Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification.

Proton magnetic resonance spectroscopy (1H-MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical 1H-MRS. Eighty-three/forty-two children with either an ependymoma (ages 4.6 ± 5.3/9.3 ± 5.4), a medulloblastoma (ages 6.9 ± 3.5/6.5 ± 4.4), or a pilocytic astrocytoma (8.0 ± 3.6/6.3 ± 5.0), recruited from four centres across England, were scanned with 1.5T/3T short-echo-time point-resolved spectroscopy. The acquired raw 1H-MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post-noise-suppression 1H-MRS showed significantly elevated signal-to-noise ratios (P < .05, Wilcoxon signed-rank test), stable full width at half-maximum (P > .05, Wilcoxon signed-rank test), and significantly higher classification accuracy (P < .05, Wilcoxon signed-rank test). Specifically, the cross-validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled 1H-MRS. The study shows that fitting-based signal-to-noise ratios of clinical 1H-MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post-noise-suppression 1H-MRS may have better diagnostic performance for paediatric brain tumours.

Characterisation of paediatric brain tumours by their MRS metabolite profiles.

1H-magnetic resonance spectroscopy (MRS) has the potential to improve the noninvasive diagnostic accuracy for paediatric brain tumours. However, studies analysing large, comprehensive, multicentre datasets are lacking, hindering translation to widespread clinical practice. Single-voxel MRS (point-resolved single-voxel spectroscopy sequence, 1.5 T: echo time [TE] 23-37 ms/135-144 ms, repetition time [TR] 1500 ms; 3 T: TE 37-41 ms/135-144 ms, TR 2000 ms) was performed from 2003 to 2012 during routine magnetic resonance imaging for a suspected brain tumour on 340 children from five hospitals with 464 spectra being available for analysis and 281 meeting quality control. Mean spectra were generated for 13 tumour types. Mann-Whitney U-tests and Kruskal-Wallis tests were used to compare mean metabolite concentrations. Receiver operator characteristic curves were used to determine the potential for individual metabolites to discriminate between specific tumour types. Principal component analysis followed by linear discriminant analysis was used to construct a classifier to discriminate the three main central nervous system tumour types in paediatrics. Mean concentrations of metabolites were shown to differ significantly between tumour types. Large variability existed across each tumour type, but individual metabolites were able to aid discrimination between some tumour types of importance. Complete metabolite profiles were found to be strongly characteristic of tumour type and, when combined with the machine learning methods, demonstrated a diagnostic accuracy of 93% for distinguishing between the three main tumour groups (medulloblastoma, pilocytic astrocytoma and ependymoma). The accuracy of this approach was similar even when data of marginal quality were included, greatly reducing the proportion of MRS excluded for poor quality. Children's brain tumours are strongly characterised by MRS metabolite profiles readily acquired during routine clinical practice, and this information can be used to support noninvasive diagnosis. This study provides both key evidence and an important resource for the future use of MRS in the diagnosis of children's brain tumours.

Metabolite profiles of medulloblastoma for rapid and non-invasive detection of molecular disease groups.

