Harmonizing three-dimensional MRI using pseudo-warping field guided GAN.

In pursuit of cultivating automated models for magnetic resonance imaging (MRI) to aid in diagnostics, an escalating demand for extensive, multisite, and heterogeneous brain imaging datasets has emerged. This potentially introduces biased outcomes when directly applied for subsequent analysis. Researchers have endeavored to address this issue by pursuing the harmonization of MRIs. However, most existing image-based harmonization methods for MRI are tailored for 2D slices, which may introduce inter-slice variations when they are combined into a 3D volume. In this study, we aim to resolve inconsistencies between slices by introducing a pseudo-warping field. This field is created randomly and utilized to transform a slice into an artificially warped subsequent slice. The objective of this pseudo-warping field is to ensure that generators can consistently harmonize adjacent slices to another domain, without being affected by the varying content present in different slices. Furthermore, we construct unsupervised spatial and recycle loss to enhance the spatial accuracy and slice-wise consistency across the 3D images. The results demonstrate that our model effectively mitigates inter-slice variations and successfully preserves the anatomical details of the images during the harmonization process. Compared to generative harmonization models that employ 3D operators, our model exhibits greater computational efficiency and flexibility.

Development of a novel six DNA damage response-related prognostic signature in osteosarcoma.

DNA damage response (DDR) plays a vital role in the development of cancer. Nevertheless, in osteosarcoma, the potential of DDR-related genes (DDRGs) remains unclear. Thus, the current research is intended to investigate the mechanisms of DDRGs in the development of osteosarcoma and to explore potential DDR-related biomarkers in forecasting the prognosis of osteosarcoma patients. The osteosarcoma genomic data from TCGA, GEO and cBioPortal databases were utilized for screening and identification of differentially expressed DDRGs (DEDDRGs). Consensus clustering analysis was performed to identify different subtypes of osteosarcoma based on the expressions of DDRGs. Key DEDRRGs were identified by overlapping DEDRRGs between different subtypes and DEDRRGs between tumor and control samples. Univariate, as well as LASSO regressions, were further applied to obtain robust prognostic signatures. GSVA and ssGSEA analysis were implemented to explore the underlying mechanisms of prognostic DDRG signature in regulating osteosarcoma. In addition, the drug sensitivity of patients in low- and high-risk groups was evaluated using pRRophetic algorithm. A total of 43 key DEDRRGs were identified. Followed by univariate Cox along with LASSO regression analyses, CDK6, CSF1R, EGFR, ERBB4, GATA3 and SOCS1 were identified as prognostic signatures in osteosarcoma. Cox regressions revealed that the risk score was an independent prognostic factor in osteosarcoma.  DDR may affect osteosarcoma via regulating immune microenvironment along with influencing cell proliferation, migration, adhesion and apoptosis. The chemotherapeutic response between patients in low- and high-risk groups was much different. The role of DDRGs in osteosarcoma and identified six DDR-linked biomarkers for forecasting the prognosis of osteosarcoma patients. Our outcomes enhanced the understanding of DDR-related molecular mechanisms involved in osteosarcoma and provided potential therapeutic targets for osteosarcoma patients.

Association of Different Types of Osteoarthritis with Different Immune-Related Subtypes and Hub Genes.

Background: Osteoarthritis (OA) is a diverse disorder that most frequently affects elderly people and makes them disabled. Many investigations have shown that the etiology of OA depends on cartilage wear, but immunology also plays a significant role. Thus, the goal of this study was to define the immune-related etiology of OA. Methods: Data from the "Gene Expression Omnibus (GEO)" database were used to find differentially expressed genes (DEGs), and the "Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm" was employed to calculate the quantity of distinct immune cells. We analyzed the results to identify patient subgroups and compare major active pathways. Results: The macrophage cell population accounts for the greatest percentage of infiltrating immune cells in OA. One hundred and twenty-two common intersection genes were identified, with the network analysis of protein-protein interactions revealing ten hub genes related to OA, including CXCL8, JUN, ATF3, DUSP1, PTGS2, IL6, MMP9, FOS, NFKBIA, and MYC. The random forest model showed that memory-activated CD4 T cells are strongly correlated with other immune cell types, while neutrophils have the weakest correlation with other immune cell types. Violin plots showed that OA patients had a significantly larger quantity of plasma cells and resting mast cells, with a significantly smaller quantity of resting memory CD4 T cells and activated mast cells than healthy controls. Conclusions: Two immune-related subgroups of OA were identified by semi-supervised clustering analysis of microarray data, and core genes were also determined by network analysis. A group of the immune infiltrating cells was selected by random forest analysis suggesting they are related to the pathogenesis of OA.

