Stacking architecture for collimated backlight using cylindrical lens sheet with linear light sources or edge-lit/direct-lit BLU

Highly collimated and directional backlights are essential for realizing advanced display technologies such as autostereoscopic 3D displays. Previously reported collimated backlights, either edge-lit or direct-lit, in general still suffer unsatisfactory form factors, directivity, uniformity, or crosstalk etc. In this work, we report a simple stacking architecture for the highly collimated and uniform backlights, by combining linear light source arrays and carefully designed cylindrical lens arrays. Experiments were conducted to validate the design and simulation, using the conventional edge-lit backlight or the direct-lit mini-LED (mLED) arrays as light sources, the NiFe (stainless steel) barrier sheets, and cylindrical lens arrays fabricated by molding. Highly collimated backlights with small angular divergence of ±1.45°∼±2.61°, decent uniformity of 93-96%, and minimal larger-angle sidelobes in emission patterns were achieved with controlled divergence of the light source and optimization of lens designs. The architecture reported here provides a convenient way to convert available backlight sources into a highly collimated backlight, and the use of optically reflective barrier also helps recycle light energy and enhance the luminance. The results of this work are believed to provide a facile approach for display technologies requiring highly collimated backlights.

Deep Learning Detection and Segmentation of Brain Arteriovenous Malformation on Magnetic Resonance Angiography

BACKGROUND

The delineation of brain arteriovenous malformations (bAVMs) is crucial for subsequent treatment planning. Manual segmentation is time-consuming and labor-intensive. Applying deep learning to automatically detect and segment bAVM might help to improve clinical practice efficiency.

PURPOSE

To develop an approach for detecting bAVM and segmenting its nidus on Time-of-flight magnetic resonance angiography using deep learning methods.

STUDY TYPE

Retrospective.

SUBJECTS

221 bAVM patients aged 7-79 underwent radiosurgery from 2003 to 2020. They were split into 177 training, 22 validation, and 22 test data.

FIELD STRENGTH/SEQUENCE

1.5 T, Time-of-flight magnetic resonance angiography based on 3D gradient echo.

ASSESSMENT

The YOLOv5 and YOLOv8 algorithms were utilized to detect bAVM lesions and the U-Net and U-Net++ models to segment the nidus from the bounding boxes. The mean average precision, F1, precision, and recall were used to assess the model performance on the bAVM detection. To evaluate the model's performance on nidus segmentation, the Dice coefficient and balanced average Hausdorff distance (rbAHD) were employed.

STATISTICAL TESTS

The Student's t-test was used to test the cross-validation results (P < 0.05). The Wilcoxon rank test was applied to compare the median for the reference values and the model inference results (P < 0.05).

RESULTS

The detection results demonstrated that the model with pretraining and augmentation performed optimally. The U-Net++ with random dilation mechanism resulted in higher Dice and lower rbAHD, compared to that without that mechanism, across varying dilated bounding box conditions (P < 0.05). When combining detection and segmentation, the Dice and rbAHD were statistically different from the references calculated using the detected bounding boxes (P < 0.05). For the detected lesions in the test dataset, it showed the highest Dice of 0.82 and the lowest rbAHD of 5.3%.

DATA CONCLUSION

This study showed that pretraining and data augmentation improved YOLO detection performance. Properly limiting lesion ranges allows for adequate bAVM segmentation.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 1.

Brain metastasis tumor segmentation and detection using deep learning algorithms: A systematic review and meta-analysis

BACKGROUND

Manual detection of brain metastases is both laborious and inconsistent, driving the need for more efficient solutions. Accordingly, our systematic review and meta-analysis assessed the efficacy of deep learning algorithms in detecting and segmenting brain metastases from various primary origins in MRI images.

METHODS

We conducted a comprehensive search of PubMed, Embase, and Web of Science up to May 24, 2023, which yielded 42 relevant studies for our analysis. We assessed the quality of these studies using the QUADAS-2 and CLAIM tools. Using a random-effect model, we calculated the pooled lesion-wise dice score as well as patient-wise and lesion-wise sensitivity. We performed subgroup analyses to investigate the influence of factors such as publication year, study design, training center of the model, validation methods, slice thickness, model input dimensions, MRI sequences fed to the model, and the specific deep learning algorithms employed. Additionally, meta-regression analyses were carried out considering the number of patients in the studies, count of MRI manufacturers, count of MRI models, training sample size, and lesion number.

RESULTS

Our analysis highlighted that deep learning models, particularly the U-Net and its variants, demonstrated superior segmentation accuracy. Enhanced detection sensitivity was observed with an increased diversity in MRI hardware, both in terms of manufacturer and model variety. Furthermore, slice thickness was identified as a significant factor influencing lesion-wise detection sensitivity. Overall, the pooled results indicated a lesion-wise dice score of 79%, with patient-wise and lesion-wise sensitivities at 86% and 87%, respectively.

CONCLUSIONS

The study underscores the potential of deep learning in improving brain metastasis diagnostics and treatment planning. Still, more extensive cohorts and larger meta-analysis are needed for more practical and generalizable algorithms. Future research should prioritize these areas to advance the field. This study was funded by the Gen. & Mrs. M.C. Peng Fellowship and registered under PROSPERO (CRD42023427776).

Federated Learning: A Cross-Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery

BACKGROUND

Deep learning-based segmentation algorithms usually required large or multi-institute data sets to improve the performance and ability of generalization. However, protecting patient privacy is a key concern in the multi-institutional studies when conventional centralized learning (CL) is used.

PURPOSE

To explores the feasibility of a proposed lesion delineation for stereotactic radiosurgery (SRS) scheme for federated learning (FL), which can solve decentralization and privacy protection concerns.

STUDY TYPE

Retrospective.

SUBJECTS

506 and 118 vestibular schwannoma patients aged 15-88 and 22-85 from two institutes, respectively; 1069 and 256 meningioma patients aged 12-91 and 23-85, respectively; 574 and 705 brain metastasis patients aged 26-92 and 28-89, respectively.