BACKGROUND: The malignant childhood brain tumour, medulloblastoma, is classified clinically into molecular groups which guide therapy. DNA-methylation profiling is the current classification 'gold-standard', typically delivered 3-4 weeks post-surgery. Pre-surgery non-invasive diagnostics thus offer significant potential to improve early diagnosis and clinical management. Here, we determine tumour metabolite profiles of the four medulloblastoma groups, assess their diagnostic utility using tumour tissue and potential for non-invasive diagnosis using in vivo magnetic resonance spectroscopy (MRS). METHODS: Metabolite profiles were acquired by high-resolution magic-angle spinning NMR spectroscopy (MAS) from 86 medulloblastomas (from 59 male and 27 female patients), previously classified by DNA-methylation array (WNT (n = 9), SHH (n = 22), Group3 (n = 21), Group4 (n = 34)); RNA-seq data was available for sixty. Unsupervised class-discovery was performed and a support vector machine (SVM) constructed to assess diagnostic performance. The SVM classifier was adapted to use only metabolites (n = 10) routinely quantified from in vivo MRS data, and re-tested. Glutamate was assessed as a predictor of overall survival. FINDINGS: Group-specific metabolite profiles were identified; tumours clustered with good concordance to their reference molecular group (93%). GABA was only detected in WNT, taurine was low in SHH and lipids were high in Group3. The tissue-based metabolite SVM classifier had a cross-validated accuracy of 89% (100% for WNT) and, adapted to use metabolites routinely quantified in vivo, gave a combined classification accuracy of 90% for SHH, Group3 and Group4. Glutamate predicted survival after incorporating known risk-factors (HR = 3.39, 95% CI 1.4-8.1, p = 0.025). INTERPRETATION: Tissue metabolite profiles characterise medulloblastoma molecular groups. Their combination with machine learning can aid rapid diagnosis from tissue and potentially in vivo. Specific metabolites provide important information; GABA identifying WNT and glutamate conferring poor prognosis. FUNDING: Children with Cancer UK, Cancer Research UK, Children's Cancer North and a Newcastle University PhD studentship.

Metabolite selection for machine learning in childhood brain tumour classification.

MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi-class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi-site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi-class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave-one-out and k-fold cross-validation. Metabolites identified as crucial in tumour classification include myo-inositol (P < 0.05, AUC=0.81±0.01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC=0.78±0.01 ) and total creatine (P < 0.05, AUC=0.77±0.01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC=0.79±0.01 ), total N-acetylaspartate (P < 0.05, AUC=0.79±0.01 ) and total choline (P < 0.05, AUC=0.75±0.01 ) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave-one-out cross-validation was 85% for 1.5 T 1 H-MRS through support vector machine and 75% for 3 T 1 H-MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours.

Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours.

1 H-magnetic resonance spectroscopy (MRS) provides noninvasive metabolite profiles with the potential to aid the diagnosis of brain tumours. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. The aim of the current study was to evaluate, prospectively, the diagnostic accuracy of a previously established classifier for diagnosing the three major childhood cerebellar tumours, and to determine added value compared with standard reporting of conventional imaging. Single-voxel MRS (1.5 T, PRESS, TE 30 ms, TR 1500 ms, spectral resolution 1 Hz/point) was acquired prospectively on 39 consecutive cerebellar tumours with histopathological diagnoses of pilocytic astrocytoma, ependymoma or medulloblastoma. Spectra were analysed with LCModel and predefined quality control criteria were applied, leaving 33 cases in the analysis. The MRS diagnostic classifier was applied to this dataset. A retrospective analysis was subsequently undertaken by three radiologists, blind to histopathological diagnosis, to determine the change in diagnostic certainty when sequentially viewing conventional imaging, MRS and a decision support tool, based on the classifier. The overall classifier accuracy, evaluated prospectively, was 91%. Incorrectly classified cases, two anaplastic ependymomas, and a rare histological variant of medulloblastoma, were not well represented in the original training set. On retrospective review of conventional MRI, MRS and the classifier result, all radiologists showed a significant increase (Wilcoxon signed rank test, p < 0.001) in their certainty of the correct diagnosis, between viewing the conventional imaging and MRS with the decision support system. It was concluded that MRS can aid the noninvasive diagnosis of posterior fossa tumours in children, and that a decision support classifier helps in MRS interpretation.

Exploring the health care utilization of children and youth in the care of child welfare: a retrospective matched cohort study.

