Publications by authors named "Wei Shao"

415 Publications

Applying interpretable deep learning models to identify chronic cough patients using EHR data.

Comput Methods Programs Biomed 2021 Sep 4;210:106395. Epub 2021 Sep 4.

Indiana University School of Medicine, 340W 10th St #6200, Indianapolis, IN 46202, United States; Regenstrief Institute, 1101W 10th Street, Indianapolis, IN, 46202, United States. Electronic address:

Background And Objective: Chronic cough (CC) affects approximately 10% of adults. Many disease states are associated with chronic cough, such as asthma, upper airway cough syndrome, bronchitis, and gastroesophageal reflux disease. The lack of an ICD code specific for chronic cough makes it challenging to identify such patients from electronic health records (EHRs). For clinical and research purposes, computational methods using EHR data are urgently needed to identify chronic cough cases. This research aims to investigate the data representations and deep learning algorithms for chronic cough prediction.

Methods: Utilizing real-world EHR data from a large academic healthcare system from October 2005 to September 2015, we investigated Natural Language Representation of the EHR data and systematically evaluated deep learning and traditional machine learning models to predict chronic cough patients. We built these machine learning models using structured data (medication and diagnosis) and unstructured data (clinical notes).

Results: The sensitivity and specificity of a transformer-based deep learning algorithm, specifically BERT with attention model, was 0.856 and 0.866, respectively, using structured data (medication and diagnosis). Sensitivity and specificity improved to 0.952 and 0.930 when we combined structured data with symptoms extracted from clinical notes. We further found that the attention mechanism of deep learning models can be used to extract important features that drive the prediction decisions. Compared with our previously published rule-based algorithm, the deep learning algorithm can identify more chronic cough patients with structured data.

Conclusions: By applying deep learning models, chronic cough patients can be reliably identified for prospective or retrospective research through medication and diagnosis data, widely available in EHR and electronic claims data, thus improving the generalizability of the patient identification algorithm. Deep learning models can identify chronic cough patients with even higher sensitivity and specificity when structured and unstructured EHR data are utilized. We anticipate language-based data representation and deep learning models developed in this research could also be productively used for other disease prediction and case identification.
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http://dx.doi.org/10.1016/j.cmpb.2021.106395DOI Listing
September 2021

Microglial lysosome dysfunction contributes to white matter pathology and TDP-43 proteinopathy in GRN-associated FTD.

Cell Rep 2021 Aug;36(8):109581

Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA; Neurobiology of Disease Graduate Program, Mayo Graduate School, Mayo Clinic College of Medicine, Rochester, MN 55902, USA. Electronic address:

Loss-of-function mutations in the progranulin gene (GRN), which encodes progranulin (PGRN), are a major cause of frontotemporal dementia (FTD). GRN-associated FTD is characterized by TDP-43 inclusions and neuroinflammation, but how PGRN loss causes disease remains elusive. We show that Grn knockout (KO) mice have increased microgliosis in white matter and an accumulation of myelin debris in microglial lysosomes in the same regions. Accumulation of myelin debris is also observed in white matter of patients with GRN-associated FTD. In addition, our findings also suggest that PGRN insufficiency in microglia leads to impaired lysosomal-mediated clearance of myelin debris. Finally, Grn KO mice that are deficient in cathepsin D (Ctsd), a key lysosomal enzyme, have augmented myelin debris and increased neuronal TDP-43 pathology. Together, our data strongly imply that PGRN loss affects microglial activation and lysosomal function, resulting in the accumulation of myelin debris and contributing to TDP-43 pathology.
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http://dx.doi.org/10.1016/j.celrep.2021.109581DOI Listing
August 2021

Effects of Lung Injury on Regional Aeration and Expiratory Time Constants: Insights From Four-Dimensional Computed Tomography Image Registration.

Front Physiol 2021 28;12:707119. Epub 2021 Jul 28.

Department of Radiology, University of Iowa, Iowa City, IA, United States.

: Intratidal changes in regional lung aeration, as assessed with dynamic four-dimensional computed tomography (CT; 4DCT), may indicate the processes of recruitment and derecruitment, thus portending atelectrauma during mechanical ventilation. In this study, we characterized the time constants associated with deaeration during the expiratory phase of pressure-controlled ventilation in pigs before and after acute lung injury using respiratory-gated 4DCT and image registration. : Eleven pigs were mechanically ventilated in pressure-controlled mode under baseline conditions and following an oleic acid model of acute lung injury. Dynamic 4DCT scans were acquired without interrupting ventilation. Automated segmentation of lung parenchyma was obtained by a convolutional neural network. Respiratory structures were aligned using 4D image registration. Exponential regression was performed on the time-varying CT density in each aligned voxel during exhalation, resulting in regional estimates of intratidal aeration change and deaeration time constants. Regressions were also performed for regional and total exhaled gas volume changes. : Normally and poorly aerated lung regions demonstrated the largest median intratidal aeration changes during exhalation, compared to minimal changes within hyper- and non-aerated regions. Following lung injury, median time constants throughout normally aerated regions within each subject were greater than respective values for poorly aerated regions. However, parametric response mapping revealed an association between larger intratidal aeration changes and slower time constants. Lower aeration and faster time constants were observed for the dependent lung regions in the supine position. Regional gas volume changes exhibited faster time constants compared to regional density time constants, as well as better correspondence to total exhaled volume time constants. : Mechanical time constants based on exhaled gas volume underestimate regional aeration time constants. After lung injury, poorly aerated regions experience larger intratidal changes in aeration over shorter time scales compared to normally aerated regions. However, the largest intratidal aeration changes occur over the longest time scales within poorly aerated regions. These dynamic 4DCT imaging data provide supporting evidence for the susceptibility of poorly aerated regions to ventilator-induced lung injury, and for the functional benefits of short exhalation times during mechanical ventilation of injured lungs.
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http://dx.doi.org/10.3389/fphys.2021.707119DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8355819PMC
July 2021

Evaluating the interaction of soil microorganisms and gut of soil fauna on the fate and spread of antibiotic resistance genes in digested sludge-amended soil ecosystem.

