ResearchPad - Bioengineering https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Transferrin-Modified Osthole PEGylated Liposomes Travel the Blood-Brain Barrier and Mitigate Alzheimer’s Disease-Related Pathology in APP/PS-1 Mice]]> https://www.researchpad.co/product?articleinfo=N69d6ab98-bd8d-4010-844a-59b94990e522

Introduction

Osthole (Ost) is a coumarin compound that strengthens hippocampal neurons and neural stem cells against Aβ oligomer-induced neurotoxicity in mice, and is a potential drug for the treatment of Alzheimer's disease (AD). However, the effectiveness of the drug is limited by its solubility and bioavailability, as well as by the low permeability of the blood-brain barrier (BBB). In this study, a kind of transferrin-modified Ost liposomes (Tf-Ost-Lip) was constructed, which could improve the bioavailability and enhance brain targeting.

Methods

Tf-Ost-Lip was prepared by thin-film hydration method. The ability of liposomal formulations to translocate across BBB was investigated using in vitro BBB model. And the protective effect of Tf-Ost-Lip was evaluated in APP-SH-SY5Y cells. In addition, we performed pharmacokinetics study and brain tissue distribution analysis of liposomal formulations in vivo. We also observed the neuroprotective effect of the varying formulations in APP/PS-1 mice.

Results

In vitro studies reveal that Tf-Ost-Lip could increase the intracellular uptake of hCMEC/D3 cells and APP-SH-SY5Y cells, and increase the drug concentration across the BBB. Additionally, Tf-Ost-Lip was found to exert a protective effect on APP-SH-SY5Y cells. In vivo studies of pharmacokinetics and the Ost distribution in brain tissue indicate that Tf-Ost-Lip prolonged the cycle time in mice and increased the accumulation of Ost in the brain. Furthermore, Tf-Ost-Lip was also found to enhance the effect of Ost on the alleviation of Alzheimer’s disease-related pathology.

Conclusion

Transferrin-modified liposomes for delivery of Ost has great potential for AD treatment.

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<![CDATA[Targeted Prodrug-Based Self-Assembled Nanoparticles for Cancer Therapy]]> https://www.researchpad.co/product?articleinfo=N2b0f5f7d-d5ab-49b6-9010-39c3b297e587

Background

Targeted prodrug has various applications as drug formulation for tumor therapy. Therefore, amphoteric small-molecule prodrug combined with nanoscale characteristics for the self-assembly of the nano-drug delivery system (DDS) is a highly interesting research topic.

Methods and Results

In this study, we developed a prodrug self-assembled nanoplatform, 2-glucosamine-fluorescein-5(6)-isothiocyanate-glutamic acid-paclitaxel (2DA-FITC-PTX NPs) by integration of targeted small molecule and nano-DDS with regular structure and perfect targeting ability. 2-glucosamine (DA) and paclitaxel were conjugated as the targeted ligand and anti-tumor chemotherapy drug by amino acid group. 2-DA molecular structure can enhance the targeting ability of prodrug-based 2DA-FITC-PTX NPs and prolong retention time, thereby reducing the toxicity of normal cell/tissue. The fluorescent dye FITC or near-infrared fluorescent dye ICG in prodrug-based DDS was attractive for in vivo optical imaging to study the behavior of 2DA-FITC-PTX NPs. In vitro and in vivo results proved that 2DA-FITC-PTX NPs exhibited excellent targeting ability, anticancer activity, and weak side effects.

Conclusion

This work demonstrates a new combination of nanomaterials for chemotherapy and may promote prodrug-based DDS clinical applications in the future.

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<![CDATA[Enzymatic Synthesis of Ricinoleyl Hydroxamic Acid Based on Commercial Castor Oil, Cytotoxicity Properties and Application as a New Anticancer Agent]]> https://www.researchpad.co/product?articleinfo=N2ee31eb3-084a-41f9-bd1c-0b276d684b2d

Background

New anticancer agents that rely on natural/healthy, not synthetic/toxic, components are very much needed.

