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Glucose is the primary source of energy for many organisms and is efficiently taken up by bacteria through a dedicated transport system that exhibits high specificity. In Escherichia coli, the glucose-specific transporter IICBGlc serves as the major glucose transporter and functions as a component of the phosphoenolpyruvate-dependent phosphotransferase system. Here, we report cryo-electron microscopy (cryo-EM) structures of the glucose-bound IICBGlc protein. The dimeric transporter embedded in lipid nanodiscs was captured in the occluded, inward- and occluded, outward-facing conformations. Together with biochemical and biophysical analyses, and molecular dynamics (MD) simulations, we provide insights into the molecular basis and dynamics for substrate recognition and binding, including the gates regulating the binding sites and their accessibility. By combination of these findings, we present a mechanism for glucose transport across the plasma membrane. Overall, this work provides molecular insights into the structure, dynamics, and mechanism of the IICBGlc transporter in a native-like lipid environment. Glucose is a key energy source for many organisms, efficiently transported in bacteria by specific systems. Here, the authors reveal cryo-EM structures of the glucose transporter IICB from E. coli, providing insights into its mechanism and dynamics. [ABSTRACT FROM AUTHOR]

Microbial ion-pumping rhodopsins (MRs) are extensively studied retinal-binding membrane proteins. However, their biogenesis, including oligomerisation and retinal incorporation, remains poorly understood. The bacterial green-light absorbing proton pump proteorhodopsin (GPR) has emerged as a model protein for MRs and is used here to address these open questions using cryo-electron microscopy (cryo-EM) and molecular dynamics (MD) simulations. Specifically, conflicting studies regarding GPR stoichiometry reported pentamer and hexamer mixtures without providing possible assembly mechanisms. We report the pentameric and hexameric cryo-EM structures of a GPR mutant, uncovering the role of the unprocessed N-terminal signal peptide in the assembly of hexameric GPR. Furthermore, certain proteorhodopsin-expressing bacteria lack retinal biosynthesis pathways, suggesting that they scavenge the cofactor from their environment. We shed light on this hypothesis by solving the cryo-EM structure of retinal-free proteoopsin, which together with mass spectrometry and MD simulations suggests that decanoate serves as a temporary placeholder for retinal in the chromophore binding pocket. Further MD simulations elucidate possible pathways for the exchange of decanoate and retinal, offering a mechanism for retinal scavenging. Collectively, our findings provide insights into the biogenesis of MRs, including their oligomeric assembly, variations in protomer stoichiometry and retinal incorporation through a potential cofactor scavenging mechanism. Microbial rhodopsins are prevalent retinal-binding membrane proteins. Here, authors reveal the roles of the N-terminal signal peptide in oligomerization and of decanoate as a retinal placeholder, providing proteorhodopsin structures and retinal incorporation mechanisms. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive "sick-care" approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients. This paper explores the approaches in the domain of five EU projects working on the creation of ethically compliant and GDPR-regulated European medical imaging platforms, focused on cancer-related data. It presents the individual approaches to the de-identification of imaging data, and describes the problems and the solutions adopted in each case. Further, lessons learned are provided, enabling future projects to optimally handle the problem of data de-identification. Critical relevance statement: This paper presents key approaches from five flagship EU projects for the de-identification of imaging and clinical data offering valuable insights and guidelines in the domain. Key Points: ΑΙ models for health imaging require access to large amounts of data. Access to large imaging datasets requires an appropriate de-identification process. This paper provides de-identification guidelines from the AI for health imaging (AI4HI) projects. [ABSTRACT FROM AUTHOR]

