Celiac Disease Presenting as Hemolytic Anemia Mimic: A Case Report

Celiac disease, an immune-mediated enteropathy, results from gluten ingestion in the form of wheat, rye, and barley in genetically susceptible individuals. It is a systemic disorder characterized by a variable combination of gluten-related signs and symptoms, and disease-specific antibodies in addition to enteropathy. The clinical presentation of celiac disease is extremely variable: a small proportion of patients presenting with severe gastrointestinal symptoms and malabsorption, and extraintestinal symptoms, and a large proportion having no symptoms at all. Owing to the varied clinical presentation, diagnosing celiac disease remains a challenge. We present a case of celiac disease presenting with severe anemia and clinical features suggestive of hemolytic anemia, making diagnosis even more difficult.

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model. We found that nonlinear models performed better than linear ones. The prediction accuracy for the total amount of catechins and for epicatechin gallate (ECG) of the extreme learning machine (ELM), based on optimal variables, reached 0.989 and 0.994, respectively, and the prediction accuracy for EGC, C, EC, and EGCG of the content support vector regression (SVR) models reached 0.972, 0.993, 0.990, and 0.994, respectively. The optimal model offers accurate prediction, and visual analysis can determine the distribution of the catechin content when fermenting leaves for different fermentation periods. The findings provide significant reference material for intelligent digital assessment of black tea during processing.

Pathophysiology and Diagnosis of Coronary Functional Abnormalities

Approximately one-half of patients undergoing diagnostic coronary angiography for angina have no significant coronary atherosclerotic stenosis. This clinical condition has recently been described as ischaemia with non-obstructive coronary arteries (INOCA). Coronary functional abnormalities are central to the pathogenesis of INOCA, including epicardial coronary spasm and coronary microvascular dysfunction composed of a variable combination of increased vasoconstrictive reactivity and/or reduced vasodilator function. During the last decade – in INOCA patients in particular – evidence for the prognostic impact of coronary functional abnormalities has accumulated and various non-invasive and invasive diagnostic techniques have enabled the evaluation of coronary vasomotor function in a comprehensive manner. In this review, the authors briefly summarise the recent advances in the understanding of pathophysiology and diagnosis of epicardial coronary artery spasm and coronary microvascular dysfunction.

Neurodevelopmental Profile in Children Affected by Ocular Albinism

Aim The aim of this study was to detail the neurodevelopmental profile of subjects affected by ocular albinism (OA) and to collect data on GPR143 gene analysis. Design The design of the study involves a retrospective longitudinal observational case series. Methods We collected data on the neurodevelopmental profile of 13 children affected by OA from clinical annual assessments conducted for a period of 6 years after the first evaluation. We described visual profile, neuromotor development and neurological examination, cognitive profile, communication and language skills and behavioral characteristics. The GPR143 gene analysis was performed as well. Results Children presented a variable combination of ocular and oculomotor disorders unchanged during the follow-up, a deficit in visual acuity and in contrast sensitivity that progressively improved. Abnormalities in pattern visual evoked potential were found. No deficits were detected at neurological examination and neuromotor development except for a mild impairment in hand-eye coordination observed in five cases. A language delay was observed in five cases, two of whom had also a developmental quotient delay at 2 years evolving to a borderline/deficit cognitive level at preschool age, difficulties in adaptive behavior and autistic-like features were found. Mutations in the GPR143 gene were identified in the two patients who presented the most severe clinical phenotype. Conclusion Children with OA may share, in addition to a variable combination of ocular signs and symptoms, a neurodevelopment impairment regarding mostly the cognitive, communicative, and social area, especially those with GPR143 mutation.

Mapping the Growing Stem Volume of the Coniferous Plantations in North China Using Multispectral Data from Integrated GF-2 and Sentinel-2 Images and an Optimized Feature Variable Selection Method

