bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow with Reference and Crowdsourced Observations
Authors: Varentsov Mikhail , Fenner Daniel , Meier Fred , Samsonov Timofey , Demuzere Matthias 
Published: December 2021

Urban climate features, such as the urban heat island (UHI), are determined by various factors characterizing the modifications of the surface by the built environment and human activity. These factors are often attributed to the local spatial scale (hundreds of meters up to several kilometers). Nowadays, more and more urban climate studies utilize the concept of the local climate zones (LCZs) as a proxy for urban climate heterogeneity. However, for modern megacities that extend to dozens of kilometers, it is reasonable to suggest a significant contribution of the larger-scale factors to the temperature and UHI climatology. In this study, we investigate the contribution of local-scale and mesoscale driving factors of the nocturnal canopy layer UHI of the Moscow megacity in Russia. The study is based on air temperature observations from a dense network consisting of around 80 reference and more than 1,500 crowdsourced citizen weather stations for a summer and a winter season. For the crowdsourcing data, an advanced quality control algorithm is proposed. Based on both types of data, we show that the spatial patterns of the UHI are shaped both by local-scale and mesoscale driving factors. The local drivers represent the surface features in the vicinity of a few hundred meters and can be described by the LCZ concept. The mesoscale drivers represent the influence of the surrounding urban areas in the vicinity of 2–20 km around a station, transformed by diffusion, and advection in the atmospheric boundary layer. The contribution of the mesoscale drivers is reflected in air temperature differences between similar LCZs in different parts of the megacity and in a dependence between the UHI intensity and the distance from the city center. Using high-resolution city-descriptive parameters and different statistical analysis, we quantified the contributions of the local- and mesoscale driving factors. For selected cases with a pronounced nocturnal UHI, their respective contributions are of similar magnitude. Our findings highlight the importance of taking both local- and mesoscale effects in urban climate studies for megacities into account. Furthermore, they underscore a need for an extension of the LCZ concept to take mesoscale settings of the urban environment into account.
Increased Nitrogen Loading Boosts Summer Phytoplankton Growth by Alterations in Resource and Zooplankton Control: A Mesocosm Study
Authors: He Hu , Chen Kunquan , Du Yingxun , Li Kuanyi , Liu Zhengwen , Jeppesen Erik , Søndergaard Martin 
Published: December 2021

The effectiveness of controlling nitrogen (N) to manage eutrophication of aquatic ecosystems remains debated. To understand the mechanisms behind phytoplankton growth in shallow lakes (resource and grazing effects) under contrasting N loading scenarios, we conducted a 70-days mesocosm experiment in summer. The mesocosms contain natural plankton communities deriving from a 10-cm layer of lake sediment and 450 L of lake water. We also added two juvenile crucian carp (Carassius carassius) in each mesocosm to simulate presence of the prevailing omni-benthivorous fish in subtropical lakes. Our results showed that N addition increased not only water N levels but also total phosphorus (TP) concentrations, which together elevated the phytoplankton biomass and caused strong dominance of cyanobacteria. Addition of N significantly lowered the herbivorous zooplankton to phytoplankton biomass ratio and promoted the phytoplankton yield per nutrient (Chl-a: TP or Chl-a: TN ratio), indicating that summer N addition likely also favored phytoplankton growth through reduced grazing by zooplankton. Accordingly, our study indicates that summer N loading may boost eutrophication via both changes in resource and grazing control in shallow lakes. Thus, alleviation of eutrophication in shallow eutrophic lakes requires a strategic approach to control both nutrients (N and P) appropriately.
Identification of an Autophagy-Related Gene Signature for the Prediction of Prognosis in Early-Stage Colorectal Cancer
Authors: Lin Xu-tao , Wu Qiu-ning , Qin Si , Fan De-jun , Lv Min-yi , Chen Xi , Cai Jia-wei , Weng Jing-rong , Zou Yi-feng , Rong Yu-ming , Gao Feng 
Published: December 2021