J Hazard Mater 2021 Jul 17;420:126672. Epub 2021 Jul 17.

CAS Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China. Electronic address:

Earthworms have shown their effectiveness in reducing the abundances of antibiotic resistance genes (ARGs) from solid waste. However, the mechanisms of the reduced ARGs by earthworm and whether the solid waste would affect the ARGs profile in earthworm gut were poorly understood. Herein, the patterns of ARGs and microbial communities in digested sludge-amended soil and earthworm gut after 80-day cultivation were investigated. Results show that the enrichment of ARGs (e.g., tetA, tetQ, and sulII) in soil caused by digested sludge-amendment was temporary and would recover to their original levels before amendment. In addition, earthworms could contribute to the further reduction of ARG abundances, which was mainly attributed to their gut digestion via shifting the microbial community (e.g., attenuating the anaerobes). However, the amended soil could significantly increase ARGs abundance in the earthworm gut, which may enhance the potential risk of ARGs spread via the food chain. These findings may provide a new sight on the control of ARGs occurrence and dissemination in sludge-amended soil ecosystem with consideration of earthworms.
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http://dx.doi.org/10.1016/j.jhazmat.2021.126672DOI Listing
July 2021

Diagnostic and Prognostic Significance of miR-675-3p in Patients With Atherosclerosis.

Clin Appl Thromb Hemost 2021 Jan-Dec;27:10760296211024754

Department of Neurosurgery, Binzhou Medical University Hospital, Binzhou, Shandong, People's Republic of China.

In recent years, a rising number of studies have confirmed that microRNA (miRNA) plays a prominent role in the early diagnosis and prognostic value assessment of cardiovascular diseases. The current study was conducted to examine the expression of miR-675-3p in atherosclerosis (AS) patients and to evaluate its clinical diagnosis and prognostic value. 110 AS patients and 70 healthy controls were included in the study. Serum miR-675-3p levels were detected by quantitative real-time PCR (qRT-PCR). The clinical diagnostic significance of serum miR-675-3p in AS patients were investigated by the receiver operating characteristic (ROC) curve. The correlation between miRNA and carotid intima-media thickness (CIMT) was analyzed by the Spearman correlation coefficient. The prognostic significance of serum miR-675-3p was evaluated by the Kaplan-Meier method and Cox regression analysis. The patient's serum miR-675-3p was significantly increased than the healthy individuals ( < 0.05). An increase of carotid intima-media thickness (CIMT) was positively correlated with the promotion of serum miR-675-3p levels. The area under the ROC curve (AUC) was 0.918, with high sensitivity and specificity. miR-675-3p is a key independent predictor of cardiovascular adverse events in AS patients (HR = 5.375, 95%CI = 1.590-18.170, = 0.007), and patients with elevated miR-675-3p were more likely to have cardiovascular adverse events (log-rank = 0.030). Increased miR-675-3p can be used as a potential marker for the diagnosis of AS, and was associated with the poor prognosis of AS.
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http://dx.doi.org/10.1177/10760296211024754DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8327005PMC
July 2021

BrcaSeg: A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images.

Genomics Proteomics Bioinformatics 2021 Jul 16. Epub 2021 Jul 16.

Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Regenstrief Institute, Indianapolis, IN 46202, USA. Electronic address:

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment. We propose BrcaSeg, an image analysis pipeline based on a convolutional neural network (CNN) model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin (H&E) stained histopathological images. The CNN model was trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas (TCGA) Program. BrcaSeg achieves a classification accuracy of 91.02%, which outperforms other state-of-the-art methods. Using this model, we generated pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data. We subsequently estimated the epithelial and stromal ratios and performed correlation analysis to model the relationship between gene expression and tissue ratios. Gene Ontology (GO) enrichment analyses of genes that were highly correlated with tissue ratios suggest that the same tissue was associated with similar biological processes in different breast cancer subtypes, whereas each subtype also had its own idiosyncratic biological processes governing the development of these tissues. Taken all together, our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors. BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.
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http://dx.doi.org/10.1016/j.gpb.2020.06.026DOI Listing
July 2021

Weakly Supervised Deep Ordinal Cox Model for Survival Prediction from Whole-slide Pathological Images.

IEEE Trans Med Imaging 2021 Jul 15;PP. Epub 2021 Jul 15.

Whole-Slide Histopathology Image (WSI) is generally considered the gold standard for cancer diagnosis and prognosis. Given the large inter-operator variation among pathologists, there is an imperative need to develop machine learning models based on WSIs for consistently predicting patient prognosis. The existing WSI-based prediction methods do not utilize the ordinal ranking loss to train the prognosis model, and thus cannot model the strong ordinal information among different patients in an efficient way. Another challenge is that a WSI is of large size (e.g., 100,000-by-100,000 pixels) with heterogeneous patterns but often only annotated with a single WSI-level label, which further complicates the training process. To address these challenges, we consider the ordinal characteristic of the survival process by adding a ranking-based regularization term on the Cox model and propose a weakly supervised deep ordinal Cox model (BDOCOX) for survival prediction from WSIs. Here, we generate amounts of bags from WSIs, and each bag is comprised of the image patches representing the heterogeneous patterns of WSIs, which is assumed to match the WSI-level labels for training the proposed model. The effectiveness of the proposed method is well validated by theoretical analysis as well as the prognosis and patient stratification results on three cancer datasets from The Cancer Genome Atlas (TCGA).
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http://dx.doi.org/10.1109/TMI.2021.3097319DOI Listing
July 2021

Amiloride ameliorates muscle wasting in cancer cachexia through inhibiting tumor-derived exosome release.

Skelet Muscle 2021 Jul 6;11(1):17. Epub 2021 Jul 6.

Key Laboratory for Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.

Background: Cancer cachexia (CAC) reduces patient survival and quality of life. Developments of efficient therapeutic strategies are required for the CAC treatments. This long-term process could be shortened by the drug-repositioning approach which exploits old drugs approved for non-cachexia disease. Amiloride, a diuretic drug, is clinically used for treatments of hypertension and edema due to heart failure. Here, we explored the effects of the amiloride treatment for ameliorating muscle wasting in murine models of cancer cachexia.

