The safety of early pharmacological venous thromboembolism prophylaxis in patients with traumatic intracranial haemorrhage: a systematic review and meta-analysis.

INTRODUCTION: In patients with traumatic intracranial haemorrhage (tICH) there is significant risk of both venous thromboembolism (VTE) and haemorrhage progression. There is a paucity of literature to inform the timing of pharmacological thromboprophylaxis (PTP) initiation. AIM: This meta-analysis aims to summarise the current literature on the timing of PTP initiation in tICH. METHODS: This meta-analysis followed the Methodological Expectations of Cochrane Intervention Reviews checklist and the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Following the literature search, studies were matched against the criteria for inclusion. Data from included studies was pooled, analysed using random-effect analysis and presented as forest plots of risk ratios, except one result reported as difference of means. The ROBINS-I tool was used to assess the risk of bias in the studies. The GRADE approach was taken to assess the quality of included studies. Heterogeneity of studies was assessed using Tau∧2. Funnel plots were generated and used in conjunction with Harbord's test and Rucker's arcsine to assess for small-study effect including publication bias. RESULTS: A total of 9927 ICH patients who received PTP were included from 15 retrospective observational cohort studies, 4807 patients received early PTP, the remaining 5120 received late PTP. The definition of early was dependent on the study but no more than 72-hours after admission. The mean age of the included cohort was 45.3 (std dev ±9.5) years, and the proportion of males was 71%. Meta-analysis indicated that there was a significant difference between early and late groups for the rate of VTE (RR, 0.544; p = 0.000), pulmonary embolus (RR, 0.538; p = 0.004), deep vein thrombosis (RR, 0.484; p = 0.000) and the intensive care unit length of stay (difference of means, -2.021; 95% CI, -2.250, -1.792; p = 0.000; Tau∧2 = 0.000), favouring the early group. However, the meta-analysis showed no significant difference between the groups for the rate of mortality (RR, 1.008; p = 0.936), tICH progression (RR, 0.853; p = 0.157), and neurosurgical intervention (RR, 0.870; p = 0.480). CONCLUSION: These findings indicated that early PTP appears to be safe and effective in patients with tICH.

Substrate specificity of human chymotrypsin-like protease (CTRL) characterized by phage display-selected small-protein inhibitors.

Chymotrypsin-like protease (CTRL) is one of the four chymotrypsin isoforms expressed in the human exocrine pancreas. Human genetic and experimental evidence indicate that chymotrypsins B1, B2, and C (CTRB1, CTRB2 and CTRC) are important not only for protein digestion but also for protecting the pancreas against pancreatitis by degrading potentially harmful trypsinogen. CTRL has not been reported to play a similar role, possibly due to its low abundance and/or different substrate specificity. To address this problem, we investigated the specificity of the substrate-binding groove of CTRL by evolving the substrate-like canonical loop of the Schistocerca gregaria proteinase inhibitor 2 (SGPI-2), a small-protein reversible chymotrypsin inhibitor to bind CTRL. We found that phage-associated SGPI-2 variants with strong affinity to CTRL were similar to those evolved previously against CTRB1, CTRB2 or bovine chymotrypsin A (bCTRA), indicating comparable substrate specificity. When tested as recombinant proteins, SGPI-2 variants inhibited CTRL with similar or slightly weaker affinity than bCTRA, confirming that CTRL is a typical chymotrypsin. Interestingly, an SGPI-2 variant selected with a Thr29His mutation in its reactive loop was found to inhibit CTRL strongly, but it was digested rapidly by bCTRA. Finally, CTRL was shown to degrade human anionic trypsinogen, however, at a much slower rate than CTRB2, suggesting that CTRL may not have a significant role in the pancreatic defense mechanisms against inappropriate trypsinogen activation and pancreatitis.

MRI-Based Radiomics in Bladder Cancer: A Systematic Review and Radiomics Quality Score Assessment.

