Hyperprolactinemia in end‐stage renal disease and effects of frequent hemodialysis

Introduction: End‐stage renal disease is associated with elevations in circulating prolactin concentrations, but the association of prolactin concentrations with intermediate health outcomes and the effects of hemodialysis frequency on changes in serum prolactin have not been examined. Methods: The FHN Daily and Nocturnal Dialysis Trials compared the effects of conventional thrice weekly hemodialysis with in‐center daily hemodialysis (6 days/week) and nocturnal home hemodialysis (6 nights/week) over 12 months and obtained measures of health‐related quality of life, self‐reported physical function, mental health and cognition. Serum prolactin concentrations were measured at baseline and 12‐month follow‐up in 70% of the FHN Trial cohort to examine the associations among serum prolactin concentrations and physical, mental and cognitive function and the effects of hemodialysis frequency on serum prolactin. Findings: Among 177 Daily Trial and 60 Nocturnal Trial participants with baseline serum prolactin measurements, the median serum prolactin concentration was 65 ng/mL (25th–75th percentile 48–195 ng/mL) and 81% had serum prolactin concentrations >30 ng/mL. While serum prolactin was associated with sex (higher in women), we observed no association between baseline serum prolactin and age, dialysis vintage, and baseline measures of physical, mental and cognitive function. Furthermore, there was no significant effect of hemodialysis frequency on serum prolactin in either of the two trials. Discussion: Serum prolactin concentrations were elevated in the large majority of patients with ESRD, but were not associated with several measures of health status. Circulating prolactin levels also do not appear to decrease in response to more frequent hemodialysis over a one‐year period.     

Recent advances in CKD and ESRD: A literature update

The past year has seen interesting publications in the fields of chronic kidney disease and end stage renal disease. This review highlights some of these important papers and places their findings in the context of clinical care.     

Carbon Nanodot‐Sensitized Modulation of Alzheimer's β‐Amyloid Self‐Assembly,Disassembly, and Toxicity

The self‐assembly of amyloidogenic peptides into β‐sheet‐rich aggregates is a general feature of many neurodegenerative diseases, including Alzheimer's disease, which signifies the need for the effective attenuation of amyloid aggregation toward alleviating amyloid‐associated neurotoxicity. This study reports that photoluminescent carbon nanodots (CDs) can effectively suppress Alzheimer's β‐amyloid (Aβ) self‐assembly and function as a β‐sheet breaker disintegrating preformed Aβ aggregates. This study synthesizes CDs using ammonium citrate through one‐pot hydrothermal treatment and passivates their surface with branched polyethylenimine (bPEI). The bPEI‐coated CDs (bPEI@CDs) exhibit hydrophilic and cationic surface characteristics, which interact with the negatively charged residues of Aβ peptides, suppressing the aggregation of Aβ peptides. Under light illumination, bPEI@CDs display a more pronounced effect on Aβ aggregation and on the dissociation of β‐sheet‐rich assemblies through the generation of reactive oxygen species from photoactivated bPEI@CDs. The light‐triggered attenuation effect of Aβ aggregation using a series of experiments, including photochemical and microscopic analysis, is verified. Furthermore, the cell viability test confirms the ability of photoactivated bPEI@CDs for the suppression of Aβ‐mediated cytotoxicity, indicating bPEI@CDs' potency as an effective anti‐Aβ neurotoxin agent.     

The Gut Microbiota,Tumorigenesis, and Liver Diseases

In recent decades, diseases concerning the gut microbiota have presented some of the most serious public health problems worldwide. The human host’s physiological status is influenced by the intestinal microbiome, thus integrating external factors, such as diet, with genetic and immune signals. The notion that chronic inflammation drives carcinogenesis has been widely established for various tissues. It is surprising that the role of the microbiota in tumorigenesis has only recently been recognized, given that the presence of bacteria at tumor sites was first described more than a century ago. Extensive epidemiological studies have revealed that there is a strong link between the gut microbiota and some common cancers. However, the exact molecular mechanisms linking the gut microbiota and cancer are not yet fully understood. Changes to the gut microbiota are instrumental in determining the occurrence and progression of hepatocarcinoma, chronic liver diseases related to alcohol, nonalcoholic fatty liver disease (NAFLD), and cirrhosis. To be specific, the gut milieu may play an important role in systemic inflammation, endotoxemia, and vasodilation, which leads to complications such as spontaneous bacterial peritonitis and hepatic encephalopathy. Relevant animal studies involving gut microbiota manipulations, combined with observational studies on patients with NAFLD, have provided ample evidence pointing to the contribution of dysbiosis to the pathogenesis of NAFLD. Given the poor prognosis of these clinical events, their prevention and early management are essential. Studies of the composition and function of the gut microbiota could shed some light on understanding the prognosis because the microbiota serves as an essential component of the gut milieu that can impact the aforementioned clinical events. As far as disease management is concerned, probiotics may provide a novel direction for therapeutics for hepatocellular carcinoma (HCC) and NAFLD, given that probiotics function as a type of medicine that can improve human health by regulating the immune system. Here, we provide an overview of the relationships among the gut microbiota, tumors, and liver diseases. In addition, considering the significance of bacterial homeostasis, we discuss probiotics in this article in order to guide treatments for related diseases.     

