Validity and reliability of the Italian version of the Oral Assessment Guide

Mucositis is a frequent side-effect of chemotherapy and radiotherapy. Assessment of oral cavity is important to detect alterations in the mouth and plan appropriate interventions. A reliable tool can help to have a better assessment of mucositis and a major knowledge about this phenomenon. Since no valid and reliable tool for the assessment of mucositis is still available in Italy, the aim of this study was to establish the validity and reliability of the Italian version of the Oral Assessment Guide (OAG). A panel of health care experts established the content validity of the tool both for the items and the descriptors. To establish the reliability of the tool, a sample of 14 inpatients with haematological diseases were recruited. Couples of dental hygienists separately performed 60 pairs of assessments (for a total of 120 assessments) on the sample. The Italian version of OAG was found to have an acceptable Content Validity Index (CVI) for items and related descriptors ranging between 0.67 and 1. Cronbach’s alpha was 0.84, agreement of assessment ranged between 0.87 and 0.65 with Cohen’s Kappa coefficient ranging from good to very good. This study showed that the Italian version of the OAG has good psychometric properties of validity and reliability to assess mucositis in patients undergoing chemotherapy. This tool will have a great importance to carry out future research in Italy aimed to improve the patient's outcomes particularly in terms of functional ability and quality of life

Continuity of Care During End of Life: An Evolutionary Concept Analysis

PURPOSE: The purpose of this study was to clarify the concept of continuity of care during the end of life with a focus on the patient’s perspective. METHODS: Rodgers’ method of evolutionary concept analysis was used. The analysis was based on literature published in English in the databases Cumulative Index for Nursing and Allied Health Literature, Medline, and PsycINFO. FINDINGS: Analysis revealed that the continuity at life’s end is a dynamic process that depends on the interaction among patients, families, and providers, and is strictly interwoven with the patient’s time perception. CONCLUSION: This analysis showed the complexities surrounding the patient’s experience of continuity at life’s end. IMPLICATION FOR NURSING: Nurses can benefit from a deeper understanding of the patient’s experience, both theoretically and in practice

Structure Modeling of All Identified G Protein–Coupled Receptors in the Human Genome

G protein–coupled receptors (GPCRs), encoded by about 5% of human genes, comprise the largest family of integral membrane proteins and act as cell surface receptors responsible for the transduction of endogenous signal into a cellular response. Although tertiary structural information is crucial for function annotation and drug design, there are few experimentally determined GPCR structures. To address this issue, we employ the recently developed threading assembly refinement (TASSER) method to generate structure predictions for all 907 putative GPCRs in the human genome. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. These features are essential for the comprehensive modeling of all human GPCRs when close homologous templates are absent. Based on a benchmarked confidence score, approximately 820 predicted models should have the correct folds. The majority of GPCR models share the characteristic seven-transmembrane helix topology, but 45 ORFs are predicted to have different structures. This is due to GPCR fragments that are predominantly from extracellular or intracellular domains as well as database annotation errors. Our preliminary validation includes the automated modeling of bovine rhodopsin, the only solved GPCR in the Protein Data Bank. With homologous templates excluded, the final model built by TASSER has a global C(α) root-mean-squared deviation from native of 4.6 Å, with a root-mean-squared deviation in the transmembrane helix region of 2.1 Å. Models of several representative GPCRs are compared with mutagenesis and affinity labeling data, and consistent agreement is demonstrated. Structure clustering of the predicted models shows that GPCRs with similar structures tend to belong to a similar functional class even when their sequences are diverse. These results demonstrate the usefulness and robustness of the in silico models for GPCR functional analysis. All predicted GPCR models are freely available for noncommercial users on our Web site (http://www.bioinformatics.buffalo.edu/GPCR)

Treatment effect of idebenone on inspiratory function in patients with Duchenne muscular dystrophy

