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Cancer is a major public health problem that ranks as the second leading cause of death. Anti-cancer drug development presents with various hurdles faced throughout the process. Nanoparticle (NP) formulations have emerged as a promising strategy for enhancing drug delivery efficiency, improving stability, and reducing drug toxicity. Previous studies have shown that the adamantyl retinoid ST1926 displays potent anti-tumor activities in several types of tumors, particularly in colorectal cancer (CRC). However, phase I clinical trials in cancer patients using ST1926 are halted due to its low bioavailability. In this manuscript, we developed ST1926-NPs using flash nanoprecipitation with polystyrene-b-poly (ethyleneoxide) as an amphiphilic stabilizer and cholesterol as a co-stabilizer. Dynamic light scattering revealed that the resulting ST1926-NPs Contin diameter was 97 nm, with a polydispersity index of 0.206. Using cell viability, cell cycle analysis, and cell death assays, we showed that ST1926-NP exhibited potent anti-tumor activities in human CRC HCT116 cells. In a CRC xenograft model, mice treated with ST1926-NP exhibited significantly lowered tumor volumes compared to controls at low drug concentrations and enhanced the delivery of ST1926 to the tumors. These findings highlight the potential of ST1926-NPs in attenuating CRC tumor growth, facilitating its further development in clinical settings.

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Prediction of indoor airflow distribution often relies on high-fidelity, computationally intensive computational fluid dynamics (CFD) simulations. Artificial intelligence (AI) models trained by CFD data can be used for fast and accurate prediction of indoor airflow, but current methods have limitations, such as only predicting limited outputs rather than the entire flow field. Furthermore, conventional AI models are not always designed to predict different outputs based on a continuous input range, and instead make predictions for one or a few discrete inputs. This work addresses these gaps using a conditional generative adversarial network (CGAN) model approach, which is inspired by current state-of-the-art AI for synthetic image generation. We create a new Boundary Condition CGAN (BC-CGAN) model by extending the original CGAN model to generate 2D airflow distribution images based on a continuous input parameter, such as a boundary condition. Additionally, we design a novel feature-driven algorithm to strategically generate training data, with the goal of minimizing the amount of computationally expensive data while ensuring training quality of the AI model. The BC-CGAN model is evaluated for two benchmark airflow cases: an isothermal lid-driven cavity flow and a non-isothermal mixed convection flow with a heated box. We also investigate the performance of the BC-CGAN models when training is stopped based on different levels of validation error criteria. The results show that the trained BC-CGAN model can predict the 2D distribution of velocity and temperature with less than 5% relative error and up to about 75,000 times faster when compared to reference CFD simulations. The proposed feature-driven algorithm shows potential for reducing the amount of data and epochs required to train the AI models while maintaining prediction accuracy, particularly when the flow changes non-linearly with respect to an input.

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Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems toward large-scale distributed AI systems that can collaboratively perform complex learning tasks. In this regard, democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. The outlined principles are meant to study a generalization in distributed learning systems that go beyond existing mechanisms such as federated learning (FL). Moreover, such learning systems rely on hierarchical self-organization of well-connected distributed learning agents who have limited and highly personalized data and can evolve and regulate themselves based on the underlying duality of specialized and generalized processes. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this article. The approach consists of a self-organizing hierarchical structuring mechanism based on agglomerative clustering, hierarchical generalization, and corresponding learning mechanism. Subsequently, hierarchical generalized learning problems in recursive forms are formulated and shown to be approximately solved using the solutions of distributed personalized learning problems and hierarchical update mechanisms. To that end, a distributed learning algorithm, namely DemLearn, is proposed. Extensive experiments on benchmark MNIST, Fashion-MNIST, FE-MNIST, and CIFAR-10 datasets show that the proposed algorithm demonstrates better results in the generalization performance of learning models in agents compared to the conventional FL algorithms. The detailed analysis provides useful observations to further handle both the generalization and specialization performance of the learning models in Dem-AI systems.

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Retinoids represent a class of chemical compounds derived from or structurally and functionally related to vitamin A. Retinoids play crucial roles in regulating a range of crucial biological processes spanning embryonic development to adult life. These include regulation of cell proliferation, differentiation, and cell death. Due to their promising characteristics, retinoids emerged as potent anti-cancer agents, and their effects were validated in vitro and in vivo preclinical models of several solid and hematological malignancies. However, their clinical translation remained limited due to poor water solubility, photosensitivity, short half-life, and toxicity. The development of retinoid delivery formulations was extensively studied to overcome these limitations. This review will summarize some preclinical and commercial synthetic retinoids in cancer and discuss their different delivery systems.

