CONSTITUTIVE EXPRESSER OF PATHOGENESIS-RELATED GENES 5 is an RNA-binding protein controlling plant immunity via an RNA processing complex.

Plant innate immunity is capable of combating diverse and ever evolving pathogens. The plasticity of innate immunity could be boosted by RNA processing. Arabidopsis thaliana CONSTITUTIVE EXPRESSER OF PATHOGENESIS-RELATED GENES 5 (CPR5), a key negative immune regulator, is a component of the nuclear pore complex. Here we further identified CPR5 as a component of RNA processing complexes. Through genetic screening, we found that RNA splicing activator NineTeen Complex and RNA polyadenylation factor CLEAVAGE AND POLYADENYLATION SPECIFICITY FACTOR, coordinately function downstream of CPR5 to activate plant immunity. CPR5 and these two regulators form a complex that is localized in nuclear speckles, an RNA processing organelle. Intriguingly, we found that CPR5 is an RNA-binding protein belonging to the Transformer 2 (Tra2) subfamily of the serine/arginine-rich family. The RNA recognition motif of CPR5 protein binds the Tra2-targeted RNA sequence in vitro and is functionally replaceable by those of Tra2 subfamily proteins. In planta, it binds RNAs of CPR5-regulated alternatively spliced genes (ASGs) identified by RNA-seq. ARGONAUTE 1 (AGO1) is one of the ASGs and, consistent with this, the ago1 mutant suppresses the cpr5 phenotype. These findings reveal that CPR5 is an RNA-binding protein linking RNA processing with plant immunity.

ABCC4 suppresses glioblastoma progression and recurrence by restraining cGMP-PKG signalling.

BACKGROUND: Cyclic nucleotides are critical mediators of cellular signalling in glioblastoma. However, the clinical relevance and mechanisms of regulating cyclic nucleotides in glioblastoma progression and recurrence have yet to be thoroughly explored. METHODS: In silico, mRNA, and protein level analyses identified the primary regulator of cyclic nucleotides in recurrent human glioblastoma. Lentiviral and pharmacological manipulations examined the functional impact of cyclic nucleotide signalling in human glioma cell lines and primary glioblastoma cells. An orthotopic xenograft mice model coupled with aspirin hydrogels verified the in vivo outcome of targeting cyclic nucleotide signalling. RESULTS: Elevated intracellular levels of cGMP, instead of cAMP, due to a lower substrate efflux from ATP-binding cassette sub-family C member 4 (ABCC4) is engaged in the recurrence of glioblastoma. ABCC4 gene expression is negatively associated with recurrence and overall survival outcomes in glioblastoma specimens. ABCC4 loss-of-function activates cGMP-PKG signalling, promoting malignancy in glioblastoma cells and xenografts. Hydrogels loaded with aspirin, inhibiting glioblastoma progression partly by upregulating ABCC4 expressions, augment the efficacy of standard-of-care therapies in orthotopic glioblastoma xenografts. CONCLUSION: ABCC4, repressing the cGMP-PKG signalling pathway, is a tumour suppressor in glioblastoma progression and recurrence. Aspirin hydrogels impede glioblastoma progression through ABCC4 restoration and constitute a viable translational approach.

Real-Time Monitoring of the Evaporation and Fission of Electrospray-Ionized Polystyrene Beads and Bacterial Pellets at Elevated Temperatures.

This study uses real-time monitoring, at microsecond time scales, with a charge-sensing particle detector to investigate the evaporation and fission processes of methanol/micrometer-sized polystyrene beads (PS beads) droplets and bacterial particles droplets generated via electrospray ionization (ESI) under elevated temperatures. By incrementally raising capillary temperatures, the solvent, such as methanol on 0.75 µm PS beads, experiences partial evaporation. Further temperature increase induces fission, and methanol molecules continue to evaporate until PS ions are detected after this range. Similar partial evaporation is observed on 3 µm PS beads. However, the shorter period of the fission temperature range is necessary compared to 0.75 µm PS beads. For the spherical-shaped bacterium, Staphylococcus aureus, the desolvation process shows a similar fission period as compared to 0.75 µm PS beads. Comparably, the rod-shaped bacteria, Escherichia coli EC11303, and E. coli strain W have shorter fission periods than S. aureus. This research provides insights into the evaporation and fission mechanisms of ESI droplets containing different sizes and shapes of micrometer-sized particles, contributing to a better understanding of gaseous macroion formation.