FIELD STRENGTH/SEQUENCE

1.5T, spin-echo, and gradient-echo [Correction added after first online publication on 21 August 2023. Field Strength has been changed to "1.5T" from "5T" in this sentence.].

ASSESSMENT

The proposed lesion delineation method was integrated into an FL framework, and CL models were established as the baseline. The effect of image standardization strategies was also explored. The dice coefficient was used to evaluate the segmentation between the predicted delineation and the ground truth, which was manual delineated by neurosurgeons and a neuroradiologist.

STATISTICAL TESTS

The paired t-test was applied to compare the mean for the evaluated dice scores (p < 0.05).

RESULTS

FL performed the comparable mean dice coefficient to CL for the testing set of Taipei Veterans General Hospital regardless of standardization and parameter; for the Taichung Veterans General Hospital data, CL significantly (p < 0.05) outperformed FL while using bi-parameter, but comparable results while using single-parameter. For the non-SRS data, FL achieved the comparable applicability to CL with mean dice 0.78 versus 0.78 (without standardization), and outperformed to the baseline models of two institutes.

DATA CONCLUSION

The proposed lesion delineation successfully implemented into an FL framework. The FL models were applicable on SRS data of each participating institute, and the FL exhibited comparable mean dice coefficient to CL on non-SRS dataset. Standardization strategies would be recommended when FL is used.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 1.

Sparse interleaved sampling for high resolution focal construct geometry X-ray tomography

We demonstrate interleaved sampling by multiplexing conical subshells within the tomosynthesis and raster scanning a phantom through a 150 kV shell X-ray beam. Each view comprises pixels sampled on a regular 1 mm grid, which is then upscaled by padding with null pixels before tomosynthesis. We show that upscaled views comprising 1% sample pixels and 99% null pixels increase the contrast transfer function (CTF) computed from constructed optical sections from approximately 0.6 line pairs/mm to 3 line pairs/mm. The driver of our method is to complement work concerning the application of conical shell beams to the measurement of diffracted photons for materials identification. Our approach is relevant to time-critical, and dose-sensitive analytical scanning applications in security screening, process control and medical imaging.

Immune thrombocytopenia and risk of stroke: Evidence from a nationwide population-based cohort study

BACKGROUND

Research investigating differences in the overall stroke risk between individuals with and without immune thrombocytopenia (ITP) is lacking.

METHODS

This real-world study used the National Health Insurance Research Database (NHIRD). Risk of stroke was compared between 13,085 individuals with ITP enrolled between 1 January 2000 and 31 December 2015 and a control cohort of 52,340 individuals without ITP (1:4 ratio propensity score-matched by age, sex, index year, relevant comorbidities, and medications). Sub-distribution hazards models were used to estimate adjusted sub-distribution hazard ratio (SHR) and 95% confidence intervals (CIs), with the non-ITP group as the control group.

RESULTS

Of the 65,425 participants, 13,085 had ITP, 63.3% were women, and the mean age was 52.59 years. The risk of both ischemic and hemorrhagic stroke was 1.14 times (adjusted SHR 1.14, 95% CI, 1.07-1.22) and 1.93 times (adjusted SHR 1.93, 95% CI, 1.70-2.20) higher in the ITP group than in controls. Patients with ITP in the 20- to 29-year subgroup had a higher risk of new-onset stroke (adjusted SHR, 4.06 (95% CI, 2.72-6.07), p value for interaction <0.01) than those aged 20-29 years without ITP. Individuals with severe ITP with splenectomy had a 1.79 times higher overall stroke risk than those without.

CONCLUSIONS

ITP is associated with increased risk of both ischemic and hemorrhagic stroke.

Lesion delineation framework for vestibular schwannoma, meningioma and brain metastasis for gamma knife radiosurgery using stereotactic magnetic resonance images

BACKGROUND AND OBJECTIVE

GKRS is an effective treatment for smaller intracranial tumors with a high control rate and low risk of complications. Target delineation in medical MR images is essential in the planning of GKRS and follow-up. A deep learning-based algorithm can effectively segment the targets from medical images and has been widely explored. However, state-of-the-art deep learning-based target delineation uses fixed sizes, and the isotropic voxel size may not be suitable for stereotactic MR images which use different anisotropic voxel sizes and numbers of slices according to the lesion size and location for clinical GKRS planning. This study developed an automatic deep learning-based segmentation scheme for stereotactic MR images.

METHODS

We retrospectively collected stereotactic MR images from 506 patients with VS, 1,069 patients with meningioma and 574 patients with BM who had been treated using GKRS; the lesion contours and individual T1W+C and T2W MR images were extracted from the GammaPlan system. The three-dimensional patching-based training strategy and dual-pathway architecture were used to manage inconsistent FOVs and anisotropic voxel size. Furthermore, we used two-parametric MR image as training input to segment the regions with different image characteristics (e.g., cystic lesions) effectively.

RESULTS

Our results for VS and BM demonstrated that the model trained using two-parametric MR images significantly outperformed the model trained using single-parametric images with median Dice coefficients (0.91, 0.05 versus 0.90, 0.06, and 0.82, 0.23 versus 0.78, 0.34, respectively), whereas predicted delineations in meningiomas using the dual-pathway model were dominated by single-parametric images (median Dice coefficients 0.83, 0.17 versus 0.84, 0.22). Finally, we combined three data sets to train the models, achieving the comparable or even higher testing median Dice (VS: 0.91, 0.07; meningioma: 0.83, 0.22; BM: 0.84, 0.23) in three diseases while using two-parametric as input.

CONCLUSIONS

Our proposed deep learning-based tumor segmentation scheme was successfully applied to multiple types of intracranial tumor (VS, meningioma and BM) undergoing GKRS and for segmenting the tumor effectively from stereotactic MR image volumes for use in GKRS planning.

