bluuebunny/crossref_metadata_2025_split huggingface dataset:
Low serum level of apolipoprotein A1 may predict the severity of COVID‐19: A retrospective study
Authors: Zhu Zhe , Yang Yayun , Fan Lingyan , Ye Shuyuan , Lou Kehong , Hua Xin , Huang Zuoan , Shi Qiaoyun , Gao Guosheng 
Published: July 2021

AbstractBackgroundDyslipidemia has been observed in patients with coronavirus disease 2019 (COVID‐19). This study aimed to investigate blood lipid profiles in patients with COVID‐19 and to explore their predictive values for COVID‐19 severity.MethodsA total of 142 consecutive patients with COVID‐19 were included in this single‐center retrospective study. Blood lipid profile characteristics were investigated in patients with COVID‐19 in comparison with 77 age‐ and gender‐matched healthy subjects, their predictive values for COVID‐19 severity were analyzed by using multivariable logistic regression analysis, and their prediction efficiencies were evaluated by using receiver operator characteristic (ROC) curves.ResultsThere were 125 and 17 cases in the non‐severe and severe groups, respectively. Total cholesterol (TC), high‐density lipoprotein cholesterol (HDL‐C), low‐density lipoprotein cholesterol (LDL‐C), and apolipoprotein A1 (ApoA1) gradually decreased across the groups in the following order: healthy controls, non‐severe group, and severe group. ApoA1 was identified as an independent risk factor for COVID‐19 severity (adjusted odds ratio [OR]: 0.865, 95% confidence interval [CI]: 0.800–0.935, p < 0.001), along with interleukin‐6 (IL‐6) (adjusted OR: 1.097, 95% CI: 1.034–1.165, p = 0.002). ApoA1 exhibited the highest area under the ROC curve (AUC) among all single markers (AUC: 0.896, 95% CI: 0.834–0.941); moreover, the risk model established using ApoA1 and IL‐6 enhanced prediction efficiency (AUC: 0.977, 95% CI: 0.932–0.995).ConclusionBlood lipid profiles in patients with COVID‐19 are quite abnormal compared with those in healthy subjects, especially in severe cases. Serum ApoA1 may represent a good indicator for predicting the severity of COVID‐19.
A dual gate material tunnel field effect transistor model incorporating two‐dimensional Poisson and Schrodinger wave equations
Authors: Mohanty Sadhana Subhadarshini , Dutta Pradipta , Das Jitendra Kumar 
Published: July 2021

AbstractThis paper presents an accurate analytical model of surface potential and drain current for a long channel dual material gate (DMG) tunneling field‐effect transistor (TFET) with the addition of nonlinear inversion charge developed at the channel/drain interface region, ambipolar characteristic, and quantum confinement. The model includes the effect of dual metal gate work function as it controls the threshold voltage without considering a fully depleted undoped channel region. The current model includes the effect of charge accumulation at the interface of two gates and an appropriate mathematical expression of nonlinear inversion charge developed at the interface of two gates has been derived from the two‐dimensional (2D) Schrodinger wave equation incorporating quantum confinement. The model is effective to capture transfer characteristics at Sub 10 nm body thickness. A high ION/IOFF ratio and less than 27 mV/decade subthreshold swing are achieved at 2 nm body thickness which is desirable for low power VLSI application. The proposed model has been compared and calculated by Sentaurus TCAD with fair accuracy and a good resemblance, including ambipolar characteristics at sub 10 nm body thickness has been achieved.
Can near‐infrared autofluorescence imaging be used for intraoperative confirmation of parathyroid tissue?
Authors: Berber Eren , Akbulut Serkan 
Published: July 2021

