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The biological effects of terahertz (THz) radiation have been observed across multiple levels of biological organization, however the sub-cellular mechanisms underlying the phenotypic changes remain to be elucidated. Filamentous protein complexes such as microtubules are essential cytoskeletal structures that regulate diverse biological functions, and these may be an important target for THz interactions underlying THz-induced effects observed at the cellular or tissue level. Here, we show disassembly of microtubules within minutes of exposure to extended trains of intense, picosecond-duration THz pulses. Further, the rate of disassembly depends on THz intensity and spectral content. As inhibition of microtubule dynamics is a mechanism of clinically-utilized anti-cancer agents, disruption of microtubule networks may indicate a potential therapeutic mechanism of intense THz pulses., Competing Interests: The authors declare no conflicts of interest., (© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.)

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.

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., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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., (©Daisy Massey, Chenxi Huang, Yuan Lu, Alina Cohen, Yahel Oren, Tali Moed, Pini Matzner, Shiwani Mahajan, César Caraballo, Navin Kumar, Yuchen Xue, Qinglan Ding, Rachel Dreyer, Brita Roy, Harlan Krumholz. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 21.06.2021.)

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.

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.

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 2 ) nanosheets on atomically flat strontium titanate (SrTiO 3 ) 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 2 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 2 adlayer to the SrTiO 3 substrate which varies against the interface conditions. More importantly, the photoluminescence of MoS 2 is significantly tailored, which is correlated with both the cleanness of the interface and the crystal orientation of the SrTiO 3 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.

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., (Copyright © 2021 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)

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.

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., (Copyright © 2020 Hong, Huang, Yang, Li, Qian and Cai.)

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 Lactococcus lactis It entails an optimized sample preparation protocol for L. lactis , 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 L. lactis 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 L. lactis This comprehensive pipeline offers a model for discovery of bioactive peptides in combination with other proteins and might be applicable to other bacteria. IMPORTANCE 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 Lactococcus lactis 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., (Copyright © 2020 Huang and Kok.)

Lactococcus lactis 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 L. lactis , 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 usp45 gene on L. lactis growth, phenotype, and cell division. Our results are in agreement with those obtained in streptococci and demonstrate that the L. lactis Usp45 protein is essential for proper cell division. We also show that the usp45 promoter is highly activated by galactose. Overall, our results indicate that Usp45 mediates cell separation, probably by acting as a peptidoglycan hydrolase. IMPORTANCE 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 Lactococcus lactis , 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., (Copyright © 2020 American Society for Microbiology.)

Molybdenum disulfide (MoS 2 ) 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 2 on various substrates. However, it remains a great challenge to fabricate high-quality MoS 2 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 3 first followed by subsequent vulcanization, to grow single-layer MoS 2 on an atomically flat rutile TiO 2 (110) (r-TiO 2 (110)) substrate. This method not only very well controls the size as well as the spatial distribution of MoS 2 nanosheets over the TiO 2 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 2 derives from the sulfurization of the singly dispersed Mo 6+ and Mo 5+ species in the surface/subsurface region instead of the aggregated MoO 3 patches on top of the TiO 2 surface. Such a mechanism may dictate a general way for synthesizing high-quality transition-metal dichalcogenides (TMDs) over a variety of functional substrates.

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.

Salmonella 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 N -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 Salmonella Enteritidis ATCC 13076 at a concentration of 0.1 μM. Salmonella population (measured by OD 600 nm ) decreased significantly ( p < 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 Salmonella 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 2+ and Mg 2+ 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 Salmonella Enteritidis ATCC 13076. These results indicate that recombinant LysSE24 represents a promising antimicrobial activity against Salmonella , especially several multidrug-resistant Salmonella 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.

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., (Copyright © 2020 Cai, Huang, Yang, Lu, Hong, Ren, Hong and Ng.)

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.

