Pneumonia Caused by Chlamydia psittaci and SARS-CoV-2 Coinfection Diagnosed Using Metagenomic Next-Generation Sequencing: A Case Report.

We report a case of pneumonia caused by coinfection with Chlamydia psittaci and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron XBB.1 variant, confirmed using metagenomic next-generation sequencing (mNGS) and quantitative polymerase chain reaction (qPCR). C. psittaci and SARS-CoV-2 were detected in bronchoalveolar lavage fluid using mNGS. Additionally, mNGS detected C. psittaci in blood and nasopharyngeal specimens and was more sensitive than qPCR. The patient recovered after treatment with moxifloxacin. This report highlights the use of coinfections of C. psittaci and SARS-CoV-2, as mNGS has already been recognized to be a diagnostic tool for identifying coinfections.

Case Report: Pneumonia Caused by Chlamydia Psittaci and Cryptococcus Co-Infection.

This study presents a rare case of pneumonia caused by a co-infection of Chlamydia psittaci and Cryptococcus, confirmed by metagenomic next-generation sequencing (mNGS). The patient, who had underlying chronic hepatitis B, had adopted a stray pigeon before the onset of the disease. The primary symptoms were fever, and a productive cough. The patient recovered following treatment with moxifloxacin and itraconazole. C. psittaci and Cryptococcus infections may both have been transmitted from the stray pigeon. This report highlights the potential for infections caused by multiple zoonotic pathogens and the value of mNGS for making the diagnosis of these infections.

PDCD5 as a Potential Biomarker for Improved Prediction of the Incidence and Remission for Patients with Rheumatoid Arthritis.

INTRODUCTION: Rheumatoid arthritis (RA) often involves an altered T-cell subpopulation, higher levels of inflammatory cytokines, and auto-antibodies. This study investigated whether PDCD5 could be a biomarker to predict the incidence and remission of RA so as to guide the therapeutic management of clinical RA. METHODS: One hundred fifty-two patients (41 being in both active status and stable remission status) who were newly diagnosed with RA and 38 healthy controls were enrolled. Basic clinical data were collected before using blood samples remaining in the clinic after routine complete blood count. The ability of PDCD5 and important indicators to predict the remission of RA was estimated based on receiver operating characteristic curve (ROC) analysis. RESULTS: PDCD5 expression was found to be significantly increased in RA patients in active status in comparison with healthy controls or those in stable remission status. Compared with anti-CCP, ESR and DAS28 score, PDCD5 was of better predictive value with an AUC of 0.846 (95% CI 0.780-0.912) for RA remission. The incidence risk of RA increased with higher levels of PDCD5 (OR = 1.73, 95% CI = 1.45-1.98, P = 0.005) in multiple logistic regression analysis, with the risk increasing by 2.94-times for high-risk group in comparison with low-risk group (OR = 2.94, 95% CI = 2.35-4.62, P < 0.001). The association between PDCD5 and RA remission showed a similar result. For correlation analysis, significant associations were eventually found between PDCD5 and indicated genes (FOXP3, TNF-α, IL-17A, IFN-γ and IL-6) as well as several important clinical parameters including IgG, RF, CRP, ESR, anti-CCP and DAS28 score. CONCLUSIONS: This study suggested that increased PDCD5 expression was significantly linked to the incidence and remission of RA. PDCD5 may be used as a novel biomarker for the prediction of RA incidence and remission, especially due to its potential involvement in the development of the condition.

Machine learning algorithm inversion experiment and pollution analysis of water quality parameters in urban small and medium-sized rivers based on UAV multispectral data.