Using angle-selective optical film to enhance the light extraction of a thin-film encapsulated 3D reflective pixel for OLED displays

Light extraction improvement is still an important issue for active-matrix organic light-emitting diode displays (AMOLEDs). In our previous work, a three-dimensional (3D) reflective pixel configuration embedding the OLED in the concave 3D reflector and patterned high-index filler had been proposed for significant enhancement of the pixel light extraction. In this work, influences of thin film encapsulation (TFE) on light extraction of such reflective 3D OLED pixels are considered as well by simulation studies. Unfortunately, the optical simulation reveals strong reduction of the light extraction efficiency induced by TFE layers. As such, an additional angle-selective optical film structure between the pixel and the encapsulation layers is introduced to control the angular distribution of the light coupled into the encapsulation layers and to solve TFE-induced optical losses. As a result, TFE-induced losses can be substantially reduced to retain much of light extraction efficiency. The results of this study are believed to provide useful insights and guides for developing even more efficient and power-saving AMOLEDs.

Acceleration of Magnetic Resonance Fingerprinting Reconstruction Using Denoising and Self-Attention Pyramidal Convolutional Neural Network

Magnetic resonance fingerprinting (MRF) based on echo-planar imaging (EPI) enables whole-brain imaging to rapidly obtain T1 and T2* relaxation time maps. Reconstructing parametric maps from the MRF scanned baselines by the inner-product method is computationally expensive. We aimed to accelerate the reconstruction of parametric maps for MRF-EPI by using a deep learning model. The proposed approach uses a two-stage model that first eliminates noise and then regresses the parametric maps. Parametric maps obtained by dictionary matching were used as a reference and compared with the prediction results of the two-stage model. MRF-EPI scans were collected from 32 subjects. The signal-to-noise ratio increased significantly after the noise removal by the denoising model. For prediction with scans in the testing dataset, the mean absolute percentage errors between the standard and the final two-stage model were 3.1%, 3.2%, and 1.9% for T1, and 2.6%, 2.3%, and 2.8% for T2* in gray matter, white matter, and lesion locations, respectively. Our proposed two-stage deep learning model can effectively remove noise and accurately reconstruct MRF-EPI parametric maps, increasing the speed of reconstruction and reducing the storage space required by dictionaries.

An unsymmetrical thermally activated delayed fluorescence emitter enables orange-red electroluminescence with 31.7% external quantum efficiency

The thermally activated delayed fluorescence (TADF) emitters based on donor-acceptor (D-A) configuration were continuously developed in the past few years, whereas an unsymmetrical TADF emitter with A-D-A' configuration has never been reported. Herein, an A-D-A' type TADF emitter of TRZ-SBA-NAI was firstly developed by simultaneously integrating 2-phenyl-1H-benzo[de]isoquinoline-1,3(2H)-dione and 2,4,6-triphenyl-1,3,5-triazine acceptors into a spirobiacridine donor core. Due to the coexistence of double charge-transfer excited states, TRZ-SBA-NAI displayed dual emission containing a dominant orange-red emission and an anti-Kasha's rule sky-blue emission shoulder in solution. As doped into the host matrix, TRZ-SBA-NAI only exhibited an orange-red emission, together with a high photoluminescence quantum yield of 87%. The linear molecular shape imparted TRZ-SBA-NAI with a high horizontal dipole ratio of 88%. As a result, the TRZ-SBA-NAI based devices achieved a record-high external quantum efficiency of 31.7% with an electroluminescence peak at 593 nm. This finding not only enriches the diversity in TADF molecular design, but also unlocks the huge potential of A-D-A' type TADF emitters for excellent device performance.

High-Efficiency Red Electroluminescence Based on a Carbene-Cu(I)-Acridine Complex

How to develop efficient red-emitting organometallics of earth-abundant copper(I) is a formidable challenge in the field of organic light-emitting diodes (OLEDs) because Cu(I) complexes have weak spin-orbit coupling and a serious excited-state reorganization effect. Here, a red Cu(I) complex, MAC*-Cu-DPAC, was developed using a rigid 9,9-diphenyl-9,10-dihydroacridine donor ligand in a carbene-metal-amide motif. The Cu(I) complex achieved satisfactory red emission, a high photoluminescence quantum yield of up to 70%, and a sub-microsecond lifetime. Thanks to a linear geometry and the acceptor and donor ligands in a coplanar conformation, the complex exhibited a high horizontal dipole ratio of 77% in the host matrix, first demonstrated for coinage metal(I) complexes. The resulting OLEDs delivered high external quantum efficiencies of 21.1% at a maximum and 20.1% at 1000 nits, together with a red emission peak at ∼630 nm. These values represent the state-of-the-art performance for red-emitting OLEDs based on coinage metal complexes.

Modified distributed Bragg reflector for protecting organic light-emitting diode displays against ultraviolet light

Sunlight/UV (ultraviolet)-induced degradation is still a critical issue for outdoor applications of organic light-emitting diode (OLED) displays. Therefore, effective UV-blocking structures that can prevent OLED displays from sunlight/UV degradation and still keep the OLED panels' display performance is necessary. In this report, modified distributed Bragg reflector (DBR) structures having UV-absorbing dielectric materials and adjusted layer/pair thicknesses were developed to realize effective UV blocking properties (nearly 0% transmittance below 400 nm), constantly high transmittance like glass in the visible range (∼92%) required for display applications, and sharp transition in transmission between the UV and the visible ranges. Furthermore, under the rigorous IEC 60068-2-5 solar test condition, it was verified that the developed modified, UV-blocking DBR can effectively enhance the OLED panel's resistance against UV/solar-induced degradation, effectively reducing voltage shifts of OLED devices after repeated solar test cycles.

Accelerated white matter lesion analysis based on simultaneous T 1 and T 2 ∗ quantification using magnetic resonance fingerprinting and deep learning

PURPOSE

To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning.