Purpose: A certain number of early-stage colorectal cancer (CRC) patients suffer tumor recurrence after initial curative resection. In this context, an effective prognostic biomarker model is constantly in need. Autophagy exhibits a dual role in tumorigenesis. Our study aims to develop an autophagy-related gene (ATG) signature-based on high-throughput data analysis for disease-free survival (DFS) prognosis of patients with stage I/II CRC.Methods: Gene expression profiles and clinical information of CRC patients extracted from four public datasets were distributed to discovery and training cohort (GSE39582), validation cohort (TCGA CRC, n = 624), and meta-validation cohort (GSE37892 and GSE14333, n = 420). Autophagy genes significantly associated with prognosis were identified.Results: Among 655 autophagy-related genes, a 10-gene ATG signature, which was significantly associated with DFS in the training cohort (HR, 2.76[1.56–4.82]; p = 2.06 × 10–4), was constructed. The ATG signature, stratifying patients into high and low autophagy risk groups, was validated in the validation (HR, 2.29[1.15–4.55]; p = 1.5 × 10–2) and meta-validation cohorts (HR, 2.5[1.03–6.06]; p = 3.63 × 10–2) and proved to be prognostic in a multivariate analysis. Functional analysis revealed enrichment of several immune/inflammatory pathways in the high autophagy risk group, where increased infiltration of T regulatory cells (Tregs) and decreased infiltration of M1 macrophages were observed.Conclusion: Our study established a prognostic ATG signature that effectively predicted DFS for early-stage CRC patients. Meanwhile, the study also revealed the possible relationship among autophagy process, immune/inflammatory response, and tumorigenesis.
Progesterone Aggravates Lung Fibrosis in a Mouse Model of Systemic Sclerosis
Authors: Vafashoar Fatemeh , Mousavizadeh Kazem , Poormoghim Hadi , Haghighi Amir , Pashangzadeh Salar , Mojtabavi Nazanin 
Published: December 2021

BackgroundGender-related factors have explained the higher prevalence of autoimmune diseases in women. Sex hormones play a key role in the immune system and parenchymal cells function; therefore, these hormones can be important in the pathogenesis of autoimmune diseases as a risk or beneficial factor. Lung fibrosis is the main cause of mortality in systemic sclerosis, a female predominant autoimmune disease. The objective of this study was to examine the effect of progesterone on lung fibrosis in a mouse model of systemic sclerosis.MethodsMice with bleomycin-induced lung fibrosis treated with progesterone subcutaneously for 21 and 28 days. Blood was collected for hormone and cytokine measurement at the end of treatment then, skin and lung tissues were harvested for histological assessment, gene expression, cytokine, hydroxyproline, and gelatinase measurement.ResultsTrichrome staining and hydroxyproline measurements showed that progesterone treatment increased the content of collagen in fibrotic and normal lung tissues. Progesterone increased α-SMA (P &lt; 0.01), TGF- β (P &lt; 0.05) and decreased MMP9 (P &lt; 0.05) in fibrotic lung tissues. Also progesterone treatment decreased the gene expression of Col1a2 (P &lt;0.05), Ctgf (P &lt;01), End1 (0.001) in bleomycin- injured lung tissues. The serum level of TNF-α was decreased, but the serum level of cortisol was increased by progesterone treatment in fibrotic mice (P&lt; 0.05).ConclusionOur results showed that progesterone aggravates lung fibrosis in a mouse model of systemic sclerosis.
Multicenter Study of Controlling Nutritional Status (CONUT) Score as a Prognostic Factor in Patients With HIV-Related Renal Cell Carcinoma
Authors: Xue Wenrui , Zhang Yu , Wang Hua , Zhang Yu , Hu Xiaopeng 
Published: December 2021

ObjectiveIn recent years, the controlled nutritional status (CONUT) score has been widely recognized as a new indicator for assessing survival in patients with urological neoplasms, including renal, ureteral, and bladder cancer. However, the CONUT score has not been analyzed in patients with HIV-related urological neoplasms. Therefore, we aimed to evaluate the prognostic significance of the CONUT score in patients with HIV-related renal cell carcinoma (RCC).MethodsA total of 106 patients with HIV-related RCC were recruited from four hospitals between 2012 and 2021, and all included patients received radical nephrectomy or partial nephrectomy. The CONUT score was calculated by serum albumin, total lymphocyte counts, and total cholesterol concentrations. Patients with RCC were divided into two groups according to the optimal cutoff value of the CONUT score. Survival analysis of different CONUT groups was performed by the Kaplan–Meier method and a log rank test. A Cox proportional risk model was used to test for correlations between clinical variables and cancer-specific survival (CSS), overall survival (OS), and disease-free survival (DFS). Clinical variables included age, sex, hypertension, diabetes, tumor grade, Fuhrman grade, histology, surgery, and CD4+ T lymphocyte count.ResultThe median age was 51 years, with 93 males and 13 females. At a median follow-up of 41 months, 25 patients (23.6%) had died or had tumor recurrence and metastasis. The optimal cutoff value for the CONUT score was 3, and a lower CONUT score was associated with the Fuhrman grade (P=0.024). Patients with lower CONUT scores had better CSS (HR 0.197, 95% CI 0.077-0.502, P=0.001), OS (HR 0.177, 95% CI 0.070-0.446, P&lt;0.001) and DFS (HR 0.176, 95% CI 0.070-0.444, P&lt;0.001). Multivariate Cox regression analysis indicated that a low CONUT score was an independent predictor of CSS, OS and DFS (CSS: HR=0.225, 95% CI 0.067-0.749, P=0.015; OS: HR=0.201, 95% CI 0.061-0.661, P=0.008; DFS: HR=0.227, 95% CI 0.078-0.664, P=0.007). In addition, a low Fuhrman grade was an independent predictor of CSS (HR 0.192, 95% CI 0.045-0.810, P=0.025), OS (HR 0.203, 95% CI 0.049-0.842, P=0.028), and DFS (HR 0.180, 95% CI 0.048-0.669, P=0.010), while other factors, such as age, sex, hypertension, diabetes, tumor grade, histology, surgery, and CD4+ T lymphocyte count, were not associated with survival outcome.ConclusionThe CONUT score, an easily measurable immune-nutritional biomarker, may provide useful prognostic information in HIV-related RCC.
Relationship Between Cerebral Hemodynamics, Tissue Oxygen Saturation, and Delirium in Patients With Septic Shock: A Pilot Observational Cohort Study
Authors: Feng Qing , Ai Meilin , Huang Li , Peng Qianyi , Ai Yuhang , Zhang Lina 
Published: December 2021