AbstractBackground and ObjectivesWhether ex vivo autofluorescence (AF) imaging findings could be quantified to intraoperatively differentiate parathyroid tissue has not been reported. Our aim was to assess the ability of AF imaging to confirm parathyroid tissue during thyroidectomy and parathyroidectomy procedures.MethodsThis was a retrospective Institutional Review Board‐approved study. AF signals of specimens imaged ex vivo before submission to pathology during thyroidectomy and parathyroidectomy procedures were quantified. Using receiver operating characteristic (ROC) curves, optimal values for sensitivity/specificity to differentiate parathyroid tissue were calculated.Results166 parathyroid and 217 non‐parathyroid specimens were analyzed. With surgical drapes as background, the optimal normalized AF intensity threshold to predict parathyroid tissue on ROC curve analysis was 1.72 (86.1% sensitivity, 84.8% specificity, and area under the curve [AUC]: 0.919). The cutoff for 97% sensitivity was 1.31 and for 99.1% specificity was 3.16. With Telfa as background, the optimal threshold to predict parathyroid tissue was 1.46 (88.4% sensitivity, 76.8% specificity, and AUC: 0.896). The threshold for 96.8% sensitivity was 1.18 and for 98.8% specificity was 2.44. If thresholds for highest specificity were used, 40% of frozen sections to confirm parathyroid tissue could have been avoided.ConclusionQuantified brightness analysis of ex vivo AF signals may have utility in intraoperative differentiation of parathyroid tissue for 40% of surgical specimens.
Influence of grazing on the structure and biological activity of dry steppe soils of the southern Russian Plain
Authors: Khomutova Tatiana E. , Fornasier Flavio , Yeltsov Maksim V. , Chernysheva Elena V. , Borisov Aleksandr V. 
Published: July 2021

AbstractA comparative analysis of physicochemical and biological properties of Kastanozems and Solonetzes, the two most common soil types in the Caspian region of the post‐Soviet territory was carried out. The carbonate, salt, Corg, mineral N (NH4+ and NO3−) contents, microbial biomass (measured by ds‐DNA and phospholipids), and activity of 17 hydrolases involved in the C, N, P, and S cycles were measured along the grazing attenuation gradient near a typical sheep farm with year‐round grazing. Heavy grazing increases the number of Solonetz areas, they occupy up to 70% of the soil cover. Further away from the farm the areas decrease, and Kastanozems dominate there instead. Both in Kastanozems and Solonetzes, heavy grazing leads to soil salinisation, accumulation of carbonates and nitrates, decrease in ammonia and Corg content, microbial biomass, and enzymatic activity. At moderate grazing, a significant increase in microbial biomass and enzymatic activity throughout the soil profile, particularly in the 0–10 cm layer was found; at light grazing, it was observed mainly in deeper soil layers, not in the upper 0–10 cm. The microbial biomass and enzymatic activity of soils did not directly follow grazing load and was maximal at moderate grazing, where negative effect of reducing phytomass is compensated by large inputs of animal excreta, stimulating development of soil microbial community. Compared to Kastanozems, Solonetzes were more resistant to pasture load, and under heavy grazing, their properties changed to a lesser extent.
Quantification of internal dosimetry in PET patients II: Individualized Monte Carlo‐based dosimetry for [18F]fluorocholine PET
Authors: Neira Sara , Guiu‐Souto Jacobo , Pais Paulino , Rodríguez Martínez de Llano Sofía , Fernández Carlos , Pubul Virginia , Ruibal Álvaro , Pombar Miguel , Gago‐Arias Araceli , Pardo‐Montero Juan 
Published: July 2021

AbstractPurposeTo obtain individualized internal doses with a Monte Carlo (MC) method in patients undergoing diagnostic [18F]FCH‐PET studies and to compare such doses with the MIRD method calculations.MethodsA patient cohort of 17 males were imaged after intravenous administration of a mean [18F]FCH activity of 244.3 MBq. The resulting PET/CT images were processed in order to generate individualized input source and geometry files for dose computation with the MC tool GATE. The resulting dose estimates were studied and compared to the MIRD method with two different computational phantoms. Mass correction of the S‐factors was applied when possible. Potential sources of uncertainty were closely examined: the effect of partial body images, urinary bladder emptying, and biokinetic modeling.ResultsLarge differences in doses between our methodology and the MIRD method were found, generally in the range ±25%, and up to ±120% for some cases. The mass scaling showed improvements, especially for non‐walled and high‐uptake tissues. Simulations of the urinary bladder emptying showed negligible effects on doses to other organs, with the exception of the prostate. Dosimetry based on partial PET/CT images (excluding the legs) resulted in an overestimation of mean doses to bone, skin, and remaining tissues, and minor differences in other organs/tissues. Estimated uncertainties associated with the biokinetics of FCH introduce variations of cumulated activities in the range of ±10% in the high‐uptake organs.ConclusionsThe MC methodology allows for a higher degree of dosimetry individualization than the MIRD methodology, which in some cases leads to important differences in dose values. Dosimetry of FCH‐PET based on a single partial PET study seems viable due to the particular biokinetics of FCH, even though some correction factors may need to be applied to estimate mean skin/bone doses.
Covalent organic frameworks: From materials design to electrochemical energy storage applications
Authors: Lin Jiamin , Zhong Yiren , Tang Lingyu , Wang Liuqi , Yang Mei , Xia Hui 
Published: July 2021

