A meta-analysis on the role of sonication in the diagnosis of cardiac implantable electronic device-related infections.

Introduction: One of the biggest obstacles in diagnosing Implant-Associated Infections is the lack of infection criteria and standardized diagnostic methods. These infections present a wide range of symptoms, and their diagnosis can be hampered by the formation of microbial biofilms on the surface of implants. This study aimed to provide insight into the performance of sonication in the diagnosis of infections associated with Cardiac Implantable Electronic Devices, to help define a consensus on the algorithm for the microbial diagnosis of these infections. Methods: We carried out a systematic review with meta-analysis. The PRISMA methodology guidelines were followed, and an advanced search was carried out in PubMed and Web of Science, which enabled 8 articles to be included in the review, in which a meta-analysis was also carried out. QUADAS-2 was used to assess the risk of bias and effect measures were calculated to assess publication bias. Results: The overall sensitivity of the method was 0.823 (95% CI: 0.682-0.910) and the specificity was 0.632 (95% CI: 0.506-0.743). Discussion: These results suggest that sonication may offer advantages in diagnosing these infections. However, it is essential to approach these findings carefully and take into account the recommendations provided in the EHRA 2019 guidelines. This study highlights the importance of more effective diagnostic approaches for implantable medical device-associated infections to improve the quality of treatment and minimize the risks associated with these challenging medical conditions.

Health care expenses impact on the disability-adjusted life years in non-communicable diseases in the European Union.

Background: Non-communicable diseases are a global health problem. The metric Disability-Adjusted Life Years was developed to measure its impact on health systems. This metric makes it possible to understand a disease's burden, towards defining healthcare policies. This research analysed the effect of healthcare expenditures in the evolution of disability-adjusted life years for non-communicable diseases in the European Union between 2000 and 2019. Methods: Data were collected for all 27 European Union countries from Global Burden of Disease 2019, Global Health Expenditure, and EUROSTAT databases. Econometric panel data models were used to assess the impact of healthcare expenses on the disability-adjusted life years. Only models with a coefficient of determination equal to or higher than 10% were analysed. Results: There was a decrease in the non-communicable diseases with the highest disability-adjusted life years: cardiovascular diseases (-2,952 years/105 inhabitants) and neoplasms (-618 years/105 inhabitants). Health expenditure significantly decreased disability-adjusted life years for all analysed diseases (p < 0.01) unless for musculoskeletal disorders. Private health expenditure did not show a significant effect on neurological and musculoskeletal disorders (p > 0.05) whereas public health expenditure did not significantly influence skin and subcutaneous diseases (p > 0.05). Conclusion: Health expenditure have proved to be effective in the reduction of several diseases. However, some categories such as musculoskeletal and mental disorders must be a priority for health policies in the future since, despite their low mortality, they can present high morbidity and disability.

Evaluation of cell membrane-derived nanoparticles as therapeutic carriers for pancreatic ductal adenocarcinoma using an in vitro tumour stroma model.

Here, we fabricated nanoparticles made solely from the membrane of cells found in the pancreatic tumour's microenvironment (TME), like the human MiaPaCa-2 cells and M2-polarized macrophages. The cell membrane-derived nanoparticles (CMNPs) deriving from the MiaPaCa-2 cells (MPC2-CMNPs) were loaded with the chemotherapeutic drug paclitaxel (PTX), and the CMNPs deriving from M2-polarized macrophages (M2-CMNPs) were loaded with the colony-stimulating factor 1 receptor inhibitor, pexidartinib (PXDB). The CMNPs' thorough morphological and physicochemical characterisation was followed by an in-depth study of their targeting ability and the endocytosis pathway involved during their internalisation. An in vitro model of the desmoplastic stroma comprising cancer-associated fibroblast-mimicking cells and M2-polarized macrophages was also developed. The model was characterised by collagen and α-smooth muscle actin (α-SMA) expression (overexpressed in desmoplasia) and was used to assess the CMNPs' ability to cross the stroma and target the tumour cells. Moreover, we assessed the effect of PXDB-loaded M2-CMNPs on the expression of M1 (CD80/CD86) and M2 (CD206/CD209) polarisation markers on activated macrophages. Finally, we evaluated the PTX and PXDB-loaded CMNPs' effect on the viability of all the used TME cell lines alone or in combination. Overall, this pilot study showed the potential of the CMNPs to cross an in vitro stroma model and act synergistically to treat PDAC.

Drosophila motor neuron boutons remodel through membrane blebbing coupled with muscle contraction.

