The added value of PSMA PET/MR radiomics for prostate cancer staging

PURPOSE

To evaluate the performance of combined PET and multiparametric MRI (mpMRI) radiomics for the group-wise prediction of postsurgical Gleason scores (psGSs) in primary prostate cancer (PCa) patients.

METHODS

Patients with PCa, who underwent [68 Ga]Ga-PSMA-11 PET/MRI followed by radical prostatectomy, were included in this retrospective analysis (n = 101). Patients were grouped by psGS in three categories: ISUP grades 1-3, ISUP grade 4, and ISUP grade 5. mpMRI images included T1-weighted, T2-weighted, and apparent diffusion coefficient (ADC) map. Whole-prostate segmentations were performed on each modality, and image biomarker standardization initiative (IBSI)-compliant radiomic features were extracted. Nine support vector machine (SVM) models were trained: four single-modality radiomic models (PET, T1w, T2w, ADC); three PET + MRI double-modality models (PET + T1w, PET + T2w, PET + ADC), and two baseline models (one with patient data, one image-based) for comparison. A sixfold stratified cross-validation was performed, and balanced accuracies (bAcc) of the predictions of the best-performing models were reported and compared through Student's t-tests. The predictions of the best-performing model were compared against biopsy GS (bGS).

RESULTS

All radiomic models outperformed the baseline models. The best-performing (mean ± stdv [%]) single-modality model was the ADC model (76 ± 6%), although not significantly better (p > 0.05) than other single-modality models (T1w: 72 ± 3%, T2w: 73 ± 2%; PET: 75 ± 5%). The overall best-performing model combined PET + ADC radiomics (82 ± 5%). It significantly outperformed most other double-modality (PET + T1w: 74 ± 5%, p = 0.026; PET + T2w: 71 ± 4%, p = 0.003) and single-modality models (PET: p = 0.042; T1w: p = 0.002; T2w: p = 0.003), except the ADC-only model (p = 0.138). In this initial cohort, the PET + ADC model outperformed bGS overall (82.5% vs 72.4%) in the prediction of psGS.

CONCLUSION

All single- and double-modality models outperformed the baseline models, showing their potential in the prediction of GS, even with an unbalanced cohort. The best-performing model included PET + ADC radiomics, suggesting a complementary value of PSMA-PET and ADC radiomics.

The FUR-like regulators PerRA and PerRB integrate a complex regulatory network that promotes mammalian host-adaptation and virulence of Leptospira interrogans

Leptospira interrogans, the causative agent of most cases of human leptospirosis, must respond to myriad environmental signals during its free-living and pathogenic lifestyles. Previously, we compared L. interrogans cultivated in vitro and in vivo using a dialysis membrane chamber (DMC) peritoneal implant model. From these studies emerged the importance of genes encoding the Peroxide responsive regulators PerRA and PerRB. First described in in Bacillus subtilis, PerRs are widespread in Gram-negative and -positive bacteria, where regulate the expression of gene products involved in detoxification of reactive oxygen species and virulence. Using perRA and perRB single and double mutants, we establish that L. interrogans requires at least one functional PerR for infectivity and renal colonization in a reservoir host. Our finding that the perRA/B double mutant survives at wild-type levels in DMCs is noteworthy as it demonstrates that the loss of virulence is not due to a metabolic lesion (i.e., metal starvation) but instead reflects dysregulation of virulence-related gene products. Comparative RNA-Seq analyses of perRA, perRB and perRA/B mutants cultivated within DMCs identified 106 genes that are dysregulated in the double mutant, including ligA, ligB and lvrA/B sensory histidine kinases. Decreased expression of LigA and LigB in the perRA/B mutant was not due to loss of LvrAB signaling. The majority of genes in the perRA and perRB single and double mutant DMC regulons were differentially expressed only in vivo, highlighting the importance of host signals for regulating gene expression in L. interrogans. Importantly, the PerRA, PerRB and PerRA/B DMC regulons each contain multiple genes related to environmental sensing and/or transcriptional regulation. Collectively, our data suggest that PerRA and PerRB are part of a complex regulatory network that promotes host adaptation by L. interrogans within mammals.

