Optical fiber-based sensing method for nanoparticle detection through supervised back-scattering analysis: A potential contributor for biomedicine

Background: In view of the growing importance of nanotechnologies, the detection/identification of nanoparticles type has been considered of utmost importance. Although the characterization of synthetic/organic nanoparticles is currently considered a priority (eg, drug delivery devices, nanotextiles, theranostic nanoparticles), there are many examples of “naturally” generated nanostructures - for example, extracellular vesicles (EVs), lipoproteins, and virus - that provide useful information about human physiology or clinical conditions. For example, the detection of tumor-related exosomes, a specific type of EVs, in circulating fluids has been contributing to the diagnosis of cancer in an early stage. However, scientists have struggled to find a simple, fast, and low-cost method to accurately detect/identify these nanoparticles, since the majority of them have diameters between 100 and 150 nm, thus being far below the diffraction limit. Methods: This study investigated if, by projecting the information provided from short-term portions of the back-scattered laser light signal collected by a polymeric lensed optical fiber tip dipped into a solution of synthetic nanoparticles into a lower features dimensional space, a discriminant function is able to correctly detect the presence of 100 nm synthetic nanoparticles in distilled water, in different concentration values. Results and discussion: This technique ensured an optimal performance (100% accuracy) in detecting nanoparticles for a concentration above or equal to 3.89 µg/mL (8.74E+10 particles/mL), and a performance of 90% for concentrations below this value and higher than 1.22E-03 µg/mL (2.74E+07 particles/mL), values that are compatible with human plasmatic levels of tumorderived and other types of EVs, as well as lipoproteins currently used as potential biomarkers of cardiovascular diseases. Conclusion: The proposed technique is able to detect synthetic nanoparticles whose dimensions are similar to EVs and other “clinically” relevant nanostructures, and in concentrations equivalent to the majority of cell-derived, platelet-derived EVs and lipoproteins physiological levels. This study can, therefore, provide valuable insights towards the future development of a device for EVs and other biological nanoparticles detection with innovative characteristics.This work was partly developed under the project NanoSTIMA, funded by the North Portugal Regional Operational Program (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). It was also funded by the Portuguese Foundation for Science and Technology (PhD research grant PD/BD/135023/2017). Rita SR Ribeiro is currently working at 4Dcell and Elvesys, Paris, France

Enabling Process Variants and Versions in Distributed Object-Aware Process Management Systems

Business process variants are common in many enterprises and properly managing them is indispensable. Some process management suites already offer features to tackle the challenges of creating and updating multiple variants of a process. As opposed to the widespread activity-centric process modeling paradigm, however, there is little to no support for process variants in other process support paradigms, such as the recently proposed artifact-centric or object-aware process support paradigm. This paper presents concepts for supporting process variants in the object-aware process management paradigm. We offer insights into the distributed object-aware process management framework PHILharmonicFlows as well as the concepts it provides for implementing variants and versioning support based on log propagation and log replay. Finally, we examine the challenges that arise from the support of process variants and show how we solved these, thereby enabling future research into related fundamental aspects to further raise the maturity level of data-centric process support paradigms

Optimizing the use of systemic corticosteroids in severe asthma (ROSA II project): a national Delphi consensus study

Although the prevalence of severe asthma is not high (5–10% of patients), it is responsible for a large part of the overall disease burden and costs (50–60% of total costs), especially if the condition remains uncontrolled (which occurs in around 40% of cases). Currently, for patients without disease control or presenting frequent exacerbations despite optimal therapy, add-on treatments, traditionally long-acting anticholinergics, oral corticosteroids (OCS), or biologic agents (monoclonal antibodies) are recommended. Nonetheless, the long-term use of oral/systemic corticosteroids (CS) is significantly associated with adverse effects, acute and chronic complications that may decrease health-related quality of life and worsen prognosis, thus requiring additional monitoring and management. Conversely, target therapies (i.e., omalizumab, mepolizumab, reslizumab, benralizumab, and more recently, dupilumab) have been developed grounded on the different phenotypes and endotypes of severe asthma, and are gradually reducing the reliance on OCS (i.e., greater specificity for achieving disease control by reducing the risk of exacerbations and requirements for rescue medication and OCS, with limited adverse events).This work was supported by AstraZeneca.info:eu-repo/semantics/publishedVersio

The percentage of CD133+ cells in human colorectal cancer cell lines is influenced by Mycoplasma hyorhinis infection