Telehealth demand is rapidly growing along with the necessity of providing wide-scale services covering multiple patients at the same time. In this work, the development of a store-and-forward (SAF) teledermoscopy system was considered. The dermoFeatures profile (DP) was proposed to decrease the size of the original dermoscopy image using its most significant features in the form of a newly generated diagonal alignment to generate a small-sized image DP, which is based on the extraction of a weighted intensity-difference frequency (WIDF) features along with morphological features (MOFs). These DPs were assembled to establish a Diagnostic Multiple-patient DermoFeature Profile (DMpDP). Different arrangements are proposed, namely the horizontally aligned, the diagonal-based, and the sequential-based DMpDPs to support the SAF systems. The DMpDPs are then embedded in a recorded patient-information signal (RPS) using a weight factor β to boost the transmitted patient-information signal. The effect of the different transform domains, β values, and number of DPs within the DMpDP were investigated in terms of the diagnostic classification accuracy at the receiver based on the extracted DPs, along with the recorded signal quality evaluation metrics of the recovered RPS. The sequential-based DMpDP achieved the highest classification accuracy, under − 5 dB additive white Gaussian noise, with a realized signal-to-noise ratio of 98.79% during the transmission of 248 DPs using β = 100, and spectral subtraction filtering. [ABSTRACT FROM AUTHOR]

Identifying individual patient characteristics that contribute to long-term mental health deterioration following diagnosis of breast cancer (BC) is critical in clinical practice. The present study employed a supervised machine learning pipeline to address this issue in a subset of data from a prospective, multinational cohort of women diagnosed with stage I–III BC with a curative treatment intention. Patients were classified as displaying stable HADS scores (Stable Group; n = 328) or reporting a significant increase in symptomatology between BC diagnosis and 12 months later (Deteriorated Group; n = 50). Sociodemographic, life-style, psychosocial, and medical variables collected on the first visit to their oncologist and three months later served as potential predictors of patient risk stratification. The flexible and comprehensive machine learning (ML) pipeline used entailed feature selection, model training, validation and testing. Model-agnostic analyses aided interpretation of model results at the variable- and patient-level. The two groups were discriminated with a high degree of accuracy (Area Under the Curve = 0.864) and a fair balance of sensitivity (0.85) and specificity (0.87). Both psychological (negative affect, certain coping with cancer reactions, lack of sense of control/positive expectations, and difficulties in regulating negative emotions) and biological variables (baseline percentage of neutrophils, thrombocyte count) emerged as important predictors of mental health deterioration in the long run. Personalized break-down profiles revealed the relative impact of specific variables toward successful model predictions for each patient. Identifying key risk factors for mental health deterioration is an essential first step toward prevention. Supervised ML models may guide clinical recommendations toward successful illness adaptation. [ABSTRACT FROM AUTHOR]

Endothelial wall shear stress (ESS) is a biomechanical force which plays a role in the formation and evolution of atherosclerotic lesions. The purpose of this study is to evaluate coronary computed tomography angiography (CCTA)-based ESS in coronary arteries without atherosclerosis, and to assess factors affecting ESS values. CCTA images from patients with suspected coronary artery disease were analyzed to identify coronary arteries without atherosclerosis. Minimal and maximal ESS values were calculated for 3-mm segments. Factors potentially affecting ESS values were examined, including sex, lumen diameter and distance from the ostium. Segments were categorized according to lumen diameter tertiles into small (< 2.6 mm), intermediate (2.6–3.2 mm) or large (≥ 3.2 mm) segments. A total of 349 normal vessels from 168 patients (mean age 59 ± 9 years, 39% men) were included. ESS was highest in the left anterior descending artery compared to the left circumflex artery and right coronary artery (minimal ESS 2.3 Pa vs. 1.9 Pa vs. 1.6 Pa, p < 0.001 and maximal ESS 3.7 Pa vs. 3.0 Pa vs. 2.5 Pa, p < 0.001). Men had lower ESS values than women, also after adjusting for lumen diameter (p < 0.001). ESS values were highest in small segments compared to intermediate or large segments (minimal ESS 3.8 Pa vs. 1.7 Pa vs. 1.2 Pa, p < 0.001 and maximal ESS 6.0 Pa vs. 2.6 Pa vs. 2.0 Pa, p < 0.001). A weak to strong correlation was found between ESS and distance from the ostium (ρ = 0.22–0.62, p < 0.001). CCTA-based ESS values increase rapidly and become widely scattered with decreasing lumen diameter. This needs to be taken into account when assessing the added value of ESS beyond lumen diameter in highly stenotic lesions. [ABSTRACT FROM AUTHOR]

