Specific Signal Patterns Research Articles

Single-cell communication patterns and their intracellular information flow in synovial fibroblastic osteoarthritis and rheumatoid arthritis

BackgroundSynovial fibroblasts are critical for maintaining homeostasis in major autoimmune diseases involving joint inflammation, including osteoarthritis and rheumatoid arthritis. However, little is known about the interactions among different cell subtypes and the specific sets of signaling pathways and activities that they trigger. MethodsUsing social network analysis, pattern recognition, and manifold learning approaches, we identified patterns of single-cell communication in OA (osteoarthritis) and RA (rheumatoid arthritis). ResultsOur results suggest that OA and RA have distinct cellular communication patterns and signaling pathways. The LAMININ (Laminin) and COLLAGEN (Collagen) pathways predominate in osteoarthritis, while the EGF (Epidermal growth factor), NT (Neurotrophin) and CDH5 (Cadherin 5) pathways predominate in rheumatoid arthritis, with a central role for THY1 (Thy-1 cell surface antigen) +CDH11 (Cadherin 11) + cells. The OA opens the PDGF (Platelet-derived growth factors) pathway (driver of bone angiogenesis), the RA opens the EGF pathway (bone formation) and the SEMA3 (Semaphorin 3A) pathway (involved in immune regulation). Interestingly, we found that OA no longer has cell types involved in the MHC complex (Major histocompatibility complex) and their activity, whereas the MHC complex functions primarily in RA in the presentation of inflammatory antigens, and that the complement system in OA has the potential to displace the function of the MHC complex. The specific signaling patterns of THY1+CDH11+ cells and their secreted ligand receptors are more conducive to cell migration and lay the foundation for promoting osteoclastogenesis. This subpopulation may also be involved in the accumulation of lymphocytes, affecting the recruitment of immune cells. Members of the collagen family (COL1A1 (Collagen Type I Alpha 1 Chain), COL6A2 (Collagen Type VI Alpha 2 Chain) and COL6A1 (Collagen Type VI Alpha 1 Chain)) and transforming growth factor (TGFB3) maintain the extracellular matrix in osteoarthritis and mediate cell migration and adhesion in rheumatoid arthritis, including the PTN (Pleiotrophin) / THBS1 (Thrombospondin 1) interaction. ConclusionIncreased understanding of the interaction networks between synovial fibroblast subtypes, particularly the shared and unique cellular communication features between osteoarthritis and rheumatoid arthritis and their hub cells, should help inform the design of therapeutic agents for inflammatory joint disease.

Identification of novel clusters of co-expressing cytokines in a diagnostic cytokine multiplex test.

Cytokines are mediators of the immune system that are essential for the maintenance, development and resolution of immune responses. Beneficial immune responses depend on complex, interdependent networks of signaling and regulatory events in which individual cytokines influence the production and release of others. Since disruptions in these signaling networks are associated with a wide spectrum of diseases, cytokines have gained considerable interest as diagnostic, prognostic and precision therapy-relevant biomarkers. However, currently individual cytokines testing has limited value because the wider immune response context is often overlooked. The aim of this study was to identify specific cytokine signaling patterns associated with different diseases. Unbiased clustering analyses were performed on a clinical cytokine multiplex test using a cohort of human plasma specimens drawn from individuals with known or suspected diseases for which cytokine profiling was considered clinically indicated by the attending physician. Seven clusters of co-expressing cytokines were identified, representing common patterns of immune activation. Common expression profiles of the cytokine clusters and preliminary associations of these profiles with specific diseases or disease categories were also identified. These findings increase our understanding of the immune environments underlying the clinical presentations of patients of inflammatory, autoimmune and neoplastic diseases, which could then improve diagnoses and the identification of evidence-based treatment targets.

