Overview of nanotherapeutics for bacterial infections

The rapid emergence of antibiotic-resistant bacteria poses one of the greatest threats to public health as conventional therapies and commercial antibiotics are dropping their effectiveness. In the race for the discovery of new strategies to prevent a scenario in which commonplace infections prove fatal, nanomaterials stand in the limelight due to their unique physicochemical properties that can be seized to overcome common resistance mechanisms. Nanoparticle-driven drug delivery emerges as a beacon of hope, shielding antibiotics from enzymatic degradation, enhancing their targeted delivery to afflicted sites in therapeutically potent concentrations, and minimising undesired side effects. Drugs can either be entrapped or chemically conjugated to nanoparticles, with the latter offering a myriad of possibilities in orchestrating spatiotemporal controlled release of the therapeutic payload. Meanwhile, nanomaterials can also display intrinsic antimicrobial properties, either by direct disruption of bacterial cell membranes (e.g., nanoparticles functionalised with cationic groups) or by instigating the generation of ROS (e.g., metallic nanoparticles). The clinical implementation of nanotherapeutics still faces considerable challenges, mainly related with their complex chemistry and polydispersity, which poses difficulties related to cost-effectiveness, scale-up, and Chemistry, Manufacturing, and Controls (CMC) management. Still, the development of computational approaches allowing a better understanding of nano-bio interactions and predictive biodistribution, pharmacokinetics, and toxicology, along with a harmonised international regulatory framework, is expected to facilitate clinical translation in the near future

µSpikeHunter: An advanced computational tool for the analysis of neuronal communication and action potential propagation in microfluidic platforms

Abstract Understanding neuronal communication is fundamental in neuroscience, but there are few methodologies offering detailed analysis for well-controlled conditions. By interfacing microElectrode arrays with microFluidics (μEF devices), it is possible to compartmentalize neuronal cultures with a specified alignment of axons and microelectrodes. This setup allows the extracellular recording of spike propagation with a high signal-to-noise ratio over the course of several weeks. Addressing these μEF devices, we developed an advanced yet easy-to-use publically available computational tool, μSpikeHunter, which provides a detailed quantification of several communication-related properties such as propagation velocity, conduction failure, spike timings, and coding mechanisms. The combination of μEF devices and μSpikeHunter can be used in the context of standard neuronal cultures or with co-culture configurations where, for example, communication between sensory neurons and other cell types is monitored and assessed. The ability to analyze axonal signals (in a user-friendly, time-efficient, high-throughput manner) opens the door to new approaches in studies of peripheral innervation, neural coding, and neuroregeneration, among many others. We demonstrate the use of μSpikeHunter in dorsal root ganglion neurons where we analyze the presence of both anterograde and retrograde signals in μEF devices. A fully functional version of µSpikeHunter is publically available for download from https://github.com/uSpikeHunter

StressMatic: a novel automated system to induce depressive- and anxiety-like phenotype in rats

Major depressive disorder (MDD) is a multidimensional psychiatric disorder that is estimated to affect around 350 million people worldwide. Generating valid and effective animal models of depression is critical and has been challenging for neuroscience researchers. For preclinical studies, models based on stress exposure, such as unpredictable chronic mild stress (uCMS), are amongst the most reliable and used, despite presenting concerns related to the standardization of protocols and time consumption for operators. To overcome these issues, we developed an automated system to expose rodents to a standard uCMS protocol. Here, we compared manual (uCMS) and automated (auCMS) stress-exposure protocols. The data shows that the impact of the uCMS exposure by both methods was similar in terms of behavioral (cognition, mood, and anxiety) and physiological (cell proliferation and endocrine variations) measurements. Given the advantages of time and standardization, this automated method represents a step forward in this field of preclinical research.This research was funded by Bn’ML—Behavioral and Molecular Lab and by the National Strategic Reference Framework (QREN). L.P. and F.V. were funded by the Portuguese Foundation for Science and Technology (FCT) (2020.02855.CEECIND to L.P.; SFRH/BD/131545/2017 to F.V.). This work was funded by the Nature Research Award for Driving Global Impact—2019 Brain Sciences (to L.P.). J.F.O. received funding from FCT (projects PTDC/MED-NEU/31417/2017) and POCI-01-0145-FEDER-016818; grants from Bial Foundation (037/18) and ”la Caixa” Foundation (LCF/PR/HR21/52410024) to J.F.O. A.J.R. was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 101003187), by “la Caixa” Foundation (ID 100010434), under the agreement LCF/PR/HR20/52400020, by FCT under the scope of the project PTDC/MED-NEU/4804/2020 (ENDOPIO). This work was also co-funded by the Life and Health Sciences Research Institute (ICVS); funded by ICVS Scientific Microscopy Platform, member of the national infrastructure PPBI—Portuguese Platform of Bioimaging (PPBI-POCI-01-0145-FEDER-022122); and funded by National funds, through the Foundation for Science and Technology (FCT)—project UIDB/50026/2020 and UIDP/50026/2020

Performance of an Adipokine Pathway-Based Multilocus Genetic Risk Score for Prostate Cancer Risk Prediction

<div><p>Few biomarkers are available to predict prostate cancer risk. Single nucleotide polymorphisms (SNPs) tend to have weak individual effects but, in combination, they have stronger predictive value. Adipokine pathways have been implicated in the pathogenesis. We used a candidate pathway approach to investigate 29 functional SNPs in key genes from relevant adipokine pathways in a sample of 1006 men eligible for prostate biopsy. We used stepwise multivariate logistic regression and bootstrapping to develop a multilocus genetic risk score by weighting each risk SNP empirically based on its association with disease. Seven common functional polymorphisms were associated with overall and high-grade prostate cancer (Gleason≥7), whereas three variants were associated with high metastatic-risk prostate cancer (PSA≥20 ng/mL and/or Gleason≥8). The addition of genetic variants to age and PSA improved the predictive accuracy for overall and high-grade prostate cancer, using either the area under the receiver-operating characteristics curves (P<0.02), the net reclassification improvement (P<0.001) and integrated discrimination improvement (P<0.001) measures. These results suggest that functional polymorphisms in adipokine pathways may act individually and cumulatively to affect risk and severity of prostate cancer, supporting the influence of adipokine pathways in the pathogenesis of prostate cancer. Use of such adipokine multilocus genetic risk score can enhance the predictive value of PSA and age in estimating absolute risk, which supports further evaluation of its clinical significance.</p> </div

Stepwise multivariate logistic regression and Bootstrap analyses.

<p>Age and PSA analyzed as continuous variables. PCa, prostate cancer. ^aStepwise multivariate logistic regression; ^bMonteCarlo simulation (1000 replications). Empirical confounding variables were independently analyzed in each model (overall prostate cancer and both restricted groups).</p

Tertiles of inclusive genetic risk score (GRS) and age-adjusted OR (CI 95%) for prostate cancer.

<p>Tertiles for all prostate cancer: T1 (<2.74897), T2 (2.74897–3.15913), T3 (≥3.15913). Tertiles for high-grade prostate cancer: T1 (<2.85839), T2 (2.85839–3.30669), T3 (≥3.30669). The genetic risk scores were computed separately derived for overall and high-grade prostate cancer. aOR, age-adjusted ORs (95%CI).</p