Tuning the correlated color temperature of white LED with a guest-host liquid crystal

We demonstrate an electro-optic method to tune the correlated color temperature (CCT) of white light-emitting-diode (WLED) with a color conversion film, consisting of fluorescent dichroic dye doped in a liquid crystal host. By controlling the molecular reorientation of dichroic dyes, the power ratio of the transmitted blue and red lights of the white light can be accurately manipulated, resulting in different CCT. In a proof-of-concept experiment, we showed that the CCT of a yellow phosphor-converted WLED can be tuned from 3200 K to 4100 K. With further optimizations, the tuning range could be enlarged to 2500 K with fairly good color performance: luminous efficacy of radiation (LER) \u3e 300 lm/W, color rendering index (CRI) \u3e 75, and Duv \u3c 0.005. Besides, the operation voltage is lower than 5 V and good angular color uniformity is achieved with remote-phosphor coating. This approach is promising for next generation smart lighting

Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls

Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies

Molecular mechanism of cleavage of SARS-CoV-2 spike protein by plasma generated RONS

Recently, it is been shown that cold atmospheric pressure plasmas Cold Atmospheric Plasma effectively inactivate the 2019-nCoV virus. Despite this promising finding, the precise mechanism of this inactivation remains unclear due to the limited number of studies conducted on the subject. Consequently, this paper focuses on the spike protein, a crucial part of the novel coronavirus, and the various reactive oxygen and nitrogen species (RONS) generated by the plasma. The study employs reactive molecular dynamics simulation and ReaxFF potential to explore the reactions between the spike protein molecules and different reactive oxygen nitrogen species (including H2O2, OH, O, O3, HOONO, and 1O2). The findings suggest that when a single RONS interacts with the spike protein, 1O2 and HOONO have the most potent ability to sever the spike protein. Additionally, the combined effect of long-lived and short-lived RONS presents a more potent decomposition impact

Structural basis of suppression of host translation termination by Moloney Murine Leukemia Virus

Retroviral reverse transcriptase (RT) of Moloney murine leukemia virus (MoMLV) is expressed in the form of a large Gag–Pol precursor protein by suppression of translational termination in which the maximal efficiency of stop codon read-through depends on the interaction between MoMLV RT and peptidyl release factor 1 (eRF1). Here, we report the crystal structure of MoMLV RT in complex with eRF1. The MoMLV RT interacts with the C-terminal domain of eRF1 via its RNase H domain to sterically occlude the binding of peptidyl release factor 3 (eRF3) to eRF1. Promotion of read-through by MoMLV RNase H prevents nonsense-mediated mRNA decay (NMD) of mRNAs. Comparison of our structure with that of HIV RT explains why HIV RT cannot interact with eRF1. Our results provide a mechanistic view of how MoMLV manipulates the host translation termination machinery for the synthesis of its own proteins

Predictive Value of Serum Uric Acid in Perioperative Acute Ischemic Stroke in Patients with Non-small Cell Lung Cancer

Background Perioperative acute ischemic stroke (POAIS) is a severe complication of surgery, which can increase surgical mortality and reduce patients' quality of life. The pathogeneses are complex and rarely explored, especially in patients with non-small cell lung cancer (NSCLC) . Objective To investigate the influencing factors of POAIS in NSCLC patients and the predictive value of serum uric acid (SUA) on the occurrence of POAIS in NSCLC patients. Methods A total of 25 NSCLC patients admitted to the Fourth Hospital of Hebei Medical University from July 2014 to April 2022, who suffered from POAIS following lung resection were selected as the case group, while 126 patients without POAIS were randomly selected as the control group after matching by age and gender. The preoperative baseline data, intraoperative data and postoperative pathology-related data of all patients were collected. Multivariate Logistic regression analysis was performed to explore the influencing factors of POAIS in the NSCLC patients, and the receiver operating characteristic (ROC) curve was plotted to evaluate the predictive value of preoperative SUA on the development of POAIS in NSCLC patients. Results The average age of the 151 patients was (64±7) years, 57.62% (87/151) of whom were male. The multivariate Logistic regression analysis showed that SUA was an influencing factor of POAIS in NSCLC patients〔OR=0.990, 95%CI (0.982, 0.998) , P=0.019〕. The ROC curve indicated that the area under the curve (AUC) of SUA to predict POAIS in NSCLC patients was 0.64, with an optimal threshold value of 307.40 μmol/L, sensitivity and specificity of 58.7% and 76.0%, respectively. Conclusion Preoperative SUA level can serve as an independent predictor of POAIS incidence in NSCLC patients. Higher SUA levels at baseline may predict a lower risk of POAIS

Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis

Introduction: Metabolomics technologies enable the identification of putative biomarkers for numerous diseases; however, the influence of confounding factors on metabolite levels poses a major challenge in moving forward with such metabolites for pre-clinical or clinical applications. Objectives: To address this challenge, we analyzed metabolomics data from a colorectal cancer (CRC) study, and used seemingly unrelated regression (SUR) to account for the effects of confounding factors including gender, BMI, age, alcohol use, and smoking. Methods: A SUR model based on 113 serum metabolites quantified using targeted mass spectrometry, identified 20 metabolites that differentiated CRC patients (n = 36), patients with polyp (n = 39), and healthy subjects (n = 83). Models built using different groups of biologically related metabolites achieved improved differentiation and were significant for 26 out of 29 groups. Furthermore, the networks of correlated metabolites constructed for all groups of metabolites using the ParCorA algorithm, before or after application of the SUR model, showed significant alterations for CRC and polyp patients relative to healthy controls. Results: The results showed that demographic covariates, such as gender, BMI, BMI2, and smoking status, exhibit significant confounding effects on metabolite levels, which can be modeled effectively. Conclusion: These results not only provide new insights into addressing the major issue of confounding effects in metabolomics analysis, but also shed light on issues related to establishing reliable biomarkers and the biological connections between them in a complex disease

Cuscutae semen alleviates CUS-induced depression-like behaviors in mice via the gut microbiota-neuroinflammation axis

Introduction: Major depressive disorder is a mental disease with complex pathogenesis and treatment mechanisms involving changes in both the gut microbiota and neuroinflammation. Cuscutae Semen (CS), also known as Chinese Dodder seed, is a medicinal herb that exerts several pharmacological effects. These include neuroprotection, anti-neuroinflammation, the repair of synaptic damage, and the alleviation of oxidative stress. However, whether CuscutaeSemen exerts an antidepressant effect remains unknown.Methods: In this study, we evaluated the effect of CS on chronic unpredictable stress (CUS)-induced depression-like behaviors in mice by observing changes in several inflammatory markers, including proinflammatory cytokines, inflammatory proteins, and gliocyte activation. Meanwhile, changes in the gut microbiota were analyzed based on 16 S rRNA sequencing results. Moreover, the effect of CS on the synaptic ultrastructure was detected by transmission electron microscopy.Results: We found that the CS extract was rich in chlorogenic acid and hypericin. And CS relieved depression-like behaviors in mice exposed to CUS. Increased levels of cytokines (IL-1β and TNF-α) and inflammatory proteins (NLRP3, NF-κB, and COX-2) induced by CUS were reversed after CS administration. The number of astrocytes and microglia increased after CUS exposure, whereas they decreased after CS treatment. Meanwhile, CS could change the structure of the gut microbiota and increase the relative abundance of Lactobacillus. Moreover, there was a significant relationship between several Lactobacilli and indicators of depression-like behaviors and inflammation. There was a decrease in postsynaptic density after exposure to CUS, and this change was alleviated after CS treatme.Conclusion: This study found that CS treatment ameliorated CUS-induced depression-like behaviors and synaptic structural defects in mice via the gut microbiota-neuroinflammation axis. And chlorogenic acid and hypericin may be the main active substances for CS to exert antidepressant effects

Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring

Colorectal cancer (CRC) is one of the most prevalent cancers worldwide and a major cause of human morbidity and mortality. In addition to early detection, close monitoring of disease progression in CRC can be critical for patient prognosis and treatment decisions. Efforts have been made to develop new methods for improved early detection and patient monitoring; however, research focused on CRC surveillance for treatment response and disease recurrence using metabolomics has yet to be reported. In this proof of concept study, we applied a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolic profiling approach focused on sequential metabolite ratio analysis of serial serum samples to monitor disease progression from 20 CRC patients. The use of serial samples reduces patient to patient metabolic variability. A partial least squares-discriminant analysis (PLS-DA) model using a panel of five metabolites (succinate, N2, N2-dimethylguanosine, adenine, citraconic acid, and 1-methylguanosine) was established, and excellent model performance (sensitivity = 0.83, specificity = 0.94, area under the receiver operator characteristic curve (AUROC) = 0.91 was obtained, which is superior to the traditional CRC monitoring marker carcinoembryonic antigen (sensitivity = 0.75, specificity = 0.76, AUROC = 0.80). Monte Carlo cross validation was applied, and the robustness of our model was clearly observed by the separation of true classification models from the random permutation models. Our results suggest the potential utility of metabolic profiling for CRC disease monitoring

One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

Magnetic resonance imaging (MRI) is a principal radiological modality that provides radiation-free, abundant, and diverse information about the whole human body for medical diagnosis, but suffers from prolonged scan time. The scan time can be significantly reduced through k-space undersampling but the introduced artifacts need to be removed in image reconstruction. Although deep learning (DL) has emerged as a powerful tool for image reconstruction in fast MRI, its potential in multiple imaging scenarios remains largely untapped. This is because not only collecting large-scale and diverse realistic training data is generally costly and privacy-restricted, but also existing DL methods are hard to handle the practically inevitable mismatch between training and target data. Here, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model. For a 2D image, the reconstruction is separated into many 1D basic problems and starts with the 1D data synthesis, to facilitate generalization. We demonstrate that training DL models on synthetic data, integrated with enhanced learning techniques, can achieve comparable or even better in vivo MRI reconstruction compared to models trained on a matched realistic dataset, reducing the demand for real-world MRI data by up to 96%. Moreover, our PISF shows impressive generalizability in multi-vendor multi-center imaging. Its excellent adaptability to patients has been verified through 10 experienced doctors' evaluations. PISF provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.Comment: 22 pages, 9 figures, 1 tabl