Deep neural networks and gradient-weighted class activation mapping to classify and analyze EEG

Deep Neural Networks (DNN) have significantly improved the capabilities for analysis and classification of data, including that of biomedical signals (eg. ElectroencephalogramEEG). Optimal classification of EEG signals from seizure onset zones has been challenging especially given the complexity of signals arising from multiple locations. Also, underlying electrophysiological abnormalities which signify epileptogenic zones have not been clearly defined. Previous studies have demonstrated, automatic feature generation based on deep learning as a useful tool for interictal epileptiform discharge (IEDs) detection. Also signals with transformations have been used in convolutional neural network (CNN) based models in the past for classifying EEG data. We explored the use of deep learning for identification of the seizure onset zones using regular dense neural network and CNN based models. After the training the model using sample data the results were validated with a smaller percentage (10%) of the data. The models were noted to be accurate in predicting the seizure onset zones with significant degree of accuracy (87–99%) with a much shorter duration of signal recorded, compared to previous studies. We further investigated model’s decision-making process with heatmapping (gradient-weighted class activation map: Grad-CAM) approach, combining with signal processing using Hilbert transform. To identify the visible features in the signal, which maximally contribute to the seizure onset zone prediction, correlation of heatmap and analytical signal of the EEG, was calculated. A high correlation between heatmap and analytical signal was noted, suggesting that the model may be utilizing the higher power regions of the data in decision making process. This study demonstrates the potential use of DNN based strategies in identifying the epileptogenic zones in the intracranial EEG. It also shows the heatmapping strategies can help in establishing how specific signal patterns may be contributing to the decision making of DNN.

Attentional State-Dependent Peripheral Electrical Stimulation During Action Observation Enhances Cortical Activations in Stroke Patients.

Brain-computer interface (BCI) is a promising technique that enables patients' interaction with computers or machines by analyzing specific brain signal patterns and provides patients with brain state-dependent feedback to assist in their rehabilitation. Action observation (AO) and peripheral electrical stimulation (PES) are conventional methods used to enhance rehabilitation outcomes by promoting neural plasticity. In this study, we assessed the effects of attentional state-dependent feedback in the combined application of BCI-AO with PES on sensorimotor cortical activation in patients after stroke. Our approach involved showing the participants a video with repetitive grasping actions under four different tasks. A mu band suppression (8-13 Hz) corresponding to each task was computed. A topographical representation showed that mu suppression of the dominant (healthy) and affected hemispheres (stroke) gradually became prominent during the tasks. There were significant differences in mu suppression in the affected motor and frontal cortices of the stroke patients. The involvement of both frontal and motor cortices became prominent in the BCI-AO+triggered PES task, in which feedback was given to the patients according to their attentive watching. Our findings suggest that synchronous stimulation according to patient attention is important for neurorehabilitation of stroke patients, which can be achieved with the combination of BCI-AO feedback with PES. BCI-AO feedback combined with PES could be effective in facilitating sensorimotor cortical activation in the affected hemispheres of stroke patients.

Functional Anti-Apoptotic Protein Dependence and Its Relation to Genomic Prognostic Markers and Response to Treatment in Chronic Lymphocytic Leukemia

