Nanomedicines: From Bench to Bedside and Beyond

Advancing nanomedicines from concept to clinic requires integration of new science with traditional pharmaceutical development. The medical and commercial success of nanomedicines is greatly facilitated when those charged with developing nanomedicines are cognizant of the unique opportunities and technical challenges that these products present. These individuals must also be knowledgeable about the processes of clinical and product development, including regulatory considerations, to maximize the odds for successful product registration. This article outlines these topics with a goal to accelerate the combination of academic innovation with collaborative industrial scientists who understand pharmaceutical development and regulatory approval requirements—only together can they realize the full potential of nanomedicines for patients.     

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.     

Variation of resting energy expenditure after the first chemotherapy cycle in acute leukemia patients

Changes in resting energy expenditure (REE) of cancer patients vary depending on type of tumor, treatment time point and kind of treatment. Little is known about REE of acute leukemia adult patients after treatment, especially with results related to body weight or fat free mass (FFM). This study aimed to assess changes in REE of acute leukemia adult patients before and after the first remission induction. Evaluation of REE was performed by indirect calorimetry and predicted REE was calculated by Harris-Benedict equation. Weight and height were measured and compared to a control group of healthy individuals. FFM was assessed by bioelectrical impedance for adjusting REE values. We evaluated 18 patients and 26 healthy individuals. At diagnosis, patients presented REE, REE/weight, and REE/FFM higher than the controls. Reductions of REE, REE/weight, and REE/FFM were also observed in patients after the first cycle of chemotherapy. The predicted REE for the patients group showed significant lower value compared with measured REE. Before the first cycle of chemotherapy REE was increased but undergoes a reduction after treatment, reaching values similar to the controls. For predictive Harris-Benedict equation, stress factors should be added to avoid underestimation of REE before and after chemotherapy.     

The relationship between childhood maltreatment and psychopathology in adults undergoing bariatric surgery

Background

A history of childhood maltreatment and psychopathology are common in adults with obesity.

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.