Hemolytic Anemia as a Provoking Factor for Recurrent Venous Thromboembolism: A Case Report.

Warm antibody autoimmune hemolytic anemia (WAIHA) is a rare disease that leads to the destruction of red blood cells in the reticuloendothelial system through the mediation of agglutinins (immunoglobulin G (IgG) type in most cases) that attach to the erythrocyte wall at 37 °C. The association of WAIHA and venous thromboembolism (VTE) seems to be higher than other hemolytic disorders classically associated with VTE and there is a current investigation aimed at clarifying this association and establishing some criteria to use anticoagulant treatment in patients with WAIHA. Despite this, WAIHA is a rare cause for the development of recurrent VTE under secondary prophylactic anticoagulant treatment, with only a few cases described in the literature. We present the case of a patient who developed a recurrence of deep vein thrombosis during a WAIHA episode despite treatment with acenocoumarol and a review of the literature.

Ethnicity and Clinical Outcomes in Patients Hospitalized for COVID-19 in Spain: Results from the Multicenter SEMI-COVID-19 Registry.

(1) Background: This work aims to analyze clinical outcomes according to ethnic groups in patients hospitalized for COVID-19 in Spain. (2) Methods: This nationwide, retrospective, multicenter, observational study analyzed hospitalized patients with confirmed COVID-19 in 150 Spanish hospitals (SEMI-COVID-19 Registry) from 1 March 2020 to 31 December 2021. Clinical outcomes were assessed according to ethnicity (Latin Americans, Sub-Saharan Africans, Asians, North Africans, Europeans). The outcomes were in-hospital mortality (IHM), intensive care unit (ICU) admission, and the use of invasive mechanical ventilation (IMV). Associations between ethnic groups and clinical outcomes adjusted for patient characteristics and baseline Charlson Comorbidity Index values and wave were evaluated using logistic regression. (3) Results: Of 23,953 patients (median age 69.5 years, 42.9% women), 7.0% were Latin American, 1.2% were North African, 0.5% were Asian, 0.5% were Sub-Saharan African, and 89.7% were European. Ethnic minority patients were significantly younger than European patients (median (IQR) age 49.1 (40.5−58.9) to 57.1 (44.1−67.1) vs. 71.5 (59.5−81.4) years, p < 0.001). The unadjusted IHM was higher in European (21.6%) versus North African (11.4%), Asian (10.9%), Latin American (7.1%), and Sub-Saharan African (3.2%) patients. After further adjustment, the IHM was lower in Sub-Saharan African (OR 0.28 (0.10−0.79), p = 0.017) versus European patients, while ICU admission rates were higher in Latin American and North African versus European patients (OR (95%CI) 1.37 (1.17−1.60), p < 0.001) and (OR (95%CI) 1.74 (1.26−2.41), p < 0.001). Moreover, Latin American patients were 39% more likely than European patients to use IMV (OR (95%CI) 1.43 (1.21−1.71), p < 0.001). (4) Conclusion: The adjusted IHM was similar in all groups except for Sub-Saharan Africans, who had lower IHM. Latin American patients were admitted to the ICU and required IMV more often.

Geometric Analysis of Signals for Inference of Multiple Faults in Induction Motors.

Multiple fault identification in induction motors is essential in industrial processes due to the high costs that unexpected failures can cause. In real cases, the motor could present multiple faults, influencing systems that classify isolated failures. This paper presents a novel methodology for detecting multiple motor faults based on quaternion signal analysis (QSA). This method couples the measured signals from the motor current and the triaxial accelerometer mounted on the induction motor chassis to the quaternion coefficients. The QSA calculates the quaternion rotation and applies statistics such as mean, variance, kurtosis, skewness, standard deviation, root mean square, and shape factor to obtain their features. After that, four classification algorithms are applied to predict motor states. The results of the QSA method are validated for ten classes: four single classes (healthy condition, unbalanced pulley, bearing fault, and half-broken bar) and six combined classes. The proposed method achieves high accuracy and performance compared to similar works in the state of the art.