BACKGROUND: Children and youth whose lives intersect with child welfare systems are amongst the most vulnerable paediatric populations. Despite the increased rates of chronic conditions, these children and youth often experience unmet health care needs. OBJECTIVES: To examine patterns of health care utilization from birth for children and youth in the care of a child welfare authority. METHODS: This retrospective matched cohort design study examined children/youth aged 0-18 who had visited a paediatric tertiary care facility from 2016 April 1 to 2017 March 31 and had "social worker" documented as their guardian. A control cohort was matched based on age and sex. Primary outcomes of interest included primary health care, emergency, outpatient, and inpatient visits. Visits for immunizations, physiological development, well-baby checks, mental health, and oral health were also examined. RESULTS: A total of 200 cases and 200 controls were included in our cohort. No statistically significant differences were found between primary care visits, well-baby checks, inpatient admissions, outpatient mental health visits, or immunizations for children in care in comparison to their controls. There was a significant difference in oral health visits, lack of physiological development, and emergency department visits for children in care when compared to their controls. CONCLUSIONS: Our study revealed disparities in health care utilization amongst children in the care of child welfare in comparison to those who are not, highlighting the need for improved practice, policy, and research initiatives. A collaborative data collection/sharing system is needed to identify and track the health care of this vulnerable population.

Children and youth whose lives intersect with child welfare systems are amongst the most vulnerable pediatric populations. Despite the increased rates of chronic health conditions, these children and youth often experience unmet health care needs. Using a retrospective matched cohort design, we sought to examine patterns of health care utilization from birth to age 18 for children and youth in the care of a child welfare authority in comparison to children/youth who were not in the care of child welfare. No statistically significant differences were found between primary care visits, well-baby checks, inpatient admissions, outpatient mental health visits, or immunizations for children in care when compared to their counterparts not in care. There was a significantly higher number of oral health visits, physiological development concerns, and emergency department visits for children in care when compared to their controls. Our study revealed differences in health care use amongst children in the care of a child welfare in comparison to those who are not, highlighting the need for improved practice, policy and research initiatives. A collaborative data collection and sharing system is needed to help accurately identify and track the health care use of this vulnerable population.

Breast Core Biopsy Information and Consent: Do we Prepare or do we Scare?

Informed consent has important ethical considerations for invasive procedures. Anecdotal evidence suggests an informed consent policy could heighten anxiety. We evaluated whether detailed information about breast biopsy prior to appointment negatively impacted patient experiences. Phase 1 surveyed patients receiving a standard appointment letter who underwent core biopsy (group A). Phase 2 surveyed two groups receiving standard letter plus biopsy leaflets: those who underwent core biopsy (group B) and those who did not (group C). The analysis included descriptive statistics and qualitative thematic analysis. Hundred percent of group A felt they were given enough information prior to biopsy and 72% felt it would not be helpful having information to read in the clinic beforehand. Hundred percent of group B and 94.1% of group C found it helpful to receive information with their letter. Common themes were good service, verbal explanation, and appreciation of written information. Despite concerns that too much information would heighten anxiety, this has not resulted in negative clinic experiences. Most patients found detailed information included with their appointment letter helpful, regardless of whether they had a biopsy or not.

Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors.

Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols.

Classification of paediatric brain tumours by diffusion weighted imaging and machine learning.

To determine if apparent diffusion coefficients (ADC) can discriminate between posterior fossa brain tumours on a multicentre basis. A total of 124 paediatric patients with posterior fossa tumours (including 55 Medulloblastomas, 36 Pilocytic Astrocytomas and 26 Ependymomas) were scanned using diffusion weighted imaging across 12 different hospitals using a total of 18 different scanners. Apparent diffusion coefficient maps were produced and histogram data was extracted from tumour regions of interest. Total histograms and histogram metrics (mean, variance, skew, kurtosis and 10th, 20th and 50th quantiles) were used as data input for classifiers with accuracy determined by tenfold cross validation. Mean ADC values from the tumour regions of interest differed between tumour types, (ANOVA P < 0.001). A cut off value for mean ADC between Ependymomas and Medulloblastomas was found to be of 0.984 × 10-3 mm2 s-1 with sensitivity 80.8% and specificity 80.0%. Overall classification for the ADC histogram metrics were 85% using Naïve Bayes and 84% for Random Forest classifiers. The most commonly occurring posterior fossa paediatric brain tumours can be classified using Apparent Diffusion Coefficient histogram values to a high accuracy on a multicentre basis.

