Development of Lipid-based Nano Formulations of Miriplatin against Lung Cancer

Lung cancer claims the highest mortality and the second most new cases in the US. Cisplatin, the first platinum-based anticancer drug, has the highest potency against lung cancer but carries many severe adverse effects. Miriplatin was discovered with a higher lipophilicity and approved in Japan for the treatment of hepatocellular carcinoma (HCC). Nanocarriers provide a promising platform to overcome the physiochemical barrier of solid tumors and to reduce the toxicity of anticancer drugs

Development of Miriplatin-loaded Nanoparticles against Non-small Cell Lung Cancer

Lung cancer claims the highest mortality and the second-most new cases among all oncological diseases. NSCLC accounts for approximately 85% of all newly diagnosed lung cancers. Although platinum-based drugs are standard first-line chemotherapy for stage IIIB/IV NSCLC, accumulating reports have shown the failure of conventional platinum-based regimens due to drug resistance. Miriplatin is a lipophilic anti-cancer drug that has been approved in Japan for transcatheter arterial chemoembolization treatment of hepatocellular carcinoma. Lipid-based nanoparticles such as liposomes, micelles, and solid lipid nanoparticles (SLNs) can encapsulate anti-cancer drugs to improve their water solubility and bioavailability

Stacking order reduction in multilayer graphene by inserting nanospacers

Toward macroscopic applications of graphene, it is desirable to preserve the superior properties of single-layer graphene in bulk scale. However, the AB-stacking structure is thermodynamically favored for multilayer graphene and causes strong interlayer interactions, resulting in property degradation. A promising approach to prevent the strong interlayer interaction is the staking order reduction of graphene, where the graphene layers are rotated in-plane to form a randomly stacking structure. In this study, we propose a strategy to effectively decrease the stacking order of multilayer graphene by incorporating nanospacers, cellulose nanofibers, or nano-diamonds (NDs) in the formation process of porous graphene sponges. We conducted an ultrahigh temperature treatment at 1500 °C with ethanol vapor for the reduction and structural repair of graphene oxide sponges with different concentrations of the nanospacers. Raman spectroscopy indicated an obvious increase in the random-stacking fraction of graphene by adding the nanospacers. The x-ray diffraction (XRD) analysis revealed that a small amount of the nanospacers induced a remarkable decrease in ordered graphene crystalline size in the stacking direction. It was also confirmed that a layer-number increase during the thermal treatment was suppressed by the nanospacers. The increase in the random-stacking fraction is attributed to the efficient formation of randomly rotated graphene through the ethanol-mediated structural restoration of relatively thin layers induced by the nanospacers. This stacking-order-reduced graphene with bulk scale is expected to be used in macroscopic applications, such as electrode materials and wearable devices.Zizhao Xu, Taiki Inoue, Yuta Nishina, and Yoshihiro Kobayashi, "Stacking order reduction in multilayer graphene by inserting nanospacers", Journal of Applied Physics 132, 174305 (2022) https://doi.org/10.1063/5.010382

UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation

Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches and realizes global communication via the self-attention mechanism. However, positional information between patches is hard to preserve in such 1D sequences, and loss of it can lead to sub-optimal performance when dealing with large amounts of heterogeneous tissues of various sizes in 3D medical image segmentation. Additionally, current methods are not robust and efficient for heavy-duty medical segmentation tasks such as predicting a large number of tissue classes or modeling globally inter-connected tissue structures. To address such challenges and inspired by the nested hierarchical structures in vision transformer, we proposed a novel 3D medical image segmentation method (UNesT), employing a simplified and faster-converging transformer encoder design that achieves local communication among spatially adjacent patch sequences by aggregating them hierarchically. We extensively validate our method on multiple challenging datasets, consisting of multiple modalities, anatomies, and a wide range of tissue classes, including 133 structures in the brain, 14 organs in the abdomen, 4 hierarchical components in the kidneys, inter-connected kidney tumors and brain tumors. We show that UNesT consistently achieves state-of-the-art performance and evaluate its generalizability and data efficiency. Particularly, the model achieves whole brain segmentation task complete ROI with 133 tissue classes in a single network, outperforming prior state-of-the-art method SLANT27 ensembled with 27 networks.Comment: 19 pages, 17 figures. arXiv admin note: text overlap with arXiv:2203.0243

Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning.

