First Measurement of η Meson Production in Neutrino Interactions on Argon with MicroBooNE

We present a measurement of η production from neutrino interactions on argon with the MicroBooNE detector. The modeling of resonant neutrino interactions on argon is a critical aspect of the neutrino oscillation physics program being carried out by the DUNE and Short Baseline Neutrino programs. η production in neutrino interactions provides a powerful new probe of resonant interactions, complementary to pion channels, and is particularly suited to the study of higher-order resonances beyond the Δ(1232). We measure a flux-integrated cross section for neutrino-induced η production on argon of 3.22±0.84(stat)±0.86(syst) 10^{-41}  cm^{2}/nucleon. By demonstrating the successful reconstruction of the two photons resulting from η production, this analysis enables a novel calibration technique for electromagnetic showers in GeV accelerator neutrino experiments.

Tissue informative cell-free DNA methylation sites in amyotrophic lateral sclerosis

Cell-free DNA (cfDNA) is increasingly recognized as a promising biomarker candidate for disease monitoring. However, its utility in neurodegenerative diseases, like amyotrophic lateral sclerosis (ALS), remains underexplored. Existing biomarker discovery approaches are tailored to a specific disease context or are too expensive to be clinically practical. Here, we address these challenges through a new approach combining advances in molecular and computational technologies. First, we develop statistical tools to select tissue-informative DNA methylation sites relevant to a disease process of interest. We then employ a capture protocol to select these sites and perform targeted methylation sequencing. Multi-modal information about the DNA methylation patterns are then utilized in machine learning algorithms trained to predict disease status and disease progression. We applied our method to two independent cohorts of ALS patients and controls (n=192). Overall, we found that the targeted sites accurately predicted ALS status and replicated between cohorts. Additionally, we identified epigenetic features associated with ALS phenotypes, including disease severity. These findings highlight the potential of cfDNA as a non-invasive biomarker for ALS.

MRI-based bone marrow radiomics for predicting cytogenetic abnormalities in multiple myeloma

AIM

To develop a radiomics signature applied to magnetic resonance imaging (MRI)-images to predict cytogenetic abnormalities in multiple myeloma (MM).

MATERIALS AND METHODS

Patients with newly diagnosed MM were enrolled retrospectively from March 2019 to September 2022. They were categorised into the high-risk cytogenetics (HRC) group and standard-risk cytogenetics (SRC) group. The patients were allocated randomly at a ratio of 7:3 into training and validation cohorts. Volumes of interest (VOI) was drawn manually on fat suppression T2-weighted imaging (FS-T2WI) and copied to the same location of the T1-weighted imaging (T1WI) sequence. Radiomics features were extracted from two sequences and selected by reproducibility and redundant analysis. The least absolute shrinkage selection operation (LASSO) algorithm was applied to build the radiomics signatures. The performance of the radiomics signatures to distinguish HRC with SRC was evaluated by ROC curves. The area under the curve (AUC), specificity, and sensitivity were also calculated.

RESULTS

A total of 105 MM patients were enrolled in this study. The four and 11 most significant and relevant features were selected separately from T1WI and FS-T2WI sequences to build the radiomics signatures based on the training cohort. Compared to the T1WI sequence, the radiomics signature based on the FS-T2WI sequence achieved better performance with AUCs of 0.896 and 0.729 in the training and validation cohorts respectively. A sensitivity of 0.833, specificity of 0.667, and Youden index of 0.500 were achieved for the FS-T2WI radiomics signature in the validation cohort.

CONCLUSIONS

The radiomics signature based on MRI provides a non-invasive and convenient tool to predict cytogenetic abnormalities in MM patients.

Ginsenoside Rb1 prevents age-related endothelial senescence by modulating SIRT1/caveolin-1/enos signaling pathway

Background

Advancing age is one of the independent risk factors for cardiovascular disorders. The Compendium of Materia Medica, a classic book on traditional Chinese medicine, states that ginseng "harmonizes the five internal organs, calming the spirit and prolonging the years of life." Considered one of the primary bioactive compounds derived from Panax ginseng, ginsenoside Rb1 (g-Rb1) has been scientifically suggested to possess anti-senescence efficacy. More research is needed to explore the vascular pharmacological activity and potential clinical application value of g-Rb1.

Aims of the study

Our previous study demonstrated that g-Rb1 could mitigate cellular senescence via the SIRT1/eNOS pathway. This study was performed to explore the exact mechanisms by which g-Rb1 modulates the SIRT1/eNOS pathway.

Materials and methods

We used human primary umbilical vein endothelial cells (HUVECs) to establish a replicative ageing model. Real-time (RT‒PCR), western blotting, small interfering RNA (siRNA), and immunoprecipitation were conducted to detect the effect of g-Rb1 on the SIRT1/caveolin-1/eNOS axis.

Results

G-Rb1 increased NO production and alleviated replicative senescence of HUVECs. The application of g-Rb1 elevated the mRNA and protein abundance of both SIRT1 and eNOS while concomitantly suppressing the expression of caveolin-1. Inhibition of SIRT1 and eNOS by siRNAs suppressed the anti-senescence function of g-Rb1, while caveolin-1 siRNA could enhance it. G-Rb1 decreased the acetylation level of caveolin-1 and increased NO production, which was suppressed by SIRT1 siRNA. Both g-Rb1 and caveolin-1 siRNA could reduce the acetylation level of eNOS and increase NO production.

Conclusion

G-Rb1 prevents age-related endothelial senescence by modulating the SIRT1/caveolin-1/eNOS signaling pathway.

Cholesterol-induced HRD1 reduction accelerates vascular smooth muscle cell senescence via stimulation of endoplasmic reticulum stress-induced reactive oxygen species

Senescence of vascular smooth muscle cells (VSMCs) is a key contributor to plaque vulnerability in atherosclerosis (AS), which is affected by endoplasmic reticulum (ER) stress and reactive oxygen species (ROS) production. However, the crosstalk between ER stress and ROS production in the pathogenesis of VSMC senescence remains to be elucidated. ER-associated degradation (ERAD) is a complex process that clears unfolded or misfolded proteins to maintain ER homeostasis. HRD1 is the major E3 ligase in mammalian ERAD machineries that catalyzes ubiquitin conjugation to the unfolded or misfolded proteins for degradation. Our results showed that HRD1 protein levels were reduced in human AS plaques and aortic roots from ApoE-/- mice fed with high-fat diet (HFD), along with the increased ER stress response. Exposure to cholesterol in VSMCs activated inflammatory signaling and induced senescence, while reduced HRD1 protein expression. CRISPR Cas9-mediated HRD1 knockout (KO) exacerbated cholesterol- and thapsigargin-induced cell senescence. Inhibiting ER stress with 4-PBA (4-Phenylbutyric acid) partially reversed the ROS production and cell senescence induced by HRD1 deficiency in VSMCs, suggesting that ER stress alone could be sufficient to induce ROS production and senescence in VSMCs. Besides, HRD1 deficiency led to mitochondrial dysfunction, and reducing ROS production from impaired mitochondria partly reversed HRD1 deficiency-induced cell senescence. Finally, we showed that the overexpression of HDR1 reversed cholesterol-induced ER stress, ROS production, and cellular senescence in VSMCs. Our findings indicate that HRD1 protects against senescence by maintaining ER homeostasis and mitochondrial functionality. Thus, targeting HRD1 function may help to mitigate VSMC senescence and prevent vascular aging related diseases. TRIAL REGISTRATION: A real-world study based on the discussion of primary and secondary prevention strategies for coronary heart disease, URL:https://www.clinicaltrials.gov, the trial registration number is [2022]-02-121-01.

