Targeting the immune checkpoint pathway has demonstrated antitumor cytotoxicity in treatment-refractory head and neck squamous cell carcinoma (HNSC). To understand the molecular mechanisms ...underpinning its antitumor response, we characterized the immune landscape of HNSC by their tumor and stromal compartments to identify novel immune molecular subgroups.
A training cohort of 522 HNSC samples from the Cancer Genome Atlas profiled by RNA sequencing was analyzed. We separated gene expression patterns from tumor, stromal, and immune cell gene using a non-negative matrix factorization algorithm. We correlated the expression patterns with a set of immune-related gene signatures, potential immune biomarkers, and clinicopathological features. Six independent datasets containing 838 HNSC samples were used for validation.
Approximately 40% of HNSCs in the cohort (211/522) were identified to show enriched inflammatory response, enhanced cytolytic activity, and active interferon-γ signaling (all, P < 0.001). We named this new molecular class of tumors the Immune Class. Then we found it contained two distinct microenvironment-based subtypes, characterized by markers of active or exhausted immune response. The Exhausted Immune Class was characterized by enrichment of activated stroma and anti-inflammatory M2 macrophage signatures, WNT/transforming growth factor-β signaling pathway activation and poor survival (all, P < 0.05). An enriched proinflammatory M1 macrophage signature, enhanced cytolytic activity, abundant tumor-infiltrating lymphocytes, high human papillomavirus (HPV) infection, and favorable prognosis were associated with Active Immune Class (all, P < 0.05). The robustness of these immune molecular subgroups was verified in the validation cohorts, and Active Immune Class showed potential response to programmed cell death-1 blockade (P = 0.01).
This study revealed a novel Immune Class in HNSC; two subclasses characterized by active or exhausted immune responses were also identified. These findings provide new insights into tailoring immunotherapeutic strategies for different HNSC subgroups.
Background
The aim of this study was to investigate the prevalence of epidemiologic and physician‐diagnosed pollen‐induced AR (PiAR) in the grasslands of northern China and to study the impact of the ...intensity and time of pollen exposure on PiAR prevalence.
Methods
A multistage, clustered and proportionately stratified random sampling with a field interviewer‐administered survey study was performed together with skin prick tests (SPT) and measurements of the daily pollen count.
Results
A total of 6043 subjects completed the study, with a proportion of 32.4% epidemiologic AR and 18.5% PiAR. The prevalence was higher in males than females (19.6% vs 17.4%, P = .024), but no difference between the two major residential and ethnic groups (Han and Mongolian) was observed. Subjects from urban areas showed higher prevalence of PiAR than rural areas (23.1% vs 14.0%, P < .001). Most PiAR patients were sensitized to two or more pollens (79.4%) with artemisia, chenopodium, and humulus scandens being the most common pollen types, which were similarly found as the top three sensitizing pollen allergens by SPT. There were significant regional differences in the prevalence of epidemiologic AR (from 18.6% to 52.9%) and PiAR (from 10.5% to 31.4%) among the six areas investigated. PiAR symptoms were positively associated with pollen counts, temperature, and precipitation (P < .05), but negatively with wind speed and pressure P < .05).
Conclusion
Pollen‐induced AR (PiAR) prevalence in the investigated region is extremely high due to high seasonal pollen exposure, which was influenced by local environmental and climate conditions.
Cross-resonance (CR) gates have emerged as a promising scheme for fault-tolerant quantum computation with fixed-frequency qubits. We experimentally implement an entangling CR gate by using a ...microwave-only control in a tunable coupling superconducting circuit, where the tunable coupler provides extra degrees of freedom to verify optimal conditions for constructing a CR gate. By developing a three-qubit Hamiltonian tomography protocol, we systematically investigate the dependency of gate fidelities on spurious qubit interactions and present the first experimental approach to the evaluation of the perturbation impact arising from spectator qubits. Our results reveal that the spectator qubits lead to reductions in CR gate fidelity dependent on Z Z interactions and particular frequency detunings between spectator and gate qubits. The target spectator demonstrates a more serious impact than the control spectator under a standard echo pulse scheme, whereas the degradation of gate fidelity is observed up to 22.5% as both the spectators are present with a modest ZZ coupling to the computational qubits. Our experiments uncover an optimal CR operation regime, and the method we develop here can readily be applied to improving other kinds of two-qubit gates in large-scale quantum circuits.
An interatomic potential for the Al-Tb alloy around the composition of Al
90
Tb
10
is developed using the deep neural network (DNN) learning method. The atomic configurations and the corresponding ...total potential energies and forces on each atom obtained from
ab initio
molecular dynamics (AIMD) simulations are collected to train a DNN model to construct the interatomic potential for the Al-Tb alloy. We show that the obtained DNN model can well reproduce the energies and forces calculated by AIMD simulations. Molecular dynamics (MD) simulations using the DNN interatomic potential also accurately describe the structural properties of the Al
90
Tb
10
liquid, such as partial pair correlation functions (PPCFs) and bond angle distributions, in comparison with the results from AIMD simulations. Furthermore, the developed DNN interatomic potential predicts the formation energies of the crystalline phases of the Al-Tb system with an accuracy comparable to
ab initio
calculations. The structure factors of the Al
90
Tb
10
metallic liquid and glass obtained by MD simulations using the developed DNN interatomic potential are also in good agreement with the experimental X-ray diffraction data. The development of short-range order (SRO) in the Al
90
Tb
10
liquid and the undercooled liquid is also analyzed and three dominant SROs,
i.e.