A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets

PURPOSE

To facilitate the demonstration of the prognostic value of radiomics, multicenter radiomics studies are needed. Pooling radiomic features of such data in a statistical analysis is however challenging, as they are sensitive to the variability in scanner models, acquisition protocols and reconstruction settings, which is often unavoidable in a multicentre retrospective analysis. A statistical harmonization strategy called ComBat was utilized in radiomics studies to deal with the "center-effect". The goal of the present work was to integrate a transfer learning (TL) technique within ComBat-and recently developed alternate versions of ComBat with improved flexibility (M-ComBat) and robustness (B-ComBat)-to allow the use of a previously determined harmonization transform to the radiomic feature values of new patients from an already known center.

MATERIAL AND METHODS

The proposed TL approach were incorporated in the four versions of ComBat (standard, B, M, and B-M ComBat). The proposed approach was evaluated using a dataset of 189 locally advanced cervical cancer patients from 3 centers, with magnetic resonance imaging (MRI) and positron emission tomography (PET) images, with the clinical endpoint of predicting local failure. The impact performance of the TL approach was evaluated by comparing the harmonization achieved using only parts of the data to the reference (harmonization achieved using all the available data). It was performed through three different machine learning pipelines.

RESULTS

The proposed TL technique was successful in harmonizing features of new patients from a known center in all versions of ComBat, leading to predictive models reaching similar performance as the ones developed using the features harmonized with all the data available.

CONCLUSION

The proposed TL approach enables applying a previously determined ComBat transform to new, previously unseen data.

Predicting the substituent effects in the optical and electrochemical properties of N,N'-substituted isoindigos

Isoindigo, the structural isomer of the well-known dye indigo, has seen a major revival recently because of the increasing interest of its use as a potential drug core structure and for the development of organic photovoltaic materials. Highly beneficial for diverse applications are its facile synthesis, straightforward functionalisation and the broad absorption band in the visible range. Moreover, its intrinsic electron deficiency renders isoindigo a promising acceptor structure in bulk heterojunction architectures. Here we present new insights into the substituent effects of N-functionalised isoindigos, developing a reliable and fast in silico screening approach of a library of compounds. Using experimental UV-Vis and electrochemical data increased the accuracy of the TD-DFT method employed. This procedure allowed us to accurately predict the optical and electrochemical properties of N-functionalised isoindigos and the elucidation of the relationship between substituent effects and electronic properties.

Lack of impact of radiation on blood physiology biomarkers of Chernobyl tree frogs

BACKGROUND

Human actions have altered natural ecosystems worldwide. Among the many pollutants released to the environment, ionizing radiation can cause severe damage at different molecular and functional levels. The accident in the Chernobyl Nuclear Power Plant (1986) caused the largest release of ionizing radiation to the environment in human history. Here, we examined the impact of the current exposure to ionizing radiation on blood physiology biomarkers of adult males of the Eastern tree frog (Hyla orientalis) inhabiting within and outside the Chernobyl Exclusion Zone. We measured the levels of eight blood parameters (sodium, potassium, chloride, ionized calcium, total carbon dioxide, glucose, urea nitrogen, and anion gap), physiological markers of homeostasis, as well as of liver and kidney function.

RESULTS

Levels of blood physiology biomarkers did not vary in function of the current exposure of tree frogs to ionizing radiation within the Chernobyl Exclusion Zone. Physiological blood levels were similar in frogs inhabiting Chernobyl (both in areas with medium-high or low radiation) than in tree frogs living outside Chernobyl exposed only to background radiation levels.

CONCLUSIONS

The observed lack of effects of current radiation levels on blood biomarkers can be a consequence of the low levels of radiation currently experienced by Chernobyl tree frogs, but also to the fact that our sampling was restricted to active breeding males, i.e. potentially healthy adult individuals. Despite the clear absence of effects of current radiation levels on physiological blood parameters in tree frogs, more research covering different life stages and ecological scenarios is still needed to clarify the impact of ionizing radiation on the physiology, ecology, and dynamics of wildlife inhabiting radioactive-contaminated areas.

Comparing the effect of cross-group friendship on generalized trust to its effect on prejudice: The mediating role of threat perceptions and negative affect

Intergroup relations theory posits that cross-group friendship reduces threat perceptions and negative emotions about outgroups. This has been argued to mitigate the negative effects of ethnic diversity on generalized trust. Yet, direct tests of this friendship-trust relation, especially including perceptions of threat and negative affect as mediators, have remained rare at the individual level. In this article, we bridge this research gap using representative data from eight European countries (Group-Focused Enmity). We employ structural equation modelling (SEM) to model mediated paths of cross-group friendship on generalized trust via perceptions of threat and negative affect. We find that both the total effect as well as the (mediated) total indirect effect of cross-group friendship on generalized trust are weak when compared with similar paths estimated for prejudice.

