Publications by authors named "Chenxi Huang"

57 Publications

Tracking self-reported symptoms and medical conditions on social media during the COVID-19 pandemic.

JMIR Public Health Surveill 2021 Aug 26. Epub 2021 Aug 26.

Center for Outcomes Research and Evaluation, Yale New Haven Hospital, 1 Church Street, Suite 200, New Haven, US.

Background: Harnessing health-related data posted on social media in real-time has the potential to offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time.

Objective: The aim of this study was to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the coronavirus disease 2019 (COVID-19) pandemic; to determine how discussion of these symptoms and medical conditions changed over time; and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, new recovered cases) in U.S.

Methods: We used natural language processing (NLP) algorithms to identify symptom and medical condition topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson's correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of U.S. daily COVID-19 statistics.

Results: Within a total of 9,807,813 posts (nearly 70% were sourced from the U.S.), we identified discussion of 120 symptom topics and 1,542 medical condition topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were: anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were: COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). Changes in posts of frequency of anxiety, generalized pain, and weight loss were statistically significant but negatively correlated with daily new COVID-19 cases in the U.S. (r=-0.49, r=-0.46, r=-0.39, respectively; P<.05). Posts of frequency of anxiety, generalized pain, weight loss, fatigue, and the changes in fatigue positively and statistically significantly correlated with U.S. daily changes of both new deaths and new active cases (r ranged: 0.39 to 0.48, P<0.05).

Conclusions: COVID-19 and symptoms of anxiety were the two most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population's mental health status and enhance public health surveillance for infectious disease.

Clinicaltrial:
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http://dx.doi.org/10.2196/29413DOI Listing
August 2021

Highly sensitive magnetic relaxation sensing method for aflatoxin B1 detection based on Au NP-assisted triple self-assembly cascade signal amplification.

Biosens Bioelectron 2021 Nov 9;192:113489. Epub 2021 Jul 9.

Institute of Quality Standard and Testing Technology for Agro-Products, Chinese Academy of Agricultural Science/Key Laboratory of Agro-Products Quality and Safety of MOA, Beijing, 100081, PR China. Electronic address:

Highly sensitive detection of aflatoxin B (AFB) is of great significance because of its high toxicity and carcinogenesis. We propose a magnetic relaxation sensing method based on gold nanoparticles (Au NPs)-assisted triple self-assembly cascade signal amplification for highly sensitive detection of AFB. Both AFB antibody and initiator DNA (iDNA) are labeled on Au NPs to form Ab-Au-iDNA probe. iDNA is enriched by Au NPs to achieve first signal amplification. Different amounts of Ab-Au-iDNA were bound with AFB antigen by indirect competitive immunoassay, and then hybridization chain reaction event was initiated by iDNA to produce long hybridization chain reaction products to enrich more horseradish peroxidase-streptavidin for the second signal amplification. Dopamine could be rapidly converted to polydopamine by HRP catalysis, which is used as the third signal amplification. The Fe solution, providing paramagnetic ions with a strong magnetic signal, could be adsorbed by the polydopamine due to the formation of coordination bonds of phenolic hydroxyl groups with Fe. This effective interaction between polydopamine and Fe significantly changes the transverse relaxation time signal of Fe supernatant solution, which can be used as a magnetic probe for highly sensitive detection of AFB. The sensor exhibited high specificity and sensitivity with a detection limit of 0.453 pg/mL owing to the Au NP-assisted triple self-assembly cascade signal amplification strategy. It has been successfully employed for AFB detection in animal feed samples with consistent results of enzyme linked immune sorbent assay and high-performance liquid chromatography.
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http://dx.doi.org/10.1016/j.bios.2021.113489DOI Listing
November 2021

A direct slicing technique for the 3D printing of implicitly represented medical models.

Comput Biol Med 2021 08 24;135:104534. Epub 2021 Jun 24.

School of Informatics, Xiamen University, Xiamen, 361005, China. Electronic address:

In conventional medical image printing methods, volumetric medical data needs to be conversed into STereo Lithography (STL) format, the most commonly used format for representing geometric models for 3D printing. However, this STL conversion process is not only time consuming, but more importantly, it often leads to the loss of accuracy. It has become a critical factor hindering the printing efficiency and precision of organ models. By examining the key characteristics of discrete medical volume data, this paper proposes a direct slicing technique for printing implicitly represented 3D medical models. The proposed method mainly consists of three algorithms: (1) A layer-based contour extraction algorithm for discrete volume data; (2) An inner shell construction algorithm based on discrete point differential indentation; (3) An infill generation algorithm based on the constructed virtual contour and scan lines. The proposed method has been applied to the slicing of several organ models for experiments, and the ratios of time cost and memory cost between the conventional method and the proposed method are about 4-100 and 1.1 to 1.4 respectively, which demonstrate that the proposed method has a great improvement in both time and space performance when compared with the conventional STL-based method. Our technique extends the direct input format of geometric models for additive manufacturing. That is, discrete volume data can be used as a direct input for additive manufacturing without conversion to STL format.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104534DOI Listing
August 2021

Engagement With COVID-19 Public Health Measures in the United States: A Cross-sectional Social Media Analysis from June to November 2020.

J Med Internet Res 2021 06 21;23(6):e26655. Epub 2021 Jun 21.

Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, United States.

Background: COVID-19 has continued to spread in the United States and globally. Closely monitoring public engagement and perceptions of COVID-19 and preventive measures using social media data could provide important information for understanding the progress of current interventions and planning future programs.

Objective: The aim of this study is to measure the public's behaviors and perceptions regarding COVID-19 and its effects on daily life during 5 months of the pandemic.

Methods: Natural language processing (NLP) algorithms were used to identify COVID-19-related and unrelated topics in over 300 million online data sources from June 15 to November 15, 2020. Posts in the sample were geotagged by NetBase, a third-party data provider, and sensitivity and positive predictive value were both calculated to validate the classification of posts. Each post may have included discussion of multiple topics. The prevalence of discussion regarding these topics was measured over this time period and compared to daily case rates in the United States.

Results: The final sample size included 9,065,733 posts, 70% of which were sourced from the United States. In October and November, discussion including mentions of COVID-19 and related health behaviors did not increase as it had from June to September, despite an increase in COVID-19 daily cases in the United States beginning in October. Additionally, discussion was more focused on daily life topics (n=6,210,255, 69%), compared with COVID-19 in general (n=3,390,139, 37%) and COVID-19 public health measures (n=1,836,200, 20%).