Methods

Ricinoleyl hydroxamic acid (RHA) was synthesized from castor oil and hydroxylamine using Lipozyme TL IM as a catalyst. To optimize the conversion, the effects of the following parameters were investigated: type of organic solvent, period of reaction, amount of enzyme, the molar ratio of reactants and temperature. The highest conversion was obtained when the reaction was carried out under the following conditions: hexane as a solvent; reaction period of 48 hours; 120 mg of Lipozyme TL IM/3 mmol oil; HA-oil ratio of 19 mmol HA/3 mmol oil; and temperature of 40°C. The cytotoxicity of the synthesized RHA was assessed using human dermal fibroblasts (HDF), and its application towards fighting cancer was assessed using melanoma and glioblastoma cancer cells over a duration of 24 and 48 hours.

Results

RHA was successfully synthesized  and it demonstrated strong anticancer activity against glioblastoma and melanoma cells at as low as a 1 µg/mL concentration while it did not demonstrate any toxicity against HDF cells.

Conclusion

This is the first report on the synthesis of RHA with great potential to be used as a new anticancer agent.

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<![CDATA[Dual Receptor-Targeted and Redox-Sensitive Polymeric Micelles Self-Assembled from a Folic Acid-Hyaluronic Acid-SS-Vitamin E Succinate Polymer for Precise Cancer Therapy]]> https://www.researchpad.co/product?articleinfo=N0333ac7a-c329-4c80-b402-750b5176061b

Purpose

Poor site-specific delivery and insufficient intracellular drug release in tumors are inherent disadvantages to successful chemotherapy. In this study, an extraordinary polymeric micelle nanoplatform was designed for the efficient delivery of paclitaxel (PTX) by combining dual receptor-mediated active targeting and stimuli response to intracellular reduction potential.

Methods

The dual-targeted redox-sensitive polymer, folic acid-hyaluronic acid-SS-vitamin E succinate (FHSV), was synthesized via an amidation reaction and characterized by 1H-NMR. Then, PTX-loaded FHSV micelles (PTX/FHSV) were prepared by a dialysis method. The physiochemical properties of the micelles were explored. Moreover, in vitro cytological experiments and in vivo animal studies were carried out to evaluate the antitumor efficacy of polymeric micelles.

Results

The PTX/FHSV micelles exhibited a uniform, near-spherical morphology (148.8 ± 1.4 nm) and a high drug loading capacity (11.28% ± 0.25). Triggered by the high concentration of glutathione, PTX/FHSV micelles could quickly release their loaded drug into the release medium. The in vitro cytological evaluations showed that, compared with Taxol or single receptor-targeted micelles, FHSV micelles yielded higher cellular uptake by the dual receptor-mediated endocytosis pathway, thus leading to significantly superior cytotoxicity and apoptosis in tumor cells but less cytotoxicity in normal cells. More importantly, in the in vivo antitumor experiments, PTX/FHSV micelles exhibited enhanced tumor accumulation and produced remarkable tumor growth inhibition with minimal systemic toxicity.

Conclusion

Our results suggest that this well-designed FHSV polymer has promising potential for use as a vehicle of chemotherapeutic drugs for precise cancer therapy.

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<![CDATA[Tissue Engineering Using Vascular Organoids From Human Pluripotent Stem Cell Derived Mural Cell Phenotypes]]> https://www.researchpad.co/product?articleinfo=Nf13c55b4-2868-4ed9-89da-fb472063def6

