Growth, seed development and genetic analysis in wild type and Def mutant of Pisum sativum L

Background

Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test.

Results

Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5.

Conclusions

When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing.     

Comparison of Cobb angles measured manually,calculated from 3-D spinal reconstruction,and estimated from torso asymmetry

While scoliotic spinal deformity is traditionally measured by the Cobb angle, we seek to estimate scoliosis severity from the torso surface without X-ray radiation. Here, we measured the Cobb angle in three ways: by protractor from postero-anterior X-ray, by computer from a 3-D digitized model of the vertebral body line, and by neural-network estimation from indices of torso surface asymmetry. The estimates of the Cobb angle by computer and by neural network were equally accurate in 153 records from 52 patients (standard deviation of 6 degrees from the Cobb angle, r=0.93), showing that torso asymmetry reliably predicted spinal deformity. Further improvements in predictive accuracy may require estimation of other 3-D indices of spinal deformity besides the Cobb angle with its wide measurement variability.     

Genetic variant of BNC2 gene is functionally associated with adolescent idiopathic scoliosis in Chinese population

Adolescent idiopathic scoliosis (AIS) is a structural curvature of the spine that was estimated to affect millions of children worldwide. Recent study shows that the functional variant rs10738445 could add to the risk of AIS through the regulation of BNC2 gene. This study aims to investigate whether the rs10738445 of BNC2 gene is a functional susceptible locus for AIS in the Chinese population and to further clarify the association of the BNC2 expression with the curve severity. SNP rs10738445 was genotyped in 1952 patients and 2492 controls, and further replicated in 693 patients and 254 controls. We found that patients have a significantly higher frequency of CC than the controls (21.9 vs. 17.7%, p?=?0.004 for stage 1; 12.6 vs. 7.9%, p?=?0.03 for stage 2). Allele C can significantly add to the risk of AIS with an OR of 1.14–1.24. AIS patients were found to have significantly higher BNC2 expression than the controls. The BNC2 expression was significantly correlated with the curve severity (r?=?0.316, p?=?0.02). In conclusion, our study suggests a functional role of BNC2 in the development and progression of the spinal deformity in AIS.     

Comparison of Cobb Angles Measured Manually,Calculated from 3-D Spinal Reconstruction,and Estimated from Torso Asymmetry

While scoliotic spinal deformity is traditionally measured by the Cobb angle, we seek to estimate scoliosis severity from the torso surface without X-ray radiation. Here, we measured the Cobb angle in three ways: by protractor from postero-anterior X-ray, by computer from a 3-D digitized model of the vertebral body line, and by neural-network estimation from indices of torso surface asymmetry. The estimates of the Cobb angle by computer and by neural network were equally accurate in 153 records from 52 patients (standard deviation of 6° from the Cobb angle, r =0.93 ), showing that torso asymmetry reliably predicted spinal deformity. Further improvements in predictive accuracy may require estimation of other 3-D indices of spinal deformity besides the Cobb angle with its wide measurement variability.     

Is decreased bone mineral density associated with development of scoliosis? A bipedal osteopenic rat model

Background

The etiology of AIS remains unclear, thus various hypotheses concerning its pathomechanism have been proposed. To date, biomechanical modeling has not been used to thoroughly study the influence of the abnormal growth profile (i.e., the growth rate of the vertebral body during the growth period) on the pathomechanism of curve progression in AIS. This study investigated the hypothesis that AIS progression is associated with the abnormal growth profiles of the anterior column of the spine.

Methods

A finite element model of the spinal column including growth dynamics was utilized. The initial geometric models were constructed from the bi-planar radiographs of a normal subject. Based on this model, five other geometric models were generated to emulate different coronal and sagittal curves. The detailed modeling integrated vertebral body growth plates and growth modulation spinal biomechanics. Ten years of spinal growth was simulated using AIS and normal growth profiles. Sequential measures of spinal alignments were compared.

Results

(1) Given the initial lateral deformity, the AIS growth profile induced a significant Cobb angle increase, which was roughly between three to five times larger compared to measures utilizing a normal growth profile. (2) Lateral deformities were absent in the models containing no initial coronal curvature. (3) The presence of a smaller kyphosis did not produce an increase lateral deformity on its own. (4) Significant reduction of the kyphosis was found in simulation results of AIS but not when using the growth profile of normal subjects.

