Evaluation of the perfusion index according to the visual analog scale in postoperative patients.

OBJECTIVES: To evaluate the possible value of the perfusion index (PI) as a tool for pain assessment. Methods: This prospective, observational study was performed with 89 patients underwent surgery with general anesthesia. The patients with visual analog scale (VAS) greater than 3 were grouped as M1, and patients with VAS≤3 and performed morphine were grouped as M2. After surgery patients with VAS greater than 3 were given 2mg morphine. Patients with VAS greater than 3 were given increments of intravenous morphine (2 mg) at 20 minute intervals until VAS less than 3. The correlation and difference between PI and VAS score values were evaluated before and after analgesic administration. Results: Significant changes were found in both PI values and VAS scores between M1 and M2 groups (2.80±0.77, 3.97±0.94, p less than 0.001; 6.60±1.20, 2.74±0.46, p less than 0.001) Despite no correlation was found between PI values and VAS scores of M1 and M2 groups, weak negative correlation was detected between differences in PI values and VAS scores among groups (r=-0.255, p=0.016). Conclusion: Perfusion index is a parameter that can be used in the assessment of postoperative pain and responses to analgesics.

The effect of patient positions on perfusion index.

BACKGROUND: The optimal position for surgery is one in which the patient is provided the best possible surgical intervention and put at minimum risk. Different surgical positions may cause changes in tissue perfusion. This study investigates the relationship between surgical patient positions and perfusion index. METHODS: A sample of 61 healthy individuals with no peripheral circulatory disorders, chronic diseases, or anemia was included in this study. Participants held six different positions: supine, prone, 45-degree sitting-supine, 45-degree supine with legs lifted, Trendelenburg (45-degrees head down), and reverse Trendelenburg (45-degrees head up). Perfusion index values were then measured and recorded after individuals held their positions for five minutes. RESULTS: Participants' perfusion index values were affected by different body positions (p < 0.05). Perfusion index was lowest in the sitting position (4.5 ± 2.5) and highest in individuals with Trendelenburg position (7.8 ± 3.8). CONCLUSION: Different body positions can cause changes in tissue perfusion. This should be considered in patient follow-up along with the perfusion index.

Information mining over heterogeneous and high-dimensional time-series data in clinical trials databases.

An effective analysis of clinical trials data involves analyzing different types of data such as heterogeneous and high dimensional time series data. The current time series analysis methods generally assume that the series at hand have sufficient length to apply statistical techniques to them. Other ideal case assumptions are that data are collected in equal length intervals, and while comparing time series, the lengths are usually expected to be equal to each other. However, these assumptions are not valid for many real data sets, especially for the clinical trials data sets. An addition, the data sources are different from each other, the data are heterogeneous, and the sensitivity of the experiments varies by the source. Approaches for mining time series data need to be revisited, keeping the wide range of requirements in mind. In this paper, we propose a novel approach for information mining that involves two major steps: applying a data mining algorithm over homogeneous subsets of data, and identifying common or distinct patterns over the information gathered in the first step. Our approach is implemented specifically for heterogeneous and high dimensional time series clinical trials data. Using this framework, we propose a new way of utilizing frequent itemset mining, as well as clustering and declustering techniques with novel distance metrics for measuring similarity between time series data. By clustering the data, we find groups of analytes (substances in blood) that are most strongly correlated. Most of these relationships already known are verified by the clinical panels, and, in addition, we identify novel groups that need further biomedical analysis. A slight modification to our algorithm results an effective declustering of high dimensional time series data, which is then used for "feature selection." Using industry-sponsored clinical trials data sets, we are able to identify a small set of analytes that effectively models the state of normal health.

Predicting the binding affinity of MHC class II peptides.

MHC (Major Histocompatibility Complex) proteins are categorized under the heterodimeric integral membrane proteins. The MHC molecules are divided into 2 subclasses, class I and class II. Two classes differ from each other in size of their binding pockets. Predicting the affinity of these peptides is important for vaccine design. It is also vital for understanding the roles of immune system in various diseases. Due to the variability of the locations of the class II peptide binding cores, predicting the affinity of these peptides is difficult. In this paper, we proposed a new method for predicting the affinity of the MHC Class II binding peptides based on their sequences. Our method classifies peptides as binding and non-binding. Our prediction method is based on a 3-step algorithm. In the first step we identify the informative n-grams based on their frequencies for both classes. In the next step, the alphabet size is reduced. At the last step, by utilizing the informative n-grams, the class of a given sequence is predicted. We have tested our method on the MHC Bench IV-b data set [13], and compared with various other methods in the literature.