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Several sesquiterpene lactones (STLs) have been tested as lead drugs in cancer clinical trials. Salograviolide-A (Sal-A) and salograviolide-B (Sal-B) are two STLs that have been isolated from Centaurea ainetensis, an indigenous medicinal plant of the Middle Eastern region. The parent compounds Sal-A and Sal-B were modified and successfully prepared into eight novel guaianolide-type STLs (compounds 1-8) bearing ester groups of different geometries. Sal-A, Sal-B, and compounds 1-8 were tested against a human colorectal cancer cell line model with differing p53 status; HCT116 with wild-type p53 and HCT116 p53-/- null for p53, and the normal-like human colon mucosa cells with wild-type p53, NCM460. IC50 values indicated that derivatization of Sal-A and Sal-B resulted in potentiation of HCT116 cell growth inhibition by 97% and 66%, respectively. The effects of the different molecules on cancer cell growth were independent of p53 status. Interestingly, the derivatization of Sal-A and Sal-B molecules enhanced their anti-growth properties versus 5-Fluorouracil (5-FU), which is the drug of choice in colorectal cancer. Structure-activity analysis revealed that the enhanced molecule potencies were mainly attributed to the position and number of the hydroxy groups, the lipophilicity, and the superiority of ester groups over hydroxy substituents in terms of their branching and chain lengths. The favorable cytotoxicity and selectivity of the potent molecules, to cancer cells versus their normal counterparts, pointed them out as promising leads for anti-cancer drug design.

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Harmful Algal Blooms (HABs) have been observed in all 50 states in the U.S., ranging from large freshwater lakes, such as the Great Lakes, to smaller inland lakes, rivers, and reservoirs, as well as marine coastal areas and estuaries. In 2014, a HAB on Lake Erie containing microcystin (a liver toxin) contaminated the municipal water supply in Toledo, Ohio, providing non-potable water to 400,000 people. Studying HABs is complicated as different cyanobacteria produce a range of toxins that impact human health, such as microcystins, saxitoxin, anatoxin-a, and cylindrospermopsin. HABs may be increasing in prevalence with rising temperatures and higher nutrient runoff. Consequently, new tools and technology are needed to rapidly detect, characterize, and respond to HABs that threaten our water security. A framework is needed to understand cyber threats to new and existing technologies that monitor and forecast our water quality. To properly detect, assess, and mitigate security threats on water infrastructure, it is necessary to envision water security from the perspective of a cyber-physical system (CPS). In doing so, we can evaluate risks and research needs for cyber-attacks on HAB-monitoring networks including data injection attacks, automated system hijacking attacks, node forgery attacks, and attacks on learning algorithms. Herein, we provide perspectives on the research needed to understand both the threats posed by HABs and the coupled cyber threats to water security in the context of HABs.

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Acute myeloid leukemia (AML) is one of the most frequent types of blood malignancies. It is a complex disorder of undifferentiated hematopoietic progenitor cells. The majority of patients generally respond to intensive therapy. Nevertheless, relapse is the major cause of death in AML, warranting the need for novel treatment strategies. Retinoids have demonstrated potent differentiation and growth regulatory effects in normal, transformed, and hematopoietic progenitor cells. All-trans retinoic acid (ATRA) is the paradigm of treatment in acute promyelocytic leukemia, an AML subtype. The majority of AML subtypes are, however, resistant to ATRA. Multiple synthetic retinoids such as ST1926 recently emerged as potent anticancer agents to overcome such resistance. Despite its lack of toxicity, ST1926 clinical development was restricted due to its limited bioavailability and rapid excretion. Here, we investigate the preclinical efficacy of ST1926 and polymer-stabilized ST1926 nanoparticles (ST1926-NP) in AML models. We show that sub-µmol/L concentrations of ST1926 potently and selectively inhibited the growth of ATRA-resistant AML cell lines and primary blasts. ST1926 induced-growth arrest was due to early DNA damage and massive apoptosis in AML cells. To enhance the drug's bioavailability, ST1926-NP were developed using Flash NanoPrecipitation, and displayed comparable anti-growth activities to the naked drug in AML cells. In a murine AML xenograft model, ST1926 and ST1926-NP significantly prolonged survival and reduced tumor burden. Strikingly, in vivo ST1926-NP antitumor effects were achieved at four fold lower concentrations than the naked drug. These results highlight the promising use of ST1926 in AML therapy and encourage its further development. Mol Cancer Ther; 16(10); 2047-57. ©2017 AACR.

