Missed opportunities for treatment of inflammatory arthritis and factors associated with non-treatment: An observational cohort study in United States Veterans with rheumatoid arthritis, psoriatic arthritis, or ankylosing spondylitis

OBJECTIVE

To identify factors associated with non-treatment with biologic and non-biologic disease modifying anti-rheumatic drugs (DMARDs) during the 12 months after initial inflammatory arthritis (IA) diagnosis.

METHODS

We identified Veterans with incident IA diagnosed in 2007-2019. We assessed time to treatment with Kaplan-Meier curves. We identified associations between non-treatment and factors relating to patients, providers, and the health system with multivariate Generalized Estimation Equation (GEE) log-Poisson. Subgroup analyses included IA subtypes (rheumatoid arthritis [RA], psoriatic arthritis [PsA], and ankylosing spondylitis [AS]) and timeframes of the initial IA diagnosis (2007-11, 2012-15, and 2016-19).

RESULTS

Of 18,318 study patients, 40.7 % did not receive treatment within 12 months after diagnosis. In all patients, factors associated with non-treatment included Black race (hazard ratio, 95 % confidence interval: 1.13, 1.08-1.19), Hispanic ethnicity (1.14, 1.07-1.22), Charlson Comorbidity Index ≥2, (1.15, 1.11-1.20), and opiate use (1.09, 1.05-1.13). Factors associated with higher frequency of DMARD treatment included married status (0.86, 0.81-0.91); erosion in joint imaging report (HR: 0.86, 0.81-0.91); female diagnosing provider (0.90, CI: 0.85-0.96), gender concordance between patient and provider (0.91, CI: 0.86-0.97), and diagnosing provider specialty of rheumatology (0.53, CI: 0.49-0.56).

CONCLUSION

A high proportion of Veterans with IA were not treated with a biologic or non-biologic DMARD within one year after their initial diagnosis. A wide range of factors were associated with non-treatment of IA that may represent missed opportunities for improving the quality of care through early initiation of DMARDs.

Arginine and its metabolites stimulate proliferation, differentiation, and physiological function of porcine trophoblast cells through β-catenin and mTOR pathways

Arginine, which is metabolized into ornithine, proline, and nitric oxide, plays an important role in embryonic development. The present study was conducted to investigate the molecular mechanism of arginine in proliferation, differentiation, and physiological function of porcine trophoblast cells (pTr2) through metabolic pathways. The results showed that arginine significantly increased cell viability (P < 0.05). The addition of arginine had a quadratic tendency to increase the content of progesterone (P = 0.06) and protein synthesis rate (P = 0.03), in which the maximum protein synthesis rate was observed at 0.4 mM arginine. Arginine quadratically increased (P < 0.05) the intracellular contents of spermine, spermidine and putrescine, as well as linearly increased (P < 0.05) the intracellular content of NO in a dose-dependent manner. Arginine showed a quadratic tendency to increase the content of putrescine (P = 0.07) and a linear tendency to increase NO content (P = 0.09) in cell supernatant. Moreover, increasing arginine activated (P < 0.05) the mRNA expressions for ARG, ODC, iNOS and PCNA. Furthermore, inhibitors of arginine metabolism (L-NMMA and DFMO) both inhibited cell proliferation, while addition of its metabolites (NO and putrescine) promoted the cell proliferation and cell cycle, the mRNA expressions of PCNA, EGF and IGF-1, and increased (P < 0.05) cellular protein synthesis rate, as well as estradiol and hCG secretion (P < 0.05). In conclusion, our results suggested that arginine could promote cell proliferation and physiological function by regulating the metabolic pathway. Further studies showed that arginine and its metabolites modulate cell function mainly through β-catenin and mTOR pathways.

[Imaging risk factors for postoperative cerebral infarction in adults with ischemic Moyamoya disease]

Objective: To investigate the imaging factors associated with postoperative cerebral infarction in adult patients aged 18 and above with ischemic Moyamoya disease. Methods: The clinical data of adult patients who underwent surgeries for ischemic Moyamoya disease in the Department of Neurosurgery at Peking University International Hospital from October 2015 to October 2020 were retrospectively analyzed. Of the 239 patients, 120 were male and 119 were female, with ages ranging from 18 to 63 (41.7±10.3) years. A total of 239 patients(290 cases) underwent direct and indirect combined revascularization (CR).Gender, age, surgical side, preoperative transient ischemic attack (TIA), presence of old cerebral infarction, and imaging features were compared between the patients with (48 cases) and without (242 cases) cerebral infarction within 1 week after surgery. Multivariate logistic binary regression model was used to analyze the imaging risk factors of postoperative cerebral infarction. Results: Cerebral infarction occurred in 48 cases(16.5%) among the 290 CR group within 1 week after surgery. The proportion of patients with TIA, old cerebral infarction, ICA stenosis, A1 segment stenosis, M1 segment stenosis, abnormal posterior cerebral artery (PCA), and unstable compensation before CR in the cerebral infarction group was higher than that in the non-cerebral infarction group (P<0.05).Preoperative TIA (OR=4.514, 95%CI: 1.920-10.611), old cerebral infarction (OR=2.856,95%CI:1.176-6.936), A1 stenosis (OR=7.027,95%CI:1.877-26.308), M1 stenosis (OR=6.968,95%CI:2.162-22.459), abnormal PCA (OR=4.114,95%CI:1.330-12.728)and unstable compensation (OR=4.488,95%CI:1.194-16.865) were risk factors for cerebral infarction after CR surgery (all P<0.05). Conclusion: Among the imaging factors, TIA, old cerebral infarction, A1 stenosis, M1 stenosis, abnormal PCA and unstable compensation were risk factors for cerebral infarction in adult patients with ischemic Moyamoya disease treated by combined revascularization.

Disturbance of mitochondrial dynamics in myocardium of broilers with pulmonary hypertension syndrome

1. The following study investigated the relationship between pulmonary hypertension syndrome (PHS) and mitochondrial dynamics in broiler cardiomyocytes.2. An animal model for PHS was established by injecting broiler chickens with CM-32 cellulose particles. Broiler myocardial cells were cultured under hypoxic conditions to establish an in vitro model. The ascites heart index, histomorphology, mitochondrial ultrastructure, and mitochondrial dynamic-related gene and protein expression were evaluated.3. The myocardial fibres from PHS broilers had wider spaces and were wavy and twisted and the number of mitochondria increased. Compared with the control group, the gene and protein expression levels were decreased for Opa1, Mfn1, and Mfn2 in the myocardium of PHS broilers. The gene and protein expression was significantly increased for Drp1 and Mff.4. This study showed that PHS in broilers may cause myocardial mitochondrial dysfunction, specifically by diminishing mitochondrial fusion and enhancing fission, causing disturbances in the mitochondrial dynamics of the heart.

Positive fluid balance and poor outcomes after initial intensive care unit admission in sepsis resuscitation: a retrospective study

Introduction

Fluid resuscitation of patients with sepsis is crucial. This study explored the role of fluid balance in the early resuscitation of sepsis patients in the intensive care unit (ICU).

Material and methods

A retrospective study of patients with sepsis using the Peking Union Medical College Hospital Intensive Care Medical Information System and Database from January 2014 to June 2020 was performed. Based on the survival status on day 28, the training cohort was divided into an alive group (n = 1,803) and a deceased group (n = 429). Univariate and multivariate analyses were used to identify risk factors, and the integrated learning XGBoost algorithm was used to construct a model for predicting outcomes. ROC and Kaplan-Meier survival curves were used to evaluate the effectiveness of the model. A verification cohort (n = 433) was used to verify the model.