Automatic segmentation of the prostate of and the prostatic zones on MRI remains one of the most compelling research areas. While different image enhancement techniques are emerging as powerful tools for improving the performance of segmentation algorithms, their application still lacks consensus due to contrasting evidence regarding performance improvement and cross-model stability, further hampered by the inability to explain models' predictions. Particularly, for prostate segmentation, the effectiveness of image enhancement on different Convolutional Neural Networks (CNN) remains largely unexplored. The present work introduces a novel image enhancement method, named RACLAHE, to enhance the performance of CNN models for segmenting the prostate's gland and the prostatic zones. The improvement in performance and consistency across five CNN models (U-Net, U-Net++, U-Net3+, ResU-net and USE-NET) is compared against four popular image enhancement methods. Additionally, a methodology is proposed to explain, both quantitatively and qualitatively, the relation between saliency maps and ground truth probability maps. Overall, RACLAHE was the most consistent image enhancement algorithm in terms of performance improvement across CNN models with the mean increase in Dice Score ranging from 3 to 9% for the different prostatic regions, while achieving minimal inter-model variability. The integration of a feature driven methodology to explain the predictions after applying image enhancement methods, enables the development of a concrete, trustworthy automated pipeline for prostate segmentation on MR images. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the safety and efficacy of innovative retinoic acid (RA) eluting stents with bioabsorbable polymer. Sixty stents divided in ten groups were implanted in the iliac arteries of 30 rabbits. Two polymers ("A", poly (lactic-co-glycolic acid) and "B", polylactic acid), and three doses ("Low", "Medium" and "High") of RA (groups: AL, AM, AH, BL, BM, BH) were used on cobalt chromium stents (Rontis Corporation), one group of bare stent (C), one group (D) of Everolimus eluting stent (Xience-Pro, Abbot Vascular), and two groups of Rontis Everolimus eluting stents coated with polymer A (EA) and B (EB). Treated arteries were explanted after 4 weeks, processed by methyl methacrylate resin and evaluated by histopathology. None of the implanted stents was related with thrombus formation or extensive inflammation. Image analysis showed limited differences between groups regarding area stenosis (BH, D and EB groups had the lower values). Group BH had lower intimal mean thickness than AH (105.1 vs 75.3 μm, p = 0.024). Stents eluting RA, a non-cytotoxic drug, were not related with thrombus formation and had an acceptable degree of stenosis 4 weeks post implantation. RA dose and type of polymer may play role in the biocompatibility of the stents. [ABSTRACT FROM AUTHOR]

Background: Dizziness and imbalance are common symptoms that are often inadequately diagnosed or managed, due to a lack of dedicated specialists. Decision Support Systems (DSS) may support first-line physicians to diagnose and manage these patients based on personalised data. Aim: To examine the diagnostic accuracy and application of the EMBalance DSS for diagnosis and management of common vestibular disorders in primary care. Methods: Patients with persistent dizziness were recruited from primary care in Germany, Greece, Belgium and the UK and randomised to primary care clinicians assessing the patients with (+ DSS) versus assessment without (− DSS) the EMBalance DSS. Subsequently, specialists in neuro-otology/audiovestibular medicine performed clinical evaluation of each patient in a blinded way to provide the "gold standard" against which the + DSS, − DSS and the DSS as a standalone tool (i.e. without the final decision made by the clinician) were validated. Results: One hundred ninety-four participants (age range 25–85, mean = 57.7, SD = 16.7 years) were assigned to the + DSS (N = 100) and to the − DSS group (N = 94). The diagnosis suggested by the + DSS primary care physician agreed with the expert diagnosis in 54%, compared to 41.5% of cases in the − DSS group (odds ratio 1.35). Similar positive trends were observed for management and further referral in the + DSS vs. the − DSS group. The standalone DSS had better diagnostic and management accuracy than the + DSS group. Conclusion: There were trends for improved vestibular diagnosis and management when using the EMBalance DSS. The tool requires further development to improve its diagnostic accuracy, but holds promise for timely and effective diagnosis and management of dizzy patients in primary care. Trial registration number: NCT02704819 (clinicaltrials.gov). [ABSTRACT FROM AUTHOR]

