Mixed effect models for predicting microbial interactions in the vaginal ecosystem

0095-1137/94/$04.00+0

CopyrightC) 1994, American Society for Microbiology

Mixed-Effect

Models for Predicting Microbial Interactions in

the

Vaginal

Ecosystem

R.A.

ROSS,'*

M.-L. T. LEE,' M. L. DELANEY,2 ANDA. B. ONDERDONK2

Channing Laboratoryand DepartmentsofMedicine' andPathology,2HarvardMedical School, and

Brigham

& Women's

Hospital,

180

Longwood

Avenue, Boston,

Massachusetts 02115

Received 20 August 1993/Returned for modification 8 November 1993/Accepted 20 December 1993

Three statistical models that predict microbial interactions within the vaginal environment are presented. A large data set was assembled from in vivo studies describing the healthy vaginal environment, and the data set wasanalyzed to determinewhetherstatistical models which would accurately predict the interactionsofthe microflora in thisenvironmentcould beformulated. Duringassembly of thedataset, two newvariableswere defined andwereaddedtothedata set, thatis, cycle (sequence of menstrual cycle) and flowstage (subdivision ofcycle determined by day ofmenstrual cycle).Concentrations of total aerobic (includes facultative) bacteria, totalanaerobicbacteria, and a Corynebacterium sp. were identified by correlation

analysis

asvariables with significant predictors. By using a regression method with a backward elimination procedure, significant predictors ofthese outcomevariables wereidentified as the concentrations of LactobaciUus spp., anaerobic Streptococcus spp., and Staphylococcus spp., respectively. For all three outcomevariables, pH andflow stage werealsoidentifiedassignificant independent variables.Because someof the datain thedatasetarerepeated measurements for a subject, a mixed-effect model that accounts for the random effects of repeated-measurement data fit best the data set forpredicting interactions betweenvarious members ofthevaginal

microflora. The predictive accuracies ofthe three models were tested by a comparison of model-predicted outcome-variablevalueswithactual mean invivooutcome-variablevalues. From these results,weconcluded thatit ispossibletoaccuratelypredict vaginal microflora interactions byusingamixed-effectmodelingsystem. Theapplicationof this type ofmodeling strategyandits futureuse arediscussed.

Knowledge of the vaginal microflora has improved

consid-erablyfrom the classic view that thevaginais colonized witha

homogeneous population oflactobacilli. More recentstudies

haveproducedthemost accurateandcomplete descriptionsof

thevaginalmicroflora, inpart,because ofimproved culturing

techniques. Itisnowknown that thevaginalenvironment is a

complexanddynamic ecosystemwith amicroflora that isnot

homogeneous byany means.

Themicroflora ofahealthy vaginaispresent at alevel of 108

to 109 CFU/g of secretion and is predominantly made up of

gram-positive organisms (3, 10, 14). The numerically

domi-nant, frequently isolated members of the vaginal microflora are Lactobacillus spp., a Corynebacterium sp., Streptococcus

spp.,Staphylococcus epidermidis and othercoagulase-negative

Staphylococcusspp.,Bacteroides spp., Eubacterium spp.,

Myco-plasma hominis, and Peptostreptococcus spp. Less frequently

isolated organisms include Micrococcus spp.,

Propionibacte-rium spp., and Veillonella spp. Rarely isolated organisms

includea Clostridium sp., Ureoplasmaurealyticum, a Fusobac-terium sp., Staphylococcus aureus, Neisseria spp., and Gard-nerella vaginalis (2,3, 8-12, 14).

Duringasingle menstrual cycle, both the phenotype and the concentration of anaerobic and aerobic bacteria can vary

significantly (2,3, 7, 8, 18,22). The concentration of anaerobes

remains constant throughout the menstrual cycle, while the total aerobic andfacultativeorganism concentrations decrease 100-foldduring the week preceding menstrual flow (2). During menstrual flow there is a decrease in the total bacterial

concentration, while an increase in the concentrations of

various individual anaerobic organisms is observed (16-18).A decrease in the concentration of Lactobacillus spp. and an

*Correspondingauthor.

increase in the concentrations of other gram-positive organ-isms, such as Staphylococcus spp., Corynebacterium spp., and

Streptococcus spp., have also been reported to occur during

menstrual flow (8, 17, 22).