Wired neurons form new presynaptic boutons in response to increased synaptic activity, however the mechanism(s) by which this occurs remains uncertain. Drosophila motor neurons (MNs) have clearly discernible boutons that display robust structural plasticity, being therefore an ideal system in which to study activity-dependent bouton genesis. Here, we show that in response to depolarization and in resting conditions, MNs form new boutons by membrane blebbing, a pressure-driven mechanism that occurs in 3-D cell migration, but to our knowledge not previously described to occur in neurons. Accordingly, F-actin is decreased in boutons during outgrowth, and non-muscle myosin-II is dynamically recruited to newly formed boutons. Furthermore, muscle contraction plays a mechanical role, which we hypothesize promotes bouton addition by increasing MN confinement. Overall, we identified a mechanism by which established circuits form new boutons allowing their structural expansion and plasticity, using trans-synaptic physical forces as the main driving force.

Blastocyst Cell Number and Allocation Affect the Developmental Potential and Transcriptome of Bovine Somatic Cell Nuclear Transfer Embryos.

Cloning cattle using somatic cell nuclear transfer (SCNT) is inefficient. Although the rate of development of SCNT embryos in vitro is similar to that of fertilized embryos, most fail to develop into healthy calves. In this study, we aimed to identify developmentally competent embryos according to blastocyst cell composition and perform transcriptome analysis of single embryos. Transgenic SCNT embryos expressing nuclear-localized HcRed gene at day 7 of development were imaged by confocal microscopy for cell counting and individually transferred to recipient heifers. Pregnancy rates were determined by ultrasonography. Embryos capable of establishing pregnancy by day 35 had an average of 117 ± 6 total cells, whereas embryos with an average of 128 ± 5 cells did not establish pregnancy (P < 0.05). A lesser average number of 41 ± 3 cells in the inner cell mass (ICM) also resulted in pregnancies (<0.05) than a greater number of 48 ± 2 cells in the ICM. Single embryos were then subjected to RNA sequencing for transcriptome analysis. Using weighted gene coexpression network analysis, we identified clusters of genes in which gene expression correlated with the number of total cells or ICM cells. Gene ontology analysis of these clusters revealed enriched biological processes in coenzyme metabolic process, intracellular signaling cascade, and glucose catabolic process, among others. We concluded that SCNT embryos with fewer total and ICM cell numbers resulted in greater pregnancy establishment rates and that these differences are reflected in the transcriptome of such embryos.

Demand for emergency services during the COVID-19 pandemic and disease burden: a case study in Portugal.

Background: The COVID-19 pandemic brought changes in the pattern of care use. A significant increase in the volume of emergencies was expected. However, a significant decrease was observed worldwide. Methods: An observational, analytical and cross-sectional study of all records of emergency episodes of patients aged 18 years or older admitted to the emergency services of the University of Porto Hospital Centre (2018-2022) were analysed. Results: During the pandemic, a significant reduction in emergency episode admissions (up to 40% during lockdowns), an increase in pre-emergency services, and discharges from Infectious Diseases and Internal Medicine was observed. The discharges from General Practice and General Practice and Family Medicine were residual. Conclusion: The lower use and type of use of emergency services during the COVID-19 pandemic had a negative impact on the disease burden. This could be prevented in future pandemics through the development of strategies to promote confidence in the use of health resources and establishing contingency plans for virtual assistance.

Neonatal Fc receptor-targeted lignin-encapsulated porous silicon nanoparticles for enhanced cellular interactions and insulin permeation across the intestinal epithelium.

Oral insulin delivery could change the life of millions of diabetic patients as an effective, safe, easy-to-use, and affordable alternative to insulin injections, known by an inherently thwarted patient compliance. Here, we designed a multistage nanoparticle (NP) system capable of circumventing the biological barriers that lead to poor drug absorption and bioavailability after oral administration. The nanosystem consists of an insulin-loaded porous silicon NP encapsulated into a pH-responsive lignin matrix, and surface-functionalized with the Fc fragment of immunoglobulin G, which acts as a targeting ligand for the neonatal Fc receptor (FcRn). The developed NPs presented small size (211 ± 1 nm) and narrow size distribution. The NPs remained intact in stomach and intestinal pH conditions, releasing the drug exclusively at pH 7.4, which mimics blood circulation. This formulation showed to be highly cytocompatible, and surface plasmon resonance studies demonstrated that FcRn-targeted NPs present higher capacity to interact and being internalized by the Caco-2 cells, which express FcRn, as demonstrated by Western blot. Ultimately, in vitro permeability studies showed that Fc-functionalized NPs induced an increase in the amount of insulin that permeated across a Caco-2/HT29-MTX co-culture model, showing apparent permeability coefficients (P app ) of 2.37 × 10-6 cm/s, over the 1.66 × 10-6 cm/s observed for their non-functionalized counterparts. Overall, these results demonstrate the potential of these NPs for oral delivery of anti-diabetic drugs.

