ENSO effects on the relationship between aerosols and evapotranspiration in the south of the Amazon biome.

The effects of the El Nino-Southern Oscillation (ENSO) events have local, regional, and global consequences for water regimes, causing floods or extreme drought events. Tropical forests are strongly affected by ENSO, and in the case of the Amazon, its territorial extension allows for a wide variation of these effects. The prolongation of drought events in the Amazon basin contributes to an increase in gas and aerosol particle emissions mainly caused by biomass burning, which in turn alter radiative fluxes and evapotranspiration rates, cyclically interfering with the hydrological regime. The ENSO effects on the interactions between aerosol particles and evapotranspiration is a critical aspect to be systematically investigated. Therefore, this study aimed to evaluate the ENSO effect on a site located on the southern portion of the Amazonian region. In addition to quantifying and testing possible differences between aerosols and evapotranspiration under different ENSO classes (El Niño, La Niña and Neutrality), this study also evaluated possible variations in evapotranspiration as a function of the aerosol load. A highly significant difference was found for air temperature, relative humidity and aerosol load between the El Niño and La Niña classes. For evapotranspiration, significant differences were found for the El Niño and La Niña classes and for El Niño and Neutrality classes. Under the Neutrality class, the aerosol load correlated significantly with evapotranspiration, explaining 20% of the phenomenon. Under the El Niño and La Niña classes, no significant linear correlation was found between aerosol load and evapotranspiration. However, the results showed that for the total data set, there is a positive and significant correlation between aerosol and evapotranspiration. It increases with a quadratic fit, i.e., the aerosol favors evapotranspiration rates up to a certain concentration threshold. The results obtained in this study can help to understand the effects of ENSO events on atmospheric conditions in the southern Amazon basin, in addition to elucidating the role of aerosols in feedback to the water cycle in the region.

Development of a new hydrogel for the prevention of allergic contact dermatitis.

Allergic contact dermatitis (ACD) is the most prevalent occupational disease and the most common form of immunotoxicity in humans. Preventing exposure to the triggering allergens is the mainstay of treatment. However, avoidance is not always possible in an occupational setting. From a pathophysiological point of view, a variety of events are involved in the development of ACD, including the formation of immunogenic complexes following the stable association of the allergen with skin proteins, which is thought to be the molecular initiating event responsible for the development of ACD. Previously, the team identified molecules that exhibited higher antiallergic potential due to their capacity to block the interaction between allergens and skin proteins. These assumptions were the starting point for the design of this work aiming to develop and characterize a new hydrogel containing the active ingredients lysine and N-acetyl cysteine under the premises of quality- and safety- by design. Two factorial plannings were established envisioning the optimization of the hydrogel in terms of mechanical and rheological properties. In vitro release and permeation studies supported its skin surface barrier effect. In addition, the selected hydrogel proved to be safe without causing human skin irritation or skin sensitization.

Deciphering specific miRNAs in brain tumors: a 5-miRNA signature in glioblastoma.

MicroRNAs are endogenous non-coding RNAs with a marked impact on the development and progression of brain tumors. However, they commonly share different expression patterns in other types of tumors, thereby exhibiting lack of tissue specificity. Here, an integrative holistic analysis of microarray data is established for deciphering dysregulated miRNAs in glioblastoma, distinguishing them from eight other CNS tumors. The identification of dysregulated miRNAs was performed in a pool of 176 patients, 118 of which diagnosed with glioblastoma. Dysregulated miRNAs commonly expressed in glioblastoma were then discriminated from those co-expressed in other CNS tumors and further characterized. Overall, 21 miRNAs were found to be commonly dysregulated in glioblastoma. Notwithstanding, 16 miRNAs also exhibited a differential expression in at least one other CNS tumor. The remaining 5, specifically, hsa-miR-21-3p, hsa-miR-338-5p, hsa-miR-485-5p, hsa-miR-491-5p and hsa-miR-1290, were solely associated to glioblastoma. This signature is in-depth characterized, with the spotlight on tumor progression, invasion and patient survival. These five endogenous molecules, differentially expressed in glioblastoma, are thus suggested as potential therapeutic targets, modulating several genes involved in major signalling pathways, including MAPK/ERK, calcium, PI3K/AKT, mTOR and Wnt. In summary, these findings lay a foundation for further research on the expression and function of specific patterns of miRNAs expression in glioblastoma, providing reference for potential novel targets.

