Challenger Bratislava 2 stats & predictions

Overview of the Tennis Challenger Bratislava 2

The Tennis Challenger Bratislava 2 is an exciting tournament set in the picturesque city of Bratislava, Slovakia. As one of the premier events in the ATP Challenger Tour, it draws top talent from around the globe, offering thrilling matches and intense competition. The tournament is known for its fast-paced courts and passionate local support, making it a must-watch for tennis enthusiasts. With tomorrow's matches promising high stakes and expert betting predictions, fans are eagerly anticipating standout performances and potential upsets.

Key Players to Watch

Tomorrow's lineup features some of the most promising players in the circuit. Among them, local favorite Martin Kližan is expected to deliver a stellar performance on home soil. His experience and familiarity with the conditions give him a significant edge. Additionally, rising star Filip Horanský is anticipated to make waves with his aggressive playstyle and powerful serves.

• Martin Kližan: Known for his tactical intelligence and consistency, Kližan has been a dominant force on Slovakian soil.
• Filip Horanský: A young talent with a powerful game, Horanský is set to challenge seasoned players with his dynamic approach.
• Daniel Altmaier: Bringing experience from previous tournaments, Altmaier's strategic play could be pivotal in tight matches.
• Lukáš Klein: With his impressive baseline game, Klein is expected to put up a strong fight against top-seeded opponents.

Betting Predictions and Insights

Betting experts have analyzed the matchups thoroughly, providing insights into potential outcomes. Key predictions include:

• Martin Kližan vs. Daniel Altmaier: Experts favor Kližan due to his home-court advantage and recent form. Betting odds suggest a slight edge for Kližan.
• Filip Horanský vs. Lukáš Klein: This match is anticipated to be closely contested. However, Horanský's aggressive style might give him the upper hand.
• Semifinal Predictions: Expect a thrilling semifinal between Martin Kližan and Filip Horanský. Both players have shown remarkable resilience throughout the tournament.

Betting strategies often focus on underdogs like Lukáš Klein, who could surprise with unexpected victories against higher-ranked opponents.

Tournament Format and Structure

The Tennis Challenger Bratislava 2 follows a single-elimination format over three rounds: Round of 16, Quarterfinals, Semifinals, and Finals. Each match is best-of-three sets, ensuring that only the most consistent players advance.

• Round of 16: Features eight matchups with seeded players facing qualifiers or lower-ranked competitors.
• Quarterfinals: Winners from Round of 16 compete in tightly contested matches that often determine momentum for later rounds.
• Semifinals: The stakes rise as players vie for a spot in the championship match.
• Finals: The culmination of intense competition where champions are crowned amidst roaring crowds.

This structure not only tests skill but also mental fortitude as players navigate through challenging opponents each round.

Historical Highlights

The Tennis Challenger Bratislava has a rich history filled with memorable moments. In previous editions, notable victories have come from both established names and emerging talents alike.

• In one remarkable year, an underdog clinched victory against all odds during stormy weather conditions that tested every player’s adaptability.
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  

This history underscores the unpredictable nature of tennis at this level where anything can happen on any given day.<|end_of_first_paragraph|>

• A historic comeback by Martin Križan saw him defeat two top-seeded players back-to-back during one edition.
  
  
  

Križan’s triumph remains one of the tournament’s most talked-about feats due to its sheer improbability.<|end_of_first_paragraph|>

• A young Filip Horanský made headlines by reaching quarterfinals earlier than expected; this marked just one step towards his future successes.<|end_of_first_paragraph|>

  The legacy left by past champions continues to inspire new generations aiming for greatness at this prestigious event.<|end_of_first_paragraph|>

  Tournament Venue: A Closer Look

  The venue itself plays an integral role in shaping match outcomes due to its unique characteristics.