To examine and analyze the applicability of UAV multispectral images to urban river monitoring, this paper, taking the Fuyang River in the urban area of Handan Municipality as the object, the orthogonal image data of the river in different seasons were acquired by unmanned aerial vehicles (UAVs) equipped with multispectral sensors, and at the same time, the water samples were collected for physical and chemical indexes detection. Based on the image data, a total of 51 modeling spectral indexes were obtained by constructing three forms of band combinations ranging from the difference index (DI), ratio index (RI), and normalization index (NDI) and combining six single-band spectral values. Through the partial least squares (PLS), random forest (RF), and lasso prediction models, six fitting models of water quality parameters were constructed: turbidity (Turb), suspended, substance (SS), chemical oxygen demand (COD), ammonia nitrogen (NH4-N), total nitrogen (TN), and total phosphorus (TP). After verifying the results and evaluating the accuracy, the following conclusions were drawn: (1) The inversion accuracy of the three types of models is generally the same-summer is better than spring, and winter is the worst. (2) Water quality parameter inversion model based on two kinds of machine learning algorithms has more prominent advantages than PLS. RF model has good performance in the inversion accuracy and generalization ability of water quality parameters in different seasons. (3) The prediction accuracy and stability of the model are positively correlated to a certain extent with the size of the standard deviation of sample values. To sum up, by using the multispectral image data acquired by UAV and adopting the prediction models built upon machine learning algorithms, water quality parameters in different seasons can be predicted in different degrees.

Nerve growth factor causes epinephrine release dysfunction by regulating phenotype alterations and the function of adrenal medullary chromaffin cells in mice with allergic rhinitis.

The presence of allergic rhinitis (AR) is an increased risk factor for the occurrence of bronchial asthma (BA). Nerve growth factor (NGF), in addition to its key role in the development and differentiation of neurons, may also be an important inflammatory factor in AR and BA. However, the pathogenesis of the progression of AR to BA remains to be elucidated. The present study aimed to investigate the ability of NGF to mediate nasobronchial interactions and explore possible underlying molecular mechanisms. In the present study, an AR mouse model was established and histology of nasal mucosa tissue injury was determined. The level of phenylethanolamine N­methyl transferase in adrenal medulla was determined by immunofluorescence. Primary adrenal medullary chromaffin cells (AMCCs) were isolated and cultured from the adrenal medulla of mice. The expression levels of synaptophysin (SYP), STAT1, JAK1, p38 and ERK in NGF­treated and untreated AMCCs were detected by reverse­transcription­quantitative PCR and western blotting. The epinephrine (EPI) and norepinephrine (NE) concentrations were measured by ELISA. It was found that the expression of SYP in AMCCs was enhanced in the presence of NGF, whereas, the concentration of EPI decreased significantly under the same conditions. Furthermore, NGF mediated the phenotypic and functional changes of AMCCs, resulting in decreased EPI secretion via JAK1/STAT1, p38 and ERK signaling. In conclusion, these findings could provide novel evidence for the role of NGF in regulating neuroendocrine mechanisms.

Spatial scale transformation-based estimation model for fresh grass yield: a case study of the Xilingol Grassland, Inner Mongolia, China.

Estimating the grass yield of a grassland area is of vital theoretical and practical significance for determining grazing capacity and maintaining ecological balance. Due to the spatial inconsistency between sampling and remote sensing data, improving the accuracy of fresh grass yield (FGY) estimation based on remote sensing is difficult. Using vegetation coverage at different spatial scales, this paper proposes a spatial scale transformation (SST)-based estimation model for FGY adopting normalized difference vegetation index (NDVI) as its estimation factor, using the grassland in Xilingol League, Inner Mongolia, as the study area. Results showed that the SST-based FGY estimation model was able to greatly improve estimation precision; the relative estimation error (REE) of the estimation models constructed using linear with intercept zero (linear-0) and power functions were 18.16% and 18.35%, respectively. The estimation models constructed using linear-0 and power functions were employed to estimate the grass yield of the grassland in Xilingol League, and the total FGYs estimated were 8.777 × 1010 kg and 8.583 × 1010 kg, respectively. The two models obtained roughly the same estimates, but there were significant differences between them in the spatial distributions of FGY per unit. Taking net primary productivity (NPP) as an example, the effectiveness of other remote sensing data as estimation factors was further verified, and the results showed that SST-based estimation for FGY also effectively improved the estimation accuracy of grass yield.

Semantic Segmentation of Hyperspectral Remote Sensing Images Based on PSE-UNet Model.