Conclusions: There was a decline in COVID-19-related social media discussion sourced mainly from the United States, even as COVID-19 cases in the United States increased to the highest rate since the beginning of the pandemic. Targeted public health messaging may be needed to ensure engagement in public health prevention measures as global vaccination efforts continue.
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http://dx.doi.org/10.2196/26655DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8218897PMC
June 2021

Toward Dynamic Risk Prediction of Outcomes After Coronary Artery Bypass Graft: Improving Risk Prediction With Intraoperative Events Using Gradient Boosting.

Circ Cardiovasc Qual Outcomes 2021 Jun 3;14(6):e007363. Epub 2021 Jun 3.

Center for Outcomes Research and Evaluation, Yale New Haven Hospital, CT (M.M., T.J.S.D., C.H., B.J.M., R.A.J, A.C., W.L.S., H.M.K).

Background: Intraoperative data may improve models predicting postoperative events. We evaluated the effect of incorporating intraoperative variables to the existing preoperative model on the predictive performance of the model for coronary artery bypass graft.

Methods: We analyzed 378 572 isolated coronary artery bypass graft cases performed across 1083 centers, using the national Society of Thoracic Surgeons Adult Cardiac Surgery Database between 2014 and 2016. Outcomes were operative mortality, 5 postoperative complications, and composite representation of all events. We fitted models by logistic regression or extreme gradient boosting (XGBoost). For each modeling approach, we used preoperative only, intraoperative only, or pre+intraoperative variables. We developed 84 models with unique combinations of the 3 variable sets, 2 variable selection methods, 2 modeling approaches, and 7 outcomes. Each model was tested in 20 iterations of 70:30 stratified random splitting into development/testing samples. Model performances were evaluated on the testing dataset using the C statistic, area under the precision-recall curve, and calibration metrics, including the Brier score.

Results: The mean patient age was 65.3 years, and 24.7% were women. Operative mortality, excluding intraoperative death, occurred in 1.9%. In all outcomes, models that considered pre+intraoperative variables demonstrated significantly improved Brier score and area under the precision-recall curve compared with models considering pre or intraoperative variables alone. XGBoost without external variable selection had the best C statistics, Brier score, and area under the precision-recall curve values in 4 of the 7 outcomes (mortality, renal failure, prolonged ventilation, and composite) compared with logistic regression models with or without variable selection. Based on the calibration plots, risk restratification for mortality showed that the logistic regression model underestimated the risk in 11 114 patients (9.8%) and overestimated in 12 005 patients (10.6%). In contrast, the XGBoost model underestimated the risk in 7218 patients (6.4%) and overestimated in 0 patients (0%).

Conclusions: In isolated coronary artery bypass graft, adding intraoperative variables to preoperative variables resulted in improved predictions of all 7 outcomes. Risk models based on XGBoost may provide a better prediction of adverse events to guide clinical care.
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http://dx.doi.org/10.1161/CIRCOUTCOMES.120.007363DOI Listing
June 2021

Single-Layer MoS Grown on Atomically Flat SrTiO Single Crystal for Enhanced Trionic Luminescence.

ACS Nano 2021 May 5;15(5):8610-8620. Epub 2021 May 5.

The elaborate interface interactions can be critical in determining the achievable functionality of a semiconductor heterojunction (SH), particularly when two-dimensional material is enclosed in the system and its thickness is at an atomic extreme. In this work, we have successfully constructed a SH model system composed of typical transition-metal chalcogenide (TMDs) and transition metal oxides (TMO) by directly growing molybdenum sulfide (MoS) nanosheets on atomically flat strontium titanate (SrTiO) single crystal substrates through a conventional chemical vapor deposition (CVD) synthetic method. Multiple measurements have demonstrated the uniform monolayer thickness and single crystallinity of the MoS nanosheets as well as the atomic flatness of the heterojunction surface, both characterizing an extremely high quality of the interface. Clear evidence have been obtained for the electron transfer from the MoS adlayer to the SrTiO substrate which varies against the interface conditions. More importantly, the photoluminescence of MoS is significantly tailored, which is correlated with both the cleanness of the interface and the crystal orientation of the SrTiO substrate. These results not only shed fresh lights on the structure-property relationship of the TMDs/TMO heterostructures but also manifest the importance of the ideal interface structure for a hybridized system.
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http://dx.doi.org/10.1021/acsnano.1c00482DOI Listing
May 2021

Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction.

JAMA Cardiol 2021 Jun;6(6):633-641

Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut.

Importance: Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights.

Objective: To evaluate whether contemporary machine learning methods can facilitate risk prediction by including a larger number of variables and identifying complex relationships between predictors and outcomes.

Design, Setting, And Participants: This cohort study used the American College of Cardiology Chest Pain-MI Registry to identify all AMI hospitalizations between January 1, 2011, and December 31, 2016. Data analysis was performed from February 1, 2018, to October 22, 2020.

Main Outcomes And Measures: Three machine learning models were developed and validated to predict in-hospital mortality based on patient comorbidities, medical history, presentation characteristics, and initial laboratory values. Models were developed based on extreme gradient descent boosting (XGBoost, an interpretable model), a neural network, and a meta-classifier model. Their accuracy was compared against the current standard developed using a logistic regression model in a validation sample.

Results: A total of 755 402 patients (mean [SD] age, 65 [13] years; 495 202 [65.5%] male) were identified during the study period. In independent validation, 2 machine learning models, gradient descent boosting and meta-classifier (combination including inputs from gradient descent boosting and a neural network), marginally improved discrimination compared with logistic regression (C statistic, 0.90 for best performing machine learning model vs 0.89 for logistic regression). Nearly perfect calibration in independent validation data was found in the XGBoost (slope of predicted to observed events, 1.01; 95% CI, 0.99-1.04) and the meta-classifier model (slope of predicted-to-observed events, 1.01; 95% CI, 0.99-1.02), with more precise classification across the risk spectrum. The XGBoost model reclassified 32 393 of 121 839 individuals (27%) and the meta-classifier model reclassified 30 836 of 121 839 individuals (25%) deemed at moderate to high risk for death in logistic regression as low risk, which were more consistent with the observed event rates.

Conclusions And Relevance: In this cohort study using a large national registry, none of the tested machine learning models were associated with substantive improvement in the discrimination of in-hospital mortality after AMI, limiting their clinical utility. However, compared with logistic regression, XGBoost and meta-classifier models, but not the neural network, offered improved resolution of risk for high-risk individuals.
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http://dx.doi.org/10.1001/jamacardio.2021.0122DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7948114PMC
June 2021

Characterization of a novel Siphoviridae Salmonella bacteriophage T156 and its microencapsulation application in food matrix.

Food Res Int 2021 02 24;140:110004. Epub 2020 Dec 24.