Lactococcus lactis subsp. cremoris 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 pepV (creating strain MGΔ pepV ) resulted in a prolonged lag phase when the mutant strain was grown with a high concentration of glycine. In addition, most MGΔ pepV 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 pepV is more resistant to vancomycin, an antibiotic targeting peptidoglycan d-Ala-d-Ala ends, which confirmed that MGΔ pepV has an abnormal PG composition. A mutant of MGΔ pepV 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 codY 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 pepVcodY R218C Transcriptome sequencing (RNA-seq) data revealed a dramatic derepression in peptide uptake and amino acid utilization in MGΔ pepVcodY R218C A model of the connections among PepV activity, CodY regulation, and PG synthesis of L. lactis is proposed. IMPORTANCE 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 L. lactis 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 L. lactis ., (Copyright © 2020 Huang et al.)

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., (Copyright © 2020 Cai, Huang, Yang, Zhang, Peng, Zhao, Hong, Ren, Hong, Xiao and Yan.)

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., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2020 The Author(s).)

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., Competing Interests: Competing interestsH.M.K. works under contract with the Centers for Medicare & Medicaid Services to support quality measurement programs; was a recipient of a research grant, through Yale, from Medtronic and the U.S. Food and Drug Administration to develop methods for post-market surveillance of medical devices; was a recipient of a research grant with Medtronic and is the recipient of a research grant from Johnson & Johnson, through Yale University, to support clinical trial data sharing; was a recipient of a research agreement, through Yale University, from the Shenzhen Center for Health Information for work to advance intelligent disease prevention and health promotion; collaborates with the National Center for Cardiovascular Diseases in Beijing; receives payment from the Arnold & Porter Law Firm for work related to the Sanofi clopidogrel litigation, from the Ben C. Martin Law Firm for work related to the Cook Celect IVC filter litigation, and from the Siegfried and Jensen Law Firm for work related to Vioxx litigation; chairs a Cardiac Scientific Advisory Board for UnitedHealth; was a participant/participant representative of the IBM Watson Health Life Sciences Board; is a member of the Advisory Board for Element Science, the Advisory Board for Facebook, and the Physician Advisory Board for Aetna; and is the co-founder of HugoHealth, a personal health information platform, and co-founder of Refactor Health, an enterprise healthcare AI-augmented data management company. Bobak Mortazavi is supported in part by the Center for Remote Health Technologies and Systems and Texas A&M University, as well as awards 1R01EB028106-01 and 1R21EB028486-01 from the National Institute for Biomedical Imaging and Bioengineering (NIBIB) for work employing machine learning on health data; received a patent US10201746B1; a patent to US20180315507A1 is pending. The other authors have no relevant disclosures., (© The Author(s) 2020.)

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 2 ). 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 2 . 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., Competing Interests: Declaration of competing interest No conflicts., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

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.

Background Characterizing and assessing the prevalence, awareness, and treatment patterns of patients with isolated diastolic hypertension (IDH) can generate new knowledge and highlight opportunities to improve their care. Methods and Results We used data from the China PEACE (Patient-centered Evaluative Assessment of Cardiac Events) Million Persons Project, which screened 2 351 035 participants aged 35 to 75 years between 2014 and 2018. IDH was defined as systolic and diastolic blood pressure of <140 and ≥90 mm Hg; awareness as self-reported diagnosis of hypertension; and treatment as current use of antihypertensive medications. Of the 2 310 184 participants included (mean age 55.7 years; 59.5% women); 73 279 (3.2%) had IDH, of whom 63 112 (86.1%) were untreated, and only 6512 (10.3%) of the untreated were aware of having hypertension. When compared with normotensives, participants who were <60 years, men, at least college educated, had body mass index of >28 kg/m 2 , consumed alcohol, had diabetes mellitus, and prior cardiovascular events were more likely to have IDH (all P <0.01). Among those with IDH, higher likelihood of awareness was associated with increased age, women, college education, body mass index of >28 kg/m 2 , higher income, diabetes mellitus, prior cardiovascular events, and Central or Eastern region (all P <0.05). Most treated participants with IDH reported taking only 1 class of antihypertensive medication. Conclusions IDH affects a substantial number of people in China, however, few are aware of having hypertension and most treated participants are poorly managed, which suggests the need to improve the diagnosis and treatment of people with IDH.