Strategies for communicating patient health information between emergency and primary care settings: a scoping review protocol.

REVIEW QUESTION: The objective of this scoping review is to explore strategies being used to communicate patient information between emergency and primary care settings. This information will be used as a first step to develop an intervention to improve information exchange and communication between emergency and primary care providers.Specifically the review questions are:i) What tools and strategies are being used to support the communication and exchange of patient information between emergency and primary care settings?ii) What models/frameworks are being used to guide the development of these strategies and tools?iii) What are the identified barriers to exchanging patient information between emergency and primary care settings?iv) What are the outcomes measures reported in these studies?

Allometry and Ecology of the Bilaterian Gut Microbiome.

Classical ecology provides principles for construction and function of biological communities, but to what extent these apply to the animal-associated microbiota is just beginning to be assessed. Here, we investigated the influence of several well-known ecological principles on animal-associated microbiota by characterizing gut microbial specimens from bilaterally symmetrical animals (Bilateria) ranging from flies to whales. A rigorously vetted sample set containing 265 specimens from 64 species was assembled. Bacterial lineages were characterized by 16S rRNA gene sequencing. Previously published samples were also compared, allowing analysis of over 1,098 samples in total. A restricted number of bacterial phyla was found to account for the great majority of gut colonists. Gut microbial composition was associated with host phylogeny and diet. We identified numerous gut bacterial 16S rRNA gene sequences that diverged deeply from previously studied taxa, identifying opportunities to discover new bacterial types. The number of bacterial lineages per gut sample was positively associated with animal mass, paralleling known species-area relationships from island biogeography and implicating body size as a determinant of community stability and niche complexity. Samples from larger animals harbored greater numbers of anaerobic communities, specifying a mechanism for generating more-complex microbial environments. Predictions for species/abundance relationships from models of neutral colonization did not match the data set, pointing to alternative mechanisms such as selection of specific colonists by environmental niche. Taken together, the data suggest that niche complexity increases with gut size and that niche selection forces dominate gut community construction.IMPORTANCE The intestinal microbiome of animals is essential for health, contributing to digestion of foods, proper immune development, inhibition of pathogen colonization, and catabolism of xenobiotic compounds. How these communities assemble and persist is just beginning to be investigated. Here we interrogated a set of gut samples from a wide range of animals to investigate the roles of selection and random processes in microbial community construction. We show that the numbers of bacterial species increased with the weight of host organisms, paralleling findings from studies of island biogeography. Communities in larger organisms tended to be more anaerobic, suggesting one mechanism for niche diversification. Nonselective processes enable specific predictions for community structure, but our samples did not match the predictions of the neutral model. Thus, these findings highlight the importance of niche selection in community construction and suggest mechanisms of niche diversification.

Application of pattern recognition techniques for classification of pediatric brain tumors by in vivo 3T 1 H-MR spectroscopy-A multi-center study.

