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强生旗下Janssen公司在研HIV-1疫苗获APPROACH研究获得了积极数据

目前全球共有3700万人患有HIV病毒感染,每年近200万的新增感染人群。由于HIV病毒的独特性质,包括其遗传多样性和迅速突变的能力,有效的HIV病毒疫苗仍然尚未获得。昨日(7月25日),强生...即将发布

日期:2017年7月26日 - 来自[新药]栏目

New Approach May Ease Severe ‘Ringing in the Ears‘

By Alan Mozes

HealthDay Reporter

FRIDAY, Dec. 6, 2013 (HealthDay News) -- Patients suffering from the intense, chronic and sometimes untreatable ringing in the ear known as tinnitus may get some relief from a new combination therapy, preliminary research suggests.

The study looked at treatment with daily targeted electrical stimulation of the body's nervous system paired with sound therapy.

Half of the procedure -- "vagus nerve stimulation" -- centers on direct stimulation of the vagus nerve, one of 12 cranial nerves that winds its way through the abdomen, lungs, heart and brain stem.

Patients are also exposed to "tone therapy" -- carefully selected tones that lie outside the frequency range of the troubling ear-ringing condition.

Indications of the new treatment's success, however, are so far based on a very small pool of patients, and relief was not universal.

"Half of the participants demonstrated large decreases in their tinnitus symptoms, with three of them showing a 44 percent reduction in the impact of tinnitus on their daily lives," said study co-author Sven Vanneste. But, "five participants, all of whom were on medications for other problems, did not show significant changes."

For those participants, drug interactions might have blocked the therapy's impact, Vanneste suggested.

"However, further research needs to be conducted to confirm this," said Vanneste, an associate professor at the School of Behavioral and Brain Sciences at the University of Texas at Dallas.

The study, conducted in collaboration with researchers at the University Hospital Antwerp, in Belgium, appeared in a recent issue of the journal Neuromodulation: Technology at the Neural Interface.

The authors disclosed that two members of the study team have a direct connection with MicroTransponder Inc., the manufacturer of the neurostimulation software used to deliver vagus nerve stimulation therapy. One researcher is a MicroTransponder employee, the other a consultant. Vanneste himself has no connection with the company.

According to the U.S. National Institute on Deafness and Other Communication Disorders, nearly 23 million American adults have at some point struggled with ear ringing for periods extending beyond three months.

Yet tinnitus is not considered to be a disease in itself, but rather an indication of trouble somewhere along the auditory nerve pathway. Noise-sparked hearing loss can set off ringing, as can ear/sinus infection, brain tumors, heart disease, hormonal imbalances, thyroid problems and medical complications.

A number of treatments are available. The two most notable are "cognitive behavioral therapy" (to promote relaxation and mindfulness) and "tinnitus retraining therapy" (to essentially mask the ringing with more neutral sounds).

In 2012, a Dutch team investigated a combination of both approaches, and found that the combined therapy process did seem to reduce impairment and improve patients' quality of life better than either intervention alone.

Additional options include neural stimulation, hearing aids, cochlear implants, dietary adjustments, and/or antidepressants and anti-anxiety medications. But there is no known cure, and some patients do not respond to any treatment.

日期:2013年12月9日 - 来自[Health News]栏目

Is the whole-diet approach better than a low-fat diet in cardiovascular risk reduction?

Dario Giugliano and Katherine Esposito

Division of Metabolic Diseases
Piazza L Miraglia 2
80138 Naples
Italy
E-mail: dario.giugliano{at}unina2.it

Dear Sir:

We read with interest 2 articles published in the November 2005 issue of the Journal (1, 2). Both studies showed that a low-fat diet (30% total energy from fat) lowered LDL-cholesterol concentrations by 7–10% in the short term (6 wk and 3 mo, respectively). However, there were some remarkable differences between the studies: HDL cholesterol decreased by 7% and triacylglycerols increased by 14% in the 6-wk trial (1), whereas there was no change in HDL cholesterol and a 12% decrease in triacylglycerols in the 12-wk trial (2). One possible explanation for the contrasting effects on the concentrations of circulating HDL cholesterol and triacylglycerol of the 2 similar low-fat diets, apart from the obvious difference in diet duration (6 wk compared with 3 mo), may be the constant weight the participants maintained throughout the 6-wk trial. In contrast, there was a mean reduction in body mass index (in kg/m2) of 1.2 (corresponding to 2 kg) in the subjects who followed the 3-mo diet. As pointed out by Ordovas (3), when low-fat diets produce weight loss, their detrimental effects (ie, reducing HDL cholesterol and increasing triacylglycerols) may no longer be evident.

Vincent-Baudry et al (2) showed no significant benefit of a Mediterranean-style diet on blood lipids compared with a standard low-fat diet, which led to the conclusion that both diets (Mediterranean-type and low fat) significantly reduced blood lipids and the calculated cardiovascular disease risk factors to an overall comparable extent. We think this message may underestimate the real advantage of the whole dietary approach in managing chronic disease. First, the follow-up may have been too short to detect a significant difference: for instance, in our study that compared a Mediterranean-style diet with a low-fat diet in subjects with the metabolic syndrome (4), the 2-y net change in lipid concentrations favored the Mediterranean-style diet [median (95% CI) changes: total cholesterol: –0.23 mmol/L (–0.44, –0.02 mmol/L); HDL cholesterol: 0.07 mmol/L (0.02, 0.14); and triacylglycerols: –0.21 mmol/L (–0.36, –0.06)]. Second, the positive effect of a Mediterranean-style diet on cardiovascular disease risk was also shown in the absence of any effect on blood lipids (5). Lastly, the global cardiovascular disease risk burden is composed of many components, of which lipids represent a huge part. However, the diet-lipids-heart hypothesis seems too reductive because it excludes many effects of diet on old and new risk factors, including blood pressure, endothelial function, vascular inflammation, insulin sensitivity, and oxidative stress (6).

ACKNOWLEDGMENTS

The authors had no conflicts of interest.

REFERENCES


日期:2008年12月28日 - 来自[2006年83卷第4期]栏目

A cellular-level approach to predicting resting energy expenditure across the adult years

ZiMian Wang, Stanley Heshka, Steven B Heymsfield, Wei Shen and Dympna Gallagher

1 From the Obesity Research Center, St Luke's–Roosevelt Hospital, Columbia University College of Physicians and Surgeons, New York.

2 Supported by the National Institutes of Health (NIDDK 42618 and R29-AG 14715) and a Weight Risk Investigators Study grant from Knoll Pharmaceuticals.

3 Reprints not available. Address correspondence to ZM Wang, Obesity Research Center, 1090 Amsterdam Avenue, 14th Floor, New York, NY 10025. E-mail: zw28{at}columbia.edu.


ABSTRACT  
Background:We previously derived a whole-body resting energy expenditure (REE) prediction model by using organ and tissue mass measured by magnetic resonance imaging combined with assumed stable, specific resting metabolic rates of individual organs and tissues. Although the model predicted REE well in young persons, it overpredicted REE by 11% in elderly adults. This overprediction may occur because of a decline in the fraction of organs and tissues as cell mass with aging.

Objective:The aim of the present study was to develop a cellular-level REE prediction model that would be applicable across the adult age span. Specifically, we tested the hypothesis that REE can be predicted from a combination of organ and tissue mass, the specific resting metabolic rates of individual organs and tissues, and the cellular fraction of fat-free mass.