AbstractCovalent organic frameworks (COFs), with large surface area, tunable porosity, and lightweight, have gained increasing attention in the electrochemical energy storage realms. In recent years, the development of high‐performance COF‐based electrodes has, in turn, inspired the innovation of synthetic methods, selection of linkages, and design of the topological structures. This review aims to present an overview of the recent advances in designing COF materials for various energy storage technologies. The fundamentals of COF materials, including synthetic chemistry, linkage diversity, and structure design, are first introduced. An in‐depth understanding of the charge storage mechanism and the structure‐property relationships of the COF electrodes is subsequently provided, highlighting their designing strategies in the latest energy storage applications. The last section of this review discusses the challenges COFs are facing and outlines new directions that could advance COF research in emerging energy technologies.
Ultrasound Imaging of Treatment‐Needed Cardiac Rejection with CD4‐Targeted Nanobubbles
Authors: Xie Yu , Xie Yuji , Chen Yihan , Deng Cheng , Fang Lingyun , Li He , Sun Zhenxing , Gao Tang , Liu Jie , Yuan Jing , Li Yuman , Jin Qiaofeng , Zhang Li , Xie Mingxing 
Published: July 2021

AbstractAcute cardiac allograft rejection (ACAR) is one of the main reasons for mortality after heart transplantation. The timely diagnosis of treatment‐needed ACAR is of great importance in the clinic. Molecular imaging may make accurate diagnoses at the early stage to visualize the activity of specific pathological processes. The amount of CD4+‐lymphocyte infiltration is a crucial index in ACAR grading. CD4‐targeted nanobubbles (NBCD4) are fabricated to assess the infiltrating CD4+ lymphocytes, while isotype control nanobubbles (NBIso) are also fabricated. Animal models with ACAR grades from 0R to 3R are established. Ultrasound molecular imaging (USMI) is performed to distinguish different rejection grades with NBCD4. In the grade 2R and 3R groups, the USMI signals of NBCD4 are significantly higher than those of NBIso. The signals of NBCD4 in the 2R group are significantly lower than those in the 3R group and significantly higher than those in the 1R and 0R groups. Moreover, the signals of NBCD4 are strongly correlated with the rejection grades and the number of infiltrating CD4+ lymphocytes. As asymptomatic rejection with 2R or higher grades should be seriously treated, USMI with NBCD4 may be a new approach to timely diagnosis of treatment‐needed ACAR.
Osteoporotic fractures and subsequent fractures: imminent fracture risk from an analysis of German real-world claims data
Authors: Hadji Peyman , Schweikert Bernd , Kloppmann Edda , Gille Patrick , Joeres Lars , Toth Emese , Möckel Luis , Glüer Claus-C. 
Published: July 2021

Abstract
Purpose
In osteoporosis, prior fracture is a strong predictor of subsequent fracture. This study aimed to assess the imminent risk of subsequent fracture following an initial fracture in osteoporosis patients in Germany, and to identify clinical and demographic characteristics that are independently associated with subsequent fracture risk.

Methods
In this retrospective, observational cohort study using German real-world claims data, male and female patients aged ≥ 50 years with osteoporosis who experienced an initial (“index”) hip/femur, vertebral, forearm/wrist/hand or shoulder/upper arm fracture between 2010 and 2014 were included. The incidence and timing of subsequent fractures during a 1-year follow-up period were analyzed. Independent risk factors for subsequent fracture were identified by multivariate regression analysis.