Introduction Due to its heterogenous biology, chronic lymphocytic leukemia (CLL) is a well-suited disease for identifying new relationships between genomic and functional biomarkers for drug response. Here, we combined a multi-omics approach and ex vivo drug-treatment data with functional characterization of apoptotic sensitivity to correlate genetic features and drug sensitivity with specific anti-apoptotic signaling patterns measured by BH3 profiling in primary CLL samples. Methods BH3 profiling is a functional assay to measure the proximity of cells to undergo apoptosis via overall priming as well as to identify specific anti-apoptotic proteins the cells are dependent on for survival. BH3 peptides and mimetic such as BIM and PUMA were used to identify overall priming or proximity to apoptosis, BAD and ABT199 were used to identify BCL-2 dependence, and FS1, MS1 and HRK were used to identify BFL-1, MCL-1 and BCL-xL dependences respectively (Ryan et al, Biol Chem, 2016). Results We analyzed primary CLL samples from 73 patients and using BH3 profiling identified the specific associations between anti-apoptotic protein dependence and genetic features (such as IGHV status, trisomy-12, del(17p) and TP53 mutation. Our univariate models demonstrated that BCL-2 dependence, as measured by ABT199 drug (p=0.001) and BAD (p=0.004) BH3 peptide, are highly correlated with mutated IGHV status, a favorable prognostic marker in CLL (Figure 1A). Using ex vivo treatment with multiple classes of small molecule inhibitors, we further identified several specific drug response or sensitivity patterns correlating with anti-apoptotic protein dependence as well as CLL prognostic factors (Figure 1B). In particular, our multivariate models (LASSO regularized) demonstrated that viability following ex vivo treatment with the MDM-2 inhibitor Nutlin-3 was positively correlated with TP53 mutation/deletion, but inversely correlated with BFL-1 (FS1 peptide) and BCL-xL (HRK peptide) dependence as well as trisomy-12 (Figure 1B), though the inverse correlation between Nutlin-3 and BFL-1 or BCL-xL dependence is relatively weak. CLL cell viability following ex vivo treatment with the dual BCL-2/BCL-xL inhibitor navitoclax was negatively correlated with BCL-2 (BAD peptide) and BCL-xL (HRK peptide) dependence, though primarily BCL-2 dependence, suggesting that sensitivity to navitoclax is highly dependent on BCL-2 (Figure 1B). At the transcriptomic level, CLL cell dependence on BCL-2 positively correlated with NOXA expression, NFκB signaling, and reduced intracellular oxidative level, various pathways that could potentially maintain BCL-2 dependence. Finally, we utilized BH3 profiling to retrospectively predict in vivo drug response, with high BCL-2 dependence (p=0.016) correlating with greater absolute lymphocyte count decrease. Conclusion Functional characterization of apoptotic signaling in CLL demonstrated that patients with mutated IGHV are more dependent on BCL-2 for survival, and that BH3 profiling could retrospectively predict in vivo sensitivity to BCL-2 inhibition. This functional precision medicine approach may help to stratify patients with CLL beyond genetic markers and should be further explored for correlation with clinical outcomes in prospective clinical trials. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Diffusion tensor imaging in unclear intramedullary tumor-suspected lesions allows separating tumors from inflammation

DesignProspective diagnostic study.ObjectivesPrimary imaging-based diagnosis of spinal cord tumor-suspected lesions is often challenging. The identification of the definite entity is crucial for dedicated treatment and therefore reduction of morbidity. The aim of this trial was to investigate specific quantitative signal patterns to differentiate unclear intramedullary tumor-suspected lesions based on diffusion tensor imaging (DTI).SettingMedical Center - University of Freiburg, Germany.MethodsForty patients with an unclear tumor-suspected lesion of the spinal cord prospectively underwent DTI. Primary diagnosis was determined by histological or clinical work-up or remained indeterminate with follow-up. DTI metrics (FA/ADC) were evaluated at the central lesion area, lesion margin, edema, and normal spinal cord and compared between different diagnostic groups (ependymomas, other spinal cord tumors, inflammations).ResultsMean DTI metrics for all spinal cord tumors (n = 18) showed significantly reduced FA and increased ADC values compared to inflammatory lesions (n = 8) at the lesion margin (p < 0.001, p = 0.001) and reduced FA at the central lesion area (p < 0.001). There were no significant differences comparing the neoplastic subgroups of ependymomas (n = 10) and other spinal cord tumors (n = 8), but remaining differences for both compared to the inflammation subgroup. We found significant higher ADC (p = 0.040) and a trend to decreased FA (p = 0.081) for ependymomas compared to inflammations at the edema.ConclusionEven if distinct differentiation of ependymomas from other spinal cord neoplasms was not possible based on quantitative DTI metrics, FA and ADC were feasible to separate inflammatory lesions. This may avoid unnecessary surgery in patients with unclear intramedullary tumor-suspected lesions.

A computational framework for modeling complex sensor network data using graph signal processing and graph neural networks in structural health monitoring

Complex networks lend themselves for the modeling of multidimensional data, such as relational and/or temporal data. In particular, when such complex data and their inherent relationships need to be formalized, complex network modeling and its resulting graph representations enable a wide range of powerful options. In this paper, we target this—connected to specific machine learning approaches on graphs for Structural Health Monitoring (SHM) from an analysis and predictive (maintenance) perspective. Specifically, we present a framework based on Complex Network Modeling, integrating Graph Signal Processing (GSP) and Graph Neural Network (GNN) approaches. We demonstrate this framework in our targeted application domain of SHM. In particular, we focus on a prominent real-world SHM use case, i. e., modeling and analyzing sensor data (strain, vibration) of a large bridge in the Netherlands. In our experiments, we show that GSP enables the identification of the most important sensors, for which we investigate a set of search and optimization approaches. Furthermore, GSP enables the detection of specific graph signal patterns (i. e., mode shapes), capturing physical functional properties of the sensors in the applied complex network. In addition, we show the efficacy of applying GNNs for strain prediction utilizing this kind of sensor data.