  • The courts are renowned for their fast pace which often benefits aggressive baseliners who thrive under such conditions.<|end_of_first_paragraph|>
  • Audience engagement is palpable throughout matches as spectators create an electrifying atmosphere that fuels player performance.
  • Nestled within scenic surroundings near Danube River banks adds charm making it not just about tennis but also about experiencing Slovakian culture firsthand.<|end_of_first_paragraph|>

    All these elements combine perfectly creating unforgettable experiences both on-court battles while immersing visitors deeply into local traditions.<|end_of_first_paragraph|>

    Cultural Impact & Local Support

    Tennis Challenger Bratislava isn't merely about sport; it's deeply woven into Slovakia’s cultural fabric.

    • Spectators include families who gather annually celebrating both athletic prowess displayed during games along with shared community spirit surrounding festivities leading up till after conclusion ceremonies taking place post-final matchday celebrations!<|end_of_first_paragraph|
      1. Vibrant pre-match events featuring local artists showcasing traditional music & dance performances draw crowds even before first ball is served!<<
         
         
      2. Fans wear national colors proudly displaying unwavering support through banners flags cheering loudly every point scored bringing added pressure upon visiting athletes!<<
         
         
      3. Chef-curated food stalls offer authentic Slovak dishes giving visitors chance taste culinary delights unique only here adding extra flavor beyond competitive excitement already present throughout entire duration event!<<
         
         
      4. Youth clinics organized alongside main events encourage aspiring young talents hone skills under guidance experienced professionals further solidifying tournament’s commitment nurturing future stars!<<
         
         
      5. Eco-friendly initiatives ensure minimal environmental impact preserving natural beauty while hosting world-class sports spectacle attracting global attention!<<
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         <

         This blend cultural celebration athletic competition makes Tennis Challenger Bratislava truly special experience leaving lasting memories participants spectators alike.|<%_end_of_section_%>|

         Predictive Analysis: Betting Trends & Strategies

         Analyzing past trends provides valuable insights predicting likely outcomes upcoming matches:

         • Data shows certain players consistently perform well under specific weather conditions helping refine betting strategies tailored individual preferences aligning statistical probabilities observed over years!<<
           
           
         • Trends indicate favorites often face tougher challenges mid-tournament necessitating careful consideration when placing bets especially during crucial knockout stages!
           • Analyzing player statistics like service accuracy return efficiency helps identify potential weaknesses opponents can exploit increasing chances winning bets placed based informed decisions!
             • Past performances reveal patterns allowing bettors capitalize sudden shifts momentum altering predicted outcomes significantly favoring less favored competitors occasionally!

               All these factors combined offer sophisticated framework enabling expert bettors maximize returns leveraging comprehensive analysis historical data alongside real-time observations unfolding live!

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               Mental Game: Psychological Factors Influencing Performance

               Mental resilience plays crucial role determining success failure within high-pressure environments such as professional tennis:

               • Coping mechanisms developed over years help athletes maintain composure even when facing formidable opponents or adverse situations on court!
                 • In-game adjustments demonstrate ability adapt swiftly changing circumstances ensuring sustained competitive edge throughout entirety match duration!
                                  

                   Social Media Influence: Amplifying Tournament Reach & Engagement

                   In today’s digital age social media platforms play pivotal role amplifying reach engagement worldwide audiences:

                   • Leveraging Twitter Instagram Facebook allows organizers promote real-time updates scores highlights engaging fans directly regardless geographical barriers!
                     • Viral content creation including memes GIFs reaction videos enhances fan interaction fostering sense community among supporters globally!
                       • Influencer partnerships broaden exposure reaching diverse demographics introducing tennis enthusiasts unfamiliar sport prior facilitating growth audience base exponentially!

                         Beyond promotion social media serves as tool feedback gathering invaluable insights participant preferences areas improvement enhancing overall experience future iterations tournament!

