Synthesis of PLGA-Lipid Hybrid Nanoparticles for siRNA Delivery Using the Emulsion Method PLGA-PEG-Lipid Nanoparticles for siRNA Delivery.

The effective delivery of small interfering RNA (siRNA) to tumor cells remains a challenge for applications in cancer therapy. The development of polymeric nanoparticles with high siRNA loading efficacy has shown great potential for cancer targets. Double emulsion solvent evaporation technique is a useful tool for encapsulation of hydrophilic molecules (e.g., siRNA). Here we describe a versatile platform for siRNA delivery based on PLGA-PEG-cationic lipid nanoparticles by using the double emulsion method. The resulting nanoparticles show high encapsulation efficiency for siRNA (up to 90%) and demonstrate effective downregulation of the target genes in vitro and vivo.

Physiologic variability at the verge of systemic inflammation: multiscale entropy of heart rate variability is affected by very low doses of endotoxin.

INTRODUCTION: Human injury or infection induces systemic inflammation with characteristic neuroendocrine responses. Fluctuations in autonomic function during inflammation are reflected by beat-to-beat variation in heart rate, termed heart rate variability (HRV). In the present study, we determine threshold doses of endotoxin needed to induce observable changes in markers of systemic inflammation, investigate whether metrics of HRV exhibit a differing threshold dose from other inflammatory markers, and investigate the size of data sets required for meaningful use of multiscale entropy (MSE) analysis of HRV. METHODS: Healthy human volunteers (n = 25) were randomized to receive placebo (normal saline) or endotoxin/lipopolysaccharide (LPS): 0.1, 0.25, 0.5, 1.0, or 2.0 ng/kg administered intravenously. Vital signs were recorded every 30 min for 6 h and then at 9, 12, and 24 h after LPS. Blood samples were drawn at specific time points for cytokine measurements. Heart rate variability analysis was performed using electrocardiogram epochs of 5 min. Multiscale entropy for HRV was calculated for all dose groups to scale factor 40. RESULTS: The lowest significant threshold dose was noted in core temperature at 0.25 ng/kg. Endogenous tumor necrosis factor α and interleukin 6 were significantly responsive at the next dosage level (0.5 ng/kg) along with elevations in circulating leukocytes and heart rate. Responses were exaggerated at higher doses (1 and 2 ng/kg). Time domain and frequency domain HRV metrics similarly suggested a threshold dose, differing from placebo at 1.0 and 2.0 ng/kg, below which no clear pattern in response was evident. By applying repeated-measures analysis of variance across scale factors, a significant decrease in MSE was seen at 1.0 and 2.0 ng/kg by 2 h after exposure to LPS. Although not statistically significant below 1.0 ng/kg, MSE unexpectedly decreased across all groups in an orderly dose-response pattern not seen in the other outcomes. CONCLUSIONS: By using repeated-measures analysis of variance across scale factors, MSE can detect autonomic change after LPS challenge in a group of 25 subjects using electrocardiogram epochs of only 5 min and entropy analysis to scale factor of only 40, potentially facilitating MSE's wider use as a research tool or bedside monitor. Traditional markers of inflammation generally exhibit threshold dose behavior. In contrast, MSE's apparent continuous dose-response pattern, although not statistically verifiable in this study, suggests a potential subclinical harbinger of infectious or other insult. The possible derangement of autonomic complexity prior to or independent of the cytokine surge cannot be ruled out. Future investigation should focus on confirmation of overt inflammation following observed decreases in MSE in a clinical setting.

On heart rate variability and autonomic activity in homeostasis and in systemic inflammation.

Analysis of heart rate variability (HRV) is a promising diagnostic technique due to the noninvasive nature of the measurements involved and established correlations with disease severity, particularly in inflammation-linked disorders. However, the complexities underlying the interpretation of HRV complicate understanding the mechanisms that cause variability. Despite this, such interpretations are often found in literature. In this paper we explored mathematical modeling of the relationship between the autonomic nervous system and the heart, incorporating basic mechanisms such as perturbing mean values of oscillating autonomic activities and saturating signal transduction pathways to explore their impacts on HRV. We focused our analysis on human endotoxemia, a well-established, controlled experimental model of systemic inflammation that provokes changes in HRV representative of acute stress. By contrasting modeling results with published experimental data and analyses, we found that even a simple model linking the autonomic nervous system and the heart confound the interpretation of HRV changes in human endotoxemia. Multiple plausible alternative hypotheses, encoded in a model-based framework, equally reconciled experimental results. In total, our work illustrates how conventional assumptions about the relationships between autonomic activity and frequency-domain HRV metrics break down, even in a simple model. This underscores the need for further experimental work towards unraveling the underlying mechanisms of autonomic dysfunction and HRV changes in systemic inflammation. Understanding the extent of information encoded in HRV signals is critical in appropriately analyzing prior and future studies.

Path length entropy analysis of diastolic heart sounds.

Early detection of coronary artery disease (CAD) using the acoustic approach, a noninvasive and cost-effective method, would greatly improve the outcome of CAD patients. To detect CAD, we analyze diastolic sounds for possible CAD murmurs. We observed diastolic sounds to exhibit 1/f structure and developed a new method, path length entropy (PLE) and a scaled version (SPLE), to characterize this structure to improve CAD detection. We compare SPLE results to Hurst exponent, Sample entropy and Multiscale entropy for distinguishing between normal and CAD patients. SPLE achieved a sensitivity-specificity of 80%-81%, the best of the tested methods. However, PLE and SPLE are not sufficient to prove nonlinearity, and evaluation using surrogate data suggests that our cardiovascular sound recordings do not contain significant nonlinear properties.

Noise detection in heart sound recordings.

Coronary artery disease (CAD) is the leading cause of death in the United States. Although progression of CAD can be controlled using drugs and diet, it is usually detected in advanced stages when invasive treatment is required. Current methods to detect CAD are invasive and/or costly, hence not suitable as a regular screening tool to detect CAD in early stages. Currently, we are developing a noninvasive and cost-effective system to detect CAD using the acoustic approach. This method identifies sounds generated by turbulent flow through partially narrowed coronary arteries to detect CAD. The limiting factor of this method is sensitivity to noises commonly encountered in the clinical setting. Because the CAD sounds are faint, these noises can easily obscure the CAD sounds and make detection impossible. In this paper, we propose a method to detect and eliminate noise encountered in the clinical setting using a reference channel. We show that our method is effective in detecting noise, which is essential to the success of the acoustic approach.