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The clinical features and immune responses of asymptomatic individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have not been well described. We studied 37 asymptomatic individuals in the Wanzhou District who were diagnosed with RT–PCR-confirmed SARS-CoV-2 infections but without any relevant clinical symptoms in the preceding 14 d and during hospitalization. Asymptomatic individuals were admitted to the government-designated Wanzhou People’s Hospital for centralized isolation in accordance with policy 1 . The median duration of viral shedding in the asymptomatic group was 19 d (interquartile range (IQR), 15–26 d). The asymptomatic group had a significantly longer duration of viral shedding than the symptomatic group (log-rank P  = 0.028). The virus-specific IgG levels in the asymptomatic group (median S/CO, 3.4; IQR, 1.6–10.7) were significantly lower ( P  = 0.005) relative to the symptomatic group (median S/CO, 20.5; IQR, 5.8–38.2) in the acute phase. Of asymptomatic individuals, 93.3% (28/30) and 81.1% (30/37) had reduction in IgG and neutralizing antibody levels, respectively, during the early convalescent phase, as compared to 96.8% (30/31) and 62.2% (23/37) of symptomatic patients. Forty percent of asymptomatic individuals became seronegative and 12.9% of the symptomatic group became negative for IgG in the early convalescent phase. In addition, asymptomatic individuals exhibited lower levels of 18 pro- and anti-inflammatory cytokines. These data suggest that asymptomatic individuals had a weaker immune response to SARS-CoV-2 infection. The reduction in IgG and neutralizing antibody levels in the early convalescent phase might have implications for immunity strategy and serological surveys. A cohort of asymptomatic patients infected with SARS-CoV-2 had significantly lower levels of virus-specific IgG antibodies compared to a cohort of age- and sex-matched symptomatic infected patients.

Oral squamous cell carcinoma (OSCC) is one of the most common malignancies and has a poor prognosis. Circular RNA (circRNA) has been increasingly recognized as a crucial contributor to carcinogenesis. circRNA_0000140 has been aberrantly expressed in OSCC, but its role in tumor growth and metastasis remains largely unclear. Sanger sequencing, actinomycin D, and RNase R treatments were used to confirm head-to-tail junction sequences and the stability of circ_0000140. In vitro cell activities, including proliferation, migration, invasion, and apoptosis, were determined by colony formation, transwell, and flow cytometry assays. The expression levels of circ_0000140, Hippo signaling pathway, and serial epithelial–mesenchymal transition (EMT) markers were measured by quantitative real-time PCR, western blotting, immunofluorescence, and immunohistochemistry. Dual luciferase reporter assays and Argonaute 2-RNA immunoprecipitation assays were performed to explore the interplay among circ_0000140, miR-31, and LATS2. Subcutaneous tumor growth was observed in nude mice, in which in vivo metastasis was observed following tail vein injection of OSCC cells. circ_0000140 is derived from exons 7 to 10 of the KIAA0907 gene. It was down-regulated in OSCC tissues and cell lines, and correlated negatively with poor prognostic outcomes in OSCC patients. Gain-of-function experiments demonstrated that circ_0000140 enhancement suppressed cell proliferation, migration, and invasion, and facilitated cell apoptosis in vitro. In xenograft mouse models, overexpression of circ_0000140 was able to repress tumor growth and lung metastasis. Furthermore, mechanistic studies showed that circ_0000140 could bind with miR-31 and up-regulate its target gene LATS2 , thus affecting OSCC cellular EMT. Our findings demonstrated the roles of circ_0000140 in OSCC tumorigenesis as well as in metastasis, and circ_0000140 exerts its tumor-suppressing effect through miR-31/LATS2 axis of Hippo signaling pathway in OSCC.

