Net-432-Gb/s/λ PS-PAM transmission based on advanced DSP and single DAC for 400†G/λ IM/DD links.

The escalating surge in datacenter traffic creates a pressing demand for augmenting the capacity of cost-effective intensity modulation and direct detection (IM/DD) systems. In this Letter, we report the demonstration of the single-lane 128-GBaud probabilistically shaped (PS)-PAM-20 IM/DD transmission using only a single digital-to-analog converter (DAC) for a net 400 G/λ system. Based on the advanced digital signal processing (DSP), we achieve net bitrates of up to 437 Gb/s for optical back-to-back and 432 Gb/s after the 0.5-km SSMF transmission in the C-band with 128-Gbaud PS-PAM-20 signals. This work is the latest demonstration on ultra-high-order PS-PAM signals achieving net bitrates exceeding 400 Gb/s despite symbol rate limitations. Notably, to the best of our knowledge, the realized net information rate ([net bitrate]/[symbol rate]) of 3.37 marks a new achievement within the domain of 400 G/λ IM/DD systems, with promising implications for enhancing bandwidth efficiency in the upcoming 1.6-Tb Ethernet scenario.

Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening.

ConspectusMolecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths. However, to meet the efficiency demands of SBVS, these algorithms and functions are often simplified, prioritizing speed over accuracy.The emergence of deep learning (DL) has exerted a profound impact on diverse fields, ranging from natural language processing to computer vision and drug discovery. DeepMind's AlphaFold2 has impressively exhibited its ability to accurately predict protein structures solely from amino acid sequences, highlighting the remarkable potential of DL in conformation prediction. This groundbreaking advancement circumvents the traditional search-scoring frameworks in LD, enhancing both accuracy and processing speed and thereby catalyzing a broader adoption of DL algorithms in binding pose prediction. Nevertheless, a consensus on certain aspects remains elusive.In this Account, we delineate the current status of employing DL to augment LD within the VS paradigm, highlighting our contributions to this domain. Furthermore, we discuss the challenges and future prospects, drawing insights from our scholarly investigations. Initially, we present an overview of VS and LD, followed by an introduction to DL paradigms, which deviate significantly from traditional search-scoring frameworks. Subsequently, we delve into the challenges associated with the development of DL-based LD (DLLD), encompassing evaluation metrics, application scenarios, and physical plausibility of the predicted conformations. In the evaluation of LD algorithms, it is essential to recognize the multifaceted nature of the metrics. While the accuracy of binding pose prediction, often measured by the success rate, is a pivotal aspect, the scoring/screening power and computational speed of these algorithms are equally important given the pivotal role of LD tools in VS. Regarding application scenarios, early methods focused on blind docking, where the binding site is unknown. However, recent studies suggest a shift toward identifying binding sites rather than solely predicting binding poses within these models. In contrast, LD with a known pocket in VS has been shown to be more practical. Physical plausibility poses another significant challenge. Although DLLD models often achieve higher success rates compared to traditional methods, they may generate poses with implausible local structures, such as incorrect bond angles or lengths, which are disadvantageous for postprocessing tasks like visualization. Finally, we discuss the future perspectives for DLLD, emphasizing the need to improve generalization ability, strike a balance between speed and accuracy, account for protein conformation flexibility, and enhance physical plausibility. Additionally, we delve into the comparison between generative and regression algorithms in this context, exploring their respective strengths and potential.

Relationship between depression and burnout among nurses in Intensive Care units at the late stage of COVID-19: a network analysis.