Design:Fifty-four healthy subjects aged 23–88 y had REE, organ and tissue mass, body cell mass, and fat-free mass measured by indirect calorimetry, magnetic resonance imaging, whole-body 40K counting, and dual-energy X-ray absoptiometry, respectively.

Results:REE predicted by the cellular-level model was highly correlated with measured REE (r = 0.92, P < 0.001). The mean difference between measured REE ( Conclusion:The present approach establishes an REE–body composition link with the use of a model at the cellular level. The combination of 2 aging-related factors (ie, decline in both the mass and the cellular fraction of organs and tissues) may account for the lower REE observed in elderly adults.

Key Words: Aging • body cell mass • body composition • fat-free mass • magnetic resonance imaging • total body potassium


INTRODUCTION  
Aging is associated with a decline in whole-body resting energy expenditure (REE) at a rate of 1–2% per decade after the second decade of life (1). The age-related lowering of REE occurs even when body weight remains stable over the same time period (2). Equations at the whole-body level for predicting REE usually include body weight, height, and age as predictor variables (3).

Fat-free mass (FFM) is an easily measured compartment that is often used to evaluate the body-composition basis of interindividual differences in REE. FFM also declines with aging, even in the presence of weight stability (4–6). Even after the relation is first controlled for FFM, REE appears to be significantly lower in the elderly (7, 8).

FFM is a heterogeneous compartment with organs and tissues differing widely in metabolic activity. The possibility exists that a lowering of REE with aging can be accounted for by a relatively greater loss of organs with a high metabolic rate. Accordingly, Gallagher et al and other investigators (9–11) measured major organs (liver, brain, heart, and kidneys) and tissues (skeletal muscle and adipose tissue) in a cohort of young adult men and women. The investigators assumed stable organ-tissue-specific resting metabolic rates and measured organ-tissue volumes by magnetic resonance imaging (MRI) to predict whole-body REE. The calculated REE for young subjects was nearly identical to values measured by indirect calorimetry (9). In contrast, the calculated values in older subjects were higher (± SD) than the measured values by 144 ± 64 kcal/d (P < 0.01) for men and 146 ± 78 kcal/d (P < 0.001) for women ( Gallagher et al's observations show that the assumed organ-tissue-specific metabolic rate values, which are based largely on young adults, may not be applicable in the elderly. Two explanations for these findings are possible. First, the elderly may have a lower REE per unit cell mass for individual organs and tissues (ie, specific metabolic rate) than do young subjects. Second, the cellular fraction of organs and tissues may differ in young and older subjects. In support of the latter explanation, well established histologic changes in liver and other tissues show a relative loss of cellularity and an expansion of the extracellular compartments (13).

The hypothesis of the present study was that the lower than predicted REE observed in the elderly may be explained by a relative loss in organ-tissue cellularity. Currently, no in vivo methods are available for measuring both organ-tissue cell mass and the corresponding specific resting metabolic rates. In the present investigation, we derive a model for the age-related decline in cell mass and apply in vivo imaging and measurement methods to specifically explore the hypothesis that the age-related lowering of REE observed in the elderly can be accounted for by a loss of organ-tissue cell mass.


SUBJECTS AND METHODS  
Study design and protocol
REE was measured by indirect calorimetry. Body cell mass (BCM) and the mass of each major organ and tissue were measured in vivo by using whole-body 40K counting and MRI, respectively. We then used currently available organ-tissue-specific resting metabolic rates (14) along with BCM and organ-tissue mass to model REE at the cellular body-composition level. We assume in this model that specific resting metabolic rates of individual cell categories are stable across the adult age years and that the age-related loss of cell mass is proportionally uniform across all organs and tissues. We compared REE estimates calculated by our model with those measured by indirect calorimetry.

On the first day, each subject completed a medical evaluation that included a physical examination and screening blood tests. Healthy free-living subjects without any diagnosed medical conditions and with normal thyroid hormone values were enrolled in the study. Total body potassium (TBK) and FFM were measured with whole-body 40K counting and dual-energy X-ray absorptiometry (DXA) systems, respectively. On the second day, liver, brain, kidney, skeletal muscle, and adipose tissue volumes were measured by using regional and whole-body MRI. Left ventricular heart volume was quantified by echocardiography. Whole-body REE was measured on the third evaluation day by indirect calorimetry after the subjects had fasted overnight.

REE prediction model
Whole-body REE can be expressed as the sum of energy expended by individual organs and tissues. The general REE model at the organ-tissue level is

RESULTS  
Baseline characteristics
The baseline characteristics of the subjects are shown in Table 2. The subject pool consisted of 54 adults (18 men and 36 women) who ranged in age from 23 to 88 y. The mean age and BMI of the men and women were not significantly different. Men as a group were heavier (P < 0.01) and taller (P < 0.001) than the women. Men also had more TBK, BCM, TBW, FFM, and liver, brain, heart, kidney, skeletal muscle, and residual mass than did the women (P < 0.05–0.001). In contrast, the women had more body fat (P < 0.01) and adipose tissue (P < 0.05).


View this table:
TABLE 2. Subject characteristics at baseline and body-composition results1

 
REE prediction by previous model
According to our previous model (ie, Equation 2), predicted REE is determined by organ and tissue mass alone (9). The mean values for REEm and REEp were 1487 ± 294 and 1605 ± 260 kcal/d for the whole group of subjects with a mean difference (ie, REEm – REEp) of –118 ± 126 kcal/d (paired Student's t test, P < 0.001). As can be seen from the regression line and 95% CI in Figure 1, the model systematically overestimates REE after early adulthood.


View larger version (22K):
FIGURE 1.. The difference between measured and predicted whole-body resting energy expenditure (REEm –REEp) versus age for men (•) and women (). The linear regression line (solid line) for all subjects [(REEm –REEp) = 0.4 – 2.60 x age; r = –0.41, P = 0.001; n = 54] and 95% CIs (dashed lines) are shown. REEp was calculated according to the organ-tissue-level model (Equation 2).

 
Age and cell fraction of FFM
Total body water was measured in 49 of the 54 evaluated subjects. The ECW, ICW, and E/I values for men and women are shown in Table 3. ECW was not significantly associated with age, but there were significant negative correlations between ICW and age (r = –0.857, P < 0.001 for men, r = –0.453, P = 0.009 for women; P = 0.015 for the difference between men and women). E/I was positively correlated with age in the pooled sample (r = 0.40, P < 0.01) and did not differ significantly between men and women.


View this table:
TABLE 3. Subject cellular-level body-composition results1

 
The BCM, FFM, and cell fraction of FFM (BCM/FFM) are also shown in Table 3. Analysis of variance of the ratio of BCM to FFM showed that BCM/FFM was higher in the men than in the women (P < 0.001) and that BCM/FFM decreased with age (P < 0.001; Figure 2). There was also a significant difference in the amount of lowering of BCM/FFM with age in men compared with women (–0.0015 compared with –0.0008 per decade, respectively; P = 0.04). Because these effects of sex and age are expressed in the BCM/FFM ratio they are implicitly taken into account by our new cellular-level model.


View larger version (19K):
FIGURE 2.. Fraction of fat-free mass (FFM) as body cell mass (BCM) versus age for men (•) and women (). The linear regression line (solid line) for all subjects (BCM/FFM = 0.573 – 0.001 x age; r = –0.48, P < 0.001; n = 54) and 95% CIs (dashed line) are shown.