Sleep deprivation (SD) is increasingly common in modern society, which can lead to the dysregulation of inflammatory responses and cognitive impairment, but the mechanisms remain unclear. Emerging evidence suggests that gut microbiota plays a critical role in the pathogenesis and development of inflammatory and psychiatric diseases, possibly via gut microbiota–brain interactions and neuroinflammation. The present study investigated the impact of SD on gut microbiota composition and explored whether alterations of the gut microbiota play a causal role in chronic inflammatory states and cognitive impairment that are induced by SD. We found that SD-induced gut dysbiosis, inflammatory responses, and cognitive impairment in humans. Moreover, the absence of the gut microbiota suppressed inflammatory response and cognitive impairment induced by SD in germ-free (GF) mice. Transplantation of the “SD microbiota” into GF mice activated the Toll-like receptor 4/nuclear factor-κB signaling pathway and impaired cognitive function in the recipient mice. Mice that harbored “SD microbiota” also exhibited increases in neuroinflammation and microglial activity in the hippocampus and medial prefrontal cortex. These findings indicate that gut dysbiosis contributes to both peripheral and central inflammatory processes and cognitive deficits that are induced by SD, which may open avenues for potential interventions that can relieve the detrimental consequences of sleep loss.

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject’s phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

In this study, a mathematical model is developed to scrutinize the transient magnetic flow of Cross nanoliquid past a stretching sheet with thermal radiation effects. Binary chemical reactions and heat source/sink effects along with convective boundary condition are also taken into the consideration. Appropriate similarity transformations are utilized to transform partial differential equations (PDE’s) into ordinary ones and then numerically tackled by shooting method. The impacts of different emerging parameters on the thermal, concentration, velocity, and micro-rotation profiles are incorporated and discussed in detail by means of graphs. Results reveal that, the escalation in magnetic parameter and Rayleigh number slowdowns the velocity and momentum of the fluid. The increase in Biot number, radiation and heat sink/source parameters upsurges the thermal boundary but, converse trend is seen for escalating Prandtl number. The density number of motile microorganisms acts as a growing function of bioconvection Lewis number and declining function of bioconvection Peclet number.

The coherent interaction between quantum emitters and photonic modes in cavities underlies many of the current strategies aiming at generating and controlling photonic quantum states. A plasmonic nanocavity provides a powerful solution for reducing the effective mode volumes down to nanometre scale, but spatial control at the atomic scale of the coupling with a single molecular emitter is challenging. Here we demonstrate sub-nanometre spatial control over the coherent coupling between a single molecule and a plasmonic nanocavity in close proximity by monitoring the evolution of Fano lineshapes and photonic Lamb shifts in tunnelling electron-induced luminescence spectra. The evolution of the Fano dips allows the determination of the effective interaction distance of ∼1 nm, coupling strengths reaching ∼15 meV and a giant self-interaction induced photonic Lamb shift of up to ∼3 meV. These results open new pathways to control quantum interference and field–matter interaction at the nanoscale. Assessing the coupling between a plasmonic nanocavity and a single quantum emitter is challenging due to the lack of spatial control at the atomic scale. Here Zhang et al . achieve control with sub-nanometre precision and demonstrate the Fano resonance and Lamb shift at the single-molecule level.

An extremely strong magnetic field (as strong as 10 15 T) is created in the off-central heavy-ion collisions by the spectator protons which \"miss\" the collisions, flying past each other rather than colliding. The magnetic field is interesting to be studied as it is expected to leave distinct imprints in the distribution of final state charged particles. In addition, novel QCD phenomena are anticipated to emerge with the presence of a strong magnetic field and the formation of charge-parity violating domains inside the medium produced in heavy-ion collisions. The aim of this article is to review two methods utilised by the experimental searches to probe the early magnetic field: the directed flow of charged hadrons (and heavy-flavour hadrons D 0 and ¯ D 0 ) and the global polarisation of Λ and ¯Λ hyperons. Furthermore, this article is also dedicated to review the searches for one of the novel QCD phenomena, the chiral magnetic effect, at the LHC and RHIC.