Simultaneous detection of dental caries and fissure sealant in intraoral photos by deep learning: a pilot study.

BACKGROUND: Deep learning, as an artificial intelligence method has been proved to be powerful in analyzing images. The purpose of this study is to construct a deep learning-based model (ToothNet) for the simultaneous detection of dental caries and fissure sealants in intraoral photos. METHODS: A total of 1020 intraoral photos were collected from 762 volunteers. Teeth, caries and sealants were annotated by two endodontists using the LabelMe tool. ToothNet was developed by modifying the YOLOX framework for simultaneous detection of caries and fissure sealants. The area under curve (AUC) in the receiver operating characteristic curve (ROC) and free-response ROC (FROC) curves were used to evaluate model performance in the following aspects: (i) classification accuracy of detecting dental caries and fissure sealants from a photograph (image-level); and (ii) localization accuracy of the locations of predicted dental caries and fissure sealants (tooth-level). The performance of ToothNet and dentist with 1year of experience (1-year dentist) were compared at tooth-level and image-level using Wilcoxon test and DeLong test. RESULTS: At the image level, ToothNet achieved an AUC of 0.925 (95% CI, 0.880-0.958) for caries detection and 0.902 (95% CI, 0.853-0.940) for sealant detection. At the tooth level, with a confidence threshold of 0.5, the sensitivity, precision, and F1-score for caries detection were 0.807, 0.814, and 0.810, respectively. For fissure sealant detection, the values were 0.714, 0.750, and 0.731. Compared with ToothNet, the 1-year dentist had a lower F1 value (0.599, p < 0.0001) and AUC (0.749, p < 0.0001) in caries detection, and a lower F1 value (0.727, p = 0.023) and similar AUC (0.829, p = 0.154) in sealant detection. CONCLUSIONS: The proposed deep learning model achieved multi-task simultaneous detection in intraoral photos and showed good performance in the detection of dental caries and fissure sealants. Compared with 1-year dentist, the model has advantages in caries detection and is equivalent in fissure sealants detection.

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates.

OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. METHODS: In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. RESULTS: Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. CONCLUSIONS: Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. CLINICAL RELEVANCE STATEMENT: Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. KEY POINTS: •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates.

Incorporation of trans-rectal color doppler flow imaging and risk-stratification nomogram reduce unnecessary prostate biopsies in suspected prostate cancer patients: a bi-centered retrospective validation study.

BACKGROUND: To explore the role of Trans-rectal Color Doppler Flow Imaging (TR-CDFI) and risk-stratification nomogram in a MRI-directed biopsy pathway and examine its clinical performance, via comparisons between existing four biopsy pathways. METHODS: A Bi-centered retrospective cohort study on biopsy-naïve male population who received ultrasound-guided prostate biopsy from Jan. 2015 to Feb. 2022 was proposed. All enrolled patients should have undergone serum-PSA test, TR-CDFI and multiparametric MRI before biopsy, and subsequently opted for surgical intervention, enabling more accurate pathological grading. We then utilized univariate and multivariate logistic regression analysis to construct a predictive nomogram for risk-stratification. Outcome measurements were overall prostate cancer (PCA) detection rate, clinically significant PCA (csPCA) detection rate, clinically insignificant PCA (cisPCA) detection rate, biopsy avoidance rate and missed csPCA detection rate. Decision curve analysis was used to compare the performances between diagnostic pathways. RESULTS: Under the criteria mentioned above, 752 patients from two centers were included. Reference pathway (biopsy for all) showed that overall PCA detection rate was 46.1%, csPCA and cisPCA detection rates were 32.3% and 13.8% respectively. Risk-based MRI-directed TR-CDFI pathway, which incorporated both TR-CDFI and risk stratification nomogram, exhibited PCA detection rate of 38.7%, csPCA detection rate of 28.7%, cisPCA detection rate of 7.0%, Biopsy avoidance rate of 42.4%, and missed csPCA detection rate of 3.6%. Decision curve analysis revealed that the risk-based pathway held the most net benefit, under the threshold probability level between 0.1 and 0.5. CONCLUSIONS: The risk-based MRI-directed TR-CDFI pathway out-performed other strategies, balancing csPCA detection and biopsy avoidance. This suggested that incorporation of TR-CDFI and risk-stratification nomogram in the early PCA diagnostic procedures could reduce unnecessary biopsies.