 
REE prediction by new model
The mean REEm was 1487 ± 294 kcal/d and REEp was 1501 ± 300 kcal/d with a nonsignificant mean difference (ie, REEm –REEp) of –14 ± 117 kcal/d for the whole group of subjects. REEm was highly correlated with REEp (r = 0.92, P < 0.001). A Bland-Altman plot showed that there was no significant trend (r = 0.053, P > 0.50) between measured and predicted REE difference versus the average of REEm and REEp (Figure 3). The difference between REEm and REEp is plotted against age for all subjects in Figure 4, which shows that the measured and predicted REE difference is not significantly associated with age.


View larger version (20K):
FIGURE 3.. The difference between measured and predicted whole-body resting energy expenditure (REEm –REEp) versus the mean of REEm and REEp for men (•) and women () or all subjects (y = 17.7 –0.02x; r = 0.053, P > 0.50; n = 54). REEp was calculated according to the cellular-level model (Equation 12). The regression line, zero difference line, and the lines representing 2 SDs for the differences (220, –248 kcal/d; indicated by the upper and lower lines) are shown.

 

View larger version (19K):
FIGURE 4.. The difference between measured and predicted whole-body resting energy expenditure (REEm –REEp) versus age for men (•) and women (). The linear regression line (solid line) for all subjects [(REEm –REEp) = –16.2 + 0.05 x age; r = 0.009, P > 0.50; n = 54] and 95% CIs (dashed lines) are shown. REEp was calculated according to the cellular-level model (Equation 12).

 

DISCUSSION  
One of the primary aims of energy metabolism research is to understand the inherent relations between REE and body composition. Several studies have investigated whether the lower REE in the elderly can be accounted for by aging-related changes at the organ-tissue level of body composition (12, 21, 27, 28). An assumption was made that the specific resting metabolic rates of individual organs and tissues (ie, Ki) are stable from young to elderly age. However, the previous organ-tissue-level model (ie, Equation 2) failed to explain the lower REE observed in the elderly, which suggests that the Ki values are not stable across the adult age span (12).

In the present study, we proposed and evaluated a cellular-level REE prediction model. Although Equation 3 is theoretically correct, this model is currently impractical to apply because of the technical difficulties in measuring cell mass and specific resting metabolic rates of individual cell groups. Equation 12, although based on simplifications and assumptions, may be the only currently operable approach for predicting REE at the cellular level.

Because specific resting metabolic rates of individual cell groups (ie, Ji) were assumed to be stable across the adult age span, there were 2 possible outcomes of the present study. The first possibility was that the new model would accurately predict REE, which would suggest that Ji values are constant from young to old age and that the decline in the fat-free cell fraction of FFM is responsible for the lower REE observed in the elderly. The second possibility was that REE in the elderly would still be overpredicted after the cellular level of body composition was taken into account; this finding would support the conclusion that changes in both Ji and the cell fraction of FFM account for the lower REE in the elderly. The results of the present study are consistent with the first alternative, ie, that the lower REE observed in elderly subjects is adequately explained by the lower cell fraction of FFM.

In the present study, we calculated Ji values on the basis of a reference man. The mass, fat, and potassium contents of individual organs and tissues are documented for a reference man (15), and the specific resting metabolic rate of organs and tissues are also documented (KRi) (3). Both (ffm/M)Ri and (bcm/ffm)Ri could thus be calculated and the JRi values derived by using Equation 6 (Table 1). Although there is a need to measure the specific resting metabolic rates of individual cell groups, noninvasive, in vivo methods for measuring Ji values remain technically demanding (29, 30). As the most advanced techniques become available [ie, methods based on magnetic resonance spectroscopy (MRS) and positron emission tomography (PET)], in vivo quantification of specific resting metabolic rates of individual cell groups may be possible.

Whole-body BCM does not remain a fixed fraction of FFM, but decreases with greater age (31, 32). In the present study, we present additional evidence that supports a decline in the cell mass fraction of FFM. The ratio of ECW to ICW, which is an index of the FFM cell fraction, was significantly correlated with age in both men and women. Additional support is also provided by histologic studies showing a low cell number per unit area in the liver of elderly humans (13). Observations suggest that the cell fractions of individual organs and tissues are smaller in older subjects than in younger ones.

In Equation 12, simplified values were used for the (BCM/FFM)R for the reference man (ie, 0.58) and woman (ie, 0.56). An accurate value of (BCM/FFM)R can be calculated as a function of the potassium and FFM content of individual organs and tissues,

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  12. Gallagher D, Allen A, Wang ZM, Heymsfield SB, Krasnow N. Smaller organ tissue mass in the elderly fails to explain lower resting metabolic rate. In: Yasumura S, Wang J, Pierson RN, eds. In vivo body composition studies. Ann N Y Acad Sci 2000;904:449–55.
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Received for publication April 2, 2004. Accepted for publication November 3, 2004.


日期:2008年12月28日 - 来自[2005年81卷第4期]栏目

Disease-Related Malnutrition: An Evidence-Based Approach To Treatment

edited by Rebecca J Stratton, Ceri J Green, and Marinos Elia, 2003, 824 pages, hardcover, $175. CABI Publishing, Wallingford, United Kingdom.

Peggy R Borum

409 FSHN, PO 110370
University of Florida
Gainesville, FL 32611
E-mail: prb{at}ufl.edu

The 11 chapters (336 pages) and 6 appendixes that make up this 824-page book are a bit overwhelming at first glance. However, the reader is soon impressed with the great care and careful organization used by the 3 authors to present both the problems of disease-related malnutrition and the approaches that have been used to treat it. The book presents a huge amount of information in a single cohesive style, which makes the work much easier to comprehend than the usual collection of works that are obviously put together by committee.

The authors state that they desired to "...collect, rationalize and highlight a wide range of information to help health care professionals make decisions..." They used standard procedures for undertaking systematic reviews and meta-analyses, and then they presented the detailed data in appendixes, which allows the evidence and the conclusions to be traceable and verifiable by the reader. In addition to the inclusion of extensive data tables, most of the chapters have summary graphics that are also presented in a unified style and that do a creditable job of giving the reader a big-picture view to go along with the detailed evidence-based approach.

Chapter one introduces a "new conceptual framework" for considering disease-related malnutrition that emphasizes the function of tissues rather than their mass. It also considers potential windows of opportunity that permit nutritional modulation of function over various periods of time. Chapter 2 summarizes an extensive clinical database of disease-related malnutrition in a variety of patients’ groups in different health care settings; data tables are provided in Appendix 1. Patients are grouped according to body mass index or according to disease diagnosis. The authors focus on the hospital and the community as the 2 predominant health care settings. Chapter 3 discusses the causes of disease-related malnutrition (data tables in Appendix 2), and chapter 4 discusses the consequences.

Chapters 5 through 9 provide a multilayered evidence base for the treatment of disease-related malnutrition. The methods used by the authors to establish the evidence base are detailed in chapter 5. Treatments involving oral nutrition supplements (data tables provided in Appendix 3 for the hospital setting and in Appendix 4 for the community setting) are presented in chapter 6, and treatments involving enteral tube feeding (data tables provided in Appendix 5 for the hospital setting and in Appendix 6 for the community setting) are presented in chapter 7. Chapter 8 provides a combined analysis of the use of oral nutrition supplements and enteral tube feeding. Chapter 9 discusses the use of parenteral nutrition to treat disease-related malnutrition, but the evidence in this chapter is less detailed and complete than is the evidence presented for oral nutrition supplements and for enteral tube feeding.

