The application of wireless near infrared spectroscopy on detecting peripheral circulation in patients with diabetes foot ulcer when doing Buerger's exercise.

BACKGROUND AND OBJECTIVES: The majority of patients with diabetes foot ulcer have peripheral arterial disease. The technique of near infrared spectroscopy (NIRS) used to monitor the dorsal foot blood circulation condition is by measuring the absorption differences between oxy-hemoglobin and deoxy-hemoglobin. STUDY DESIGNS/MATERIALS AND METHODS: Patients with diabetic foot ulcer (DFU) treated in our wound care center were divided into two sub-groups based on the Duplex ultrasound or angiography. Patients with peripheral arterial disease (PAD) and non-peripheral arterial disease were divided into group A1 and A2, respectively. We applied wireless NIRS on patients' dorsal foot to assess the peripheral circulation by continuously detect the signal penetrating the tissue while patients were asked to do Buerger's exercise. The same procedure was also repeated on 15 healthy participants as control group B. RESULTS: From January to August 2015, 30 patients with DFU (Group A) were enrolled in the study, 9 patients in Group A1 and 21 patients in Group A2. Tissue concentration of oxygenated hemoglobin (HbO2) in each group varied in a similar trend among five stages of Buerger's exercise. HbO2 concentration increases at post-exercise stages in all groups (P = 0.006 in Group A1, P = 0.223 in Group A2, P = 0.03 in Group B), however, there were significant difference in both pre- and post-exercise stages (P = 0.001 and P = 0.01, respectively) between Group A and B. Moreover, significant differences (P = 0.0009) of HbO2 were also found between Group A1 and A2 in pre-exercise status, but no significant difference (P = 0.294) was found in post-exercise status. Similar results were also found in total hemoglobin (Hb) concentration analyses. CONCLUSIONS: Buerger's exercise could increase peripheral HbO2 and total Hb circulation in patients with diabetes foot ulcer. The NIRS could continuously and quantitatively monitor real-time peripheral circulation in postural changes and is novel to rehabilitation program. A larger scale study to prove the accuracy of NIRS system in detecting peripheral circulation could be worthwhile. Lasers Surg. Med. 49:652-657, 2017. © 2017 Wiley Periodicals, Inc.

Using Bayesian networks to discover relations between genes, environment, and disease.

We review the applicability of Bayesian networks (BNs) for discovering relations between genes, environment, and disease. By translating probabilistic dependencies among variables into graphical models and vice versa, BNs provide a comprehensible and modular framework for representing complex systems. We first describe the Bayesian network approach and its applicability to understanding the genetic and environmental basis of disease. We then describe a variety of algorithms for learning the structure of a network from observational data. Because of their relevance to real-world applications, the topics of missing data and causal interpretation are emphasized. The BN approach is then exemplified through application to data from a population-based study of bladder cancer in New Hampshire, USA. For didactical purposes, we intentionally keep this example simple. When applied to complete data records, we find only minor differences in the performance and results of different algorithms. Subsequent incorporation of partial records through application of the EM algorithm gives us greater power to detect relations. Allowing for network structures that depart from a strict causal interpretation also enhances our ability to discover complex associations including gene-gene (epistasis) and gene-environment interactions. While BNs are already powerful tools for the genetic dissection of disease and generation of prognostic models, there remain some conceptual and computational challenges. These include the proper handling of continuous variables and unmeasured factors, the explicit incorporation of prior knowledge, and the evaluation and communication of the robustness of substantive conclusions to alternative assumptions and data manifestations.