Results

Univariate analysis showed that fluid balance is an important covariate. Based on the scatterplot distribution, a significant difference in mortality was determined between groups stratified with a balance of 1000 ml. There were associations in the multivariate analysis between poor outcomes and sex, PO2/FiO2, serum creatinine, FiO2, platelets, respiratory rate, SPO2, temperature, and total fluid volume (1000 ml). Among these variables, total fluid balance (1000 ml) had an OR of 1.98 (CI: 1.41-2.77, p < 0.001). Therefore, the model was built with these nine factors using XGBoost. Cross validation was used to verify generalizability. This model performed better than the SOFA and APACHE II models. The result was well verified in the verification cohort. A causal forest model suggested that patients with hypoxemia may suffer from positive fluid balance.

Conclusions

Sepsis fluid resuscitation in the ICU should be a targeted and goal-oriented treatment. A new prognostic prediction model was constructed and indicated that a 6-hour positive fluid balance after ICU initial admission is a risk factor for poor outcomes in sepsis patients. A 6-hour fluid balance above 1000 ml should be performed with caution.

Study of High-Transverse-Momentum Higgs Boson Production in Association with a Vector Boson in the qqbb Final State with the ATLAS Detector

This Letter presents the first study of Higgs boson production in association with a vector boson (V=W or Z) in the fully hadronic qqbb final state using data recorded by the ATLAS detector at the LHC in proton-proton collisions at sqrt[s]=13  TeV and corresponding to an integrated luminosity of 137  fb^{-1}. The vector bosons and Higgs bosons are each reconstructed as large-radius jets and tagged using jet substructure techniques. Dedicated tagging algorithms exploiting b-tagging properties are used to identify jets consistent with Higgs bosons decaying into bb[over ¯]. Dominant backgrounds from multijet production are determined directly from the data, and a likelihood fit to the jet mass distribution of Higgs boson candidates is used to extract the number of signal events. The VH production cross section is measured inclusively and differentially in several ranges of Higgs boson transverse momentum: 250-450, 450-650, and greater than 650 GeV. The inclusive signal yield relative to the standard model expectation is observed to be μ=1.4_{-0.9}^{+1.0} and the corresponding cross section is 3.1±1.3(stat)_{-1.4}^{+1.8}(syst)  pb.

[Pathogenetic analysis of transfusion-related acute lung injury caused by human leukocytes antigen antibody against human leukocyte antigen]

From September 2019 to October 2020, pathogenetic analysis of three patients clinically diagnosed as transfusion-related acute lung injury (TRALI) caused by human leukocyte antibodies was conducted by Guangzhou Blood Centre, including 2 males and 1 female, aged 56, 50 and 20 years old, respectively. Solid phase agglutination, anti-human globulin test and flow cytometry method were used to detect the presence of antibodies against patients. Sequencing-based human leukocyte antigen (HLA-SBT) typing technique was used to detect the human leukocyte antigen (HLA) genotypes of patients. Lifecodes single antigen class Ⅰ/Ⅱ kit (LSA-Ⅰ/Ⅱ) were used to detect the specificity of HLA-class Ⅰ and class Ⅱ antibodies in donor blood by Luminex 200 liquid suspension chip system. The HLA specific antibodies and corresponding epitopes in donors were also analyzed. The results showed that　HLA class Ⅰ or class Ⅱ specific antibodies against TRALI patients were detected in the blood donors. The plasma of donor 3 received by patient 1 contained antibodies against the patient's HLA-DRB1*09∶01 antigen, and the epitopes mediating the antibody reaction of the donor and recipient were 70R, 31I, 70QA. There were antibodies against the HLA-A*11∶02, HLA-A*11∶01, DRB1*12∶02, and DRB1*09∶01 antigens of patient 2 in the plasma of donor 4, and the associated antigenic epitopes were 151AHA, 57V, and 16Y. Antibodies against the HLA-DRB1*14∶04, DRB1*11∶01, and DPB1*05∶01 antigens of patient 3 were present in the plasma of donor 6 and donor 7, and the associated epitopes were 96HK, 140TV, 13SE, and 111K.　Three cases of TRALI were confirmed to be caused by HLA antibodies through laboratory analysis, and human leukocyte antibody detection should be paid attention in clinically suspected cases of TRALI, and targeted diagnosis and treatment should be given.

[Ophthalmic surgical robot-assisted retinal puncture and injection for submacular hemorrhage caused by polypoid choroidal vasculopathy: a case report]

A 65-year-old man presented with decreased visual acuity in the left eye for 1 month. The diagnosis of hemorrhagic retinal detachment (submacular hemorrhage), which was caused by idiopathic polypoid choroidal vasculopathy, was confirmed by the ultra-wide-angle fundus examination, optical coherence tomography, and B-ultrasound. A vitrectomy combined with an ophthalmic surgical robot-assisted retinal puncture and injection was performed. The recombinant tissue plasminogen activator was injected accurately by the ophthalmic surgical robot between the retinal nerve epithelium and retinal pigment epithelium through a micro-injection needle. During the 2-month follow-up, the subretinal hemorrhage was significantly regressive, the visual acuity of the left eye was improved from hand movement to 0.1, and no other complications were observed.

Measurement of the Centrality Dependence of the Dijet Yield in p+Pb Collisions at sqrt[s_{NN}]=8.16 TeV with the ATLAS Detector

ATLAS measured the centrality dependence of the dijet yield using 165  nb^{-1} of p+Pb data collected at sqrt[s_{NN}]=8.16  TeV in 2016. The event centrality, which reflects the p+Pb impact parameter, is characterized by the total transverse energy registered in the Pb-going side of the forward calorimeter. The central-to-peripheral ratio of the scaled dijet yields, R_{CP}, is evaluated, and the results are presented as a function of variables that reflect the kinematics of the initial hard parton scattering process. The R_{CP} shows a scaling with the Bjorken x of the parton originating from the proton, x_{p}, while no such trend is observed as a function of x_{Pb}. This analysis provides unique input to understanding the role of small proton spatial configurations in p+Pb collisions by covering parton momentum fractions from the valence region down to x_{p}∼10^{-3} and x_{Pb}∼4×10^{-4}.

Search for New Phenomena in Two-Body Invariant Mass Distributions Using Unsupervised Machine Learning for Anomaly Detection at sqrt[s]=13 TeV with the ATLAS Detector

Searches for new resonances are performed using an unsupervised anomaly-detection technique. Events with at least one electron or muon are selected from 140  fb^{-1} of pp collisions at sqrt[s]=13  TeV recorded by ATLAS at the Large Hadron Collider. The approach involves training an autoencoder on data, and subsequently defining anomalous regions based on the reconstruction loss of the decoder. Studies focus on nine invariant mass spectra that contain pairs of objects consisting of one light jet or b jet and either one lepton (e,μ), photon, or second light jet or b jet in the anomalous regions. No significant deviations from the background hypotheses are observed. Limits on contributions from generic Gaussian signals with various widths of the resonance mass are obtained for nine invariant masses in the anomalous regions.

Metabolomic analysis identifies dysregulation of lipid metabolism in the immune clearance phase of chronic hepatitis B patients

There is an accelerated progression of liver necroinflammation and fibrosis in the liver during the immune clearance (IC) phase of Chronic hepatitis B virus (HBV) infection, which are critical indicators of antiviral treatment for chronic hepatitis B (CHB) infection. This study applied serum metabolomics to identify the potential metabolite biomarkers for differential diagnosis between the CHB immune tolerance (IT) and Immune clearance (IC) phases. A liquid chromatography-mass spectrometry (LC-MS)-based approach was applied to evaluate and compared the serum metabolic profiles of 28 patients in IT phase and 33 patients in IC phase and appropriate statistical methods with MetaboAnalystR 2.0 R package to analyze those metabolites. The differential metabolites between IT and TC groups were classified and the top altered classification were lipids and lipid-like molecules and fatty acyls, clearly indicating that there were differences in the lipid metabolomic profile of HBV-infected patients with IT vs. IR phase. We identified the top 10 potential metabolite biomarkers for differential diagnosis between IT and IR. There were four lipid metabolites among them and the AUC of two of them, octadecadienoyl-sn-glycero-3-phosphocholine and 3-Cycloheptene-l-acetic acid, were 0.983 and 0.933. octadecadienoyl-sn-glycero-3-phosphocholine is Diacylglycerol (18:2n6/18:0) and 3-Cycloheptene-l-acetic acid is hydroxy fatty acids, both of which were associated with lipid metabolism. This study not only provides the potential metabolic biomarkers but also insight into the mechanism of CHB progression during IT clearance phase.

