Decoding Immuno-Competence: A Novel Analysis of Complete Blood Cell Count Data in COVID-19 Outcomes.

BACKGROUND: While 'immuno-competence' is a well-known term, it lacks an operational definition. To address this omission, this study explored whether the temporal and structured data of the complete blood cell count (CBC) can rapidly estimate immuno-competence. To this end, one or more ratios that included data on all monocytes, lymphocytes and neutrophils were investigated. MATERIALS AND METHODS: Longitudinal CBC data collected from 101 COVID-19 patients (291 observations) were analyzed. Dynamics were estimated with several approaches, which included non-structured (the classic CBC format) and structured data. Structured data were assessed as complex ratios that capture multicellular interactions among leukocytes. In comparing survivors with non-survivors, the hypothesis that immuno-competence may exhibit feedback-like (oscillatory or cyclic) responses was tested. RESULTS: While non-structured data did not distinguish survivors from non-survivors, structured data revealed immunological and statistical differences between outcomes: while survivors exhibited oscillatory data patterns, non-survivors did not. In survivors, many variables (including IL-6, hemoglobin and several complex indicators) showed values above or below the levels observed on day 1 of the hospitalization period, displaying L-shaped data distributions (positive kurtosis). In contrast, non-survivors did not exhibit kurtosis. Three immunologically defined data subsets included only survivors. Because information was based on visual patterns generated in real time, this method can, potentially, provide information rapidly. DISCUSSION: The hypothesis that immuno-competence expresses feedback-like loops when immunological data are structured was not rejected. This function seemed to be impaired in immuno-suppressed individuals. While this method rapidly informs, it is only a guide that, to be confirmed, requires additional tests. Despite this limitation, the fact that three protective (survival-associated) immunological data subsets were observed since day 1 supports many clinical decisions, including the early and personalized prognosis and identification of targets that immunomodulatory therapies could pursue. Because it extracts more information from the same data, structured data may replace the century-old format of the CBC.

Data structuring may prevent ambiguity and improve personalized medical prognosis.

Topics expected to influence personalized medicine (PM), where medical decisions, practices, and treatments are tailored to the individual patient, are reviewed. Lack of discrimination due to different biological conditions that express similar values of numerical variables (ambiguity) is regarded to be a major potential barrier for PM. This material explores possible causes and sources of ambiguity and offers suggestions for mitigating the impacts of uncertainties. Three causes of ambiguity are identified: (1) delayed adoption of innovations, (2) inadequate emphases, and (3) inadequate processes used when new medical practices are developed and validated. One example of the first problem is the relative lack of medical research on "compositional data" -the type that characterizes leukocyte data. This omission results in erroneous use of data abundantly utilized in medicine, such as the blood cell differential. Emphasis on data output ‒not biomedical interpretation that facilitates the use of clinical data‒ exemplifies the second type of problems. Reliance on tools generated in other fields (but not validated within biomedical contexts) describes the last limitation. Because reductionism is associated with these problems, non-reductionist alternatives are reviewed as potential remedies. Data structuring (converting data into information) is considered a key element that may promote PM. To illustrate a process that includes data-information-knowledge and decision-making, previously published data on COVID-19 are utilized. It is suggested that ambiguity may be prevented or ameliorated. Provided that validations are grounded on biomedical knowledge, approaches that describe certain criteria - such as non-overlapping data intervals of patients that experience different outcomes, immunologically interpretable data, and distinct graphic patterns - can inform, at personalized bases, earlier and/or with fewer observations.

Geo-temporal patterns to design cost-effective interventions for zoonotic diseases -the case of brucellosis in the country of Georgia.

Introduction: Control of zoonosis can benefit from geo-referenced procedures. Focusing on brucellosis, here the ability of two methods to distinguish disease dissemination patterns and promote cost-effective interventions was compared. Method: Geographical data on bovine, ovine and human brucellosis reported in the country of Georgia between 2014 and 2019 were investigated with (i) the Hot Spot (HS) analysis and (ii) a bio-geographical (BG) alternative. Results: More than one fourth of all sites reported cases affecting two or more species. While ruminant cases displayed different patterns over time, most human cases described similar geo-temporal features, which were associated with the route used by migrant shepherds. Other human cases showed heterogeneous patterns. The BG approach identified small areas with a case density twice as high as the HS method. The BG method also identified, in 2018, a 2.6-2.99 higher case density in zoonotic (human and non-human) sites than in non-zoonotic sites (which only reported cases affecting a single species) -a finding that, if corroborated, could support cost-effective policy-making. Discussion: Three dissemination hypotheses were supported by the data: (i) human cases induced by sheep-related contacts; (ii) human cases probably mediated by contaminated milk or meat; and (iii) cattle and sheep that infected one another. This proof-of-concept provided a preliminary validation for a method that may support cost-effective interventions oriented to control zoonoses. To expand these findings, additional studies on zoonosis-related decision-making are recommended.