Augmenting Neutrophil Extracellular Traps with Carbonized Polymer Dots: A Potential Treatment for Bacterial Sepsis.

Sepsis is a life-threatening condition that can progress to septic shock as the body's extreme response to pathogenesis damages its own vital organs. Staphylococcus aureus (S. aureus) accounts for 50% of nosocomial infections, which are clinically treated with antibiotics. However, methicillin-resistant strains (MRSA) have emerged and can withstand harsh antibiotic treatment. To address this problem, curcumin (CCM) is employed to prepare carbonized polymer dots (CPDs) through mild pyrolysis. Contrary to curcumin, the as-formed CCM-CPDs are highly biocompatible and soluble in aqueous solution. Most importantly, the CCM-CPDs induce the release of neutrophil extracellular traps (NETs) from the neutrophils, which entrap and eliminate microbes. In an MRSA-induced septic mouse model, it is observed that CCM-CPDs efficiently suppress bacterial colonization. Moreover, the intrinsic antioxidative, anti-inflammatory, and anticoagulation activities resulting from the preserved functional groups of the precursor molecule on the CCM-CPDs prevent progression to severe sepsis. As a result, infected mice treated with CCM-CPDs show a significant decrease in mortality even through oral administration. Histological staining indicates negligible organ damage in the MRSA-infected mice treated with CCM-CPDs. It is believed that the in vivo studies presented herein demonstrate that multifunctional therapeutic CPDs hold great potential against life-threatening infectious diseases.

Equalization system of low differential mode delay few-mode fibers based on the neural network MIMO algorithm.

In recent years, with the development of information networks, higher requirements for transmission capacity have been recommended. Yet, at the same time, the capacity of single-mode fiber is rapidly approaching the theoretical limit. The multidimensional multiplexing technique is an effective way to solve this problem. Since the high differential mode delay (DMD) of transmission fiber increases the complexity of demultiplexing in equalization algorithms, we use an intelligent design method to optimize the trench-assisted gradient refractive index structure in this paper. The maximum DMD of the optimized optical fiber structure is 19.6 ps/km. A least mean squares-feedforward neural network constant modulus algorithm (LMS-FNNCMA) is also designed by using the theory of the least mean squares (LMS), constant modulus algorithm (CMA), and the multiple input multiple output (MIMO) neural networks. In order to verify the accuracy of the algorithm, a polarization division multiplexing-wavelength division multiplexing-mode division multiplexing (PDM-WDM-MDM) optical transmission system is constructed through simulation. The algorithm successfully realizes the de-crosstalk over a transmission distance of 1200 km at a rate of 1.2 Tbps under simulation conditions.

End-to-end learning strategy based on a frequency domain feature decoupling network emulator with joint probabilistic shaping and equalization for a 300-Gbit/s OAM mode division multiplexing transmission.