PURPOSE This work attempts to decode the discriminative information in dopamine transporter (DAT) imaging using deep learning for the differential diagnosis of parkinsonism. METHODS This study involved 1017 subjects who underwent DAT PET imaging ([11C]CFT) including 43 healthy subjects and 974 parkinsonian patients with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA) or progressive supranuclear palsy (PSP). We developed a 3D deep convolutional neural network to learn distinguishable DAT features for the differential diagnosis of parkinsonism. A full-gradient saliency map approach was employed to investigate the functional basis related to the decision mechanism of the network. Furthermore, deep-learning-guided radiomics features and quantitative analysis were compared with their conventional counterparts to further interpret the performance of deep learning. RESULTS The proposed network achieved area under the curve of 0.953 (sensitivity 87.7%, specificity 93.2%), 0.948 (sensitivity 93.7%, specificity 97.5%), and 0.900 (sensitivity 81.5%, specificity 93.7%) in the cross-validation, together with sensitivity of 90.7%, 84.1%, 78.6% and specificity of 88.4%, 97.5% 93.3% in the blind test for the differential diagnosis of IPD, MSA and PSP, respectively. The saliency map demonstrated the most contributed areas determining the diagnosis located at parkinsonism-related regions, e.g., putamen, caudate and midbrain. The deep-learning-guided binding ratios showed significant differences among IPD, MSA and PSP groups (P < 0.001), while the conventional putamen and caudate binding ratios had no significant difference between IPD and MSA (P = 0.24 and P = 0.30). Furthermore, compared to conventional radiomics features, there existed average above 78.1% more deep-learning-guided radiomics features that had significant differences among IPD, MSA and PSP. CONCLUSION This study suggested the developed deep neural network can decode in-depth information from DAT and showed potential to assist the differential diagnosis of parkinsonism. The functional regions supporting the diagnosis decision were generally consistent with known parkinsonian pathology but provided more specific guidance for feature selection and quantitative analysis

The abundance and host-seeking behavior of culicine species (Diptera: Culicidae) and Anopheles sinensis in Yongcheng city, people's Republic of China

<p>Abstract</p> <p>Background</p> <p>The knowledge of mosquito species diversity and the level of anthropophily exhibited by each species in a region are of great importance to the integrated vector control. Culicine species are the primary vectors of Japanese encephalitis (JE) virus and filariasis in China. <it>Anopheles sinensis </it>plays a major role in the maintenance of <it>Plasmodium vivax </it>malaria transmission in China. The goal of this study was to compare the abundance and host-seeking behavior of culicine species and <it>An. sinensis </it>in Yongcheng city, a representative region of <it>P. vivax </it>malaria. Specifically, we wished to determine the relative attractiveness of different animal baits versus human bait to culicine species and <it>An. sinensis</it>.</p> <p>Results</p> <p><it>Culex tritaeniorhynchus </it>was the most prevalent mosquito species and <it>An. sinensis </it>was the sole potential vector of <it>P. vivax </it>malaria in Yongcheng city. There were significant differences (P < 0.01) in the abundance of both <it>An. sinensis </it>and <it>Cx. tritaeniorhynchus </it>collected in distinct baited traps. The relative attractiveness of animal versus human bait was similar towards both <it>An. sinensis </it>and <it>Cx. tritaeniorhynchus</it>. The ranking derived from the mean number of mosquitoes per bait indicated that pigs, goats and calves frequently attracted more mosquitoes than the other hosts tested (dogs, humans, and chickens). These trends were similar across all capture nights at three distinct villages. The human blood index (HBI) of female <it>An. sinensis </it>was 2.94% when computed with mixed meals while 3.70% computed with only the single meal. 19:00~21:00 was the primary peak of host-seeking female <it>An. sinensis </it>while 4:00~5:00 was the smaller peak at night. There was significant correlation between the density of female <it>An. sinensis </it>and the average relative humidity (P < 0.05) in Wangshanzhuang village.</p> <p>Conclusions</p> <p>Pigs, goats and calves were more attractive to <it>An. sinensis </it>and <it>Cx. tritaeniorhynchus </it>than dogs, humans, and chickens. Female <it>An. sinensis </it>host-seeking activity mainly occurred from 19:00 to 21:00. Thus, we propose that future vector control against <it>An. sinensis </it>and <it>Cx. tritaeniorhynchus </it>in the areas along the Huang-Huai River of central China should target the interface of human activity with domestic animals and adopt before human hosts go to bed at night.</p