The microflora of the vaginal environment significantly

affects the host'sgeneral stateof health byacting (not unlike mucosal surfacesor skin) as abiological barriertoinfectious agents (5). While most studies of the vaginal environment focus on describing the microflora, few studies attempt to

unravel the complex microbial interactions inherent to this barrier. Weset out toidentifythesemicrobial interactions and use themtoformulatestatistical models thatpredictthestate

ofthisprotectivemicrobial barrier. Usingdata

describing

the

vaginal ecosystems of nonpregnant, menarcheal women, we formulated three mixed-effect models thatpredict the interac-tions between microorganismswithin thevaginal ecosystem.

MATERLILSAND METHODS

Data set. The data set assembled for the present study

consisted ofa subset of information selected from adatabase

containing 1,890 data records and 39 variables from in vivo

studies (13, 16, 17). Each record consisted of data obtained from thesamplingofasubject. Samplescould be obtained from

a subject once ormultiple times during a study. This

charac-teristic of the datasetwas taken into account during analysis when themixed-effect modelwasused forregression analysis. Theexclusion criteria for the in vivo studieswere thorough

to ensure that onlywomenwith a healthy vaginal microflora were included (13, 17). Criteria for exclusion included preg-nancy,genital abnormalities, useofcontraceptive spermicides,

vaginal infectionsincludingbacterialvaginosis detected

clini-cally, abnormalPapanicolaou test results, hysterectomy,

anti-microbialtherapyordouching1month orless before thestart

871

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ofsampling, positivity for human immunodeficiency virus or hepatitis B virus antibody, or a history of pelvicinflammatory disease or ectopic pregnancy. Pelvic examinations were per-formedoneachwomanpriortothestartof theprotocol, and samples for culture of Chlamydia trachomatis and Trichomonas

vaginalis were obtained. Specificcultures for Neisseria

gonor-rhoeaewere notperformed because the culture methods used in all thestudies were adequate to detect this organism if it was present.

Bacterial concentrations were expressed as

log1o

CFU per gramof sample. Because some of the in vivo studies involved altering the vaginal environment, such as douching, only pretreatment data wereincluded in the dataset.

Two new variables, cycle (indicating the sequence of the menstrualcycles ofasubject)andflow stage (asubdivision of

cycle, determined by analysis ofvariance ofoutcomevariables

between cycledays),were addedto the dataset.

Bacteriological analysis.The sampling and culturing methods used in the studies are described in detail elsewhere (17). In brief, the duplicate-swab technique was used to sample the vaginal vault. Serial dilutions of the sample were plated onto various selective and nonselective media for the recovery of facultative andanaerobic microorganisms. For the recovery of anaerobicbacteria, the following media were used: prereduced Brucella base agar with5% sheep blood containing hemin and vitamin K1, each at 10 mg/liter(BMB); BMBwith 150 jig of neomycin sulfate per ml; and prereduced Brucella base agar with5% laked sheep blood, 100 ,ug of kanamycin per ml, 7.5 ,ug ofvancomycin per ml, and hemin and vitamin K1, each at 10 mg/liter. The media used for the recovery of facultative

organ-ismswere5%sheep blood in tryptic soy agar, mannitol salt agar, andMacConkey agar.Chocolate agar was used for the recovery of fastidiousorganisms(Adams Scientific,Inc.,Fiskeville, R.I.).

All colony types were isolated andidentifiedby established criteria (16-18). Briefly, facultative gram-positive cocci were

identified by usingconventional media and dichotomous keys

(1). Members of the family Enterobacteriaceae and

gram-negativebacilliwereidentifiedwitheithertheAPI20E system

ortheAMSVitek system(bioMerieux Vitek,Inc.,Hazelwood, Mo.). Aerobic, gram-positive, spore-forming, catalase-positive

rods were classified as Bacillus spp. Catalase-positive,

gram-positive, pleomorphic rodswere classifiedasCorynebacterium

spp. No further classification was performed for facultative

Corynebacterium spp. orBacillusspp. Gram-positiveor

gram-variable, catalase-negative, pleomorphic rods showing

beta-hemolysisonhuman bloodbilayeragarwithTweenagarwere presumptively identifiedasG. vaginalis.Thispreliminary

iden-tificationwasconfirmedbyusingamodificationof thecriteria

established by Piot et al. (20) or the rapid identification

method G.vaginaliskit(Austin Biological Labs, Austin, Tex.).