A Stepwise Framework for the Systematic Development of Lipid Nanoparticles.

A properly designed nanosystem aims to deliver an optimized concentration of the active pharmaceutical ingredient (API) at the site of action, resulting in a therapeutic response with reduced adverse effects. Due to the vast availability of lipids and surfactants, producing stable lipid dispersions is a double-edged sword: on the one hand, the versatility of composition allows for a refined design and tuning of properties; on the other hand, the complexity of the materials and their physical interactions often result in laborious and time-consuming pre-formulation studies. However, how can they be tailored, and which premises are required for a "right at first time" development? Here, a stepwise framework encompassing the sequential stages of nanoparticle production for disulfiram delivery is presented. Drug in lipid solubility analysis leads to the selection of the most suitable liquid lipids. As for the solid lipid, drug partitioning studies point out the lipids with increased capacity for solubilizing and entrapping disulfiram. The microscopical evaluation of the physical compatibility between liquid and solid lipids further indicates the most promising core compositions. The impact of the outer surfactant layer on the colloidal properties of the nanosystems is evaluated recurring to machine learning algorithms, in particular, hierarchical clustering, principal component analysis, and partial least squares regression. Overall, this work represents a comprehensive systematic approach to nanoparticle formulation studies that serves as a basis for selecting the most suitable excipients that comprise solid lipid nanoparticles and nanostructured lipid carriers.

Time-crystalline eigenstate order on a quantum processor.

Quantum many-body systems display rich phase structure in their low-temperature equilibrium states1. However, much of nature is not in thermal equilibrium. Remarkably, it was recently predicted that out-of-equilibrium systems can exhibit novel dynamical phases2-8 that may otherwise be forbidden by equilibrium thermodynamics, a paradigmatic example being the discrete time crystal (DTC)7,9-15. Concretely, dynamical phases can be defined in periodically driven many-body-localized (MBL) systems via the concept of eigenstate order7,16,17. In eigenstate-ordered MBL phases, the entire many-body spectrum exhibits quantum correlations and long-range order, with characteristic signatures in late-time dynamics from all initial states. It is, however, challenging to experimentally distinguish such stable phases from transient phenomena, or from regimes in which the dynamics of a few select states can mask typical behaviour. Here we implement tunable controlled-phase (CPHASE) gates on an array of superconducting qubits to experimentally observe an MBL-DTC and demonstrate its characteristic spatiotemporal response for generic initial states7,9,10. Our work employs a time-reversal protocol to quantify the impact of external decoherence, and leverages quantum typicality to circumvent the exponential cost of densely sampling the eigenspectrum. Furthermore, we locate the phase transition out of the DTC with an experimental finite-size analysis. These results establish a scalable approach to studying non-equilibrium phases of matter on quantum processors.

Information scrambling in quantum circuits.

Interactions in quantum systems can spread initially localized quantum information into the exponentially many degrees of freedom of the entire system. Understanding this process, known as quantum scrambling, is key to resolving several open questions in physics. Here, by measuring the time-dependent evolution and fluctuation of out-of-time-order correlators, we experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor. We engineer quantum circuits that distinguish operator spreading and operator entanglement and experimentally observe their respective signatures. We show that whereas operator spreading is captured by an efficient classical model, operator entanglement in idealized circuits requires exponentially scaled computational resources to simulate. These results open the path to studying complex and practically relevant physical observables with near-term quantum processors.

Sorting hidden patterns in nanoparticle performance for glioblastoma using machine learning algorithms.