PURPOSE: 3T magnetic resonance scanners have boosted clinical application of 1 H-MR spectroscopy (MRS) by offering an improved signal-to-noise ratio and increased spectral resolution, thereby identifying more metabolites and extending the range of metabolic information. Spectroscopic data from clinical 1.5T MR scanners has been shown to discriminate between pediatric brain tumors by applying machine learning techniques to further aid diagnosis. The purpose of this multi-center study was to investigate the discriminative potential of metabolite profiles obtained from 3T scanners in classifying pediatric brain tumors. METHODS: A total of 41 pediatric patients with brain tumors (17 medulloblastomas, 20 pilocytic astrocytomas, and 4 ependymomas) were scanned across four different hospitals. Raw spectroscopy data were processed using TARQUIN. Borderline synthetic minority oversampling technique was used to correct for the data skewness. Different classifiers were trained using linear discriminative analysis, support vector machine, and random forest techniques. RESULTS: Support vector machine had the highest balanced accuracy for discriminating the three tumor types. The balanced accuracy achieved was higher than the balanced accuracy previously reported for similar multi-center dataset from 1.5T magnets with echo time 20 to 32 ms alone. CONCLUSION: This study showed that 3T MRS can detect key differences in metabolite profiles for the main types of childhood tumors. Magn Reson Med 79:2359-2366, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Diffusion-weighted MRI and intravoxel incoherent motion model for diagnosis of pediatric solid abdominal tumors.

BACKGROUND: Pediatric retroperitoneal tumors in the renal bed are often large and heterogeneous, and their diagnosis based on conventional imaging alone is not possible. More advanced imaging methods, such as diffusion-weighted (DW) MRI and the use of intravoxel incoherent motion (IVIM), have the potential to provide additional biomarkers that could facilitate their noninvasive diagnosis. PURPOSE: To assess the use of an IVIM model for diagnosis of childhood malignant abdominal tumors and discrimination of benign from malignant lesions. STUDY TYPE: Retrospective. POPULATION: Forty-two pediatric patients with abdominal lesions (n = 32 malignant, n = 10 benign), verified by histopathology. FIELD STRENGTH/SEQUENCE: 1.5T MRI system and a DW-MRI sequence with six b-values (0, 50, 100, 150, 600, 1000 s/mm2 ). ASSESSMENT: Parameter maps of apparent diffusion coefficient (ADC), and IVIM maps of slow diffusion coefficient (D), fast diffusion coefficient (D*), and perfusion fraction (f) were computed using a segmented fitting model. Histograms were constructed for whole-tumor regions of each parameter. STATISTICAL TESTS: Comparison of histogram parameters of and their diagnostic performance was determined using Kruskal-Wallis, Mann-Whitney U, and receiver-operating characteristic (ROC) analysis. RESULTS: IVIM parameters D* and f were significantly higher in neuroblastoma compared to Wilms' tumors (P < 0.05). The ROC analysis showed that the best diagnostic performance was achieved with D* 90th percentile (area under the curve [AUC] = 0.935; P = 0.002; cutoff value = 32,376 × 10-6 mm2 /s) and f mean values (AUC = 1.00; P < 0.001; cutoff value = 14.7) in discriminating between neuroblastoma (n = 11) and Wilms' tumors (n = 8). Discrimination between tumor types was not possible with IVIM D or ADC parameters. Malignant tumors revealed significantly lower ADC, D, and higher D* values than in benign lesions (all P < 0.05). DATA CONCLUSION: IVIM perfusion parameters could distinguish between malignant childhood tumor types, providing potential imaging biomarkers for their diagnosis. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1475-1486.

The importance of health advocacy in Canadian postgraduate medical education: current attitudes and issues.

BACKGROUND: Health advocacy is currently a key component of medical education in North America. In Canada, Health Advocate is one of the seven roles included in the Royal College of Physicians and Surgeons of Canada's CanMEDS competency framework. METHOD: A literature search was undertaken to determine the current state of health advocacy in Canadian postgraduate medical education and to identify issues facing educators and learners with regards to health advocacy training. RESULTS: The literature revealed that the Health Advocate role is considered among the least relevant to clinical practice by educators and learners and among the most challenging to teach and assess. Furthermore learners feel their educational needs are not being met in this area. A number of key barriers affecting health advocacy education were identified including limited published material on the subject, lack of clarity within the role, insufficient explicit role modeling in practice, and lack of a gold standard for assessment. Health advocacy is defined and its importance to medical practice is highlighted, using pediatric emergency medicine as an example. CONCLUSIONS: Increased published literature and awareness of the role, along with integration of the new 2015 CanMEDS framework, are important going forward to address concerns regarding the quality of postgraduate health advocacy education in Canada.

