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Drug candidates with poor solubility have been recognized as the cause of many drug development failures, owing to the fact that low solubility is unfavorable for physicochemical, pharmacokinetic (PK) and pharmacodynamic (PD) properties. Given the imperative role of solubility during drug development, we herein summarize various strategies for solubility optimizations from a medicinal chemistry perspective, including introduction of polar group, salt formation, structural simplification, disruption of molecular planarity and symmetry, optimizations on the solvent exposed region as well as prodrug design. In addition, methods for solubility assessment and prediction are reviewed. Besides, we have deeply discussed the strategies for solubility improvement. This paper is expected to be beneficial for the development of drug-like molecules with good solubility.

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Objective.Hybrid proton-photon radiotherapy (RT) is a cancer treatment option to broaden access to proton RT. Additionally, with a refined treatment planning method, hybrid RT has the potential to offer superior plan quality compared to proton-only or photon-only RT, particularly in terms of target coverage and sparing organs-at-risk (OARs), when considering robustness to setup and range uncertainties. However, there is a concern regarding the underestimation of the biological effect of protons on OARs, especially those in close proximity to targets. This study seeks to develop a hybrid treatment planning method with biological dose optimization, suitable for clinical implementation on existing proton and photon machines, with each photon or proton treatment fraction delivering a uniform target dose.Approach.The proposed hybrid biological dose optimization method optimized proton and photon plan variables, along with the number of fractions for each modality, minimizing biological dose to the OARs and surrounding normal tissues. To mitigate underestimation of hot biological dose spots, proton biological dose was minimized within a ring structure surrounding the target. Hybrid plans were designed to be deliverable separately and robustly on existing proton and photon machines, with enforced uniform target dose constraints for the proton and photon fraction doses. A probabilistic formulation was utilized for robust optimization of setup and range uncertainties for protons and photons. The nonconvex optimization problem, arising from minimum monitor unit constraint and dose-volume histogram constraints, was solved using an iterative convex relaxation method.Main results.Hybrid planning with biological dose optimization effectively eliminated hot spots of biological dose, particularly in normal tissues surrounding the target, outperforming proton-only planning. It also provided superior overall plan quality and OAR sparing compared to proton-only or photon-only planning strategies.Significance.This study presents a novel hybrid biological treatment planning method capable of generating plans with reduced biological hot spots, superior plan quality to proton-only or photon-only plans, and clinical deliverability on existing proton and photon machines, separately and robustly.

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In cyanobacteria, carbonic anhydrase (zinc metalloenzyme) is a major enzyme that converts CO2 to HCO3- maintaining the carbon concentration around the vicinity of RuBisCo, leading to cyanobacterial biomass generation. Anthropogenic activities, disposal of leached micro nutrients effluents from industries into the aquatic environment results in cyanobacterial blooms. The harmful cyanobacteria release cyanotoxins in open-water system which on ingression through oral route causes major health issues like hepatotoxicity and immunotoxicity. A database was prepared consisting of approximately 3k phytochemicals curated from previous literatures, earlier identified by GC-MS analysis. The phytochemicals were subjected to online servers to identify the novel lead molecules which followed ADMET and drug-like candidates. The identified leads were optimized by density functional theory method using B3YLP/G* level of theory. Carbonic anhydrase chosen as target to observe the binding interaction through molecular docking simulations. From the molecules included in the database the highest binding energy exhibited by alpha-tocopherol succinate and mycophenolic acid were found to be -9.23 kcal/mol and -14.41 kcal/mol and displayed interactions with GLY A102, GLN B30, ASP A41, LYS A105 including Zn2+ and their adjacent amino acids CYS 101, HIS 98, CYS 39 in both chain A and chain A-B of carbonic anhydrase. The Identified molecular orbitals decipher computed global electrophilicity values (Energy gap, electrophilicity and Softness) of alpha-tocopherol succinate and mycophenolic acid were found to be (5.262, 1.948, 0.380) eV and (4.710, 2.805, 0.424) eV demonstrates both molecules are effective and stable. The identified leads may serve as a better anti-carbonic anhydrase agent because they accommodate in the binding site and hampers the catalytic activity of Carbonic anhydrase thus inhibiting the generation of cyanobacterial biomass. This identified lead molecules may serve as a substructure to design novel phytochemicals against carbonic anhydrase present in cyanobacteria. Further in vitro study is necessary to evaluate the efficacy of these molecules.

