Microcirculatory changes and skeletal muscle oxygenation measured at rest by non-infrared spectroscopy in patients with and without diabetes undergoing haemodialysis

Introduction: Haemodialysis has direct and indirect effects on skin and muscle microcirculatory regulation that are severe enough to worsen tolerance to physical exercise and muscle asthenia in patients undergoing dialysis, thus compromising patients' quality of life and increasing the risk of mortality. In diabetes these circumstances are further complicated, leading to an approximately sixfold increase in the incidence of critical limb ischaemia and amputation. Our aim in this study was to investigate in vivo whether haemodialysis induces major changes in skeletal muscle oxygenation and blood flow, microvascular compliance and tissue metabolic rate in patients with and without diabetes. Methods: The study included 20 consecutive patients with and without diabetes undergoing haemodialysis at Sant Andrea University Hospital, Rome from March to April 2007. Near-infrared spectroscopy (NIRS) quantitative measurements of tissue haemoglobin concentrations in oxygenated [HbO(2)] and deoxygenated forms [HHb] were obtained in the calf once hourly for 4 hours during dialysis. Consecutive venous occlusions allowed one to obtain muscular blood flow (mBF), microvascular compliance and muscle oxygen consumption (mVO(2)). The tissue oxygen saturation (StO(2)) and content (CtO(2)) as well as the microvascular bed volume were derived from the haemoglobin concentration. Nonparametric tests were used to compare data within each group and among the groups and with a group of 22 matched healthy controls. Results: The total haemoglobin concentration and [HHb] increased significantly during dialysis in patients without and with diabetes. Only in patients with diabetes, dialysis involved a [HbO(2)], CtO(2) and mVO(2) increase but left StO(2) unchanged. Multiple regression analysis disclosed a significant direct correlation of StO(2) with HbO(2) and an inverse correlation with mVO(2). Dialysis increased mBF only in diabetic patients. Microvascular compliance decreased rapidly and significantly during the first hour of dialysis in both groups. Conclusions: Our NIRS findings suggest that haemodialysis in subjects at rest brings about major changes in skeletal muscle oxygenation, blood flow, microvascular compliance and tissue metabolic rate. These changes differ in patients with and without diabetes. In all patients haemodialysis induces changes in tissue haemoglobin concentrations and microvascular compliance, whereas in patients with diabetes it alters tissue blood flow, tissue oxygenation (CtO(2), [HbO(2)]) and the metabolic rate (mVO(2)). In these patients the mVO(2) is correlated to the blood supply. The effects of haemodialysis on cell damage remain to be clarified. The absence of StO(2) changes is probably linked to an opposite [HbO(2)] and mVO(2) pattern

Vasoactive agents affect growth and protein synthesis of cultured rat mesangial cells

Vasoactive agents affect growth and protein synthesis of cultured rat mesangial cells. Mesangial cell (MC) proliferation and extracellular matrix (ECM) formation are hallmarks of chronic glomerular disease. The present in vitro study examined the effects of the vasoactive agents angiotensin II (Ang II), arginine vasopressin (AVP), and serotonin (5-HT) on growth and protein biosynthesis of cultured rat MCs after 72 hours of incubation. AVP and 5-HT (10-6 M) significantly increased DNA synthesis and growth of quiescent subconfluent MCs to levels of 25 and 45%, respectively, of the optimal stimulatory effect of 10% fetal calf serum (FCS) (both P < 0.001). The mitogenic effect of Ang II was 10% of the 10% FCS effect (P < 0.01). ECM production was studied by ELISA assay for fibronectin (FN) secreted into the culture medium (SeFN) and cell-associated FN, that is, intra- and pericellular FN (CaFN). In all incubations, highly significant negative linear relationships were found between the numbers of MCs per well and quantities of both SeFN and CaFN after normalization of the data by logarithmic transformation (SeFN: r values > -0.9705; CaFN: r < -0.9620; P < 0.001). Thus, increasing cell densities progressively suppressed ECM formation by MCs. The ECM production was found to be independent of growth activity. AVP significantly increased SeFN (P < 0.05) and decreased CaFN (P < 0.001) in subconfluent cultures; Ang II and 5-HT had no effect. Metabolic labeling with 35S-methionine (18 hr, 200 µCi/ml medium) and 2-D electrophoresis of MC lysates resulted in resolution of >500 different radiolabeled intracellular proteins in molecular weight from 110 to 20 Kd over an isoelectric interval of 5.0 to 7.0. Computerized video densitometry and scintillation counting of excised spots revealed prominent upregulation of 10 different MC proteins in response to AVP, and enhanced expression of five proteins in response to 5-HT, events characteristic of cellular activation. Ang II caused weakly increased expression of only one protein. The stimulatory effects of AVP and 5-HT on growth and protein synthesis of MCs in-vitro imply a possible in vivo role for these factors in glomerular disease