Methods: The CT26 and LLC tumor cells were subcutaneously injected into mice to induce colon cancer cachexia and lung cancer cachexia, respectively. Amiloride was intraperitoneally injected daily once tumors were formed. Cachexia features of the CT26 model and the LLC model were separately characterized by phenotypic, histopathologic and biochemical analyses. Plasma exosomes and muscle atrophy-related proteins were quantitatively analyzed. Integrative NMR-based metabolomic and transcriptomic analyses were conducted to identify significantly altered metabolic pathways and distinctly changed metabolism-related biological processes in gastrocnemius.

Results: The CT26 and LLC cachexia models displayed prominent cachexia features including decreases in body weight, skeletal muscle, adipose tissue, and muscle strength. The amiloride treatment in tumor-bearing mice distinctly alleviated muscle atrophy and relieved cachexia-related features without affecting tumor growth. Both the CT26 and LLC cachexia mice showed increased plasma exosome densities which were largely derived from tumors. Significantly, the amiloride treatment inhibited tumor-derived exosome release, which did not obviously affect exosome secretion from non-neoplastic tissues or induce observable systemic toxicities in normal healthy mice. Integrative-omics revealed significant metabolic impairments in cachectic gastrocnemius, including promoted muscular catabolism, inhibited muscular protein synthesis, blocked glycolysis, and impeded ketone body oxidation. The amiloride treatment evidently improved the metabolic impairments in cachectic gastrocnemius.

Conclusions: Amiloride ameliorates cachectic muscle wasting and alleviates cancer cachexia progression through inhibiting tumor-derived exosome release. Our results are beneficial to understanding the underlying molecular mechanisms, shedding light on the potentials of amiloride in cachexia therapy.
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http://dx.doi.org/10.1186/s13395-021-00274-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8258996PMC
July 2021

[Research progress and application of retention time prediction method based on deep learning].

Se Pu 2021 Mar;39(3):211-218

School of Basic Medicine, Anhui Medical University, Hefei 230032, China.

In "shotgun" proteomics strategy, the proteome is explained by analyzing tryptic digested peptides using liquid chromatography-mass spectrometry. In this strategy, the retention time of peptides in liquid chromatography separation can be predicted based on the peptide sequence. This is a useful feature for peptide identification. Therefore, the prediction of the retention time has attracted much research attention. Traditional methods calculate the physical and chemical properties of the peptides based on their amino acid sequence to obtain the retention time under certain chromatography conditions; however, these methods cannot be directly adopted for other chromatography conditions, nor can they be used across laboratories or instrument platforms. To solve this problem, in recent years, deep learning was introduced to proteomics research for retention time prediction. Deep learning is an advanced machine-learning method that has extraordinary capability to learn complex relationships from large-scale data. By stacking multiple hidden neural networks, deep learning can ingest raw data without manually designed features. Transfer learning is an important method in deep learning. It improves the learning process a new task through the transfer of knowledge from an already-learned related task. Transfer learning allows models trained using large datasets to be utilized across conditions by fine-tuning on smaller datasets, instead of retraining the whole model. Many retention time prediction methods have been developed. In the process of training the model, the sequences of peptides are encoded to represent peptide information. Deep learning considers the relationship between the characteristics of the peptides and their corresponding retention times without the need for manual input of the physical and chemical properties of the peptides. Compared with traditional methods, deep learning methods have higher accuracy and can be easily used under different chromatography conditions by transfer learning. If there are not enough datasets to train a new model, a trained model from other datasets can be used as a replacement after calibration with small datasets obtained from these chromatography conditions. While the retention times of modified peptides can also be predicted, the predictions are inadequate for complex modifications such as glycosylation, and this is one of the main problems to be solved. The predicted retention times were used to control the quality of peptide identification. With high accuracy, the predicted retention times can be considered as actual retention times. Therefore, the difference between predicted and observed retention times can serve as an effective and unbiased quantitative metric for evaluating the quality of peptide-spectrum matches (PSMs) reported using different peptide identification methods. Combined with fragment ion intensity prediction, retention time prediction is used to generate spectral libraries for data-independent acquisition (DIA)-based mass spectrometry analysis. Generally, DIA methods identify peptides using specific spectrum libraries obtained from data-dependent acquisition (DDA) experiments. As a result, only peptides detected in the DDA experiments can be present in the libraries and detected in DIA. Furthermore, it takes a lot of time and effort to build libraries from DDA experiments, and typically, they cannot be adopted across different laboratories or instrument platforms. In contrast, the pseudo spectral libraries generated by retention times and fragment ion intensity prediction can overcome these shortcomings. The pseudo spectral libraries generate theoretical spectra of all possible peptides without the need for DDA experiments. This paper reviews the research progress of deep learning methods in the prediction of retention time and in related applications in order to provide references for retention time prediction and protein identification. At the same time, the development direction and application trend of retention time prediction methods based on deep learning are discussed.
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http://dx.doi.org/10.3724/SP.J.1123.2020.08015DOI Listing
March 2021

Geodesic density regression for correcting 4DCT pulmonary respiratory motion artifacts.

Med Image Anal 2021 08 21;72:102140. Epub 2021 Jun 21.

Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242 USA; Department of Radiation Oncology, University of Iowa, Iowa City, IA 52242 USA. Electronic address:

Pulmonary respiratory motion artifacts are common in four-dimensional computed tomography (4DCT) of lungs and are caused by missing, duplicated, and misaligned image data. This paper presents a geodesic density regression (GDR) algorithm to correct motion artifacts in 4DCT by correcting artifacts in one breathing phase with artifact-free data from corresponding regions of other breathing phases. The GDR algorithm estimates an artifact-free lung template image and a smooth, dense, 4D (space plus time) vector field that deforms the template image to each breathing phase to produce an artifact-free 4DCT scan. Correspondences are estimated by accounting for the local tissue density change associated with air entering and leaving the lungs, and using binary artifact masks to exclude regions with artifacts from image regression. The artifact-free lung template image is generated by mapping the artifact-free regions of each phase volume to a common reference coordinate system using the estimated correspondences and then averaging. This procedure generates a fixed view of the lung with an improved signal-to-noise ratio. The GDR algorithm was evaluated and compared to a state-of-the-art geodesic intensity regression (GIR) algorithm using simulated CT time-series and 4DCT scans with clinically observed motion artifacts. The simulation shows that the GDR algorithm has achieved significantly more accurate Jacobian images and sharper template images, and is less sensitive to data dropout than the GIR algorithm. We also demonstrate that the GDR algorithm is more effective than the GIR algorithm for removing clinically observed motion artifacts in treatment planning 4DCT scans. Our code is freely available at https://github.com/Wei-Shao-Reg/GDR.
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http://dx.doi.org/10.1016/j.media.2021.102140DOI Listing
August 2021

van der Waals Epitaxial Growth of Borophene on a Mica Substrate toward a High-Performance Photodetector.

ACS Appl Mater Interfaces 2021 Jul 2;13(27):31808-31815. Epub 2021 Jul 2.

The State Key Laboratory of Mechanics and Control of Mechanical Structures, Laboratory of Intelligent Nano Materials and Devices of Ministry of Education, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

The emergence of borophene has triggered soaring interest in the investigation of its superior structural anisotropy, a novel photoelectronic property for diverse potential applications. However, the structural instability and need of a metal substrate for depositing borophene restrict its large-scale applications toward high-performance electronic and optoelectric devices. van der Waals epitaxy is regarded as an efficient technique for growing superb two-dimensional materials onto extensive functional substrates, but the preparation of stable and controllable borophene on nonmetallic substrates is still not reported. Here, we demonstrate that borophene films can be synthesized onto a mica substrate by van der Waals epitaxy, where hydrogen and NaBH are respectively used as the carrier gas and the boron source. The lattice structure of the as-synthesized borophene coincides with the predicted α'-boron sheet. The borophene-based photodetector shows an excellent photoresponsivity of 1.04 A W and a specific detectivity of 1.27 × 10 Jones at a reversed bias of 4 V under illumination of a 625 nm light-emitting diode, which are remarkably superior to those of reported boron nanosheets. This work facilitates further studies of borophene toward its attractive properties and applications in novel optoelectronic devices and integrated circuits.
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http://dx.doi.org/10.1021/acsami.1c03146DOI Listing
July 2021

Effects of Feedback on Students' Motor Skill Learning in Physical Education: A Systematic Review.

Int J Environ Res Public Health 2021 06 10;18(12). Epub 2021 Jun 10.

School of Physical Education and Training, Shanghai University of Sport, Shanghai 200438, China.

Objectives: Feedback is an essential factor that may affect students' motor skill learning during physical education (PE) classes. This review aimed to (1) systematically examine the evidence for the effectiveness of feedback on students' skill learning during PE classes and (2) summarize the evidence for the effects of feedback elements (i.e., format and content).

Methods: A systematic search was conducted on seven electronic databases to identify studies that explored the effects of feedback on student learning during PE classes. Twenty-three studies were selected, and the study quality was evaluated using the Physiotherapy Evidence Database scale. The levels of evidence were determined with the best evidence synthesis.

Results: Strong evidence indicates the effectiveness of feedback intervention on students' skill learning compared with those who received no feedback. Limited evidence was found for the effect of visual feedback compared with verbal feedback. There were mixed results for the effectiveness of information feedback compared with praise or corrective feedback.

Conclusion: The current evidence suggests that feedback is useful for skill learning during PE classes. Emergent questions still need to be addressed, such as those regarding the efficiency of using different formats and contents for feedback delivery to enhance motor skill learning during PE classes.
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http://dx.doi.org/10.3390/ijerph18126281DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8296044PMC
June 2021

Sustainable, Highly Efficient and Superhydrophobic Fluorinated Silica Functionalized Chitosan Aerogel for Gravity-Driven Oil/Water Separation.

Gels 2021 Jun 2;7(2). Epub 2021 Jun 2.

Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China.

A superhydrophobic fluorinated silica functionalized chitosan (F-CS) aerogel is constructed and fabricated by a simple and sustainable method in this study in order to achieve highly efficient gravity-driven oil/water separation performance. The fluorinated silica functionalization invests the pristine hydrophilic chitosan (CS) aerogel with promising superhydrophobicity with a water contact angle of 151.9°. This novel F-CS aerogel possesses three-dimensional structure with high porosity as well as good chemical stability and mechanical compression property. Moreover, it also shows striking self-cleaning performance and great oil adsorption capacity. Most importantly, the as-prepared aerogels exhibits fast and efficient separation of oil/water mixture by the gravity driven process with high separation efficiency. These great performances render the prepared F-CS aerogel a good candidate for oil/water separation in practical industrial application.
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http://dx.doi.org/10.3390/gels7020066DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293147PMC
June 2021

A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer.

Front Oncol 2021 14;11:690129. Epub 2021 Jun 14.

School of Basic Medical Science, Cheeloo College of Medicine, Shandong University, Jinan, China.

Background: Gastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.

Methods: Weighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.

Results: WGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.

Conclusions: Our results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.
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http://dx.doi.org/10.3389/fonc.2021.690129DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8238374PMC
June 2021

Identification of the molecular mechanisms underlying brisket disease in Holstein heifers via microbiota and metabolome analyses.

AMB Express 2021 Jun 12;11(1):86. Epub 2021 Jun 12.

College of Animal Science, Xinjiang Agricultural University, Urumqi, 830052, China.