BACKGROUND: Mental health problems are critical and common in medical staff working in Intensive Care Units (ICU) even at the late stage of COVID-19, particularly for nurses. There is little research to explore the inner relationships between common syndromes, such as depression and burnout. Network analysis (NA) was a novel approach to quantified the correlations between mental variables from the perspective of mathematics. This study was to investigate the interactions between burnout and depression symptoms through NA among ICU nurses. METHOD: A cross-sectional study with a total of 616 Chinese nurses in ICU were carried out by convenience sampling from December 19, 2022 to January19, 2023 via online survey. Burnout symptoms were measured by Maslach Burnout Inventory-General Survey (MBI-GS) (Chinese version), and depressive symptoms were assessed by the 9-item Patient Health Questionnaire (PHQ-9). NA was applied to build interactions between burnout and depression symptoms. We identified central and bridge symptoms by R package qgraph in the network model. R package bootnet was used to examined the stability of network structure. RESULTS: The prevalence of burnout and depressive symptoms were 48.2% and 64.1%, respectively. Within depression-burnout network, PHQ4(Fatigue)-MBI2(Used up) and PHQ4(Fatigue)-MBI5(Breakdown) showed stronger associations. MBI2(Used up) had the strongest expected influence central symptoms, followed by MBI4(Stressed) and MBI7 (Less enthusiastic). For bridge symptoms. PHQ4(Fatigue), MBI5(Breakdown) and MBI2(Used up) weighed highest. Both correlation stability coefficients of central and bridge symptoms in the network structure were 0.68, showing a high excellent level of stability. CONCLUSION: The symptom of PHQ4(Fatigue) was the bridge to connect the emotion exhaustion and depression. Targeting this symptom will be effective to detect mental disorders and relieve mental syndromes of ICU nurses at the late stage of COVID-19 pandemic.

Neural-network-based carrier-less amplitude phase modulated signal generation and end-to-end optimization for fiber-terahertz integrated communication system.

In fiber-terahertz integrated communication systems, nonlinear distortion and inter-symbol interference (ISI) will degrade transmission performance. Pre-compensation is an efficient method to handle the channel distortion as it can avoid noise boosting during channel compensation and reduce receiver side signal processing algorithmic complexity at user-end (UE) considering the asymmetric access scenario. In this paper, we propose and experimentally demonstrate a neural-network (NN)-based carrier-less amplitude phase (CAP) modulated signal generation and end-to-end optimization method for a fiber-terahertz integrated communication system. The CAP signal is generated directly from quadrature amplitude modulation symbols and pre-compensated through a transmitter NN, which allows the receiver to demodulate the signal with simple linear digital signal process (DSP). In generating the CAP signal, the NN based transmitter learns a group of filters, which can generate, up-convert, and pre-compensate the signals. Based on the proposed method, a fiber-terahertz integration access system at 220 GHz is demonstrated and a sensitivity gain of 1.2 dB is achieved at a transmission speed of 50 Gbps and the forward error correction (FEC) bit error rate (BER) threshold of 1 × 10-2 compared with the baseline after 10-km fiber transmission and 1-m wireless delivering.

A metasurface-based full-color circular auto-focusing Airy beam transmitter for stable high-speed underwater wireless optical communications.

Due to its unique intensity distribution, self-acceleration, and beam self-healing properties, Airy beam holds great potential for optical wireless communications in challenging channels, such as underwater environments. As a vital part of 6G wireless network, the Internet of Underwater Things requires high-stability, low-latency, and high-capacity underwater wireless optical communication (UWOC). Currently, the primary challenge of UWOC lies in the prevalent time-varying and complex channel characteristics. Conventional blue Gaussian beam-based systems face difficulties in underwater randomly perturbed links. In this work, we report a full-color circular auto-focusing Airy beams metasurface transmitter for reliable, large-capacity and long-distance UWOC links. The metasurface is designed to exhibits high polarization conversion efficiency over a wide band (440-640 nm), enabling an increased data transmission rate of 91% and reliable 4 K video transmission in wavelength division multiplexing (WDM) based UWOC data link. The successful application of this metasurface in challenging UWOC links establishes a foundation for underwater interconnection scenarios in 6G communication.

Preparation of Ti3C2Tx modified rare earth doped PbO2 electrodes for efficient removal of sulfamethoxazole.