Diffusion is a limiting factor in regenerating large tissues (100–200 μm) due to reduced nutrient supply and waste removal leading to low viability of the regenerating cells as neovascularization of the implant by the host is a slow process. Thus, generating prevascularized tissue engineered constructs, in which endothelial (ECs) and mural (MCs) cells, such as smooth muscle cells (SMCs), and pericytes (PCs), are preassembled into functional in vitro vessels capable of rapidly connecting to the host vasculature could overcome this obstacle. Toward this purpose, using feeder-free and low serum conditions, we developed a simple, efficient and rapid in vitro approach to induce the differentiation of human pluripotent stem cells-hPSCs (human embryonic stem cells and human induced pluripotent stem cells) to defined SMC populations (contractile and synthetic hPSC-SMCs) by extensively characterizing the cellular phenotype (expression of CD44, CD73, CD105, NG2, PDGFRβ, and contractile proteins) and function of hPSC-SMCs. The latter were phenotypically and functionally stable for at least 8 passages, and could stabilize vessel formation and inhibit vessel network regression, when co-cultured with ECs in vitro. Subsequently, using a methylcellulose-based hydrogel system, we generated spheroids consisting of EC/hPSC-SMC (vascular organoids), which were extensively phenotypically characterized. Moreover, the vascular organoids served as focal starting points for the sprouting of capillary-like structures in vitro, whereas their delivery in vivo led to rapid generation of a complex functional vascular network. Finally, we investigated the vascularization potential of these vascular organoids, when embedded in hydrogels composed of defined extracellular components (collagen/fibrinogen/fibronectin) that can be used as scaffolds in tissue engineering applications. In summary, we developed a robust method for the generation of defined SMC phenotypes from hPSCs. Fabrication of vascularized tissue constructs using hPSC-SMC/EC vascular organoids embedded in chemically defined matrices is a significant step forward in tissue engineering and regenerative medicine.

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<![CDATA[Therapeutic Potential of Extracellular Vesicles in Degenerative Diseases of the Intervertebral Disc]]> https://www.researchpad.co/product?articleinfo=N96062283-4421-4f07-8486-ec0c1acc7b4d

Extracellular vesicles (EVs) are lipid membrane particles carrying proteins, lipids, DNA, and various types of RNA that are involved in intercellular communication. EVs derived from mesenchymal stem cells (MSCs) have been investigated extensively in many different fields due to their crucial role as regeneration drivers, but research for their use in degenerative diseases of the intervertebral disc (IVD) has only started recently. MSC-derived EVs not only promote extracellular matrix synthesis and proliferation in IVD cells, but also reduce apoptosis and inflammation, hence having multifunctional beneficial effects that seem to be mediated by specific miRNAs (such as miR-233 and miR-21) within the EVs. Aside from MSC-derived EVs, IVD-derived EVs (e.g., stemming from notochordal cells) also have important functions in IVD health and disease. This article will summarize the current knowledge on MSC-derived and IVD-derived EVs and will highlight areas of future research, including the isolation and analysis of EV subpopulations or exposure of MSCs to cues that may enhance the therapeutic potential of released EVs.

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<![CDATA[Gene Editing Regulation and Innovation Economics]]> https://www.researchpad.co/product?articleinfo=Na9b0e3e9-b736-41c0-b802-b8dc3a213d39

Argentina was the first country that enacted regulatory criteria to assess if organisms resulting from new breeding techniques (NBTs) are to be regarded as genetically modified organisms (GMOs) or not. The country has now accumulated 4 year of experience applying such criteria, reaching a considerable number of cases, composed mostly of gene-edited plants, animals, and microorganisms of agricultural use. This article explores the effects on economic innovation of such regulatory experience. This is done by comparing the cases of products derived from gene editing and other NBTs that have been presented to the regulatory system, against the cases of GMOs that have been deregulated in the country. Albeit preliminary, this analysis suggests that products from gene editing will have different profiles and market release rates compared with the first wave of products from the so called “modern biotechnology.” Gene editing products seems to follow a much faster development rate from bench to market. Such development is driven by a more diverse group of developers, and led mostly by small and medium enterprises (SMEs) and public research institutions. In addition, product profiles are also more diversified in terms of traits and organisms. The inferences of these findings for the agricultural and biotechnology sectors, particularly in developing countries, are discussed.

