a schulte table is a grid with randomly distributed numbers or letters used for development of speed reading , peripheral vision , attention and visual perception .

schulte table

also there is myth that schulte table must be played only statically without clicking anything , this is wrong , because if your goal is to upgrade yourself , be more productive , find and analyze information much faster , improve attention and performance , then statically schulte table will not help , it will only increase your vision area that this version of schulte table can do also .
- is online game , that contains most powerful tools to upgrade your brain abilities based on schulte table , and other methods to train your brain .
this is a program of schulte table . you can use it to improve your abilities . if you practice super speed reading , this program may help you .
focus on the center of the table , try to see whole table while playing and try also not to move your eyes while searching numbers and clicking on them .
game clicker or schulte tables . rules and information about the game . develop attention training on urltoken
va . group wants to unseat pilgrims at history ' s table ( washingtonpost . com )
after the table is completed , certain results interpretation is possible . the following operating status indicators are calculated :
table 5 - 2 blood concentrations for cadmium in the u . s . population aged 1 year and older
table 5 - 3 urine concentrations for cadmium in the u . s . population aged 6 years and older
the most common version of the table is 5 columns and 5 rows , and cells are the numbers from 1 to 25 .
schulte , p et al . ( 2009 ) : ( table 2 ) representative analyses of fe–mg - ti - rich globules and lath - shaped pseudomorphologically - replaced crystallites , as well as the enclosing spherule matrix from two smectite spherules of the spherule deposit in odp hole 207 - 1258b
schulte , p . a . , and f . p . perera . 1993 . molecular epidemiology : principle and practices . new york : academy press .
williams is joined by students jada alexia hampton as ermina crump , avery howard as godfrey crump , kenyana trambles as lily ann green and hannah michelle as gerte schulte .
crumbs from the table of joy
intertwines the struggle — both inwardly and outwardly — of the journey toward a better life and comfort .
search for numbers ascending from 1 to 25 , mentally noting the results . the distance from the eyes to the table must be equal to the distance at which the person is used to read . the table must be clearly and fully visible - typically at a distance of 35 - 40 cm .
imagine a thanksgiving day without pilgrims . no turkey , no cranberries , no happy celebrations with family and friends crammed around the extended dining - room table .
particular importance is the work with schulte tables within general training of neurolinguistic programming . due to exercises , working capacity increases , as well as skill and speed of execution of consecutive actions . that is , the table is teaching the person to disable critical perception of reality and move into some apathetic state , which is ideal for working with the logical sequence of operations .
schulte , p . a . , and m . waters . 1999 . using molecular epidemiology in assessing exposure for risk assessment . ann . ny acad . sci . 895 : 101 - 111 .
lauded as a way of reducing uncertainty ( goldstein 1996 ; schulte and waters 1999 ; perera 2000 ; vainio 2001 ; who 2001 ; maier et al . 2004 ) , there are relatively few such cases .
p . s . i prefer to use mouse in my training , you can also use touch - screen but i don’t like when fingers are preventing me seeing all the table .
table 5 - 5 shows that the biomonitoring - based exposure estimate for dehp had a median of 0 . 71 , a 95th percentile value of 3 . 6 , and a maxi -
working with tables helps in quick and easy developing of some useful skills that will significantly improve the ability to work hard and intellectual load . regular training with schulte tables is the most effective speed reading and concentration training .
the tester has the standard online versions of the table , but the more difficult levels or certain types of tables can be either downloaded or passed on brainapps with extensive interpretation of the results .
table 5 - 5 estimated exposures ( µg / kg / day ) to the general population based on extrapolated intake from urinary metabolites in 289 individuals measured by blount et al . ( 2000 )
+ new game modes : 1 . gorbov - schulte 2 . practice + new app design + new app icon + statsitics has more information + next symbol is being said + sound and vibration feedback + new board size 4x4
) . putative criteria were sorted according to the resulting overall sensitivities and specificities . from all band combinations resulting in the same specificity , only those with the highest sensitivities were extracted for presentation in table
schulte , p . a . , and g . talaska . 1995 . validity criteria for the use of biological markers of exposure to chemical agents in environmental epidemiology . toxicology 101 ( 1 - 2 ) : 73 - 88 .
an interesting and complicated analog is represented by red and black schulte tables , which provide blocks of numbers from 1 to 25 . they can significantly diversify the training , since there may be more options , for example , searching for numbers of red and black by turn , or finding all the numbers of only one color , and so on . learn more about the rules for training with red and black tables in the section of the gorbova - schulte tables .
schulte tables are successfully used both individually and in groups , for example , on collective training . the fastest and most noticeable effect of this training is given to the developing organism - children , for whom specially modified tables are developed .
the most convenient way to work with tables is the online mode . a mini - trainer will give the user a random version of the table and allow a to fix completion time with a simple key click .