Results
A total of 18,354 patients (mean age: 77 years; standard deviation: 9.8) were included. Of these, 2918 (15.9%) suffered a subsequent fracture during the 1-year follow-up period. The incidence of subsequent fracture was higher following an index vertebral fracture (18.0%) than after an index forearm/wrist/hand fracture (14.1%) or index hip/femur fracture (12.1%). Subsequent 1-year fracture incidence was generally higher in older patients. Index fracture type, age, epilepsy/use of antiepileptics, and heart failure were all independently associated with subsequent fracture risk.

Conclusion
Osteoporosis patients in Germany are at imminent risk of subsequent fracture during the first year following an initial fracture. They should be targeted for immediate post-fracture treatment to reduce the risk of further fractures, especially in the presence of specific risk factors such as old age or index vertebral fracture.
Does delaying curative surgery for colorectal cancer influence long-term disease-free survival? A cohort study
Authors: Garcia-Botello Stephanie , Martín-Arevalo J. , Cozar-Lozano C. , Benitez-Riesco A. , Moro-Valdezate D. , Pla-Martí V. , Espí-Macías A. 
Published: July 2021

Abstract
Background
Surgical wait list time is a major problem in many health-care systems and its influence on survival is unclear. The aim of this study is to assess the impact of wait list time on long-term disease-free survival in patients scheduled for colorectal cancer resection.

Materials and methods
A prospective study was carried out in patients with colorectal cancer scheduled for surgery at a tertiary care center. Wait list time was defined as the time from completion of diagnostic workup to definitive surgery and divided into 2-week intervals from 0 to 6 weeks. The outcome variables were 2-year and 5-year disease-free survival.

Results
A total of 602 patients, 364 (60.5%) male, median age 73 years (range = 71) were defined. The median wait list time was 28 days (range = 99). Two and 5-year disease-free survival rates were 521 (86.5%) and 500 (83.1%) respectively. There were no differences in 2-year or 5-year disease-free survival for the whole cohort or by tumor stage between wait list time intervals except for AJCC stage II tumors which showed a higher 5-year disease-free survival for the 2–4 and 4–6-week wait list time interval (p = 0.021).

Conclusions
Time from diagnosis to definitive surgery up to 6 weeks is not associated with a decrease in 2-year or 5-year disease-free survival (DFS) in AJCC stage I through III colorectal cancer patients. These are important findings in the light of the COVID-19 pandemic and offer a window of opportunity for preoperative optimization and prehabilitation.
Local-aware spatio-temporal attention network with multi-stage feature fusion for human action recognition
Authors: Hou Yaqing , Yu Hua , Zhou Dongsheng , Wang Pengfei , Ge Hongwei , Zhang Jianxin , Zhang Qiang 
Published: July 2021

AbstractIn the study of human action recognition, two-stream networks have made excellent progress recently. However, there remain challenges in distinguishing similar human actions in videos. This paper proposes a novel local-aware spatio-temporal attention network with multi-stage feature fusion based on compact bilinear pooling for human action recognition. To elaborate, taking two-stream networks as our essential backbones, the spatial network first employs multiple spatial transformer networks in a parallel manner to locate the discriminative regions related to human actions. Then, we perform feature fusion between the local and global features to enhance the human action representation. Furthermore, the output of the spatial network and the temporal information are fused at a particular layer to learn the pixel-wise correspondences. After that, we bring together three outputs to generate the global descriptors of human actions. To verify the efficacy of the proposed approach, comparison experiments are conducted with the traditional hand-engineered IDT algorithms, the classical machine learning methods (i.e., SVM) and the state-of-the-art deep learning methods (i.e., spatio-temporal multiplier networks). According to the results, our approach is reported to obtain the best performance among existing works, with the accuracy of 95.3% and 72.9% on UCF101 and HMDB51, respectively. The experimental results thus demonstrate the superiority and significance of the proposed architecture in solving the task of human action recognition.
Profile of urinary amino acids and their post-translational modifications (PTM) including advanced glycation end-products (AGEs) of lysine, arginine and cysteine in lean and obese ZSF1 rats
Authors: Baskal Svetlana , Büttner Petra , Werner Sarah , Besler Christian , Lurz Philipp , Thiele Holger , Tsikas Dimitrios 
Published: July 2021