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<![CDATA[Synthetic Biology Tools for Genome and Transcriptome Engineering of Solventogenic Clostridium]]> https://www.researchpad.co/product?articleinfo=N64df6521-f3a0-47bc-86eb-824714de9ad4

Strains of Clostridium genus are used for production of various value-added products including fuels and chemicals. Development of any commercially viable production process requires a combination of both strain and fermentation process development strategies. The strain development in Clostridium sp. could be achieved by random mutagenesis, and targeted gene alteration methods. However, strain improvement in Clostridium sp. by targeted gene alteration method was challenging due to the lack of efficient tools for genome and transcriptome engineering in this organism. Recently, various synthetic biology tools have been developed to facilitate the strain engineering of solventogenic Clostridium. In this review, we consolidated the recent advancements in toolbox development for genome and transcriptome engineering in solventogenic Clostridium. Here we reviewed the genome-engineering tools employing mobile group II intron, pyrE alleles exchange, and CRISPR/Cas9 with their application for strain development of Clostridium sp. Next, transcriptome engineering tools such as untranslated region (UTR) engineering and synthetic sRNA techniques were also discussed in context of Clostridium strain engineering. Application of any of these discussed techniques will facilitate the metabolic engineering of clostridia for development of improved strains with respect to requisite functional attributes. This might lead to the development of an economically viable butanol production process with improved titer, yield and productivity.

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<![CDATA[Predicting Knee Joint Instability Using a Tibio-Femoral Statistical Shape Model]]> https://www.researchpad.co/product?articleinfo=Ncfa0bac3-6ba9-4eae-8689-54452b8ef41e

Statistical shape models (SSMs) are a well established computational technique to represent the morphological variability spread in a set of matching surfaces by means of compact descriptive quantities, traditionally called “modes of variation” (MoVs). SSMs of bony surfaces have been proposed in biomechanics and orthopedic clinics to investigate the relation between bone shape and joint biomechanics. In this work, an SSM of the tibio-femoral joint has been developed to elucidate the relation between MoVs and bone angular deformities causing knee instability. The SSM was built using 99 bony shapes (distal femur and proximal tibia surfaces obtained from segmented CT scans) of osteoarthritic patients. Hip-knee-ankle (HKA) angle, femoral varus-valgus (FVV) angle, internal-external femoral rotation (IER), tibial varus-valgus (TVV) angles, and tibial slope (TS) were available across the patient set. Discriminant analysis (DA) and logistic regression (LR) classifiers were adopted to underline specific MoVs accounting for knee instability. First, it was found that thirty-four MoVs were enough to describe 95% of the shape variability in the dataset. The most relevant MoVs were the one encoding the height of the femoral and tibial shafts (MoV #2) and the one representing variations of the axial section of the femoral shaft and its bending in the frontal plane (MoV #5). Second, using quadratic DA, the sensitivity results of the classification were very accurate, being all >0.85 (HKA: 0.96, FVV: 0.99, IER: 0.88, TVV: 1, TS: 0.87). The results of the LR classifier were mostly in agreement with DA, confirming statistical significance for MoV #2 (p = 0.02) in correspondence to IER and MoV #5 in correspondence to HKA (p = 0.0001), FVV (p = 0.001), and TS (p = 0.02). We can argue that the SSM successfully identified specific MoVs encoding ranges of alignment variability between distal femur and proximal tibia. This discloses the opportunity to use the SSM to predict potential misalignment in the knee for a new patient by processing the bone shapes, removing the need for measuring clinical landmarks as the rotation centers and mechanical axes.

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<![CDATA[Comparative Transcriptome Analysis Reveals New lncRNAs Responding to Salt Stress in Sweet Sorghum]]> https://www.researchpad.co/product?articleinfo=Nda266021-f0b6-42f5-951c-1a0cb7ff7a4e

Long non-coding RNAs (lncRNAs) can enhance plant stress resistance by regulating the expression of functional genes. Sweet sorghum is a salt-tolerant energy crop. However, little is known about how lncRNAs in sweet sorghum respond to salt stress. In this study, we identified 126 and 133 differentially expressed lncRNAs in the salt-tolerant M-81E and the salt-sensitive Roma strains, respectively. Salt stress induced three new lncRNAs in M-81E and inhibited two new lncRNAs in Roma. These lncRNAs included lncRNA13472, lncRNA11310, lncRNA2846, lncRNA26929, and lncRNA14798, which potentially function as competitive endogenous RNAs (ceRNAs) that influence plant responses to salt stress by regulating the expression of target genes related to ion transport, protein modification, transcriptional regulation, and material synthesis and transport. Additionally, M-81E had a more complex ceRNA network than Roma. This study provides new information regarding lncRNAs and the complex regulatory network underlying salt-stress responses in sweet sorghum.

