Association between Postmenopausal Osteoporosis and IL-6、TNF-α: A Systematic Review and A Meta-analysis.

BACKGROUND: Postmenopausal osteoporosis (PMOP) greatly increases the risk of bone fracture in postmenopausal women, seriously affects the quality of life of patients, and is an important global public health problem. Persistent chronic systemic inflammation may be involved in the change process of PMOP, and many cytokines, such as TNF-alpha and Interleukin-6, play an important role in the inflammatory response. Therefore, This study takes commonly representative inflammatory factors as indicators to better determine their role in PMOP patients by means of databases from multiple studies for use in Meta-analysis. METHOD: Systematic review of studies on the relationship between PMOP and markers of inflammation: interleukin-6 (IL-6) and tumour necrosis factor-α (TNF-α). Each effect size was expressed with a 95% confidence interval (CI), and I2 quantified the heterogeneity. The final results were aggregated and evaluated using random or fixed effects models. RESULTS: Twenty-one original studies were identified. There were twenty studies involving IL-6 and eleven involving TNF-α. Overall, The levels of IL-6[MD=23.93, 95%CI (19.65, 28.21)] and TNF-α[MD=2.9, 95%CI (2.37, 3.44)] were increased in PMOP patients compared with postmenopausal women without osteoporosis; The levels of IL-6[MD=42.4, 95%CI (38.62, 46.19)] and TNF-α[MD=0.40, 95%CI (0.36, 0.44)] were significantly higher than those of premenopausal healthy women. CONCLUSIONS: The levels of inflammatory cytokines IL-6 and TNF-α were significantly increased in PMOP patients compared with controls, suggesting that persistent chronic inflammatory reaction exists in PMOP patients, which may be an important cause of aggravated osteoporosis in postmenopausal women. Therefore, the level of IL-6 and TNF-α indexes may be of great significance for the early prevention, diagnosis, treatment and prognosis assessment of PMOP.

Vocal Cord Leukoplakia Classification Using Siamese Network Under Small Samples of White Light Endoscopy Images.

OBJECTIVE: Accurate vocal cord leukoplakia classification is instructive for clinical diagnosis and surgical treatment. This article introduces a reliable very deep Siamese network for accurate vocal cord leukoplakia classification. STUDY DESIGN: A study of a classification network based on a retrospective database. SETTING: Academic university and hospital. METHODS: The white light image datasets of vocal cord leukoplakia used in this article were classified into 6 classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia, and squamous cell carcinoma. The classification performance was assessed by comparing it with 6 classical deep learning models, including AlexNet, VGG Net, Google Inception, ResNet, DenseNet, and Vision Transformer. RESULTS: Experiments show the superior classification performance of our proposed network compared to state-of-the-art methods. The overall accuracy is 0.9756. The values of sensitivity and specificity are very high as well. The confusion matrix provides information for the 6-class classification task and demonstrates the superiority of our proposed network. CONCLUSION: Our very deep Siamese network can provide accurate classification results of vocal cord leukoplakia, which facilitates early detection, clinical diagnosis, and surgical treatment. The excellent performance obtained in white light images can reduce the cost for patients, especially those living in developing countries.

GGNpTCR: A Generative Graph Structure Neural Network for Predicting Immunogenic Peptides for T-cell Immune Response.

Identifying the interactions between T-cell receptor (TCRs) and human antigens is a crucial step in developing new vaccines, diagnostics, and immunotherapy. Current methods primarily focus on learning binding patterns from known TCR binding repertoires by using sequence information alone without considering the binding specificity of new antigens or exogenous peptides that have not appeared in the training set. Furthermore, the spatial structure of antigens plays a critical role in immune studies and immunotherapy, which should be addressed properly in the identification of interacting TCR-antigen pairs. In this study, we introduced a novel deep learning framework based on generative graph structures, GGNpTCR, for predicting interactions between TCR and peptides from sequence information. Results of real data analysis indicate that our model achieved excellent prediction for new antigens unseen in the training data set, making significant improvements compared to existing methods. We also applied the model to a large COVID-19 data set with no antigens in the training data set, and the improvement was also significant. Furthermore, through incorporation of additional supervised mechanisms, GGNpTCR demonstrated the ability to precisely forecast the locations of peptide-TCR interactions within 3D configurations. This enhancement substantially improved the model's interpretability. In summary, based on the performance on multiple data sets, GGNpTCR has made significant progress in terms of performance, universality, and interpretability.

Vocal cord leukoplakia classification using deep learning models in white light and narrow band imaging endoscopy images.

BACKGROUND: Accurate vocal cord leukoplakia classification is critical for the individualized treatment and early detection of laryngeal cancer. Numerous deep learning techniques have been proposed, but it is unclear how to select one to apply in the laryngeal tasks. This article introduces and reliably evaluates existing deep learning models for vocal cord leukoplakia classification. METHODS: We created white light and narrow band imaging (NBI) image datasets of vocal cord leukoplakia which were classified into six classes: normal tissues (NT), inflammatory keratosis (IK), mild dysplasia (MiD), moderate dysplasia (MoD), severe dysplasia (SD), and squamous cell carcinoma (SCC). Vocal cord leukoplakia classification was performed using six classical deep learning models, AlexNet, VGG, Google Inception, ResNet, DenseNet, and Vision Transformer. RESULTS: GoogLeNet (i.e., Google Inception V1), DenseNet-121, and ResNet-152 perform excellent classification. The highest overall accuracy of white light image classification is 0.9583, while the highest overall accuracy of NBI image classification is 0.9478. These three neural networks all provide very high sensitivity, specificity, and precision values. CONCLUSION: GoogLeNet, ResNet, and DenseNet can provide accurate pathological classification of vocal cord leukoplakia. It facilitates early diagnosis, providing judgment on conservative treatment or surgical treatment of different degrees, and reducing the burden on endoscopists.

Evaluation and Impact Mechanism of High-Quality Development in China's Coastal Provinces.

With economic expansion having moderated to a "new normal" pace, the eastern coastal provinces have been given a new historical task of high-quality development and become a window and frontier of China's high-quality development. By designing and optimizing an index system of high-quality development levels and using the entropy-TOPSIS method, the study selected 21 indicators, include economic vitality, residents' living standards, innovation efficiency and green development, and took China's eastern coastal provinces as an example to study the characteristics of spatial-temporal variations in the high-quality development level from 2010 to 2020. Then, the study used the obstacle degree model to explore the factors that are obstacles to high-quality development. The results show that the high-quality development of the eastern coastal provinces presents an "up-down-up" fluctuation, with an increase of 40.1%. In particular, the development level of the residents' living standards dimension is higher, and the high-quality development level of each province shows different degrees of growth and gradually tends to balanced development, with the high-quality development of Shanghai, Jiangsu Province and Zhejiang Province in a dominant position. The spatial pattern of high-quality development in the study areas shows a spatial distribution pattern of "high in the east and low in the west, high in the north and low in the south", in which the bipolar spatial effect of the innovation efficiency dimension is becoming more and more prominent, while the regional synergistic development effect of the residents' living standard dimension is more obvious, and the high-quality development spatial pattern shows a "core-periphery" structure, and there is a path-dependent effect in time change, and agglomeration is produced by trickle-down effect in space. The obstacles to residents' living standards are high, and the main obstacle factor has gradually changed from insufficient output in innovation to a reduction in the scale of foreign trade. In addition, the problems of unreasonable industrial structure and shortage of per capita public cultural resources still exist. In provinces with a high-quality development level and a relatively developed economy, the biggest obstacle factors are economic vitality and residents' living standards. In provinces with a low level of high-quality development and a relatively backward economy, the biggest obstacle factors are green development and innovation efficiency, and there are both similarities and differences in the main obstacle factors among provinces.

CPGL: Prediction of Compound-Protein Interaction by Integrating Graph Attention Network With Long Short-Term Memory Neural Network.

Recent advancements of artificial intelligence based on deep learning algorithms have made it possible to computationally predict compound-protein interaction (CPI) without conducting laboratory experiments. In this manuscript, we integrated a graph attention network (GAT) for compounds and a long short-term memory neural network (LSTM) for proteins, used end-to-end representation learning for both compounds and proteins, and proposed a deep learning algorithm, CPGL (CPI with GAT and LSTM) to optimize the feature extraction from compounds and proteins and to improve the model robustness and generalizability. CPGL demonstrated an excellent predictive performance and outperforms recently reported deep learning models. Based on 3 public CPI datasets, C.elegans, Human and BindingDB, CPGL represented 1 - 5% improvement compared to existing deep-learning models. Our method also achieves excellent results on datasets with imbalanced positive and negative proportions constructed based on the C.elegans and Human datasets. More importantly, using 2 label reversal datasets, GPCR and Kinase, CPGL showed superior performance compared to other existing deep learning models. The AUC were substantially improved by 20% on the Kinase dataset, indicative of the robustness and generalizability of CPGL.

Efficacy of the Chinese herbal medicine Jintiange capsules in the postoperative treatment of osteoporotic vertebral compression fractures: a systematic review and meta-analysis.

Background: In traditional Chinese medicine, Jintiange capsules are frequently used to treat metabolic bone diseases and strengthen bones and tendons. The main component of Jintiange capsules is bionic tiger bone powder. However, the active ingredients and proteins are derived from other animal bones, with chemical profiles similar to that of natural tiger bone. This study aimed to explore the efficacy of Jintiange capsules, a Chinese herbal medicine, in the postoperative treatment of osteoporotic vertebral compression fractures (OVCFs). Methods: In this systematic review, literature was retrieved using PubMed, the Cochrane Library, the Chinese National Knowledge Infrastructure, the Web of Science, the Wanfang Database, the Chinese Biomedical Literature Database, and the Chinese VIP Database from inception to July 2023. The primary outcome measures were the bone mineral density (BMD) and effective rate. The secondary outcome measures were the visual analog pain score (VAS), Oswestry disability index (ODI), Cobb's angle, serum osteocalcin, serum alkaline phosphatase, and adverse events. RevMan 5.4 and STATA 17.0 software were used for data analysis. Results: We enrolled randomized controlled trials (RCTs) focusing on 1,642 patients in the meta-analysis. The meta-analysis illustrated that Jintiange capsules significantly increased the BMD of the lumbar spine (p < 0.00001), femoral neck (p = 0.0005), and whole body (p = 0.01). The subgroup analysis of Jintiange capsules combination therapy showed that the BMD of the lumbar spine and whole body was significantly improved with Jintiange capsules (p < 0.00001). The test for the overall effect showed that Jintiange capsules had a significantly higher effective rate than the control groups (p = 0.003). Additionally, the overall effect test showed that Jintiange capsules decreased the VAS and ODI (p < 0.00001) and Cobb's angle (p = 0.02), and improved serum OC and ALP (p < 0.00001) compared with the controls. Furthermore, the pooled analysis of adverse reactions showed no serious impacts on the treatment of OVCFs. Conclusion: Jintiange capsules demonstrate high safety and efficacy in the treatment of OVCFs, including increasing BMD, the lift effect rate, serum OC levels, and pain relief, decreasing the ODI, serum ALP levels, and adverse events, and improving Cobb's angle. Additional research is required to validate the efficacy of Jintiange capsules for the postoperative treatment of OVCFs.Systematic review registration: https://www.crd.york.ac.uk/PROSPERO.

Trade-Off Analyses of Multiple Ecosystem Services and Their Drivers in the Shandong Yellow River Basin.

With the intensification of conflicts between different ecosystem services, how to achieve a win-win situation between socio-economic development and ecological protection is an important issue that needs to be addressed nowadays. In particular, how to better quantify and assess the intensity of ecosystem service trade-offs and their relative benefits, and to identify the influencing factors are issues that need to be studied in depth. Based on the INVEST model, this paper analyzed the evolution of spatial and temporal patterns of ecosystem services such as Carbon Storage (CS), Food Production (FP), Habitat Quality (HQ), and Water Yield (WY) in the Shandong Yellow River Basin (SYRB) in 2000, 2010 and 2020. Next, we quantitatively measured the trade-off intensity and revealed the key influencing factors of the trade-off intensity evolution using automatic linear models, root mean square deviation, and geographically weighted regressions. Subsequently, we further analyzed the impact of the correlation between environmental and socio-economic factors on the trade-off intensity of ecosystem services. The results indicated that the temporal and spatial changes of the four main ecosystem services in SYRB area were inconsistent. WY showed a fluctuating trend, with a large interannual gap. CS and FP are on the rise, while HQ is on the decline. Spatially, WY and HQ showed a decreasing distribution from the center to the periphery, while FP and CS showed a decreasing distribution from the southwest to the northeast. The location characteristics of SYRB's four ecosystem services and their trade-offs were obvious. FP had absolute location advantage in ecosystem service trade-offs. Most of the four ecosystem services showed significant trade-offs, and the trade-off intensity had significant spatial heterogeneity, but the trade-off between FP and CS was relatively weak. At the same time, there were also differences in the trends of trade-off intensities. Counties with low trade-off intensity were mostly located in mountainous areas; these areas are less disturbed by human activities, and most of them are areas without prominent services. Counties with high trade-off intensities were mostly concentrated in areas with relatively developed agriculture; these areas are more disturbed by human activities and are mostly prominent in FP. The trade-off intensity of ecosystem services in SYRB was affected by several factors together, and there were difference characteristics in the degree and direction of influence of each factor. Moreover, these influencing factors have gradually changed over 20 years. In terms of the spatial distribution at the county scale, the influence areas of the dominant drivers of different trade-off types varied greatly, among which the areas with NDVI, CON, and PRE as the dominant factors were the largest. In the future, in effectively balancing regional economic development and ecological environmental protection, quantifiable correspondence strategies should be developed from the administrative perspective of counties and regions based on comprehensive consideration of the locational advantages of each ecosystem service and changes in trade-offs.

Multiscale segmentation- and error-guided iterative convolutional neural network for cerebral neuron segmentation in microscopic images.

This article uses microscopy images obtained from diverse anatomical regions of macaque brain for neuron semantic segmentation. The complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset increase the difficulty of neuron semantic segmentation. To address this problem, we propose a multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) to improve the semantic segmentation performance in major anatomical regions of the macaque brain. After evaluating microscopic images from 17 anatomical regions, the semantic segmentation performance of neurons is improved by 10.6%, 4.0%, 1.5%, and 1.2% compared with Random Forest, FCN-8s, U-Net, and UNet++, respectively. Especially for neurons with brighter staining intensity in the anatomical regions such as lateral geniculate, globus pallidus and hypothalamus, the performance is improved by 66.1%, 23.9%, 11.2%, and 6.7%, respectively. Experiments show that our proposed method can efficiently segment neurons with a wide range of staining intensities. The semantic segmentation results are of great significance and can be further used for neuron instance segmentation, morphological analysis and disease diagnosis. Cell segmentation plays a critical role in extracting cerebral information, such as cell counting, cell morphometry and distribution analysis. Accurate automated neuron segmentation is challenging due to the complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset. The proposed multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) improve the segmentation performance in 17 major anatomical regions of the macaque brain. HIGHLIGHTS: Cell segmentation plays a critical role in extracting cerebral information, such as cell counting, cell morphometry and distribution analysis. Accurate automated neuron segmentation is challenging due to the complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset. The proposed multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) improve the segmentation performance in 17 major anatomical regions of the macaque brain.

Network Pharmacological Study on Mechanism of the Therapeutic Effect of Modified Duhuo Jisheng Decoction in Osteoporosis.

Background: Modified Duhuo Jisheng Decoction (MDHJSD) is a traditional Chinese medicine prescription for the treatment of osteoporosis (OP), but its mechanism of action has not yet been clarified. This study aims to explore the mechanism of MDHJSD in OP through a combination of network pharmacology analysis and experimental verification. Methods: The active ingredients and corresponding targets of MDHJSD were acquired from the Traditional Chinese Medicine System Pharmacology (TCMSP) database. OP-related targets were acquired from databases, including Genecards, OMIM, Drugbank, CTD, and PGKB. The key compounds, core targets, major biological processes, and signaling pathways of MDHJSD that improve OP were identified by constructing and analysing the relevant networks. The binding affinities between key compounds and core targets were verified using AutoDock Vina software. A rat model of ovariectomized OP was used for the experimental verification. Results: A total of 100 chemical constituents, 277 targets, and 4734 OP-related targets of MDHJSD were obtained. Subsequently, five core components and eight core targets were identified in the analysis. Pathway enrichment analysis revealed that overlapping targets were significantly enriched in the tumour necrosis factor-alpha (TNF-α) signaling pathway, an inflammation signaling pathway, which contained six of the eight core targets, including TNF-α, interleukin 6 (IL-6), transcription factor AP-1, mitogen-activated protein kinase 3, RAC-alpha serine/threonine-protein kinase, and caspase-3 (CASP3). Molecular docking analysis revealed close binding of the six core targets of the TNF signaling pathway to the core components. The results of experimental study show that MDHJSD can protect bone loss, inhibit the inflammatory response, and downregulate the expression levels of TNF-α, IL-6, and CASP3 in ovariectomized rats. Conclusion: The mechanism of MDHJSD in the treatment of OP may be related to the regulation of the inflammatory response in the bone tissue.

Stable isotopes of deep soil water retain long-term evaporation loss on China's Loess Plateau.

Evaporation from the land surface enriches heavy isotope ratios (2H/1H and 18O/16 O) in shallow soils, and downward water movement will carry the fractionation signal to deep soils. However, how to acquire the evaporation from water stable isotopes in deep soils remains untested. Here, we measured water stable isotope composition in the deep soils (2-10 m) across 20 sites on China's Loess Plateau. Our results show that the line-conditioned excess (lc-excess) in deep soils of these sites was invariable with depth at each site, but ranged between -14.0‰ and - 4.1‰ among these sites, indicating differing degree of enrichment in heavy water isotopes between sites. Moreover, the mean lc-excess in deep soils water was significantly correlated to mean annual precipitation (R2 = 0.57), potential evapotranspiration (R2 = 0.25), and the Budyko dryness (R2 = 0.68), indicating that deep soil water lc-excess reflects land surface climate conditions. Furthermore, the deep soils correspond to a timescale of approximately 100 years at one site and more than 27 years at the remaining sites. These results together indicate that stable isotopes of deep soil water retained long-term land surface evaporation effects. Further, by implementing the steady-state isotope mass balance model into the lc-excess framework, we derived a new method to estimate evaporation loss fraction (f). Our f estimates at these sites varied between 5% and 15%, which may represent the lower bound of the actual evaporation to precipitation ratio. Nevertheless, our work suggests that in these and the other similar regions, deep soil is a novel archive for long-term soil evaporation loss, and f may be estimated through a snapshot field campaign of stable isotope measurements.

Efficient algorithms for covariate analysis with dynamic data using nonlinear mixed-effects model.

Nonlinear mixed-effects modeling is one of the most popular tools for analyzing repeated measurement data, particularly for applications in the biomedical fields. Multiple integration and nonlinear optimization are the two major challenges for likelihood-based methods in nonlinear mixed-effects modeling. To solve these problems, approaches based on empirical Bayesian estimates have been proposed by breaking the problem into a nonlinear mixed-effects model with no covariates and a linear regression model without random effect. This approach is time-efficient as it involves no covariates in the nonlinear optimization. However, covariate effects based on empirical Bayesian estimates are underestimated and the bias depends on the extent of shrinkage. Marginal correction method has been proposed to correct the bias caused by shrinkage to some extent. However, the marginal approach appears to be suboptimal when testing covariate effects on multiple model parameters, a situation that is often encountered in real-world data analysis. In addition, the marginal approach cannot correct the inaccuracy in the associated p-values. In this paper, we proposed a simultaneous correction method (nSCEBE), which can handle the situation where covariate analysis is performed on multiple model parameters. Simulation studies and real data analysis showed that nSCEBE is accurate and efficient for both effect-size estimation and p-value calculation compared with the existing methods. Importantly, nSCEBE can be >2000 times faster than the standard mixed-effects models, potentially allowing utilization for high-dimension covariate analysis for longitudinal or repeated measured outcomes.

[Analysis of Hydrogen and Oxygen Stable Isotope Characteristics and Vapor Sources of Precipitation in the Guanzhong Plain].

To improve the understanding of hydrogen and oxygen stable isotope characteristics and vapor sources in the Guanzhong Plain, we collected 98 precipitation samples and corresponding meteorological data between 2015 and 2018 in Yangling, Shanxi Province, which is located in the central area of the Guanzhong Plain. The composition characteristics of the local hydrogen and oxygen stable isotopes of precipitation (δ2H, δ18O, and δ17O) and their environmental controls were analyzed, and the local meteoric water line (LMWL) and the meteoric water line of the triple oxygen isotopes were established. Three indicators (δ18O, d-excess, and 17O-excess) were used to explore the possible vapor sources of local precipitation and to quantify the contributions of ocean-source and inland-source water vapor to the precipitation. The results showed that there were obvious seasonal changes in the hydrogen and oxygen stable isotopes of precipitation in the Yangling area:water isotopes were depleted in the wet season (May to October) and enriched in the dry season (November to April of the next year). Both the slope (7.7) and intercept (9.1) of the LMWL were lower than those of the global meteoric water line (GMWL), indicating that the annual precipitation in the research area experienced variable degrees of secondary evaporation under cloud cover. The slope of the meteoric water line of the triple oxygen isotopes is 0.528, which is between that of seawater equilibrium fractionation (0.529) and water vapor diffusion into dry air (0.518), consistent with the fact that the Guanzhong area is located on the migration path of marine air mass to inland arid regions. Comprehensive analysis of δ18O, d-excess, and 17O-excess confirmed that the precipitation in the study area is jointly contributed to by the warm and humid air mass from the southeast monsoon and the dry and cold air mass from the westerly wind. Of these, approximately 55%-79% of the precipitation water vapor comes from the ocean, mainly in June to August, and about 21%-45% of the water vapor comes from inland and local evaporation, mainly from October to April. The water vapor sources of precipitation in May and September are complex and may intermittently originate from ocean and inland water vapor.

Dose Response to Transnasal Pulmonary Administration of Bronchodilator Aerosols via Nasal High-Flow Therapy in Adults with Stable Chronic Obstructive Pulmonary Disease and Asthma.

BACKGROUND: There has been increasing interest in transnasal pulmonary aerosol administration, but the dose-response relationship has not been reported. OBJECTIVES: To determine the accumulative bronchodilator dose at which patients with stable mild-to-moderate asthma and chronic obstructive pulmonary disease (COPD) achieve similar spirometry responses before and after bronchodilator tests using albuterol via a metered dose inhaler with a valved holding chamber (MDI + VHC). METHOD: Adult patients who met ATS/ERS criteria for bronchodilator responses in pulmonary function laboratory were recruited and consented to participate. After a washout period, patients received escalating doubling dosages (0.5, 1, 2, and 4 mg) of albuterol in a total volume of 2 mL delivered by vibrating mesh nebulizer via a nasal cannula at 37°C with a flow rate of 15-20 L/min using a Venturi air entrainment device. Spirometry was measured at baseline and after each dose. Titration was stopped when an additional forced expiratory volume in 1 second (FEV1) improvement was <5%. RESULTS: 42 patients (16 males) with stable mild-to-moderate asthma (n = 29) and COPD (n = 13) were enrolled. FEV1 increment after a cumulative dose of 1.5 mg of albuterol via nasal cannula at 15-20 L/min was similar to 4 actuations of MDI + VHC (0.34 ± 0.18 vs. 0.34 ± 0.12 L, p = 0.878). Using ATS/ERS criteria of the bronchodilator test, 33.3% (14/42) and 69% (29/42) of patients responded to 0.5 and 1.5 mg of albuterol, respectively. CONCLUSIONS: With a nasal cannula at 15-20 L/min, transnasal pulmonary delivery of 1.5 mg albuterol resulted in similar bronchodilator response as 4 actuations of MDI + VHC.