- is schulte tables . there are many paper and online variations of these tables . interestingly , this test is so popular that its name is already read as an abbreviation , a psychiatrist who first invented the technique , is almost not remembered .
the results for all scales can be assessed as good , or requiring training . for each age , normal readings are different , as well as for each variant of the table . find more information about results interpretation here .
rothman , n . , w . f . stewart , and p . a . schulte . 1995 . incorporating biomarkers into cancer epidemiology : a matrix of biomarker and study design categories . cancer epidemiol . biomarkers prev . 4 ( 4 ) : 301 - 311 .
usi ' s production of ' crumbs ' showcases history through memory
crumbs from the table of joy
intertwines the struggle — both inwardly and outwardly — of the journey toward a better life and comfort . check out this story on urltoken urltoken
jauris - heipke , s . , b . roessle , g . wanner , c . habermann , d . roessler , v . fingerle , g . lehnert , r . lobentanzer , i . pradel , b . hillenbrand , u . schulte - spechtel , and b . wilske .
they can be used to evaluate not only static , but also dynamic results of working with the table . when calculating the results with their further interpretation , the speed of finding all the numbers is taken into account ( at least 4 indicators ) .
the case studies presented in this chapter and in appendix b exemplify a variety of interpretive options . table 5 - 6 and the associated text summarize the case - study biomarkers in terms of their utility , supporting data , and the interpretive option that they exemplify .
when working with tables , the main aspect is the correct passage of training . since the emphasis is made on the improvement of peripheral vision , the training begins with the focus on the central cell of the table , while the others are evaluated only in the periphery .
if there is no internet or you wish to have a table at your fingertips in a printed version , it can be downloaded in various formats . in addition , printed exercises will allow to carry out mass training at work or , for example , in children groups .
while working with schulte tables , the most important role is played by two points : the speed of finding all the numbers in ascending order ( or descending order , depending on the setting ) and correct exercising . only in this case , positive training effect will take place , and you will improve peripheral vision .
( each must take less than 60 sec , but if 60 sec isn ' t enough for you than don ' t worry , you soon will be able to solve it much faster ) , so on the beginning of third lesson you will have 10 lines on each of your result table .
smaller versions of the tables that are simpler due to fewer numbers , are perfect for training children . for the younger ones , exercises with numbers from 1 to 9 will come in good , and for older children , tables of 12 , 16 and 20 cells are provided . children schulte tables allow to train the peripheral vision since primary school age . learn more about them here .
as shown in table 5 - 4 , the nhanes median values observed for the four metals in the u . s . population correspond , on the average , to about 4 % of the beis , whereas the 95th percentile values reach up to over 30 % of the bei . mercury is a special case because occupational exposures to mercury
) for wb for igm with both strains and for wb for igg with pko . for wb for igg with pbi , p30 was included in the band combination proposed previously . wb is used as a confirmatory assay ; therefore , specificities of at least 96 % were required . by applying these criteria to the panel of sera provided by eucalb , high sensitivities were obtained for sera from patients with all manifestations of lb ( table
< 0 . 001 ) . p41 of both strains was the band most frequently recognized by wb for igg by the group a samples but was also recognized by many control group sera . for group a , recognition of none of the other bands of pbi reached sensitivities of 25 % or more by wb for igg , whereas the unique proteins of pko , p43 , p17 , and p14 , were recognized considerably more frequently . from table
the ability to define a reference population can be a substantial limitation . there may be insufficient information regarding the exposure history of the reference population to a particular contaminant of interest . it is important to know the range of values of a given biomarker in a “normal” population ( nrc 1991 ) , where normal is regarded as without occupational exposure or without observed or hypothetical environmental exposure . however , schulte and talaska ( 1995 ) point out that pristine populations are rare , so “nonexposed” populations generally have some exposure of widely varied extent .
if done correctly , it will seem at first that the task is completed very slowly , because the eyes are not accustomed to work effectively in periphery . however , after one - two weeks you will feel positive result . in addition , at the beginning it is difficult to keep the view on the central cell - it will continually slide to habitually locate the desired cell by central vision . it is important to hold it in one place - in the center of the table .