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<![CDATA[Dynamic Modeling of CHO Cell Metabolism Using the Hybrid Cybernetic Approach With a Novel Elementary Mode Analysis Strategy]]> https://www.researchpad.co/product?articleinfo=Nac8afc15-f00e-4d03-8f37-9b20004340ce

Chinese hamster ovary (CHO) cell culture has a major importance on the production of biopharmaceuticals, including recombinant therapeutic proteins such as monoclonal antibodies (MAb). Mathematical modeling of biological systems can successfully assess metabolism complexity while providing logical and systematic methods for relevant genetic target and culture parameter identification toward cell growth and productivity improvements. Most modeling approaches on CHO cells have been performed under stationary constraints, and only a few dynamic models have been presented on simplified reaction sets, due to substantial overparameterization problems. The hybrid cybernetic modeling (HCM) approach has been recently used to describe the dynamic behavior by incorporating regulation between different metabolic states by elementary mode participation control, with sets of equations evaluated by objective functions. However, as metabolic networks evaluated are constructed toward a genomic scale, and cell compartmentalization is considered, identification of the active set becomes more difficult as EM number exponentially grows. Thus, the development of robust approaches for EM active set selection and analysis with smaller computational requirements is required to impulse the use of cybernetic modeling on larger up to genome-scale networks. In this report, a novel elementary mode selection strategy, based on a polar representation of the convex solution space is presented and coupled to a cybernetic approach to model the dynamic physiologic and metabolic behavior of CHO-S cell cultures. The proposed Polar Space Yield Analysis (PSYA) was compared to other reported elementary mode selection approaches derived from Common Metabolic Objective Analysis (CMOA) used in Flux Balance Analysis (FBA), Yield Space Analysis (YSA), and Lumped Yield Space Analysis (LYSA). For this purpose, exponential growth phase dynamic metabolic models were calculated using kinetic rate equations based on previously modeled growth parameters. Finally, complete culture dynamic metabolic flux models were constructed using the HCM approach with selected elementary mode sets. The yield space elementary mode- and the polar space elementary mode- hybrid cybernetic models presented the best fits and performances. Also, a flux reaction perturbation prediction approach based on the polar yield solution space resulted useful for metabolic network flux distribution capability analysis and identification of potential genetic modifications targets.

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<![CDATA[Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis]]> https://www.researchpad.co/product?articleinfo=N73d9aa5f-5f1f-407c-9ac4-96b244119030

Concussion is a significant public health problem affecting 1.6–2.4 million Americans annually. An alternative to reducing the burden of concussion is to reduce its incidence with improved protective equipment and injury mitigation systems. Finite element (FE) models of the brain response to blunt trauma are often used to estimate injury potential and can lead to improved helmet designs. However, these models have yet to incorporate how the patterns of brain connectivity disruption after impact affects the relay of information in the injured brain. Furthermore, FE brain models typically do not consider the differences in individual brain structural connectivities and their purported role in concussion risk. Here, we use graph theory techniques to integrate brain deformations predicted from FE modeling with measurements of network efficiency to identify brain regions whose connectivity characteristics may influence concussion risk. We computed maximum principal strain in 129 brain regions using head kinematics measured from 53 professional football impact reconstructions that included concussive and non-concussive cases. In parallel, using diffusion spectrum imaging data from 30 healthy subjects, we simulated structural lesioning of each of the same 129 brain regions. We simulated lesioning by removing each region one at a time along with all its connections. In turn, we computed the resultant change in global efficiency to identify regions important for network communication. We found that brain regions that deformed the most during an impact did not overlap with regions most important for network communication (Pearson's correlation, ρ = 0.07; p = 0.45). Despite this dissimilarity, we found that predicting concussion incidence was equally accurate when considering either areas of high strain or of high importance to global efficiency. Interestingly, accuracy for concussion prediction varied considerably across the 30 healthy connectomes. These results suggest that individual network structure is an important confounding variable in concussion prediction and that further investigation of its role may improve concussion prediction and lead to the development of more effective protective equipment.

