TREM-1 triggers necroptosis of macrophages through mTOR-dependent mitochondrial fission during acute lung injury.

BACKGROUND: Necroptosis of macrophages is a necessary element in reinforcing intrapulmonary inflammation during acute lung injury (ALI). However, the molecular mechanism that sparks macrophage necroptosis is still unclear. Triggering receptor expressed on myeloid cells-1 (TREM-1) is a pattern recognition receptor expressed broadly on monocytes/macrophages. The influence of TREM-1 on the destiny of macrophages in ALI requires further investigation. METHODS: TREM-1 decoy receptor LR12 was used to evaluate whether the TREM-1 activation induced necroptosis of macrophages in lipopolysaccharide (LPS)-induced ALI in mice. Then we used an agonist anti-TREM-1 Ab (Mab1187) to activate TREM-1 in vitro. Macrophages were treated with GSK872 (a RIPK3 inhibitor), Mdivi-1 (a DRP1 inhibitor), or Rapamycin (an mTOR inhibitor) to investigate whether TREM-1 could induce necroptosis in macrophages, and the mechanism of this process. RESULTS: We first observed that the blockade of TREM-1 attenuated alveolar macrophage (AlvMs) necroptosis in mice with LPS-induced ALI. In vitro, TREM-1 activation induced necroptosis of macrophages. mTOR has been previously linked to macrophage polarization and migration. We discovered that mTOR had a previously unrecognized function in modulating TREM-1-mediated mitochondrial fission, mitophagy, and necroptosis. Moreover, TREM-1 activation promoted DRP1Ser616 phosphorylation through mTOR signaling, which in turn caused surplus mitochondrial fission-mediated necroptosis of macrophages, consequently exacerbating ALI. CONCLUSION: In this study, we reported that TREM-1 acted as a necroptotic stimulus of AlvMs, fueling inflammation and aggravating ALI. We also provided compelling evidence suggesting that mTOR-dependent mitochondrial fission is the underpinning of TREM-1-triggered necroptosis and inflammation. Therefore, regulation of necroptosis by targeting TREM-1 may provide a new therapeutic target for ALI in the future.

Spontaneous tendon rupture in a patient with systemic sclerosis: a case report.

BACKGROUND: Systemic sclerosis (SSc) is an incurable autoimmune disease characterized by progressive skin fibrosis and organ failure. Tenosynovitis is a common musculoskeletal manifestation, but tendon rupture has seldom reported in SSc. CASE PRESENTATION: We present a rare case of a 49-year-old female with SSc who has suffered from bilateral tendon rupture of the fourth and fifth digits with positive antinuclear antibody (ANA) and anti-centromere B antibody, but negative rheumatoid factor in serum. In the extensor tendons of the patient's hands, inflammation, edema, hypertrophy and tendon interruption were detected with ultrasound and magnetic resonance imaging(MRI). Tendon transfer repair surgery was performed and 10 mg/week methotrexate was then used in this patient. Her hand function was improved well with methotrexate and rehabilitation treatment postoperatively. CONCLUSIONS: Early detection of tenosynovitis is necessary to prevent tendon rupture in SSc patients. Ultrasound and Magnetic Resonance Imaging appear to be useful examinations for evaluating tendon pathology for early detection.

To Combat Multiclass Imbalanced Problems by Aggregating Evolutionary Hierarchical Classifiers.

Real-world datasets are often imbalanced, posing frequent challenges to canonical machine learning algorithms that assume a balanced class distribution. Moreover, the imbalance problem becomes more complicated when the dataset is multiclass. Although many approaches have been presented for imbalanced learning (IL), research on the multiclass imbalanced problem is relatively limited and deficient. To alleviate these issues, we propose a forest of evolutionary hierarchical classifiers (FEHC) method for multiclass IL (MCIL). FEHC can be seen as a classifier fusion framework with a forest structure, and it aggregates several evolutionary hierarchical multiclassifiers (EHMCs) to reduce generalization error. Specifically, a multichromosome genetic algorithm (MCGA) is designed to simultaneously select (sub)optimal features, classifiers, and hierarchical structures when generating these EHMCs. The MCGA adopts a dynamic weighting module to learn difficult classes and promote the diversity of FEHC. We also present the "stratified underbagging" (SUB) strategy to address class imbalance and the "soft tree traversal" (STT) strategy to make FEHC converge faster and better. We thoroughly evaluate the proposed algorithm using 14 multiclass imbalanced datasets with various properties. Compared with popular and state-of-the-art approaches, FEHC obtains better performance under different evaluation metrics. Codes have been made publicly available on GitHub.https://github.com/CUHKSZ-NING/FEHCClassifier.