Mode coupling and device nonlinear impairment appear to be a long-standing challenge in the orbital angular momentum (OAM) mode division multiplexing (MDM) of intensity modulation direct detection (IM/DD) transmission systems. In this paper, we propose an end-to-end (E2E) learning strategy based on a frequency domain feature decoupling network (FDFDnet) emulator with joint probabilistic shaping (PS) and equalization for an OAM-MDM IM/DD transmission with three modes. Our FDFDnet emulator can accurately build a complex nonlinear model of an OAM-MDM system by separating the signal into features from different frequency domains. Furthermore, a FDFDnet-based E2E strategy for joint PS and equalization is presented with the aim of compensating the signal impairment for the OAM-MDM IM/DD system. An experiment is carried out on a 300 Gbit/s carrierless amplitude phase-32 (CAP-32) signal with three OAM modes over a 10 km ring-core fiber transmission, and the results show that the proposed FDFDnet emulator outperforms the traditional CGAN emulator, with improvements in the modelling accuracy of 30.8%, 26.3% and 31% for the three OAM modes. Moreover, the receiver sensitivity of the proposed E2E learning strategy is higher than for the CGAN emulator by 3, 2.5, 2.2 dBm and the real channel by 5.5, 5.1, and 5.3 dBm for the three OAM modes, respectively. Our experimental results demonstrate that the proposed FDFDnet emulator-based E2E learning strategy is a promising contender for achieving ultra-high-capacity interconnectivity between data centers.

OAM mode-division multiplexing IM/DD transmission at 4.32†Tbit/s with a low-complexity adaptive-network-based fuzzy inference system nonlinear equalizer.

Stochastic nonlinear impairment is the primary factor that limits the transmission performance of high-speed orbital angular momentum (OAM) mode-division multiplexing (MDM) optical fiber communication systems. This Letter presents a low-complexity adaptive-network-based fuzzy inference system (LANFIS) nonlinear equalizer for OAM-MDM intensity-modulation direct-detection (IM/DD) transmission with three OAM modes and 15 wavelength division multiplex (WDM) channels. The LANFIS equalizer could adjust the probability distribution functions (PDFs) of the distorted pulse amplitude modulation (PAM) symbols to fit the statistical characteristics of the WDM-OAM-MDM transmission channel. Therefore, although the transmission symbols in the WDM-OAM-MDM system are subjected to a stochastic nonlinear impairment, the proposed LANFIS equalizer can effectively compensate the distorted signals. The proposed equalizer outperforms the Volterra equalizer with improvements in receiver sensitivity of 2, 1.5, and 1.3 dB for three OAM modes at a wavelength of 1550.12 nm, respectively. It also outperforms a CNN equalizer, with improvements in receiver sensitivity of 1, 0.5, and 0.3 dB, respectively. Moreover, complexity reductions of 67%, 74%, and 99.9% are achieved for the LANFIS equalizer compared with the Volterra, CNN, and ANFIS equalizers, respectively. The proposed equalizer has high performance and low complexity, making it a promising candidate for a high-speed WDM-OAM-MDM system.

DACNN-aided nonlinear equalizer for a probabilistic shaping coherent optical communication system.

The probabilistic shaping (PS) technique is a key technology for fiber optic communication systems to further approach the Shannon limit. To solve the problem that nonlinear equalizers are ineffective for probabilistic shaping optical communication systems with non-uniform distribution, a distribution alignment convolutional neural network (DACNN)-aided nonlinear equalizer is proposed. The approach calibrates the equalizer using the probabilistic shaping prior distribution, which reduces the training complexity and improves the performance of the equalizer simultaneously. Experimental results show nonlinear equalization of 120 Gb/s PS 64QAM signals in a 375 km transmission scenario. The proposed DACNN equalizer improves the receiver sensitivity by 2.6 dB and 1.1 dB over the Volterra equalizer and convolutional neural network (CNN) equalizer, respectively. Meanwhile, DACNN converges with fewer training epochs than CNN, which provides great potential for mitigating the nonlinear distortion of PS signals in fiber optic communication systems.

A Deep Learning Based Platform for Remote Sensing Images Change Detection Integrating Crowdsourcing and Active Learning.