In this study, we deposited Ti3C2Tx-modified, rare-earth-doped PbO2 on the surface of a carbon fabric via electrodeposition. The surface morphology and electronic structure of the electrode were characterized with SEM, XRD and XPS. The layered Ti3C2Tx did not change the structure of ß-PbO2, and at the same time, it improved the crystallinity of the material and reduced the grains of PbO2. Electrochemical experiments showed that the addition of Ti3C2Tx increased the electrochemical activity of the electrode and produced more H2O2, which contributed to the degradation of pollutants. The efficiency of sulfamethoxazole (SMX) degradation reached 95% after 120 min at pH 3 with a current density of 50 mA/cm2. Moreover, the electrode has good cycling performance, and the degradation efficiency was still 80% after 120 min after 10 cycles of recycling. Based on the intermediates identified by HPLC‒MS, a mechanism for SMX degradation was proposed. Our results will provide a new idea for the development of efficient electrocatalytic degradation of antibiotics.

Dry-processed technology for flexible and high-performance FeS2-based all-solid-state lithium batteries at low stack pressure.

All-solid-state lithium batteries (ASSLBs) are considered promising energy storage systems due to their high energy density and inherent safety. However, scalable fabrication of ASSLBs based on transition metal sulfide cathodes through the conventional powder cold-pressing method with ultrahigh stacking pressure remains challenging. This article elucidates a dry process methodology for preparing flexible and high-performance FeS2-based ASSLBs under low stack pressure by utilizing polytetrafluoroethylene (PTFE) binder. In this design, fibrous PTFE interweaves Li6PS5Cl particles and FeS2 cathode components, forming flexible electrolyte and composite cathode membranes. Beneficial to the robust adhesion, the composite cathode and Li6PS5Cl membranes are tightly compacted under a low stacking pressure of 100 MPa which is a fifth of the conventional pressure. Moreover, the electrode/electrolyte interface can sustain adequate contact throughout electrochemical cycling. As expected, the FeS2-based ASSLBs exhibit outstanding rate performance and cyclic stability, contributing a reversible discharged capacity of 370.7 mAh g-1 at 0.3C after 200 cycles. More importantly, the meticulous dQ/dV analysis reveals that the three-dimensional PTFE binder effectively binds the discharge products with sluggish kinetics (Li2S and Fe) to the ion-electron conductive network in the composite cathode, thereby preventing the electrochemical inactivation of products and enhancing electrochemical performance. Furthermore, FeS2-based pouch-type cells are fabricated, demonstrating the potential of PTFE-based dry-process technology to scale up ASSLBs from laboratory-scale mold cells to factory-scale pouch cells. This feasible dry-processed technology provides valuable insights to advance the practical applications of ASSLBs.

Increased visceral fat area to skeletal muscle mass ratio is positively associated with the risk of metabolic dysfunction-associated steatotic liver disease in a Chinese population.

BACKGROUND: The diagnosis and comprehension of nonalcoholic fatty liver disease (NAFLD), recently redefined as metabolic dysfunction-associated steatotic liver disease (MASLD) are gaining a better understanding. In this study, we examined the association between visceral fat area and skeletal muscle mass ratio (VSR) and the prevalence of MASLD in a Chinese population. METHODS: A cross-sectional study was conducted involving 10,916 individuals who underwent bioelectrical impedance analysis, along with anthropometric and biochemical measurements, from January 2022 to June 2023. According to the VSR distribution, sex-specific quartiles of VSR within the study population were defined. Linear trend tests were performed for the categorized VSR variables. Logistic regression models were performed to estimate the odds ratio and 95% confidence intervals between VSR distribution and MASLD prevalence stratified by sex. RESULTS: The prevalence of MASLD was 37.94% in the overall population (56.34% male), and it gradually increased with higher VSR levels in both genders (P < 0.001). Logistic regression analysis demonstrated a significant association between VSR and MASLD prevalence after adjusting for confounders. The odds ratio (95% confidence interval) for MASLD, comparing the lowest to the highest VSR quartile, was 3.159 (2.671, 3.736) for men and 2.230 (1.764, 2.819) for women (all P < 0.001). Restricted cubic splines also indicated significant non-linear relationships between VSR and MASLD prevalence. CONCLUSIONS: VSR is positively associated with the prevalence of MASLD in this Chinese population, with a notably higher risk for men as VSR increases compared to women.

Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor.

BACKGROUND: Preoperative knowledge of mutational status of gastrointestinal stromal tumors (GISTs) is essential to guide the individualized precision therapy. AIM: To develop a combined model that integrates clinical and contrast-enhanced computed tomography (CE-CT) features to predict gastric GISTs with specific genetic mutations, namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions. METHODS: A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio. The models were constructed using selected clinical features, conventional CT features, and radiomics features extracted from abdominal CE-CT images. Three models were developed: ModelCT sign, modelCT sign + rad, and model CTsign + rad + clinic. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis and the Delong test. RESULTS: The ROC analyses revealed that in the training cohort, the area under the curve (AUC) values for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic for predicting KIT exon 11 mutation were 0.743, 0.818, and 0.915, respectively. In the validation cohort, the AUC values for the same models were 0.670, 0.781, and 0.811, respectively. For predicting KIT exon 11 codons 557-558 deletions, the AUC values in the training cohort were 0.667, 0.842, and 0.720 for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic, respectively. In the validation cohort, the AUC values for the same models were 0.610, 0.782, and 0.795, respectively. Based on the decision curve analysis, it was determined that the modelCT sign + rad + clinic had clinical significance and utility. CONCLUSION: Our findings demonstrate that the combined modelCT sign + rad + clinic effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions. This combined model has the potential to be valuable in assessing the genotype of GISTs.

Achieving room temperature plasticity in brittle ceramics through elevated temperature preloading.

Ceramic materials with high strength and chemical inertness are widely used as engineering materials. However, the brittle nature limits their applications as fracture occurs before the onset of plastic yielding. There has been limited success despite extensive efforts to enhance the deformability of ceramics. Here we report a method for enhancing the room temperature plastic deformability of ceramics by artificially introducing abundant defects into the materials via preloading at elevated temperatures. After the preloading treatment, single crystal (SC) TiO2 exhibited a substantial increase in deformability, achieving 10% strain at room temperature. SC α-Al2O3 also showed plastic deformability, 6 to 7.5% strain, by using the preloading strategy. These preinjected defects enabled the plastic deformation process of the ceramics at room temperature. These findings suggest a great potential for defect engineering in achieving plasticity in ceramics at room temperature.

Is RNA editing truly absent in the complex thalloid liverworts (Marchantiopsida)? Evidence of extensive RNA editing from Cyathodium cavernarum.

RNA editing is a crucial modification in plants' organellar transcripts that converts cytidine to uridine (C-to-U; and sometimes uridine to cytidine) in RNA molecules. This post-transcriptional process is controlled by the PLS-class protein with a DYW domain, which belongs to the pentatricopeptide repeat (PPR) protein family. RNA editing is widespread in land plants; however, complex thalloid liverworts (Marchantiopsida) are the only group reported to lack both RNA editing and DYW-PPR protein. The liverwort Cyathodium cavernarum (Marchantiopsida, Cyathodiaceae), typically found in cave habitats, was newly found to have 129 C-to-U RNA editing sites in its chloroplast and 172 sites in its mitochondria. The Cyathodium genus, specifically C. cavernarum, has a large number of PPR editing factor genes, including 251 DYW-type PPR proteins. These DYW-type PPR proteins may be responsible for C-to-U RNA editing in C. cavernarum. Cyathodium cavernarum possesses both PPR DYW proteins and RNA editing. Our analysis suggests that the remarkable RNA editing capability of C. cavernarum may have been acquired alongside the emergence of DYW-type PPR editing factors. These findings provide insight into the evolutionary pattern of RNA editing in land plants.