With the development of deep learning, the use of convolutional neural networks (CNN) to improve the land cover classification accuracy of hyperspectral remote sensing images (HSRSI) has become a research hotspot. In HSRSI semantics segmentation, the traditional dataset partition method may cause information leakage, which poses challenges for a fair comparison between models. The performance of the model based on "convolutional-pooling-fully connected" structure is limited by small sample sizes and high dimensions of HSRSI. Moreover, most current studies did not involve how to choose the number of principal components with the application of the principal component analysis (PCA) to reduce dimensionality. To overcome the above challenges, firstly, the non-overlapping sliding window strategy combined with the judgment mechanism is introduced, used to split the hyperspectral dataset. Then, a PSE-UNet model for HSRSI semantic segmentation is designed by combining PCA, the attention mechanism, and UNet, and the factors affecting the performance of PSE-UNet are analyzed. Finally, the cumulative variance contribution rate (CVCR) is introduced as a dimensionality reduction metric of PCA to study the Hughes phenomenon. The experimental results with the Salinas dataset show that the PSE-UNet is superior to other semantic segmentation algorithms and the results can provide a reference for HSRSI semantic segmentation.

Clinical characteristics of 14 cases of severe Chlamydia psittaci pneumonia diagnosed by metagenomic next-generation sequencing: A case series.

INTRODUCTION: The objective of this study was to explore the clinical, laboratory, and imaging features of severe Chlamydia psittaci pneumonia in order to improve early diagnosis and treatment success rates. METHODS: We conducted a retrospective record review of 14 cases of severe Chlamydia psittaci pneumonia diagnosed by metagenomic next-generation sequencing technology in our hospital. We extracted and analyzed data on the clinical symptoms and signs, contact history, laboratory investigations, chest computed tomography, treatment, and clinical outcomes. RESULTS: Of the 14 patients, 12 (86%) were male and two (14%) were female, with a mean age of 57 years (SD: 7 years). Eleven patients (79%) had a history of poultry contact. The main clinical manifestations were fever (n = 14, 100%), flu-like symptoms (n = 10, 71%), cough, sputum (n = 9, 64%), and dyspnea (n = 5, 36%). Blood tests revealed marked elevation of neutrophil percentage, C-reactive protein, procalcitonin, brain natriuretic peptide, and creatine kinase levels; slight elevation of aspartate aminotransferase, creatinine, urea, fibrinogen, and D-dimer levels; and decreased albumin, sodium, and calcium levels. Chest computed tomography showed bilateral lesions (n = 7, 50%), middle-lower lobe lesions (n = 10, 71%), lesions in multiple lobes (n = 9, 64%), consolidation shadows (n = 11, 79%), and pleural effusions (n = 11, 79%). The median time from disease onset to hospital admission was 4.5 days (interquartile range: 1-17 days); the mean length of hospital stay was 20.9 ±â€Š8.5 days, and the mean time from admission to diagnosis was 5.1 ±â€Š2.6 days. After diagnosis, patients were either treated with doxycycline alone or doxycycline combined with quinolones. All 14 patients developed respiratory failure and received invasive mechanical ventilation; two (14%) received veno-venous extracorporeal membrane oxygenation, four (29%) received continuous renal replacement therapy, and three (21%) died. DISCUSSION AND CONCLUSION: A poultry contact history and typical flu-like symptoms are early indicators of Chlamydia psittaci pneumonia. Substantial elevations in procalcitonin, creatine kinase, and brain natriuretic peptide indicate severe disease. Metagenomic next-generation sequencing is useful for diagnosis. Early empirical antibiotic therapy with quinolones can reduce the mortality in critically ill patients.

Mucosa-associated lymphoid tissue lymphoma translocation protein 1 in rheumatoid arthritis: Longitudinal change after treatment and correlation with treatment efficacy of tumor necrosis factor inhibitors.