(1): Background: With the recent introduction of vesical imaging reporting and data system (VI-RADS), magnetic resonance imaging (MRI) has become the main imaging method used for the preoperative local staging of bladder cancer (BCa). However, the VI-RADS score is subject to interobserver variability and cannot provide information about tumor cellularity. These limitations may be overcome by using a quantitative approach, such as the new emerging domain of radiomics. (2) Aim: To systematically review published studies on the use of MRI-based radiomics in bladder cancer. (3) Materials and Methods: We performed literature research using the PubMed MEDLINE, Scopus, and Web of Science databases using PRISMA principles. A total of 1092 papers that addressed the use of radiomics for BC staging, grading, and treatment response were retrieved using the keywords "bladder cancer", "magnetic resonance imaging", "radiomics", and "textural analysis". (4) Results: 26 papers met the eligibility criteria and were included in the final review. The principal applications of radiomics were preoperative tumor staging (n = 13), preoperative prediction of tumor grade or molecular correlates (n = 9), and prediction of prognosis/response to neoadjuvant therapy (n = 4). Most of the developed radiomics models included second-order features mainly derived from filtered images. These models were validated in 16 studies. The average radiomics quality score was 11.7, ranging between 8.33% and 52.77%. (5) Conclusions: MRI-based radiomics holds promise as a quantitative imaging biomarker of BCa characterization and prognosis. However, there is still need for improving the standardization of image preprocessing, feature extraction, and external validation before applying radiomics models in the clinical setting.

A Light, 3D UNet-based Architecture for Fully Automatic Segmentation of Prostate Lesions from T2-MRI Images.

INTRODUCTION: Prostate magnetic resonance imaging (MRI) has been recently integrated into the pathway of diagnosis of prostate cancer (PCa). However, the lack of an optimal contrast-to-noise ratio hinders automatic recognition of suspicious lesions, thus developing a solution for proper delimitation of the tumour and its separation from the healthy parenchyma, which is of primordial importance. METHOD: As a solution to this unmet medical need, we aimed to develop a decision support system based on artificial intelligence, which automatically segments the prostate and any suspect area from the 3D MRI images. We assessed retrospective data from all patients diagnosed with PCa by MRI-US fusion prostate biopsy, who underwent prostate MRI in our department due to a clinical or biochemical suspicion of PCa (n=33). All examinations were performed using a 1.5 Tesla MRI scanner. All images were reviewed by two radiologists, who performed manual segmentation of the prostate and all lesions. A total of 145 augmented datasets were generated. The performance of our fully automated end-to-end segmentation model based on a 3D UNet architecture and trained in two learning scenarios (on 14 or 28 patient datasets) was evaluated by two loss functions. RESULTS: Our model had an accuracy of over 90% for automatic segmentation of prostate and PCa nodules, as compared to manual segmentation. We have shown low complexity networks, UNet architecture with less than five layers, as feasible and to show good performance for automatic 3D MRI image segmentation. A larger training dataset could further improve the results. CONCLUSION: Therefore, herein, we propose a less complex network, a slim 3D UNet with superior performance, being faster than the original five-layer UNet architecture.

Fully automated bladder tumor segmentation from T2 MRI images using 3D U-Net algorithm.

Introduction: Bladder magnetic resonance imaging (MRI) has been recently integrated in the diagnosis pathway of bladder cancer. However, automatic recognition of suspicious lesions is still challenging. Thus, development of a solution for proper delimitation of the tumor and its separation from the healthy tissue is of primordial importance. As a solution to this unmet medical need, we aimed to develop an artificial intelligence-based decision support system, which automatically segments the bladder wall and the tumor as well as any suspect area from the 3D MRI images. Materials: We retrospectively assessed all patients diagnosed with bladder cancer, who underwent MRI at our department (n=33). All examinations were performed using a 1.5 Tesla MRI scanner. All images were reviewed by two radiologists, who performed manual segmentation of the bladder wall and all lesions. First, the performance of our fully automated end-to-end segmentation model based on a 3D U-Net architecture (by considering various depths of 4, 5 or 6 blocks) trained in two data augmentation scenarios (on 5 and 10 augmentation datasets per original data, respectively) was tested. Second, two learning setups were analyzed by training the segmentation algorithm with 7 and 14 MRI original volumes, respectively. Results: We obtained a Dice-based performance over 0.878 for automatic segmentation of bladder wall and tumors, as compared to manual segmentation. A larger training dataset using 10 augmentations for 7 patients could further improve the results of the U-Net-5 model (0.902 Dice coefficient at image level). This model performed best in terms of automated segmentation of bladder, as compared to U-Net-4 and U-Net-6. However, in this case increased time for learning was needed as compared to U-Net-4. We observed that an extended dataset for training led to significantly improved segmentation of the bladder wall, but not of the tumor. Conclusion: We developed an intelligent system for bladder tumors automated diagnostic, that uses a deep learning model to segment both the bladder wall and the tumor. As a conclusion, low complexity networks, with less than five-layers U-Net architecture are feasible and show good performance for automatic 3D MRI image segmentation in patients with bladder tumors.

