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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. A deep reinforcement learning (DRL) model was developed to learn the optimal adaptive NPIs strategies to mitigate the outbreak of RIDs after COVID-19-targeted NPIs are lifted with minimal health and economic cost. The performance was validated by simulations of 1000 days starting August 29, 2022. We also extended the model to Beijing context. Findings Without any NPIs, Hong Kong experienced a major COVID-19 resurgence far exceeding the hospital bed capacity. Simulation results showed that the proposed DRL-based adaptive NPIs successfully suppressed the outbreak of COVID-19 and other RIDs to lower than capacity. DRL carefully controlled the epidemic curve to be close to the full capacity so that herd immunity can be reached in a relatively short period with minimal cost. DRL derived more stringent adaptive NPIs in Beijing. Interpretation DRL is a feasible method to identify the optimal adaptive NPIs that lead to minimal health and economic cost by facilitating gradual herd immunity of COVID-19 and mitigating the other RIDs outbreaks without overwhelming the hospitals. The insights can be extended to other countries/regions. [ABSTRACT FROM AUTHOR]

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. After differentially expressed gene (DEG) analysis, we successfully identified 97 upregulated and 172 downregulated DEGs. They are mainly concentrated in actin binding, regulation of cytokine production, muscle cell promotion, chemokine signaling pathway, and cytokine-cytokine receiver interaction. According to the diagram of protein-protein interaction (PPI), we obtained 10 hub genes: CCL5, CXCL10, EGFR, HMOX1, IL12B, CCL7, TBX21, XCL1, PGR, and ITGA1. Expression analysis showed that Egfr, Hmox1, and Pgr were significantly upregulated in estrogen-treated osteoporotic patients, while Ccl5, Cxcl10, Il12b, Ccl7, Tbx21, Xcl1, and Itga1 were significantly downregulated. In addition, the analysis results of Pearson's correlation revealed that CCL7 has a strong positive association with IL12b, TBX21, and CCL5 and so was CCL5 with IL12b. Conversely, EGFR has a strong negative association with XCL1 and CXCL10. In conclusion, this study screened 10 hub genes related to OP based on the GEO database, laying a biological foundation for further research on new biomarkers and potential therapeutic targets in OP. [ABSTRACT FROM AUTHOR]

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. There will be outbreaks of all RIDs with staggered peak times after lifting COVID-19-targeted NPIs. Such a large-scale resurgence of RID patients will impose a significant risk of overwhelming the health system. With a strict NPI strategy, a TCM-initiated threshold of 600 severe infections can ensure a sufficient supply of hospital beds for all hospitalized severely infected patients. The proposed TCM identifies effective dynamic NPIs, which facilitate future NPI relaxation policymaking. [ABSTRACT FROM AUTHOR]

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. The model was constructed based on epidemiological characteristics of COVID-19 in China and accounting for the heterogeneity of social contact networks. The early community outbreaks in Wuhan were reconstructed and fitted with the actual data. We used this model to assess epidemic control measures for asymptomatic cases in three dimensions. The 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. Management of asymptomatic cases can help flatten the infection curve. Tracing 75% of the asymptomatic cases corresponds to a 32.5% overall reduction in new cases (compared with tracing no asymptomatic cases). Regardless of population-wide measures, household transmission is higher than other types of transmission, accounting for an estimated 50% of all cases. The magnitude of tracing of asymptomatic cases is more important than the timing; when all symptomatic patients were traced, tested, and isolated in a timely manner, the overall epidemic was not sensitive to the time of implementing the measures to trace asymptomatic patients. Disease control and prevention within families should be emphasized during an epidemic. Note to Practitioners—This article addresses the urgent need to assess the risk of another COVID-19 outbreak caused by asymptomatic cases and to find the optimal, most practical approach to asymptomatic case management. Previous studies mostly focused on the clinical and statistical characteristics of asymptomatic cases; few have evaluated the impact of asymptomatic case measures using mathematical modeling at the community scale. This study proposed a Susceptible, Exposed, Infectious, No symptoms, Hospitalized and reported, Recovered, Death (SEINRHD) propagation model based on local community structures and social contact networks, according to the development characteristics and trend of COVID-19 in a Chinese community. The conclusion provides theoretical support for emergency work of relevant departments in different periods of an epidemic. In the early stages of the epidemic, timely detection and isolation of symptomatic patients should be a priority. Where there are surplus resources for epidemic prevention, the authorities should consider increasing the proportion of asymptomatic patients being traced. Epidemic prevention measures among family members should be a primary focus of attention. This combination of strategies can help reduce the rate of viral transmission and result in extinguishing the epidemic. [ABSTRACT FROM AUTHOR]

