Is there any interaction of resistin and adiponectin levels with protein-energy wasting among patients with chronic kidney disease.

The aim of this study was to evaluate the effects of adipocytokines including adiponectin, leptin, resistin, neuropeptide Y and ghrelin in chronic kidney disease (CKD) patients on appearance of protein-energy wasting (PEW). One hundred fifty patients with mean age of 45.4 ± 15.9 years, without active infections or chronic inflammatory conditions were recruited into the study. Study groups were control group (consisting of 30 healthy volunteers with normal kidney functions), hemodialysis group, predialysis group, peritoneal dialysis group and kidney transplant group. Fasting morning serum leptin, ghrelin, acylated ghrelin, neuropeptide Y, adiponectin, resistin levels of all of the groups were measured. Anthropometric and nutritional assessments of all patients were obtained. Diagnosis of PEW was made according to definition recommended by the International Society of Renal Nutrition and Metabolism. Presence of PEW in hemodialysis (23.3%) and peritoneal dialysis (26.7%) groups were significantly higher than those of predialysis (3.3%), and transplantation (0%) groups. Adiponectin and resistin levels in predialysis, peritoneal dialysis and hemodialysis patients were significantly higher than control group (p: 0.0001). This study had given significant positive correlations between presence of PEW and serum resistin (r: 0.267, p: 0.001), and serum adiponectin levels (r: 0.349, p: 0.0001). There were no relationship between presence of PEW and ghrelin, acylated-ghrelin, neuropeptide Y, and leptin levels of the groups. CKD patients except transplant patients had higher adiponectin and resistin levels than control group. PEW was found to be linearly correlated with resistin and adiponectin. High serum resistin and adiponectin levels might have a role in development of PEW among dialysis patients.

The role of adipocytokines on depressive symptoms of patients with chronic kidney disease.

OBJECTIVE: The aim of this study is to evaluate depression and anxiety scores among chronic kidney disease (CKD) patients and to search the changes of serum concentrations of adipokines with respect to emotional disturbances of CKD patients. PATIENTS AND METHODS: 150 patients recruited into this cross-sectional analytic study. Study groups were control, hemodialysis, predialysis, peritoneal dialysis and kidney transplantation groups. Fasting morning serum leptin, ghrelin, acylated ghrelin, neuropeptide Y, adiponectin, resistin levels of all of the groups were measured using ELISA (Sandwich) method. A screening interview based on the Structured Clinical Interview for DSM-IV and self-report scales (The Beck Depression [BDI] and The Beck Anxiety Inventory [BAI] and Brief Symptom Inventory [BSI] which is self report scales) were administered and conducted by a trained interviewer. RESULTS: BDI scores were significantly higher in hemodialysis and predialysis groups compared to control group (p = 0.009). Somatization sub scores of BSI were significantly higher in hemodialysis and peritoneal dialysis groups compared to control group (p = 0.041). Also positive symptom distress index scores of BSI were significantly higher in hemodialysis and transplantation groups compared to control group (p = 0.047). BDI score were significantly negatively correlated with duration of education (r = -0.165, p = 0.045), positively correlated with presence of protein energy wasting (r = 0.198, p = 0.016), and resistin levels (r = 0.233, p = 0.004). CONCLUSION: CKD patients had higher BDI, BSI-somatization, BSI-positive symptom distress index scores compared to control group. High serum resistin levels, presence of protein energy wasting might have a role in development of depressive disorders of patients with chronic kidney disease.

Modeling individual tree mortality for crimean pine plantations.

Individual tree mortality model was developed for crimean pine (Pinus nigra subsp. pallasiana) plantations in Turkey. Data came from 5 year remeasurements of the permanent sample plots. The data comprises of 115 sample plots with 5029 individual trees. Parameters of the logistic equation were estimated using weighted nonlinear regression analysis. Approximately 80% of the observations were used for model development and 20% for validation. The explicatory variables in the model were ratio of diameter of the subject tree and basal area mean diameter of the sample plot as measure of competition index for individual trees, basal area and site index. All parameter estimates were found highly significant (p < 0.001) in predicting mortality model. Chi-square statistics indicate that the most important variable is d / d(q), the second most important is site index, and the third most important predictor is stand basal area. Examination of graphs of observed vs. predicted mortality rates reveals that the mortality model is well behaved and match the observed mortality rates quite well. Although the phenomenon of mortality is a stochastic, rare and irregular event, the model fit was fairly good. The logistic mortality model passed a validation test on independent data not used in parameter estimation. The key ingredient for obtaining a good mortality model is a data set that is both large and representative of the population under study and the data satisfy both requirements. The mortality model presented in this paper is considered to have an appropriate level of reliability.

Characterization of soil erosion and its implication to forest management.

Forests have traditionally been managed to maximize timber production or economic profit, completely neglecting other forest values. Nowadays, however, forests are being managed for multiple uses. The basic requirement of multiple use forestry is to identify and quantify forest values and to determine management objectives. The priorities of management objectives, however, must be decided. In this study, a model predicting the soil loss for multi objective forest management was developed. The model was based on data from remeasurement of permanent sample plots. The data were gathered from 132 sample plots. Approximately 80% of the observations were used for model development and 20% for validation. The model was designed for even aged and uneven aged forests, as well as for forests with mixed and pure species composition. The explicatory variables in the model were mean diameter and number of trees. All parameter estimates were found highly significant (p < 0.001) in predicting soil loss. The model fit and validation tests were fairly good. The soil loss model presented in this paper was considered to have an appropriate level of reliability. It can be used in the overall multi-objective forest management planning, but, it should be limited to the conditions for which the data were gathered.

Developing a multi-objective forest planning process with goal programming: a case study.

In this study, multi-objective forest management planning process and application were realized in a case study involving faculty research forest. Forest values were quantitatively determined. Goal programming was used in developing multi objective forest management planning model. Forest function considered as forest management objectives (goals) in the model include wood production, soil protection and water production. Four different models containing different goals combinations were developed. Goals were prioritized differently in these models. The goals were fully achieved. Moreover, for the tree goals, the optimum GP solutions presented wanted deviations with respect to the initial target values. Forest function maps were prepared according to models solutions in the end of this study.

A yield prediction model for Crimean pine plantations.

Crimean pine is one of the most important plantation and natural species in Turkey. In spite of this importance, information on the growth and yield of this species is lacking. For this reason, a model for growth and yield in crimean pine plantations is described, in which stand development is derived from relation to age and site index. Data were collected from 115 temporary sample plots from plantations ranging in age from 3 to 50 years. Site index curves and empirical yield tables were developed and presented. Site index curves were used to classify plantations into site classes of I, II and III, in order of decreasing productivity. Yield functions indicate that crimean pine can be grown on biologically optimum rotations of 35, 36 and 37 years on site classes I, II and III, respectively. The models can be calibrated on permanent field plot data and tested against independent long term growth and yield records.