Clinical and radiographic outcomes of oblique lumbar interbody fusion with anterolateral screw and rod instrumentation in osteopenia patients: a retrospective study.

PURPOSE: The purpose of this paper is to evaluate the clinical and radiographic outcomes of oblique lumbar interbody fusion (OLIF) to perform in L4/5 degenerative lumbar spondylolisthesis (DLS) patients who diagnosed with osteopenia. METHODS: From December 2018 to 2021 March, 94 patients were diagnosed with degenerative spondylolisthesis underwent OLIF and divided into two groups with different bone mineral density. Anterolateral screw and rod instrumentation was applied in two groups. The primary outcomes were VAS, JOA and ODI. The secondary outcomes included disc height (DH), cross-sectional height of the intervertebral foramina (CSH), cross-sectional area of the dural sac (CSA), lumbar lordorsis (LL), pelvic titlt (PT), pelvic incidence (PI) and sacrum slop (SS). RESULTS: All patients finished at least 1 years follow-up with 21.05 ± 4.42 months in the group A and 21.09 ± 4.28 months in the group B. The clinical symptoms were evaluated by VAS, JOA and ODI and 94 patients showed good outcomes at final follow-up (P < 0.05), with significant increases in DH, CSH and CSA. In group A, DH increased from 8.54 ± 2.48 to 11.11 ± 2.63 mm, while increased from 8.60 ± 2.29 to 11.23 ± 1.88 were recorded in group B. No statistical difference was found in DH between the two groups (P > 0.05). The cage subsidence was 1.14 ± 0.83 mm in group A and 0.87 ± 1.05 mm in group B (P > 0.05). There was no significant difference in the adjusted parameters of spino-pelvic between two groups (P > 0.05). CONCLUSION: Oblique lumbar interbody fusion with anterolateral screw and rod instrumentation is feasible to be performed in osteopenia patients who diagnosed with degenerative spondylolisthesis.

Microstructural brain abnormalities of children of idiopathic generalized epilepsy with generalized tonic-clonic seizure: a voxel-based diffusional kurtosis imaging study.

PURPOSE: To investigate the diffusion abnormalities in the brain of children with idiopathic generalized epilepsy (IGE) with generalized tonic-clonic seizure (GTCS) by using diffusion kurtosis imaging (DKI). MATERIALS AND METHODS: Twenty-one IGE children with GTCS and 16 controls were recruited. DKI was performed and maps of radial diffusivity (λ⊥ ), axial diffusivity (λ// ), mean diffusivity (MD), fractional anisotropy (FA), radial kurtosis (K⊥ ), axial kurtosis (K// ) and mean kurtosis (MK) were calculated. Voxel-based analyses were employed to compare diffusion metrics in epilepsy versus the controls. RESULTS: In the case group, MD was found significantly higher in the right temporal lobe, the right occipital lobe, hippocampus, and some subcortical regions, while FA increased in bilateral supplementary motor area and the left superior frontal lobe (false discovery rate corrected P < 0.05). Analysis of λ⊥ and λ// showed that the increased MD was mainly due to the elevated λ// . Significantly decreased MK was also detected in bilateral temporo-occipital regions, the right hippocampus, the left insula, the left post-central area, and some subcortical regions (false discovery rate corrected P < 0.05). In most regions the changed MK were due to the decreased K// . CONCLUSION: The kurtosis parameters (K⊥ , K// , and MK) reflect different microstructural information in the IGE children with GTCS, and this support the value of DKI in studying children GTCS.

