Methodological research to support searching for those doing systematic reviews of epidemiological studies is a relatively neglected area. Our aim was to determine how many databases it is necessary to search to ensure a comprehensive coverage of the literature in diabetes epidemiology, with the aim of examining the efficiency of searching in support of systematic reviews of the epidemiology of diabetesThree approaches were used. First, we defined a set of English language diabetes journals and examined their coverage in bibliographic databases. Second, we searched extensively for diabetes epidemiology articles  to determine which are the most useful databases; and third, we analysed the scattering of these articles to determine the core journals in the area.Diabetes Care contributing 14.3% of the articles, followed by Diabetic Medicine (5.0%); Diabetes Research & Clinical Practice (4.1%); Diabetologia (4.0%); Diabetes & Metabolism (2.4%) and Diabetes (2.0%).The overlap between MEDLINE and Embase for diabetes journals was 59%. A search for diabetes epidemiology articles across both MEDLINE and Embase, showed that MEDLINE alone retrieved about 94% of the total articles. Searching for diabetes epidemiology studies beyond MEDLINE and Embase retrieved no additional English language journal articles. The only diabetes epidemiology studies found by searching beyond MEDLINE and Embase were found in LILACS, and were Spanish or Portuguese language studies from Latin America; no additional English language studies were found. Only 30% of the meeting abstracts were converted to full publication after three years. One third of journal articles were published in just six journals, with Our results show that when searching for articles on diabetes epidemiology, MEDLINE and Embase would suffice for English language papers, with LILACS giving some additional non-English articles from Latin America. Although a MEDLINE-only search will retrieve the vast majority of the relevant literature, Embase and LILACs should also be searched to ensure the search is comprehensive. Searching for meeting abstracts is recommended to alert reviewers to unpublished work. The low rate of full publication of meeting abstracts has the danger of producing bias in reviews. Our findings on scattering show that the core literature in this field is concentrated in just six journals. Review articles have a valuable role in the medical literature, because the volume of journals and articles is such that keeping up to date is very difficult. Reviews are much more valuable if they are systematic reviews done to internationally agreed standards, as non-systematic reviews are known to be subject to bias ,2.Dickersin noted that there was a shortage of systematic reviews in epidemiology, and called for more reviews, and more research into the methodology relating to reviews in epidemiology . A studyOne of the key quality criteria for systematic reviews is the comprehensiveness of the searching, as failure to identify all relevant reports can result in selection bias. The usuUlrich's International Periodicals Directory as a gold standard, found that a MEDLINE search in psychiatry would retrieve only about half the relevant journals [A study, using journals . SimilarThe scattering of the journal literature in a subject area can provide a useful insight into the number and range of journals needed to capture the key literature in a field. Bradford's Law of Scattering states that on any one subject, a small group of 'core' journals (Zone 1) will provide about one third of the articles on that subject, a medium number of less-core journals (Zone 2) will provide another third, and a large number of peripheral journals (Zone 3) will provide the final third of the articles ,10.The aim of this study was to examine the efficiency of searching in support of systematic reviews of the incidence and prevalence of diabetes by providing empirical data to answer the following questions:1. Which databases index diabetes journals (restricted to English language)?2. Which databases outside MEDLINE and Embase index diabetes epidemiology journal articles and grey literature?3. How are diabetes epidemiology articles scattered across the journals, and what are the core journals in this area?Accordingly, this study was divided into three parts..For the purposes of this study, epidemiology articles were defined as studies of incidence or prevalence of diabetes, or of factors affecting those . Basic science studies, e.g. biological mechanisms of disease, were not included.We started from a position that both MEDLINE and Embase should always both be searched, since the overlap between these databases has been estimated to range from 10% to 87% ,11-17. AHence, a three-part approach was used to investigate literature searching to support systematic reviews of diabetes epidemiology, and address each of our aims in turn.Ulrich's Periodical Directory 2003  [Diabetes journals were identified from the 'Medical Sciences – Endocrinology' section of e world) . This wae world)  for jourUlrich's, the contents pages for the past five years, and where necessary, abstracts or the full journal articles, were examined by a diabetes epidemiologist.The inclusion criteria for the journals were: i) the word stem 'diabet' in the title, ii) contains original scientific studies of an academic or scholarly nature, iii) currently in print, and iv) published in English. If inclusion could not be decided on the basis of the information provided in List of Serials Indexed for Online Users [Journals which fulfilled all the above criteria were then checked against the ne Users  to see iIf any journals were not indexed in either MEDLINE or Embase, searches of BIOSIS, BNI, CINAHL, SCI were done to determine if the journals were indexed in any of these databases.Databases searched were: AMED, ASSIA, BIOSIS (abstracts only), BNI, CINAHL, Conference Papers Index, Dissertation Abstracts US, Health Management Information Consortium (HMIC), Index to Theses UK, ISI Proceedings, PsycINFO, NLM Gateway, LILACS , National Research Register (NRR), SIGLE, SCI (abstracts only), SSCI, and Zetoc.The search strategy used was 'diabetes and ' in the Title (TI) field and restricted to publication years 1998 to 2003.The titles (and abstracts when available) of all records were checked by an expert in diabetes epidemiology, in order to determine their potential usefulness for those doing systematic reviews.MEDLINE and Embase were searched using the search strategy: 'Diabetes Mellitus as the major subject heading, with the sub-heading 'Epidemiology' assigned , and restricted to publication years 1998 to 2003'. All languages were included. Duplicates found in both MEDLINE and Embase were removed. The journal titles in which the articles were found were ranked according to the number of articles contributed by each journal. The cumulated numbers of articles and journals were calculated and plotted. This was used to identify Bradford zones; that is, the number of journals needed to cover about one third, two thirds or all the relevant articles in the field.Ulrich's Periodicals Directory and PubList.com initially identified four English language journals that were of potential interest but not indexed by MEDLINE or Embase. On closer inspection, three of these, Clinical Diabetes, Journal of Diabetes Nursing, and Diabetes and Primary Care were excluded as they did not appear to contain any primary research; the articles were mainly educational, professional news and views, opinions, or narrative reviews. The fourth journal, The Diabetic Foot, contained primary research, but did not appear to contain studies useful for epidemiological reviews of diabetes itself (as opposed to complications). It is indexed by CINAHL only.Searches of As shown in Table The four diabetes journals unique to MEDLINE were all published in the USA. By contrast, only one of the seven journals unique to Embase was published in the USA.The results are summarised below:No English language journals articles that were not also indexed in MEDLINE or Embase were identified.There were 23 Spanish and Portuguese language articles identified in LILACs. On the basis of the English translation of the titles, they all reported studies done in Latin America.We defined grey literature as any literature not published in a peer reviewed journal. After removing duplicates, there were 51 dissertations identified from searches of Dissertations Abstracts US, Index to Theses UK, and SIGLE. The research presented in the vast majority (92%) of the dissertations appeared to have been written up as articles in journals indexed in MEDLINE or Embase. No grey literature studies of any format other than dissertations were retrieved from SIGLE, so there was very little additional information gained by these searches.The National Research Register  gave brief details of 18 projects in progress that had not been otherwise identified. Searching the NRR might be useful if unpublished results could be included in the review, but its main value would be to indicate when the review was likely to need updating.The search of the Conference Proceedings Index retrieved 25 articles, none of which appeared to have been published as journal articles after five years. The Zetoc Conference Search found eight articles, of which 50% had been published as full journal articles in MEDLINE or Embase.The search of Science Citation Index (SCI), restricted to meeting abstracts only, found 171 relevant studies. The time to publication of the SCI abstracts was examined by checking how many had subsequently been published as journal articles indexed in MEDLINE or Embase. It was found that 30% had reached full publication after three years.Diabetes and Metabolism and Diabetes Research and Clinical Practice. Of these, 11 (12%) had been published in journals. The average time delay from the date of publication of the abstract to full publication was 1.4 years.A search of BIOSIS, restricted to meeting abstracts only, retrieved 71 additional relevant abstracts that were not in SCI. Most (65%) of these 71 abstracts came from the supplements of These included AMED, BNI, HMIC, NLM Gateway meeting abstracts, PsychINFO, and SSCI,In summary, the data indicate that when searching for English language journal articles on diabetes epidemiology, searches of MEDLINE and Embase would suffice. The exception would be for studies from Latin America, where LILACS should also be searched. Searching for meeting abstracts may alert reviewers to forthcoming or unpublished work.The searches for diabetes epidemiology articles in MEDLINE and Embase resulted in 2923 articles being found in 696 different journal titles; 39% were found to be in 'diabetes journals' and 14% were non-English language.Figure Applying Bradford's Law of Scattering gives three zones, each providing one-third of the articles.Diabetes Care (contained 14.3% of the articles); Diabetic Medicine (5.0%); Diabetes Research & Clinical Practice (4.1%); Diabetologia (4.0%); Diabetes & Metabolism (2.4%), Diabetes (2.0%) These six journals represent 0.9% (of the 696) total journals, and all are indexed in MEDLINE.The first