BACKGROUND: Mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1) correlates with treatment outcomes in inflammatory bowel disease and rheumatoid arthritis (RA). This study aimed to further evaluate the MALT1 longitudinal change and its relationship with tumor necrosis factor inhibitors (TNFi) response in RA patients. METHODS: Seventy-one RA patients receiving TNFi [etanercept (n = 42) or adalimumab (n = 29)] were enrolled. MALT1 was detected by RT-qPCR in peripheral blood samples of RA patients before treatment (W0), at week (W)4, W12, and W24 after treatment. RA patients were divided into response/non-response, remission/non-remission patients according to their treatment outcome at W24. Meanwhile, MALT1 was also detected by RT-qPCR in 30 osteoarthritis patients and 30 healthy controls (HCs). RESULTS: Mucosa-associated lymphoid tissue lymphoma translocation protein 1 was elevated in RA patients compared with HCs (Z=-6.392, p < 0.001) and osteoarthritis patients (Z = -5.020, p < 0.001). In RA patients, MALT1 was positively correlated with C-reactive protein (rs  = 0.347, p = 0.003), but not other clinical characteristics, treatment history, or current TNFi category. Meanwhile, MALT1 decreased from W0 to W12 in total RA patients (x2  = 86.455, p < 0.001), etanercept subgroup (x2  = 46.636, p < 0.001), and adalimumab subgroup (x2  = 41.291, p < 0.001). Moreover, MALT1 at W24 (p = 0.012) was decreased in response patients compared with non-response patients; MALT1 at W12 (p = 0.027) and W24 (p = 0.010) were reduced in remission patients than non-remission patients. In etanercept subgroup, MALT1 at W24 (p = 0.013) was decreased in response patients compared with non-response patients. In adalimumab subgroup, MALT1 at W24 (p = 0.015) was lower in remission patients than non-remission patients. CONCLUSION: Mucosa-associated lymphoid tissue lymphoma translocation protein 1 reduction after treatment is associated with response and remission to TNFi in RA patients.

MALT1 positively relates to Th17 cells, inflammation/activity degree, and its decrement along with treatment reflects TNF inhibitor response in ankylosing spondylitis patients.

BACKGROUND: Mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1) facilitates CD4+ T-cell differentiation, immune response, inflammation, and osteoclastogenesis. This study aimed to explore the relation between MALT1 and treatment efficacy to tumor necrosis factor inhibitor (TNFi) in ankylosing spondylitis (AS) patients. METHODS: This study recruited 73 AS patients underwent adalimumab treatment. Peripheral blood mononuclear cell (PBMC) was obtained at Week (W) 0, W4, W8, and W12 after treatment initiation; then, MALT1 was measured using RT-qPCR. Furthermore, PBMC and serum at W0 were proposed to flow cytometry and ELISA for Th1 cells, Th17 cells, IFN-γ, and IL-17A levels measurement. Besides, 20 osteoarthritis patients and 20 healthy controls (HCs) were enrolled to detect MALT1. RESULTS: Mucosa-associated lymphoid tissue lymphoma translocation protein 1 expression was higher in AS patients compared with HCs (p < 0.001) and osteoarthritis patients (p < 0.001). Besides, MALT1 expression was positively linked with CRP (p = 0.002), BASDAI (p = 0.026), PGADA (p = 0.040), ASDASCRP (p = 0.028), Th17 cells (p = 0.020), and IL-17A (p = 0.017) in AS patients, but did not relate to other clinical features, Th1 cells or IFN-γ (all p>0.050). MALT1 was decreased along with treatment only in AS patients with ASAS40 response (p < 0.001), but not in those without ASAS40 response (p = 0.064). Notably, MALT1 expression was of no difference at W0 (p = 0.328), W4 (p = 0.280), and W8 (p = 0.080), but lower at W12 (p = 0.028) in AS patients with ASAS40 response compared with those without ASAS40 response. CONCLUSION: Mucosa-associated lymphoid tissue lymphoma translocation protein 1 positively correlates with Th17 cells, inflammatory, and activity degree; meanwhile, its decrement along with treatment reflects the response to TNF inhibitor in AS patients.

A study on water quality parameters estimation for urban rivers based on ground hyperspectral remote sensing technology.