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<![CDATA[Bioengineering of Pulmonary Epithelium With Preservation of the Vascular Niche]]> https://www.researchpad.co/product?articleinfo=N33218ec5-a5a1-497e-a794-d97a9758a778

The shortage of transplantable donor organs directly affects patients with end-stage lung disease, for which transplantation remains the only definitive treatment. With the current acceptance rate of donor lungs of only 20%, rescuing even one half of the rejected donor lungs would increase the number of transplantable lungs threefold, to 60%. We review recent advances in lung bioengineering that have potential to repair the epithelial and vascular compartments of the lung. Our focus is on the long-term support and recovery of the lung ex vivo, and the replacement of defective epithelium with healthy therapeutic cells. To this end, we first review the roles of the lung epithelium and vasculature, with focus on the alveolar-capillary membrane, and then discuss the available and emerging technologies for ex vivo bioengineering of the lung by decellularization and recellularization. While there have been many meritorious advances in these technologies for recovering marginal quality lungs to the levels needed to meet the standards for transplantation – many challenges remain, motivating further studies of the extended ex vivo support and interventions in the lung. We propose that the repair of injured epithelium with preservation of quiescent vasculature will be critical for the immediate blood supply to the lung and the lung survival and function following transplantation.

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<![CDATA[Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning]]> https://www.researchpad.co/product?articleinfo=Nb2ade9be-1de4-4798-8883-e2c43ab411af

Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Therefore, the future of human movement analysis requires procedures that enhance the classification of movement patterns into relevant groups and support practitioners in their decisions. In this regard, the use of data-driven techniques seems to be particularly suitable to generate classification models. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution, e.g., in increasing the classification performance. In order to ensure the generalizability of the machine-learning models, different data preprocessing steps are usually carried out to process the measured raw data before the classifications. In the past, various methods have been used for each of these preprocessing steps. However, there are hardly any standard procedures or rather systematic comparisons of these different methods and their impact on the classification performance. Therefore, the aim of this analysis is to compare different combinations of commonly applied data preprocessing steps and test their effects on the classification performance of gait patterns. A publicly available dataset on intra-individual changes of gait patterns was used for this analysis. Forty-two healthy participants performed 6 sessions of 15 gait trials for 1 day. For each trial, two force plates recorded the three-dimensional ground reaction forces (GRFs). The data was preprocessed with the following steps: GRF filtering, time derivative, time normalization, data reduction, weight normalization and data scaling. Subsequently, combinations of all methods from each preprocessing step were analyzed by comparing their prediction performance in a six-session classification using Support Vector Machines, Random Forest Classifiers, Multi-Layer Perceptrons, and Convolutional Neural Networks. The results indicate that filtering GRF data and a supervised data reduction (e.g., using Principal Components Analysis) lead to increased prediction performance of the machine-learning classifiers. Interestingly, the weight normalization and the number of data points (above a certain minimum) in the time normalization does not have a substantial effect. In conclusion, the present results provide first domain-specific recommendations for commonly applied data preprocessing methods and might help to build more comparable and more robust classification models based on machine learning that are suitable for a practical application.

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<![CDATA[Preservation of Microalgae, Lignocellulosic Biomass Blends by Ensiling to Enable Consistent Year-Round Feedstock Supply for Thermochemical Conversion to Biofuels]]> https://www.researchpad.co/product?articleinfo=N9018a82a-34e3-478a-93da-cf1c08b555d8