Analysis and classification of drone sounds from digital media

AbstractThe use of the quadcopter‐type drone is now at a mature and practical stage, and many major manufacturers are expanding their range of applications. Because of the high maneuverability and practicality of this flying object, how we ensure it is used for benign purposes in urban life has become a major issue. Most such drones are small enough to avoid many current airborne detection methods and cheap enough to be disposable. We were able to gather enough amount of multimedia sources including drone‐related sources using open online platforms, such as YouTube and other similar services. For a subset of commercial quadcopter drones, we collected sound sample data consisting of recordings and YouTube videos and built a trained simple nonlinear neural network filter to classify them. We used Mel‐frequency cepstral coefficients to reduce the amount of data as the input signal and created two‐dimensional spectrogram images to minimize the loss of temporal information in the sample, as suggested by previous research. Most of the acoustic signal energy in the drone sound existed in specific frequency bands and residual signal patterns were spread up to around 15 kHz. We designed and tested several recurrent and convolutional neural network models and one of the simple neural network models based on the LeNet architecture could classify over 10 different types of drones from acoustic data with over 97% accuracy including an additional path for the detection of general acoustic drone patterns.

Extensive Ossification of the Achilles Tendon with and without Acute Fracture: A Scoping Review

Extensive Ossification of the Achilles Tendon (EOAT) is an uncommon condition characterized by the presence of heterotopic ossification within the substance of the Achilles Tendon and is distinct from other tendinopathies associated with tendon mineralization. The purpose of this scoping review of the literature on EOAT is to describe the pathogenesis, patient population, presentation, management, and outcomes of this rare condition. Fifty-four articles were included in the scoping review after screening and selection. According to the literature, EOAT often presents with pain and swelling around the Achilles Tendon and is frequently associated with acute trauma. EOAT is more common in men, and although the exact mechanisms of the pathology are not fully understood, EOAT may demonstrate specific molecular signaling patterns. The lack of knowledge regarding the molecular mechanism may be a significant hindrance to the management of the condition. Even though a standard treatment regimen for EOAT does not exist, conservative management for six months in patients without complications is recommended. Those who have an acute fracture of the ossification should be managed more aggressively and will often require surgical repair with autograft, although there is no standardized procedure at this time. Clinicians should be aware of the typical presentation, risk factors, and management options of patients with EOAT. Additionally, they should be cautious when selecting treatment strategies and conduct a thorough evaluation of long-term outcomes with various treatment modalities, which this review provides. Most important, this review highlights the need for further research to determine the best course of clinical treatment of EOAT injuries, in order to establish a standard treatment regimen.

Direct Comparison of the Tau PET Tracers 18F-Flortaucipir and 18F-MK-6240 in Human Subjects.

Tau PET tracers exhibit varying levels of specific signal and distinct off-target binding patterns that are more diverse than amyloid PET tracers. This study compared 2 frequently used tau PET tracers, 18F-flortaucipir and 18F-MK-6240, in the same subjects. Methods:18F-flortaucipir and 18F-MK-6240 scans were collected within 2 mo in 15 elderly subjects varying in clinical diagnosis and cognition. FreeSurfer, version 5.3, was applied to 3-T MR images to segment Braak pathologic regions (I-VI) for PET analyses. Off-target binding was assessed in the choroid plexus, meninges, and striatum. SUV ratio (SUVR) outcomes were determined over 80-100 min (18F-flortaucipir) or 70-90 min (18F-MK-6240) normalized to cerebellar gray matter. Masked visual interpretation of images was performed by 5 raters for both the medial temporal lobe and the neocortex, and an overall (majority) rating was determined. Results: Overall visual ratings showed complete concordance between radiotracers for both the medial temporal lobe and the neocortex. SUVR outcomes were highly correlated (r2 > 0.92; P ≪ 0.001) for all Braak regions except Braak II. The dynamic range of SUVRs in target regions was approximately 2-fold higher for 18F-MK-6240 than for 18F-flortaucipir. Cerebellar SUVs were similar for 18F-MK-6240 and 18F-flortaucipir, suggesting that differences in SUVRs are driven by specific signals. Apparent off-target binding was observed often in the striatum and choroid plexus with 18F-flortaucipir and most often in the meninges with 18F-MK-6240. Conclusion: Both 18F-MK-6240 and 18F-flortaucipir are capable of quantifying signal in a common set of brain regions that develop tau pathology in Alzheimer disease; these tracers perform equally well in visual interpretations. Each also shows distinct patterns of apparent off-target binding. 18F-MK-6240 showed a greater dynamic range in SUVR estimates, which may be an advantage in detecting early tau pathology or in performing longitudinal studies to detect small interval changes.