METHODS

MRF using echo-planar imaging (EPI) scans with varying repetition and echo times were acquired for whole brain quantification of T 1 and T 2 ∗ in 50 subjects with multiple sclerosis (MS) and 10 healthy volunteers along 2 centers. MRF T 1 and T 2 ∗ parametric maps were distortion corrected and denoised. A CNN was trained to reconstruct the T 1 and T 2 ∗ parametric maps, and the WM and GM probability maps.

RESULTS

Deep learning-based postprocessing reduced reconstruction and image processing times from hours to a few seconds while maintaining high accuracy, reliability, and precision. Mean absolute error performed the best for T 1 (deviations 5.6%) and the logarithmic hyperbolic cosinus loss the best for T 2 ∗ (deviations 6.0%).

CONCLUSIONS

MRF is a fast and robust tool for quantitative T 1 and T 2 ∗ mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning.

Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery

Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that AI will soon be used to optimize strategies for the clinical management of patients based on intensive imaging follow-up. Our objective in this study was to establish an algorithm by which to automate the volumetric measurement of vestibular schwannoma (VS) using a series of parametric MR images following radiosurgery. Based on a sample of 861 consecutive patients who underwent Gamma Knife radiosurgery (GKRS) between 1993 and 2008, the proposed end-to-end deep-learning scheme with automated pre-processing pipeline was applied to a series of 1290 MR examinations (T1W+C, and T2W parametric MR images). All of which were performed under consistent imaging acquisition protocols. The relative volume difference (RVD) between AI-based volumetric measurements and clinical measurements performed by expert radiologists were + 1.74%, - 0.31%, - 0.44%, - 0.19%, - 0.01%, and + 0.26% at each follow-up time point, regardless of the state of the tumor (progressed, pseudo-progressed, or regressed). This study outlines an approach to the evaluation of treatment responses via novel volumetric measurement algorithm, and can be used longitudinally following GKRS for VS. The proposed deep learning AI scheme is applicable to longitudinal follow-up assessments following a variety of therapeutic interventions.

Integrating molecular rigidity and chirality into thermally activated delayed fluorescence emitters for highly efficient sky-blue and orange circularly polarized electroluminescence

By integrating high molecular rigidity and stable chirality, two pairs of D*-A type circularly polarized thermally activated delayed fluorescence (CP-TADF) emitters with an almost absolute quasi-equatorial conformer geometry and excellent photoluminescence quantum efficiencies (PLQYs) are developed, achieving state-of-the-art electroluminescence performance among blue and orange circularly polarized organic light-emitting diodes (CP-OLEDs).

Prediction of pseudoprogression and long-term outcome of vestibular schwannoma after Gamma Knife radiosurgery based on preradiosurgical MR radiomics

BACKGROUND AND PURPOSE

Gamma Knife radiosurgery (GKRS) is a safe and effective treatment modality with a long-term tumor control rate over 90% for vestibular schwannoma (VS). However, numerous tumors may undergo a transient pseudoprogression during 6-18 months after GKRS followed by a long-term volume reduction. The aim of this study is to determine whether the radiomics analysis based on preradiosurgical MRI data could predict the pseudoprogression and long-term outcome of VS after GKRS.

MATERIALS AND METHODS

A longitudinal dataset of patients with VS treated by single GKRS were retrospectively collected. Overall 336 patients with no previous craniotomy for tumor removal and a median of 65-month follow-up period after radiosurgery were finally included in this study. In total 1763 radiomic features were extracted from the multiparameteric MRI data before GKRS followed by the machine-learning classification.

RESULTS

We constructed a two-level machine-learning model to predict the long-term outcome and the occurrence of transient pseudoprogression after GKRS separately. The prediction of long-term outcome achieved an accuracy of 88.4% based on five radiomic features describing the variation of T2-weighted intensity and inhomogeneity of contrast enhancement in tumor. The prediction of transient pseudoprogression achieved an accuracy of 85.0% based on another five radiomic features associated with the inhomogeneous hypointensity pattern of contrast enhancement and the variation of T2-weighted intensity.

CONCLUSION

The proposed machine-learning model based on the preradiosurgical MR radiomics provides a potential to predict the pseudoprogression and long-term outcome of VS after GKRS, which can benefit the treatment strategy in clinical practice.

Acceptor plane expansion enhances horizontal orientation of thermally activated delayed fluorescence emitters

Manipulating orientation of organic emitters remains a formidable challenge in organic light-emitting diodes (OLEDs). Here, expansion of the acceptor plane of thermally activated delayed fluorescence (TADF) emitters was demonstrated to selectively modulate emitting dipole orientation. Two proof-of-the-concept molecules, PXZPyPM and PXZTAZPM, were prepared by introducing a planar 2-phenylpyridine or 2,4,6-triphenyl-1,3,5-triazine substituent into a prototypical molecule (PXZPM) bearing a pyrimidine core and two phenoxazine donors. This design approach suppressed the influence of substituents on electronic structures and associated optoelectronic properties. Accordingly, PXZPyPM and PXZTAZPM preserved almost the same excited states and similar emission characteristics as PXZPM. The expanded acceptor plane of PXZPyPM and PXZTAZPM resulted in a 15 to 18% increase in horizontal ratios of emitting dipole orientation. PXZPyPM supported its green device exhibiting an external quantum efficiency of 33.9% and a power efficiency of 118.9 lumen per watt, competitive with the most efficient green TADF OLEDs reported so far.