Search for Heavy Neutral Leptons in Electron-Positron and Neutral-Pion Final States with the MicroBooNE Detector

We present the first search for heavy neutral leptons (HNLs) decaying into νe^{+}e^{-} or νπ^{0} final states in a liquid-argon time projection chamber using data collected with the MicroBooNE detector. The data were recorded synchronously with the NuMI neutrino beam from Fermilab's main injector corresponding to a total exposure of 7.01×10^{20} protons on target. We set upper limits at the 90% confidence level on the mixing parameter |U_{μ4}|^{2} in the mass ranges 10≤m_{HNL}≤150  MeV for the νe^{+}e^{-} channel and 150≤m_{HNL}≤245  MeV for the νπ^{0} channel, assuming |U_{e4}|^{2}=|U_{τ4}|^{2}=0. These limits represent the most stringent constraints in the mass range 35 Collapse

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Affiliation(s)

P Abratenko Tufts University, Medford, Massachusetts 02155, USA
O Alterkait Tufts University, Medford, Massachusetts 02155, USA
D Andrade Aldana Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
L Arellano The University of Manchester, Manchester M13 9PL, United Kingdom
J Asaadi University of Texas, Arlington, Texas 76019, USA
A Ashkenazi Tel Aviv University, Tel Aviv, Israel, 69978
S Balasubramanian Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
B Baller Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
G Barr University of Oxford, Oxford OX1 3RH, United Kingdom
D Barrow University of Oxford, Oxford OX1 3RH, United Kingdom
J Barrow Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA Tel Aviv University, Tel Aviv, Israel, 69978
V Basque Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
O Benevides Rodrigues Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
S Berkman Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA Michigan State University, East Lansing, Michigan 48824, USA
A Bhanderi The University of Manchester, Manchester M13 9PL, United Kingdom
A Bhat University of Chicago, Chicago, Illinois 60637, USA
M Bhattacharya Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
M Bishai Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
A Blake Lancaster University, Lancaster LA1 4YW, United Kingdom
B Bogart University of Michigan, Ann Arbor, Michigan 48109, USA
T Bolton Kansas State University (KSU), Manhattan, Kansas 66506, USA
J Y Book Harvard University, Cambridge, Massachusetts 02138, USA
M B Brunetti University of Warwick, Coventry CV4 7AL, United Kingdom
L Camilleri Columbia University, New York, New York 10027, USA
Y Cao The University of Manchester, Manchester M13 9PL, United Kingdom
D Caratelli University of California, Santa Barbara, California 93106, USA
F Cavanna Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
G Cerati Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
A Chappell University of Warwick, Coventry CV4 7AL, United Kingdom
Y Chen SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
J M Conrad Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
M Convery SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
L Cooper-Troendle University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
J I Crespo-Anadón Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid E-28040, Spain
R Cross University of Warwick, Coventry CV4 7AL, United Kingdom
M Del Tutto Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
S R Dennis University of Cambridge, Cambridge CB3 0HE, United Kingdom
P Detje University of Cambridge, Cambridge CB3 0HE, United Kingdom
A Devitt Lancaster University, Lancaster LA1 4YW, United Kingdom
R Diurba Universität Bern, Bern CH-3012, Switzerland
Z Djurcic Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
R Dorrill Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
K Duffy University of Oxford, Oxford OX1 3RH, United Kingdom
S Dytman University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
B Eberly University of Southern Maine, Portland, Maine 04104, USA
P Englezos Rutgers University, Piscataway, New Jersey 08854, USA
A Ereditato University of Chicago, Chicago, Illinois 60637, USA Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
J J Evans The University of Manchester, Manchester M13 9PL, United Kingdom
R Fine Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
O G Finnerud The University of Manchester, Manchester M13 9PL, United Kingdom
W Foreman Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
B T Fleming University of Chicago, Chicago, Illinois 60637, USA
D Franco University of Chicago, Chicago, Illinois 60637, USA
A P Furmanski University of Minnesota, Minneapolis, Minnesota 55455, USA
F Gao University of California, Santa Barbara, California 93106, USA
D Garcia-Gamez Universidad de Granada, Granada E-18071, Spain
S Gardiner Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
G Ge Columbia University, New York, New York 10027, USA
S Gollapinni Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
E Gramellini The University of Manchester, Manchester M13 9PL, United Kingdom
P Green University of Oxford, Oxford OX1 3RH, United Kingdom
H Greenlee Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
L Gu Lancaster University, Lancaster LA1 4YW, United Kingdom
W Gu Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
R Guenette The University of Manchester, Manchester M13 9PL, United Kingdom
P Guzowski The University of Manchester, Manchester M13 9PL, United Kingdom
L Hagaman University of Chicago, Chicago, Illinois 60637, USA
O Hen Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
C Hilgenberg University of Minnesota, Minneapolis, Minnesota 55455, USA
G A Horton-Smith Kansas State University (KSU), Manhattan, Kansas 66506, USA
Z Imani Tufts University, Medford, Massachusetts 02155, USA
B Irwin University of Minnesota, Minneapolis, Minnesota 55455, USA
M Ismail University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
C James Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
X Ji Nankai University, Nankai District, Tianjin 300071, China
J H Jo Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
R A Johnson University of Cincinnati, Cincinnati, Ohio 45221, USA
Y-J Jwa Columbia University, New York, New York 10027, USA
D Kalra Columbia University, New York, New York 10027, USA
N Kamp Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
G Karagiorgi Columbia University, New York, New York 10027, USA
W Ketchum Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
M Kirby Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
T Kobilarcik Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
I Kreslo Universität Bern, Bern CH-3012, Switzerland
M B Leibovitch University of California, Santa Barbara, California 93106, USA
I Lepetic Rutgers University, Piscataway, New Jersey 08854, USA
J-Y Li University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
K Li Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
Y Li Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
K Lin Rutgers University, Piscataway, New Jersey 08854, USA
B R Littlejohn Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
H Liu Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
W C Louis Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
X Luo University of California, Santa Barbara, California 93106, USA
C Mariani Center for Neutrino Physics, Virginia Tech, Blacksburg, Viriginia 24061, USA
D Marsden The University of Manchester, Manchester M13 9PL, United Kingdom
J Marshall University of Warwick, Coventry CV4 7AL, United Kingdom
N Martinez Kansas State University (KSU), Manhattan, Kansas 66506, USA
D A Martinez Caicedo South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA
S Martynenko Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
A Mastbaum Rutgers University, Piscataway, New Jersey 08854, USA
I Mawby University of Warwick, Coventry CV4 7AL, United Kingdom
N McConkey University College London, London WC1E 6BT, United Kingdom
V Meddage Kansas State University (KSU), Manhattan, Kansas 66506, USA
J Micallef Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA Tufts University, Medford, Massachusetts 02155, USA
K Miller University of Chicago, Chicago, Illinois 60637, USA
A Mogan Colorado State University, Fort Collins, Colorado 80523, USA
T Mohayai Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA Indiana University, Bloomington, Indiana 47405, USA
M Mooney Colorado State University, Fort Collins, Colorado 80523, USA
A F Moor University of Cambridge, Cambridge CB3 0HE, United Kingdom
C D Moore Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
L Mora Lepin The University of Manchester, Manchester M13 9PL, United Kingdom
M M Moudgalya The University of Manchester, Manchester M13 9PL, United Kingdom
S Mulleriababu Universität Bern, Bern CH-3012, Switzerland
D Naples University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
A Navrer-Agasson The University of Manchester, Manchester M13 9PL, United Kingdom
N Nayak Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
M Nebot-Guinot University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
J Nowak Lancaster University, Lancaster LA1 4YW, United Kingdom
N Oza Columbia University, New York, New York 10027, USA
O Palamara Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
N Pallat University of Minnesota, Minneapolis, Minnesota 55455, USA
V Paolone University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
A Papadopoulou Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
V Papavassiliou New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
H B Parkinson University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
S F Pate New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
N Patel Lancaster University, Lancaster LA1 4YW, United Kingdom
Z Pavlovic Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
E Piasetzky Tel Aviv University, Tel Aviv, Israel, 69978
I Pophale Lancaster University, Lancaster LA1 4YW, United Kingdom
X Qian Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
J L Raaf Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
V Radeka Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
A Rafique Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
M Reggiani-Guzzo University of Edinburgh, Edinburgh EH9 3FD, United Kingdom The University of Manchester, Manchester M13 9PL, United Kingdom
L Ren New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
L Rochester SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
J Rodriguez Rondon South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA
M Rosenberg Tufts University, Medford, Massachusetts 02155, USA
M Ross-Lonergan Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
C Rudolf von Rohr Universität Bern, Bern CH-3012, Switzerland
I Safa Columbia University, New York, New York 10027, USA
G Scanavini Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
D W Schmitz University of Chicago, Chicago, Illinois 60637, USA
A Schukraft Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
W Seligman Columbia University, New York, New York 10027, USA
M H Shaevitz Columbia University, New York, New York 10027, USA
R Sharankova Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
J Shi University of Cambridge, Cambridge CB3 0HE, United Kingdom
E L Snider Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
M Soderberg Syracuse University, Syracuse, New York 13244, USA
S Söldner-Rembold The University of Manchester, Manchester M13 9PL, United Kingdom
J Spitz University of Michigan, Ann Arbor, Michigan 48109, USA
M Stancari Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
J St John Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
T Strauss Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
A M Szelc University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
W Tang University of Tennessee, Knoxville, Tennessee 37996, USA
N Taniuchi University of Cambridge, Cambridge CB3 0HE, United Kingdom
K Terao SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
C Thorpe Lancaster University, Lancaster LA1 4YW, United Kingdom The University of Manchester, Manchester M13 9PL, United Kingdom
D Torbunov Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
D Totani University of California, Santa Barbara, California 93106, USA
M Toups Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
Y-T Tsai SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
J Tyler Kansas State University (KSU), Manhattan, Kansas 66506, USA
M A Uchida University of Cambridge, Cambridge CB3 0HE, United Kingdom
T Usher SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
B Viren Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
M Weber Universität Bern, Bern CH-3012, Switzerland
H Wei Louisiana State University, Baton Rouge, Louisiana 70803, USA
A J White University of Chicago, Chicago, Illinois 60637, USA
S Wolbers Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
T Wongjirad Tufts University, Medford, Massachusetts 02155, USA
M Wospakrik Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
K Wresilo University of Cambridge, Cambridge CB3 0HE, United Kingdom
W Wu University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
E Yandel University of California, Santa Barbara, California 93106, USA
T Yang Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
L E Yates Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
H W Yu Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
G P Zeller Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
J Zennamo Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
C Zhang Brookhaven National Laboratory (BNL), Upton, New York 11973, USA

