Neuroimaging anomalies in asymptomatic middle cerebral artery steno-occlusive disease with normal-appearing white matter.

Background: Asymptomatic chronic cerebrovascular steno-occlusive disease is common, but the cognitive function and alterations in the brain's structural and functional profiles have not been well studied. This study aimed to reveal whether and how patients with asymptomatic middle cerebral artery (MCA) steno-occlusive disease and normal-appearing white matter differ in brain structural and functional profiles from normal controls and their correlations with cognitive function. Methods: In all, 26 patients with asymptomatic MCA steno-occlusive disease and 22 healthy controls were compared for neurobehavioral assessments, brain volume, cortical thickness, fiber connectivity density (FiCD) value, and resting-state functional connectivity (FC) using multimodal MRI. We also investigated the associations between abnormal cortical thicknesses, FiCD values, and functional connectivities with the neurobehavioral assessments. Results: Patients performed worse on memory tasks (Auditory Verbal Learning Test-Huashan version) compared with healthy controls. Patients were divided into two groups: the right group (patients with right MCA steno-occlusive disease) and the left group (patients with left MCA steno-occlusive disease). The left group showed significant cortical thinning in the left superior parietal lobule, while the right group showed significant cortical thinning in the right superior parietal lobule and caudal portion of the right middle frontal gyrus. Increased FiCD values in the superior frontal region of the left hemisphere were observed in the left group. In addition, a set of interhemispheric and intrahemispheric FC showed a significant decrease or increase in both the left and right groups. Many functional connectivity profiles were positively correlated with cognitive scores. No correlation was found between cortical thickness, FiCD values, and cognitive scores. Conclusion: Even if the patients with MCA steno-occlusive disease were asymptomatic and had normal-appearing white matter, their cognitive function and structural and functional profiles had changed, especially the FC. Alterations in FC may be an important mechanism underlying the neurodegenerative process in patients with asymptomatic MCA steno-occlusive disease before structural changes occur, so FC assessment may promote the detection of network alterations, which may be used as a biomarker of disease progression and therapeutic efficacy evaluation in these patients.

Shoulder MRI-based radiomics for diagnosis and severity staging assessment of surgically treated supraspinatus tendon tears.

OBJECTIVE: To develop and validate MRI-based radiomics models capable of evaluating supraspinatus tendon tears within the shoulder joints by using arthroscopy as the reference standard. METHODS: A total of 432 patients (332 in the training set and 100 in the external validation set) with intact supraspinatus tendon (n = 202) and supraspinatus tendon tear (n = 230, 130 full-thickness tears and 100 partial-thickness tears) were enrolled. Radiomics features were extracted from fat-saturated T2-weighted coronal images. Two radiomics signature models for detecting supraspinatus tendon abnormalities (tear or not), and stage lesion severity (full- or partial-thickness tear) and radiomics scores (Rad-score), were constructed and calculated using multivariate logistic regression analysis. The diagnostic performance of the two models was validated using ROC curves on the training and validation datasets. RESULTS: For the radiomics model of no tears or tears, thirteen features from MR images were used to build the radiomics signature with an AUC value of 0.98 in the training set, 0.97 in the internal validation set, and 0.98 in the external validation set. For the radiomics model of full- or partial-thickness tears, thirteen features from MR images were used to build the radiomics signature with an AUC value of 0.79 in the training set, 0.69 in the internal validation set, and 0.77 in the external validation set. CONCLUSION: The proposed radiomics models in this study can accurately rule out supraspinatus tendon tears and are capable of assessing the severity staging of tears with moderate accuracy based on shoulder MR images. KEY POINTS: • The radiomics model of no tears or tears achieved a high overall accuracy of 93.6%, sensitivity of 91.6%, and specificity of 95.2% for supraspinatus tendon tears. • The radiomics model of full- or partial-thickness tears displayed moderate performance with an accuracy of 76.4%, a sensitivity of 79.2%, and a specificity of 74.3% for supraspinatus tendon tears severity staging.