L-lactate is an important metabolite, energy source, and signaling molecule in health and disease. In mammals, its transport across biological membranes is mediated by monocarboxylate transporters (MCTs) of the solute carrier 16 (SLC16) family. Malfunction, overexpression or absence of transporters of this family are associated with diseases such as cancer and type 2 diabetes. Moreover, lactate acts as a signaling molecule and virulence factor in certain bacterial infections. Here, we report the rational, structure-guided identification of potent, nanomolar affinity inhibitors acting on an L-lactate-specific SLC16 homologue from the bacterium Syntrophobacter fumaroxidans (SfMCT). High-resolution crystal structures of SfMCT with bound inhibitors uncovered their interaction mechanism on an atomic level and the role of water molecules in inhibitor binding. The presented systematic approach is a valuable procedure for the identification of L-lactate transport inhibitors. Furthermore, identified inhibitors represent potential tool compounds to interfere with monocarboxylate transport across biological membranes mediated by MCTs. The L-lactate-specific transporter SfMCT is a model SLC16 family homologue for the determination of inhibitors and potential drugs. Here, starting from a weakly-binding non-transported ligand, nanomolar affinity inhibitors of SfMCT are developed and crystal structures of the transporter-ligand complexes are presented unveiling interactions at the atomic level. [ABSTRACT FROM AUTHOR]

Novel approaches in synthetic biology focus on the bottom-up modular assembly of natural, modified natural or artificial components into molecular systems with functionalities not found in nature. A possible application for such techniques is the bioremediation of natural water sources contaminated with small organic molecules (e.g., drugs and pesticides). A simple molecular system to actively accumulate and degrade pollutants could be a bionanoreactor composed of a liposome or polymersome scaffold combined with energizing- (e.g., light-driven proton pump), transporting- (e.g., proton-driven transporter) and degrading modules (e.g., enzyme). This work focuses on the engineering of a transport module specific for β-lactam antibiotics. We previously solved the crystal structure of a bacterial peptide transporter, which allowed us to improve the affinity for certain β-lactam antibiotics using structure-based mutagenesis combined with a bacterial uptake assay. We were able to identify specific mutations, which enhanced the affinity of the transporter for antibiotics containing certain structural features. Screening of potential compounds allowed for the identification of a β-lactam antibiotic ligand with relatively high affinity. Transport of antibiotics was evaluated using a solid-supported membrane electrophysiology assay. In summary, we have engineered a proton-driven β-lactam antibiotic translocation module, contributing to the growing toolset for bionanotechnological applications. [ABSTRACT FROM AUTHOR]