Concatemers to re-investigate the role of α5 in α4β2 nicotinic receptors

Nicotinic acetylcholine receptors (nAChRs) are pentameric ion channels expressed in the central nervous systems. nAChRs containing the α4, β2 and α5 subunits are specifically involved in addictive processes, but their functional architecture is poorly understood due to the intricacy of assembly of these subunits. Here we constrained the subunit assembly by designing fully concatenated human α4β2 and α4β2α5 receptors and characterized their properties by two-electrodes voltage-clamp electrophysiology in Xenopus oocytes. We found that α5-containing nAChRs are irreversibly blocked by methanethiosulfonate (MTS) reagents through a covalent reaction with a cysteine present only in α5. MTS-block experiments establish that the concatemers are expressed in intact form at the oocyte surface, but that reconstitution of nAChRs from loose subunits show inefficient and highly variable assembly of α5 with α4 and β2. Mutational analysis shows that the concatemers assemble both in clockwise and anticlockwise orientations, and that α5 does not contribute to ACh binding from its principal (+) site. Reinvestigation of suspected α5-ligands such as galantamine show no specific effect on α5-containing concatemers. Analysis of the α5-D398N mutation that is linked to smoking and lung cancer shows no significant effect on the electrophysiological function, suggesting that its effect might arise from alteration of other cellular processes. The concatemeric strategy provides a well-characterized platform for mechanistic analysis and screening of human α5-specific ligands.

Ranking and Rating Bicycle Helmet Safety Performance in Oblique Impacts Using Eight Different Brain Injury Models

Bicycle helmets are shown to offer protection against head injuries. Rating methods and test standards are used to evaluate different helmet designs and safety performance. Both strain-based injury criteria obtained from finite element brain injury models and metrics derived from global kinematic responses can be used to evaluate helmet safety performance. Little is known about how different injury models or injury metrics would rank and rate different helmets. The objective of this study was to determine how eight brain models and eight metrics based on global kinematics rank and rate a large number of bicycle helmets (n=17) subjected to oblique impacts. The results showed that the ranking and rating are influenced by the choice of model and metric. Kendall’s tau varied between 0.50 and 0.95 when the ranking was based on maximum principal strain from brain models. One specific helmet was rated as 2-star when using one brain model but as 4-star by another model. This could cause confusion for consumers rather than inform them of the relative safety performance of a helmet. Therefore, we suggest that the biomechanics community should create a norm or recommendation for future ranking and rating methods.

Towards designing reactive glasses for alkali activation: Understanding the origins of alkaline reactivity of Na-Mg aluminosilicate glasses

Alkali-activated materials (AAMs), sometimes called geopolymers, are eco-friendly cementitious materials with reduced carbon emissions when compared to ordinary Portland cement. However, the availability of most precursors used for AAM production may decline in the future because of changes in industrial sectors. Thus, new precursors must be developed. Recently there has been increased interest in synthetic glass precursors. One major concern with using synthetic glasses is ensuring that they react sufficiently under alkaline conditions. Reactivity is a necessary, although not sufficient, requirement for a suitable precursor for AAMs. This work involves the synthesis, characterization, and estimation of alkaline reactivity of Na-Mg aluminosilicate glasses. Structural characterization showed that replacing Na with Mg led to more depolymerization. Alkaline reactivity studies indicated that, as Mg replaced Na, reactivity of glasses increased at first, reached an optimal value, and then declined. This trend in reactivity could not be explained by the conventional parameters used for estimating glass reactivity: the non-bridging oxygen fraction (which predicts similar reactivity for all glasses) and optical basicity (which predicts a decrease in reactivity with an increase in Mg replacement). The reactivity of the studied glasses was found to depend on two main factors: depolymerization (as indicated by structural characterization) and optical basicity. Depolymerization dominated initially, which led to an increase in reactivity, while the effect of optical basicity dominated later, leading to a decrease in reactivity. Hence, while designing reactive synthetic glasses for alkali activation, structural study of glasses should be given due consideration in addition to the conventional factors.