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INTRODUCTION: The challenge of territorial hospital groups is to develop coherent care pathways for optimal patient care. Following the creation of a territorial pharmaceutical team, a common prescription review process was initiated in our health area. The objective of this study is to analyze the uses of statins in the elderly. METHOD: The study included all statin-treated patients older than 75 years at the five participating institutions (including long-term nursing homes). In a prospective multicenter study, the benefit/risk ratio of statin prescription has been assessed up. Depending on the clinical situation, a proposal to stop or adjust the dosage could be made. RESULTS: Nine hundred and forty-seven patients were included. Among them, 184 were treated with a statin. Forty-seven patients (26%) are treated in primary prevention and 137 patients (74%) in secondary prevention. Dosages are lower for long stays. Fifteen treatments interruption were accepted out of 44 proposals, mostly for long stays. The reasons given to continue treatment are the need for a new evaluation by a cardiologist or a high cardiovascular risk. CONCLUSION: The variability of results according to the type healthcare institution makes territorial medical and pharmaceutical collaboration relevant. The challenge is to develop a coherent care pathway for optimal care of elderly patients, with congruent objectives.

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After the outbreak of COVID-19, the freight demand fell briefly, and as production resumed, the trucking share rate increased again, further increasing energy consumption and environmental pollution. To optimize the sudden changing freight structure, the study aims on developing an evolution model based on Markov's theory to estimate the freight structure post-COVID-19. The current study applies economic cybernetics to establish a freight structural adjustment path optimization model and solve the problem of how much freight transportation should increase each year under the premise that the total turnover of the freight industry continues to grow, and how many years it will take at least to reach a reasonable freight structure. The freight transport structure of China is used to examine the feasibility of the proposed model. The finding indicates that the development of China's freight transport structure is at an adjustment period and should enter a stable period by 2035 and the COVID-19 makes it harder to adjust the freight structure. Increasing the growth rate of the freight volume of railway and waterway transportation is the key to realizing the optimization of the freight structure, and the freight structure path optimization method can realize the rationalization of the freight structure in advance.

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To better guarantee the quality of the thawed meat, maximize the thawing rate, and minimize the system energy consumption, the multiparameter and multi-objective coupling optimizations for the newly proposed vacuum sublimation-rehydration thawing (VSRT) process was conducted. The polynomial nonlinear regression equations of single and comprehensive objectives were established by the central composite rotatable design, and the corresponding test of fitting degree and the analysis of influencing factors order were carried out. Furthermore, the interaction effects of influencing factors were investigated through the response surface methodology and were experimentally validated to obtain the optimal process parameters. The results showed that the established regression equations were in good agreement with the experimental values. For the different objectives, there were great differences in the influence order and interaction of factors. In the sublimation and rehydration stages, there existed an optimal region in the response surface to achieve a better value for the single and comprehensive objectives. When the sublimation time was 19 min, the heating plate temperature was 26°C, the rehydration water volume was 1634 ml, the rehydration water temperature was 29°C, the thawing time was relatively short (1.00 h), and the thawing loss (1.19%), the total color difference (1.02), and the system-specific energy consumption (0.026 kW h/kg) were relatively low. The comprehensive performance of the VSRT system reached the best state. PRACTICAL APPLICATION: The purpose of this work is to make the novel vacuum sublimation-rehydration thawing method not only better guarantee the quality of thawed meat but also maximize the thawing rate and minimize the energy consumption of the system, which can provide a new idea and reference for the development of new high-efficiency thawing equipment.

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Incorporating pigments into polymers can be done for various purposes, including the introduction of color, interfacial effects, or aesthetics. If these pigments are to disperse properly, then the process of extrusion must be optimized. During polymer compounding extrusion, three effective processing factors were investigated: feed rate (FR), speed (Sp.) and temperature (temp.) for a colored compounded polycarbonate (PC) grade (30/70%). The processing design techniques were obtained by applying design experiments in a response surface methodology (RSM) to blend two polycarbonates with pigments and optimize the processing temperatures at center points. The first study decided to utilize the response surface approach of Box-Behnken design (BBD) to design an experiment to optimize the process parameters. Statistical significance was demonstrated by the model passing all diagnostic tests. Furthermore, the three processing factors strongly impacted the characteristics of the tri-stimulus color, according to the results from a variance analysis. The second study identified process variables for the same PC grade at the center level, 25 kg/h FR, 750 rpm speed, and (255 °C) temp. The characterization and scanning morphology were examined using MicroCtscanner image analysis, SEM, DOM, rheology, FT-IR, and color-pigmented values were measured using a color spectrometer. The output response was significantly impacted when excellent color dispersion was observed with few agglomerates and less differences in colors at the center point. By characterizing these results and having good insight into color difference output and processing condition relationships, which have an adverse effect on color variation characteristics and minimize recycling compounds of different grades, results in cleaner environments benefits.