Molecular Dynamics Simulation as a Tool to Identify Mutual Synergistic Folding Proteins.

Mutual synergistic folding (MSF) proteins belong to a recently emerged subclass of disordered proteins, which are disordered in their monomeric forms but become ordered in their oligomeric forms. They can be identified by experimental methods following their unfolding, which happens in a single-step cooperative process, without the presence of stable monomeric intermediates. Only a limited number of experimentally validated MSF proteins are accessible. The amino acid composition of MSF proteins shows high similarity to globular ordered proteins, rather than to disordered ones. However, they have some special structural features, which makes it possible to distinguish them from globular proteins. Even in the possession of their oligomeric three-dimensional structure, classification can only be performed based on unfolding experiments, which are frequently absent. In this work, we demonstrate a simple protocol using molecular dynamics simulations, which is able to indicate that a protein structure belongs to the MSF subclass. The presumption of the known atomic resolution quaternary structure is an obvious limitation of the method, and because of its high computational time requirements, it is not suitable for screening large databases; still, it is a valuable in silico tool for identification of MSF proteins.

Lung vascular changes as biomarkers of severity in systemic sclerosis-associated interstitial lung disease.

OBJECTIVES: It has recently become possible to assess lung vascular and parenchymal changes quantitatively in thoracic CT images using automated software tools. We investigated the vessel parameters of patients with SSc, quantified by CT imaging, and correlated them with interstitial lung disease (ILD) features. METHODS: SSc patients undergoing standard of care pulmonary function testing and CT evaluation were retrospectively evaluated. CT images were analysed for ILD patterns and total pulmonary vascular volume (PVV) extents with Imbio lung texture analysis. Vascular analysis (volumes, numbers and densities of vessels, separating arteries and veins) was performed with an in-house developed software. A threshold of 5% ILD extent was chosen to define the presence of ILD, and commonly used cut-offs of lung function were adopted. RESULTS: A total of 79 patients [52 women, 40 ILD, mean age 56.2 (s.d. 14.2) years, total ILD extent 9.5 (10.7)%, PVV/lung volume % 2.8%] were enrolled. Vascular parameters for total and separated PVV significantly correlated with functional parameters and ILD pattern extents. SSc-associated ILD (SSc-ILD) patients presented with an increased number and volume of arterial vessels, in particular those between 2 and 4 mm of diameter, and with a higher density of arteries and veins of <6 mm in diameter. Considering radiological and functional criteria concomitantly, as well as the descriptive trends from the longitudinal evaluations, the normalized PVVs, vessel numbers and densities increased progressively with the increase/worsening of ILD extent and functional impairment. CONCLUSION: In SSc patients CT vessel parameters increase in parallel with ILD extent and functional impairment, and may represent a biomarker of SSc-ILD severity.

Arg236 in human chymotrypsin B2 (CTRB2) is a key determinant of high enzyme activity, trypsinogen degradation capacity, and protection against pancreatitis.

Pancreatic chymotrypsins (CTRs) are digestive proteases that in humans include CTRB1, CTRB2, CTRC, and CTRL. The highly similar CTRB1 and CTRB2 are the products of gene duplication. A common inversion at the CTRB1-CTRB2 locus reverses the expression ratio of these isoforms in favor of CTRB2. Carriers of the inversion allele are protected against the inflammatory disorder pancreatitis presumably via their increased capacity for CTRB2-mediated degradation of harmful trypsinogen. To reveal the protective molecular determinants of CTRB2, we compared enzymatic properties of CTRB1, CTRB2, and bovine CTRA (bCTRA). By evolving substrate-like Schistocerca gregaria proteinase inhibitor 2 (SGPI-2) inhibitory loop variants against the chymotrypsins, we found that the substrate binding groove of the three enzymes had overlapping specificities. Based on the selected sequences, we produced eight SGPI-2 variants. Remarkably, CTRB2 and bCTRA bound these inhibitors with significantly higher affinity than CTRB1. Moreover, digestion of peptide substrates, beta casein, and human anionic trypsinogen unequivocally confirmed that CTRB2 is a generally better enzyme than CTRB1 while the potency of bCTRA lies between those of the human isoforms. Unexpectedly, mutation D236R alone converted CTRB1 to a CTRB2-like high activity protease. Modeling indicated that in CTRB1 Met210 partially obstructed the substrate binding groove, which was relieved by the D236R mutation. Taken together, we identify CTRB2 Arg236 as a key positive determinant, while CTRB1 Asp236 as a negative determinant for chymotrypsin activity. These findings strongly support the concept that in carriers of the CTRB1-CTRB2 inversion allele, the superior trypsinogen degradation capacity of CTRB2 protects against pancreatitis.