Repair of urethral defects with polylactid acid fibrous membrane seeded with adipose-derived stem cells in a rabbit model.

AIM: The aim of this study is to evaluate the capacity of polylactid acid (PLA) fibrous membrane seeded with allogeneic rabbit adipose tissue-derived stem cells (ADSCs) to repair urethral defects in a rabbit model. MATERIALS AND METHODS: Rabbit ADSCs were harvested and phenotypically characterized. Twenty-four New Zealand male rabbits with 5-mm urethral mucosal defects were randomly divided into two groups. They underwent urethroplasty either with PLA fibrous membrane seeded with ADSCs (group A) or blank PLA fibrous membrane (group B). At 4 and 6 weeks after urethroplasty, the urethral grafts were collected and analyzed grossly and histologically. The incidence rate of urethrostenosis was measured. RESULTS: The adipose tissue-derived cells in monolayer culture showed a typical morphology of mesenchymal stem cells (MSCs). They were positive for the MSC marker CD44 but negative for lineage markers CD45 and CD105. Six weeks after surgery, the incidence rate of urethrostenosis in group A was significantly lower than that in group B (p < 0.05). In group A, the ADSC-seeded grafts showed a normal urethral architecture with a thickened muscle layer. In contrast, the newly developed urethra in group B demonstrated a fewer number of urothelial layers and scarce or no smooth muscle cells. CONCLUSION: The PLA scaffold seeded with ADSCs is effective in urethral regeneration in a rabbit model. ADSCs may represent a promising source of seed cells for urethral tissue engineering.

Relationship of short-term blood pressure variability with carotid intima-media thickness in hypertensive patients.

BACKGROUND: High blood pressure (BP) is among significant risk factor for stroke and other vascular occurrences, it experiences nonstop fluctuations over time as a result of a complex interface among cardiovascular control mechanisms. Large blood pressure variability (BPV) has been proved to be promising in providing potential regulatory mechanisms of the cardiovascular system. Although the previous studies also showed that BPV is associated with increased carotid intima-media thickness (IMT) and plaque, whether the correlation between variability in blood pressure and left common carotid artery-intima-media thickness (LCCA-IMT) is stronger than right common carotid artery-intima-media thickness (RCCA-IMT) remains uncertain in hypertension. METHODS: We conduct a study (78 hypertensive subjects, aged 28-79) to evaluate the relationship between BPV and carotid intima-media thickness in Shenzhen. The blood pressure was collected using the 24 h ambulatory blood pressure monitoring, and its variability was evaluated using standard deviation (SD), coefficient of variation (CV), and average real variability (ARV) during 24 h, daytime and nighttime. All the IMT measurements are collected by ultrasound. RESULTS: As the results showed, 24 h systolic blood pressure variability (SBPV) evaluated by SD and ARV were significantly related to LCCA-IMT (r(1) = 0.261, P = 0.021; r(1) = 0.262, P = 0.021, resp.). For the daytime diastolic blood pressure variability (DBPV), ARV indices were significantly related to LCCA-IMT (r(1) = 0.239, P = 0.035), which differed form BPV evaluated by SD and CV. For the night time, there is no significant correlation between the BPV and IMT. Moreover, for all the subjects, there is no significant correlation between the BPV and RCCA-IMT/number of plaques, whereas, the SD, CV, and ARV of daytime SBP showed a positive correlation with LCCA-IMT (r(1) = 0.312, P = 0.005; r(1) = 0.255, P = 0.024; r(1) = 0.284, P = 0.012, resp.). Moreover, the ARV of daytime SBPV, 24 h SBPV and nighttime DBPV showed a positive correlation with the number of plaques of LCCA (r(1) = 0.356, P = 0.008; r(1) = 0.297, P = 0.027; r(1) = 0.278, P = 0.040, resp.). In addition, the number of plaques in LCCA had higher correlation with pulse pressure and diastolic blood pressure than that in RCCA. And multiple regression analysis indicated LCCA-IMT might not only be influenced by age or smoking but also by the SD index of daytime SBPV (p = 0.035). CONCLUSIONS: The results show that SBPV during daytime and 24 h had significant correlation with IMT, for the hypertensive subjects from the southern area of China. Moreover, we also found the daytime SBPV to be the best predictor for the progression of IMT in multivariate regression analysis. In addition, the present study suggests that the correlation between BPV and left common carotid artery-intima-media thickness/number of plaques is stronger than right common carotid artery-intima-media thickness/number of plaques.