Synthesis of CsPbBr3 Quantum Dots for Photodetectors

Thesis (Master's)--University of Washington, 2020Metal halide perovskites (MHP) represent a flourishing area of research, which is driven by both their potential applications in photovoltaics and optoelectronics and by the fundamental science behind their unique optoelectronic properties. we synthesized CsPbBr3 quantum dots (QDs) via the LARP method at RT and mixed them with conjugate polymer to fabricate nanocomposite photodetectors. We found that the size and optical properties of CsPbBr3 QDs are affected by varying the amount of precursor injected into antisolvent, ligands in precursor or antisolvent, and centrifugation speed. Besides, we have fabricated the conventional sandwich structure photodiode by using CsPbBr3 QDs blended with conjugated polymer Poly[(9,9-dioctylfluorenyl-2,7-diyl)-alt-co-(bithiophene)] F8T2 as the active layer. The dark current might depend on the weight ratio of F8T2:CsPbBr3 and the structure of device, which we will focus on in the future

Development of Lipid-based Nano Formulations of Miriplatin Against Lung Cancer

Cancer is the second leading cause of death and is responsible for approximately 9.6 million deaths worldwide in 2018. Among all oncological diseases, lung cancer claims the highest mortality (male: 23.5%; female: 22%) and the second most new cases (male: 13%; female: 12%) in the US. Approximately 40% of newly diagnosed lung cancer patients are in the advanced stage IV, for which platinum-based chemotherapy is the first-line treatment, either by itself or in combination with surgery or radiotherapy. Cisplatin, the first-generation platinum-based anticancer chemotherapeutic agent, has the highest potency against lung cancer but carries many severe adverse effects. Cisplatin also induces drug resistance during long-term chemotherapy. Many more platinum complexes have been investigated as better alternatives, which led to the approval of carboplatin and oxaliplatin by Food and Drug Administration (FDA). In addition, miriplatin suspended in iodolipds (lipiodolization) was approved in Japan for the treatment of hepatocellular carcinoma (HCC) in 2009. Miriplatin has the same non-leaving group as oxaliplatin but different leaving groups of two myristate chains, which make it highly lipophilic. Several characteristics of solid tumors in lung cancer constitute a physiochemical barrier to the homogenous distribution and deep penetration of chemotherapy agents. Nanocarriers provide a promising platform to overcome the physiochemical barrier and to reduce the systemic toxicity of anticancer chemotherapy. In this study, miriplatin is formulated with various lipid-based nanocarriers including micelles and solid lipid nanoparticles (SLNs) thanks to its highly lipophilic structure. The goal of this thesis is to develop and evaluate miriplatin-loaded nano formulations against lung cancer. Miriplatin-loaded formulations were prepared by different methods, including thin film hydration and several scale-up methods including chloroform dripping, chloroform injection, chloroform evaporation, co-solvent evaporation, chloroform slow evaporation and co-solvent slow evaporation. Between the two types of nano formulations under this study, micelles were much smaller (~10 nm in diameter) and more homogeneous (PDI \u3c 0.3), while SLNs were bigger (~ 100 nm in diameter) and more heterogeneous (PDI ~0.8). A quantification method of miriplatin was established using inductively coupled plasma-optical emission spectrometry (ICP-OES). The quantification of platinum recovery from different miriplatin-loaded nano formulations was facilitated by digestion with 70% nitric acid and heating. The co-solvent slow evaporation method to prepare miriplatin-loaded nano formulations improved the platinum recovery prominently from 10% to 70%. Thus, co-solvent slow evaporation has been established as a pharmaceutically viable scale-up method to prepare nano formulations of miriplatin. Miriplatin-loaded nano formulations of different compositions were negatively stained with uranyl acetate and then imaged by transmission electron microscopy (TEM), which showed the formulations’ size and morphology that were consistent with the size and PDI data from dynamic light scattering studies by the Malvern Zetasizer. In the TEM studies, micelles showed a morphology of spherical dots at around 10 nm in diameter while SLNs showed both spherical and rod structures with a size distribution from 50 to 150 nm. A three-dimensional multicellular spheroid (3D MCS) model of A549-iRFP cells was used for in vitro evaluation of the nano formulations’ activity against lung cancer. A549-iRFP cells were engineered from the common lung cancer cell line A549 to stably express the near-infrared fluorescent protein (iRFP). The viability of A549-iRFP 3D MCS after exposure to cisplatin or nano formulations was similar to A549 3D MCS. The anticancer activity of miriplatin-loaded nano formulations against 3D MCS was positively associated with the platinum recovery as quantified by ICP-OES. The miriplatin-loaded nano formulations that had been prepared by the co-solvent slow evaporation method showed substantial anticancer activities against A549 3D MCS and A549-iRFP 3D MCS, which were comparable to cisplatin. Taken together, miriplatin-loaded nano formulations were successfully prepared by co-solvent slow evaporation. The formulations were developed to carry favorable physiochemical properties to enhance the activities of platinum drugs against lung cancer