Panax notoginseng is the most important valued and endemic medicinal herb to China. It belongs to the Araliaceae family, which has a longer medical history in China. The complete chloroplast genome of Panax notoginseng is 156,387 bp in size and displays a typical quadripartite structure of the large single-copy region (LSC, 86,112 bp), small single-copy region (SSC, 18,005 bp) that separate by a pair of inverted repeat regions (IRs, each for 26,135 bp). The base nucleotide composition of the cpDNA is 30.8% of A, 31.1% of T, 19.9% of C, and 18.2% of G, with a total G + C content of 38.1%. The whole chloroplast genome of P. notoginseng contains 134 genes, including 89 protein-coding genes (PCGs), 37 transfer RNA (tRNAs) genes, and eight ribosomal RNA (rRNAs) genes species. Phylogenetic relationship analysis based on 37 medicinal herbs species confirmed the position of P. notoginseng closely related to Panax japonicus and Panax vietnamensis ., Competing Interests: No potential conflict of interest was reported by the authors., (© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

Importance: Better prediction of major bleeding after percutaneous coronary intervention (PCI) may improve clinical decisions aimed to reduce bleeding risk. Machine learning techniques, bolstered by better selection of variables, hold promise for enhancing prediction., Objective: To determine whether machine learning techniques better predict post-PCI major bleeding compared with the existing National Cardiovascular Data Registry (NCDR) models., Design, Setting, and Participants: This comparative effectiveness study used the NCDR CathPCI Registry data version 4.4 (July 1, 2009, to April 1, 2015), machine learning techniques were used (logistic regression with lasso regularization and gradient descent boosting [XGBoost, version 0.71.2]), and output was then compared with the existing simplified risk score and full NCDR models. The existing models were recreated, and then performance was evaluated through additional techniques and variables in a 5-fold cross-validation in analysis conducted from October 1, 2015, to October 27, 2017. The setting was retrospective modeling of a nationwide clinical registry of PCI. Participants were all patients undergoing PCI. Percutaneous coronary intervention procedures were excluded if they were not the index PCI of admission, if the hospital site had missing outcomes measures, or if the patient underwent subsequent coronary artery bypass grafting., Exposures: Clinical variables available at admission and diagnostic coronary angiography data were used to determine the severity and complexity of presentation., Main Outcomes and Measures: The main outcome was in-hospital major bleeding within 72 hours after PCI. Results were evaluated by comparing C statistics, calibration, and decision threshold-based metrics, including the F score (harmonic mean of positive predictive value and sensitivity) and the false discovery rate., Results: The post-PCI major bleeding rate among 3 316 465 procedures (patients' median age, 65 years; interquartile range, 56-73 years; 68.1% male) was 4.5%. The existing full model achieved a mean C statistic of 0.78 (95% CI, 0.78-0.78). The use of XGBoost and full range of selected variables achieved a C statistic of 0.82 (95% CI, 0.82-0.82), with an F score of 0.31 (95% CI, 0.30-0.31). XGBoost correctly identified an additional 3.7% of cases identified as high risk who experienced a bleeding event and an overall improvement of 1.0% of cases identified as low risk who did not experience a bleeding event. The data-driven decision threshold helped improve the false discovery rate of the existing techniques. The existing simplified risk score model improved the false discovery rate from more than 90% to 78.7%. Modifying the model and the data decision threshold improved this rate from 78.7% to 73.4%., Conclusions and Relevance: Machine learning techniques improved the prediction of major bleeding after PCI. These techniques may help to better identify patients who would benefit most from strategies to reduce bleeding risk.

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.

