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.

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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Clinicopathological and survival analysis of primary spindle cell carcinoma of the breast in Chinese patients

Due to the low prevalence of Spindle cell carcinoma (SpCC) of the breast, the perception of the disease was limited. The aim of our study was to analyze the clinicopathological features, survival outcomes and prognostic factors of SpCC of the breast among Chinese. Patients diagnosed with SpCC of the breast in Cancer Hospital of Chinese Academy of Medical Sciences between 2004 to 2021 were retrospectively analyzed. Additionally, we searched Chinese databases and Pubmed websites for literature on breast SpCC in Chinese patients. The clinicalpathological characteristics, survival outcomes and prognostic factors were evaluated. A total of 160 eligible cases were enrolled, including 23 patients in our center and 137 cases from the literature search. The median age was 52 years old (range, 22-88). 84.8% (101/119) cases were in the early stage (stage I and II). 15.0% (20/133) had axillary lymph node involvement. The majority of patients were HR-HER2- (85.4%, 98/137). 77.5% (79/102) patients received adjuvant chemotherapy. 36.9% (31/84) of patients received adjuvant radiation. Of 126 patients available for a median follow-up with 38 (range, 1-211) months, 58 cases (46.0%, 58/126) recurred, including 31.0% (18/58) who had local recurrence and 69.0% (40/58) who had distant metastasis. The most common distant metastatic site was the lung (41.4%, 24/58). Most patients (91.5%) had recurrence within 3 years. The Kaplan-Meier curves showed that the 3-year and 5-year disease-free survival (DFS) were 55.9% and 46.8%, and the 3-year and 5-year overall survival (OS) were 67.0% and 54.9%, respectively. T stage was an independent prognostic factor for OS (T1-2 vs T3-4, HR=0.362, 95% CI: 0.139-0.945, P=0.038). Although SpCC of the breast was often diagnosed in the early stage with low lymph node involvement, the prognosis was poor. T stage was an indicator of prognosis for OS. Better treatments need to be explored to prevent recurrence and improve survival.

rTMS reduces spatial learning and memory deficits induced by sleep deprivation possibly via suppressing the expression of kynurenine 3-monooxygenase in rats

INTRODUCTION

Impairment of learning and memory caused by sleep deprivation is a common symptom that significantly affects quality of life. Repetitive transcranial magnetic stimulation (rTMS) is a promising approach to exert a positive effect on cognitive impairment. However, there is less known about the mechanism of rTMS for learning and memory induced by chronic REM sleep deprivation (CRSD). This study was to detect the effects of rTMS on spatial learning and memory deficits by CRSD and explore possible mechanism.

METHODS

Sixty male Sprague-Dawley rats were randomly divided into four groups: wide platform (Control), sleep deprivation (SD), sleep deprivation + rTMS (TMS), and sleep deprivation + sham rTMS (Sham-TMS). Morris water maze (MWM) and open field test (OFT) assessed spatial learning and memory and anxiety of rats with pre/post-intervention. Golgi staining and transmission electron microscope (TEM) were used to observe structural variations of synapses in the hippocampus. The alteration in gene expression of different groups was analyzed by RNA-sequencing (RNA-Seq), and the key gene was screened and identified by quantitative polymerase chain reaction (qPCR) and subsequently verified with western blotting and immunofluorescence.

RESULTS

The behavioral test showed spatial learning and memory decreased and anxiety increased in the SD group compared to the Control and TMS groups. Moreover, rTMS improved spine density, ultrastructural damage, and quantities of synapses. In accordance with RNA-Seq, 56 differentially expressed genes (DEGs) were identified by comparing alternations in four groups and concentrated on kynurenine 3-monooxygenase (KMO). The expression of KMO increased significantly in rats of the SD group compared to the Control and TMS groups identified by qPCR, western blotting, and immunofluorescence.

CONCLUSION

1 Hz rTMS alleviated spatial learning and memory deficits induced by CRSD probably via down-regulating the expression of KMO and improving the structure and quantity of synapses in the hippocampus of rats.

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.

Impact of body mass index and its change on survival outcomes in patients with early breast cancer: A pooled analysis of individual-level data from BCIRG-001 and BCIRG-005 trials

INTRODUCTION

The relationships between body mass index (BMI) and survival rates are complex, and have not been thoroughly investigated in breast cancer patients who received adjuvant chemotherapy.