Combining analysis of multi-parametric MR images into a convolutional neural network: Precise target delineation for vestibular schwannoma treatment planning

Manual delineation of vestibular schwannoma (VS) by magnetic resonance (MR) imaging is required for diagnosis, radiosurgery dose planning, and follow-up tumor volume measurement. A rapid and objective automatic segmentation method is required, but problems have been encountered due to the low through-plane resolution of standard VS MR scan protocols and because some patients have non-homogeneous cystic areas within their tumors. In this study, we retrospectively collected multi-parametric MR images from 516 patients with VS; these were extracted from the Gamma Knife radiosurgery planning system and consisted of T1-weighted (T1W), T2-weighted (T2W), and T1W with contrast (T1W + C) images. We developed an end-to-end deep-learning-based method via an automatic preprocessing pipeline. A two-pathway U-Net model involving two sizes of convolution kernel (i.e., 3 × 3 × 1 and 1 × 1 × 3) was used to extract the in-plane and through-plane features of the anisotropic MR images. A single-pathway model that adopted the same architecture as the two-pathway model, but used a kernel size of 3 × 3 × 3, was also developed for comparison purposes. In addition, we used multi-parametric MR images with different image contrasts as the model training input in order to effectively segment tumors with solid as well as cystic parts. The results of the automatic segmentation demonstrated that (1) the two-pathway model outperformed single-pathway model in terms of dice scores (0.90 ± 0.05 versus 0.87 ± 0.07); both of them having been trained using the T1W, T1W + C and T2W anisotropic MR images, (2) the optimal single-parametric two-pathway model (dice score: 0.88 ± 0.06) was then trained using the T1W + C images, and (3) the two-pathway models trained using bi-parametric (T1W + C and T2W) and tri-parametric (T1W, T2W, and T1W + C) images outperformed the model trained using the single-parametric (T1W + C) images (dice scores: 0.89 ± 0.05 and 0.90 ± 0.05, respectively, larger than 0.88 ± 0.06) because it showed improved segmentation of the non-homogeneous parts of the tumors. The proposed two-pathway U-Net model outperformed the single-pathway U-Net model when segmenting VS using anisotropic MR images. The multi-parametric models effectively improved on the defective segmentation obtained using the single-parametric models by separating the non-homogeneous tumors into their solid and cystic parts.

New Service Model for Common Mental Disorders in Hong Kong: a Retrospective Outcome Study

OBJECTIVE

To review the first 8-month outcome of the Common Mental Disorder Clinic model in Hong Kong in terms of patient exit status and improvement in depressive and anxiety symptoms.

METHODS

During the first appointment, patients were interviewed by a multidisciplinary team comprising a psychiatrist, a psychiatric nurse, and an occupational therapist. A multidisciplinary case conference was conducted to discuss clinical observations, diagnosis, issues of concern, and the optimal individualised treatment plan. Low-intensity interventions by nurses and/or occupational therapists were provided, as were optional, time-limited, protocol-based interventions by clinical psychologists for those with mild to moderate depressive and anxiety symptoms. Pharmacological intervention may be used when indicated. Upon completion of the treatment plan, patients were reassessed by the treating psychiatrist. Discharge options included discharge without psychiatric follow-up, step-up to psychiatric outpatient clinics, and step-down services. The self-administered Patient Health Questionnaire-9 (PHQ-9) and Generalised Anxiety Disorder 7-item scale (GAD-7) were used to assess the past 2 weeks' depressive and anxiety symptoms, respectively, at baseline and at each session.

RESULTS

From July 2015 to February 2016, 1325 Chinese patients received the new service. Of them, 170 men and 363 women (mean age, 52.6 years) completed the treatment plan. After treatment, their mean PHQ-9 score decreased from 11.06 to 7.55 (p < 0.001), and the mean GAD-7 score decreased from 9.94 to 6.54 (p < 0.001). After treatment, 42.4% and 48.2% of the patients were within the normal range of PHQ-9 and GAD-7 scores, respectively, compared with 16.9% and 20.8% before treatment. The mean time to implementation of the individualised treatment plan was 82.33 days. Of the patients, 54.4% were discharged without any need for medical or psychiatric follow-up; 28% were stepped up to psychiatric outpatient clinics; and 17.3% were stepped down. The predictors of exit status were whether psychiatric medication was prescribed during initial intake (p = 0.011), whether psychiatric medication was prescribed at last follow-up (p < 0.001), the service period (p = 0.010), and the GAD-7 final score (p = 0.005).

CONCLUSIONS

The first 8-month outcome of the new service model was encouraging, with shortened waiting time, reduced severity of symptoms, and better exit status (high recovery and step-down rates).

Remote ischaemic conditioning for prevention of acute kidney injury after valvular heart surgery: a randomised controlled trial

BACKGROUND

Repeated remote ischaemic conditioning (RIC) during weaning from cardiopulmonary bypass and in the early postoperative period may confer protection against acute kidney injury (AKI). We evaluated the effect of repeated RIC on the incidence of AKI in patients undergoing valvular heart surgery.

METHODS

Patients were randomised into either the RIC (n=120) or control (n=124) group. A pneumatic tourniquet was placed on each patient's thigh. Upon removal of the aortic cross-clamp, three cycles of inflation for 5 min at 250 mm Hg (with 5 min intervals) were applied in the RIC group. Additionally, three cycles of RIC were repeated at postoperative 12 and 24 h. AKI was diagnosed based on the Kidney Disease: Improving Global Outcomes guideline. The incidences of renal replacement therapy, permanent stroke, sternal wound infection, newly developed atrial fibrillation, mechanical ventilation >24 h, and reoperation for bleeding during hospitalisation were recorded.

RESULTS

The incidences of AKI were not significantly different between the control (19.4%) and RIC (15.8%) groups (a difference of 3.5 percentage points; 95% confidence interval: -6.8%-13.9%; P=0.470). Perioperative serum creatinine concentrations were similar in the control and RIC groups (P=0.494). Fluid balance, urine output, blood loss, transfusion, and vasopressor/inotropic requirements were not significantly different between the groups (all P>0.05). The occurrences of a composite of morbidity and mortality endpoints were not significantly different between the control (46.0%) and RIC (39.2%) groups (a difference of 6.8 percentage points; 95% confidence interval: -6.4%-20.0%; P=0.283).

CONCLUSIONS

The results of our study do not support repeated RIC to decrease the incidence of AKI after valvular heart surgery.

CLINICAL TRIAL REGISTRATION

NCT02720549.