one-third of articles were from six journals. They are in rank order: Practical Diabetes International; Diabetologia Polska; Diabetes und Stoffwechsel and Journal of the Japan Diabetes Society. Hence, 94% of Zone 2 journals are covered by MEDLINE.The second one-third of articles were from 62 journals, representing 9.1% of the total journals. The four journals in Embase only were: The final one-third of articles were in 628 journals, representing 90.2% of the total journals. MEDLINE indexed 88% of these journals.Overall, for the three zones, the search of MEDLINE and Embase for diabetes epidemiology articles revealed that MEDLINE indexed 89% of the total journals, and these contained 94% of the articles.Our results showed that there was an overlap of only 59% in current English language 'diabetes journals' indexed by both MEDLINE and Embase. Also, a search for diabetes epidemiology articles across both MEDLINE and Embase showed that MEDLINE alone retrieved about 94% of the total articles; therefore, both databases should be searched. Embase appears to index more diabetes journals published outside the USA. Therefore, if searching is limited to MEDLINE only  this could potentially introduce a bias. Also, duplication of searching can be useful, as due to differences in indexing practices, a search of one database may retrieve something missed by the other.We also found that despite a wide range of additional databases searched after MEDLINE and Embase, no additional English language journal articles on diabetes epidemiology were identified. The LILACS database was a useful source of Spanish and Portuguese language articles on the epidemiology of diabetes in Latin American countries.Meeting abstracts appeared to be valuable sources of information on forthcoming studies, but their inclusion in systematic reviews is contentious. Some reviewers exclude abstracts on the grounds that the quality of the study cannot be judged because of the inevitably limited detail. However, others include them on the grounds that abstracts provide the most up-to-date information.Nearly all the dissertations identified had been published as journal articles. However, it was found that only 30% of the meeting abstracts were converted to full publication after three years, which is considerably lower than the figure for RCTs, which is 56% . This haDiabetes Care alone containing about 14% of the articles. A similar concentration effect in journals was also shown in a study of 3400 science journals in the SCI database, where just 100 journals accounted for 22% of the published articles and 100 journals also accounted for 44% of cited articles [Scattering of the diabetes epidemiology articles revealed that the 'core' literature in this field is concentrated in just six journals, with articles .This study has a number of limitations. The search to identify diabetes journals was restricted to English language journals only, as we were unable to assess articles in other languages. We did not compare the quality of the articles identified from databases outside MEDLINE and Embase. Also, when searching for articles, we were necessarily limited to the range of databases available to us.Finally, there may be databases inaccessible or unknown to us that cover foreign language and regional journals not indexed in MEDLINE and Embase. Such journals may carry studies of incidence which may seem of primarily local interest, but which may be useful contributions to the international body of evidence because they may show large variations in incidence, or in its relationship to possible aetiological risk factors.It is often useful to study the epidemiology of a disease where it is rare, as well as where it is common. However it is likely that studies which report high incidence are more likely to be published than those which report low incidence. Similarly with risk factors; a study which finds no link between factor x and disease y may be less likely to be published than one which does show a correlation ,23.There is a need for further research to see whether our findings apply to searching for epidemiological reviews of other diseases, and on measuring the sensitivity and specificity of various search filters to retrieve epidemiological studies in MEDLINE and Embase.We endorse Dickersin's suggestion of an international collaborative effort to establish an 'epidemiological Cochrane-like database' to identify all relevant studies and to begin systematically reviewing available data for important epidemiological questions .Searching MEDLINE and Embase appears to provide comprehensive coverage of the English language journal literature in diabetes epidemiology. LILACs is a useful source of Spanish and Portuguese language articles on diabetes epidemiology done in Latin American countries and published in regional journals not indexed in MEDLINE and Embase. Searching for meeting abstracts is recommended to alert reviewers to unpublished work.The volume of literature on diabetes epidemiology makes it impossible for one person to read everything. However the provision of systematic reviews makes keeping up with research manageable, and more reviews are needed. Our findings on scattering shows that the core literature in diabetes epidemiology is concentrated in a small number of core journals, and that in the absence of reviews, one can follow the field by reading these journals. It may also be reassuring that a good MEDLINE-only search will retrieve the vast majority of the relevant literature.The author(s) declare that they have no competing interests.PR and NW conceived the study and drafted the initial manuscript. PR and LB collected and analysed the data. All authors contributed to and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Calcium makes up just 2% of the human body, and 99% of it is sequestered in bones and teeth. The remainder exists within and around cells, influencing a variety of cellular processes, from transcription and cell survival to nerve signaling and muscle contraction. Calcium inhabits the intra- and extracellular space as a gradient, with extracellular concentrations some 1,000 times greater than those inside the cell. These gradients are maintained by calcium pumps. Calcium signaling operates mostly through voltage- and ligand-gated calcium channels , both in the plasma membrane of the cell and in the membranes enclosing intracellular organelles. Signaling is typically initiated by an influx of calcium across the plasma membrane or by the release of calcium from an organelle, such as the endoplasmic reticulum. In neuron-to-neuron signaling, calcium signaling helps convert electrical signals into chemical signals in the form of neurotransmitters. The arrival of an electrical signal at a nerve terminal opens the many calcium channels in the nerve terminal, admitting a stream of calcium ions. The increased intracellular calcium concentration in turn releases the resident neurotransmitters accumulated in the nerve terminal, converting the electrical signal into a chemical signal. As neurotransmitters bind to their receptors in the next target neuron, they change the cell's membrane potential, prompting the neuron to generate an electrical signal, thereby converting the chemical signal back into an electrical one and completing the signaling circuit. Since calcium dynamics mediates most neuronal information flow, it can be used as a general measure of neural activity.Through an elaborate network of electrical activity, the brain encodes, combines, and interprets signals to process information about the world. Simultaneous measurement of this activity in multiple brain locations has provided valuable insight into how neural networks function. But since electrical recordings can't pinpoint activity in the fine branches of individual neurons or pick up biochemical  signals, researchers are increasingly turning to approaches that measure calcium concentrations as a proxy for neuron activity. Now Mazahir Hasan et al. have created mice engineered to stably express two different kinds of fluorescent calcium indicator proteins (FCIPs) in the brain (the fluorescence produced by these proteins can be seen when the brain is viewed with a two-photon microscope). Because the indicators are incorporated into the mouse genomes, this approach offers the possibility of targeting specific cells (by using promoters that specify which cells the genes should be activated in), allowing researchers to map the activity of select neuron populations.Fluorescent proteins can be incorporated into a gene of interest to help researchers track that gene's protein in living tissue. FCIPs report calcium concentrations by changing fluorescence when they bind to calcium. Introducing calcium indicators into neural tissues was largely impractical, often failing to target specific cell types, until these genetically engineered indicators were developed in the late 1990s, allowing the desired specificity. While FCIPs have been used to good effect in worms, fruitflies, and zebrafish—and just recently in mouse muscle—they had not been stably and functionally expressed in the mammalian brain until now.To deliver the FCIPs to the mouse brain, Hasan et al. used a regulatory promoter called Ptet (in the tetracycline system), which offers the possibility of targeting the expression of the FCIPs in different neural populations. To test the functionality of the proteins, they used fluorescence microscopy to analyze neurons from mouse lines and found high levels of FCIP expression. The real test, however, was whether the FCIPs could fulfill their promise as a probe for calcium activity. When the authors electrically stimulated brain slices from a mouse line expressing moderate to high levels of FCIP , fluorescence increased rapidly following the stimulus. Significant changes in FCIP fluorescence were also observed when live mice responded to odor stimulation. That “fast and robust” FCIP signals were detected in live animals responding to sensory stimulation, the authors argue, proves the promise of FCIPs as a reporter on the activity of select neural populations in living systems. And since these indicator proteins retain stable functional expression over time (8- to 12-week-old mice continued to express the proteins), they could help researchers track neuronal activity over extended periods.While a variety of bugs remain to be worked out with FCIPs—it's unclear, for example, why only the Ptet promoter generates high levels of FCIP expression in the brain and why not all neurons in a given population express the proteins—Hasan et al. demonstrate that the tetracycline system supports stable expression of the calcium indicators. The FCIP approach avoids the complications of invasive techniques like surgically administering dyes and produces a more interpretable signal, since the cell populations are already known. Because FCIPS can be used in living animals, they can reveal where and when neurons are firing. And because FCIP mice can be crossed with mice containing mutations in genes important for neural function, this method could reveal how specific genes contribute to the construction of neural networks.