Abstract P797: Sex Specific Plasma Protein Changes During Thrombectomy

Introduction: Sex differences in stroke have been apparent with premenopausal females having a lower incidence of stroke with better outcomes than postmenopausal females and males. We examined sex-specific outcomes and changes in plasma proteins following emergent large vessel occlusions. The previously published Blood and Clot Thrombectomy Registry and Collaboration (BACTRAC), clinicaltrials.gov NCT03153683, allows for analysis of plasma proteins both systemically and distal to the thrombus. Methods: Plasma samples, processed in accordance with the BACTRAC protocol, were sent to Olink to run cardiometabolic and inflammatory panels. Demographics are reported as mean±SEM. Significance determined in Prism with Mann-Whitney, t-test, or pair mixed-effect analysis. Results: We evaluated 34 subjects, &gt;18 yrs old (20 females, 14 males) enrolled in BACTRAC. There was no significant difference in age (68.9±2.7, 65.4±4.5 yrs, respectively) or comorbidities (hypertension, diabetes, cholesterolemia). Interestingly, males had a larger (p&lt;0.1) change in Modified Rankin Scale (mRS, premorbid-discharge, 3.4±1.8, 2.2±1.6, respectively) with larger infarcts (86,666±30,889 mm 3 , 36,228±10,943 mm 3 , respectively). This coincided with a lower (p&lt;0.05) CTA collateral scores for males compared to females (0.64±0.67, 1.1±0.13, respectively). 12 proteins were significantly (p&lt;0.1) higher in females, compared to males (5 proteins upregulated in both the systemic and intracranial, 3 systemic specific, and 4 intracranial specific). Males had 15 proteins significantly higher than females (3 proteins upregulated in both the systemic and intracranial, 12 systemic specific, and 0 intracranial specific). The most significant intracranial protein for females is coagulation factor XI (F11) and males is transforming growth factor beta-1 (TGFB1). Analysis of an additional 16 subjects has begun to validate the sex specific proteins. Conclusions: Unexpectedly, males have larger infarcts and less independence following large vessel occlusions in BACTRAC. We hypothesize this is due to fewer collaterals which leads to sex specific signaling patterns. Additional analysis of the plasma and subjects in BACTRAC are needed to target sex specific therapeutic.

Self-assembly of differentiated progenitor cells facilitates spheroid human skin organoid formation and planar skin regeneration.

Self-assembly of solid organs from single cells would greatly expand applicability of regenerative medicine. Stem/progenitor cells can self-organize into micro-sized organ units, termed organoids, partially modelling tissue function and regeneration. Here we demonstrated 3D self-assembly of adult and induced pluripotent stem cell (iPSC)-derived fibroblasts, keratinocytes and endothelial progenitors into both, planar human skin in vivo and a novel type of spheroid-shaped skin organoids in vitro, under the aegis of human platelet lysate.Methods: Primary endothelial colony forming cells (ECFCs), skin fibroblasts (FBs) and keratinocytes (KCs) were isolated from human tissues and polyclonally propagated under 2D xeno-free conditions. Human tissue-derived iPSCs were differentiated into endothelial cells (hiPSC-ECs), fibroblasts (hiPSC-FBs) and keratinocytes (hiPSC-KCs) according to efficiency-optimized protocols. Cell identity and purity were confirmed by flow cytometry and clonogenicity indicated their stem/progenitor potential. Triple cell type floating spheroids formation was promoted by human platelet-derived growth factors containing culture conditions, using nanoparticle cell labelling for monitoring the organization process. Planar human skin regeneration was assessed in full-thickness wounds of immune-deficient mice upon transplantation of hiPSC-derived single cell suspensions.Results: Organoids displayed a distinct architecture with surface-anchored keratinocytes surrounding a stromal core, and specific signaling patterns in response to inflammatory stimuli. FGF-7 mRNA transfection was required to accelerate keratinocyte long-term fitness. Stratified human skin also self-assembled within two weeks after either adult- or iPSC-derived skin cell-suspension liquid-transplantation, healing deep wounds of mice. Transplant vascularization significantly accelerated in the presence of co-transplanted endothelial progenitors. Mechanistically, extracellular vesicles mediated the multifactorial platelet-derived trophic effects. No tumorigenesis occurred upon xenografting.Conclusion: This illustrates the superordinate progenitor self-organization principle and permits novel rapid 3D skin-related pharmaceutical high-content testing opportunities with floating spheroid skin organoids. Multi-cell transplant self-organization facilitates development of iPSC-based organ regeneration strategies using cell suspension transplantation supported by human platelet factors.