Preparation of “Open/closed” pores of PLGA-microsphere for controlled release of protein drug

Poly(D,L-lactic-co-glycolic acid)  has been extensively used as a controlled release carrier for drug delivery due to its good biocompatibility, biodegradability, and mechanical strength. In this study, porous PLGA microspheres were fabricated by an emulsion-solvent evaporation technique using poly ethylene glycol (PEG) as an extractable porogen and loaded with  protein (lysozyme) by suspending them in protein solution. For controlled release of protein, porous microspheres containing lysozyme were treated with water-miscible solvents in aqueous phase for production of pore-closed microspheres. The surface morphology of microspheres were investigated using scanning electron microscopy (SEM) for confirmation of its porous microstructure structure. Protein property after release was observed by enzymatic activity assay. The pore-closing process resulted in nonporous microspheres which exhibited sustained release patterns over an extended period.

Achieving Nearly 30% External Quantum Efficiency for Orange-Red Organic Light Emitting Diodes by Employing Thermally Activated Delayed Fluorescence Emitters Composed of 1,8-Naphthalimide-Acridine Hybrids

The combination of rigid acridine donor and 1,8-naphthalimide acceptor has afforded two orange-red emitters of NAI-DMAC and NAI-DPAC with high rigidity in molecular structure and strongly pretwisted charge transfer state. Endowed with high photoluminescence quantum yields (Φ_PL ), distinct thermally activated delayed fluorescence (TADF) characteristics, and preferentially horizontal emitting dipole orientations, these emitters afford record-high orange-red TADF organic light-emitting diodes (OLEDs) with external quantum efficiencies of up to 21-29.2%, significantly surpassing all previously reported orange-to-red TADF OLEDs. Notably, the influence of microcavity effect is verified to support the record-high efficiency. This finding relaxes the usually stringent material requirements for effective TADF emitters by comprising smaller radiative transition rates and less than ideal Φ_PL s.

Triboluminescence and Metal Phosphor for Organic Light-Emitting Diodes: Functional Pt(II) Complexes with Both 2-Pyridylimidazol-2-ylidene and Bipyrazolate Chelates

We report the utilization of both pyrid-2-yl-imidazolylidene and dianionic bipz chelates as constituents in syntheses of a new series of charge-neutral Pt(II) complexes 1-4, among which complex 4 revealed remarkable triboluminescence, i.e., generation of photoemission upon grinding or cracking of the solid sample. The triboluminescence is found to be sensitive to the subtle changes of the associated substituents of pyrid-2-yl-imidazolylidene chelate, as verified by the disappearance of the triboluminescence for complexes 1-3. Alternatively, the well-ordered solid packing of 3, as indicated by the grazing incidence X-ray scattering experiment, serves as an ideal emitter for the fabrication of highly efficient OLEDs, rendering high external quantum efficienciy (25.9%) and luminesce efficiency (90 cd A^-1) at the practical brightness of 100 cd m^-2. The rather low roll-off in efficiency (24.4%, 85 cd A^-1 at high brightness of 1000 cd m^-2) is attributed to the short excited-state lifetime of 3 (∼800 ns) in the solid state, which in turn is associated with the MMLCT transition character.

Nephrolithiasis among male patients with newly diagnosed gout

INTRODUCTION

An elevated serum urate level is recognised as a cause of gouty arthritis and uric acid stone. The level of serum uric acid that accelerates kidney stone formation, however, has not yet been clarified. This study aimed to find out if a high serum urate level is associated with nephrolithiasis.

METHODS

Patients were recruited from the rheumatology clinic of Taipei City Hospital (Renai and Zhongxing branches) in Taiwan from March 2015 to February 2016. A total of 120 Chinese male patients with newly diagnosed gout and serum urate concentration of >7 mg/dL and no history of kidney stones were divided into two groups according to their serum urate level: <10 mg/dL (group 1, n=80) and ≥10 mg/dL (group 2, n=40). The mean body mass index, blood urea nitrogen level, creatinine level, urinary pH, and kidney ultrasonography were compared between the two groups.

RESULTS

There were no significant differences in blood urea nitrogen or creatinine level between the two groups. The urine pH in both groups was similar and not statistically significant. Kidney stone formation was detected via ultrasonography in 6.3% (5/80) and 82.5% (33/40) of patients in groups 1 and 2, respectively (P<0.05).

CONCLUSION

A serum urate level of ≥10 mg/dL may precipitate nephrolithiasis. Further studies are warranted to substantiate the relationship between serum urate level and kidney stone formation.

Sky-Blue Organic Light Emitting Diode with 37% External Quantum Efficiency Using Thermally Activated Delayed Fluorescence from Spiroacridine-Triazine Hybrid

Extremely efficient sky-blue organic electroluminescence with external quantum efficiency of ≈37% is achieved in a conventional planar device structure, using a highly efficient thermally activated delayed fluorescence emitter based on the spiroacridine-triazine hybrid and simultaneously possessing nearly unitary (100%) photoluminescence quantum yield, excellent thermal stability, and strongly horizontally oriented emitting dipoles (with a horizontal dipole ratio of 83%).

Association of bone mineral density and diabetic retinopathy in diabetic subjects: the 2008-2011 Korea National Health and Nutrition Examination Survey

Because diabetic retinopathy increases fracture risk, we studied the association between bone mineral density (BMD) and diabetic retinopathy in a nationally representative sample. A significant association between the presence of diabetic retinopathy and low BMD was observed. Therefore, diabetic retinopathy might be considered as a marker of low BMD.

INTRODUCTION

Several diabetic complications, including nephropathy, retinopathy, and peripheral neuropathy, are associated with a higher fracture risk in diabetic subjects. However, in contrast to diabetic nephropathy and peripheral neuropathy, which are associated with low bone mineral density (BMD), little is known about the association between BMD and diabetic retinopathy. The aim of the present study was to determine whether the prevalence of diabetic retinopathy is associated with BMD.