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Black soldier flies have been studied as an alternative animal feed. On the other hand, they could be used to yield an abundance of fatty acids. Their omnivorous diet and low space requirements allow for the mass breeding of black soldier flies, using widely available food wastes as feedstock. This study simulates the industrial upscaling of an extraction process for black soldier fly larvae using SuperPro™ 9.5 simulation software. This software contains an extensive material library that regulated physical data for the chemical composition of the larvae and the products. It also bundled several types of bioreactors utilized in bioprocessing. The scheduling of the plant was aided by SchedulePro, which allows for the generation of batch durations and Gantt charts. Four fatty acids were chosen as the main revenue source, with simulated proteins assigned as by-products of the plant. Ash and cellulose were the wastes of the plant, and were separated through multiple filters. The plants were later assessed for their economic feasibility. The kitchen waste plant was the most profitable, and the control variable was the only unprofitable plant. These results may have been impacted by the waste content found in the control variable and the abundance of revenue products in the kitchen-waste-fed larvae.

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Genetic optimization of Nucleic Acid immunogens is important for potentially improving their immune potency. A COVID-19 DNA vaccine is in phase III clinical trial which is based on a promising highly developable technology platform. Here, we show optimization in mice generating a pGX-9501 DNA vaccine encoding full-length spike protein, which results in induction of potent humoral and cellular immune responses, including neutralizing antibodies, that block hACE2-RBD binding of live CoV2 virus in vitro. Optimization resulted in improved induction of cellular immunity by pGX-9501 as demonstrated by increased IFN-γ expression in both CD8+ and CD4 + T cells and this was associated with more robust antiviral CTL responses compared to unoptimized constructs. Vaccination with pGX-9501 induced subsequent protection against virus challenge in a rigorous hACE2 transgenic mouse model. Overall, pGX-9501 is a promising optimized COVID-19 DNA vaccine candidate inducing humoral and cellular immunity contributing to the vaccine's protective effects.

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Supramolecular helices that arise from the self-assembly of small organic molecules via non-covalent interactions play an important role in the structure and properties of the corresponding materials. Here we study the supramolecular helical aggregation of oligo(phenyleneethynylene) monomers from a theoretical point of view, always guiding the studies with experimentally available data. In this way, by systematically increasing the number of monomer units, optimized n-mer geometries are obtained along with the corresponding absorption and circular dichroism spectra. For the geometry optimizations we use density functional theory together with the B3LYP-D3 functional and the 6-31G** basis set. For obtaining the spectra we resort to time-dependent density functional theory using the CAM-B3LYP functional and the 3-21G basis set. These combinations of density functional and basis set were selected after systematic convergence studies. The theoretical results are analyzed and compared to the experimentally available spectra, observing a good agreement.

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The new approach of Dittrich [IUCrJ (2021). 8, 305-318] towards describing, understanding and modelling disorders is discussed.

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Distinguishing disorder into static and dynamic based on multi-temperature X-ray or neutron diffraction experiments is the current state of the art, but is only descriptive, not predictive. Here, several disordered structures are revisited from the Cambridge Crystallographic Data Center 'drug subset', the Cambridge Structural Database and own earlier work, where experimental intensities of Bragg diffraction data were available. Using the molecule-in-cluster approach, structures with distinguishable conformations were optimized separately, as extracted from available or generated disorder models of the respective disordered crystal structures. Re-combining these 'archetype structures' by restraining positional and constraining displacement parameters for conventional least-squares refinement, based on the optimized geometries, then often achieves a superior fit to the experimental diffraction data compared with relying on experimental information alone. It also simplifies and standardizes disorder refinement. Ten example structures were analysed. It is observed that energy differences between separate disorder conformations are usually within a small energy window of RT (T = crystallization temperature). Further computations classify disorder into static or dynamic, using single experiments performed at one single temperature, and this was achieved for propionamide.