concern its inorganic forms almost exclusively and the beis apply only to these forms . in contrast , the general population is exposed largely to the organic forms . the comparisons presented in table 5 - 4 are therefore of limited value for this specific metal . for parathion and pentachlorophenol , the 95th percentile values are below 1 % of the bei . pahs are also assessed in nhanes and acgih proposes only one biomarker , 1 - hydroxypyrene . however , acgih does not recommend a specific limit value for this determinant .
knowledge of the world is not possible without attention . any activity is accompanied by concentration and a continuous analysis of the state of the object , phenomena , properties . one of the most popular methods for the development of attention , and this is the basis of our games
clicker
and
click on boxes from 1 to 25 ( or in reversed order ) as fast as you can . in reaction mode click on red box .
for even better results - you have to treat this game as daily meditation practice .
i also recommend you while playing listen to special music for meditation , you can listen to radio for meditation right here . ( click play button in buttom left corner )
after 1 year of training you will be surprised , how everything comes easily to you .
if you are googling something you don’t just stare at your screen , you type and click , find and click , and after working out here you would be able to do it much faster .
if you are programmer , you will be able to see more of your code at once , find functions , procedures , classes , needed information in documentation much faster and be more productive .
even if you are gamer , this training will help you to be faster , see sharp more area of the screen , you will get more frags and headshots : ) .
in the left buttom corner you will find your daily progress , every day minimum is to make it 100 % .
if you want to make progress in your training you have to play all modes of the game : reaction , classic light , classic light reverse , classic original , classic original reverse and memory .
? click
f - facebook log in
to use your facebook user to log in . if you don ' t have facebook , enter user and password which you want to use and click
ok
. press
x
to play without statistics and tasks , as anonymous .
the app is now available in german , spanish , french and chinese languages .
i wish instructions just said that you concentrate on the middle square and then click the numbers in ascending order from 1 to 20 something . that ' s it very challenging and enjoying once i figured that out ! !
my only suggestion would be to add an alert once you reach the daily 10 . also , a notification to do this daily !
strangely relaxing . i don ' t ever do it 10 times a day but i do enjoy playing a few tables at a time . i am much better at letters than numbers
requires ios 9 . 0 or later . compatible with iphone , ipad , and ipod touch .
with family sharing set up , up to six family members can use this app .
by purchasing this item , you are transacting with google payments and agreeing to the google payments terms of service and privacy notice .
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urltoken
my tide times is the only tide tables application you ' ll ever need .
migrate from github to sourceforge with this tool . check out all of sourceforge ' s recent improvements .
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57 cores ; 453 . 9 m cored ; 7 m drilled ; 76 . 3 % recovery
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human vision works so that the most accurate concentration is in the central zone of the retina . the peripheral zone works much less efficiently , therefore , the angle and width of a person viewing is quite limited .
quick information search ( working with tables teaches to cover a large amount of text while looking at the point , making finding information in the block must easier ) .
passing the test also allows you to assess current skills , to see if they need improvement .
completion speed is also an important aspect , but you should not reach it by moving your eyes over all cells , as the effect of the training will be lost . therefore , the next image shows how you shouldn ' t pass the exercise .
besides to standard 5x5 variants , there are simpler tables for beginners - 3x3 and 4x4 . also , the following variants were specially developed :
to get more information about this book , to buy it in print , or to download it as a free pdf .
. washington , dc : the national academies press . doi : 10 . 17226 / 11700 .
this chapter describes various options for interpreting biomonitoring results with respect to those two questions and discusses how the analysis and interpretation can be used in different biomonitoring settings . the settings in which biomonitoring results may need interpretation include the workplace , the doctor’s office , screening of the general population , and study of specific subpopulations . the purpose and use of biomonitoring data may vary among those scenarios , but the options for interpreting the data are generally similar .
other questions that are alluded to but not addressed in detail in this chapter include , how did the exposures occur ? are there means to decrease the exposures ? these questions involve interpretation of biomonitoring data but also extend into risk - management issues .
figure 5 - 1 is a flow diagram of the information provided in this chapter . when biomonitoring data become available , one must determine the
interpretive options for evaluating them . the options include descriptive approaches that involve comparisons among biomonitoring datasets and risk - based approaches that describe the degree of risk associated with a given biomonitoring result . throughout this chapter and in appendix b , case studies are used to illustrate the applications of biomonitoring data to understanding of risk . the case studies are intended solely as illustrations and are not judgments about the data or risks associated with the chemicals discussed .