The green-light absorbing proteorhodopsin (GPR) is the archetype of bacterial light-driven proton pumps. Here, we present the 2.9 Å cryo-EM structure of pentameric GPR, resolving important residues of the proton translocation pathway and the oligomerization interface. Superposition with the structure of a close GPR homolog and molecular dynamics simulations reveal conformational variations, which regulate the solvent access to the intra- and extracellular half channels harbouring the primary proton donor E109 and the proposed proton release group E143. We provide a mechanism for the structural rearrangements allowing hydration of the intracellular half channel, which are triggered by changing the protonation state of E109. Functional characterization of selected mutants demonstrates the importance of the molecular organization around E109 and E143 for GPR activity. Furthermore, we present evidence that helices involved in the stabilization of the protomer interfaces serve as scaffolds for facilitating the motion of the other helices. Combined with the more constrained dynamics of the pentamer compared to the monomer, these observations illustrate the previously demonstrated functional significance of GPR oligomerization. Overall, this work provides molecular insights into the structure, dynamics and function of the proteorhodopsin family that will benefit the large scientific community employing GPR as a model protein. The cryo-EM structure of pentameric green-light absorbing proteorhodopsin together with molecular dynamics simulations and functional studies provides insights into the proton translocation pathway and oligomerization, and a protonation-dependent mechanism for intracellular half channel hydration. [ABSTRACT FROM AUTHOR]

Objectives: Application of computational fluid dynamics (CFD) to three-dimensional CTCA datasets has been shown to provide accurate assessment of the hemodynamic significance of a coronary lesion. We aim to test the feasibility of calculating a novel CTCA-based virtual functional assessment index (vFAI) of coronary stenoses > 30% and ≤ 90% by using an automated in-house-developed software and to evaluate its efficacy as compared to the invasively measured fractional flow reserve (FFR).Methods and Results: In 63 patients with chest pain symptoms and intermediate (20-90%) pre-test likelihood of coronary artery disease undergoing CTCA and invasive coronary angiography with FFR measurement, vFAI calculations were performed after 3D reconstruction of the coronary vessels and flow simulations using the finite element method. A total of 74 vessels were analyzed. Mean CTCA processing time was 25(± 10) min. There was a strong correlation between vFAI and FFR, (R = 0.93, p < 0.001) and a very good agreement between the two parameters by the Bland-Altman method of analysis. The mean difference of measurements from the two methods was 0.03 (SD = 0.033), indicating a small systematic overestimation of the FFR by vFAI. Using a receiver-operating characteristic curve analysis, the optimal vFAI cutoff value for identifying an FFR threshold of ≤ 0.8 was ≤ 0.82 (95% CI 0.81 to 0.88).Conclusions: vFAI can be effectively derived from the application of computational fluid dynamics to three-dimensional CTCA datasets. In patients with coronary stenosis severity > 30% and ≤ 90%, vFAI performs well against FFR and may efficiently distinguish between hemodynamically significant from non-significant lesions.Key Points: Virtual functional assessment index (vFAI) can be effectively derived from 3D CTCA datasets. In patients with coronary stenoses severity > 30% and ≤ 90%, vFAI performs well against FFR. vFAI may efficiently distinguish between functionally significant from non-significant lesions. [ABSTRACT FROM AUTHOR]

This study aims at presenting a nonlinear, recursive, multivariate prediction model of the subcutaneous glucose concentration in type 1 diabetes. Nonlinear regression is performed in a reproducing kernel Hilbert space, by either the fixed budget quantized kernel least mean square (QKLMS-FB) or the approximate linear dependency kernel recursive least-squares (KRLS-ALD) algorithm, such that a sparse model structure is accomplished. A multivariate feature set (i.e., subcutaneous glucose, food carbohydrates, insulin regime and physical activity) is used and its influence on short-term glucose prediction is investigated. The method is evaluated using data from 15 patients with type 1 diabetes in free-living conditions. In the case when all the input variables are considered: (i) the average root mean squared error (RMSE) of QKLMS-FB increases from 13.1 mg dL-1 (mean absolute percentage error (MAPE) 6.6%) for a 15-min prediction horizon (PH) to 37.7 mg dL-1 (MAPE 20.8%) for a 60-min PH and (ii) the RMSE of KRLS-ALD, being predictably lower, increases from 10.5 mg dL-1 (MAPE 5.2%) for a 15-min PH to 31.8 mg dL-1 (MAPE 18.0%) for a 60-min PH. Multivariate data improve systematically both the regularity and the time lag of the predictions, reducing the errors in critical glucose value regions for a PH ≥ 30 min. Graphical abstract ᅟ. [ABSTRACT FROM AUTHOR]

