing the number of deceased cases. Our simulation results provide novel insights into the city-specific implementation of NPIs with minimal social disruption considering the locations and population characteristics.

Role of asymptomatic COVID-19 cases in viral transmission: Findings from a hierarchical community contact network model

BackgroundAs part of on-going efforts to contain the COVID-19 pandemic, understanding the role of asymptomatic patients in the transmission system is essential to infection control. However, optimal approach to risk assessment and management of asymptomatic cases remains unclear. MethodsThis study involved a SEINRHD epidemic propagation model, constructed based on epidemiological characteristics of COVID-19 in China, accounting for the heterogeneity of social network. We assessed epidemic control measures for asymptomatic cases on three dimensions. Impact of asymptomatic cases on epidemic propagation was examined based on the effective reproduction number, abnormally high transmission events, and type and structure of transmission. ResultsManagement of asymptomatic cases can help flatten the infection curve. Tracking 75% of asymptomatic cases corresponds to an overall reduction in new cases by 34.3% (compared to tracking no asymptomatic cases). Regardless of population-wide measures, family transmission is higher than other types of transmission, accounting for an estimated 50% of all cases. ConclusionsAsymptomatic case tracking has significant effect on epidemic progression. When timely and strong measures are taken for symptomatic cases, the overall epidemic is not sensitive to the implementation time of the measures for asymptomatic cases.

Development of a predictive risk model for severe COVID-19 disease using population-based administrative data

BackgroundRecent studies have reported numerous significant predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk score for prompt risk stratification. The objective is to develop a simple risk score for severe COVID-19 disease using territory-wide healthcare data based on simple clinical and laboratory variables. MethodsConsecutive patients admitted to Hong Kongs public hospitals between 1st January and 22nd August 2020 diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8th September 2020. ResultsCOVID-19 testing was performed in 237493 patients and 4445 patients (median age 44.8 years old, 95% CI: [28.9, 60.8]); 50% male) were tested positive. Of these, 212 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, hypertension, stroke, diabetes mellitus, ischemic heart disease/heart failure, respiratory disease, renal disease, increases in neutrophil count, monocyte count, sodium, potassium, urea, alanine transaminase, alkaline phosphatase, high sensitive troponin-I, prothrombin time, activated partial thromboplastin time, D-dimer and C-reactive protein, as well as decreases in lymphocyte count, base excess and bicarbonate levels. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. ConclusionsA simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results.