A novel multi-feature learning model for disease diagnosis using face skin images.

BACKGROUND: Facial skin characteristics can provide valuable information about a patient's underlying health conditions. OBJECTIVE: In practice, there are often samples with divergent characteristics (commonly known as divergent samples) that can be attributed to environmental factors, living conditions, or genetic elements. These divergent samples significantly degrade the accuracy of diagnoses. METHODOLOGY: To tackle this problem, we propose a novel multi-feature learning method called Multi-Feature Learning with Centroid Matrix (MFLCM), which aims to mitigate the influence of divergent samples on the accurate classification of samples located on the boundary. In this approach, we introduce a novel discriminator that incorporates a centroid matrix strategy and simultaneously adapt it to a classifier in a unified model. We effectively apply the centroid matrix to the embedding feature spaces, which are transformed from the multi-feature observation space, by calculating a relaxed Hamming distance. The purpose of the centroid vectors for each category is to act as anchors, ensuring that samples from the same class are positioned close to their corresponding centroid vector while being pushed further away from the remaining centroids. RESULTS: Validation of the proposed method with clinical facial skin dataset showed that the proposed method achieved F1 scores of 92.59%, 83.35%, 82.84% and 85.46%, respectively for the detection the Healthy, Diabetes Mellitus (DM), Fatty Liver (FL) and Chronic Renal Failure (CRF). CONCLUSION: Experimental results demonstrate the superiority of the proposed method compared with typical classifiers single-view-based and state-of-the-art multi-feature approaches. To the best of our knowledge, this study represents the first to demonstrate concept of multi-feature learning using only facial skin images as an effective non-invasive approach for simultaneously identifying DM, FL and CRF in Han Chinese, the largest ethnic group in the world.

Multiple color representation and fusion for diabetes mellitus diagnosis based on back tongue images.

Tongue images have been proved to be effective in diabetes mellitus (DM) diagnosis. Without requirement of collecting blood sample, tongue image based diagnosis approach is non-invasive and convenient for the patients. Meanwhile, the colors of tongues play an important in aiding accurate diagnosis. However, the tongues' colors fall on a small color gamut that makes it difficult for the existing color descripts to identify and distinguish the tiny difference of the tongues. To tackle this problem, we introduce a novel color descriptor by representing the colors with the clustering centers, namely color centroid points, of the color points sampled from tongue images. In order to boost the capacity of the descriptor, we extend it into three color spaces, i.e., RGB, HSV and LAB to mine a rich set of color information and exploit the complementary information among the three spaces. Since there exist correlation and complementarity among the features extracted from the three color spaces, we propose a novel multiple color features fusion method for DM diagnosis. Particularly, two projections are learned to project the multiple features to their corresponding shared and specific subspaces, in which their similarity and diversity are firstly measured by the Euclidean Distance and Hilbert Schmidt Independence Criterion (HSIC), respectively. To fully exploit the similar and complementary information, the two components are jointly transformed to their label vector, efficiently embedding the discriminant prior into the model, leading to significant improvement in the diagnosis outcomes. Experimental results on clinical tongue dataset substantiated the effectiveness of our proposed clustering-based color descriptor and the proposed multiple colors fusion approach. Overall, the proposed pipeline for the diagnosis of DM using back tongue images, achieved an average accuracy of up to 93.38%, indicating its potential toward realization of a clinical diagnostic tool for DM. Without loss generality, we also assessed the performance of the novel multiple features fusion method on two public datasets. The experiments prove the superiority of our multiple features learning model on general real-life application.

Pressure wrist pulse signal analysis by sparse decomposition using improved Gabor function.

BACKGROUND AND OBJECTIVE: In traditional Chinese medicine and Ayurvedic medicine, wrist pulse wave fluctuations are an important indicator for distinguishing different health states. Owing to the development of modern sensing technology, computational methods have been used in the analysis of pulse wave signals. The description and quantification of the peaks in the pulse wave is significant for the identification of health status. METHODS: In this study, we decomposed the pressure pulse waveform of the radial artery into several components by sparse decomposition with an improved Gabor function. To better represent the position, shape, and relationship of the peaks, we designed an improved Gabor function structure based on the characteristics of the pulse waveform to generate a time-frequency dictionary. Compared with conventional representation methods, the shape of the Gabor function is more variable. In addition, owing to the limitation of windowing, the Gabor function can reduce the influence on other positions when it represents a specific position. Feature vectors consisting of decomposed components can be used for computerized pulse signal analysis and disease diagnosis. RESULTS: In the binary classification of healthy and diseased pulse signals, the proposed method achieved the best results for health/diabetes, health/cardiac disease, health/hypertension, and health/nephropathy with accuracies of 93.54%, 73.42%, 88.42%, and 82.28%, respectively. The multi-classification performance of the different types of features was evaluated by six classifiers, and the proposed method obtained the highest classification performance with support vector machine-radial basis function for both balanced and imbalanced data. CONCLUSIONS: The results indicated that the proposed method enabled to obtain a smaller representation error and exhibited superior performance in distinguishing between the signals collected from patients and healthy individuals. Moreover, for the multi-classification of the pulse signals, the proposed method performed better than the state-of-the-art methods.

