Rituximab Treatment in a Patient with Kimura Disease and Membranous Nephropathy: Case Report

Kimura disease (KD) is a chronic, inflammatory disorder with slowly developing subcutaneous tumor-like swellings, often occurring in the head and neck region. KD is diagnosed based on histology, elevated levels of immunoglobulin type E, and increased peripheral eosinophil granulocytes. KD may coexist with glomerular renal diseases, and this case report is based on a patient with KD-associated membranous nephropathy. Patients with membranous nephropathy without KD have demonstrated responsiveness to treatment with monoclonal anti-CD20 antibodies. This case report is the first to investigate the effect of rituximab treatment in a patient with KD-associated membranous nephropathy. A 30-year-old Italian man living in Denmark was diagnosed with Kimura’s disease based on subcutaneous nodules with eosinophil angiolymphoid hyperplasia. The patient was admitted to the hospital due to nephrotic syndrome. Serology showed eosinophil granulocytosis and negative PLA2-receptor antibody. Renal biopsy showed membranous nephropathy, and the patient was treated with systemic methylprednisolone followed by cyclosporin and then cyclophosphamide with only partial remission. Ultimately, treatment with intravenous rituximab was initiated, which resulted in overall remission and no nephrotic relapses at 30 months of follow-up. Thus, intravenous rituximab effectively decreased proteinuria and prevented nephrotic relapses in a patient with treatment-refractory membranous nephropathy due to KD

Pharmacologic Atrial Natriuretic Peptide Reduces Human Leg Capillary Filtration

Atrial natriuretic peptide (ANP) is produced and secreted by atrial cells. We measured calf capillary filtration rate with prolonged venous-occlusion plethys-mography of supine health male subjects during pharmacologic infusion of ANP (48 pmol/kg/min for 15 min; n equals 6) and during placebo infusion (n equals 7). Results during infusions were compared to prior control measurements. ANP infusion increased plasma (ANP) from 30 plus or minus 4 to 2,568 plus or minus 595 pmol/L. Systemic hemoconcentration occurred during ANP infusion; mean hematocrit and plasma colloid osmotic pressure increased 4.6 and 11.3 percent respectively, relative to pre-infusion baseline values (p is less than 0.05). Mean calf filtration, however was significantly reduced from 0.15 to 0.08 ml/100 ml/min with ANP. Heart rate increased 20 percent with ANP infusion, wheras blood pressure was unchanged. Calf conductance (blood flow/arterial pressure) and venous compliance were unaffected by ANP infusion. Placebo infusion had no effect relative to prior baseline control measurements. Although ANP induced systemic capillary filtration, in the calf, filtration was reduced with ANP. Therefore, phamacologic ANP infusion enhances capillary filtration from the systemic circulation, perhaps at upper body or splanchic sites or both, while having the opposite effect in the leg

Deep phenotyping of the unselected COPSAC2010 birth cohort study

BACKGROUND: We hypothesize that perinatal exposures, in particular the human microbiome and maternal nutrition during pregnancy, interact with the genetic predisposition to cause an abnormal immune modulation in early life towards a trajectory to chronic inflammatory diseases such as asthma and others. OBJECTIVE: The aim of this study is to explore these interactions by conducting a longitudinal study in an unselected cohort of pregnant women and their offspring with emphasis on deep clinical phenotyping, exposure assessment, and biobanking. Exposure assessments focus on the human microbiome. Nutritional intervention during pregnancy in randomized controlled trials are included in the study to prevent disease and to be able to establish causal relationships. METHODS: Pregnant women from eastern Denmark were invited during 2008–2010 to a novel unselected ‘COPSAC(2010)’ cohort. The women visited the clinic during pregnancy weeks 24 and 36. Their children were followed at the clinic with deep phenotyping and collection of biological samples at nine regular visits until the age of 3 and at acute symptoms. Randomized controlled trials of high‐dose vitamin D and fish oil supplements were conducted during pregnancy, and a trial of azithromycin for acute lung symptoms was conducted in the children with recurrent wheeze. RESULTS: Seven hundred and thirty‐eight mothers were recruited from week 24 of gestation, and 700 of their children were included in the birth cohort. The cohort has an over‐representation of atopic parents. The participant satisfaction was high and the adherence equally high with 685 children (98%) attending the 1 year clinic visit and 667 children (95%) attending the 2 year clinic visit. CONCLUSIONS: The COPSAC(2010) birth cohort study provides longitudinal clinical follow‐up with highly specific end‐points, exposure assessments, and biobanking. The cohort has a high adherence rate promising strong data to elucidate the interaction between genomics and the exposome in perinatal life leading to lifestyle‐related chronic inflammatory disorders such as asthma

Post-intervention Status in Patients With Refractory Myasthenia Gravis Treated With Eculizumab During REGAIN and Its Open-Label Extension