Observation of WZγ Production in pp Collisions at sqrt[s]=13 TeV with the ATLAS Detector

This Letter reports the observation of WZγ production and a measurement of its cross section using 140.1±1.2  fb^{-1} of proton-proton collision data recorded at a center-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. The WZγ production cross section, with both the W and Z bosons decaying leptonically, pp→WZγ→ℓ^{'}^{±}νℓ^{+}ℓ^{-}γ (ℓ^{(^{'})}=e, μ), is measured in a fiducial phase-space region defined such that the leptons and the photon have high transverse momentum and the photon is isolated. The cross section is found to be 2.01±0.30(stat)±0.16(syst)  fb. The corresponding standard model predicted cross section calculated at next-to-leading order in perturbative quantum chromodynamics and at leading order in the electroweak coupling constant is 1.50±0.06  fb. The observed significance of the WZγ signal is 6.3σ, compared with an expected significance of 5.0σ.

Role of Cisplatin in Inducing Acute Kidney Injury and Pyroptosis in Mice via the Exosome miR-122/ELAVL1 Regulatory Axis

Although cisplatin is an effective chemotherapy drug for the treatment of various cancers, its clinical use is limited due to its side effects, especially nephrotoxicity. Unfortunately, acute kidney injury (AKI) caused by cisplatin remains one of the main challenges in effective cancer treatment. Evidence increasingly suggests that renal inflammation and pyroptotic inflammatory cell death of renal tubular epithelial cells (RTECs) mainly determine the progression and outcome of cisplatin-induced AKI. However, it is not clear how cisplatin regulates the pyroptosis of RTECs cells in AKI. The current study aimed to determine the regulation mechanism of AKI induced by cisplatin. We used cisplatin to induce AKI in vivo. We performed H&E staining of mouse kidney tissue sections and evaluated serological indicators of kidney injury (including blood urea nitrogen (BUN), serum creatinine, and tumor necrosis factor-alpha (TNF-alpha)). We used immunohistochemistry and western blot to detect the important substrate protein gasdermin D (GSDMD) and key target caspase-1 of pyroptosis, respectively. Cisplatin induced mouse AKI and RTECs pyroptosis. HK2 cell-derived exosomes treated with cisplatin influenced pyroptosis of the surrounding HK2 cells. Cisplatin-treated HK2 cells exosome-derived miR-122 regulated pyroptosis in the surrounding cells. Exosome-derived miR-122 affected cisplatin-induced AKI and HK2 cells pyroptosis by regulating the expression of embryonic lethal abnormal vision (ELAVL1). These results suggest that exosome miR-122 inhibited pyroptosis and AKI by targeting ELAVL1 under cisplatin treatment, and this offers a potential target for the treatment of AKI.

Combined Measurement of the Higgs Boson Mass from the H→γγ and H→ZZ^{*}→4ℓ Decay Channels with the ATLAS Detector Using sqrt[s]=7, 8, and 13 TeV pp Collision Data

A measurement of the mass of the Higgs boson combining the H→ZZ^{*}→4ℓ and H→γγ decay channels is presented. The result is based on 140  fb^{-1} of proton-proton collision data collected by the ATLAS detector during LHC run 2 at a center-of-mass energy of 13 TeV combined with the run 1 ATLAS mass measurement, performed at center-of-mass energies of 7 and 8 TeV, yielding a Higgs boson mass of 125.11±0.09(stat)±0.06(syst)=125.11±0.11  GeV. This corresponds to a 0.09% precision achieved on this fundamental parameter of the Standard Model of particle physics.

Search for Dark Photons in Rare Z Boson Decays with the ATLAS Detector

A search for events with a dark photon produced in association with a dark Higgs boson via rare decays of the standard model Z boson is presented, using 139  fb^{-1} of sqrt[s]=13  TeV proton-proton collision data recorded by the ATLAS detector at the Large Hadron Collider. The dark boson decays into a pair of dark photons, and at least two of the three dark photons must each decay into a pair of electrons or muons, resulting in at least two same-flavor opposite-charge lepton pairs in the final state. The data are found to be consistent with the background prediction, and upper limits are set on the dark photon's coupling to the dark Higgs boson times the kinetic mixing between the standard model photon and the dark photon, α_{D}ϵ^{2}, in the dark photon mass range of [5, 40] GeV except for the ϒ mass window [8.8, 11.1] GeV. This search explores new parameter space not previously excluded by other experiments.

Discovery and bioassay of disubstituted β-elemene-NO donor conjugates: synergistic enhancement in the treatment of leukemia

Natural products are essential sources of antitumor drugs. One such molecule, β-elemene, is a potent antitumor compound extracted from Curcuma wenyujin. In the present investigation, a series of novel 13,14-disubstituted nitric oxide (NO)-donor β-elemene derivatives were designed, with β-elemene as the foundational compound, and subsequently synthesized to evaluate their therapeutic potential against leukemia. Notably, the derivative labeled as compound 13d demonstrated a potent anti-proliferative activity against the K562 cell line, with a high NO release. In vivo studies indicated that compound 13d could effectively inhibit tumor growth, exhibiting no discernible toxic manifestations. Specifically, a significant tumor growth inhibition rate of 62.9% was observed in the K562 xenograft tumor mouse model. The accumulated data propound the potential therapeutic application of compound 13d in the management of leukemia.

Association between Storage Time of Transfused Red Blood Cells and Infection after Clean-contaminated Surgery: A Retrospective Cohort Study

OBJECTIVE

The aim of this study was to investigate the association between storage time of transfused red blood cells and risks of infections after clean-contaminated surgery.

SUMMARY BACKGROUND DATA

Storage lesions of red blood cells can aggravate transfusion-related immunomodulation. Very few randomized controlled trials have investigated the impacts of storage time on postoperative outcomes in non-cardiac patients.

METHODS

We included adult patients who had undergone clean-contaminated surgery from 2014 to 2018 and received allogeneic red blood cell transfusion. In transfusion episode-level analysis, the exposure was the storage time of each transfusion episode. In patient-level analysis, the exposures were the mean, weighted mean, maximum storage time, and Scalar Age of Blood Index of red blood cells transfused into each patient. The primary outcome was infections that developed after transfusions within postoperative Day 30.

RESULTS

The 4046 included patients received 11604 transfusion episodes. Of these, 1025 (25.3%) patients developed postoperative infections. An increased storage time of transfused red blood cells was not associated with increased odds of postoperative infections in either transfusion episode-level analysis [odds ratio (OR) 1.03 per five days, 95% confidence interval (CI) 0.95 to 1.11] or patient-level analysis (mean: OR 1.02, 95% CI 0.95 to 1.10; weighted mean: OR 1.02, 95% CI 0.95 to 1.10; maximum: OR 1.06, 95% CI 0.98 to 1.14; Scalar Age of Blood Index: OR 0.99, 95% CI 0.96 to 1.03), after adjusting 17 confounders.

CONCLUSIONS

Prolonged storage time of transfused red blood cells was not associated with increased risks of infections after clean-contaminated surgery.