Adaptive Neural Cooperative Control of Multirobot Systems With Input Quantization.

This article develops the adaptive neural cooperative control scheme for a group of mobile robots with a limited sensing range in presence of input quantization by a dynamic surface control technique. First, to make the controller design feasible, the original robotic system is transformed into a new fully actuated system using a transverse function. Then, taking into consideration the effects of a hysteresis quantizer, an adaptive neural cooperative controller is developed based on the universal approximation property of the radial basis function neural networks and the connectivity preservation strategy. Furthermore, the proposed control scheme can guarantee that all closed-loop signals are semi-globally uniformly ultimately bounded. Meanwhile, desired constraints are not breached and tracking errors are within the predefined domains. Finally, several simulation results are carried out to testify the feasibility and efficiency of the theoretical findings revealed in this article.

Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots.

The applications of self-assembled InAs/GaAs quantum dots (QDs) for lasers and single photon sources strongly rely on their density and quality. Establishing the process parameters in molecular beam epitaxy (MBE) for a specific density of QDs is a multidimensional optimization challenge, usually addressed through time-consuming and iterative trial-and-error. Here, we report a real-time feedback control method to realize the growth of QDs with arbitrary density, which is fully automated and intelligent. We develop a machine learning (ML) model named 3D ResNet 50 trained using reflection high-energy electron diffraction (RHEED) videos as input instead of static images and providing real-time feedback on surface morphologies for process control. As a result, we demonstrate that ML from previous growth could predict the post-growth density of QDs, by successfully tuning the QD densities in near-real time from 1.5 × 1010 cm-2 down to 3.8 × 108 cm-2 or up to 1.4 × 1011 cm-2. Compared to traditional methods, our approach can dramatically expedite the optimization process and improve the reproducibility of MBE. The concepts and methodologies proved feasible in this work are promising to be applied to a variety of material growth processes, which will revolutionize semiconductor manufacturing for optoelectronic and microelectronic industries.

Universal Deoxidation of Semiconductor Substrates Assisted by Machine Learning and Real-Time Feedback Control.

Substrate oxidation is inevitable when exposed to ambient atmosphere during semiconductor manufacturing, which is detrimental to the fabrication of state-of-the-art devices. Optimizing the deoxidation process in molecular beam epitaxy (MBE) for random substrates poses a multidimensional challenge and is sometimes controversial. Due to variations in substrates and growth processes, the determination of the deoxidation condition heavily relies on the individual's expertise, yielding inconsistent results. This study employs a machine learning model that integrates interpolation and vision transformer (Interpolation-ViT) techniques. The model utilizes reflection high-energy electron diffraction videos as input to predict the status of the substrate, enabling automated deoxidation within a controlled architecture for various substrates. Furthermore, we highlight the potential of models trained on data from specific MBE equipment to achieve high-accuracy deployment on different pieces of equipment. In contrast to traditional methods, our approach holds exceptional value, as it standardizes deoxidation temperatures across diverse equipment and substrates. This significantly advances the standardization of the semiconductor process. The concepts and methods presented are expected to revolutionize semiconductor manufacturing processes in the optoelectronic and microelectronic industries.

Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes.