Artificial Intelligence to Analyze the Cortical Thickness Through Age.

In this study, Artificial Intelligence was used to analyze a dataset containing the cortical thickness from 1,100 healthy individuals. This dataset had the cortical thickness from 31 regions in the left hemisphere of the brain as well as from 31 regions in the right hemisphere. Then, 62 artificial neural networks were trained and validated to estimate the number of neurons in the hidden layer. These neural networks were used to create a model for the cortical thickness through age for each region in the brain. Using the artificial neural networks and kernels with seven points, numerical differentiation was used to compute the derivative of the cortical thickness with respect to age. The derivative was computed to estimate the cortical thickness speed. Finally, color bands were created for each region in the brain to identify a positive derivative, that is, a part of life with an increase in cortical thickness. Likewise, the color bands were used to identify a negative derivative, that is, a lifetime period with a cortical thickness reduction. Regions of the brain with similar derivatives were organized and displayed in clusters. Computer simulations showed that some regions exhibit abrupt changes in cortical thickness at specific periods of life. The simulations also illustrated that some regions in the left hemisphere do not follow the pattern of the same region in the right hemisphere. Finally, it was concluded that each region in the brain must be dynamically modeled. One advantage of using artificial neural networks is that they can learn and model non-linear and complex relationships. Also, artificial neural networks are immune to noise in the samples and can handle unseen data. That is, the models based on artificial neural networks can predict the behavior of samples that were not used for training. Furthermore, several studies have shown that artificial neural networks are capable of deriving information from imprecise data. Because of these advantages, the results obtained in this study by the artificial neural networks provide valuable information to analyze and model the cortical thickness.

Generating X-ray Images from Point Clouds Using Conditional Generative Adversarial Networks.

Simulating medical images such as X-rays is of key interest to reduce radiation in non-diagnostic visualization scenarios. Past state of the art methods utilize ray tracing, which is reliant on 3D models. To our knowledge, no approach exists for cases where point clouds from depth cameras and other sensors are the only input modality. We propose a method for estimating an X-ray image from a generic point cloud using a conditional generative adversarial network (CGAN). We train a CGAN pix2pix to translate point cloud images into X-ray images using a dataset created inside our custom synthetic data generator. Additionally, point clouds of multiple densities are examined to determine the effect of density on the image translation problem. The results from the CGAN show that this type of network can predict X-ray images from points clouds. Higher point cloud densities outperformed the two lowest point cloud densities. However, the networks trained with high-density point clouds did not exhibit a significant difference when compared with the networks trained with medium densities. We prove that CGANs can be applied to image translation problems in the medical domain and show the feasibility of using this approach when 3D models are not available. Further work includes overcoming the occlusion and quality limitations of the generic approach and applying CGANs to other medical image translation problems.

Learning Conflicts for C-arm Kinematic Modeling using Artificial Intelligence.

During common surgical tasks related to orthopedic applications, it is necessary to carefully manipulate a mobile C-arm device to achieve the desired position. In this work, we propose the application of learning conflicts analysis to improve the performance of an artificial neural network to compute the inverse kinematics of a C-arm device. Using the forward kinematics equations of a C-arm device (and the respective patient table) a training set for machine learning was generated. However, as an inverse kinematics problem may have multiple solutions, it is likely that training a neural network using forward kinematics data may generate machine learning conflicts. In this sense, we show that it is possible to eliminate those C-arm positions that may represent a learning conflict for the neural network, and thus, improve the accuracy of the model. Finally, we randomly generated a suitable validation set to verify the performance of our proposed model with data different from those used for training.

Acute pancreatitis in Chile: A multicenter study on epidemiology, etiology and clinical outcome. Retrospective analysis of clinical files.

BACKGROUND: Epidemiology of acute pancreatitis (AP) is variable in different geographical regions. OBJECTIVES: To compare etiology and severity of AP to published data from South America and the rest of world, study impact of demographical factors and treatment on its outcome in Chilean hospitals. METHODS: Multicenter observational study. Data of consecutive patients with AP were collected at the moment of discharge from 11 centers and retrospectively analyzed. RESULTS: Data of 962 patients were included in the analysis, 447 men and 515 women. Mean age was 48,2 years. Biliary etiology was significantly more frequent in women (70%) than in men (52%). Conversely, alcohol was responsible for about 17% of AP in men but exceptional in women. Mild AP was seen in 73.4%, moderately severe in 14.1% and severe in 13%. The overall mortality was 2.5% (24 of 962): 0.3%, 3.1% and 15.1% in mild, moderately severe and severe cases, respectively. No difference was found in the mortality and severity of biliary versus alcoholic AP, while hypertriglyceridemia induced AP was more severe, without increased mortality. Severity and mortality increased with age. ERCP was performed in 16% of biliary pancreatitis. Adherence to main guidelines was heterogeneous: more than half of mild AP patients were admitted to critical care units and antibiotics were used in about 25% them. CONCLUSION: This is the first multicenter study in Chile on AP. When compared to literature, we found similar severity distribution and an acceptably low mortality. Biliary etiology was dominant, but alcohol was also important in men.

pix2xray: converting RGB images into X-rays using generative adversarial networks.

PURPOSE: We propose a novel methodology for generating synthetic X-rays from 2D RGB images. This method creates accurate simulations for use in non-diagnostic visualization problems where the only input comes from a generic camera. Traditional methods are restricted to using simulation algorithms on 3D computer models. To solve this problem, we propose a method of synthetic X-ray generation using conditional generative adversarial networks (CGANs). METHODS: We create a custom synthetic X-ray dataset generator to generate image triplets for X-ray images, pose images, and RGB images of natural hand poses sampled from the NYU hand pose dataset. This dataset is used to train two general-purpose CGAN networks, pix2pix and CycleGAN, as well as our novel architecture called pix2xray which expands upon the pix2pix architecture to include the hand pose into the network. RESULTS: Our results demonstrate that our pix2xray architecture outperforms both pix2pix and CycleGAN in producing higher-quality X-ray images. We measure higher similarity metrics in our approach, with pix2pix coming in second, and CycleGAN producing the worst results. Our network performs better in the difficult cases which involve high occlusion due to occluded poses or large rotations. CONCLUSION: Overall our work establishes a baseline that synthetic X-rays can be simulated using 2D RGB input. We establish the need for additional data such as the hand pose to produce clearer results and show that future research must focus on more specialized architectures to improve overall image clarity and structure.

Bilateral Deep Vein Thrombosis, Vena Cava Agenesis, and Renal Abnormalities: KILT Syndrome-A Case Report and Literature Review.

KILT (kidney and IVC abnormalities with leg thrombosis) syndrome is a very rare condition that associates inferior vena cava abnormalities, renal defects, and venous thrombosis. These vascular disorders appear in 0.6-2% of patients with cardiovascular events and condition a venous stasis that contributes to the formation of thrombus in the lower limbs. Only a few cases of KILT syndrome have been published in the literature and the genesis, epidemiology, and natural history of the disease are yet unknown. We present a case of a 39-year-old man with no medical background who developed thrombosis of the inferior vena cava in its infrarrenal portion to both common iliac veins, all associated with agenesis of the rest of the vascular structure and an atrophic right kidney. The patient was treated with full anticoagulation, without the development of renal failure, postthrombotic syndrome, nor other complications. Thrombophilia study was normal, including mutations of prothrombin gene, factor V Leiden, and antiphospholipid antibodies. We hypothesize the benefit of an early diagnosis to improve the management of this condition. KILT syndrome must be taken into account in cases of proximal venous thrombosis, especially in young patients without risk factors for the development of thrombosis.

Peak Systolic Velocity for Calcified Plaques Fails to Estimate Carotid Stenosis Degree.