Glucose concentration in type 1 diabetes is a function of biological and environmental factors which present high inter-patient variability. The objective of this study is to evaluate a number of features, which are extracted from medical and lifestyle self-monitoring data, with respect to their ability to predict the short-term subcutaneous (s.c.) glucose concentration of an individual. Random forests (RF) and RReliefF algorithms are first employed to rank the candidate feature set. Then, a forward selection procedure follows to build a glucose predictive model, where features are sequentially added to it in decreasing order of importance. Predictions are performed using support vector regression or Gaussian processes. The proposed method is validated on a dataset of 15 type diabetics in real-life conditions. The s.c. glucose profile along with time of the day and plasma insulin concentration are systematically highly ranked, while the effect of food intake and physical activity varies considerably among patients. Moreover, the average prediction error converges in less than d/2 iterations (d is the number of features). Our results suggest that RF and RReliefF can find the most informative features and can be successfully used to customize the input of glucose models. [ABSTRACT FROM AUTHOR]

Coronary stents have revolutionized the treatment of coronary artery disease. Improvement in clinical outcomes requires detailed evaluation of the performance of stent biomechanics and the effectiveness as well as safety of biomaterials aiming at optimization of endovascular devices. Stents need to harmonize the hemodynamic environment and promote beneficial vessel healing processes with decreased thrombogenicity. Stent design variables and expansion properties are critical for vessel scaffolding. Drug-elution from stents, can help inhibit in-stent restenosis, but adds further complexity as drug release kinetics and coating formulations can dominate tissue responses. Biodegradable and bioabsorbable stents go one step further providing complete absorption over time governed by corrosion and erosion mechanisms. The advances in computing power and computational methods have enabled the application of numerical simulations and the in silico evaluation of the performance of stent devices made up of complex alloys and bioerodible materials in a range of dimensions and designs and with the capacity to retain and elute bioactive agents. This review presents the current knowledge on stent biomechanics, stent fatigue as well as drug release and mechanisms governing biodegradability focusing on the insights from computational modeling approaches. [ABSTRACT FROM AUTHOR]

Microarrays provide a simple way to measure the level of hybridization of known probes of interest with one or more samples under different conditions. The rapid development of microarray technology requires the implementation of smart and flexible algorithms to deal either with the great amount of data or with the variations of the used hardware. In this paper, a generalized methodology for spot addressing and gridding of microarray images is presented. The methodology can cope with both rectangular and hexagonal grids, which are used for the probes placement onto the substrate. Initially, the methodology identifies the structure of the image, and an efficient spot-by-spot approach has been developed for the detection of all spots in the image. The evaluation of the methodology was performed using both rectangular and hexagonal structured images, merged in a single dataset. The methodology results in high accuracy in the spots detection, ranging from 92.8 to 99.8 % depending on the dataset used. [ABSTRACT FROM AUTHOR]

Diabetes is a chronic metabolic disorder requiring the active and continuous participation of diabetic individuals, their caregivers and physicians in order to be managed and well-controlled. Diabetes care has evolved considerably over the years with the development of advanced medical technologies and therapeutics. In the first part of this paper, we present the state of the art in wearable medical devices for monitoring and controlling blood glucose levels with special emphasis on continuous glucose monitoring (CGM) devices and insulin pumps. Moreover, a description is made of the significant steps taken towards a portable fully automated closed-loop glucose control system (artificial pancreas) suitable for outpatient use. The second part of this paper is concerned with mobile self-management support interventions in diabetes care. Advances in mobile computing and wireless communications have enabled the development of efficient mobile applications for self-monitoring diabetes and providing feedback. Both literature and digital distribution stores demonstrate a plethora of such paradigms with, admittedly, obvious variety in their functionalities, design and validity. The way in which mobile health technology has contributed to improvements in diabetes health outcomes and life is also discussed and future directions for research are identified. [ABSTRACT FROM AUTHOR]