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HRD1 reduction promotes cholesterol-induced vascular smooth muscle cell phenotypic change via endoplasmic reticulum stress

Phenotypic change of vascular smooth muscle cells (VSMCs) is the main contributor of vascular pathological remodeling in atherosclerosis. The endoplasmic reticulum (ER) is critical for maintaining VSMC function through elimination of misfolded proteins that impair VSMC cellular function. ER-associated degradation (ERAD) is an ER-mediated process that controls protein quality by clearing misfolded proteins. One of the critical regulators of ERAD is HRD1, which also plays a vital role in lipid metabolism. However, the function of HRD1 in VSMCs of atherosclerotic vessels remains poorly understood. The level of HRD1 expression was analyzed in aortic tissues of mice fed with a high-fat diet (HFD). The H&E and EVG (VERHOEFF'S VAN GIESON) staining were used to demonstrate pathological vascular changes. IF (immunofluorescence) and WB (western blot) were used to explore the signaling pathways in vivo and in vitro. The wound closure and transwell assays were also used to test the migration rate of VSMCs. CRISPR gene editing and transcriptomic analysis were applied in vitro to explore the cellular mechanism. Our data showed significant reduction of HRD1 in aortic tissues of mice under HFD feeding. VSMC phenotypic change and HRD1 downregulation were detected by cholesterol supplement. Transcriptomic and further analysis of HRD1-KO VSMCs showed that HRD1 deficiency induced the expression of genes related to ER stress response, proliferation and migration, but reduced the contractile-related genes in VSMCs. HRD1 deficiency also exacerbated the proliferation, migration and ROS production of VSMCs induced by cholesterol, which promoted the VSMC dedifferentiation. Our results showed that HRD1 played an essential role in the contractile homeostasis of VSMCs by negatively regulating ER stress response. Thus, HRD1 in VSMCs could serve as a potential therapeutic target in metabolic disorder-induced vascular remodeling.

SBC (Sanhuang Xiexin Tang combined with Baihu Tang plus Cangzhu) alleviates NAFLD by enhancing mitochondrial biogenesis and ameliorating inflammation in obese patients and mice

In the context of non-alcoholic fatty liver disease (NAFLD), characterized by dysregulated lipid metabolism in hepatocytes, the quest for safe and effective therapeutics targeting lipid metabolism has gained paramount importance. Sanhuang Xiexin Tang (SXT) and Baihu Tang (BHT) have emerged as prominent candidates for treating metabolic disorders. SXT combined with BHT plus Cangzhu (SBC) has been used clinically for Weihuochisheng obese patients. This retrospective analysis focused on assessing the anti-obesity effects of SBC in Weihuochisheng obese patients. We observed significant reductions in body weight and hepatic lipid content among obese patients following SBC treatment. To gain further insights, we investigated the effects and underlying mechanisms of SBC in HFD-fed mice. The results demonstrated that SBC treatment mitigated body weight gain and hepatic lipid accumulation in HFD-fed mice. Pharmacological network analysis suggested that SBC may affect lipid metabolism, mitochondria, inflammation, and apoptosis-a hypothesis supported by the hepatic transcriptomic analysis in HFD-fed mice treated with SBC. Notably, SBC treatment was associated with enhanced hepatic mitochondrial biogenesis and the inhibition of the c-Jun N-terminal kinase (JNK)/nuclear factor-kappa B (NF-κB) and extracellular signal-regulated kinase (ERK)/NF-κB pathways. In conclusion, SBC treatment alleviates NAFLD in both obese patients and mouse models by improving lipid metabolism, potentially through enhancing mitochondrial biogenesis. These effects, in turn, ameliorate inflammation in hepatocytes.

Discriminating minimal residual disease status in multiple myeloma based on MRI: utility of radiomics and comparison of machine-learning methods

AIM

To explore the possibility of discriminating minimal residual disease (MRD) status in multiple myeloma (MM) based on magnetic resonance imaging (MRI) and identify optimal machine-learning methods to optimise the clinical treatment regimen.

MATERIALS AND METHODS

A total of 83 patients were analysed retrospectively. They were divided randomly into training and validation cohorts. The regions of interest were segmented and radiomics features were extracted and analysed on two sequences, including T1-weighted imaging (WI) and fat saturated (FS)-T2WI, and then radiomics models were built in the training cohort and evaluated in the validation cohort. Clinical characteristics were calculated to build a traditional model. A combined model was also built using the clinical characteristics and radiomics features. Classification accuracy was assessed using area under the curve (AUC) and F1 score.

RESULTS

In the training cohort, only the bone marrow (BM) infiltrate ratio (p=0.005) was retained after univariate and multivariable logistic regression analysis. In T1WI, the linear support vector machine (SVM) achieved the best performance compared to other classifiers, with AUCs of 0.811 and 0.708 and F1 scores of 0.792 and 0.696 in the training and validation cohorts, respectively. Similarly, in FS-T2WI sequence, linear SVM achieved the best performance with AUCs of 0.833 and 0.800 and F1 score of 0.833 and 0.800. The combined model constructed by the FS-T2WI-linear SVM and BM infiltrate ratio outperformed the traditional model (p=0.050 and 0.012, Delong test), but showed no significant difference compared with the radiomics model (p=0.798 and 0.855).

CONCLUSION

The linear SVM-based machine-learning method can offer a non-invasive tool for discriminating MRD status in MM.

First Double-Differential Measurement of Kinematic Imbalance in Neutrino Interactions with the MicroBooNE Detector

We report the first measurement of flux-integrated double-differential quasielasticlike neutrino-argon cross sections, which have been made using the Booster Neutrino Beam and the MicroBooNE detector at Fermi National Accelerator Laboratory. The data are presented as a function of kinematic imbalance variables which are sensitive to nuclear ground-state distributions and hadronic reinteraction processes. We find that the measured cross sections in different phase-space regions are sensitive to different nuclear effects. Therefore, they enable the impact of specific nuclear effects on the neutrino-nucleus interaction to be isolated more completely than was possible using previous single-differential cross section measurements. Our results provide precision data to help test and improve neutrino-nucleus interaction models. They further support ongoing neutrino-oscillation studies by establishing phase-space regions where precise reaction modeling has already been achieved.

Final Measurement of the ^{235}U Antineutrino Energy Spectrum with the PROSPECT-I Detector at HFIR

This Letter reports one of the most precise measurements to date of the antineutrino spectrum from a purely ^{235}U-fueled reactor, made with the final dataset from the PROSPECT-I detector at the High Flux Isotope Reactor. By extracting information from previously unused detector segments, this analysis effectively doubles the statistics of the previous PROSPECT measurement. The reconstructed energy spectrum is unfolded into antineutrino energy and compared with both the Huber-Mueller model and a spectrum from a commercial reactor burning multiple fuel isotopes. A local excess over the model is observed in the 5-7 MeV energy region. Comparison of the PROSPECT results with those from commercial reactors provides new constraints on the origin of this excess, disfavoring at 2.0 and 3.7 standard deviations the hypotheses that antineutrinos from ^{235}U are solely responsible and noncontributors to the excess observed at commercial reactors, respectively.

Alirocumab effect on preventing periprocedural ischaemic events in coronary heart disease patients undergoing coronary stenting (APPEASE trial): study protocol of a multicentre, open-label, randomised controlled trial

INTRODUCTION

Percutaneous coronary intervention (PCI)-related myocardial infarction (type 4a MI) and major periprocedural myocardial injury have been demonstrated leading to poor prognosis of patients with coronary heart disease (CHD) undergoing elective PCI and still remain high occurrence even after the therapy of dual antiplatelet agents and statins. Proprotein convertase subtilisin/kexin type 9 inhibitor alirocumab has been shown to be effectively in reducing the risk of acute MI (AMI). However, the effect of alirocumab on preventing PCI-related MI or major periprocedural myocardial injury in patients with CHD undergoing elective PCI remains uncertain.