Sleepiness, fatigue, and risk of obstructive sleep apnea using the STOP-BANG questionnaire in multiple sclerosis: a pilot study.

PURPOSE: This study aims: (1) to identify patients with multiple sclerosis (MS) who are at high risk for obstructive sleep apnea (OSA) by utilizing the STOP-BANG questionnaire and (2) to evaluate the relationship between OSA risk as determined by the STOP-BANG questionnaire and self-reported sleepiness and fatigue using the Epworth Sleepiness Scale (ESS) and the Fatigue Severity Scale (FSS), respectively. METHODS: A total of 120 consecutive patients presenting to the UC Davis Neurology MS Clinic were invited to participate in an anonymous survey. The exclusion criteria were: age <18 years, indefinite MS diagnosis, or incomplete survey. RESULTS: There were 103 subjects included in our study: 42% of subjects (n = 43) met the criteria for high-risk OSA, 69% of subjects (n = 71) screened high for fatigue (FSS ≥ 4), but only 24 subjects (23%) screened high for excessive daytime sleepiness (ESS > 10). In males, 44% of the variation in ESS scores and 63% in FSS scores were explained by the STOP-BANG components. However, only 17% of the variation in ESS scores and 15% of the variation in FSS scores was explained by the STOP-BANG components in females. CONCLUSIONS: Over 40% of MS patients were identified as high risk for OSA based on the STOP-BANG questionnaire. The STOP-BANG questionnaire offers clinicians an efficient and objective tool for improving detection of OSA risk in MS patients.

Improving the translation of research into primary care practice: results of a national quality improvement demonstration project.

BACKGROUND: There is widespread evidence of inadequate translation of research findings into primary care practice. Theoretically sound demonstrations of how health care organizations can overcomes these deficiencies are needed. A demonstration project was conducted from January 1, 2003, through June 30, 2006, to evaluate the impact of a multicomponent intervention and improvement models intended to enhance adherence to clinical practice guidelines across eight broad clinical areas. METHODS: The demonstration project involving 530 clinicians and staff members from 99 primary practices in 36 states entailed practice performance reports (audit and feedback), practice site visits for academic detailing and participatory planning, and network meetings for sharing 4 of "best practice" approaches. Data from electronic medical records (EMRs) of 847,073 patients were abstracted to identify 31 process and 5 outcome quality measures for prevention and treatment of cardiovascular disease and diabetes, cancer screening, adult immunization, respiratory and infectious disease, mental health and substance abuse, obesity and nutrition, safe medication prescribing in the elderly, and a summary measure, the Summary Quality Index (SQUID). RESULTS: The yearly adjusted absolute improvement in the SQUID was 2.43% (95% confidence interval [C.I.], 2.24%-2.63%). Clinically and statistically significant improvements occurred for 29 of the 36 quality measures, including all 5 outcome measures. DISCUSSION: The findings suggest that a multicomponent quality improvement intervention involving audit and feedback, academic detailing and participatory planning activities, and sharing of "best practice" approaches in practices with EMRs can have a robust impact in quality of care for Americans seen in primary care practices.

Alcohol screening and brief counseling in a primary care hypertensive population: a quality improvement intervention.