Remote sensing images change detection technology has become a popular tool for monitoring the change type, area, and distribution of land cover, including cultivated land, forest land, photovoltaic, roads, and buildings. However, traditional methods which rely on pre-annotation and on-site verification are time-consuming and challenging to meet timeliness requirements. With the emergence of artificial intelligence, this paper proposes an automatic change detection model and a crowdsourcing collaborative framework. The framework uses human-in-the-loop technology and an active learning approach to transform the manual interpretation method into a human-machine collaborative intelligent interpretation method. This low-cost and high-efficiency framework aims to solve the problem of weak model generalization caused by the lack of annotated data in change detection. The proposed framework can effectively incorporate expert domain knowledge and reduce the cost of data annotation while improving model performance. To ensure data quality, a crowdsourcing quality control model is constructed to evaluate the annotation qualification of the annotators and check their annotation results. Furthermore, a prototype of automatic detection and crowdsourcing collaborative annotation management platform is developed, which integrates annotation, crowdsourcing quality control, and change detection applications. The proposed framework and platform can help natural resource departments monitor land cover changes efficiently and effectively.

Imaging Extreme Ultraviolet Radiation Using Nanodiamonds with Nitrogen-Vacancy Centers.

Extreme ultraviolet (EUV) radiation with wavelengths of 10-121 nm has drawn considerable attention recently for its use in photolithography to fabricate nanoelectronic chips. This study demonstrates, for the first time, fluorescent nanodiamonds (FNDs) with nitrogen-vacancy (NV) centers as scintillators to image and characterize EUV radiations. The FNDs employed are ∼100 nm in size; they form a uniform and stable thin film on an indium-tin-oxide-coated slide by electrospray deposition. The film is nonhygroscopic and photostable and can emit bright red fluorescence from NV0 centers when excited by EUV light. An FND-based imaging device has been developed and applied for beam diagnostics of 50 nm and 13.5 nm synchrotron radiations, achieving a spatial resolution of 30 µm using a film of ∼1 µm thickness. The noise equivalent power density is 29 µW/(cm2 Hz1/2) for the 13.5 nm radiation. The method is generally applicable to imaging EUV radiation from different sources.

A highly sensitive and selective colorimetric aptasensor for detecting sulfadiazine in river waters based on gold nanoparticles synthesized from discarded Longan seed extract.

Gold nanoparticles (AuNPs) were extensively employed for in-situ detection sulfadiazine (SDZ) residues, yet current synthesis methods suffer from complex procedures, reagent pollution of the environment, and low particle quality. This study presents a novel synthesis method using discarded longan seed extract as a reducing agent to synthesized high-quality AuNPs, and then can be used for in-situ SDZ detection. Response surface methodology (RSM) was employed to optimize synthesis parameters, which resulted in five optimal combinations that enhanced the flexibility of synthesis. These AuNPs, ranging in size from 18.26 nm to 33.8 nm with zeta potentials from - 29.5 mV to - 14.3 mV, were successfully loaded with functional groups from longan seed extract. In the detection of SDZ, the colorimetric aptasensor demonstrated excellent sensitivity and selectivity over other antibiotics with a limit of detection and quantification at 70.98 ng·mL-1 and 236.59 ng·mL-1 in the concentration range of 200-800 ng·mL-1. Recoveries of spiked SDZ samples ranged from 97.90% to 106.7%, with RSD values below 9.25%. Meanwhile, the aptasensor exhibited exceptional diagnostic efficacy (AUC: 0.976) compared to UV absorption methods in the ROC evaluation. In conclusion, this study highlights the potential of using AuNPs synthesized from longan seed extract coupled with aptamer technology as a straightforward detection method for SDZ in river water, offering promising applications in environmental monitoring.

Antibody-Conjugated Nanodiamonds as Dual-Functional Immunosensors for In Vitro Diagnostics.

