Connectome-based predictive modeling for functional recovery of acute ischemic stroke.

Patients of acute ischemic stroke possess considerable chance of recovery of various levels in the first several weeks after stroke onset. Prognosis of functional recovery is important for decision-making in poststroke patient care and placement. Poststroke functional recovery has conventionally been based on demographic and clinical variables such as age, gender, and severity of stroke impairment. On the other hand, the concept of connectome has become a basis of interpreting the functional impairment and recovery of stroke patients. In this research, the connectome-based predictive modeling was used to provide predictive models for prognosing poststroke functional recovery. Predictive models were developed to use the brain connectivity at stroke onset to predict functional assessment scores at one or three months later, or to use the brain connectivity one-month poststroke to predict functional assessment scores at three months after stroke onset. The brain connectivity was computed from the resting-state fMRI signals. The functional assessment scores used in this research included modified Rankin Scale (mRS) and Barthel Index (BI). This research found significant models that used the brain connectivity at onset to predict the mRS one-month poststroke and to predict the BI three-month poststroke for patients with supratentorial infarction, as well as predictive models that used the brain connectivity one-month poststroke to predict the mRS three-month poststroke for patients with supratentorial infarction in the right hemisphere. The connectome-based predictive modeling could provide clinical value in prognosis of acute ischemic stroke.

Automated Cerebral Infarct Detection on Computed Tomography Images Based on Deep Learning.

The limited accuracy of cerebral infarct detection on CT images caused by the low contrast of CT hinders the desirable application of CT as a first-line diagnostic modality for screening of cerebral infarct. This research was aimed at utilizing convolutional neural network to enhance the accuracy of automated cerebral infarct detection on CT images. The CT images underwent a series of preprocessing steps mainly to enhance the contrast inside the parenchyma, adjust the orientation, spatially normalize the images to the CT template, and create a t-score map for each patient. The input format of the convolutional neural network was the t-score matrix of a 16 × 16-pixel patch. Non-infarcted and infarcted patches were selected from the t-score maps, on which data augmentation was conducted to generate more patches for training and testing the proposed convolutional neural network. The convolutional neural network attained a 93.9% patch-wise detection accuracy in the test set. The proposed method offers prompt and accurate cerebral infarct detection on CT images. It renders a frontline detection modality of ischemic stroke on an emergent or regular basis.

High-frequency power of heart rate variability can predict the outcome of thoracic surgical patients with acute respiratory distress syndrome on admission to the intensive care unit: a prospective, single-centric, case-controlled study.

BACKGROUND: The morbidity and mortality of acute respiratory distress syndrome (ARDS) remains high, and the strategic focus of ARDS research has shifted toward identifying patients at high risk of mortality early in the course of illness. This study intended to identify the heart rate variability (HRV) measure that can predict the outcome of patients with ARDS on admission to the surgical intensive care unit (SICU). METHODS: Patients who had lung or esophageal cancer surgery were included either in the ARDS group (n = 21) if they developed ARDS after surgery or in the control group (n = 11) if they did not. The ARDS patients were further stratified into survivors and non-survivors subgroups according to their outcomes. HRV measures of the patients were used for statistical analysis. RESULTS: The mean RR interval (mRRI), high-frequency power (HFP) and product of low-/high-frequency power ratio tidal volume and tidal volume (LHR*VT) were significantly lower (p < 0.05), while the normalized HFP to VT ratio (nHFP/VT) was significantly higher in the ARDS patients (p = 0.011). The total power (TP), low-frequency power (LFP), HFP and HFP/VT were all significantly higher in the non-survived ARDS patients, whereas Richmond Agitation-Sedation Scale (RASS) was significantly lower in the non-survived ARDS patients. After adjustment for RASS, age and gender, firth logistic regression analysis identified the HFP, TP as the significant independent predictors of mortality for ARDS patients. CONCLUSIONS: The vagal modulation of thoracic surgical patients with ARDS was enhanced as compared to that of non-ARDS patients, and the non-survived ARDS patients had higher vagal activity than those of survived ARDS patients. The vagal modulation-related parameters such as TP and HFP were independent predictors of mortality in patients with ARDS on admission to the SICU, and the HFP was found to be the best predictor of mortality for those ARDS patients. Increased vagal modulation might be an indicator for poor prognosis in critically ill patients following thoracic surgery.

Development of carbon-graphene-based aptamer biosensor for EN2 protein detection.