Biologically grounded scientific methods: The challenges ahead for combating epidemics.

The protracted COVID 19 pandemic may indicate failures of scientific methodologies. Hoping to facilitate the evaluation and/or update of methods relevant in Biomedicine, several aspects of scientific processes are here explored. First, the background is reviewed. In particular, eight topics are analyzed: (i) the history of Higher Education models in reference to the pursuit of science and the type of student cognition pursued, (ii) whether explanatory or actionable knowledge is emphasized depending on the well- or ill-defined nature of problems, (iii) the role of complexity and dynamics, (iv) how differences between Biology and other fields influence methodologies, (v) whether theory, hypotheses or data drive scientific research, (vi) whether Biology is reducible to one or a few factors, (vii) the fact that data, to become actionable knowledge, require structuring, and (viii) the need of inter-/trans-disciplinary knowledge integration. To illustrate how these topics interact, a second section describes four temporal stages of scientific methods: conceptualization, operationalization, validation and evaluation. They refer to the transition from abstract (non-measurable) concepts (such as 'health') to the selection of concrete (measurable) operations (such as 'quantification of ́anti-virus specific antibody titers'). Conceptualization is the process that selects concepts worth investigating, which continues as operationalization when data-producing variables viewed to reflect critical features of the concepts are chosen. Because the operations selected are not necessarily valid, informative, and may fail to solve problems, validations and evaluations are critical stages, which require inter/trans-disciplinary knowledge integration. It is suggested that data structuring can substantially improve scientific methodologies applicable in Biology, provided that other aspects here mentioned are also considered. The creation of independent bodies meant to evaluate biologically oriented scientific methods is recommended.

Test positivity - Evaluation of a new metric to assess epidemic dispersal mediated by non-symptomatic cases.

Epidemic control may be hampered when the percentage of asymptomatic cases is high. Seeking remedies for this problem, test positivity was explored between the first 60 to 90 epidemic days in six countries that reported their first COVID-19 case between February and March 2020: Argentina, Bolivia, Chile, Cuba, Mexico, and Uruguay. Test positivity (TP) is the percentage of test-positive individuals reported on a given day out of all individuals tested the same day. To generate both country-specific and multi-country information, this study was implemented in two stages. First, the epidemiologic data of the country infected last (Uruguay) were analyzed. If at least one TP-related analysis yielded a statistically significant relationship, later assessments would investigate the six countries. The Uruguayan data indicated (i) a positive correlation between daily TP and daily new cases (r = 0.75); (ii) a negative correlation between TP and the number of tests conducted per million inhabitants (TPMI, r = -0.66); and (iii) three temporal stages, which differed from one another in both TP and TPMI medians (p < 0.01) and, together, revealed a negative relationship between TPMI and TP. No significant relationship was found between TP and the number of active or recovered patients. The six countries showed a positive correlation between TP and the number of deaths/million inhabitants (DMI, r = 0.65, p < 0.01). With one exception -a country where isolation was not pursued-, all countries showed a negative correlation between TP and TPMI (r = 0.74). The temporal analysis of country-specific policies revealed four patterns, characterized by: (1) low TPMI and high DMI, (2) high TPMI and low DMI; (3) an intermediate pattern, and (4) high TPMI and high DMI. Findings support the hypothesis that test positivity may guide epidemiologic policy-making, provided that policy-related factors are considered and high-resolution geographical data are utilized.

Early network properties of the COVID-19 pandemic - The Chinese scenario.