Accurate measurement of forest growing stem volume (GSV) is important for forest resource management and ecosystem dynamics monitoring. Optical remote sensing imagery has great application prospects in forest GSV estimation on regional and global scales as it is easily accessible, has a wide coverage, and mature technology. However, their application is limited by cloud coverage, data stripes, atmospheric effects, and satellite sensor errors. Combining multi-sensor data can reduce such limitations as it increases the data availability, but also causes the multi-dimensional problem that increases the difficulty of feature selection. In this study, GaoFen-2 (GF-2) and Sentinel-2 images were integrated, and feature variables and data scenarios were derived by a proposed adaptive feature variable combination optimization (AFCO) program for estimating the GSV of coniferous plantations. The AFCO algorithm was compared to four traditional feature variable selection methods, namely, random forest (RF), stepwise random forest (SRF), fast iterative feature selection method for k-nearest neighbors (KNN-FIFS), and the feature variable screening and combination optimization procedure based on the distance correlation coefficient and k-nearest neighbors (DC-FSCK). The comparison indicated that the AFCO program not only considered the combination effect of feature variables, but also optimized the selection of the first feature variable, error threshold, and selection of the estimation model. Furthermore, we selected feature variables from three datasets (GF-2, Sentinel-2, and the integrated data) following the AFCO and four other feature selection methods and used the k-nearest neighbors (KNN) and random forest regression (RFR) to estimate the GSV of coniferous plantations in northern China. The results indicated that the integrated data improved the GSV estimation accuracy of coniferous plantations, with relative root mean square errors (RMSErs) of 15.0% and 19.6%, which were lower than those of GF-2 and Sentinel-2 data, respectively. In particular, the texture feature variables derived from GF-2 red band image have a significant impact on GSV estimation performance of the integrated dataset. For most data scenarios, the AFCO algorithm gained more accurate GSV estimates, as the RMSErs were 30.0%, 23.7%, 17.7%, and 17.5% lower than those of RF, SRF, KNN-FIFS, and DC-FSCK, respectively. The GSV distribution map obtained by the AFCO method and RFR model matched the field observations well. This study provides some insight into the application of optical images, optimization of the feature variable combination, and modeling algorithm selection for estimating the GSV of coniferous plantations.

Can Celiac Disease Be Prevented?

Celiac disease (CD) is an autoimmune disorder triggered by gluten in genetically susceptible individuals characterized by a variable combination of gluten-dependent symptoms, presence of specific autoantibodies and enteropathy. The health burden of CD is considerable, as it reduces quality of life and, at a societal level, has extensive negative economic consequences. Prevention strategies are based on the identification of at-risk subjects and identification and elimination of risk factors. A number of prospective observational and interventional studies conducted on the general population, and more often in subjects at-risk, have given important information on the natural history of the disease. Both genetic and environmental factors have been identified with the former, in particular histocompatibility genes, playing a major role. Environmental factors, some operating already before birth, have been identified, with feeding pattern in the first year of life (breast feeding, amount and time of introduction of gluten) and infections being the most relevant. Prospective studies have also allowed the identification of biomarkers predictive of the disease which in perspective could better define the population on which to intervene. Interventions have been so far limited to modifications of feeding patterns. However, as also learnt from diseases that share with CD genetic risk factors and mechanisms of damage, such as type 1 diabetes (T1D), future strategies may be envisaged based on protection from infections, manipulation of microbiota, intervention on T cells.

Pathophysiological Bases of Comorbidity in Migraine

Despite that it is commonly accepted that migraine is a disorder of the nervous system with a prominent genetic basis, it is comorbid with a plethora of medical conditions. Several studies have found bidirectional comorbidity between migraine and different disorders including neurological, psychiatric, cardio- and cerebrovascular, gastrointestinal, metaboloendocrine, and immunological conditions. Each of these has its own genetic load and shares some common characteristics with migraine. The bidirectional mechanisms that are likely to underlie this extensive comorbidity between migraine and other diseases are manifold. Comorbid pathologies can induce and promote thalamocortical network dysexcitability, multi-organ transient or persistent pro-inflammatory state, and disproportionate energetic needs in a variable combination, which in turn may be causative mechanisms of the activation of an ample defensive system with includes the trigeminovascular system in conjunction with the neuroendocrine hypothalamic system. This strategy is designed to maintain brain homeostasis by regulating homeostatic needs, such as normal subcortico-cortical excitability, energy balance, osmoregulation, and emotional response. In this light, the treatment of migraine should always involves a multidisciplinary approach, aimed at identifying and, if necessary, eliminating possible risk and comorbidity factors.

Donnai–Barrow syndrome in nephrology practice

Donnai–Barrow syndrome is a multi-system disorder characterized by a variable combination of congenital anomalies, progressive myopia, sensorineural hearing loss, intellectual disability and renal disease. The article describes clinical cases of children with different phenotypes of the syndrome, including different renal disorders. One patient had isolated low-molecular-weight proteinuria, another patient suffered from proteinuria, hypercalciuria, nephrocalcinosis. Disruption of megaline-mediated endocytosis, retrograde endosomal transport of ligands, mitochondrial dysfunction, stress of the endoplasmic reticulum can lead to a different spectrum and various degrees of severity of tubular dysfunction in Donnai-Barrow syndrome. A variety of clinical manifestations of the disease can lead to a low diagnosis of Donnai-Barrow syndrome and inadequate patient management.