Strategies for subtyping primary aldosteronism.

Adrenal venous sampling (AVS) is a crucial method for the lateralization of primary aldosteronism (PA). It is advised to halt the use of the patient's antihypertensive medications and correct hypokalemia prior to undergoing AVS. Hospitals equipped to conduct AVS should establish their own diagnostic criteria based on current guidelines. If the patient's antihypertensive medications cannot be discontinued, AVS can be performed as long as the serum renin level is suppressed. The Task Force of Taiwan PA recommends using a combination of adrenocorticotropic hormone stimulation, quick cortisol assay, and C-arm cone-beam computed tomography to maximize the success of AVS and minimize errors by using the simultaneous sampling technique. If AVS is not successful, an NP-59 (131 I-6-ß-iodomethyl-19-norcholesterol) scan can be used as an alternative method to lateralize PA. We depicted the details of the lateralization procedures (mainly AVS, and alternatively NP-59) and their tips and tricks for confirmed PA patients who would consider to undergo surgical treatment (unilateral adrenalectomy) if the subtyping shows unilateral disease.

The role of confirmatory tests in the diagnosis of primary aldosteronism.

Confirmatory tests for diagnosis of primary aldosteronism (PA) play an important role in sparing patients with a false-positive aldosterone-to-renin ratio (ARR) screening test from undergoing invasive subtyping procedures. We recommend that patients with a positive ARR test should undergo at least one confirmatory test to confirm or exclude the diagnosis of PA before directly proceeding to subtype studies, except for patients with significant PA phenotypes, including spontaneous hypokalemia, plasma aldosterone concentration >20 ng/dL plus plasma renin activity below a detectable level. Although a gold standard confirmatory test has not been identified, we recommend that saline infusion test and captopril challenge test, which were widely used in Taiwan. Patients with PA have been reported to have a higher prevalence of concurrent autonomous cortisol secretion (ACS). ACS is a biochemical condition of mild cortisol overproduction from adrenal lesions, but without the typical clinical features of overt Cushing's syndrome. Concurrent ACS may result in incorrect interpretation of adrenal venous sampling (AVS) and may lead to adrenal insufficiency after adrenalectomy. We recommend screening for ACS in patients with PA scheduled for AVS examinations as well as for adrenalectomy. We recommend the 1-mg overnight dexamethasone suppression test as screening method to detect ACS.

Comparison of fatal traumatic medico-legal cases with postmortem computed tomography and autopsy: A pilot study in Taiwan.

In order to determine the performance of postmortem computed tomography (PMCT) in identifying traumatic-relevant macroscopic findings in medico-legal cases, this retrospective observational pilot study involving nine trauma casualties who had received PMCT prior to autopsy. The comparison of these findings in six anatomical regions as dictated in Injury Severity Score (ISS) were performed. 104 traumatic-relevant findings were identified with achievement of 51% congruent findings. PMCT and autopsy had additionally found 22 and 29 findings respectively. PMCT had highest sensitivity for extremity injury (81.82%), followed by chest (73.91%), head, neck and face (71.43%), and abdomino-pelvic area (50%). It had excellent detection rate in abnormal air collection, fracture, foreign body localization, internal ballistic and intracranial pathology. However, the solid organ and vascular injuries as well as integumentary lesions were the major drawback. In conclusion, incorporation of PMCT to autopsy in medico-legal investigation helps to preserve the most abundant traumatic-relevant injuries compared to either modality.

Embedding Biometric Information in Interpolated Medical Images with a Reversible and Adaptive Strategy.

How to hide messages in digital images so that messages cannot be discovered and tampered with is a compelling topic in the research area of cybersecurity. The interpolation-based reversible data hiding (RDH) scheme is especially useful for the application of medical image management. The biometric information of patients acquired by biosensors is embedded into an interpolated medical image for the purpose of authentication. The proposed scheme classifies pixel blocks into complex and smooth ones according to each block's dynamic range of pixel values. For a complex block, the minimum-neighbor (MN) interpolation followed by DIM embedding is applied, where DIM denotes the difference between the block's interpolated pixel values and the maximum pixel values. For a smooth block, the block mean (BM) interpolation is followed by a prediction error histogram (PEH) embedding and a difference expansion (DE) embedding is applied. Compared with previous methods, this adaptive strategy ensures low distortion due to embedding for smooth blocks while it provides a good payload for complex blocks. Our scheme is suitable for both medical and general images. Experimental results confirm the effectiveness of the proposed scheme. Performance comparisons with state-of-the-art schemes are also given. The peak signal to noise ratio (PSNR) of the proposed scheme is 10.32 dB higher than the relevant works in the best case.