Intravascular ultrasound (IVUS)-based reconstructions have been traditionally used to examine the effect of endothelial shear stress (ESS) on neointimal formation. The aim of this analysis is to compare the association between ESS and neointimal thickness (NT) in models obtained by the fusion of optical coherence tomography (OCT) and coronary angiography and in the reconstructions derived by the integration of IVUS and coronary angiography. We analyzed data from six patients implanted with an Absorb bioresorbable vascular scaffold that had biplane angiography, IVUS and OCT investigation at baseline and 6 or 12 months follow-up. The IVUS and OCT follow-up data were fused separately with the angiographic data to reconstruct the luminal morphology at baseline and follow-up. Blood flow simulation was performed on the baseline reconstructions and the ESS was related to NT. In the OCT-based reconstructions the ESS were lower compared to the IVUS-based models (1.29 ± 0.66 vs. 1.87 ± 0.66 Pa, P = 0.030). An inverse correlation was noted between the logarithmic transformed ESS and the measured NT in all the OCT-based models which was higher than the correlation reported in five of the six IVUS-derived models (-0.52 ± 0.19 Pa vs. -0.10 ± 0.04, P = 0.028). Fusion of OCT and coronary angiography appears superior to IVUS-based reconstructions; therefore it should be the method of choice for the study of the effect of the ESS on neointimal proliferation. [ABSTRACT FROM AUTHOR]

In this paper we present a novel methodology for sequence classification, based on sequential pattern mining and optimization algorithms. The proposed methodology automatically generates a sequence classification model, based on a two stage process. In the first stage, a sequential pattern mining algorithm is applied to a set of sequences and the sequential patterns are extracted. Then, the score of every pattern with respect to each sequence is calculated using a scoring function and the score of each class under consideration is estimated by summing the specific pattern scores. Each score is updated, multiplied by a weight and the output of the first stage is the classification confusion matrix of the sequences. In the second stage an optimization technique, aims to finding a set of weights which minimize an objective function, defined using the classification confusion matrix. The set of the extracted sequential patterns and the optimal weights of the classes comprise the sequence classification model. Extensive evaluation of the methodology was carried out in the protein classification domain, by varying the number of training and test sequences, the number of patterns and the number of classes. The methodology is compared with other similar sequence classification approaches. The proposed methodology exhibits several advantages, such as automated weight assignment to classes using optimization techniques and knowledge discovery in the domain of application. [ABSTRACT FROM AUTHOR]

We report a patient with coronary dilatation 10 weeks after paclitaxel-eluting stent implantation in the right coronary artery (RCA). Coronary angiography demonstrated a double-locular lumen dilatation in the body of the stented segment of the RCA, and intravascular ultrasound interrogation revealed the corresponding region with evidence of tissue regression behind the stent struts, incomplete stent apposition, and lumen enlargement. After three-dimensionally (3D) reconstructing the RCA, we applied computational fluid dynamics to the 3D RCA model, representing the lumen of the artery at the moment of stent implantation, and investigated the correlation of both neointimal thickness and tissue regression depth with shear stress in the stent. Neointimal thickness was found to be inversely related to shear stress, but there was no significant association of tissue regression depth with shear stress. Post-procedural stent malapposition, thrombus dissolution, and less possibly, a localized hypersensitivity reaction are potential mechanisms for the lumen enlargement in this case. [ABSTRACT FROM AUTHOR]