The authors identify many limitations in the design and execution of studies in the published literature. Chapter 10 provides an excellent practical guide to planning, undertaking, and reporting clinical nutrition trials. It has just the right amount of broad principles and philosophy blended with detailed suggestions and lists. For example, the chapter directs the reader to the CONSORT statement website (Internet: www.consort-statement.org), which is an important research tool that takes an evidence-based approach to improve the quality of reports of randomized trials.

Chapter 11 provides an overview of disease-related malnutrition and some suggested directions for future research. Because of the geographic location of the 3 authors, it is not surprising that many of the nutrition products discussed are more representative of the United Kingdom and Europe than of other locations. The book represents a very complete work as of December 2001, the time at which the data collection was completed. It is the completeness of this work that makes it outstanding; however, the book would probably be more widely used if its content were also available in an online database format that could be easily searched and updated.


日期:2008年12月28日 - 来自[2004年79卷第6期]栏目

An Evidence-based Approach to Vitamins and Minerals: Health Implications and Intake Recommendations,

by Jane Higdon, 2003, 268 pages, hardcover, $59. Thieme, New York.

Donald B McCormick

2245 Deer Ridge Drive
Stone Mountain, GA 30087
E-mail: biocdbm{at}emory.edu

This book comes from and is endorsed by the Linus Pauling Institute (LPI) at Oregon State University. As stated by Higdon in the preface, the LPI is "dedicated to training and supporting new researchers in the interdisciplinary science of nutrition and optimum health, as well as to educating the public about the science of optimum nutrition." The author further states that the goal of the book is "to provide clinicians and consumers with a practical evidence-based reference to the rapidly expanding field of micronutrient nutrition." The underlying tenet of the book, expressed in the Preface, is that there is "the potential for micronutrients to prevent and treat chronic diseases at intakes higher than those required to prevent deficiency." Yet it is generally recognized that there is real concern about exaggerated health claims from numerous supplement manufacturers and salespersons. In the foreword, Bruce Ames, a biochemist who is a strong proponent of the consumption of micronutrients in amounts greater than the recommended dietary allowances (RDAs), states that there are "potential health benefits of tuning up people's micronutrient metabolism." He also writes that there is a "need to educate the public about the crucial importance of optimal nutrition and the potential health benefits of something as simple and affordable as a daily multivitamin/multimineral supplement."

From the above, it is clear that the author and her colleagues recognize that some persons may benefit from intakes of micronutrients that are greater than those currently recommended by the committees that adjudicate the RDAs via the Food and Nutrition Board of the Institute of Medicine. This extra need, which a small fraction of the US population is known to have, leads the author to include in each chapter and again in the closing section, "The Linus Pauling Institute's Prescription for Healthy Living," an LPI recommendation that essentially every person should take a daily supplement containing 100% of the RDA of most micronutrients in addition to whatever is provided in his or her diet.

It is important to understand that RDAs are meant to be sufficient (not necessarily optimal) to meet the needs of nearly all healthy persons in a particular life-stage group, and, indeed, they represent amounts that are only modestly larger than those known to prevent deficiency. It is equally important, however, to recognize that, for most if not all nutrients, we do not yet know what is "optimal" for any life-stage group. The quantitative meaning of optimal as applied to nutrition is arguable. Hence, it may be somewhat presumptuous to believe the LPI recommendations will help in "tuning up people's micronutrient metabolism" and lead to "optimal nutrition," whatever that might be. In fact, it can be imagined that the concentrations of some micronutrients that are achieved with LPI recommendations, although well below known toxic amounts, may enrich the blood and other tissues of a person to the point of making him or her a better nutrient source for some pathogenic organisms that also require the micronutrient. Despite the current vagueness and general uncertainty about the amount of a specific micronutrient that is optimal for anyone, the LPI-recommended intake of a supplement in addition to dietary acquisition probably would rarely be harmful and may be helpful to some who require more micronutrients because of defects in absorption or utilization.

This book is written in an organized and thoughtful manner and provides an up-to-date synopsis of our knowledge of micronutrients. Overall, it is a good summary of what clinicians and consumers may want to know.


日期:2008年12月28日 - 来自[2004年79卷第5期]栏目

Rapid determination of renal filtration function using an optical ratiometric imaging approach

【摘要】  Glomerular filtration rate (GFR), which measures the amount of plasma filtered through the kidney within a given time, is an essential and clinically important indicator of kidney function. Here, we report a new ratiometric measurement technique based on intravital fluorescence microscopy that allows rapid evaluations of renal function in rodent models. By using this technique, plasma clearance rates of a fluorescent GFR marker can be measured in less than 5 min following a bolus infusion of a fluorescent dye mixture into the bloodstream. The plasma clearance kinetics of the GFR marker showed consistent values when measured in healthy animals at locations both in the kidney and from the skin. In addition, by using this technique, we were able to rapidly determine renal function with acute renal failure animal models and with other animal models where kidney filtration functions were altered. The measured plasma clearance kinetics using this technique correlated with expected changes in kidney function. We found this ratiometric approach offers improved accuracy and speed for quantifying renal function compared with the approach using single fluorescent probes, and the measurement can be done noninvasively from the skin. This approach also offers a high sensitivity for determining plasma clearance rate of a fluorescent compound. This feature is important for rapidly quantifying small differences in plasma clearance when kidney function is changing.

【关键词】  GFR plasma clearance rate intravital microscopy ischemia acute renal failure twophoton excitation dextran multiphoton


DEVELOPMENT OF INTRAVITAL microscopy has opened up new windows for studying the kidney ( 6, 13 - 15 ), the associated disease processes ( 12, 22 - 24 ), and molecular trafficking and metabolism ( 19, 26 ). Following an intravenous infusion of fluorescent dye molecules, one can monitor processes such as apoptosis and necrosis of tubular cells, vascular permeability defects, abnormal blood flow patterns and glomerular filtration of molecules with different sizes with regard to kidney injuries by performing intravital kidney imaging ( 7, 13, 23 ). A wealth of information can be retrieved from analysis of these in vivo images. It is apparent that to understand the dynamic processes of the kidney, quantitative analysis of in vivo images is essential. Recently, we have developed a ratiometric imaging technique by using the generalized polarity concept for quantitative analysis of multicolor fluorescence images and demonstrated its potential power for detecting relative concentration changes of multiple probes both spatially and temporally ( 28, 29 ). The filtration property of a glomerulus and specific reabsorptive characteristics of a tubular section for different fluorescent molecules are among those of particular interests that can be quantified. We began to address important questions specifically related to glomerular permselectivity or tubular reabsorption by using this quantitative approach ( 29 ). What is unique about imaging intact tissues from live animals vs. isolated tissues or cell culture models is the ability to access the kinetics of different biological processes systemically within local tissues. Here, we report a technique that allows rapid measurement of plasma clearance rates of a particular molecule and glomerular filtration rate (GFR) using intravital microscopy.


The gold standard of determining GFR is via measurement of plasma clearance of a marker molecule that can be filtered freely through the glomerular filtration barrier, not protein bound or metabolized, and is not reabsorbed. Inulin is one good example and an often used GFR marker. Results based on plasma clearance using a single injection of inulin, or other marker molecules, correlate well in patient tests ( 5, 8 ). Techniques applying plasma clearance allow rapid determination of GFR in both normal patients as well as patients with acute renal failure (ARF) in a matter of 15 min ( 5, 17, 18 ). This is an attractive advantage for assessing renal function of ARF patients over methods requiring a multiple-hour period of urine collection that are not applicable in these patients. This imaging technique we developed and tested on rodents maintains this important feature for rapid determination of kidney filtration function.


