PLGA Nanoparticle-Based Formulations to Cross the Blood–Brain Barrier for Drug Delivery: From R&D to cGMP

The blood–brain barrier (BBB) is a natural obstacle for drug delivery into the human brain, hindering treatment of central nervous system (CNS) disorders such as acute ischemic stroke, brain tumors, and human immunodeficiency virus (HIV)-1-associated neurocognitive disorders. Poly(lactic-co-glycolic acid) (PLGA) is a biocompatible polymer that is used in Food and Drug Administration (FDA)-approved pharmaceutical products and medical devices. PLGA nanoparticles (NPs) have been reported to improve drug penetration across the BBB both in vitro and in vivo. Poly(ethylene glycol) (PEG), poly(vinyl alcohol) (PVA), and poloxamer (Pluronic) are widely used as excipients to further improve the stability and effectiveness of PLGA formulations. Peptides and other linkers can be attached on the surface of PLGA to provide targeting delivery. With the newly published guidance from the FDA and the progress of current Good Manufacturing Practice (cGMP) technologies, manufacturing PLGA NP-based drug products can be achieved with higher efficiency, larger quantity, and better quality. The translation from bench to bed is feasible with proper research, concurrent development, quality control, and regulatory assurance

An integrative Raman microscopy-based workflow for rapid in situ analysis of microalgal lipid bodies

Additional file 1. Schematic diagram of Raman imaging technique

A novel metagenome-derived viral RNA polymerase and its application in a cell-free expression system for metagenome screening

The mining of genomes from non-cultivated microorganisms using metagenomics is a powerful tool to discover novel proteins and other valuable biomolecules. However, function-based metagenome searches are often limited by the time-consuming expression of the active proteins in various heterologous host systems. We here report the initial characterization of novel single-subunit bacteriophage RNA polymerase, EM1 RNAP, identified from a metagenome data set obtained from an elephant dung microbiome. EM1 RNAP and its promoter sequence are distantly related to T7 RNA polymerase. Using EM1 RNAP and a translation-competent Escherichia coli extract, we have developed an efficient medium-throughput pipeline and protocol allowing the expression of metagenome-derived genes and the production of proteins in cell-free system is sufficient for the initial testing of the predicted activities. Here, we have successfully identified and verified 12 enzymes acting on bis(2-hydroxyethyl) terephthalate (BHET) in a completely clone-free approach and proposed an in vitro high-throughput metagenomic screening method

Restoration of heart function using transplantation of human umbilical cord matrix-derived cardiomyocytes and vascular endothelial growth factor

Objectives: In the previous study, although it has been shown that intramyocardial injection of human umbilical cord matrix stem cell (hUCM) improved cardiac function 4 weeks post MI, but angiogenesis has not been observed. Angiogenesis and replacing lost cardiomyocytes with new, live cardiomyocytes are considered as two key agents in cardiac repair. To achieve the above two factors we examined the effects of combination of stem cell and angiogenic therapy approaches by simultaneously injection of hUCM-derived cardiomyocytes with vascular endothelial growth factor (VEGF) in cardiac repair. Methods: MI-induced animals(by ligation of LAD) received 50 μl PBS, 5 � 106 differentiated hUCM cells (dhUCM), 5μg VEGF in normal saline and 5 � 106 dhUCM cells combined with 5μg VEGF in normal saline, intramyocardialy. MI group, with no other intervention, served as a control group. We were assessed survival, migration and integration of dhUCM cells, as well as angiogenesis eight weeks post MI induction. Results: Eight weeks post MI, although dhUCM and VEGF groups have shown that LVEF and LVFS improved significantly, but animals in dhUCM+VEGF group have the highest rise in LVEF and LVFS in comparison to the other MI-induced groups (p<0.05). Histological and morphological analysis have revealed that myocardium of animals in dhUCM+VEGF group have the highest vascular density and the lowest fibrosis tissue in comparison to the other MI-induced groups (p<0.05). Immunohistological assessments revealed that transplanted dhUCM cells have survived, migrated to infarcted area and integrated with recipient cardiac tissue. Conclusion: we have found that intramyocardial administration of dhUCM cells combined with VEGF improved cardiac function, enhanced angiogenesis and reduced fibrosis tissue formation after MI, eight weeks post MI. © Kaveh et al