Difference of mean Hounsfield units (dHU) between follow-up and initial noncontrast CT scan predicts 90-day poor outcome in spontaneous supratentorial acute intracerebral hemorrhage with deep convolutional neural networks.

OBJECTIVES: This study aimed to investigate the usefulness of a new non-contrast CT scan (NCCT) sign called the dHU, which represented the difference in mean Hounsfield unit values between follow-up and the initial NCCT for predicting 90-day poor functional outcomes in acute supratentorial spontaneous intracerebral hemorrhage(sICH) using deep convolutional neural networks. METHODS: A total of 377 consecutive patients with sICH from center 1 and 91 patients from center 2 (external validation set) were included. A receiver operating characteristic (ROC) analysis was performed to determine the critical value of dHU for predicting poor outcome at 90 days. Modified Rankin score (mRS) >3 or >2 was defined as the primary and secondary poor outcome, respectively. Two multivariate models were developed to test whether dHU was an independent predictor of the two unfavorable functional outcomes. RESULTS: The ROC analysis showed that a dHU >2.5 was a critical value to predict the poor outcomes (mRS >3) in sICH. The sensitivity, specificity, and accuracy of dHU >2.5 for poor outcome prediction were 37.5%, 86.0%, and 70.6%, respectively. In multivariate models developed after adjusting for all elements of the ICH score and hematoma expansion, dHU >2.5 was an independent predictor of both primary and secondary poor outcomes (OR = 2.61, 95% CI [1.32,5.13], P = 0.006; OR = 2.63, 95% CI [1.36,5.10], P = 0.004, respectively). After adjustment for all possible significant predictors (p < 0.05) by univariate analysis, dHU >2.5 had a positive association with primary and secondary poor outcomes (OR = 3.25, 95% CI [1.52,6.98], P = 0.002; OR = 3.42, 95% CI [1.64,7.15], P = 0.001). CONCLUSIONS: The dHU of hematoma based on serial CT scans is independently associated with poor outcomes after acute sICH, which may help predict clinical evolution and guide therapy for sICH patients.

Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis.

Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing a deep neural network to synthesize virtual EUS (V-EUS) from conventional B-mode images. A total of 4580 breast tumor cases were collected from 15 medical centers, including a main cohort with 2501 cases for model establishment, an external dataset with 1730 cases and a portable dataset with 349 cases for testing. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5% after V-EUS is equipped.

Differentiation of Parkinson's disease and Parkinsonism predominant multiple system atrophy in early stage by morphometrics in susceptibility weighted imaging.

Background and purpose: We previously established a radiological protocol to discriminate multiple system atrophy-parkinsonian subtype (MSA-P) from Parkinson's disease (PD). However, we do not know if it can differentiate early stage disease. This study aimed to investigate whether the morphological and intensity changes in susceptibility weighted imaging (SWI) of the lentiform nucleus (LN) could discriminate MSA-P from PD at early stages. Methods: We retrospectively enrolled patients with MSA-P, PD and sex- and age-matched controls whose brain MRI included SWI, between January 2015 and July 2020 at the Movement Disorder Center. Two specialists at the center reviewed the medical records and made the final diagnosis, and two experienced neuroradiologists performed MRI analysis, based on a defined and revised protocol for conducting morphological measurements of the LN and signal intensity. Results: Nineteen patients with MSA-P and 19 patients with PD, with less than 2 years of disease duration, and 19 control individuals were enrolled in this study. We found that patients with MSA- P presented significantly decreased size in the short line (SL) and corrected short line (cSL), ratio of the SL to the long line (SLLr) and corrected SLLr (cSLLr) of the LN, increased standard deviation of signal intensity (SIsd_LN, cSIsd_LN) compared to patients with PD and controls (P < 0.05). With receiver operating characteristic (ROC) analysis, this finding had a sensitivity of 89.5% and a specificity of 73.7% to distinguish MSA- P from PD. Conclusion: Compared to PD and controls, patients with MSA-P are characterized by a narrowing morphology of the posterior region of the LN. Quantitative morphological changes provide a reference for clinical auxiliary diagnosis.

A Pilot Study of Radiomic Based on Routine CT Reflecting Difference of Cerebral Hemispheric Perfusion.