BACKGROUND: Duplex ultrasonography (DUS), although consolidated as the primary tool for the estimation of carotid stenosis, may be impaired by calcified plaques that promote acoustic shadow (AcS). AcS seems to hamper the quantification of the main parameter used in the determination of percentage stenosis, that is, the maximal peak systolic velocity (PSV) at the lesion site. The aim of our study was to compare the degrees of carotid artery stenosis in DUS/PSV and computed tomography angiography (CTA) in the presence of AcS. METHODS: During 36 months, 1,178 carotid DUS tests were performed. A total of 164 carotids in 139 patients showed AcS resulting from calcified plaques. Carotids with AcS were referred for a second imaging examination; thus, 62 carotids were analyzed by both DUS/PSV and CTA. CTA measured the area reduction at the lesion site to calculate the percent stenosis. PSV was measured immediately after the end of the AcS. According to velocities-based DUS criteria, stenoses were classified as mild (PSV < 125 cm/s), moderate (125 ≤ PSV ≤ 230 cm/s), and severe (PSV > 230). CTA and DUS/PSV measurements were compared to determine the accuracy of PSV in characterizing the severity of carotid stenosis with AcS. RESULTS: Of the 62 lesions, PSV characterized 10 as severe, 21 as moderate, and 31 as mild. According to the CTA study, there were 36 severe, 8 moderate, and 18 mild lesions. PSV underestimated in 27.79% the incidence of cases of severe carotid artery stenosis detected by the CTA. In addition, PSV overestimated the incidence of the cases of moderate and mild stenosis in 61.91% and 37.78%, respectively. The agreement ratio between the imaging examinations used in this study was 50%. DUS/PSV discretely correlated with CTA (r = 0.668, P < 0.0001, 95% confidence interval = 0.502-0.786). Using PSVs >125 and >230 as predictors of carotid lesions higher than 50% and 70%, respectively, the sensitivities were 63.3% and 27.8%, the specificities were 100%, the positive predictive values were 100%, and the negative predictive values were 71.9% and 50%. CONCLUSION: PSV alone is inadequate to quantify carotid stenosis in the presence of calcified plaques and AcS. Another image tool, such as CTA, could be recommendable for clinical decision-making.

A Fast Image Dehazing Algorithm Using Morphological Reconstruction.

Outdoor images are used in a vast number of applications, such as surveillance, remote sensing, and autonomous navigation. The greatest issue with these types of images is the effect of environmental pollution: haze, smog, and fog originating from suspended particles in the air, such as dust, carbon and water drops, which cause degradation to the image. The elimination of this type of degradation is essential for the input of computer vision systems. Most of the state-of-the-art research in dehazing algorithms is focused on improving the estimation of transmission maps, which are also known as depth maps. The transmission maps are relevant because they have a direct relation to the quality of the image restoration. In this paper, a novel restoration algorithm is proposed using a single image to reduce the environmental pollution effects, and it is based on the dark channel prior and the use of morphological reconstruction for the fast computing of transmission maps. The obtained experimental results are evaluated and compared qualitatively and quantitatively with other dehazing algorithms using the metrics of the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index; based on these metrics, it is found that the proposed algorithm has improved performance compared to recently introduced approaches.

Analysis of the Cluster Prominence Feature for Detecting Calcifications in Mammograms.

In mammograms, a calcification is represented as small but brilliant white region of the digital image. Earlier detection of malignant calcifications in patients provides high expectation of surviving to this disease. Nevertheless, white regions are difficult to see by visual inspection because a mammogram is a gray-scale image of the breast. To help radiologists in detecting abnormal calcification, computer-inspection methods of mammograms have been proposed; however, it remains an open important issue. In this context, we propose a strategy for detecting calcifications in mammograms based on the analysis of the cluster prominence (cp) feature histogram. The highest frequencies of the cp histogram describe the calcifications on the mammography. Therefore, we obtain a function that models the behaviour of the cp histogram using the Vandermonde interpolation twice. The first interpolation yields a global representation, and the second models the highest frequencies of the histogram. A weak classifier is used for obtaining a final classification of the mammography, that is, with or without calcifications. Experimental results are compared with real DICOM images and their corresponding diagnosis provided by expert radiologists, showing that the cp feature is highly discriminative.