Key Laboratory of Environment Correlative Dietology, Huazhong Agricultural University, Wuhan 430070, China; College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, China. Electronic address:

Salmonella is one of the most common causes of foodborne diseases and can cause severe economic loss. Increased antibiotic resistance, particularly of multidrug-resistant strains, has led to the use of phages as substitute agents. However, bacteriophages are usually sensitive to harsh environments. At present, microencapsulation is one of the methods to solve this problem. But there are few studies on the application of microencapsulated bacteriophages in food matrix. In this study, a novel Salmonella phage T156 was firstly studied for its biological characteristics. T156 belongs to the T5-like Siphoviridae family, with broad host spectrum and potent lytic ability against tested Salmonella strains, including multiple antibiotic-resistant Salmonella. It also showed valuable characteristics such as high pH (3.0-12.0), thermal tolerances (30-50 °C) and a short latent period (10 min). Genome analysis indicated T156 genome comprised a 123,849 bp DNA with 176 putative open reading frames, of which 56 ORFs were annotated to known functions. No genes associated with antibiotic resistance, virulence factor and lysogeny were found in T156 genome. Then, orifice-coagulation bath method was used to microencapsulate bacteriophage T156. Microencapsulated bacteriophage can effectively inhibit the growth of Salmonella in artificially contaminated milk and lettuce at 4 °C and 25 °C. At 25 °C, the maximum antibacterial efficacy of phage in milk and lettuce were 57.93% and 55.47%, respectively. This is the first report about microencapsulated bacteriophage infecting Salmonella in food matrix. It can provide insights into fundamental researches on microencapsulated bacteriophage for future utilization in food.
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http://dx.doi.org/10.1016/j.foodres.2020.110004DOI Listing
February 2021

The Association of COVID-19 With Acute Kidney Injury Independent of Severity of Illness: A Multicenter Cohort Study.

Am J Kidney Dis 2021 04 8;77(4):490-499.e1. Epub 2021 Jan 8.

Section of Nephrology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT; Clinical and Translational Research Accelerator, Department of Internal Medicine, Yale School of Medicine, New Haven, CT. Electronic address:

Rationale & Objective: Although coronavirus disease 2019 (COVID-19) has been associated with acute kidney injury (AKI), it is unclear whether this association is independent of traditional risk factors such as hypotension, nephrotoxin exposure, and inflammation. We tested the independent association of COVID-19 with AKI.

Study Design: Multicenter, observational, cohort study.

Setting & Participants: Patients admitted to 1 of 6 hospitals within the Yale New Haven Health System between March 10, 2020, and August 31, 2020, with results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing via polymerase chain reaction of a nasopharyngeal sample.

Exposure: Positive test for SARS-CoV-2.

Outcome: AKI by KDIGO (Kidney Disease: Improving Global Outcomes) criteria.

Analytical Approach: Evaluated the association of COVID-19 with AKI after controlling for time-invariant factors at admission (eg, demographic characteristics, comorbidities) and time-varying factors updated continuously during hospitalization (eg, vital signs, medications, laboratory results, respiratory failure) using time-updated Cox proportional hazard models.

Results: Of the 22,122 patients hospitalized, 2,600 tested positive and 19,522 tested negative for SARS-CoV-2. Compared with patients who tested negative, patients with COVID-19 had more AKI (30.6% vs 18.2%; absolute risk difference, 12.5% [95% CI, 10.6%-14.3%]) and dialysis-requiring AKI (8.5% vs 3.6%) and lower rates of recovery from AKI (58% vs 69.8%). Compared with patients without COVID-19, patients with COVID-19 had higher inflammatory marker levels (C-reactive protein, ferritin) and greater use of vasopressors and diuretic agents. Compared with patients without COVID-19, patients with COVID-19 had a higher rate of AKI in univariable analysis (hazard ratio, 1.84 [95% CI, 1.73-1.95]). In a fully adjusted model controlling for demographic variables, comorbidities, vital signs, medications, and laboratory results, COVID-19 remained associated with a high rate of AKI (adjusted hazard ratio, 1.40 [95% CI, 1.29-1.53]).

Limitations: Possibility of residual confounding.

Conclusions: COVID-19 is associated with high rates of AKI not fully explained by adjustment for known risk factors. This suggests the presence of mechanisms of AKI not accounted for in this analysis, which may include a direct effect of COVID-19 on the kidney or other unmeasured mediators. Future studies should evaluate the possible unique pathways by which COVID-19 may cause AKI.
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http://dx.doi.org/10.1053/j.ajkd.2020.12.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7791318PMC
April 2021

Predicting Human Intention-Behavior through EEG signal analysis using Multi-Scale CNN.

IEEE/ACM Trans Comput Biol Bioinform 2020 Nov 23;PP. Epub 2020 Nov 23.

At present, the application of Electroencephalogram (EEG) signal classification to human intention-behavior prediction has become a hot topic in the brain computer interface (BCI) research field. In recent studies, the introduction of convolutional neural networks (CNN) has contributed to substantial improvements in the EEG signal classification performance. However, there is still a key challenge with the existing CNN-based EEG signal classification methods, the accuracy of them is not very satisfying. This is because most of the existing methods only utilize the feature maps in the last layer of CNN for EEG signal classification, which might miss some local and detailed information for accurate classification. To address this challenge, this paper proposes a multi-scale CNN model-based EEG signal classification method. In this method, first, the EEG signals are preprocessed and converted to time-frequency images using the short-time Fourier Transform (STFT) technique. Then, a multi-scale CNN model is designed for EEG signal classification, which takes the converted time-frequency image as the input. Especially, in the designed multi-scale CNN model, both the local and global information is taken into consideration. The performance of the proposed method is verified on the benchmark data set 2b used in the BCI contest IV.
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http://dx.doi.org/10.1109/TCBB.2020.3039834DOI Listing
November 2020

FFA-DMRI: A Network Based on Feature Fusion and Attention Mechanism for Brain MRI Denoising.

Front Neurosci 2020 16;14:577937. Epub 2020 Sep 16.

School of Informatics, Xiamen University, Xiamen, China.

