Clinical classification of tetanus patients

The authors propose a clinical classification to monitor the evolution of tetanus patients, ranging from grade I to IV according to severity. It was applied on admission and repeated on alternate days up to the 10th day to patients aged > or = 12 years admitted to the State University Hospital, Recife, Brazil. Patients were also classified upon admission according to three prognostic indicators to determine if the proposed classification is in agreement with the traditionally used indicators. Upon admission, the distribution of the 64 patients among the different levels of the proposed classification was similar for the groups of better and worse prognosis according to the three indicators (P > 0.05), most of the patients belonging to grades I and II of the proposed classification. In the later reclassifications, severe forms of tetanus (grades III and IV) were more frequent in the categories of worse prognosis and these differences were statistically significant. There was a reduction in the proportion of mild forms (grades I and II) of tetanus with time for the categories of worse prognostic indicators (chi-square for trend: P = 0.00006, 0.03, and 0.00000) whereas no such trend was observed for the categories of better prognosis (grades I and II). This serially used classification reflected the prognosis of the traditional indicators and permitted the comparison of the dynamics of the disease in different groups. Thus, it becomes a useful tool for monitoring patients by determining clinical category changes with time, and for assessing responses to different therapeutic measures.

Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS) can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA) was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis) and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.

Manual and semi-automatic quantification of in vivo ¹H-MRS data for the classification of human primary brain tumors

In vivo proton magnetic resonance spectroscopy (¹H-MRS) is a technique capable of assessing biochemical content and pathways in normal and pathological tissue. In the brain, ¹H-MRS complements the information given by magnetic resonance images. The main goal of the present study was to assess the accuracy of ¹H-MRS for the classification of brain tumors in a pilot study comparing results obtained by manual and semi-automatic quantification of metabolites. In vivo single-voxel ¹H-MRS was performed in 24 control subjects and 26 patients with brain neoplasms that included meningiomas, high-grade neuroglial tumors and pilocytic astrocytomas. Seven metabolite groups (lactate, lipids, N-acetyl-aspartate, glutamate and glutamine group, total creatine, total choline, myo-inositol) were evaluated in all spectra by two methods: a manual one consisting of integration of manually defined peak areas, and the advanced method for accurate, robust and efficient spectral fitting (AMARES), a semi-automatic quantification method implemented in the jMRUI software. Statistical methods included discriminant analysis and the leave-one-out cross-validation method. Both manual and semi-automatic analyses detected differences in metabolite content between tumor groups and controls (P < 0.005). The classification accuracy obtained with the manual method was 75% for high-grade neuroglial tumors, 55% for meningiomas and 56% for pilocytic astrocytomas, while for the semi-automatic method it was 78, 70, and 98%, respectively. Both methods classified all control subjects correctly. The study demonstrated that ¹H-MRS accurately differentiated normal from tumoral brain tissue and confirmed the superiority of the semi-automatic quantification method.

Classification of different degrees of adiposity in sedentary rats

In experimental studies, several parameters, such as body weight, body mass index, adiposity index, and dual-energy X-ray absorptiometry, have commonly been used to demonstrate increased adiposity and investigate the mechanisms underlying obesity and sedentary lifestyles. However, these investigations have not classified the degree of adiposity nor defined adiposity categories for rats, such as normal, overweight, and obese. The aim of the study was to characterize the degree of adiposity in rats fed a high-fat diet using cluster analysis and to create adiposity intervals in an experimental model of obesity. Thirty-day-old male Wistar rats were fed a normal (n=41) or a high-fat (n=43) diet for 15 weeks. Obesity was defined based on the adiposity index; and the degree of adiposity was evaluated using cluster analysis. Cluster analysis allowed the rats to be classified into two groups (overweight and obese). The obese group displayed significantly higher total body fat and a higher adiposity index compared with those of the overweight group. No differences in systolic blood pressure or nonesterified fatty acid, glucose, total cholesterol, or triglyceride levels were observed between the obese and overweight groups. The adiposity index of the obese group was positively correlated with final body weight, total body fat, and leptin levels. Despite the classification of sedentary rats into overweight and obese groups, it was not possible to identify differences in the comorbidities between the two groups.

Characterization and classification of pequi trees (Caryocar brasiliense Camb.) based on the profile of volatile constituents using headspace solid-phase microextraction - gas chromatography - mass spectrometry and multivariate analysis

In order to determine the variability of pequi tree (Caryocar brasiliense Camb.) populations, volatile compounds from fruits of eighteen trees representing five populations were extracted by headspace solid-phase microextraction and analyzed by gas chromatography-mass spectrometry. Seventy-seven compounds were identified, including esters, hydrocarbons, terpenoids, ketones, lactones, and alcohols. Several compounds had not been previously reported in the pequi fruit. The amount of total volatile compounds and the individual compound contents varied between plants. The volatile profile enabled the differentiation of all of the eighteen plants, indicating that there is a characteristic profile in terms of their origin. The use of Principal Component Analysis and Cluster Analysis enabled the establishment of markers (dendrolasin, ethyl octanoate, ethyl 2-octenoate and β-cis-ocimene) that discriminated among the pequi trees. According to the Cluster Analysis, the plants were classified into three main clusters, and four other plants showed a tendency to isolation. The results from multivariate analysis did not always group plants from the same population together, indicating that there is greater variability within the populations than between pequi tree populations.