In this study, we developed a screen-printed carbon-graphene-based electrochemical biosensor for EN2 protein detection. The engrailed-2 (EN2) protein, a biomarker for prostate cancer, is known to be a strong binder to a specific DNA sequence (5'-TAATTA-3') to regulate transcription. To take advantage of this intrinsic property, aptamer probes with TAATTA sequence was immobilized onto the screen-printed carbon-graphene electrode surface via EDC-NHS coupling approach. Cyclic voltammetry (CV) of the electrochemical measurement technique was employed for the quantitative detection of EN2 protein. The hindrance to the redox reaction of potassium ferricyanide on the biosensor surface due to the binding of the immobilized aptamer with its target EN2 protein quantified the protein concentration. Under optimum conditions, the aptamer biosensor can detect EN2 protein over a linear range from 35 to 185 nM with a detection limit of 38.5 nM.

Left-Right Asymmetry in Spectral Characteristics of Lung Sounds Detected Using a Dual-Channel Auscultation System in Healthy Young Adults.

Though lung sounds auscultation is important for the diagnosis and monitoring of lung diseases, the spectral characteristics of lung sounds have not been fully understood. This study compared the spectral characteristics of lung sounds between the right and left lungs and between healthy male and female subjects using a dual-channel auscultation system. Forty-two subjects aged 18-22 years without smoking habits and any known pulmonary diseases participated in this study. The lung sounds were recorded from seven pairs of auscultation sites on the chest wall simultaneously. We found that in four out of seven auscultation pairs, the lung sounds from the left lung had a higher total power (PT) than those from the right lung. The PT of male subjects was higher than that of female ones in most auscultation pairs. The ratio of inspiration power to expiration power (RI/E) of lung sounds from the right lung was greater than that from the left lung at auscultation pairs on the anterior chest wall, while this phenomenon was reversed at auscultation pairs on the posterior chest wall in combined subjects, and similarly in both male and female subjects. Though the frequency corresponding to maximum power density of lung sounds (FMPD) from the left and right lungs was not significantly different, the frequency that equally divided the power spectrum of lung sounds (F50) from the left lung was significantly smaller than that from the right lung at auscultation site on the anterior and lateral chest walls, while it was significantly larger than that of from the right lung at auscultation site on the posterior chest walls. In conclusion, significant differences in the PT, FMPD, F50, and RI/E between the left and right lungs at some auscultation pairs were observed by using a dual-channel auscultation system in this study. Structural differences between the left and the right lungs, between the female and male subjects, and between anterior and posterior lungs might account for the observed differences in the spectral characteristics of lung sounds. The dual-channel auscultation system might be useful for future development of digital stethoscopes and power spectral analysis of lung sounds in patients with various kinds of cardiopulmonary diseases.

Reduced enhancement of high-frequency component in the cross spectrum of ECG and nostril airflow signals in patients with chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a chronic airway disease with increased airway resistance. This study investigated the common characteristics of electrocardiographic (ECG) and nostril airflow signals in COPD patients using cross-spectral analysis. Heart rate variability (HRV) measures and cross-spectral (cs) measures of ECG and nostril airflow were compared in COPD patients and normal subjects, and correlated with their clinical characteristics. We found that cross-spectral analysis can lead to a significant increase in normalized high-frequency power (nHFPcs) and a significant decrease in normalized very low-frequency power (nVLFPcs), normalized low-frequency power (nLFPcs), and low-/high-frequency power ratio (LHRcs) in both normal subjects and COPD patients, as compared with their corresponding HRV measures. Further analysis showed that the percentage increase in nHFP (%nHFP) and the percentage decrease in LHR (%LHR) due to cross-spectral analysis in COPD patients were significantly smaller than those of normal subjects. All cross-spectral measures of ECG and nostril airflow in COPD patients did not significantly correlate with their pulmonary function characteristics. However, the nHFPcs correlated significantly and negatively with body mass index (BMI) in both normal subjects and COPD patients, and the %nHFP correlated significantly and negatively with BMI in COPD patients. We conclude that cross-spectral analysis of ECG and nostril airflow signals could lead to reduced enhancement in the high-frequency component in the cross spectrum of COPD patients. The magnitude of reduced enhancement in the high-frequency component in the cross-spectrum was related to the BMI of the patients. Cross-spectral analysis of ECG and nostril airflow might be used to assess the cardiovascular-related functions of COPD patients.

Screen-printed carbon electrode-based electrochemical immunosensor for rapid detection of microalbuminuria.