interpreting biomonitoring results depends on the availability of various types of information , including data on exposure , toxicity , and toxicokinetics . if toxicity information is unavailable , the results cannot be put into a risk
context . if exposure information is unavailable , it may not be possible to determine where and how the exposures that produced the measured biomarker concentrations occurred . however , the starting point for interpreting biomonitoring data is an assessment of the quality of the biomonitoring data . if they are of low quality , there is little point in considering whether exposure or toxicity information is available . but high - quality biomonitoring data may be applied to a variety of interpretive options , as outlined below . chapter 4 reviews the considerations relevant to the design of biomonitoring studies to ensure scientific quality and integrity .
two main options for interpreting biomonitoring results—descriptive and risk - based approaches—appear in figure 5 - 1 . this figure is organized from simplest to most complex approaches , with the potential for interpreting health risks also increasing from top to bottom . the expectation is that the quicker , descriptive approaches would be used first and then , depending on the level of concern and data availability , risk - based approaches would be used . the final interpretation of biomonitoring data would probably have elements of both .
another descriptive approach characterizes a chemical’s use pattern in society at large . the information is used to interpret biomonitoring data in terms of how long the chemical may have been detected in bodily fluids and whether its concentration may be going up or down with changing use . it is not uncommon for the public to consider a new biomarker as evidence of new exposure . but it is possible that exposure has been going on for de -
cades and the biomarker became available only recently . because new or increasing exposure generally prompts greater concern , the context is important . this question is best answered by analysis of biomonitoring results that span several years of sample collection . however , if biomonitoring results are available only for a single sampling round , temporal trends cannot be known . in such a case , historical data on chemical production rates and trends may be useful ( if they are available ) .
the most data - intensive approaches are those which evaluate the risk associated with a biomonitoring result . evaluation of risk may be a desirable outcome , given the importance of the “how risky is this blood concentration” question and the fact that the descriptive approaches only provide relative information and do not assess risk . figure 5 - 2 illustrates the various risk - based options discussed in the report .
ments . those assessments characterize human exposure with a pathways analysis , accounting for concentrations in air , food , water , and soil to estimate human dose in milligrams per kilogram per day . the dose is then used to calculate risk on the basis of reference doses or cancer slope factors ( see figure 5 - 2b ) . using existing risk assessments for interpreting biomonitoring data can help to put biomonitoring results into a broad risk
context that makes maximal use of the underlying exposure and toxicology data but falls short of actually calculating risk .
another option attempts to convert biomonitoring results into a form that is directly useful for risk assessment . the chapter describes both the human pharmacokinetic ( pk ) modeling used to relate internal concentration to dose and the development of exposure - response relationships in animal studies that use biomarker concentrations rather than applied dose ( see figure 5 - 2c ) . finally , the chapter describes how biomonitoring studies can augment and help to interpret traditional risk assessments .
many communication challenges stem from collection , interpretation , and reporting of biomonitoring results . this chapter indicates where communication issues arise in relation to the interpretation of biomonitoring results ; these issues are explored more fully in chapter 6 .
when data gaps are filled , there may be disagreement about how to apply the data for interpreting biomonitoring results . for example , the biomarker–toxicity relationship for methylmercury has been controversial because of the differences in results among major epidemiology studies ( appendix b ) . although a national consensus has emerged after the national research council review of methylmercury ( nrc 2000 ) , there may not be an opportunity for such a comprehensive analysis of other biomarkers as data gaps are addressed and risk assessors use existing data .
the case studies in this chapter and in appendix b are presented to illustrate particular points and are not intended to be exhaustive in their review or analysis of a chemical .
abbreviations : pbde = polybrominated diphenyl ether ; tce = trichloroethylene ; pfoa = perfluorooctanoic acid ; tcp = trichloro - 2 - pyridinol .
biomonitoring results can be interpreted at different levels of complexity ( figure 5 - 1 ) . the reference - range approach represents the least complex level . it is only descriptive , offering a statistical presentation of data ( tables 5 - 2 and 5 - 3 ) for comparison with data from other populations or individuals but with no conclusions about risk potential . however , this approach is often the first stage in the more complex risk - related analyses discussed in the remainder of the chapter .
in the reference - range approach , reference ranges ( or intervals ) , 1 are established , and biomonitoring values from individuals or subgroups are compared with them . the validity and utility of biomonitoring values for use as reference ranges depends on study design and data quality , with special attention to the availability and comparability of data on the reference population in relation to the study population .
the overview below focuses on two fundamental elements of the reference - range approach : establishing a reference range and interpreting biomonitoring data in comparison with it . the remainder of this section details methods , principles , and issues related to data quality and reference - population selection and comments on regulatory uses of this approach and related cautions .
