Big data in healthcare:Conceptual network structure,key challenges and opportunities

Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources systematically.Big data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease prediction.This research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare sector.With this goal in mind,4857 documents from the Web of Science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT software.The bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research fields.The strategic diagram yielded 20 clusters and their relative importance in terms of centrality and density.The thematic evolution structure presented the most important themes and how it changes over time.Lastly,we presented the main challenges and future opportunities of big data in healthcare.

Internet of things:Conceptual network structure,main challenges and future directions

Internet of Things(IoT)is a key technology trend that supports our digitalized society in applications such as smart countries and smart cities.In this study,we investigate the existing strategic themes,thematic evolution structure,key challenges,and potential research opportunities associated with the IoT.For this study,we conduct a Bibliometric Performance and Network Analysis(BPNA),supplemented by an exhaustive Systematic Literature Review(SLR).Specifically,in BPNA,the software SciMAT is used to analyze 14,385 documents and 30,381 keywords in the Web of Science(WoS)database,which was released between 2002 and 2019.The results reveal that 31 clusters are classified according to their importance and development,and the conceptual structures of key clusters are presented,along with their performance analysis and the relationship with other subthemes.The thematic evolution structure describes the important cluster(s)over time.For the SLR,23 documents are analyzed.The SLR reveals key challenges and limitations associated with the IoT.We expect the results will form the basis of future research and guide decision-making in the IoT and other supporting industries.

Gut microbiota and nutrient interactions with skin in psoriasis:A comprehensive review of animal and human studies

The intestinal tract(i.e.,the gut),is where the body’s nutrients are absorbed,and is simultaneously inhabited by numerous microbes.An increasing body of literature suggests a crucial role for the gut microbiome in modulating systemic inflammatory disease.Psoriasis is a chronic systemic inflammatory disease and its pathogenesis is related to the interaction between genetic susceptibility,immune response and environmental triggers.The omics era has allowed physicians to assess different aspects of psoriasis pathogenesis such as the microbiome,infectome,and autoinfectome.Furthermore,diet appears to play an important role in modulating disease activity,perhaps by influencing gut microbes.Given these observations,we aimed to summarize the current knowledge regarding skin-microbiome-gut-nutrients and psoriasis.

新型冠状病毒肺炎疫情数据挖掘与离散随机传播动力学模型分析

新型冠状病毒肺炎(COVID-19)疫情已经蔓延至全国各地,包括陕西省在内很多省份的早期疫情均以输入病例为主,后期的疫情在严格的防控措施下也已呈下降趋势.评价防控措施的有效性、分析人口流动对疫情的影响对于研究陕西省(或其他以输入病例为主的地区)疫情和未来应对突发性传染病有着重要的意义.根据陕西省卫生健康委员会(简称卫健委)公布的详实数据信息可以挖掘传播链(感染树),得到从发病到首诊、入院、确诊的中位持续时间,每日潜伏者类、感染者类、治疗者类的具体人数和感染者状态转移的空间分布.本文计算确定COVID-19疫情的控制再生数(1.48–1.69),并发展新的统计推断方法获得陕西省严控措施下的有效再生数;进而提出一个全新的融入了公共卫生干预和输入病例的离散随机COVID-19疫情传播模型,通过多源数据实现了模型的参数化,分析不同的流动模式、输入人口中感染者的比例对二次暴发风险的影响.主要结论显示,间歇性的人口流动、密切关注和有效隔离流动人口中的感染者能有效降低二次暴发的风险,为有序组织复工、复学提供决策支持.

An updated estimation of the risk of transmission of the novel coronavirus(2019-nCov)

The basic reproduction number of an infectious agent is the average number of infections one case can generate over the course of the infectious period,in a naïve,uninfected population.It is well-known that the estimation of this number may vary due to several methodological issues,including different assumptions and choice of parameters,utilized models,used datasets and estimation period.With the spreading of the novel coronavirus(2019-nCoV)infection,the reproduction number has been found to vary,reflecting the dynamics of transmission of the coronavirus outbreak as well as the case reporting rate.Due to significant variations in the control strategies,which have been changing over time,and thanks to the introduction of detection technologies that have been rapidly improved,enabling to shorten the time from infection/symptoms onset to diagnosis,leading to faster confirmation of the new coronavirus cases,our previous estimations on the transmission risk of the 2019-nCoV need to be revised.By using time-dependent contact and diagnose rates,we refit our previously proposed dynamics transmission model to the data available until January 29th,2020 and re-estimated the effective daily reproduction ratio that better quantifies the evolution of the interventions.We estimated when the effective daily reproduction ratio has fallen below 1 and when the epidemics will peak.Our updated findings suggest that the best measure is persistent and strict self-isolation.The epidemics will continue to grow,and can peak soon with the peak time depending highly on the public health interventions practically implemented.