METHODS AND ANALYSIS

Alirocumab effect on Preventing Periprocedural ischaemic Events in coronary heart diseAse patients undergoing coronary StEnting trial is a multicentre, open-label, randomised controlled trial aiming to determine whether alirocumab could reduce the incidence of type 4a MI or major periprocedural myocardial injury in patients with CHD undergoing elective PCI. In total, 422 non-AMI CHD patients planned to undergo elective PCI will be randomly assigned to receive standard pharmacotherapy of CHD (control group) or additional use of subcutaneous alirocumab 75 mg 1 day before procedure (alirocumab group). The primary outcome is type 4a MI or major periprocedural myocardial injury defined as high-sensitivity cardiac troponin elevating above 5×99 th percentile upper reference limit in 48 hours after PCI. Patients will continue receiving standard pharmacotherapy or additional biweekly subcutaneous alirocumab 75 mg for 3 months according to the initial randomisation group. We will follow up for 3 months and record all the major adverse cardiovascular events (MACEs). Incidence of PCI-related MI or major periprocedural myocardial injury, and MACE in 3 months after PCI will be compared between control group and alirocumab group.

ETHICS AND DISSEMINATION

Ethics approval has been obtained from the Medical Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University with approval number: (2022)02-140-01. The results of this study will be reported through peer-reviewed journals and conference presentations.

TRIAL REGISTRATION NUMBER

ChiCTR2200063191.

Differential diagnosis of secondary hypertension based on deep learning

Secondary hypertension is associated with higher risks of target organ damage and cardiovascular and cerebrovascular disease events. Early aetiology identification can eliminate aetiologies and control blood pressure. However, inexperienced doctors often fail to diagnose secondary hypertension, and comprehensively screening for all causes of high blood pressure increases health care costs. To date, deep learning has rarely been involved in the differential diagnosis of secondary hypertension. Relevant machine learning methods cannot combine textual information such as chief complaints with numerical information such as the laboratory examination results in electronic health records (EHRs), and the use of all features increases health care costs. To reduce redundant examinations and accurately identify secondary hypertension, we propose a two-stage framework that follows clinical procedures. The framework carries out an initial diagnosis process in the first stage, on which basis patients are recommended for disease-related examinations, followed by differential diagnoses of different diseases based on the different characteristics observed in the second stage. We convert the numerical examination results into descriptive sentences, thus blending textual and numerical characteristics. Medical guidelines are introduced through label embedding and attention mechanisms to obtain interactive features. Our model was trained and evaluated using a cross-sectional dataset containing 11,961 patients with hypertension from January 2013 to December 2019. The F1 scores of our model were 0.912, 0.921, 0.869 and 0.894 for primary aldosteronism, thyroid disease, nephritis and nephrotic syndrome and chronic kidney disease, respectively, which are four kinds of secondary hypertension with high incidence rates. The experimental results show that our model can powerfully use the textual and numerical data contained in EHRs to provide effective decision support for the differential diagnosis of secondary hypertension.

First Measurement of Quasielastic Λ Baryon Production in Muon Antineutrino Interactions in the MicroBooNE Detector

We present the first measurement of the cross section of Cabibbo-suppressed Λ baryon production, using data collected with the MicroBooNE detector when exposed to the neutrinos from the main injector beam at the Fermi National Accelerator Laboratory. The data analyzed correspond to 2.2×10^{20} protons on target running in neutrino mode, and 4.9×10^{20} protons on target running in anti-neutrino mode. An automated selection is combined with hand scanning, with the former identifying five candidate Λ production events when the signal was unblinded, consistent with the GENIE prediction of 5.3±1.1 events. Several scanners were employed, selecting between three and five events, compared with a prediction from a blinded Monte Carlo simulation study of 3.7±1.0 events. Restricting the phase space to only include Λ baryons that decay above MicroBooNE's detection thresholds, we obtain a flux averaged cross section of 2.0_{-1.7}^{+2.2}×10^{-40}  cm^{2}/Ar, where statistical and systematic uncertainties are combined.

ICARUS at the Fermilab Short-Baseline Neutrino program: initial operation

The ICARUS collaboration employed the 760-ton T600 detector in a successful 3-year physics run at the underground LNGS laboratory, performing a sensitive search for LSND-like anomalous ν e appearance in the CERN Neutrino to Gran Sasso beam, which contributed to the constraints on the allowed neutrino oscillation parameters to a narrow region around 1 eV2 . After a significant overhaul at CERN, the T600 detector has been installed at Fermilab. In 2020 the cryogenic commissioning began with detector cool down, liquid argon filling and recirculation. ICARUS then started its operations collecting the first neutrino events from the booster neutrino beam (BNB) and the Neutrinos at the Main Injector (NuMI) beam off-axis, which were used to test the ICARUS event selection, reconstruction and analysis algorithms. ICARUS successfully completed its commissioning phase in June 2022. The first goal of the ICARUS data taking will be a study to either confirm or refute the claim by Neutrino-4 short-baseline reactor experiment. ICARUS will also perform measurement of neutrino cross sections with the NuMI beam and several Beyond Standard Model searches. After the first year of operations, ICARUS will search for evidence of sterile neutrinos jointly with the Short-Baseline Near Detector, within the Short-Baseline Neutrino program. In this paper, the main activities carried out during the overhauling and installation phases are highlighted. Preliminary technical results from the ICARUS commissioning data with the BNB and NuMI beams are presented both in terms of performance of all ICARUS subsystems and of capability to select and reconstruct neutrino events.

Improved Measurement of the Evolution of the Reactor Antineutrino Flux and Spectrum at Daya Bay

Reactor neutrino experiments play a crucial role in advancing our knowledge of neutrinos. In this Letter, the evolution of the flux and spectrum as a function of the reactor isotopic content is reported in terms of the inverse-beta-decay yield at Daya Bay with 1958 days of data and improved systematic uncertainties. These measurements are compared with two signature model predictions: the Huber-Mueller model based on the conversion method and the SM2018 model based on the summation method. The measured average flux and spectrum, as well as the flux evolution with the ^{239}Pu isotopic fraction, are inconsistent with the predictions of the Huber-Mueller model. In contrast, the SM2018 model is shown to agree with the average flux and its evolution but fails to describe the energy spectrum. Altering the predicted inverse-beta-decay spectrum from ^{239}Pu fission does not improve the agreement with the measurement for either model. The models can be brought into better agreement with the measurements if either the predicted spectrum due to ^{235}U fission is changed or the predicted ^{235}U, ^{238}U, ^{239}Pu, and ^{241}Pu spectra are changed in equal measure.

Resveratrol intervention attenuates chylomicron secretion via repressing intestinal FXR-induced expression of scavenger receptor SR-B1

Two common features of dietary polyphenols have hampered our mechanistic understanding of their beneficial effects for decades: targeting multiple organs and extremely low bioavailability. We show here that resveratrol intervention (REV-I) in high-fat diet (HFD)-challenged male mice inhibits chylomicron secretion, associated with reduced expression of jejunal but not hepatic scavenger receptor class B type 1 (SR-B1). Intestinal mucosa-specific SR-B1^-/- mice on HFD-challenge exhibit improved lipid homeostasis but show virtually no further response to REV-I. SR-B1 expression in Caco-2 cells cannot be repressed by pure resveratrol compound while fecal-microbiota transplantation from mice on REV-I suppresses jejunal SR-B1 in recipient mice. REV-I reduces fecal levels of bile acids and activity of fecal bile-salt hydrolase. In Caco-2 cells, chenodeoxycholic acid treatment stimulates both FXR and SR-B1. We conclude that gut microbiome is the primary target of REV-I, and REV-I improves lipid homeostasis at least partially via attenuating FXR-stimulated gut SR-B1 elevation.

Modifiable predictors of type 2 diabetes mellitus and roles of insulin resistance and β-cell function over a 6-year study and 30-year follow-up

PURPOSE

This study aimed to examine the modifiable predictors of T2DM and the roles of insulin resistance (IR) and β-cell function over a 6-year study and 30-year follow-up.

METHODS

A total of 462 non-diabetic participants, 282 with impaired glucose tolerance (IGT), and 180 with normal glucose tolerance (NGT) were enrolled in this analysis. The Matsuda IR index and area under the curve of insulin-to-glucose ratio (AUCI/G-R) were used as IR and β-cell function indices in the analysis.