Recent research has reported the application of image fusion technologies in medical images in a wide range of aspects, such as in the diagnosis of brain diseases, the detection of glioma and the diagnosis of Alzheimer's disease. In our study, a new fusion method based on the combination of the shuffled frog leaping algorithm (SFLA) and the pulse coupled neural network (PCNN) is proposed for the fusion of SPECT and CT images to improve the quality of fused brain images. First, the intensity-hue-saturation (IHS) of a SPECT and CT image are decomposed using a non-subsampled contourlet transform (NSCT) independently, where both low-frequency and high-frequency images, using NSCT, are obtained. We then used the combined SFLA and PCNN to fuse the high-frequency sub-band images and low-frequency images. The SFLA is considered to optimize the PCNN network parameters. Finally, the fused image was produced from the reversed NSCT and reversed IHS transforms. We evaluated our algorithms against standard deviation (SD), mean gradient (Ḡ), spatial frequency (SF) and information entropy (E) using three different sets of brain images. The experimental results demonstrated the superior performance of the proposed fusion method to enhance both precision and spatial resolution significantly.

Study Objectives: Investigation into sleep and coronary heart disease (CHD) has predominantly been focused on sleep disturbances as a risk factor for developing CHD. Objectively measured and self-reported sleep at a patient level has only been sparsely and not systematically reported. Therefore, we set out to review the literature for studies using objectively measured and self-reported sleep in patients with CHD. The review focuses on patients with acute coronary syndrome (ACS) and stable CHD., Methods: A systematic review performed in four databases adhering to the PRISMA guidelines applying a qualitative synthesis of evidence., Results: Following ACS, we found sleep architecture to be significantly disturbed with changes normalizing over a period of up to 6 months. With increasing severity of CHD, sleep disturbances were more pronounced; however, the modulating effects of sleep-disordered breathing and ejection fraction on sleep in patients with CHD are conflicting. Overall, studies were predominantly cross-sectional in design and of low methodological quality. Polysomnography was the predominant outcome assessment tool and validated self-reported assessment tools were limited., Conclusions: Future investigations in sleep and CHD applying both a longitudinal design and investigating objective and self-reported sleep assessments are warranted., Systematic Review Registration: Registry: PROSPERO, Title: Sleep measures in relation to coronary heart disease: a systematic review, Identifier: CRD42017056377, URL: https://www.crd.york.ac.uk/PROSPERO/display&#95;record.php?RecordID=56377., (© 2019 American Academy of Sleep Medicine.)

Background: The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI., Methods and Findings: We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749-0.754) versus 0.711 (95% CI 0.708-0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615-0.0618) versus 0.0636 (95% CI 0.0634-0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988-1.028) versus 1.036 (95% CI 1.015-1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015-March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors., Conclusions: Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: SSD is supported by the Department of Veterans Affairs. WLS is a consultant for Hugo, a personal health information platform. CIM is a consultant for Cook, Bard, Medtronic, Abbott and Cardinal Health. FPW is supported by the National Science Foundation grant R01DK113191. JSR is the Chief Innovation Officer for the American College of Cardiology. HMK is a recipient of research agreements from Medtronic and from Johnson & Johnson (Janssen), through Yale University, to develop methods of clinical trial data sharing; was the recipient of a grant from the Food and Drug Administration and Medtronic to develop methods for postmarket surveillance of medical devices; works under contract with the Centers for Medicare and Medicaid Services to develop and maintain performance measures; chairs a cardiac scientific advisory board for UnitedHealth; is a member of the Advisory Board for Element Science and the Physician Advisory Board for Aetna; is a participant/participant representative of the IBM Watson Health Life Sciences Board; and is the founder of Hugo, a personal health information platform. JAS is supported by grants from Gilead, Genentech, Lilly, Amorcyte, and EvaHeart and has a patent for the Seattle Angina Questionnaire with royalties paid. He also owns the copyright to the Seattle Angina Questionnaire. He is the PI of an Analytic Center for the American College of Cardiology Foundation and has an equity interest in Health Outcomes Sciences. FAM has a contract (through his primary institution) for his role as Chief Science Officer of the NCDR. BJM is an associate editor for PLOS ONE, which is involved in this special issue. He has a relationship with the American College of Cardiology in selecting and pursuing innovative research based upon their registry data (unrelated to this paper). He has a pending patent application for an EHR-based prediction tool in Yale New Haven Health, as well as two funded studies, one by the DoD-Advanced Research Projects Agency and one with the NSF to support student travel to conferences in the body sensor networks field. American College of Cardiology may incorporate this work, or future iterations, into its registry. No other organisation named above has a competing interest in relation to this work. The other authors report no potential competing interests.