More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis-A Systematic Review.

(1) Introduction: Multiparametric magnetic resonance imaging (mpMRI) is the main imagistic tool employed to assess patients suspected of harboring prostate cancer (PCa), setting the indication for targeted prostate biopsy. However, both mpMRI and targeted prostate biopsy are operator dependent. The past decade has been marked by the emerging domain of radiomics and artificial intelligence (AI), with extended application in medical diagnosis and treatment processes. (2) Aim: To present the current state of the art regarding decision support tools based on texture analysis and AI for the prediction of aggressiveness and biopsy assistance. (3) Materials and Methods: We performed literature research using PubMed MeSH, Scopus and WoS (Web of Science) databases and screened the retrieved papers using PRISMA principles. Articles that addressed PCa diagnosis and staging assisted by texture analysis and AI algorithms were included. (4) Results: 359 papers were retrieved using the keywords "prostate cancer", "MRI", "radiomics", "textural analysis", "artificial intelligence", "computer assisted diagnosis", out of which 35 were included in the final review. In total, 24 articles were presenting PCa diagnosis and prediction of aggressiveness, 7 addressed extracapsular extension assessment and 4 tackled computer-assisted targeted prostate biopsies. (5) Conclusions: The fusion of radiomics and AI has the potential of becoming an everyday tool in the process of diagnosis and staging of the prostate malignancies.

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer.

BACKGROUND: Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine. METHODS: Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types. RESULTS: Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 ± 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 ± 0.03) or SERS data (AUC = 0.84 ± 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 ± 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 ± 0.04) or SERS (AUC = 0.92 ± 0.05) individually, although SERS alone performed better in terms of classification accuracy. CONCLUSION: miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged.

SERS Liquid Biopsy Profiling of Serum for the Diagnosis of Kidney Cancer.

Renal cancer (RC) represents 3% of all cancers, with a 2% annual increase in incidence worldwide, opening the discussion about the need for screening. However, no established screening tool currently exists for RC. To tackle this issue, we assessed surface-enhanced Raman scattering (SERS) profiling of serum as a liquid biopsy strategy to detect renal cell carcinoma (RCC), the most prevalent histologic subtype of RC. Thus, serum samples were collected from 23 patients with RCC and 27 controls (CTRL) presenting with a benign urological pathology such as lithiasis or benign prostatic hypertrophy. SERS profiling of deproteinized serum yielded SERS band spectra attributed mainly to purine metabolites, which exhibited higher intensities in the RCC group, and Raman bands of carotenoids, which exhibited lower intensities in the RCC group. Principal component analysis (PCA) of the SERS spectra showed a tendency for the unsupervised clustering of the two groups. Next, three machine learning algorithms (random forest, kNN, naïve Bayes) were implemented as supervised classification algorithms for achieving discrimination between the RCC and CTRL groups, yielding an AUC of 0.78 for random forest, 0.78 for kNN, and 0.76 for naïve Bayes (average AUC 0.77 ± 0.01). The present study highlights the potential of SERS liquid biopsy as a diagnostic and screening strategy for RCC. Further studies involving large cohorts and other urologic malignancies as controls are needed to validate the proposed SERS approach.

SERS-based DNA methylation profiling allows the differential diagnosis of malignant lymphadenopathy.