the cdc sampling plan follows a “complex , stratified , multistage , probability cluster design to select a representative sample of the civilian noninstitutionalized population of the united states . ” relevant details are
poulsen et al . ( 1994 ) appear to use the term reference interval as synonymous with reference range . in a paper titled “trace element reference values … , ” the authors emphasize that “knowledge of the reference intervals ( baseline data ) for the trace elements in human body fluids and tissues is of paramount importance . ”
a lod = limit of detection , which may vary for some chemicals by year and by individual sample .
b not calculated . proportion of results below limit of detection was too high to provide valid result .
as shown in tables 5 - 2 and 5 - 3 , the data on each group include survey period , geometric mean , population sample size , and the biomarker concentration at the 50th , 75th , 90th , and 95th percentiles of the population distribution .
a not calculated . proportion of results below limit of detection was too high to provide valid result .
pared with that in a reference population . that approach depends on a suitable reference population and a body of biomonitoring data collected in comparable fashion that can serve as a reference range . ( for discussion of an appropriate comparison population , see chapter 4 . )
figure 5 - 3 illustrates the distribution of biomarker concentrations in a generic reference population , expressed as cumulative frequency . as is commonly done in a clinical test , the 95th percentile of the distribution can be used to determine the upper limit value of this test result . however , a different percentile may be chosen , depending on the circumstances , the characteristics of the reference population , the distribution of the results , and the intended purpose of the study . it is important to be aware that a particular cut point does not represent a bright line that automatically separates the population into typical vs highly exposed , or no risk vs high risk ( when , for example , beis or risk - based targets are used ) . rather , it is a guideline to point out where in the population distribution exposures may require more detailed analysis of sources and health risks .
figure 5 - 3 distribution of biomarker concentrations in generic reference population . ulv is the upper limit value .
finally , if the 95th percentile is highly unstable or uncertain because of high variability ( for example , if the distribution has a long tail ) , a more stable percentile would be preferable .
pertinent to the above comparisons is the assumption that the data are from a single study that used a uniform sampling design and method , which initially treated the sampled group as a single population . however , biomonitoring can also study subgroups suspected of having unique exposures—for example , because they lived next to a potential source—in a cross - sectional design . those results can be informative when compared with the reference range and can help to answer the question of whether a
a recent report illustrates the utility of the reference - range approach . in a study designed to collect descriptive information on urinary concentrations of organophosphorus ( op ) metabolite during and soon after pregnancy , bradman et al . ( 2005 ) collected samples from 600 pregnant low - income women living in california’s salinas valley . some 28 % were employed as farm field workers during pregnancy . for 81 % of these women , at least one member of their household worked in agriculture .
suspected contaminant source is , in fact , leading to increased exposures and body burden . box 5 - 1 presents a case example of the utility and limitations of reference ranges .
ideally , reference ranges consist of biomonitoring values developed according to scientifically rigorous study design and quality - control procedures ( see chapter 4 ) . the utility of reference ranges depends on attention to reference populations and data quality .
the reference - range approach depends on data availability and data comparability for both the reference and test populations . ideally , the two populations are comparable in age , race or ethnicity , sex , and other demographic factors and were analyzed for the same end point in the same tissues or fluids ( see introduction to this chapter and chapter 4 ) .
interpreting biomonitoring data from individuals or groups depends on the characteristics of the reference group ( viau et al . 2000 ) . the importance of comparability cannot be overstated . for example , comparative data on
figure 5 - 4 cumulative frequency distribution of the urinary excretion of 1 - hydroxypyrene in people living in two rural districts of burundi ( ▲ ) , in bujumbura ( • ) and in a reference group at the university of montreal ( ■ ) . source : viau et al . 2000 . reprinted with permission ; copyright 2000 , international archives of occupational and environmental health .
urban residents between continents ( bujumbura vs north american populations ) . in other words , the reference group must always be selected on the basis of the study’s objectives and a priori knowledge of exposure of the reference group .
as another example , if biomonitoring results on people who live near a contamination source ( proximity subgroup ) are being evaluated , these results can be lumped in with those on the remainder of the population to create an overall distribution . however , the contribution of the group may shift the distribution , and unless there are large exposure differences ( as in a bimodal distribution ) , it may be difficult to distinguish the proximity group from the rest of the population . keeping the proximity group separate would allow more direct comparisons with the reference population and more relevant interpretation of study results .
ideally , reference ranges are developed from biomonitoring data that conform to the study design and to data - quality considerations ( see chapter 4 ) . for example , valid comparisons depend on comparable sampling methods for collecting data from reference and test populations . similarly , comparisons are meaningful only if comparable statistical methods are applied in analyzing and reporting the data .