Background: To explore the effectiveness of radiomics features based on routine CT to reflect the difference of cerebral hemispheric perfusion. Methods: We retrospectively recruited 52 patients with severe stenosis or occlusion in the unilateral middle cerebral artery (MCA), and brain CT perfusion showed an MCA area with deficit perfusion. Radiomics features were extracted from the stenosis side and contralateral of the MCA area based on precontrast CT. Two different region of interest drawing methods were applied. Then the patients were randomly grouped into training and testing sets by the ratio of 8:2. In the training set, ANOVA and the Elastic Net Regression with fivefold cross-validation were conducted to filter and choose the optimized features. Moreover, different machine learning models were built. In the testing set, the area under the receiver operating characteristic (AUC) curve, calibration, and clinical utility were applied to evaluate the predictive performance of the models. Results: The logistic regression (LR) for the triangle-contour method and artificial neural network (ANN) for the semiautomatic-contour method were chosen as radiomics models for their good prediction efficacy in the training phase (AUC = 0.869, 0.873) and the validation phase (AUC = 0.793, 0.799). The radiomics algorithms of the triangle-contour and semiautomatic-contour method were implemented in the whole training set (AUC = 0.870, 0.867) and were evaluated in the testing set (AUC = 0.760, 0.802). According to the optimal cutoff value, these two methods can classify the vascular stenosis side class and normal side class. Conclusion: Radiomic predictive feature based on precontrast CT image could reflect the difference of cerebral hemispheric perfusion to some extent.

Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer-A Multicenter Study.

Purpose: To develop a risk stratification system that can predict axillary lymph node (LN) metastasis in invasive breast cancer based on the combination of shear wave elastography (SWE) and conventional ultrasound. Materials and Methods: A total of 619 participants pathologically diagnosed with invasive breast cancer underwent breast ultrasound examinations were recruited from a multicenter of 17 hospitals in China from August 2016 to August 2017. Conventional ultrasound and SWE features were compared between positive and negative LN metastasis groups. The regression equation, the weighting, and the counting methods were used to predict axillary LN metastasis. The sensitivity, specificity, and the areas under the receiver operating characteristic curve (AUC) were calculated. Results: A significant difference was found in the Breast Imaging Reporting and Data System (BI-RADS) category, the "stiff rim" sign, minimum elastic modulus of the internal tumor and peritumor region of 3 mm between positive and negative LN groups (p < 0.05 for all). There was no significant difference in the diagnostic performance of the regression equation, the weighting, and the counting methods (p > 0.05 for all). Using the counting method, a 0-4 grade risk stratification system based on the four characteristics was established, which yielded an AUC of 0.656 (95% CI, 0.617-0.693, p < 0.001), a sensitivity of 54.60% (95% CI, 46.9%-62.1%), and a specificity of 68.99% (95% CI, 64.5%-73.3%) in predicting axillary LN metastasis. Conclusion: A 0-4 grade risk stratification system was developed based on SWE characteristics and BI-RADS categories, and this system has the potential to predict axillary LN metastases in invasive breast cancer.

Radiomics for predicting revised hematoma expansion with the inclusion of intraventricular hemorrhage growth in patients with supratentorial spontaneous intraparenchymal hematomas.