This study highlights the potential of surface-enhanced Raman scattering (SERS) to differentiate between B-cell lymphoma (BCL), T-cell lymphoma (TCL), lymph node metastasis of melanoma (Met) and control (Ctr) samples based on the specific SERS signal of DNA extracted from lymph node tissue biopsy. Differences in the methylation profiles as well as the specific interaction of malignant and non-malignant DNA with the metal nanostructure are captured in specific variations of the band at 1005 cm-1, attributed to 5-methylcytosine and the band at 730 cm-1, attributed to adenine. Thus, using the area ratio of these two SERS marker bands as input for univariate classification, an area under the curve (AUC) of 0.70 was achieved in differentiating between malignant and non-malignant DNA. In addition, DNA from the BCL and TCL groups exhibited differences in the area of the SERS band at 730 cm-1, yielding an AUC of 0.84 in differentiating between these two lymphadenopathies. Lastly, using multivariate data analysis techniques, an overall accuracy of 94.7% was achieved in the differential diagnosis between the BCL, TCL, Met and Ctr groups. These results pave the way towards the implementation of SERS as a novel tool in the clinical setting for improving the diagnosis of malignant lymphadenopathy.

Apelin-17 to diagnose idiopathic pulmonary arterial hypertension: A biomarker study.

Background: NT-proBNP and GDF-15 are established blood-derived biomarkers for risk assessment in pulmonary hypertension (PH), despite limited sensitivity and specificity. Apelin has a crucial function in endothelial homeostasis, thus it might represent a new biomarker for PH. However, there are numerous circulating apelin isoforms, and their potential role in this setting is unknown. This study evaluated different apelin isoforms in PH patients and prospectively evaluated the role of apelin-17 in comparison with NT-proBNP and GDF-15 as diagnostic marker in idiopathic pulmonary arterial hypertension (IPAH). Methods: Based on our pilot study, we performed a power calculation for apelin-13, apelin-17, apelin-36, as predictor of IPAH vs healthy controls. Apelin-17 provided the best discriminatory power, and accordingly, we enrolled n = 31 patients with IPAH and n = 31 matched healthy controls in a prospective study. NT-proBNP and GDF-15 was determined in all patients. ROC curve analysis was performed to assess the diagnostic value of the markers and their combinations. Results: Apelin-17, NT-proBNP, and GDF-15 were significantly elevated in IPAH patients as compared to controls (p < .001). Apelin-17 detected IPAH with a sensitivity of 68% and a specificity of 93% at a cut-off value of >1,480 pg/ml (AUC 0.86, 95%CI:0.76-0.95) as compared to GDF-15 (sensitivity 86%; specificity 72%, AUC 0.81 (95%CI:0.7-0.92)) and NT-proBNP (sensitivity 86%; specificity 72% (AUC 0.85, 95%CI:0.75-0.95)). Combinations of these markers could be used to increase either specificity or sensitivity. Conclusion: Apelin-17 appears to be suitable blood derived diagnostic marker for idiopathic pulmonary arterial hypertension.

Correlation between Volumes Determined by Echocardiography and Cardiac MRI in Controls and Atrial Fibrillation Patients.

AIMS: We aimed to compare cardiac volumes measured with echocardiography (echo) and cardiac magnetic resonance imaging (MRI) in a mixed cohort of healthy controls (controls) and patients with atrial fibrillation (AF). MATERIALS AND METHODS: In total, 123 subjects were included in our study; 99 full datasets were analyzed. All the participants underwent clinical evaluation, EKG, echo, and cardiac MRI acquisition. Participants with full clinical data were grouped into 63 AF patients and 36 controls for calculation of left atrial volume (LA Vol) and 51 AF patients and 30 controls for calculation of left ventricular end-diastolic volume (LV EDV), end-systolic volume (ESV), and LV ejection fraction (LV EF). RESULTS: No significant differences in LA Vol were observed (p > 0.05) when measured by either echo or MRI. However, echo provided significantly lower values for left ventricular volume (p < 0.0001). The echo LA Vol of all the subjects correlated well with that measured by MRI (Spearmen correlation coefficient r = 0.83, p < 0.0001). When comparing the two methods, significant positive correlations of EDV (all subjects: r = 0.55; Controls: r = 0.71; and AF patients: r = 0.51) and ESV (all subjects: r = 0.62; Controls: r = 0.47; and AF patients: r = 0.66) were found, with a negative bias for values determined using echo. For a subgroup of participants with ventricular volumes smaller than 49.50 mL, this bias was missing, thus in this case echocardiography could be used as an alternative for MRI. CONCLUSION: Good correlation and reduced bias were observed for LA Vol and EF determined by echo as compared to cardiac MRI in a mixed cohort of patients with AF and healthy volunteers. For the determination of volume values below 49.50 mL, an excellent correlation was observed between values obtained using echo and MRI, with comparatively reduced bias for the volumes determined by echo. Therefore, in certain cases, echocardiography could be used as a less expensive, less time-consuming, and contraindication free alternative to MRI for cardiac volume determination.