It has been previously reported that by using a fluorescent marker molecule, such as FITC-inulin, following a single bolus injection, GFR can be determined by measuring plasma clearance kinetics based on fluorescence intensity decay ( 16 ). The measured results correlated well with renal functions under various controlled experimental conditions where the kidney function was known to change ( 16 ). When animal models with superficial glomeruli are used, glomerular filtration can be visualized with two-photon excitation microscopy at near real-time speeds ( 28 ) and subsequently single-nephron GFR (snGFR) can be measured when fluorescent GFR markers were used ( 9 ). So far, these fluorescence techniques for measuring GFR are all based on the quantification of fluorescence intensity values from a single fluorescence source. It is understandable that measurements using intensity values alone, from a single source, are subject to errors caused by experimental conditions such as ambient light, fluctuations from excitation light source, and the high-voltage supply of the detectors. The fluorescence intensity value can be further affected by the nonuniformity of the imaging detector and intensity attenuation at different imaging depths ( 3 ). The measurement technique we report here is based on the use of two fluorescent molecules and the ratio between their intensity values. Using intensity ratioing, the ratio values are much less sensitive to signal fluctuations caused by environmental and experimental conditions ( 28 ). Most importantly, as we will discuss later, applications of intensity ratio techniques result in better defined kinetic curves of plasma clearance and improved accuracy for determining the rate constants of the marker molecules.


MATERIALS AND METHODS


Animal preparation. Experimental procedures using animals were approved by the Institutional Animal Care and Use Committee and performed in accordance with the Guide for the Care and Use of Laboratory Animals (Washington, DC: National Academy Press, 1996). Male Sprague-Dawley rats (6-8 wk, Harlan, Indianapolis, IN), 250 g in body weight, were used. Animals were anesthetized with an intraperitoneal injection of thiobutabarbital (130 mg/kg, Sigma, St. Louis, MO), shaved, and placed on a homeothermic table to maintain the body temperature at 37°C. After adequate anesthesia was ensured, a femoral venous catheter was placed using a 27-gauge cannula for injection of fluorescent dye solutions. One of the kidneys was exteriorized for microscopy imaging via a 10- to 15-mm lateral incision made dorsally under sterile conditions ( 6 ). The surgical procedure was only to exteriorize the kidney. Kidney blood flow was not interrupted during surgery, except when we deliberately occluded blood flow to model renal ischemic injury. For studies in which kidney ischemia was induced, the renal artery and vein were occluded with a nontraumatic microaneurysm clamp for 30 min. For studies in which the whole kidney nephrectomy was performed, the renal artery and vein were first ligated with a nonabsorbable no. 2 (metric size) silk suture before the kidney/kidneys were removed. For studies in which bilateral ureteral ligations were performed, both the left and right ureters were ligated with no. 2 silk sutures each at two points to ensure that there was no urine flow. During all procedures, core body temperature of the animal was maintained at 37°C by using a homeothermic table and monitored with a rectal thermometer.


Fluorescent probes. The fluorescent solution we used for intravenous injection included mixtures of 1 ) FITC-inulin (3-5 kDa, Sigma) and 500-kDa Texas red-dextran and 2 ) 3-kDa tetramethylrhodamine (TMR)-dextran and 500-kDa FITC-dextran. FITC-inulin was dissolved in 0.9% saline and dialyzed overnight to remove unbound fluorescent molecules (1-kDa cutoff). The 3-kDa TMR-dextran, 500-kDa FITC-dextran, and 500-kDa Texas red-dextran were purchased from Invitrogen (Eugene, OR) and used directly by dissolving in 0.9% saline.


Fluorescence microscopy. A 0.5-ml saline solution containing 3.2 mg FITC-inulin (3-5 kDa) and 1.6 mg 500-kDa Texas red-dextran was infused through the femoral venous catheter immediately before microscopic imaging. Live images of the animal organ were captured with a two-photon laser scanning fluorescence microscope system (Bio-Rad MRC-1024MP, Hercules, CA). The external detectors were used for acquiring fluorescence signals from 500 to 550 nm (green channel for FITC-inulin) and 560-650 nm (red channel for Texas red-dextran), respectively. A water-circulation heating pad was placed on the microscope stage to preheat it to 37°C. The exposed kidney of the animal was held in a 50-mm diameter tissue culture dish with a no. 1.5 cover-glass bottom (WillCo Wells, Amsterdam, The Netherlands) for imaging. The tissue culture dish was filled with 0.9% saline to maintain the moisture of the kidney. In cases of bilateral whole kidney nephrectomy, the liver was exposed and held in the tissue culture dish for imaging. Live images of the kidney/liver were taken as functions of time for plasma clearance analysis. Baseline kidney/liver images were collected before fluorescent dye infusion to record autofluorescence signals within the tissue under investigation. Typically, we started to collect images synchronized with dye infusion, and continuously for 5 min at a frame rate of 1.22 s. Images at later time points were collected when needed. All intravital kidney/liver images were acquired using a x 60/1.2-NA water objective and external nonscan detectors. A Ti-sapphire laser (Spectra-Physics, Mountain View, CA) was tuned to 800 nm for excitation. The excitation laser power on the sample was attenuated to between 2 and 28 mW using neutral-density filters. During all imaging procedures, the body temperature of the animal was maintained at 37°C.


Image data analysis. Intensity ratio images R (t) were calculated using Meta Imaging Series (version 6, Universal Imaging, West Chester, PA) on a personal computer as follows


where I small ( t ) and I large ( t ) are fluorescence intensities of the smaller (GFR marker) and the larger size molecules at a particular pixel of an image taken at different time points, respectively. A threshold level for each detection channel was set according to the average pixel value of an area without significant autofluorescence from images taken before dye infusion. The average pixel values of intensity ratio R from a region of blood vessel lumen (regions of interest, ROI) were exported into PSI-PLOT (version 6, Salt Lake City, UT) for analysis. The image-processing procedures were performed in an equivalent manner for all images to ensure the results were comparable.


The time series intensity ratio values of a ROI from an image time series were extracted and fit nonlinearly with the following equation using the Marquardt method ( 2 ) to retrieve the (apparent) plasma clearance rate constant k A


where R vessel ( t ) is the average pixel value of the intensity ratio from a given blood vessel lumen region extracted at different time point, A is the amplitude or the preexponential factor, and C is a constant.