Nanodiamonds (NDs) are carbon nanoparticles with a large refractive index, a high density, and exceptional chemical stability. When excited by green light, they can emit bright red fluorescence from implanted nitrogen-vacancy (NV) centers. Taking advantage of these properties, we have developed antibody-conjugated NDs as in vitro diagnostic sensors for two complementary assays: particle-enhanced turbidimetric immunoassay (PETIA) and spin-enhanced lateral flow immunoassay (SELFIA). To achieve this goal, monocrystalline diamond powders (∼100 nm in diameter) with or without NV implantation were first treated in molten KNO3 to reduce their size and shape inhomogeneity, followed by surface carboxylation in strong oxidative acids and non-covalent conjugation with antibodies in water. PETIA and SELFIA were carried out separately with a microplate reader and a magnetically modulated fluorescence analyzer. Using C-reactive protein (CRP) as the target antigen, we found that anti-CRP-conjugated NDs exhibited high colloidal stability over 1 month at 4 °C in buffer solution. The limits of detection for 3 µL of CRP sample solution were 0.06 µg/mL and 1 ng/mL with variation coefficients of less than 10 and 15% for PETIA and SELFIA, respectively. These two methods together provide a detection range of 1 ng/mL-10 µg/mL, potentially useful for clinical applications. This work represents the first practical use of rounded monocrystalline NDs as in vitro diagnostic reagents.

Syntenin-1-mediated arthritogenicity is advanced by reprogramming RA metabolic macrophages and Th1 cells.

OBJECTIVES: Syntenin-1, a novel endogenous ligand, was discovered to be enriched in rheumatoid arthritis (RA) specimens compared with osteoarthritis synovial fluid and normal synovial tissue (ST). However, the cellular origin, immunoregulation and molecular mechanism of syntenin-1 are undescribed in RA. METHODS: RA patient myeloid and lymphoid cells, as well as preclinical models, were used to investigate the impact of syntenin-1/syndecan-1 on the inflammatory and metabolic landscape. RESULTS: Syntenin-1 and syndecan-1 (SDC-1) co-localise on RA ST macrophages (MΦs) and endothelial cells. Intriguingly, blood syntenin-1 and ST SDC-1 transcriptome are linked to cyclic citrullinated peptide, erythrocyte sedimentation rate, ST thickness and bone erosion. Metabolic CD14+CD86+GLUT1+MΦs reprogrammed by syntenin-1 exhibit a wide range of proinflammatory interferon transcription factors, monokines and glycolytic factors, along with reduced oxidative intermediates that are downregulated by blockade of SDC-1, glucose uptake and/or mTOR signalling. Inversely, IL-5R and PDZ1 inhibition are ineffective on RA MΦs-reprogrammed by syntenin-1. In syntenin-1-induced arthritis, F4/80+iNOS+RAPTOR+MΦs represent glycolytic RA MΦs, by amplifying the inflammatory and glycolytic networks. Those networks are abrogated in SDC-1-/- animals, while joint prorepair monokines are unaffected and the oxidative metabolites are moderately replenished. In RA cells and/or preclinical model, syntenin-1-induced arthritogenicity is dependent on mTOR-activated MΦ remodelling and its ability to cross-regulate Th1 cells via IL-12 and IL-18 induction. Moreover, RA and joint myeloid cells exposed to Syntenin-1 are primed to transform into osteoclasts via SDC-1 ligation and RANK, CTSK and NFATc1 transcriptional upregulation. CONCLUSION: The syntenin-1/SDC-1 pathway plays a critical role in the inflammatory and metabolic landscape of RA through glycolytic MΦ and Th1 cell cross-regulation (graphical abstract).

Bayesian generative adversarial network emulator based end-to-end learning strategy of the probabilistic shaping for OAM mode division multiplexing IM/DD transmission.