Obligate anaerobes and gram-positive, catalase-negative,

mi-croaerophilic bacilli were classified by gas-liquid

chromato-graphic analysis of glucose fermentationproducts and

antibi-oticsusceptibilitypatterns,whichwereperformed bystandard

procedures (6, 23).Final identification included theuseof the

Anastat II systemof biochemical tests anda computer

data-base (AdamsScientific) and the Microbial Identification

Sys-tem(MIDI, Inc., Newark,

Del.).

Selectionofindependentvariables.Significant (Pc0.1000)

independent variables wereidentified for each outcome vari-able by means of a regression method with a backward eliminationprocedure.Variablesweredroppedfrom the

mul-tiple regression analysisinastepwise fashion,startingwith the

least significant independent variable. Because this was an initial step in theanalysisof the data set, a Pvalueof 0.10was used.

Mixed-effect model. The following mixed-effect modelwas

used for statistical evaluation

(11).

Foreach

subject

i,

condi-tioning on random effects

bi,

the observation

Yi

=

(Y.,...,

Yi,)

with the covariate matrix of

Xi

is modeled

by

Yi

1

bi

-N(X,B

+

Zibi, a'2

X),

whereBisa

fixed-population

parameter vector,and

bi

isarandom-effectvectorforsubject i,with

bi

-N(O,

&D).

This mixed-effect modelis availableas apackageof Fortran subroutines which implements the maximum likeli-hood and restricted maximum likelilikeli-hood estimationmethods describedbyLindstrom and Bates

(11).

Itis distributed under thetermsof the GNU GeneralPublic Licenseas

published by

theFree SoftwareFoundation.

RESULTS

Restructuring ofdata set. Results of one-way analysis of variance and Sheffe'stestdocumenteda significantdifference in the meanconcentrations of total aerobic

(includes

faculta-tive)

bacteria

(TAB) (P

= 0.0014), mean concentrations of total anaerobic bacteria

(TAnB) (P

=

0.0001),

andmeanpH

values(P=0.0001)forcycle days2 to5(a cycle beginsoncycle day 1, which is the first dayof menstrual bleeding). Further

analysisrevealed asignificantdifference in thesemeanvalues

betweencycle days2to3 andcycle days4 to5. Forcycle days

6 to 28, mean TAB concentrations, mean TAnB

concentra-tions,

andmean pHvalues didnotdiffer

significantly.

Conse-quently,eachcyclewassubdivided into thefollowingthreeflow

stagesaccordingtocycle day: flow stage 1 (cycle days2 to

3),

flowstage2(cycle days4to5), andflow stage 3 (cycle days6 to

28).

Since thecorrespondingflow stages incycles1to4displayed

no significant difference inmean TABconcentrations, TAnB

concentrations, or pH values (Fig.

1),

the data from each

individual flow stage within cycles 1 to 4 were combined.

Analysisof these combined data showed that forflow stages 1

to 3 there was no significant difference among mean TAB

concentrations, but there was a significant difference among

meanTAnBconcentrations(P=0.0152)and amongmeanpH

values(P=

0.0001).

Model formulation. After

adjustment

for

cycle

and flow-stage

effects,

the dataset was analyzed

by using

acorrelation matrix to identify outcome variables and their correlated

independentvariables. The concentration of Lactobacillus spp.

wasfoundtobepositivelycorrelated with theTAB

concentra-tion

(r

=

0.4560),

theconcentration of anaerobicStreptococcus

spp. was found to be

positively

correlated with the TAnB concentration

(r

=

0.3720),

and theconcentration of

Staphy-lococcus spp. was found to be

positively

correlated with the concentration of theCorynebacterium sp.

(r

=

0.6898).

More-over,pHand flow stagewerefoundtobe

significant

indepen-dent variablesinall threemodels(Pc

0.1000).

Therefore,we

had threeoutcome variables

(TAnB,

TAB, and

Corynebacte-rium sp.

concentrations)

that could be modeled

by

using

a

number ofindependentvariables.

Otherstatistical modelsweretested

(multiple

linear

regres-sion, regression

with

damped

exponential

correlation

struc-ture),

but the mixed-effect model for

repeated-measure

data best fit the data set for

predicting

bacterial interactions

by

using

covariate variables. Inthe mixed-effect

model,

data for each

subject

weremodeledas a

parametric

function,

in which some of the parameters or "effects" were random variables with a multivariate normal distribution and some were fixed

(11).