<p>Abstract</p> <p>Background</p> <p><it>Mollicutes </it>contamination is recognized to be a critical issue for the cultivation of continuous cell lines. In this work we characterized the effect of <it>Mycoplasma hyorhinis </it>contamination on CD133 expression in human colon cancer cell lines.</p> <p>Methods</p> <p>MycoAlert^®and mycoplasma agar culture were used to detect mycoplasma contamination on GEO, SW480 and HT-29 cell lines. Restriction fragment length polymorphism assay was used to determine mycoplasma species. All cellular models were decontaminated by the use of a specific antibiotic panel (Enrofloxacin, Ciprofloxacin, BM Cyclin 1 and 2, Mycoplasma Removal Agent and MycoZap^®). The percentage of CD133 positive cells was analyzed by flow cytometry on GEO, SW480 and HT-29 cell lines, before and after <it>Mycoplasma hyorhinis </it>eradication.</p> <p>Results</p> <p><it>Mycoplasma hyorhinis </it>infected colon cancer cell lines showed an increased percentage of CD133+ cells as compared to the same cell lines rendered mycoplasma-free by effective exposure to antibiotic treatment. The percentage of CD133 positive cells increased again when mycoplasma negative cells were re-infected by <it>Mycoplasma hyorhinis</it>.</p> <p>Conclusions</p> <p><it>Mycoplasma hyorhinis </it>infection has an important role on the quality of cultured human colon cancer cell lines giving a false positive increase of cancer stem cells fraction characterized by CD133 expression. Possible explanations are (i) the direct involvement of Mycoplasma on CD133 expression or (ii) the selective pressure on a subpopulation of cells characterized by constitutive CD133 expression.</p> <p>In keeping with United Kingdom Coordinating Committee on Cancer Research (UKCCCR) guidelines, the present data indicate the mandatory prerequisite, for investigators involved in human colon cancer research area, of employing mycoplasma-free cell lines in order to avoid the production of non-reproducible or even false data.</p

Comparing families of dynamic causal models

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data

FADS2 Function Loss at the Cancer Hotspot 11q13 Locus Diverts Lipid Signaling Precursor Synthesis to Unusual Eicosanoid Fatty Acids

Background: Genes coding for the fatty acid desaturases (FADS1, 2, 3) localized at the cancer genomic hotspot 11q13 locus are required for the biosynthesis of 20 carbon polyunsaturated fatty acids (PUFA) that are direct eicosanoid precursors. In several cancer cell lines, FADS2 encoded D6 and D8 desaturation is not functional. Methodology/Principal Findings: Analyzing MCF7 cell fatty acids with detailed structural mass spectrometry, we show that in the absence of FADS2 activity, the FADS1 product D5-desaturase operates to produce 5,11,14–20:3 and 5,11,14,17–20:4. These PUFA are missing the 8–9 double bond of the eicosanoid signaling precursors arachidonic acid (5,8,11,14–20:4) and eicosapentaenoic acid (5,8,11,14,17–20:5). Heterologous expression of FADS2 restores D6 and D8-desaturase activity and normal eicosanoid precursor synthesis. Conclusions/Significance: The loss of FADS2-encoded activities in cancer cells shuts down normal PUFA biosynthesis, deleting the endogenous supply of eicosanoid and downstream docosanoid precursors, and replacing them with unusual butylene-interrupted fatty acids. If recapitulated in vivo, the normal eicosanoid and docosanoid cell signaling milieu would be depleted and altered due to reduction and substitution of normal substrates with unusual substrates, with unpredictable consequences for cellular communication

Prognostic value of discharge heart rate in acute heart failure patients: More relevant in atrial fibrillation?

Aims: The prognostic impact of heart rate (HR) in acute heart failure (AHF) patients is not well known especially in atrial fibrillation (AF) patients. The aim of the study was to evaluate the impact of admission HR, discharge HR, HR difference (admission-discharge) in AHF patients with sinus rhythm (SR) or AF on long- term outcomes. Methods: We included 1398 patients consecutively admitted with AHF between October 2013 and December 2014 from a national multicentre, prospective registry. Logistic regression models were used to estimate the association between admission HR, discharge HR and HR difference and one- year all-cause mortality and HF readmission. Results: The mean age of the study population was 72+/-12years. Of these, 594 (42.4%) were female, 655 (77.8%) were hypertensive and 655 (46.8%) had diabetes. Among all included patients, 745 (53.2%) had sinus rhythm and 653 (46.7%) had atrial fibrillation. Only discharge HR was associated with one year all-cause mortality (Relative risk (RR)=1.182, confidence interval (CI) 95% 1.024-1.366, p=0.022) in SR. In AF patients discharge HR was associated with one year all cause mortality (RR=1.276, CI 95% 1.115-1.459, p</=0.001). We did not observe a prognostic effect of admission HR or HRD on long-term outcomes in both groups. This relationship is not dependent on left ventricular ejection fraction. Conclusions: In AHF patients lower discharge HR, neither the admission nor the difference, is associated with better long-term outcomes especially in AF patients