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Recently, there has been a growing interest in identifying suitable routes for the disposal of pharmaceutical wastes. This study investigates the potential of matrix materials composed of recycled polyethylene/polypropylene reclaimed from municipal solid wastes at immobilizing pharmaceutical solid wastes. Diclofenac (DF) drug product was embedded in boards of recycled plastic material, and leaching in water was assessed at various temperatures. DF concentrations were determined by high-performance liquid chromatography and revealed a maximum leachable fraction of 4% under accelerated conditions of 70°C, and less than 0.3% following 39 days of exposure at 20°C. The Ensemble Kalman Filter was employed to characterize the leaching behavior of DF. The filter verified the occurrence of leaching through diffusion, and was successful in predicting the leaching behavior of DF at 50°C and 70°C.

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The impact of residual pharmaceuticals on the aquatic environment has gained widespread attention over the past years. Various studies have established the occurrence of pharmaceutical compounds in different water bodies throughout the world. In view of the absence of occurrence data in a number of developing world countries, and given the limited availability of analytical resources in these countries, it is prudent to devise methodologies to prioritize pharmaceuticals for environmental monitoring purposes that are site specific. In this work, several prioritization approaches are used to rank the 88 most commonly consumed pharmaceuticals in Lebanon. A simultaneous multi-criteria decision analysis method utilizing the exposure, persistence, bioaccumulation, and toxicity (EPBT) approach is applied to a smaller subset of the original list (69 pharmaceuticals). Several base cases are investigated and sensitivity analysis is applied to one of these base case runs. The similarities and differences in the overall ranking of individual, and classes of, pharmaceuticals for the base cases and the sensitivity runs are elucidated. An environmental risk assessment (ERA), where predicted environmental concentrations (PEC) and risk quotients (RQ) are determined at different dilution factors, is performed as an alternative method of prioritization for a total of 84 pharmaceuticals. The ERA results indicate that metformin and amoxicillin have the highest PECs while 17ß-estradiol, naftidrofuryl and dimenhydrinate have the highest RQs. The two approaches, EPBT prioritization and ERA, are compared and a priority list consisting of 26 pharmaceuticals of various classes is developed. Nervous system and alimentary tract and metabolism pharmaceuticals (9/26 and 5/26 respectively) constitute more than half of the numbers on the priority list with the balance consisting of anti-infective (4/26), musculo-skeletal (3/26), genito-urinary (2/26), respiratory (2/26) and cardiovascular (1/26) pharmaceuticals. This list will serve as a basis for the selection of candidate compounds to focus on for future monitoring campaigns.

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The wide application and production of nanotechnology have increased the interest in studying the toxicity of nano- and micro-sized particles escaping into air from various aspects of the production process. Metal oxides (MOs) are one particular class of particles that exist abundantly in ambient PM. Studies show an emphasis on biological mechanisms by which inhalation exposure to MOs leads to disease. However, different biological assays provide different redox activity rankings making it difficult to assess the contributions of various MOs to measures of aggregate toxicity in multi-pollutant systems such as ambient PM. Therefore, research to evaluate the chemical interaction between these particles and molecules that are relevant to cellular redox activity can help in establishing indicators of reactivity. In particular, this study assesses the redox activity of six MOs mainly emitted from anthropogenic industrial activities using the dithiothreitol (DTT) assay. DTT is commonly used in acellular assays due to its analogous structure to cellular glutathione. The structural and chemical behaviors between active MOs and DTT were elucidated using FTIR, NMR, and BET methods. The results indicate that the health risk (redox activity) associated with MOs is mainly a function of their surface reactivity demonstrated by the ability of the oxidized (S-H) bond in DTT to form a stable bond with the MO surface.

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Amiodarone has been widely used in treating cardiac arrhythmias. Drug-induced lupus due to amiodarone has been rarely reported. Here, we report the rare occurrence of drug-induced lupus in a 37-year-old male shortly after the initiation of amiodarone. After cessation of drug exposure, the patient recovered completely.