Cationic compounds have been described to readily penetrate cell membranes. Assigning positive charge to nanosystems, e.g. lipid nanoparticles, has been identified as a key feature to promote electrostatic binding and design ligand-based constructs for tumour targeting. However, their intrinsic high cytotoxicity has hampered their biomedical application. This paper seeks to establish which cationic compounds and properties are compelling for interface modulation, in order to improve the design of tumour targeted nanoparticles against glioblastoma. How can intrinsic features (e.g. nature, structure, conformation) shape efficacy outcomes? In the quest for safer alternative cationic compounds, we evaluate the effects of two novel glycerol-based lipids, GLY1 and GLY2, on the architecture and performance of nanostructured lipid carriers (NLCs). These two molecules, composed of two alkylated chains and a glycerol backbone, differ only in their polar head and proved to be efficient in reversing the zeta potential of the nanosystems to positive values. The use of unsupervised and supervised machine learning (ML) techniques unraveled their structural similarities: in spite of their common backbone, GLY1 exhibited a better performance in increasing zeta potential and cytotoxicity, while decreasing particle size. Furthermore, NLCs containing GLY1 showed a favorable hemocompatible profile, as well as an improved uptake by tumour cells. Summing-up, GLY1 circumvents the intrinsic cytotoxicity of a common surfactant, CTAB, is effective at increasing glioblastoma uptake, and exhibits encouraging anticancer activity. Moreover, the use of ML is strongly incited for formulation design and optimization.

On speeding up factoring with quantum SAT solvers.

There have been several efforts to apply quantum SAT solving methods to factor large integers. While these methods may provide insight into quantum SAT solving, to date they have not led to a convincing path to integer factorization that is competitive with the best known classical method, the Number Field Sieve. Many of the techniques tried involved directly encoding multiplication to SAT or an equivalent NP-hard problem and looking for satisfying assignments of the variables representing the prime factors. The main challenge in these cases is that, to compete with the Number Field Sieve, the quantum SAT solver would need to be superpolynomially faster than classical SAT solvers. In this paper the use of SAT solvers is restricted to a smaller task related to factoring: finding smooth numbers, which is an essential step of the Number Field Sieve. We present a SAT circuit that can be given to quantum SAT solvers such as annealers in order to perform this step of factoring. If quantum SAT solvers achieve any asymptotic speedup over classical brute-force search for smooth numbers, then our factoring algorithm is faster than the classical NFS.

Peptide-lipid nanoconstructs act site-specifically towards glioblastoma growth impairment.

Ultra-small nanostructured lipid carriers (usNLCs) have been hypothesized to promote site-specific glioblastoma (GB) drug delivery. Envisioning a multitarget purpose towards tumor cells and microenvironment, a surface-bioconjugated usNLC prototype is herein presented. The comeback of co-delivery by repurposing atorvastatin and curcumin, as complementary therapy, was unveiled and characterized, considering colloidal properties, stability, and drug release behavior. Specifically, the impact of the surface modification of usNLCs with hyaluronic acid (HA) conjugates bearing the cRGDfK and H7k(R2)2 peptides, and folic acid (FA) on GB cells was sequentially evaluated, in terms of cytotoxicity, internalization, uptake mechanism and hemolytic character. As proof-of-principle, the biodistribution, tolerability, and efficacy of the nanocarriers were assessed, the latter in GB-bearing mice through magnetic resonance imaging and spectroscopy. The hierarchical modification of the usNLCs promotes a preferential targeting behavior to the brain, while simultaneously sparing the elimination by clearance organs. Moreover, usNLCs were found to be well tolerated by mice and able to impair tumor growth in an orthotopic xenograft model, whereas for mice administered with the non-encapsulated therapeutic compounds, tumor growth exceeded 181% in the same period. Relevant biomarkers extracted from metabolic spectroscopy were ultimately identified as a potential tumor signature.

Biomimeting ultra-small lipid nanoconstructs for glioblastoma treatment: A computationally guided experimental approach.