it is important to note that short half - life chemicals do have the potential to be useful biomarkers for establishing the reference range if exposure is fairly frequent at about the same concentrations . in this case , rapid clearance can be balanced by frequent exposure to yield a stable blood or urinary concentration . biomonitoring examples with cotinine and phthalates , as described in chapter 4 , illustrate this point .
appropriate reference populations are not always available . for example , recruiting children , especially newborns and infants , is difficult . when it is possible , the samples are usually small ( european commission 2004 ) .
reference - range information permits officials to compare exposures in specific geographic or demographic groups with those in the general ( reference ) population . the reference range may be chosen at a fixed time to facilitate evaluation of temporal trends and the effects of regulatory interventions . because , a reference range can be critical in deciding whether public - health action is warranted ( box 5 - 2 ) or in evaluating the effectiveness of regulatory initiatives , informed attention to the construction of statistically valid and representative reference ranges is imperative ( gao 2000 ) .
in one community where citizens were concerned about exposure to dioxins from nearby chemical manufacturing plants , agency for toxic substance and disease registry ( atsdr ) officials had cdc’s laboratory analyze blood samples and found that some residents had concentrations of several dioxins above the highest in a cdc - atsdr - developed reference range . in response , atsdr helped residents to obtain assistance from medical professionals expert in dioxins and , working with state and federal environmental agencies , began environmental testing to locate exposure sources ( gao 2000 ) .
utility of biomonitoring data . over 60 % of state officials responding to a general accounting office ( now government accountability office ) survey said that the lack of reference - range data prevented them from using human exposure data in their work ( gao 2000 ) . that situation can arise when datasets are biased in some way , background exposures in the control population are not well characterized , or other sampling requirements are not fully met .
as part of nhanes iii , a subsample of about 1 , 000 people provided blood and urine to determine reference ranges for 32 volatile organic compounds ( blood ) and 12 pesticides ( urine ) . demographic subgroups were defined by urban - rural status , region of the country , age , sex , race or ethnicity , and so on . the urinary measurements included metabolites of pesticides , such as carbaryl , naphthlalene , propoxur , carbofuran , parathion , and chlorpyriphos ( pirkle et al . 1995 ) . the datasets demonstrate the feasibility of developing reference ranges for emerging analytes .
in sampling design and overall methodology between reference and subpopulation datasets are especially relevant .
as stated at the outset , reference ranges do not provide conclusions on safety or risk . presenting that fact and other limitations is an essential aspect of communicating reference - range information to individuals , the general public , and organizational decision - makers—a topic developed more fully in chapter 6 .
the use of reference ranges has been considered as a way to compare the exposure in an individual or group against a reference group , generally taken to mean a random sample of the general population . another type of comparison group is workers , who represent a convenient point of reference because a number of biologic reference values have been established for this population ; these values are biomonitoring criteria , typically blood or urinary concentrations , that if exceeded indicate worker overexposure to an occupational toxicant .
the frequently cited sources of biomarker reference values include acgih ( 2005 ) , the deutsche forschungsgemeinschaft ( dfg ) ( dfg 2004 ) , and lauwerys and hoet ( 2001 ) . in the acgih sourcebook on tlvs and beis ( acgih 2005 ) , beis were proposed for 42 substances or groups of substances ( such as methemoglobin inducers ) . because a number of the substances have more than one determinant ( biomarker ) , a total of 71 determinants were reported .
a nhanes results for mercury are for women 16 - 49 years old . beis for mercury apply only to exposure to inorganic forms , whereas main population exposure is usually to organic forms .
the committee cannot make a generic recommendation on applying occupational limit values to the general population . specifically , acgih explicitly states that “the values are inappropriate to use for the general population or for nonoccupational exposures” ( acgih 2003 ) . similarly , dfg indicates that “bat values are not suitable for the derivation , by means of fixed conversion factors , of biological threshold values for long - term nonoccupational exposure such as from air pollution or contaminants in food” ( dfg 2004 ) .
thus , although it may be tempting to lower a bei by time - weighting and uncertainty factors that extrapolate from workers to the general public , the factors listed below must be carefully considered , and such extrapolations should be used with caution . they may be appropriate in specific situations .