RESULTS AND DISCUSSION


Figure 1 is a montage of color-combined fluorescence intensity images of the kidney from a live and healthy male Sprague-Dawley rat. These images were taken as a function of time after a bolus intravenous infusion of a dye mixture containing FITC-inulin and 500-kDa dextran labeled with Texas red. The fluorescence intensity signal from FITC-inulin is shown in green, and the 500-kDa Texas red-dextran intensity is shown in red. At about 7.3 s after dye infusion ( Fig. 1 B ), the fluorescence intensity was mainly from the blood vessels of the kidney. The yellowish color of the blood vessel indicates that both FITC-inulin and 500-kDa Texas red-dextran were in these blood vessels. At 12.2 s ( Fig. 1 C ), the intensity from both dye molecules increased as a result of increased plasma concentrations of both molecules. At 24.4 s ( Fig. 1 D ), FITC-inulin was already observed in the proximal tubule lumen (in green and indicated in Fig. 1, D and E ), as a result of immediate plasma clearance (glomerular filtration) of this molecule by the kidneys. At 95.2 s ( Fig. 1 E ), the FITC-inulin appeared to accumulate in the distal tubule lumen (in green and indicated in Fig. 1, E and F ), while its concentration in the proximal tubule was decreased compared with that at 24.4 s ( Fig. 1 D ). At the same time, there was less FITC-inulin in the bloodstream compared with that at 24.4 s. Consequently, the strength of the red color from the blood vessels increases, indicating a relative increase in the 500-kDa Texas red-dextran-to-FITC-inulin concentration ratio due to plasma clearance of FITC-inulin. At 244 s ( Fig. 1 F ), the FITC-inulin concentration continues to decrease from the bloodstream due to further clearance. This was accompanied by intensity decreases from both the proximal and distal tubule lumens ( Fig. 1 F ). This type of time-series image collection contains dynamic information about a given molecule passing through the glomerular filtration barrier of the kidney and becoming part of the filtrate. This is the basis for measuring plasma clearance kinetics and glomerular filtration function.


Fig. 1. Montage of color-combined fluorescence intensity time-series in vivo images of the kidney following a bolus infusion of a dye mixture. Green, FITC-inulin; red, 500-kDa Texas red-dextran. A : autofluorescence of the kidney at 3.66 s postinfusion. Dye molecules had not reached local tissue yet at this time point. As a result of glomerular filtration of FITC-inulin, it appears in both the proximal and distal tubule lumens over time ( B - F ), and the color of the capillary blood vessels becomes increasingly red due to plasma clearance of FITC-inulin and increased fractional concentration of the 500-kDa Texas red-dextran. The image frame size is 200 x 200 µm 2.


To quantify the molecular filtration rate, we used the intensity ratio ( Eq. 1 ) of the FITC-inulin and the 500-kDa Texas red-dextran ( Fig. 2 ). The intensity ratio from the blood vessels (shown in cyan in Fig. 2 ) changed in time with a change of relative concentrations of the two dyes. Since the 500-kDa dextran is not cleared by the kidneys, due to its large size, it stays in the bloodstream for a very long time (days) after infusion. Typically, there is no noticeable intensity drop from the 500-kDa dextran within the time period we follow a dye infusion (anywhere between 5 and 30 min). Figure 3 is an example of the intensity time series of the 500-kDa Texas red-dextran measured from a blood vessel, following a bolus infusion, for up to 79 min. The initial intensity spike (see inset ) was due to dye injection and fast distribution of the dye molecules into the whole blood volume. There was no significant intensity drop for the rest of the curve. Effectively, the decrease in the FITC-inulin-to-500-kDa Texas red-dextran intensity ratio from the blood correlates with the concentration decrease in FITC-inulin.


Fig. 2. Fluorescence intensity ratio images (using the same data set as in Fig. 1 ) of the kidney following a bolus infusion of dye mixture showing the regions of the capillary blood vessels. The decreasing of the ratio value over time is visible. The image frame size is 200 x 200 µm 2.


Fig. 3. Fluorescence intensity of the 500-kDa Texas Red-dextran measured from within the plasma as a function of time following a bolus infusion. Inset : the first 250 s after infusion. There was no significant intensity drop over the 79-min time period the intravital imaging measurements were performed.


In Fig. 4, we plot the FITC-inulin-to-500-kDa Texas red-dextran intensity ratio as a function of time after a bolus infusion along with the result of a least squares fit. Each data point in Fig. 4 was the average ratio value of the same region from a blood vessel extracted from an image time series (such as the images shown in Fig. 2 ). We plot data points every 1.22 s up to 120 s, then every 6.1 s until 244 s. The data had two phases ( Fig. 4 ): the initial phase and the clearance phase (or the filtration phase/elimination phase). The signal increase of the initial phase was due to relative dye distributions and accumulations in the kidney. The highest point (at 15 s) of the curve, which marks the starting point of the clearance phase, correlates with the beginning of the appearance of FITC-inulin in the proximal tubule in this case. The data points of the clearance phase fit well with a single exponential ( Eq. 2 ). We obtained a FITC-inulin plasma clearance rate constant, k (or k A ), of 0.0097 (s -1 ) with 2% error (using 95% confidence limits) ( 2 ).


Fig. 4. Example of FITC-inulin plasma clearance curve measured from the blood plasma using intensity ratio. Open squares: data points; solid line: a fit to the elimination phase using Eq. 2. A single exponential fits the clearance phase data points very well, and the fitting curve goes through the initial points of the elimination phase as well as points at the tail.


Plasma clearance rates measured using various animal models. Measurement results of plasma clearance of a single fluorescent (GFR) marker molecule have been shown to correlate with renal function ( 16 ). Using the intensity ratio of two fluorescent molecules, as in our case, should not change this fact. To prove this point and to demonstrate the capability of this ratiometric microscopic measurement technique for evaluations of abnormal kidney functions, we performed kinetic imaging experiments in a number of animal models where their GFRs were altered.


All plasma clearance rate constants plotted in Fig. 5 were measured using Sprague-Dawley rats. The clearance rate constant in Fig. 5 A was measured from control animals where the kidney function was not altered. In the case of unilateral kidney ischemia ( Fig. 5 B ), intravital microscopic images were taken from healthy kidneys of live rats. At 24 h after a 30-min unilateral kidney ischemia, the clearance rate constant was down to k = 0.0082 ± 0.0002 s -1. This reduction of plasma clearance rate was expected ( 11 ). Significant reductions of the plasma clearance rate constants were obtained at 24 h after a 30-min bilateral kidney ischemia ( Fig. 5 D, k = 0.0035 ± 0.001 s -1 ). This result correlated with the blood urea nitrogen (BUN) and creatinine measurements reported in the literature using similar kidney ischemia models ( 10, 27 ).


Fig. 5. Comparison between measured plasma clearance rate constants using the ratiometric imaging approach in different animal models with their kidney functions altered. A, controls. B, 24 h after a 30-min unilateral ischemia. C, unilateral nephrectomy. D, 24 h after a 30 min bilateral ischemia. E, bilateral nephrectomy. F, 15 min after bilateral ureter ligations. G, 1 h after bilateral ureter ligations. H, 1.5 h after bilateral ureter ligations. For A - E, n = 3. F, G, and H were measured from the same animal and each rate constant value k was an average from analyzing ratio data of 3 individual blood vessel regions.


As negative controls we measured the changes in the plasma clearance rate constant following whole-kidney nephrectomy. The reduction of the clearance rate constant measured immediately after unilateral nephrectomy (within <30 min, Fig. 5 C ) was as expected. The nonzero rate constant after bilateral nephrectomy ( Fig. 5 E ) indicates nonrenal "clearance" (due to nonspecific tissue distribution) of FITC-inulin that needs to be corrected for calculating the GFR ( Eqs. 3 and 4; discussed in the following sections).