An ultracentrifugation – hollow-fiber flow field-flow fractionation orthogonal approach for the purification and mapping of extracellular vesicle subtypes

In the course of their life span, cells release a multitude of different vesicles in the extracellular matrix (EVs), constitutively and/or upon stimulation, carrying signals either inside or on their membrane for intercellular communication. As a natural delivery tool, EVs present many desirable advantages, such as biocompatibility and low toxicity. However, due to the complex biogenesis of EVs and their high heterogeneity in size distribution and composition, the characterization and quantification of EVs and their subpopulations still represents an enticing analytical challenge.Centrifugation methods allow to obtain different subpopulations in an easy way from cell culture conditioned medium and biological fluids including plasma, amniotic fluid and urine, but they still present some drawbacks and limitations. An unsatisfactory isolation can limit their downstream analysis and lead to wrong conclusions regarding biological activities. Isolation and characterization of biologically relevant nanoparticles like EVs is crucial to investigate specific molecular and signaling patterns and requires new combined approaches.Our work was focused on HF5 (miniaturized, hollow-fiber flow field-flow fractionation), and its hyphenation to ultracentrifugation techniques, which are the most assessed techniques for vesicle isolation. We exploited model samples obtained from culture medium of murine myoblasts (C2C12), known to release different subsets of membrane-derived vesicles.Large and small EVs (LEVs and SEVs) were isolated by differential ultracentrifugation (UC). Through an HF5 method employing UV, fluorescence and multi-angle laser scattering as detectors, we characterized these subpopulations in terms of size, abundance and DNA/protein content; moreover, we showed that microvesicles tend to hyper-aggregate and partially release nucleic matter. The quali-quantitative information we obtained from the fractographic profiles was improved with respect to Nano Tracking Analysis (NTA) estimation.The SEV population was then further separated using density gradient centrifugation (DGC), and four fractions were submitted again to HF5-multidetection. This technique is based on a fully orthogonal principle, since F4 does not separate by density, and provided uncorrelated information for each of the fractions processed. The “second dimension” achieved with HF5 showed good promise in sorting particles with both different size and content, and allowed to identify the presence of fibrilloid nucleic matter. This analytical bidimensional approach proved to be effective for the characterization of highly complex biological samples such as mixtures of EVs and could provide purified fractions for further biological characterization.

Confocal laser microscopy without fluorescent dye in minimal-invasive thoracic surgery: an ex-vivo pilot study in lung cancer.

Cancer will be the leading cause of death in a few decades. In line with minimal invasive lung cancer surgery, surgeons loose most of their tactile tissue information and need an additional tool of intraoperative tissue navigation during surgery. Confocal laser microscopy is a well-established method of tissue investigation. In this ex-vivo pilot study, we evaluated an endoscopic confocal laser microscope (eCLM) that does not need any fluorescent dye as a diagnostic tool in non-malignant and malignant pulmonary tissue and distal stapler resection margins, respectively. In seven cases, an eCLM was used for examining pulmonary tissue ex-vivo. Images of non-malignant and non-small cell lung cancer tissue and distal stapler resection margins were characterized in terms of specific signal-patterns. No fluorescent dye was used. Correlations to findings in conventional histology were systematically recorded and described. Healthy lung tissue showed hyperreflectoric alveolar walls with dark alveolar spaces. Hyperreflective nets indicated the tumor stroma; whereas the hyperreflective areas indicated the tumor cell clusters. Compared to adenocarcinoma tissue, tissue from squamous cell carcinoma showed more distinctive hyperreflective stroma nets. eCLM characteristics seen in non-malignant and malignant tissue were also visible in distal stapler resection margins and so therefore it was feasible to distinguish between healthy lung tissue and lung cancer. This pilot study shows that the assessment of pulmonary tissue with this eCLM for minimally invasive surgical approach without any fluorescent dye is feasible. It enables to differentiate between benign and malignant tissue in pulmonary specimen by easy to evaluate and reproducible parameters.