Seasonal variation in microalgae productivity is a significant barrier to economical production of algae biofuels and chemicals. Summer production can be 3–5 times higher than in the winter resulting in uneven feedstock supplies at algae biorefineries. A portion of the summer production must be preserved for conversion in the winter in order to maintain a biorefinery running at capacity. Ensiling, a preservation process that utilizes lactic acid fermentation to limit microbial degradation, has been demonstrated to successfully stabilize algae biomass (20% solids) and algae-lignocellulosic blends (40% algae-60% lignocellulosic biomass, dry basis) for over 6 months, resulting in fuel production cost savings with fewer emissions. Preservation of algae as blends could be beneficial to biorefineries that utilize thermochemical approaches to fuel production as co-processing of algae and lignocellulosic biomass has been observed to enhance biocrude yield and improve oil quality. This study conducts a resource assessment of biomass residues in the southern United States to identify materials available during peak algae productivity and in sufficient quantity to meet the algae storage needs of an algae biofuel industry. Eight feedstocks met the quantity threshold but only three, distillers grains, haylage, and yard waste, were also available in season. Storage experiments utilizing both freshwater and marine strains of microalgae – Scenedesmus acutus, Chlorella vulgaris, Chlorella zofingiensis, Nannochloropsis gaditana, and Porphyridium purpureum – and yard waste were conducted for 30 days. Storage losses were less than 10% in all but one case, and the pH of all but one blend was reduced to less than 4.7, indicating that yard waste is a suitable feedstock for blending with algae prior to storage. To better understand whether the benefits to conversion realized by processing blends might be affected by storage, elemental analysis and bomb calorimetry of pre- and post-storage algae-yard waste blends were conducted to characterize changes occurring during storage. Storing algae biomass as blends with lignocellulosic biomass could be an effective method of mitigating seasonal variability in algae biomass production while retaining the synergistic effect of co-processing algae blends in thermochemical conversion.

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<![CDATA[An Engineered Pathway for Production of Terminally Sialylated N-glycoproteins in the Periplasm of Escherichia coli]]> https://www.researchpad.co/product?articleinfo=N48ea37a6-5e57-473a-9833-b67556c37ddc

Terminally sialylated N-glycoproteins are of great interest in therapeutic applications. Due to the inability of prokaryotes to carry out this post-translational modification, they are currently predominantly produced in eukaryotic host cells. In this study, we report a synthetic pathway to produce a terminally sialylated N-glycoprotein in the periplasm of Escherichia coli, mimicking the sialylated moiety (Neu5Ac-α-2,6-Gal-β-1,4-GlcNAc-) of human glycans. A sialylated pentasaccharide, Neu5Ac-α-2,6-Gal-β-1,4-GlcNAc-β-1,3-Gal-β-1,3-GlcNAc-, was synthesized through the activity of co-expressed glycosyltransferases LsgCDEF from Haemophilus influenzae, Campylobacter jejuni NeuBCA enzymes, and Photobacterium leiognathi α-2,6-sialyltransferase in an engineered E. coli strain which produces CMP-Neu5Ac. C. jejuni oligosaccharyltransferase PglB was used to transfer the terminally sialylated glycan onto a glyco-recognition sequence in the tenth type III cell adhesion module of human fibronectin. Sialylation of the target protein was confirmed by lectin blotting and mass spectrometry. This proof-of-concept study demonstrates the successful production of terminally sialylated, homogeneous N-glycoproteins with α-2,6-linkages in the periplasm of E. coli and will facilitate the construction of E. coli strains capable of producing terminally sialylated N-glycoproteins in high yield.

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<![CDATA[Characterization and Classification of Electrophysiological Signals Represented as Visibility Graphs Using the Maxclique Graph]]> https://www.researchpad.co/product?articleinfo=N41692e8b-86f0-42ec-8d7d-0a0f284eee06

Detection, characterization and classification of patterns within time series from electrophysiological signals have been a challenge for neuroscientists due to their complexity and variability. Here, we aimed to use graph theory to characterize and classify waveforms within biological signals using maxcliques as a feature for a deep learning method. We implemented a compact and easy to visualize algorithm and interface in Python. This software uses time series as input. We applied the maxclique graph operator in order to obtain further graph parameters. We extracted features of the time series by processing all graph parameters through K-means, one of the simplest unsupervised machine learning algorithms. As proof of principle, we analyzed integrated electrical activity of XII nerve to identify waveforms. Our results show that the use of maxcliques allows identification of two distinct types of waveforms that match expert classification. We propose that our method can be a useful tool to characterize and classify other electrophysiological signals in a short time and objectively. Reducing the classification time improves efficiency for further analysis in order to compare between treatments or conditions, e.g., pharmacological trials, injuries, or neurodegenerative diseases.

