Protargol Synthesis: An In-House Protocol

The protargol staining method has proved to be indispensable for revealing the cellular structures of a variety of protozoa, especially the flagellates and ciliates. Protargol provides permanent stains of a variety of cellular structures: nuclei, extrusomes, basal bodies, and microfilamentous constituents of cells. Together with the older silver nitrate methods, protargol impregnations have provided the basis for the detailed descriptions of nearly all ciliates to date. The performance of commercially available preparations has varied widely. Recently, suppliers have stopped stocking the effective forms of protargol resulting in a worldwide shortage. Thus, it has become necessary for protistologists to explore on-site synthesis of this critically important agent. An optimum protocol for synthesis of protargol should be rapid, relatively inexpensive, simple enough to be done by non-chemists, and achievable without specialized equipment. In this article, the authors briefly review the interesting history of protargol and describe a protocol, based on the early studies of neuroanatomists, that yields a protargol producing impregnations of ciliates comparable to those obtained with previously available commercial preparations

Attenuating Immune Response of Macrophage by Enhancing Hydrophilicity of Ti Surface

Immune responses can determine the in vivo fate of implanted materials. The strategy for developing implants has shifted towards using materials with immunomodulatory activity. However, the immunoregulatory effect of hydrophilicity of titanium surface on the macrophage behavior and its underlying mechanism remain poorly understood. Here, the Ti surface hydrophilicity-dependent behavior of murine RAW264.7 macrophages was investigated in vitro. Two laboratory models with significantly different surface hydrophilicity and similar roughness were established with Ti-polished and Ti-H2O2 surfaces. The results of cell morphology observation showed that the Ti-H2O2 surface yielded enhanced cell adhesion and less multinucleated cell formation. CCK-8 assay indicated that the growth rate of macrophage on Ti-H2O2 surface is higher than that of Ti-polished. ELISA assay result revealed lower level of proinflammatory factor TNF-α and higher level of anti-inflammatory factor IL-10 on the Ti-H2O2 surface compared to Ti-polished. Subsequently, immunofluorescence and western blotting analysis showed that activation of the NF-κB-TNF-α pathway might be involved in the modulation of the immune response by surface hydrophilicity. Together, these results suggested that relative high hydrophilic Ti surface might attenuate the immune response of macrophage by activating NF-κB signaling. These findings could provide new insights into designing implant devices for orthopedic applications

Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.

Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning (DL) method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a T2D relevant tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million genetic variants. We anticipate that DL methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies

Optimization of a tri-drug treatment against lung cancer using orthogonal design in preclinical studies

A growing body of evidence suggests that anesthetics impact the outcome of patients with cancer after surgical intervention. However, the optimal dose and underlying mechanisms of co-administered anesthetics in lung tumor therapy have been poorly studied. Here, we aimed to investigate the role of combined anesthetics propofol, sufentanil, and rocuronium in treating lung cancer using an orthogonal experimental design and to explore the optimal combination of anesthetics. First, we evaluated the effects of the three anesthetics on the proliferation and invasion of A-549 cells using Cell Counting Kit 8 and Transwell migration and invasion assays. Subsequently, we applied the orthogonal experimental design (OED) method to screen the appropriate concentrations of the combined anesthetics with the most effective antitumor activity. We found that all three agents inhibited the proliferation of A-549 cells in a dose- and time-dependent manner when applied individually or in combination, with the highest differences in the magnitude of inhibition occurring 24 h after combined drug exposure. The optimal combination of the three anesthetics that achieved the strongest reduction in cell viability was 1.4 µmol/L propofol, 2 nmol/L sufentanil, and 7.83 µmol/L rocuronium. This optimal 3-drug combination produced a more beneficial result at 24 h than either single drug. Our results provide a theoretical basis for improving the efficacy of lung tumor treatment and optimizing anesthetic strategies