BLINDED VS ULTRASOUND-GUIDED CORTICOSTEROID INJECTIONS for the TREATMENT of the GREATER TROCHANTERIC PAIN SYNDROME (SDPT): A RANDOMIZED CONTROLLED TRIAL

Universidade Federal de São Paulo, Div Rheumatol, São Paulo, BrazilUniversidade Federal de São Paulo, Div Rheumatol, São Paulo, BrazilWeb of Scienc

Local fluctuations in quantum critical metals

We show that spatially local, yet low-energy, fluctuations can play an essential role in the physics of strongly correlated electron systems tuned to a quantum critical point. A detailed microscopic analysis of the Kondo lattice model is carried out within an extended dynamical mean-field approach. The correlation functions for the lattice model are calculated through a self-consistent Bose-Fermi Kondo problem, in which a local moment is coupled both to a fermionic bath and to a bosonic bath (a fluctuating magnetic field). A renormalization-group treatment of this impurity problem--perturbative in $\epsilon=1-\gamma$, where $\gamma$ is an exponent characterizing the spectrum of the bosonic bath--shows that competition between the two couplings can drive the local-moment fluctuations critical. As a result, two distinct types of quantum critical point emerge in the Kondo lattice, one being of the usual spin-density-wave type, the other ``locally critical.'' Near the locally critical point, the dynamical spin susceptibility exhibits $\omega/T$ scaling with a fractional exponent. While the spin-density-wave critical point is Gaussian, the locally critical point is an interacting fixed point at which long-wavelength and spatially local critical modes coexist. A Ginzburg-Landau description for the locally critical point is discussed. It is argued that these results are robust, that local criticality provides a natural description of the quantum critical behavior seen in a number of heavy-fermion metals, and that this picture may also be relevant to other strongly correlated metals.Comment: 20 pages, 12 figures; typos in figure 3 and in the main text corrected, version as publishe

Aptamer-based field-effect biosensor for tenofovir detection

During medical treatment it is critical to maintain the circulatory concentration of drugs within their therapeutic range. A novel biosensor is presented in this work to address the lack of a reliable point-of-care drug monitoring system in the market. The biosensor incorporates high selectivity and sensitivity by integrating aptamers as the recognition element and field-effect transistors as the signal transducer. The drug tenofovir was used as a model small molecule. The biointerface of the sensor is a binary self-assembled monolayer of specific thiolated aptamer and 6-mercapto-1-hexanol (MCH), whose ratio was optimized by electrochemical impedance spectroscopy measurements to enhance the sensitivity towards the specific target. Surface plasmon resonance, performed under different buffer conditions, shows optimum specific and little non-specific binding in phosphate buffered saline. The dose-response behavior of the field-effect biosensor presents a linear range between 1 nM and 100 nM of tenofovir and a limit of detection of 1.2 nM. Two non-specific drugs and one non-specific aptamer, tested as stringent control candidates, caused negligible responses. The applications were successfully extended to the detection of the drug in human serum. As demonstrated by impedance measurements, the aptamer-based sensors can be used for real-time drug monitoring

Statistical learning leads to persistent memory: evidence for one-year consolidation

Statistical learning is a robust mechanism of the brain that enables the extraction of environmental patterns, which is crucial in perceptual and cognitive domains. However, the dynamical change of processes underlying long-term statistical memory formation has not been tested in an appropriately controlled design. Here we show that a memory trace acquired by statistical learning is resistant to inference as well as to forgetting after one year. Participants performed a statistical learning task and were retested one year later without further practice. The acquired statistical knowledge was resistant to interference, since after one year, participants showed similar memory performance on the previously practiced statistical structure after being tested with a new statistical structure. These results could be key to understand the stability of long-term statistical knowledge

Hacking into bacterial biofilms: a new therapeutic challenge

Microbiologists have extensively worked during the past decade on a particular phase of the bacterial cell cycle known as biofilm, in which single-celled individuals gather together to form a sedentary but dynamic community within a complex structure, displaying spatial and functional heterogeneity. In response to the perception of environmental signals by sensing systems, appropriate responses are triggered, leading to biofilm formation. This process involves various molecular systems that enable bacteria to identify appropriate surfaces on which to anchor themselves, to stick to those surfaces and to each other, to construct multicellular communities several hundreds of micrometers thick, and to detach from the community. The biofilm microbial community is a unique, highly competitive, and crowded environment facilitating microevolutionary processes and horizontal gene transfer between distantly related microorganisms. It is governed by social rules, based on the production and use of "public" goods, with actors and recipients. Biofilms constitute a unique shield against external aggressions, including drug treatment and immune reactions. Biofilm-associated infections in humans have therefore generated major problems for the diagnosis and treatment of diseases. Improvements in our understanding of biofilms have led to innovative research designed to interfere with this process

Proteases of haematophagous arthropod vectors are involved in blood-feeding, yolk formation and immunity : a review

Ticks, triatomines, mosquitoes and sand flies comprise a large number of haematophagous arthropods considered vectors of human infectious diseases. While consuming blood to obtain the nutrients necessary to carry on life functions, these insects can transmit pathogenic microorganisms to the vertebrate host. Among the molecules related to the blood-feeding habit, proteases play an essential role. In this review, we provide a panorama of proteases from arthropod vectors involved in haematophagy, in digestion, in egg development and in immunity. As these molecules act in central biological processes, proteases from haematophagous vectors of infectious diseases may influence vector competence to transmit pathogens to their prey, and thus could be valuable targets for vectorial control

Killing by type VI secretion drives genetic phase separation and correlates with increased cooperation

By nature of their small size, dense growth and frequent need for extracellular metabolism, microbes face persistent public goods dilemmas. Genetic assortment is the only general solution stabilizing cooperation, but all known mechanisms structuring microbial populations depend on the availability of free space, an often unrealistic constraint. Here we describe a class of self-organization that operates within densely packed bacterial populations. Through mathematical modelling and experiments with Vibrio cholerae, we show how killing adjacent competitors via the Type VI secretion system (T6SS) precipitates phase separation via the ‘Model A' universality class of order-disorder transition mediated by killing. We mathematically demonstrate that T6SS-mediated killing should favour the evolution of public goods cooperation, and empirically support this prediction using a phylogenetic comparative analysis. This work illustrates the twin role played by the T6SS, dealing death to local competitors while simultaneously creating conditions potentially favouring the evolution of cooperation with kin

Advancing microbial sciences by individual-based modelling

Remarkable technological advances have revealed ever more properties and behaviours of individual microorganisms, but the novel data generated by these techniques have not yet been fully exploited. In this Opinion article, we explain how individual-based models (IBMs) can be constructed based on the findings of such techniques and how they help to explore competitive and cooperative microbial interactions. Furthermore, we describe how IBMs have provided insights into self-organized spatial patterns from biofilms to the oceans of the world, phage-CRISPR dynamics and other emergent phenomena. Finally, we discuss how combining individual-based observations with IBMs can advance our understanding at both the individual and population levels, leading to the new approach of microbial individual-based ecology (μIBE)