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Introduction: Data overlapping of different biological conditions prevents personalized medical decision-making. For example, when the neutrophil percentages of surviving septic patients overlap with those of non-survivors, no individualized assessment is possible. To ameliorate this problem, an immunological method was explored in the context of sepsis., Methods: Blood leukocyte counts and relative percentages as well as the serum concentration of several proteins were investigated with 4072 longitudinal samples collected from 331 hospitalized patients classified as septic (n=286), non-septic (n=43), or not assigned (n=2). Two methodological approaches were evaluated: (i) a reductionist alternative, which analyzed variables in isolation; and (ii) a non-reductionist version, which examined interactions among six (leukocyte-, bacterial-, temporal-, personalized-, population-, and outcome-related) dimensions., Results: The reductionist approach did not distinguish outcomes: the leukocyte and serum protein data of survivors and non-survivors overlapped. In contrast, the non-reductionist alternative differentiated several data groups, of which at least one was only composed of survivors (a finding observable since hospitalization day 1). Hence, the non-reductionist approach promoted personalized medical practices: every patient classified within a subset associated with 100% survival subset was likely to survive. The non-reductionist method also revealed five inflammatory or disease-related stages (provisionally named 'early inflammation, early immunocompetence, intermediary immuno-suppression, late immuno-suppression, or other'). Mortality data validated these labels: both 'suppression' subsets revealed 100% mortality, the 'immunocompetence' group exhibited 100% survival, while the remaining sets reported two-digit mortality percentages. While the 'intermediary' suppression expressed an impaired monocyte-related function, the 'late' suppression displayed renal-related dysfunctions, as indicated by high concentrations of urea and creatinine., Discussion: The data-driven differentiation of five data groups may foster early and non-overlapping biomedical decision-making, both upon admission and throughout their hospitalization. This approach could evaluate therapies, at personalized level, earlier. To ascertain repeatability and investigate the dynamics of the 'other' group, additional studies are recommended., Competing Interests: ALR and AH are co-inventors of the temporary guides used to recognize data patterns European Union patent number 2959295, US patent number 10,429,389 B2. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Pappa, Rivas, Iandiorio, Hoogesteijn, Fair, Rojas Gil, Burriel, Bagos, Chatzipanagiotou and Ioannidis.)

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.

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., Competing Interests: MK was employed by KB One Health LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewers SR and AS declared a shared affiliation with the authors KG, JB, CW to the handling editor at the time of review., (Copyright © 2023 Rivas, Smith, Basiladze, Chaligava, Malania, Burjanadze, Chichinadze, Suknidze, Bolashvili, Hoogesteijn, Gilbertson, Bertram, Fair, Webb, Imnadze and Kosoy.)

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., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., (Copyright © 2021 Elsevier Inc. All rights reserved.)

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., (Copyright © 2021. Published by Elsevier Inc.)

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., (© 2020 Wiley Periodicals, Inc.)

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., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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.

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.

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.