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. Here, an integrated network model is developed, which integrates a metapopulation model and a threshold model, to investigate the shortage contagion patterns. The metapopulation model captures disease contagion across countries. The threshold model captures the shortage contagion on the global PPE trade network. Due to the Pareto distribution in global exports, the shortage contagion pattern is mainly determined by the export restriction policies of the top exporters. Export restrictions exacerbate the shortages of PPE and cause the shortage contagion to transmit even faster than the disease contagion. To some extent, export restrictions can provide benefits for self‐sufficient countries, at the sacrifice of immediate economic shocks at not‐self‐sufficient countries. With export restrictions, a large amount of PPE is hoarded instead of being distributed to where it is most needed, particularly at the early stage. Cooperation between countries plays an essential role in preventing global shortages of PPE regardless of the production level. Except for promoting global cooperation, governments and international organizations should take actions to reduce supply chain barriers and work together to increase global PPE production. [ABSTRACT FROM AUTHOR]

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. Kaplan-Meyer survival plots and COX regression analysis were conducted for the assessment into the MSH2's impact on tumor patients' clinical prognosis. In an investigation to the association of MSH2 expression with immune infiltration level of various tumors and a similar study on tumor immune neoantigens, microsatellite instability was subsequently taken. It was found that high expression of MSH2 is prevalent in most cancers. MSH2's efficacy on clinical prognosis as well as immune infiltration in tumor patients revealed a fact that expression of MSH2 in prostate adenocarcinoma (PRAD), brain lower-grade glioma (LGG), breast-invasive carcinoma (BRCA), and head and neck squamous cell carcinoma (HNSC) posed a significant correlation with the immune cell infiltration level of patients. Likewise as above, MSH2's expression comes in a similar trend with tumor immune neoantigens and microsatellite instability. MSH2's expression in the majority of tumors is a direct factor to the activation of tumor-associated pathways as well as immune-associated pathways. MSH2's early screening or even therapeutic target role for sarcoma (SARC) diagnosis is contributing to the efficiency of early screening and overall survival in SARC patients. [ABSTRACT FROM AUTHOR]

Resilience problems from cyber-attacks on information communication technologies exist under their wide usage. False data injection (FDI) judiciously designed by attackers may cause severe consequences such as uneconomic operation and blackouts, particularly multivector energy distribution systems (MEDS), which are closely linked and interdependent. This article addresses the cyber resilient issues of an MEDS caused by FDI, considering the uncertainty from renewable resources. A novel two-stage distributionally robust optimization (DRO) is proposed to realize the day-ahead and real-time resilience improvement. The ambiguity set is based on both the Wasserstein distance and moment information. Compared to robust optimization which considers the worst case, DRO yields less-conservative solutions and thus provides more economic operation schemes. The Wasserstein metric-based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Case studies are demonstrated on two representative MEDS networked with energy hubs, illustrating the effectiveness of the proposed cybersecured model. The produced adaptive robust economic operation for MEDS can reduce load shedding and enhance system resilience against severe cyberattacks. [ABSTRACT FROM AUTHOR]