Brisket disease (BD) is common among Holstein heifers in high-altitude environments, and this disease may result in serious economic loss. At present, no effective treatment is available for brisket disease. In this study, liver and cecum samples were collected from five heifers with BD and five healthy heifers (HH) for analyses of the metabolome and microbiota. The mean pulmonary arterial pressure and systolic blood pressure were significantly higher in BD heifers, whereas the average breathing rate, blood oxygen saturation, and glucose level were significantly lower in BD group than in the HH group. Further, 16S rDNA data showed that the abundance of Firmicutes was significantly lower and that of Bacteroidetes was significantly higher in BD group than in the HH group. At the genus level, the BD group heifers harbored fewer Ruminococcaceae and Lachnospiraceae than the HH group. Several metabolites, including beta-D-fructose, D-ribose, 1,4-beta-D-glucan, sucrose, and glucose-6-phosphate were present at low levels in BD heifers. Moreover, the mean pulmonary arterial pressure was negatively correlated with beta-D-fructose (r =  - 0.74; P = 0.013), D-ribose (r =  - 0.72; P = 0.018), and acetyl-tyrosine-ethyl-ester (r =  - 0.71; P = 0.022). We also found that mean pulmonary arterial pressure was negatively correlated with most of the genera, including those in the families of Lachnospiraceae and Ruminococcaceae. In summary, the decreased levels of metabolites and microbial genera might affect BD by limiting the energy supply. This study may help us better understand the role of the microbiota in BD and provide new insights into the management of feeding to decrease the rate of BD in Holstein dairy cows in the Qinghai-Tibetan plateau.
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http://dx.doi.org/10.1186/s13568-021-01246-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8241945PMC
June 2021

The Pyroptosis-Related Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Gastric Cancer.

Front Cell Dev Biol 2021 11;9:676485. Epub 2021 Jun 11.

Key Laboratory for Experimental Teratology of The Chinese Ministry of Education, Department of Microbiology, School of Basic Medical Science, Cheeloo College of Medicine, Shandong University, Jinan, China.

Gastric cancer (GC) is one of the leading causes of cancer-related deaths and shows high levels of heterogeneity. The development of a specific prognostic model is important if we are to improve treatment strategies. Pyroptosis can arise in response to , a primary carcinogen, and also in response to chemotherapy drugs. However, the prognostic evaluation of GC to pyroptosis is insufficient. Consensus clustering by pyroptosis-related regulators was used to classify 618 patients with GC from four GEO cohorts. Following Cox regression with differentially expressed genes, our prognosis model (PS-score) was built by LASSO-Cox analysis. The TCGA-STAD cohort was used as the validation set. ESTIMATE, CIBERSORTx, and EPIC were used to investigate the tumor microenvironment (TME). Immunotherapy cohorts by blocking / were used to investigate the treatment response. The subtyping of GC based on pyroptosis-related regulators was able to classify patients according to different clinical traits and TME. The difference between the two subtypes identified in this study was used to develop a prognosis model which we named "PS-score." The PS-score could predict the prognosis of patients with GC and his/her overall survival time. A low PS-score implies greater inflammatory cell infiltration and better response of immunotherapy by / blockers. Our findings provide a foundation for future research targeting pyroptosis and its immune microenvironment to improve prognosis and responses to immunotherapy.
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http://dx.doi.org/10.3389/fcell.2021.676485DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8226259PMC
June 2021

HIVIntact: a python-based tool for HIV-1 genome intactness inference.

Retrovirology 2021 Jun 27;18(1):16. Epub 2021 Jun 27.

HIV Dynamics and Replication Program, CCR, NCI-Frederick, Frederick, MD, USA.

The characterisation of the HIV-1 reservoir, which consists of replication-competent integrated proviruses that persist on antiretroviral therapy (ART), is made difficult by the rarity of intact proviruses relative to those that are defective. While the only conclusive test for the replication-competence of HIV-1 proviruses is carried out in cell culture, genetic characterization of genomes by near full-length (NFL) PCR and sequencing can be used to determine whether particular proviruses have insertions, deletions, or substitutions that render them defective. Proviruses that are not excluded by having such defects can be classified as genetically intact and, possibly, replication competent. Identifying and quantifying proviruses that are potentially replication-competent is important for the development of strategies towards a functional cure. However, to date, there are no programs that can be incorporated into deep-sequencing pipelines for the automated characterization and annotation of HIV genomes. Existing programs that perform this work require manual intervention, cannot be widely installed, and do not have easily adjustable settings. Here, we present HIVIntact, a python-based software tool that characterises genomic defects in NFL HIV-1 sequences, allowing putative intact genomes to be identified in-silico. Unlike other applications that assess the genetic intactness of HIV genomes, this tool can be incorporated into existing sequence-analysis pipelines and applied to large next-generation sequencing datasets.
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http://dx.doi.org/10.1186/s12977-021-00561-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237426PMC
June 2021

Molecular Design of Conjugated Small Molecule Nanoparticles for Synergistically Enhanced PTT/PDT.

Nanomicro Lett 2020 Jul 13;12(1):147. Epub 2020 Jul 13.

Institute of Pharmaceutics and Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China.

Simultaneous photothermal therapy (PTT) and photodynamic therapy (PDT) is beneficial for enhanced cancer therapy due to the synergistic effect. Conventional materials developed for synergistic PTT/PDT are generally multicomponent agents that need complicated preparation procedures and be activated by multiple laser sources. The emerging monocomponent diketopyrrolopyrrole (DPP)-based conjugated small molecular agents enable dual PTT/PDT under a single laser irradiation, but suffer from low singlet oxygen quantum yield, which severely restricts the therapeutic efficacy. Herein, we report acceptor-oriented molecular design of a donor-acceptor-donor (D-A-D) conjugated small molecule (IID-ThTPA)-based phototheranostic agent, with isoindigo (IID) as selective acceptor and triphenylamine (TPA) as donor. The strong D-A strength and narrow singlet-triplet energy gap endow IID-ThTPA nanoparticles (IID-ThTPA NPs) high mass extinction coefficient (18.2 L g cm), competitive photothermal conversion efficiency (35.4%), and a dramatically enhanced singlet oxygen quantum yield (84.0%) comparing with previously reported monocomponent PTT/PDT agents. Such a high PTT/PDT performance of IID-ThTPA NPs achieved superior tumor cooperative eradicating capability in vitro and in vivo.
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http://dx.doi.org/10.1007/s40820-020-00474-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7770699PMC
July 2020

NIR-II Upconversion Photoluminescence of Er Doped LiYF and NaY(Gd)F Core-Shell Nanoparticles.