RESULTS

In all participants, multivariable analysis showed that BMI, glucose status, Matsuda IR index and systolic blood pressure (SBP) at baseline were independently associated with an increased risk of T2DM over 30 years, whereas lifestyle intervention and AUCI/G-R were inversely associated with this risk. The predictive effect of the Matsuda IR index and AUCI/G-R in participants with IGT was consistent with the results of all participants, whereas in those with NGT, only the Matsuda IR index, not the AUCI/G-R, predicted the development of T2DM (HR = 1.42, 95% CI 1.07-1.89 vs HR = 1.09, 95% CI 0.76-1.56). The predictive effect of the Matsuda IR index on T2DM existed even in participants with BMI < 25 (p = 0.049).

CONCLUSION

The modifiable predictors of T2DM in Chinese adults were high BMI, hypertension, mild hyperglycaemia, IR, and β-cell dysfunction. Both IR and β-cell function contributed to the development of T2DM in the long term; however, IR remains the initial and long-standing key risk factor for T2DM.

Precision Measurement of Reactor Antineutrino Oscillation at Kilometer-Scale Baselines by Daya Bay

We present a new determination of the smallest neutrino mixing angle θ_{13} and the mass-squared difference Δm_{32}^{2} using a final sample of 5.55×10^{6} inverse beta-decay (IBD) candidates with the final-state neutron captured on gadolinium. This sample is selected from the complete dataset obtained by the Daya Bay reactor neutrino experiment in 3158 days of operation. Compared to the previous Daya Bay results, selection of IBD candidates has been optimized, energy calibration refined, and treatment of backgrounds further improved. The resulting oscillation parameters are sin^{2}2θ_{13}=0.0851±0.0024, Δm_{32}^{2}=(2.466±0.060)×10^{-3}  eV^{2} for the normal mass ordering or Δm_{32}^{2}=-(2.571±0.060)×10^{-3}  eV^{2} for the inverted mass ordering.

Re-Visiting Antioxidant Therapy in Murine Advanced Atherosclerosis with Brussels Chicory, a Typical Vegetable in Mediterranean Diets

Brussels chicory, a typical vegetable in Mediterranean diets, has been recently reported to stabilize advanced atherosclerotic plaques in the brachiocephalic artery of apoE-deficient (Apoe-/-) mice. Herein, we investigated whether Brussels chicory can stabilize advanced plaques in the aorta via improving oxidative stress. Thirty week old Apoe-/- mice were fed the AIN-93G diet or supplemented with 0.5% freeze-dried Brussels chicory for twenty weeks. Aortic plaque size and stability, aortic relaxation, monocyte adhesion to aortic endothelium, free radicals, and enzymatic and non-enzymatic factors involved in free radical production and elimination in aorta and serum were measured. Brussels chicory consumption did not alter aortic plaque size, however, it stabilized aortic plaques, promoted aortic relaxation, and also inhibited monocyte adhesion to aortic endothelium. Moreover, this administration reduced oxidized LDL (ox-LDL) and 4-hydroxynonenal (4-HNE) content in aortic plaques, associated with inhibited aortic NADPH oxidase (NOX) and uncoupled endothelial nitric oxide synthase (eNOS)-mediated free radical production. However, Brussels chicory consumption did not appreciably alter aortic and serum superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) activities, aortic glutathione (GSH), as well as serum non-enzymatic antioxidants, such as bilirubin, uric acid, and GSH. Collectively, improved oxidative stress might contribute to the atheroprotective effect of Brussels chicory, supporting the prospect of the antioxidant therapy in advanced atherosclerosis progression.

First Constraints on Light Sterile Neutrino Oscillations from Combined Appearance and Disappearance Searches with the MicroBooNE Detector

We present a search for eV-scale sterile neutrino oscillations in the MicroBooNE liquid argon detector, simultaneously considering all possible appearance and disappearance effects within the 3+1 active-to-sterile neutrino oscillation framework. We analyze the neutrino candidate events for the recent measurements of charged-current ν_{e} and ν_{μ} interactions in the MicroBooNE detector, using data corresponding to an exposure of 6.37×10^{20} protons on target from the Fermilab booster neutrino beam. We observe no evidence of light sterile neutrino oscillations and derive exclusion contours at the 95% confidence level in the plane of the mass-squared splitting Δm_{41}^{2} and the sterile neutrino mixing angles θ_{μe} and θ_{ee}, excluding part of the parameter space allowed by experimental anomalies. Cancellation of ν_{e} appearance and ν_{e} disappearance effects due to the full 3+1 treatment of the analysis leads to a degeneracy when determining the oscillation parameters, which is discussed in this Letter and will be addressed by future analyses.

The J-shape Association between Total Bilirubin and Stroke in Older Patients with Obstructive Sleep Apnea Syndrome: A Multicenter Study

OBJECTIVES

To explore the relationship between total bilirubin (TBil) and stroke risk in older patients with obstructive sleep apnea syndrome (OSAS).

METHODS

A total of 1,007 patients with OSAS without stroke history aged ≥ 60 years and with complete serum TBil records were enrolled in this study. The median follow-up was 42 months. Participants were divided into four groups based on the quartile of the baseline serum TBil concentration. Multivariate Cox proportional hazards analysis and restricted cubic spline (RCS) were used to investigate the association of TBil with the incidence of new-onset stroke.

RESULTS

The PRIMARY part: the third quantile TBil level group had the lowest prevalence of stroke among the four groups. The RCS functions depicted a J-type curve relationship between TBil (3.3-33.3 µmol/L) and stroke (nonlinear P < 0.05). When the TBil level was in the range of 3.3 to 11.5 µmol/L, the possible protective influence of bilirubin against stroke in patients with OSAS enhanced with an increasing TBil level. However, when the TBil level exceeded 11.5 µmol/L and gradually increased, the effect of TBil on stroke risk became more and more pronounced. The SECONDARY part: for every 1 µmol/L increase in TBil levels in the range of 11.5 to 33.3 µmol/L, the risk of stroke in patients with OSAS increased by 16.2% (P < 0.001). In addition, there was a higher risk in women with OSAS (hazard ratio (HR)=1.292, 95% confidence interval (95%CI): 1.093-1.528; P = 0.003). Moreover, an increased TBil level alone was significantly associated with stroke in subjects aged < 75 years (HR: 1.190, 95%CI: 1.069-1.324), patients with mild-to-moderate OSAS (HR: 1.215, 95%CI: 1.083-1.364), and individuals without atrial fibrillation (AF) (HR: 1.179, 95%CI: 1.083-1.285) within a TBil level in the range of 11.5 to 33.3 µmol/L.

CONCLUSIONS

Both lower and higher bilirubin levels may increase the risk of stroke in older persons with OSAS, and there was a J-type dose-response relationship. The risk of stroke was lowest when the TBil level was approximately 11.5 µmol/L.

LASSO Regression-Based Diagnosis of Acute ST-Segment Elevation Myocardial Infarction (STEMI) on Electrocardiogram (ECG)

Electrocardiogram (ECG) is an important tool for the detection of acute ST-segment elevation myocardial infarction (STEMI). However, machine learning (ML) for the diagnosis of STEMI complicated with arrhythmia and infarct-related arteries is still underdeveloped based on real-world data. Therefore, we aimed to develop an ML model using the Least Absolute Shrinkage and Selection Operator (LASSO) to automatically diagnose acute STEMI based on ECG features. A total of 318 patients with STEMI and 502 control subjects were enrolled from Jan 2017 to Jun 2019. Coronary angiography was performed. A total of 180 automatic ECG features of 12-lead ECG were input into the model. The LASSO regression model was trained and validated by the internal training dataset and tested by the internal and external testing datasets. A comparative test was performed between the LASSO regression model and different levels of doctors. To identify the STEMI and non-STEMI, the LASSO model retained 14 variables with AUCs of 0.94 and 0.93 in the internal and external testing datasets, respectively. The performance of LASSO regression was similar to that of experienced cardiologists (AUC: 0.92) but superior (p < 0.05) to internal medicine residents, medical interns, and emergency physicians. Furthermore, in terms of identifying left anterior descending (LAD) or non-LAD, LASSO regression achieved AUCs of 0.92 and 0.98 in the internal and external testing datasets, respectively. This LASSO regression model can achieve high accuracy in diagnosing STEMI and LAD vessel disease, thus providing an assisting diagnostic tool based on ECG, which may improve the early diagnosis of STEMI.