Fusobacterium necrophorum may cause a number of clinical syndromes, collectively known as necrobacillosis. Meningitis is a significant cause of mortality, rarely reported in the adult population.Fusobacterium necrophorum, secondary to otitis media in an alcoholic male. Diagnosis was delayed due to the typical slow growth of the organism. The clinical course was complicated by encephalitis and by hydrocephalus. The patient failed to respond to metronidazole and penicillin. The patient died on day 12 from increased intracranial pressure and brain stem infarction.We report a fatal case of meningitis, caused by Fusobacterium necrophorum meningitis. We recommend the use of appropriate anaerobic culture techniques and antimicrobial coverage for anaerobic organisms when the gram stain shows gram negative bacilli.This case emphasizes the need for a high index of clinical suspicion to make the diagnosis of  Fusobacterium necrophorum may cause a number of clinical syndromes, collectively known as necrobacillosis. Meningitis is a significant cause of mortality, rarely reported in the adult population. Diagnosis is often delayed by difficulties encountered in isolating the organism. Here, we report a fatal case of meningitis from complicated otitis media caused by this organism.A 51 year male was brought to the emergency department (ED) by his family for confusion and shaking episodes. The patient was very lethargic in the ED and was intubated for airway protection. Family reported that he had not been well for several months, but the family was not able to define any specific symptoms until the past few days when he reported right ear pain. The patient had been given a prescription for erythromycin within the previous week for a diagnosis of otitis media; the patient had not taken any doses for at least two days. The only significant past medical history was of ongoing alcohol abuse without intravenous drug use. The family reported that he had no alcohol intake over the two days prior to presentation. Vitals on presentation: temperature 98.5°F (36.9°C), heart rate 125/min, respirations 25/min, blood pressure 219/121, Oxygen saturation 96 % on room air. On physical exam he was lethargic, the right tympanic membrane was erythematous with decreased movement on pneumatic otoscopy. The neurological exam revealed no focal deficits.F. necrophorum. Penicillin G was added with discontinuation of ceftazidime and levofloxacin. The patient further deteriorated and the neurological exam revealed absent corneal reflexes, and no response to painful stimulus. EEG was done to assess brain function, which revealed evidence of brain death. The caloric test also confirmed brain death.A non-contrasted head CT scan showed no abnormalities; there was no evidence of an intracranial bleed or elevated intracranial pressure. Lumbar puncture revealed markedly purulent CSF with 338,400 white cells per cubic millimeter (94% neutrophils), glucose was less than 2 mg/dl and protein was 3544 mg/dl. Gram stain showed numerous gram negative bacilli. He was initially treated with high dose dexamethasone, along with ceftriaxone, ampicillin and vancomycin. When gram negative bacilli were identified in the CSF, the antibiotic regimen was revised to ceftazidime, levofloxacin and metronidazole. There was a decline in mental status despite aggressive antibiotic therapy. He continued to require complete ventilatory and pressor support to maintain adequate blood pressure and oxygenation. Physical examination revealed up-going plantar reflexes on day 6. MRI brain showed extensive infarction of the brain stem, obstructive hydrocephalus, severe basilar meningitis, and ventriculitis (see figures Fusobacterium is an anaerobic, non spore forming gram negative rod which belongs to the family of Bacteroidaceae. It is a part of normal flora which is found in mouth, upper respiratory tract, gastrointestinal tract and vagina. It can cause local infections like pharyngitis, tonsillitis, mastoiditis or can cause severe bacteremic illness like meningitis. Other central nervous system complications caused by Fusobacterium include cranial nerve palsy, sinus venous thrombosis, and brain abscess [F. necrophorum was the most common anaerobe isolated, found in 33% of patients [F. necrophorum. Lemierre syndrome has been described to progress through three stages [ abscess ,2. In onF. necrophorum meningitis, middle ear infection was the source for 75% of the cases [Fusobacterium [In one series of he cases . Other pacterium ,4,6-19, acterium . Althougacterium . It has F. necrophorum. Anaerobic organisms should be considered as potential causative agents of meningitis when routine cultures are negative. Routine cultures of cerebrospinal fluid do not include the use of anaerobic growth media. Therefore, appropriate anaerobic culture techniques should be employed when sinus, otitic or mastoid symptoms precede or accompany the onset of meningitis in children or adults. The presence of irregularly stained gram negative rods in the CSF or meningitis unresponsive to empiric antibiotics should also raise the suspicion of anaerobic infection. The addition of metronidazole should be considered in these cases.This case shows the severity of illness that can result from infection with None declared.AI cared for the patient. SG drafted the manuscript. TH cared for the patient. All authors reviewed and approved the final manuscript.The pre-publication history for this paper can be accessed here:


There is considerable interest in the literature on disease mapping to interpolate estimates of disease occurrence or risk of disease from a regional database onto a continuous surface. In addition to many interpolation techniques available the geostatistical method of kriging has been used but also criticised.To circumvent these critics one may use kriging along with already smoothed regional estimates, where smoothing is based on empirical Bayes estimates, also known as shrinkage estimates. The empirical Bayes step has the advantage of shrinking the unstable and often extreme estimates to the global or local mean, and also has a stabilising effect on variance by borrowing strength, as well. Negative interpolates are prevented by choice of the appropriate kriging method. The proposed mapping method is applied to the North Carolina SIDS data example as well as to an example data set from veterinary epidemiology. The SIDS data are modelled without spatial trend. And spatial interpolation is based on ordinary kriging. The second example is included to demonstrate the method when the phenomenon under study exhibits a spatial trend and interpolation is based on universal kriging.Interpolation of the regional estimates overcomes the areal bias problem and the resulting isopleth maps are easier to read than choropleth maps. The empirical Bayesian estimate for smoothing is related to internal standardization in epidemiology. Therefore, the proposed concept is easily communicable to map users. As with the analysis of any set of data, it is always good practice to begin by producing and inspecting graphs. A feel for the data can then be obtained and any outstanding features identified. In spatial epidemiology this is called disease mapping. Bithel , Diggle There are three basic types of disease maps corresponding to certain types of data. These are dot maps for point (or case-event) data, choropleth maps for regional data , and lastly, isopleth maps for geostatistical data  representing spatially continuous phenomena at a limited number of sampling locations. Spatial epidemiology is mainly concerned with the analysis of two types of data: case-event data and regional summary data, generally leading to dot maps and choropleth maps, respectively. However, epidemiology is also concerned with the identification of unknown risk factors, which may be part of the environment such as air pollution, radiation or magnetic fields. And such factors vary spatially continuously. Thus it is important to produce an isopleth map of disease occurrence or risk. Furthermore, as has been often pointed out in the literature For spatial point case-control data Bithell  introducRegional data arise from summarising individual information for administrative regions such as census tracts. The basic model for the individual data, i.e. spatial point data, is the spatial Poisson process. In case of a rare disease, aggregation of the data over distinct regions results again in Poisson distributed data. For a more common disease the regional counts may be binomially distributed.When spatial interpolation of regional data is the objective of the disease mapping study a grid of surface interpolant co-ordinates must be provided. Then a number of techniques can be used to automatically interpolate the data by use of deterministic methods or to predict the values statistically at the grid co-ordinates. Among the possible choices are kernel smoothing, splines, loess and running medians; see  for a diAnother approach for the interpolation of regional data onto a continuous surface is the geostatistical prediction method of kriging. Some implementations of kriging have been proposed to obtain a risk surface ,34,35,38The problems with the kriging method for generating isopleth maps of disease occurrence, i.e. the spatial risk function, are: (1) heterogeneous variances in the regional estimates and (2) the potential of negative interpolations. The first problem can be ameliorated by the use of empirical Bayes estimation to smooth the data prior to kriging. Appropriate geostatistical modelling can solve the second problem.N disjunctive sub regions indexed by i . Define ni as the size of the 'at-risk' population of the i-th region, and denote the number of cases by mi. The proportion pi = mi/ ni is the crude estimate for the parameter of interest θi.Let the study area be divided into Mapping raw estimates of disease occurrence can lead to spurious spatial features. To overcome this problem Cressie and Read  have expClayton and Kaldor  proposedi are independently Poisson distributed with the unknown parameter θi which has an unknown prior distribution associated with expectation E(θi) = π and variance Var(θi) = φ2. Then the totals of cases from the i-th region, e.g. mi , are distributed as followsAssume the cases in every region mi | θi, ni ~ Po(ni θi)θi ~  for the prior mean and  for the prior variance, where  denotes the mean regional 'at-risk' population and the summation is over the range of i. The empirical Bayesian estimates then becomes  with shrinkage weights .The MME of the unknown hyperparameters are pi has the side-effect of stabilising the variances for the regional empirical Bayes estimates, i.e. . Heuristically this is clear, because the empirical Bayes estimates are based on the whole sample information and not just on the individual regional sample, and thus are more stable. But this is difficult to prove, because this requires an analytical expression for the corresponding variance or an approximate variance estimate. Morris ., generally have a smaller associated variance than the corresponding frequentist estimates, i.e. pi Aside from some positive and negative extreme values the general appearance of the residual process is Gaussian. The potential outliers are regions with steep gradients in risk and hence part of disease clusters (or their surroundings) which were previously identified  and are Echinococcus multilocularis (E.m.) is a tapeworm occurring in the northern hemisphere, including endemic regions in central Europe, most of northern and central Eurasia and parts of North America. In central Europe the red fox is the main definitive host with rodents such as mice or muskrats serving as intermediate hosts [te hosts . The parThe federal state of Lower Saxony is part of northern Germany and contains the federal city-state of Bremen as an enclave. During the period from 1991 to 1997, 5365 red foxes were sampled in Lower Saxony and examined for infections with E.m.. The data are given in Table Figure The map indicates high period prevalences in the south and north of Lower Saxony. Extraordinarily high prevalences were observed in the southern regions, which indicate the presence of a positive disease cluster that is identified by use of the spatial scan statistic .Figure s = ' denote an arbitrary location in the study region, where x and y are the co-ordinates to the east and north. By inspection of the variogram cloud [μ(s) = β0 + β1y + β2y2, with , , . Instead of OLSE one could use iterated WLSE, but iteration may lead to biased estimates. The residuals of the trend surface fit to the empirical Bayes smoothed regional period prevalences were then used to model a spherical semivariogram by weighted least squares estimation of the robustly estimated empirical semivariogram. Figure Figure am cloud  based onThe normal probability plot of the kriging cross-validation residuals in Figure Geostatistical modelling of the empirical Bayesian smoothed regional prevalences allows the calculation of an error map representing the kriging standard errors. The darker the error map in Figure In this paper two sophisticated statistical methods were combined to solve an open problem in disease mapping. Mapping regional data using choropleth maps holds on to several problems that are overcome by isopleth mapping. To interpolate the regional, i.e. spatially discrete information, the geostatistical method of kriging is used here. Kriging requires variance homogeneous data. However, regional risk estimates are generally based on varying sample sizes and consequently turn up spatially varying standard errors. Therefore the spatial risk estimates are smoothed using linear empirical Bayes estimation. By borrowing-strength-from-the-ensemble, the impact of outliers is reduced and standard errors are stabilized over space. This cannot achieve variance homogeneity entirely nor could the tendency be shown analytically. Bootstrapping methods for spatially dependent data  promise The purpose of exploratory disease mapping is to provide insight, as opposed to precise estimates of location, spread or trends . EmphasiKriging is known to be a smoothing method and it may be argued that the proposed mapping method results in double or over-smoothing. But here kriging is based on a semivariogram model without nugget effect, which is known to be a direct interpolation method . TherefoThe proposed disease mapping approach is a two-step procedure. The corresponding error map Figure , howeverThe empirical Bayes method as described here does not take into account spatial autocorrelation, but a modification to overcome this lack has been proposed . The metInterpolation of the regional disease risk estimates overcomes the areal bias and related problems of choropleth disease mapping. Consequently, isopleth maps are easier to read and interpret than choropleth maps. The geostatistical method of kriging is appropriate for this task when based on linear empirical Bayesian smoothed data. Unlike non-parametric and mathematical interpolation methods, the spatial model underlying the exploratory spatial risk map offers ways of interpretation.Finally, the proposed concept for exploratory spatial risk mapping is easily communicable to map users. The Bayesian smoothing estimator is related to internal standardization in epidemiology. Also kriging can be viewed as weighted moving averaging.