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 (chunyun), to the exceptional calm of COVID-19 lockdown, and then to the recovery period. We observed that cross-city movements, which increased substantially in chunyun and then dropped sharply during the lockdown, are primarily dependent on travel distance and the socio-economic development of cities. Following the Lunar New Year holiday, national mobility remained low until mid-February, and COVID-19 interventions delayed more than 72.89 million people returning to large cities. Mobility network analysis revealed clusters of highly connected cities, conforming to the social-economic division of urban agglomerations in China. While the mass migration back to large cities was delayed, smaller cities connected more densely to form new clusters. During the recovery period after travel restrictions were lifted, the netflows of over 55% city pairs reversed in direction compared to before the lockdown. These findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response, transportation planning and regional economic development, among others. [ABSTRACT FROM AUTHOR]

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. 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 × 10 6 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 has been split into 4905 500 × 500 m 2 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 Google's 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 propose 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 effectiveness of common NPIs and the proposed targeted interventions are evaluated by 100 extensive 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. [ABSTRACT FROM AUTHOR]

Seismic events can severely damage both electricity and natural gas systems, causing devastating consequences. Ensuring the secure and reliable operation of the integrated energy system (IES) is of high importance to avoid potential damage to the infrastructure and reduce economic losses. This article proposes a new optimal two-stage optimization to enhance the reliability of IES planning and operation against seismic attacks. In the first stage, hardening investment on the IES is conducted, featuring preventive measures for seismic attacks. The second stage minimizes the expected operation cost of emergency response. The random seismic attack is modeled as uncertainty, which is realized after the first stage. An explicit damage assessment model is developed to define the budget set of the uncertain seismic activity. Based on the survivability of transmission lines and gas pipelines of IES, an optimal system investment plan is developed. The problem is formulated as a two-stage distributionally robust optimization (DRO) model, which is tested on an integrated IEEE 30-bus system and 20-node gas network. Case studies demonstrate that the two-stage DRO outperforms robust optimization and a single-stage optimization model in terms of minimizing the investment cost and expected economic loss. This article can help system operators to make economical hardening and operation strategies to improve the reliability of IES under seismic attacks, thus managing a more robust and secure energy system. [ABSTRACT FROM AUTHOR]

The wide implementation of information and communication technologies (ICT) cause power system operations exposed to cyber-attacks. Meanwhile, the tendency of integrated multi energy vectors has worsened this issue with multiple energy coupled. This paper proposes a two-stage risk-averse mitigation strategy for water-energy systems (WESs), incorporating power, natural gas and water systems against false data injection attacks (FDIA) under water-energy nexus. The FDIA on individual sub-systems is modelled through hampering false data integrity to the systems. An innovative two-stage risk-averse distributionally robust optimization (RA-DRO) is proposed to mitigate uneconomic operation and provides a coordinated optimal load shedding scheme for the nexus system security. A coherent risk measure, Conditional Value-at-Risk is incorporated into the RA-DRO to model risk. A Benders decomposition method is used to solve the original NP-hard RA-DRO problem. Case studies are demonstrated on a WES under water-energy nexus and results show that the effectiveness of the method to mitigate risks from potential FDIA and renewable uncertainties. This research provides WES operators an economic system operation tool by optimally coordinating energy infrastructures and implementing reasonable load shedding to enhance cybersecurity. [ABSTRACT FROM AUTHOR]

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. In this paper, we propose a novel method to model the spread of ASFV in China by integrating the data of pork import/export, transportation networks, and pork distribution centers. We first empirically analyze the overall spatiotemporal patterns of ASFV spread and conduct extensive experiments to evaluate the efficacy of a number of geographic distance measures. These empirical analyses of ASFV spread within China indicate that the first occurrence of ASFV has not been purely dependent on the geographical distance from existing infected regions. Instead, the pork supply–demand patterns have played an important role. Predictions based on a new distance measure achieve better performance in predicting ASFV spread among Chinese provinces and thus have the potential to enable the design of more effective control interventions. [ABSTRACT FROM AUTHOR]