Multi-Feature Complementary Learning for Diabetes Mellitus Detection Using Pulse Signals.

Computational pulse diagnosis is a convenient, non-invasive, and effective Diabetes Mellitus (DM) detection technique. Generally, diverse pulse features are extracted from different views to represent pulse signals and then used for achieving the pulse diagnosis. However, current pulse-based DM detection methods only used one pulse feature for detection, ignoring the fact that diverse pulse features can be combined together to boost the diagnosis performance. To this end, we propose a novel Multi-Feature Complementary Learning (MFCL) model for DM detection. By designing feature-specific projections, multiple features are separately projected into a shared observation space and effectively fused into one vector. Besides, a mapping function is built to correlate the fused vectors to category labels to make the fused vectors suitable for classification. Inspired by the graph Laplacian matrix, which effectively preserves the correlations among samples from different categories, we integrate it in MFCL and design a discriminative prior to make the fused vectors sufficiently discriminative. Finally, an optimization algorithm is proposed to alternatively optimize the projection variables and then generate fused feature vectors. The proposed method reaches an accuracy of 92.85% in DM detection, outperforming state-of-the-art methods.

Wrist pulse signal acquisition and analysis for disease diagnosis: A review.

Pulse diagnosis (PD) plays an indispensable role in healthcare in China, India, Korea, and other Orient countries. It requires considerable training and experience to master. The results of pulse diagnosis rely heavily on the practitioner's subjective analysis, which means that the results from different physicians may be inconsistent. To overcome these drawbacks, computational pulse diagnosis (CPD) is used with advanced sensing techniques and analytical methods. Focusing on the main processes of CPD, this paper provides a systematic review of the latest advances in pulse signal acquisition, signal preprocessing, feature extraction, and signal recognition. The most relevant principles and applications are presented along with current progress. Extensive comparisons and analyses are conducted to evaluate the merits of different methods employed in CPD. While much progress has been made, a lack of datasets and benchmarks has limited the development of CPD. To address this gap and facilitate further research, we present a benchmark to evaluate different methods. We conclude with observations of the status and prospects of CPD.

Role of NLRP3 inflammasome in systemic sclerosis.

Systemic sclerosis (SSc) is an autoimmune rheumatic disease with high mortality, which is featured by inflammation, vascular damage, and aggressive fibrosis. To date, the pathogenesis of SSc remains unclear and effective treatments are still under research. Active NLRP3 recruits downstream proteins such as ASC and caspase-1 and assembles into inflammasome, resulting in excretion of inflammatory cytokines including IL-1ß and IL-18, as well as in pyroptosis mediated by gasdermin D. Various studies demonstrated that NLRP3 inflammasome might be involved in the mechanism of tenosynovitis, arthritis, fibrosis, and vascular damage. The pathophysiological changes might be due to the activation of proinflammatory Th2 cells, profibrotic M2 macrophages, B cells, fibroblasts, and endothelial cells. Here, we review the studies focused on NLRP3 inflammasome activation, its association with innate and adaptive immune cells, endothelium injury, and differentiation of fibroblasts in SSc. Furthermore, we summarize the prospect of therapy targeting NLRP3 pathway.

Contribution to the peripheral vasculopathy and endothelial cell dysfunction by CXCL4 in Systemic Sclerosis.