Front Chem 2021 31;9:690833. Epub 2021 May 31.

College of Chemical Engineering and State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology, Zhejiang University of Technology, Hangzhou, China.

The availability of colloidal nano-materials with high efficiency, stability, and non-toxicity in the near infrared-II range is beneficial for biological diagnosis and therapy. Rare earth doped nanoparticles are ideal luminescent agents for bio-applications in the near infrared-II range due to the abundant energy level distribution. Among them, both excitation and emission range of Er ions can be tuned into second biological window range. Herein, we report the synthesis of ∼15 nm LiYF, NaYF, and NaGdF nanoparticles doped with Er ions and their core-shell structures. The luminescent properties are compared, showing that Er ions with single-doped LiYF and NaYF nanoparticles generate stronger luminescence than Er ions with doped NaGdF, despite the difference in relative intensity at different regions. By epitaxial growth an inert homogeneous protective layer, the surface luminescence of the core-shell structure is further enhanced by about 5.1 times, 6.5 times, and 167.7 times for LiYF, NaYF, and NaGdF, respectively. The excellent luminescence in both visible and NIR range of these core-shell nanoparticles makes them potential candidate for bio-applications.
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http://dx.doi.org/10.3389/fchem.2021.690833DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201074PMC
May 2021

2020 SARS-CoV-2 diversification in the United States: Establishing a pre-vaccination baseline.

medRxiv 2021 Jun 4. Epub 2021 Jun 4.

In 2020, SARS-CoV-2 spread across the United States (U.S.) in three phases distinguished by peaks in the numbers of infections and shifting geographical distribution. We investigated the viral genetic diversity in each phase using sequences publicly available prior to December 15 , 2020, when vaccination was initiated in the U.S. In Phase 1 (winter/spring), sequences were already dominated by the D614G Spike mutation and by Phase 3 (fall), genetic diversity of the viral population had tripled and at least 54 new amino acid changes had emerged at frequencies above 5%, several of which were within known antibody epitopes. These findings highlight the need to track the evolution of SARS-CoV-2 variants in the U.S. to ensure continued efficacy of vaccines and antiviral treatments.

One Sentence Summary: SARS-CoV-2 genetic diversity in the U.S. increased 3-fold in 2020 and 54 emergent nonsynonymous mutations were detected.
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http://dx.doi.org/10.1101/2021.06.01.21258185DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202437PMC
June 2021

MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification.

Nat Commun 2021 06 8;12(1):3445. Epub 2021 Jun 8.

Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.

To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems.
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http://dx.doi.org/10.1038/s41467-021-23774-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8187432PMC
June 2021

CpG Methylation Profiles of HIV-1 Pro-Viral DNA in Individuals on ART.

Viruses 2021 04 29;13(5). Epub 2021 Apr 29.

Department of Molecular Biology and Microbiology, Tufts University, Boston, MA 02129, USA.

The latent HIV-1 reservoir is comprised of stably integrated and intact proviruses with limited to no viral transcription. It has been proposed that latent infection may be maintained by methylation of pro-viral DNA. Here, for the first time, we investigate the cytosine methylation of a replication competent provirus (AMBI-1) found in a T cell clone in a donor on antiretroviral therapy (ART). Methylation profiles of the AMBI-1 provirus were compared to other proviruses in the same donor and in samples from three other individuals on ART, including proviruses isolated from lymph node mononuclear cells (LNMCs) and peripheral blood mononuclear cells (PBMCs). We also evaluated the apparent methylation of cytosines outside of CpG (i.e., CpH) motifs. We found no evidence for methylation in AMBI-1 or any other provirus tested within the 5' LTR promoter. In contrast, CpG methylation was observed in the overlapping reading frame. In addition, we found evidence for differential provirus methylation in cells isolated from LNMCs vs. PBMCs in some individuals, possibly from the expansion of infected cell clones. Finally, we determined that apparent low-level methylation of CpH cytosines is consistent with occasional bisulfite reaction failures. In conclusion, our data do not support the proposition that latent HIV infection is associated with methylation of the HIV 5' LTR promoter.
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http://dx.doi.org/10.3390/v13050799DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8146454PMC
April 2021

Atomic-level engineering of two-dimensional electrocatalysts for CO reduction.

Nanoscale 2021 Apr 13;13(15):7081-7095. Epub 2021 Apr 13.

Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China.

Carbon dioxide (CO) from the excessive consumption of fossil fuels has exhibited a huge threat to the planet's ecosystem. Electrocatalytic CO reduction into value-added chemicals has been regarded as a promising strategy in CO utilization and needs the development of advanced electrocatalysts for lowering the activation energy and enhancing selectivity in CO reduction. Two-dimensional (2D) materials, benefiting from their unique geometrical structures, have been extensively studied in the electrocatalytic CO reduction reaction (CORR). In this review, we systematically overview atomic-level engineering strategies in 2D electrocatalysts for the CORR, including thickness control, elemental doping, vacancy engineering, heterostructure construction, and single-atom loading. Meanwhile, we analyze the relationship between structures and activity in electrocatalysis, and present the future challenges and opportunities in the electrocatalytic CORR, and we hope that this review will offer helpful guidance for developing electrocatalysts for the CORR.
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http://dx.doi.org/10.1039/d1nr00649eDOI Listing
April 2021

Atomistic Insight into the Effects of Depositional Nanoparticle on Nanoscale Liquid Film Evaporation.

Langmuir 2021 May 21;37(17):5202-5212. Epub 2021 Apr 21.

Institute of Thermal Science and Technology, Shandong University, Jinan 250061, Shandong Province China.