Spatio-temporal variation of the pink shrimp Farfantepenaeus paulensis (Crustacea, Decapoda, Penaeidae) associated to the seasonal overture of the sandbar in a subtropical lagoon

On the southern Brazilian coast, the penaeid pink shrimp Farfantepenaeus paulensis (Pérez-Farfante, 1967) is intensively captured by both artisanal and industrial fisheries. In the Ibiraquera Lagoon, artisanal fishing of juvenile stocks has declined sharply over the last 15 years. The traditional management methods used by fishermen are no longer sustainable due to conflicts with the demands of tourism and weak environmental and public agencies. The dynamics of the timing of the artificial opening and natural reformation of a sandbar across the mouth of the lagoon are regarded as a central socio-ecological problem. We evaluated the abundance and biomass variation of F. paulensis throughout the year and along a spatial gradient measured from the sandbar into the lagoon. We also explored the influence of opening the sandbar on the shrimp population. Samples were collected monthly from February 2004 to February 2005, using a 27 mm mesh cast net, in four different areas, with three sites being sampled per area. Small numbers of large shrimps were captured in the upper area, while greater biomass and abundance of small shrimps were observed near the sandbar, suggesting the role of the inner area as a refuge for growing. The highest records for biomass and abundance were recorded during warm months, regardless if the lagoon was open or closed by the sandbar. The Ibiraquera Lagoon could be more productive for fishermen if the sandbar opening was coincident to the period of natural post-larvae influx. Furthermore, coastal stocks could be improved by opening the sandbar again during summer in conjunction with fishing quotas.

An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology

Understanding the different background landscapes in which malaria transmission occurs is fundamental to understanding malaria epidemiology and to designing effective local malaria control programs. Geology, geomorphology, vegetation, climate, land use, and anopheline distribution were used as a basis for an ecological classification of the state of Roraima, Brazil, in the northern Amazon Basin, focused on the natural history of malaria and transmission. We used unsupervised maximum likelihood classification, principal components analysis, and weighted overlay with equal contribution analyses to fine-scale thematic maps that resulted in clustered regions. We used ecological niche modeling techniques to develop a fine-scale picture of malaria vector distributions in the state. Eight ecoregions were identified and malaria-related aspects are discussed based on this classification, including 5 types of dense tropical rain forest and 3 types of savannah. Ecoregions formed by dense tropical rain forest were named as montane (ecoregion I), submontane (II), plateau (III), lowland (IV), and alluvial (V). Ecoregions formed by savannah were divided into steppe (VI, campos de Roraima), savannah (VII, cerrado), and wetland (VIII, campinarana). Such ecoregional mappings are important tools in integrated malaria control programs that aim to identify specific characteristics of malaria transmission, classify transmission risk, and define priority areas and appropriate interventions. For some areas, extension of these approaches to still-finer resolutions will provide an improved picture of malaria transmission patterns.

Pediatric patient classification system: improvement of an instrument

Objective Improve the content validity of the instrument for classification of pediatric patients and evaluate its construct validity. Method A descriptive exploratory study in the measurement of the content validity index, and correlational design for construct validation through exploratory factor analysis. Results The content validity index for indicators was 0.99 and it was 0.97 for graded situations. Three domains were extracted in the construct validation, namely: patient, family and therapeutic procedures, with 74.97% of explained variance. The instrument showed evidences of content and construct validity. Conclusion The validation of the instrument occurred under the approach of family-centered care, and allowed incorporating some essential needs of childhood such as playing, interaction and affection in the content of the instrument.


Classification and fingerprinting of different berries based on biochemical profiling and antioxidant capacity

The objective of this work was to evaluate the biochemical composition of six berry types belonging to Fragaria, Rubus, Vaccinium and Ribes genus. Fruit samples were collected in triplicate (50 fruit each) from 18 different species or cultivars of the mentioned genera, during three years (2008 to 2010). Content of individual sugars, organic acids, flavonols, and phenolic acids were determined by high performance liquid chromatography (HPLC) analysis, while total phenolics (TPC) and total antioxidant capacity (TAC), by using spectrophotometry. Principal component analysis (PCA) and hierarchical cluster analysis (CA) were performed to evaluate the differences in fruit biochemical profile. The highest contents of bioactive components were found in Ribes nigrum and in Fragaria vesca, Rubus plicatus, and Vaccinium myrtillus. PCA and CA were able to partially discriminate between berries on the basis of their biochemical composition. Individual and total sugars, myricetin, ellagic acid, TPC and TAC showed the highest impact on biochemical composition of the berry fruits. CA separated blackberry, raspberry, and blueberry as isolate groups, while classification of strawberry, black and red currant in a specific group has not occurred. There is a large variability both between and within the different types of berries. Metabolite fingerprinting of the evaluated berries showed unique biochemical profiles and specific combination of bioactive compound contents.