Background: Septic shock patients have tendencies toward impairment in cerebral autoregulation and imbalanced cerebral oxygen metabolism. Tissue Oxygen Saturation (StO2) and Transcranial Doppler (TCD) monitoring were undertaken to observe the variations of cerebral hemodynamic indices and cerebral/peripheral StO2 to find risk factors that increase the sepsis-associated delirium (SAD).Materials and Methods: The research cohort was chosen from septic shock patients received in the Department of Critical Care Medicine, Xiangya Hospital, Central South University between May 2018 and March 2019. These patients were separated into two groups, SAD and non-SAD as assessed by using the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Comparisons were made between the two groups in terms of peripheral StO2, fluctuations in regional cerebral oxygen saturation (rSO2), cerebral vascular automatic regulation function [Transient Hyperemic Response Ratio (THRR) index], cerebral hemodynamic index, organ function indicators, blood gas analysis indices, and patient characteristics.Results: About 39% of the patients (20/51) suffered from SAD. Nearly 43% of the patients died within 28 days of admission (22/51). Individuals in the SAD cohort needed a longer period of mechanical ventilation [5 (95% CI 2, 6) vs. 1 days (95% CI 1, 4), p = 0.015] and more time in ICU [9 (95% CI 5, 20) vs. 5 days (95% CI 3, 9), p = 0.042]; they also experienced more deaths over the 28-day period (65 vs. 29%, p = 0.011). The multivariate regression analysis indicated that independent variables associated with SAD were THRR index [odds ratio (OR) = 5.770, 95% CI: 1.222–27.255; p = 0.027] and the mean value for rSO2 was &lt; 55% (OR = 3.864, 95% CI: 1.026–14.550; p = 0.046).Conclusion: Independent risk factors for SAD were mean cerebral oxygen saturation below 55% and cerebrovascular dysregulation (THRR &lt; 1.09).
Case Reports: Bronchial Mucosal Vasculature Is Also Involved in the Acute Vascular Distress Syndrome of COVID-19
Authors: Jounieaux Vincent , Basille Damien , Toublanc Bénédicte , Andrejak Claire , Rodenstein Daniel Oscar , Mahjoub Yazine 
Published: December 2021

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which targets the pulmonary vasculature is supposed to induce an intrapulmonary right to left shunt with an increased pulmonary blood flow. We report here what may be, to the best of our knowledge, the first videoendoscopic descriptions of an hypervascularization of the bronchial mucosa in two patients hospitalized for coronavirus disease 2019 (COVID-19) pneumonia.Cases Presentation: Two patients, 27- and 37-year-old, were addressed to our Pneumology department for suspicion of COVID-19 pneumonia. Their symptoms (fever, dry cough, and dyspnoea), associated to pulmonary ground glass opacities on thoracic CT, were highly suggestive of a COVID-19 disease despite repeated negative pharyngeal swabs RT-PCR. In both patients, bronchoscopy examination using white light was unremarkable but NBI bronchoscopy revealed a diffuse hypervascularization of the mucosa from the trachea to the sub-segmental bronchi, associated with dilated submucosal vessels. RT-PCR performed in bronchoalveolar lavage (BAL) confirmed the presence of Sars-CoV-2.Conclusions: These two case reports highlight the crucial importance of the vascular component of the viral disease. We suggest that such bronchial hypervascularization with dilated vessels contributes, at least in part, to the intrapulmonary right to left shunt that characterizes the COVID-19 related Acute Vascular Distress Syndrome (AVDS). The presence of diffuse bronchial hypervascularization in the context of COVID-19 pandemic should prompt the search for Sars-CoV-2 in BAL samples.
Poly(I:C)-Induced Mesenchymal Stem Cells Protect the Kidney Against Ischemia/Reperfusion Injury via the TLR3/PI3K Pathway
Authors: Chen Tian , Jiang Yamei , Xu Shihao , Cheuk Yin Celeste , Wang Jiyan , Yang Cheng , Rong Ruiming 
Published: December 2021