A urinary microalbumin test is used to detect early signs of kidney damage in people who have a risk of chronic kidney disease, such as those with diabetes or hypertension. In this study, we developed a screen-printed carbon electrode-based immunosensor for the detection of microalbumin in urine. Anti-human albumin antibodies were immobilized on the screen-printed sensor surface by the covalent immobilization method. Cyclic voltammetry (CV) and scanning electron microscopy with an energy dispersive spectroscopical (SEM-EDS) analysis demonstrated that the modification process was well performed. Chronoamperometric (CA) electrochemical measurement technique was employed for the quantitative detection of albumin. The electrochemical measurements performed with some possible interfering compounds normally present in urine (ascorbic acid, uric acid, glucose and creatinine samples) demonstrated a high specificity and selectivity of this immunosensor in albumin detection. Under optimum conditions, the immunosensor can detect urinary albumin in a wide linear range from 10 µg/ml to 300 µg/ml with a detection limit of 9.7 µg/ml. The excellent performance of this immunosensor was confirmed by analyzing microalbumin in urine samples; the results were in good agreement with those obtained by the standard immunoturbidimetric method. The biosensor proposed herein is easy to prepare and can be used for low-cost, rapid, and sensitive screening of microalbuminuria. This approach provides a promising platform for developing clinical point-of-care diagnostic applications.

Impedimetric method for measuring ultra-low E. coli concentrations in human urine.

In this study, we developed an interdigitated gold microelectrode-based impedance sensor to detect Escherichia coli (E. coli) in human urine samples for urinary tract infection (UTI) diagnosis. E. coli growth in human urine samples was successfully monitored during a 12-h culture, and the results showed that the maximum relative changes could be measured at 10Hz. An equivalent electrical circuit model was used for evaluating the variations in impedance characteristics of bacterial growth. The equivalent circuit analysis indicated that the change in impedance values at low frequencies was caused by double layer capacitance due to bacterial attachment and formation of biofilm on electrode surface in urine. A linear relationship between the impedance change and initial E. coli concentration was obtained with the coefficient of determination R(2)>0.90 at various growth times of 1, 3, 5, 7, 9 and 12h in urine. Thus our sensor is capable of detecting a wide range of E. coli concentration, 7×10(0) to 7×10(8) cells/ml, in urine samples with high sensitivity.

Resonant efficiency improvement design of piezoelectric biosensor for bacteria gravimetric sensing.

The piezoelectric biosensor have been widely used in ultra-small mass detection of biomolecular, based on PZT piezoelectric material can create a variety of compositions geometrically; it could widely develop a high-frequency resonator and measure the change of the slightest mass while improve the limited detection simultaneously. Therefore, the piezoelectric biosensor of this study was fabricated by a spin-coating method and backside etching process for improving the characteristic of piezoelectric biosensor. The result exhibited that the 250 µm × 250 µm working size has the most favorable piezoelectric characteristic. The tunability was approximately 38.56 % and it showed that reducing the substrate thickness could obtain a clear resonance signal in a range of 60 to 380 MHz. In theory calculated for gravimetric sensing, it could achieve 0.1 ng sensing sensitivity. In gravimetric sensing, the sensing range was between 50,000~100,000 CFU/ml. Sensing range was lower in clinical urinary tract infection (100,000 CFU/ml), thus demonstrating its usefulness for preventive medicine. It can understand the piezoelectric sensor of this study has potential application in the future for biomedical gravimetric sensing.

Automated segmentation and quantification of white matter hyperintensities in acute ischemic stroke patients with cerebral infarction.

White matter hyperintensities (WMHs) of presumed vascular origin are common in ageing population, especially in patients with acute cerebral infarction and the volume has been reported to be associated with mental impairment and the risk of hemorrhage from antithrombotic agents. WMHs delineation can be computerized to minimize human bias. However, the presence of cerebral infarcts greatly degrades the accuracy of WMHs detection and thus limits the application of computerized delineation to patients with acute cerebral infarction. We propose a computer-assisted segmentation method to depict WMHs in the presence of cerebral infarcts in combined T1-weighted, fluid attenuation inversion recovery, and diffusion-weighted magnetic resonance imaging (MRI). The proposed method detects WMHs by empirical threshold and atlas information, with subtraction of white matter voxels affected by acute infarction. The method was derived using MRI from 25 hemispheres with WMHs only and 13 hemispheres with both WMHs and cerebral infarcts. Similarity index (SI) and correlation were utilized to assess the agreement between the new automated method and a gold standard visually guided semi-automated method done by an expert rater. The proposed WMHs segmentation approach produced average SI, sensitivity and specificity of 83.142±11.742, 84.154±16.086 and 99.988±0.029% with WMHs only and of 68.826±14.036, 74.381±18.473 and 99.956±0.054% with both WMHs and cerebral infarcts in the derivation cohort. The performance of the proposed method with an external validation cohort was also highly consistent with that of the experienced rater.