METHODS

This cross-sectional study included a nationally representative sample consisting of 4357 men aged 50 years and older and 4392 postmenopausal women who participated in the Korea National Health and Nutritional Examination Survey (KNHANES) from 2008 to 2011 and underwent BMD measurement by dual-energy X-ray absorptiometry (DXA) and diabetic retinopathy assessments using seven standard gradable photographs.

RESULTS

The diabetic women with retinopathy had lower mean BMD at all measured sites than those without retinopathy, although the BMD difference between the two groups was small (3-5 %). In addition, the diabetic women with retinopathy were 2.27 times more likely to have osteoporosis following adjustments for all clinically relevant covariates. However, the prevalence of diabetes mellitus (DM) or diabetic retinopathy was not associated with the prevalence of osteoporosis in men.

CONCLUSIONS

This study has shown that the presence of diabetic retinopathy is significantly associated with a reduced BMD and increased prevalence of osteoporosis in diabetic women.

Analyses of optical out-coupling of organic light-emitting devices having micromesh indium tin oxide and conducting polymer as composite transparent electrode

We report the characterization and analyses of organic light-emitting devices (OLEDs) using microstructured composite transparent electrodes consisting of the high-index ITO (indium tin oxide) micromesh and the low-index conducting polymer

PEDOT

PSS [poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)], that are fabricated by the facile and convenient microsphere lithography and are useful for enhancing light extraction. The rigorous electromagnetic simulation based on the three-dimensional finite-difference time-domain (FDTD) method was conducted to study optical properties and mechanisms in such devices. It provides a different but consistent viewpoint/insight of how this microstructured electrode enhances optical out-coupling of OLEDs, compared to that provided by ray optics simulation in previous works. Both experimental and simulation studies indicate such a microstructured electrode effectively enhances coupling of internal radiation into the substrate, compared to devices with the typical planar ITO electrode. By combining this internal extraction structure and the external extraction scheme (e.g. by attaching extraction lens) to further extract radiation into the substrate, a rather high external quantum efficiency of 46.8% was achieved with green phosphorescent OLEDs, clearly manifesting its high potential.

Bis-Tridentate Ir(III) Complexes with Nearly Unitary RGB Phosphorescence and Organic Light-Emitting Diodes with External Quantum Efficiency Exceeding 31%

A new class of neutral bis-tridentate Ir(III) metal complexes that show nearly unitary red, green, and blue emissions in solution is prepared and employed for the fabrication of both monochrome and white-emitting organic light-emitting diodes, among which a green device gives external quantum efficiency exceeding 31%.

Tuberculosis in Malaysia: predictors of treatment outcomes in a national registry

OBJECTIVES

To determine treatment outcomes and associated predictors of all patients registered in 2012 with the Malaysian National Tuberculosis (TB) Surveillance Registry.

METHODS

Sociodemographic and clinical data were analysed. Unfavourable outcomes included treatment failure, transferred out and lost to follow-up, treatment defaulters, those not evaluated and all-cause mortality.

RESULTS

In total, 21 582 patients were registered. The mean age was 42.36 ± 17.77 years, and 14.2% were non-Malaysians. The majority were new cases (93.6%). One fifth (21.5%) had unfavourable outcomes; of these, 46% died, 49% transferred out or defaulted and 1% failed treatment. Predictors of unfavourable outcomes were older age, male sex, foreign citizenship, lower education, no bacille Calmette-Guérin (BCG) vaccination scar, treatment in tertiary settings, smoking, previous anti-tuberculosis treatment, human immunodeficiency virus infection, not receiving directly observed treatment, advanced chest radiography findings, multidrug-resistant TB (MDR-TB) and extra-pulmonary TB. For all-cause mortality, predictors were similar except for rural dwelling and nationality (higher mortality among locals). Absence of BCG scar, previous treatment for TB and MDR-TB were not found to be predictors of all-cause mortality. Indigenous populations in East Malaysia had lower rates of unfavourable treatment outcomes.

CONCLUSIONS

One fifth of TB patients had unfavourable outcomes. Intervention strategies should target those at increased risk of unfavourable outcomes and all-cause mortality.

Ballistocardiogram of baby during sleep

Ballistocardiogram (BCG), which displays the mechanical activity of heart, has been a subject of interest for several years due to its advantages in taking unobtrusive physiological measurements. In the field of sleep science, researchers actively study sleep architecture and clinically apply various sleep-related conditions through BCG-derived biological information such as the heartbeat, respiration and body movements of subjects. However, most of these studies have involved only adults. This area of research may be even more important with babies to monitor their biological signals without confinement. For this reason, we developed a physiological signal monitoring bed for baby by using a load cell. Heartbeat and respiration information was assessed with average respective performance errors of 1.53% and 2.53% compared to commercial equipment. The results showed the possibility of applying BCG technology to baby. Therefore, we expect that BCG-derived signals can be extensively applied to analyze sleep architecture and clinical applications in baby as they are with adults.

Association between prolonged breastfeeding and bone mineral density and osteoporosis in postmenopausal women: KNHANES 2010-2011

This study showed that a negative correlation between duration of breastfeeding and bone mineral density (BMD) in the lumbar spine and prolonged breastfeeding is an independent risk for osteoporosis in postmenopausal women. The present study suggests that postmenopausal women with a history of prolonged breastfeeding require more careful screening for osteoporosis.

INTRODUCTION

Several studies suggest that breastfeeding and childbirth lead to maternal calcium loss and a decline in BMD. While the association between breastfeeding and BMD immediately after weaning is well-established, the effects of breastfeeding on postmenopausal women have been controversial. The aim of this study was to examine the effects of breastfeeding on bone mineral density (BMD) and the prevalence of osteoporosis in postmenopausal women.

METHODS

The present study was a cross-sectional survey based on the Korea National Health and Nutrition Examination Survey (KNHANES) 2010 and 2011 data. The association between breastfeeding and BMD and osteoporosis was examined in 1222 postmenopausal women.