Ultra-small nanostructured lipid carriers (usNLCs) are stable, biocompatible and biodegradable colloidal systems, claiming a broad set of advanced features suitable for cancer drug delivery. To unleash their potential in glioblastoma research and therapy, we have developed an usNLC prototype able to co-encapsulate atorvastatin calcium and curcumin, as repurposed drugs previously screened from molecular dynamics simulations. The novelty not only relies on the drug repositioning approach, but also on a robust computational methodology utilized for formulation optimization, under the umbrella of multivariate analysis and full factorial designs. A coating procedure with red blood cell membranes is ultimately hypothesized, aiming at integrating the biomimetic concept into usNLCs for glioblastoma therapeutics. The formulation composition and process parameters, that demonstrated a high-risk level for the final quality and performance of the usNLCs, include the solid:liquid lipid ratio, type and concentration of liquid lipids and surfactants, along with the type of production method. Particles with an average diameter of ca. 50 nm, and a polydispersity index lower than 0.3 were produced, exhibiting high stability, up-scalability, drug protection and sustained co-release properties, meeting the suitable critical quality attributes for intravenous administration. Also, a Taguchi design was successfully applied to optimizing usNLCs as cell membrane-coating technology.

Neuron dynamics on directional surfaces.

Geometrical features play a very important role in neuronal growth and the formation of functional connections between neuronal cells. Here, we analyze the dynamics of axonal growth for neuronal cells cultured on micro-patterned polydimethylsiloxane surfaces. We utilize fluorescence microscopy to image axons, quantify their dynamics, and demonstrate that periodic geometrical patterns impart strong directional bias to neuronal growth. We quantify axonal alignment and present a general stochastic approach that quantitatively describes the dynamics of the growth cones. Neuronal growth is described by a general phenomenological model, based on a simple automatic controller with a closed-loop feedback system. We demonstrate that axonal alignment on these substrates is determined by the surface geometry, and it is quantified by the deterministic part of the stochastic (Langevin and Fokker-Planck) equations. We also show that the axonal alignment with the surface patterns is greatly suppressed by the neuron treatment with Blebbistatin, a chemical compound that inhibits the activity of myosin II. These results give new insight into the role played by the molecular motors and external geometrical cues in guiding axonal growth, and could lead to novel approaches for bioengineering neuronal regeneration platforms.

Development and full validation of an HPLC methodology to quantify atorvastatin and curcumin after their intranasal co-delivery to mice.

There is increasing interest in atorvastatin and curcumin owing to their potential anticancer activity. A new, accurate and sensitive HPLC method was developed, for the first time, to simultaneously quantify atorvastatin and curcumin in mouse plasma and brain, liver, lung and spleen tissues following protein precipitation sample preparation. The chromatographic separation was achieved in 13 min on a C18 column, at 35°C, using a mobile phase composed of acetonitrile-methanol-2% (v/v) acetic acid (37.5:2.5:60, v/v/v) at a flow rate of 1.0 mL/min. The detection of analytes and internal standard was carried out at 247, 425 and 250 nm, respectively. According to international guidelines, the method was shown to be selective, with lower limits of quantification ranging from 10 to 500 ng/mL for curcumin, and from 100 to 600 ng/mL for atorvastatin, linear over a wide concentration range (r2 ≥ 0.9971) and with acceptable accuracy (bias ± 12.29%) and precision (coefficient of variation ≤13.15%). The analytes were reproducibly recovered at a percentage >81.10% and demonstrated to be stable under various experimental conditions in all biological matrices. This method can be easily applied to in vivo biodistribution studies related to the intranasal administration of atorvastatin and curcumin, separately or simultaneously.

Anomalous diffusion for neuronal growth on surfaces with controlled geometries.

Geometrical cues are known to play a very important role in neuronal growth and the formation of neuronal networks. Here, we present a detailed analysis of axonal growth and dynamics for neuronal cells cultured on patterned polydimethylsiloxane surfaces. We use fluorescence microscopy to image neurons, quantify their dynamics, and demonstrate that the substrate geometrical patterns cause strong directional alignment of axons. We quantify axonal growth and report a general stochastic approach that quantitatively describes the motion of growth cones. The growth cone dynamics is described by Langevin and Fokker-Planck equations with both deterministic and stochastic contributions. We show that the deterministic terms contain both the angular and speed dependence of axonal growth, and that these two contributions can be separated. Growth alignment is determined by surface geometry, and it is quantified by the deterministic part of the Langevin equation. We combine experimental data with theoretical analysis to measure the key parameters of the growth cone motion: speed and angular distributions, correlation functions, diffusion coefficients, characteristics speeds and damping coefficients. We demonstrate that axonal dynamics displays a cross-over from Brownian motion (Ornstein-Uhlenbeck process) at earlier times to anomalous dynamics (superdiffusion) at later times. The superdiffusive regime is characterized by non-Gaussian speed distributions and power law dependence of the axonal mean square length and the velocity correlation functions. These results demonstrate the importance of geometrical cues in guiding axonal growth, and could lead to new methods for bioengineering novel substrates for controlling neuronal growth and regeneration.