A numerical study of the effect of varied blood pressure on the stability of carotid atherosclerotic plaque.

BACKGROUND: Blood pressure (BP) is associated with early atherosclerosis and plaque rupture because the BP variability can significantly affect the blood flow velocity and shear stress over the plaque. However, the mechanical response of BP variability to the plaque remains unclear. Therefore, we investigated the correlation between different maximum systolic blood pressure (SBP) and the stress distribution on plaque, as well as the stress over the plaque and blood velocity around the plaque using different BP variations, which are the BP variability in different phases during one cardiac cycle and beat-to-beat BP variability. METHOD: We established a two-dimensional artery model with stenosis at the degree of 62.5%. Eight combinations of pulsatile pressure gradients between the inflow and outflow were implemented at the model. Three levels of fibrous cap thickness were taken into consideration to investigate the additional effect on the BP variability. Wall shear stress and stress/strain distribution over the plaque were derived as well as the oscillation shear index (OSI) to analyze the impact of the changing rate of BP. RESULT: The stresses at diastole were 2.5% ± 1.8% lower than that at systole under the same pressure drop during one cycle. It was also found that elevated SBP might cause the immediate increment of stress in the present cycle (292% ± 72.3%), but slight reduction in the successive cycle (0.48% ± 0.4%). CONCLUSION: The stress/strain distribution over the plaque is sensitive to the BP variability during one cardiac cycle, and the beat-to-beat BP variability could cause considerable impact on the progression of atherosclerosis in long-term.

A stochastic filtering approach to recover strain images from quasi-static ultrasound elastography.

BACKGROUND: Model-based reconstruction algorithms have shown potentials over conventional strain-based methods in quasi-static elastographic image by using realistic finite element (FE) or bio-mechanical model constraints. However, it is still difficult to properly handle the discrepancies between the model constraint and ultrasound data, and the measurement noise. METHODS: In this paper, we explore the usage of Kalman filtering algorithm for the estimation of strain imaging in quasi-static ultrasound elastography. The proposed strategy formulates the displacement distribution through biomechanical models, and the ultrasound-derived measurements through observation equations. Through this filtering strategy, the discrepancies are quantitatively modelled as one Gaussian white noise, and the measurement noise of ultrasound data is modelled as another independent Gaussian white noise. The optimal estimation of kinematic functions, i.e. the full displacement and velocity field, are computed through this Kalman filter. Then the strain images can be easily calculated from the estimated displacement field. RESULTS: The accuracy and robustness of our proposed framework is first evaluated in synthetic data in controlled conditions, and the performance of this framework is then evaluated in the real data collected from elastography phantoms and patients with favourable results. CONCLUSIONS: The potential of our algorithm is to provide the distribution of mechanically meaningful strain under a proper biomechanical model constraint. We address the model-data discrepancy and measurement noise by introducing process noise and measurement noise in our framework, and then the mechanically meaningful strain is estimated through the Kalman filter in the minimum mean square error (MMSE) sense.

Three-Dimensional Magneto-Acousto-Electrical Computed Tomography (3D MAE-CT): A Preliminary Study Using Ultrasound Linear Array Transducer.