SERS liquid biopsy: An emerging tool for medical diagnosis.

Surface-enhanced Raman scattering (SERS) is emerging as a novel strategy for biofluid analysis. In this review, we delineate four experimental SERS protocols that are frequently used for the profiling of biofluids: 1) liquid SERS for the detection of purine metabolites; 2) iodide-modified liquid SERS for the detection of proteins; 3) dried SERS for the detection of both purine metabolites and proteins; 4) resonant Raman for the detection of carotenoids. To explain the selectivity of each experimental SERS protocol, we introduce a heuristic model for the chemisorption of analytes mediated by adsorbed ions (adions) onto the SERS substrate. Next, we show that the promising results of SERS liquid biopsy stem from the fact that the concentration levels of purine metabolites, proteins and carotenoids are informative of the cellular turnover rate, inflammation, and oxidative stress, respectively. These processes are perturbed in virtually every disease, from cancer to autoimmune maladies. Finally, we review recent SERS liquid biopsy studies and discuss future steps that are required for translating SERS in the clinical setting.

A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm Optimization.

Edge detection is a fundamental image analysis task, as it provides insight on the content of an image. There are weaknesses in some of the edge detectors developed until now, such as disconnected edges, the impossibility to detect branching edges, or the need for a ground truth that is not always accessible. Therefore, a specialized detector that is optimized for the image particularities can help improve edge detection performance. In this paper, we apply transfer learning to optimize cellular automata (CA) rules for edge detection using particle swarm optimization (PSO). Cellular automata provide fast computation, while rule optimization provides adaptability to the properties of the target images. We use transfer learning from synthetic to medical images because expert-annotated medical data is typically difficult to obtain. We show that our method is tunable for medical images with different properties, and we show that, for more difficult edge detection tasks, batch optimization can be used to boost the quality of the edges. Our method is suitable for the identification of structures, such as cardiac cavities on medical images, and could be used as a component of an automatic radiology decision support tool.

Butterfly Effect in Chaotic Image Segmentation.

The exploitation of the important features exhibited by the complex systems found in the surrounding natural and artificial space will improve computational model performance. Therefore, the purpose of the current paper is to use cellular automata as a tool simulating complexity, able to bring forth an interesting global behaviour based only on simple, local interactions. We show that, in the context of image segmentation, a butterfly effect arises when we perturb the neighbourhood system of a cellular automaton. Specifically, we enhance a classical GrowCut cellular automaton with chaotic features, which are also able to improve its performance (e.g., a Dice coefficient of 71% in case of 2D images). This enhanced GrowCut flavor (referred to as Band-Based GrowCut) uses an extended, stochastic neighbourhood, in which randomly-selected remote neighbours reinforce the standard local ones. We demonstrate the presence of the butterfly effect and an increase in segmentation performance by numerical experiments performed on synthetic and natural images. Thus, our results suggest that, by having small changes in the initial conditions of the performed task, we can induce major changes in the final outcome of the segmentation.

Assessment of Gold-Coated Iron Oxide Nanoparticles as Negative T2 Contrast Agent in Small Animal MRI Studies.