The purpose of this research is to seek a better inversion algorithm. And on this basis, it explores the feasibility of using hyperspectral monitoring technology instead of laboratory physical and chemical index test and evaluates the prediction effect of inversion model on water quality change. So as to be more convenient, more economical and extensive monitoring methods for water quality monitoring of urban internal river are provided. This paper takes the water samples collected in Fuyang River in downtown Handan as the research object and obtains original spectral data of the samples by the ASD FieldSpec 4 field hyperspectral spectrometer. After the smoothing filter pretreatment by the Savitzky-Golay (SG) method and specified mathematical transformations, the modeling spectral indicators of various water quality parameters are selected and determined by calculating the maximum mean of absolute values for correlation coefficients of various spectral indicators and measured values in the wavelength range from 400 to 950 nm. By introducing partial least squares (PLS), random forest (RF), and Lasso (least absolute shrinkage and selection operator), six water quality parameter fitting models were constructed including turbidity (Turb), suspended substance (SS), chemical oxygen demand (COD), NH4-N, total nitrogen (TN), and total phosphorus (TP), which are also testified and evaluated through hyperspectral data. The results show that different spectral transformation methods highlight different information inversion effects. The first derivative of reciprocal logarithm of spectral data after SG smoothing has a good modeling effect on four water quality parameters including Turb, COD, NH4-N, and TP; and the first derivative of smoothed spectral data has a good modeling effect on both water quality parameters of SS and TN. Among the three models, the PLS model has a good prediction effect, with the [Formula: see text] for COD, TN, and TP ranging from 0.74 to 0.80, while that for Turb and SS shows relatively poorer prediction effect, followed by even worse effect on HN4-H. Both machine learning algorithms of RF and Lasso have respectively obtained the best prediction models for different water quality parameters. The Lasso model has a [Formula: see text] value above 0.8 for water body organic pollutants COD, TN, and TP, and the decrease value for [Formula: see text] and [Formula: see text] is below 0.1, which indicates that the model has high prediction accuracy and strong generalization ability, but the results of SS and NH4-N do not meet the expected accuracy. In the inversion model of RF for COD, [Formula: see text] is higher than [Formula: see text], which shows excellent performance, and has certain prediction ability for SS and NH4-N. The RF model and Lasso model complement each other effectively in applicability and prediction accuracy. Compared with the traditional regression model PLS, machine learning has obvious overall advantages, making it more suitable for classified inversion prediction of urban river water quality parameters.

Severe Chlamydia psittaci Pneumonia Complicated by Rhabdomyolysis: A Case Series.

Purpose: To explore the clinical characteristics, diagnosis, and treatment of severe Chlamydia psittaci pneumonia complicated by rhabdomyolysis and to improve the success rate of treatment. Patients and Methods: The clinical characteristics, diagnosis, treatment, and outcomes of four patients with severe C. psittaci pneumonia complicated by rhabdomyolysis diagnosed by metagenomic next-generation sequencing (mNGS) in our hospital were analyzed retrospectively. Results: All four patients were male, aged 46-64 years, and all had a history of bird contact. All patients had fever, fatigue, tea-colored urine, myalgia, and two patients were unable to walk. C. psittaci DNA was found by mNGS of the bronchoalveolar lavage fluid of all four patients. Their creatine kinase was >1000 U/L, and myoglobin, C-reactive protein, procalcitonin, and brain natriuretic peptide were significantly increased. The McMahon score of three patients was >6 points, of whom one patient suffered from acute kidney injury; he was treated with continuous renal replacement therapy and eventually died. After diagnosis, three patients were treated with doxycycline and quinolones and were discharged after recovery. Conclusion: Psittacosis complicated by rhabdomyolysis is characterized by fever, fatigue, myalgia, and tea-colored urine, with significant increases in creatine kinase and myoglobin. The McMahon score should be applied early to assess the risk of acute kidney injury, and renal replacement therapy and renal protection therapy should be initiated in the early stage. Among severely ill patients, early use of empirical antibiotics, including quinolones, may improve the prognosis.

Spatial heterogeneity of changes in cropland ecosystem water use efficiency and responses to drought in China.

Understanding cropland ecosystem water use efficiency (eWUE) responses to drought is important for sustainable water resource management and food security. Today in China, the spatiotemporal patterns of eWUE and responses to drought across different cropland classes remain poorly quantified. In this study, we characterized the spatial temporal variability in cropland eWUE and response to drought in China from 1982 to 2017 using the satellite-retrieved evapotranspiration (ET), gross primary production (GPP), and self-calibrating Palmer Drought Severity Index (scPDSI), in conjunction with the Global Food Security-support Analysis Data product for Crop Dominance (GFSAD1KCD) data. Results indicated that (1) mean annual cropland eWUE had a spatial range from 0 to 9.94 g C kg-1 H2O, with higher values (2.06 g C kg-1 H2O) in class 4 (rainfed: wheat, rice, and soybeans dominant), whereas the lowest eWUE (1.58 g C kg-1 H2O) occurred in class 2 (irrigated mixed crop 1: wheat, rice, barley, and soybeans). (2) Annual eWUE, GPP, and ET values for croplands in China increased significantly between 1982 and 2017. Class 1 (irrigated wheat and rice) had the highest trend of 0.011 g C kg-1 H2O yr-1, and class 6 (rainfed: corn and soybeans) had the lowest of 0.0007 g C kg-1 H2O yr-1. Apart from class 4, annual GPP and ET were enhanced in most cropland classes from 1982 to 2017 (p<0.01). (3) Rainfed croplands generally had higher eWUE, GPP, and ET values than irrigated croplands. Except for rainfed cropland eWUE, all other cropland variables increased significantly (p<0.001) from 1982 to 2017. (4) Correlation analysis found that the 19.66% (15.62%) of cropland had significant negative (positive) correlations between eWUE and current-year scPDSI. The legacy effects of drought on cropland eWUE indicated that previous and current-year drought impacts on cropland eWUE were in the same direction. Our results provide insights into variability in cropland eWUE and its response to drought in China, where there is a growing demand for agricultural water resource management.