METHODS

We collected data on 2394 patients from two randomized, phase III clinical trials that investigated adjuvant chemotherapy in breast cancer identified in Project Data Sphere. The objective was to examine the effect of baseline BMI, BMI after adjuvant chemotherapy, and BMI change from baseline to post-adjuvant chemotherapy on disease-free survival (DFS) and overall survival (OS). Restricted cubic splines were used to examine potential non-linear associations between continuous BMI value and survival. Stratified analyses involved chemotherapy regimens.

RESULTS

Severe obesity (BMI≥40.0 kg/m2) at baseline was independently associated with worse DFS (hazard ration [HR] = 1.48, 95% confidence interval [CI] 1.02-2.16, P = 0.04) and OS (HR = 1.79, 95%CI 1.17-2.74, P = 0.007) compared with underweight/normal weight (BMI≤24.9 kg/m2). A BMI loss >10% was also an independent prognostic factor for adverse OS (HR = 2.14, 95%CI 1.17-3.93, P = 0.014). Stratified analyses revealed that severe obesity adversely affected DFS (HR = 2.38, 95%CI 1.26-4.34, P = 0.007) and OS (HR = 2.90, 95%CI 1.46-5.76, P = 0.002) in the docetaxel-based group, but not in the non-docetaxel-based group. Restricted cubic splines revealed a "J-shaped" association of baseline BMI with risk of recurrence or all-cause death, and this relationship was more pronounced in the docetaxel-based group.

CONCLUSIONS

In early breast cancer patients treated with adjuvant chemotherapy, baseline severe obesity was significantly linked to worse DFS and OS, and a BMI loss over 10% from baseline to post-adjuvant chemotherapy also negatively affected OS. Moreover, the prognostic role of BMI might differ between docetaxel-based and non-docetaxel-based groups.

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.

Survival Outcomes and Efficacy of Platinum in Early Breast Cancer Patients with Germline BRCA1 or BRCA2 Mutation: A Multicenter Retrospective Cohort Study

Purpose

The study aimed to compare the survival outcomes and efficacy of platinum in early breast cancer patients with BRCA1 and BRCA2 mutations.

Methods

Patients diagnosed with stage I-III breast cancer and carrying germline pathogenic/likely pathogenic BRCA mutations in three medical institutions in China from April 2016 to January 2021 were retrospectively analyzed. Data on clinical and pathological characteristics, treatment information, pathogenic variants of BRCA, and survival outcomes were collected for all eligible patients.

Outcomes

One hundred and sixty-nine patients with BRCA mutations were enrolled, including BRCA1 mutation (53.3%, n = 90) and BRCA2 mutation (46.7%, n = 79). The median age was 39 years, and most patients (68.1%, n = 115) were stage I-II. Patients with BRCA1 mutations were characterized by histological grade III (55.6%) and higher Ki-67 index (Ki-67 ≥ 30%, 78.9%) compared with patients with BRCA2 mutations (27.8%, 58.2%). BRCA1 mutation patients accounted for a significantly higher proportion of triple negative breast cancer than BRCA2 mutation patients (71.1% vs 19.0%, P < 0.0001). A total of 142 (84.0%) patients received neo/adjuvant chemotherapy, including anthracycline and/or taxane-based regimens (55.6%) or platinum-based regimens (27.2%). Median follow-up was 33.2 months. Three-year DFS (disease-free survival) and DRFS (distant recurrence-free survival) had no significant differences between patients with BRCA1 and BRCA2 mutations (82.0% vs 85.4%, P = 0.35; 94.3% vs 94.6%, P = 0.39). The 3-year DFS rate in BRCA1 mutation cohort of patients received platinum regimen was significantly higher than patients received non-platinum regimen (96.0% vs 75.2%, P = 0.01). No differences between DFS and DRFS were observed in patients with BRCA2 mutation received platinum regimen and non-platinum regimen.

Conclusion

Similar survival outcomes were observed in early breast cancer patients with BRCA1 and BRCA2 mutation, though they had different biological characteristics. Patients with BRCA1 mutations are more benefit from platinum-regimen. The value of platinum-regimen for early breast cancer patients with BRCA1 and BRCA2 needs to be verified further.

Epidemiology and clinicopathologic features of breast cancer in China and the United States

Background

Breast cancer has kept increasing since the past decades and the incidence rate is the highest among all neoplasms nowadays. China, as well as other countries, faces severe burden from the increasing population with breast cancer. This study aimed to analyze the epidemiology and clinicopathologic features of breast cancer in China and the United States (US).