Bilateral ureteral ligation is an alternative way to reduce and stop glomerular filtration. Plasma clearance rate constants in Fig. 5, F - H, were measured in the same animal, at 15, 60, and 90 min after bilateral ureter ligations. The clearance rate constant of the first measurement ( Fig. 5 F ), 15 min after ligation, was the same as the control ( Fig. 5 A ), indicating normal kidney function at this time point. Kidney filtration started to decrease when urine began to back up from the ureters upstream. As a result, the clearance rate constant was decreased to k = 0.0081 ± 0.0016 s -1 at 1 h ( Fig. 5 G ) and further down to k = 0.0046 ± 0.0021 s -1 at 90 min ( Fig. 5 H ). This rate constant is similar to those of the bilateral nephrectomy ( Fig. 5 E ) and bilateral ischemia observations at 24 h postinjury ( Fig. 5 D ).


Plasma clearance rate constant vs. GFR. The nonspecific tissue (nonrenal) distribution rate constant k T was determined from double whole-kidney nephrectomy experiments. The part of plasma clearance related to kidney function can be determined as k P = k A - k T. Therefore, we can calculate the GFR as follows


where k P is the rate constant of the plasma clearance of the marker molecule related to kidney function, and V D is the distribution volume. As a first approximation, V D equals the whole plasma volume. According to Eq. 3, it is equivalent to use plasma clearance rate or GFR for kidney function evaluation. Calculating GFR requires the knowledge of a distribution volume V D, which varies from individual to individual and may also vary over time. The plasma clearance rate constant of a GFR marker molecule, on the other hand, is a more direct measure of kidney function and independent of blood volumes of individual animals, in other words independent of the size and weight variations of the animals one uses. Here, we directly compare the plasma clearance rate constants obtained in different animal experiments and show the compiled results of measured rate constants in Fig. 5. It should be pointed out that in a need of estimating GFR from plasma clearance measurements, typically the plasma volume can be calculated directly from the body weight ( 16 ) which is simple and practical. It is a common practice in the clinics to evaluate renal function by scaling the actual measurement results with body weights or surface areas.


From results in Fig. 5, by considering the nonrenal clearance rate constant k T ( 0.0047 s -1 obtained from the bilateral nephrectomy Fig. 5 E ), we can calculate GFR for an individual animal; e.g., we obtained GFR = 2.38 ± 0.8 ml/min for an animal of 229 g in body weight, with healthy kidneys, having a total plasma volume of 6.3 ml (50% of the total blood volume figured at 5.5% of the body weight) ( 1 ). In fact, this rate is close to the rate reported in the literature using magnetic resonance imaging techniques ( 20 ) to measure plasma clearance of a GFR marker. With the removal of one kidney, we obtained GFR = 1.34 ± 0.5 ml/min measured with a 302-g animal having a total plasma volume of 8.3 ml. This GFR value was 56% of that of the above-mentioned control animal, as one expected.


By using a bolus infusion of GFR markers, the nonspecific tissue distributions of the marker molecules contribute to the overall plasma disappearance kinetics of the marker molecules. This is a well-documented phenomenon and an issue associated with methods using plasma clearance to quantify renal filtration function. To remove this "nonrenal clearance" component, we have experimentally determined its kinetic rate constant by using a bilateral nephrectomy model. The fact that the rate constants of the nonrenal clearance of the marker molecules are very close when renal filtration functions are stopped ( Fig. 5, E and H ) suggest that it has a well-defined value for the animal models we used. It should be noted that the nonrenal distribution rate is likely not a constant in different cases, such as in acute or chronic renal insufficiency, and should be investigated accordingly. However, in each one of these cases the corresponding renal plasma clearance rate constant still needs to be corrected using Eq. 4.


Alternatively, one can use a single exponential model with two rate constants to fit the overall kinetic curve and directly retrieve the rate constants of both the renal and nonrenal clearance of the marker molecules as follows


where k p and k T are rate constants of renal and nonrenal plasma clearance, respectively, and A and C are constants. The use of a single expression ( Eq. 5 ) and Eqs. 2 and 4 is mathematically equivalent. Typically, a multicompartment model with double exponentials is used to account for the kinetics of GFR marker distribution in the blood volume after a bolus injection and the clearance of the marker molecules, respectively. Since we are using intensity ratio, the fraction of the distribution phase is negligible, and therefore what's left is a single exponential term to account for the clearance kinetics. However, the overall rate constant of the clearance phase is a sum of individual rate constants of all processes contributing to the clearance kinetics. For a general case when the GFR marker distribution phase is present, the plasma clearance kinetics can be described as follows


with is the rate constant of any individual nonrenal clearance processes, k D is the rate constant of marker distribution process, and A, B, and C are constants. The use of a multicompartment model to fit the data and retrieve the plasma clearance rate constants can be helpful in human tests ( 4, 25 ) when the fraction of the distribution phase becomes more prominent.


Inulin clearance rate measured by imaging the skin. Since we measure the clearance rate of the marker molecule from the blood space, we should be able to determine the plasma clearance rate constant by making measurements at any bodily location wherever there is blood flow. To demonstrate that the plasma clearance rate constants measured by imaging the kidney represent renal function systemically, we performed in vivo kinetic imaging of plasma clearance on the skin. Figure 6 is a montage of color-combined intensity images of a skin blood vessel from a healthy male Sprague-Dawley rat. These images were acquired as a function of time following intravenous infusion of a FITC-inulin and 500-kDa Texas red-dextran mixture. Other than placement of a catheter in the vein, there were no surgeries involved. A skin area of male genitalia of the rat was imaged.


Fig. 6. Montage of color-combined fluorescence time-series images of a blood vessel from a rat skin area following a bolus infusion of a dye mixture. Green, FITC-inulin, Red, 500-kDa Texas red-dextran. The color of the blood vessel becomes increasingly red over time due to plasma clearance of FITC-inulin and increased fractional concentration of the 500-kDa Texas Red-dextran. Imaging was done on the rat penis. Image frame size is 100 x 150 µm 2.


The clearance of FITC-inulin (in green) from the blood was visible as the green color gradually disappeared over time. At the end of 360 s after infusion, the fluorescent molecule left in the blood was mainly the 500-kDa Texas red-dextran (in red). The FITC-inulin-to-Texas red-dextran intensity ratio value of the whole blood vessel region was calculated from individual images and plotted as a function of time ( Fig. 7 ). We obtained an inulin plasma clearance rate constant k = 0.0097 ± 0.0010 (s -1 ) by fitting the data to Eq. 2. This rate constant value is almost identical to what was measured from imaging the kidney ( Fig. 4 ). These results suggest there is no difference between imaging the kidney vasculature and imaging the skin in measuring inulin plasma clearance rates.


Fig. 7. Plasma clearance of FITC-inulin measured from within a blood vessel (same data set as in Fig. 6 ) as a function of time using the FITC-inulin-to-Texas red-dextran (500 kDa) intensity ratio., Data points. The solid line is a fit to the elimination phase using Eq. 2.


In principle, plasma clearance rates can be measured at any bodily location where there is representative blood flow. We have experimented to image blood flow from the skin of the ear, using a skin flap (requires surgery), from the lips, and the genital areas of Sprague-Dawley rats. We found the genital area is relatively simple to image, involving fewer procedures to perform, not having the problem of the hair (highly fluorescent and a problem associated with imaging the ear), or interference with essential physiological functions (e.g., blocking or interfere with the airway, a problem in imaging the lips). Imaging the genitalia of the animal allows us to directly experiment with the Sprague-Dawley model commonly used for different studies e.g., ischemia, without needing to use special animals such as the nude rats.


