Effects of oral administration of a fuel cell product water to Macaca mulatta Final report, Jan. - Feb. 1965

Fuel cell product water given to monkeys as sole source of fluid intake for 14-day perio

Validation of the CAchexia SCOre (CASCO). Staging cancer patients: The use of miniCASCO as a simplified tool

The CAchexia SCOre (CASCO) was described as a tool for the staging of cachectic cancer patients. The aim of this study is to show the metric properties of CASCO in order to classify cachectic cancer patients into three different groups, which are associated with a numerical scoring. The final aim was to clinically validate CASCO for its use in the classification of cachectic cancer patients in clinical practice. We carried out a case -control study that enrolled prospectively 186 cancer patients and 95 age-matched controls. The score includes five components: (1) body weight loss and composition, (2) inflammation/metabolic disturbances/immunosuppression, (3) physical performance, (4) anorexia, and (5) quality of life. The present study provides clinical validation for the use of the score. In order to show the metric properties of CASCO, three different groups of cachectic cancer patients were established according to the results obtained with the statistical approach used: mild cachexia (15 â\u89¤ Ã\u97 â\u89¤ 28), moderate cachexia (29 â\u89¤ Ã\u97 â\u89¤ 46), and severe cachexia (47 â\u89¤ Ã\u97 â\u89¤ 100). In addition, a simplified version of CASCO, MiniCASCO (MCASCO), was also presented and it contributes as a valid and easy-to-use tool for cachexia staging. Significant statistically correlations were found between CASCO and other validated indexes such as Eastern Cooperative Oncology Group (ECOG) and the subjective diagnosis of cachexia by specialized oncologists. A very significant estimated correlation between CASCO and MCASCO was found that suggests that MCASCO might constitute an easy and valid tool for the staging of the cachectic cancer patients. CASCO and MCASCO provide a new tool for the quantitative staging of cachectic cancer patients with a clear advantage over previous classifications

Cognitive tests used in chronic adult human randomised controlled trial micronutrient and phytochemical intervention studies

In recent years there has been a rapid growth of interest in exploring the relationship between nutritional therapies and the maintenance of cognitive function in adulthood. Emerging evidence reveals an increasingly complex picture with respect to the benefits of various food constituents on learning, memory and psychomotor function in adults. However, to date, there has been little consensus in human studies on the range of cognitive domains to be tested or the particular tests to be employed. To illustrate the potential difficulties that this poses, we conducted a systematic review of existing human adult randomised controlled trial (RCT) studies that have investigated the effects of 24 d to 36 months of supplementation with flavonoids and micronutrients on cognitive performance. There were thirty-nine studies employing a total of 121 different cognitive tasks that met the criteria for inclusion. Results showed that less than half of these studies reported positive effects of treatment, with some important cognitive domains either under-represented or not explored at all. Although there was some evidence of sensitivity to nutritional supplementation in a number of domains (for example, executive function, spatial working memory), interpretation is currently difficult given the prevailing 'scattergun approach' for selecting cognitive tests. Specifically, the practice means that it is often difficult to distinguish between a boundary condition for a particular nutrient and a lack of task sensitivity. We argue that for significant future progress to be made, researchers need to pay much closer attention to existing human RCT and animal data, as well as to more basic issues surrounding task sensitivity, statistical power and type I error

PREDICT-CP: study protocol of implementation of comprehensive surveillance to predict outcomes for school-aged children with cerebral palsy

Objectives: Cerebral palsy (CP) remains the world’s most common childhood physical disability with total annual costs of care and lost well-being of $A3.87b. The PREDICT-CP (NHMRC 1077257 Partnership Project: Comprehensive surveillance to PREDICT outcomes for school age children with CP) study will investigate the influence of brain structure, body composition, dietary intake, oropharyngeal function, habitual physical activity, musculoskeletal development (hip status, bone health) and muscle performance on motor attainment, cognition, executive function, communication, participation, quality of life and related health resource use costs. The PREDICT-CP cohort provides further follow-up at 8–12 years of two overlapping preschool-age cohorts examined from 1.5 to 5 years (NHMRC 465128 motor and brain development; NHMRC 569605 growth, nutrition and physical activity). Methods and analyses: This population-based cohort study undertakes state-wide surveillance of 245 children with CP born in Queensland (birth years 2006–2009). Children will be classified for Gross Motor Function Classification System; Manual Ability Classification System, Communication Function Classification System and Eating and Drinking Ability Classification System. Outcomes include gross motor function, musculoskeletal development (hip displacement, spasticity, muscle contracture), upper limb function, communication difficulties, oropharyngeal dysphagia, dietary intake and body composition, participation, parent-reported and child-reported quality of life and medical and allied health resource use. These detailed phenotypical data will be compared with brain macrostructure and microstructure using 3 Tesla MRI (3T MRI). Relationships between brain lesion severity and outcomes will be analysed using multilevel mixed-effects models. Ethics and dissemination: The PREDICT-CP protocol is a prospectively registered and ethically accepted study protocol. The study combines data at 1.5–5 then 8–12 years of direct clinical assessment to enable prediction of outcomes and healthcare needs essential for tailoring interventions (eg, rehabilitation, orthopaedic surgery and nutritional supplements) and the projected healthcare utilisation