OBJECTIVES: To control epidemics, sites more affected by mortality should be identified. METHODS: Defining epidemic nodes as areas that included both most fatalities per time unit and connections, such as highways, geo-temporal Chinese data on the COVID-19 epidemic were investigated with linear, logarithmic, power, growth, exponential, and logistic regression models. A z-test compared the slopes observed. RESULTS: Twenty provinces suspected to act as epidemic nodes were empirically investigated. Five provinces displayed synchronicity, long-distance connections, directionality and assortativity - network properties that helped discriminate epidemic nodes. The rank I node included most fatalities and was activated first. Fewer deaths were reported, later, by rank II and III nodes, while the data from rank I-III nodes exhibited slopes, the data from the remaining provinces did not. The power curve was the best fitting model for all slopes. Because all pairs (rank I vs. rank II, rank I vs. rank III, and rank II vs. rank III) of epidemic nodes differed statistically, rank I-III epidemic nodes were geo-temporally and statistically distinguishable. CONCLUSIONS: The geo-temporal progression of epidemics seems to be highly structured. Epidemic network properties can distinguish regions that differ in mortality. This real-time geo-referenced analysis can inform both decision-makers and clinicians.

Where and when to vaccinate? Interdisciplinary design and evaluation of the 2018 Tanzanian anti-rabies campaign.

OBJECTIVES: Hoping to improve health-related effectiveness, a two-phase vaccination against rabies was designed and executed in northern Tanzania in 2018, which included geo-epidemiological and economic perspectives. METHODS: Considering the local bio-geography and attempting to rapidly establish a protective ring around a city at risk, the first phase intervened on sites surrounding that city, where the population density was lower than in the city at risk. The second phase vaccinated a rural area. RESULTS: No rabies-related case has been reported in the vaccinated areas for over a year post-immunisation; hence, the campaign is viewed as highly cost-effective. Other metrics included: rapid implementation (concluded in half the time spent on other campaigns) and the estimated cost per protected life, which was 3.28 times lower than in similar vaccinations. CONCLUSIONS: The adopted design emphasised local bio-geographical dynamics: it prevented the occurrence of an epidemic in a city with a higher demographic density than its surrounding area and it also achieved greater effectiveness than average interventions. These interdisciplinary, policy-oriented experiences have broad and immediate applications in settings of limited and/or time-sensitive (expertise, personnel, and time available to intervene) resources and conditions.

Birth weight, birth order, and age at first solid food introduction influence child growth and body composition in 6- to 8-year-old Maya children: The importance of the first 1000 days of life.

OBJECTIVES: To analyze the relationship of birth weight, birth order, breastfeeding duration, and age of introduction of solid foods with height, fat mass, and fat-free mass in a sample of Maya children when aged 6 to 8 years old. METHODS: We collected data on anthropometry, body composition, children's birth weight, birth order, early feeding practices, and household socioeconomic characteristics in a sample of 260 Maya children aged 6 to 8 years living in Merida and Motul, two cities in Yucatan, Mexico. Multiple regression models were performed to identify variables associated with height-for-age (HAZ), fat mass index (FMI), and fat-free mass index (FFMI). The predictors included in the models were birth weight (kg), birth order, duration of breastfeeding (months), age at introduction of solid foods (months), maternal age (years), and height (cm). Models were adjusted for the influence of children's age and sex, maternal educational level, and household overcrowding. RESULTS: HAZ was positively associated with child birthweight and maternal height and age, but inversely associated with birth order and age of introduction of solid foods. FMI was positively associated with birth weight, maternal age, and height, and negatively associated with birth order. FFMI was positively associated with maternal age and birth weight. CONCLUSIONS: These results are evidence of the importance of the first 1000 days of life for the growth and body composition of Maya children and contributed to understand the development of nutritional dual burden in this population.

Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data.

Investigating disease pathogenesis and personalized prognostics are major biomedical needs. Because patients sharing the same diagnosis can experience different outcomes, such as survival or death, physicians need new personalized tools, including those that rapidly differentiate several inflammatory phases. To address these topics, a pattern recognition-based method (PRM) that follows an inverse problem approach was designed to assess, in <10 min, eight concepts: synergy, pleiotropy, complexity, dynamics, ambiguity, circularity, personalized outcomes, and explanatory prognostics (pathogenesis). By creating thousands of secondary combinations derived from blood leukocyte data, the PRM measures synergic, pleiotropic, complex and dynamic data interactions, which provide personalized prognostics while some undesirable features-such as false results and the ambiguity associated with data circularity-are prevented. Here, this method is compared to Principal Component Analysis (PCA) and evaluated with data collected from hantavirus-infected humans and birds that appeared to be healthy. When human data were examined, the PRM predicted 96.9 % of all surviving patients while PCA did not distinguish outcomes. Demonstrating applications in personalized prognosis, eight PRM data structures sufficed to identify all but one of the survivors. Dynamic data patterns also distinguished survivors from non-survivors, as well as one subset of non-survivors, which exhibited chronic inflammation. When the PRM explored avian data, it differentiated immune profiles consistent with no, early, or late inflammation. Yet, PCA did not recognize patterns in avian data. Findings support the notion that immune responses, while variable, are rather deterministic: a low number of complex and dynamic data combinations may be enough to, rapidly, unmask conditions that are neither directly observable nor reliably forecasted.