Towards Transparency by Design for Artificial Intelligence

In this article, we develop the concept of Transparency by Design that serves as practical guidance in helping promote the beneficial functions of transparency while mitigating its challenges in automated-decision making (ADM) environments. With the rise of artificial intelligence (AI) and the ability of AI systems to make automated and self-learned decisions, a call for transparency of how such systems reach decisions has echoed within academic and policy circles. The term transparency, however, relates to multiple concepts, fulfills many functions, and holds different promises that struggle to be realized in concrete applications. Indeed, the complexity of transparency for ADM shows tension between transparency as a normative ideal and its translation to practical application. To address this tension, we first conduct a review of transparency, analyzing its challenges and limitations concerning automated decision-making practices. We then look at the lessons learned from the development of Privacy by Design, as a basis for developing the Transparency by Design principles. Finally, we propose a set of nine principles to cover relevant contextual, technical, informational, and stakeholder-sensitive considerations. Transparency by Design is a model that helps organizations design transparent AI systems, by integrating these principles in a step-by-step manner and as an ex-ante value, not as an afterthought.

Characterization of Worldwide Olive Germplasm Banks of Marrakech (Morocco) and Córdoba (Spain): Towards management and use of olive germplasm in breeding programs

Olive (Olea europaea L.) is a major fruit crop in the Mediterranean Basin. Ex-situ olive management is essential to ensure optimal use of genetic resources in breeding programs. The Worldwide Olive Germplasm Bank of Córdoba (WOGBC), Spain, and Marrakech (WOGBM), Morocco, are currently the largest existing olive germplasm collections. Characterization, identification, comparison and authentication of all accessions in both collections could thus provide useful information for managing olive germplasm for its preservation, exchange within the scientific community and use in breeding programs. Here we applied 20 microsatellite markers (SSR) and 11 endocarp morphological traits to discriminate and authenticate 1091 olive accessions belonging to WOGBM and WOGBC (554 and 537, respectively). Of all the analyzed accessions, 672 distinct SSR profiles considered as unique genotypes were identified, but only 130 were present in both collections. Combining SSR markers and endocarp traits led to the identification of 535 cultivars (126 in common) and 120 authenticated cultivars. No significant differences were observed between collections regarding the allelic richness and diversity index. We concluded that the genetic diversity level was stable despite marked contrasts in varietal composition between collections, which could be explained by their different collection establishment conditions. This highlights the extent of cultivar variability within WOGBs. Moreover, we detected 192 mislabeling errors, 72 of which were found in WOGBM. A total of 228 genotypes as molecular variants of 74 cultivars, 79 synonyms and 39 homonyms as new cases were identified. Both collections were combined to define the nested core collections of 55, 121 and 150 sample sizes proposed for further studies. This study was a preliminary step towards managing and mining the genetic diversity in both collections while developing collaborations between olive research teams to conduct association mapping studies by exchanging and phenotyping accessions in contrasted environmental sites.

A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG

Early detection of coronary heart disease (CHD) has the potential to prevent the millions of deaths that this disease causes worldwide every year. However, there exist few automatic methods to detect CHD at an early stage. A challenge in the development of these methods is the absence of relevant datasets for their training and validation. Here, the ten Tusscher-Panfilov 2006 model and the O’Hara-Rudy model for human myocytes were used to create two populations of models that were in concordance with data obtained from healthy individuals (control populations) and included inter-subject variability. The effects of ischemia were subsequently included in the control populations to simulate the effects of mild and severe ischemic events on single cells, full ischemic cables of cells and cables of cells with various sizes of ischemic regions. Action potential and pseudo-ECG biomarkers were measured to assess how the evolution of ischemia could be quantified. Finally, two neural network classifiers were trained to identify the different degrees of ischemia using the pseudo-ECG biomarkers. The control populations showed action potential and pseudo-ECG biomarkers within the physiological ranges and the trends in the biomarkers commonly identified in ischemic patients were observed in the ischemic populations. On the one hand, inter-subject variability in the ischemic pseudo-ECGs precluded the detection and classification of early ischemic events using any single biomarker. On the other hand, the neural networks showed sensitivity and positive predictive value above 95%. Additionally, the neural networks revealed that the biomarkers that were relevant for the detection of ischemia were different from those relevant for its classification. This work showed that a computational approach could be used, when data is scarce, to validate proof-of-concept machine learning methods to detect ischemic events.