BACKGROUND: Previous radiomics analyses of hematoma expansion have been based on the traditional definition, which only focused on changes in intraparenchymal volume. However, the ability of radiomics-related models to predict revised hematoma expansion (RHE) with the inclusion of intraventricular hemorrhage expansion remains unclear. To develop and validate a noncontrast computed tomography (NCCT)-based clinical- semantic-radiomics nomogram to identify supratentorial spontaneous intracerebral hemorrhage (sICH) patients with RHE on admission. METHODS: In this double-center retrospective study, data from 376 patients with sICH (training set: n=299; test set: n=77; external validation cohort: n=91) were reviewed. A radiomics model, a clinical-semantic model, and a combined model were then constructed based on the logistic regression machine learning approach. Radiomics features were extracted and selected by least absolute shrinkage and selection operator (LASSO) with 5-fold cross validation. Furthermore, the classical BRAIN scoring system was also constructed to predict RHE. Discriminative performance of the models was evaluated on the training and test set with area under the curve (AUC) and decision curve analysis (DCA). RESULTS: The addition of radiomics to clinical-semantic factors significantly improved the prediction performance of RHE compared with the clinical-semantic model alone in the training (AUC, 0.94 vs. 0.81, P<0.05) and test (AUC, 0.84 vs. 0.71, P<0.05) sets, with similar results in the validation set (AUC, 0.83 vs. 0.69, P<0.05). Moreover, the discrimination efficacy of the BRAIN score was significantly lower than the other 3 models (AUC of 0.71 in the training set, P<0.05). CONCLUSIONS: The clinical-semantic-radiomics combined model had the greatest potential for discriminating RHE, and significantly outperformed the classical BRAIN scoring system.

Weight loss in children undergoing allogeneic hematopoietic stem cell transplantation within the first 100 days: Its influencing factors and impact on clinical outcomes.

Purpose/Objective: This study aimed to evaluate the nutritional status of children subjected to allogeneic hematopoietic stem cell transplantation (alloHSCT) in the first 100 days. Objectives were to clarify the effect of weight loss on clinical outcomes, and to analyze factors influencing weight loss. Methods: Eighty pediatric patients receiving alloHSCT were enrolled in the study. Body mass index (BMI) z-scores and weight for age (WFA) z-scores were collected. A multivariate regression model was set up to investigate factors affecting weight loss. Post-transplant clinical outcomes relative to weight loss on 100 days after transplantation were analyzed. Results: At admission, eight patients (10%) were underweight, the number had increased to 23 (30.67%) by 100 days post-HSCT. On day + 100, only nutrition screening tool for childhood cancer (SCAN) scores ≥ 3 (OR: 4.474, 95% CI: 1.215, 16.472; P = 0.024) and acute graft versus host disease (aGVHD) (OR: 9.915, 95% CI: 3.302, 29.771; P < 0.001) were regarded as significant influencing factors of weight loss. The Weight loss ≥ 5% group was associated with longer hospital stays (P = 0.001), greater cost of inpatient treatment (P = 0.001), and a higher incidence of 100-day re-admission and intensive care unit (ICU) transfer (P = 0.03 and P = 0.033, respectively). Cumulative number of fever days (P = 0.023) and antibiotic use (P = 0.007) also increased significantly. The Weight loss ≥ 5% group had a significantly lower one-year overall survival rate compared with the Weight loss < 5% group (P = 0.015). Conclusion: Pediatric patients' nutritional status declined significantly after HSCT. Weight loss within the first 100 days influenced short-term clinical outcomes and one-year overall survival.

Automated detection of 3D midline shift in spontaneous supratentorial intracerebral haemorrhage with non-contrast computed tomography using deep convolutional neural networks.

Deep learning (DL)-based convolutional neural networks facilitate more accurate detection and rapid analysis of MLS. Our objective was to assess the feasibility of applying a DL-based convolutional neural network to non-contrast computed tomography (CT) for automated 2D/3D brain midline shift measurement and outcome prediction after spontaneous intracerebral haemorrhage. In this retrospective study, 140 consecutive patients were referred for CT assessment of sICH from January 2014 to April 2019. The level of consciousness of patients was evaluated using the Glasgow Coma Scale (GCS) score, and the Glasgow Outcome Scale (GOS) score was calculated to classify the outcome. The distance of midline shift (MLS-D) and volume of midline shift (MLS-V) were automatically measured via DL methods. Patients were divided into three groups based on GCS scores: mild degree (GCS score: 13-15), moderate degree (GCS score: 9-12), and severe degree (GCS score: 3-8). Spearman's correlation analysis revealed statistically significant (P<0.01) positive correlation between GCS and MLS-D (r=0.709) and MLS-V (r=0.754). The AUC of MLS-V was slightly larger than that of MLS-D (0.831 vs 0.799, P=0.318) in the midline shifting group. The AUC of MLS-V was significantly larger than that of MLS-D (0.854 vs 0.736, P=0.03) in patients with severe degree GCS scores. The DL-based measurements of both MLS-D and MLS-V enable the assessment of consciousness and the prediction of the outcome of sICH. Compared to MLS-D, MLS-V measurement can better indicate mass effect and predict outcomes, particularly in severe cases.