Objective: To investigate the effect and protective mechanism of mesenchymal stem cell subpopulations on acute kidney injury by establishing a mouse model of renal ischemia-reperfusion injury.Methods: Male C57BL/6 mice were randomly divided into five groups, namely, sham-operation group and those treated with normal saline, untreated mesenchymal stem cells, mesenchymal stem cells treated with lipopolysaccharide (LPS, pro-inflammatory phenotype) and mesenchymal stem cells treated with polyinosinic-polycytidylic acid (poly[I:C], anti-inflammatory phenotype) respectively. The renal function, histopathological damage, circulating inflammation levels and antioxidant capacity of mice were evaluated. The PI3 kinase p85 (PI3K) inhibitor was added into the conventional mesenchymal stem cell cultures in vitro to observe its effects on the secretion of anti-inflammatory cytokines.Results: Mesenchymal stem cells treated with poly(I:C) (anti-inflammatory phenotype) could effectively reduce serum creatinine and blood urea nitrogen, attenuate histopathological damage and apoptosis level, decrease the level of circulating pro-inflammatory cytokines and increase the level of circulating anti-inflammatory cytokines, enhance peroxidase activity and reduce malondialdehyde content at each time point. After the addition of the PI3K inhibitor, the mRNA expression and protein secretion of indoleamine 2,3-dioxygenase 1 and heme oxygenase 1 of various mesenchymal stem cells were significantly reduced, and that of mesenchymal stem cells treated with poly(I:C) (anti-inflammatory phenotype) was more obvious.Conclusions: Polyriboinosinic-polyribocytidylic acid (poly[I:C]), a synthetic double-stranded RNA, whose pretreatment induces mesenchymal stem cells to differentiate into the anti-inflammatory phenotype. Anti-inflammatory mesenchymal stem cells induced by poly(I:C) can better protect renal function, alleviate tissue damage, reduce circulating inflammation levels and enhance antioxidant capacity, and achieve stronger anti-inflammatory effects through the TLR3/PI3K pathway.
Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI
Authors: Li Qingling , Zhu Yanhua , Chen Minglin , Guo Ruomi , Hu Qingyong , Lu Yaxin , Deng Zhenghui , Deng Songqing , Zhang Tiecheng , Wen Huiquan , Gao Rong , Nie Yuanpeng , Li Haicheng , Chen Jianning , Shi Guojun , Shen Jun , Cheung Wai Wilson , Liu Zifeng , Guo Yulan , Chen Yanming 
Published: December 2021

Background: It is often difficult to diagnose pituitary microadenoma (PM) by MRI alone, due to its relatively small size, variable anatomical structure, complex clinical symptoms, and signs among individuals. We develop and validate a deep learning -based system to diagnose PM from MRI.Methods: A total of 11,935 infertility participants were initially recruited for this project. After applying the exclusion criteria, 1,520 participants (556 PM patients and 964 controls subjects) were included for further stratified into 3 non-overlapping cohorts. The data used for the training set were derived from a retrospective study, and in the validation dataset, prospective temporal and geographical validation set were adopted. A total of 780 participants were used for training, 195 participants for testing, and 545 participants were used to validate the diagnosis performance. The PM-computer-aided diagnosis (PM-CAD) system consists of two parts: pituitary region detection and PM diagnosis. The diagnosis performance of the PM-CAD system was measured using the receiver operating characteristics (ROC) curve and area under the ROC curve (AUC), calibration curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score.Results: Pituitary microadenoma-computer-aided diagnosis system showed 94.36% diagnostic accuracy and 98.13% AUC score in the testing dataset. We confirm the robustness and generalization of our PM-CAD system, the diagnostic accuracy in the internal dataset was 96.50% and in the external dataset was 92.26 and 92.36%, the AUC was 95.5, 94.7, and 93.7%, respectively. In human-computer competition, the diagnosis performance of our PM-CAD system was comparable to radiologists with &gt;10 years of professional expertise (diagnosis accuracy of 94.0% vs. 95.0%, AUC of 95.6% vs. 95.0%). For the misdiagnosis cases from radiologists, our system showed a 100% accurate diagnosis. A browser-based software was designed to assist the PM diagnosis.Conclusions: This is the first report showing that the PM-CAD system is a viable tool for detecting PM. Our results suggest that the PM-CAD system is applicable to radiology departments, especially in primary health care institutions.
Trajectory of Estimated Glomerular Filtration Rate and Malnourishment Predict Mortality and Kidney Failure in Older Adults With Chronic Kidney Disease
Authors: Weng Shuo-Chun , Chen Chyong-Mei , Chen Yu-Chi , Wu Ming-Ju , Tarng Der-Cherng 
Published: December 2021