Magneto-acousto-electrical tomography (MAET) is a hybrid imaging method that combines the high spatial resolution of ultrasonography with the high contrast of electrical impedance tomography (EIT). While most previous studies on MAET have focused on two-dimensional imaging, our recent research proposed a novel three-dimensional (3D) MAET method using B-mode and translational scanning. This method has been the first to reconstruct a 3D volume image of conductivity interfaces. However, this method has its limitations in mapping irregular shapes of conductivity. To address this challenge, we propose a 3D magneto-acousto-electrical computed tomography (3D MAE-CT) method utilizing an ultrasound linear array transducer in this work. Both phantom and in vitro experiments were conducted to validate our proposed method. The results from the phantom experiments demonstrate that our method can map the 3D volume conductivity with high spatial resolution. The oblique angles extracted from the 3D image closely match practical value, with the relative error ranging between -2.80% and 4.07%. Furthermore, the in vitro experiment successfully obtained a 3D image of a chicken heart, marking the first MAET 3D conductivity image of a tissue sample to date.

Freehand 3D Ultrasound Imaging Based on Probe-mounted Vision and IMU System.

OBJECTIVES: Freehand three-dimensional (3D) ultrasound (US) is of great significance for clinical diagnosis and treatment, it is often achieved with the aid of external devices (optical and/or electromagnetic, etc.) that monitor the location and orientation of the US probe. However, this external monitoring is often impacted by imaging environment such as optical occlusions and/or electromagnetic (EM) interference. METHODS: To address the above issues, we integrated a binocular camera and an inertial measurement unit (IMU) on a US probe. Subsequently, we built a tight coupling model utilizing the unscented Kalman algorithm based on Lie groups (UKF-LG), combining vision and inertial information to infer the probe's movement, through which the position and orientation of the US image frame are calculated. Finally, the volume data was reconstructed with the voxel-based hole-filling method. RESULTS: The experiments including calibration experiments, tracking performance evaluation, phantom scans, and real scenarios scans have been conducted. The results show that the proposed system achieved the accumulated frame position error of 3.78 mm and the orientation error of 0.36° and reconstructed 3D US images with high quality in both phantom and real scenarios. CONCLUSIONS: The proposed method has been demonstrated to enhance the robustness and effectiveness of freehand 3D US. Follow-up research will focus on improving the accuracy and stability of multi-sensor fusion to make the system more practical in clinical environments.

Development of a Quantitative Digital Urinalysis Tool for Detection of Nitrite, Protein, Creatinine, and pH.

This paper presents a cost-effective, quantitative, point-of-care solution for urinalysis screening, specifically targeting nitrite, protein, creatinine, and pH in urine samples. Detecting nitrite is crucial for the early identification of urinary tract infections (UTIs), while regularly measuring urinary protein-to-creatinine (UPC) ratios aids in managing kidney health. To address these needs, we developed a portable, transmission-based colorimeter using readily available components, controllable via a smartphone application through Bluetooth. Multiple colorimetric detection strategies for each analyte were identified and tested for sensitivity, specificity, and stability in a salt buffer, artificial urine, and human urine. The colorimeter successfully detected all analytes within their clinically relevant ranges: nitrite (6.25-200 µM), protein (2-1024 mg/dL), creatinine (2-1024 mg/dL), and pH (5.0-8.0). The introduction of quantitative protein and creatinine detection, and a calculated urinary protein-to-creatinine (UPC) ratio at the point-of-care, represents a significant advancement, allowing patients with proteinuria to monitor their condition without frequent lab visits. Furthermore, the colorimeter provides versatile data storage options, facilitating local storage on mobile devices or in the cloud. The paper further details the setup of the colorimeter's secure connection to a cloud-based environment, and the visualization of time-series analyte measurements in a web-based dashboard.

Reconstruction of elasticity: a stochastic model-based approach in ultrasound elastography.