Methods

Data of hospitalized patients diagnosed with primary breast cancer between 1 January 1999 and 31 December 2014 in the Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS) were reviewed. Clinical and demographic data were extracted from medical history systems, and the sixteen-year trends were analyzed. Meanwhile, retrieved data from the Surveillance, Epidemiology, and End Results (SEER) database from 1999 to 2014 were used for comparisons.

Results

A total of 18,768 breast cancer patients were included from CHCAMS, China, with 18,685 female cases (99.57%) and 81 male cases (0.43%). A total of 762,954 breast cancer patients were included from the SEER database, US, with 757,357 female cases (99.27%) and 5,597 male cases (0.73%). The peak age of breast cancer was 45-49 years old from 1999 to 2014 in China, while the peak age was 55-59 years from 1999 to 2006 and 60-64 years from 2007 to 2014 in the US. There were more young (<35 years, 6.56% vs. 1.97%, P<0.001), less elderly (≥65 years, 9.99% vs. 40.88%, P<0.001), less stage I (24.93% vs. 48.84%, P<0.001) and more stage III (21.00% vs. 12.35%, P<0.001) breast cancer patients in China than in the US. Patients aged 30-49 years old had a decreased trend (P<0.001), while 55-64 years old patients had an increased trend (P<0.001) from 1999 to 2014 in China, the same trend was also observed in the US. Mucinous carcinoma and histological grade I breast cancer patients increased with age both in China and the US (P<0.001).

Conclusions

The unique epidemiology and clinicopathologic features of breast cancer (earlier peak age, more younger patients, more advanced stage, etc.), as well as the typical trend in China, should be seriously recognized, so as to guide future prevention and management strategies.

Unveiling the drives behind tetracycline adsorption capacity with biochar through machine learning

This study aimed to develop a robust predictive model for tetracycline (TC) adsorption onto biochar (BC) by employing machine learning techniques to investigate the underlying driving factors. Four machine learning algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANN), were used to model the adsorption of TC on BC using the data from 295 adsorption experiments. The analysis revealed that the RF model had the highest predictive accuracy (R² = 0.9625) compared to ANN (R² = 0.9410), GBDT (R² = 0.9152), and XGBoost (R² = 0.9592) models. This study revealed that BC with a specific surface area (S (BET)) exceeding 380 cm³·g^-1 and particle sizes ranging between 2.5 and 14.0 nm displayed the greatest efficiency in TC adsorption. The TC-to-BC ratio was identified as the most influential factor affecting adsorption efficiency, with a weight of 0.595. The concentration gradient between the adsorbate and adsorbent was demonstrated to be the principal driving force behind TC adsorption by BC. A predictive model was successfully developed to estimate the sorption performance of various types of BC for TC based on their properties, thereby facilitating the selection of appropriate BC for TC wastewater treatment.

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.

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.

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.

Biomarkers predicting clinical outcomes in nasopharyngeal cancer patients receiving immune checkpoint inhibitors: A systematic review and meta-analysis

Background

Optimal biomarkers to select patients who will benefit most from immunotherapy remain lacking in nasopharyngeal cancer (NPC). This systematic review and meta-analysis aimed to evaluate the association between various biomarkers and clinical outcomes in NPC patients treated with immune checkpoint inhibitors (ICIs).

Methods

Systematic searches of PubMed, Embase, Cochrane Library, and Web of Science databases were performed up to October 2022. Studies evaluating the association between biomarkers and intended outcomes of ICIs were included. The pooled odds ratio (OR) and hazard ratio (HR) with 95% confidence intervals (CIs) were calculated, respectively, for the objective response rate (ORR) and progression-free survival (PFS) under fixed or random-effect models.