Orbital angular momentum (OAM) mode division multiplexing (MDM) has emerged as a new multiplexing technology that can significantly increase transmission capacity. In addition, probabilistic shaping (PS) is a well-established technique that can increase the transmission capacity of an optical fiber to close to the Shannon limit. However, both the mode coupling and the nonlinear impairment lead to a considerable gap between the OAM-MDM channel and the conventional additive white Gaussian noise (AWGN) channel, meaning that existing PS technology is not suitable for an OAM-MDM intensity-modulation direct-detection (IM-DD) system. In this paper, we propose a Bayesian generative adversarial network (BGAN) emulator based on an end-to-end (E2E) learning strategy with probabilistic shaping (PS) for an OAM-MDM IM/DD transmission with two modes. The weights and biases of the BGAN emulator are treated as a probability distribution, which can be accurately matched to the stochastic nonlinear model of OAM-MDM. Furthermore, a BGAN emulator based on an E2E learning strategy is proposed to find the optimal probability distribution of PS for an OAM-MDM IM/DD system. An experiment was conducted on a 200 Gbit/s two OAM modes carrierless amplitude phase-32(CAP-32) signal over a 5 km ring-core fiber transmission, and the results showed that the proposed BGAN emulator outperformed a conventional CGAN emulator, with improvements in modelling accuracy of 29.3% and 26.3% for the two OAM modes, respectively. Moreover, the generalized mutual information (GMI) of the proposed E2E learning strategy outperformed the conventional MB distribution and the CGAN emulator by 0.31 and 0.33 bits/symbol and 0.16 and 0.2 bits/symbol for the two OAM modes, respectively. Our experimental results demonstrate that the proposed E2E learning strategy with the BGAN emulator is a promising candidate for OAM-MDM IM/DD optic fiber communication.

400 Gbit/s 4 mode transmission for IM/DD OAM mode division multiplexing optical fiber communication with a few-shot learning-based AffinityNet nonlinear equalizer.

Nonlinear impairment in a high-speed orbital angular momentum (OAM) mode-division multiplexing (MDM) optical fiber communication system presents high complexity and strong stochasticity due to the massive optoelectronic devices. In this paper, we propose an Affinity Network (AffinityNet) nonlinear equalizer for an OAM-MDM intensity-modulation direct-detection (IM/DD) transmission with four OAM modes. The labeled training and testing signals from the OAM-MDM system can be regarded as "small sample" and "large target", respectively. AffinityNet can be used to build an accurate nonlinear model using "small sample" based on few-shot learning and can predict the stochastic characteristic nonlinearity of OAM-MDM with a high level of generalization. As a result, the AffinityNet nonlinear equalizer can effectively compensate the stochastic nonlinearity in the OAM-MDM system, despite the large difference between the training and testing signals due to the stochastic nonlinear impairment. An experiment was conducted on a 400 Gbit/s transmission with four OAM modes using a pulse amplitude modulation-8 (PAM-8) signal over a 2 km ring-core fiber (RCF). Our experimental results show that the proposed nonlinear equalizer outperformed the conventional Volterra equalizer with improvements in receiver sensitivity of 1.7, 1.8, 3, and 3.3 dB for the four OAM modes at the 15% forward error correction (FEC) threshold, respectively. In addition, the proposed equalizer outperformed a convolutional neural network (CNN) equalizer with improvements in receiver sensitivity of 0.8, 0.5, 0.9, and 1.4 dB for the four OAM modes at the 15% FEC threshold. In the experiment, a complexity reduction of 37% and 83% of the AffinityNet equalizer is taken compared to the conventional Volterra equalizer and CNN equalizer, respectively. The proposed equalizer is a promising candidate for a high-speed OAM-MDM optical fiber communication system.

Hidden conditional random field-based equalizer for the 3D-CAP-64 transmission of OAM mode-division multiplexed ring-core fiber communication.