AbstractHeart failure with preserved ejection fraction (HFpEF) is associated with high mortality and has an increasing prevalence associated with the demographic change and limited therapeutic options. Underlying mechanisms are largely elusive and need to be explored to identify specific biomarkers and new targets, which mirror disease progression and intervention success. Obese ZSF1 (O-ZSF1) rats are a useful animal model, as they spontaneously develop hypertension, hyperlipidemia and glucose intolerance and finally HFpEF. The urinary profile of amino acids and their metabolites of post-translational modifications (PTM), including the advanced glycation end-products (AGEs) of lysine, arginine and cysteine, are poorly investigated in HFpEF and ZSF1 rats. The aim of the present study was to characterize the status of free amino acids and their metabolites of PTM and glycation in lean ZSF1 (L-ZSF1) and O-ZSF1 rats in urine aiming to find possible effects of glucose on the excretion of native and modified amino acids. In the urine of twelve L-ZSF1 and twelve O-ZFS1 rats collected at the age of 20 weeks, we measured the concentration of native and modified amino acids by reliable previously validated stable-isotope dilution gas chromatography-mass spectrometry (GC–MS) approaches. Serum glucose was 1.39-fold higher in the O-ZSF1 rats, while urinary creatinine concentration was 2.5-fold lower in the O-ZSF1 rats. We observed many differences in urinary amino acids excretion between L-ZSF1 and O-ZSF1 rats. The creatinine-corrected homoarginine excretion was twofold lower in the O-ZSF1 rats. We also observed distinct associations between the concentrations of serum glucose and urinary amino acids including their PTM and AGE metabolites in the L-ZSF1 and O-ZSF1 rats. Our study shows that PTM metabolites and AGEs are consistently lower in the L-ZSF1 than in the O-ZSF1 rats. Serum malondialdehyde (MDA) concentration was higher in the O-ZSF1 rats. These results suggest that hyperglycemia, hyperlipidemia and elevated oxidative stress in the O-ZSF1 rats favor PTM methylation of arginine and lysine and the glycation of lysine and cysteine. The area under the receiver operation characteristic (ROC) curve values were 0.996 for serum glucose, 0.951 for urinary creatinine, 0.939 for serum MDA, 0.885 for Nε-carboxyethyl-lysine, 0.830 for carboxyethyl-cysteine, and 0.792 for monomethyl-lysine. Non-invasive measurement of methylation and glycation products of arginine, lysine and cysteine residues in proteins in urine of L-ZSF1 and O-ZSF1 rats may be useful in studying pathophysiology and pharmacology of HFpEF.
ChoiceNet: CNN learning through choice of multiple feature map representations
Authors: Rayhan Farshid , Galata Aphrodite , Cootes Tim F. 
Published: July 2021

AbstractWe introduce a new architecture called ChoiceNet where each layer of the network is highly connected with skip connections and channelwise concatenations. This enables the network to alleviate the problem of vanishing gradients, reduces the number of parameters without sacrificing performance and encourages feature reuse. We evaluate our proposed architecture on three independent tasks: classification, segmentation and facial landmark localisation. For this, we use benchmark datasets such as ImageNet, CIFAR-10, CIFAR-100, SVHN CamVid and 300W.
Stochastic radiotherapy appointment scheduling
Authors: Braune Roland , Gutjahr Walter J. , Vogl Petra 
Published: July 2021

AbstractWhen scheduling the starting times for treatment appointments of patients in hospitals or outpatient clinics such as radiotherapy centers, minimizing patient waiting time and simultaneously maximizing resource usage is crucial. Significant uncertainty in the treatment durations makes scheduling those activities particularly challenging. In addition to the treatments themselves, also preparation times and exiting times have to be considered, which are uncertain as well. To address and analyze this type of problems, the current study develops a model for planning appointment times under uncertain activity durations for a medical unit with a single “core resource” (in our application case a radiotherapy beam device), several treatment rooms, and required preparation and exiting phases for each patient. We employ a novel buffer concept based on quantiles of duration distributions and introduce a reactive procedure that adapts a pre-determined baseline schedule to the actual patient flow. For heuristically solving the resulting stochastic optimization model, a combination of a Genetic Algorithm and Monte Carlo simulation is proposed. A case study uses real-world data on activity durations gathered from an ion beam therapy facility in Austria. Experimental results comparing different variants of the method are carried out. In particular, comparisons of the stochastic optimization approach to a simpler deterministic approach are given.