Expert System for Nutrition Care Process of Older Adults

This paper presents an expert system for a nutrition care process tailored for the specific needs of elders. Dietary knowledge is defined by nutritionists and encoded as Nutrition Care Process Ontology, and then used as underlining base and standardized model for the nutrition care planning. An inference engine is developed on top of the ontology, providing semantic reasoning infrastructure and mechanisms for evaluating the rules defined for assessing short and long term elders’ self-feeding behaviours, to identify unhealthy dietary patterns and detect the early instauration of malnutrition. Our expert system provides personalized intervention plans covering nutrition education, diet prescription and food ordering adapted to the older adult’s specific nutritional needs, health conditions and food preferences. In-lab evaluation results are presented proving the usefulness and quality of the expert system as well as the computational efficiency, coupling and cohesion of the defined ontology

Formative evaluation of a mobile liquid portion size estimation interface for people with varying literacy skills

Chronically ill people, especially those with low literacy skills, often have difficulty estimating portion sizes of liquids to help them stay within their recommended fluid limits. There is a plethora of mobile applications that can help people monitor their nutritional intake but unfortunately these applications require the user to have high literacy and numeracy skills for portion size recording. In this paper, we present two studies in which the low- and the high-fidelity versions of a portion size estimation interface, designed using the cognitive strategies adults employ for portion size estimation during diet recall studies, was evaluated by a chronically ill population with varying literacy skills. The low fidelity interface was evaluated by ten patients who were all able to accurately estimate portion sizes of various liquids with the interface. Eighteen participants did an in situ evaluation of the high-fidelity version incorporated in a diet and fluid monitoring mobile application for 6 weeks. Although the accuracy of the estimation cannot be confirmed in the second study but the participants who actively interacted with the interface showed better health outcomes by the end of the study. Based on these findings, we provide recommendations for designing the next iteration of an accurate and low literacy- accessible liquid portion size estimation mobile interface

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Neurological, psychological, and cognitive disorders in patients with chronic kidney disease on conservative and replacement therapy

Chronic kidney disease (CKD) is a highly prevalent condition in the world. Neurological, psychological, and cognitive disorders, related to CKD, could contribute to the morbidity, mortality, and poor quality of life of these patients. The aim of this study was to assess the neurological, psychological, and cognitive imbalance in patients with CKD on conservative and replacement therapy. Seventy-four clinically stable patients affected by CKD on conservative therapy, replacement therapy (hemodialysis (HD), peritoneal dialysis (PD)), or with kidney transplantation (KT) and 25 healthy controls (HC), matched for age and sex were enrolled. Clinical, laboratory, and instrumental examinations, as renal function, inflammation and mineral metabolism indexes, electroencephalogram (EEG), psychological (MMPI-2, Sat P), and cognitive tests (neuropsychological tests, NPZ5) were carried out. The results showed a significant differences in the absolute and relative power of delta band and relative power of theta band of EEG (P=0.008, P<0.001, P=0.051), a positive correlation between relative power of delta band and C-reactive protein (CRP) (P< 0.001) and a negative correlation between estimated glomerular filtration rate (eGFR) (P<0.001) and 1,25-dihydroxyvitamin D3 (1,25-(OH)2D3) (P<0.001), in all the samples. Qualitative analysis of EEG showed alterations of Grade 2 (according to Parsons-Smith classification) in patients on conservative therapy, and Grade 2-3 in KT patients. The scales of MMPI-2 hysteria and paranoia, are significantly correlated with creatinine, eGFR, serum nitrogen, CRP, 1,25-(OH)2D3, intact parathyroid hormone (iPTH), phosphorus, and cynical and hysterical personality, are correlated with higher relative power of delta (P=0.016) and theta band (P= 0.016). Moreover, all NPZ5 scores showed a significant difference between the means of nephropathic patients and the means of the HC, and a positive correlation with eGFR, serum nitrogen, CRP, iPTH, and vitamin D. In CKD patients, simple and noninvasive instruments, as EEG, and cognitive-psychological tests, should be performed and careful and constant monitoring of renal risk factors, probably involved in neuropsychological complications (inflammation, disorders of mineral metabolism, electrolyte disorders, etc.), should be carried out. Early identification and adequate therapy of neuropsychological, and cognitive disorders, might enable a better quality of life and a major compliance with a probable reduction in the healthcare costs