Therefore, ACEI use was protective of the severe disease outcome in COVID-19 patients receiving steroid therapy. Interaction effects between angiotensin-converting enzyme inhibitors or angiotensin receptor blockers and steroid or antiviral therapies in COVID-19: A population-based study Interaction effects between ACEIs, ARBs, and individual drugs in these classes with antiviral therapies or steroids were assessed in the unmatched cohort (Table 2). A noteworthy point is that the renin-angiotensin-aldosterone system may interact with the Kinin-Kallikrein system and coagulation cascade.11 Therefore, at the very least, interactions aside, prevention of thromboembolic phenomena may improve outcomes in COVID-19 patients. [Extracted from the article]

The water system management problem has been widely investigated. However, the interdependencies between water and energy systems are significant and the effective co-optimization is required considering strong interconnections. This paper proposes a two-stage distributionally robust operation model for integrated water-energy nexus systems including power, gas and water systems networked with energy hub systems at a distribution level considering wind uncertainty. The presence of wind power uncertainty inevitably leads to risks in the optimization model. Accordingly, a coherent risk measure, i.e., conditional value-at-risk, is combined with the optimization objective to determine risk-averse operation schemes. This two-stage mean-risk distributionally robust optimization is solved by Bender's decomposition method. Both the day-ahead and real-time operation cost are minimized with an optimal set of scheduling the multi-energy infrastructures. Case studies focus on investigating the strong interdependencies among the four interconnected energy systems. Numerical results validate the economic effectiveness of IES through optimally coordinating the multi-energy infrastructures. The proposed model can provide system operators a powerful two-stage operation scheme to minimise operation cost under water-energy nexus considering risk caused by renewable uncertainties, thus benefiting customers with lower utility bills. [ABSTRACT FROM AUTHOR]

This paper develops a novel two-stage coordinated volt-pressure optimization (VPO) for integrated energy systems (IES) networked with energy hubs considering renewable energy sources. The promising power-to-gas (P2G) facilities are used for improving the interdependency of the IES. The proposed VPO contains the traditional volt-VAR optimization functionality to mitigate the voltage deviation while ensuring a satisfying gas quality due to the hydrogen mixture. In addition to the conventional voltage regulating devices, i.e., on-load tap changers and capacitor banks, P2G converter and gas storage are used to address the voltage fluctuation problem caused by renewable penetration. Moreover, an effective two-stage distributionally robust optimization (DRO) based on Wasserstein metric is utilized to capture the renewable uncertainty with tractable robust counterpart reformulations. The Wasserstein-metric based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Extensive case studies are conducted in a modified IEEE 33-bus system connected with a 20-node gas system. The proposed VPO provides a voltage-regulated economic operation scheme with gas quality ensured that contributes to high-quality but low-cost multi-energy supply to customers. [ABSTRACT FROM AUTHOR]

Volt-VAR optimization (VVO) has been investigated extensively in power systems. However, under the era of integrated energy systems (IES), the growing interdependencies between different energy systems complicate traditional VVO. This is further hardened by incurred gas quality problems due to the hydrogen injection in IES, produced by widely applied power-to-gas (P2G) facilitates that couple between power and gas systems. This paper develops a two-stage volt-VAR-pressure optimization (VVPO) model for PV-penetrated IES to manage the variation of system voltages while managing gas quality indices. In addition to the traditional voltage regulating devices, P2G facilities, which can mitigate fluctuating PV output via converting the surplus generation into hydrogen, are also used for voltage management. A two-stage distributionally robust optimization (DRO) based on moment information is utilized to model PV uncertainty. A semidefinite programming model is formulated and finally solved by the constraint generation algorithm. A 33-bus-20-node IES is used to verify the effectiveness of the proposed VVPO on voltage management, ensured gas quality with high economic efficiency. The proposed VVPO is applicable to injecting other green gases into gas systems while ensuring power quality and enable system operators to provide low-cost but high-quality multi-energy to customers. [ABSTRACT FROM AUTHOR]