Far more species exist in the fossil record than inhabit Earth today. An estimated 94% of all bird species that ever lived, for example, are now extinct. So why is species extinction of urgent concern today? Though species come and go over evolutionary time, mass extinctions are relatively rare. Biologists believe they have occurred only five times, arising from relatively short-lived cataclysmic natural forces like astronomical or volcanic events. We are now on the brink of the Sixth Great Extinction, and we humans are largely to blame. For thousands of years, humans have retooled the landscape, an endeavor that has rarely coincided with the life history needs of local flora and fauna: over 150 bird species alone have vanished since 1500.halt biodiversity loss by that time. To meet this goal, biologists need reliable metrics to monitor global trends in biodiversity. Stuart Butchart et al. describe a new model for generating such indices to measure trends in extinction risk for complete classes of organisms, starting with the world's 10,000 bird species. Their “Red List Index” measures changes in overall extinction risk over time for all bird species worldwide. Similar indices are already being developed to track other groups, including mammals and amphibians, and in the future will hopefully be developed for some plant and invertebrate groups.As our capacity to alter the landscape has mushroomed, species have started disappearing faster than biologists can identify and document them. Mindful of this crisis, nearly 200 countries  agreed to staunch the loss of biodiversity by 2010, with the European Union raising the bar to In 2002, the CBD proposed that efforts to monitor global trends in biodiversity start by developing indicators to evaluate trends in biodiversity components, such as ecosystems and habitats, abundance and distribution of selected species, and change in threat status of species. Butchart et al. focus on evaluating trends in changes in threat status (extinction risk), relying on categories developed by the World Conservation Union (IUCN) Red List. Species are placed in categories on the Red List ranging from extinct to “least concern,” according to criteria that take into account their population size, population trends, and range size. Thousands of scientists from around the world feed these assessments, which have been widely used to measure the degree of degradation of biodiversity.To use the Red List to track biodiversity trends over time, Butchart et al. collected data from four complete assessments of the world's birds over sixteen years, supplemented by other sources. The number of threatened and near threatened species increased from 1,664 species in 1988 to 1,990 species in 2004, but many species moved between categories. To calculate the real net increase in extinction risk for the world's birds over this time, the authors first identified reasons for these category switches to remove biases introduced by factors irrelevant to genuine changes in species status; category changes owing to better knowledge, for example, do not reflect real changes in conservation status. They also accounted for time lags between status changes, and category changes owing to delays in knowledge becoming available to Red List assessors.The authors argue that the Red List Index provides a simple measure of trends in the status of avian species worldwide, in terms of their overall extinction risk. Overall, the index shows “a steady and continuing deterioration in the threat status of the world's birds between 1988 and 2004.” Though the extinction risk has improved for some species, it has deteriorated for others, with “particularly steep declines” in recent years for Asian birds—resulting from massive deforestation in Indonesia—and for seabirds such as albatrosses and petrels, which drown on the hooks of commercial long-line fisheries.Butchart et al. argue that Red List Indices complement indicators based on population trends, because although the indices show coarser temporal resolution, they have much better geographic representation; they're based on nearly all species in a group worldwide rather than on a potentially biased subset. Both types of species-based indicators show finer ecological resolution in tracking biodiversity loss than indicators like habitat or biome trends. Thus, the Red List Index provides a baseline for tracking progress toward the 2010 target. But having a reliable indicator is only the first step. Without an international commitment to halt the advancing extinction crisis, biodiversity will continue to decrease. The United States is the only industrialized nation that has not signed on to this effort.


Pseudomonas aeruginosa (PA) is the most important bacterial pathogen in patients with cystic fibrosis (CF) patients. Currently, routine bacteriological culture on selective/non- selective culture media is the cornerstone of microbiological detection. The aim of this study was to compare isolation rates of PA by conventional culture and molecular (PCR) detection directly from sputum.Pseudomonas isolation agar, Blood agar & McConkey agar). In addition, from the same specimen, genomic bacterial DNA was extracted (1 ml) and was amplified employing two sequence-specific targets, namely (i) the outer membrane protein (oprL) gene locus and (ii) the exotoxin A (ETA) gene locus.Adult patients (n = 57) attending the regional adult CF centre in Northern Ireland, provided fresh sputum following airways clearance exercise. Following processing of the specimen with sputasol (1:1 vol), the specimen was examined for the presence of PA by plating onto a combination of culture media , we followed these patients and recorded that 5 of these patients converted to being culture-positive at times ranging from 4–17 months later, with a mean lag time of 4.5 months.By sputum culture, there were 30 patients positive for PA, whereas by molecular techniques, there were 35 positive patients. In 39 patients (22 PA +ve & 17 PA -ve), there was complete agreement between molecular and conventional detection and with both PCR gene loci. The oprL gene target, is a useful technique in the early detection of PA, gaining on average 4.5 months over conventional culture. It now remains to be established whether aggressive antibiotic intervention at this earlier stage, based on PCR detection, has any significant benefits on clinical outcome.This study indicates that molecular detection of PA in sputum employing the  Pseudomonas aeruginosa, Burkholderia cepacia and Stenotrophomonas maltophilia. However, with modern antibiotic management with improved antimicrobial agents, such as the aminoglycosides and carbapenems, CF patients have an improved survival, resulting in more adults in employment.Cystic fibrosis [CF] is the most commonly inherited fatal disease in persons originating from a white and European background, currently affecting approximately 30,000 adults and children in the US . The defPseudomonas aeruginosa in CF patients.[P. aeruginosa,[P. aeruginosa in CF patients [P. aeruginosa directly from the sputum of CF patients in the UK and Ireland is uncommon. Most clinical microbiology laboratories in the UK and Ireland, which support a CF centre, have developed standard operating procedures (SOPs) for the isolation of this organism from patients' sputum. Hence, it was the aim of this study to compare the conventional and molecular detection of P. aeruginosa from the sputum of adult patients attending the adult centre in Northern Ireland, as well as to estimate the lag time of conventional culture to detection, compared with molecular (PCR) detection.Previous studies have described alternative laboratory markers to conventional bacteriological culture for the detection of patients.,5 These ruginosa, as well Pseudomonas aeruginosa. Processed sputa (10 μ l) were inoculated and incubated, onto several selective media for the isolation of Pseudomonas aeruginosa, including:- Columbia Blood Agar (Oxoid CM0331) supplemented with 5% (v/v) defribinated horse blood, MacConkey Agar (Oxoid CM0007) and Pseudomonas Isolation Agar (PIA) (Oxoid CM0559 + SR0102). All media were incubated aerobically at 37°C for 48 h, unless otherwise stated. The PIA plates were incubated at room temperature for a further three days following initial 48 hrs incubation. In addition, all different phenotypes from the sputum of each patient were identified phenotypically employing a combination of conventional identification methods (e.g. oxidase), as well as the API Identification schemes  .Duplicate sputa (1 ml minimum) specimens were collected from 57 adult patients with a well characterized history of CF in sterile (100 ml) plastic disposable containers. Sputum was collected immediately after a standardized session of physiotherapy and was stored at ambient temperature and was processed within 4 h from collection. Fresh sputum (1 ml min) was mixed with an equal mass (1:1) of Sputasol  and was incubated in a water bath at 37°C for 15 min, before further qualitative processing for the detection of et al[Pseudomonas aeruginosa  ATCC 27853, by employment of the Roche High Purity PCR Template Preparation Kit , in accordance with the manufacturer's instructions. Extracted DNA was stored at -80°C prior to PCR amplification. For each batch of extractions, a negative extraction control containing all reagents minus sputum, was performed, as well as an extraction positive control with P. aeruginosa.All DNA isolation procedures were carried out in a Class II Biological Safety Cabinet  in a room physically separated from that used to set up nucleic acid amplification reaction mixes and also from the "post-PCR" room in accordance with the Good Molecular Diagnostic Procedures (GMDP) guidelines of Millar et al, in orde2, 200 μM (each) dATP, dCTP, dGTP and dTTP; 1.25 U of Taq DNA polymerase , 0.1 μM (each) of the each set of primers (exoA &oprL) (Table P. aeruginosa ATCC 27853 DNA) and multiple negative (water) amplification controls were included in every set of PCR reactions. In addition, a broad-range/universal PCR was employed with each sputum to demonstrate successful extraction of bacterial DNA from the specimen, as well as lack of PCR inhibition, by employing the highly conserved 16S rDNA primers, PSL/PSR  were set up as follows:-10 mM Tris-HCl, pH 8.3, 50 mM KCl, 2.5 mM MgCl00 μl werexoA or oprL loci, the specimen was considered positive for P. aeruginosa.Following amplification, aliquots (10 μl) were removed from each reaction mixture and examined by electrophoresis  in gels composed of 2% (w/v) agarose  in TAE buffer , stained with ethidium bromide (5 μg/100 ml). Gels were visualised under UV illumination using a gel image analysis system  and all images archived as digital graphic files (*.bmp). Where a band was visualized at the correct expected size for either P. aeruginosa in patients' sputum by conventional and molecular techniques is shown , there was complete agreement between molecular and conventional detection techniques. The oprL locus was more sensitive than the ETA locus, as the former was positive in 10 more patients and there were no patients where the ETA was positive and the oprL target negative, However, there were five patients who were culture positive but PCR -ve by both gene targets. Given that the universal 16S rDNA PCR was positive for these sputum specimens and that the appropriate molecular controls were working optimally, this may accounted for by potential phenotypic misidentification of P. aeruginosa, which has been recently described.