Conclusion

Results from this analysis suggest that accelerated growth profiles may encourage supplementary scoliotic progression and, thus, may pose as a progressive risk factor.     

Pelvis morphology, trunk posture and standing imbalance and their relations to the Cobb angle in moderate and severe untreated AIS

Adolescent idiopathic scoliosis (AIS) is the most common form of scoliosis and usually affects young girls. Studies mostly describe the differences between scoliotic and non-scoliotic girls and focus primarily on a single set of parameters derived from spinal and pelvis morphology, posture or standing imbalance. No study addressed all these three biomechanical aspects simultaneously in pre-braced AIS girls of different scoliosis severity but with similar curve type and their interaction with scoliosis progression. The first objective of this study was to test if there are differences in these parameters between pre-braced AIS girls with a right thoracic scoliosis of moderate (less than 27°) and severe (more than 27°) deformity. The second objective was to identify which of these parameters are related to the Cobb angle progression either individually or in combination of thereof. Forty-five scoliotic girls, randomly selected by an orthopedic surgeon from the hospital scoliosis clinic, participated in this study. Parameters related to pelvis morphology, pelvis orientation, trunk posture and quiet standing balance were measured. Generally moderate pre-brace idiopathic scoliosis patients displayed lower values than the severe group characterized by a Cobb angle greater than 27°. Only pelvis morphology and trunk posture were statistically different between the groups while pelvis orientation and standing imbalance were similar in both groups. Statistically significant Pearson coefficients of correlation between individual parameters and Cobb angle ranged between 0.32 and 0.53. Collectively trunk posture, pelvis morphology and standing balance parameters are correlated with Cobb angle at 0.82. The results suggest that spinal deformity progression is not only a question of trunk morphology distortion by itself but is also related to pelvis asymmetrical bone growth and standing neuromuscular imbalance.     

Detection of somatic mutations by high-resolution DNA melting (HRM) analysis in multiple cancers

Identification of somatic mutations in cancer is a major goal for understanding and monitoring the events related to cancer initiation and progression. High resolution melting (HRM) curve analysis represents a fast, post-PCR high-throughput method for scanning somatic sequence alterations in target genes. The aim of this study was to assess the sensitivity and specificity of HRM analysis for tumor mutation screening in a range of tumor samples, which included 216 frozen pediatric small rounded blue-cell tumors as well as 180 paraffin-embedded tumors from breast, endometrial and ovarian cancers (60 of each). HRM analysis was performed in exons of the following candidate genes known to harbor established commonly observed mutations: PIK3CA, ERBB2, KRAS, TP53, EGFR, BRAF, GATA3, and FGFR3. Bi-directional sequencing analysis was used to determine the accuracy of the HRM analysis. For the 39 mutations observed in frozen samples, the sensitivity and specificity of HRM analysis were 97% and 87%, respectively. There were 67 mutation/variants in the paraffin-embedded samples, and the sensitivity and specificity for the HRM analysis were 88% and 80%, respectively. Paraffin-embedded samples require higher quantity of purified DNA for high performance. In summary, HRM analysis is a promising moderate-throughput screening test for mutations among known candidate genomic regions. Although the overall accuracy appears to be better in frozen specimens, somatic alterations were detected in DNA extracted from paraffin-embedded samples.     

Kinetic Curve Type Assessment for Classification of Breast Lesions Using Dynamic Contrast-Enhanced MR Imaging

ObjectiveThe aim of this study was to employ a kinetic model with dynamic contrast enhancement-magnetic resonance imaging to develop an approach that can efficiently distinguish malignant from benign lesions.ResultsAn average sensitivity of 82%, a specificity of 65%, an area under the receiver operating characteristic curve of 0.76, and a positive predictive value of 82% and negative predictive value of 63% was shown with the kinetic model (p = 0.017, 0.052, 0.068), as compared to an average sensitivity of 80%, a specificity of 55%, an area under the receiver operating characteristic of 0.69, and a positive predictive value of 79% and negative predictive value of 57% with the time-signal intensity curve method (p = 0.003, 0.004, 0.008). The diagnostic consistency of the three radiologists was shown by the κ-value, 0.857 (p<0.001) with the method based on the time-signal intensity curve and 0.826 (p<0.001) with the method of the kinetic model.ConclusionsAccording to the statistic results based on the 46 lesions, the kinetic modeling curve method showed higher sensitivity, specificity, positive and negative predictive values as compared with the time-signal intensity curve method in lesion classification.     