Managing River Fish Biodiversity Generates Substantial Economic Benefits in Four European Countries

Ecosystems and biodiversity produce benefits to society, but many of them are hard to quantify. For example, it is unclear whether European societies gain benefits from experiencing rivers that host high native biodiversity. Without such knowledge, monetary investments into ecologically oriented river management plans are difficult to justify. The objective of this study was to reveal how the public in four European countries values ecological characteristics of domestic rivers and the outcomes of hypothetical river basin management plans designed to improve river ecosystems, particularly fish biodiversity. We conducted a choice experiment among the populations in Norway, Sweden, Germany, and France. We found similar preference structures in all countries with high marginal willingness-to-pay for improvements of abiotic river attributes (increased accessiblity of the river banks, improved bathing water quality, decreased river fragmentation). Citizens also benefited from certain fish species occurring in a river with native salmonid species being more valued than nonnatives, particularly in Norway, and from the degree of a river's native biodiversity. Welfare measures calculated for selected river basin management plans (policy scenarios) revealed societal benefits that were primarily derived from ecological river management whereas a scenario focusing on hydroelectricity production generated the lowest utility. We conclude that ecological river management may produce high nonmarket economic benefits in all study countries, particularly through the management of abiotic river attributes and the restoration of declining or extinct fish species. Our results help to inform decisions on restoration efforts by showcasing the benefits that these measures have for the public.

Refining wet lab experiments with in silico searches: A rational quest for diagnostic peptides in visceral leishmaniasis

Background

The search for diagnostic biomarkers has been profiting from a growing number of high quality sequenced genomes and freely available bioinformatic tools. These can be combined with wet lab experiments for a rational search. Improved, point-of-care diagnostic tests for visceral leishmaniasis (VL), early case detection and surveillance are required. Previous investigations demonstrated the potential of IgG1 as a biomarker for monitoring clinical status in rapid diagnostic tests (RDTs), although using a crude lysate antigen (CLA) as capturing antigen. Replacing the CLA by specific antigens would lead to more robust RDTs.

Methodology

Immunoblots revealed L. donovani protein bands detected by IgG1 from VL patients. Upon confident identification of these antigens by mass spectrometry (MS), we searched for evidence of constitutive protein expression and presence of antigenic domains or high accessibility to B-cells. Selected candidates had their linear epitopes mapped with in silico algorithms. Multiple high-scoring predicted epitopes from the shortlisted proteins were screened in peptide arrays. The most promising candidate was tested in RDT prototypes using VL and nonendemic healthy control (NEHC) patient sera.

Results

Over 90% of the proteins identified from the immunoblots did not satisfy the selection criteria and were excluded from the downstream epitope mapping. Screening of predicted epitope peptides from the shortlisted proteins identified the most reactive, for which the sensitivity for IgG1 was 84% (95% CI 60—97%) with Sudanese VL sera on RDT prototypes. None of the sera from NEHCs were positive.

Conclusion

We employed in silico searches to reduce drastically the output of wet lab experiments, focusing on promising candidates containing selected protein features. By predicting epitopes in silico we screened a large number of peptides using arrays, identifying the most promising one, for which IgG1 sensitivity and specificity, with limited sample size, supported this proof of concept strategy for diagnostics discovery, which can be applied to the development of more robust IgG1 RDTs for monitoring clinical status in VL.

Visceral leishmaniasis (VL) is a neglected tropical disease caused by protozoan parasites of the Leishmania donovani complex. Without treatment, VL is fatal. Although diagnostic techniques, mainly based on the detection of anti-Leishmania antibodies are available, invasive procedures such as microscopy from spleen or bone marrow aspirates are still required for the diagnosis of seronegative VL suspects, for the detection of recurrent cases and to confirm cure after successful treatment. Previous investigations showed the potential of IgG1 as a biomarker of post-chemotherapeutic relapse for VL in rapid diagnostic tests (RDTs) sensitised with crude lysate antigen (CLA). Here we employed in silico tools to search for desired protein features in a large number of L. donovani antigens detected by human IgG1 in western blots. We then employed prediction algorithms to profile epitopes from the shortlisted proteins. We screened a panel of high-scoring peptides in a high-throughput manner using arrays, with low reagent consumption. The most reactive peptide was adapted to RDTs, showing promising results of both sensitivity and specificity. This peptide has the potential of replacing the CLAs in IgG1 RDTs. Thus we believe that in silico tools can be used to optimise wet lab experiments for a rational search of biomarkers.