Deep learning assessment of left ventricular hypertrophy based on electrocardiogram

Background

Current electrocardiogram (ECG) criteria of left ventricular hypertrophy (LVH) have low sensitivity. Deep learning (DL) techniques have been widely used to detect cardiac diseases due to its ability of automatic feature extraction of ECG. However, DL was rarely applied in LVH diagnosis. Our study aimed to construct a DL model for rapid and effective detection of LVH using 12-lead ECG.

Methods

We built a DL model based on convolutional neural network-long short-term memory (CNN-LSTM) to detect LVH using 12-lead ECG. The echocardiogram and ECG of 1,863 patients obtained within 1 week after hospital admission were analyzed. Patients were evenly allocated into 3 sets at 3:1:1 ratio: the training set (n = 1,120), the validation set (n = 371) and the test set 1 (n = 372). In addition, we recruited 453 hospitalized patients into the internal test set 2. Different DL model of each subgroup was developed according to gender and relative wall thickness (RWT).

Results

The LVH was predicted by the CNN-LSTM model with an area under the curve (AUC) of 0.62 (sensitivity 68%, specificity 57%) in the test set 1, which outperformed Cornell voltage criteria (AUC: 0.57, sensitivity 48%, specificity 72%) and Sokolow-Lyon voltage (AUC: 0.51, sensitivity 14%, specificity 96%). In the internal test set 2, the CNN-LSTM model had a stable performance in predicting LVH with an AUC of 0.59 (sensitivity 65%, specificity 57%). In the subgroup analysis, the CNN-LSTM model predicted LVH by 12-lead ECG with an AUC of 0.66 (sensitivity 72%, specificity 60%) for male patients, which performed better than that for female patients (AUC: 0.59, sensitivity 50%, specificity 71%).

Conclusion

Our study established a CNN-LSTM model to diagnose LVH by 12-lead ECG with higher sensitivity than current ECG diagnostic criteria. This CNN-LSTM model may be a simple and effective screening tool of LVH.

First Measurement of High-Energy Reactor Antineutrinos at Daya Bay

This Letter reports the first measurement of high-energy reactor antineutrinos at Daya Bay, with nearly 9000 inverse beta decay candidates in the prompt energy region of 8-12 MeV observed over 1958 days of data collection. A multivariate analysis is used to separate 2500 signal events from background statistically. The hypothesis of no reactor antineutrinos with neutrino energy above 10 MeV is rejected with a significance of 6.2 standard deviations. A 29% antineutrino flux deficit in the prompt energy region of 8-11 MeV is observed compared to a recent model prediction. We provide the unfolded antineutrino spectrum above 7 MeV as a data-based reference for other experiments. This result provides the first direct observation of the production of antineutrinos from several high-Q_{β} isotopes in commercial reactors.

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6  ×  6  ×  6 m 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties.

Search for an Excess of Electron Neutrino Interactions in MicroBooNE Using Multiple Final-State Topologies

We present a measurement of ν_{e} interactions from the Fermilab Booster Neutrino Beam using the MicroBooNE liquid argon time projection chamber to address the nature of the excess of low energy interactions observed by the MiniBooNE Collaboration. Three independent ν_{e} searches are performed across multiple single electron final states, including an exclusive search for two-body scattering events with a single proton, a semi-inclusive search for pionless events, and a fully inclusive search for events containing all hadronic final states. With differing signal topologies, statistics, backgrounds, reconstruction algorithms, and analysis approaches, the results are found to be either consistent with or modestly lower than the nominal ν_{e} rate expectations from the Booster Neutrino Beam and no excess of ν_{e} events is observed.

[The relationship between insulin resistance and risk of long-term mortality in people without diabetes: a 30-year follow-up of the Daqing Diabetes Study]

Objective: To determine whether insulin resistance is associated with all-cause mortality in subjects without diabetes. Methods: A total of 505 participants without diabetes, 198 with normal glucose tolerance (NGT) and 307 with impaired glucose tolerance (IGT), were recruited from the Daqing Diabetes Study. The participants were followed up for 30 years. They were stratified into three groups (tertiles) according to baseline homeostasis model assessment of insulin resistance(HOMA-IR) levels, as the HOMA-IR 0, the HOMA-IR 1 and the HOMA-IR 2 groups, to assess the predictive effect of insulin resistance on risk of all-cause mortality. Results: During the 30-year follow-up, 52, 56 and 78 participants died across the three HOMA-IR groups, respectively. The corresponding mortality per 1 000 person-years (95%CI) were 12.12 (9.56-15.01), 13.10 (10.46-16.03) and 19.91 (16.73-23.15), respectively. Participants in the HOMA-IR 2 group had a significantly higher risk of death than those in the HOMA-IR 0 group after adjustment of age, sex and smoking status (HR=1.97,95%CI 1.38-2.81, P<0.001). Cox analyses showed that a one standard deviation increase in HOMA-IR was associated with a 22% increase in the mortality after adjustment of potential confounders (HR=1.22, 95%CI 1.08-1.39, P=0.002). Conclusions: Insulin resistance is associated with increased risk of all-cause death in Chinese people without diabetes, suggesting that improving insulin resistance could be beneficial for people without diabetic in reducing risk of long-term all-cause mortality.

Five-year mortality of heart failure with preserved, mildly reduced, and reduced ejection fraction in a 4880 Chinese cohort

Aims

Available evidence is incomplete and inconsistent in the outcomes of heart failure (HF) patients with preserved ejection fraction (HFpEF), mildly reduced ejection fraction (HFmrEF), and reduced ejection fraction (HFrEF). There are also limited data on the proportions and long‐term prognosis among the three HF phenotypes in China. We aimed to characterize the 5 year prognosis in three HF phenotypes according to EF in a cohort of hospitalized HF patients undergoing coronary angiography in southern China.

Methods and results

Hospitalized patients with HF were enrolled from the Cardiorenal ImprovemeNt registry (CIN; ClinicalTrials.gov NCT04407936) between January 2007 and December 2014. HF phenotypes were defined as HFpEF (EF ≥ 50%), HFmrEF (EF 41–49%), and HFrEF (EF ≤ 40%). Kaplan–Meier and Cox proportional hazards models were constructed to examine differences in 5 year outcomes in HF patients with different phenotypes. A total of 4880 HF patients [mean age: 61.8 ± 10.3, male: 3156 (64.7%)] were included: 2768 (57%) had HFpEF, 1015 (21%) had HFmrEF, and 1097 (22%) had HFrEF. Patients with HFrEF were older than those with HFpEF (62.5 ± 10.6 vs. 61.3 ± 10.1, P < 0.001) and more likely to be male (78.0% vs. 55.9%, P < 0.001). With 5 year follow‐up through the end of December 2019, 1624 (27.6%) patients died. Controlling confounding variables, declined EF category was independently associated with increased 5 year mortality {HFrEF 25.2% vs. HFpEF 13.4%, adjusted hazard ratio [aHR]: 1.85 [95% confidence interval (CI): 1.45 to 2.35]; HFmrEF 18.1% vs. HFpEF 13.4%, aHR: 1.40 [95% CI: 1.08 to 1.81]; HFrEF 25.2% vs. HFmrEF 18.1%, aHR: 1.32 [95% CI: 1.02 to 1.71]}.

Conclusions

In this Chinese cohort, patients with HFrEF account for less than a fourth of HF patients. One‐sixth individuals with HF died in 5 years. HFrEF was associated with a nearly two‐fold increased risk of 5 year mortality than HFpEF. Further studies are needed to prospectively evaluate the efficacy of improving treatment on outcomes in all three HF phenotypes.

First Measurement of Energy-Dependent Inclusive Muon Neutrino Charged-Current Cross Sections on Argon with the MicroBooNE Detector

We report a measurement of the energy-dependent total charged-current cross section σ(E_{ν}) for inclusive muon neutrinos scattering on argon, as well as measurements of flux-averaged differential cross sections as a function of muon energy and hadronic energy transfer (ν). Data corresponding to 5.3×10^{19} protons on target of exposure were collected using the MicroBooNE liquid argon time projection chamber located in the Fermilab booster neutrino beam with a mean neutrino energy of approximately 0.8 GeV. The mapping between the true neutrino energy E_{ν} and reconstructed neutrino energy E_{ν}^{rec} and between the energy transfer ν and reconstructed hadronic energy E_{had}^{rec} are validated by comparing the data and Monte Carlo (MC) predictions. In particular, the modeling of the missing hadronic energy and its associated uncertainties are verified by a new method that compares the E_{had}^{rec} distributions between data and a MC prediction after constraining the reconstructed muon kinematic distributions, energy, and polar angle to those of data. The success of this validation gives confidence that the missing energy in the MicroBooNE detector is well modeled and underpins first-time measurements of both the total cross section σ(E_{ν}) and the differential cross section dσ/dν on argon.