Observation of Single-Top-Quark Production in Association with a Photon Using the ATLAS Detector

This Letter reports the observation of single top quarks produced together with a photon, which directly probes the electroweak coupling of the top quark. The analysis uses 139  fb^{-1} of 13 TeV proton-proton collision data collected with the ATLAS detector at the Large Hadron Collider. Requiring a photon with transverse momentum larger than 20 GeV and within the detector acceptance, the fiducial cross section is measured to be 688±23(stat) _{-71}^{+75}(syst)  fb, to be compared with the standard model prediction of 515_{-42}^{+36}  fb at next-to-leading order in QCD.

Comparison of Ultrasound and CT Imaging for the Diagnosis of Coronavirus Disease and Influenza A Pneumonia

OBJECTIVE

The outbreak of coronavirus disease (COVID-19) coincided with the season of influenza A pneumonia, a common respiratory infectious disease. Therefore, this study compared ultrasonography and computed tomography (CT) for the diagnosis of the two diseases.

METHODS

Patients with COVID-19 or influenza A infection hospitalized at our hospital were included. The patients were examined by ultrasonography every day. The CT examination results within 1 day before and after the day of the highest ultrasonography score were selected as the controls. The similarities and differences between the ultrasonography and CT results in the two groups were compared.

RESULTS

There was no difference between the ultrasonography and CT scores (P = .307) for COVID-19, while there was a difference between ultrasonography and CT scores for influenza A pneumonia (P = .024). The ultrasonography score for COVID-19 was higher than that for influenza A pneumonia (P = .000), but there was no difference between the CT scores (P = .830). For both diseases, there was no difference in ultrasonography and CT scores between the left and right lungs; there were differences between the CT scores of the upper and middle lobes, as well as between the upper and lower lobes of the lungs; however, there was no difference between the lower and middle lobes of the lungs.

CONCLUSION

Ultrasonography is equivalent to the gold standard CT for diagnosing and monitoring the progression of COVID-19. Because of its convenience, ultrasonography has important application value. Furthermore, the diagnostic value of ultrasonography for COVID-19 is higher than that for influenza A pneumonia.

Measurement of Suppression of Large-Radius Jets and Its Dependence on Substructure in Pb+Pb Collisions at sqrt[s_{NN}]=5.02 TeV with the ATLAS Detector

This letter presents a measurement of the nuclear modification factor of large-radius jets in sqrt[s_{NN}]=5.02  TeV Pb+Pb collisions by the ATLAS experiment. The measurement is performed using 1.72  nb^{-1} and 257  pb^{-1} of Pb+Pb and pp data, respectively. The large-radius jets are reconstructed with the anti-k_{t} algorithm using a radius parameter of R=1.0, by reclustering anti-k_{t} R=0.2 jets, and are measured over the transverse momentum (p_{T}) kinematic range of 158 Collapse

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Measurement of the Sensitivity of Two-Particle Correlations in pp Collisions to the Presence of Hard Scatterings

A key open question in the study of multiparticle production in high-energy pp collisions is the relationship between the "ridge"-i.e., the observed azimuthal correlations between particles in the underlying event that extend over all rapidities-and hard or semihard scattering processes. In particular, it is not known whether jets or their soft fragments are correlated with particles in the underlying event. To address this question, two-particle correlations are measured in pp collisions at sqrt[s]=13  TeV using data collected by the ATLAS experiment at the LHC, with an integrated luminosity of 15.8  pb^{-1}, in two different configurations. In the first case, charged particles associated with jets are excluded from the correlation analysis, while in the second case, correlations are measured between particles within jets and charged particles from the underlying event. Second-order flow coefficients, v_{2}, are presented as a function of event multiplicity and transverse momentum. These measurements show that excluding particles associated with jets does not affect the measured correlations. Moreover, particles associated with jets do not exhibit any significant azimuthal correlations with the underlying event, ruling out hard processes contributing to the ridge.

Observation of an Excess of Dicharmonium Events in the Four-Muon Final State with the ATLAS Detector

A search is made for potential ccc[over ¯]c[over ¯] tetraquarks decaying into a pair of charmonium states in the four muon final state using proton-proton collision data at sqrt[s]=13  TeV, corresponding to an integrated luminosity of 140  fb^{-1} recorded by the ATLAS experiment at LHC. Two decay channels, J/ψ+J/ψ→4μ and J/ψ+ψ(2S)→4μ, are studied. Backgrounds are estimated based on a hybrid approach involving Monte Carlo simulations and data-driven methods. Statistically significant excesses with respect to backgrounds dominated by the single parton scattering are seen in the di-J/ψ channel consistent with a narrow resonance at 6.9 GeV and a broader structure at lower mass. A statistically significant excess is also seen in the J/ψ+ψ(2S) channel. The fitted masses and decay widths of the structures are reported.

Observation of the γγ→ττ Process in Pb+Pb Collisions and Constraints on the τ-Lepton Anomalous Magnetic Moment with the ATLAS Detector

This Letter reports the observation of τ-lepton-pair production in ultraperipheral lead-lead collisions Pb+Pb→Pb(γγ→ττ)Pb and constraints on the τ-lepton anomalous magnetic moment a_{τ}. The dataset corresponds to an integrated luminosity of 1.44  nb^{-1} of LHC Pb+Pb collisions at sqrt[s_{NN}]=5.02  TeV recorded by the ATLAS experiment in 2018. Selected events contain one muon from a τ-lepton decay, an electron or charged-particle track(s) from the other τ-lepton decay, little additional central-detector activity, and no forward neutrons. The γγ→ττ process is observed in Pb+Pb collisions with a significance exceeding 5 standard deviations and a signal strength of μ_{ττ}=1.03_{-0.05}^{+0.06} assuming the standard model value for a_{τ}. To measure a_{τ}, a template fit to the muon transverse-momentum distribution from τ-lepton candidates is performed, using a dimuon (γγ→μμ) control sample to constrain systematic uncertainties. The observed 95% confidence-level interval for a_{τ} is -0.057 Collapse

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Dose Painting Radiotherapy Guided by Diffusion-Weighted Magnetic Resonance vs. 18F-FDG-PET/CT in Locoregionally Advanced Nasopharyngeal Carcinoma: A Randomized, Controlled Clinical Trial

PURPOSE/OBJECTIVE(S)

This phase II randomized controlled trial aimed at comparing the efficacy and toxicity of diffusion-weighted magnetic resonance imaging (DWI)-guided dose painting radiotherapy (DP-RT), FDG-PET/CT-guided DP-RT, and conventional MRI-based radiotherapy (RT) in locoregionally advanced nasopharyngeal carcinoma (NPC).

MATERIALS/METHODS

A total of 330 patients with stage III-IVa NPC disease were randomly assigned in a 1:1:1 ratio to receive induction chemotherapy followed by concurrent chemoradiotherapy by DWI-guided DP-RT (group A, n = 110), FDG-PET/CT-guided DP-RT (group B, n = 110), or conventional MRI-based RT (group C, n = 110). All patients received volumetric modulated arc therapy (VMAT). In group A, subvolume GTVnx-DWI (gross tumor volume of nasopharynx in DWI) was defined as the areas within the GTVnx (gross tumor volume of nasopharynx) with an apparent diffusion coefficient (ADC) below the mean ADC (ADC < mean). In group B, subvolume GTVnx-PET (gross tumor volume of nasopharynx in PET images) was defined within GTVnx as the SUV50%max isocontour. The doses to GTVnx-DWI in group A and GTVnx-PET in group B were escalated to 75.2 Gy/32 fx in patients with T1-2 disease and to 77.55 Gy/33 fx in those with T3-4 disease in 2.35 Gy per fraction. In group C, planning gross tumor volume of nasopharynx (PGTVnx) was irradiated at 70.4 to 72.6 Gy/32 to 33 fx in 2.2 Gy per fraction. This trial is registered with chictr.org.cn (ChiCTR2200057476).

