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For decades there have been anecdotal claims of synergistic interactions between plant-parasitic nematodes and soil-borne fungi causing decline of productivity of passion fruit (Passiflora edulis) orchards. An empirical confirmation of these disease complexes would impact disease management and plant breeding for resistance. To test those claims, we subjected passion fruit plants to single or concomitant parasitism by Meloidogyne javanica or M. incognita and Fusarium nirenbergiae or Neocosmospora sp. under controlled conditions. Non-inoculated plants served as control for the assays. The severity of shoot symptoms and variables related to plant growth, the extent of fungal lesions, and nematode reproduction were assessed to characterize the interactions. The shoot symptoms and effect on plant growth induced by the pathogens varied, but no synergy between the pathogens was observed. Moreover, the volume of tissue lesioned by the fungi was not affected by co-parasitism of the nematodes. Conversely, plant resistance to the nematodes was not affected by co-parasitism of the fungi. The interactions M. incognita-F. nirenbergiae, M. incognita-Neocosmospora sp., M. javanica-F. nirenbergiae, and M. javanica-Neocosmospora sp. were not synergistic as previously claimed, but instead neutral.

There are several equations to predict maximum oxygen consumption (VO2max) from ergometric test variables on different ergometers. However, a similar equation using ventilatory thresholds of ergospirometry in a submaximal test on a cycle ergometer is unavailable. The aim of the present study was to assess the accuracy of VO2max prediction models based on indicators of submaximal effort. Accordingly, 4,640 healthy, nonathlete women ages 20 years and older volunteered to be tested on a cycle ergometer using a maximum incremental protocol. The subjects were randomly assigned to 2 groups: group A (estimation) and group B (validation). From the independent variables of weight in kilograms, the second workload threshold (WT2), and heart rate of the second threshold (HRT2), it was possible to build a multiple linear regression model to predict maximal oxygen consumption (VO2max = 40.302 - 0.497 [Weight] - 0.001 [HRT2] + 0.239 [WT2] in mL O2/kg/min(-1); r = 0.995 and standard error of the estimate [SEE] = 0.68 mL O2/kg/min(-1)). The cross-validation method was used in group B with group A serving as the basis for building the model and the validation dataset. The results showed that, in healthy nonathlete women, it is possible to predict VO2max with a minimum of error (SEE = 1.00%) from submaximal indicators obtained in an incremental test. [ABSTRACT FROM AUTHOR]

Abstract Background The importance of achieving and maintaining an appropriate metabolic control in patients with type 1 diabetes mellitus (DM1) has been established in many studies aiming to prevent the development of chronic complications. The carbohydrate counting method can be recommended as an additional tool in the nutritional treatment of diabetes, allowing patients with DM1 to have more flexible food choices. This study aimed to evaluate the influence of nutrition intervention and the use of multiple short-acting insulin according to the carbohydrate counting method on clinical and metabolic control in patients with DM1. Methods Our sample consisted of 51 patients with DM1, 32 females, aged 25.3 ± 1.55 years. A protocol of nutritional status evaluation was applied and laboratory analysis was performed at baseline and after a three-month intervention. After the analysis of the food records, a balanced diet was prescribed using the carbohydrate counting method, and short-acting insulin was prescribed based on the total amount of carbohydrate per meal (1 unit per 15 g of carbohydrate). Results A significant decrease in A1c levels was observed from baseline to the three-month evaluation after the intervention (10.40 ± 0.33% and 9.52 ± 0.32%, respectively, p = 0.000). It was observed an increase in daily insulin dose after the intervention (0.99 ± 0.65 IU/Kg and 1.05 ± 0.05 IU/Kg, respectively, p = 0.003). No significant differences were found regarding anthropometric evaluation (BMI, waist, hip or abdominal circumferences and waist to hip ratio) after the intervention period. Conclusions The use of short-acting insulin based on the carbohydrate counting method after a short period of time resulted in a significant improvement of the glycemic control in patients with DM1 with no changes in body weight despite increases in the total daily insulin doses.

Abstract Background Detection of circulating tumor cells (CTC) in the blood of cancer patients may have prognostic and predictive significance. However, background expression of 'tumor specific markers' in peripheral blood mononuclear cells (PBMC) may confound these studies. The goal of this study was to identify the origin of Cytokeratin 19 (CK19) and HER-2 signal in PBMC and suggest an approach to enhance techniques involved in detection of CTC in breast cancer patients. Methods PBMC from healthy donors were isolated and fractionated into monocytes, lymphocytes, natural killer cells/granulocytes and epithelial populations using immunomagnetic selection and fluorescent cell-sorting for each cell type. RNA isolated from each fraction was analyzed for CK19, HER2 and Beta 2 microglobulin (B2M) using real-time qRT-PCR. Positive selection for epithelial cells and negative selection for NK/granulocytes were used in an attempt to reduce background expression of CK19 and HER2 markers. Results In normal PBMC, CK19 was expressed in the lymphocyte population while HER-2 expression was highest in the NK/granulocyte population. Immunomagnetic selection for epithelial cells reduced background CK19 signal to a frequency of Conclusion We present a novel method to improve the specificity of the traditional method of detecting CTC by identifying the source of the background signals and reducing them by negative immunoselection. Further studies are warranted to improve sensitivity and specificity of methods of detecting CTC will prove to be useful tools for clinicians in determining prognosis and monitoring treatment responses of breast cancer patients.