Neural networks for parameter estimation in microstructural MRI: Application to a diffusion-relaxation model of white matter.

Specific features of white matter microstructure can be investigated by using biophysical models to interpret relaxation-diffusion MRI brain data. Although more intricate models have the potential to reveal more details of the tissue, they also incur time-consuming parameter estimation that may converge to inaccurate solutions due to a prevalence of local minima in a degenerate fitting landscape. Machine-learning fitting algorithms have been proposed to accelerate the parameter estimation and increase the robustness of the attained estimates. So far, learning-based fitting approaches have been restricted to microstructural models with a reduced number of independent model parameters where dense sets of training data are easy to generate. Moreover, the degree to which machine learning can alleviate the degeneracy problem is poorly understood. For conventional least-squares solvers, it has been shown that degeneracy can be avoided by acquisition with optimized relaxation-diffusion-correlation protocols that include tensor-valued diffusion encoding. Whether machine-learning techniques can offset these acquisition requirements remains to be tested. In this work, we employ artificial neural networks to vastly accelerate the parameter estimation for a recently introduced relaxation-diffusion model of white matter microstructure. We also develop strategies for assessing the accuracy and sensitivity of function fitting networks and use those strategies to explore the impact of the acquisition protocol. The developed learning-based fitting pipelines were tested on relaxation-diffusion data acquired with optimal and sub-optimal acquisition protocols. Networks trained with an optimized protocol were observed to provide accurate parameter estimates within short computational times. Comparing neural networks and least-squares solvers, we found the performance of the former to be less affected by sub-optimal protocols; however, model fitting networks were still susceptible to degeneracy issues and their use could not fully replace a careful design of the acquisition protocol.

Combined diffusion-relaxometry microstructure imaging: Current status and future prospects.

Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T1 , T2 , and T2∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.

Requirements for Animal Experiments: Problems and Challenges.

In vivo models remain a principle screening tool in the drug discovery pipeline. Here, the challenges associated with the need for animal experiments, as well as their impact on research, individual/societal, and economic contexts are discussed. A number of alternatives that, with further development, optimization, and investment, may replace animal experiments are also revised.

Computing and visualising intra-voxel orientation-specific relaxation-diffusion features in the human brain.

Diffusion MRI techniques are used widely to study the characteristics of the human brain connectome in vivo. However, to resolve and characterise white matter (WM) fibres in heterogeneous MRI voxels remains a challenging problem typically approached with signal models that rely on prior information and constraints. We have recently introduced a 5D relaxation-diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo-times to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation-diffusion distributions where contributions from different sub-voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre-specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation-specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along with distinct fibre bundles. If combined with fibre-tracking algorithms, the methodology presented in this work has potential for increasing the depth of characterisation of microstructural properties along individual WM pathways.

Diffusion tensor distribution imaging of an in vivo mouse brain at ultrahigh magnetic field by spatiotemporal encoding.

Diffusion tensor distribution (DTD) imaging builds on principles from diffusion, solid-state and low-field NMR spectroscopies, to quantify the contents of heterogeneous voxels as nonparametric distributions, with tensor "size", "shape" and orientation having direct relations to corresponding microstructural properties of biological tissues. The approach requires the acquisition of multiple images as a function of the magnitude, shape and direction of the diffusion-encoding gradients, leading to long acquisition times unless fast image read-out techniques like EPI are employed. While in previous in vivo human brain studies performed at 3 T this proved a viable option, porting these measurements to very high magnetic fields and/or to heterogeneous organs induces B0 - and B1 -inhomogeneity artifacts that challenge the limits of EPI. To overcome such challenges, we demonstrate here that high spatial resolution DTD of mouse brain can be carried out at 15.2 T with a surface-cryoprobe, by relying on SPatiotemporal ENcoding (SPEN) imaging sequences. These new acquisition and data-processing protocols are demonstrated with measurements on in vivo mouse brain, and validated with synthetic phantoms designed to mimic the diffusion properties of white matter, gray matter and cerebrospinal fluid. While still in need of full extensions to 3D mappings and of scanning additional animals to extract more general physiological conclusions, this work represents another step towards the model-free, noninvasive in vivo characterization of tissue microstructure and heterogeneity in animal models, at ≈0.1 mm resolutions.