in the occupational setting , inhalation is the main route of entry of numerous chemical substances . for some solvents ( such as dimethylformamide ) , dermal exposure may also be a major route of entry . ingestion usually comes last , although poor hygiene in the workplace may result in substantial exposure to aerosolized or particulate contaminants by this route . in comparison , ingestion is often the principal exposure pathway for the general population . assuming that an objective is to protect the general population from the same systemic toxic effects as workers , the importance of potentially different routes of entry must be examined . for example , the liver first - pass effect on ingested substances might alter the proportion of metabolites formed from a parent chemical . the pharmacokinetics of a biomarker may also be modified by different rates of absorption after exposure by various routes .
the workplace airborne concentration of a substance and the biomarker concentration in exposed workers typically vary as illustrated in figure 5 - 5 . although such relationships aid in interpreting occupational biomonitoring data on a group basis , the effect of such variability ( see chapter 4 ) must be considered , as must the shape of the dose–biomarker concentration relationship at the low - dose end of the correlation . the dispersion of data points around the regression line may be due to biologic variability and partial inadequacy of air - concentration measurements for inferring actual exposure
figure 5 - 5 daily average workplace xylene air and urinary methylhippuric acid ( mha ) in exposed workers . source : jang et al . 2001 . reprinted with permission ; copyright 2001 , international archives of occupational and environmental health .
( viau 2005 ) . indeed , such factors as varying ventilation rates associated with varying workloads and dermal exposure are unaccounted for by the air measurement ; this might make the biomarker a better metric of the actual exposure than the air - concentration measurement of the parent chemical .
furthermore , whereas biologic limit values are set for workers exposed 8 hours / day , 5 days / week for a working lifetime , the potential exposure of the general population is generally assumed to be 24 hours / day , 7 days / week for an entire lifetime . acgih ( 2003 ) addresses that point as follows :
“although modified work schedules are sometimes used in various occupations , acgih does not recommend that any adjustment or correction factor be applied to the beis ( i . e . , the beis should be used as listed , regardless of the work schedule ) . ”
one of the main advantages of using occupational limit values to derive reference points for the general population is that the former apply to humans , although animal data are sometimes used in their derivation . one might therefore consider that occupational limit values alleviate the need for interspecies extrapolations . the other advantage is that the values have been used for preventing work - related diseases for years and so offer some degree of validation of the reference values .
some of the limitations presented above can be given appropriate consideration in setting a reference value for the general population . for example , biomarkers with long half - lives are less prone to limitations in sampling time and are also probably reasonably related to cumulative exposure . in such cases , it might be tempting to apply a correction factor of 4
or be used in developing a pk model . those approaches , particularly the latter , can be useful in interpreting population biomonitoring data .
although biomonitoring data constitute a key body of knowledge about the distribution of exposure , relatively few risk assessments have been based on biomarker - response relationships established in epidemiologic studies ( who 2001 ) . in a recent informal survey of leading risk - assessment prac -
figure 5 - 6 evolution of risk assessment and risk management . framework showing research , risk assessment , and risk management . source : omenn 2003 . reprinted with permission ; copyright 2003 , human and ecological risk assessment .
another advantage of the use of lead and mercury biomarkers is that exposure to both chemicals is more readily quantified through measurement of the biomarkers than through collection of questionnaire data or environmental measures ( who 2001 ) . in addition , both compounds have relatively long half - lives and therefore provide relatively stable metrics that integrate dose over long exposure periods . the lead example , which follows , details the development and utility of this blood biomarker . similar information on mercury is presented in appendix b .
however , more recent epidemiologic investigations suggest that there may not be a threshold for the adverse neurodevelopmental effects of lead in children and that the dose - response curve may in fact be steeper at blood lead concentrations less than 10 µg / dl , than at higher concentrations ( lanphear 2005 ; bellinger and needleman 2003 ; canfield et al . 2003 ) . in 2005 , cdc concluded that “since no safe blood lead level in children has been identified , emphasis should be placed on efforts to control or eliminate lead in children’s environment before children are exposed” ( cdc 2005 ) .
one can compare the biomarker - based risk derived for lead with population - based biomonitoring results . data from nhanes 2000 - 2001 can be used to estimate the number of children in the united states who have increased blood lead ( cdc 2005 ) . blood lead in u . s . children declined dramatically after the removal of lead from gasoline—from a median of 15 µg / dl in 1978 to 2 µg / dl in 1999 ( rogan and ware 2003 ) . nonetheless , an estimated 1 . 6 % of u . s . children 1 - 5 years old had blood lead greater than 10 µg / dl in 1999 - 2002 , according to nhanes data ( cdc 2005 ) . the major exposure sources of lead for u . s . children are deterioration of lead - based paint and the resulting dust and soil contamination ( cdc 2005 ) .