Parasympathetic activity and total fibrotic kidney in autosomal-dominant polycystic kidney disease patients: a pilot study

PurposeRenin-angiotensin system hyperactivation in autosomal-dominant polycystic kidney disease (ADPKD) patients leads to early hypertension. Cystic enlargement probably causes parenchymal hypoxia, renin secretion, and endothelial dysfunction. Sympathetic and parasympathetic balance is altered in this condition, especially during the night, also affecting blood pressure circadian rhythm. Aim of this study was to evaluate sympathetic/parasympathetic balance using heart rate variability (HRV) parameters and find a correlation between HRV and renal damage progression, as total kidney volume enlargement, in ADPKD patients.MethodsSixteen adult ADPKD patients were enrolled in the study. Eleven patients (68.8%) were male, and the median age was 42 years (IQR 36-47.5). HRV parameters were calculated using 24 h-ECG Holter. A kidney magnetic resonance imaging (MRI) scan 3 Tesla was performed to evaluate total kidney volume (TKV) and total fibrotic volume (TFV).ResultsA statistically significant positive linear correlation was observed between length of kidneys and frequency domain parameters as low frequency (LF) (r = 0.595, p &lt; 0.05) and LFday (r = 0.587, p &lt; 0.05). Moreover, a statistically significant positive linear correlation exists between high frequency (HF) and TFV (r = 0.804, p &lt; 0.01) or height-adjusted (ha) TFV (r = 0.801, p &lt; 0.01). Finally, we found a statistically significant positive linear correlation between HFnight and TKV (r = 0.608, p &lt; 0.05), ha-TKV (r = 0.685, p &lt; 0.01), TFV (r = 0.594, p &lt; 0.05), and ha-TFV (r = 0.615, p &lt; 0.05).ConclusionWe suppose that the increase in TKV and TFV could lead to a parasympathetic tone hyperactivation, probably in response to hypoxic stress and vasoconstriction due to cystic enlargement

Incremental Peritoneal Dialysis Favourably Compares with Hemodialysis as a Bridge to Renal Transplantation

Background. The value of incremental peritoneal dialysis (PD) as a bridge to renal transplantation (Tx) has not been specifically addressed. Methods. All consecutive Stage 5 CKD patients with at least 1 year predialysis followup, starting incremental PD or HD under our care and subsequently receiving their first renal Tx were included in this observational cohort study. Age, gender, BMI, underlying nephropathy, residual renal function (RRF) loss rate before dialysis and RRF at RRT start, comorbidity, RRT schedules and adequacy measures, dialysis-related morbidity, Tx waiting time, RRF at Tx, incidence of delayed graft function (DGF), in-hospital stay for Tx, serum creatinine at discharge and one year later were collected and compared between patients on incremental PD or HD before Tx. Results. Seventeen patients on incremental PD and 24 on HD received their first renal Tx during the study period. Age, underlying nephropathy, RRF loss rate in predialysis, RRF at the start of RRT and comorbidity did not differ significantly. While on dialysis, patients on PD had significantly lower epoetin requirements, serum phosphate, calciumxphosphate product and better RRF preservation. Delayed graft function (DGF) occurred in 12 patients (29%), 1 on incremental PD and 11 on HD. Serum creatinine at discharge and 1 year later was significantly higher in patients who had been on HD. Conclusions. In patients receiving their first renal Tx, previous incremental PD was associated with low morbidity, excellent preservation of RRF, easier attainment of adequacy targets and significantly better immediate and 1-year graft function than those observed in otherwise well-matched patients previously treated with HD

Contrast-Induced Acute Kidney Injury and Endothelial Dysfunction: The Role of Vascular and Biochemical Parameters