Information transmission from NFkB signaling dynamics to gene expression.

The dynamic signal encoding paradigm suggests that information flows from the extracellular environment into specific signaling patterns (encoding) that are then read by downstream effectors to control cellular behavior. Previous work empirically quantified the information content of dynamic signaling patterns. However, whether this information can be faithfully transmitted to the gene expression level is unclear. Here we used NFkB signaling as a model to understand the accuracy of information transmission from signaling dynamics into gene expression. Using a detailed mathematical model, we simulated realistic NFkB signaling patterns with different degrees of variability. The NFkB patterns were used as an input to a simple gene expression model. Analysis of information transmission between ligand and NFkB and ligand and gene expression allows us to determine information loss in transmission between receptors to dynamic signaling patterns and between signaling dynamics to gene expression. Information loss could occur due to biochemical noise or due to a lack of specificity. We found that noise-free gene expression has very little information loss suggesting that gene expression can preserve specificity in NFkB patterns. As expected, the addition of noise to the gene expression model results in information loss. Interestingly, this effect can be mitigated by a specific choice of parameters that can substantially reduce information loss due to biochemical noise during gene expression. Overall our results show that the cellular capacity for information transmission from dynamic signaling patterns to gene expression can be high enough to preserve ligand specificity and thereby the accuracy of cellular response to environmental cues.

An Individual Specific Electroencephalography Signal Pattern Verification Model Based on Machine Learning and Convolutional Neural Network

An Individual Specific Electroencephalography Signal Pattern Verification Model Based on Machine Learning and Convolutional Neural Network

Saliva-coated titanium biosensor detects specific bacterial adhesion and bactericide caused mass loading upon cell death

Bacteria adhering to implanted medical devices can cause invasive microbial infections, of e.g. skin, lung or blood. In dentistry, Streptococcus gordonii is an early oral colonizer initiating dental biofilm formation and also being involved in life-threatening infective endocarditis. To treat oral biofilms, antibacterial mouth rinses are commonly used. Such initial biomaterial-bacteria interactions and the influence of antibacterial treatments are poorly understood and investigated here in situ by quartz crystal microbalance with dissipation monitoring (QCM-D). A saliva-coated titanium (Ti) biosensor is applied to analyze possible specific signal patterns indicating microbial binding mechanisms and bactericide-caused changes in bacterial film rigidity or cell leakage caused by a clinically relevant antibacterial agent (ABA), i.e., a mouth rinse comprising chlorhexidine (CHX) and cetylpyridinium chloride (CPC). Apparent missing mass effects during the formation of microscopically proven dense and vital bacterial films indicate punctual, specific binding of S. gordonii to the saliva-coated biosensor, compared to unspecific adhesion to pure Ti. Coincidentally to ABA-induced killing of surface-adhered bacteria, an increase of adsorbed dissipative mass can be sensed, contrary to the prior mass-loss. This suggests the acoustic sensing of the leakage of cellular content caused by bacterial cell wall rupturing and membrane damage upon the bactericidal attack. The results have significant implications for testing bacterial adhesion mechanisms and cellular integrity during interaction with antibacterial agents.

Confocal laser microscopy as novel approach for real-time and in-vivo tissue examination during minimal-invasive surgery in colon cancer.