Brain charts for the human lifespan

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

Search for Neutrino-Induced Neutral-Current Δ Radiative Decay in MicroBooNE and a First Test of the MiniBooNE Low Energy Excess under a Single-Photon Hypothesis

We report results from a search for neutrino-induced neutral current (NC) resonant Δ(1232) baryon production followed by Δ radiative decay, with a ⟨0.8⟩  GeV neutrino beam. Data corresponding to MicroBooNE's first three years of operations (6.80×10^{20} protons on target) are used to select single-photon events with one or zero protons and without charged leptons in the final state (1γ1p and 1γ0p, respectively). The background is constrained via an in situ high-purity measurement of NC π^{0} events, made possible via dedicated 2γ1p and 2γ0p selections. A total of 16 and 153 events are observed for the 1γ1p and 1γ0p selections, respectively, compared to a constrained background prediction of 20.5±3.65(syst) and 145.1±13.8(syst) events. The data lead to a bound on an anomalous enhancement of the normalization of NC Δ radiative decay of less than 2.3 times the predicted nominal rate for this process at the 90% confidence level (C.L.). The measurement disfavors a candidate photon interpretation of the MiniBooNE low-energy excess as a factor of 3.18 times the nominal NC Δ radiative decay rate at the 94.8% C.L., in favor of the nominal prediction, and represents a greater than 50-fold improvement over the world's best limit on single-photon production in NC interactions in the sub-GeV neutrino energy range.

Deep Learning Networks Accurately Detect ST-Segment Elevation Myocardial Infarction and Culprit Vessel

Early diagnosis of acute ST-segment elevation myocardial infarction (STEMI) and early determination of the culprit vessel are associated with a better clinical outcome. We developed three deep learning (DL) models for detecting STEMIs and culprit vessels based on 12-lead electrocardiography (ECG) and compared them with conclusions of experienced doctors, including cardiologists, emergency physicians, and internists. After screening the coronary angiography (CAG) results, 883 cases (506 control and 377 STEMI) from internal and external datasets were enrolled for testing DL models. Convolutional neural network-long short-term memory (CNN-LSTM) (AUC: 0.99) performed better than CNN, LSTM, and doctors in detecting STEMI. Deep learning models (AUC: 0.96) performed similarly to experienced cardiologists and emergency physicians in discriminating the left anterior descending (LAD) artery. Regarding distinguishing RCA from LCX, DL models were comparable to doctors (AUC: 0.81). In summary, we developed ECG-based DL diagnosis systems to detect STEMI and predict culprit vessel occlusion, thus enhancing the accuracy and effectiveness of STEMI diagnosis.

Joint Determination of Reactor Antineutrino Spectra from ^{235}U and ^{239}Pu Fission by Daya Bay and PROSPECT

A joint determination of the reactor antineutrino spectra resulting from the fission of ^{235}U and ^{239}Pu has been carried out by the Daya Bay and PROSPECT Collaborations. This Letter reports the level of consistency of ^{235}U spectrum measurements from the two experiments and presents new results from a joint analysis of both data sets. The measurements are found to be consistent. The combined analysis reduces the degeneracy between the dominant ^{235}U and ^{239}Pu isotopes and improves the uncertainty of the ^{235}U spectral shape to about 3%. The ^{235}U and ^{239}Pu antineutrino energy spectra are unfolded from the jointly deconvolved reactor spectra using the Wiener-SVD unfolding method, providing a data-based reference for other reactor antineutrino experiments and other applications. This is the first measurement of the ^{235}U and ^{239}Pu spectra based on the combination of experiments at low- and highly enriched uranium reactors.

Joint Measurement of the ^{235}U Antineutrino Spectrum by PROSPECT and STEREO

The PROSPECT and STEREO collaborations present a combined measurement of the pure ^{235}U antineutrino spectrum, without site specific corrections or detector-dependent effects. The spectral measurements of the two highest precision experiments at research reactors are found to be compatible with χ^{2}/ndf=24.1/21, allowing a joint unfolding of the prompt energy measurements into antineutrino energy. This ν[over ¯]_{e} energy spectrum is provided to the community, and an excess of events relative to the Huber model is found in the 5-6 MeV region. When a Gaussian bump is fitted to the excess, the data-model χ^{2} value is improved, corresponding to a 2.4σ significance.

Predictors and Mortality for Worsening Left Ventricular Ejection Fraction in Patients With HFpEF

Background

Definitions of declined left ventricular ejection fraction (LVEF) vary across studies and research results concerning the association of mortality with declined LVEF are inconsistent. Thus, this study aimed to assess the impact of early worsening LVEF on mortality in patients with heart failure (HF) with preserved ejection fraction (HFpEF) and to establish independent predictors of early worsening LVEF.

Methods and Results

A total of 1,418 consecutive patients with HFpEF with LVEF remeasurement from the Cardiorenal Improvement registry were included in this study. Worsening LVEF was defined as an absolute decline ≥ 5% from baseline LVEF within 3 to 12 months after discharge. The Cox and logistic regression analyses were performed to assess prognostic effects and predictors for worsening LVEF, respectively. Among 1,418 patients with HFpEF, 457 (32.2%) patients exhibited worsening LVEF. During a median follow-up of 3.2 years (interquartile range: 2.3–4.0 years), 92 (6.5%) patients died. Patients with HFpEF with worsening LVEF had higher mortality relative to those with nonworsening LVEF [9.2 vs. 5.2%; adjusted hazard ratio (aHR): 2.18, 95% CI: 1.35–3.52]. In the multivariate binary logistic regression analysis, baseline left ventricular end-diastolic dimension (LVEDD), LVEF, high-density lipoprotein cholesterol (HDL-C), atrial fibrillation (AF), and diabetes mellitus (DM) emerged as predictive factors of worsening LVEF.

Conclusion

This study demonstrated that about one out of three patients with HFpEF experiences worsening LVEF during follow-up, which is associated with 2.2-fold increased mortality. Increased LVEDD and LVEF, low HDL-C levels, AF, and DM were predictors of worsening LVEF. Further studies are needed to prospectively assess the efficacy of early active management on prognosis in patients with HF with worsening LVEF.

Registration

ClinicalTrials.gov, identifier NCT04407936.

[Clinical value of tumor-associated autoantibodies in diagnosis of early non-small cell lung cancer]

Objective: To investigate the auxiliary diagnostic value of seven tumor-associated autoantibodies (AABs) P53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGEA1 and CAGE in early non-small cell lung cancer (NSCLC). Methods: The case-control study enrolled 195 patients with early NSCLC [71 males and 124 females, aged (55.70±11.78) years old], 114 patients with benign lung disease [44 males and 70 females, aged (52.85±12.31) years old] and 100 healthy subjects [39 males and 61 females, aged (53.62±9.97) years old] from the First Affiliate Hospital of Nanjing Medical University from June 2020 to December 2020. AABs were detected by enzyme-linked-immunosorbent serologic assay (ELISA), and carcinoembryonic antigen (CEA),cytokeratin 19-fragments (CYFRA21-1) and neuron specific enolase (NSE) were detected by electrochemiluminescence. The levels of AABs,CEA,CYFRA21-1 and NSE in the 3 groups were compared. Patients with benign lung diseases and healthy subjects were combined into the control group, and the positive rate of each indicator in the NSCLC group and the control group was compared. The diagnostic efficacy of single and combined tests for NSCLC were obtained using receiver operating characteristic (ROC) curves. Besides, the relationship between the levels of AABs, CEA, CYFRA21-1 and NSE and their clinicopathological features and preoperative imaging parameters in NSCLC patients was assessed. Results: The levels of SOX2 [0.70 (0.10, 2.40) U/mL] and GBU4-5 [1.30 (0.30, 8.90) U/mL] in NSCLC group were higher than those in benign disease group [SOX2: 0.50 (0.10, 1.60) U/mL, GBU4-5: 0.80 (0.10, 2.30) U/mL, Z values were 27.258 and 45.797; P values were all<0.05] and health control group [SOX2: 0.45 (0.10, 1.08) U/mL, GBU4-5: 0.75 (0.20, 1.78) U/mL, Z values were 32.551 and 40.456; P values were all<0.05], and there was no significant difference between benign disease group and health control group (Z values were 5.293 and 5.340, P values were all>0.05). The levels of CEA [1.75 (1.08, 2.72) U/mL] and CYFRA21-1 [1.81 (1.41, 2.36) U/mL] in NSCLC group were higher than those in healthy control group [CEA: 1.22 (0.68, 1.81) U/mL, CYFRA21-1: 1.43 (1.14, 1.74) U/mL, Z values were 64.100 and 37.597; P values were all<0.05], but there was no significant difference between NSCLC group and benign group [CEA: 1.74 (1.01, 2.51) U/mL, CYFRA21-1: 1.82 (1.45, 2.46) U/mL, Z values were 7.275 and 10.621; P values were all>0.05]. The positive rates of P53, SOX2, GAGE7, GBU4-5 and CEA in NSCLC group were higher than those in the control group [P53: 10.3% vs 0.9%, SOX2: 11.3% vs 2.3%, GAGE7: 11.3% vs 0.5%, GBU4-5: 30.1% vs 5.6%, CEA: 9.7% vs 0.9%, χ² values were 17.420, 13.242, 22.485, 43.211, 16.255, respectively; P values were all<0.05]. The diagnostic efficiency of the combined detection of seven AABs was better than that of single detection. Seven AABs combined with CEA [area under curve (AUC): 0.732, sensitivity: 64.10%] and with CYFRA21-1 (AUC: 0.737, sensitivity: 58.97%) greatly improved the diagnostic efficiency and sensitivity of CEA (AUC: 0.583, sensitivity: 50.77%) and CYFRA21-1 (AUC: 0.552, sensitivity: 44.10%). The levels of SOX2 and CEA in NSCLC patients were correlated with the degree of tumor invasion (H values were 6.436 and 14.071; P values were all<0.05); the levels of GAGE7 and CEA were correlated with the nodule density (H values were 7.643 and 12.268; P values were all<0.05); and the levels of SOX2, GAGE7, CEA and CYFRA21-1 were all correlated with the nodule size (H values were 10.837, 11.528, 31.835, 20.338; P values were all<0.05). Conclusion: The detection of AABs combined with CEA and CYFRA21-1 is helpful for the early auxiliary diagnosis of NSCLC, and plays an important role in prevention and screening for early lung cancer.