RESULTS

Group A and B showed significant higher complete response (CR) rates than group C (100%, 100%, and 96.4% for group A, B and C, respectively, p = 0.036). In groups A, B and C, the 1-year local recurrence-free survival (LRFS) rates were 100%, 100%, and 94.5%, respectively (p = 0.002). The 1-year disease-free survival (DFS) rates were 100%, 99.1%, and 92.7%, respectively (p = 0.001). The 1-year distant metastasis-free survival (DMFS) rates were 100%, 99.1%, and 93.6%, respectively (p = 0.004). The 1-year overall survival (OS) rates were 100%, 100%, and 95.4%, respectively (p = 0.006). Group A and B had significantly higher 1-year LRFS, DFS, DMFS, and OS than those in group C. No significant differences were observed in LRFS, DFS, DMFS and OS between group A and B. Group B (PET/CT group) had a higher incidence of grade 3-4 acute ototoxicity (3.6%) than group A (0%) and group C (0%, p = 0.036). No significant differences in other grade 3-4 acute adverse events and late toxic effects were observed among the three groups, and no patient had grade 5 toxicities. Multivariate analysis showed that dose painting (DWI-guided DP-RT and PET/CT-guided DP-RT vs conventional MRI-based RT) was associated with improved LRFS, DFS, DMFS and OS.

CONCLUSION

Both DWI-guided DP-RT and PET/CT-guided DP-RT plus chemotherapy are associated with improved LRFS, DFS, DMFS and OS compared with conventional MRI-based RT among patients with locoregionally advanced NPC. DWI-guided DP-RT does not increase toxicities, but PET/CT-guided DP-RT has higher incidence of acute ototoxicity.

Associations of Hyponatremia with Cognition Function and All-Cause Mortality: Post Hoc Analysis of the Systolic BP Intervention Trial

Key Points

Incident hyponatremia is associated with probable dementia. Incident hyponatremia does not seem to be associated with mild cognitive impairment or death.

Background

Acute neurologic effects of severe hyponatremia are well-known. However, the long-term association of hyponatremia with cognitive impairment is unclear.

Methods

In this post hoc analysis of the Systolic Blood Pressure Intervention Trial, we examined whether incident hyponatremia is a risk factor of mild cognitive impairment (MCI) or probable dementia (PD). In those with baseline serum sodium level ≥130 mmol/L, we defined incident hyponatremia in the first 6 months as a Systolic Blood Pressure Intervention Trial safety alert for serum sodium level <130 mmol/L from randomization to the 6-month visit. In multivariate Cox regression models adjusted for baseline cognitive function and other variables, we related incident hyponatremia in the first 6 months with subsequent MCI or PD in 8540 participants with cognitive outcomes data and with all-cause mortality (ACM) in 9135 participants with mortality data.

Results

Incident hyponatremia in the first 6 months was noted in 116 participants (1.4%). Older age, female sex, non-Black race, lower body mass index, and randomization to intensive systolic BP control were associated with incident hyponatremia. Compared with those without hyponatremia, those with incident hyponatremia had higher risk of PD (2.1 versus 0.9 events/100 person-years; hazard ratio [HR], 3.08; 95% confidence interval [CI], 1.48 to 6.41) but not MCI (3.1 versus 3.6 events/100 person-years; HR, 0.95; 95% CI, 0.54 to 1.68) and the composite of MCI/PD (5.0 versus 4.2 events/100 person-years; HR, 1.28; 95% CI, 0.82 to 2.0). There were no significant differences in ACM (HR, 1.84; 95% CI, 0.90 to 3.73).

Conclusions

Biologic plausibility for the association of incident hyponatremia with PD but not MCI or death is unclear. The association of incident hyponatremia with PD could reflect a chance finding or noncausal biologic association or causal relationship.

Deep Learning-Based Multi-Modality Segmentation of Primary Gross Tumor Volume in CT and MRI for Nasopharyngeal Carcinoma

PURPOSE/OBJECTIVE(S)

The delineation of primary gross tumor volume (GTV) of nasopharyngeal carcinoma (NPC) is an essential step for radiotherapy planning. In clinical practice, radiation oncologists manually delineate the GTV in planning CT with the help of diagnostic MRI. This is because NPC tumors are closely adjacent to many important anatomic structures, and CT and MRI provide complementary strength to accurately determine the tumor extension boundary. Manual delineation is time-consuming with the potential registration errors between MRI and CT decreasing the delineation accuracy. In this study, we propose a fully automated GTV segmentation method based on CT and MRI by first aligning MRI to CT, and then, segmenting the GTV using a multi-modality deep learning model.

MATERIALS/METHODS

We collected 104 nasopharyngeal carcinoma patients with both planning CT and diagnostic MRI scans (T1 & T2 phases). An experienced radiation oncologists manually delineated the GTV, which was further examined by another senior radiation oncologist. Then, a coarse to fine cross-modality registration from MRI to CT was conducted as follows: (1) A rigid transformation was performed on MRI to roughly align MRI to CT with similar anatomic position. (2) Then, the region of interest (RoI) on both CT and rigid-transformed MRI were cropped. (3) A leading cross-modality deformable registration algorithm, named DEEDS, was applied on the cropped MRI and CT RoIs to find an accurate local alignment. Next, using CT and registered MRI as the combined input, a multi-modality deep segmentation network based on nnUNet was trained to generate the GTV prediction. 20% patients were randomly selected as the unseen testing set to quantitatively evaluate the performance.

RESULTS

The quantitative NPC GTV segmentation performance is summarized in Table 1. The deep segmentation model using CT alone achieved reasonable high performance with 76.6% Dice score and 1.34mm average surface distance (ASD). When both CT and registered MRI were used, the segmentation model further improved the performance by 0.9% Dice score increase and 11% relative ASD error reduction, demonstrating the complementary strength of CT and MRI in determining NPC GTV. Notably, the achieved 77.5% Dice score and 1.19mm ASD by the multimodality model is among the top performing results reported in recent automatic NPC GTV segmentation using either CT or MRI modality.

CONCLUSION

We developed a fully automated multi-modal deep-learning model for NPC GTV segmentation. The developed model can segment the NPC GTV in high accuracy. With further optimization and validation, this automated model has potential to standardize the NPC GTV segmentation and significantly decrease the workload of radiation oncologists in clinical practice.

Efficacy and Toxicity of Different Target Volume Delineations of Radiotherapy Based on the Updated RTOG/NRG and EORTC Guidelines in Patients with High Grade Glioma: A Randomized, Controlled Clinical Trial

PURPOSE/OBJECTIVE(S)

Postoperative radiotherapy with concomitant and adjuvant temozolomide (TMZ) is the standard of care for newly diagnosed high grade glioma, but the optimal method for target volume delineations for intensity modulated radiation therapy (IMRT) is still unclear. We hypothesized that compared with the EORTC guidelines, IMRT based on the updated RTOG/NRG guidelines was equally effective, without increasing toxicities for patients with high-grade glioma. The purpose of this randomized phase 2 study was to compare the efficacy and toxicity of IMRT based on different target volume delineations (updated RTOG/NRG versus EORTC guidelines) with concomitant and adjuvant TMZ for patients with high grade glioma.