Scalable, ambient atmosphere roll-to-roll manufacture of encapsulated large area, flexible organic tandem solar cell modules

Inline printing and coating methods have been demonstrated to enable a high technical yield of fully roll-to-roll processed polymer tandem solar cell modules. We demonstrate generality by employing different material sets and also describe how the ink systems must be carefully co-developed in order to reach the ambitious objective of a fully printed and coated 14-layer flexible tandem solar cell stack. The roll-to-roll methodologies involved are flexographic printing, rotary screen printing, slot-die coating, X-ray scattering, electrical testing and UV-lamination. Their combination enables the manufacture of completely functional devices in exceptionally high yields. Critical to the ink and process development is a carefully chosen technology transfer to industry method where first a roll coater is employed enabling contactless stack build up, followed by a small roll-to-roll coater fitted to an X-ray machine enabling in situ studies of wet ink deposition and drying mechanisms, ultimately elucidating how a robust inline processed recombination layer is key to a high technical yield. Finally, the transfer to full roll-to-roll processing is demonstrated

Roadmap on Photovoltaic Absorber Materials for Sustainable Energy Conversion

Photovoltaics (PVs) are a critical technology for curbing growing levels of anthropogenic greenhouse gas emissions, and meeting increases in future demand for low-carbon electricity. In order to fulfil ambitions for net-zero carbon dioxide equivalent (CO2eq) emissions worldwide, the global cumulative capacity of solar PVs must increase by an order of magnitude from 0.9 TWp in 2021 to 8.5 TWp by 2050 according to the International Renewable Energy Agency, which is considered to be a highly conservative estimate. In 2020, the Henry Royce Institute brought together the UK PV community to discuss the critical technological and infrastructure challenges that need to be overcome to address the vast challenges in accelerating PV deployment. Herein, we examine the key developments in the global community, especially the progress made in the field since this earlier roadmap, bringing together experts primarily from the UK across the breadth of the photovoltaics community. The focus is both on the challenges in improving the efficiency, stability and levelized cost of electricity of current technologies for utility-scale PVs, as well as the fundamental questions in novel technologies that can have a significant impact on emerging markets, such as indoor PVs, space PVs, and agrivoltaics. We discuss challenges in advanced metrology and computational tools, as well as the growing synergies between PVs and solar fuels, and offer a perspective on the environmental sustainability of the PV industry. Through this roadmap, we emphasize promising pathways forward in both the short- and long-term, and for communities working on technologies across a range of maturity levels to learn from each other.Comment: 160 pages, 21 figure

Learning-based multilabel random walks for image segmentation containing translucent overlapped objects

Supervised image segmentation methods usually start with information extracted from the learning phase to separate an image into non-overlapping regions. We have used user input information or seeds in our previous work to segment partially overlapped translucent regions. However providing a lot of seeds might sometimes be too time consuming that might make the method perform poorly or not work at all. Machine learning algorithms consist of two major phases: learning phase where the information will be generated based on the data, and test phase where the generated information will be used to improve the performance of the method. In previous work user guided labels were used as hard seeds in the RW algorithm. In this paper we extend our previous work to be able to segment multilabel translucent overlapped objects using soft seed information. We first map each segment as a class on a 25D manifold in the learning phase. Then the probability of assigning each of the image pixels to the segments, data term, is obtained by calculating the geodesic distance between the pixels’ features and these classes on the manifold. This data term is then used as soft seeds in the RW algorithm instead of user predefined labels. Experimental results on synthetic images show the strength of our proposed method comparing to our previous algorithm with more than 95% segmentation accuracy

Deep learning methods for image segmentation containing translucent overlapped objects

Convolutional neural networks(CNN) are a subset of deep learning methods recently used widely for image segmentation. SegNet network [4] has shown interesting results for semantic segmentation, but it is designed to segment images with non-overlapped objects. However in some data translucent regions partially overlap. Having overlapped regions will cause methods not designed for overlapped objects to perform poorly or not work at all. To our knowledge no CNN has been designed yet to segment partially overlapped translucent objects.In this paper, we have designed a CNN to segment partially overlapped translucent regions. We used SegNet [4] as transfer learning for our overlapped image segmentation method. We also designed a new CNN with a simpler network for our data. Results on synthetic images give more than 95% segmentation accuracy for both methods