Klotho in pregnancy and intrauterine development-potential clinical implications: a review from the European Renal Association CKD-MBD Working Group.

Intrauterine development is crucial for life-long health; therefore, elucidation of its key regulators is of interest for their potential prognostic and therapeutic implications. Originally described as a membrane-bound anti-aging protein, Klotho has evolved as a regulator of numerous functions in different organ systems. Circulating Klotho is generated by alternative splicing or active shedding from cell membranes. Recently, Klotho was identified as a regulator of placental function, and while Klotho does not cross the placental barrier, increased levels of circulating α-Klotho have been identified in umbilical cord blood compared with maternal blood, indicating that Klotho may also play a role in intrauterine development. In this narrative review, we discuss novel insights into the specific functions of the Klotho proteins in the placenta and in intrauterine development, while summarizing up-to-date knowledge about their structures and functions. Klotho plays a role in stem cell functioning, organogenesis and haematopoiesis. Low circulating maternal and foetal levels of Klotho are associated with preeclampsia, intrauterine growth restriction, and an increased perinatal risk for newborns, indicating a potential use of Klotho as biomarker and therapeutic target. Experimental administration of Klotho protein indicates a neuro- and nephroprotective potential, suggesting a possible future role of Klotho as a therapeutic agent. However, the use of Klotho as intervention during pregnancy is as yet unproven. Here, we summarize novel evidence, suggesting Klotho as a key regulator for healthy pregnancies and intrauterine development with promising potential for clinical use.

Pancreatic steatosis is an independent risk factor for post-transplant diabetes mellitus in kidney transplant patients.

BACKGROUND AND AIM: Post-transplant diabetes mellitus (PTDM) is associated with an increased risk of post-transplant cardiovascular diseases, and several risk factors of PTDM have been shown in the literature. Yet, the relationship between hepatic and pancreatic steatosis with post-transplant diabetes mellitus remains vague. We aimed to evaluate pancreatic steatosis, a novel component of metabolic syndrome, and hepatic steatosis association with post-transplant diabetes mellitus in a single-center retrospective cohort study conducted on kidney transplant recipients. METHOD: We have performed a single-center retrospective cohort study involving all kidney transplant recipients. We have utilized pretransplant Fibrosis-4, nonalcoholic fatty liver disease fibrosis score, and abdominal computed tomography for the assessment of visceral steatosis status. RESULTS: We have included 373 kidney transplant recipients with a mean follow-up period of 32 months in our final analysis. Post-transplant diabetes mellitus risk is associated with older age (p < .001), higher body-mass index (p < .001), nonalcoholic fatty liver disease-fibrosis score (p = .002), hepatic (p < .001) or pancreatic (p < .001) steatosis on imaging and higher pre-transplant serum triglyceride (p = .003) and glucose levels (p = .001) after multivariate analysis. CONCLUSION: Our study illustrates that recipients' pancreatic steatosis is an independent predictive factor for post-transplant diabetes mellitus including in kidney transplant patients.

Social isolation and loneliness: Undervalued risk factors for disease states and mortality.

Social isolation and loneliness are two common but undervalued conditions associated with a poor quality of life, decreased overall health and mortality. In this review, we aim to discuss the health consequences of social isolation and loneliness. We first provide the potential causes of these two conditions. Then, we explain the pathophysiological processes underlying the effects of social isolation and loneliness in disease states. Afterwards, we explain the important associations between these conditions and different non-communicable diseases, as well as the impact of social isolation and loneliness on health-related behaviours. Finally, we discuss the current and novel potential management strategies for these conditions. Healthcare professionals who attend to socially isolated and/or lonely patients should be fully competent in these conditions and assess their patients thoroughly to detect and properly understand the effects of isolation and loneliness. Patients should be offered education and treatment alternatives through shared decision-making. Future studies are needed to understand the underlying mechanisms better and to improve the treatment strategies for both social isolation and loneliness.

Lung Ultrasound Can Influence the Clinical Treatment of Pregnant Women With COVID-19.