, Al-centered distorted icosahedron (DISICO) and Tb-centered '3661' and '15551' clusters, respectively, are identified.
The developed deep neural network (DNN) potential can describe the structural properties of the Al
90
Tb
10
liquid and the formation energies of Al-Tb crystals with the accuracy of
ab initio
calculations.
Mechanically driven magnetoelectric antennas are a promising new technology that enable a reduction in antenna size by many orders of magnitude, as compared to conventional antennas. The ...magnetoelastic coupling in these antennas, a phenomenon playing a direct role in determining performance, has been modeled using approaches that are severely lacking in both accuracy and tractability. In response to this problem, we take a physics-based approach to the analysis of magnetoelastic coupling. We find that certain directions of applied stress will maximize the coupling and we derive general expressions to quantify it. Our results are applied in comprehensive simulations that demonstrate the dynamic nature of the coupling as well as the impact of various operating conditions and material properties. Our work contributes analytical expressions and associated insight that can serve not only as guidelines for the design of mechanically driven magnetoelectric antennas, but also as stepping stones towards the development of more accurate models.
To explore the clinical characteristics and prognosis of the new coronavirus 2019-nCoV patients combined with cardiovascular disease （CVD).
A retrospective analysis was performed on 112 COVID-19 ...patients with CVD admitted to the western district of Union Hospital in Wuhan, from January 20, 2020 to February 15, 2020. They were divided into critical group (ICU,
=16) and general group (
=96) according to the severity of the disease and patients were followed up to the clinical endpoint. The observation indicators included total blood count, C-reactive protein (CRP), arterial blood gas analysis, myocardial injury markers, coagulation function, liver and kidney function, electrolyte, procalcitonin (PCT), B-type natriuretic peptide (BNP), blood lipid, pulmonary CT and pathogen detection.
Compared with the general group, the lymphocyte count (0.74 (0.34, 0.94)×10
/L vs. 0.99 (0.71, 1.29)×10
/L,
=0.03) was extremely lower in the critical group, CRP (106.98 (81.57, 135.76) mg/L vs. 34.34 (9.55,76.54) mg/L,
<0.001) and PCT (0.20 (0.15,0.48) μg/L vs. 0.11 (0.06,0.20) μg/L,
<0.001) were significantly higher in the critical group. The BMI of the critical group was significantly higher than that of the general group (25.5 (23.0, 27.5) kg/m
vs. 22.0 (20.0, 24.0) kg/m
，
=0.003). Patients were further divided into non-survivor group (17, 15.18%) group and survivor group (95, 84.82%). Among the non-survivors, there were 88.24% (15/17) patients with BMI> 25.0 kg/m
, which was significantly higher than that of survivors (18.95% (18/95),
<0.001). Compared with the survived patients, oxygenation index (130 (102, 415) vs. 434 (410, 444),
<0.001) was significantly lower and lactic acid (1.70 (1.30, 3.00) mmol/L vs. 1.20 (1.10, 1.60) mmol/L,
<0.001) was significantly higher in the non-survivors. There was no significant difference in the proportion of ACEI/ARB medication between the critical group and the general group or between non-survivors and survivors （all
>0.05).
COVID-19 patients combined with CVD are associated with a higher risk of mortality. Critical patients are characterized with lower lymphocyte counts. Higher BMI are more often seen in critical patients and non-survivor. ACEI/ARB use does not affect the morbidity and mortality of COVID-19 combined with CVD. Aggravating causes of death include fulminant inflammation, lactic acid accumulation and thrombotic events.
Recently, the antibacterial properties of oestrogen and progestogen were discovered. The aim of this study was to find the cross-sectional association between oral contraceptive use and Helicobacter ...pylori seroprevalence. Data were obtained from the US National Health and Nutrition Examination Survey (NHANES). The H. pylori immunoglobulin G (IgG) enzyme-linked immunosorbent assays were used to categorise participants as seropositive or seronegative. The study population included 799 female participants who had information on H. pylori seroprevalence and all other covariates and had not been taking any medications (except oral contraceptives). The bivariate Rao–Scott chi-square test indicated a significant association between H. pylori seroprevalence and contraceptive use (P < 0.01). The variables of race, education, poverty income ratio, smoking, and blood lead and cadmium levels were also significantly associated with H. pylori seroprevalence (P < 0.01). Multiple logistic regression analysis of the age-adjusted model revealed that contraceptive users are 65% less likely of being H. pylori seropositive as compared to non-contraceptive users (odds ratio (OR): 0.35, 95% confidence interval (CI): 0.18–0.68). This association is stronger with the final multivariate model (OR: 0.46, 95% CI: 0.23–0.89). Conclusions: This finding reveals the potential protective effect of oral contraceptives against H. pylori infection and serves as a foundation study for further investigations.
A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the ...assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded RFormula: see text of Formula: see text and Formula: see text between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved RFormula: see text of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.
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