Aim: Multiple sclerosis is a severe brain and/or spinal cord disease. It may lead to a wide range of symptoms. Hence, the early diagnosis and treatment is quite important. Method: This study proposed a 14-layer convolutional neural network, combined with three advanced techniques: batch normalization, dropout, and stochastic pooling. The output of the stochastic pooling was obtained via sampling from a multinomial distribution formed from the activations of each pooling region. In addition, we used data augmentation method to enhance the training set. In total 10 runs were implemented with the hold-out randomly set for each run. Results: The results showed that our 14-layer CNN secured a sensitivity of 98.77 ± 0.35%, a specificity of 98.76 ± 0.58%, and an accuracy of 98.77 ± 0.39%. Conclusion: Our results were compared with CNN using maximum pooling and average pooling. The comparison shows stochastic pooling gives better performance than other two pooling methods. Furthermore, we compared our proposed method with six state-of-the-art approaches, including five traditional artificial intelligence methods and one deep learning method. The comparison shows our method is superior to all other six state-of-the-art approaches.

Application of bacteriophages to eliminate foodborne pathogens in food matrices is an emerging research field. In this study, a promising phage candidate specific for Salmonella strains was screened and its ability to decrease Salmonella counts in some food, such as milk, sausage, and lettuce, was investigated. A total of 58 Salmonella phages were isolated from a wastewater treatment plant, sewage near a river, farm ditch near a lake, and poultry house. Among them, phages LPST10, LPST18, and LPST23 were highly efficient in infecting Salmonella Typhimurium ATCC 14028. In particular, phage LPST10 could infect all the tested Salmonella Typhimurium and Salmonella Enteritidis strains with high efficiency. Bacterial challenge tests revealed that phage LPST10 and its combination with phages LPST18 and LPST23 could consistently inhibit the growth of multiple strains. Phage LPST10 presented a lysis time of about 50 min with a burst size of 101 PFU/CFU, exhibited two distinct phases in the one-step growth curve, and was stable at a pH range of 3-13 that corresponds to the pH of most of the foods (pH 3.5-7.5) and at temperatures between 30 °C and 60 °C. Transmission electron microscopy demonstrated that phage LPST10 belongs to the Siphoviridae family, with an icosahedral head with a diameter of 83.26 nm and tail length and width of approximately 144.89 nm and 10.9 nm, respectively. A significant decrease in the bacterial counts (0.92-5.12 log 10 CFU/sample) and an increase in phage titers (0-2.96 log 10 PFU/sample) were observed in different food matrices tested. These results demonstrated that phage LPST10 is a promising candidate for controlling Salmonella contamination in foods owing to its safety and effectiveness., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Surgical patients are at high risk of developing postoperative cognitive dysfunction (POCD) and postoperative delirium (POD). POCD and POD are associated with increased morbidity and mortality and worsening functional outcomes leading to severe socioeconomic consequences for the patient and the society in general. Magnetic resonance imaging (MRI) offers a unique opportunity to study the anatomy and function of the brain. MRI thus plays an important role in elucidating the neuronal component of POCD and POD. Our aim has been to systematically gather MRI findings that are related to POCD and POD. Systematic searches were conducted in PubMed, EMBASE, and PsycINFO: MRI studies investigating patients with POCD as identified by perioperative cognitive testing or patients with delirium identified postoperatively by the Confusion Assessment Method. A total of ten eligible papers were included with a total of 269 surgical patients, 36 patient controls, and 55 healthy controls who all underwent MRI examination. These studies suggested that reduction of thalamic and hippocampal volumes and reduction of cerebral blood flow may be associated with POCD, while presurgery/preexisting and postoperative white matter pathology may be associated with POD. However, the evidence from these studies is rather weak. Future MRI studies are warranted to verify the current findings.