Nanoscale liquid film evaporation plays an essential role in many engineering applications. This study carries out molecular dynamics simulations on the effects of the depositional nanoparticle's wettability and volume in base fluid on the evaporation process to understand how the depositional nanoparticle affects the evaporation heat transfer. Increasing the nanoparticle's wettability can enhance the evaporation heat transfer process, and the enhancement effect of the hydrophobic surface is more remarkable than that of the hydrophilic surface. This because the increasing wettability causes more significant solid-liquid interaction. However, the potential energy of argon atoms at the liquid-vapor interface is almost unaffected by wettability. Moreover, when the depositional nanoparticle locates below the free liquid film, increasing the nanoparticle volume has a better heat transfer performance. As the volume increases, the heat transfer through the nanoparticle becomes more obvious, which effectively enhances the heat transfer at the solid-liquid interface and the liquid-vapor interface. The latent heat of phase change at the liquid-vapor interface is almost unchanged so that the evaporation can be enhanced. This research provides an understanding of the effects of depositional nanoparticles on nanoscale evaporation, which can impact several engineering applications, including devices' cooling and fluid transport.
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http://dx.doi.org/10.1021/acs.langmuir.1c00149DOI Listing
May 2021

Deep Learning Improves Speed and Accuracy of Prostate Gland Segmentations on Magnetic Resonance Imaging for Targeted Biopsy.

J Urol 2021 09 21;206(3):604-612. Epub 2021 Apr 21.

Department of Urology, Stanford University School of Medicine, Stanford, California.

Purpose: Targeted biopsy improves prostate cancer diagnosis. Accurate prostate segmentation on magnetic resonance imaging (MRI) is critical for accurate biopsy. Manual gland segmentation is tedious and time-consuming. We sought to develop a deep learning model to rapidly and accurately segment the prostate on MRI and to implement it as part of routine magnetic resonance-ultrasound fusion biopsy in the clinic.

Materials And Methods: A total of 905 subjects underwent multiparametric MRI at 29 institutions, followed by magnetic resonance-ultrasound fusion biopsy at 1 institution. A urologic oncology expert segmented the prostate on axial T2-weighted MRI scans. We trained a deep learning model, ProGNet, on 805 cases. We retrospectively tested ProGNet on 100 independent internal and 56 external cases. We prospectively implemented ProGNet as part of the fusion biopsy procedure for 11 patients. We compared ProGNet performance to 2 deep learning networks (U-Net and holistically-nested edge detector) and radiology technicians. The Dice similarity coefficient (DSC) was used to measure overlap with expert segmentations. DSCs were compared using paired t-tests.

Results: ProGNet (DSC=0.92) outperformed U-Net (DSC=0.85, p <0.0001), holistically-nested edge detector (DSC=0.80, p <0.0001), and radiology technicians (DSC=0.89, p <0.0001) in the retrospective internal test set. In the prospective cohort, ProGNet (DSC=0.93) outperformed radiology technicians (DSC=0.90, p <0.0001). ProGNet took just 35 seconds per case (vs 10 minutes for radiology technicians) to yield a clinically utilizable segmentation file.

Conclusions: This is the first study to employ a deep learning model for prostate gland segmentation for targeted biopsy in routine urological clinical practice, while reporting results and releasing the code online. Prospective and retrospective evaluations revealed increased speed and accuracy.
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http://dx.doi.org/10.1097/JU.0000000000001783DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352566PMC
September 2021

Predicting Students' Attitudes Toward Collaboration: Evidence From Structural Equation Model Trees and Forests.

Front Psychol 2021 26;12:604291. Epub 2021 Mar 26.

School of Psychology, South China Normal University, Guangzhou, China.

Numerous studies have shed some light on the importance of associated factors of collaborative attitudes. However, most previous studies aimed to explore the influence of these factors in isolation. With the strategy of data-driven decision making, the current study applied two data mining methods to elucidate the most significant factors of students' attitudes toward collaboration and group students to draw a concise model, which is beneficial for educators to focus on key factors and make effective interventions at a lower cost. Structural equation model trees (SEM trees) and structural equation model forests (SEM forests) were applied to the Program for International Student Assessment 2015 dataset (a total of 9,769 15-year-old students from China). By establishing the most important predictors and the splitting rules, these methods constructed multigroup common factor models of collaborative attitudes. The SEM trees showed that home educational resources (split by "above-average or not"), home possessions (split by "disadvantaged or not"), mother's education (split by "below high school or not"), and gender (split by "male or female") were the most important predictors among the demographic variables, drawing a 5-group model. Among all the predictors, achievement motivation (split by "above-average or not") and sense of belonging at school (split by "above-average or not" and "disadvantaged or not") were the most important, drawing a 6-group model. The SEM forest findings proved the relative importance of these variables. This paper discusses various interpretations of these results and their implications for educators to formulate corresponding interventions. Methodologically, this research provides a data mining approach to discover important information from large-scale educational data, which might be a complementary approach to enhance data-driven decision making in education.
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http://dx.doi.org/10.3389/fpsyg.2021.604291DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8033009PMC
March 2021

Early Emergence and Long-Term Persistence of HIV-Infected T-Cell Clones in Children.

mBio 2021 04 8;12(2). Epub 2021 Apr 8.

HIV Dynamics and Replication Program, CCR, National Cancer Institute, Frederick, Maryland, USA.

Little is known about the emergence and persistence of human immunodeficiency virus (HIV)-infected T-cell clones in perinatally infected children. We analyzed peripheral blood mononuclear cells (PBMCs) for clonal expansion in 11 children who initiated antiretroviral therapy (ART) between 1.8 and 17.4 months of age and with viremia suppressed for 6 to 9 years. We obtained 8,662 HIV type 1 (HIV-1) integration sites from pre-ART samples and 1,861 sites from on-ART samples. Expanded clones of infected cells were detected pre-ART in 10/11 children. In 8 children, infected cell clones detected pre-ART persisted for 6 to 9 years on ART. A comparison of integration sites in the samples obtained on ART with healthy donor PBMCs infected showed selection for cells with proviruses integrated in and Our analyses indicate that, despite marked differences in T-cell composition and dynamics between children and adults, HIV-infected cell clones are established early in children, persist for up to 9 years on ART, and can be driven by proviral integration in proto-oncogenes. HIV-1 integrates its genome into the DNA of host cells. Consequently, HIV-1 genomes are copied with the host cell DNA during cellular division. Pediatric immune systems differ significantly from adults, consisting primarily of naive T cells, which have low expression of the HIV-1 coreceptor CCR5. This difference may result in variances in the number or size of infected cell clones that persist in children on ART. Here, we provide the most extensive analysis of the integration landscape of HIV-1 in children. We found that, despite the largely naive cell populations in neonatal immune systems, patterns of HIV-1 integration and the size of infected cell clones are as large and widespread as those in adults. Furthermore, selection for integration events in proto-oncogenes were observed in children despite early ART. If such cell clones persist for the life span of these individuals, there may be long-term consequences that have yet to be realized.
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http://dx.doi.org/10.1128/mBio.00568-21DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8092253PMC
April 2021