Substantia Nigra Radiomics Feature Extraction of Parkinson's Disease Based on Magnitude Images of Susceptibility-Weighted Imaging.

BACKGROUND: It is reported that radiomic features extracted from quantitative susceptibility mapping (QSM) had promising clinical value for the diagnosis of Parkinson's disease (PD). We aimed to explore the usefulness of radiomics features based on magnitude images to distinguish PD from non-PD controls. METHODS: We retrospectively recruited PD patients and controls who underwent brain 3.0T MR including susceptibility-weighted imaging (SWI). A total of 396 radiomics features were extracted from the SN of 95 PD patients and 95 non-PD controls based on SWI. Intra-/inter-observer correlation coefficients (ICCs) were applied to measure the observer agreement for the radiomic feature extraction. Then the patients were randomly grouped into training and validation sets in a ratio of 7:3. In the training set, the maximum correlation minimum redundancy algorithm (mRMR) and the least absolute shrinkage and selection operator (LASSO) were conducted to filter and choose the optimized subset of features, and a radiomics signature was constructed. Moreover, radiomics signatures were constructed by different machine learning models. Area under the ROC curves (AUCs) were applied to evaluate the predictive performance of the models. Then correlation analysis was performed to evaluate the correlation between the optimized features and clinical factors. RESULTS: The intro-observer CC ranged from 0.82 to 1.0, and the inter-observer CC ranged from 0.77 to 0.99. The LASSO logistic regression model showed good prediction efficacy in the training set [AUC = 0.82, 95% confidence interval (CI, 0.74-0.88)] and the validation set [AUC = 0.81, 95% CI (0.68-0.91)]. One radiomic feature showed a moderate negative correlation with Hoehn-Yahr stage (r = -0.49, P = 0.012). CONCLUSION: Radiomic predictive features based on SWI magnitude images could reflect the Hoehn-Yahr stage of PD to some extent.

An MSCT-based radiomics nomogram combined with clinical factors can identify Crohn's disease and ulcerative colitis.

BACKGROUND: We established and evaluated a radiomics nomogram based on multislice computed tomography (MSCT) arterial phase contrast-enhanced images to distinguish between Crohn's disease (CD) and ulcerative colitis (UC) objectively, quantitatively, and reproducibly. METHODS: MSCT arterial phase-enhancement images of 165 lesions (99 CD, 66 UC) in 87 patients with inflammatory bowel disease (IBD) confirmed by endoscopy or surgical pathology were retrospectively analyzed. A total of 132 lesions (80%) were selected as the training cohort and 33 lesions (20%) as the test cohort. A total of 1648 radiomic features were extracted from each region of interest (ROI), and the Pearson correlation coefficient and tree-based method were used for feature selection. Five machine learning classifiers, including logistic regression (LR), support vector machine (SVM), random forest (RF), stochastic gradient descent (SGD), and linear discriminative analysis (LDA), were trained. The best classifier was evaluated and obtained, and the results were transformed into the Rscore. Three clinical factors were screened out from 8 factors by univariate analysis. The logistic regression method was used to synthesize the significant clinical factors and the Rscore to generate the nomogram, which was compared with the clinical model and LR model. RESULTS: Among all machine learning classifiers, LR performed the best (AUC =0.8077, accuracy =0.697, sensitivity =0.8, specificity =0.5385), SGD model had the second best performance (AUC =0.8, accuracy =0.6667, sensitivity =0.75, specificity =0.5385), and the DeLong test results showed that there was no significant difference between LR and SGD (P=0.465>0.05), while the other models performed poorly. Texture features had the greatest impact on classification results among all imaging features. The significant features of the LR model were used to calculate the Rscore. The 3 significant clinical factors were perienteric edema or inflammation, CT value of arterial phase-enhancement (AP-CT value), and lesion location. Finally, a nomogram was constructed based on the 3 significant clinical factors and the Rscore, whose AUC (0.8846) was much higher than that of the clinical model (0.6154) and the LR model (0.8077). CONCLUSIONS: The nomogram is expected to provide a new auxiliary tool for radiologists to quickly identify CD and UC.