Spatiotemporal dynamics of ecosystem water use efficiency over the Chinese Loess Plateau base on long-time satellite data.

Ecosystem water use efficiency (eWUE), defined as the ratio between carbon gains and water loss from the system, has been recognized as an important characteristic of carbon and water balances. The long-lasting "Grain for Green" Program (GFGP) initiated in 1999 in China's Loess Plateau (CLP) is a large-scale ecological program in the world, which aims to improve the CLP's ecosystem resilience by enhancing vegetation cover and productivity. Understanding how the GFGP can affect eWUE is imperative to ensuring sustainable water resources and to promoting sustainable management strategies. In this study, we evaluated the spatiotemporal variability of growing-season eWUE and examined its response to both climate change and vegetation coverage from 1982 to 2017. Our results indicate that growing-season eWUE, gross primary productivity (GPP), and evapotranspiration (ET) in CLP area increased significantly from 1982 to 2017. Specifically, eWUE, GPP, and ET increased more rapidly after China established the program. The most significant growth area of eWUE was found in main areas conducting GFGP project, including the Loess hilly and gully area (LHGA). Spatially, eWUE, GPP, and ET in the growing season increased from northwest to southeast, and higher eWUE was found in areas with high vegetation cover. The spatial and temporal variability of eWUE was related to vegetation cover (expressed as leaf area index, LAI) and climatic variability. Significant positive correlations were observed between growing-season LAI, temperature, and eWUE, because the LAI and temperature have a greater effect on photosynthesis than ET. Our results suggested that the GFGP was the main driving force that causes the spatial-temporal variability of eWUE in CLP.

Spatiotemporal analysis of interactions between seasonal water, climate, land use, policy, and socioeconomic changes: Hulun-Buir Steppe as a Case Study.

Increased grazing and agricultural production, industrialization, population growth, and consequent land use land cover (LULC) changes considerably increase water consumption. Global climate change exaggerates the uncertainty of water sources and supplies. Unfortunately, most current examinations are either confined within disciplinary silos or not integrated for considering wide-ranging socioenvironmental, management, and policy factors. The paper develops an integrated regional water environment modeling framework, examining how climate, LULC, socioenvironmental, and policy factors interact with the water environment. It also adopts a block-based econometric panel data analysis to quantify this framework. The paper extracts seasonal water area and LULC data through image processing from 2000 to 2014 in the Hulun-Buir watershed, Inner Mongolia of China. The paper quantitatively analyzed the interactions between seasonal water changes and major driving factors, such as climatic, land-use, socioeconomic, policy, space, and time. Many of these driving factors were interacting with the seasonal water environment and showing long-term causal relationships. The socioeconomic variables explained 71% of the variance of seasonal water change, the environmental and climatic factors about 9%, the regional disparities around 13%, and the yearly differences about 4%. The findings confirm that it is critical to carry out a time-series examination of causal relationships between seasonal water change and its manifold driving factors at the scale of regional watershed studies. This integrated watershed modeling framework is suitable for adaptation in other geographic areas or for integrated studies of other socio-environmental systems.

COVID-19 infection in patients with connective tissue disease: A multicity study in Hubei province, China.