Multimodal Discrimination of Alzheimer’s Disease Based on Regional Cortical Atrophy and Hypometabolism

Structural MR image (MRI) and ¹⁸F-Fluorodeoxyglucose-positron emission tomography (FDG-PET) have been widely employed in diagnosis of both Alzheimer’s disease (AD) and mild cognitive impairment (MCI) pathology, which has led to the development of methods to distinguish AD and MCI from normal controls (NC). Synaptic dysfunction leads to a reduction in the rate of metabolism of glucose in the brain and is thought to represent AD progression. FDG-PET has the unique ability to estimate glucose metabolism, providing information on the distribution of hypometabolism. In addition, patients with AD exhibit significant neuronal loss in cerebral regions, and previous AD research has shown that structural MRI can be used to sensitively measure cortical atrophy. In this paper, we introduced a new method to discriminate AD from NC based on complementary information obtained by FDG and MRI. For accurate classification, surface-based features were employed and 12 predefined regions were selected from previous studies based on both MRI and FDG-PET. Partial least square linear discriminant analysis was employed for making diagnoses. We obtained 93.6% classification accuracy, 90.1% sensitivity, and 96.5% specificity in discriminating AD from NC. The classification scheme had an accuracy of 76.5% and sensitivity and specificity of 46.5% and 89.6%, respectively, for discriminating MCI from AD. Our method exhibited a superior classification performance compared with single modal approaches and yielded parallel accuracy to previous multimodal classification studies using MRI and FDG-PET.     

Smart garment for trunk posture monitoring: A preliminary study

Background

Hueter-Volkmann's law regarding growth modulation suggests that increased pressure on the end plate of bone retards the growth (Hueter) and conversely, reduced pressure accelerates the growth (Volkmann). Literature described the same principle in Rat-tail model. Human spine and its deformity i.e. scoliosis has also same kind of pattern during the growth period which causes wedging in disc or vertebral body.

Methods

This cross sectional study in 150 patients of adolescent idiopathic scoliosis was done to evaluate vertebral body and disc wedging in scoliosis and to compare the extent of differential wedging of body and disc, in thoracic and lumbar area. We measured wedging of vertebral bodies and discs, along with two adjacent vertebrae and disc, above and below the apex and evaluated them according to severity of curve (curve < 30° and curve > 30°) to find the relationship of vertebral body or disc wedging with scoliosis in thoracic and lumbar spine. We also compared the wedging and rotations of vertebrae.

Results

In both thoracic and lumbar curves, we found that greater the degree of scoliosis, greater the wedging in both disc and body and the degree of wedging was more at apex supporting the theory of growth retardation in stress concentration area. However, the degree of wedging in vertebral body is more than the disc in thoracic spine while the wedging was more in disc than body in lumbar spine. On comparing the wedging with the rotation, we did not find any significant relationship suggesting that it has no relation with rotation.

Conclusion

From our study, we can conclude that wedging in disc and body are increasing with progression on scoliosis and maximum at apex; however there is differential wedging of body and disc, in thoracic and lumbar area, that is vertebral body wedging is more profound in thoracic area while disc wedging is more profound in lumbar area which possibly form 'vicious cycle' by asymmetric loading to spine for the progression of curve.     