Ginsenoside Rb1 attenuates age-associated vascular impairment by modulating the Gas6 pathway

CONTEXT

Ginsenoside Rb1 (Rb1) exerts many beneficial effects and protects against cardiovascular disease.

OBJECTIVE

To investigate whether Rb1 could attenuate age-related vascular impairment and identify the mechanism.

MATERIALS AND METHODS

Female C57BL/6J mice aged 2 and 18 months, randomly assigned to Young, Young + 20 mg/kg Rb1, Old + vehicle, Old + 10 mg/kg Rb1 and Old + 20 mg/kg Rb1 groups, were daily intraperitoneal injected with vehicle or Rb1 for 3 months. The thoracic aorta segments were used to inspect the endothelium-dependent vasorelaxation. Left thoracic aorta tissues were collected for histological or molecular expression analyses, including ageing-related proteins, markers relevant to calcification and fibrosis, and expression of Gas6/Axl.

RESULTS

We found that in Old + vehicle group, the expression of senescence proteins and cellular adhesion molecules were significantly increased, with worse endothelium-dependent thoracic aorta relaxation (58.35% ± 2.50%) than in Young group (88.84% ± 1.20%). However, Rb1 treatment significantly decreased the expression levels of these proteins and preserved endothelium-dependent relaxation in aged mice. Moreover, Rb1 treatment also reduced calcium deposition, collagen deposition, and the protein expression levels of collagen I and collagen III in aged mice. Furthermore, we found that the downregulation of Gas6 protein expression by 41.72% and mRNA expression by 52.73% in aged mice compared with young mice was abrogated by Rb1 treatment. But there was no significant difference on Axl expression among the groups.

CONCLUSIONS

Our study confirms that Rb1 could ameliorate vascular injury, suggesting that Rb1 might be a potential anti-ageing related vascular impairment agent.

Search for a Higgs Portal Scalar Decaying to Electron-Positron Pairs in the MicroBooNE Detector

We present a search for the decays of a neutral scalar boson produced by kaons decaying at rest, in the context of the Higgs portal model, using the MicroBooNE detector. We analyze data triggered in time with the Fermilab NuMI neutrino beam spill, with an exposure of 1.93×10^{20} protons on target. We look for monoenergetic scalars that come from the direction of the NuMI hadron absorber, at a distance of 100 m from the detector, and decay to electron-positron pairs. We observe one candidate event, with a standard model background prediction of 1.9±0.8. We set an upper limit on the scalar-Higgs mixing angle of θ<(3.3-4.6)×10^{-4} at the 95% confidence level for scalar boson masses in the range (100-200)  MeV/c^{2}. We exclude, at the 95% confidence level, the remaining model parameters required to explain the central value of a possible excess of K_{L}^{0}→π^{0}νν[over ¯] decays reported by the KOTO collaboration. We also provide a model-independent limit on a new boson X produced in K→πX decays and decaying to e^{+}e^{-}.

Hepatic Fibroblast Growth Factor 21 Is Involved in Mediating Functions of Liraglutide in Mice With Dietary Challenge

BACKGROUND AND AIMS

Several studies have shown that expression of hepatic fibroblast growth factor 21 (FGF21) can be stimulated by glucagon-like peptide 1 (GLP-1)-based diabetes drugs. As GLP-1 receptor (GLP-1R) is unlikely to be expressed in hepatocytes, we aimed to compare such stimulation in mice and in mouse hepatocytes, determine the involvement of GLP-1R, and clarify whether FGF21 mediates certain functions of the GLP-1R agonist liraglutide.

APPROACH AND RESULTS

Liver FGF21 expression was assessed in mice receiving a daily liraglutide injection for 3 days or in mouse primary hepatocytes (MPHs) undergoing direct liraglutide treatment. The effects of liraglutide on metabolic improvement and FGF21 expression were then assessed in high-fat diet (HFD)-fed mice and compared with the effects of the dipeptidyl-peptidase 4 inhibitor sitagliptin. Animal studies were also performed in Glp1r^-/- mice and liver-specific FGF21-knockout (lFgf21-KO) mice. In wild-type mouse liver that underwent RNA sequencing and quantitative reverse-transcription PCR, we observed liraglutide-stimulated hepatic Fgf21 expression and a lack of Glp1r expression. In MPHs, liraglutide did not stimulate Fgf21. In mice with HFD-induced obesity, liraglutide or sitagliptin treatment reduced plasma triglyceride levels, whereas their effect on reducing body-weight gain was different. Importantly, increased hepatic FGF21 expression was observed in liraglutide-treated mice but was not observed in sitagliptin-treated mice. In HFD-fed Glp1r^-/- mice, liraglutide showed no beneficial effects and could not stimulate Fgf21 expression. In lFgf21-KO mice undergoing dietary challenge, the body-weight-gain attenuation and lipid homeostatic effects of liraglutide were lost or significantly reduced.

CONCLUSIONS

We suggest that liraglutide-stimulated hepatic Fgf21 expression may require GLP-1R to be expressed in extrahepatic organs. Importantly, we revealed that hepatic FGF21 is required for liraglutide to lower body weight and improve hepatic lipid homeostasis. These observations advanced our mechanistic understanding of the function of GLP-1-based drugs in NAFLD.

Gene regulatory network analysis identifies key genes and regulatory mechanisms involved in acute myocardial infarction using bulk and single cell RNA-seq data

Cardiovascular and cerebrovascular diseases are leading causes of death worldwide, accounting for more than 40% of all deaths in China. Acute myocardial infarction (AMI) is a common cardiovascular disease and traditionally divided into ST-segment (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI), which are known with different prognoses and treatment strategies. However, key regulatory genes and pathways involved in AMI that may be used as potential biomarker for prognosis are unknown. In this study, we employed both bulk and single-cell RNA-seq to construct gene regulatory networks and cell-cell communication networks. We first constructed weighted gene co-expression networks for differential expressed genes between STEMI and NSTEMI patients based on whole-blood RNA-seq transcriptomics. Network topological attributes (e.g., node degree, betweenness) were analyzed to identify key genes involved in different functional network modules. Furthermore, we used single-cell RNA-seq data to construct multilayer signaling network to infer regulatory mechanisms of the above key genes. PLAUR (receptor for urokinase plasminogen activator) was found to play a vital role in transducing inter-cellular signals from endothelial cells and fibroblast cells to intra-cellular pathways of myocardial cells, leading to gene expression involved in cellular response to hypoxia. Our study sheds lights on identifying molecular biomarkers for diagnosis and prognosis of AMI, and provides candidate key regulatory genes for further experimental validation.

Cosmic Ray Background Removal With Deep Neural Networks in SBND

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons, and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying deep learning on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, on a pixel-by-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.