Self-Supervised Learning Framework toward State-of-the-Art Iris Image Segmentation.

Iris segmentation plays a pivotal role in the iris recognition system. The deep learning technique developed in recent years has gradually been applied to iris recognition techniques. As we all know, applying deep learning techniques requires a large number of data sets with high-quality manual labels. The larger the amount of data, the better the algorithm performs. In this paper, we propose a self-supervised framework utilizing the pix2pix conditional adversarial network for generating unlimited diversified iris images. Then, the generated iris images are used to train the iris segmentation network to achieve state-of-the-art performance. We also propose an algorithm to generate iris masks based on 11 tunable parameters, which can be generated randomly. Such a framework can generate an unlimited amount of photo-realistic training data for down-stream tasks. Experimental results demonstrate that the proposed framework achieved promising results in all commonly used metrics. The proposed framework can be easily generalized to any object segmentation task with a simple fine-tuning of the mask generation algorithm.

Allometric scaling and growth: Evaluation and applications in subadult body mass estimation.

OBJECTIVES: Previously developed methods in subadult body mass estimation have not been tested in populations other than European-American or African-American. This study uses a contemporary Taiwanese sample to test these methods. Through evaluating their accuracy and bias, we addressed whether the allometric relationships between body mass and skeletal traits commonly used in subadult body mass estimation are conserved among different populations. MATERIALS AND METHODS: Computed tomography scans of lower limbs from individuals aged 0-17 years old of both sexes were collected from National Taiwan University Hospital along with documented body weight. Polar second moment of area, distal femoral metaphyseal breadth, and maximum superior/inferior femoral head diameter were collected either directly from the scans or from reconstructed 3D models. Estimated body mass was compared with documented body mass to assess the performance of the equations. RESULTS: Current methods provided good body mass estimates in Taiwanese individuals, with accuracy and bias similar to those reported in other validation studies. A tendency for increasing error with increasing age was observed for all methods. Reduced major axis regression showed the allometric relationships between different skeletal traits and body mass across different age categories can all be summarized using a common fitted line. A revised, maximum likelihood-based approach was proposed for all skeletal traits. DISCUSSION: The results suggested that the allometric relationships between body mass and different skeletal traits are largely conserved among populations. The revised method provided improved applicability with strong underlying theoretical justifications, and potential for future improvements.

Taiwan mini-frontier of primary aldosteronism: Updating detection and diagnosis.

BACKGROUND: To update information about the internationally accepted standards and clinical recommendations for the detection and diagnosis of primary aldosteronism (PA). METHODS: The Taiwan Society of Aldosteronism (TSA) Task Force reviewed the latest literature and reached a consensus after group meetings. The nine critical issues were recognized to provide updated information and internationally acceptable protocols. RESULTS: When screening for PA by using the plasma aldosterone concentration (PAC) to plasma renin activity (PRA) ratio (ARR), withdrawal or adjustment of antihypertensive medication is not always necessary on the first patient visit. Hypokalemia should be corrected before ARR screening. In spontaneous hypokalemia, plasma renin below detection levels, and PAC higher than 20 ng/dL (550 pmol/L), further confirmatory testing is unnecessary for PA diagnosis. Direct renin concentration (DRC) could be used for PA diagnosis if PRA is unavailable. Although additional confirmatory tests are suggested, the result of a single test is still reliable. For patient safety, discontinuation or adjustment of antihypertensive medications is indicated before adrenal venous sampling (AVS). ACTH could be beneficial for successful adrenal vein cannulation but is not necessary for determining lateralization in AVS. Simultaneous technique is preferred for AVS. Adrenal NP-59 scintigraphy integrated with SPECT/CT could guide PA management. CONCLUSION: With introduction of these new concepts to the clinicians, we expect better identification, management and treatment of PA patients.