Cyclic peptides have emerged as a highly promising class of therapeutic molecules owing to their favorable pharmacokinetic properties, including stability and permeability. Currently, many clinically approved cyclic peptides are derived from natural products or their derivatives, and the development of molecular docking techniques for cyclic peptide discovery holds great promise for expanding the applications and potential of this class of molecules. Given the availability of numerous docking programs, there is a pressing need for a systematic evaluation of their performance, specifically on protein-cyclic peptide systems. In this study, we constructed an extensive benchmark data set called CPSet, consisting of 493 protein-cyclic peptide complexes. Based on this data set, we conducted a comprehensive evaluation of 10 docking programs, including Rosetta, AutoDock CrankPep, and eight protein-small molecule docking programs (i.e., AutoDock, AudoDock Vina, Glide, GOLD, LeDock, rDock, MOE, and Surflex). The evaluation encompassed the assessment of the sampling power, docking power, and scoring power of these programs. The results revealed that all of the tested protein-small molecule docking programs successfully sampled the binding conformations when using the crystal conformations as the initial structures. Among them, rDock exhibited outstanding performance, achieving a remarkable 94.3% top-100 sampling success rate. However, few programs achieved successful predictions of the binding conformations using tLEaP-generated conformations as the initial structures. Within this scheme, AutoDock CrankPep yielded the highest top-100 sampling success rate of 29.6%. Rosetta's scoring function outperformed the others in selecting optimal conformations, resulting in an impressive top-1 docking success rate of 87.6%. Nevertheless, all the tested scoring functions displayed limited performance in predicting binding affinity, with MOE@Affinity dG exhibiting the highest Pearson's correlation coefficient of 0.378. It is therefore suggested to use an appropriate combination of different docking programs for given tasks in real applications. We expect that this work will offer valuable insights into selecting the appropriate docking programs for protein-cyclic peptide complexes.

Synchronously Polishing the Lead-Rich Surface and Passivating Surface Defects of CsPb(Br/I)3 Quantum Dots for High-Performance Pure-Red PeLEDs.

Mixed-halide CsPb(Br/I)3 perovskite quantum dots (QDs) are regarded as one of the most promising candidates for pure-red perovskite light-emitting diodes (PeLEDs) due to their precise spectral tuning property. However, the lead-rich surface of these QDs usually results in halide ion migration and nonradiative recombination loss, which remains a great challenge for high-performance PeLEDs. To solve the above issues, we employ a chelating agent of 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid hydrate (DOTA) to polish the lead-rich surface of the QDs and meanwhile introduce a new ligand of 2,3-dimercaptosuccinic acid (DMSA) to passivate surface defects of the QDs. This synchronous post-treatment strategy results in high-quality CsPb(Br/I)3 QDs with suppressed halide ion migration and an improved photoluminescence quantum yield, which enables us to fabricate spectrally stable pure-red PeLEDs with a peak external quantum efficiency of 23.2%, representing one of the best performance pure-red PeLEDs based on mixed-halide CsPb(Br/I)3 QDs reported to date.

On-chip multi-trap optical tweezers based on a guided wave-driven metalens.

Optical tweezer arrays (OTAs) have emerged as a powerful tool for quantum simulation, quantum computation, and quantum many-body physics. Conventional OTAs require bulky and costly optical components to generate multiple optical traps, such as spatial light modulators (SLMs). An integrated way to achieve on-chip OTAs is a sought-after goal for compact optical manipulation. In this Letter, we have numerically demonstrated compact on-chip multi-trap optical tweezers based on a guided wave-driven metalens. The presented on-chip optical tweezers are capable of capturing multiple polystyrene nanospheres in parallel. Moreover, we proposed an analytical design method to generate customized focal points from the integrated photonics chip into free space. Different trapping patterns are demonstrated to validate our proposed off-chip emission scheme. Our approach offers a promising solution to realize on-chip optical tweezers and provides a prospective way to realize elaborate emission control of guided waves into free-space beams.

Hybrid Grid Pattern Star Identification Algorithm Based on Multi-Calibration Star Verification.