Objective: The trajectory patterns of estimated glomerular filtration rates (eGFR) in chronic kidney disease (CKD) older adults with malnourishment and their association with subsequent patient outcomes have not been elucidated. We aimed to assess the eGFR trajectory patterns for predicting patient survival and kidney failure in the elderly without or with malnourishment.Materials and Methods: Based on a prospective longitudinal cohort, CKD patients aged 65 years or older were enrolled from 2001 to 2013. Among the 3,948 patients whose eGFR trajectory patterns were analyzed, 1,872 patients were stratified by the absence or presence of malnourishment, and 765 patients were identified and categorized as having malnourishment. Four eGFR trajectory patterns [gradual decline (T0), early non-decline and then persistent decline (T1), persistent increase (T2), and low baseline and then progressive increase (T3)] were classified by utilizing a linear mixed-effect model with a quadratic term in time. The malnourishment was defined as body mass index &lt; 22 kg/m2, serum albumin &lt; 3.0 mg/dL, or Geriatric Nutritional Risk Index (GNRI) &lt; 98. This study assessed the effectiveness of eGFR trajectory patterns in a median follow-up of 2.27 years for predicting all-cause mortality and kidney failure.Results: The mean age was 76.9 ± 6.7 years, and a total of 82 (10.7%) patients with malnourishment and 57 (5.1%) patients without malnourishment died at the end of the study. Compared with the reference trajectory T0, the overall mortality of T1 was markedly reduced [adjusted hazard ratio (aHR) = 0.52, 95% confidence interval (CI) 0.32–0.83]. In patients with trajectory, T3 was associated with a high risk for kidney failure (aHR = 5.68, 95% CI 3.12–10.4) compared with the reference, especially higher risk in the presence of malnourishment. Patients with high GNRI values were significantly associated with a lower risk of death and kidney failure, but patients with malnourishment and concomitant alcohol consumption had a higher risk of kidney failure.Conclusions: Low baseline eGFR and progressively increasing eGFR trajectory were high risks for kidney failure in CKD patients. These findings may be attributed to multimorbidity, malnourishment, and decompensation of renal function.
Artificial Intelligence to Detect Meibomian Gland Dysfunction From in-vivo Laser Confocal Microscopy
Authors: Zhang Ye-Ye , Zhao Hui , Lin Jin-Yan , Wu Shi-Nan , Liu Xi-Wang , Zhang Hong-Dan , Shao Yi , Yang Wei-Feng 
Published: December 2021

Background: In recent years, deep learning has been widely used in a variety of ophthalmic diseases. As a common ophthalmic disease, meibomian gland dysfunction (MGD) has a unique phenotype in in-vivo laser confocal microscope imaging (VLCMI). The purpose of our study was to investigate a deep learning algorithm to differentiate and classify obstructive MGD (OMGD), atrophic MGD (AMGD) and normal groups.Methods: In this study, a multi-layer deep convolution neural network (CNN) was trained using VLCMI from OMGD, AMGD and healthy subjects as verified by medical experts. The automatic differential diagnosis of OMGD, AMGD and healthy people was tested by comparing its image-based identification of each group with the medical expert diagnosis. The CNN was trained and validated with 4,985 and 1,663 VLCMI images, respectively. By using established enhancement techniques, 1,663 untrained VLCMI images were tested.Results: In this study, we included 2,766 healthy control VLCMIs, 2,744 from OMGD and 2,801 from AMGD. Of the three models, differential diagnostic accuracy of the DenseNet169 CNN was highest at over 97%. The sensitivity and specificity of the DenseNet169 model for OMGD were 88.8 and 95.4%, respectively; and for AMGD 89.4 and 98.4%, respectively.Conclusion: This study described a deep learning algorithm to automatically check and classify VLCMI images of MGD. By optimizing the algorithm, the classifier model displayed excellent accuracy. With further development, this model may become an effective tool for the differential diagnosis of MGD.