BACKGROUND: CXCL4, a chemokine with anti-angiogenic property, is involved in systemic sclerosis (SSc) related pulmonary arterial hypertension (PAH). OBJECTIVE: To investigated the contribution of CXCL4 to SSc development by focusing on the correlation of circulatory CXCL4 levels with their peripheral vasculopathy, and the effect of CXCL4 on endothelial cell dysfunction and the potential signaling. METHODS: We measured the plasma CXCL4 levels in 58 patients with SSc, 10 patients with the very early diagnosis of SSc (VEDOSS), and 80 healthy controls (HCs). Then, CXCL4 concentrations were correlated with clinical features, especially the peripheral vasculopathy. These observations were further validated in an additional cohort. Moreover, we studied the anti-angiogenic effects of CXCL4 and the underlying downstream signaling in human umbilical vein endothelial cells (HUVECs) in vitro. RESULTS: Circulating CXCL4 levels were 103.62 % higher in patients with SSc and 201.51 % higher in patients with VEDOSS than matched HCs, which were confirmed in two independent cohorts. CXCL4 levels were associated with digital ulcers (DU) and nailfold videocapillaroscopy (NVC) abnormalities in SSc. The proliferation, migration, and tube formation of HUVECs were inhibited by CXCL4 or SSc derived plasma, which reversed by CXCL4 neutralizing antibody, but failed by CXCR3 inhibitor. CXCL4 downregulated the transcription factor Friend leukaemia integration factor-1 (Fli-1) via c-Abl signaling. Furthermore, CXCL4 blocked the transforming growth factor (TGF) -ß or platelet-derived growth factor (PDGF) induced cell proliferation of HUVECs. CONCLUSIONS: CXCL4 may contribute to peripheral vasculopathy in SSc by downregulating Fli-1 via c-Abl signaling in endothelial cells and interfering angiogenesis.

Promotion of Myofibroblast Differentiation and Tissue Fibrosis by the Leukotriene B4 -Leukotriene B4 Receptor Axis in Systemic Sclerosis.

OBJECTIVE: To investigate the role of the inflammatory lipid mediator leukotriene B4 (LTB4 ) and its receptor, BLT1, in the development and progression of systemic sclerosis (SSc). METHODS: Serum levels of LTB4 were compared in 64 patients with SSc and 80 healthy controls. Skin and lung tissue sections from patients with SSc and healthy donors were immunostained for leukotriene A4 hydrolase (LTA4 H), the critical enzyme for LTB4 synthesis, and BLT1, in combination with different cell markers. In mouse models of SSc using bleomycin or angiotensin II challenge or immunization with the DNA topoisomerase I, genetic or pharmacologic interruption of the LTB4 -BLT1 axis in mice was carried out to assess its effects on systemic disease features and myofibroblast markers. Immunoblotting was performed to examine the signaling pathway in fibroblasts and endothelial cells following stimulation with LTB4 or with serum from SSc patients. RESULTS: Serum LTB4 levels were 44.93% higher in patients with SSc than in matched healthy controls (mean ± SD 220.3 ± 74.75 pg/ml versus 152.0 ± 68.05 pg/ml; P < 0.0001), and this was associated with the patient subsets of SSc-associated interstitial lung disease and diffuse cutaneous SSc. Levels of LTA4 H and BLT1 were increased in lesional areas of the skin and lungs of SSc patients, and both were abundant in myofibroblasts and endothelial cells. Interruption of the LTB4 -BLT1 axis in mouse models of SSc significantly mitigated dermal and pulmonary fibrosis, with 54.00% and 52.65% fewer α-smooth muscle actin-positive myofibroblasts accumulating in the skin and lungs of mice, respectively, after bleomycin challenge. Immunoblotting of cultures with recombinant LTB4 -stimulated fibroblasts and endothelial cells or with serum from SSc patients showed that fibroblast-myofibroblast and endothelial-mesenchymal transitions were promoted via BLT1, and that this was dependent on activation of the phosphatidylinositol 3-kinase (PI3K)/Akt/mechanistic target of rapamycin (mTOR) pathway but independent of the release of transforming growth factor ß (TGFß) by fibroblasts or endothelial cells. CONCLUSION: The LTB4 -BLT1 axis may contribute to fibrosis in SSc by directly promoting myofibroblast differentiation via the PI3K/Akt/mTOR pathway, and this appears to operate independently of autocrine secretion of TGFß.

Radial artery pulse waveform analysis based on curve fitting using discrete Fourier series.