As a key technique for achieving ultra-high capacity optical fiber communications, orbital angular momentum (OAM) mode-division multiplexing (MDM) is affected by severe nonlinear impairments, including modulation related nonlinearities, square-law nonlinearity and mode-coupling-induced nonlinearity. In this paper, an equalizer based on a hidden conditional random field (HCRF) is proposed for the nonlinear mitigation of OAM-MDM optical fiber communication systems with 20 GBaud three-dimensional carrierless amplitude and phase modulation-64 (3D-CAP-64) signals. The HCRF equalizer extracts the stochastic nonlinear feature of the OAM-MDM 3D-CAP-64 signals by estimating the conditional probabilities of the hidden variables, thereby enabling the signals to be classified into subclasses of constellation points. The nonlinear impairment can then be mitigated based on the statistical probability distribution of the hidden variables of the OAM-MDM transmission channel in the HCRF equalizer. Our experimental results show that compared with a convolutional neural network (CNN)-based equalizer, the proposed HCRF equalizer improves the receiver sensitivity by 2 dB and 1 dB for the two OAM modes used here, with l = + 2 and l = + 3, respectively, at the 7% forward error correction (FEC) threshold. When compared with a Volterra nonlinear equalizer (VNE) and CNN-based equalizer, the computational complexity of the proposed HCRF equalizer was found to be reduced by 30% and 41%, respectively. The bit error ratio (BER) performance and reduction in computational complexity indicate that the proposed HCRF equalizer has great potential to mitigate nonlinear distortions in high-speed OAM-MDM fiber communication systems.

Novel Classification System of Adenoids Based on Appearance and Its Relationship with Drug Therapy.

INTRODUCTION: Adenoidectomy is a common procedure in children who have adenoid hypertrophy (AH), but anesthesia risks should be considered. We proposed a novel classification system for adenoids based on their appearance. Additionally, we explored whether the novel classification of adenoids correlates with the response to therapy and thus might be helpful for further treatment recommendations. METHODS: We used fiberoptic nasal endoscopy to determine the degree and appearance of AH. Obstructive Sleep Apnea Questionnaire (OSA-18) was used to assess the quality of life of children with AH. The adenoids were divided into three types: edematous type, common type, and fibrous type. In adenoid tissues, the eosinophils were counted. Immunohistochemistry and Western blot were done to determine the expression of CysLTR1, CysLTR2, CGR-α, and CGR-ß in different types of adenoids. RESULTS: 70.67% (106/150) of AH patients presented with allergic rhinitis (AR), and of them, 68% (72/106) of adenoids were the edematous type. The expressions of CGR-α, CGR-ß, and eosinophil count were higher in the edematous compared with the common and fibrous types. The expression of the leukotriene receptor was similar in all types. Upon montelukast combined with nasal glucocorticoid therapy, improvement of OSA-18 scores and AH grade was significantly compared to montelukast monotherapy for edematous type. There was not any statistically significant difference between the scores upon montelukast combined with nasal glucocorticoid and montelukast monotherapy for common and fibrous type. We observed a positive correlation between eosinophil count in the blood and in the adenoid tissue. CONCLUSION: AR was the risk factor for the development of edematous AH. All subtypes of AH responded to montelukast, while there was an additional effect of nasal glucocorticoid in the edematous type. A combination therapy of nasal glucocorticoid with leukotriene receptor antagonist can be recommended for AH patients with AR, patients with edematous adenoids, and/or patients with increased eosinophils in blood routine.

Synthesis of Fluorescent C-C Bonded Triazole-Purine Conjugates.

A design toward C-C bonded 2,6-bis(1H-1,2,3-triazol-4-yl)-9H-purine and 2-piperidinyl-6-(1H-1,2,3-triazol-4-yl)-9H-purine derivatives was established using the combination of Mitsunobu, Sonogashira, copper (I) catalyzed azide-alkyne cycloaddition, and SNAr reactions. 11 examples of 2,6-bistriazolylpurine and 14 examples of 2-piperidinyl-6-triazolylpurine intermediates were obtained, in 38-86% and 41-89% yields, respectively. Obtained triazole-purine conjugates expressed good fluorescent properties which were studied in the solution and in the thin layer film for the first time. Quantum yields reached up to 49% in DMSO for bistriazolylpurines and up to 81% in DCM and up to 95% in DMSO for monotriazolylpurines. Performed biological studies in mouse embryo fibroblast, human keratinocyte, and transgenic adenocarcinoma of the mouse prostate cell lines showed that most of obtained triazole-purine conjugates are not cytotoxic. The 50% cytotoxic concentration of the tested derivatives was in the range from 59.6 to 1528.7 µM.