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This research has examined the ability of two forecasting methods to forecast Bitcoin's price trends. The research is based on Bitcoin-USA dollar prices from the beginning of 2012 until the end of March 2020. Such a long period of time that includes volatile periods with strong up and downtrends introduces challenges to any forecasting system. We use particle swarm optimization to find the best forecasting combinations of setups. Results show that Bitcoin's price changes do not follow the "Random Walk" efficient market hypothesis and that both Darvas Box and Linear Regression techniques can help traders to predict the bitcoin's price trends. We also find that both methodologies work better predicting an uptrend than a downtrend. The best setup for the Darvas Box strategy is six days of formation. A Darvas box uptrend signal was found efficient predicting four sequential daily returns while a downtrend signal faded after two days on average. The best setup for the Linear Regression model is 42 days with 1 standard deviation.

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Previously, we introduced DFT-D3(BJ)ωB97X-V and ωB97M-V functionals and assessed them for the GMTKN55 database [Najibi and Goerigk, J Chem. Theory Comput. 2018, 14, 5725]. In this study, we present DFT-D4 damping parameters to build the DFT-D4 counterparts of these functionals and assess these in comparison. We extend our analysis beyond GMTKN55 and especially turn our attention to enzymatically catalyzed and metal-organic reactions. We find that B97M-D4 is now the second-best performing meta-generalized-gradient approximation functional for the GMTKN55 database and it can provide noticeably better organometallic reaction energies compared to B97M-D3(BJ). Moreover, the aforementioned DFT-D3(BJ)-based functionals have not been thoroughly assessed for geometries and herein we close this gap by analyzing geometries of noncovalently bound dimers and trimers, peptide conformers, water hexamers and transition-metal complexes. We find that several of the B97(M)-based methods-particularly the DFT-D4 versions-surpass the accuracy of previously studied methods for peptide conformer, water hexamer, and transition-metal complex geometries, making them safe-to-use, cost-efficient alternatives to the original methods. The DFT-D4 variants can be easily used with ORCA4.1 and above.

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Predictive gait simulations currently do not account for environmental or internal noise. We describe a method to solve predictive simulations of human movements in a stochastic environment using a collocation method. The optimization is performed over multiple noisy episodes of the trajectory, instead of a single episode in a deterministic environment. Each episode used the same control parameters. The method was verified on a torque-driven pendulum swing-up problem. A different optimal trajectory was found in a stochastic environment than in the deterministic environment. Next, it was applied to gait to show its application in predictive simulation of human movement. We show that, unlike in a deterministic model, a nonzero minimum foot clearance during swing is predicted by a minimum-effort criterion in a stochastic environment. The predicted amount of foot clearance increased with the noise amplitude.

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Hydrodynamic limb vein injection is an in vivo locoregional gene delivery method. It consists of administrating a large volume of solution containing nucleic acid constructs in a limb with both blood inflow and outflow temporarily blocked using a tourniquet. The fast, high pressure delivery allows the musculature of the whole limb to be reached. The skeletal muscle is a tissue of choice for a variety of gene transfer applications, including gene therapy for Duchenne muscular dystrophy or other myopathies, as well as for the production of antibodies or other proteins with broad therapeutic effects. Hydrodynamic limb vein delivery has been evaluated with success in a large range of animal models. It has also proven to be safe and well-tolerated in muscular dystrophy patients, thus supporting its translation to the clinic. However, some possible limitations may occur at different steps of the delivery process. Here, we have highlighted the interests, bottlenecks and potential improvements that could further optimize non-viral gene transfer following hydrodynamic limb vein injection.

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This study describes the determination of lead at trace levels by slotted quartz tube flame atomic absorption spectrophotometry (SQT-FAAS) after preconcentration by the help of stearic acid coated magnetic nanoparticle (SAC-MNPs) based sonication assisted dispersive solid phase extraction (SA-DSPE). SAC-MNPs were used due to their easy separation advantages by the application of external magnetic field. All extraction parameters were optimized by response surface methodology based experimental design. The experimented data was evaluated by the analysis of variance. Under the optimum conditions, about 31 folds enhancement in detection power was obtained over the conventional FAAS. The recovery results obtained for samples spiked at 60 and 120 ng mL-1 were 106.6 and 102.6%, respectively, validating the method as accurate and applicable to the red pepper matrix. The percent relative standard deviations of the results were under 5.0% even at low concentrations that established high precision for replicate extractions and instrumental readings.