Role of geometrical cues in neuronal growth.

Geometrical cues play an essential role in neuronal growth. Here, we quantify axonal growth on surfaces with controlled geometries and report a general stochastic approach that quantitatively describes the motion of growth cones. We show that axons display a strong directional alignment on micropatterned surfaces when the periodicity of the patterns matches the dimension of the growth cone. The growth cone dynamics on surfaces with uniform geometry is described by a linear Langevin equation with both deterministic and stochastic contributions. In contrast, axonal growth on surfaces with periodic patterns is characterized by a system of two generalized Langevin equations with both linear and quadratic velocity dependence and stochastic noise. We combine experimental data with theoretical analysis to measure the key parameters of the growth cone motion: angular distributions, correlation functions, diffusion coefficients, characteristics speeds, and damping coefficients. We demonstrate that axonal dynamics displays a crossover from an Ornstein-Uhlenbeck process to a nonlinear stochastic regime when the geometrical periodicity of the pattern approaches the linear dimension of the growth cone. Growth alignment is determined by surface geometry, which is fully quantified by the deterministic part of the Langevin equation. These results provide insight into the role of curvature sensing proteins and their interactions with geometrical cues.

Repurposing drugs for glioblastoma: From bench to bedside.

Glioblastoma multiforme is the most common, aggressive and lethal type of brain tumor. It is a stage IV cancer disease with a poor prognosis, as the current therapeutic options (surgery, radiotherapy and chemotherapy) are not able to eradicate tumor cells. The approach to treat glioblastoma has not suffered major changes over the last decade and temozolomide (TMZ) remains the mainstay for chemotherapy. However, resistance mechanisms to TMZ and other chemotherapeutic agents are becoming more frequent. The lack of effective options is a reality that may be counterbalanced by repositioning known and commonly used drugs for other diseases. This approach takes into consideration the available pharmacokinetic, pharmacodynamic, toxicity and safety data, and allows a much faster and less expensive drug and product development process. In this review, an extensive literature search is conducted aiming to list drugs with repurposing usage, based on their preferential damage in glioblastoma cells through various mechanisms. Some of these drugs have already entered clinical trials, exhibiting favorable outcomes, which sparks their potential application in glioblastoma treatment.

Hydrogel-Based Drug Delivery Nanosystems for the Treatment of Brain Tumors.

Chemotherapy is commonly associated with limited effectiveness and unwanted side effects in normal cells and tissues, due to the lack of specificity of therapeutic agents to cancer cells when systemically administered. In brain tumors, the existence of both physiological barriers that protect tumor cells and complex resistance mechanisms to anticancer drugs are additional obstacles that hamper a successful course of chemotherapy, thus resulting in high treatment failure rates. Several potential surrogate therapies have been developed so far. In this context, hydrogel-based systems incorporating nanostructured drug delivery systems (DDS) and hydrogel nanoparticles, also denoted nanogels, have arisen as a more effective and safer strategy than conventional chemotherapeutic regimens. The former, as a local delivery approach, have the ability to confine the release of anticancer drugs near tumor cells over a long period of time, without compromising healthy cells and tissues. Yet, the latter may be systemically administered and provide both loading and targeting properties in their own framework, thus identifying and efficiently killing tumor cells. Overall, this review focuses on the application of hydrogel matrices containing nanostructured DDS and hydrogel nanoparticles as potential and promising strategies for the treatment and diagnosis of glioblastoma and other types of brain cancer. Some aspects pertaining to computational studies are finally addressed.