Centiloid cut-off values for optimal agreement between PET and CSF core AD biomarkers

BACKGROUND

The Centiloid scale has been developed to standardize measurements of amyloid PET imaging. Reference cut-off values of this continuous measurement enable the consistent operationalization of decision-making for multicentre research studies and clinical trials. In this study, we aimed at deriving reference Centiloid thresholds that maximize the agreement against core Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers in two large independent cohorts.

METHODS

A total of 516 participants of the ALFA+ Study (N = 205) and ADNI (N = 311) underwent amyloid PET imaging ([18F]flutemetamol and [18F]florbetapir, respectively) and core AD CSF biomarker determination using Elecsys® tests. Tracer uptake was quantified in Centiloid units (CL). Optimal Centiloid cut-offs were sought that maximize the agreement between PET and dichotomous determinations based on CSF levels of Aβ42, tTau, pTau, and their ratios, using pre-established reference cut-off values. To this end, a receiver operating characteristic analysis (ROC) was conducted, and Centiloid cut-offs were calculated as those that maximized the Youden's J Index or the overall percentage agreement recorded.

RESULTS

All Centiloid cut-offs fell within the range of 25-35, except for CSF Aβ42 that rendered an optimal cut-off value of 12 CL. As expected, the agreement of tau/Aβ42 ratios was higher than that of CSF Aβ42. Centiloid cut-off robustness was confirmed even when established in an independent cohort and against variations of CSF cut-offs.

CONCLUSIONS

A cut-off of 12 CL matches previously reported values derived against postmortem measures of AD neuropathology. Together with these previous findings, our results flag two relevant inflection points that would serve as boundary of different stages of amyloid pathology: one around 12 CL that marks the transition from the absence of pathology to subtle pathology and another one around 30 CL indicating the presence of established pathology. The derivation of robust and generalizable cut-offs for core AD biomarkers requires cohorts with adequate representation of intermediate levels.

TRIAL REGISTRATION

ALFA+ Study, NCT02485730 ALFA PET Sub-study, NCT02685969.

The long journey of Orthotrichum shevockii (Orthotrichaceae, Bryopsida): From California to Macaronesia

Biogeography, systematics and taxonomy are complementary scientific disciplines. To understand a species’ origin, migration routes, distribution and evolutionary history, it is first necessary to establish its taxonomic boundaries. Here, we use an integrative approach that takes advantage of complementary disciplines to resolve an intriguing scientific question. Populations of an unknown moss found in the Canary Islands (Tenerife Island) resembled two different Californian endemic species: Orthotrichum shevockii and O. kellmanii. To determine whether this moss belongs to either of these species and, if so, to explain its presence on this distant oceanic island, we combined the evaluation of morphological qualitative characters, statistical morphometric analyses of quantitative traits, and molecular phylogenetic inferences. Our results suggest that the two Californian mosses are conspecific, and that the Canarian populations belong to this putative species, with only one taxon thus involved. Orthotrichum shevockii (the priority name) is therefore recognized as a morphologically variable species that exhibits a transcontinental disjunction between western North America and the Canary Islands. Within its distribution range, the area of occupancy is limited, a notable feature among bryophytes at the intraspecific level. To explain this disjunction, divergence time and ancestral area estimation analyses are carried out and further support the hypothesis of a long-distance dispersal event from California to Tenerife Island.

High time resolution and high signal-to-noise monitoring of the bacterial growth kinetics in the presence of plasmonic nanoparticles

BACKGROUND

Emerging concepts for designing innovative drugs (i.e., novel generations of antimicrobials) frequently include nanostructures, new materials, and nanoparticles (NPs). Along with numerous advantages, NPs bring limitations, partly because they can limit the analytical techniques used for their biological and in vivo validation. From that standpoint, designing innovative drug delivery systems requires advancements in the methods used for their testing and investigations. Considering the well-known ability of resazurin-based methods for rapid detection of bacterial metabolisms with very high sensitivity, in this work we report a novel optimization for tracking bacterial growth kinetics in the presence of NPs with specific characteristics, such as specific optical properties.