[P. aeruginosa colonization with a false-negative culture result due to sample overgrowth by other bacteria or to the presence of non-cultivable organisms or auxotrophic mutations in the organism. Where a PCR +ve/culture -ve result was recorded (10 patients), we followed these patients and recorded that five of these patients converted to being culture-positive at times ranging from 4–17 months later, with a mean lag time of 4.5 months, whereas the remaining five patients remained negative for P. aeruginosa.A comparison of the occurrence of wn Table . By cultP. aeruginosa directly from the sputum of patients with CF. Two gene loci were targeted, as specific markers of P. aeruginosa, namely the exotoxin A (ETA) gene and the outer membrane lipoprotein (oprL) gene. ETA is produced by the majority of P. aeruginosa strains and can inhibit eucaryotic protein biosynthesis at the level of polypeptide chain elongation factor 2, similarly to diphtheria toxin.[In this study, two PCR assays were performed individually for the molecular detection of ia toxin. OprL is ia toxin.Pseudomonas aeruginosa is the most important bacterial pathogen in patients with CF [Pseudomonas colonisation of the major airways leading to delibating exacerbations of pulmonary infection, is the major cause of morbidity and mortality in patients with CF, hence it is important to be able to reliably detect P. aeruginosa from patients' sputum. Emerson et al. [P. aeruginosa was a major predictor of morbidity and mortality, whereby the 8-year risk of death parameter was 2.6 times higher in patients who had positive sputum cultures for this organism, as well as having a significantly lower percent predicted forced expiratory volume (FEV1). These workers suggested that early interventions may help decrease associated morbidity and mortality of young patients with CF. with CF , as demon et al.  recentlyet al. [P. aeruginosa infection in CF. These workers suggested that chronic lower airway infection with P. aeruginosa is associated with significant morbidity and mortality among CF patients [P. aeruginosa does not appear to cause an immediate and rapid decline in lung function, as early isolates are generally non-mucoid, antibiotic-sensitive and present at low densities, suggesting a possible "window of opportunity" for early intervention, as they describe [More recently, Rosenfeld et al.  describepatients . Howeverdescribe . This stdescribe Pseudomonas colonization as early as possible, so that (i) aggressive antibiotic regimes may be considered,(ii). the patient is managed optimally, in an attempt to avoid early biofilm formation and chronic colonization with Pseudomonas and (iii) appropriate infection control precautions are considered. More recently, West et al. showed by a combination of serum IgG, IgA and IgM anti P. aeruginosa antibodies, in conjunction with these authors' Wisconsis Cystic Fibrosis Radiograph score, that P. aeruginosa infections occurred approximately 6–12 months before the organism was recovered from respiratory secretions.[P. aeruginosa acquisition.It is therefore important that primary diagnostic bacteriology laboratories have the ability to detect transient and early cretions. In additPseudomonas aeruginosa, either from culture plates or patients' sputum, small numbers of Pseudomonas colonies (n = 1–2) may therefore be missed when present in the early stages of colonization preceding infection of the patient's airways, particularly where such single colonies are mixed alongside other phenotypically similar genera on the primary culture plate.[Pseudomonas colonization. Although such assays are not generally available in most clinical diagnostic laboratories, these laboratories generally do have access to such technology at regional specialist microbiology centres and it may therefore be prudent to establish routine analysis of children's sputum, particularly from patients with no or an intermittent history by culture of Pseudomonas colonization, at annual review. Furthermore, it is very important to follow up any such patients, which demonstrate a PCR +ve/culture -ve finding from their sputum, in order to establish whether there is transient infection in patients, which never result in established colonization leading to chronic infection. Additionally, it is important to monitor such PCR +ve/culture -ve patients in terms of optimal antibiotic management and infection control.Given that not all laboratories employ molecular detection methods for re plate. PragmatiP. aeruginosa were able to detect P. aeruginosa at an early stage, on average 4.5 months sooner than culture detection. However, it remains unclear what this "window of opportunity" potentially offers in terms of a decrease in P. aeruginosa-associated morbidity and mortality.Overall, our study demonstrated that molecular assays for the detection of JEM, PGM and JSE jointly conceived the study and designed the experiments. JX executed and analyzed all practical aspects of the study, as well as analyzing the data. JEM executed certain molecular components of experimentation, analyzed the data and prepared the manuscript. BCM helped guide the molecular aspects of experimentation and was involved in drafting of the manuscript. PGM provided expert microbiological analysis and interpretation of data. JSE provided clinical expertise, interacted with the CF patients and was involved in interpretation of data, as well as critically reviewing the final manuscript. All authors read and approved the final manuscript.


Changes in cellular functions in response to drug therapy are mediated by specific transcriptional profiles resulting from the induction or repression in the activity of a number of genes, thereby modifying the preexisting gene activity pattern of the drug-targeted cell(s). Recombinant human interferon beta (rIFNβ) is routinely used to control exacerbations in multiple sclerosis patients with only partial success, mainly because of adverse effects and a relatively large proportion of nonresponders. We applied advanced data-mining and predictive modeling tools to a longitudinal 70-gene expression dataset generated by kinetic reverse-transcription PCR from 52 multiple sclerosis patients treated with rIFNβ to discover higher-order predictive patterns associated with treatment outcome and to define the molecular footprint that rIFNβ engraves on peripheral blood mononuclear cells. We identified nine sets of gene triplets whose expression, when tested before the initiation of therapy, can predict the response to interferon beta with up to 86% accuracy. In addition, time-series analysis revealed potential key players involved in a good or poor response to interferon beta. Statistical testing of a random outcome class and tolerance to noise was carried out to establish the robustness of the predictive models. Large-scale kinetic reverse-transcription PCR, coupled with advanced data-mining efforts, can effectively reveal preexisting and drug-induced gene expression signatures associated with therapeutic effects. By studying gene expression in patients with multiple sclerosis before and after therapy with beta interferon, it is possible to identify gene expression signatures that are associated with therapeutic effects Interferons are small, inducible proteins secreted by nucleated cells in response to viral infection and other stimuli. They act in a paracrine fashion on other cells in their immediate vicinity, triggering a state of growth arrest, so that infected cells cannot be forced to produce viral proteins, and activating the process of programmed cell death, so that infected cells can be removed . InterfeWe generated and analyzed longitudinal patterns of gene expression from interferon beta (IFNβ)–treated patients suffering from MS with the aim of identifying preexisting and drug-induced signatures that would predict or explain the clinical response to the drug.Fifty-two patients with relapsing-remitting MS were followed for at least 2 y after initiation of therapy with IFNβ. Clinical follow-up included a neurological examination every 3 mo and at the time of relapse. At each visit, a blood sample was obtained by venipuncture. After the 2-y endpoint, patients were classified as either good or poor responders based on strict criteria, as described in MX1 as the single best discriminating variable between samples obtained at baseline (T = 0) and at 3 mo after initiation of therapy (T = 3) with a classification accuracy of 79% (data not shown). Given that MX1 is a known marker of IFN bioavailability [Using linear discriminant analysis–based integrated Bayesian inference system (IBIS), we were able to detect the gene lability , this reT = 0 correlated with a good or poor outcome of therapy at the 2-y endpoint. This process exhaustively carried out sample classification by searching through all 54,740 possible three-gene combinations of 70 genes. Higher-order combinatorial searches beyond combinations of three genes are possible using IBIS and are currently under investigation. However, higher-order predictive variable combinations do require the support of many more samples to prevent overfitting of the model.To search for expression signatures associated with therapeutic outcome (good or poor responder), we conducted clustering of samples using normalized data for all 70 genes at each time point . DespiteWe implemented a stringent method for examining the statistical validity of our classification results that consisted in testing the obtained classifier in an independent set of samples not previously “seen” by the program. All of the following statistical analyses were thus performed on split datasets, namely, training (75% of the samples) and test (25%), each reflecting the same relative proportion of classes (63% good and 37% poor responders). We started by conducting 3D IBIS searches using expression data from only the training set and selected the top nine scoring triplets . For each gene triplet, and using the training data only, a committee of classifiers was built based on an internal cross-validation scheme. Subsequently, the classifiers were used to predict the outcome of an independent test set of samples. Gene triplets were ranked on the basis of the prediction accuracies of the classifiers on this independent test set. We identified nine gene triplets with a predictive accuracy of at least 80% . We consCaspase 2, Caspase 10, and FLIP, which yielded a predictive accuracy of 86% in the original split. The bell-shaped distribution resulting from 100 tests for this triplet displayed a mean accuracy of 87.8% and a tenth percentile of 78.6%, meaning that if the prediction were performed multiple times, in 90% of these instances an accuracy of almost 79% or better would be obtained. This histogram only reflects the range of accuracies obtained, should the initial data split be different. Notably, the genes in the top-scoring triplet were Caspase 2, Caspase 10, and FLIP—three apoptosis-related molecules. The second-highest-scoring triplet was that of Caspase 2, Caspase 3, and IRF4 (86.8% mean accuracy after 100 splits). Other high-scoring triplets included IL4Ra and MAP3K1, in addition to other apoptotic molecules   .Caspase 2, Caspase 10, and FLIP; 3D model) was compared with those obtained with the single-gene (1D) and gene-pair (2D) models. We observed that the classification