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

SummaryO_ST_ABSBackgroundC_ST_ABSResearch papers related to COVID-19 have exploded. We aimed to explore the academic value of preprints through comparing with peer-reviewed publications, and synthesize the parameter estimates of the two kinds of literature. MethodWe collected papers regarding the estimation of four key epidemiological parameters of the COVID-19 in China: the basic reproduction number (R0), incubation period, infectious period, and case-fatality-rate (CFR). PubMed, Google Scholar, medRxiv, bioRxiv, arRxiv, and SSRN were searched by 20 March, 2020. Distributions of parameters and timeliness of preprints and peer-reviewed papers were compared. Further, four parameters were synthesized by bootstrap, and their validity was verified by susceptible-exposed-infectious-recovered-dead-cumulative (SEIRDC) model based on the context of China. Findings106 papers were included for analysis. The distributions of four parameters in two literature groups were close, despite that the timeliness of preprints was better. Four parameter estimates changed over time. Synthesized estimates of R0 (3{middle dot}18, 95% CI 2{middle dot}85-3{middle dot}53), incubation period (5{middle dot}44 days, 95% CI 4{middle dot}98-5{middle dot}99), infectious period (6{middle dot}25 days, 95% CI 5{middle dot}09-7{middle dot}51), and CFR (4{middle dot}51%, 95% CI 3{middle dot}41%-6{middle dot}29%) were obtained from the whole parameters space, all with p<0{middle dot}05. Their validity was evaluated by simulated cumulative cases of SEIRDC model, which matched well with the onset cases in China. InterpretationPreprints could reflect the changes of epidemic situation sensitively, and their academic value shouldnt be neglected. Synthesized results of literatures could reduce the uncertainty and be used for epidemic decision making. FundingThe National Natural Science Foundation of China and Beijing Municipal Natural Science Foundation. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSSince its outbreak, scientific articles about the COVID-19 have greatly surged, with a significant portion as non-peer-reviewed preprints. Although preprints captured great attention, the credibility of preprints was widely debated. We searched PubMed and Google on March 20, 2020, for publications that discussed the preprints during the COVID-19 pandemic, using the terms ("preprints" AND "COVID-19"). We identified 12 papers and news, and found that scientists were skeptical of preprints mainly because rigorous peer review is absent and thus the conclusions of preprints may not be reliable. However, scientists opinions could have been biased towards limited data, and there is few knowledges about the validity of the results reported in the preprints. Further, to examine how scientists utilize results of preprints, taking the epidemiological parameter estimation as the objects, we searched reviews on Google using the terms ("epidemiology" AND ("meta-analysis" OR "reviews") AND "COVID-19") on May 23, 2020. Nine papers were identified. We found that existing meta-analysis and reviews included few preprints. This may be due to the fact that the quality of preprints was not recognized, and thus their academic value was underestimated. Overall, the validity of the results as reported in the preprints should be further examined and the potential of synthesizing preprints with formally published papers should be explored. Added value of this studyOur study adds value in four main ways. First, we collected preprints and peer-reviewed papers on estimations of the four most important epidemiological parameters (the basic reproduction number, incubation period, infectious period, and case-fatality-rate) for the COVID-19 outbreak in China. 106 papers were included and available data were extracted. Second, we quantitatively compared the differences and timeliness between preprints and peer-reviewed publications in the estimation of the four parameters, and found that the validity of the preprints estimations was largely consistent with that of the peer-reviewed group. Third, we synthesized the estimations of the two groups of literatures using bootstrap method, and found that the values of infectious period and case-fatality-rate decreased over time, indicating that the synthesized results timely reflected the changing trend of the COVID-19 in China. Finally, the practicability of the synthesized parameter estimations was verified by the data of confirmed cases in China. The cumulative infection curve simulated using synthesized parameters fitted the real data well. Implications of all the available evidenceResults of our study indicate that the validity of the COVID-19 parameter estimations of the preprints is on par with that of peer-reviewed publications, and the preprints are relatively timelier. Further, the synthesized parameters of the two literature groups can effectively reduce the uncertainty and capture the patterns of epidemics. These results provide data-driven insights into the academic value of preprints, which have been arguably underestimated. The scientific community should actively capitalize the collective wisdom generated by the huge amount of preprints, particularly during the emerging infectious diseases like the COVID-19.

Identifying main and interaction effects of risk factors to predict intensive care admission in patients hospitalized with COVID-19: a retrospective cohort study in Hong Kong

BackgroundThe coronavirus disease 2019 (COVID-19) has become a pandemic, placing significant burdens on the healthcare systems. In this study, we tested the hypothesis that a machine learning approach incorporating hidden nonlinear interactions can improve prediction for Intensive care unit (ICU) admission. MethodsConsecutive patients admitted to public hospitals between 1st January and 24th May 2020 in Hong Kong with COVID-19 diagnosed by RT-PCR were included. The primary endpoint was ICU admission. ResultsThis study included 1043 patients (median age 35 (IQR: 32-37; 54% male). Nineteen patients were admitted to ICU (median hospital length of stay (LOS): 30 days, median ICU LOS: 16 days). ICU patients were more likely to be prescribed angiotensin converting enzyme inhibitors/angiotensin receptor blockers, anti-retroviral drugs lopinavir/ritonavir and remdesivir, ribavirin, steroids, interferon-beta and hydroxychloroquine. Significant predictors of ICU admission were older age, male sex, prior coronary artery disease, respiratory diseases, diabetes, hypertension and chronic kidney disease, and activated partial thromboplastin time, red cell count, white cell count, albumin and serum sodium. A tree-based machine learning model identified most informative characteristics and hidden interactions that can predict ICU admission. These were: low red cells with 1) male, 2) older age, 3) low albumin, 4) low sodium or 5) prolonged APTT. A five-fold cross validation confirms superior performance of this model over baseline models including XGBoost, LightGBM, random forests, and multivariate logistic regression. ConclusionsA machine learning model including baseline risk factors and their hidden interactions can accurately predict ICU admission in COVID-19.