Background: The aim of the present study was to report the trajectory of heart rate variability (HRV) indices during a low-grade acute inflammation and their associations to biomarkers for infection., Methods: Twelve patients with uncomplicated acute diverticulitis completed this observational study, which composed of 3 sessions of continuous HRV recording from 9 PM to 8 AM during ongoing diverticulitis and at complete remission (baseline). The blood samples were collected at each study session measuring C-reactive protein (CRP) and leukocytes., Results: This study showed that the trajectories of the HRV indices were decreased both in time and frequency domains during acute diverticulitis compared to baseline. In particular, the indices reflecting the balance of sympathetic and parasympathetic activities were affected: standard deviation of normal-to-normal beats (P = .003), low-frequency power (P < .001), and total power (P = .001). These HRV changes indicate alterations in the autonomic nervous system during acute inflammation. All reductions of mean HRV indices had significant (P < .001) correlations to increased CRP correlations to increased CRP levels during diverticulitis suggesting inflammatory involvement in the observed HRV alterations., Conclusion: We found substantial HRV depression in relation to acute uncomplicated diverticulitis, and this was associated with the elevated CRP levels., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Introduction: Most institutions perform percutaneous liver biopsy with a post-biopsy patient observation period lasting up to eight hours, which is resource-demanding. This study aimed to evaluate the safety of liver biopsy performed in a fast-track set-up with an only one-hour post-biopsy observation time., Methods: Patients referred to our institution underwent fast-track ultrasound-guided 18-gauge Tru-Cut liver biopsy procedures. Each single biopsy procedure was followed by a post-procedure observational period of one hour and an additional focused assessment with sonography for trauma before patient discharge. All patients underwent a clinical follow-up programme at revisit in order to register any delayed onset of major complications., Results: Out of 200 completed biopsy procedures, two major complications were registered post biopsy and they were treated appropriately. All patients were safely discharged from our institution. No fatality or long-term complications were found during this study., Conclusion: The fast-track approach reported herein is a feasible option when adequate patient information is given. Besides the obvious, positive effect on patient logistics and departmental throughput, this approach may also reduce diagnostic work-up time and bring financial benefits. Therefore, we encourage the use of this approach in institutions comparable to our own., Funding: not relevant., Trial Registration: not relevant.

Sleep disturbances are a prevalent and disabling problem for patients with cancer. Sleep disturbances are present throughout the cancer trajectory, especially during oncological treatment. Previously sleep disturbances have primarily been quantified with subjective rating scales. Actigraphy is an easy to use, non-invasive method for objective measurement of sleep. We systematically reviewed the literature for studies using actigraphy to measure sleeping habits of patients with cancer, undergoing oncological treatment. Our study furthermore reviewed studies with interventions designed to reduce sleep disturbances in the patient population. 19 studies were included in the final review of which 13 had a descriptive study design and six included some kind of intervention. The studies were primarily performed in patients with breast cancer undergoing chemotherapy. We found that sleep disturbances are prevalent, and persistent in patients with cancer. The sleep disturbances seem to be aggravated by chemotherapy treatment and accumulate as the treatment continues. Sleep disturbances need further attention among clinicians working with patients with cancer., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Introduction: Dictation of scientific articles has been recognised as an efficient method for producing high-quality, first article drafts. However, standardised transcription service by a secretary may not be available for all researchers and voice recognition software (VRS) may therefore be an alternative. The purpose of this study was to evaluate the out-of-the-box accuracy of VRS., Methods: Eleven young researchers without dictation experience dictated the first draft of their own scientific article after thorough preparation according to a pre-defined schedule. The dictate transcribed by VRS was compared with the same dictate transcribed by an experienced research secretary, and the effect of adding words to the vocabulary of the VRS was investigated. The number of errors per hundred words was used as outcome. Furthermore, three experienced researchers assessed the subjective readability using a Likert scale (0-10). Dragon Nuance Premium version 12.5 was used as VRS., Results: The median number of errors per hundred words was 18 (range: 8.5-24.3), which improved when 15,000 words were added to the vocabulary. Subjective readability assessment showed that the texts were understandable with a median score of five (range: 3-9), which was improved with the addition of 5,000 words., Conclusion: The out-of-the-box performance of VRS was acceptable and improved after additional words were added. Further studies are needed to investigate the effect of additional software accuracy training.