The following

equations,

with numbers of

equations

in

parentheses

atthefar

right,

arethethreemixed-effect models:

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CU

Cu

7.5- 7- 6.5- 6- 5. 5- 4.5- 4- 3.5- 3-5.J

i

2

Cycle1

3 1 2

Cycle2

3 1 2

Cycle3

3

1C

4

Cycle4

Flow

Stages from Cycles 1

-

4

FIG. 1. Mean pH values fromcycles 1to4. Foreachcycle,themeanpHvaluesobservedinflowstages 1 to3arereported.Thedashedline indicates thereturnofpHtothe valueatthebeginningofthenextcycle.

TABconcentration =(4.12+

bo)

+ [(0.46 +

bl)

x (1)

Lactobacillus spp. concentration] + [(0.06 + b2) x pH]

- (0.06 x flow stage 1) + (0.08 x flow stage2);

TAnBconcentration = (6.13 +

bo)

+ [(0.39 +

bl)

X

(2)

anaerobicStreptococcusspp.concentration] - [(0.07+ b2) x

pH] - (0.28 x flow stage 1) - (0.04 x flowstage2);

Corynebacteriumsp.concentration = (2.50 +

bo)

+ (3)

[(0.55 +

bl)

x Staphylococcusspp.concentration] + [(0.07 +

b2) x pH] + (0.15 x flowstage 1) + (0.38 x flowstage

2).

In all three models,

bo,

b1,

and b2 are random effects associated with each subject. Either 0 or 1 is substituted for flow stages 1 and 2 according to the

flow-stage

status of the

data, asfollows: 0 if the data arenotfrom theparticularflow

stage and 1 if data are fromthe particularflow stage. Inthe caseofflow stage 3data, 0issubstituted for both flow stage1 andflow stage 2.

Testing of data set variability. The distributions of the random-effect coefficients (RECs)

bo, bl,

and b2 for each model were calculated to determine the variability between eachsubject used in the analysis. Figure 2 displays the distri-butions of the RECs for theintercept

(bo),

theStaphylococcus

spp. concentration

(bl),

andthe pH (b2) in the

Corynebacte-rium sp.model (equation 3).Thedistributions of RECs in the

TAB(equation 1)and TAnB(equation 2)models weresimilar

(datanot shown).

Testingofmodel predictions. The mixed-effect models for the determination ofTAB (equation 1),TAnB (equation 2),

and Corynebacterium sp. (equation 3) concentrations were

tested for accuracy by comparison of actual mean in vivo concentrations of TAB, TAnB, and Corynebacterium sp. with predicted values. The predicted and actual mean values for

TAB concentration were the same (8.2

log1o

CFU/g). The predicted TAnB concentration was 8.8

loglo

CFU/g, while the actualmeanvaluewas 8.5

log1o

CFU/g. The predicted

Coryne-bacterium sp. concentration was 0.2

loglo

units lower thanthe actual mean value of 6.7

logl0

CFU/g.

DISCUSSION

The primary aim of the present study was to formulate statistical models with whichto predict microbial interactions in thevaginal ecosystem. In the process of modelconstruction,

two factors, pH and flow stage, were identifiedas significant

independent variables. These findings were not unexpected since in vivo studies hadsuggested that both variables could have an impact on thevaginal ecosystem. Other researchers haveproposedpH as aprimary mechanism in the control of thevaginal environment, since pH has been associated with changes in the microflora (19, 21). Flow-stage (i.e., rate of menstrual fluidsecretion)effectisnot aswelldefined and may represent the effect of menstrual fluid on thevaginal ecosys-tem. The menstrual cycle has been implicated as a factor

influencingthecompositionof thevaginalmicroflora(15),with

the most numerous changes occurring during menstrual flow (8, 16, 17,22). Thevaginalenvironment may bealteredbythe introduction of degenerated cells, hemin, and other blood

proteins during menstrual flow (4); however, the specific

flow-stagefactorsaffectingthe ecosystem havenotbeen

eluci-dated.

