Patient-reported outcomes of parenteral somatostatin analogue injections in 195 patients with acromegaly.

This work is licensed under a Creative Commons Attribution 3.0 Unported LicenseBACKGROUND: Long-acting somatostatin analogues delivered parenterally are the most widely used medical treatment in acromegaly. This patient-reported outcomes survey was designed to assess the impact of chronic injections on subjects with acromegaly. METHODS: The survey was conducted in nine pituitary centres in Germany, UK and The Netherlands. The questionnaire was developed by endocrinologists and covered aspects of acromegaly symptoms, injection-related manifestations, emotional and daily life impact, treatment satisfaction and unmet medical needs. RESULTS: In total, 195 patients participated, of which 112 (57%) were on octreotide (Sandostatin LAR) and 83 (43%) on lanreotide (Somatuline Depot). The majority (>70%) of patients reported acromegaly symptoms despite treatment. A total of 52% of patients reported that their symptoms worsen towards the end of the dosing interval. Administration site pain lasting up to a week following injection was the most frequently reported injection-related symptom (70% of patients). Other injection site reactions included nodules (38%), swelling (28%), bruising (16%), scar tissue (8%) and inflammation (7%). Injection burden was similar between octreotide and lanreotide. Only a minority of patients received injections at home (17%) and 5% were self-injecting. Over a third of patients indicated a feeling of loss of independence due to the injections, and 16% reported repeated work loss days. Despite the physical, emotional and daily life impact of injections, patients were satisfied with their treatment, yet reported that modifications that would offer major improvement over current care would be 'avoiding injections' and 'better symptom control'. CONCLUSION: Lifelong injections of long-acting somatostatin analogues have significant burden on the functioning, well-being and daily lives of patients with acromegaly.Chiasma, Inc. 60 Welles Ave, Newton, MA 02 459, USA

Prednisolone is associated with a worse bone mineral density in primary adrenal insufficiency

Context: Patients with primary adrenal insufficiency (PAI) or congenital adrenal hyperplasia (CAH) receive life-long glucocorticoid (GC) therapy. Daily GC doses are often above the physiological cortisol production rate and can cause long-term morbidities such as osteoporosis. No prospective trial has investigated the long-term effect of different GC therapies on bone mineral density (BMD) in those patients. Objectives: To determine if patients on hydrocortisone (HC) or prednisolone show changes in BMD after follow-up of 5.5 years. To investigate if BMD is altered after switching from immediate- to modified-release HC. Design and patients: Prospective, observational, longitudinal study with evaluation of BMD by DXA at visit1, after 2.2 ± 0.4 (visit2) and after 5.5 ± 0.8 years (visit3) included 36 PAI and 8 CAH patients. Thirteen patients received prednisolone (age 52.5 ± 14.8 years; 8 women) and 31 patients received immediate-release HC (age 48.9 ± 15.8 years; 22 women). Twelve patients on immediate-release switched to modified-release HC at visit2. Results: Prednisolone showed significantly lower Z-scores compared to HC at femoral neck (−0.85 ± 0.80 vs −0.25 ± 1.16, P < 0.05), trochanter (−0.96 ± 0.62 vs 0.51 ± 1.07, P < 0.05) and total hip (−0.78 ± 0.55 vs 0.36 ± 1.04, P < 0.05), but not at lumbar spine, throughout the study. Prednisolone dose decreased by 8% over study time, but no significant effect was seen on BMD. BMD did not change significantly after switching from immediate- to modified-release HC. Conclusions: The use of prednisolone as hormone replacement therapy results in significantly lower BMD compared to HC. Patients on low-dose HC replacement therapy showed unchanged Z-scores within the normal reference range during the study period

BclI polymorphism of the glucocorticoid receptor and adrenal crisis in primary adrenal insufficiency

Context: Patients with primary adrenal insufficiency (PAI) or congenital adrenal hyperplasia (CAH) are at a high risk of adrenal crisis (AC). Glucocorticoid sensitivity is at least partially genetically determined by polymorphisms of the glucocorticoid receptor (GR). Objectives: To determine if a number of intercurrent illnesses and AC are associated with the GR gene polymorphism BclI in patients with PAI and CAH. Design and patients: This prospective, longitudinal study over 37.7 ± 10.1 months included 47 PAI and 25 CAH patients. During the study period, intercurrent illness episodes and AC were documented. Results: The study period covered 223 patient years in which 21 AC occurred (9.4 AC/100 pat years). There were no significant differences between BclI polymorphisms (CC (n = 29), CG (n = 34) and GG (n = 9)) regarding BMI, hydrocortisone equivalent daily dose and blood pressure. We did not find a difference in the number of intercurrent illnesses/patient year among BclI polymorphisms (CC (1.5 ± 1.4/pat year), CG (1.2 ± 1.2/pat year) and GG (1.6 ± 2.2/pat year)). The occurrence of AC was not significantly different among the homozygous (GG) genotype (32.5 AC/100 pat years), the CC genotype (6.7 AC/100 pat years) and the CG genotype (4.9 AC/100 pat years). Concomitant hypothyroidism was the highest in the GG genotype group (5/9), compared to others (CC (11/29) and CG (11/34)). Conclusions: Although sample sizes were relatively small and results should be interpreted with caution, this study suggests that the GR gene polymorphism BclI may not be associated with the frequencies of intercurrent illnesses and AC

Gut–Liver Axis: How Do Gut Bacteria Influence the Liver?