Using intensity from a single probe vs. intensity ratio from two probes. After fluorescent molecules are intravenously infused into the animal, they will distribute in the plasma and eventually reach concentration equilibrium. This dye distribution process usually requires a given period of time to complete. However, on the other hand, the filtration function of kidneys is a continuous function. At the moment a GFR marker molecule is being injected into the bloodstream, it is filtered by the kidneys almost instantaneously on reaching the kidney glomeruli. As a result of this kinetic process, if one uses the signal from a single GFR marker, the kinetics of plasma distribution and plasma clearance processes are convoluted. Different from using intensity values from a single source, by infusing two probes at the same time, the initial plasma distributions of the two molecules happen at the same time and the contribution, due to plasma distribution, to the overall kinetic signals is minimized using the ratio values of one probe being filtered and the other being retained in the blood. This difference is illustrated in Fig. 8. The fluorescence intensity value (from FITC-inulin) peaked at 20 s ( Fig. 8 ) after infusion was significantly delayed compared with the time when the ratio value reached its peak (at 7 s). At 20 s postinfusion, FITC-inulin had already been filtered 10 s ( Fig. 1 ). Clearly, the kinetic curve using FITC-inulin intensity ( Fig. 8 ) alone misaligns in time with the filtration process. As a result, the initial data points were either not acquired or not used for retrieving the rate constants of plasma clearance using signals from single GFR markers alone ( 16 ). On the contrary, the peak of the kinetic curve using intensity ratio data ( Fig. 8 ) matches more closely the appearance of dextran mixture in the renal peritubular capillaries (usually within the time period of acquiring one image frame, which is 1.22 s under our experimental conditions). Therefore, the initial data points related to the (plasma) clearance phase can be used for curve fitting using a single exponential. Consequently, the decay trace using intensity ratio data better represents the kinetics of plasma clearance, simplifies the mathematical model used for fitting, and allows rapid renal function analysis.


Fig. 8. Comparison between using FITC-inulin-to-500-kDa Texas red-dextran intensity ratio ( ) and directly using FITC-inulin intensity alone ( ). Both curves are normalized to their highest values, respectively. The peak of the intensity-alone curve is delayed with respect to the peak of the ratio curve. Other than that, both curves are significantly overlapped. The lines are used to guide the eyes.


In addition, a comparison of the kinetic data between using the intensity ratio and directly using the intensity value of a 3-kDa tetramethylrhodamine-conjugated dextran (3-kDa TMR-dextran) alone is shown in Fig. 9. The differences include the following.


Fig. 9. Example showing that the intensity-alone data can be very noisy and the points are scattered, but the ratio data points are much tighter with a well-defined clearance curve. A : 3-kDa tetramethylrhodamine (TMR)-dextran-to-500-kDa FITC-dextran intensity ratio. B : 3-kDa TMR-dextran intensity alone. In this case, a mixture of 1.6 mg of the 3-kDa TMR-dextran and 1.6 mg of the 500-kDa FITC-dextran dissolved in 0.5 ml saline was used for infusion. The solid fit lines in both A and B are for guiding the eyes.


The intensity fluctuation of the 3-kDa TMR-dextran alone was quite significant ( Fig. 9 B ). Consequently, the fitting result 15%) and is less defined. In contrast, the intensity ratio (between 3-kDa TMR-dextran and 500-kDa FITC-dextran) has significantly less noise, and the measured clearance rate constant is much better defined with significantly less error. This is partially because fluorescence intensity is typically very sensitive to even a slight change in focus and movement of the sample. Intensity ratio, on the other hand, is insensitive to minor changes in imaging depth and motion. We are focusing on the fluorescence signals from the blood. The intensity signal of a dye from the blood can change when the blood flow rate changes. However, the relative intensity ratio between two molecules does not change even when the blood flow rate or blood volume changes (assuming there is no clearance). This is because both dye molecules are present in the blood and move together.


As discussed earlier, the separation between the initial dye distribution and the clearance phase is well defined using the intensity ratio. In the case of using the intensity of a single dye alone, it is more difficult to determine at what time point the clearance phase begins. The highest data point in the intensity curve typically does not correlate in time with the appearance of the smaller molecule in the proximal tubule lumen. Therefore, the dye distribution and the filtration phases are convoluted in the intensity only curve ( 16, 21 ). If we fit the data using the same model as the one to which we fit the ratio curve, we may miscalculate the plasma clearance rate.


Although the data shown in Figs. 8 and Fig. 9 are results using a fluorescent GFR marker, the associated disadvantages of using the signals from single marker molecules exist in other plasma clearance-based GFR measurement techniques, when single nonfluorescent marker molecules, e.g., [ 125 I]iothalamate, technetium-labeled diethylenetriamine penta-acetate, or 51 Cr-EDTA, are used. In these cases when ratios are not used, one has to apply more complicated models to describe the whole plasma clearance kinetics to retrieve the clearance rate constant of the marker molecules.


In conclusion, as demonstrated in the animal experiments, the ratiometric microscopic imaging technique we have developed provides a quantitative means for determining kidney function with improved accuracy and sensitivity compared with techniques using signals from single GFR marker molecules alone. Consequently, it allows rapid evaluations of kidney function and as a result the initial data points of the clearance phase immediately postinfusion can be used effectively with a simple mathematical model, which provides a tool for quantifying renal function even when it is severally reduced. This ratiometric technique eliminates the need for continuous infusion, urine collection, and other procedures used for measuring renal function, and it can be also applied to improve other techniques used to determine glomerular filtration function based on plasma disappearance rates.


GRANTS


This work was supported by startup funds from an Indiana Genomics Initiative (INGEN) Grant from the Eli Lilly and Company Foundation to the Indiana University School of Medicine (W. Yu) and a pilot study fund from an National Institutes of Health (NIH) O?Brien Center of Excellence Grant (W. Yu). The Indiana Center for Biological Imaging is supported by grant funds from INGEN and an NIH O?Brien Center of Excellence Award (P50 DK-61594) to B. A. Molitoris.


ACKNOWLEDGMENTS


The authors thank Dr. Silvia B. Campos for performing animal surgeries, Xianming Chen and Ashley M. Dutton for technical support, and the Indiana Center for Biological Microscopy for using its microscopic imaging facility. The authors also thank Drs. Timothy Sutton, Pierre Dagher, and Robert Bacallao for support and stimulating discussions.

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作者单位:Nephrology Division, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana

日期:2008年7月4日 - 来自[2007年第290卷第6期]栏目

学术报告:A powerful approach via forest to identifying gene and gene-gene interactions


主讲人:Heping ZhangPh.D.

        Department of Epidemiology and Public Health Yale University School of Medicine

Director of the Collaborative Center for Statistics in Science

主持人:Xueqin Wang, Ph.D.

Professor of Statistics School of mathematics & computational science  Zhongshan school of medicine

   间:20080617日(星期二)下1600

  点:中山大学北校区永生楼四楼讲学厅

 

Heping ZhangPh.D.简介:

Educational background

 

Ph.D. in Statistics, with minor in Computer Sciences

Stanford University, Stanford, 1991.

 

Research Interests

* Recursive partitioning (trees and splines) in health sciences

* Nonparametric analysis of longitudinal (continuous and discrete) data

* Linkage and association analyses, mapping quantitative trait loci

* FMR imaging analysis

* Analysis of gene expression data

 

欢迎各位师生踊跃参加!

 

   中山医学院

 

日期:2008年6月14日 - 来自[中山医科大学(中山大]栏目
共 4 页,当前第 1 页 9 1 2 3 4 :


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