Quantum Chromodynamics permits the formation of charge conjugation parity violating domains inside the medium produced in heavy-ion collisions, resulting in an imbalanced quark chirality. With the presence of a strong magnetic field (as strong as 10 15 T) produced by the spectator protons in offcentral heavy-ion collisions, this would lead to an electric-charge separation along the direction of the magnetic field, known as the Chiral Magnetic Effect (CME). Experimental searches commonly utilise strategies involving charge-dependent correlators to measure the charge separation. These correlators are, however, dominated by a large background proportional to the elliptic flow v 2 . This article presents a systematic study of the correlators used experimentally to probe the CME by using the Anomalous Viscous Fluid Dynamics (AVFD) model in Pb-Pb and Xe-Xe collisions at √ s NN = 5.02 TeV and √ s NN = 5.44 TeV, respectively. The results from AVFD suggest that Xe-Xe collisions are consistent with a background-only scenario and a significant non-zero value of axial current density (imbalanced quark chirality) is required to describe the measurements in Pb-Pb collisions.

In the automatic detection of epileptic seizures, the monitoring of critically ill patients with time varying EEG signals is an essential procedure in intensive care units. There is an increasing interest in using EEG analysis to detect seizure, and in this study we aim to get a better understanding of how to visualize the information in the EEG time-frequency feature, and design and train a novel random forest algorithm for EEG decoding, especially for multiple-levels of illness. Here, we propose an automatic detection framework for epileptic seizure based on multiple time-frequency analysis approaches; it involves a novel random forest model combined with grid search optimization. The short-time Fourier transformation visualizes seizure features after normalization. The dimensionality of features is reduced through principal component analysis before feeding them into the classification model. The training parameters are optimized using grid search optimization to improve detection performance and diagnostic accuracy by in the recognition of three different levels epileptic of conditions (healthy subjects, seizure-free intervals, seizure activity). Our proposed model was used to classify 500 samples of raw EEG data, and multiple cross-validations were adopted to boost the modeling accuracy. Experimental results were evaluated by an accuracy, a confusion matrix, a receiver operating characteristic curve, and an area under the curve. The evaluations indicated that our model achieved the more effective classification than some previous typical methods. Such a scheme for computer-assisted clinical diagnosis of seizures has a potential guiding significance, which not only relieves the suffering of patient with epilepsy to improve quality of life, but also helps neurologists reduce their workload.

Infectious diseases, including COVID-19, are crucial public health issues and may lead to considerable fear among the general public and stigmatization of, and discrimination against, specific populations. This meta-analysis aimed to estimate the pooled prevalence of stigma in infectious disease epidemics. We systematically searched PubMed, PsycINFO, Embase, MEDLINE, Web of Science, and Cochrane databases since inception to June 08, 2021, and reported the prevalence of stigma towards people with infectious diseases including SARS, H1N1, MERS, Zika, Ebola, and COVID-19. A total of 50 eligible articles were included that contributed 51 estimates of prevalence in 92722 participants. The overall pooled prevalence of stigma across all populations was 34% [95% CI: 28−40%], including enacted stigma (36% [95% CI: 28−44%]) and perceived stigma (31% [95% CI: 22−40%]). The prevalence of stigma in patients, community population, and health care workers, was 38% [95% CI: 12− 65%], 36% [95% CI: 28−45%], and 30% [95% CI: 20−40%], respectively. The prevalence of stigma in participants from low- and middle-income countries was 37% [95% CI: 29−45%], which is higher than that from high-income countries (27% [95% CI: 18−36%]) though this difference was not statistically significant. A similar trend of prevalence of stigma was also observed in individuals with lower education (47% [95% CI: 23−71%]) compared to higher education level (33% [95% CI: 23−4%]). These findings indicate that stigma is a significant public health concern, and effective and comprehensive interventions are needed to counteract the damaging effects of the infodemics during infectious disease epidemics, including COVID-19, and reduce infectious disease-related stigma.