Nature and Consequences of Biological Reductionism for the Immunological Study of Infectious Diseases.

Evolution has conserved "economic" systems that perform many functions, faster or better, with less. For example, three to five leukocyte types protect from thousands of pathogens. To achieve so much with so little, biological systems combine their limited elements, creating complex structures. Yet, the prevalent research paradigm is reductionist. Focusing on infectious diseases, reductionist and non-reductionist views are here described. The literature indicates that reductionism is associated with information loss and errors, while non-reductionist operations can extract more information from the same data. When designed to capture one-to-many/many-to-one interactions-including the use of arrows that connect pairs of consecutive observations-non-reductionist (spatial-temporal) constructs eliminate data variability from all dimensions, except along one line, while arrows describe the directionality of temporal changes that occur along the line. To validate the patterns detected by non-reductionist operations, reductionist procedures are needed. Integrated (non-reductionist and reductionist) methods can (i) distinguish data subsets that differ immunologically and statistically; (ii) differentiate false-negative from -positive errors; (iii) discriminate disease stages; (iv) capture in vivo, multilevel interactions that consider the patient, the microbe, and antibiotic-mediated responses; and (v) assess dynamics. Integrated methods provide repeatable and biologically interpretable information.

Preventing Data Ambiguity in Infectious Diseases with Four-Dimensional and Personalized Evaluations.

BACKGROUND: Diagnostic errors can occur, in infectious diseases, when anti-microbial immune responses involve several temporal scales. When responses span from nanosecond to week and larger temporal scales, any pre-selected temporal scale is likely to miss some (faster or slower) responses. Hoping to prevent diagnostic errors, a pilot study was conducted to evaluate a four-dimensional (4D) method that captures the complexity and dynamics of infectious diseases. METHODS: Leukocyte-microbial-temporal data were explored in canine and human (bacterial and/or viral) infections, with: (i) a non-structured approach, which measures leukocytes or microbes in isolation; and (ii) a structured method that assesses numerous combinations of interacting variables. Four alternatives of the structured method were tested: (i) a noise-reduction oriented version, which generates a single (one data point-wide) line of observations; (ii) a version that measures complex, three-dimensional (3D) data interactions; (iii) a non-numerical version that displays temporal data directionality (arrows that connect pairs of consecutive observations); and (iv) a full 4D (single line-, complexity-, directionality-based) version. RESULTS: In all studies, the non-structured approach revealed non-interpretable (ambiguous) data: observations numerically similar expressed different biological conditions, such as recovery and lack of recovery from infections. Ambiguity was also found when the data were structured as single lines. In contrast, two or more data subsets were distinguished and ambiguity was avoided when the data were structured as complex, 3D, single lines and, in addition, temporal data directionality was determined. The 4D method detected, even within one day, changes in immune profiles that occurred after antibiotics were prescribed. CONCLUSIONS: Infectious disease data may be ambiguous. Four-dimensional methods may prevent ambiguity, providing earlier, in vivo, dynamic, complex, and personalized information that facilitates both diagnostics and selection or evaluation of anti-microbial therapies.

Comparative Study of Lead Concentration in Feathers of Urban and Rural Passerines in Merida, Mexico.

Lead is a commonly monitored heavy metal because of potential health effects on exposed organisms. We quantified lead in secondary feathers of two passerine bird species, clay-colored thrushes (Turdus grayi) and great-tailed grackles (Quiscalus mexicanus), from an urban and a rural site in the municipality of Merida, Yucatan. Urban lead concentration was significantly higher than its rural counterpart for both species (p < 0.05). In the urban site, lead concentration was similar in both species (p = 0.14). However, data from the rural site showed that lead concentration was significantly higher in thrush feathers (p < 0.05). Lead levels herein presented are among the lowest ever reported suggesting that either lead accumulation or absorption is limited. Finally, our data seem to support the hypothesis that species feeding ecology plays a major role in lead accumulation.

Beyond numbers: the informative patterns of immuno-staphylococcal dynamics.

To evaluate new drugs, the immune system should be considered. Here we evaluated a proof-of-concept that uncovers bacterial-leukocyte interactions. Analyzing longitudinal leukocyte data from bovines infected with either methicillin-resistant (MRSA) or methicillin-susceptible (MSSA) Staphylococcus aureus, two methods were investigated: (i) an approach that assesses lymphocytes, monocytes, or neutrophils, separately, and (ii) a method that, using dimensionless indicators (products, ratios, or combinations derived from leukocyte data), explores the dynamics of leukocyte relationships in three-dimensional (3D) space and identifies data subsets of informative value. The classic approach not always distinguished infected from non-infected cows. In contrast, the alternative approach differentiated noninfected from infected animals and distinguished early MRSA from early MSSA and late MRSA infections. Discrimination was associated with the use of dimensionless indicators. When measured in 3D space, such indicators generated a very large number of combinations, which helped detect data subsets usually unobserved, such as non-overlapping infection-negative and -positive subsets, and several disease stages. The validity of such data subsets was determined with biologically interpretable data. This graphic, pattern recognition-based information system included but did not depend on any one number or variable. Because it can detect functions (relationships that involve two or more elements), in real time, if shown reproducible, the analysis of complex host-microbial dynamics could be used to evaluate antimicrobials.

Cleaning products, environmental awareness and risk perception in Mérida, Mexico.

Cleaning products are associated with many health and environmental problems. Contamination of water resources by cleaning products is more likely to occur with septic tanks as sewage treatment systems especially in karstic terrains. We explored women's ideas about water sources and the risk cleaning products pose to health and sewage in Mérida, a city in the Yucatán peninsula of Mexico. Women were unaware of the city's water management system. We found a positive and statistically significant association between risk perception and environmental awareness, education level and employment status. We suggest developing education and risk communication strategies to inform residents about the hydro-geological features in the Yucatán, the vulnerability of its karstic aquifer and the health and environmental risks associated with cleaning agents.

Feedback-based, system-level properties of vertebrate-microbial interactions.

BACKGROUND: Improved characterization of infectious disease dynamics is required. To that end, three-dimensional (3D) data analysis of feedback-like processes may be considered. METHODS: To detect infectious disease data patterns, a systems biology (SB) and evolutionary biology (EB) approach was evaluated, which utilizes leukocyte data structures designed to diminish data variability and enhance discrimination. Using data collected from one avian and two mammalian (human and bovine) species infected with viral, parasite, or bacterial agents (both sensitive and resistant to antimicrobials), four data structures were explored: (i) counts or percentages of a single leukocyte type, such as lymphocytes, neutrophils, or macrophages (the classic approach), and three levels of the SB/EB approach, which assessed (ii) 2D, (iii) 3D, and (iv) multi-dimensional (rotating 3D) host-microbial interactions. RESULTS: In all studies, no classic data structure discriminated disease-positive (D+, or observations in which a microbe was isolated) from disease-negative (D-, or microbial-negative) groups: D+ and D- data distributions overlapped. In contrast, multi-dimensional analysis of indicators designed to possess desirable features, such as a single line of observations, displayed a continuous, circular data structure, whose abrupt inflections facilitated partitioning into subsets statistically significantly different from one another. In all studies, the 3D, SB/EB approach distinguished three (steady, positive, and negative) feedback phases, in which D- data characterized the steady state phase, and D+ data were found in the positive and negative phases. In humans, spatial patterns revealed false-negative observations and three malaria-positive data classes. In both humans and bovines, methicillin-resistant Staphylococcus aureus (MRSA) infections were discriminated from non-MRSA infections. CONCLUSIONS: More information can be extracted, from the same data, provided that data are structured, their 3D relationships are considered, and well-conserved (feedback-like) functions are estimated. Patterns emerging from such structures may distinguish well-conserved from recently developed host-microbial interactions. Applications include diagnosis, error detection, and modeling.

