简介：Thepresentresearcheffortwasaimedtodevelopcolontargeteddrugdeliverysystem(CTDDS)ofrhubarb,herbaldrugusingamixedfilmofpectinandethylcellulose(EC).Pectinandethylcelluloseweremixedinvariousproportionstocoatthecoretablettotargetcolon.Themethanolicextractofrhubarbwasusedandthedoseoftheextractineachformulationwasfinalizedbyestimatingtheemodincontentinitbyhighperformacethinlayerchromatography(HPTLC).Invitrodrugrelease,erosionstudyinpresenceandabsenceofpectinaseenzymeandreleaseconstant(K)ofzeroorderwasmeasuredforeachformulation.Theformulationwasoptimizedbyusing32fullfactorialdesign.Formulationhaving50%pectinasacoatingpolymerwith12%coatweightwasselectedasanoptimizedformulation(OF)onthebasisof%similaritywithmaximumdesirabilitybutthisformulationwasnotabletoretardthereleaseofdruginstomachandupperintestinefully.SoitwasfurthercoatedwithEudragitS100(ES)(3%coatweight).Theoptimizedformulation,coatedwithESindicatedsignificantlaxativeactivityonloperamideinducedconstipationinrats.TheresultsrevealedthatCTDDSofrhubarbusingtwocombinedapproachesofbiodegrablemicroflora-activatedsystemandpH-sensitivesystemexhibitedapromisingcolontargetingperformance.

简介：AbstractFollowing the emergence of COVID-19 outbreak, numbers of studies have been conducted to curtail the global spread of the virus by identifying epidemiological changes of the disease through developing statistical models, estimation of the basic reproduction number, displaying the daily reports of confirmed and deaths cases, which are closely related to the present study. Reliable and comprehensive estimation method of the epidemiological data is required to understand the actual situation of fatalities caused by the epidemic. Case fatality rate (CFR) is one of the cardinal epidemiological parameters that adequately explains epidemiology of the outbreak of a disease. In the present study, we employed two statistical regression models such as the linear and polynomial models in order to estimate the CFR, based on the early phase of COVID-19 outbreak in Nigeria (44 days since first reported COVID-19 death). The estimate of the CFR was determined based on cumulative number of confirmed cases and deaths reported from 23 March to 30 April, 2020. The results from the linear model estimated that the CFR was 3.11% (95% CI: 2.59% - 3.80%) with R2 value of 90% and p-value of < 0.0001. The findings from the polynomial model suggest that the CFR associated with the Nigerian outbreak is 3.0% and may range from 2.23% to 3.42% with R2 value of 93% and p-value of <0.0001. Therefore, the polynomial regression model with the higher R2 value fits the dataset well and provides better estimate of CFR for the reported COVID-19 cases in Nigeria.

简介：AbstractBackground:Being able to predict with confidence the early onset of type 2 diabetes from a suite of signs and symptoms (features) displayed by potential sufferers is desirable to commence treatment promptly. Late or inconclusive diagnosis can result in more serious health consequences for sufferers and higher costs for health care services in the long run.Methods:A novel integrated methodology is proposed involving correlation, statistical analysis, machine learning, multi-K-fold cross-validation, and confusion matrices to provide a reliable classification of diabetes-positive and -negative individuals from a substantial suite of features. The method also identifies the relative influence of each feature on the diabetes diagnosis and highlights the most important ones. Ten statistical and machine learning methods are utilized to conduct the analysis.Results:A published data set involving 520 individuals (Sylthet Diabetes Hospital, Bangladesh) is modeled revealing that a support vector classifier generates the most accurate early-onset type 2 diabetes status predictions with just 11 misclassifications (2.1% error). Polydipsia and polyuria are among the most influential features, whereas obesity and age are assigned low weights by the prediction models.Conclusion:The proposed methodology can rapidly predict early-onset type 2 diabetes with high confidence while providing valuable insight into the key influential features involved in such predictions.

简介：Thisstudyadaptedastatisticalprobabilisticanatomicalmapofthebrainforsinglephotonemissioncomputedtomographyimagesofdepressiveend-stagerenaldiseasepatients.Thisresearchaimedtoinvestigatetherelationshipbetweensymptomclusters,diseaseseverity,andcerebralbloodflow.Twenty-sevenpatients(16males,11females)withstages4and5end-stagerenaldiseasewereenrolled,alongwith25healthycontrols.Allpatientsunderwentdepressivemoodassessmentandbrainsinglephotonemissioncomputedtomography.Thestatisticalprobabilisticanatomicalmapimageswereusedtocalculatethebrainsinglephotonemissioncomputedtomographycounts.AsymmetricindexwasacquiredandPearsoncorrelationanalysiswasperformedtoanalyzethecorrelationbetweensymptomfactors,severity,andregionalcerebralbloodflow.ThedepressionfactorsoftheHamiltonDepressionRatingScaleshowedanegativecorrelationwithcerebralbloodflowintheleftamygdale.Theinsomniafactorshowednegativecorrelationswithcerebralbloodflowintheleftamygdala,rightsuperiorfrontalgyrus,rightmiddlefrontalgyrus,andleftmiddlefrontalgyrus.Theanxietyfactorshowedapositivecorrelationwithcerebralglucosemetabolisminthecerebellarvermisandanegativecorrelationwithcerebralglucosemetabolismintheleftglobuspallidus,rightinferiorfrontalgyrus,bothtemporalpoles,andleftparahippocampus.Theoveralldepressionseverity(totalscoresofHamiltonDepressionRatingScale)wasnegativelycorrelatedwiththestatisticalprobabilisticanatomicalmapresultsintheleftamygdalaandrightinferiorfrontalgyrus.Inconclusion,ourresultsdemonstratedthatthediseaseseverityandextentofcerebralbloodflowquantifiedbyaprobabilisticbrainatlaswasrelatedtovariousbrainareasintermsoftheoverallseverityandsymptomfactorsinend-stagerenaldiseasepatients.