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). Numerical experiments from our model show that the control policies implemented in NYC reduced the number of infections by 72% [interquartile range (IQR) 53–95] and the number of deceased cases by 76% (IQR 58–96) by the end of 2020. Among all the NPIs, social distancing for the entire population and protection for the elderly in 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. [ABSTRACT FROM AUTHOR]

There are often multiple diseases with cross-immunity competing for vaccination resources. Here we investigate the optimal vaccination program in a two-layer Susceptible-Infected-Removed (SIR) model, where two diseases with cross-immunity spread in the same population, and vaccines for both diseases are available. We identify three scenarios of the optimal vaccination program, which prevents the outbreaks of both diseases at the minimum cost. We analytically derive a criterion to specify the optimal program based on the costs for different vaccines. [ABSTRACT FROM AUTHOR]

Introduction: In order to improve the prediction accuracy of dengue fever incidence, we constructed a prediction model with interactive effects between meteorological factors, based on weekly dengue fever cases in Guangdong, China from 2008 to 2016. Methods: Dengue fever data were derived from statistical data from the China National Notifiable Infectious Disease Reporting Information System. Daily meteorological data were obtained from the China Integrated Meteorological Information Sharing System. The minimum temperature for transmission was identified using data fitting and the Ross-Macdonald model. Correlations and interactive effects were examined using Spearman's rank correlation and multivariate analysis of variance. A probit regression model to describe the incidence of dengue fever from 2008 to 2016 and forecast the 2017 incidence was constructed, based on key meteorological factors, interactive effects, mosquito-vector factors, and other important factors. Results: We found the minimum temperature suitable for dengue transmission was ≥18°C, and as 97.91% of cases occurred when the minimum temperature was above 18 °C, the data were used for model training and construction. Epidemics of dengue are related to mean temperature, maximum/minimum and mean atmospheric pressure, and mean relative humidity. Moreover, interactions occur between mean temperature, minimum atmospheric pressure, and mean relative humidity. Our weekly probit regression prediction model is 0.72. Prediction of dengue cases for the first 41 weeks of 2017 exhibited goodness of fit of 0.60. Conclusion: Our model was accurate and timely, with consideration of interactive effects between meteorological factors. [ABSTRACT FROM AUTHOR]

Background: The benefit of school‐based influenza vaccination policy has not been fully addressed in Beijing. Objectives: To evaluate the benefit of school‐based influenza vaccination policy launched in Beijing. Methods: Using existing surveillance and immunization data, we developed a dynamic transmission model to assess the impact of influenza vaccination in school‐going children. The outcome was defined as the averted number of medically attended influenza illnesses and the prevented disease fraction to all children aged 5‐14 years for the 2013/14, 2014/15, and 2015/16 seasons. Results: We estimated that during the three consecutive influenza seasons, the averted number of medically attended influenza illnesses among children aged 5‐14 years was around 104 000 (95% CI: 101 000‐106 000), 23 000 (95% CI: 22 000‐23 000), and 21 000 (95% CI: 21 000‐22 000), respectively. Corresponding prevented fractions to all children aged 5‐14 years were 76.3%, 38.5%, and 43.9%. Conclusions: In Beijing, school‐based vaccinations reduced a substantial number of medically attended influenza illnesses despite seasonal variation in the prevented fraction. This is strong supportive evidence for the continuation of school‐based vaccination programs to reduce the influenza burden in this age group. [ABSTRACT FROM AUTHOR]

