A low fermentable oligo-di-mono saccharides and polyols (FODMAP) diet reduced pain and improved daily life in fibromyalgia patients

Fibromyalgia (FM) is a chronic, rheumatic disease characterized by widespread myofascial pain, of unknown aetiology, having a major impact on quality of life (QOL). Available pharmacotherapy for FM is marginally effective. FM is associated with co-morbidities of gastrointestinal (GI) disorders and Irritable Bowel Syndrome (IBS). There is growing evidence that diets low in FODMAPs, “fermentable oligo-, di- or mono-saccharides and polyols” [Low FODMAP Diet (LFD)], are effective in treating IBS. The aim of this pilot study was to examine the effects of LFDs on symptoms of FM, especially with regard to pain, QOL and GI disorders. Methods A longitudinal study using LFD intervention was performed on 38, 51 ± 10 year-old, female patients diagnosed with FM for an average of 10 years, based on ACR (American College of Rheumatology) 2010 criteria. The study was conducted from January through May, 2015, using a four-week, repeated-assessment model, as follows: Moment 0 – introduction of the protocol to participants; Moment 1 – first assessment and delivery of individual LFD dietary plans; Moment 2 – second assessment and reintroduction of FODMAPs; Moment 3 – last assessment and final nutritional counselling. Assessment tools used were the following: RFIQ (Revised Fibromyalgia Impact Questionnaire), FSQ (Fibromyalgia Survey Questionnaire), IBS-SSS (Severity Score System), EQ-5D (Euro-QOL quality of life instrument), and VAS (Visual Analogue Scale). Daily consumption of FODMAPs was quantified based on published food content analyses. Statistical analyses included ANOVA, non-parametric Friedman, t-student and Chi-square tests, using SPSS 22 software. Results The mean scores of the 38 participants at the beginning of the study were: FSQ (severity of FM, 0–31) – 22 ± 4.4; RFIQ (0–100) – 65 ± 17; IBS-SSS (0–500) – 275 ± 101; and EQ-5D (0–100) – 48 ± 19. Mean adherence to dietary regimens was 86%, confirmed by significant difference in FODMAP intakes (25 g/day vs. 2.5 g/day; p < 0.01). Comparisons between the three moments of assessment showed significant (p < 0.01) declines in scores in VAS, FSQ, and RFIQ scores, in all domains measured. An important improvement was observed with a reduction in the severity of GI symptoms, with 50% reduction in IBS scores to 138 ± 117, following LFD therapy. A significant correlation (r = 0.36; p < 0.05) was found between improvements in FM impact (declined scores) and gastrointestinal scores. There was also a significant correlation (r = 0.65; p < 0.01) between “satisfaction with improvement” after introduction of LFDs and “diet adherence”, with satisfaction of the diet achieving 77% among participants. A significant difference was observed between patients who improved as compared to those that did not improve (Chi-square χ2 = 6.16; p < .05), showing that the probability of improvement, depends on the severity of the RFIQ score. Conclusions Implementation of diet therapy involving FODMAP restrictions, in this cohort of FM patients, resulted in a significant reduction in GI disorders and FM symptoms, including pain scores. These results need to be extended in future larger studies on dietary therapy for treatment of FM. Implications According to current scientific knowledge, these are the first relevant results found in an intervention with LFD therapy in FM and must be reproduced looking for a future dietetic approach in FM.

Assessment of the spatial variability in Tall wheatgrass forage using LANDSAT 8 satellite imagery to delineate potential management zones

Little information is available on the degree of within-field variability of potential production of Tall wheatgrass (Thinopyrum ponticum) forage under unirrigated conditions. The aim of this study was to characterize the spatial variability of the accumulated biomass (AB) without nutritional limitations through vegetation indexes, and then use this information to determine potential management zones. A 27-×-27-m grid cell size was chosen and 84 biomass sampling areas (BSA), each 2 m(2) in size, were georeferenced. Nitrogen and phosphorus fertilizers were applied after an initial cut at 3 cm height. At 500 °C day, the AB from each sampling area, was collected and evaluated. The spatial variability of AB was estimated more accurately using the Normalized Difference Vegetation Index (NDVI), calculated from LANDSAT 8 images obtained on 24 November 2014 (NDVInov) and 10 December 2014 (NDVIdec) because the potential AB was highly associated with NDVInov and NDVIdec (r (2) = 0.85 and 0.83, respectively). These models between the potential AB data and NDVI were evaluated by root mean squared error (RMSE) and relative root mean squared error (RRMSE). This last coefficient was 12 and 15 % for NDVInov and NDVIdec, respectively. Potential AB and NDVI spatial correlation were quantified with semivariograms. The spatial dependence of AB was low. Six classes of NDVI were analyzed for comparison, and two management zones (MZ) were established with them. In order to evaluate if the NDVI method allows us to delimit MZ with different attainable yields, the AB estimated for these MZ were compared through an ANOVA test. The potential AB had significant differences among MZ. Based on these findings, it can be concluded that NDVI obtained from LANDSAT 8 images can be reliably used for creating MZ in soils under permanent pastures dominated by Tall wheatgrass.

An Integrated Soft Computing Approach to Hughes Syndrome Risk Assessment

The AntiPhospholipid Syndrome (APS) is an acquired autoimmune disorder induced by high levels of antiphospholipid antibodies that cause arterial and veins thrombosis, as well as pregnancy-related complications and morbidity, as clinical manifestations. This autoimmune hypercoagulable state, usually known as Hughes syndrome, has severe consequences for the patients, being one of the main causes of thrombotic disorders and death. Therefore, it is required to be preventive; being aware of how probable is to have that kind of syndrome. Despite the updated of antiphospholipid syndrome classification, the diagnosis remains difficult to establish. Additional research on clinically relevant antibodies and standardization of their quantification are required in order to improve the antiphospholipid syndrome risk assessment. Thus, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a Logic Programming approach to knowledge representation and reasoning, complemented with a computational framework based on Artificial Neural Networks. The proposed model allows for improving the diagnosis, classifying properly the patients that really presented this pathology (sensitivity higher than 85%), as well as classifying the absence of APS (specificity close to 95%).