Modeling analysis of the relationship between atherosclerosis and related inflammatory factors

Objective: To establish early diagnosis model of inflammatory factors for atherosclerosis (AS), providing theoretical evidence for early detection of AS and development of plaques. Methods: Serum samples were collected to detect the inflammatory factors including CysC, Hcy, hs-CRP, UA, FIB, D-D, LP (a), IL-6, SAA, sCD40L and MDA. Using Logistic regression analysis, the inflammatory factors used for modeling were screened out, and then the AS early diagnosis models were established based on receiver operating characteristic (ROC) curve, support vector machine and BP neural network respectively. Results: No significant difference exists between the general materials of two groups. All 11 inflammatory factors had higher level in AS group than in control group. As shown in ROC curve, all inflammatory factors were helpful in AS diagnosis. In terms of sensitivity, UA ranked first (98) and FIB ranked last (55.5); in terms of specificity, UA ranked first (99) and FIB ranked last (78); in terms of area under the curve, UA and SAA ranked first (both were 0.995) and FIB ranked last (0.721). Based on Logistic regression equation, six factors were screened out, including Hcy, Hs-CRP, IL-6, D-D, CysC and MDA. According to classification, the final sixth steps had a prediction accuracy of 99%. When six inflammatory factors included in Logistic regression equation were detected jointly, the sensitivity, specificity and area under the curve were 57%, 97% and 0.821 respectively, while those of the model excluding D-D were 64%, 90% and 0.828, generally superior to results of joint detection including six factors. The ROC curve based on Hcy, Hs-CRP and MDA had a sensitivity of 87%, a specificity of 94% and an area under the curve of 0.869, being inferior to those of the ROC curve based on IL-6, D-D and Cys C, which were 87%, 92% and 0.936 respectively. The accuracy of SVM-AS diagnosis model and BP neural network model were 82.5% and 77.5% respectively. Conclusion: All 11 inflammatory factors are valuable in AS diagnosis. AS early diagnosis models based on Logistic regression analysis, ROC curve, support vector machine and BP neural network possess diagnostic value and can provide reference for clinical diagnosis.     

The flying buttress construct for posterior spinopelvic fixation: a technical note

Background

School screening for adolescent idiopathic scoliosis (AIS) is discussed. The aim of the present study was to describe the point prevalence of AIS and to evaluate the effectiveness of school screening in 12-year- old children.

Methods

Community nurses and physical therapists in the Southern Health region of Norway including about 12000 school children aged 12 years were invited to participate. All participating community nurses and physical therapists fulfilled an educational course to improve their knowledge about AIS and learn the screening procedure including the Adam Forward Bending Test and measurement of gibbus using a scoliometer.

Results

Sub-regions including 4000 school children participated. The prevalence of idiopathic scoliosis defined as a positive Adam Forward Bending Test, gibbus > 7° and primary major curve on radiographs > 10°, was 0.55%. Five children (0.13%) had a major curve > 20°. Bracing was not indicated in any child; all children were post menarche; four had Risser sign of 4, and one with Risser 1 did not have curve progression > 5° at later follow-up. In one of these 5 children however, the major curve progressed to 45° within 7 months after screening and the girl was operated.

Conclusion

The point prevalence of AIS in 12- year old children is in agreement or slightly lower than previous studies. The screening model employed demonstrates acceptable sensitivity and specificity and low referral rates. Screening at the age of 12 years only was not effective for detecting patients with indication for brace treatment.     

Unique corneal tomography features of allergic eye disease identified by OCT imaging and artificial intelligence

The purpose of this study was to assess unique corneal tomographic parameters of allergic eye disease (AED) using optical coherence tomography (OCT) and artificial intelligence (AI). A total of 57 eyes diagnosed with AED were included. The curvature and aberrations of the air‐epithelium (A‐E) and epithelium‐Bowman's layer (E‐B) interfaces were calculated. Random forest AI models were built combing this data with the parameters of healthy, forme fruste keratoconus (FFKC) and KC eyes. The AI models were cross‐validated with 3‐fold random sampling. Each model was limited to 10 trees. The AI model incorporating both A‐E and E‐B parameters provided the best classification of AED eyes (area under the curve = 0.958, sensitivity = 80.7%, specificity = 98.5%, precision = 88.2%). Further, the E‐B interface parameters provided the highest information gain in the AI model. A few AED eyes (n = 9) had tomography parameters similar to FFKC and KC eyes and may be at risk of progression to KC.     

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ₂, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.     