MATERIALS/METHODS

A total of 302 patients with newly diagnosed high-grade glioma (WHO grade 3-4) were randomly assigned (1:1) to receive postoperative IMRT based on either updated RTOG/NRG guidelines (RTOG/NRG group, n = 151) or EORTC guideline (EORTC group, n = 151), with concomitant and adjuvant TMZ. In the RTOG/NRG group, an initial volume consisting of enhancement, postoperative cavity, plus surrounding edema (or fluid-attenuated inversion recovery [FLAIR] abnormality defined by magnetic resonance imaging [MRI]) and a 2-cm margin received 46 Gy in 23 fractions followed by a boost of 14 Gy in 7 fractions to the area of enhancement plus the cavity and a 2-cm margin. In the EORTC group, a single planning volume was used to deliver 60 Gy in 30 fractions to the area of enhancement and the cavity with a 2-cm margin. The primary end point was overall survival (OS). Secondary end points included progression-free survival (PFS) and toxicities associated with each treatment.

RESULTS

No statistically significant differences were observed between groups for 1-year OS (71.8% for RTOG/NRG group and 69.9% for EORTC group, respectively; P = 0.759) or 1-year PFS (46.7% for RTOG/NRG group and 43.6% for EORTC group, respectively; P = 0.674). Efficacy did not differ by MGMT methylation status. There were no differences in grade 3-4 toxicities (leukopenia, lymphopenia, neutropenia, thrombocytopenia, fatigue, nausea and vomiting) between the two groups. No grade 5 toxicities were observed in both groups. Multivariate analyses showed that tumor MGMT status (methylated vs unmethylated) and WHO grade (grade 3 vs grade 4) were associated with OS and PFS. However, radiation type (RTOG/NRG group vs EORTC), sex, age, and Karnofsky scale did not significantly influence OS or PFS.

CONCLUSION

Compared with EORTC guidelines for postoperative radiotherapy, IMRT based on RTOG/NRG guidelines was equally effective, without increasing toxicities for patients with high-grade glioma. This trial is registered with chictr.org.cn, number ChiCTR2100046667.

Homeostatic Balance of Gut Microbiota in Head and Neck Squamous Cell Carcinoma Patients during Radiotherapy

PURPOSE/OBJECTIVE(S)

Radiotherapy is the major treatment for head and neck squamous cell carcinoma (HNSCC). Oral microbiota changes have been described before. However, gut microbiota changes in HNSCC patients who received local radiotherapy remain unclear. We aim to investigate the dynamic change of gut microbiota composition in HNSCC patients undergoing radiotherapy and to construct the radiotherapy related gut microbiota database in HNSCC patients.

MATERIALS/METHODS

We enrolled 47 HNSCC patients who scheduled with radiotherapy solely. Intensity-modulated radiotherapy (IMRT) was the standard radiotherapy technique for all the enrolled patients. The field was irradiated with a total dose of 60-66Gy in 30-33 fractions. Fecal pellets were collected at three time points. Bacterial genomic DNA was isolated using magnetic beads and then analyzed by the Illumina MiSeq Sequencing System based on the V3-V4 hypervariable regions of the 16S rRNA gene.

RESULTS

A total of 194 genera which belonged to 27 phyla were found in 141 samples. Increased abundance of microbiota in diversity and richness was observed in mid-radiotherapy group. Moreover, Bacteroides, Blautia, and Phascolarctobacterium were three main genera in all three groups and the mid-radiotherapy group had the highest relative abundance of Phascolarctobacterium. What's more, most significantly altered bacteria shared the same variation pattern which was increased in mid-radiotherapy while decreased to the almost same level of as pre-radiotherapy in post-radiotherapy group.

CONCLUSION

Local radiotherapy can affect the composition of the gut microbiota in HNSCC patients during the mid-term of radiotherapy. However, self-stabilized ability maintained the gut microbiota homeostasis in the end.

Deep Learning for Automatic Prediction of Lymph Node Station Metastasis in Esophageal Cancer Patients from Contrast-Enhanced CT

PURPOSE/OBJECTIVE(S)

The diagnosis of lymph node (LN) metastasis in computed tomography (CT) is an essential yet challenging task in esophageal cancer staging and treatment planning. Although criteria (e.g., RECIST, morphological/texture features) are proposed to predict LN metastasis, the diagnostic accuracy remains low with sensitivity <50% and specificity <75%, as reported in previous studies. Deep learning (DL) has the potential to address this issue by learning from large-scale labeled data. However, due to the practical surgery procedure in lymph node dissection, it is difficult to pair the metastasis of individual LN reported in the pathology report to the LN instance found in the CT image. Hence, in this study, we first use pathology reports to determine the LNS metastasis, then develop a multiple instance deep learning (MIDL) model to predict lymph node station (LNS) metastasis.

MATERIALS/METHODS

We collected 1200 esophageal cancer patients with preoperative contrast-enhanced CT before surgery. A recently developed automatic mediastinal LNS segmentation model was first applied to segment LNS of 1 to 8 based on the IASLC protocol. For each LNS, the local CT region of interest (ROI) was cropped to generate a station-wise CT patch, where the LNS was labeled as metastatic if at least one metastatic LN was indicated in the pathology report. Using the station-wise CT patch and LNS label, we train a 3D MIDL model, MobileNetV3, to predict LNS metastasis. To better provide the LN position priors in MIDL, LN instances (with a short axis >4mm) were also segmented using an automatic LN detection algorithm and were added to the MIDL model as an auxiliary input. Five-fold cross-validation was conducted to evaluate the MIDL performance.

RESULTS

The MIDL model's performance is summarized in Table 1. The MIDL model incorporating an additional LN instance mask demonstrated a superior overall AUC of 0.7539, surpassing the model without the LN mask input by 2.93%. The specificity was evaluated at a threshold resulting in a recall of 0.7, and the best model outperformed the CT input model in terms of specificity by 2.11%. This highlights the value of including the LN position prior to the MIDL model. Notably, when a threshold was set to result in a specificity of 75%, the best MIDL model demonstrated a significantly higher recall compared to the previously reported clinical diagnostic recall (39.7% vs. 63.21%).

CONCLUSION

We developed a MIDL classification model to predict LNS metastasis using CT scans of 1200 patients. Our findings suggest that the MIDL model can substantially improve LNS metastasis prediction and has the potential to play an essential role in cancer staging, treatment planning, and prognostic analysis.

Anatomy-Guided Deep Learning Model for Accurate and Robust Gross Tumor Volume Segmentation in Lung Cancer Radiation Therapy

PURPOSE/OBJECTIVE(S)

In lung cancer radiation therapy, clinicians must outline the gross tumor volume (GTV) precisely on the planning computed tomography (pCT) for accurate radiation dose delivery. However, due to the limited contrast between tumor and normal tissues in lung parenchyma, accurate delineation of tumor boundaries is difficult leading to large inter-observer variation. In this study, we develop an anatomy-guided lung GTV deep segmentation model using a training cohort of multi-center datasets. The quantitative segmentation performance is evaluated on an independent dataset, where the inter-observer delineation variation is also assessed.

MATERIALS/METHODS

We collected and curated four publicly available lung datasets with GTV annotations (Lung-PET-CT-Dx, LIDC-IDRI, NSCLC-Radiogenomics and RIDER-CT) for deep learning model development. A total of 871 CT scans of patients, who were diagnosed with T1-T4 NSCLC, were available for training after data curation. The GTV annotations of primary tumor were examined and edited by two experienced radiation oncologists following the RTOG 1106 protocol. An anatomy-guided deep learning model was proposed, which consisted two deep networks. The first deep network used CT scan as input and segmented 4 anatomic organs (airway, heart, pulmonary artery and pulmonary vein), while the second deep network took both CT scan and these pre-segmented 4 organs as input and segmented the lung GTV. With the help of anatomic priors from 4 pre-segmented organs, the second deep network could more easily locate the GTV. We used nnUNet as the deep segmentation network. For evaluation, we used NSCLC-Radiomics as the testing dataset, which contains 20 CT scans each annotated by 5 radiation oncologists. The auto-segmented GTV were compared against each of the manual GTV reference. Inter-observer variation was also assessed using the 5 manual GTV references.