Integration in oncogenes plays only a minor role in determining the in vivo distribution of HIV integration sites before or during suppressive antiretroviral therapy.

PLoS Pathog 2021 04 7;17(4):e1009141. Epub 2021 Apr 7.

National Cancer Institute, Frederick, Maryland, United States of America.

HIV persists during antiretroviral therapy (ART) as integrated proviruses in cells descended from a small fraction of the CD4+ T cells infected prior to the initiation of ART. To better understand what controls HIV persistence and the distribution of integration sites (IS), we compared about 15,000 and 54,000 IS from individuals pre-ART and on ART, respectively, with approximately 395,000 IS from PBMC infected in vitro. The distribution of IS in vivo is quite similar to the distribution in PBMC, but modified by selection against proviruses in expressed genes, by selection for proviruses integrated into one of 7 specific genes, and by clonal expansion. Clones in which a provirus integrated in an oncogene contributed to cell survival comprised only a small fraction of the clones persisting in on ART. Mechanisms that do not involve the provirus, or its location in the host genome, are more important in determining which clones expand and persist.
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http://dx.doi.org/10.1371/journal.ppat.1009141DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055010PMC
April 2021

Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging.

Med Phys 2021 Jun 3;48(6):2960-2972. Epub 2021 May 3.

Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, USA.

Purpose: While multi-parametric magnetic resonance imaging (MRI) shows great promise in assisting with prostate cancer diagnosis and localization, subtle differences in appearance between cancer and normal tissue lead to many false positive and false negative interpretations by radiologists. We sought to automatically detect aggressive cancer (Gleason pattern 4) and indolent cancer (Gleason pattern 3) on a per-pixel basis on MRI to facilitate the targeting of aggressive cancer during biopsy.

Methods: We created the Stanford Prostate Cancer Network (SPCNet), a convolutional neural network model, trained to distinguish between aggressive cancer, indolent cancer, and normal tissue on MRI. Ground truth cancer labels were obtained by registering MRI with whole-mount digital histopathology images from patients who underwent radical prostatectomy. Before registration, these histopathology images were automatically annotated to show Gleason patterns on a per-pixel basis. The model was trained on data from 78 patients who underwent radical prostatectomy and 24 patients without prostate cancer. The model was evaluated on a pixel and lesion level in 322 patients, including six patients with normal MRI and no cancer, 23 patients who underwent radical prostatectomy, and 293 patients who underwent biopsy. Moreover, we assessed the ability of our model to detect clinically significant cancer (lesions with an aggressive component) and compared it to the performance of radiologists.

Results: Our model detected clinically significant lesions with an area under the receiver operator characteristics curve of 0.75 for radical prostatectomy patients and 0.80 for biopsy patients. Moreover, the model detected up to 18% of lesions missed by radiologists, and overall had a sensitivity and specificity that approached that of radiologists in detecting clinically significant cancer.

Conclusions: Our SPCNet model accurately detected aggressive prostate cancer. Its performance approached that of radiologists, and it helped identify lesions otherwise missed by radiologists. Our model has the potential to assist physicians in specifically targeting the aggressive component of prostate cancers during biopsy or focal treatment.
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http://dx.doi.org/10.1002/mp.14855DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360053PMC
June 2021

Identification of common genetic variants associated with serum concentrations of p, p'-DDE in non-occupational populations in eastern China.

Environ Int 2021 07 20;152:106507. Epub 2021 Mar 20.

Key Lab of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China; State Key Lab of Reproductive Medicine, Institute of Toxicology, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China. Electronic address:

Dichlorodiphenyldichloroethylene (DDE) is the major and most stable toxic metabolite of dichlorodiphenyltrichloroethane (DDT), a well-known organochlorine pesticide banned worldwide in the 1980s. However, it remains easy to detect in humans, and internal levels vary widely among individuals. In the present study, a genome-wide association study (GWAS) (511 subjects) and two replications (812 and 1030 subjects) were performed in non-occupational populations in eastern China. An estimated dietary intake (EDI) of p, p'-DDT and p, p'-DDE was calculated by a food frequency questionnaire (FFQ) and the determination of 195 food and 85 drinking water samples. In addition, functional verifications of susceptible loci were performed by dual-luciferase reporter, immunoblotting and metabolic activity assays in vitro. p, p'-DDT and p, p'-DDE were measured using gas chromatography-tandem mass spectrometry (GC-MS/MS). A common loci rs3181842 (high linkage equilibrium with rs2279345) in CYP2B6 at 19p13.2 were found to be strongly associated with low serum levels of p, p'-DDE in this population in GWAS and were verified by two replications and combined analysis of 2353 subjects (P = 1.00 × 10). In addition, p, p'-DDE levels were significantly lower in subjects with the rs3181842 C allele than in those carrying the normal genotype, even in individuals with similar EDIs of p, p'-DDT. Furthermore, the rs3181842 C allele functionally led to low CYP2B6 expression and activity, resulting in a low metabolic capacity for the formation of p, p'-DDE from p, p'-DDT. The study highlighted that CYP2B6 variants were more relevant than environmental exposure to internal p, p'-DDE exposure, which is important information for DDT risk assessments.
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http://dx.doi.org/10.1016/j.envint.2021.106507DOI Listing
July 2021
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