Magnetic Resonance Imaging (MRI) is an indispensable tool in the diagnosis of brain diseases due to painlessness and safety. Nevertheless, Rician noise is inevitably injected during the image acquisition process, which leads to poor observation and interferes with the treatment. Owing to the complexity of Rician noise, using the elimination method of Gaussian to remove it does not perform well. Therefore, the feature fusion and attention network (FFA-DMRI) is proposed to separate noise from observed MRI. Inspired by the attention-guided CNN network (ADNet) and Convolutional block attention module (CBAM), a spatial attention mechanism has been specially designed to obtain the area of interest in MRI. Furthermore, the feature fusion block concatenates local with global information, which makes full use of the multilevel structure and boosts the expressive ability of network. The comprehensive experiments on Alzheimer's disease neuroimaging initiative dataset (ADNI) have demonstrated high effectiveness of FFA-DMRI with maintaining the crucial brain details. Moreover, in terms of visual inspections, the denoising results are also consistent with human perception.
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http://dx.doi.org/10.3389/fnins.2020.577937DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7525046PMC
September 2020

Editing of the Proteolytic System of Lactococcus lactis Increases Its Bioactive Potential.

Appl Environ Microbiol 2020 09 1;86(18). Epub 2020 Sep 1.

Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands

Large-scale mass spectrometry-based peptidomics for bioactive-peptide discovery is relatively unexplored because of challenges in intracellular peptide extraction and small-peptide identification. Here, we present an analytical pipeline for large-scale intracellular peptidomics of It entails an optimized sample preparation protocol for , used as an "enzyme complex" to digest β-casein, an extraction method for its intracellular peptidome, and a peptidomics data analysis and visualization procedure. In addition, we proofread the publicly available bioactive-peptide databases and obtained an optimized database of bioactive peptides derivable from bovine β-casein. We used the pipeline to examine cultures of MG1363 and a set of 6 isogenic multiple peptidase mutants incubated with β-casein. We observed a clearly strain-dependent accumulation of peptides with several bioactivities, such as angiotensin-converting enzyme (ACE)-inhibitory, dipeptidyl peptidase 4 (DPP-IV)-inhibitory, and immunoregulatory functions. The results suggest that both the number of different bioactive peptides and the bioactivity diversity can be increased by editing the proteolytic system of This comprehensive pipeline offers a model for discovery of bioactive peptides in combination with other proteins and might be applicable to other bacteria. Lactic acid bacteria (LAB) are very important for the production of safe and healthy human and animal fermented foods and feed and, increasingly more, in the functional food industry. The intracellular peptidomes of LAB are promising reservoirs of bioactive peptides. We show here that targeted genetic engineering of the peptide degradation pathway allows steering the composition of the peptide pool of the LAB and production of peptides with interesting bioactivities. Our work could be used as a guideline for modifying proteolytic systems in other LAB to further explore their potential as cell peptide factories.
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http://dx.doi.org/10.1128/AEM.01319-20DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480361PMC
September 2020

Mo Doping Assisting the CVD Synthesis of Size-Controlled, Uniformly Distributed Single-Layer MoS on Rutile TiO(110).

ACS Appl Mater Interfaces 2020 Jul 17;12(30):34378-34387. Epub 2020 Jul 17.

Molybdenum disulfide (MoS) has attracted considerable interest due to its superior electronic and optical properties, which have seen promising applications in optoelectronics and catalysis. Chemical vapor deposition (CVD) has been successfully applied in synthesizing MoS on various substrates. However, it remains a great challenge to fabricate high-quality MoS sheets with well-controlled micro/nano size and homogeneous distribution over the functional substrates such as active metal oxides. Herein, we have developed a two-step synthetic strategy via depositing MoO first followed by subsequent vulcanization, to grow single-layer MoS on an atomically flat rutile TiO(110) (r-TiO(110)) substrate. This method not only very well controls the size as well as the spatial distribution of MoS nanosheets over the TiO surface but also averts the formation of contaminative species at the heterojunction while maintaining the atomic structure of the substrate surface. The extensive characterizations reveal that the formation of MoS derives from the sulfurization of the singly dispersed Mo and Mo species in the surface/subsurface region instead of the aggregated MoO patches on top of the TiO surface. Such a mechanism may dictate a general way for synthesizing high-quality transition-metal dichalcogenides (TMDs) over a variety of functional substrates.
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http://dx.doi.org/10.1021/acsami.0c07997DOI Listing
July 2020

Identification of Key Genes and Long Noncoding RNA-Associated Competing Endogenous RNA (ceRNA) Networks in Early-Onset Preeclampsia.

Biomed Res Int 2020 5;2020:1673486. Epub 2020 Jun 5.

Department of Medical Research Center, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052 Henan Province, China.

Background: Preeclampsia (PE) is a pregnancy-specific hypertension syndrome and is one of the leading causes of maternal and perinatal morbidity and mortality. Long noncoding RNAs (lncRNAs) have been reported to be abnormally expressed in many diseases, including preeclampsia. The present study is aimed at identifying the key genes and lncRNA-associated competing endogenous RNA (ceRNA) networks in early-onset preeclampsia (EOPE).

Methods: We investigated expression profiles of differentially expressed lncRNAs (DElncRNAs) and genes (DEGs) in placental tissues of EOPE and healthy controls with Human LncRNA Array v4. The potential functions of DEGs and DElncRNAs were predicted using the clusterProfiler package. The lncRNA-mRNA coexpression network was constructed via Pearson's correlation coefficient. The protein-protein interaction (PPI) network of DEGs was constructed, and the hub genes were obtained using the STRING database and Cytoscape. The ceRNA networks were constructed based on miRWalk and LncBase v2. qRT-PCR was performed to confirm the expression of lncRNA MIR193BHG, PROX1-AS1, and GATA3-AS1. ROC curves were performed to assess the clinical value of lncRNA MIR193BHG, PROX1-AS1, and GATA3-AS1 in the diagnosis of EOPE.

Results: We found 6 hub genes (SPP1, CCR2, KIT, ENG, ACKR1, and FLT1) altered in placental tissues of EOPE and constructed a ceRNA network, including 21 lncRNAs, 3 mRNAs, and 69 miRNAs. The expression of lncRNA MIR193BHG and GATA3-AS1 were elevated and showed good clinical values for diagnosing EOPE.

Conclusion: This study provides novel insights into the lncRNA-related ceRNA network in EOPE and identified two lncRNAs as potential prognostic biomarkers in EOPE.
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http://dx.doi.org/10.1155/2020/1673486DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293732PMC
March 2021

Another Breaker of the Wall: the Biological Function of the Usp45 Protein of Lactococcus lactis.

Appl Environ Microbiol 2020 08 3;86(16). Epub 2020 Aug 3.

Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands

is a Gram-positive bacterium that is widely used as a cell factory for the expression of heterologous proteins that are relevant in the pharmaceutical and nutraceutical fields. The signal peptide of the major secreted protein of , Usp45, has been employed extensively in engineering strategies to secrete proteins of interest. However, the biological function of Usp45 has remained obscure despite more than 25 years of research. Studies on Usp45 homologs in other Gram-positive bacteria suggest that Usp45 may play a role in cell wall turnover processes. Here, we show the effect of inactivation and overexpression of the gene on growth, phenotype, and cell division. Our results are in agreement with those obtained in streptococci and demonstrate that the Usp45 protein is essential for proper cell division. We also show that the promoter is highly activated by galactose. Overall, our results indicate that Usp45 mediates cell separation, probably by acting as a peptidoglycan hydrolase. The cell wall, composed mainly of peptidoglycan, is key to maintaining the cell shape and protecting the cell from bursting. Peptidoglycan degradation by peptidoglycan hydrolysis and autolysins occurs during growth and cell division. Since peptidoglycan hydrolases are important for virulence, envelope integrity, and regulation of cell division, it is valuable to investigate their function and regulation. Notably, PcsB-like proteins such as Usp45 have been proposed as new targets for antimicrobial drugs and could also be target for the development of food-grade suicide systems. In addition, although various other expression and secretion systems have been developed for use in , the most-used signal peptide for protein secretion in this bacterium is that of the Usp45 protein. Thus, elucidating the biological function of Usp45 and determining the factors affecting its expression would contribute to optimize several applications.
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http://dx.doi.org/10.1128/AEM.00903-20DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414944PMC
August 2020

Heterogeneity in Trajectories of Systolic Blood Pressure among Young Adults in Qingdao Port Cardiovascular Health Study.

Glob Heart 2020 03 2;15(1):20. Epub 2020 Mar 2.

Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale School of Medicine, New Haven, CT, US.

Background: Although increased age is associated with higher systolic blood pressure (SBP) in general, there may be variation across individuals in how SBP changes over time. The goal of this paper is to identify heterogeneity in SBP trajectories among young adults with similar initial values and identify personal characteristics associated with different trajectory patterns. This may have important implications for prevention and prognosis.

Methods: A cohort of 12,468 individuals aged 18-35 years in the Qingdao Port Cardiovascular Health Study in China was followed yearly during 2000-2011. Individuals were categorized into three strata according to their baseline SBP: ≤110 mmHg, 111-130 mmHg, and >130 mmHg. Within each stratum, group-based trajectory analyses were conducted to identify distinct SBP trajectory patterns, and their association with sociodemographic and baseline health characteristics was assessed by ordinal logistic regression.

Results: Five distinct groups of individuals exhibiting divergent patterns of increasing, stable or decreasing SBP trends were identified within each stratum. This is a first report to identify a subgroup with decreasing trend in SBP. Individuals with more advanced age, having less than high school education, family history of cardiovascular diseases, greater body mass index, greater waist circumference, and hyperlipidemia at baseline were more likely to experience trajectories of higher SBP within each stratum.

Conclusions: The diverging trajectories among young adults with similar initial SBP highlight the need for prevention and feasibility of effective blood pressure control, while the identified risk factors may inform targeted interventions.
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http://dx.doi.org/10.5334/gh.764DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7218791PMC
March 2020

An Endolysin LysSE24 by Bacteriophage LPSE1 Confers Specific Bactericidal Activity against Multidrug-Resistant Strains.

Microorganisms 2020 May 15;8(5). Epub 2020 May 15.

Key Laboratory of Environment Correlative Dietology, Huazhong Agricultural University, Wuhan 430070, China.

is responsible for a wide range of infections and is a constant threat to public health, particularly in light of emerging antibiotic resistance. The use of bacteriophages and phage endolysins as specific antibacterial agents is a promising strategy to control this bacterial infection. Endolysins are important proteins during the process of bacteria lysis by bacteriophages. In this study, we identify a novel endolysin, named LysSE24. LysSE24 was predicted to possess -acetylmuramidases activity, with a molecular mass of ca. 17.4 kDa and pI 9.44. His-tagged LysSE24 was heterologously expressed and purified by Ni-NTA chromatography. LysSE24 exhibited optimal bactericidal activity against Enteritidis ATCC 13076 at a concentration of 0.1 μM. population (measured by OD) decreased significantly ( < 0.05) after 10 min of incubation in combination with the outer membrane permeabilizer in vitro. It also showed antibacterial activity against a panel of 23 tested multidrug-resistant strains. Bactericidal activity of LysSE24 was evaluated in terms of pH, temperature, and ionic strength. It was very stable with different pH (4.0 to 10.0) at different temperatures (20 to 60 °C). Both K and Na at concentrations between 0.1 to 100 mM showed no effects on its bactericidal activity, while a high concentration of Ca and Mg showed efficacy. Transmission electron microscopy revealed that exposure to 0.1 μM LysSE24 for up to 5 min caused a remarkable modification of the cell shape of Enteritidis ATCC 13076. These results indicate that recombinant LysSE24 represents a promising antimicrobial activity against , especially several multidrug-resistant strains. Further studies can be developed to improve its bactericidal activity without the need for pretreatment with outer membrane-destabilizing agents by synthetic biology methods.
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http://dx.doi.org/10.3390/microorganisms8050737DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7284969PMC
May 2020

Altered Patterns of Functional Connectivity and Causal Connectivity in Salience Subnetwork of Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment.

Front Neurosci 2020 21;14:288. Epub 2020 Apr 21.

School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore.

The subjective cognitive decline (SCD) may last for decades prior to the onset of dementia and has been proposed as a risk population for development to amnestic mild cognitive impairment (aMCI) and Alzheimer disease (AD). Disruptions of functional connectivity and causal connectivity (CC) in the salience network (SN) are generally perceived as prominent hallmarks of the preclinical AD. Nevertheless, the alterations in anterior SN (aSN), and posterior SN (pSN) remain unclear. Here, we hypothesized that both the functional connectivity (FC) and CC of the SN subnetworks, comprising aSN and pSN, were distinct disruptive in the SCD and aMCI. We utilized resting-state functional magnetic resonance imaging to investigate the altered FC and CC of the SN subnetworks in 28 healthy controls, 23 SCD subjects, and 29 aMCI subjects. In terms of altered patterns of FC in SN subnetworks, aSN connected to the whole brain was significantly increased in the left orbital superior frontal gyrus, left insula lobule, right caudate lobule, and left rolandic operculum gyrus (ROG), whereas decreased FC was found in the left cerebellum superior lobule and left middle temporal gyrus when compared with the HC group. Notably, no prominent statistical differences were obtained in pSN. For altered patterns of CC in SN subnetworks, compared to the HC group, the aberrant connections in aMCI group were separately involved in the right cerebellum inferior lobule (CIL), right supplementary motor area (SMA), and left ROG, whereas the SCD group exhibited more regions of aberrant connection, comprising the right superior parietal lobule, right CIL, left inferior parietal lobule, left post-central gyrus (PG), and right angular gyrus. Especially, SCD group showed increased CC in the right CIL and left PG, whereas the aMCI group showed decreased CC in the left pre-cuneus, corpus callosum, and right SMA when compared to the SCD group. Collectively, our results suggest that analyzing the altered FC and CC observed in SN subnetworks, served as impressible neuroimaging biomarkers, may supply novel insights for designing preclinical interventions in the preclinical stages of AD.
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http://dx.doi.org/10.3389/fnins.2020.00288DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7189119PMC
April 2020

HIF‑1α affects trophoblastic apoptosis involved in the onset of preeclampsia by regulating FOXO3a under hypoxic conditions.