All-in-one microfluidic assembly of insulin-loaded pH-responsive nano-in-microparticles for oral insulin delivery.

Here, a continuous two-step glass-capillary microfluidic technique to produce a multistage oral delivery system is reported. Insulin is successfully encapsulated into liposomes, which are coated with chitosan to improve their mucoadhesion. The encapsulation in an enteric polymer offers protection from the harsh gastric conditions. Insulin permeability is enhanced across an intestinal monolayer.

Accuracy and precision of statistical descriptors obtained from multidimensional diffusion signal inversion algorithms.

In biological tissues, typical MRI voxels comprise multiple microscopic environments, the local organization of which can be captured by microscopic diffusion tensors. The measured diffusion MRI signal can, therefore, be written as the multidimensional Laplace transform of an intravoxel diffusion tensor distribution (DTD). Tensor-valued diffusion encoding schemes have been designed to probe specific features of the DTD, and several algorithms have been introduced to invert such data and estimate statistical descriptors of the DTD, such as the mean diffusivity, the variance of isotropic diffusivities, and the mean squared diffusion anisotropy. However, the accuracy and precision of these estimations have not been assessed systematically and compared across methods. In this article, we perform and compare such estimations in silico for a one-dimensional Gamma fit, a generalized two-term cumulant approach, and two-dimensional and four-dimensional Monte-Carlo-based inversion techniques, using a clinically feasible tensor-valued acquisition scheme. In particular, we compare their performance at different signal-to-noise ratios (SNRs) for voxel contents varying in terms of the aforementioned statistical descriptors, orientational order, and fractions of isotropic and anisotropic components. We find that all inversion techniques share similar precision (except for a lower precision of the two-dimensional Monte Carlo inversion) but differ in terms of accuracy. While the Gamma fit exhibits infinite-SNR biases when the signal deviates strongly from monoexponentiality and is unaffected by orientational order, the generalized cumulant approach shows infinite-SNR biases when this deviation originates from the variance in isotropic diffusivities or from the low orientational order of anisotropic diffusion components. The two-dimensional Monte Carlo inversion shows remarkable accuracy in all systems studied, given that the acquisition scheme possesses enough directions to yield a rotationally invariant powder average. The four-dimensional Monte Carlo inversion presents no infinite-SNR bias, but suffers significantly from noise in the data, while preserving good contrast in most systems investigated.

Peripheral axonal ensheathment is regulated by RalA GTPase and the exocyst complex.

Axon ensheathment is fundamental for fast impulse conduction and the normal physiological functioning of the nervous system. Defects in axonal insulation lead to debilitating conditions, but, despite its importance, the molecular players responsible are poorly defined. Here, we identify RalA GTPase as a key player in axon ensheathment in Drosophila larval peripheral nerves. We demonstrate through genetic analysis that RalA action through the exocyst complex is required in wrapping glial cells to regulate their growth and development. We suggest that the RalA-exocyst pathway controls the targeting of secretory vesicles for membrane growth or for the secretion of a wrapping glia-derived factor that itself regulates growth. In summary, our findings provide a new molecular understanding of the process by which axons are ensheathed in vivo, a process that is crucial for normal neuronal function.

Dual-peptide functionalized acetalated dextran-based nanoparticles for sequential targeting of macrophages during myocardial infarction.

The advent of nanomedicine has recently started to innovate the treatment of cardiovascular diseases, in particular myocardial infarction. Although current approaches are very promising, there is still an urgent need for advanced targeting strategies. In this work, the exploitation of macrophage recruitment is proposed as a novel and synergistic approach to improve the addressability of the infarcted myocardium achieved by current peptide-based heart targeting strategies. For this purpose, an acetalated dextran-based nanosystem is designed and successfully functionalized with two different peptides, atrial natriuretic peptide (ANP) and linTT1, which target, respectively, cardiac cells and macrophages associated with atherosclerotic plaques. The biocompatibility of the nanocarrier is screened on both macrophage cell lines and primary macrophages, showing high safety, in particular after functionalization of the nanoparticles' surface. Furthermore, the system shows higher association versus uptake ratio towards M2-like macrophages (approximately 2-fold and 6-fold increase in murine and human primary M2-like macrophages, respectively, compared to M1-like). Overall, the results demonstrate that the nanosystem has potential to exploit the "hitchhike" effect on M2-like macrophages and potentially improve, in a dual targeting strategy, the ability of the ANP peptide to target infarcted heart.

Transferring principles of solid-state and Laplace NMR to the field of in vivo brain MRI.