Results

A total of 15 studies involving 1,407 patients were included. The pooled analysis indicated that NPC patients with lower plasma Epstein-Barr virus (EBV) DNA level at baseline (OR = 2.14, 95% CI: 1.46-3.14, P &lt; 0.001), decreased EBV DNA load during immunotherapy (OR = 4.57, 95% CI: 2.24-9.34, P = 0.002) and higher programmed cell death-ligand 1 (PD-L1) expression (OR = 2.35, 95% CI: 1.36-4.09, P = 0.002) had superior ORR than the counterparts. No significant differences of ORR were observed between positive PD-L1 expression and negative PD-L1 expression (OR = 1.50, 95% CI: 0.92-2.45, P = 0.104), as well as higher tumor mutation burden (TMB) and lower TMB (OR = 1.62, 95% CI: 0.41-6.44, P = 0.494). Patients with lower plasma EBV DNA level at baseline obtained a significant benefit on PFS than those with higher plasma EBV DNA level (HR = 0.52, 95% CI: 0.42-0.63, P &lt; 0.001). There were no differences in PFS between decreased EBV DNA load and increased EBV DNA load during immunotherapy (HR = 0.51, 95% CI: 0.22-1.17, P = 0.109), higher PD-L1 expression and lower PD-L1 expression (HR = 0.65, 95% CI: 0.42-1.01, P = 0.054), positive PD-L1 expression and negative PD-L1 expression (HR = 0.90, 95% CI: 0.64-1.26, P = 0.531), lower TMB and higher TMB (HR = 0.84, 95% CI: 0.51-1.38, P = 0.684).

Conclusion

Lower baseline plasma EBV DNA level, decreased plasma EBV DNA during immunotherapy, and higher PD-L1 expression are reliable biomarkers predicting better response to ICIs treatment. Lower baseline plasma EBV DNA level was also associated with longer PFS. It is warranted to further explore and better illuminate the utility of these biomarkers in future clinical trials and real-world practice.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022324434.

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.

Preparation and characterization of antioxidant and pH-sensitive films based on arrowhead (Sagittaria sagittifolia) starch, κ-carrageenan and black chokeberry (Aronia melanocarpa) extract for monitoring spoilage of chicken wings

In this study, we firstly developed an antioxidant and pH-sensitive film based on arrowhead starch (AS), κ-carrageenan (KC) and black chokeberry extract (BCE) and its physical and structural properties were investigated. We found BCE showed different colors in different pH solutions and incorporation with KC and BCE could significantly decrease light transmittance, increase thickness, elongation at break and pH-sensitive property of AS film. The results of structural property assay indicated that there were some intermolecular interactions between BCE and AS/KC in AS-KC-BCE films. Secondly, we investigated the rheological property of AS, AS-KC and AS-KC-BCE suspensions and found the suspensions showed an obvious shear-thinning behavior with high apparent viscosity. Finally, the functional properties of AS-KC-BCE films were investigated and AS-KC-BCE films showed strong scavenging activity on DPPH free radical and presented visible colour changes in response to the changes of the chicken wing qualities. The results suggest that AS-KC-BCE films can be used in active and intelligent packaging of food industry.

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.

The protective effects of lipoxin A4 on type 2 diabetes mellitus: A Chinese prospective cohort study

BACKGROUND

Several cellular and animal studies have suggested that lipoxin A4 (LXA4) has a protective effect on type 2 diabetes mellitus (T2DM) development. However, little is known about whether LXA4 influences T2DM development at the population level.

METHODS

We included 2755 non-diabetic participants from a cohort study in China who were followed for about seven years. Cox proportional hazards model was used to estimate hazard ratios (HRs) and 95% confidence intervals (CI) for the association between LXA4 and incident T2DM. Mediation models were used to examine how serum lipids as mediators impact the association between LXA4 and T2DM.

RESULTS

In total, 172 newly diagnosed T2DM cases were identified. Multivariate-adjusted HR for T2DM in the fourth compared with the first quartile of LXA4 was 0.62 (95% CI: 0.40-0.96). When used the optimal cutoff value determined by the receiver operating characteristic curve, the results showed participants with LXA4 > 2.84 ng/mL had a decreased T2DM risk compared to those with LXA4 ≤ 2.84 ng/mL (HR: 0.63, 95% CI: 0.45-0.89). The effect of LXA4 on incident T2DM was significantly modified by gender (P _-interaction = 0.024) and family history of diabetes (P _-interaction = 0.025). Additionally, the association between LXA4 and incident T2DM was partially suppressed by the TyG and TG/HDL-c ratio, with a suppression proportion of 22.2% and 16.0%, respectively.

CONCLUSIONS

Higher LXA4 levels are significantly associated with a lower risk of T2DM development. The present findings would be helpful in understanding the effect of LXA4 on T2DM development at the population level.