accuracy improves as more genes are added to the classifier. We next plotted the samples of a test dataset (25% of samples) on the predictive probability model shown in Caspase 2/Caspase 10 and Caspase 10/FLIP gene pairs show significant predictive capability, but that all three genes are required to provide the highest level of model accuracy and robustness.In To validate the specificity and predictive capability of the top-scoring gene triplet (for the good and poor responding classes) and its associated classifiers, we examined the model exhibiting the best performance on a “default” expression dataset. This null dataset was built keeping the original gene expression data and randomly permuting the class labels of the outcomes 1,000 times . The prediction accuracies for all the gene triplets obtained with this dataset dropped dramatically as the means ranged from 49.2% to 53.6% (data not shown), emphasizing the specificity of the classifiers. In addition, for the top-scoring gene triplet (for good and poor responder classes), we calculated the probability of achieving, under the null hypothesis, an equal or better accuracy than that obtained in the original prediction (86%), as previously described . This acFinally, we tested the robustness of each of these gene sets as predictors of IFNβ response by simulating experimental measurement error. To accomplish this, we first calculated the standard deviation of the expression measurements for all genes as an estimation of the overall experimental error. We then added a fixed amount of Gaussian noise corresponding to one standard deviation (taken from 20 random deviations) to each expression value and repeated the classification/prediction in 30 different splits of the data . Notably, the mean drop in predictive accuracy after the addition of noise was less than 10%, denoting a significant tolerance to reasonable measurement errors .Because all the patients in this study were systematically followed up for a period of 2 y, we were able to perform a longitudinal analysis. Using a repeated-measures analysis of variance (ANOVA), we searched for genes with significantly different expression patterns based on models that tested for responder effect, time effect, and interaction effect (time × response).IRF4 (p = 0.03), IL4Ra (p = 0.01), Caspase 10 (p = 0.0008), Caspase 7 (p = 0.01), IRF2 (p = 0.02), and IRF6 (p = 0.03) are among the 12 genes that best predict response at T = 0  was observed for the poor responders.Significant responder effect for 20 genes  and signAlthough we successfully identified informative, combinatorial relationships, establishing the causality of the association between gene expression and outcome to therapy is beyond of the scope of this work, and these genes are therefore considered surrogate markers. Moreover, although extensive in vivo and in vitro experiments have been conducted, the full mechanism of action of IFNβ in MS remains unknown.Transcription profiling experiments have involved IFNβ in the regulation of apoptosis in both cancer and MS ,16,17,18IRF4, a gene essential for mature T and B lymphocyte function and homeostasis, and a transcription factor with dual function (activator/repressor) that regulates transcription of IL4 through physical interaction with NFATc2 [IRF4 is a repressor of other IFN-induced genes [IRF4 observed in the poor responders before initiation of therapy.Other differentially expressed transcripts included h NFATc2 . Remarkaed genes , an obseMX1 showed a significant time effect independent of clinical response (p = 0.01). This result is in agreement with previous findings indicating substantial MX1 upregulation in response to type I IFNs [MX1, which occurs minutes after IFN stimulation [MX1 as the best single classifier for samples from patients before (T = 0) and after (T = 3) initiation of therapy. In fact, as MX1 comes from the difference between T = 0 and T = 3.As expected, the gene e I IFNs . Interesmulation , is sustIFNAR1 and STAT2 , their upregulation on administration of rIFNβ is likely related to mechanistic aspects of IFN signaling. Our results suggest that poor response is associated with downstream signaling events rather than deficient recognition or metabolism of the drug. Our previous finding that IFN receptor polymorphisms do not affect therapeutic response in this same set of patients partially supports this hypothesis [Caspase 10 (p = 0.01) and MAP3K1 (p = 0.01) were part of any predictor set . These results highlight the involvement of these genes in the response to IFN both at T = 0 and once therapy has started.Also of interest, a significant time effect (but not responder effect) was observed for nd STAT2 B. Becauspothesis . Only twctor set B. In addHere we combined large-scale, function-oriented gene expression with advanced data mining to identify a set of markers that accurately and robustly predict the response to rIFNβ therapy. Although larger, prospective studies are needed to confirm these findings, our results suggest that the underlying gene activity profile of an individual at the verge of therapy harbors sufficient information to allow investigators to estimate the chances of experiencing satisfactory therapeutic effects. As analytical tools to predict clinical outcomes based on molecular evidence evolve, these types of studies are likely to become a substantial aid to the physician, taking the paradigm of personalized medicine one step further.All studies were approved by the respective Committees of Human Research at Hospital Vall d'Hebron, Barcelona, Spain, and the University of California, San Francisco, United States. Informed consent was obtained for all study participants. All patients were examined by a trained neurologist at the CNI Unit, Vall d'Hebron Hospital. Inclusion criteria for this study were clinically definite MS (Poser's criteria), disease in relapsing-remitting phase, age between 18 and 65 y, recorded history of at least two clearly identified relapses within the preceding 24 mo, and expanded disability status scale between zero and 5.5 (inclusive). Detailed information about clinical aspects of these patients has been recently reported elsewhere [n = 33) if they experienced a total suppression of relapses and no increase in the expanded disability status scale after a 2-y follow-up period. Poor responders (n = 19) were defined as having suffered two or more relapses or having a confirmed increase of one point in the expanded disability status scale score. Patients with intermediate phenotypes were excluded from this study. Blood specimens were taken following institutional guidelines at approximately the same time of the day just before the administration of the first dose of rIFNβ and every 3 mo thereafter during the neurological examination, with the exception of T = 15 and T = 21 mo. Altogether, 336 samples were tested . Immediately after being drawn, all blood samples were spun over Ficoll-Paque  gradients to enrich the sample for mononuclear cells. After three washes with PBS, aliquots of 5 × 106 cells were frozen in RPMI1640 containing 20% DMSO and 20% fetal calf serum.Patients were categorized as good responders . Plates were kept frozen at −70 °C until needed.Peripheral blood mononuclear cells were thawed at 37 °C for 1 min, and RNA lysis buffer was added immediately. RNA was purified with the Strataprep kit  and finally resuspended in nuclease-free water . One-microliter RNA aliquots were subjected to fluorescence-based quantitation (in duplicate) using the Ribogreen reagent  in a Spectra Max Gemini fluorometer . Samples were diluted to 1 ng μlA master mix was prepared essentially as described previously , with thA custom Microsoft Excel  worksheet was prepared for handling reaction data import and performing initial statistics. Normalized data were imported as a .csv file into GeneLinker Platinum  for preprocessing and clustering analysis. Quadratic discriminant analysis–based IBIS implemented for 3D searches was carried out at the School of Computing, Queen's University, Kingston, Ontario, Canada, and at Biosystemix, Sydenham, Ontario, Canada. IBIS is a data-mining algorithm that searches through the gene space for a single gene (or group of genes) that can predict the outcome class . This algorithm incorporates a complete 10-fold cross-validation method with several independently trained classifiers to predict an outcome on the basis of a voting scheme (see below). We used MSE and classification accuracy to assess how well the classification predictions matched the true response of the patients to therapy. The top-performing gene triplets were selected on the basis of a mixed threshold for low MSE levels and high accuracy rates.i,g two Gaussian functions are fitted to the distributions of the observed expression levels in good responding and poor responding patients . Our fitted distribution, Dg(x), denotes the probability of a patient having an expression level of i = x,g given that this patient is a good responder. The fundamental question we are aiming to answer using data-mining methods (here using IBIS particularly) is as follows: what is the probability of a patient being a good responder given the observed expression level of a gene for that patient? Taking advantage of the fitted distributions, a classifier applies Bayes' formula to answer the fundamental question. According to this formula:IBIS identifies genes (or gene pairs or groups of genes) that are highly predictive of the outcome based on probability distributions of those genes in different outcome classes. For example, for a given gene P1= P((i = xg)|Pagood_responder)P(Pagood_responder). In fact, P((i = xg)|Pagood_responder) is the distribution function fitted to the observed gene expression values of ig above (Dg), and P(Pagood_responder), the probability of a patient being a good responder, can be easily calculated using the total and good responding patient counts. The term P2 is strictly analogous to P1 but applies to poor responders. Therefore, according to where To obtain a reliable classifier that is generalizable to all patient samples obtained under similar conditions, the Gaussian distributions and the classifier were only trained on a subsample of the patient data (training set). The results of the classification , however, were tested on patient samples never seen by the classifier before (test set). This ensures limitation of the classifier overfitting. Further, a complete 10-fold cross-validation scheme was built into the training phase. In IBIS, linear and quadratic classifiers correspond to classifiers built using Gaussian distributions with equal or different covariance matrices, respectively. The prediction results of IBIS are visualized graphically within the observed gene expression space by presenting the probabilities of a patient being a good or poor responder as a background color see  and 5. Tiobserved − responseiexpected)2 averaged over all patients. For a given patient, the clinical response determined by the end of the 2-y monitoring period is denoted by responseiobserved and responseiexpected and represents the probability of that patient being a good responder to rIFNβ therapy, using the Bayes' formula above. Classification accuracy simply expresses the percentage of patients that were correctly predicted as being good or poor responders.Several measures were used to assess how well the calculated probabilities matched the true patient responses to