Magnetic resonance imaging (MRI) is the primary method for noninvasive investigations of the human brain in health, disease, and development but yields data that are difficult to interpret whenever the millimeter-scale voxels contain multiple microscopic tissue environments with different chemical and structural properties. We propose a novel MRI framework to quantify the microscopic heterogeneity of the living human brain as spatially resolved five-dimensional relaxation-diffusion distributions by augmenting a conventional diffusion-weighted imaging sequence with signal encoding principles from multidimensional solid-state nuclear magnetic resonance (NMR) spectroscopy, relaxation-diffusion correlation methods from Laplace NMR of porous media, and Monte Carlo data inversion. The high dimensionality of the distribution space allows resolution of multiple microscopic environments within each heterogeneous voxel as well as their individual characterization with novel statistical measures that combine the chemical sensitivity of the relaxation rates with the link between microstructure and the anisotropic diffusivity of tissue water. The proposed framework is demonstrated on a healthy volunteer using both exhaustive and clinically viable acquisition protocols.

Microfluidic Nanoassembly of Bioengineered Chitosan-Modified FcRn-Targeted Porous Silicon Nanoparticles @ Hypromellose Acetate Succinate for Oral Delivery of Antidiabetic Peptides.

Microfluidics technology is emerging as a promising strategy in improving the oral delivery of proteins and peptides. Herein, a multistage drug delivery system is proposed as a step forward in the development of noninvasive therapies. Undecylenic acid-modified thermally hydrocarbonized porous silicon (UnPSi) nanoparticles (NPs) were functionalized with the Fc fragment of immunoglobulin G for targeting purposes. Glucagon-like peptide-1 (GLP-1) was loaded into the NPs as a model antidiabetic drug. Fc-UnPSi NPs were coated with mucoadhesive chitosan and ultimately entrapped into a polymeric matrix with pH-responsive properties by microfluidic nanoprecipitation. The final formulation showed a controlled and narrow size distribution. The pH-responsive matrix remained intact in acidic conditions, dissolving only in intestinal pH, resulting in a sustained release of the payload. The NPs presented high cytocompatibility and increased levels of interaction with intestinal cells when functionalized with the Fc fragment, which was supported by the validation of the Fc-fragment integrity after conjugation to the NPs. Finally, the Fc-conjugated NPs showed augmented GLP-1 permeability in an intestinal in vitro model. These results highlight the potential of microfluidics as an advanced technique for the preparation of multistage platforms for oral administration. Moreover, this study provides new insights on the potential of the Fc receptor transcytotic capacity for the development of targeted therapies.

Sexual compulsivity, anxiety, depression, and sexual risk behavior among treatment-seeking men in São Paulo, Brazil

Objective: There is a lack of studies on negative mood states and sexual risk behavior in men of all sexual orientations who seek treatment for excessive sexual behavior (ESB). We aim to examine sexual compulsivity (SC), anxiety, depression, and sexual risk behavior in a treatment-seeking sample of men and controls. Methods: We enrolled 88 (37 [42%] gay or bisexual and 51 [58%] heterosexual) ESB outpatients and 64 controls. Assessments included the Sexual Compulsivity Scale (SCS), the Beck Anxiety Inventory (BAI), the Beck Depression Inventory (BDI), and sexual risk behaviors. Results: Compared to controls, ESB outpatients showed increased SC, anxiety, and depression, which were correlated. Regarding sex with casual partners, ESB outpatients reported more sexual intercourse, a greater number of partners, more anal intercourse, and unprotected anal intercourse. Anxiety, depression, and SC were associated with protected vaginal intercourse with a main partner, whereas they were associated with unprotected anal intercourse with a casual partner. Depression was associated with unprotected vaginal intercourse with a casual partner. Condomless anal intercourse was predicted by SC and was also reported by the heterosexual ESB outpatients (36%). Conclusion: The data contribute to the field by providing information on men of all sexual orientations who are searching for mental healthcare. The connections among these psychopathological factors and sexual risk behavior have implications for public health, clinicians, and research.

Profiling Maternal mRNA Translation During Oocyte Development.

With the progress in our understanding of germ cell development, there is an emerging need to investigate the mechanisms of mRNA translation functioning in these cells. Indeed, posttranscriptional regulations of gene expression drive the most important transitions of the germ cell life cycle. Here we describe a strategy to measure mRNA translation in the oocyte, taking advantage of an approach originally developed to identify the transcriptome of a subgroup of cells in a complex cell mixture. This technique takes advantage of the "RiboTag" approach to express an HA-tag on the large ribosomal subunit of the ribosomes in the oocyte. Immunoprecipitation of the extracts followed by qPCR or RNAseq is used to identify mRNAs actively translated.