Development of a brain nucleus involved in song production in zebra finches (Taeniopygia guttata) is disrupted by Aroclor 1248.

We studied whether polychlorinated biphenyls (PCBs) may alter the development of song control brain nuclei in zebra finch (Taeniopygia guttata) offspring of pulse-exposed hens. We orally administered 40 microg of Aroclor 1248 to adult female finches before egg laying. When the progeny were 50 d old, we measured the volumes of the song control nuclei robustus arcopallialis (RA) and higher vocal center (HVC) using light microscopy. Both male and female progeny of exposed birds had a significantly smaller RA than control birds (36 and 16%, respectively; p < or = 0.05). The HVC did not differ in either sex between exposed and control groups. Perhaps impaired development of RA was caused by PCB action on steroid receptors. We conclude that animals living in contaminated areas may be at risk of neurological damage in hormone-sensitive brain areas and that changes in brain nuclei related to song may be a sensitive indicator of low-level PCB exposure.

Reproductive impairment in zebra finches (Taeniopygia guttata).

The effects of polychlorinated biphenyls (PCBs) as compounds that may disrupt endocrine activity and, consequently, alter reproductive performance were investigated in altricial zebra finches (Taeniopygia guttata). The breeding performance and breeding cycle of zebra finches differed significantly between nonexposed birds and those experimentally pulse-exposed to Aroclor 1248, a PCB compound (40 microg/bird). Aroclor-exposed birds showed significantly increased numbers of clutches laid, nests constructed per pair, incubation time per pair, and percentage of hatchling mortality compared to controls. Not all reproductive parameters were affected. Those traditionally regarded as indicators of reproductive capacity (number of eggs laid per clutch, number of eggs laid per pair, hatchlings per clutch, and fledglings per clutch) did not differ statistically between exposed and control birds. Findings support the hypothesis that very low PCB doses may be associated with endocrine disruption. It is suggested that evaluation of reproductive parameters related to parental care is more adequate to assess endocrine disruption than is evaluation of reproductive success parameters. Given its short breeding cycle, altricial breeding behavior, and other advantages not possessed by precocial birds, we propose using the zebra finch for evaluations of chemicals with endocrine-disruptive activity.

Identification of geographic factors associated with early spread of foot-and-mouth disease.

OBJECTIVE: To explore whether early analysis of spatial data may result in identification of variables associated with epidemic spread of foot and mouth disease. SAMPLE POPULATION: 37 farms with infected cattle (ie, case farms) reported within the first 6 days of the 2001 Uruguayan foot-and-mouth disease epidemic. PROCEDURE: A georeferenced database was created and retrospective analysis was performed on case farm location in relation to farm density, cattle density, farm type (ie, beef vs dairy cattle production), road density, case farm distance to the nearest road, farm size, farm ownership, and day of infection. Mean or median results of 1 to 3 day versus 4 to 6 day spatial data were compared. Spatial-temporal associations were investigated by correlation analysis. RESULTS: Comparison of mean or median values between the first 3 days and days 4 to 6 of the epidemic and results of correlation analysis indicated a significant increase in road density, cattle density, and dairy cattle production and a significant decrease in farm size and case farm distance to the nearest road that developed over time. A route that linked most case farms by the shortest possible distance and also considered significantly associated variables was created. It included 86.1% of all case farms reported by 60 days into the epidemic. CONCLUSIONS AND CLINICAL RELEVANCE: Epidemic direction can be assessed on the basis of road density and other spatial variables as early as 6 days into an epidemic. Epidemic control areas may be more effectively identified if local and regional georeferenced data are considered.

Oral treatment of avian lead intoxication with meso-2,3-dimercaptosuccinic acid.

The efficacy of meso-dimercaptosuccinic acid (DMSA) (succimer) in treating avian lead intoxication was studied in a retrospective, nonrandomized, longitudinal study. Nineteen birds with moderate to high blood lead concentration and neurologic signs compatible with lead toxicity were treated with DMSA (30 mg/kg p.o., b.i.d.; n = 15) for a minimum of 7 days. In cases with severe neurologic signs, DMSA was supplemented with a single dose of edetate calcium disodium (<50.0 mg/kg of body weight i.m.; n = 4). Blood lead concentrations were measured two or more times (before and after treatment). Median blood lead concentration decreased (87%), neurologic signs were resolved, and there were no apparent adverse secondary effects.