Diet and nutrition against inflammatory bowel disease: Trick or treat(ment)?

Even if the relationships between nutrition and inflammatory bowel disease (IBD) remain underexplored, the current literature is providing, day by day, much more evidence on the effects of various diets in both prevention and treatment of such illnesses. Wrong dietary habits, together with other environmental factors such as pollution, breastfeeding, smoke, and/or antibiotics, are among the theoretical pathogenetic causes of IBD, whose multifactorial aetiology has been already confirmed. While some of these risk factors are potentially reversible, some others cannot be avoided, and efficient treatments become necessary to prevent IBD spread or recurrence. Furthermore, the drugs currently available for treatment of such disease provide low-to-no effect against the symptoms, making the illnesses still strongly disabling. Whether nutrition and specific diets will prove to effectively interrupt the course of IBD has still to be clarified and, in this sense, further research concerning the applications of such dietary interventions is still needed.     

Investigation of the mechanisms of Genkwa Flos hepatotoxicity by a cell metabolomics strategy combined with serum pharmacology in HL-7702 liver cells

1. To investigate Genkwa Flos hepatotoxicity, a cell metabolomics strategy combined with serum pharmacology was performed on human HL-7702 liver cells in this study.

2. Firstly, cell viability and biochemical indicators were determined and the cell morphology was observed to confirm the cell injury and develop a cell hepatotoxicity model. Then, with the help of cell metabolomics based on UPLC-MS, the Genkwa Flos group samples were completely separated from the blank group samples in the score plots and seven upregulated as well as two down-regulated putative biomarkers in the loading plot were identified and confirmed. Besides, two signal molecules and four enzymes involved in biosynthesis pathway of lysophosphatidylcholine and the sphingosine kinase/sphingosine-1-phosphate pathway were determined to investigate the relationship between Genkwa Flos hepatotoxicity and these two classic pathways. Finally, the metabolic pathways related to specific biomarkers and two classic metabolic pathways were analyzed to explain the possible mechanism of Genkwa Flos hepatotoxicity.

3. Based on the results, lipid peroxidation and oxidative stress, phospholipase A₂/lysophosphatidylcholine pathway, the disturbance of sphingosine-1-phosphate metabolic profile centered on sphingosine kinase/sphingosine-1-phosphate pathway and fatty acid metabolism might be critical participators in the progression of liver injury induced by Genkwa Flos.     

Factors influencing choice of medical vs. surgical treatment of pediatric appendicitis

PurposeTo examine what proportion of caregivers, if given a choice, would choose medical versus surgical treatment of appendicitis and what factors would be important in their decision.MethodsA survey was devised and given to the caregivers of children presenting to the pediatrician for a routine visit in community and academic pediatric clinics. The survey presented a summary of outcomes after medical (non-operative) and surgical treatment of uncomplicated appendicitis. Participants were then asked to choose medical versus surgical treatment if their child were to develop appendicitis. They were also asked to rate the importance of certain factors in their decision ? 1 being “not important” and 5 being “very important”.ResultsFour hundred surveys were distributed with an 86.2% (345/400) response rate. Six percent (21/342) of respondents reported a history of appendicitis and 49.4% (168/340) reported having known someone who had appendicitis. The majority of respondents, 85.3% (284/333), were mothers. A minority of respondents, 41.7% (95% CI: 36.7, 47.0), chose medical treatment over surgery for appendicitis. There was no statistical difference in the proportion of mothers (41.6%) versus fathers who chose medical treatment (41.3%). Caregivers who chose medical treatment were more likely to rate time in hospital (p = .008) and time out of school (p = 05) as important in decision making when compared with those who chose surgery. Those who chose surgical treatment were more likely to rate risk of recurrent appendicitis (p < .001) as important to decision making. In the multivariate analysis, those who rated time in hospital as very important had more than twice the odds of choosing medical therapy (OR 2.20, p = 0.02) when compared with those who rated it as less important. Not knowing someone who has had appendicitis was significantly associated with choosing medical therapy when compared with those who do know someone who has had appendicitis, OR 2.3, p = .002. Rating pain as very important was also significantly associated with choosing medical therapy, when compared to those rating pain 1–3, OR 3.38, p = .03.ConclusionsIn this survey of caregivers of children presenting for routine care, 41.7% would choose medical, or non-operative, therapy for their children with acute appendicitis. The risk of recurrence, time in hospital, and time out of school, pain, and knowing someone who has had appendicitis were all important factors that families may consider when making a decision. These data may be useful for surgeons counseling patients on which treatment to pursue.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.