therapy. Two of these measures were MSE and classification accuracy. MSE was calculated as the sum of . Only the training set was used for identifying the best predictive gene triplets with the IBIS method, as well as for building the classifier. A committee of classifiers was then generated using a 10-fold cross-validation scheme during training. The training data themselves were divided into ten parts, and each time, a classifier was built using only nine parts of the data. That classifier's predictive capability was determined by its accuracy over the one-tenth of the data withheld. A committee of ten classifiers was assembled from the results of this training stage; this committee was then applied to the test data (which have thus far been hidden from the classifiers). For a patient sample in the test data, each classifier in the committee made a prediction. A majority voting scheme then decided as to which class the sample would be assigned.Given the initial data split into training and test sets, it was important to rule out the role of fortuitous idiosyncrasies in this split and the resulting accuracy rates. To address this point, we created 100 random splits of the data into training and test subgroups. A committee of classifiers was trained on the training set for each data split, and the accuracies were calculated over the blind test set. A histogram of the test set accuracies was then built, representing the expected ranges of accuracies had the initial data split been different. This histogram is not representative of the estimated or idealized distribution of the accuracies for a gene triplet in a machine learning sense. Rather, it is a coarse approximation of the possible range of gene triplet outcome–prediction accuracies that could be expected.p-value, and indicates the number of times in 1,000 trials for which accuracies of 86% or higher can be achieved under the “no predictive capability” null hypothesis.To assess the significance and specificity of the top-scoring gene triplets and their corresponding trained committee of classifiers, a null dataset was created by keeping the same expression levels of genes in the dataset and randomly permuting the class labels of the patients 1,000 times . Classifiers were built using the training null data, and accuracies were calculated on the corresponding test sets. The mean of these accuracies for all the top-performing gene triplets was around 50%. The achieved significance level, which represents the probability of achieving accuracy levels better than or equal to that of the nonpermuted classification, was calculated to be 0.009. This value can be considered a significance level, or Time-series analysis was performed using SAS version 8.0 . Permutation analysis and histogram graphic outputs were produced with Matlab .Dataset S1Gene expression values for all samples at all time points. This is the raw file from which all analyses were performed.(491 KB XLS).Click here for additional data file.Table S1Gene names, symbols, and LocusLink and GenBank accession numbers, as well as primer sequences, are listed for all targets.(160 KB DOC).Click here for additional data file.http://www.ncbi.nlm.nih.gov/projects/LocusLink/) accession numbers for the genes discussed in this paper are BAX (LLID 581), Caspase 10 (LLID 843), Caspase 2 (LLID 835), Caspase 3 (LLID 836), Caspase 7 (LLID 840), FLIP (LLID 8837), IFNAR1 (LLID 3454), IL4 (LLID 3565), IL4Ra (LLID 3566), IRF2 (LLID 3660), IRF4 (LLID 3662), IRF6 (LLID 3664), MAP3K1 (LLID 4214), MX1 (LLID 4599), NFATc2 (LLID 4773), STAT2 (LLID 6773), and TRADD (LLID 8717).The LocusLink (


Learning the exact demographic characteristics of a neighborhood in which a public library serves, assists the collection development librarian in building an appropriate collection. Gathering that demographic information can be a lengthy process, and then formatting the information for the neighborhood in question becomes arduous.As society ages and the methods for health care evolve, people may take charge of their own health. With this prospectus, public libraries should consider creating a consumer health collection to assist the public in their health care needs. Using neighborhood demographic information can inform the collection development librarians as to the dominant age groups, sex, and races within the neighborhood. With this information, appropriate consumer health materials may be assembled in the public library. the data was manipulated with ArcView GIS and queried to produce maps displaying the requested neighborhood demographics to view in respect to libraries.In order to visualize the demographics of a neighborhood, the computer program ArcView GIS (geographic information systems) was used to create maps for specified areas. The neighborhood data was taken from the U.S. Census Department's annual census and library addresses were accumulated through a free database. After downloading the census block information from ArcView GIS produced maps displaying public libraries and requested demographics. After viewing the maps the collection development librarian can see exactly what populations are served by the library and adjust the library's collection accordingly.ArcView GIS can be used to produce maps displaying the communities that libraries serve, spot boundaries, be it "man-made or natural," that exist prohibiting customer service, and assist collection development librarians in justifying their purchases for a dedicated consumer health collection or resources in general. Libraries have the objective to build collections that support the communities they serve. To build a viable collection the collection development librarian, outreach/marketing librarian, and others, must determine exactly what populations reside in the neighborhood, in respect to race, spoken language, educational level, and age groups. One method collection development librarians use to gather demographic information is to physically visit the communities and integrate into the neighborhoods. They may attend events within the community in order to analyze the attendees or walk around the neighborhood to get a feel for the community. Another method for collection development is to perform an informal survey with people that visit the library and learn about their preferences and/or what they like to read or look for on the Internet. Using census information can be a third way to gather community/neighborhood information. The US Census Bureau takes a census of the United States every 10 years and publishes the results on the Internet. Taking the Census information and transforming it into a graphical format provides an objective view of the communities surrounding a library. One way to visualize the census data is through the use of GIS, Geographic Information Systems. GIS are databases arranged by spatial coordinates that, when programmed, can produce maps ."Many disciplines have used GIS and many large academic libraries even support GIS by establishing a GIS department and employing a librarian to specialize in GIS information. In the field of library science articles have been published describing the establishment of GIS departments in libraries and what GIS librarians do -7, but lGovernments use GIS to visualize land use planning, tax appraisal, utility and infrastructure planning and more . BusinesBuilding a collection for a library has become a sophisticated art. Johnson states that "collection building consists of four steps: identifying the relevant items, assessing the item to decide if it is appropriate for the collection and evaluating its quality, deciding to purchase, and preparing an order ." Each sThis paper reports the steps taken to use GIS in assisting a large metropolitan public library system in visualizing neighborhoods that surround branch libraries in order to make an educated decision on whether a dedicated consumer health collection should be established to support the community. The objective of the project was to determine if GIS could be used to improve collection development.After comparing large library systems in major metropolitan areas,  Chicago was selected as a convenient sample to be mapped with GIS because of its size, number of public libraries, their geographic distribution within the urban area, and ease of access to data. Compared to other large cities, the New York Public Library system has 86 libraries and Los Angeles has 67 public libraries . The citWith the selection of the city of Chicago, the next step was to create a map of the metropolitan area. ESRI ArcGIS v9.0 geographic information software (GIS)  was seleIn order to create the city map of Chicago, data files were obtained from the U.S. Census Bureau website. The Census Bureau produces TIGER  Files available for free to the public . These fThe next step required mapping distances between neighborhoods and neighborhood demographics. In order to achieve this, files were obtained from the U.S. Census Bureau website  which di. The output of this search was imported into Excel, cleaned, and converted into a dBase file. The dBase file was imported into ArcView to coordinate the library addresses to the street addresses of the base map. This enabled the software to place an image for each public library in the city map of Chicago.The final piece of information needed to build a map showing neighborhoods in respect to public library locations, was to map the public libraries within Chicago. Because the base map of Cook County was a street map, addresses of public libraries were entered into ArvView to mark all the public libraries within the city of Chicago. The state of Illinois provides a database available on the Internet that permits searching types of libraries and locations of libraries, With the city of Chicago and its public libraries now in a GIS application, the location of libraries was displayed in respect to neighborhoods and distances from library to library . The age ranges presented in the maps varied by query and census information. The groups analyzed were: women, men, white women (group age 40–49), African American women (group age 40–49), Asian . The color schemes used represent the number of people that live within the census block as to the specified GIS query. In Figure In this project ArcGIS was used to build maps of neighborhoods in the city of Chicago, display public libraries and spatially represent distances from libraries to populations. The US Census demographic information was incorporated into ArcGIS and queried to produce 15 different maps. Queries were built to display neighborhood demographics through colors showing the percentages of the population(s). Each query produced a map for a specific age or race. Once the map was produced the public libraries were added to the map and analysis was made regarding collection needs according to the neighborhood population surrounding the library.To decide if a public library should establish a consumer health collection, a map showing the breakdown of women in their 40s was built. As in Figure By creating maps showing the demographic breakdown of the city, a public library can build, adjust, or verify its collection in respect to the neighborhoods it supports . It is eMany studies have been conducted analyzing what people search for on the Internet and who is searching. Studies have found that women in their 40s search for and utilize health information from the Internet more than men -24. UsinUsing ArcGIS can assist in defining the exact make-up of the population that a library serves. By turning the census information into a graphic the library is provided a chance to see how far  their services may reach. Library systems that have branch libraries may have one collection development librarian who buys for each library. By using ArcGIS the central library can analyze the communities surrounding each library to purchase materials appropriately without having to spend time in each library learning the environment. Purchase decisions may now be justified with hard data from ArcGIS. For example, if ArcGIS shows that the largest population surrounding a library speaks a minority language, materials should be purchased in the dominant language to best meet the needs of the community.There is a fairly high learning curve to use ESRI Arcview GIS. There are five programs in ESRI Arcview and each program has separate features that must be used within the specific program to be imported into the primary map. Learning what each program does and then how to incorporate the data into the map requires hours of reading. Acquiring the necessary data to build a map requires background study of the geographic area in order to assure that the map is configured correctly. Upon attaining all necessary data for the map, one must build queries to manipulate the data according to the information need, and then configure the map accordingly.While GIS presents a new technique to use in performing collection development, there was not a way to test the differences from a collection built using GIS information and the current methods of collection development.In addition, research has discovered that knowing the service area radius of the library doesn't mean that people within the service area will actually use or belong to the neighborhood library ,25. WhenWhile GIS can take the census information and demonstrate the population surrounding a library, the census does not describe the specific users of the library . To builBudget constraints will add a need for justification of purchases for a library's collection. Using GIS may provide one method to justify additions to collections. As health care changes and individuals take more control of their health, libraries need to have materials that provide valid information in an age and language appropriate format and be culturally sensitive. Branch libraries respond to the demands of the neighborhood  and knowGIS = geographical information systems


Chronic ethanol exposure has been shown to result in changes in neuronal cyto-architecture such as aberrant sprouting and alteration of neurite outgrowth. In PC12 cells, chronic ethanol treatment produces an increase in Nerve Growth Factor (NGF)-induced neurite outgrowth that appears to require the epsilon, but not delta, isoform of Protein Kinase C (PKC). Neurites contain a core of microtubules that are formed from polymerization of free-tubulin. Therefore, it would be expected that an increase in neurite outgrowth would correlate with an increase in microtubule content. We examined the effect of chronic ethanol exposure on microtubule content in PC12 cells and the role of PKC epsilon and delta in ethanol's effect on microtubule levels.Chronic ethanol exposure of wild-type and vector control PC12 cells resulted in a significant increase in microtubule content and a corresponding decrease in free tubulin. There was also a significant increase in microtubule content in PC12 cells expressing a dominate-negative inhibitor of epsilon PKC; cells which have previously been shown to have no ethanol-induced increase in neurite outgrowth. In contrast, ethanol had no effect on microtubule content in PC12 cells expressing a dominate-negative inhibitor of delta PKC.These results suggest that chronic ethanol exposure alters the relative ratio of free tubulin to microtubule-associated tubulin, an important component of the cytoskeleton. Further, the data from the PKC dominant-negative cell lines suggest that the effects of ethanol on microtubule content do not correlate with the effects of ethanol on neurite outgrowth. The delta isoform of PKC appears to be necessary for the ethanol-induced increase in microtubule content. These studies demonstrate an effect of chronic ethanol exposure which may contribute to previously documented alterations of neuronal cyto-architecture. Membranes were then incubated in primary antibody  overnight at 4°C. Following incubation in the primary antibody, membranes were washed with TBS/0.05% Tween-20 and incubated in secondary antibody . Membranes were again washed and tubulin bands were visualized by Enhanced Chemiluminescence (ECL) using standard luminol reagents captured using a BioRad Gel Doc 1000. The computer analysis software Molecular Analyst  was used to quantify tubulin concentrations . All blots were very clean and no filtering was needed.Tubulin concentrations in both the free-tubulin and microtubule fractions were expressed as a percent control relative density and presented as mean (+/-SEM). The data represent multiple replicates of the same experiment . Student's t-tests were used to make comparisons between control and ethanol-treated PC12 cells.Each individual band analyzed was an independent extract from the same experiment, with the experiment being replicated 5 times. Each replication of the experiment was composed of 2 separate wells of cells for each treatment condition. For each of the 5 replications, the 2 control bands were averaged and each sample, including the controls, from that replication normalized to the average. The data was combined after all 5 replications.CKR-F conceived and performed the experiments and participated in writing the manuscript. DPD participated in the design of the study and writing of the manuscript. Both authors read and approved the final manuscript.


High prevalence rates for rheumatoid arthritis, spondyloarthopathies, and systemic lupus erythematosus have been described in American Indian and Alaskan Native adults. The impact of these diseases on American Indian children has not been investigated.We used International Classification of Diseases-9 (ICD-9) codes to search two Indian Health Service (IHS) patient registration databases over the years 1998–2000, searching for individuals 19 years of age or younger with specific ICD-9-specified diagnoses. Crude estimates for disease prevalence were made based on the number of individuals identified with these diagnoses within the database.Rheumatoid arthritis (RA) / juvenile rheumatoid arthritis (JRA) was the most frequent diagnosis given. The prevalence rate for JRA in the Oklahoma City Area was estimated as 53 per 100,000 individuals at risk, while in the Billings Area, the estimated prevalence was nearly twice that, at 115 per 100,000. These rates are considerably higher than those reported in the most recent European studies.Chronic arthritis in childhood represents an important, though unrecognized, chronic health challenge within the American Indian population living in the United States. As a group, the rheumatic diseases of childhood represent one of the most common chronic disease conditions in children . These iPrevalence rates of rheumatic disease in North American Indian/First Nations populations have been reported in small studies from single tribes. From these studies, significantly higher prevalence rates for rheumatoid arthritis (RA) have been reported in adults from tribes living in the Great Lakes region , the PacTo our knowledge, a comprehensive survey of rheumatic diseases affecting children, adolescents, and young adults has not been reported in any non-Caucasian population. Because both our clinical experience here in Oklahoma suggests that rheumatic diseases in children may also be more prevalent in the American Indian population compared with Caucasians, we undertook a search of the Oklahoma City Indian Health Service (IHS) user population databases in order to develop prevalence estimates of rheumatic diseases in American Indian children and adolescents. We performed the same queries from the database in the Billings Area IHS office as a basis of comparison.The Oklahoma City Area IHS serves a population of 291,288 individuals, most of whom reside in Oklahoma, with small numbers living in the neighboring states of Kansas and Texas . IHS serThe Billings Area IHS serves a population of 72,591 individuals. The tribes in this service area, a significant proportion of whom live on 8 reservations located in Montana and Wyoming, consist largely of northern plains tribes .In both Areas, the population is younger than the population of the United States as a whole, with 40 percent of the population 19 years of age or younger .International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9) codes were used to search the Oklahoma City and Billings Area IHS National Patient Information Reporting Systems and Patient Registration user databases  over a tOutpatient data over this three-year period was gathered for the user population ≤ 19 years of age and included: patient chart number (with the chart number scrambled to protect patient identity), date of birth, sex, date of visit and diagnosis . The codes used and the diagnoses denoted by those codes are listed in Table A case was defined as any person who was 19 years of age or younger on January 1,1998, January 1, 1999 and January 1, 2000 and whose diagnoses included at least one of the entities listed in Table service population, which includes all individuals who are eligible to receive IHS services. Estimation of disease prevalence was based on three assumptions: (1) that the diagnoses recorded were, in fact, accurate; (2) that the population at risk did not change significantly over the three-year period; (3) that the three-year mortality rate for the diseases of interest was no greater in the IHS user population than for all races in the United States.The population at risk was defined as the number of individuals ≤19 years of age within the IHS user population  . The useFor the Oklahoma City Area, accuracy of the IHS database information was assessed by matching the IHS identification number of known JRA cases followed at the Children's Hospital of Oklahoma with the same number in the IHS patient databank to be sure that known patients were identified and coded accurately.et al, manuscript in preparation). The absence of a prevalence peak in the preschool years may reflect the almost complete absence of children with monoarticular or pauciarticular JRA in the IHS user population, consistent with previous studies in non-European populations . Using such an approach, these authors reported a prevalence rate of 4 per 1,000 for JRA, significantly higher than any previous estimates. We are now preparing a similar project involving American Indian communities on the northern plains.We conclude that the rheumatic diseases of childhood may represent a significant burden of morbidity in these two IHS user populations. More detailed studies with rigorous case ascertainment are required to follow up these preliminary data.None declared.Dan Cameron provided data from the IHS database in Oklahoma City, and Diane Jeannotte provided the Billings Area data. Joyce Mauldin and Glenn Solomon performed the database searches. Dr. Jarvis directed this study and assisted in data analysis and interpretation.The pre-publication history for this paper can be accessed here: