CMFHMDA: a prediction framework for human disease-microbe associations based on cross-domain matrix factorization.

Predicting associations between microbes and diseases opens up new avenues for developing diagnostic, preventive, and therapeutic strategies. Given that laboratory-based biological tests to verify these associations are often time-consuming and expensive, there is a critical need for innovative computational frameworks to predict new microbe-disease associations. In this work, we introduce a novel prediction algorithm called Predicting Human Disease-Microbe Associations using Cross-Domain Matrix Factorization (CMFHMDA). Initially, we calculate the composite similarity of diseases and the Gaussian interaction profile similarity of microbes. We then apply the Weighted K Nearest Known Neighbors (WKNKN) algorithm to refine the microbe-disease association matrix. Our CMFHMDA model is subsequently developed by integrating the network data of both microbes and diseases to predict potential associations. The key innovations of this method include using the WKNKN algorithm to preprocess missing values in the association matrix and incorporating cross-domain information from microbes and diseases into the CMFHMDA model. To validate CMFHMDA, we employed three different cross-validation techniques to evaluate the model's accuracy. The results indicate that the CMFHMDA model achieved Area Under the Receiver Operating Characteristic Curve scores of 0.9172, 0.8551, and 0.9351$\pm $0.0052 in global Leave-One-Out Cross-Validation (LOOCV), local LOOCV, and five-fold CV, respectively. Furthermore, many predicted associations have been confirmed by published experimental studies, establishing CMFHMDA as an effective tool for predicting potential disease-associated microbes.

Predicting potential microbe-disease associations based on dual branch graph convolutional network.

Studying the association between microbes and diseases not only aids in the prevention and diagnosis of diseases, but also provides crucial theoretical support for new drug development and personalized treatment. Due to the time-consuming and costly nature of laboratory-based biological tests to confirm the relationship between microbes and diseases, there is an urgent need for innovative computational frameworks to anticipate new associations between microbes and diseases. Here, we propose a novel computational approach based on a dual branch graph convolutional network (GCN) module, abbreviated as DBGCNMDA, for identifying microbe-disease associations. First, DBGCNMDA calculates the similarity matrix of diseases and microbes by integrating functional similarity and Gaussian association spectrum kernel (GAPK) similarity. Then, semantic information from different biological networks is extracted by two GCN modules from different perspectives. Finally, the scores of microbe-disease associations are predicted based on the extracted features. The main innovation of this method lies in the use of two types of information for microbe/disease similarity assessment. Additionally, we extend the disease nodes to address the issue of insufficient features due to low data dimensionality. We optimize the connectivity between the homogeneous entities using random walk with restart (RWR), and then use the optimized similarity matrix as the initial feature matrix. In terms of network understanding, we design a dual branch GCN module, namely GlobalGCN and LocalGCN, to fine-tune node representations by introducing side information, including homologous neighbour nodes. We evaluate the accuracy of the DBGCNMDA model using five-fold cross-validation (5-fold-CV) technique. The results show that the area under the receiver operating characteristic curve (AUC) and area under the precision versus recall curve (AUPR) of the DBGCNMDA model in the 5-fold-CV are 0.9559 and 0.9630, respectively. The results from the case studies using published experimental data confirm a significant number of predicted associations, indicating that DBGCNMDA is an effective tool for predicting potential microbe-disease associations.

Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective study.

Background: Patients with non-small cell lung cancer (NSCLC) and patients with NSCLC combined with chronic obstructive pulmonary disease (COPD) have similar physiological conditions in early stages, and the latter have shorter survival times and higher mortality rates. The purpose of this study was to develop and compare machine learning models to identify future diagnoses of COPD combined with NSCLC patients based on the patient's disease and routine clinical data. Methods: Data were obtained from 237 patients with COPD combined with NSCLC as well as NSCLC admitted to Ningxia Hui Autonomous Region People's Hospital from October 2013 to July 2022. Six machine learning algorithms (K-nearest neighbor, logistic regression, eXtreme gradient boosting, support vector machine, naïve Bayes, and artificial neural network) were used to develop prediction models for NSCLC combined with COPD. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, Mathews correlation coefficient (MCC), Kappa, area under the receiver operating characteristic curve (AUROC)and area under the precision-recall curve (AUPRC) were used as performance indicators to evaluate the performance of the models. Results: 135 patients with NSCLC combined with COPD, 102 patients with NSCLC were included in the study. The results showed that pulmonary function and emphysema were important risk factors and that the support vector machine-based identification model showed optimal performance with accuracy:0.946, recall:0.940, specificity:0.955, precision:0.972, npv:0.920, F1 score:0.954, MCC:0.893, Kappa:0.888, AUROC:0.975, AUPRC:0.987. Conclusion: The use of machine learning tools combining clinical symptoms and routine examination data features is suitable for identifying the risk of concurrent NSCLC in COPD patients.

CNN-LSTM Model for Recognizing Video-Recorded Actions Performed in a Traditional Chinese Exercise.

Identifying human actions from video data is an important problem in the fields of intelligent rehabilitation assessment. Motion feature extraction and pattern recognition are the two key procedures to achieve such goals. Traditional action recognition models are usually based on the geometric features manually extracted from video frames, which are however difficult to adapt to complex scenarios and cannot achieve high-precision recognition and robustness. We investigate a motion recognition model and apply it to recognize the sequence of complicated actions of a traditional Chinese exercise (ie, Baduanjin). We first developed a combined convolutional neural network (CNN) and long short-term memory (LSTM) model for recognizing the sequence of actions captured in video frames, and applied it to recognize the actions of Baduanjin. Moreover, this method has been compared with the traditional action recognition model based on geometric motion features in which Openpose is used to identify the joint positions in the skeletons. Its performance of high recognition accuracy has been verified on the testing video dataset, containing the video clips from 18 different practicers. The CNN-LSTM recognition model achieved 96.43% accuracy on the testing set; while those manually extracted features in the traditional action recognition model were only able to achieve 66.07% classification accuracy on the testing video dataset. The abstract image features extracted by the CNN module are more effective on improving the classification accuracy of the LSTM model. The proposed CNN-LSTM based method can be a useful tool in recognizing the complicated actions.

Inhibition of miR-4640-5p alleviates pulmonary hypertension in chronic obstructive pulmonary disease patients by regulating nitric oxide synthase 1.

BACKGROUND: Pulmonary hypertension (PH) is a devastating disease characterized by vasoconstriction and vascular remodeling, leading to right ventricular failure and death. PH is a common complication of chronic obstructive pulmonary disease (COPD). Accumulating evidence demonstrate that microRNAs participate in the pathobiology of PH in COPD patients. In this study, we aimed to evaluate the expression and function of microRNA-4640-5p (miR-4640-5p) in PH. METHODS: The mRNA and protein levels were determined by quantitative polymerase chain reaction (qPCR) and western blot, separately. Functional assays and western blot were performed to determine the effects of miR-4640-5p and NOS1 on cell growth, migration. Besides, the dual-luciferase reporter assays were used to validate miR-4640-5p and NOS1 interactions. RESULTS: We found that miR-4640-5p expression was significantly higher in the lung tissues of COPD-PH patients than in the healthy controls while higher expression of miR-4640-5p was correlated with more severe COPD-PH. By using pulmonary artery smooth muscle cell (PASMC) in in vitro assays, we demonstrated that inhibition of miR-4640-5p suppressed cell proliferation and migration of PASMC via regulating mTOR/S6 signaling. Bioinformatics analysis and validation experiments revealed that nitric oxide synthase 1 (NOS1) was a direct downstream target of miR-4640-5p. Overexpression of NOS1 partially antagonized the effect of miR-4640-5p in regulating PASMC cell proliferation and migration. In addition, our findings suggested that miR-4640-5p/NOS1 axis regulated mitochondrial dynamics in PASMCs. Furthermore, in the hypoxia-induced PH rat model, inhibition of miR-4640-5p ameliorated PH with reduced right ventricular systolic pressure and Fulton index. CONCLUSIONS: miR-4640-5p regulates PH via targeting NOS1, which provides a potential diagnostic biomarker and therapeutic target for COPD-PH patients.

Nano Parthenolide Improves Intestinal Barrier Function of Sepsis by Inhibiting Apoptosis and ROS via 5-HTR2A.

Background: Intestinal barrier dysfunction is an important complication of sepsis, while the treatment is limited. Recently, parthenolide (PTL) has attracted much attention as a strategy of sepsis, but whether nano parthenolide (Nano PTL) is therapeutic in sepsis-induced intestinal barrier dysfunction is obscured. Methods: In this study, cecal ligation and puncture (CLP)-induced sepsis rats and lipopolysaccharide (LPS)-stimulated intestinal epithelial cells (IECs) were used to investigate the effect of PTL on intestinal barrier dysfunction. Meanwhile, we synthesized Nano PTL and compared the protective effect of Nano PTL with ordinary PTL on intestinal barrier function in septic rats and IECs. Network pharmacology and serotonin 2A (5-HTR2A) inhibitor were used to explore the mechanism of PTL on the intestinal barrier function of sepsis. Results: The encapsulation rate of Nano PTL was 95±1.5%, the drug loading rate was 11±0.5%, and the average uptake rate of intestinal epithelial cells was 94%. Ordinary PTL and Nano PTL improved the survival rate and survival time of septic rats, reduced the mean arterial pressure and the serum level of inflammatory cytokines, and protected the liver and kidney functions in vivo, and increased the value of transmembrane resistance (TEER) reduced the reactive oxygen species (ROS) and apoptosis in IECs in vitro through 5-HTR2A. Nano PTL had better effect than ordinary PTL. Conclusion: Ordinary PTL and Nano PTL can protect the intestinal barrier function of septic rats by inhibiting apoptosis and ROS through up-regulating 5-HTR2A, Nano PTL is better than ordinary PTL.

Safety and efficacy of sofosbuvir-velpatasvir: A meta-analysis.

INTRODUCTION: The sofosbuvir-velpatasvir single-tablet regimen (Epclusa) is a newly FDA-approved inhibitor of hepatitis C virus (HCV). This meta-analysis aimed to investigate the safety and efficacy of velpatasvir-sofosbuvir in the treatment of chronic HCV infection. METHODS: A comprehensive literature search of PubMed, Cochrane CENTRAL, EMBASE and Web of Science was conducted. Data from eligible studies were pooled in a fixed-effect meta-analysis model, using Open-Meta and RevMan software's. RESULTS: Pooled data showed that velpatasvir-sofosbuvir achieved sustained virological response (SVR12) rates of 94.2% (95% CI 90.7-97.7%, P < .001) in 1277 patients. The addition of ribavirin did not significantly increase the SVR12 (RR = 1.03, 95%CI [0.95, 1.11]) in HCV genotype-1 patients and the SVR12 (RR = 1.09, 95%CI [0.86, 1.38]) in HCV genotype-2 patients. However, adding ribavirin significantly increased SVR12 (RR = 1.13, 95% CI [1.04, 1.23]) in genotype-3 patients. CONCLUSION: In conclusion, the 12-week regimen of sofosbuvir-velpatasvir was highly effective in HCV patients. Except for genotype-3, adding ribavirin was not associated with significant improvements in SVR12 rates.

MLST/MVLST Analysis and Antibiotic Resistance of Vibrio cholerae in Shandong Province of China.

BACKGROUND: Vibrio cholerae is an important bacterium causing profuse watery diarrhea. Cholera had swept the whole Shandong province from 1975 to 2013. METHODS: From epidemiological data and pulsed-field gel electrophoresis data, we selected 86 V. cholerae isolates appearing in Shandong Province in China from 1975 to 2013 and characterized them by multilocus sequence typing (MLST)/multi-virulence locus sequence typing (MVLST), antibiogram and analysis of genes related to antibiotic resistance. RESULTS: Combined MLST/MVLST data revealed 33 sequence types and a major group. Within the group, 3 subgroups (ST1, ST24 and ST29) were revealed, prevalent in the strains isolated during the 1980s, 1990s and 21st century, respectively. All the O1 isolates after 1990 were found to be El Tor variants harboring the classical ctxB gene. The tcpA gene of O139 strains had a mutation at amino acid position 62 (N→D). Antibiotic resistance of V. cholerae increased over time. Most El Tor variants between 1998 and 1999 were resistant to trimethoprim/sulfamethoxazole. The O139 strain, since its appearance in 1997, had significantly broader spectrum of antibiotic resistance than O1 variants. The presence of the SXT element corresponds to the trend of growing drug resistance. CONCLUSION: The analysis of genotypic polymorphism and enhanced resistance of V. cholerae indicated continuous variation and evolution of this pathogenic agent in Shandong Province.

Clinical Inertia and 2-Year Glycaemic Trajectories in Patients with Non-Newly Diagnosed Type 2 Diabetes Mellitus in Primary Care: A Retrospective Cohort Study.

OBJECTIVE: To analyse diabetes treatment, treatment change and self-management behaviours in association with 2-year glycaemic trajectories in patients with non-newly diagnosed type 2 diabetes mellitus in Chinese primary care. METHODS: This was an observational, multi-centre, longitudinal, retrospective cohort study. Clinical data of 4690 subjects were extracted from electronic medical records, including serial glycated haemoglobin A1c (HbA1c) measurements, antidiabetic medication records and compliance to exercise, diet, medications and self-monitoring of blood glucose (SMBG). Patterns of longitudinal HbA1c trajectories were identified using the percentage of HbA1c measurements <7.5% from the second available HbA1c measurement. Clinical relevance of the clusters was assessed through multivariable analysis. RESULTS: Approximately half of the participants demonstrated good glycaemic control; of these, 34.5% demonstrated stable, good control, and 13.7% demonstrated relatively good control. About 16.2% demonstrated moderate control, and 35.6% demonstrated poor control. From the good to poor control groups, the percentage of subjects treated with insulin at baseline and during the follow-up period increased gradually, while the percentage of subjects adhering to exercise, diet, medications and SMBG decreased gradually. Compared with baseline, the adherence to exercise, diet, medications and SMBG improved significantly. Approximately 50% and 26% of subjects in the two poorest control groups, respectively, experienced treatment changes. After multivariable adjustments, baseline HbA1c ≥7.5%, HbA1c change ≥-0.5% from baseline to visit 1, insulin treatment, treatment change, poor adherence to diet, exercise, SMBG during the follow-up period and HbA1c measurements <3 per year were significantly associated with poorer glycaemic control. CONCLUSION: We identified four longitudinal HbA1c trajectories in patients with non-newly diagnosed type 2 diabetes. Even if baseline HbA1c is suboptimal, aggressive treatment changes, good adherence during the follow-up period, ≥3 HbA1c measurements per year and reducing HbA1c levels to a certain extent by the first follow-up visit were important for good, stable, long-term glycaemic control.

Prediction of gold stage in patients hospitalized with COPD exacerbations using blood neutrophils and demographic parameters as risk factors.

BACKGROUND: Patients hospitalized with chronic obstructive pulmonary disease (COPD) exacerbations are unable to complete the pulmonary function test reliably due to their poor health conditions. Creating an easy-to-use instrument to identify the Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage will offer valuable information that assists clinicians to choose appropriate clinical care to decrease the mortality in these patients. The objective of this study was to develop a prediction model to identify the GOLD stage in the hospitalized exacerbation of chronic obstructive pulmonary disease (ECOPD) patients. METHODS: This prospective study involved 155 patients hospitalized for ECOPD. All participants completed lung function tests and the collection of blood neutrophils and demographic parameters. Receiver operating characteristic (ROC) curve was plotted based on the data of 155 patients, and was used to analyze the disease severity predictive capability of blood neutrophils and demographic parameters. A support vector regression (SVR) based GOLD stage prediction model was built using the training data set (75%), whose accuracy was then verified by the testing data set (25%). RESULTS: The percentage of blood neutrophils (denoted as NEU%) combined with the demographic parameters was associated with a higher risk to severe episode of ECOPD. The area under the ROC curve was 0.84. The SVR model managed to predict the GOLD stage with an accuracy of 90.24%. The root-mean-square error (RMSE) of the forced expiratory volume in one second as the percentage of the predicted value (denoted as FEV1%pred) was 8.84%. CONCLUSIONS: The NEU% and demographic parameters are associated with the pulmonary function of the hospitalized ECOPD patients. The established prediction model could assist clinicians in diagnosing GOLD stage and planning appropriate clinical care.

Long Distance Transmission of SARS-CoV-2 from Contaminated Cold Chain Products to Humans - Qingdao City, Shandong Province, China, September 2020.

What is already known about this topic? Though coronavirus disease 2019 (COVID-19) has largely been controlled in China, several outbreaks of COVID-19 have occurred from importation of cases or of suspected virus-contaminated products. Though several outbreaks have been traced to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) isolated on the outer packaging of cold chain products, live virus has not been obtained. What is added by this report? In September 2020, two dock workers were detected as having asymptomatic SARS-CoV-2 infection using throat swabs during routine screening in Qingdao, China. Epidemiological information showed that the two dock workers were infected after contact with contaminated outer packaging, which was confirmed by genomic sequencing. Compared to the Wuhan reference strain, the sequences from the dock workers and the package materials differed by 12-14 nucleotides. Furthermore, infectious virus from the cold chain products was isolated by cell culture, and typical SARS-CoV-2 particles were observed under electron microscopy. What are the implications for public health practice? The international community should pay close attention to SARS-CoV-2 transmission mode through cold chain, build international cooperative efforts in response, share relevant data, and call on all countries to take effective prevention and control measures to prevent virus contamination in cold-chain food production, marine fishing and processing, transportation, and other operations.

Spurious fringe processing for dielectric metasurface profile measurement using white-light scanning interferometry.

Dielectric metasurfaces, which are capable of manipulating incident light, have been a novel branch of flat optics. This modulation ability is realized by nanostructures with space-variant geometrical parameters such as height and diameter. Therefore, accurate profile measurement of metasurfaces is of great importance. White-light scanning interferometry is widely used for profile measurement. The step height is retrieved by locating the envelope's peak. However, spurious fringes attached to the desired fringes were observed at the measured area near the edge of nanostructures. Their amplitude distributions vary with the density of nanostructures as well as distance to the edge. Further, anomalous coherence signals with two fringe envelopes are produced, which result in inaccurate measurement results. We attributed this phenomenon to the complex light modulation by the nanostructures. When referring to the anomalous coherence signals for the top of the nanostructures, one envelope is produced by the top, and the other is produced by the bottom; however, it is difficult to distinguish these two, which is the same case for the bottom of the nanostructures. To automatically solve these obstacles, a signal processing method, which integrates the image segmentation technology to identify and divide the anomalous coherence signals, along with a Morlet wavelet transform to extract the fringe envelope, suitable for any measured area of the dielectric metasurface, is proposed. One metasurface belt consisting of seven kinds of nanopillars with varying arrayed densities that produce different coherence signals is measured. The diameter distribution ranges from 500 to 1250 nm with a constant height of 1850 nm. The local periods in the X and Y directions are 3020 and 1740 nm, respectively. Measurement results demonstrate the validity of the proposed method for spurious fringes processing.

The Association Between the Decline of eGFR and a Reduction of Hemoglobin A1c in Type 2 Diabetic Patients.

Objective: This study aimed to explore the relationship between short-term (≤12 months) changes in the estimated glomerular filtration rate (eGFR) and hemoglobin A1c (HbA1c) in patients with type 2 diabetes (T2D). Method: A total of 2,599 patients with T2D were enrolled if they were registered in the Diabetes Sharecare Information System, were aged 18-75 years, and had 2-3 HbA1c and eGFR measurements within the preceding 12 months. The studied patients were categorized into five groups based on eGFR, i.e., the relatively stable (RS), fast decline (FD), modest decline (MD), modest increase (MI), and fast increase (FI) groups. Results: The median eGFR changes from baseline were -22.14, -6.44, 0.00, 6.32, and 20.00 ml/min per 1.73 m2 for patients in the FD, MD, RS, MI, and FI groups, respectively. Up to 1,153 (44.4%) subjects experienced an eGFR decline of ≥3.5 ml/min per 1.73 m2, including 821 (31.6%) FD subjects and 332 (12.8%) MD subjects. A decreased trend was found between the eGFR change and HbA1c decrease category, even after multivariable adjustment. In general, an eGFR FD was frequently found in patients who had an HbA1c reduction of ≥3.00% and a baseline HbA1c ≥8.0%; alternatively, such a result was also observed for a urinary albumin-to-creatinine ratio (UACR) of 30.0-300.0 mg/g, regardless of a diabetes duration of <10.0 or ≥10.0 years, or in patients who had an HbA1c reduction of ≥1.00% accompanied by hyperfiltration. Conclusions: Some patients with T2D experienced an eGFR FD or MD during the ≤12-month follow-up period. A significant downward trend in eGFR change was demonstrated alongside an HbA1c reduction, independent of UACR stage, diabetes duration, and hyperfiltration. Sustained monitoring and cautious interpretation of the HbA1c and eGFR changes will be needed in clinical practice.

Absence of SARS-CoV-2 in semen of a COVID-19 patient cohort.

BACKGROUND: Since SARS-CoV-2 infection was first identified in December 2019, the novel coronavirus-induced pneumonia COVID-19 spread rapidly and triggered a global pandemic. Recent bioinformatics evidence suggested that angiotensin-converting enzyme 2-the main cell entry target of SARS-CoV-2-was predominantly enriched in spermatogonia, Leydig and Sertoli cells, which suggests the potential vulnerability of the male reproductive system to SARS-CoV-2 infection. OBJECTIVES: To identify SARS-CoV-2 RNA in seminal plasma and to determine semen characteristics from male patients in the acute and recovery phases of infection. METHODS: From February 26 to April 2, 2020, 23 male patients with COVID-19 were recruited. The clinical characteristics, laboratory findings and chest computed tomography scans of all patients were recorded in detail. We also investigated semen characteristics and the viral RNA load in semen from these patients in the acute and recovery phases of SARS-CoV-2 infection using approved methods. RESULTS: The age range of the 23 patients was 20-62 years. All patients tested negative for SARS-CoV-2 RNA in semen specimens. Among them, the virus had been cleared in 11 patients, as they tested negative. The remaining 12 patients tested negative for SARS-CoV-2 RNA in semen samples, but were positive in sputum and fecal specimens. The median interval from diagnosis to providing semen samples was 32 days, when total sperm counts, total motile sperm counts, and sperm morphology of the patients were within normal ranges. DISCUSSION AND CONCLUSION: In this cohort of patients with a recent infection or recovering from COVID-19, there was no SARS-CoV-2 RNA detected in semen samples, which indicates the unlikely possibility of sexual transmission through semen at about 1 month after first detection.

Huaier extract suppresses non-small cell lung cancer progression through activating NLRP3-dependent pyroptosis.

Recent studies have reported the anticancer activity of huaier extract in various human malignancies. However, little is known about the effect of huaier extract in non-small cell lung cancer (NSCLC) and its underlying mechanism. The current study aimed to investigate whether huaier extract affects the progression of NSCLC. mRNA and proteins expression of pyroptotic-related genes (NLRP3, caspase-1, IL-1ß, and IL-18) in NSCLC tissues and cells were, respectively, detected by qRT-PCR and western blot. The effects of huaier extract on NSCLC cell viability and cytotoxicity were evaluated by CCK-8 assay, colony formation assay, and LDH detection kit. Besides, we established a xenograft model to assess the antitumor effect of huaier extract on tumor growth in vivo. Our results showed that the expression of pyroptotic-related genes was downregulated in NSCLC tissues and cell lines. Huaier extract pretreatment inhibited cell viability and the percentage of colony formation of H520 and H358 cells, and upregulated the expression of pyroptotic-related genes. Mechanistically, huaier extract exhibited antitumor effect in NSCLC via inducing NLRP3-dependent pyroptosis in vitro and in vivo. In conclusion, our finding confirmed that huaier extract played an antitumor role in NSCLC progression through promoting pyroptotic cell death, which provided a new potential strategy for NSCLC clinical treatment.

Prediction models for pulmonary function during acute exacerbation of chronic obstructive pulmonary disease.

OBJECTIVE: The pulmonary function test is an effort-dependent test; however, during acute exacerbation of chronic obstructive pulmonary disease (AECOPD), patients are unable to effectively cooperate due to poor health. The present study aimed to establish prediction models that only require demographic and inflammatory parameters to predict pulmonary function indexes: forced expiratory volume in one second (FEV1) and forced vital capacity (FVC). APPROACH: The goal was to establish prediction models based on multi-output support vector regression. A total of 143 subjects received a peripheral blood examination and pulmonary function test. The demographic and inflammatory parameters were used as input features, and FEV1 and FVC were used as the target features in prediction models. Three models (mixed model, severe model and nonsevere model) were established with FEV1 < 1 l as the threshold of severe episodes of AECOPD. The values of FEV1 and FVC from the pulmonary function tests were compared with the prediction models to validate the performances of the developed prediction models. MAIN RESULTS: The severe and nonsevere models' prediction performances were better than that of the mixed model. The mean squared errors were lower than 0.05 l2, and the decision coefficients (R 2) were higher than 0.40. The two-tailed t-test results showed that for both severe and nonsevere models, the absolute percentage errors of FEV1 and FVC were within 10%. SIGNIFICANCE: Our study shows the feasibility of predicting the pulmonary function indexes FEV1 and FVC with demographic and inflammatory parameters when the pulmonary function test fails to be implemented, which is beneficial for the treatment of AECOPD.

Hypoxia induces pulmonary artery smooth muscle dysfunction through mitochondrial fragmentation-mediated endoplasmic reticulum stress.

Pulmonary arterial hypertension (PAH) is characterized by pulmonary artery smooth muscle cell (PASMC) dysfunction. However, the underlying mechanisms of PASMC dysfunction remain largely unknown. Here, we show that mitochondrial fragmentation contributes to PASMC dysfunction through enhancement of endoplasmic reticulum (ER) stress. PASMC dysfunction accompanied by mitochondrial fragmentation and ER stress was observed in the pulmonary arteries of hypoxia-induced rats with PAH, as well as isolated PASMCs under hypoxia. Treatment with Mdivi-1 inhibited mitochondrial fragmentation and ER stress and improved PASMC function in isolated PASMCs under hypoxia, while Drp1 overexpression increased mitochondrial fragmentation and ER stress, impairing PASMC function in isolated PASMCs under normoxia. However, inhibition of ER stress using ER stress inhibitors showed a negligible effect on mitochondrial morphology but improved PASMC function during hypoxia. Additionally, we found that mitochondrial fragmentation-promoted ER stress was dependent on mitochondrial reactive oxygen species. Furthermore, inhibition of mitochondrial fragmentation using Mdivi-1 attenuated mitochondrial fragmentation and ER stress in hypoxic PASMCs and improved the pulmonary artery smooth muscle function in hypoxic rats. These results suggest that hypoxia induces pulmonary artery smooth muscle dysfunction through mitochondrial fragmentation-mediated ER stress and that mitochondrial morphology is a potential target for treatment of hypoxia-induced pulmonary artery smooth muscle dysfunction.

Automated fast computational adaptive optics for optical coherence tomography based on a stochastic parallel gradient descent algorithm.

The transverse resolution of optical coherence tomography is decreased by aberrations introduced from optical components and the tested samples. In this paper, an automated fast computational aberration correction method based on a stochastic parallel gradient descent (SPGD) algorithm is proposed for aberration-corrected imaging without adopting extra adaptive optics hardware components. A virtual phase filter constructed through combination of Zernike polynomials is adopted to eliminate the wavefront aberration, and their coefficients are stochastically estimated in parallel through the optimization of the image metrics. The feasibility of the proposed method is validated by a simulated resolution target image, in which the introduced aberration wavefront is estimated accurately and with fast convergence. The computation time for the aberration correction of a 512 × 512 pixel image from 7 terms to 12 terms requires little change, from 2.13 s to 2.35 s. The proposed method is then applied for samples with different scattering properties including a particle-based phantom, ex-vivo rabbit adipose tissue, and in-vivo human retina photoreceptors, respectively. Results indicate that diffraction-limited optical performance is recovered, and the maximum intensity increased nearly 3-fold for out-of-focus plane in particle-based tissue phantom. The SPGD algorithm shows great potential for aberration correction and improved run-time performance compared to our previous Resilient backpropagation (Rprop) algorithm when correcting for complex wavefront distortions. The fast computational aberration correction suggests that after further optimization our method can be integrated for future applications in real-time clinical imaging.

Nonnull interferometric testing of spherical gratings under Littrow conditions with opposite diffraction orders.

Diffracted wavefront measurements are qualitative and comprehensive verifications for the spherical grating that was manufactured to specifications. Direct interferometric testing of the diffracted wavefront is convenient and implemented by tilting the spherical grating at a Littrow angle to obtain autoreflection and then results in a nonnull interferometric testing configuration. The diffracted wavefront of the spherical grating contains not only wavefront errors induced by the manufacturing imperfections but also inherent wavefront contributions from the autoreflection testing setup. The magnitudes of the latter are affected by both the spherical substrate and the groove pattern. Through the analysis of geometric aberrations of spherical gratings, the groove pattern contributions are demonstrated to be contrary for the opposite diffraction orders. A nonnull interferometric testing of spherical gratings is proposed without foreknowledge of the groove pattern, in which the wavefront errors contributed only by the manufacturing imperfections are derived from dual measurements under Littrow conditions with opposite diffraction orders. Simulations are implemented for varied line spacing (VLS) spherical gratings with an F-number slower than 1.5 and groove density varying from 150 to 300 lp/mm, and the residual error less than 0.004λ RMS is obtained. The residual misalignment error after conventionally removing defocus and tilt is further analyzed and discussed. A VLS grating in which the NA is 0.13 and groove density is 200 lp/mm is chosen as an experimental sample, and the diffracted wavefront error with 0.018λ RMS is obtained.

Subaperture stitching interferometry based on the combination of the phase correlation and iterative gradient methods.

Subaperture stitching interferometry (SAS) is an important method for map testing of large aperture optical components, in which a mechanical structure is often employed for the testing of each subaperture. By eliminating the phase deviation of the corresponding points in the overlapping regions of every adjacent subaperture, the whole aperture map can be obtained. Accurate subaperture positioning is an important guarantee for precise stitching. In this paper, a hybrid optimization algorithm is proposed to realize subpixel-level positioning accuracy in SAS based on the combination of the phase correlation and iterative gradient methods. The phase correlation method is adopted to calculate the pixel-level positioning deviation first, and the subpixel deviation is derived and then corrected by iterative optimization through the gradient method. The subpixel-level positioning accuracy of the proposed optimization algorithm is verified by simulations and a 76.2 mm off-axis parabolic mirror is chosen as an experimental testing sample. The surface map obtained from the proposed hybrid optimization method is consistent with the full aperture testing result, which also verifies that the proposed optimization algorithm is a powerful tool with subpixel-level positioning accuracy in SAS testing.