Histological analysis of surgical specimen is the gold standard for cancer classification. In particular, frozen histological diagnosis of vague peritoneal spots or uncertain excision of tumors plays a crucial role in the decision to proceed with or abandon an operation. Confocal laser microscopy (CLM) enables in-vivo and real-time high-resolution tissue analysis. This method has already been used during endoscopic assessments analyzing transformation of esophageal or colon mucosa. We examined whether a CLM device enables to distinguish between non-malignant and malignant tissue in vivo and real time and enables to assign peritoneal carcinomatosis spots to their primary tumor. In addition, we investigated whether the newly developed CLM camera device causes any tissue damage. CC531 colon carcinoma cells were implanted on the serosa side of the colon and intraperitoneally in Wag/Rija rats via laparotomy. After 7days of tumor growth, confocal laser microscopy in vivo was performed by re-laparotomy. Images of non-malignant and malignant tissue were characterized in terms of specific signal pattern. No fluorescent dye was used. Correlations to findings in conventional histology were systematically recorded and described. Potential tissue damage was examined by conventional histology. All animals survived the operative procedure and could be evaluated 7days following surgery. No unexpected death occurred after surgery. Non-malignant colon is defined by small cycles of the microvilli of the colon. There is repetitive deregulated structure in colon carcinoma. Peritoneal carcinomatosis showed the same structural pattern as in primary colon carcinoma. In all examined cases, it was possible to differentiate between peritoneal carcinomatosis spots and non-malignant peritoneum. The CLM device did not cause any tissue damage. The CLM camera device reported here is feasible to identify peritoneal carcinomatosis spots, assign these spots to the primary tumor, as well as distinguish between non-malignant and malignant tissue in without using any fluorescent dye.

Delayed hemodynamic responses associated with a history of suicide attempts in bipolar disorder: a multichannel near-infrared spectroscopy study

Bipolar disorder (BD) is associated with a high risk of suicide compared with other psychiatric disorders. Recent studies using near-infrared spectroscopy (NIRS) reported frontotemporal functional abnormalities in BD. However, it remains unclear whether NIRS signal changes are associated with vulnerability toward suicide in BD. We recruited 20 patients with depressed BD with a history of suicide attempts (suicide attempters; SAs) and 28 control patients with BD who did not have a history of suicide attempts (non-attempters; NAs). Regional hemodynamic responses during a verbal fluency task were monitored using NIRS. Compared with the NA group, the SA group exhibited significantly reduced activation during VFT in the bilateral precentral and superior temporal gyri and left supramarginal, inferior frontal, postcentral, and middle temporal gyri. Furthermore, compared with the NA group, the SA group exhibited delayed activation timing of the NIRS signal in the prefrontal region. In BD patients, current suicide risk was noted to be significantly and positively associated with delayed activation timing of the NIRS signal in the prefrontal region. The findings of this study suggest that the observed specific NIRS signal pattern in BD patients is associated with vulnerability toward suicide.

Learning-Based Just-Noticeable-Quantization- Distortion Modeling for Perceptual Video Coding.

Conventional predictive video coding-based approaches are reaching the limit of their potential coding efficiency improvements, because of severely increasing computation complexity. As an alternative approach, perceptual video coding (PVC) has attempted to achieve high coding efficiency by eliminating perceptual redundancy, using just-noticeable-distortion (JND) directed PVC. The previous JNDs were modeled by adding white Gaussian noise or specific signal patterns into the original images, which were not appropriate in finding JND thresholds due to distortion with energy reduction. In this paper, we present a novel discrete cosine transform-based energy-reduced JND model, called ERJND, that is more suitable for JND-based PVC schemes. Then, the proposed ERJND model is extended to two learning-based just-noticeable-quantization-distortion (JNQD) models as preprocessing that can be applied for perceptual video coding. The two JNQD models can automatically adjust JND levels based on given quantization step sizes. One of the two JNQD models, called LR-JNQD, is based on linear regression and determines the model parameter for JNQD based on extracted handcraft features. The other JNQD model is based on a convolution neural network (CNN), called CNN-JNQD. To our best knowledge, our paper is the first approach to automatically adjust JND levels according to quantization step sizes for preprocessing the input to video encoders. In experiments, both the LR-JNQD and CNN-JNQD models were applied to high efficiency video coding (HEVC) and yielded maximum (average) bitrate reductions of 38.51% (10.38%) and 67.88% (24.91%), respectively, with little subjective video quality degradation, compared with the input without preprocessing applied.