Skeletal Muscle Lipid Droplets and the Athlete's Paradox.

The lipid droplet (LD) is an organelle enveloped by a monolayer phospholipid membrane with a core of neutral lipids, which is conserved from bacteria to humans. The available evidence suggests that the LD is essential to maintaining lipid homeostasis in almost all organisms. As a consequence, LDs also play an important role in pathological metabolic processes involving the ectopic storage of neutral lipids, including type 2 diabetes mellitus (T2DM), atherosclerosis, steatosis, and obesity. The degree of insulin resistance in T2DM patients is positively correlated with the size of skeletal muscle LDs. Aerobic exercise can reduce the occurrence and development of various metabolic diseases. However, trained athletes accumulate lipids in their skeletal muscle, and LD size in their muscle tissue is positively correlated with insulin sensitivity. This phenomenon is called the athlete's paradox. This review will summarize previous studies on the relationship between LDs in skeletal muscle and metabolic diseases and will discuss the paradox at the level of LDs.

Many Is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images.

Accurate lung segmentation is an essential step in developing a computer-aided lung disease diagnosis system. However, because of the high variability of computerized tomography (CT) images, it remains a difficult task to accurately segment lung tissue in CT slices using a simple strategy. Motived by the aforementioned, a novel CT lung segmentation method based on the integration of multiple strategies was proposed in this paper. Firstly, in order to avoid noise, the input CT slice was smoothed using the guided filter. Then, the smoothed slice was transformed into a binary image using an optimized threshold. Next, a region growing strategy was employed to extract thorax regions. Then, lung regions were segmented from the thorax regions using a seed-based random walk algorithm. The segmented lung contour was then smoothed and corrected with a curvature-based correction method on each axis slice. Finally, with the lung masks, the lung region was automatically segmented from a CT slice. The proposed method was validated on a CT database consisting of 23 scans, including a number of 883 2D slices (the number of slices per scan is 38 slices), by comparing it to the commonly used lung segmentation method. Experimental results show that the proposed method accurately segmented lung regions in CT slices.

A Feature Selection Approach Based on Interclass and Intraclass Relative Contributions of Terms.

Feature selection plays a critical role in text categorization. During feature selecting, high-frequency terms and the interclass and intraclass relative contributions of terms all have significant effects on classification results. So we put forward a feature selection approach, IIRCT, based on interclass and intraclass relative contributions of terms in the paper. In our proposed algorithm, three critical factors, which are term frequency and the interclass relative contribution and the intraclass relative contribution of terms, are all considered synthetically. Finally, experiments are made with the help of kNN classifier. And the corresponding results on 20 NewsGroup and SougouCS corpora show that IIRCT algorithm achieves better performance than DF, t-Test, and CMFS algorithms.

Receiver operating characteristics of impulse oscillometry parameters for predicting obstructive sleep apnea in preobese and obese snorers.