Using an Optimization Algorithm to Detect Hidden Waveforms of Signals.

Source signals often contain various hidden waveforms, which further provide precious information. Therefore, detecting and capturing these waveforms is very important. For signal decomposition (SD), discrete Fourier transform (DFT) and empirical mode decomposition (EMD) are two main tools. They both can easily decompose any source signal into different components. DFT is based on Cosine functions; EMD is based on a collection of intrinsic mode functions (IMFs). With the help of Cosine functions and IMFs respectively, DFT and EMD can extract additional information from sensed signals. However, due to a considerably finite frequency resolution, EMD easily causes frequency mixing. Although DFT has a larger frequency resolution than EMD, its resolution is also finite. To effectively detect and capture hidden waveforms, we use an optimization algorithm, differential evolution (DE), to decompose. The technique is called SD by DE (SDDE). In contrast, SDDE has an infinite frequency resolution, and hence it has the opportunity to exactly decompose. Our proposed SDDE approach is the first tool of directly applying an optimization algorithm to signal decomposition in which the main components of source signals can be determined. For source signals from four combinations of three periodic waves, our experimental results in the absence of noise show that the proposed SDDE approach can exactly or almost exactly determine their corresponding separate components. Even in the presence of white noise, our proposed SDDE approach is still able to determine the main components. However, DFT usually generates spurious main components; EMD cannot decompose well and is easily affected by white noise. According to the superior experimental performance, our proposed SDDE approach can be widely used in the future to explore various signals for more valuable information.

Screen-Shooting Resilient Watermarking Scheme via Learned Invariant Keypoints and QT.

This paper proposes a screen-shooting resilient watermarking scheme via learned invariant keypoints and QT; that is, if the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the photo. A screen-shooting resilient watermarking algorithm should meet the following two basic requirements: robust keypoints and a robust watermark algorithm. In our case, we embedded watermarks by combining the feature region filtering model to SuperPoint (FRFS) neural networks, quaternion discrete Fourier transform (QDFT), and tensor decomposition (TD). First we applied FRFS to locate the embedding feature regions which are decided by the keypoints that survive screen-shooting. Second, we structured watermark embedding regions centered at keypoints. Third, the watermarks were embedded by the QDFT and TD (QT) algorithm, which is robust for capturing process attacks. In a partial shooting scenario, the watermark is repeatedly embedded into different regions in an image to enhance robustness. Finally, we extracted the watermarks from at least one region at the extraction stage. The experimental results showed that the proposed scheme is very robust for camera shooting (including partial shooting) different shooting scenarios, and special attacks. Moreover, the efficient mechanism of screen-shooting resilient watermarking could have propietary protection and leak tracing applications.

Object Detection and Depth Estimation Approach Based on Deep Convolutional Neural Networks.

In this paper, we present a real-time object detection and depth estimation approach based on deep convolutional neural networks (CNNs). We improve object detection through the incorporation of transfer connection blocks (TCBs), in particular, to detect small objects in real time. For depth estimation, we introduce binocular vision to the monocular-based disparity estimation network, and the epipolar constraint is used to improve prediction accuracy. Finally, we integrate the two-dimensional (2D) location of the detected object with the depth information to achieve real-time detection and depth estimation. The results demonstrate that the proposed approach achieves better results compared to conventional methods.

Reversible Data Hiding in Encrypted Image Based on (7, 4) Hamming Code and UnitSmooth Detection.

With the development of cloud storage and privacy protection, reversible data hiding in encrypted images (RDHEI) plays the dual role of privacy protection and secret information transmission. RDHEI has a good application prospect and practical value. The current RDHEI algorithms still have room for improvement in terms of hiding capacity, security and separability. Based on (7, 4) Hamming Code and our proposed prediction/ detection functions, this paper proposes a Hamming Code and UnitSmooth detection based RDHEI scheme, called HUD-RDHEI scheme for short. To prove our performance, two database sets-BOWS-2 and BOSSBase-have been used in the experiments, and peak signal to noise ratio (PSNR) and pure embedding rate (ER) are served as criteria to evaluate the performance on image quality and hiding capacity. Experimental results confirm that the average pure ER with our proposed scheme is up to 2.556 bpp and 2.530 bpp under BOSSBase and BOWS-2, respectively. At the same time, security and separability is guaranteed. Moreover, there are no incorrect extracted bits during data extraction phase and the visual quality of directly decrypted image is exactly the same as the cover image.