RESULTS

The duration of breastfeeding and BMD in the lumbar spine showed a negative correlation. The association between duration of breastfeeding and BMD remained significant after adjustment for reproductive factors and other confounding factors (P = 0.008). However, the number of deliveries and age at the time of delivery did not correlate with BMD at any site after adjustment. Moreover, the prevalence of osteoporosis in postmenopausal women with a history of prolonged breastfeeding was significantly higher than that in women with a short history of breastfeeding (≥37 months, OR = 3.292; 95 % CI 1.485-7.299). The prevalence of lumbar spine fracture showed a significant increasing trend with the increase in the duration of breastfeeding.

CONCLUSION

Prolonged breastfeeding was significantly associated with low BMD in the lumbar spine and higher prevalence of osteoporosis. However, the number of deliveries or age at the time of childbirth did not influence BMD.

Prevalence of Psychiatric Morbidity in Chinese Subjects with Knee Osteoarthritis in a Hong Kong Orthopaedic Clinic

OBJECTIVES

To identify the prevalence and associated factors of psychiatric morbidities and to evaluate the effectiveness of the Hospital Anxiety and Depression Scale (HADS) in screening for psychiatric morbidity in patients with knee osteoarthritis in a local orthopaedic clinic.

METHODS

A cross-sectional study was conducted from October 2013 to June 2014 to examine patients with knee osteoarthritis at a specialist orthopaedic clinic. They were evaluated for psychiatric diagnoses using the Structured Clinical Interview for the DSM-IV Axis I Disorders (SCID-I). Socio-demographic and clinical characteristics were compared between patients with and without psychiatric morbidity. Patients were also assessed using the HADS with accuracy compared with the psychiatric diagnoses made by the SCID-I.

RESULTS

Of the 115 patients, 39% had current psychiatric disorders. Logistic regression analysis revealed that limitation of physical activity, perceived absence of confidant, a history of psychiatric illness, and follow-up in orthopaedic clinic for ≥ 5 years were independent factors associated with psychiatric disorders.

CONCLUSIONS

The prevalence of psychiatric morbidity in patients with knee osteoarthritis was significant. Recognition of independent associated factors can aid in early identification and intervention in patients who are at risk of developing psychiatric morbidity. The HADS is a useful means to screen for psychiatric morbidity in these patients.

Enhancing Optical Out-Coupling of Organic Light-Emitting Devices with Nanostructured Composite Electrodes Consisting of Indium Tin Oxide Nanomesh and Conducting Polymer

A nanostructured composite electrode consisting of a high-index indium-tin-oxide nanomesh and low-index high-conductivity conducting polymer effectively enhances coupling of internal radiation of organic light-emitting devices into their substrates. When combining this internal extraction structure and the external extraction scheme, a very high external quantum efficiency of nearly 62% is achieved with a green phosphorescent device.

Psychiatric Morbidity in Chinese Adults with Type 1 Diabetes in Hong Kong

OBJECTIVES

To determine the prevalence of psychiatric morbidity and identify the correlates, as well as to evaluate the effectiveness of the Hospital Anxiety and Depression Scale and the 12-item General Health Questionnaire to screen for psychiatric morbidity in Chinese adults with type 1 diabetes.

METHODS

Subjects were recruited from a local public diabetes specialist outpatient clinic from August 2013 to January 2014. Demographic and clinical factors were recorded. Psychiatric diagnosis was established using the Chinese-bilingual version of the Structured Clinical Interview for the DSM-IV Axis I disorders. Scores for the Hospital Anxiety and Depression Scale and the 12-item General Health Questionnaire were compared with the psychiatric diagnosis.

RESULTS

Of the 136 patients, the point prevalence of overall psychiatric, depressive, and anxiety disorders was 39.7%, 23.5%, and 25.7%, respectively. Family history of mental illness, smoking status, history of mental illness, presence of social problems, perceived absence of confidant, presence of neuropathy and hyperlipidaemia, as well as higher level of glycosylated haemoglobin were found to be the associated factors.

CONCLUSION

Psychiatric disorders were common in Chinese adults with type 1 diabetes. Finding out associated factors and using the Hospital Anxiety and Depression Scale and the 12-item General Health Questionnaire as a screening tool helped to identify patients in a diabetes clinic with psychiatric disorders.

A genetic variation in microRNA target site of KRT81 gene is associated with survival in early-stage non-small-cell lung cancer

BACKGROUND

MicroRNAs (miRNAs) have a key role in carcinogenesis through negative regulation of their target genes. Therefore, genetic variations in miRNAs or their target sites may affect miRNA-mRNA interactions, thereby result in altered expression of target genes. This study was conducted to investigate the associations between single-nucleotide polymorphisms (SNP) located in the miRNA target sites (poly-miRTSs) and survival of patients with early-stage non-small-cell lung cancer (NSCLC).

METHODS

Using public SNP database and miRNA target sites prediction program, 354 poly-miRTSs were selected for genotyping. Among these, 154 SNPs applicable to Sequenom's MassARRAY platform were investigated in 357 patients. A replication study was carried out on an independent patient population (n = 479). Renilla luciferase assay and reverse transcription-polymerase chain reaction were conducted to examine functional relevance of potentially functional poly-miRTSs.

RESULTS

Of the 154 SNPs analyzed in a discovery set, 14 SNPs were significantly associated with survival outcomes. Among these, KRT81 rs3660G>C was found to be associated with survival outcomes in the validation cohort. In the combined analysis, patients with the rs3660 GC + CC genotype had a significantly better overall survival compared with those with GG genotype [adjusted hazard ratio (aHR) for OS, 0.65; 95% confidence interval (CI) 0.50-0.85; P = 0.001]. An increased expression of the reporter gene for the C allele of rs3660 compared with the G allele was observed by luciferase assay. Consistently, the C allele was associated with higher relative expression level of KRT81 in tumor tissues.

CONCLUSION

The rs3660G>C affects KRT81 expression and thus influences survival in early-stage NSCLC. The analysis of the rs3660G>C polymorphism may be useful to identify patients at high risk of a poor disease outcome.