Accuracy of i-Scan for Optical Diagnosis of Colonic Polyps: A Meta-Analysis

Backgroundi-Scan is a novel virtual chromoendoscopy system designed to enhance surface and vascular patterns to improve optical diagnostic performance. Numerous prospective studies have been done to evaluate the accuracy of i-Scan in differentiating colonic neoplasms from non-neoplasms. i-Scan could be an effective endoscopic technique for optical diagnosis of colonic polyps.ObjectiveOur aim of this study was to perform a meta-analysis of published data to establish the diagnostic accuracy of i-Scan for optical diagnosis of colonic polyps.MethodsWe searched PubMed, Medline, Elsevier ScienceDirect and Cochrane Library databases. We used a bivariate meta-analysis following a random effects model to summarize the data and plotted hierarchical summary receiver-operating characteristic (HSROC) curves. The area under the HSROC curve (AUC) serves as an indicator of the diagnostic accuracy.ResultsThe meta-analysis included a total of 925 patients and 2312 polyps. For the overall studies, the area under the HSROC curve was 0.96. The summary sensitivity was 90.4% (95%CI 85%-94.1%) and specificity was 90.9% (95%CI 84.3%-94.9%). In 11 studies predicting polyps histology in real-time, the summary sensitivity and specificity was 91.5% (95%CI 85.7%-95.1%) and 92.1% (95%CI 84.5%-96.1%), respectively, with the AUC of 0.97. For three different diagnostic criteria (Kudo, NICE, others), the sensitivity was 86.3%, 93.0%, 85.0%, respectively and specificity was 84.8%, 94.4%, 91.8%, respectively.ConclusionsEndoscopic diagnosis with i-Scan has accurate optical diagnostic performance to differentiate neoplastic from non-neoplastic polyps with an area under the HSROC curve exceeding 0.90. Both the sensitivity and specificity for diagnosing colonic polyps are over 90%.     

The analysis of placement values for evaluating discriminatory measures

The idea of using measurements such as biomarkers, clinical data, or molecular biology assays for classification and prediction is popular in modern medicine. The scientific evaluation of such measures includes assessing the accuracy with which they predict the outcome of interest. Receiver operating characteristic curves are commonly used for evaluating the accuracy of diagnostic tests. They can be applied more broadly, indeed to any problem involving classification to two states or populations (D= 0 or 1). We show that the ROC curve can be interpreted as a cumulative distribution function for the discriminatory measure Y in the affected population (D= 1) after Y has been standardized to the distribution in the reference population (D= 0). The standardized values are called placement values. If the placement values have a uniform(0, 1) distribution, then Y is not discriminatory, because its distribution in the affected population is the same as that in the reference population. The degree to which the distribution of the standardized measure differs from uniform(0, 1) is a natural way to characterize the discriminatory capacity of Y and provides a nontraditional interpretation for the ROC curve. Statistical methods for making inference about distribution functions therefore motivate new approaches to making inference about ROC curves. We demonstrate this by considering the ROC-GLM regression model and observing that it is equivalent to a regression model for the distribution of placement values. The likelihood of the placement values provides a new approach to ROC parameter estimation that appears to be more efficient than previously proposed methods. The method is applied to evaluate a pulmonary function measure in cystic fibrosis patients as a predictor of future occurrence of severe acute pulmonary infection requiring hospitalization. Finally, we note the relationship between regression models for the mean placement value and recently proposed models for the area under the ROC curve which is the classic summary index of discrimination.     

The transformation of spinal curvature into spinal deformity: pathological processes and implications for treatment

Background

Chêneau-Brace treatment of a certain standard reduces the rate of surgery, prevents progression and in a certain patient population leads to marked improvement of Cobb angle and cosmetic appearance. During the last two years a patient refusing surgery with a double major curvature of initially 60° showed a clear cosmetic improvement and a clear radiological progression at the same time. The findings of this patient have been reviewed in order to find out how cosmetic appearance and Cobb angle can develop differently.

