Reports show that AKI is a common complication of severe coronavirus disease 2019 (COVID-19) in hospitalized patients. Studies have also observed proteinuria and microscopic hematuria in such ...patients. Although a recent autopsy series of patients who died with severe COVID-19 in China found acute tubular necrosis in the kidney, a few patient reports have also described collapsing glomerulopathy in COVID-19.
We evaluated biopsied kidney samples from ten patients at our institution who had COVID-19 and clinical features of AKI, including proteinuria with or without hematuria. We documented clinical features, pathologic findings, and outcomes.
Our analysis included ten patients who underwent kidney biopsy (mean age: 65 years); five patients were black, three were Hispanic, and two were white. All patients had proteinuria. Eight patients had severe AKI, necessitating RRT. All biopsy samples showed varying degrees of acute tubular necrosis, and one patient had associated widespread myoglobin casts. In addition, two patients had findings of thrombotic microangiopathy, one had pauci-immune crescentic GN, and another had global as well as segmental glomerulosclerosis with features of healed collapsing glomerulopathy. Interestingly, although the patients had confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by RT-PCR, immunohistochemical staining of kidney biopsy samples for SARS-CoV-2 was negative in all ten patients. Also, ultrastructural examination by electron microscopy showed no evidence of viral particles in the biopsy samples.
The most common finding in our kidney biopsy samples from ten hospitalized patients with AKI and COVID-19 was acute tubular necrosis. There was no evidence of SARS-CoV-2 in the biopsied kidney tissue.
Carbon price fluctuations significantly impact the development of industries, energy, agriculture, and stock investments. The carbon price possesses the features of nonlinearity, non-stationarity, ...and high complexity as a time series. To overcome the negative impact of these characteristics on prediction and to improve the prediction accuracy of carbon price series, a combination prediction model named Lp-CNN-LSTM, which utilizes both convolutional neural networks and long short-term memory networks, has been proposed. Strategy one involved establishing distinct models of CNN-LSTM and LSTM to analyze high-frequency and low-frequency carbon price sequences; the combination of output was integrated to predict carbon prices more precisely. Strategy two comprehensively considered the economic and technical indicators of carbon price sequences based on the Pearson correlation coefficient, while the Multi-CNN-LSTM model selected explanatory variables that strongly correlated with carbon prices. Finally, a predictive model for a combination of carbon prices was developed using Lp-norm. The empirical study focused on China’s major carbon markets, including Hubei, Guangdong, and Shanghai. According to the error indicators, the performance of the Lp-CNN-LSTM model was superior to individual strategy prediction models. The Lp-CNN-LSTM model has excellent accuracy, superiority, and robustness in predicting carbon prices, which can provide a necessary basis for revising carbon pricing strategies, regulating carbon trading markets, and making investment decisions.
The estimation of large functional and longitudinal data, which refers to the estimation of mean function, estimation of covariance function, and prediction of individual trajectory, is one of the ...most challenging problems in the field of high-dimensional statistics. Functional Principal Components Analysis (FPCA) and Functional Linear Mixed Model (FLMM) are two major statistical tools used to address the estimation of large functional and longitudinal data; however, the former suffers from a dramatically increasing computational burden while the latter does not have clear asymptotic properties. In this paper, we propose a computationally effective estimator of large functional and longitudinal data within the framework of FLMM, in which all the parameters can be automatically estimated. Under certain regularity assumptions, we prove that the mean function estimation and individual trajectory prediction reach the minimax lower bounds of all nonparametric estimations. Through numerous simulations and real data analysis, we show that our new estimator outperforms the traditional FPCA in terms of mean function estimation, individual trajectory prediction, variance estimation, covariance function estimation, and computational effectiveness.
Abstract Aldo-keto reductase 1B10 (AKR1B10) has relatively specific lipid substrates including carbonyls, retinal and farnesal/geranylgeranial. Metabolizing these lipid substrates appears crucial to ...carcinogenesis, particularly for farnesal/geranylgeranial that involves protein prenylation. Mutant Kras is a most common active oncogene in pancreatic cancer, and its activation requires protein prenylation. To directly determine the role of AKR1B10 in pancreatic carcinogenesis, we knocked down AKR1B10 in CD18 human pancreatic carcinoma cells using shRNA approach. Silencing AKR1B10 resulted in a significant inhibition of anchor-dependent growth (knockdown cells vs. vector-control cells: 67 ± 9.5 colonies/HPF vs. 170 ± 3.7 colonies/HPF, p < 0.01), invasion index (0.27 vs. 1.00, p < 0.05), and cell migration (at 16 hours 9.2 ± 1.2% vs. 14.0 ± 1.8%, at 24 hours 21.0 ± 1.1% vs. 30.5 ± 3.5%, and at 48 hours 51.9 ± 5.7% vs. 88.9 ± 3.0%, p < 0.01). Inhibition of AKR1B10 by oleanolic acid (OA) showed a dose-dependent inhibition of cell growth with IC50 at 30 µM. Kras pull-down and Western blot analysis revealed a significant down-regulation of active form Kras and phosphorylated C-Raf, and Erk, as well as an up-regulation of E-cadherin. A significant reduction of in vivo tumor growth was observed in nude mice implanted with the CD18 pancreatic carcinoma cells with AKR1B10 knockdown (tumor weight: 0.25 ± 0.06 g vs. 0.52 ± 0.07 g, p = 0.01), and with OA treatment (tumor weight: 0.35 ± 0.05 g vs. 0.52 ± 0.07 g, p = 0.05). Our findings indicate AKR1B10 is a unique enzyme involved in pancreatic carcinogenesis via modulation of the Kras–E-cadherin pathway.