RESULTS

Arginine-functionalized gold composite (HAp/Au/arginine) NPs, used as the NP model for validation of the method, possess plasmonic properties and are characterized by intensive absorption in the UV/vis region with a surface plasmon resonance maximum at 540 nm. Due to the specific optical properties, the NP absorption intensively interferes with the light absorption measured during the evaluation of bacterial growth (optical density; OD600). The results confirm substantial nonspecific interference by NPs in the signal detected during a regular turbidity study used for tracking bacterial growth. Instead, during application of a resazurin-based method (Presto Blue), when a combination of absorption and fluorescence detection is applied, a substantial increase in the signal-to-noise ratio is obtained that leads to the improvement of the accuracy of the measurements as verified in three bacterial strains tested with different growth rates (E. coli, P. aeruginosa, and S. aureus).

CONCLUSIONS

Here, we described a novel procedure that enables the kinetics of bacterial growth in the presence of NPs to be followed with high time resolution, high sensitivity, and without sampling during the kinetic study. We showed the applicability of the Presto Blue method for the case of HAp/Au/arginine NPs, which can be extended to various types of metallic NPs with similar characteristics. The method is a very easy, economical, and reliable option for testing NPs designed as novel antimicrobials.

Expression and secretion of a lytic polysaccharide monooxygenase by a fast-growing cyanobacterium

BACKGROUND

Cyanobacteria have the potential to become next-generation cell factories due to their ability to use CO₂, light and inorganic nutrients to produce a range of biomolecules of commercial interest. Synechococcus elongatus UTEX 2973, in particular, is a fast-growing, genetically tractable, cyanobacterium that has garnered attention as a potential biotechnological chassis. To establish this unique strain as a host for heterologous protein production, we aimed to demonstrate expression and secretion of the industrially relevant TfAA10A, a lytic polysaccharide monooxygenase from the Gram-positive bacterium Thermobifida fusca.

RESULTS

Two variations of TfAA10A were successfully expressed in S. elongatus UTEX 2973: One containing the native N-terminal, Sec-targeted, signal peptide and a second with a Tat-targeted signal peptide from the Escherichia coli trimethylamine-N-oxide reductase (TorA). Although the TorA signal peptide correctly targeted the protein to the plasma membrane, the majority of the TorA-TfAA10A was found unprocessed in the plasma membrane with a small fraction of the mature protein ultimately translocated to the periplasm. The native Sec signal peptide allowed for efficient secretion of TfAA10A into the medium with virtually no protein being found in the cytosol, plasma membrane or periplasm. TfAA10A was demonstrated to be correctly cleaved and active on the model substrate phosphoric acid swollen cellulose. Additionally, expression and secretion only had a minor impact on cell growth. The secretion yield was estimated at 779 ± 40 µg L^-1 based on densitometric analysis. To our knowledge, this is the highest secretion yield ever registered in cyanobacteria.

CONCLUSIONS

We have shown for the first time high-titer expression and secretion of an industrially relevant and catalytically active enzyme in S. elongatus UTEX 2973. This proof-of-concept study will be valuable for the development of novel and sustainable applications in the fields of bioremediation and biocatalysis.

Brain haemorrhage detection using a SVM classifier with electrical impedance tomography measurement frames

Brain haemorrhages often require urgent treatment with a consequent need for quick and accurate diagnosis. Therefore, in this study, we investigate Support Vector Machine (SVM) classifiers for detecting brain haemorrhages using Electrical Impedance Tomography (EIT) measurement frames. A 2-layer model of the head, along with a series of haemorrhages, is designed as both numerical models and physical phantoms. EIT measurement frames, taken from an electrode array placed on the head surface, are used to train and test linear SVM classifiers. Various scenarios are implemented on both platforms to examine the impact of variables such as noise level, lesion location, lesion size, variation in electrode positioning, and variation in anatomy, on the classifier performance. The classifier performed well in numerical models (sensitivity and specificity of 90%+) with signal-to-noise ratios of 60 dB+, was independent of lesion location, and could detect lesions reliably down to the tested minimum volume of 5 ml. Slight variations in electrode layout did not affect performance. Performance was affected by variations in anatomy however, emphasising the need for large training sets covering different anatomies. The phantom models proved more challenging, with maximal sensitivity and specificity of 75% when used with the linear SVM. Finally, the performance of two more complex classifiers is briefly examined and compared to the linear SVM classifier. These results demonstrate that a radial basis function (RBF) SVM classifier and a neural network classifier can improve detection accuracy. Classifiers applied to EIT measurement frames is a novel approach for lesion detection and may offer an effective diagnostic tool clinically. A challenge is to translate the strong results from numerical models into real world phantoms and ultimately human patients, as well as the selection and development of optimal classifiers for this application.