Serum vitamin D deficiency in children and adolescents is associated with type 1 diabetes mellitus.

BACKGROUND: To investigate the relationship 25-hydroxy vitamin D (25OHD) level among children and in children with type 1 diabetes mellitus (T1DM). METHODS: A case-control study was conducted to compare the serum 25OHD levels between cases and controls. This study recruited 296 T1DM children (106 newly diagnosed T1DM patients and 190 established T1DM patients), and 295 age- and gender-matched healthy subjects as controls. RESULTS: The mean serum 25OHD in T1DM children was 48.69 ± 15.26 nmol/L and in the controls was 57.93 ± 19.03 nmol/L. The mean serum 25OHD in T1DM children was lower than that of controls (P < 0.01). The mean serum 25OHD level (50.42 ± 14.74 nmol/L) in the newly diagnosed T1DM children was higher than that (47.70 ± 15.50 nmol/L) in the established T1DM children but the difference was not statistically significant (P = 0.16). HbA1c values were associated with 25OHD levels in established T1DM children (r = 0.264, P < 0.01), and there was no association between 25OHD and HbA1c in newly diagnosed T1DM children (r = 0.164; P > 0.05). CONCLUSION: Vitamin D deficiency is common in T1DM children, and it should be worthy of attention on the lack of vitamin D in established T1DM children.

CORRELATION OF SERUM VITAMIN D LEVEL WITH TYPE 1 DIABETES MELLITUS IN CHILDREN: A META-ANALYSIS.

OBJECTIVE: to assess the relationship between serums vitamin D level and type 1 diabetes mellitus (T1DM) in children. METHODS: the following electronic databases were searched until Sep 2014 to identify relevant studies that assessed the relationship between serum vitamin D with T1DM: PubMed\EMbase\Medline\Central Register of Controlled Trials\CBM\Chinese National Knowledge Infrastructure (CNKI)\wangfang; The NOS scale was used to evaluate the quality of studies, and the statistical tests were performed by Stata 11.0 software. RESULT: a total of 10 studies were included in this study. Our results showed that serum vitamin D was significantly lower in children with T1DM than in healthy controls (MD = -0.60, P < 0.05). No evidence support publication bias in present study. CONCLUSION: the meta-analysis suggests that serum vitamin D level is associated with T1DM in children.

Objetivo: evaluar la relación entre sueros de vitamina D y la diabetes mellitus tipo 1 (T1DM) en niños. Métodos: fueron utilizadas las siguientes bases de datos electrónicas hasta septiembre de 2014 para identificar los estudios que evaluaron la relación entre la vitamina D y la T1DM: PubMed, EMBASE, MEDLINE, Central Register of Controlled Trials, CBM, China National Knowledge Infrastructure (CNKI), wangfang; las enmiendas a la escala se utilizan para evaluar la calidad de los estudios, y los ensayos estadísticos fueron realizados mediante el software Stata 11.0. Resultado: fueron incluidos un total de 10 estudios. Nuestros resultados mostraron que los niveles séricos de vitamina D fueron significativamente menores en los niños con T1DM que en los controles sanos (MD = -0,60, P < 0,05). No hay evidencia de sesgo de publicación en el presente estudio. Conclusión: el metaanálisis sugiere que la vitamina D está asociada con la T1DM en niños.

Foldable and portable triboelectric-electromagnetic generator for scavenging motion energy and as a sensitive gas flow sensor for detecting breath personality.