Mol Med Rep 2020 Jun 1;21(6):2484-2492. Epub 2020 Apr 1.

Department of Clinical Laboratory, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, P.R. China.

Preeclampsia (PE) is a pregnancy-specific syndrome that has severe implications on perinatal mortality and morbidity. Excessive apoptosis of trophoblasts induced by hypoxia may be associated with the development of PE, but the exact pathogenesis is unknown. Forkhead box O transcription factor 3a (FOXO3a) is activated under hypoxic conditions. Furthermore, hypoxia‑inducible factor‑1α (HIF‑1α) is sensitive to variations in partial oxygen pressure. Thus, the aims of the present study were to investigate the expression levels of HIF‑1α and FOXO3a in placental samples of early onset severe PE, and their effect on trophoblastic apoptosis under hypoxic conditions. Cobalt chloride was used to establish the hypoxic model. The present study examined the expression levels of HIF‑1α and FOXO3a in the placental tissues and HTR8/SVneo cells under hypoxic conditions. It was found that HIF‑1α and FOXO3a were highly expressed in placental tissues of patients with PE and in HTR8/SVneo cells under hypoxic conditions. Furthermore, knockdown of FOXO3a using a specific small interfering RNA (siRNA) decreased apoptosis in HTR8/SVneo cells. Moreover, it was found that after knockdown of HIF‑1α using siRNA, FOXO3a expression and the apoptotic rate were reduced in HTR8/SVneo cells. Therefore, the present results indicated that the elevated expression of HIF‑1α increased trophoblastic apoptosis by regulating FOXO3a, which may be involved in the pathogenesis of PE.
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http://dx.doi.org/10.3892/mmr.2020.11050DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7185296PMC
June 2020

Altered Patterns of Phase Position Connectivity in Default Mode Subnetwork of Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment.

Front Neurosci 2020 20;14:185. Epub 2020 Mar 20.

School of Informatics, Xiamen University, Xiamen, China.

Alzheimer's disease (AD), which most commonly occurs in the elder, is a chronic neurodegenerative disease with no agreed drugs or treatment protocols at present. Amnestic mild cognitive impairment (aMCI), earlier than AD onset and later than subjective cognitive decline (SCD) onset, has a serious probability of converting into AD. The SCD, which can last for decades, subjectively complains of decline impairment in memory. Distinct altered patterns of default mode network (DMN) subnetworks connected to the whole brain are perceived as prominent hallmarks of the early stages of AD. Nevertheless, the aberrant phase position connectivity (PPC) connected to the whole brain in DMN subnetworks remains unknown. Here, we hypothesized that there exist distinct variations of PPC in DMN subnetworks connected to the whole brain for patients with SCD and aMCI, which might be acted as discriminatory neuroimaging biomarkers. We recruited 27 healthy controls (HC), 20 SCD and 28 aMCI subjects, respectively, to explore aberrant patterns of PPC in DMN subnetworks connected to the whole brain. In anterior DMN (aDMN), SCD group exhibited aberrant PPC in the regions of right superior cerebellum lobule (SCL), right superior frontal gyrus of medial part (SFGMP), and left fusiform gyrus (FG) in comparison of HC group, by contrast, no prominent difference was found in aMCI group. It is important to note that aMCI group showed increased PPC in the right SFGMP in comparison with SCD group. For posterior DMN (pDMN), SCD group showed decreased PPC in the left superior parietal lobule (SPL) and right superior frontal gyrus (SFG) compared to HC group. It is noteworthy that aMCI group showed decreased PPC in the left middle frontal gyrus of orbital part (MFGOP) and right SFG compared to HC group, yet increased PPC was found in the left superior temporal gyrus of temporal pole (STGTP). Additionally, aMCI group exhibited decreased PPC in the left MFGOP compared to SCD group. Collectively, our results have shown that the aberrant regions of PPC observed in DMN are related to cognitive function, and it might also be served as impressible neuroimaging biomarkers for timely intervention before AD occurs.
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http://dx.doi.org/10.3389/fnins.2020.00185DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7099636PMC
March 2020

Surgeons: Buyer beware-does "universal" risk prediction model apply to patients universally?

J Thorac Cardiovasc Surg 2020 Jul 19;160(1):176-179.e2. Epub 2020 Feb 19.

Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Conn; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Conn; Department of Health Policy and Management, Yale School of Public Health, New Haven, Conn. Electronic address:

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http://dx.doi.org/10.1016/j.jtcvs.2019.11.144DOI Listing
July 2020

Leveraging the Electronic Health Records for Population Health: A Case Study of Patients With Markedly Elevated Blood Pressure.

J Am Heart Assoc 2020 04 23;9(7):e015033. Epub 2020 Mar 23.

Center for Outcomes Research and Evaluation Yale-New Haven Hospital New Haven CT.

Background The digital transformation of medical data provides opportunities to perform digital population health surveillance and identify people inadequately managed in usual care. We leveraged the electronic health records of a large health system to identify patients with markedly elevated blood pressure and characterize their follow-up care pattern. Methods and Results We included 373 861 patients aged 18 to 85 years, who had at least 1 outpatient encounter in the Yale New Haven Health System between January 2013 and December 2017. We described the prevalence and follow-up pattern of patients with at least 1 systolic blood pressure (SBP) ≥160 mm Hg or diastolic blood pressure (DBP) ≥100 mm Hg and patients with at least 1 SBP ≥180 mm Hg or DBP ≥120 mm Hg. Of 373 861 patients included, 56 909 (15.2%) had at least 1 SBP ≥160 mm Hg or DBP ≥100 mm Hg, and 10 476 (2.8%) had at least 1 SBP ≥180 mm Hg or DBP ≥120 mm Hg. Among patients with SBP ≥160 mm Hg or DBP ≥100 mm Hg, only 28.3% had a follow visit within 1 month (time window of follow-up recommended by the guideline) and 19.9% subsequently achieved control targets (SBP <130 mm Hg and DBP <80 mm Hg) within 6 months. Follow-up rate at 1 month and control rate at 6 months for patients with SBP ≥180 mm Hg or DBP ≥120 mm Hg was 31.9% and 17.2%. Conclusions Digital population health surveillance with an electronic health record identified a large number of patients with markedly elevated blood pressure and inadequate follow-up. Many of these patients subsequently failed to achieve control targets.
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http://dx.doi.org/10.1161/JAHA.119.015033DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7428633PMC
April 2020

Sketch Augmentation-Driven Shape Retrieval Learning Framework Based on Convolutional Neural Networks.

