A procedure to characterize geographic distributions of rare disorders in cohorts

<p>Abstract</p> <p>Background</p> <p>Individual point data can be analyzed against an entire cohort instead of only sampled controls to accurately picture the geographic distribution of populations at risk for low prevalence diseases. Analyzed as individual points, many smaller clusters with high relative risks (RR) and low empirical p values are indistinguishable from a random distribution. When points are aggregated into areal units, small clusters may result in a larger cluster with a low RR or be lost if divided into pieces included in units of larger populations that show no increased prevalence. Previous simulation studies showed lowered validity of spatial scan tests for true clusters with low RR. Using simulations, this study explored the effects of low cluster RR and areal unit size on local area clustering test (LACT) results, proposing a procedure to improve accuracy of cohort spatial analysis for rare events.</p> <p>Results</p> <p>Our simulations demonstrated the relationship of true RR to observed RR and p values with various, randomly located, cluster shapes, areal unit sizes and scanning window shapes in a diverse population distribution. Clusters with RR < 1.7 had elevated observed RRs and high p values.</p> <p>We propose a cluster identification procedure that applies parallel multiple LACTs, one on point data and three on two distinct sets of areal units created with varying population parameters that minimize the range of population sizes among units. By accepting only clusters identified by all LACTs, having a minimum population size, a minimum relative risk and a maximum p value, this procedure improves the specificity achieved by any one of these tests alone on a cohort study of low prevalence data while retaining sensitivity for small clusters. The procedure is demonstrated on two study regions, each with a five-year cohort of births and cases of a rare developmental disorder.</p> <p>Conclusion</p> <p>For truly exploratory research on a rare disorder, false positive clusters can cause costly diverted research efforts. By limiting false positives, this procedure identifies 'crude' clusters that can then be analyzed for known demographic risk factors to focus exploration for geographically-based environmental exposure on areas of otherwise unexplained raised incidence.</p

Spin-photon interface and spin-controlled photon switching in a nanobeam waveguide

Access to the electron spin is at the heart of many protocols for integrated and distributed quantum-information processing [1-4]. For instance, interfacing the spin-state of an electron and a photon can be utilized to perform quantum gates between photons [2,5] or to entangle remote spin states [6-9]. Ultimately, a quantum network of entangled spins constitutes a new paradigm in quantum optics [1]. Towards this goal, an integrated spin-photon interface would be a major leap forward. Here we demonstrate an efficient and optically programmable interface between the spin of an electron in a quantum dot and photons in a nanophotonic waveguide. The spin can be deterministically prepared with a fidelity of 96\%. Subsequently the system is used to implement a "single-spin photonic switch", where the spin state of the electron directs the flow of photons through the waveguide. The spin-photon interface may enable on-chip photon-photon gates [2], single-photon transistors [10], and efficient photonic cluster state generation [11]

Delineation of Two Clinically and Molecularly Distinct Subgroups of Posterior Fossa Ependymoma

Despite the histological similarity of ependymomas from throughout the neuroaxis, the disease likely comprises multiple independent entities, each with a distinct molecular pathogenesis. Transcriptional profiling of two large independent cohorts of ependymoma reveals the existence of two demographically, transcriptionally, genetically, and clinically distinct groups of posterior fossa (PF) ependymomas. Group A patients are younger, have laterally located tumors with a balanced genome, and are much more likely to exhibit recurrence, metastasis at recurrence, and death compared with Group B patients. Identification and optimization of immunohistochemical (IHC) markers for PF ependymoma subgroups allowed validation of our findings on a third independent cohort, using a human ependymoma tissue microarray, and provides a tool for prospective prognostication and stratification of PF ependymoma patients

A procedure to characterize geographic distributions of rare disorders in cohorts-0

columns, from the left, represent the areal unit sets (Set 1 (maximum unit pop. = 1000), Set 2 (maximum unit pop. = 2000). The underlying green and blue points indicate the qualifying SaTScan point clusters. All other located births are gray points. On Row 1, the Episcan display uses a heat map to indicate the most likely cluster area within reported significant clusters. In Row 3, Column1, SaTScan areal Set 1 had two overlapping significant clusters, both shown here in different colors.<p><b>Copyright information:</b></p><p>Taken from "A procedure to characterize geographic distributions of rare disorders in cohorts"</p><p>http://www.ij-healthgeographics.com/content/7/1/26</p><p>International Journal of Health Geographics 2008;7():26-26.</p><p>Published online 28 May 2008</p><p>PMCID:PMC2430550.</p><p></p