Bio-Inspired Pangolin Design for Self-Healable Flexible Perovskite Light-Emitting Diodes

Despite tremendous developments in the luminescene performance of perovskite light-emitting diodes (PeLEDs), the brittle nature of perovskite crystals and their poor crystallinity on flexible substrates inevitably lead to inferior performance. Inspired by pangolins' combination of rigid scales and soft flesh, we propose a bionic structure design for self-healing flexible PeLEDs by employing a polymer-assisted crystal regulation method with a soft elastomer of diphenylmethane diisocyanate polyurethane (MDI-PU). The crystallinity and flexural strain resistance of such perovskite films on plastics with silver-nanowire-based flexible transparent electrodes are highly enhanced. The detrimental cracks induced during repeated deformation can be effectively self-healed under heat treatment via intramolecular/intermolecular hydrogen bonds with MDI-PU. Upon collective optimization of the perovskite films and device architecture, the blue-emitting flexible PeLEDs can achieve a record external quantum efficiency of 13.5% and high resistance to flexural strain, which retain 87.8 and 80.7% of their initial efficiency after repeated bending and twisting operations of 2000 cycles, respectively.

Clinical N3 is an independent risk factor of recurrence for breast cancer patients achieving pathological complete response and near-pathological complete response after neoadjuvant chemotherapy

Background

Although achieving pathological complete response (pCR) and near-pathological complete response (near-pCR) after neoadjuvant chemotherapy (NAC) in breast cancer predicts a better outcome, some patients still experience recurrence. The aim of our study was to investigate the predictive factors of recurrence in the pCR and near-pCR population.

Methods

We reviewed 1,209 breast cancer patients treated with NAC between January 2010 and April 2021 in the Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS). A total of 292 patients achieving pCR and near-pCR were included in our analysis. pCR was defined as ypT0N0/ypTisN0. Near-pCR was defined as ypT1mi/1a/1bN0 or ypT0/isN1mi. Clinical features and follow-up information were collected. Survival and predictive factors of recurrence were analyzed.

Results

Of the 292 patients, 173 were pCR and 119 were near-pCR. The median age was 46 years (range, 23–75 years). The predominant tumor subtypes were human epidermal growth factor receptor type 2 (HER2)-positive breast cancer (49.0%) and triple-negative breast cancer (TNBC) (30.8%). The median duration of follow-up was 53 months (range, 9–138 months). A total of 25 (8.6%) patients developed recurrence, with 9 (5.2%) in the pCR group and 16 (13.4%) in the near-pCR group. The vast majority of recurrence occurred within 36 months from onset of NAC. The 5-year recurrence-free survival (RFS) rate of patients achieving pCR was significantly higher than that of patients achieving near-pCR (94.6% vs. 85.6%, p = 0.008). However, the 5-year overall survival (OS) rate between the two cohorts had no statistical difference (94.3% vs. 89.6%, p = 0.304). Clinical N3 (cN3) before NAC was an independent risk factor of recurrence in patients who achieved pCR (p = 0.003) and near-pCR (p = 0.036). Tumor size before NAC, subtypes of breast cancer, and chemotherapy regimens showed no significant association with RFS both for patients who achieved pCR and for those who achieved near-pCR (p > 0.05).

Conclusions

cN3 before NAC was an independent risk factor of recurrence in patients who achieved pCR and near-pCR. It is worthwhile to closely monitor patients with cN3, especially in the first 3 years.

Prospective pilot study of tirofiban in progressive stroke after intravenous thrombolysis

Background

Intravenous thrombolysis (IVT) is a standard procedure for the treatment of patients with acute ischemic stroke (AIS). Improving the therapeutic efficacy of IVT is an important task for neurologists. The aim of this study was to evaluate the efficacy and safety of early low-dose tirofiban treatment in AIS patients with early neurological deterioration (END) after IVT.

Methods

In this prospective and randomized pilot study, 73 AIS patients with END were recruited from a local hospital in China. Of these, 14 patients were treated with regular antiplatelet agents (aspirin plus clopidogrel) and 59 patients were treated with tirofiban within 24 h of IVT, followed by regular antiplatelet therapy. Neurological deficits and functional recovery were assessed with NIHSS and modified Rankin Scale (mRS) at 7 and 90 days. During the 90-day follow-up period, both hemorrhagic (e.g., intracerebral hemorrhage) and non-hemorrhagic (e.g., pneumonia) events were recorded.