Methods

The patient entered conservative treatment at the age of 13 years, premenarchial with Tanner II and a Cobb angle of 60° thoracic and 59° lumbar. The angle of trunk rotation (ATR; Scoliometer) was 13° thoracic and 13° lumbar. We have documented the findings of this patient (Surface topography, ATR, Cobb angles and angles of vertebral rotation (according to Raimondi) during the treatment period (27 Month) until 2 years after the onset of menarche.

Results

After a treatment time of 27 Month the Cobb angle increased to 74° thoracic and 65° lumbar. The angles of vertebral rotation according to Raimondi increased slightly from 26° thoracic and 28° lumbar to 30° thoracic and 28° lumbar. The ATR improved to 12° thoracic and 5° lumbar while Lateral deviation improved from 22,4 mm to 4,6 mm and average surface rotation improved from 10,6° to 6°. In the X-rays a reduction of decompensation was visible. The patient felt comfortable with the cosmetic result.

Conclusion

Conservative treatment may improve cosmetic appearance while the curve progresses radiologically. This could be explained by assuming that (1) the Rigo Chêneau brace is able to improve cosmetic appearance by changing the shape of the thorax when the curve itself is too stiff to be corrected by a brace, that (2) reduction of decompensation leads to significant cosmetical improvements or (3) that the patient gained weight and therefore the deformation is masked. However, the weight the patient gained cannot explain the cosmetical improvement in this case. Conservative treatment with a certain standard of quality seems a viable alternative for patients with Cobb angles of > 60° when surgical treatment is refused. Specialists in scoliosis management should be aware of the fact that curve progression can occur even if the clinical measurements show an improvement.     

Ear examination

Adolescent idiopathic scoliosis is the single most common form of spinal deformity seen in orthopedic practice. Our knowledge about the epidemiology, etiology, natural history, and treatment has recently increased dramatically. The incidence of small curves is rather high (2% of the population), whereas severe curves are much less common (<0.1%), but we cannot always predict which curve will progress. Abnormalities of the neuromuscular system and of calcium metabolism, and certain growth, genetic, and mechanical factors may all play roles in the pathogenesis of the disorder. The physiologic secondary effects of severe scoliosis relate to restrictive lung disease, but most patients do not have a deformity great enough to affect their cardiorespiratory function. The psychological and social effects of scoliosis are significant for patients but difficult to quantitate. For most patients with moderate scoliosis—that is, more than 25 to 30 degrees—treatment with an underarm brace or electrical stimulation is adequate to “control” progression of the curve. Surgical fusion allows actual correction of the curve but is indicated in only a small percentage of patients—usually those with more than 50 degrees of deformity.     

Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features.     

Roles of ST2, IL‐33 and BNP in predicting major adverse cardiovascular events in acute myocardial infarction after percutaneous coronary intervention

This study investigated roles of serum ST2, IL‐33 and BNP in predicting major adverse cardiovascular events (MACEs) in acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI). Blood samples were collected from the included AMI patients (n = 180) who underwent PCI. All patients were divided into the MACEs and MACEs‐free groups. Enzyme‐linked immunosorbent assay was performed to measure serum levels of ST2, IL‐33 and BNP. Severity of coronary artery lesion was evaluated by Gensini score. Pearson correlation analysis was used. A receiver operating characteristics curve was drawn to evaluate the potential roles of ST2, IL‐33 and BNP in predicting MACEs, and Kaplan–Meier curve to analyse the 1‐year overall survival rate. Logistic regression analysis was conducted to analyse the independent risk factors for MACEs. Compared with the MACEs‐free group, the serum levels of ST2, IL‐33 and BNP were significantly higher in the MACEs group. Serum levels of ST2, IL‐33 and BNP were positively correlated with each other and positively correlated with Gensini score. The area under curves of ST2, IL‐33 and BNP, respectively, were 0.872, 0.675 and 0.902. The relative sensitivity and specificity were, respectively, 76.27% and 85.92%, 69.49% and 58.68%, as well as, 96.61% and 77.69%. Serum levels of ST2, IL‐33 and BNP were independent risk factors for MACEs. The 1‐year overall survival rate was higher in AMI patients with lower serum levels of ST2, IL‐33 and BNP. In conclusion, serum levels of ST2, IL‐33 and BNP have potential value in predicting MACEs in AMI patients undergoing PCI.