Preoperative non-stimulated adrenal venous sampling index for predicting outcomes of adrenalectomy for unilateral primary aldosteronism.

BACKGROUND/PURPOSE: The aim of this study was to predict outcomes of adrenalectomy for unilateral primary aldosteronism (PA) using non-stimulated adrenal venous sampling (AVS) indices and the standardized Primary Aldosteronism Surgical Outcome (PASO) criteria. METHODS: Patients with unilateral PA who underwent adrenalectomy based on non-stimulated AVS and had follow-up data regarding surgical outcomes between 2011 and 2016 were enrolled. Demographic data and non-stimulated AVS indices, including lateralization index (LI) and contralateral suppression, were collected for analysis. RESULTS: This study included 54 patients who underwent adrenalectomy. Clinical and biochemical outcomes were evaluated in all patients and in 52 (96.3%) patients, respectively. Complete clinical and biochemical success was achieved in 31 (57.4%) of 54 patients and 42 (80.8%) of 52 patients, respectively. An LI > 4 was significantly associated with complete clinical and biochemical success (OR = 4.30, 95% CI 1.18-15.68, p = 0.03, and OR = 7.55, 95% CI 1.28-44.47, p = 0.03, respectively). Contralateral suppression was an independent predictor of complete biochemical success (OR = 17.27, 95% CI 1.95-153.21, p = 0.01). CONCLUSION: Non-stimulated AVS indices including LI and contralateral suppression are reliable preoperative determinants for predicting the outcomes of adrenalectomy in patients with unilateral PA. Our findings provide more evidence and confidence to clinicians when applying non-stimulated AVS to determine PA treatment.

An Efficient Data-Hiding Scheme Based on Multidimensional Mini-SuDoKu.

The massive Internet of Things (IoT) connecting various types of intelligent sensors for goods tracking in logistics, environmental monitoring and smart grid management is a crucial future ICT. High-end security and low power consumption are major requirements in scaling up the IoT. In this research, we propose an efficient data-hiding scheme to deal with the security problems and power saving issues of multimedia communication among IoT devises. Data hiding is the practice of hiding secret data into cover images in order to conceal and prevent secret data from being intercepted by malicious attackers. One of the established research streams of data-hiding methods is based on reference matrices (RM). In this study, we propose an efficient data-hiding scheme based on multidimensional mini-SuDoKu RM. The proposed RM possesses high complexity and can effectively improve the security of data hiding. In addition, this study also defines a range locator function which can significantly improve the embedding efficiency of multidimensional RM. Experimental results show that our data-hiding scheme can not only obtain better image quality, but also achieve higher embedding capacity than other related schemes.

A Maze Matrix-Based Secret Image Sharing Scheme with Cheater Detection.

Secret image sharing is a technique for sharing a secret message in such a fashion that stego image shadows are generated and distributed to individual participants. Without the complete set of shadows shared among all participants, the secret could not be deciphered. This technique may serve as a crucial means for protecting private data in massive Internet of things applications. This can be realized by distributing the stego image shadows to different devices on the Internet so that only the ones who are authorized to access these devices can extract the secret message. In this paper, we proposed a secret image sharing scheme based on a novel maze matrix. A pair of image shadows were produced by hiding secret data into two distinct cover images under the guidance of the maze matrix. A two-layered cheat detection mechanism was devised based on the special characteristics of the proposed maze matrix. In addition to the conventional joint cheating detection, the proposed scheme was able to identify the tampered shadow presented by a cheater without the information from other shadows. Furthermore, in order to improve time efficiency, we derived a pair of Lagrange polynomials to compute the exact pixel values of the shadow images instead of resorting to time-consuming and computationally expensive conventional searching strategies. Experimental results demonstrated the effectiveness and efficiency of the proposed secret sharing scheme and cheat detection mechanism.