An easily foldable and portable triboelectric-electromagnetic generator (TEMG) based on two polymer/Al layers and one copper coil has been designed to harvest ambient mechanical energy, where the copper coil is used both as a spring to achieve contact and separation of triboelectric layers and as a circuit to collect electromagnetic-induced electricity. The output performance of the TEMG is approximately reproducible after being folded many times. The working mechanism is discussed. The output performance of individual triboelectric generator (TEG) and electromagnetic generator (EMG) are systematically investigated. The maximum output current, voltage, and power are obtained to be 32.2 µA, 500 V, and 2 mW for the TEG, and 4.04 mA, 30 mV, and 15.8 µW for the EMG, respectively. The TEG with a higher internal resistance can be used as a current source, while the EMG with a lower resistance can be used as a voltage source. It can be used as a mobile light source via integrating the TEMG in clothes or bags, and as a self-powered gas flow sensor for detecting respiratory rate, which has a potential application in medical diagnoses. The simple structure and easy portability of the TEMG could be used widely in daily life to harvest ambient energy for electronic devices.

Correlation of serum vitamin D level with type 1 diabetes mellitus in children: a meta-analysis / Correlación de los niveles de vitamina D en suero y la diabetes mellitus tipo 1: un metaanálisis

Objective: to assess the relationship between serums vitamin D level and type 1 diabetes mellitus (T1DM) in children. Methods: the following electronic databases were searched until Sep 2014 to identify relevant studies that assessed the relationship between serum vitamin D with T1DM: PubMed, EMbase, Medline, Central Register of Controlled Trials, CBM Chinese National Knowledge Infrastructure (CNKI) wangfang; The NOS scale was used to evaluate the quality of studies, and the statistical tests were performed by Stata 11.0 software. Result: a total of 10 studies were included in this study. Our results showed that serum vitamin D was significantly lower in children with T1DM than in healthy controls (MD = -0.60, P < 0.05). No evidence support publication bias in present study. Conclusion: the meta-analysis suggests that serum vitamin D level is associated with T1DM in children (AU)

Objetivo: evaluar la relación entre sueros de vitamina D y la diabetes mellitus tipo 1 (T1DM) en niños. Métodos: fueron utilizadas las siguientes bases de datos electrónicas hasta septiembre de 2014 para identificar los estudios que evaluaron la relación entre la vitamina D y la T1DM: PubMed, EMBASE, MEDLINE, Central Register of Controlled Trials, CBM, China National Knowledge Infrastructure (CNKI), wangfang; las enmiendas a la escala se utilizan para evaluar la calidad de los estudios, y los ensayos estadísticos fueron realizados mediante el software Stata 11.0. Resultado: fueron incluidos un total de 10 estudios. Nuestros resultados mostraron que los niveles séricos de vitamina D fueron significativamente menores en los niños con T1DM que en los controles sanos (MD = -0,60, P < 0,05). No hay evidencia de sesgo de publicación en el presente estudio. Conclusión: el metaanálisis sugiere que la vitamina D está asociada con la T1DM en niños (AU)

Novel Spiral-Like Electrode Structure Design for Realization of Two Modes of Energy Harvesting.

A planar spiral-like electrodes (PSE) based triboelectric generator has been designed for harvesting rotary mechanical energy to translate into electricity. The performance of the PSE-triboelectric generator with different cycles of spiral-like electrode strip at different rotating speeds is investigated, which demonstrates the open-circuit voltage and short-circuit current of 470 V and 9.0 µA at rotating speed of 500 r/min with three cycles. In addition, a novel coaxially integrated multilayered PSE-triboelectric generator is built, which can enhance the output of the power effectively. The short-circuit current, the open-circuit voltage, and output power reach to 41.55 µA, 500 V, and 11.73 mW, respectively, at rotating speed of 700 r/min. The output power of the multilayered PSE-triboelectric generator can drive 200 LEDs connected in antiparallel and charge a 110 µF commercial capacitor to 6 V in 23 s. Besides, due to the spiral-like electrode structure, the PSE-generator can work simultaneously in the modes of triboelectricity and electromagnetic induced electricity by sticking a small magnet on the rotating disk. The electromagnetic induced output power reaches to 21 µW at a loading resistance of 2 Ω at a rotating rate of 200 r/min. The spiral-like electrode structure not only broadens the electrode structure design but also adds a new function to the electrode.