Fast Parabola Detection Using Estimation of Distribution Algorithms.

This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.

Morphological and hemodynamic patterns of carotid stenosis treated by endarterectomy with patch closure versus stenting: a duplex ultrasound study.

OBJECTIVES: A duplex ultrasound study was performed to investigate morphological and hemodynamic patterns of carotid stenoses treated by endarterectomy with patch closure versus stenting. MATERIALS AND METHOD: Twenty-nine carotid stenoses were treated with stenting and 65 with patch closure. Duplex ultrasound parameters (luminal diameter, mm; peak systolic velocity and end-diastolic velocity, cm/s) were measured 24 hours after the procedures and also at 12 months post-procedure. Residual stenoses (immediately postprocedure) and restenoses (within 12 months of procedure) were defined as narrowings of ≥ 50% on duplex ultrasound examination. RESULTS: In stented patients, the luminal diameter of the proximal internal carotid artery increased in the interval between the 24-hour and 12-month post-procedure studies, while in the patch closure patients, the diameter decreased. Carotid hemodynamics normalized immediately after both patching and stenting and remained relatively stable thereafter up to 12 months. No statistically elevated flow velocities (in the absence of residual stenosis or restenosis) were observed in the patched or stented carotid arteries. No significant differences in residual stenosis rates were observed between the stenting group (3 cases, 10.34%) and the patch closure group (1 case, 1.53%, P = 0.08). At 12 months, 2 stenting patients (6.88%) and 2 patch closure patients (3.07%) had $50% restenosis (P = 0.58). One case of late stroke due to restenosis was observed in the stenting group; the patient died 12 months postoperatively, before receiving new intervention. CONCLUSION: Measurements over time in luminal diameter signalized differences in arterial remodeling mechanisms between patched and stented carotids. Both stenting and patch closure were associated with carotid patency and flow restoration. This study does not support a general approach to new velocity criteria indiscriminately applied to stented or patched carotids.

Morphological and hemodynamic patterns of carotid stenosis treated by endarterectomy with patch closure versus stenting: a duplex ultrasound study

OBJECTIVES: A duplex ultrasound study was performed to investigate morphological and hemodynamic patterns of carotid stenoses treated by endarterectomy with patch closure versus stenting. MATERIALS AND METHOD: Twenty-nine carotid stenoses were treated with stenting and 65 with patch closure. Duplex ultrasound parameters (luminal diameter, mm; peak systolic velocity and end-diastolic velocity, cm/s) were measured 24 hours after the procedures and also at 12 months post-procedure. Residual stenoses (immediately postprocedure) and restenoses (within 12 months of procedure) were defined as narrowings of >50 percent on duplex ultrasound examination. RESULTS: In stented patients, the luminal diameter of the proximal internal carotid artery increased in the interval between the 24-hour and 12-month post-procedure studies, while in the patch closure patients, the diameter decreased. Carotid hemodynamics normalized immediately after both patching and stenting and remained relatively stable thereafter up to 12 months. No statistically elevated flow velocities (in the absence of residual stenosis or restenosis) were observed in the patched or stented carotid arteries. No significant differences in residual stenosis rates were observed between the stenting group (3 cases, 10.34 percent) and the patch closure group (1 case, 1.53 percent, P = 0.08). At 12 months, 2 stenting patients (6.88 percent) and 2 patch closure patients (3.07 percent) had $50 percent restenosis (P = 0.58). One case of late stroke due to restenosis was observed in the stenting group; the patient died 12 months postoperatively, before receiving new intervention. CONCLUSION: Measurements over time in luminal diameter signalized differences in arterial remodeling mechanisms between patched and stented carotids. Both stenting and patch closure were associated with carotid patency and flow restoration. This study does not support a general approach to new velocity criteria indiscriminately applied to stented or patched carotids.

Pancreatitis aguda como forma de presentación de una fibrosis quística / Acute pancreatitis as a first manifestation of cystic fibrosis

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