A Secure IoT-Based Authentication System in Cloud Computing Environment.

The Internet of Things (IoT) is currently the most popular field in communication and information techniques. However, designing a secure and reliable authentication scheme for IoT-based architectures is still a challenge. In 2019, Zhou et al. showed that schemes pro-posed by Amin et al. and Maitra et al. are vulnerable to off-line guessing attacks, user tracking attacks, etc. On this basis, a lightweight authentication scheme based on IoT is proposed, and an authentication scheme based on IoT is proposed, which can resist various types of attacks and realize key security features such as user audit, mutual authentication, and session security. However, we found weaknesses in the scheme upon evaluation. Hence, we proposed an enhanced scheme based on their mechanism, thus achieving the security requirements and resisting well-known attacks.

Comparison of single and tandem ureteral stenting for malignant ureteral obstruction: a prospective study of 104 patients.

OBJECTIVES: The aim of this study was to compare single and tandem ureteral stenting in the management of malignant ureteral obstruction (MUO). METHODS: Our hospital's institutional review board approved this prospective study. Between November 2014 and June 2017, single ureteral stenting was performed in 56 patients (94 renal units) and tandem ureteral stenting in 48 patients (63 renal units) for MUO. A comparative analysis of the technical success rate, patient survival, stent patency, and complications was performed. RESULTS: Similar demographic data were observed in patients receiving either single or tandem ureteral stenting. The technical success rate was 93.6% (88/94) for single ureteral stenting and 95.2% (60/63) for tandem ureteral stenting. There was no difference in overall survival between patients receiving single or tandem ureteral stenting (p = 0.41), but the duration of stent patency in tandem ureteral stenting was significantly longer (p = 0.022). The mean patency time was 176.7 ± 21.3 days for single ureteral stenting, and 214.7 ± 21.0 days for tandem ureteral stenting. The complications of ureteral stenting were urinary tract infection (n = 18), lower urinary tract symptoms (n = 5), haematuria (n = 3), and stent migration (n = 1). CONCLUSIONS: Tandem ureteral stenting is a safe and feasible treatment for MUO, and had better efficacy compared to single ureteral stenting. KEY POINTS: • Ureteral stenting is an established treatment for the management of malignant ureteral obstruction (MUO) • Prospective single-centre study showed that tandem ureteral stenting is a safe and feasible treatment for MUO • Tandem ureteral stenting provides longer stent patency compared to single ureteral stenting in patient with MUO.

Targeted treatment of primary aldosteronism - The consensus of Taiwan Society of Aldosteronism.

BACKGROUND/PURPOSE: Even with the increasing recognition of primary aldosteronism (PA) as a cause of refractory hypertension and an issue of public health, the consensus of its optimal surgical or medical treatment in Taiwan has not been reached. Our objective was to develop a clinical practice guideline that is feasible for real-world management of PA patients in Taiwan. METHODS: The Taiwan Society of Aldosteronism (TSA) Task Force recognized the above-mentioned issues and reached this Taiwan PA consensus at its inaugural meeting, in order to provide updated information of internationally acceptable standards, and also to incorporate our local disease characteristics and constraints into PA management. RESULTS: In patients with lateralized PA, including aldosterone producing adenoma (APA), laparoscopic adrenalectomy is the 'gold standard' of treatment. Mini-laparoscopic and laparoendoscopic single-site approaches are feasible only in highly experienced surgeons. Patients with bilateral adrenal hyperplasia or those not suitable for surgery should be treated by mineralocorticoid receptor antagonists. The outcome data of PA patient management from the literature, especially from PA patients in Taiwan, are reviewed. Mental health screening is helpful in early detection and management of psychopathology among PA patients. CONCLUSION: We hope this consensus will provide a guideline to help medical professionals to manage PA patients in Taiwan to achieve a better quality of care.

PET/MRI for evaluating subclinical inflammation of ulcerative colitis.