Results

Treatment with tirofiban compared with regular antiplatelet therapy: (1) improved functional recovery of AIS patients to mRS (≤2) at both 7 and 90 days (odds ratios [ORs], 1.37 and 1.64; 95% confidence interval [CI], 1.16–1.61 and 1.26–2.12; P = 0.008 and < 0.001, respectively), and (2) reduced NIHSS scores from 11.14 ± 2.38 to 5.95 ± 3.48 at day 7 (P < 0.001) and from 8.14 ± 2.74 to 4.08 ± 3.50 at day 90 (P < 0.001). Tirofiban treatment did not increase the risk of hemorrhagic complications. Multivariate regression analysis showed that tirofiban treatment independently predicted a favorable functional outcome (P ≤ 0.001).

Conclusion

Early treatment with low-dose tirofiban in AIS patients with neurologic deterioration after IVT potentially improved functional recovery and attenuated neurologic deficits as early as 7 days and did not increase the risk of various hemorrhagic complications. However, the therapeutic efficacy of tirofiban treatment in END patients needs to be determined by future randomized clinical trials with a large study population.

Clinical trial registration

http://www.chictr.org.cn/, Identifier ChiCTR2200058513.

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.

Spatial Resolution of Mycobacterium tuberculosis Bacteria and Their Surrounding Immune Environments Based on Selected Key Transcripts in Mouse Lungs

Mycobacterium tuberculosis (Mtb) bacilli are the causative agent of tuberculosis (TB), a major killer of mankind. Although it is widely accepted that local interactions between Mtb and the immune system in the tuberculous granuloma determine whether the outcome of infection is controlled or disseminated, these have been poorly studied due to methodological constraints. We have recently used a spatial transcriptomic technique, in situ sequencing (ISS), to define the spatial distribution of immune transcripts in TB mouse lungs. To further contribute to the understanding of the immune microenvironments of Mtb and their local diversity, we here present two complementary automated bacteria-guided analysis pipelines. These position 33 ISS-identified immune transcripts in relation to single bacteria and bacteria clusters. The analysis was applied on new ISS data from lung sections of Mtb-infected C57BL/6 and C3HeB/FeJ mice. In lungs from C57BL/6 mice early and late post infection, transcripts that define inflammatory macrophages were enriched at subcellular distances to bacteria, indicating the activation of infected macrophages. In contrast, expression patterns associated to antigen presentation were enriched in non-infected cells at 12 weeks post infection. T-cell transcripts were evenly distributed in the tissue. In Mtb-infected C3HeB/FeJ mice, transcripts characterizing activated macrophages localized in apposition to small bacteria clusters, but not in organized granulomas. Despite differences in the susceptibility to Mtb, the transcript patterns found around small bacteria clusters of C3HeB/FeJ and C57BL/6 mice were similar. Altogether, the presented tools allow us to characterize in depth the immune cell populations and their activation that interact with Mtb in the infected lung.

Clinical N3 as an independent risk factor of recurrence for breast cancer patients achieving pathological complete response and near-pathological complete response after neoadjuvant chemotherapy

e12619

Background: Although achieving pathological complete response (pCR) and near-pathological complete response (near-pCR) after neoadjuvant chemotherapy (NAC) in breast cancer predicts a better outcome, some patients still experience recurrence. The aim of our study was to analyse the predictive factors of recurrence in the pCR and near-pCR population. Methods: We reviewed 1209 breast cancer patients treated with NAC. A total of 292 patients achieving pCR and near-pCR between January 2010 and April 2021 in the Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS) were included in our analysis. pCR was defined as ypT0N0/ypTisN0. Near-pCR was defined as ypT1mi/1a/1bN0 or ypT0/isN1mi. The Kaplan-Meier method with the log rank test was used to estimate recurrence analysis. Results: Of the 292 patients,173 were pCR and 119 were near-pCR. The median age was 46 years (range 23-75 years). The predominant tumor subtype was human epidermal growth factor receptor type 2 (HER2) positive (49.0%) and triple-negative breast cancer (TNBC) (30.8%). The median duration of follow-up was 53 months (range 9-138 months). A total of 16 (8.9%) patients developed recurrence, with 9 (5.2%) in the pCR group and 16 (13.4%) in the near-pCR group. The vast majority of recurrence occurred within 36 months from onset of NAC. The 5-year recurrence-free survival (RFS) rate of patients achieving pCR was significantly higher than that of patients achieving near-pCR (94.6% vs.85.6%, P=0.008). Clinical N3 (cN3) before NAC was an independent factor of higher risk for recurrence in patients who achieved pCR ( P=0.003) and near-pCR ( P=0.036). Tumor size before NAC, subtype of breast cancer and chemotherapy regimens showed no significant association with RFS both for patients who achieved pCR and near-pCR ( P ≥0.05). Conclusions: cN3 before NAC was an independent factor of higher risk for recurrence in patients who achieved pCR and near-pCR. It is worthwhile to monitor closely for patients with cN3, especially in the first 3 years.