Novel Coronavirus disease 2019 (COVID-19) has spread rapidly around the world. Individuals with immune dysregulation and/or on immunosuppressive therapy, such as rheumatic patients, are considered at greater risk for infections. However, the risks of patients with each subcategory of rheumatic diseases have not been reported. Here, we identified 100 rheumatic patients from 18,786 COVID-19 patients hospitalized in 23 centers affiliated to Hubei COVID-19 Rheumatology Alliance between January 1 and April 1, 2020. Demographic information, medical history, length of hospital stay, classification of disease severity, symptoms and signs, laboratory tests, disease outcome, computed tomography, and treatments information were collected. Compared to gout and ankylosing spondylitis (AS) patients, patients with connective tissue disease (CTD) tend to be more severe after COVID-19 infection (p = 0.081). CTD patients also had lower lymphocyte counts, hemoglobin, and platelet counts (p values were 0.033, < 0.001, and 0.071, respectively). Hydroxychloroquine therapy and low- to medium-dose glucocorticoids before COVID-19 diagnosis reduced the progression of COVID-19 to severe/critical conditions (p = 0.001 for hydroxychloroquine; p = 0.006 for glucocorticoids). Our data suggests that COVID-19 in CTD patients may be more severe compared to patients with AS or gout.

Evaluating the cumulative and time-lag effects of drought on grassland vegetation: A case study in the Chinese Loess Plateau.

The increased frequency of drought events in recent years is known to be responsible for significantly altering plant biodiversity in many of Earth's ecosystems, though the specifics of vegetation-drought interactions, especially the cumulative and time-lag responses, remains unclear. This study aimed to quantitatively investigate how grassland vegetation over the Chinese Loess Plateau (CLP) reacts to drought, specifically the observed cumulative and time-lag effects which are caused, using a combination of the Normalized Difference Vegetation Index (NDVI) and a multiple time-scale drought index (Standardized Precipitation and Evapotranspiration Index, SPEI). Our results revealed that while drought conditions have widespread cumulative impacts on grass growth in the CLP, the time lag effect of drought covered about half of the total area of the CLP. The cumulative effect of drought on grass was found to take place over various time scales, ranging from 5 to 10 months, while the time lag effect occurred within 2-3 months. The different response time of vegetation growth to the cumulative effect of drought in the CLP was found to be highly related to different water conditions. The accumulated months and mean rmax-cum both had a significant negative correlation with the mean annual SPEI (R2 = 0.90, P < 0.001; R2 = 0.70, P < 0.001, respectively). The lagged months and mean rmax-lag were also found to be negatively correlated with the mean annual SPEI (R2 = 0.547, P < 0.05; R2 = 0.785, P < 0.01, respectively).

HMGB1-induced asthmatic airway inflammation through GRP75-mediated enhancement of ER-mitochondrial Ca2+ transfer and ROS increased.

Imbalanced T-helper (TH)1/Th2 response contributes significantly to asthma pathogenesis. Our study indicated that HMGB1 play an important role in the release of Th2-associated cytokines of asthma. However, the specific mechanism about HMGB1-induced imbalanced TH1/Th2 response is not known. In vivo, an OVA-induced asthma mouse model was set up and mice treated with anti-HMGB1 IgG. The mice treated with the anti-HMGB1 IgG ameliorated airway hyper-reactivity, disruption of Th1/Th2 balance and the upregulation of GRP75 induced by OVA. In vitro, the exposure of normal human bronchial epithelial cells to HMGB1 resulted in the upregulation of GRP75, proinflammatory cytokine production, enhanced ER-Mitochondrial Ca2+ transfer, and enhancement of reactive oxygen species (ROS). While HMGB1-induced these changes were attenuated by GRP75 siRNA treatment. Sequentially, pretreatment with 2-APB, SKF960365 (SKF) and Ru360 which inhibit ER-Mitochondrial Ca2+ transfer significantly lowered HMGB1-induced the generation of ROS and the release of Th2 cytokines in 16HBE cells. Meanwhile, N-acetylcysteine (NAC) significantly attenuated the HMGB1-mediated pro-inflammatory cytokines release. Therefore, these results indicate that GRP75-mediated ER-Mitochondrial Ca2+ transfer may be an important contributor in imbalanced of Th1/Th2 balance of asthma. Moreover, HMGB1 specifically induces the release of Th2 cytokines through GRP75-mediated enhancement of ER-Mitochondrial Ca2+ transfer and ROS increased.