Purpose: Estimation of left ventricular ejection fraction (LVEF) with equilibrium 99MTc-HSA equilibrium radionuclide angiography (MUGA) is frequently used for assessing cardiac function. The purpose of this study was to compare intra- and interobserver variation between three different gamma cameras., Materials and Methods: Eighty-two patients, scanned in the same sequential order on the three cameras. Each acquisition was analyzed twice by two technologists. Inter- and intraobserver variations were calculated as the coefficient of variation and the 95% confidence interval for limits of agreement between each sequence of analyses for each of the three cameras., Results: The lowest intraobserver variations in LVEF for the two NaI-detector cameras were 3.1% (-4.0% to 3.5%) for the planar and 3.4% (-4.2% to 4.5%) for SPECT (P ≤ 0.001-0.019), the highest result for the CZT SPECT camera was 2.6% (-2.9% to 3.1%). Similarly, interobserver variation was 4.8% (-4.8% to 6.4%) and 4.9% (-5.4% to 7.5%), respectively, for each of the NaI-detector cameras and 3.3% (-3.4% to 4.3%) for the CZT SPECT camera (P ≤ 0.001-0.008)., Discussion: The CZT detector camera was superior to both NaI detector cameras regarding intra- and interobserver variation. The CZT SPECT camera may identify changes in LVEF with greater certainty than its NaI detector-equipped counterparts.

Objective: To clarify the incidence and survival of colorectal carcinoma (CRC) patients among permanent residents in Yangpu district of Shanghai, from 2002 to 2012., Methods: Data of CRC patients in permanent residents of Yangpu district were collected from the database of the registration and management system in Shanghai city. Temporal trend in the incidence of CRC was analyzed by using Annual Percent Change (APC) model. Kaplan-Meier analysis with Log-rank testing was employed to estimate the survival. Incidence and mortality rates were standardized on age composition of standard population from 2000 nationwide census., Results: A total of 5 881 CRC cases were diagnosed from 2002 to 2012 with crude incidence as 50.60/10(5) and standardized one as 31.21/10(5). The crude incidence rates of colon cancer and rectal cancer were 31.09/10(5) and 18.27/10(5), respectively and the standardized rates were 14.49/10(5) and 8.83/10(5), respectively. The incidence rates of colon cancer were not significantly different between different gender. However, the incidence of rectal cancer in males was significantly higher than in females (P < 0.001). The incidence rates of CRC significantly increased in the age group older than 50 years. However, the standardized incidence rate did not change significantly(APC = 0.39). A total of 3 735 cases died of CRC in this period. The annual crude mortality of CRC was 31.35/10(5) with the standardized rate as 13.72/10(5). The mortality kept increasing significantly in the population older than 60 years. Mortality of rectal cancer was significantly higher in males than in females (P < 0.001). The 5-year survival rates in both colon cancer and rectal cancer patients were 48.88% and 54.16%, respectively. Survivals were significantly higher in those who had received surgical treatments than in those without surgical treatments (colon cancer: 58.10% vs. 37.22%, P < 0.001; rectal cancer:66.18% vs. 39.37%, P < 0.001)., Conclusion: Age seemed to have contributed to an increased morbidity and mortality of CRC in Yangpu district of Shanghai. The mortality of CRC appeared higher than the incidence. Surgical treatment could significantly prolong the survival of CRC patients.

Single Particle Reconstruction (SPR) for Cryogenic Electron Microscopy (cryo-EM) aligns and averages the images extracted from micrographs to improve the Signal-to-Noise ratio (SNR). Outliers compromise the fidelity of the averaging. We propose a robust cross-correlation-like w-estimator for combating the effect of outliers on the average images in cryo-EM. The estimator accounts for the natural variation of signal contrast among the images and eliminates the need for a threshold for outlier rejection. We show that the influence function of our estimator is asymptotically bounded. Evaluations of the estimator on simulated and real cryo-EM images show good performance in the presence of outliers.