IEEE Trans Vis Comput Graph 2021 08 30;27(8):3558-3570. Epub 2021 Jun 30.

In this article, we present a deep learning approach to sketch-based shape retrieval that incorporates a few novel techniques to improve the quality of the retrieval results. First, to address the problem of scarcity of training sketch data, we present a sketch augmentation method that more closely mimics human sketches compared to simple image transformation. Our method generates more sketches from the existing training data by (i) removing a stroke, (ii) adjusting a stroke, and (iii) rotating the sketch. As such, we generate a large number of sketch samples for training our neural network. Second, we obtain the 2D renderings of each 3D model in the shape database by determining the view positions that best depict the 3D shape: i.e., avoiding self-occlusion, showing the most salient features, and following how a human would normally sketch the model. We use a convolutional neural network (CNN) to learn the best viewing positions of each 3D model and generates their 2D images for the next step. Third, our method uses a cross-domain learning strategy based on two Siamese CNNs that pair up sketches and the 2D shape images. A joint Bayesian measure is used to measure the output similarity from these CNNs to maximize inter-class similarity and minimize intra-class similarity. Extensive experiments show that our proposed approach comprehensively outperforms many existing state-of-the-art methods.
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http://dx.doi.org/10.1109/TVCG.2020.2975504DOI Listing
August 2021

A Deep Segmentation Network of Multi-Scale Feature Fusion Based on Attention Mechanism for IVOCT Lumen Contour.

IEEE/ACM Trans Comput Biol Bioinform 2021 Jan-Feb;18(1):62-69. Epub 2021 Feb 3.

Recently, coronary heart disease has attracted more and more attention, where segmentation and analysis for vascular lumen contour are helpful for treatment. And intravascular optical coherence tomography (IVOCT) images are used to display lumen shapes in clinic. Thus, an automatic segmentation method for IVOCT lumen contour is necessary to reduce the doctors' workload while ensuring diagnostic accuracy. In this paper, we proposed a deep residual segmentation network of multi-scale feature fusion based on attention mechanism (RSM-Network, Residual Squeezed Multi-Scale Network) to segment the lumen contour in IVOCT images. Firstly, three different data augmentation methods including mirror level turnover, rotation and vertical flip are considered to expand the training set. Then in the proposed RSM-Network, U-Net is contained as the main body, considering its characteristic of accepting input images with any sizes. Meanwhile, the combination of residual network and attention mechanism is applied to improve the ability of global feature extraction and solve the vanishing gradient problem. Moreover, the pyramid feature extraction structure is introduced to enhance the learning ability for multi-scale features. Finally, in order to increase the matching degree between the actual output and expected output, the cross entropy loss function is also used. A series of metrics are presented to evaluate the performance of our proposed network and the experimental results demonstrate that the proposed RSM-Network can learn the contour details better, contributing to strong robustness and accuracy for IVOCT lumen contour segmentation.
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http://dx.doi.org/10.1109/TCBB.2020.2973971DOI Listing
February 2021

Expression of ACKR2 in placentas from different types of preeclampsia.

Placenta 2020 01 24;90:121-127. Epub 2019 Dec 24.

Department of Clinical Laboratory, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China.

Objective: The purpose of this study was to investigate the expression of atypical chemokine receptor 2 (ACKR2, D6) in different types of preeclampsia (PE) and its effects on trophoblast proliferation and apoptosis.

Methods: The subjects were divided into four groups: early-onset PE group (EOPE, n = 30), late-onset PE group (LOPE, n = 30), preterm birth group (PB, n = 30), and normal group (N, n = 30). The expression of ACKR2 in placentas was evaluated using immunohistochemistry, qRT-PCR, and Western blot. The trophoblast cell line JAR was cultured to detect the expression of ACKR2 after simulating hypoxic conditions with cobalt chloride (CoCl). The effects on cell proliferation, apoptosis, and expression of the chemokine CCL2 were analyzed after silencing ACKR2 with siRNA.

Results: ACKR2 was decreased in placentas of EOPE and PB groups at the protein and mRNA level,compared to the normal group. No statistical differences were found between EOPE and PB groups, or between LOPE and normal groups. In our in vitro work, we found that the expression of ACKR2 decreased after treatment with 150 μmol/L, 200 μmol/L, and 250 μmol/L of CoCl. After ACKR2 was silenced, the degree of cellular proliferation decreased, while apoptosis and CCL2 expression increased.

Conclusion: The changes of ACKR2 expression in placentas of PE may be related to gestational weeks. Hypoxia inhibits the expression of ACKR2 in placentas. Abnormal expression of ACKR2 in PE may lead to dysfunction of trophoblast, and ACKR2 is an essential player in the immunoregulation of the placental chemokine CCL2.
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http://dx.doi.org/10.1016/j.placenta.2019.12.015DOI Listing
January 2020

The National Institutes of Health funding for clinical research applying machine learning techniques in 2017.

NPJ Digit Med 2020 31;3:13. Epub 2020 Jan 31.

1Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT USA.

Machine learning (ML) techniques have become ubiquitous and indispensable for solving intricate problems in most disciplines. To determine the extent of funding for clinical research projects applying ML techniques by the National Institutes of Health (NIH) in 2017, we searched the NIH Research Portfolio Online Reporting Tools Expenditures and Results (RePORTER) system using relevant keywords. We identified 535 projects, which together received a total of $264 million, accounting for 2% of the NIH extramural budget for clinical research.
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http://dx.doi.org/10.1038/s41746-020-0223-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6994580PMC
January 2020

Lysis of a Lactococcus lactis Dipeptidase Mutant and Rescue by Mutation in the Pleiotropic Regulator CodY.

Appl Environ Microbiol 2020 04 1;86(8). Epub 2020 Apr 1.