BACKGROUND AND OBJECTIVES: Radial artery pulse diagnosis has been playing an important role in traditional Chinese medicine (TCM). For its non-invasion and convenience, the pulse diagnosis has great significance in diseases analysis of modern medicine. The practitioners sense the pulse waveforms in patients' wrist to make diagnoses based on their non-objective personal experience. With the researches of pulse acquisition platforms and computerized analysis methods, the objective study on pulse diagnosis can help the TCM to keep up with the development of modern medicine. METHODS: In this paper, we propose a new method to extract feature from pulse waveform based on discrete Fourier series (DFS). It regards the waveform as one kind of signal that consists of a series of sub-components represented by sine and cosine (SC) signals with different frequencies and amplitudes. After the pulse signals are collected and preprocessed, we fit the average waveform for each sample using discrete Fourier series by least squares. The feature vector is comprised by the coefficients of discrete Fourier series function. RESULTS: Compared with the fitting method using Gaussian mixture function, the fitting errors of proposed method are smaller, which indicate that our method can represent the original signal better. The classification performance of proposed feature is superior to the other features extracted from waveform, liking auto-regression model and Gaussian mixture model. CONCLUSIONS: The coefficients of optimized DFS function, who is used to fit the arterial pressure waveforms, can obtain better performance in modeling the waveforms and holds more potential information for distinguishing different psychological states.

Point-spread imaging for measurement of skin translucency and scattering.

BACKGROUND/PURPOSE: The translucency of skin has long been identified as an important cue for healthy and youthful looking skin. There is currently no universal definition for skin translucency let alone a measurement method. We propose that skin translucency is the light scattering beneath skin surface. We demonstrate the use of polarization gated point spreading imaging for non-invasive, in vivo measurement of the translucency and the reduced scattering coefficient m's of skin. METHODS: We developed a polarization-gated point-spread imaging system to measure the spread of the incident pencil-thin laser beam on the skin. Skin translucency was calculated as the spread of the laser beam. From the measurement of the shift of the light diffuse center from the light injection point, the reduced scattering coefficient m's of the skin was calculated. We validated the measurement technique with milk as an in vitro model for skin. RESULTS AND CONCLUSION: The measured m's of milk solution was found to be linearly proportional to the milk concentration, in agreement with Beer's law. The calculated translucency decreased as the milk concentration increased or as the reduced scattering coefficient m's increased. It was also found that the translucency decreased as the absorption coefficient of the milk solution increased. The measured translucency of a set of custom made clay tiles correlated well with the consumer perception of the incremental ranking of the translucency. In vivo measurement of skin translucency and the reduced scattering coefficient m's were carried out on several volunteers. The measured reduced scattering coefficient m's was in agreement with those in the literature. The measured skin translucency for different skin ethnicities of Caucasian, North Asian, South Asian and African American were in line with the expectation that skin translucency decreases as the skin color gets darker.

Polyneuropathy as Novel Initial Manifestation in a Case of "Nonsecretory" POEMS Syndrome with Sjögren's Syndrome.

POEMS syndrome (polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy, and skin changes) is a paraneoplastic syndrome driven by plasma cell dyscrasias. We report a patient with novel initial manifestation of polyneuropathy, which was considered due to Sjögren's syndrome but with poor response to methylprednisolone (120 mg/d) and intravenous immunoglobulin (IVIg). Further investigation by imaging tests and following biopsy eventually confirmed the diagnosis of POEMS syndrome secondary to solitary plasmocytoma. To our knowledge, this is the first reported case of POEMS syndrome with Sjögren's syndrome occurring in the absence of a peripheral monoclonal gammopathy, highlighting the diagnostic challenges posed by this disease and reviewing the diagnostic role of (18) F-FDG PET/CT in POEMS syndrome.

Clinical Association of Chemokine (C-X-C motif) Ligand 1 (CXCL1) with Interstitial Pneumonia with Autoimmune Features (IPAF).

The term "interstitial pneumonia with autoimmune features" (IPAF) has been recently proposed. We here investigate the clinical characteristics of IPAF and evaluate the clinical implications of CXCL1-CXCR2 axis in IPAF. An increased plasma level of CXCL1 was exhibited in IPAF compared to idiopathic interstitial pneumonia (IIP), chronic obstructive pulmonary disease (COPD), and healthy controls. Additionally, plasma CXCL1 levels were clinically associated with diffusing capacity of the lungs for carbon monoxide (DLCO), erythrocyte sedimentation rate (ESR), and involved parenchyma extension in IPAF. Furthermore, circulating CXCL1 levels were highest in IPAF patients with acute exacerbations. CXCR2, the chemokine receptor for CXCL1, was readily observed in inflammatory aggregates and endothelial cells in IPAF lungs, but was lower in IIP lungs and healthy lungs. Interestingly, increased CXCL1 concentrations in BALF paralleled neutrophil counts in IPAF. Overall, the plasma concentrations of CXCL1 indicated the disease activity and prognosis in IPAF. Thus, the CXCL1/CXCR2 axis appears to be involved in the progression of IPAF.