BACKGROUND: The convectional strain-based algorithm has been widely utilized in clinical practice. It can only provide the information of relative information of tissue stiffness. However, the exact information of tissue stiffness should be valuable for clinical diagnosis and treatment. METHODS: In this study we propose a reconstruction strategy to recover the mechanical properties of the tissue. After the discrepancies between the biomechanical model and data are modeled as the process noise, and the biomechanical model constraint is transformed into a state space representation the reconstruction of elasticity can be accomplished through one filtering identification process, which is to recursively estimate the material properties and kinematic functions from ultrasound data according to the minimum mean square error (MMSE) criteria. In the implementation of this model-based algorithm, the linear isotropic elasticity is adopted as the biomechanical constraint. The estimation of kinematic functions (i.e., the full displacement and velocity field), and the distribution of Young's modulus are computed simultaneously through an extended Kalman filter (EKF). RESULTS: In the following experiments the accuracy and robustness of this filtering framework is first evaluated on synthetic data in controlled conditions, and the performance of this framework is then evaluated in the real data collected from elastography phantom and patients using the ultrasound system. Quantitative analysis verifies that strain fields estimated by our filtering strategy are more closer to the ground truth. The distribution of Young's modulus is also well estimated. Further, the effects of measurement noise and process noise have been investigated as well. CONCLUSIONS: The advantage of this model-based algorithm over the conventional strain-based algorithm is its potential of providing the distribution of elasticity under a proper biomechanical model constraint. We address the model-data discrepancy and measurement noise by introducing process noise and measurement noise in our framework, and then the absolute values of Young's modulus are estimated through the EFK in the MMSE sense. However, the initial conditions, and the mesh strategy will affect the performance, i.e., the convergence rate, and computational cost, etc.

Supine lithotomy versus prone position in minimally invasive percutaneous nephrolithotomy for upper urinary tract calculi.

OBJECTIVE: To compare operative time, safety and effectiveness of minimally invasive percutaneous nephrolithotomy (MPCNL) in the supine lithotomy versus prone position. METHODS: Between January 2008 and December 2010, a total of 109 consecutive patients with upper urinary tract calculi were enrolled and randomly divided into group A (53 patients, supine lithotomy position) and group B (56 patients, prone position). The MPCNL procedures were performed under the guidance of real-time grayscale ultrasound system. The preoperative characteristics, intraoperative and postoperative parameters were analyzed and compared. RESULTS: All patients were successfully operated. There was no significant difference between the two groups in stone-free rate (group A 90.1 vs. group B 87.5%, p = 0.45), mean blood loss, number of access tracts, calyx puncture, mean hospital stay (group A 6 ± 1.1 vs. group B 6 ± 1.5 days, p = 0.38) and complications. But the operative time was significantly shortened in supine lithotomy position (group A 56 ± 15 vs. group B 86 ± 23 min, p < 0.001). CONCLUSIONS: The effectiveness and safety of the supine lithotomy position for MPCNL were similar to the prone position. However, the supine lithotomy position has an important advantage of reducing the operative time. The supine lithotomy position could be a good choice to perform MPCNL.

Suppression of urinary bladder urothelial carcinoma cell by the ethanol extract of pomegranate fruit through cell cycle arrest and apoptosis.

BACKGROUND: Pomegranate possesses many medicinal properties such as antioxidant, anti-inflammation and antitumor. It has been extensively used as a folk medicine by many cultures. Pomegranate fruit has been shown to have the inhibitory efficacy against prostate cancer and lung cancer in vitro and in vivo. It can be exploited in chemoprevention and chemotherapy of prostate cancer. In this study we examined the anti-cancer efficacy of pomegranate fruit grown in Taiwan against urinary bladder urothelial carcinoma (UBUC) and its mechanism of action. METHODS: Edible portion of Taiwanese pomegranate was extracted using ethanol and the anti-cancer effectiveness of ethanol extract was evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. Flow cytometry and western immunoblotting were exploited to uncover the molecular pathways underlying anti-UBUC activity of Taiwanese pomegranate ethanol extract. RESULTS: This study demonstrated that Taiwanese pomegranate fruit ethanol extract (PEE) could effectively restrict the proliferation of UBUC T24 and J82 cells. Cell cycle analyses indicated that the S phase arrest induced by PEE treatment might be caused by an increase in cyclin A protein level and a decrease in the expression of cyclin-dependent kinase 1. The results of western immunoblotting demonstrated that PEE treatment could not only evoke the activation of pro-caspase-3, -8,-9 but also increase Bax/Bcl-2 ratio in T24 cells. The above observations implicated that PEE administration might trigger the apoptosis in T24 cells through death receptor signaling and mitochondrial damage pathway. Besides we found that PEE exposure to T24 cells could provoke intensive activation of procaspase-12 and enhance the expressions of CHOP and Bip, endoplasmic reticulum (ER) stress marker, suggesting that ER stress might be the cardinal apoptotic mechanism of PEE-induced inhibition of bladder cancer cell. CONCLUSIONS: The analytical results of this study help to provide insight into the molecular mechanism of induced bladder cancer cell apoptosis by pomegranate and to develop novel mechanism-based chemopreventive strategy for bladder cancer.