Background: To extract more information, the properties of infectious disease data, including hidden relationships, could be considered. Here, blood leukocyte data were explored to elucidate whether hidden information, if uncovered, could forecast mortality., Methods: Three sets of individuals (n = 132) were investigated, from whom blood leukocyte profiles and microbial tests were conducted (i) cross-sectional analyses performed at admission (before bacteriological tests were completed) from two groups of hospital patients, randomly selected at different time periods, who met septic criteria [confirmed infection and at least three systemic inflammatory response syndrome (SIRS) criteria] but lacked chronic conditions (study I, n = 36; and study II, n = 69); (ii) a similar group, tested over 3 days (n = 7); and (iii) non-infected, SIRS-negative individuals, tested once (n = 20). The data were analyzed by (i) a method that creates complex data combinations, which, based on graphic patterns, partitions the data into subsets and (ii) an approach that does not partition the data. Admission data from SIRS+/infection+ patients were related to 30-day, in-hospital mortality., Results: The non-partitioning approach was not informative: in both study I and study II, the leukocyte data intervals of non-survivors and survivors overlapped. In contrast, the combinatorial method distinguished two subsets that, later, showed twofold (or larger) differences in mortality. While the two subsets did not differ in gender, age, microbial species, or antimicrobial resistance, they revealed different immune profiles. Non-infected, SIRS-negative individuals did not express the high-mortality profile. Longitudinal data from septic patients displayed the pattern associated with the highest mortality within the first 24 h post-admission. Suggesting inflammation coexisted with immunosuppression, one high-mortality sub-subset displayed high neutrophil/lymphocyte ratio values and low lymphocyte percents. A second high-mortality subset showed monocyte-mediated deficiencies. Numerous within- and between-subset comparisons revealed statistically significantly different immune profiles., Conclusion: While the analysis of non-partitioned data can result in information loss, complex (combinatorial) data structures can uncover hidden patterns, which guide data partitioning into subsets that differ in mortality rates and immune profiles. Such information can facilitate diagnostics, monitoring of disease dynamics, and evaluation of subset-specific, patient-specific therapies.

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.

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 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.

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.

When an exotic infectious disease invades a susceptible environment, protection zones are enforced. Historically, such zones have been shaped as circles of equal radius (ER), centred on the location of infected premises. Because the ER policy seems to assume that epidemic dissemination is driven by a similar number of secondary cases generated per primary case, it does not consider whether local features, such as connectivity, influence epidemic dispersal. Here we explored the efficacy of ER protection zones. By generating a geographically explicit scenario that mimicked an actual epidemic, we created protection zones of different geometry, comparing the cost-benefit estimates of ER protection zones to a set of alternatives, which considered a pre-existing connecting network (CN) - the road network. The hypothesis of similar number of cases per ER circle was not substantiated: the number of units at risk per circle differed up to four times among ER circles. Findings also showed that even a small area (of <115 km(2) ) revealed network properties. Because the CN policy required 20% less area to be protected than the ER policy, and the CN-based protection zone included a 23.8% greater density of units at risk/km(2) than the ER-based alternative, findings supported the view that protection zones are likely to be less costly and more effective if they consider connecting structures, such as road, railroad and/or river networks. The analysis of local geographical factors (contacts, vectors and connectivity) may optimize the efficacy of control measures against epidemics., (© 2012 Blackwell Verlag GmbH.)

The daily progression of the 2006 (January-June) Nigerian avian influenza (AI H5N1) epidemic was assessed in relation to both spatial variables and the generation interval of the invading virus. Proximity to the highway network appeared to promote epidemic dispersal: from the first AI generation interval onwards > 20% of all cases were located at < 5 km from the nearest major road. Fifty-seven per cent of all cases were located 31 km from three highway intersections. Findings suggest that the spatial features of emerging infections could be key in their control. When the spatial location of a transmission factor is well known, such as that of the highway network, and a substantial percentage of cases (e.g. > 20%) are near that factor, early interventions focusing on transmission factors, such as road blocks that prevent poultry trade, may be more efficacious than interventions applied only to the susceptible population.

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.

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.

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.

The time available to implement successful control measures against epidemics was estimated. Critical response time (CRT), defined as the time interval within which the number of epidemic cases remains stationary (so that interventions implemented within CRT may be the most effective or least costly), was assessed during the early epidemic phase, when the number of cases grows linearly over time. The CRT was calculated from data of the 2001 foot-and-mouth disease (FMD) epidemic that occurred in Uruguay. Significant regional CRT differences (ranging from 1.4 to 2.7 days) were observed. The CRT may facilitate selection of control measures. For instance, a CRT equal to 3 days would support the selection of measures, such as stamping-out, implementable within 3 days, but rule out measures, such as post-outbreak vaccination, because intervention and immunity building require more than 3 days. Its use in rapidly disseminating diseases, such as FMD, may result in regionalized decision-making.

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.