Inhibition of IRAK4 dysregulates SARS-CoV-2 spike protein-induced macrophage inflammatory and glycolytic reprogramming.

Escalated innate immunity plays a critical role in SARS-CoV-2 pathology; however, the molecular mechanism is incompletely understood. Thus, we aim to characterize the molecular mechanism by which SARS-CoV-2 Spike protein advances human macrophage (MÏ´) inflammatory and glycolytic phenotypes and uncover novel therapeutic strategies. We found that human MÏ´s exposed to Spike protein activate IRAK4 phosphorylation. Blockade of IRAK4 in Spike protein-stimulated MÏ´s nullifies signaling of IRAK4, AKT, and baseline p38 without affecting ERK and NF-κB activation. Intriguingly, IRAK4 inhibitor (IRAK4i) rescues the SARS-CoV-2-induced cytotoxic effect in ACE2+HEK 293 cells. Moreover, the inflammatory reprogramming of MÏ´s by Spike protein was blunted by IRAK4i through IRF5 and IRF7, along with the reduction of monokines, IL-6, IL-8, TNFα, and CCL2. Notably, in Spike protein-stimulated MÏ´s, suppression of the inflammatory markers by IRAK4i was coupled with the rebalancing of oxidative phosphorylation over metabolic activity. This metabolic adaptation promoted by IRAK4i in Spike protein-activated MÏ´s was shown to be in part through constraining PFKBF3, HIF1α, cMYC, LDHA, lactate expression, and reversal of citrate and succinate buildup. IRAK4 knockdown could comparably impair Spike protein-enhanced inflammatory and metabolic imprints in human MÏ´s as those treated with ACE2, TLR2, and TLR7 siRNA. Extending these results, in murine models, where human SARS-CoV-2 Spike protein was not recognized by mouse ACE2, TLRs were responsible for the inflammatory and glycolytic responses instigated by Spike protein and were dysregulated by IRAK4i therapy. In conclusion, IRAK4i may be a promising strategy for severe COVID-19 patients by counter-regulating ACE2 and TLR-mediated MÏ´ hyperactivation. IRAK4i therapy counteracts MÏ´ inflammatory and glycolytic reprogramming triggered by Spike protein. This study illustrates that SARS-CoV-2 Spike protein activates IRAK4 signaling via ACE2 as well as TLR2 and TLR7 sensing in human MÏ´s. Remarkably, IRAK4i treatment can dysregulate both ACE-dependent and independent (via TLR sensing) SARS-CoV-2 Spike protein-activated inflammatory and metabolic imprints.

Low-complexity characterized-long-short-term-memory-aided channel modeling for optical fiber communications.

In this work, a low-complexity data-driven characterized-long-short-term-memory (C-LSTM)-aided channel modeling technique is proposed for optical single-mode fiber (SMF) communications. To fully utilize the sequence correlation learning ability of traditional long short-term memory (LSTM) networks and solve the gradient explosion problem, the feature information is introduced into the traditional LSTM input layer to better characterize the intersymbol interference caused by dispersion in SMF modeling. The simulation results show that the proposed C-LSTM can effectively alleviate the gradient explosion problem with a stable and ultimately lower mean square error (MSE) than traditional LSTM. Compared with the split-step Fourier method (SSFM) and the conditional generative adversarial network (CGAN), the proposed C-LSTM has superior computational complexity. Moreover, due to the sequence correlation learning ability inherent to C-LSTM, coupled with the flexibility of feature information selection, the proposed C-LSTM-aided modeling technique has a higher modeling accuracy than traditional LSTM. Moreover, the C-LSTM-aided modeling technique can be effectively extended to other channel modeling applications with strong sequence correlations.