[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.

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.

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.

Optimal encephalitis/meningitis roadmap via precise diagnosis and treatment (IMPROVE): a study protocol for a randomized controlled trial

BACKGROUND

Encephalitis/meningitis brings a heavy disease burden, and the origin of disease remains unknown in 30-40% of patients. It is greatly significant that combinations of nucleic acid amplification and autoimmune antibody testing improves the diagnosis and treatment of encephalitis/meningitis. Moreover, though several diagnostic methods are in clinical use, a recognized and unified diagnosis and treatment process for encephalitis management remains unclear.

METHODS

IMPROVE is a multicenter, open label, randomized controlled clinical trial that aims to evaluate the diagnostic performance, applications, and impact on patient outcomes of a new diagnostic algorithm that combines metagenomic next-generation sequencing (mNGS), multiplex polymerase chain reaction (PCR) and autoimmune antibody testing. The enrolled patients will be grouped into two parallel groups, multiplex PCR test plus autoimmune antibody group (Group I) or the mNGS plus autoimmune antibody group (Group II) with a patient ratio of 1:1. Both groups will be followed up for 12 months. The primary outcomes include the initial time of targeted treatment and the modified Rankin scale score on the 30th day of the trial. The secondary outcomes are the cerebrospinal fluid index remission rate on the 14th day, mortality rate on the 30th day, and an evaluation of diagnostic efficacy. The two groups are predicted to comprise of 484 people in total.

DISCUSSION

To optimize the roadmap of encephalitis/meningitis, precise diagnosis, and treatment are of great significance. The effect of rapid diagnosis undoubtedly depends on the progression of new diagnostic tests, such as the new multiplex PCR, mNGS, and examination of broad-spectrum autoimmune encephalitis antibodies. This randomized-controlled study could allow us to obtain an accurate atlas of the precise diagnostic ability of these tests and their effect on the treatment and prognosis of patients. Trial registration ClinicalTrial.gov, NCT04946682. Registered 29 June 2021, 'Retrospectively registered', https://clinicaltrials.gov/ct2/show/NCT04946682?term=NCT04946682&draw=2&rank=1.

Amine-functionalized MCM-41 as an efficient catalyst for the synthesis of sulfur/dinitrogen-fused heterocycles via [3+3] cycloaddition in water

The sulfur/dinitrogen-containing heterocycle is an important chemical motif and is found widely in natural products, drug molecules, catalysts and functional materials.

[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.

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^{-}.

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.

[Risk factors and risk model of lymph node metastasis in early gastric cancer]

To investigate the risk factors of lymph node metastasis in early gastric cancer (EGC) and to develop a risk model for the presence of lymph node metastasis. A total of 172 EGC patients, with a median age of 62(52, 68) years, who underwent gastric cancer resection in the First Affiliated Hospital of Nanjing Medical University from January 2017 to June 2019 were selected. Clinical data of the patients were collected through the case system. Logistic regression analysis was used to determine the variables significantly related to lymph node metastasis. ROC curve and calibration curve were used to evaluate the risk model. The results showed that the lymph node metastasis rate of 172 EGC patients was 19.19% (33/172). Tumor size, depth of invasion, degree of differentiation and vascular tumor thrombus were associated with lymph node metastasis (P<0.05), but age ≥ 60 years (OR=5.556, 95%CI: 1.757-17.569, P=0.004), invasion depth (OR=4.218,95%CI:1.418-12.548, P=0.010) and vascular cancer embolus (OR=13.878,95%CI:4.081-47.196,P<0.001) were independent risk factors for lymph node metastasis of EGC. The consistency index of the risk model based on the above risk factors was 0.8835 (95%CI: 0.818 8-0.948 2). The calibration curve shows that the risk assessment model is in good agreement with the actual results, indicating that the model has high accuracy and discrimination.The most common site of metastasis was group 3, followed by group 4. Therefore, patients over 60 years old with submucosal invasion and vascular tumor thrombus may have a higher risk of lymph node metastasis.