AIMS: To determine the effect of an intervention to improve alcohol screening and brief counseling for hypertensive patients in primary care. DESIGN: Two-year randomized, controlled trial. SETTING/PARTICIPANTS: Twenty-one primary care practices across the United States with a common electronic medical record. INTERVENTION: To promote alcohol screening and brief counseling. Intervention practices received site visits from study personnel and were invited to annual network meetings to review the progress of the project and share improvement strategies. MEASUREMENTS: Main outcome measures included rates of documented alcohol screening in hypertensive patients and brief counseling administered in those diagnosed with high-risk drinking, alcohol abuse or alcohol dependence. Secondary outcomes included change in blood pressure among patients with these diagnoses. FINDINGS: Hypertensive patients in intervention practices were significantly more likely to have been screened after 2 years than hypertensive patients in control practices [64.5% versus 23.5%; adjusted odds ratio (OR) = 8.1; 95% confidence interval (CI) 1.7-38.2; P < 0.0087]. Patients in intervention practices diagnosed with high-risk drinking, alcohol abuse or alcohol dependence were more likely than those in control practices to have had alcohol counseling documented (50.5% versus 29.6%; adjusted OR = 5.5, 95% CI 1.3-23.3). Systolic (adjusted mean decline = 4.2 mmHg, P = 0.036) and diastolic (adjusted mean decline = 3.3 mmHg, P = 0.006) blood pressure decreased significantly among hypertensive patients receiving alcohol counseling. CONCLUSIONS: Primary care practices receiving an alcohol-focused intervention over 2 years improved rates of alcohol screening for their hypertensive population. Implementation of alcohol counseling for high-risk drinking, alcohol abuse or alcohol dependence also improved and led to changes in patient blood pressures.

Stem cells act through multiple mechanisms to benefit mice with neurodegenerative metabolic disease.

Intracranial transplantation of neural stem cells (NSCs) delayed disease onset, preserved motor function, reduced pathology and prolonged survival in a mouse model of Sandhoff disease, a lethal gangliosidosis. Although donor-derived neurons were electrophysiologically active within chimeric regions, the small degree of neuronal replacement alone could not account for the improvement. NSCs also increased brain beta-hexosaminidase levels, reduced ganglioside storage and diminished activated microgliosis. Additionally, when oral glycosphingolipid biosynthesis inhibitors (beta-hexosaminidase substrate inhibitors) were combined with NSC transplantation, substantial synergy resulted. Efficacy extended to human NSCs, both to those isolated directly from the central nervous system (CNS) and to those derived secondarily from embryonic stem cells. Appreciating that NSCs exhibit a broad repertoire of potentially therapeutic actions, of which neuronal replacement is but one, may help in formulating rational multimodal strategies for the treatment of neurodegenerative diseases.

'Trigger' events precede calcium puffs in Xenopus oocytes.

The liberation of calcium ions sequestered in the endoplasmic reticulum through inositol 1,4,5-trisphosphate receptors/channels (IP(3)Rs) results in a spatiotemporal hierarchy of calcium signaling events that range from single-channel openings to local Ca(2+) puffs believed to arise from several to tens of clustered IP(3)Rs to global calcium waves. Using high-resolution confocal linescan imaging and a sensitive Ca(2+) indicator dye (fluo-4-dextran), we show that puffs are often preceded by small, transient Ca(2+) elevations that we christen "trigger events". The magnitude of triggers is consistent with their arising from the opening of a single IP(3) receptor/channel, and we propose that they initiate puffs by recruiting neighboring IP(3)Rs within the cluster by a regenerative process of Ca(2+)-induced Ca(2+) release. Puff amplitudes (fluorescence ratio change) are on average approximately 6 times greater than that of the triggers, suggesting that at least six IP(3)Rs may simultaneously be open during a puff. Trigger events have average durations of approximately 12 ms, as compared to 19 ms for the mean rise time of puffs, and their spatial extent is approximately 3 times smaller than puffs (respective widths at half peak amplitude 0.6 and 1.6 micro m). All these parameters were relatively independent of IP(3) concentration, although the proportion of puffs showing resolved triggers was greatest (approximately 80%) at low [IP(3)]. Because Ca(2+) puffs constitute the building blocks from which cellular IP(3)-mediated Ca(2+) signals are constructed, the events that initiate them are likely to be of fundamental importance for cell signaling. Moreover, the trigger events provide a useful yardstick by which to derive information regarding the number and spatial arrangement of IP(3)Rs within clusters.