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<![CDATA[A Machine Learning Approach to Estimate Hip and Knee Joint Loading Using a Mobile Phone-Embedded IMU]]> https://www.researchpad.co/product?articleinfo=N41881cba-485a-4da0-bec8-2824eeebd586

Hip osteoarthritis patients exhibit changes in kinematics and kinetics that affect joint loading. Monitoring this load can provide valuable information to clinicians. For example, a patient's joint loading measured across different activities can be used to determine the amount of exercise that the patient needs to complete each day. Unfortunately, current methods for measuring joint loading require a lab environment which most clinicians do not have access to. This study explores employing machine learning to construct a model that can estimate joint loading based on sensor data obtained solely from a mobile phone. In order to learn such a model, we collected a dataset from 10 patients with hip osteoarthritis who performed multiple repetitions of nine different exercises. During each repetition, we simultaneously recorded 3D motion capture data, ground reaction force data, and the inertial measurement unit data from a mobile phone attached to the patient's hip. The 3D motion and ground reaction force data were used to compute the ground truth joint loading using musculoskeletal modeling. Our goal is to estimate the ground truth loading value using only the data captured by the sensors of the mobile phone. We propose a machine learning pipeline for learning such a model based on the recordings of a phone's accelerometer and gyroscope. When evaluated for an unseen patient, the proposed pipeline achieves a mean absolute error of 29% for the left hip and 36% for the right hip. While our approach is a step in the direction of using a minimal number of sensors to estimate joint loading outside the lab, developing a tool that is accurate enough to be applicable in a clinical context still remains an open challenge. It may be necessary to use sensors at more than one location in order to obtain better estimates.

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<![CDATA[Identification and Validation of Novel Long Non-coding RNA Biomarkers for Early Diagnosis of Oral Squamous Cell Carcinoma]]> https://www.researchpad.co/product?articleinfo=Nc92f7751-26a7-4339-b3b2-854e9b720e2b

Long non-coding RNAs (lncRNAs) are recently emerging as a novel promising biomarker for cancer diagnosis and prognosis. Despite these previous investigations, the expression pattern and diagnostic role of lncRNAs in oral squamous cell carcinoma (OSCC) remain unclear. In this study, we identified six novel lncRNA biomarkers (LINC01697, LINC02487, LOC105376575, AC005083.1, SLC8A1-AS1, and U62317.1) from a list of 29 differentially expressed lncRNAs using the least absolute shrinkage and selection operator (LASSO) method in the discovery dataset of 167 OSCC samples and 45 normal oral tissues. Using the logistic regression method, we constructed a six lncRNAs-based diagnostic risk model (6lncRNAScore) which was able to differentiate between OSCC samples and control samples with high performance with AUC of 0.995 and high diagnostic specificity of 88.9% and sensitivity of 98.2% in the discovery dataset. The diagnostic performance of the 6lncRNAScore was further validated in another two independent OSCC dataset with AUC of 0.968 and 1.0. Functional enrichment analysis for lncRNA biomarkers-related mRNAs suggested that lncRNAs biomarkers tended to be involved in the lipid metabolic process. Together, our study highlighted the importance of lncRNAs in OSCC and demonstrated the utility of lncRNA expression as a promising biomarker for early diagnosis of OSCC.

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<![CDATA[Fruits By-Products – A Source of Valuable Active Principles. A Short Review]]> https://www.researchpad.co/product?articleinfo=Ned77042a-89b7-455f-a8fe-477c6cb98018

The growing demand for more sustainable, alternative processes leading to production of plant-derived preparations imposes the use of plants waste generated mainly by agri-food and pharmaceutical industries. These mostly unexploited but large quantities of plants waste also increase the interest in developing alternative approaches for sustainable production of therapeutic molecules. In order to reduce the amount of plant waste by further processing, different novel extraction techniques can be applied. Fruits and their industrial by-products are rich sources of different classes of compounds with therapeutic properties. The processed fruits waste can be reused and lead to novel pharmaceuticals, food supplements or functional foods. This review intends to briefly summarize recent aspects regarding the production of different active compounds from fruit by-products, and their therapeutic properties. The potential use of fruits by-products in different industries will be also discussed.

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