Reciprocal Peer Support for Post-partum Patients with Diabetes: A Needs Assessment for the Diabetes Buddy Program

While peer support has been investigated in multiple clinical contexts, its application to the postpartum setting is unknown. The aim was to assess acceptability of a postpartum peer support program for women with diabetes. Observational survey-based needs assessment of forty low-income women with diabetes, receiving care at a major medical institution. Mean age and gravidity were 30.7 years and 3.15 ± 1.67 respectively. 45 % expressed interest in a “buddy.” There was no significant difference between groups desiring and not desiring this program. A majority of respondents desired telephone, text messaging, and in-person contacts (79.2, 72.1, 83.8 %), with 72.5 % of patients desiring diabetes-related activities during clinic waiting time. Many women desire a postpartum diabetes reciprocal peer program for support outside of clinician visits. Patients are receptive to educational services during their wait and outside of clinic time, a potentially valuable opportunity to share important health information.     

Comparative outcomes of vascular access in patients older than 70 years with end-stage renal disease

Objective

The advantage of arteriovenous fistulas (AVFs) in older patients requiring dialysis is controversial. We reviewed our vascular access experience in patients ≥70 years of age (older group) compared with younger patients.

Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis

A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.     

Assessment of toxicity using dehydrogenases activity and mathematical modeling

Dehydrogenase activity is frequently used to assess the general condition of microorganisms in soil and activated sludge. Many studies have investigated the inhibition of dehydrogenase activity by various compounds, including heavy metal ions. However, the time after which the measurements are carried out is often chosen arbitrarily. Thus, it can be difficult to estimate how the toxic effects of compounds vary during the reaction and when the maximum of the effect would be reached. Hence, the aim of this study was to create simple and useful mathematical model describing changes in dehydrogenase activity during exposure to substances that inactivate enzymes. Our model is based on the Lagergrens pseudo-first-order equation, the rate of chemical reactions, enzyme activity, and inactivation and was created to describe short-term changes in dehydrogenase activity. The main assumption of our model is that toxic substances cause irreversible inactivation of enzyme units. The model is able to predict the maximum direct toxic effect (MDTE) and the time to reach this maximum (T_MDTE). In order to validate our model, we present two examples: inactivation of dehydrogenase in microorganisms in soil and activated sludge. The model was applied successfully for cadmium and copper ions. Our results indicate that the predicted MDTE and T_MDTE are more appropriate than EC₅₀ and IC₅₀ for toxicity assessments, except for long exposure times.