RESULTS

The proposed anatomic-guided lung GTV segmentation model achieved a mean Dice score of 82.4% and 95% Hausdorff distance (HD95) of 6.9mm when averaged cross 20 patients and 5 GTV references (Table 1), which outperformed the basic deep GTV segmentation model by markedly reducing 19.4% HD95 error. The performance of proposed model was also comparable to the inter-observer variation (Dice score: 82.4% vs. 81.9%, HD95 6.9 vs. 6.4mm), indicating that our model had similar reproducibility as human observers.

CONCLUSION

We developed and tested an anatomy-guided deep learning model for segmenting GTV in NSCLC patients. The model achieves high quantitative segmentation performance, which is comparable to the human observer variation. It can be potentially used in radiotherapy practice to improve GTV delineation consistency and reduce workloads of radiation oncologists.

Strong Constraints on Jet Quenching in Centrality-Dependent p+Pb Collisions at 5.02 TeV from ATLAS

Jet quenching is the process of color-charged partons losing energy via interactions with quark-gluon plasma droplets created in heavy-ion collisions. The collective expansion of such droplets is well described by viscous hydrodynamics. Similar evidence of collectivity is consistently observed in smaller collision systems, including pp and p+Pb collisions. In contrast, while jet quenching is observed in Pb+Pb collisions, no evidence has been found in these small systems to date, raising fundamental questions about the nature of the system created in these collisions. The ATLAS experiment at the Large Hadron Collider has measured the yield of charged hadrons correlated with reconstructed jets in 0.36  nb^{-1} of p+Pb and 3.6  pb^{-1} of pp collisions at 5.02 TeV. The yields of charged hadrons with p_{T}^{ch}>0.5  GeV near and opposite in azimuth to jets with p_{T}^{jet}>30 or 60 GeV, and the ratios of these yields between p+Pb and pp collisions, I_{pPb}, are reported. The collision centrality of p+Pb events is categorized by the energy deposited by forward neutrons from the struck nucleus. The I_{pPb} values are consistent with unity within a few percent for hadrons with p_{T}^{ch}>4  GeV at all centralities. These data provide new, strong constraints that preclude almost any parton energy loss in central p+Pb collisions.

Search for Heavy Neutral Leptons in Decays of W Bosons Using a Dilepton Displaced Vertex in sqrt[s]=13 TeV pp Collisions with the ATLAS Detector

A search for a long-lived, heavy neutral lepton (N) in 139  fb^{-1} of sqrt[s]=13  TeV pp collision data collected by the ATLAS detector at the Large Hadron Collider is reported. The N is produced via W→Nμ or W→Ne and decays into two charged leptons and a neutrino, forming a displaced vertex. The N mass is used to discriminate between signal and background. No signal is observed, and limits are set on the squared mixing parameters of the N with the left-handed neutrino states for the N mass range 3  GeV Collapse

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Test of CP Invariance in Higgs Boson Vector-Boson-Fusion Production Using the H→γγ Channel with the ATLAS Detector

A test of CP invariance in Higgs boson production via vector-boson fusion has been performed in the H→γγ channel using 139  fb^{-1} of proton-proton collision data at sqrt[s]=13  TeV collected by the ATLAS detector at the LHC. The optimal observable method is used to probe the CP structure of interactions between the Higgs boson and electroweak gauge bosons, as described by an effective field theory. No sign of CP violation is observed in the data. Constraints are set on the parameters describing the strength of the CP-odd component in the coupling between the Higgs boson and the electroweak gauge bosons in two effective field theory bases: d[over ˜] in the HISZ basis and c_{HW[over ˜]} in the Warsaw basis. The results presented are the most stringent constraints on CP violation in the coupling between Higgs and weak bosons. The 95% C.L. constraint on d[over ˜] is derived for the first time and the 95% C.L. constraint on c_{HW[over ˜]} has been improved by a factor of 5 compared to the previous measurement.

Performance of Off-the-Shelf Machine Learning Architectures and Biases in Detection of Low Left Ventricular Ejection Fraction

Artificial intelligence - machine learning (AI-ML) is a computational technique that has been demonstrated to be able to extract meaningful clinical information from diagnostic data that are not available using either human interpretation or more simple analysis methods. Recent developments have shown that AI-ML approaches applied to ECGs can accurately predict different patient characteristics and pathologies not detectable by expert physician readers. There is an extensive body of literature surrounding the use of AI-ML in other fields, which has given rise to an array of predefined open-source AI-ML architectures which can be translated to new problems in an "off-the-shelf" manner. Applying "off-the-shelf" AI-ML architectures to ECG-based datasets opens the door for rapid development and identification of previously unknown disease biomarkers. Despite the excellent opportunity, the ideal open-source AI-ML architecture for ECG related problems is not known. Furthermore, there has been limited investigation on how and when these AI-ML approaches fail and possible bias or disparities associated with particular network architectures. In this study, we aimed to: (1) determine if open-source, "off-the-shelf" AI-ML architectures could be trained to classify low LVEF from ECGs, (2) assess the accuracy of different AI-ML architectures compared to each other, and (3) to identify which, if any, patient characteristics are associated with poor AI-ML performance.

Evaluation of the mid- and long-term effects of a private car driving-restriction policy under the carbon emission peak target

To investigate the medium- and long-term effects of a driving-restriction policy under the guidance of carbon emission peak goals, Beijing was taken as an example to construct a private-car energy-conservation and emission-reduction model with dual-driving factors from the perspective of the economy, environment, technology, and energy. Through causal analysis, the negative effects of a driving-restriction policy were revealed. The simulation results revealed the following. (1) A driving-restriction policy is effective in the short term, but in the long run, it cannot effectively reduce CO2 emissions and the quantity of fuel used in private cars (e.g., the increase in the amount of CO2 pollution and degree of parking space tension, as well as the rebound of air pollution). (2) Compared with the driving-restriction policy, the combined policy reduces the amount of CO2 pollution by approximately 52.45%, the PM2.5 concentration by 21.98%, and the quantity of fuel in private cars by 33.42% by 2035, while improving the air quality by approximately 144.79%. Therefore, this article provides a theoretical basis for optimizing driving-restriction policies and achieving the carbon emission peak target.

Modeling and simulation analysis of vehicle pollution and carbon reduction management model based on system dynamics

The vehicle exhaust pollution has become an important source of air pollutant and CO2 emissions, with the continuous growth of the number of vehicles. Focusing on the increasingly serious problems of vehicle exhaust pollution and CO2 emissions, a management model of vehicle pollution reduction and carbon reduction was established by using system dynamics. Taking Beijing as the case study city, different emission reduction scenarios were designed. Different scenarios are analyzed, and the results reveal the following: (1) Although the carbon tax policy for motor vehicles can play a role in vehicle pollution reduction and carbon reduction to a certain extent, but as the simulation time goes on, the policy effect is gradually weakened. The emission reduction effect of new energy vehicle promotion policy is not significant, and there is a "lag effect" and a "seesaw effect." (2) The science and technology policy has multiple effects of environmental, economic, and health. It can significantly reduce vehicle pollution and carbon emissions, and achieve the peak carbon by 2030. (3) It is not that more policies are better for CO2 emission reduction, and there is a "crowding out effect" in the CS. (4) From the perspectives of long term, the science and technology policy is a more effective way to achieve the co-control of CO2 and PM2.5 and achieve the carbon peaking goal compared with other emission reduction scenarios. These results can provide reference for relevant departments to formulate emission reduction policies and realize the management of motor vehicle pollution reduction and carbon reduction.

1067. Predicting next 7-day Discharges of Hospitalized COVID-19 Patients Using Ensemble Learning

Background

COVID-19 pandemic, especially during resurgences of cases in hard-hit areas, led to significant shortage of hospital beds. Such shortages may be alleviated through timely and effective forecasting of hospital discharges. The objective of this study is to predict next 7-day discharges of hospitalized COVID-19 patients using daily-based electronic health records (EHR) data.