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Drug nanocarrier clearance by the immune system must be minimized to achieve targeted delivery to pathological tissues. There is considerable interest in finding in vitro tests that can predict in vivo clearance outcomes. In this work, we produce nanocarriers with dense PEG layers resulting from block copolymer-directed assembly during rapid precipitation. Nanocarriers are formed using block copolymers with hydrophobic blocks of polystyrene (PS), poly-ε-caprolactone (PCL), poly-D,L-lactide (PLA), or poly-lactide-co-glycolide (PLGA), and hydrophilic blocks of polyethylene glycol (PEG) with molecular weights from 1 kg/mol to 9 kg/mol. Nanocarriers with paclitaxel prodrugs are evaluated in vivo in Foxn1(nu) mice to determine relative rates of clearance. The amount of nanocarrier in circulation after 4h varies from 10% to 85% of initial dose, depending on the block copolymer. In vitro complement activation assays are conducted to correlate in vivo circulation to the protection of the nanocarrier surface from complement binding and activation. Guidelines for optimizing block copolymer structure to maximize circulation of nanocarriers formed by rapid precipitation and directed assembly are proposed, relating to the relative sizes of the hydrophilic and hydrophobic blocks, the hydrophobicity of the anchoring block, the absolute size of the PEG block, and polymer crystallinity. The in vitro results distinguish between the poorly circulating PEG(5k)-PCL(9 k) and the better circulating nanocarriers, but could not rank the better circulating nanocarriers in order of circulation time. Analysis of PEG surface packing on monodisperse 200 nm latex spheres indicates that the size of the hydrophobic PCL, PS, and PLA blocks are correlated with the PEG blob size. Suggestions for next steps for in vitro measurements are made.

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Nanoparticles have significant potential in therapeutic applications to improve the bioavailability and efficacy of active drug compounds. However, the retention of nanometer sizes during concentrating or drying steps presents a significant problem. We report on a new concentrating and drying process for poly(ethylene glycol) (PEG) stabilized nanoparticles, which relies upon the unique pH sensitive hydrogen bonding interaction between PEG and polyacid species. In the hydrogen bonding coacervate precipitation (HBCP) process, PEG protected nanoparticles rapidly aggregate into an easily filterable precipitate upon the addition various polyacids. When the resulting solid is neutralized, the ionization of the acid groups eliminates the hydrogen bonded structure and the approximately 100 nm particles redisperse back to within 10% of their original size when poly(acrylic acid) and citric acid are used and 45% when poly(aspartic acid) is used. While polyacid concentrations of 1-5 wt % were used to form the precipitates, the incorporation of the acid into the PEG layer is approximately 1:1 (acid residue):(ethylene oxide unit) in the final dried precipitate. The redispersion of dried beta-carotene nanoparticles protected with PEG-b-poly(lactide-co-glycolide) polymers dried by HBCP was compared with the redispersion of particles dried by freeze-drying with sucrose as a cryprotectant, spray freeze-drying, and normal drying. Freeze-drying with 0, 2, and 12 wt % sucrose solutions resulted in size increases of 350%, 50%, and 6%, respectively. Spray freeze-drying resulted in particles with increased sizes of 50%, but no cryoprotectant and only moderate redispersion energy was required. Conventional drying resulted in solids that could not be redispersed back to nanometer size. The new HBCP process offers a promising and efficient way to concentrate or convert nanoparticle dispersions into a stable dry powder form.

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A series of paclitaxel prodrugs designed for formulation in lipophilic nanoparticles are described. The hydrophobicity of paclitaxel was increased by conjugating a succession of increasingly hydrophobic lipid anchors to the drug using succinate or diglycolate cross-linkers. The prodrugs were formulated in well defined block copolymer-stabilized nanoparticles. These nanoparticles were shown to have an elimination half-life of approximately 24 h in vivo. The rate at which the prodrug was released from the nanoparticles could be controlled by adjusting the hydrophobicity of the lipid anchor, resulting in release half-lives ranging from 1 to 24 h. The diglycolate and succinate cross-linked prodrugs were 1-2 orders of magnitude less potent than paclitaxel in vitro. Nanoparticle formulations of the succinate prodrugs showed no evidence of efficacy in HT29 human colorectal tumor xenograph models. Efficacy of diglycolate prodrug nanoparticles increased as the anchor hydrophobicity increased. Long circulating diglycolate prodrug nanoparticles provided significantly enhanced therapeutic activity over commercially formulated paclitaxel at the maximum tolerated dose.

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Ostwald ripening, the interfacial-energy-driven dissolution and reprecipitation of solutes, becomes an increasingly significant problem for nanoparticle formulations. We present the first quantitative study of Ostwald ripening for nanoparticle dispersions. The Lifshitz-Slyozov-Wagner (LSW) theory of particle growth driven by diffusion is applied to study beta-carotene nanoparticles with sizes of O(100 nm) formed by our block-copolymer protected Flash Nanoprecipitation process. A numerical implementation of the LSW theory that accounts for the original particle size distribution is presented. The predicted particle sizes from the numerical simulation are compared with the experimental results measured by dynamical light scattering. The results show quantitative agreement with no adjustable parameters. The addition of antisolvent results in the reduction of the ripening rate by dramatically decreasing bulk solubility.

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