Other significant independent variables identified during modelformulationinclude the Lactobacillus sp. concentration for predicting the TAB concentration (equation 1) and the anaerobicStreptococcussp. concentration forpredictingTAnB concentration (equation 2). Both of these independent vari-ablescomprise themajorityof totalbacterialcounts.Thismay explain why these factorsaresignificant. Aninterestingfinding wastherecognition that theStaphylococcus sp. concentration

is predictiveof the Corynebacterium sp. concentration

(equa-tion 3); thisunanticipated microbial interaction deserves

fur-therinvestigation.

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76 A

6-5 U

3

4 U

2

-1~~~~~~~~~~~~~~~~~~~~~~~~~~~--- --- -

-3 U

-4U

0 10 20 30 40 50 60 70 80 90 100 1I610 212

SubjectNumber

0.8-0.7

B

0.6-0.4

0.3- - U

0.2-,. ,,_---...

---0.1 U.

-0.3 U

-0.24 a

-04--0.5

-.6 -.7

0 10 20 30 40 50 60 70 80 90 100 110 120

SubjectNumber

0.8-0.6_

0.5-0.4

0.3-0.2

-0.1 *mI r

-0.2-

---U--

-0.3--0.4 U

-0.5 -0.6 -0.7 0.8

A

secondary

aim of the present study was to demonstrate that the datasetthatweusedresulted inaccuratepredictions for the microflora. This goal was accomplished, in part, by calculation of the RECs for each subject by using equations 1 to3. Forthe Corynebacterium sp.model (equation 3) (Fig.2), the RECs

b(,

b,,

andb2 for each subject had normal distribu-tions, within1 standarderrorof themean.The minor degree ofvariability observed in the RECs for each subject indicates theaccuracyof the data set.

The precisions of the three statistical models and the reconstructed data set were tested by comparison of the predicted values of theoutcomevariables with the actualmean values observed in vivo. The similarity of the predicted and actual values for TAnB and Corynebacterium sp. concentra-tions and the identities of thepredicted and actual values for TABconcentrationsuggestthatthe mixed-effect models satis-factorily predict microbial concentrations in vivo.

Both the determination of RECs and thecomparisons of the predicted and actualmeanvalues for theoutcomevariables of the three mixed-effect models verify the accuracy of the restructured dataset.The precision of the datasetis important to the success of future studies, which will focus on the identification of the microbial interactions involved in the prevention of infection and the formulation of predictive models that will differentiate between ahealthyvaginal eco-system and one that is at risk for infection or disease. We conclude that theuseof the mixed-effect model for repeated-measuredata isanovel approachtothestatistical modelingof

ahost-microbe ecosystem.

20

0 10 20 30 40 50 60 70 80 90 100 110 120

SubjectNumber

FIG. 2. Distributions ofRECs for theintercept (A),the

Staphylo-coccussp.concentration(B),and thepH (C)inthe mixed-effectmodel predicting the Corynebacterium sp. concentration to determine the

degreeofvariabilitybetweeneachsubject.The smaller thedegreeof variability observed in the distribution of each subject's RECs, the

moreaccurate the data set. ThemeanREC value is indicatedbyasolid

line. The dashed lines above and below the solid line indicate 1

standarderrorofthemean.

ACKNOWLEDGMENTS

We thank David Friedmanfor valuable assistance in data entry.

This research was funded in part by grants from SmithKline

Beecham and Tambrands, Inc.

REFERENCES

1. Balows, A., W. J. Hausler, Jr., K. L. Herrmann, H. D.Isenberg, and H.J. Shadomy(ed.). 1991. Manual of clinical microbiology, 5th ed. AmericanSocietyforMicrobiology, Washington,D.C.

2. Bartlett, J. G., A. B. Onderdonk, E. Drude, C. Goldstein, M.

Anderka, S. Alpert, and W. M. McCormack. 1977. Quantitative

bacteriology of the vaginal flora. J. Infect. Dis. 136:271-277. 3. Bartlett, J. G., and B. F. Polk. 1989. Normal vaginal flora in

relation tovaginitis.Obstet.Gynecol. Clin. N. Am.16:329-336.

4. Hahn, L. 1980.Compositionof menstrualblood,p.107-137. In E. Diczfalusy, I. S. Frazer, and F. T. G. Webb (ed.), Endometrial

bleeding and steroidal contraception. Pitman Press Ltd., Bath,

England.

5. Hill, G. B., D. A. Eschenbach, and K. K. Holmes. 1984.

Bacteri-ologyof thevagina. Scand. J. Urol.Nephrol. Suppl.86:23-39.