Methods

Using EHR data of hospitalized COVID-19 patients from 03/2020-08/2021, we employed ensemble learning to predict next 7-day discharges of individual patients. We used both baseline and daily inpatient features for model training, validation, and test. Baseline features include demographic and clinical characteristics, and comorbidities. The daily inpatient features were vital signs, laboratory tests, medications administered, acute physiological scores, use of ventilator, and use of intensive care unit.

1832 hospitalized patients were identified (12,397 hospital days). Samples were randomly split at patient level (7:2:1) into training set (N=1,283 patients with 8,704 hospital days), validation set (N=366 patients with 2,524 days), and test/holdout set (patient N=183, and 1,169 days). Prediction models were trained on the training set and the validation set. We conducted the model training separately on the samples of admission day and the samples of days after admission day.

The predictions were based on the ensemble learning from decision tree, XGBoost, logistic regression, and multilayer perceptron, long short-term memory (LSTM), bi-directional LSTM, and convolutional neural network.

The combination of ensemble learning on the test/holdout set was used for final next 7-day predictions based on ‘hard’ voting (by majority). Where there was a tie, we used ‘soft’ voting (sum of probabilities) to break the tie. Figure 1.Data Processing PipelineFigure 2.Ensemble Model Architecture

Results

The overall average hospital length of stay was 8.7 (SD=10.5) days. The ensemble learning accuracies for admission-day samples and after-admission-day samples were 0.781 and 0.793, and the F1-scores for were 0.761 and 0.789, respectively.

Conclusion

EHR data of hospitalized COVID-19 patients can be used to predict next 7-day hospital discharges. Additional inpatient features and more advanced machine learning techniques are needed for prediction accuracy improvement.

Disclosures

All Authors: No reported disclosures.

Screening and analysis of differentially expressed circRNAs and miRNAs in chronic diabetic extremity wounds

Increasing studies have shown that circular RNAs (circRNAs) and microRNAs (miRNAs) are related to the development of endocrine and metabolic diseases. However, there are few reports on the expression of circRNAs and miRNAs and their related co-expression and the expression of competitive endogenous RNA (ceRNA) in diabetic chronic refractory wounds. In this study, we compared the differential expression of circRNAs and miRNAs in diabetes chronic refractory wounds and normal skin tissues by high-throughput gene sequencing, and screened the differentially expressed circRNAs and miRNAs. Five abnormally expressed circRNAs and seven abnormally expressed miRNAs were detected by reverse transcription quantitative polymerase chain reaction PCR (RT-qPCR)to verify the results of RNA sequencing. We applied gene ontology (GO) to enrich and analyze dysregulated genes and elucidated their main functions via the Kyoto encyclopedia of genes and genomes analysis (KEGG). We constructed coding noncoding gene co-expression networks and ceRNA networks based on significantly abnormally expressed genes. According to the results of coding noncoding gene co-expression network analysis, hsa_circRNA_104175, hsa_circRNA_ 001588, hsa_circRNA_104330, hsa_circRNA_ 100141, hsa_circRNA_103107, and hsa_ circRNA_102044 may be involved in the regulation of the chronic intractable wound healing process in diabetes mellitus. This is particularly true in the regulation of vascular smooth muscle contraction-related pathways and the actin cytoskeleton, which affect the healing of chronic intractable wounds in diabetes. MiR-223-5p, miR-514a-3p, miR-205-5p, and miR-203-3p, which each have a targeting relationship with the above circRNAs, regulate the metabolism of nitrogen compounds in wound tissue by regulating NOD-like receptor signaling pathways, signaling pathways regulating the pluripotency of stem cells, microRNAs in cancer, and ECM-receptor interaction. This study showed circRNAs, miRNAs, and their network are associated with the development of chronic intractable wounds in diabetes, and our research identified the goals for new molecular biomarkers and gene therapy.

[High expression of CCBE1 in adjacent tissues of tongue squamous cell carcinoma is correlated with pericancerous lymphatic vessel proliferation and poor 5-year survival outcomes]

OBJECTIVE

To examine the correlation of CCBE1 expression in adjacent tissues of tongue squamous cell carcinoma (TSCC) with pericancerous lymphatic vessel proliferation, cervical lymph node metastasis and survival outcomes of the patients.

METHODS

Lymphatic vessel density was quantified in pericancerous tissue sections of 44 cases of cT_1-2N₀ TSCC using D2-40 as the lymphatic vessel endothelial marker for calibration and counting of the lymphatic vessels. Of these 44 cases, 22 showed a relatively low lymphatic vessel density (group A) and the other 22 had a high lymphatic vessel density (group B), and the expression levels of CCBE1 in the adjacent tissues determined using immunohistochemistry, immunofluorescence assay and Western blotting were compared between the two groups. The expression level of CCBE1 was also measured in another 90 patients with TSCC using immunohistochemistry, and all the patients were followed up for their survival outcomes.

RESULTS

Immunohistochemistry and Western blotting showed a significantly lower rate of high CCBE1 expression in group A than in group B (P < 0.05). Immunofluorescence assay showed co-localization of CCBE1 and D2-40 in the adjacent tissues of TSCC. In the 90 TSCC patients with complete follow-up data, a high expression of CCBE1 was found to correlate with lymph node metastasis and a poor 5-year survival outcomes of the patients (P < 0.05).

CONCLUSION

A high expression of CCBE1 in the adjacent tissues of TSCC is closely related with pericancerous lymphatic vessel proliferation, cervical lymph node metastasis and a poor 5-year survival of the patients, suggesting the value of CCBE1 as a potential prognostic predictor for TSCC.

Observation of WWW Production in pp Collisions at sqrt[s]=13 TeV with the ATLAS Detector

This Letter reports the observation of WWW production and a measurement of its cross section using 139  fb^{-1} of proton-proton collision data recorded at a center-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. Events with two same-sign leptons (electrons or muons) and at least two jets, as well as events with three charged leptons, are selected. A multivariate technique is then used to discriminate between signal and background events. Events from WWW production are observed with a significance of 8.0 standard deviations, where the expectation is 5.4 standard deviations. The inclusive WWW production cross section is measured to be 820±100 (stat)±80 (syst)  fb, approximately 2.6 standard deviations from the predicted cross section of 511±18  fb calculated at next-to-leading-order QCD and leading-order electroweak accuracy.

A detailed map of Higgs boson interactions by the ATLAS experiment ten years after the discovery

The standard model of particle physics1-4 describes the known fundamental particles and forces that make up our Universe, with the exception of gravity. One of the central features of the standard model is a field that permeates all of space and interacts with fundamental particles5-9. The quantum excitation of this field, known as the Higgs field, manifests itself as the Higgs boson, the only fundamental particle with no spin. In 2012, a particle with properties consistent with the Higgs boson of the standard model was observed by the ATLAS and CMS experiments at the Large Hadron Collider at CERN10,11. Since then, more than 30 times as many Higgs bosons have been recorded by the ATLAS experiment, enabling much more precise measurements and new tests of the theory. Here, on the basis of this larger dataset, we combine an unprecedented number of production and decay processes of the Higgs boson to scrutinize its interactions with elementary particles. Interactions with gluons, photons, and W and Z bosons-the carriers of the strong, electromagnetic and weak forces-are studied in detail. Interactions with three third-generation matter particles (bottom (b) and top (t) quarks, and tau leptons (τ)) are well measured and indications of interactions with a second-generation particle (muons, μ) are emerging. These tests reveal that the Higgs boson discovered ten years ago is remarkably consistent with the predictions of the theory and provide stringent constraints on many models of new phenomena beyond the standard model.