Objective: To objectively and precisely define the spatial distribution of osteonecrosis and to investigate the influence of various factors including etiology. Design: A volumetric method is presented to describe the size and spatial distribution of necrotic lesions of the femoral head, using MRI scans. The technique is based on the definition of an equivalent sphere model for the femoral head. Patients: The gender, age, number of hips involved, disease duration, pain intensity, limping disability and etiology were correlated with the distribution of the pathologic bone. Seventy-nine patients with 122 hips affected by osteonecrosis were evaluated. Results: The lesion size ranged from 7% to 73% of the sphere equivalent. The lateral octants presented considerable variability, ranging from wide lateral lesions extending beyond the lip of the acetabulum, to narrow medial lesions, leaving a lateral supporting pillar of intact bone. Patients with sickle cell disease and steroid administration presented the largest lesions. The extent of the posterior superior medial octant involvement correlated with the symptom intensity, a younger age and male gender. Conclusion: The methodology presented here has proven a reliable and straightforward imaging tool for precise assessment of necrotic lesions. It also enables us to target accurately the drilling and grafting procedures. [ABSTRACT FROM AUTHOR]

Atherosclerosis is the one of the major causes of mortality worldwide, urging the need for prevention strategies. In this work, a novel computational model is developed, which is used for simulation of plaque growth to 94 realistic 3D reconstructed coronary arteries. This model considers several factors of the atherosclerotic process even mechanical factors such as the effect of endothelial shear stress, responsible for the initiation of atherosclerosis, and biological factors such as the accumulation of low and high density lipoproteins (LDL and HDL), monocytes, macrophages, cytokines, nitric oxide and formation of foams cells or proliferation of contractile and synthetic smooth muscle cells (SMCs). The model is validated using the serial imaging of CTCA comparing the simulated geometries with the real follow-up arteries. Additionally, we examine the predictive capability of the model to identify regions prone of disease progression. The results presented good correlation between the simulated lumen area (P < 0.0001), plaque area (P < 0.0001) and plaque burden (P < 0.0001) with the realistic ones. Finally, disease progression is achieved with 80% accuracy with many of the computational results being independent predictors. [ABSTRACT FROM AUTHOR]

In human and other mammalian cells, transport of L-lactate across plasma membranes is mainly catalyzed by monocarboxylate transporters (MCTs) of the SLC16 solute carrier family. MCTs play an important role in cancer metabolism and are promising targets for tumor treatment. Here, we report the crystal structures of an SLC16 family homologue with two different bound ligands at 2.54 and 2.69 Å resolution. The structures show the transporter in the pharmacologically relevant outward-open conformation. Structural information together with a detailed structure-based analysis of the transport function provide important insights into the molecular working mechanisms of ligand binding and L-lactate transport. The transport of L-lactate across plasma membranes is catalyzed by proton-driven monocarboxylate transporters (MCTs) of the SLC16 solute carrier family. Here, the authors present the crystal structures of a bacterial SLC16 homologue with the bound substrate L-lactate and ligand thiosalicylate both in an outward-open conformation and discuss the L-lactate transport mechanism. [ABSTRACT FROM AUTHOR]

Most studies characterizing the folding, structure, and function of membrane proteins rely on solubilized or reconstituted samples. Whereas solubilized membrane proteins lack the functionally important lipid membrane, reconstitution embeds them into artificial lipid bilayers, which lack characteristic features of cellular membranes including lipid diversity, composition and asymmetry. Here, we utilize outer membrane vesicles (OMVs) released from Escherichia coli to study outer membrane proteins (Omps) in the native membrane environment. Enriched in the native membrane of the OMV we characterize the assembly, folding, and structure of OmpG, FhuA, Tsx, and BamA. Comparing Omps in OMVs to those reconstituted into artificial lipid membranes, we observe different unfolding pathways for some Omps. This observation highlights the importance of the native membrane environment to maintain the native structure and function relationship of Omps. Our fast and easy approach paves the way for functional and structural studies of Omps in the native membrane. Johannes Thoma et al. overexpress outer membrane proteins (Omps) in Escherichia coli and collect the expelled outer membrane vesicles (OMVs) to study Omp assembly, folding and structure. They find that Omps in OMVs show different unfolding pathways compared to Omps reconstituted in artificial lipid membranes. [ABSTRACT FROM AUTHOR]