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The three-dimensional geometry of a micromixer with an asymmetrical split-and-recombine mechanism was optimized to enhance the fluid-mixing capability at a Reynolds number of 20. Single and multi-objective optimizations were carried out by using particle swarm optimization and a genetic algorithm on a modeled surrogate surface. Surrogate modeling was performed using the computational results for the mixing. Mixing and flow analyses were carried out by solving the convection-diffusion equation in combination with the three-dimensional continuity and momentum equations. The optimization was carried out with two design variables related to dimensionless geometric parameters. The mixing effectiveness was chosen as the objective function for the single-objective optimization, and the pressure drop and mixing index at the outlet were chosen for the multi-objective optimization. The sampling points in the design space were determined using a design of experiment technique called Latin hypercube sampling. The surrogates for the objective functions were developed using a Kriging model. The single-objective optimization resulted in 58.9% enhancement of the mixing effectiveness compared to the reference design. The multi-objective optimization provided Pareto-optimal solutions that showed a maximum increase of 48.5% in the mixing index and a maximum decrease of 55.0% in the pressure drop in comparison to the reference design.

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On 2018-01-17 two electron crystallography structures (with PDB entries 6AXZ, 6BTK) on a prion protofibril of bank vole PrP(168-176) (a segment in the PrP ß2-α2 loop) were released into the PDB Bank. The paper published by Gallagher-Jones et al. (Nat Struct Mol Biol 25(2):131-134, 2018) reports some polar clasps for these two crystal structures, and "an intersheet hydrogen bond between Tyr169 and the backbone carbonyl of Asn171 on an opposing strand."-this hydrogen bond is not directly between the neighboring chain B and chain A. In addition, by revisiting the polar clasps, we found another two hydrogen bonds (B.Asn171@H-A.Gln172@OE1, B.Tyr169@OH-A.Gln172@N) between the strand A of one sheet and the opposing strand B of the mating sheet. For the neighboring two single beta-sheets AB, the two new hydrogen bonds are completely different from the experimental one (an intersheet hydrogen bond between Tyr169 and the backbone carbonyl of Asn171 on an opposing strand) in (Nat Struct Mol Biol 25(2):131-134, 2018).

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BACKGROUND: It is well recognized that unplanned readmissions following total joint arthroplasty (TJA) are more prevalent in patients with comorbidities. However, few investigators have delayed surgery and medically optimized patients prior to surgery. In its current form, the Perioperative Orthopedic Surgical Home (POSH) is a surgeon-led screening and optimization initiative targeting 8 common modifiable comorbidities. METHODS: A total of 4188 patients who underwent TJA between January 2014 and December 2016 were retrospectively screened by the Readmission Risk Assessment tool (RRAT) score. one thousand one hundred and ninety four subjects had a preoperative RRAT score ≥3 and were eligible for inclusion. Patients were then separated into 2 cohorts based on whether they were enrolled into the POSH initiative (POSH; n = 216) or continued with surgery (non-POSH; n = 978) despite their risk. RESULTS: Since the implementation of the POSH initiative, patients with RRAT scores ranging from 3 to 5 have experienced lower 30-day (1.6% vs 5.3%, P = .03) and 90-day (3.2% vs 7.4%, P < .05) readmission rates when compared to the non-POSH cohort. Only 15.3% of medically optimized patients enrolled in the POSH initiative were discharged to a post-acute care facility, whereas 23.4% of non-POSH patients were discharged to a post-acute care facility (P = .01). There were no differences in length of stay and infection rates between the 2 cohorts. Moreover, 90-day episode-of-care costs were 14.9% greater among non-POSH Medicare TJA recipients and 32.6% higher if a readmission occurred. CONCLUSION: The identification and medical optimization of comorbidities prior to surgical intervention may enhance the value of care TJA candidates receive. A standardized multidisciplinary approach to the medical optimization of high-risk TJA candidates may improve patient engagement and perioperative outcomes, while reducing cost associated with TJA. LEVEL OF EVIDENCE: Level III, Retrospective Cohort Study.

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