An increasing number of studies indicate that microRNAs (miRNAs) may exert an oncogenic or tumor suppressive role in diverse types of cancer. MicroRNA (miR)-142-3p has been detected to be downregulated in a number of cancer types, and it may function as a tumor suppressor. However, the expression profile and potential role of miR-142-3p in gastric cancer remain unknown. In the present study, the expression of miR-142-3p in numerous gastric cancer samples was investigated. It was observed that miR-142-3p was markedly downregulated in cancer tissues compared with normal tissues. Furthermore, a low expression level of miR-142-3p was associated with higher tumor stages. The overexpression of miR-142-3p was able to inhibit the proliferation, invasion and migration of gastric cancer cells. A further investigation into the mechanism underlying the effect of miR-142-3p identified cyclin T2 (CCNT2) as a target of miR-142-3p in gastric cancers. miR-142-3p may exert its tumor suppressor role partially by downregulating CCNT2. These results suggested that the abnormal downregulation of miR-142-3p and the subsequent increase in CCNT2 expression may have an important role in gastric cancer carcinogenesis. [ABSTRACT FROM AUTHOR]

Background Antibiotic-loaded carriers were developed because of their ability to fill cavities and deliver antibiotics locally following implantation. However, the most commonly used antibiotic carrier, polymethyl methacrylate, has many shortcomings, such as heat production, non-bioresorbable and lack of bone regeneration or conduction. Bone-like hydroxyapatite/poly(amino acid) scaffolds have been shown to have controllable biodegradability, nontoxicity, some osteogenic and osteoconductive properties, which has great potential as a carrier for local delivery of antibiotics. Vancomycin-loaded bone-like hydroxyapatite/poly(amino acid) was successfully fabricated by a homogeneous method using a diffusion control system. In this study, bone regeneration using this scaffold was observed both in vitro and in vivo. Methods In vitro tests, MG63 cells were incubated with the vancomycin-loaded scaffold to observe its effects on the activation of osteoblasts. In vivo tests, the scaffolds were implanted into rabbit models of chronic osteomyelitis, including regular and methicillin-resistant Staphylococcus aureus. The effects were evaluated by gross observation, X-ray and histological observation. Results After incubating with the scaffold, MG63 cells exhibited good proliferative activity, and increased calcium and alkaline phosphatase synthesis compared with blank control group. In vivo tests, the experimental group showed increased bone growth in infectious bone defects compared with the control groups, regardless of the type of Staphylococcus aureus. Conclusion Vancomycin-loaded bone-like hydroxyapatite/poly(amino acid) scaffold has good potential for the repair of infectious bone defects because of its ability to deliver antibiotics and promote bone regeneration. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is a major public health problem and usually associated with various comorbidities. Base on electronic health records of inpatients, aged 40 to 80 years old, from 169 hospitals located across China between 2013 and 2014, this paper summarized 27 comorbidities of COPD and compared the time and economic burdens of COPD patients to non-COPD patients. The 17 comorbidities included in Charlson Comorbidity Index (CCI) and 10 additional comorbidities were employed while odds ratios were considered to compare differences. The results disclosed that COPD patients had higher comorbidity burdens than non-COPD patients did. Moreover, COPD patients had 6.40 % more hospital stay and 12.96 % lower medical cost. Seven morbidities had positive correlations with hospital stay and medical cost, which indicated that COPD patients should be paid more attention to those comprehensive comorbidities when making therapeutic plans. [ABSTRACT FROM AUTHOR]

Computational social science plays an important role in emergency management from a quantitative perspective. Reconstructing an individual-based social computing environment is crucial for both accurate computational experiments and determining optimal decisions. Here, the authors propose a formalization method to define basic componential models in the artificial society and the inner logic in these models. A detailed generation process is presented, and multisource statistical data, social interactive behavior, and multilayer social networks are integrated together. As an evaluation of the proposed framework, a virtual city of Beijing is reconstructed. Each citizen is endowed with demographic attributes, including age, gender, social role, correlated geographic locations, and multiple social relations. The generated synthetic population is statistically equivalent to the real population. Transmission experiments of influenza are performed in the reconstructed computational environment, and individual daily interacting behavior is tracked and analyzed. The results indicate that the framework can provide an effective methodology to reconstruct the computing environment in high resolution by using low-resolution statistical data, leading to better prediction and management of emergencies. [ABSTRACT FROM PUBLISHER]