PURPOSE: To explore the utility of integrated positron emission tomography (PET) / magnetic resonance imaging (MRI) for evaluating subclinical inflammation in patients with ulcerative colitis (UC). MATERIALS AND METHODS: This prospective study was approved by the Institutional Review Board and informed consent was obtained. Between November 2015 and April 2016, 19 consecutive patients with UC in clinical remission were enrolled. These patients underwent 18F-fluorodeoxyglucose PET/MRI (3T) and colonoscopy. Serum high-sensitivity C-reactive protein (hs-CRP) and fecal calprotectin (FC) levels were also obtained. The findings of colonoscopy were graded using the Mayo endoscopic subscore. Quantitative (minimum apparent diffusion coefficient [ADCmin ] and maximum standardized uptake value [SUVmax ]), semiquantitative, and qualitative parameters of PET/MRI were evaluated and correlated with colonoscopic findings. RESULTS: In per-segment analysis, ADCmin was significantly lower and SUVmax and ratio of SUVmax to ADCmin were significantly higher in the colonic segments with active inflammation (Mayo endoscopic subscore ≥2) (P < 0.05). Qualitative MRI score, PET activity grade, and PET/MRI score were also significantly higher in the colonic segments with active inflammation (P < 0.05). Among these parameters, the ratio of SUVmax to ADCmin exhibited the highest area under the receiver operating characteristic curve (AUC) (0.763). In per-patient analysis, the AUC of PET activity grade was 0.778, higher than those of hs-CRP (0.589) and FC (0.722). Using a combined index of FC and PET, an even higher AUC (0.867) was achieved. CONCLUSION: PET/MRI is a potentially useful tool in identifying subclinical inflammation in patients with UC. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:737-745.

Risk of sepsis in patients with primary aldosteronism.

BACKGROUND: The interaction between hyperaldosteronism and immune dysfunction has been reported and glucocorticoid co-secretion is frequently found in primary aldosteronism (PA). The aforementioned conditions raise the possibility of the infection risk; however, clinical episodes of sepsis have not been reported in PA. METHODS: Using Taiwan's National Health Insurance Research Database between 1997 and 2009, we identified PA and aldosterone-producing adenoma (APA) matched with essential hypertension (EH) at a 1:1 ratio by propensity scores. The incidences of sepsis and mortality after the index date were evaluated, and the risk factors of outcomes were identified using adjusted Cox proportional hazards models and taking mortality as a competing risk. RESULTS: We enrolled 2448 patients with PA (male, 46.08%; mean age, 48.4 years). There were 875 patients who could be ascertained as APA. Taking mortality as the competing risk, APA patients had a lower incidence of sepsis than their matched EH patients (hazard ratio (HR) 0.29; P < 0.001) after target treatments. Patients receiving adrenalectomy showed a benefit of decreasing the risk of sepsis (PA vs EH, HR 0.14, P = 0.001; APA vs EH, HR 0.16, P = 0.003), but mineralocorticoid receptor antagonist treatment may differ. Compared with matched control cohorts, patients with APA had a lower risk of all-cause mortality (PA, adjusted HR 0.84, P = 0.050; APA, adjusted HR 0.31, P < 0.001) after target treatments. CONCLUSIONS: Our study demonstrated that patients with PA/APA who underwent adrenalectomy could attenuate the risk of sepsis compared with their matched EH patients. We further found that APA patients with target treatments could decrease all-cause mortality compared with EH patients.

Optimization of CNN through Novel Training Strategy for Visual Classification Problems.

The convolution neural network (CNN) has achieved state-of-the-art performance in many computer vision applications e.g., classification, recognition, detection, etc. However, the global optimization of CNN training is still a problem. Fast classification and training play a key role in the development of the CNN. We hypothesize that the smoother and optimized the training of a CNN goes, the more efficient the end result becomes. Therefore, in this paper, we implement a modified resilient backpropagation (MRPROP) algorithm to improve the convergence and efficiency of CNN training. Particularly, a tolerant band is introduced to avoid network overtraining, which is incorporated with the global best concept for weight updating criteria to allow the training algorithm of the CNN to optimize its weights more swiftly and precisely. For comparison, we present and analyze four different training algorithms for CNN along with MRPROP, i.e., resilient backpropagation (RPROP), Levenberg-Marquardt (LM), conjugate gradient (CG), and gradient descent with momentum (GDM). Experimental results showcase the merit of the proposed approach on a public face and skin dataset.