Department of Molecular Genetics, University of Groningen, Groningen Biomolecular Sciences and Biotechnology Institute, Groningen, the Netherlands

subsp. MG1363 is a model for the lactic acid bacteria (LAB) used in the dairy industry. The proteolytic system, consisting of a proteinase, several peptide and amino acid uptake systems, and a host of intracellular peptidases, plays a vital role in nitrogen metabolism and is of eminent importance for flavor formation in dairy products. The dipeptidase PepV functions in the last stages of proteolysis. A link between nitrogen metabolism and peptidoglycan (PG) biosynthesis was underlined by the finding that deletion of the dipeptidase gene (creating strain MGΔ) resulted in a prolonged lag phase when the mutant strain was grown with a high concentration of glycine. In addition, most MGΔ cells lyse and have serious defects in their shape. This phenotype is due to a shortage of alanine, since adding alanine can rescue the growth and shape defects. Strain MGΔ is more resistant to vancomycin, an antibiotic targeting peptidoglycan d-Ala-d-Ala ends, which confirmed that MGΔ has an abnormal PG composition. A mutant of MGΔ was obtained in which growth inhibition and cell shape defects were alleviated. Genome sequencing showed that this mutant has a single point mutation in the gene, resulting in an arginine residue at position 218 in the DNA-binding motif of CodY being replaced by a cysteine residue. Thus, this strain was named MGΔ Transcriptome sequencing (RNA-seq) data revealed a dramatic derepression in peptide uptake and amino acid utilization in MGΔ A model of the connections among PepV activity, CodY regulation, and PG synthesis of is proposed. Precise control of peptidoglycan synthesis is essential in Gram-positive bacteria for maintaining cell shape and integrity as well as resisting stresses. Although neither the dipeptidase PepV nor alanine is essential for MG1363, adequate availability of either ensures proper cell wall synthesis. We broaden the knowledge about the dipeptidase PepV, which acts as a linker between nitrogen metabolism and cell wall synthesis in .
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http://dx.doi.org/10.1128/AEM.02937-19DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7117943PMC
April 2020

Fast Hyperspectral Diffuse Optical Imaging Method with Joint Sparsity.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:4758-4761

Diffuse optical tomography (DOT) is an important functional imaging modality in clinical diagnosis and treatment. As the number of wavelengths in the acquired DOT data grows, it becomes very challenging to reconstruct diffusion and absorption coefficients of tissue, i.e., a DOT image. In this paper, we consider the hyperspectral DOT (hyDOT) inverse problem as a multiple-measurement vector (MMV) problem by exploiting the joint sparsity of the images to be reconstructed. Then we propose a fast stochastic greedy algorithm based on the MMV stochastic gradient matching pursuit (MStoGradMP) and the mini-batching technique. Numerical results show that the proposed algorithm can achieve higher reconstruction accuracy with significantly reduced running time than the related gradient descent method with sparsity regularization.
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http://dx.doi.org/10.1109/EMBC.2019.8857069DOI Listing
July 2019

Development and Validation of a Model for Predicting the Risk of Acute Kidney Injury Associated With Contrast Volume Levels During Percutaneous Coronary Intervention.

JAMA Netw Open 2019 11 1;2(11):e1916021. Epub 2019 Nov 1.

Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut.

Importance: Determining the association of contrast volume during percutaneous coronary intervention (PCI) with the risk of acute kidney injury (AKI) is important for optimizing PCI safety.

Objective: To quantify how the risk of AKI is associated with contrast volume, accounting for the possibility of nonlinearity and heterogeneity among different baseline risks.

Design, Setting, And Participants: This prognostic study used data from the American College of Cardiology National Cardiovascular Data Registry CathPCI Registry for 1694 US hospitals. Derivation analysis included 2 076 694 individuals who underwent PCI from July 1, 2011, to June 30, 2015. Validation analysis included 961 863 individuals who underwent PCI from July 1, 2015, to March 31, 2017. Data analysis took place from July 2018 to May 2019.

Exposure: Contrast volume during PCI.

Main Outcomes And Measures: Acute kidney injury was defined using 3 thresholds for preprocedure to postprocedure creatinine level increase (ie, ≥0.3 mg/dL, ≥0.5 mg/dL, and ≥1.0 mg/dL). A model quantifying the association of contrast volume with AKI was developed, and the existence of nonlinearity and heterogeneity were examined by likelihood ratio tests. The model was derived in the training set (a random 50% of the derivation cohort), and performance was evaluated in the test set (the remaining 50% of the derivation cohort) and an independent validation set by area under the receiver operating characteristic curve (AUC) and calibration slope of observed vs predicted risks.

Results: The 2 076 694 patients in the derivation set had a mean (SD) age of 65.1 (12.1) years, and 662 525 (31.9%) were women; 133 306 (6.4%) had creatinine level increases of at least 0.3 mg/dL, 66 626 (3.2%) had creatinine level increases of at least 0.5 mg/dL, and 28 378 (1.4%) had creatinine level increases of at least 1.0 mg/dL. In the validation set of 961 843 patients (mean [SD] age, 65.7 [12.1] years; 305 577 [31.8%] women), these rates were 62 913 (6.5%), 34 229 (3.6%), and 15 555 (1.6%), respectively. The association of contrast volume and AKI risk was nonlinear (χ226 = 1436.2; P < .001) and varied by preprocedural risk (χ220 = 105.6; P < .001). In the test set, the model yielded an AUC of 0.777 (95% CI, 0.775-0.779) for predicting risk of a creatinine level increase of at least 0.3 mg/dL, 0.839 (95% CI, 0.837-0.841) for predicting risk of a creatinine level increase of at least 0.5 mg/dL, and 0.870 (95% CI, 0.867-0.873) for predicting risk of a creatinine level increase of at least 1.0 mg/dL; it achieved a calibration slope of 0.998 (95% CI, 0.989-1.007), 0.999 (95% CI, 0.989-1.008), and 0.986 (95% CI, 0.973-0.998), respectively, for the AKI severity levels. The model had similar performance in the validation set (creatinine level increase of ≥0.3 mg/dL: AUC, 0.794; 95% CI, 0.792-0.795; calibration slope, 1.039; 95% CI, 1.030-1.047; creatinine level increase of ≥0.5 mg/dL: AUC, 0.845; 95% CI, 0.843-0.848; calibration slope, 1.063; 95% CI, 1.054-1.074; creatinine level increase of ≥1.0 mg/dL: AUC, 0.872; 95% CI, 0.869-0.875; calibration slope, 1.103; 95% CI, 1.089-1.117).

Conclusions And Relevance: The association of contrast volume with AKI risk is complex, varies by baseline risk, and can be predicted by a model. Future research to evaluate the effect of the model on AKI is needed.
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http://dx.doi.org/10.1001/jamanetworkopen.2019.16021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6902830PMC
November 2019
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