Assessing Ebola-related web search behaviour: insights and implications from an analytical study of Google Trends-based query volumes

Background:The 2014 Ebola epidemic in West Africa has attracted public interest worldwide,leading to millions of Ebola-related Internet searches being performed during the period of the epidemic.This study aimed to evaluate and interpret Google search queries for terms related to the Ebola outbreak both at the global level and in all countries where primary cases of Ebola occurred.The study also endeavoured to look at the correlation between the number of overall and weekly web searches and the number of overall and weekly new cases of Ebola.Methods:Google Trends(GT)was used to explore Internet activity related to Ebola.The study period was from 29 December 2013 to 14 June 2015.Pearson’s correlation was performed to correlate Ebola-related relative search volumes(RSVs)with the number of weekly and overall Ebola cases.Multivariate regression was performed using Ebola-related RSV as a dependent variable,and the overall number of Ebola cases and the Human Development Index were used as predictor variables.Results:The greatest RSV was registered in the three West African countries mainly affected by the Ebola epidemic.The queries varied in the different countries.Both quantitative and qualitative differences between the affected African countries and other Western countries with primary cases were noted,in relation to the different flux volumes and different time courses.In the affected African countries,web query search volumes were mostly concentrated in the capital areas.However,in Western countries,web queries were uniformly distributed over the national territory.In terms of the three countries mainly affected by the Ebola epidemic,the correlation between the number of new weekly cases of Ebola and the weekly GT index varied from weak to moderate.The correlation between the number of Ebola cases registered in all countries during the study period and the GT index was very high.Conclusion:Google Trends showed a coarse-grained nature,strongly correlating with global epidemiological data,but was weaker at country level,as it was prone to distortions induced by unbalanced media coverage and the digital divide.Global and local health agencies could usefully exploit GT data to identify disease-related information needs and plan proper communication strategies,particularly in the case of health-threatening events.

Nanotechnology meets atopic dermatitis: Current solutions, challenges and future prospects. Insights and implications from a systematic review of the literature

Atopic dermatitis is a chronic,relapsing,non-contiguous,exudative eczema/dermatitis,which represents a complex,multi-factorial disorder,due to an impairment of the stratum corneum barrier.Currently available drugs have a low skin bioavailability and may give rise to severe adverse events.Nanotechnologies,including nanoparticles,liposomes,nano-gels,nano-mixtures,nano-emulsions and other nano-carriers,offer unprecedented solutions to these issues,enabling:i)the management of different clinical forms of atopic dermatitis,especially the recalcitrant ones,i)a better bio-availability and trans-dermal drug targeted delivery at the inflammation site,ii)dose control,iii)significant improvements both in clinical symptoms and immune responses,iv)with less adverse events being reported and a better safety profile.However,some nano-sized structures could amplify and even worsen symptoms in particularly susceptible individuals.Furthermore,most studies included in the present systematic review have been conducted in-vitro or in-vivo,with few randomized controlled clinical trials(RCTs).Future investigations should adopt this design in order to enable scholars achieving robust findings and evidence.Therefore,given the above-mentioned shortcomings,further research in the field is urgently warranted.

Prevalence of hepatitis C virus among street children in Iran

Background:Street children are forced to spend a lot of time away from their homes and some of them do not have homes at all,due to economic and family problems,which makes them exposed to many health problems,such as the hepatitis C virus(HCV)infection.Iran,like many other countries in the world,experiences the burden of street children,however,the rate of HCV among street children is virtually unknown.This study aimed to determine the prevalence of HCV among street children in Iran.Main body:This systematic review and meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.The study protocol of this review was registered in PROSPERO under identification term CRD42018082336.A comprehensive literature search was conducted to identify published studies reporting on the prevalence of HCV among street children in Iran.Several international scholarly databases,including Web of Science,PubMed^(■)/MEDLINE^(■),Embase,Scopus®,Google Scholar and Directory of Open Access Journals,as well as Iranian databases such as MagIran and Barakathns were searched.Studies published between 1988 and December 2017 with any of the following keywords were selected:(street OR homeless OR labour)AND(children OR child OR infant)AND(hepatitis C OR hepatitis C virus OR HCV OR viral hepatitis OR hepatitis OR hepacivirus)AND Iran.Moreover,a grey literature search was performed in order to obtain other potentially relevant studies.The search was carried out without any language restrictions.Four studies,surveying a total of 1691 street children,conducted between 2006 and 2017 were found to be eligible for inclusion in the review and therefore analysed.Three studies were conducted in Tehran and one in Isfahan.The prevalence of HCV among street children in Iran was found to be high,at 2.4%(95%CI:1.8-3.3).Conclusions:Since the prevalence of HCV among street children in Iran is quite high,health decision-and policy-makers should pay more attention to street children and widen support channels,both social and economic.Further studies should be conducted among street children in different cities of Iran to add to the knowledge base of HCV among street children in the country.The health system should provide facilities for street children to be screened to quickly diagnose illnesses and prevent them from developing complications.

Canada needs to rapidly escalate public health interventions for its COVID-19 mitigation strategies

Background:After the declaration of COVID-19 pandemic on March 11th,2020,local transmission chains starting in different countries including Canada are forcing governments to take decisions on public health interventions to mitigate the spread of the epidemic.Methods:We conduct data-driven and model-free estimations for the growth rates of the COVID-19 epidemics in Italy and Canada,by fitting an exponential curve to the daily reported cases.We use these estimates to predict epidemic trends in Canada under different scenarios of public health interventions.Results:In Italy,the initial growth rate(0.22)has reduced to 0.1 two weeks after the lockdown of the country on March 8th,2020.This corresponds to an increase of the doubling time from about 3.15 to almost 7 days.In comparison,the growth rate in Canada has increased from 0.13 between March 1st and 13th,to 0.25 between March 13th to 22nd.This current growth rate corresponds to a doubling time of 2.7 days,and therefore,unless further public health interventions are escalated in Canada,we project 15,000 cases by March 31st.However,the case number may be reduced to 4000 if escalated public health interventions could instantly reduce the growth rate to 0.1,the same level achieved in Italy.Interpretation:Prompt and farsighted interventions are critical to counteract the very rapid initial growth of the COVID-19 epidemic in Canada.Mitigation plans must take into account the delayed effect of interventions by up to 2-weeks and the short doubling time of 3e4 days.