Assessment of dynamic inspiratory function may provide valuable information about the degree and progression of pulmonary involvement in patients with Duchenne muscular dystrophy (DMD). The aims of this study were to characterize inspiratory function and to assess the efficacy of idebenone on this pulmonary function outcome in a large and well‐characterized cohort of 10–18 year‐old DMD patients not taking glucocorticoid steroids (GCs) enrolled in the phase 3 randomized controlled DELOS trial. We evaluated the effect of idebenone on the highest flow generated during an inspiratory FVC maneuver (maximum inspiratory flow; V'I,max(FVC)) and the ratio between the largest inspiratory flow during tidal breathing (tidal inspiratory flow; V'I,max(t)) and the V'I,max(FVC). The fraction of the maximum flow that is not used during tidal breathing has been termed inspiratory flow reserve (IFR). DMD patients in both treatment groups of DELOS (idebenone, n = 31; placebo: n = 33) had comparable and abnormally low V'I,max(FVC) at baseline. During the study period, V'I,max(FVC) further declined by −0.29 L/sec in patients on placebo (95%CI: −0.51, −0.08; P = 0.008 at week 52), whereas it remained stable in patients on idebenone (change from baseline to week 52: 0.01 L/sec; 95%CI: −0.22, 0.24; P = 0.950). The between‐group difference favoring idebenone was 0.27 L/sec (P = 0.043) at week 26 and 0.30 L/sec (P = 0.061) at week 52. In addition, during the study period, IFR improved by 2.8% in patients receiving idebenone and worsened by −3.0% among patients on placebo (between‐group difference 5.8% at week 52; P = 0.040). Although the clinical interpretation of these data is currently limited due to the scarcity of routine clinical practice experience with dynamic inspiratory function outcomes in DMD, these findings from a randomized controlled study nevertheless suggest that idebenone preserved inspiratory muscle function as assessed by V'I,max(FVC) and IFR in patients with DMD

A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features

Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD–AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a “mixed VD–AD dementia” (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations

Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts

Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify—CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. / Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. / Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from “slight” to “significant” in 80% of the cases. / Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology

Correlation of circulating CD133+ progenitor subclasses with a mild phenotype in Duchenne muscular dystrophy patients.

Various prognostic serum and cellular markers have been identified for many diseases, such as cardiovascular diseases and tumor pathologies. Here we assessed whether the levels of certain stem cells may predict the progression of Duchenne muscular dystrophy (DMD)

Longitudinal ambulatory measurements of gait abnormality in dystrophin-deficient dogs

Chantier qualité GAInternational audienceABSTRACT: BACKGROUND: This study aimed to measure the gait abnormalities in GRMD (Golden retriever muscular dystrophy) dogs during growth and disease progression using an ambulatory gait analyzer (3D-accelerometers) as a possible tool to assess the effects of a therapeutic intervention. METHODS: Six healthy and twelve GRMD dogs were evaluated twice monthly, from the age of two to nine months. The evolution of each gait variable previously shown to be modified in control and dystrophin-deficient adults was assessed using two-ways variance analysis (age, clinical status) with repeated measurements. A principal component analysis (PCA) was applied to perfect multivariate data interpretation. RESULTS: Speed, stride length, total power and force significantly already decreased (p < 0.01) at the age of 2 months. The other gait variables (stride frequency, relative power distributions along the three axes) became modified at later stages. Using the PCA analysis, a global gait index taking into account the main gait variables was calculated, and was also consistent to detect the early changes in the GRMD gait patterns, as well as the progressive degradation of gait quality. CONCLUSION: The gait variables measured by the accelerometers were sensitive to early detect and follow the gait disorders and mirrored the heterogeneity of clinical presentations, giving sense to monitor gait in GRMD dogs during progression of the disease and pre-clinical therapeutic trials

"I have got something positive out of this situation": psychological benefits of caregiving in relatives of young people with muscular dystrophy.

This paper focuses on the psychological benefits of caregiving in key relatives of patients with muscular dystrophies (MD), a group of rare diseases characterized by progressive weakness and restriction of the patient's functional abilities. We describe whether relatives perceived caregiving to be a positive experience and test whether relatives' perceptions vary in relation to their view of the patient as a valued person, the degree of involvement in care, and the level of support provided by social network and professionals. The study sample included 502 key relatives of patients aged 4-25 years, suffering from Duchenne, Becker, or limb-girdle MD, in treatment for at least 6 months to one of the eight participating centers, living with at least one relative aged 18-80 years. Of key relatives, 88 % stated that they had gotten something positive out of the situation, 96 % considered their patients to be sensitive, and 94 % viewed their patients as talented. Positive aspects of caregiving were more recognized by key relatives who were more convinced that the patient was sensitive and who perceived that they received higher level of professional help and psychological social support. These results suggest that most key relatives consider that their caregiving experience has had a positive impact on their lives, despite the practical difficulties of caring for patients with MD. Professionals should help relatives to identify the benefits of caregiving without denying its difficulties. Clinicians themselves should develop positive attitudes towards family involvement in the care of patients with long-term diseases