Background. Risk of progressive disease of gastrointestinal stromal tumors (GISTs) relies on mitotic index, size, and location of the tumor. However, manual mitotic counting on hematoxylin and ...eosin–stained slides (MMC-HE) is inefficient with low reproducibility. Manual count of phospho-histone H3 (MC-PHH3)-positive cells on immunohistochemical stained slides has been shown to have comparable reliability with MMC-HE. This study aims to confirm the reliability of MC-PHH3 in GISTs compared with MMC-HE and then to further compare MC-PHH3 with computer-assisted image analysis of PHH3-positive cells (Comp-PHH3). Methods. The study included 119 patients with GISTs. PHH3 stains were performed. MC-PHH3 was assessed as counts/5 mm2 high-power fields. Whole slide images were captured and the tumor area with greatest mitotic activity was manually identified. The PHH3-positive cells were automatically counted in 0.5 mm2 using Ventana Virtuoso software. Results. MMC-HE ranged from 0 to 157/5 mm2. MC-PHH3 ranged from 0 to 35.6/5 mm2. Comp-PHH3 ranged from 0 to 66/0.5 mm2. Interclass correlation coefficient (ICC) indicates good agreement between the 3 pathologists for MC-PHH3 (ICC = 0.74, P = .42). There is a strong correlation between MMC-HE and MC-PHH3. The Spearman correlation coefficient was 0.63 (P < .0001). Lin’s concordance further indicated a moderate diagnostic agreement between MC-PHH3 and Comp-PHH3. Conclusion. MC-PHH3 is proposed as a superior alternative to MMC-HE with potential application in GIST reporting and prognostication. Furthermore, Comp-PHH3 may be a valid alternative to MC-PHH3.
Machine reading comprehension is a crucial and challenging task in natural language processing (NLP). Recently, knowledge graph (KG) embedding has gained massive attention as it can effectively ...provide side information for downstream tasks. However, most previous knowledge-based models do not take into account the structural characteristics of the triples in KGs, and only convert them into vector representations for direct accumulation, leading to deficiencies in knowledge extraction and knowledge fusion. In order to alleviate this problem, we propose a novel deep model KCF-NET, which incorporates knowledge graph representations with context as the basis for predicting answers by leveraging capsule network to encode the intrinsic spatial relationship in triples of KG. In KCF-NET, we fine-tune BERT, a highly performance contextual language representation model, to capture complex linguistic phenomena. Besides, a novel fusion structure based on multi-head attention mechanism is designed to balance the weight of knowledge and context. To evaluate the knowledge expression and reading comprehension ability of our model, we conducted extensive experiments on multiple public datasets such as WN11, FB13, SemEval-2010 Task 8 and SQuAD. Experimental results show that KCF-NET achieves state-of-the-art results in both link prediction and MRC tasks with negligible parameter increase compared to BERT-Base, and gets competitive results in triple classification task with significantly reduced model size.
There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported ...interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci (6 independent signals) interacted with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrate that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated heritability is significant (P < 0.02) for low-density lipoprotein cholesterol and triglycerides in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.
Crescentic IgA nephropathy (IgAN), defined as >50% crescentic glomeruli on kidney biopsy, is one of the most common causes of rapidly progressive GN. However, few studies have characterized this ...condition. To identify risk factors and develop a prediction model, we assessed data from patients ≥ 14 years old with crescentic IgAN who were followed ≥ 12 months. The discovery cohort comprised 52 patients from one kidney center, and the validation cohort comprised 61 patients from multiple centers. At biopsy, the mean serum creatinine (SCr) level ± SD was 4.3 ± 3.4 mg/dl, and the mean percentage of crescents was 66.4%± 15.8%. The kidney survival rates at years 1, 3, and 5 after biopsy were 57.4%± 4.7%, 45.8%± 5.1%, and 30.4%± 6.6%, respectively. Multivariate Cox regression revealed initial SCr as the only independent risk factor for ESRD (hazard ratio HR, 1.32; 95% confidence interval CI, 1.10 to 1.57; P=0.002). Notably, the percentage of crescents did not associate independently with ESRD. Logistic regression showed that the risk of ESRD at 1 year after biopsy increased rapidly at SCr>2.7 mg/dl and reached 90% at SCr>6.8 mg/dl (specificity=98.5%, sensitivity=64.6% for combined cohorts). In both cohorts, patients with SCr>6.8 mg/dl were less likely to recover from dialysis. Analyses in additional cohorts revealed a similar association between initial SCr and ESRD in patients with antiglomerular basement membrane disease but not ANCA-associated systemic vasculitis. In conclusion, crescentic IgAN has a poor prognosis, and initial SCr concentration may predict kidney failure in patients with this disease.
Answer generation is one of the most important tasks in natural language processing, and deep learning-based methods have shown their strength over traditional machine learning based methods. ...However, most previous deep learning-based answer generation models were built on traditional recurrent neural networks or convolutional neural networks. The former model cannot well exploit contextual correlation preserved in paragraphs due to their inherent computation complexity. For the latter, since the size of the convolutional kernel is fixed, the model cannot extract complete semantic information features. In order to alleviate this problem, based on multi-layer Transformer aggregation coder, we propose an end-to-end answer generation model (AG-MTA). AG-MTA consists of a multi-layer attention Transformer unit and a multi-layer attention Transformer aggregation encoder (MTA). It can focus on information representation at different positions and aggregate nodes at same layer to combine the context information. Thereby, it fuses semantic information from base layer to top layer, enhancing the information representation of the encoder. Furthermore, based on trigonometric function, a novel position encoding method is also proposed. Experiments are conducted on public datasets SQuAD. AG-MTA reaches the state-of-the-art performance, EM score achieves 71.1 and F1 score achieves 80.3.
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