In order to solve the star identification problem in the lost space mode for scientific cameras with small fields of view and higher instruction magnitudes, this paper proposes a star identification algorithm based on a hybrid grid pattern. The application of a hybrid pattern generated by multi-calibration stars in the initial matching enables the position distribution features of neighboring stars around the main star to be more comprehensively described and avoids the interference of position noise and magnitude noise as much as possible. Moreover, calibration star filtering is adopted to eliminate incorrect candidates and pick the true matched navigation star from candidate stars in the initial match. Then, the reference star image is utilized to efficiently verify and determine the final identification results of the algorithm via the nearest principle. The performance of the proposed algorithm in simulation experiments shows that, when the position noise is 2 pixels, the identification rate of the algorithm is 96.43%, which is higher than that of the optimized grid algorithm by 2.21% and the grid algorithm by 4.05%; when the magnitude noise is 0.3 mag, the star identification rate of the algorithm is 96.45%, which is superior to the optimized grid algorithm by 2.03% and to the grid algorithm by 3.82%. In addition, in the actual star image test, star magnitude values of ≤12 mag can be successfully identified using the proposed algorithm.

An optimized high-throughput SARS-CoV-2 dual reporter trans-complementation system for antiviral screening in vitro and in vivo.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still epidemic around the world. The manipulation of SARS-CoV-2 is restricted to biosafety level 3 laboratories (BSL-3). In this study, we developed a SARS-CoV-2 ΔN-GFP-HiBiT replicon delivery particles (RDPs) encoding a dual reporter gene, GFP-HiBiT, capable of producing both GFP signal and luciferase activities. Through optimal selection of the reporter gene, GFP-HiBiT demonstrated superior stability and convenience for antiviral evaluation. Additionally, we established a RDP infection mouse model by delivering the N gene into K18-hACE2 KI mouse through lentivirus. This mouse model supports RDP replication and can be utilized for in vivo antiviral evaluations. In summary, the RDP system serves as a valuable tool for efficient antiviral screening and studying the gene function of SARS-CoV-2. Importantly, this system can be manipulated in BSL-2 laboratories, decreasing the threshold of experimental requirements.

The role of lncRNAKCNQ1OT1/miR-301b/Tcf7 axis in cardiac hypertrophy.

OBJECTIVE: Cardiac hypertrophy, acting as a pathologic process of chronic hypertension and coronary disease, and its underlying mechanisms still need to be explored. Long non-coding RNA (LncRNA) potassium voltage-gated channel subfamily Q member 1 Transcript 1 (KCNQ1OT1) has been implicated in myocardial infarction. However, its role in cardiac hypertrophy remains reported. METHOD: To explore the regulated effect of lncRNAKCNQ1OT1 and miR-301b in cardiac hypertrophy, gain-and-lose function assays were tested. The expression of lncRNAKCNQ1OT1 and miR-301b were tested by quantitative real time polymerase chain reaction (qRT-PCR). The levels of transcription factor 7 (Tcf7), Proto-oncogene c-myc (c-myc), Brainnatriureticpeptide (BNP) and ß-myosin heavy chain (ß-MHC) were detected by Western blot. Additionally, luciferase analysis revealed interaction between lncRNAKCNQ1OT1, BNPß-MHCmiR-301b, and Tcf7. RESULT: LncRNAKCNQ1OT1 overexpression significantly induced cardiac hypertrophy. Furthermore, lncRNAKCNQ1OT1 acts as a sponge for microRNA-301b, which exhibited lower expression in cardiac hypertrophy model, indicating an anti-hypertrophic role. Furthermore, the BNP and ß-MHC expression increased, as well as cardiomyocyte surface area, with Ang II treatment, while the effect was repealed by miR-301b. Moreover, the protein expression of Tcf7 was inversely regulated by miR-301b and Antisense miRNA oligonucleotides (AMO)-301b. CONCLUSION: Our study has shown that overexpression of lncRNAKCNQ1OT1 could promote the development of cardiac hypertrophy by regulating miR-301b and Tcf7. Therefore, inhibition of lncRNAKCNQ1OT1 might be a potential therapeutic strategy for cardiac hypertrophy.