Comparative use patterns of plant resources in rural areas of South Africa and Zimbabwe

Documentation of use patterns of plants across national boundaries is of relevance in understanding the importance of plant resources to livelihood strategies of different ethnic groups. Plant resources have gained prominence as a natural asset through which families derive food, firewood, income, medicines and timber, enabling particularly poor communities to achieve self-sufficiency. The objective of this study was to investigate the trends in plant usage in South Africa and Zimbabwe. An ethnobotanical investigation was conducted between January 2012 and January 2013 in the Limpopo Province, South Africa and the Midlands Province, Zimbabwe. The study used questionnaire surveys and interviews with a total of 143 participants to explore plant use patterns in South Africa and Zimbabwe. A total of 98 plant species were identified, with Zimbabwe contributing 70 species and 47 species from South Africa. The uses were classified into 15 categories, major use categories were firewood, food plants, medicine and timber. Food plant was a major plant use category in Zimbabwe, contributing 55.1%, followed by medicinal plants (36.8%), firewood (35.7%) and timber (31.6%). In contrast, firewood was the major plant use category in South Africa, contributing 18.4%, followed by food plants (17.3%), medicinal (14.3%) and timber (1.0%). Comparison of the two countries demonstrated remarkable differences in plant use patterns. The results showed that rural households in Zimbabwe were more reliant on plant resources than their counterparts in South Africa. Such a trend could be attributed to a close relationship between the local people, and their natural and agricultural environment leading to a rich knowledge base on plants, plant use and related practices. This comparative analysis strengthens the firm belief that utilization of plant resources represents an important shared heritage, preserved over the centuries, which must be exploited in order to provide further new and useful body of ethnobotanical knowledge.     

Reduced proportions of natural killer T cells are present in the relatives of lupus patients and are associated with autoimmunity

Introduction

Systemic lupus erythematosus is a genetically complex disease. Currently, the precise allelic polymorphisms associated with this condition remain largely unidentified. In part this reflects the fact that multiple genes, each having a relatively minor effect, act in concert to produce disease. Given this complexity, analysis of subclinical phenotypes may aid in the identification of susceptibility alleles. Here, we used flow cytometry to investigate whether some of the immune abnormalities that are seen in the peripheral blood lymphocyte population of lupus patients are seen in their first-degree relatives.

Methods

Peripheral blood mononuclear cells were isolated from the subjects, stained with fluorochrome-conjugated monoclonal antibodies to identify various cellular subsets, and analyzed by flow cytometry.

Results

We found reduced proportions of natural killer (NK)T cells among 367 first-degree relatives of lupus patients as compared with 102 control individuals. There were also slightly increased proportions of memory B and T cells, suggesting increased chronic low-grade activation of the immune system in first-degree relatives. However, only the deficiency of NKT cells was associated with a positive anti-nuclear antibody test and clinical autoimmune disease in family members. There was a significant association between mean parental, sibling, and proband values for the proportion of NKT cells, suggesting that this is a heritable trait.

Conclusions

The findings suggest that analysis of cellular phenotypes may enhance the ability to detect subclinical lupus and that genetically determined altered immunoregulation by NKT cells predisposes first-degree relatives of lupus patients to the development of autoimmunity.     

A European focus on proteomics

A report on the First International Symposium of the Austrian Proteomics Platform, Seefeld, Austria, 26-29 January 2004.     

Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models

Background  

With the explosion of microarray studies, an enormous amount of data is being produced. Systematic integration of gene expression data from different sources increases statistical power of detecting differentially expressed genes and allows assessment of heterogeneity. The challenge, however, is in designing and implementing efficient analytic methodologies for combination of data generated by different research groups.