Lung ultrasound (LUS) is an effective tool to detect and monitor patients infected with 2019 coronavirus disease (COVID-19). The use of LUS on pregnant women is an emerging trend, considering its effectiveness during the outbreak. Eight pregnant women with a diagnosis of COVID-19 confirmed by nasal/throat real-time reverse transcription polymerase chain reaction testing who underwent point-of-care LUS examinations after routine obstetric ultrasound are described. A routinely performed LUS examination revealed serious lung involvement in 7 cases: 2 were initially asymptomatic; 3 have chest computed tomography; 1 had initial negative real-time reverse transcription polymerase chain reaction results; and 1 had initial negative computed tomographic findings. Treatment for COVID-19 was either commenced or changed in 87.5% of the patients (n = 7 of 8) on LUS findings. Among patients with abnormal LUS findings, treatment was commenced in 5 patients (71.5%) and changed in 2 patients (28.5%). One normal and 7 abnormal LUS cases indicate the impact of routine LUS on the clinical outcome and treatment of pregnant women.

Smartphone-based colorimetric detection via machine learning.

We report the application of machine learning to smartphone-based colorimetric detection of pH values. The strip images were used as the training set for Least Squares-Support Vector Machine (LS-SVM) classifier algorithms that were able to successfully classify the distinct pH values. The difference in the obtained image formats was found not to significantly affect the performance of the proposed machine learning approach. Moreover, the influence of the illumination conditions on the perceived color of pH strips was investigated and further experiments were conducted to study the effect of color change on the learning model. Non-integer pH levels are identified as their nearest integer pH values, whereas the test results for integer pH levels using JPEG, RAW and RAW-corrected image formats captured under different lighting conditions lead to perfect classification accuracy, sensitivity and specificity, which proves that colorimetric detection using machine learning based systems is able to adapt to various experimental conditions and is a great candidate for smartphone-based sensing in paper-based colorimetric assays.

Klotho: a potential therapeutic target in aging and neurodegeneration beyond chronic kidney disease-a comprehensive review from the ERA CKD-MBD working group.

Klotho, a multifunctional protein, acts as a co-receptor in fibroblast growth factor 23 and exerts its impact through various molecular pathways, including Wnt, hypoxia-inducible factor and insulin-like growth factor 1 pathways. The physiological significance of Klotho is the regulation of vitamin D and phosphate metabolism as well as serving as a vital component in aging and neurodegeneration. The role of Klotho in aging and neurodegeneration in particular has gained considerable attention. In this narrative review we highlight several key insights into the molecular basis and physiological function of Klotho and synthesize current research on the role of Klotho in neurodegeneration and aging. Klotho deficiency was associated with cognitive impairment, reduced growth, diminished longevity and the development of age-related diseases in vivo. Serum Klotho levels showed a decline in individuals with advanced age and those affected by chronic kidney disease, establishing its potential diagnostic significance. Additionally, multiple medications have been demonstrated to influence Klotho levels. Therefore, this comprehensive review suggests that Klotho could open the door to novel interventions aimed at addressing the challenges of aging and neurodegenerative disorders.

A new immune disease: systemic hypertension.

Systemic hypertension is the most common medical comorbidity affecting the adult population globally, with multiple associated outcomes including cerebrovascular diseases, cardiovascular diseases, vascular calcification, chronic kidney disease, metabolic syndrome and mortality. Despite advancements in the therapeutic field approximately one in every five adult patients with hypertension is classified as having treatment-resistant hypertension, indicating the need for studies to provide better understanding of the underlying pathophysiology and the need for more therapeutic targets. Recent pre-clinical studies have demonstrated the role of the innate and adaptive immune system including various cell types and cytokines in the pathophysiology of hypertension. Moreover, pre-clinical studies have indicated the potential beneficial effects of immunosuppressant medications in the control of hypertension. Nevertheless, it is unclear whether such pathophysiological mechanisms and therapeutic alternatives are applicable to human subjects, while this area of research is undoubtedly a rapidly growing field.

Early aging and premature vascular aging in chronic kidney disease.

Aging is the progressive decline of body functions and a number of chronic conditions can lead to premature aging characterized by frailty, a diseased vasculature, osteoporosis, and muscle wasting. One of the major conditions associated with premature and accelerated aging is chronic kidney disease (CKD), which can also result in early vascular aging and the stiffening of the arteries. Premature vascular aging in CKD patients has been considered as a marker of prognosis of mortality and cardiovascular morbidity and therefore requires further attention. Oxidative stress, inflammation, advanced glycation end products, fructose, and an aberrant gut microbiota can contribute to the development of early aging in CKD patients. There are several key molecular pathways and molecules which play a role in aging and vascular aging including nuclear factor erythroid 2-related factor 2 (Nrf-2), AMP-activated protein kinase (AMPK), sirtuin 1 (SIRT1), and klotho. Potential therapeutic strategies can target these pathways. Future studies are needed to better understand the importance of premature aging and early vascular aging and to develop therapeutic alternatives for these conditions.