Chronic liver diseases are a major cause of morbidity and mortality worldwide. Recently, gut dysbiosis was identified as an important factor in the pathogenesis of liver diseases. The relationship between gut microbiota and the liver is still not well understood; however, dysfunction of the gut mucosal barrier (&ldquo;leaky gut&rdquo;) and increased bacterial translocation into the liver via the gut&ndash;liver axis probably play crucial roles in liver disease development and progression. The liver is an important immunological organ, and, after exposure to gut-derived bacteria via portal circulation, it responds with activation of the innate and adaptive immune system, leading to hepatic injury. A better understanding of the pathophysiological links among gut dysbiosis, the integrity of the gut barrier, and the hepatic immune response to gut-derived factors is essential for the development of new therapies to treat chronic liver diseases

Exercise Intervention Studies in Patients with Peripheral Neuropathy: A Systematic Review

Introduction Peripheral neuropathies (PNPs) encompass a large group of disorders of heterogeneous origin which can manifest themselves with sensory and/or motor deficits depending on the predominantly affected nerve fiber modality. It represents a highly prevalent disease group which can be associated with significant disability and poor recovery. Exercise has the potential to improve side effects of PNP. Objective Our objective in this systematic review was to analyze exercise interventions for neuropathic patients in order to evaluate the possible benefits of exercise. Methods Three independent reviewers used PubMed, MEDPILOT (R) (MEDLINE), Cochrane, and relevant reference lists to obtain the data. Relevant studies were graded according to the Oxford Levels of Evidence. Results Eighteen studies (ten randomized controlled trials and eight controlled clinical trials) met all inclusion criteria. Three (diabetic) studies were ranked very high quality [1b (A)], nine high quality (four diabetes, one cancer, four others) [2b (B)], while six (four diabetes, two others) showed low quality (4/C). Current data suggests that exercise is a feasible, safe, and promising supportive measure for neuropathic patients. This is best documented for patients with diabetic peripheral neuropathy (DPN), suggesting that endurance training has the potential to prevent the onset of and reduce the progression of DPN. In general, balance exercises showed the highest effect on the motor as well as sensory symptoms in all types of PNP. Conclusion Overall, balance training appears to be the most effective exercise intervention. Studies focusing exclusively on strength, or a combination of endurance and strength, appear to have a lower impact. For metabolicallyinduced neuropathies, endurance training also plays an important role. Further research with high methodological quality needs to be conducted in order to establish evidence-based clinical recommendations for neuropathic patients

U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets

Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer’s. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper—quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset

Motivation for and adherence to growth hormone replacement therapy in adults with hypopituitarism: the patients‘ perspective

Introduction!#!While reasons for non-adherence in children requiring growth hormone (GH) replacement (GH-Rx) are well researched, few studies have investigated adherence in adult GH deficient patients. Against the background of the adverse medical sequelae of untreated severe GH deficiency (GHD) in adults, we explored adherence to GH-Rx and associated factors in this patient group.!##!Method!#!Cross-sectional analysis including 107 adult patients with severe GHD on GH-Rx, 15 untreated GDH patients and 19 who had discontinued therapy. Patients completed self-developed ad hoc surveys on adherence to medication and GH-Rx, specific beliefs about GH-Rx, side effects and burden of injection, reasons for never receiving or dropping out of therapy, respectively.!##!Results!#!Adherence to GH-Rx was high (mean 15.8/18 points on the self-developed adherence score) and significantly correlated with general medication adherence. Higher age was significantly associated with better adherence to GH-Rx, while injection side effects, duration of treatment or device used were not. The most frequent reasons for not being on GH-Rx apart from medical reasons included fear of side effects, lack of belief in treatment effects and dislike of injections. In patients not on GH-Rx, the proportion of patients in employment was significantly smaller than in the treatment group, despite similar age and comorbidities.!##!Conclusions!#!Adherence to GH-Rx was high for those patients on therapy. Instead of focusing on improving adherence in those adults already on GH-Rx, efforts should be undertaken to ally fear of side effects and provide education on positive treatment effects for those eligible but not receiving therapy

S1 Appendix -

Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer’s. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper—quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.</div

The overall process overview of the proposed methodology, used to extract blood vessels from MRI image stacks using an U-Net segmentation model.

The overall process overview of the proposed methodology, used to extract blood vessels from MRI image stacks using an U-Net segmentation model.</p