Introduction: Contrast-induced acute kidney injury (CIAKI) is one of the main causes of acute renal failure in hospitalized patients, following the administration of iodinated contrast medium used for CT scans and angiographic procedures. CIAKI determines a high cardiovascular risk and appears to be one of the most feared complications of coronary angiography, causing a notable worsening of the prognosis with high morbidity and mortality. Aim: To evaluate a possible association between the renal resistive index (RRI) and the development of CIAKI, as well as an association with the main subclinical markers of atherosclerosis and the main cardiovascular risk factors. Materials and Methods: We enrolled 101 patients with an indication for coronary angiography. Patients underwent an assessment of renal function (serum nitrogen and basal creatinine, 48 and 72 h after administration of contrast medium), inflammation (C reactive protein (CRP), serum calcium and phosphorus, intact parathormone (iPTH), 25-hydroxyvitaminD (25-OH-VitD), serum uric acid (SUA), total cholesterol, serum triglycerides, serum glucose and insulin). All patients also carried out an evaluation of RRI, intima-media thickness (IMT), interventricular septum (IVS) and the ankle-brachial index (ABI). Results: 101 patients (68 male), with a mean age of 73.0 ± 15.0 years, were enrolled for the study; 35 are affected by type 2 diabetes mellitus. A total of 19 cases of CIAKI were reported (19%), while among diabetic patients we reported an incidence of 23% (8 patients). In our study, patients with CIAKI had significantly higher RRI (p &lt; 0.001) and IMT (p &lt; 0.001) with respect to the patients who did not develop CIAKI. Furthermore, patients with CIAKI had significantly higher CRP (p &lt; 0.001) and SUA (p &lt; 0.006). Conclusions: We showed a significant difference in RRI, IMT, SUA and CRP values between the population developing CIAKI and patients without CIAKI. This data appears relevant considering that RRI and IMT are low-cost, non-invasive and easily reproducible markers of endothelial dysfunction and atherosclerosis

An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar

Background and objectives: Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients' clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent.Methods: DB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) mod-els based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo-and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality.Results: The dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (&gt; 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets.Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker ( &lt;1 min versus 7 +/- 3 min), and required minimal user interaction. Conclusions: Our solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report.(c) 2022 Elsevier B.V. All rights reserved

A Characterization of Scale Invariant Responses in Enzymatic Networks

An ubiquitous property of biological sensory systems is adaptation: a step increase in stimulus triggers an initial change in a biochemical or physiological response, followed by a more gradual relaxation toward a basal, pre-stimulus level. Adaptation helps maintain essential variables within acceptable bounds and allows organisms to readjust themselves to an optimum and non-saturating sensitivity range when faced with a prolonged change in their environment. Recently, it was shown theoretically and experimentally that many adapting systems, both at the organism and single-cell level, enjoy a remarkable additional feature: scale invariance, meaning that the initial, transient behavior remains (approximately) the same even when the background signal level is scaled. In this work, we set out to investigate under what conditions a broadly used model of biochemical enzymatic networks will exhibit scale-invariant behavior. An exhaustive computational study led us to discover a new property of surprising simplicity and generality, uniform linearizations with fast output (ULFO), whose validity we show is both necessary and sufficient for scale invariance of enzymatic networks. Based on this study, we go on to develop a mathematical explanation of how ULFO results in scale invariance. Our work provides a surprisingly consistent, simple, and general framework for understanding this phenomenon, and results in concrete experimental predictions

An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar

BACKGROUND AND OBJECTIVES: Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients' clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent. METHODS: DB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) models based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo- and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality. RESULTS: The dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (> 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets. Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker (<1 min versus 7 ± 3 min), and required minimal user interaction. CONCLUSIONS: Our solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report

Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems

Objective:We aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest CT scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID-19 in comparison to semi-quantitative visual scoring systems.Methods:A deep-learning algorithm was utilized to quantify the pneumonia burden, while semi-quantitative pneumonia severity scores were estimated through visual means. The primary outcome was clinical deterioration, the composite end point including admission to the intensive care unit, need for invasive mechanical ventilation, or vasopressor therapy, as well as in-hospital death.Results:The final population comprised 743 patients (mean age 65  ±  17 years, 55% men), of whom 175 (23.5%) experienced clinical deterioration or death. The area under the receiver operating characteristic curve (AUC) for predicting the primary outcome was significantly higher for AI-assisted quantitative pneumonia burden (0.739, p = 0.021) compared with the visual lobar severity score (0.711, p &lt; 0.001) and visual segmental severity score (0.722, p = 0.042). AI-assisted pneumonia assessment exhibited lower performance when applied for calculation of the lobar severity score (AUC of 0.723, p = 0.021). Time taken for AI-assisted quantification of pneumonia burden was lower (38 ± 10 s) compared to that of visual lobar (328 ± 54 s, p &lt; 0.001) and segmental (698 ± 147 s, p &lt; 0.001) severity scores.Conclusion:Utilizing AI-assisted quantification of pneumonia burden from chest CT scans offers a more accurate prediction of clinical deterioration in patients with COVID-19 compared to semi-quantitative severity scores, while requiring only a fraction of the analysis time.Advances in knowledge:Quantitative pneumonia burden assessed using AI demonstrated higher performance for predicting clinical deterioration compared to current semi-quantitative scoring systems. Such an AI system has the potential to be applied for image-based triage of COVID-19 patients in clinical practice