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Magnetic resonance imaging (MRI) is increasingly used in the diagnosis of Alzheimer's disease (AD) in order to identify abnormalities in the brain. Indeed, cortical atrophy, a powerful biomarker for AD, can be detected using structural MRI (sMRI), but it cannot detect impairment in the integrity of the white matter (WM) preceding cortical atrophy. The early detection of these changes is made possible by the novel MRI modality known as diffusion tensor imaging (DTI). In this study, we integrate DTI and sMRI as complementary imaging modalities for the early detection of AD in order to create an effective computer-assisted diagnosis tool. The fused Bag-of-Features (BoF) with Speeded-Up Robust Features (SURF) and modified AlexNet convolutional neural network (CNN) are utilized to extract local and deep features. This is applied to DTI scalar metrics (fractional anisotropy and diffusivity metric) and segmented gray matter images from T1-weighted MRI images. Then, the classification of local unimodal and deep multimodal features is first performed using support vector machine (SVM) classifiers. Then, the majority voting technique is adopted to predict the final decision from the ensemble SVMs. The study is directed toward the classification of AD versus mild cognitive impairment (MCI) versus cognitively normal (CN) subjects. Our proposed method achieved an accuracy of 98.42% and demonstrated the robustness of multimodality imaging fusion.     

Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases

Abstract: The aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS-SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10-fold cross-validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS-SVM classifier and the AIRS classifier respectively using 10-fold cross-validation. It is shown that the LS-SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases.     

Understanding dengue fever dynamics: a study of seasonality in vector-borne disease models

Dengue fever dynamics show seasonality, with the disease transmission being higher during the warmer seasons. In this paper, we analyse seasonally forced epidemic models with and without vector dynamics. We assume small seasonal effects and obtain approximations for the real response of each state variable and also for the corresponding amplitude and phase via decomposition of the sinusoidal forcing into imaginary exponential functions. The analysis begins with the simplest susceptible-infected-susceptible (SIS) model, followed by the simplest model with vector dynamics, susceptible-infected-susceptible for hosts and uninfected-vector (SISUV). Finally, we compare the more complex susceptible-infected-recovered (SIR) and susceptible-infected-recovered for hosts and uninfected-vector (SIRUV) models and conclude that the models give basically the same information when we replace, in the SIR model, the human infectivity by a function of both human and mosquito infectivities.     

Fuzzy risk assessment of mortality after coronary surgery using combination of adaptive neuro‐fuzzy inference system and K‐means clustering

In this paper, a fuzzy expert system based on adaptive neuro‐fuzzy inference system (ANFIS) is introduced to assess the mortality after coronary bypass surgery. In preprocessing phase, the attributes were reduced using a univariant analysis in order to make the classifier system more effective. Prognostic factors with a p‐value of less than 0.05 in chi‐square or t‐student analysis were given to inputs ANFIS classifier. The correct diagnosis performance of the proposed fuzzy system was calculated in 824 samples. To demonstrate the usefulness of the proposed system, the study compared the performance of fuzzy system based on ANFIS method through the binary logistic regression with the same attributes. The experimental results showed that the fuzzy model (accuracy: 96.4%; sensitivity: 66.6%; specificity: 97.2%; and area under receiver operating characteristic curve: 0.82) consistently outperformed the logistic regression (accuracy: 89.4%; sensitivity: 47.6%; specificity: 89.4%; and area under receiver operating characteristic curve: 0.62). The obtained classification accuracy of fuzzy expert system was very promising with regard to the traditional statistical methods to predict mortality after coronary bypass surgery such as binary logistic regression model.     

用ENDEAVOUR软件鉴定肥胖症致病基因GAD2

鉴别复杂疾病致病基因对于治疗和预防疾病作用非常重要,致病基因预测软件是鉴别致病基因的有效工具。位于染色体10p12的基因GAD2是尚有争议的肥胖基因,许多研究者用实验方法研究基因GAD2,却得出了不一致的结论。基于生物计算的致病基因预测软件能弥补实验的不足,已成为揭示疾病发病机理和预测致病基因的有效途径,本文采用ENDEAVOUR软件来验证基因GAD2是否为肥胖症致病基因,该软件运用多种生物数据,根据每一个候选基因与已知致病基因的相似程度以排序,结果在35个候选基因中,基因GAD2排在最前面,表明基因GAD2最可能是肥胖症致病基因。预测结果有利于重新解释基因GAD2的生物功能,促使肥胖症药物的研制及治疗水平的提高。