A procedure to characterize geographic distributions of rare disorders in cohorts-2

columns, from the left, represent the areal unit sets (Set 1 (maximum unit pop. = 1000), Set 2 (maximum unit pop. = 2000). The underlying green and blue points indicate the qualifying SaTScan point clusters. All other located births are gray points. On Row 1, the Episcan display uses a heat map to indicate the most likely cluster area within reported significant clusters. In Row 3, Column1, SaTScan areal Set 1 had two overlapping significant clusters, both shown here in different colors.<p><b>Copyright information:</b></p><p>Taken from "A procedure to characterize geographic distributions of rare disorders in cohorts"</p><p>http://www.ij-healthgeographics.com/content/7/1/26</p><p>International Journal of Health Geographics 2008;7():26-26.</p><p>Published online 28 May 2008</p><p>PMCID:PMC2430550.</p><p></p

A procedure to characterize geographic distributions of rare disorders in cohorts-3

are gray.<p><b>Copyright information:</b></p><p>Taken from "A procedure to characterize geographic distributions of rare disorders in cohorts"</p><p>http://www.ij-healthgeographics.com/content/7/1/26</p><p>International Journal of Health Geographics 2008;7():26-26.</p><p>Published online 28 May 2008</p><p>PMCID:PMC2430550.</p><p></p

Loss of Consciousness and Altered Mental State as Predictors of Functional Recovery Within 6 Months Following Mild Traumatic Brain Injury.

OBJECTIVE: The authors tested the hypothesis that a combination of loss of consciousness (LOC) and altered mental state (AMS) predicts the highest risk of incomplete functional recovery within 6 months after mild traumatic brain injury (mTBI), compared with either condition alone, and that LOC alone is more strongly associated with incomplete recovery, compared with AMS alone. METHODS: Data were analyzed from 407 patients with mTBI from RESULTS: A gradient of risk of incomplete functional recovery at 1, 3, and 6 months postinjury was noted, moving from neither LOC nor AMS, to LOC or AMS alone, to both. LOC was associated with incomplete functional recovery at 1 and 3 months (odds ratio=2.17, SE=0.46, p CONCLUSIONS: These findings highlight the need to include symptom-focused clinical variables that pertain to the injury itself when assessing who might be at highest risk of incomplete functional recovery post-mTBI

Cognitive impairment in older patients with breast cancer before systemic therapy: is there an interaction between cancer and comorbidity?

PURPOSE: To determine if older patients with breast cancer have cognitive impairment before systemic therapy. PATIENTS AND METHODS: Participants were patients with newly diagnosed nonmetastatic breast cancer and matched friend or community controls age > 60 years without prior systemic treatment, dementia, or neurologic disease. Participants completed surveys and a 55-minute battery of 17 neuropsychological tests. Biospecimens were obtained for APOE genotyping, and clinical data were abstracted. Neuropsychological test scores were standardized using control means and standard deviations (SDs) and grouped into five domain z scores. Cognitive impairment was defined as any domain z score two SDs below or ≥ two z scores 1.5 SDs below the control mean. Multivariable analyses evaluated pretreatment differences considering age, race, education, and site; comparisons between patient cases also controlled for surgery. RESULTS: The 164 patient cases and 182 controls had similar neuropsychological domain scores. However, among patient cases, those with stage II to III cancers had lower executive function compared with those with stage 0 to I disease, after adjustment (P = .05). The odds of impairment were significantly higher among older, nonwhite, less educated women and those with greater comorbidity, after adjustment. Patient case or control status, anxiety, depression, fatigue, and surgery were not associated with impairment. However, there was an interaction between comorbidity and patient case or control status; comorbidity was strongly associated with impairment among patient cases (adjusted odds ratio, 8.77; 95% CI, 2.06 to 37.4; P = .003) but not among controls (P = .97). Only diabetes and cardiovascular disease were associated with impairment among patient cases. CONCLUSION: There were no overall differences between patients with breast cancer and controls before systemic treatment, but there may be pretreatment cognitive impairment within subgroups of patient cases with greater tumor or comorbidity burden