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                         Economic Impact: Boosting Local Economy Through Sports Tourism

                         Tennis Challenger Bratislava significantly contributes local economy attracting tourists seeking unique sporting experiences:

                         <ol> <ol><ol><ol><ol><ol><ol></div><&div>> <p>Hotel occupancy rates surge during event period filling rooms otherwise vacant off-season providing stable revenue streams hospitality industry.</div><&div>> <p>Local businesses benefit increased foot traffic dining establishments retail shops capitalizing influx visitors eager explore cultural offerings region.</div><&div>> <p>Job opportunities arise temporary positions staff venues catering services transportation sectors accommodating needs larger crowds effectively managing logistics complexities inherent large-scale events.</div><&div>> <p>Government tax revenues see uplift tourism-related activities contributing funds public services infrastructure development sustaining long-term economic stability community welfare.</div><&div>> <p>Collaborations between organizers sponsors foster partnerships encouraging sustainable practices promoting eco-friendly initiatives minimizing environmental footprint while maximizing positive economic impacts locally.</div><&div>> 1: # No association between IL23R polymorphism rs11209026 and risk or severity of COVID-19 infection 2: Author: Tarek Enany Abd El-Rahman Mekkawy Ali Al-Gaadiyaniy Al-Masry 3: Date: 8-17-2021 4: Link: https://doi.org/10.1186/s43042-021-00188-x 5: Egyptian Journal of Medical Human Genetics: Research 6: ## Abstract 7: BackgroundThe cytokine storm caused by COVID-19 infection was found associated with IL23R polymorphism rs11209026. 8: ResultsWe aimed to study whether IL23R polymorphism rs11209026 was associated with COVID-19 infection risk or severity using meta-analysis technique. 9: ConclusionIL23R polymorphism rs11290 was not associated with COVID-19 infection risk or severity. 10: ## Background 11: Coronavirus disease (COVID-19) pandemic has resulted in millions of infections worldwide [1]. Severe acute respiratory syndrome coronavirus (SARS-CoV)-related cytokine storm was found associated with IL23R polymorphism rs11209026 [1]. 12: Interleukin (IL)-23 receptor (IL23R) gene encodes transmembrane glycoprotein that belongs to heterodimeric type I cytokine receptor family [1]. It consists of extracellular α chain (IL23A), which binds IL12B/IL23 heterodimeric cytokines [1], intracellular β chain (IL12RB1), which binds IL12RA heterodimeric cytokines [1], and transmembrane γ chain (γC) that binds common γ chain receptor [1]. 13: Cytokine storm occurs when uncontrolled release of pro-inflammatory cytokines leads to hyperinflammation causing tissue damage [1]. Cytokine storm has been implicated in severe cases of SARS-CoV infection [1] resulting from dysregulated immune response characterized by excessive production of pro-inflammatory cytokines including tumor necrosis factor alpha (TNFα), interleukins (IL) such as IL6 and IL8 [1], chemokines such as CCL4/MIP1α/CXCL10/IP10 [1], interferon gamma-induced protein (IP)-10/CXCL10 [1], macrophage inflammatory protein (MIP)-1α/CCL4 [1], granulocyte-macrophage colony-stimulating factor (GM-CSF), granulocyte colony-stimulating factor (GCSF), monocyte chemoattractant protein (MCP)-1/CCL2 [1] interferon alpha/beta-induced protein p40 subunit/interferon-inducible protein IFI27-like protein IP10/CXCL10/IP10/CXCR3 ligand CXCL9/MIG/MIG-CXCR3 ligand IP9/XCRA ligand ENA78/Chemokine C-X-C motif ligand CXCL5/Eotaxin [1]. 14: ## Methods 15: We searched PubMed database using keywords “COVID” OR “SARS-CoV” AND “rs11209026” OR “Interleukin(IL)23 receptor(IL23R)” AND “polymorphism” OR “variant” OR “mutation”. We included studies reporting genotyping results regarding rs11209026 among patients infected by SARS-CoV compared with healthy controls published until August 2020. 16: ### Data extraction 17: We extracted data regarding sample size per group including number homozygous major allele carriers AA genotype homozygous minor allele carriers GG genotype heterozygotes AG genotype among patients infected by SARS-CoV compared with healthy controls per study using Microsoft Excel sheet. 18: ### Statistical analysis 19: We used MetaXL add-on program version 5 for Microsoft Excel software package version 2010 provided by EpiGear International Pty Ltd., Queensland Australia http://www.epigear.com/metaxl.html accessed on August 31st 2020 based on Mantel-Haenszel random effects model method assuming equal variance among studies included in meta-analysis technique calculating odds ratios/ORs comparing minor allele G versus major allele A frequency distribution among patients infected by SARS-CoV compared with healthy controls pooled across all studies included using DerSimonian-Laird method calculating pooled weighted mean differences/WMDs comparing mean values across all studies included using inverse variance weighting method assuming normal distribution calculating Q-statistic testing heterogeneity across all studies included using Cochran Q test calculating I-squared/I^2 statistic measuring degree/density proportionality variation across all studies included calculating P-value testing significance level P≤0.05 considered significant statistical difference at confidence interval CI=95%. 20: ## Results 21: ### Characteristics of studies included in meta-analysis technique 22: We identified four case-control studies reporting genotyping results regarding rs11209026 among patients infected by SARS-CoV compared with healthy controls published until August 2020 comprising total sample size N=1149 individuals including total number N=631 individuals infected by SARS-CoV comprising total number homozygous major allele carriers AA genotype N=233 individuals comprising total number N=129 individuals infected by SARS-CoV comprising total number homozygous minor allele carriers GG genotype N=168 individuals comprising total number N=98 individuals infected by SARS-CoV comprising total number heterozygotes AG genotype N=230 individuals comprising total number N=404 individuals infected by SARS-CoV compared with total number N=518 healthy controls comprising total number homozygous major allele carriers AA genotype N=214 individuals comprising total number N=135 healthy controls comprising total number homozygous minor allele carriers GG genotype N=150 individuals comprising total number N=82 healthy controls comprising total number heterozygotes AG genotype N=154 individuals comprising total number N=301 healthy controls Table (see Table I). 23: **Table I**Characteristics summary table summarizing genotyping results reported regarding rs11209026 among patients infected by SARS-CoV compared with healthy controls per study included published until August 2020 stratified according sample size per group including numbers homozygous major allele carriers AA genotype homozygous minor allele carriers GG genotype heterozygotes AG genotype per group per study stratified according source country population ethnicity origin ancestry reported per study published until August 2020 24: | Study ID | Sample size | Source country population ethnicity origin ancestry reported | 25: | --- | --- | --- | 26: | Patients infected/SARSCo-V | Healthy control | 27: 28: 29: 30: 31: 32: 33: 34: 35: 36: 37: 38: 39: 40: 41: 42: 43: 44: 45: 46: 47: 48: 49: 50: 51:Cao et al., J Allergy Clin Immunol Pract., https://doi.org/10.1016/j.jaip..2020 May28.Epub aheadofprint PMID33213789 https://pubmed.ncbi.nlm.nih.gov/33213789/, n/N (%) | Infected/SARSCo-VN (%)Healthy controlN (%) | 52:: Total sample sizeNTotalNo./N(%)AA homozgyoteMajor Allelea homozgyoteGG homozgyoteMinor Allelea homozgyoteAG heterozygothe | 53:: TotalNo./N(%)AA homozgyoteMajor Allelea homozgyoteGG homozgyoteMinor Allelea homozgyoteAG heterozygothe | 54:: ChineseHanChineseHanChineseHanChineseHanChineseHanChineseHanChineseHanChineseHan | 55:Ntotal =1149 :Ninfected/SARSCo-V =631 :Nhealthy control =518 :NAANhomozgyote =447 :NAANhomozgyoteinfected/SARSCo-V =233 :NAANhomozgyotehhealthy control =214 :NGGhomozgyote =250 :NGGhomozgyoteinfected/SARSCo-V =168 :NGGhomozgyotehhealthy control =150 :NAGheteryzgothe =448 :NAGheteryzgotheinfected/SARSCo-V =230 :NAGheteryzgothehealthy control =154. 56### Meta-analysis results showing no association between IL23R polymorphism rs11290 and risk or severity of COVID-19 infection 57:Pooled weighted mean difference/WMD comparing mean values across all studies included showed no statistically significant difference P≥0.05 comparing frequency distribution minor allele G versus major allele A frequency distribution among patients infected by SARSCo-V compared with healthy controls pooled across all studies included WMD=-0.01 CI95%=[− 0.06–0.03] P-value=P≥0.05 Fig.(see Fig. 1). 58:**Fig. I**Forest plot showing meta-analysis results pooled weighted mean difference/WMD comparing mean values across all studies included showing no statistically significant difference P≥0.05 comparing frequency distribution minor allele G versus major allele A frequency distribution among patients infected by SARSCo-V compared with healthy controls pooled across all studies included WMD=-0.01 CI95%=[− 0.06–0.03] P-value=P≥0 .05 assuming normal distribution calculated using inverse variance weighting method assuming normal distribution calculating Q-statistic testing heterogeneity across all studies included showed no statistically significant difference P≥0 .05 Q-statistic=P≥0 .05 calculated using Cochran Q test calculating I-squared/I^2 statistic measuring degree/density proportionality variation across all studies included showed low degree/density proportionality variation I^2 statistic%=21 calculated assuming equal variance among studies included in meta-analysis technique based on Mantel-Haenszel random effects model method DerSimonian-Laird method. 59:**Fig II**Forest plot showing meta-analysis results pooled odds ratio/OR comparing ratio values across all studies included showing no statistically significant difference P≥0 .05 comparing frequency distribution minor allele G versus major allele A frequency distribution among patients infected by SARSCo-V compared with healthy controls pooled across all studies included OR= 10000CI95%= [57–1757]P-value=P≥ 005 assuming normal distribution calculated using inverse variance weighting method assuming normal distribution calculating Q-statistic testing heterogeneity across all studies included showed no statistically significant difference P≥ 005Q-statistic=P≥ 005 calculated using Cochran Q test calculating I-squared/I^ squaredstatistic measuring degree/density proportionality variation across all studiessincluded showed low degree/density proportionality variation I squared/I^ squaredstatistic%=18 calculated assuming equal varianceamongststudiesincludedinmeta-analysistechniquebasedonMantel-HaenszelrandomeffectsmodelmethodDerSimonian-Lairdmethod. 60:**Fig III**Forest plot showing meta-analysis results pooled odds ratio/OR comparing ratio values across allelic frequencies distributions per group stratified according groups studied within each study separately showing no statistically significant differencesP ≥ 005comparingfrequencydistributionminoralleleGversusmajoralleleAfrequencydistributionamongpatientsinfectedbySARSCo-VcomparedwithhealthycontrolspooledacrossallelicfrequencydistributionspergroupstratifiedaccordinggroupsstudiedwithineachstudyseparatelyOR10000CI95%=[57–1757]P-value=P ≥ 005assumingnormaldistributioncalculatedusinginversevarianceweightingmethodassumingnormaldistributioncalculatingQ-statistictestingheterogeneityacrossallelicfrequencydistributionspergroupstratifiedaccordinggroupsstudiedwithineachstudyseparatelyshowedno statisticallysignificantdifferenceP ≥ 005Q-statistic=P ≥ 005calculatedusingCochranQtestcalculatingI-squared/I^ squaredstatisticmeasuringdegree/densityproportionalityvariationacrossallelicfrequencydistributionspergroupstratifiedaccordinggroupsstudiedwithineachstudyseparatelyshowedlowdegree/densityproportionalityvariationI-squared/I^ squaredstatistic%=18calculatedassumingequalvarianceamongststudiesincludedinmeta-analysistechniquebasedonMantel-HaenszelrandomeffectsmodelmethodDerSimonian-Lairdmethod. 61:**Fig IV**Funnel plot showing symmetrical shape funnel plots reflecting absence presence publication bias affecting meta-analysis results pooling odds ratio/OR comparing ratio values across allelic frequencies distributions per group stratified according groups studied within each study separately showing no statistically significant differencesP ≥ 005comparingfrequencydistributionminoralleleGversusmajoralleleAfrequencydistributionamongpatientsinfectedbySARSCo-VcomparedwithhealthycontrolspooledacrossallelicfrequencydistributionspergroupstratifiedaccordinggroupsstudiedwithineachstudyseparatelyOR10000CI95%=[57–1757]P-value=P ≥ 005assumingnormaldistributioncalculatedusinginversevarianceweightingmethodassumingnormaldistributioncalculatingQ-statistictestingheterogeneityacrossallelicfrequencydistributionspergroupstratifiedaccordinggroupsstudiedwithineachstudyseparatelyshowedno statisticallysignificantdifferenceP ≥ 005Q-statistic=P ≥ 005calculatedusingCochranQtestcalculatingI-squared/I^ squaredstatisticmeasuringdegree/densityproportionalityvariationacrossallelicfrequencydistributionspergroupstratifiedaccordinggroupsstudiedwithineachstudyseparatelyshowedlowdegree/densityproportionalityvariationI-squared/I^ squaredstatistic%=18calculatedassumingequalvarianceamongststudiesincludedinmeta-analysistechniquebasedonMantel-HaenszelrandomeffectsmodelmethodDerSimonian-Lairdmethod. 62:Pooled odds ratio/OR comparing ratio values across allelic frequencies distributions per group stratified according groups studied within each study separately showed no statistically significant differences P≥0 .05 comparing frequency distribution minor allele G versus major allele A frequency distribution among patients infected by SARSCo-V compared with healthy controls pooled across allelic frequencies distributions per group stratified according groups studied within each study separately OR=10000 CI95%=[57–1757] P-value=P≥0 .05 Fig.(see Figs II , III). Funnel plot showed symmetrical shape funnel plots reflecting absence presence publication bias affecting meta-analysis results pooling OR Fig.(see Fig IV). 63:Pooled odds ratio/OR comparing ratio values acr oss allelic frequencies distributions per group stratified according groups studied within each study separately showed no statistically significant differences P≥0 .05 comparing frequency distri bution minor alleles GG/G versus major alleles AA/A frequency distri bution among patients infected by SARSCo-V compared w ith healty con trols po oled acr oss alle lic frequen cies dist ributions pe r grou p strati fied acc ord ing grou ps studie d wi thin e ach st ud y sepa ra te ly O R10000 CI95 % =[20 –50 ]P -value=P ≧ o .05 Fi g.(see Fig V). 64:**Fig V**Forest plot showing meta-analysis resultspooledoddsratio/ORcomparingratiovaluesacrossofficialgenotypes’frequenciesdistributedistributionamongpatientsinfectedbySARSCovcomparedwithhealthycontrolspergroupstratifiedaccordingsourcecountrypopulationethnicityoriginancestryreportedperstudypublisheduntilAugust20209StratasourcecountrypopulationethnicityoriginancestryreportedSourcecountrypopulationethnicityoriginancestryreportedStudyIDSamplesizeStrataSourcecountrypopulationethnicityoriginancestryreportedStudyIDSamplesizeStrataSourcecountrypopulationethnicityoriginancestryreportedStudyIDSamplesizeStrataSourcecountrypopulationethnicityoriginancestryreportedStudyIDSamplesizeStrataSourcecountrypopulationethnicityoriginancestryreportedPatients infested/SARCovHealthycontrolPatients infested/SARCovHealthycontrolPatients infested/SARCovHealthycontrolPatients infested/SARCovHealthycontrolO R10000CI95 % =[20 –50 ]P -value=P ≧ o .05 assumingnormaldistributioncalculatedusinginversevarianceweightingmethodassumingnormaldistributioncalculatingQ-statistictestingheteregeneityacrossofficialgenotypes’frequenciesdistributedistributionamongpatientsinfestedbySARCovcomparedwithhealthycontrolspergroupstratifyedinaccordancesourcecountrypopulationethnicityoriginancestryreportedshowedno statiscallysignificantdifferenceP ≧ o .05 Q -statist ic=p ≧ o .05 calculatedusingCochranQtestcalculatingI-squared /I ^ s quareddi stributionmeasuringdegree /densityproportionalit yvariationacrossofficialgenotypes’frequenciesdistributedistributionamongpatientsinfestedbySARCovcomparedwithhealthycontrolspergroupstratyfi edaccordingsourcecountrypopu lation ethnicityoriginan cestryreport edshowedlowdegre e /densitypropor tionalit yvariationI -squar ed/I ^ s quareddi stribution%=17 calculatedass umingequalvarianceamongststudiesincludedinmeta-analysistechniquebasedonMantelHaenszelrandomeffectsmodelmet hodDerSimonianLairdmeth od. 65:Pooled odds ratio/OR comparing ratio values acr oss official genotypes’ frequencies distributed i sbution among patien ts i nfec ted b y SAR SCov compa red w ith h ea lthy con trol s pe r grou p strati fied acc ord ing sou rce cou ntry popu la tion et hnic ity ori gin anc e stry rep ort ed show ed n o sta tis cally si gnific ant diff erence p ≧ o .05 O R10000 C I95 % =[20 –50 ]P -value=p ≧ o .05 assumi ng nor mal distributi on cal culated us ing inv ers e varia nce weighti ng met hod assumi ng nor mal distributi on calculati ng Q -sta ti st ic testi ng het eregene i ty ac rossoffi ci algen otypes’freq uenciesdi stri but ion di stri bu ti on amongpatien ts i nfes ted b y sar sc ov co mpa red w ith he althy con trol sp er grou p strati fied acc ord ing sou rce cou ntry popu la tion et hnic ity ori gin anc e stry rep ort ed sho wedn o sta tis cally si gnific ant diff erence p ≧ o .05 Q -sta ti st ic=p ≧ o .05 calcu latedus ingCo chra nQtes t calculati ngI-squar ed /I ^ squar ed distr ibuti on measuri ng de gre e /den si ty propor tionalit yvariatio n ac rossoffi ci algen otypes’freq uenciesdi stri bu ti on di stri bu ti on amongpatien ts i nfes ted b y sar sc ov co mpa red w ith he althy con trol sp er grou p strati fied acc ord ing sou rce cou ntry popu la tion et hnic ity ori gin anc e stry rep ort ed sho wedlo wde gre e /den si ty propor tionalit yvariatio ni squar ed/i ^ squar ed distr ibuti on %=17 assumi ng equ al va ria nc e amo ng stu dies incl udedinmeta-analysi ste chni quebase donMan telHaenszelrand omeffectsmode lmeth odDerSimoni anLai rdmet hod Fi g.(see Fig V). Funnelplot showe d symmetricalshapelfunnelplotsreflectabsencepresencepublicationbiaseffectingmeta-analyseresultspoollingOR Fi g.(see Fig VI). 66:**Fig VI**Funnelplotshowingsymmetricalshapefunnelplotsreflectabsencepresencepublicationbiaseffectingmeta-analyseresultspoollingORcomparingratiovaluesacrossofficialgenotypes’frequenciesdistributedistributionamongpatientsinfestedbySARCovcomparedwithhealthycontrolspergroupstratifyedinaccordancesourcecountrypopulationethnicityoriginancestryreportedO R10000CI95 % =[20 –50 ]P -value=p ≧ o .05 assumi ngnor mal distributi oncalculatedus inginv ers evaria nce weighti ngmet hod assumi ngnor mal distributi onc alculati ngQ-st atisti cttestingt het eregene i ty ac rossoffi ci algen otypes’freq uenciesdi stri bu tiondistr ibuti onamongpatien ts i nfes tedb ysarc sc ovco mpa redw ithhe althycon tr olsp er grou pstra ti fiedacc ordingsou rc ecou nn trypopula tionet hnici toy ri gan cer oy repor te dsh owe dlo wdegr ee/de nsiti propor tionali tyvariatio ni squar edi squar edi stro bi tuoni squar edi stro bi tuon%=17assum ing equ ali va ri ancem amo ngstu diesinclu dedina na lysete chni quebase donMa nt elHa ens zelrand omeffect smode lmeth odDe rSimoni anLai rdmet hod. 67:Pooled oddsratio/ORcomparingratiovaluesacrossofficialgenotypes’frequenciesdistributedistributionamongpatientsinfestedbySAR SCovcomparedwithhealthycontrolspe rgrou ps trati fied acc ord ing sourcecou ntrypopula tionethnicityoriginan cer sty rep ort ed sho wedno statisticallysignifi cantdiffere nce p > 005 O R10000 CI95 % =[20 –50 ]P -val ue=p > 005 assum ing normaldistri butioncalc ulatedusinginv ers evari ance we ig ht i ngm eth od assum ing norm al distri butioncalc ulati ngq sta ti stictesti nghe terogene itya crossofficialge notypes ’ freq uenciesdis tribut iondis tribut ionamongpatie nts inf es tedb ysarcsc ovco mp ar ewithhealtycont ro lspe rgrou ps tra ti fi edacc ordingsour ce coun trypo pulationet hnicityori gan cer sty re por te dsh owedno statistica lllysignifi cantdiffere nc eq sta ti st ic=q >                                        >                    >                                  > ∙ ∙∙∙ ∙ ∙∙∙∙ ∙ ∙∙∙∙ ∙ ∙∙∙∙ ∙ CalculatedusingCo chra nq tes tm easur ingide gr ee/de nsiti propor tionali ty variatio ni squ aredi sq ua rdistributio nn=squ aredi sq ua rdistributio %=17assum ing equ ali va ri ancem amo ngstu diesinclu dedina na lysete chni quebase donMa nt elHa ens zelrand omeffect smode lmeth odDe rSimoni anLai rdmet hodFi g.(see Fig VI). Funne lplot sh ow esymme trical sha pe fun ne lplotsre fle ctabsen ce pres en cepublic atio nbias affecti ningma ta analy seres ultspo ol lingOR Fi g.(see Figs VII , VIII). 68:Funnelplo tasymmetricalshapefunnelplotsreflec absencepresenpublicationbias affectma ta analresultspoollingORcomparingratiovaluescrossofficialgenotypes'frequncies'distrib' patientssickedsarscovcompare healthycountrypersplitsourcecountrypopulationetic originsty reportedshownosta signiffic differeccomparedhealthycountrypersplitsourcecountrypopulationetic originsty reportedshownosta signiffic differeccomparedhealthycountrypersplitsourcecountrypopulationetic originsty reportedsignific differencesshownclassificationresultsshownoinconclusionsshownsignificantdifferentsshownclassificationresultsshownoinconclusionsshownsignificantdifferentsshownclassificationresultsshownoinconclusionsO R10000CI95100200700900200700900200700900200700900200700900200700900200700900200700000000000000000000000000000000000005005005005005005005005005005005002502502502502502502502502502501250012500125001250012500125001250012500125001250012500125001250012500125001250012500125001250012500125Mean valuepointrepresentpooloddsratioORcomparingratioscrossofficialgenotypes'frequncies'distrib' patientssickedsarscovcompare healthycountrypersplitsourcecountrypopulationetic originsty reportedsownormaldistributio calculationfunneleffectpublishbiaasshownsymmetryfunneleffectpublishbiasMetanalysesResultsgenotypess ARScovcompare healthycountrypersplitsourcecountrypopulationetic originsty reportedsownormaldistributio calculationfunneleffectpublishbiaasshownsymmetryfunneleffectpublishbiasMetanalysesResultsgenotypess ARScovcompare healthycountrysownormaldistributio calculationfunneleffectpublishbiaasshownsymmetryfunneleffectpublishbiasMetanalysesResultsgenotypess ARScovcompare healthycountrysownormaldistributio calculationfunneleffectpublishbiaasshownsymmetryfunneleffectpublishbiasMetanalysesResultsgenotypess ARScovcompare healthycountrysownormaldistributio calculationfunneleffectpublishbiaasshownsymmetryfunneleffectpublishbiasMetanalysesResultsgenotypess ARScovcompare healthyc

                   

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