Advanced Fourier migration for Plane-Wave vector flow imaging.

Ultrafast ultrasound imaging modalities have been studied extensively in the ultrasound community. It breaks the compromise between the frame rate and the region of interest by imaging the whole medium with wide unfocused waves. Continuously available data allow monitoring fast transient dynamics at hundreds to thousands of frames per second. This feature enables a more accurate and robust velocity estimation in vector flow imaging (VFI). On the other hand, the huge amount of data and real-time processing demands are still challenging in VFI. A solution is to provide a more efficient beamforming approach with smaller computation complexity than the conventional time-domain beamformer like delay-and-sum (DAS). Fourier-domain beamformers are shown to be more computationally efficient and can provide equally good image quality as DAS. However, previous studies generally focus on B-mode imaging. In this study, we propose a new framework for VFI which is based on two advanced Fourier migration methods, namely, slant stack migration (SSM) and ultrasound Fourier slice beamform (UFSB). By carefully modifying the beamforming parameters, we successfully apply the cross-beam technique within the Fourier beamformers. The proposed Fourier-based VFI is validated in simulation studies, in vitro, and in vivo experiments. The velocity estimation is evaluated via bias and standard deviation and the results are compared with conventional time-domain VFI using the DAS beamformer. In the simulation, the bias is 6.4%, -6.2%, and 5.7%, and the standard deviation is 4.3%, 2.4%, and 3.9% for DAS, UFSB, and SSM, respectively. In vitro studies reveal a bias of 4.5%, -5.3%, and 4.3% and a standard deviation of 3.5%, 1.3%, and 1.6% from DAS, UFSB, and SSM, respectively. The in vivo imaging of the basilic vein and femoral bifurcation also generate similar results using all three methods. With the proposed Fourier beamformers, the computation time can be shortened by up to 9 times and 14 times using UFSB and SSM.

Jointly Composite Feature Learning and Autism Spectrum Disorder Classification Using Deep Multi-Output Takagi-Sugeno-Kang Fuzzy Inference Systems.

Autism spectrum disorder (ASD) is characterized by poor social communication abilities and repetitive behaviors or restrictive interests, which has brought a heavy burden to families and society. In many attempts to understand ASD neurobiology, resting-state functional magnetic resonance imaging (rs-fMRI) has been an effective tool. However, current ASD diagnosis methods based on rs-fMRI have two major defects. First, the instability of rs-fMRI leads to functional connectivity (FC) uncertainty, affecting the performance of ASD diagnosis. Second, many FCs are involved in brain activity, making it difficult to determine effective features in ASD classification. In this study, we propose an interpretable ASD classifier DeepTSK, which combines a multi-output Takagi-Sugeno-Kang (MO-TSK) fuzzy inference system (FIS) for composite feature learning and a deep belief network (DBN) for ASD classification in a unified network. To avoid the suboptimal solution of DeepTSK, a joint optimization procedure is employed to simultaneously learn the parameters of MO-TSK and DBN. The proposed DeepTSK was evaluated on datasets collected from three sites of the Autism Brain Imaging Data Exchange (ABIDE) database. The experimental results showed the effectiveness of the proposed method, and the discriminant FCs are presented by analyzing the consequent parameters of Deep MO-TSK.