Hutchinson-Gilford progeria syndrome: oral and craniofacial phenotypes

Objective:  Hutchinson-Gilford progeria syndrome (HGPS) is a rare early-onset accelerated senescence syndrome. In HGPS, a recently identified de novo dominant mutation of the lamin A gene ( LMNA ) produces abnormal lamin A, resulting in compromised nuclear membrane integrity. Clinical features include sclerotic skin, cardiovascular and bone abnormalities, and marked growth retardation. Craniofacial features include 'bird-like' facies, alopecia, craniofacial disproportion, and dental crowding. Our prospective study describes dental, oral soft tissue, and craniofacial bone features in HGPS.
Methods:  Fifteen patients with confirmed p.G608G LMNA mutation (1–17 years, seven males, eight females) received comprehensive oral evaluations. Anomalies of oral soft tissue, gnathic bones, and dentition were identified.
Results:  Radiographic findings included hypodontia ( n  = 7), dysmorphic teeth ( n  = 5), steep mandibular angles ( n  = 11), and thin basal bone ( n  = 11). Soft tissue findings included ogival palatal arch ( n  = 8), median sagittal palatal fissure ( n  = 7), and ankyloglossia ( n  = 7). Calculated dental ages (9 months to 11 years 2 months) were significantly lower than chronological ages (1 year 6 months to 17 years 8 months) ( P  = 0.002). Eleven children manifested a shorter mandibular body, anterior/posterior cranial base and ramus, but a larger gonial angle, compared to age/gender/race norms.
Conclusion:  Novel oral-craniofacial phenotypes and quantification of previously reported features are presented. Our findings expand the HGPS phenotype and provide additional insight into the complex pathogenesis of HGPS.     

Civil defence in a nuclear disaster

Any nuclear war would be horrific and our main aim should be universal abolition of nuclear weapons. Civil defence, with its medical attributes, could certainly increase the survival rate should a disaster occur. The effect of the explosion of a nuclear warhead is outlined, together with what could be done to reduce casualties. Nuclear winter is discussed and it is suggested that the results of computerization of doubtful surmises have been treated too much as proven facts. The possibility of its occurrence should not deter emergency planning. Civil defence can save lives, and the fact that medicine as we know it would cease to exist in an all‐out nuclear war does not excuse us from doing what we can to increase the survival rate, if the worst should happen. Action should therefore be taken now to plan decentralization of resources and to instruct the public in protection, first aid and self‐sufficiency.     

Determinants of Outbreak Detection Performance

Objective

To predict the performance of outbreak detection algorithms under different circumstances which will guide the method selection and algorithm configuration in surveillance systems, to characterize the dependence of the performance of detection algorithms on the type and severity of outbreak, to develop quantitative evidence about determinants of detection performance.

Introduction

The choice of outbreak detection algorithm and its configuration can result in important variations in the performance of public health surveillance systems. Our work aims to characterize the performance of detectors based on outbreak types. We are using Bayesian networks (BN) to model the relationships between determinants of outbreak detection and the detection performance based on a significant study on simulated data.

Methods

The simulated surveillance data that we used was generated by Surveillance Lab of McGill University using Simulation Analysis Platform [1] considering surveillance in an urban area to detect waterborne outbreaks due to the failure of a water treatment plant. We focus on predicting the performance of the C-family of algorithms, because they are widely used, state-of-art outbreak detection algorithms [2]. We investigate the influence of algorithm characteristics and outbreak characteristics in determining outbreak detection performance. The C1, C2, and C3 are distinguished by the configuration of 2 parameters,the guardband and memory. Generally, gradually increasing outbreaks can bias the test statistic upward, so the detection algorithm will fail to flag the outbreak. To avoid this situation, the C2 and C3 use a 2-day gap, guardband, between the baseline interval and the test interval. The C3 includes 2 recent observations, called memory, in the computation of the test statistic. The W2 algorithm is a modified version of the C2 which takes weekly patterns of surveillance time series into account [3]. In the W2, the baseline data is stratified to 2 distinct baselines: one for weekdays, the other for weekends. The W3 includes 2 recent observations of each baseline while calculating the test statistic in the corresponding baseline.We ran the C1, C2, C3, W2, and W3 on 18k simulated time series and measured the sensitivity and specificity of detection. Then we created the training data set of 5400000 instances. Each instance was the result of performance evaluation of an outbreak detection algorithm with a specific setting of parameters. In order to investigate the determinants of detection performance and reveal their effects quantitatively, we used BN to predict the performance based on algorithm characteristics and outbreak characteristics.