Healthcare-resource-adjusted vulnerabilities towards the 2019-nCoV epidemic across China

We integrate the human movement and healthcare resource data to identify cities with high vulnerability towards the 2019-nCoV epidemic with respect to available health resources. The results inform public health responses in multiple ways.

Incorporating Human Movement Data to Improve Epidemiological Estimates for 2019-nCoV

Estimating the key epidemiological features of the novel coronavirus (2019-nCoV) epidemic proves to be challenging, given incompleteness and delays in early data reporting, in particular, the severe under-reporting bias in the epicenter, Wuhan, Hubei Province, China. As a result, the current literature reports widely varying estimates. We developed an alternative geo-stratified debiasing estimation framework by incorporating human mobility with case reporting data in three stratified zones, i.e., Wuhan, Hubei Province excluding Wuhan, and mainland China excluding Hubei. We estimated the latent infection ratio to be around 0.12% (18,556 people) and the basic reproduction number to be 3.24 in Wuhan before the citys lockdown on January 23, 2020. The findings based on this debiasing framework have important implications to prioritization of control and prevention efforts. One Sentence SummaryA geo-stratified debiasing approach incorporating human movement data was developed to improve modeling of the 2019-nCoV epidemic.

Estimating the effective reproduction number of the 2019-nCoV in China

We estimate the effective reproduction number for 2019-nCoV based on the daily reported cases from China CDC. The results indicate that 2019-nCoV has a higher effective reproduction number than SARS with a comparable fatality rate. Article Summary LineThis modeling study indicates that 2019-nCoV has a higher effective reproduction number than SARS with a comparable fatality rate.

Clinical application of short-segment nail-stick system combined with annular fusion in lumbar spondylolisthesis / 重庆医学

Objective To investigate the clinical effect of the bone graft fusion of the posterior lumbar interbody fusion (PLIF) combined with posterolateral fusion(PLF) in treating lumbar spondylolisthesis .Methods 63 cases of lumbar spondylolisthesis were performed the posterior unilateral or bilateral vertebral plate resection ,nerve root canal decompression ,clearing the slippage space , reduction ,fixation short-segment vertebral pedicle nail-stick system for reduction and fixation ,bone graft fusion with the interverte-bral space Cage and posterior-lateral bone graft fusion ,vertebral pedicle isthmus cracking was performed the clearance and bone graft fusion(PLIF combined with PLF ) .The JOA scores ,lumbar lordosis ,segment lordosis ,intervertebral space height ,slippage rate and complications were recorded before operation ,in postoperative 1 week ,6 ,12 months .Results All cases had no serious complications .The JOA scores were increased to some different degrees from the beginning of postoperative 1 week ,with the reha-bilitation time extension ,the JOA scores were gradually increased ,the improvement rate of the postoperative JOA score averaged 85 .00% .The lumbar lordosis ,segment lordosis ,intervertebral space height and slippage rate after operation were significantly im-proved compared with before operation ,the fusion failure rate was 4 .76% .Conclusion The bone graft fusion of PLIF combined with PLF is one of ideal methods to treat lumbar spondylolisthesis .

Effects of tetrandrine on fibroblast's proliferation derived from hypertrophic scar / 重庆医科大学学报

Objective:To probe into the effects of tetrandrine on fibroblast proliferation derived from hypertrophic scars and evaluate the role of tetrandrine in the treatment of scars.Methods:Taking the cultured fibroblasts derived from human hypertrophic scars as model,the effects of tetrandrine on fibroblast proliferation and content of extracellular collagen were observed and analyzed by MTT reduction assay,flow cytometry after propidium iodide staining and modified chloraseptine T oxidizing assay.Besides,their relationship was analyzed by linear correlation.Results:Growth curve descent,TD prolonging and extracellular collagen reduction in a dosage and time dependent manner were observed.Moreover,they changed in positive correlation with each other.Conclusion:Tetrandrine can inhibit fibroblast biological action derived from hypertrophic scars,which is one of the mechanism of anti-scarring action of tetrandrine.