6. Holdeman, L. V., E. P. Cato, and W. E. C. Moore (ed.). 1977.

Anaerobe laboratory manual, 4th ed. Virginia Polytechnic Insti-tute andStateUniversity, Blacksburg.

7. Johnson, S. R., C. R. Petzold, and R. P. Galask. 1985.Qualitative

andquantitative changesof thevaginalmicrobial floraduringthe

menstrualcycle. Am. J. Reprod. Immunol. Microbiol. 9:1-5.

8. Larsen, B., and R. P. Galask. 1982. Vaginal microbial flora:

composition and influencesof hostphysiology.Ann. Intern. Med. 92:926-930.

9. Levinson,M.E.,L. C.Corman, E. R.Carrington, and D. Kaye.

1977. Quantitative microflora of the vagina. Am. J. Obstet.

Gynecol. 127:80-85.

10. Lindner, J.G. E. M.,and F. H. F. Plantema. 1978. Quantitative studies of thevaginalflora ofhealthywomenand ofobstetric and

gynecological patients. J. Med. Microbiol. 11:233-241.

11. Lindstrom, M.J.,and D. M. Bates. 1988.Newton-Raphson and

EM algorithms for linear mixed-effects models for

repeated-measuresdata. J. Am.Statist. Assoc. 83:1014-1022.

12. Ohm, M. J.,and R. P.Galask. 1975. Bacterial flora of the cervix 0)

4-ci

0 w

a:

ci

c 0

0

QI

U)

01

a

C.)

0 cn

0

IL

0.

0 w

cr

C)

cr

on May 15, 2020 by guest

http://jcm.asm.org/

from 100 prehysterectomy patients. Am. J. Obstet. Gynecol. 122:683-687.

13. Onderdonk, A. B., M. L. Delaney, P. L. Hinkson, and A. M. DuBois. 1992. Quantitative and qualitative effects of douche preparationsonvaginal microflora. Obstet. Gynecol. 80:333-338. 14. Onderdonk, A. B., B. F. Polk, N. E. Moon, B. Goren, and J. G. Bartlett. 1977. Methodsfor quantitative vaginal flora studies.Am. J.Obstet.Gynecol. 128:777-781.

15. Onderdonk, A. B., and K. Wissemann.1993. Normalvaginal flora,

p. 285-304. In P. Elsner and J. Martius (ed.), Vulvovaginitis.

Marcel Dekker,Inc., NewYork.

16. Onderdonk, A. B.,G.R. Zamarchi, M. L. Rodriguez, M. L. Hirsch, A. Munoz, and E. H. Kass. 1987. Quantitative assessment of vaginal microflora duringuseoftamponsof various compositions. Appl.Environ. Microbiol. 53:2774-2778.

17. Onderdonk, A. B., G. R. Zamarchi, M. L. Rodriguez, M. L. Hirsch, A. Munoz, and E. H. Kass.1987.Qualitativeassessmentof vaginal microflora during useoftamponsof various compositions. Appl.

Environ. Microbiol. 53:2779-2784.

18. Onderdonk, A. B.,G. R. Zamarchi, J. A. Walsh, R. D. Mellor,A. Munoz, and E. H. Kass. 1986. Methods for quantitative and qualitative evaluation of vaginal microflora during menstruation. AppI.Environ. Microbiol. 51:333-339.

19. Paavonen, J.1983.Physiology and ecology of the vagina. Scand. J. Infect. Dis. Suppl. 40:31-35.

20. Piot, P., E. Van Dyck, P. A. Totten, and K. K. Holmes. 1982. Identification of Gardnerella (Haemophilus) vaginalis. J. Clin. Microbiol. 15:19-24.

21. Redondo-Lopez, V., R. L.Cook,and J. D.Sobel. 199t). Emerging role of lactobacilli in the control and maintenance of vaginal bacterial microflora. Rev. Infect. Dis. 12:856-872.

22. Sauter,R.L., andW.J. Brown. 1980. Sequential vaginal cultures from normalyoung women.J. Clin. Microbiol. 11:479-484. 23. Sutter, V. L.,V. L.Vargo,and S. M. Finegold. 1975. Wadsworth

anaerobic bacteriology manual, 2nd ed. University of California Press, Los Angeles.

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http://jcm.asm.org/