Results

We developed 2 BN models in the Weka machine learning software [4] using 5-fold cross-validation. The first BN determines the effect of the guardband, memory, alerting threshold, and the weekly pattern indicator (0 for C-algorithms, 1 for W-algorithms) and outbreak characteristics (contamination level and duration) on the sensitivity of detection. The value of sensitivity was mapped to 4 classes: (0, 0.3], (0.3, 0.6], (0.6, 0.9], (0.9, 1]. The developed BN correctly classified 67.74% of instances. The misclassification error was 0.9407. The second BN for predicting the specificity of detection correctly classified 95.895% of instances in 10 classes and the misclassification error was 0.2975.

Conclusions

The contamination level and duration of outbreaks, alerting threshold, memory, guardband, and whether the weekly pattern was considered or not influence the sensitivity and specificity of outbreak detection and given the C-algorithm parameter settings, we can predict outbreak detection performance quantitatively. In future work, we plan to investigate other predictors of performance and study how these predictions can be used in algorithm and policy choices.     

红细胞保存期影响早产儿疗效的随机对照试验:研究设计

尽管目前趋于逐渐降低输血阅值,并开发技术避免输注异体血液,但异体红细胞输注仍是罹患疾病和贫血的新生儿重症监护患者一个重要的支持和挽救生命的方法.     

Citrus auraptene suppresses cyclin D1 and significantly delays N-methyl nitrosourea induced mammary carcinogenesis in female Sprague-Dawley rats

Background  

Breast cancer is a major problem in the United States leading to tens of thousands of deaths each year. Although citrus auraptene suppresses cancer in numerous rodent models, its role in breast cancer prevention previously has not been reported. Thus, our goal was to determine the anticarcinogenic effects of auraptene against breast cancer.     

Deoxygenation-induced changes in sickle cell-sickle cell adhesion

The tendency for sickle cells to adhere to each other is increased in oxygenated sickle blood in parallel with cell density. The increased adherence of these cells occurred despite their reduced deformability and diminished ability to form rouleaux. Using a method developed in our laboratory, we measured the yield stress: a sensitive index of cell- cell adhesion of deoxygenated suspensions of sickle cells. Deoxygenation of whole sickle blood to 30 to 50 mm Hg caused a significant increase in yield stress of all sickle blood samples. Deoxygenation caused a significant increase in yield stress of both dense and light sickle cells. Deoxygenation-induced increases in yield stress occurred at higher oxygen tensions for dense (> 55 mm Hg) than for light sickle cells (< 45 mm Hg). The increase in yield stress on deoxygenation was correlated with hemoglobin polymerization as assessed morphologically by sickling or by changes in relative viscosity. Thus, deoxygenation-induced cell sticking must involve small areas of strong membrane adhesion because the changes in yield stress occurred despite a reduction in rouleaux formation and surface area of membrane contact. Sickle trait red blood cells also exhibited increased yield stress on deoxygenation but only under hypertonic conditions where sickling occurred. Thus, deoxygenation-induced cell adhesion did not require prior membrane damage because it occurred in sickle trait cells. No change in yield stress was seen when deoxygenated sickle cells were suspended in buffer, but the addition of physiologic amounts of fibrinogen to buffer restored the deoxygenation-induced increase in cell adhesion. We speculate that the increase in sticking among sickle cells on deoxygenation results from spicule formation and may involve interaction of fibrinogen and possibly other plasma proteins with the cell membrane.