OBJECTIVE: To evaluate whether eculizumab helps patients with anti-acetylcholine receptor-positive (AChR+) refractory generalized myasthenia gravis (gMG) achieve the Myasthenia Gravis Foundation of America (MGFA) post-intervention status of minimal manifestations (MM), we assessed patients' status throughout REGAIN (Safety and Efficacy of Eculizumab in AChR+ Refractory Generalized Myasthenia Gravis) and its open-label extension. METHODS: Patients who completed the REGAIN randomized controlled trial and continued into the open-label extension were included in this tertiary endpoint analysis. Patients were assessed for the MGFA post-intervention status of improved, unchanged, worse, MM, and pharmacologic remission at defined time points during REGAIN and through week 130 of the open-label study. RESULTS: A total of 117 patients completed REGAIN and continued into the open-label study (eculizumab/eculizumab: 56; placebo/eculizumab: 61). At week 26 of REGAIN, more eculizumab-treated patients than placebo-treated patients achieved a status of improved (60.7% vs 41.7%) or MM (25.0% vs 13.3%; common OR: 2.3; 95% CI: 1.1-4.5). After 130 weeks of eculizumab treatment, 88.0% of patients achieved improved status and 57.3% of patients achieved MM status. The safety profile of eculizumab was consistent with its known profile and no new safety signals were detected. CONCLUSION: Eculizumab led to rapid and sustained achievement of MM in patients with AChR+ refractory gMG. These findings support the use of eculizumab in this previously difficult-to-treat patient population. CLINICALTRIALSGOV IDENTIFIER: REGAIN, NCT01997229; REGAIN open-label extension, NCT02301624. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that, after 26 weeks of eculizumab treatment, 25.0% of adults with AChR+ refractory gMG achieved MM, compared with 13.3% who received placebo

Minimal Symptom Expression' in Patients With Acetylcholine Receptor Antibody-Positive Refractory Generalized Myasthenia Gravis Treated With Eculizumab

The efficacy and tolerability of eculizumab were assessed in REGAIN, a 26-week, phase 3, randomized, double-blind, placebo-controlled study in anti-acetylcholine receptor antibody-positive (AChR+) refractory generalized myasthenia gravis (gMG), and its open-label extension

The exercise metaboreflex is maintained in the absence of muscle acidosis: insights from muscle microdialysis in humans with McArdle's disease

In McArdle's disease, muscle glycogenolysis is blocked, which results in absent lactate and enhanced ammonia production in working muscle. Using McArdle patients as an experimental model, we studied whether lactate and ammonia could be mediators of the exercise pressor reflex.Changes in muscle interstitial ammonia and lactate were compared with changes in blood pressure and muscle sympathetic nerve activity (MSNA) during static arm flexor exercise at 30 % of maximal contraction force. Muscle interstitial changes in lactate and ammonia were assessed by microdialysis of the biceps muscle, and MSNA by peroneal nerve microneurography, in six McArdle patients and 11 healthy, matched controls. One McArdle patient also had myoadenylate deaminase deficiency, a condition associated with abolished ammonia production in exercise.Exercise-induced increases were higher in McArdle patients vs. controls for MSNA (change of 164 ± 71 vs. 59 ± 19 %) and blood pressure (change of 47 ± 7 vs. 38 ± 4 mmHg). Interstitial lactate increased in controls (peak change 1.3 ± 0.2 mmol l−1) and decreased in McArdle patients (peak change -0.5 ± 0.1 mmol l−1) during and after exercise. Interstitial ammonia did not change during exercise in either group, but was higher post-exercise in McArdle patients, except in the patient with myoadenylate deaminase deficiency who had a flat ammonia response. This patient had an increase in MSNA and blood pressure comparable to other patients. MSNA and blood pressure responses were maintained during post-exercise ischaemia in both groups, indicating that sympathetic activation was caused, at least partly, by a metaboreflex.In conclusion, changes in muscle interstitial lactate and ammonia concentrations during and after exercise are temporally dissociated from changes in MSNA and blood pressure in both patients with McArdle's disease and healthy control subjects. This suggests that muscle acidification and changes in interstitial ammonia concentration are not mediators of sympathetic activation during exercise

Boosting for high-dimensional two-class prediction

Background In clinical research prediction models are used to accurately predict the outcome of the patients based on some of their characteristics. For high-dimensional prediction models (the number of variables greatly exceeds the number of samples) the choice of an appropriate classifier is crucial as it was observed that no single classification algorithm performs optimally for all types of data. Boosting was proposed as a method that combines the classification results obtained using base classifiers, where the sample weights are sequentially adjusted based on the performance in previous iterations. Generally boosting outperforms any individual classifier, but studies with high-dimensional data showed that the most standard boosting algorithm, AdaBoost.M1, cannot significantly improve the performance of its base classier. Recently other boosting algorithms were proposed (Gradient boosting, Stochastic Gradient boosting, LogitBoost)they were shown to perform better than AdaBoost.M1 but their performance was not evaluated for high-dimensional data. Results In this paper we use simulation studies and real gene-expression data sets to evaluate the performance of boosting algorithms when data are high-dimensional. Our results confirm that AdaBoost.M1 can perform poorly in this setting, often failing to improve the performance of its base classifier. We provide the explanation for this and propose a modification, AdaBoost.M1.ICV, which uses cross-validated estimates of the prediction errors and outperforms the original algorithm when data are high-dimensional. The use of AdaBoost.M1.ICV is advisable when the base classifier overfits the training data: the number of variables is large, the number of samples is small, and/or the difference between the classes is large. To a lesser extent also Gradient boosting suffers from similar problems. Contrary to the findings for the low-dimensional data, shrinkage does not improve the performance of Gradient boosting when data are high-dimensional, however it is beneficial for Stochastic Gradient boosting, which outperformed the other boosting algorithms in our analyses. LogitBoost suffers from overfitting and generally performs poorly. Conclusions The results show that boosting can substantially improve the performance of its base classifier also when data are high-dimensional. However, not all boosting algorithms perform equally well. LogitBoost, AdaBoost.M1 and Gradient boosting seem less useful for this type of data. Overall, Stochastic Gradient boosting with shrinkage and AdaBoost.M1.ICV seem to be the preferable choices for high-dimensional class-prediction