BACKGROUND: Inability to maintain upper-airway patency during sleep is a cause of obstructive sleep apnea (OSA) and its sequelae. The associated syndrome (OSAS) is common in obese populations, currently, nocturnal polysomnography is the gold standard for diagnosing this conditions, but the diagnostic procedures are expensive and time-consuming. Therefore, identification of new markers of OSAS would be useful. This study aims to examine the receiver operating characteristics of impulse oscillometry (IOS) parameters for the prediction of OSAS in preobese and obese snoring patients. METHODS: In total, 230 patients with normal spirometric values were included in this cross-sectional study. Full laboratory polysomnography was performed and IOS measurements were determined in sitting and supine positions to obtain respiratory impedance (Zrs), resistance (Rrs), and reactance (Xrs) parameters. The respiratory resistance at zero-frequency (Rrs0) was extrapolated by linear regression analysis of Rrs versus low-oscillatory-frequencies and its inverse, respiratory conductance (Grs), was calculated. RESULTS: In both the sitting and supine positions Rrs0, Zrs, and Rrs at five oscillatory-frequencies (Hz) and Grs, the reciprocal of Zrs5 (Gz), and Xrs at 5 Hz all had significant positive or negative correlations with OSAS severity as defined by the Respiratory disturbance index (RDI). The correlation coefficients between Rrs0, Zrs5, Rrs5, Grs, Gz, Xrs5 measured in the supine and RDI were 0.425, 0.395, 0.378, -0.425, -0.395, and -0.517, respectively (all p < 0.001). The receiver operating characteristics curves showed that Xrs at 5 Hz (reactance) in the supine position was the best for predicting OSAS with a sensitivity of 73 % and specificity of 84 % at the optimal cut-off point of -0.23 (kPa s L(-1)). The other parameters also showed acceptable discriminating power. A logistic-regression model based on respiratory function abnormalities revealed that reactance combined with patient sex and lung volume yielded a specificity of 83.3 % with a sensitivity of 76.8 % for indicating OSAS. CONCLUSION: Respiratory resistance and reactance measured by IOS are abnormal in preobese and obese OSAS patients, and these parameters are moderate to closely correlated with OSAS severity. IOS might be a useful screening tool for detecting OSAS in clinic based populations.

Decentralized Dimensionality Reduction for Distributed Tensor Data Across Sensor Networks.

This paper develops a novel decentralized dimensionality reduction algorithm for the distributed tensor data across sensor networks. The main contributions of this paper are as follows. First, conventional centralized methods, which utilize entire data to simultaneously determine all the vectors of the projection matrix along each tensor mode, are not suitable for the network environment. Here, we relax the simultaneous processing manner into the one-vector-by-one-vector (OVBOV) manner, i.e., determining the projection vectors (PVs) related to each tensor mode one by one. Second, we prove that in the OVBOV manner each PV can be determined without modifying any tensor data, which simplifies corresponding computations. Third, we cast the decentralized PV determination problem as a set of subproblems with consensus constraints, so that it can be solved in the network environment only by local computations and information communications among neighboring nodes. Fourth, we introduce the null space and transform the PV determination problem with complex orthogonality constraints into an equivalent hidden convex one without any orthogonality constraint, which can be solved by the Lagrange multiplier method. Finally, experimental results are given to show that the proposed algorithm is an effective dimensionality reduction scheme for the distributed tensor data across the sensor networks.

What can impulse oscillometry and pulmonary function testing tell us about obstructive sleep apnea: a case-control observational study?

PURPOSE: This study aims to determine whether functional residual capacity (FRC) in obese patients with obstructive sleep apnea (OSA) decreases more than in patients without OSA because of decreased outward recoil from chest wall mass loading as well as increased lung inward recoil. METHODS: Subjects who were overweight and obese to various degrees with normal spirometric values underwent overnight polysomnography to determine the presence or absence of OSA and were labeled as cases or controls. Lung volume and respiratory mechanical properties were measured by plethysmograph and impulse oscillometry, respectively. RESULTS: A total of 76 men and 31 women were diagnosed with OSA (cases); 64 men and 33 women without OSA were confirmed as controls. Expiratory reserve volume and FRC were significantly decreased in cases compared with controls. Respiratory impedance and resistance at 5 Hz were significantly higher in cases than in controls, although reactance at low frequencies was significantly lower in cases than in controls. Reactance at 5 Hz (Xrs5) was found to be independently highly correlated with the severity of OSA as defined by the Apnea-Hypopnea Index and was significantly correlated with FRC. CONCLUSIONS: FRC is significantly decreased in overweight or obese patients with OSA compared with those without OSA, which may be attributed to an increase in lung elastic recoil. The stronger correlation between Xrs5 and OSA severity might indicate upper airway stenosis, and abnormally increased lung elastic recoil may contribute to OSA.