PURPOSE: Magnetic resonance imaging (MRI) contrast agents are pharmaceuticals that enable a better visualization of internal body structures. In this study, we present the synthesis, MRI signal enhancement capabilities, in vitro as well as in vivo cytotoxicity results of gold-coated iron oxide nanoparticles (Fe3O4@AuNPs) as potential contrast agents. METHODS: Fe3O4@AuNPs were obtained by synthesizing iron oxide nanoparticles and gradually coating them with gold. The obtained Fe3O4@AuNPs were characterized by spectroscopies, transmission electron microscopy (TEM) and energy dispersive X-ray diffraction. The effect of the nanoparticles on the MRI signal was tested using a 7T Bruker PharmaScan system. Cytotoxicity tests were made in vitro on Fe3O4@AuNP-treated retinal pigment epithelium cells by WST-1 tests and in vivo by following histopathological changes in rats after injection of Fe3O4@AuNPs. RESULTS: Stable Fe3O4@AuNPs were successfully prepared following a simple and fast protocol (<1h worktime) and identified using TEM. The cytotoxicity tests on cells have shown biocompatibility of Fe3O4@AuNPs at small concentrations of Fe (<1.95×10-8 mg/cell). Whereas, at higher Fe concentrations (eg 7.5×10-8 mg/cell), cell viability decreased to 80.88±5.03%, showing a mild cytotoxic effect. MRI tests on rats showed an optimal Fe3O4@AuNPs concentration of 6mg/100g body weight to obtain high-quality images. The histopathological studies revealed significant transient inflammatory responses in the time range from 2 hours to 14 days after injection and focal cellular alterations in several organs, with the lung being the most affected organ. These results were confirmed by hyperspectral microscopic imaging of the same, but unstained tissues. In most organs, the inflammatory responses and sublethal cellular damage appeared to be transitory, except for the kidneys, where the glomerular damage indicated progression towards glomerular sclerosis. CONCLUSION: The obtained stable, gold covered, iron oxide nanoparticles with reduced cytotoxicity, gave a negative T2 signal in the MRI, which makes them suitable for candidates as contrast agent in small animal MRI applications.

SERS-based quantification of albuminuria in the normal-to-mildly increased range.

The lack of an accurate point-of-care detection system for microalbuminuria represents an important unmet medical need that contributes to the morbidity and mortality of patients with kidney diseases. In this proof-of-concept study, we used SERS spectroscopy to detect urinary albumin concentrations in the normal-to-mildly increased albuminuria range, a strategy that could be useful for the early diagnosis of renal impairment due to uncontrolled hypertension, cardiovascular disease or diabetes. We analyzed 27 urine samples by SERS, using iodide-modified silver nanoparticles and we could discriminate between groups with high and low albumin concentrations with an overall accuracy of 89%, 93% and 89%, using principal component analysis-linear discriminant analysis and cut-off values of 3, 6 and 10 µg mL-1 for urinary albumin concentrations, respectively. We achieved a detection limit of 3 µg mL-1 for human serum albumin based on the 1002 cm-1 SERS band, attributed to the ring breathing vibration of phenylalanine. Our detection limit is similar to that of the immunoturbidimetric assays and around one order of magnitude below the detection limit of urinary dipsticks used to detect microalbuminuria. We used principal least squares regression for building a spectral model for quantifying albumin. Using an independent prediction set, the R2 and root mean squared error of prediction between predicted and reference values of human serum albumin concentrations were 0.982 and 2.82, respectively. Here, we show that direct SERS spectroscopy has the sensitivity required for detecting clinically relevant concentrations of urinary albumin, a strategy that could be used in the future for the point-of-care screening of microalbuminuria.

Docking of Meprin α to Heparan Sulphate Protects the Endothelium from Inflammatory Cell Extravasation.

Pulmonary arterial hypertension (PAH) is a rare disease characterized by increased pulmonary pressure and vascular remodelling as a consequence of smooth muscle cell proliferation, endothelial cell dysfunction and inflammatory infiltrates. Meprin α is a metalloproteinase whose substrates include adhesion and cell-cell contact molecules involved in the process of immune cell extravasation. In this study, we aimed to unravel the role of meprin α in PAH-induced vascular remodelling. Our results showed that meprin α was present in the apical membrane of endothelial cells in the lungs and pulmonary arteries of donors and idiopathic PAH (IPAH) patients. Elevated circulating meprin α levels were detected in the plasma of IPAH patients. In vitro binding assays and electron microscopy confirmed binding of meprin α to the glycocalyx of human pulmonary artery endothelial cells (hPAECs). Enzymatic and genetic approaches identified heparan sulphate (HS) as an important determinant of the meprin α binding capacity to hPAEC. Meprin α treatment protected from excessive neutrophil infiltration and the protective effect observed in the presence of neutrophils was partially reversed by removal of HS from hPAEC. Importantly, HS levels in pulmonary arteries were decreased in IPAH patients and binding of meprin α to HS was impaired in IPAH hPAEC. In summary, our results suggest a role of HS in docking meprin α to the endothelium and thus in the modulation of inflammatory cell extravasation. In IPAH, the decreased endothelial HS results in the reduction of meprin α binding which might contribute to enhanced inflammatory cell extravasation and potentially to pathological vascular remodelling.