Resilience during the COVID-19 pandemic among people living with HIV: A cross-sectional study.

BACKGROUND: This study aims to investigate the factors shaping resilience during the COVID-19 pandemic among people living with HIV (PLWH). METHODS: A total of 341 participants were included in this cross-sectional study. The online survey included scales of resilience, depression & anxiety, loneliness, social support, COVID-19 fear, stress, and sociodemographic information. RESULTS: Logistic regression test results showed loneliness (OR = 2.548, 95% CI = 1.251, 5.189), social support (OR = 2.217, 95% CI = 1.148, 4.279), income (OR = 2.581, 95% CI = 1.217, 5.472), sexual orientation (OR = 2.707, 95% CI = 1.004, 7.300), age (OR = 1.044, 95% CI = 1.006, 1.083) and COVID-19 fear (OR = 0.891, 95% CI = 0.840, 0.944) were statistically significant factors associated with resilience among PLWH. CONCLUSION: In conclusion, reducing COVID-19 fear by providing the correct information about the COVID-19 pandemic, fortifying the level of social support satisfaction, as well as minimising the level of loneliness have the potential to improve psychological resilience among PLWH.

Hilbert Vibration Decomposition-based epileptic seizure prediction with neural network.

Epilepsy is one of the most prominent brain disorders in the world, and epileptic patients suffer from sudden seizures that have a substantial negative impact on their lives. A seizure prediction system, therefore, is essential in overcoming the difficulties that epileptic individuals experience. This study designs and demonstrates a non-patient specific seizure prediction system that uses the Hilbert Vibration Decomposition (HVD) method on surface EEG recordings of 10 patients from the CHB-MIT database. EEG signals with 18 channels are decomposed to 7 subcomponents with the HVD in sliding windows. These subcomponents from all channels are then used to calculate features to be fed into an MLP classifier. The classification process is performed for all patients simultaneously and without relaying information concerning patient identity to the classifier. After the classification stage, an alarm algorithm that evaluates the frequency of preictal predictions is developed. The classification sensitivity was, on average, 19.89% across patients. This sensitivity was increased to, on average 89.8% within 120 min and an average false alarm rate of 0.081/h was achieved with a seizure prediction horizon of 4 min across patients after alarm creation.

Single-Image-Referenced Colorimetric Water Quality Detection Using a Smartphone.

In this paper, we present a smartphone platform for colorimetric water quality detection based on the use of built-in camera for capturing a single-use reference image. A custom-developed app processes this image for training and creates a reference model to be used later in real experimental conditions to calculate the concentration of the unknown solution. This platform has been tested on four different water quality colorimetric assays with various concentration levels, and results show that the presented platform provides approximately 100% accuracy for colorimetric assays with noticeable color difference. This portable, cost-effective, and user-friendly platform is promising for application in water quality monitoring.

A signal-processing-based approach to time-varying graph analysis for dynamic brain network identification.

In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not sufficient to represent the long term communication patterns of the brain and is considered as an unreliable snapshot of functional connectivity. In this paper, we propose a dynamic network summarization approach to describe the time-varying evolution of connectivity patterns in functional brain activity. The proposed approach is based on first identifying key event intervals by quantifying the change in the connectivity patterns across time and then summarizing the activity in each event interval by extracting the most informative network using principal component decomposition. The proposed method is evaluated for characterizing time-varying network dynamics from event-related potential (ERP) data indexing the error-related negativity (ERN) component related to cognitive control. The statistically significant connectivity patterns for each interval are presented to illustrate the dynamic nature of functional connectivity.

Inferring effective connectivity in the brain from EEG time series using dynamic Bayesian networks.

Effective connectivity, defined as the influence of a neuronal population on another, is known to have great significance for understanding the organization of the brain. Disruptions in the effective connectivity patterns occur in the case of neurological and psychopathological diseases. Therefore, it is important to develop models of effective brain connectivity from non-invasive neuroimaging data. In this paper, we propose to use dynamic Bayesian networks (DBN) to learn effective brain connectivity from electroencephalogram (EEG) data. DBNs use first order Markov chain to model EEG time series obtained from multiple electrodes. We explore effective brain connectivity in healthy and schizophrenic subjects using this framework. Fourier bootstrapping technique is used to identify the statistically significant pairs of interactions among electrodes.