of scenarios that involve high - end exposures and susceptible populations ( such as young children ) to point out whether particular groups are important to include in biomonitoring studies .
appendix b provides brief case studies of two pesticides , glyphosate and permethrin , for which a pre - existing risk assessment can help to put biomonitoring results into perspective . in both cases , the environmental protection agency ( epa ) has evaluated risks for a wide array of exposure scenarios as part of the reregistration process , and there are biomonitoring data whose interpretation could benefit from these risk assessments .
biomonitoring - led risk assessment is needed when the biomonitoring and toxicology databases are robust but epidemiologic data are not adequate to establish the biomarker - response relationship and there are few exposure data . in such cases , biomonitoring results may raise important health questions that cannot be answered without knowing more about exposure . there are three main options for converting biomonitoring data into a format that can be used as exposure information in risk assessments :
use human pk modeling ( box 5 - 4 ) to convert the biomonitoring data into a human - exposure dose that can be related to an rfd , cancer potency value , or other toxicity value .
use animal pk modeling to convert the dose - response relationship seen in toxicity studies ( applied dose ) to a dose - response relationship based on internal dose , using a dose metric derived from human biomonitoring data . this approach fosters the development of a biomarker - response relationship and biomarker - based toxicity values .
collect sufficient biomarker data in animals to express the dose - response relationship in key toxicology studies in terms of a biomarker - response relationship , in addition to an applied dose - response relationship .
pbpk models rely on a series of simultaneous differential equations that simulate chemical delivery to tissues via the arterial circulation and removal via the venous circulation . the models are run in time steps such that the entire course of chemical disposition can be presented for calculation of the area - under - the - curve ( auc ) dose , often a key metric for chronic risk assessment . the physiologic parameters can be adapted for different species , sexes , age groups , and genetic variants to facilitate extrapolation from one type of receptor to another .
pseudosteady state ; and short - half - life chemicals that do not approach steady state .
even though chemicals with long half - lives and high storage in lipid tend to have stable concentrations , they may not be at steady state . for example , dioxin body burdens apparently increased in the early part of the 20th century and then declined over the last 2 decades . that pattern repre -
figure 5 - 7 conversion of biomonitoring data to daily dose on the basis of one - compartment ( body - burden ) model .
sents a nonuniform exposure and thus a non - steady - state condition . it can still be addressed with simple one - compartment modeling as described above , but the exposure dose is varied to match the temporal trends in the biomonitoring data ( pinsky and lorber 1998 ) . the dose profile that best matches the biomonitoring data can then be used for risk assessment .
chemicals that are not highly lipid - soluble and that have relatively short half - lives ( hours to days ) do not build up a stable long - term reservoir . however , a pseudosteady state can be attained if exposure is nearly uniform and constant ( figure 5 - 8 ) . pseudosteady state refers to the case in which blood or tissue concentrations are changing and therefore are not completely stable . these blood or tissue concentrations fluctuate slightly and in a regular pattern around the average concentration .
if that is the case , blood concentrations may be relatively stable , and there is a potential to convert biomonitoring results into exposure estimates in a manner analogous to that described above for tcdd . the difference is that starting from a blood or serum concentration , one needs the volume of distribution ( vd ) to estimate the total amount in the body . the general framework is as shown in figure 5 - 9 .
figure 5 - 8 blood concentrations of rapidly cleared chemical to which there is frequent and nearly uniform exposure . highlighted line ( ≈—≈ ) is mean blood concentration . under these exposure conditions , biomarker concentration will be within a factor of 2 of mean after first several hours . simplifying assumption of pseudosteady state ( mean concentration is approximated by concentration found at any sampling time ) may suffice for estimating exposure dose from blood concentration under these circumstances .
figure 5 - 9 conversion of biomonitoring data to daily dose on basis of one - compartment model for non - lipid - soluble chemicals at steady state .
derivation of vd relies on the ratio of dose and blood concentration immediately after intravenous administration . two - compartment models involve more complex calculations to estimate vd at steady state that reflect the transport of chemical between compartments as well as out of the body .
in summary , conversion of biomonitoring data to exposure dose requires knowledge of chemical elimination rate and vd and requires that conditions be approximately pseudosteady state . it may be useful to estimate dose from body burden ; however , this cannot be used to interpret an individual’s biomonitoring result , because the elimination rate and vd would not be known . reasonable bounds on elimination rate and vd could be used to calculate an upper end of daily dose that is still compatible with the biomonitoring results ( for example , when both vd and elimination rate are high ) .