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This study applies the Apriori algorithm to analyse patterns, syntactic structures, and thematic clusters in Chinese studies data from various genres. This study aims to identify recurring linguistic elements in order to shed light on the dynamic nature of the Chinese language across different contexts and time periods. The Apriori algorithm is used to identify frequent item sets and establish associations between linguistic constructs in large datasets (over 20 years). This study examines the complexity of the Chinese language by analysing co-occurrence patterns, syntactic tendencies, and thematic categorizations. This study examines the evolution of language, regional word choices, and cultural nuances. Thematic clusters and sensory associations establish the relationship between language and culture. The study of Chinese language patterns and cultural implications utilises data to advance computational linguistics and theory. Computational models prioritize cultural and historical context analysis for more comprehensive language processing. Theoretical implications help researchers understand language evolution and culture, while practical implications improve language technology tools. The conclusion provides support for research in computational linguistics, cultural studies, and linguistic theory-based holistic language analysis and application.

Collaborative problem-solving (CPS) involves the interaction and interdependence of students' social and cognitive skills, making it a complex learning process. To delve into the complex dynamics of CPS, previous research has categorized socio-cognitive roles, providing insights into social-cognitive frameworks. However, despite the specific cognitive and social interaction structures employed by roles to engage in CPS interactions, most existing research primarily focuses on individual roles, neglecting inter-role interactions. To fill this gap, twelve triad groups were formed by engaging 36 undergraduate students in online CPS activities to examine differences in social and cognitive interaction structures across different roles and group compositions. Additionally, analyze the differences in CPS processes among various group compositions. The analyses identified five roles ("Lurkers," "Followers," "Drivers," "Influential Actors," and "Innovators") and three group compositions ("Balanced groups," "Decentralized groups," and "Power Struggle groups"). The socio-cognitive structure of Balanced groups, along with other evidence, indicates effective information sharing and negotiation interactions. In contrast, "Decentralized" and "Power Struggle groups" exhibited various deficiencies in their socio-cognitive structures, negatively impacting group collaboration processes. These insights provide educators with a comprehensive guide to fostering effective group compositions and role dynamics in online CPS settings, thereby enhancing the overall success of CPS. Additionally, possible activity design considerations and scaffolding strategies are also discussed.

This study aims to track college students' on-task rate during the teaching process and to analyze the influence of instructional strategies on on-task rate through the aspects of observable and internal engagement indicators. Thirty-six undergraduate students at a higher education institution in China participated in the study. Students' behaviors and their EEG signals were recorded from fifty-one learning activities. Analyses have been focused on identifying the determinants of student's engagement levels and revealing the impacts of behavioral sequences and cognitive sequences on student's engagement levels. The results show that: (1) instructional strategies, classroom behaviors, and cognitive states were significant predictors of students' on-task rate; (2) the continuity of classroom behaviors improved the on-task rate; and (3) the standard deviations of attention and cognitive load were positively correlated with the on-task rate. This study describes a case of integrating multimodal data analysis in classroom teaching and discusses practical implications for improving classroom teaching.

Abstract Background To develop an artificial intelligence (AI)-based model using Radiomics, deep learning (DL) features extracted from 18F-fluorodeoxyglucose (18F-FDG) Positron emission tomography/Computed Tomography (PET/CT) images of tumor and cervical lymph node with clinical feature for predicting cervical lymph node metastasis (CLNM) in patients with esophageal squamous cell carcinoma (ESCC). Methods The study included 300 ESCC patients from the First Affiliated Hospital of Zhengzhou University who were divided into a training cohort and an internal testing cohort with an 8:2 ratio. Another 111 patients from Shanghai Chest Hospital were included as the external cohort. For each sample, we extracted 428 PET/CT-based Radiomics features from the gross tumor volume (GTV) and cervical lymph node (CLN) delineated layer by layer and 256 PET/CT-based DL features from the maximum cross-section of GTV and CLN images We input these features into seven different machine learning algorithms and ultimately selected logistic regression (LR) as the model classifier. Subsequently, we evaluated seven models (Clinical, Radiomics, Radiomics-Clinical, DL-Clinical, DL-Radiomics, DL-Radiomics-Clinical) using Radiomics features, DL features and clinical feature. Results The DL-Radiomics-Clinical (DRC) model demonstrated higher AUC of 0.955 and 0.916 compared to the other six models in both internal and external testing cohorts respectively. The DRC model achieved the highest accuracy among the seven models in both the internal and external test sets, with scores of 0.951 and 0.892, respectively. Conclusions Through the combination of Radiomics features and DL features from PET/CT imaging and clinical feature, we developed a predictive model exhibiting exceptional classification capabilities. This model can be considered as a non-invasive method for predication of CLNM in patients with ESCC. It might facilitate decision-making regarding to the extend of lymph node dissection, and to select candidates for postoperative adjuvant therapy.

Context Achyranthes bidentata Blume (ABB), a plant of Amaranthaceae family, has been one of the more commonly used phytomedicine remedies for thousands of years, and recent studies have highlighted the efficacy of its extracts in the treatment of osteoporosis. Nonetheless, a thorough analysis of its benefits is currently absent.Objective This meta-analysis assessed the effects of ABB root extract (ABBRE) on osteoporotic rats and provides a rationale for future clinical studies.Methods Searches were conducted in seven different Chinese and English databases, and the search period was from their establishment to January 2024. This study was registered in PROSPERO (CRD42023418917). Selected research regarding the ABBRE treatment of osteoporotic rats, and the corresponding research has distinctly reported outcomes, and the data on the bone mineral density (BMD), bone histomorphometrics, biomechanical parameters, and bone biochemical markers of osteoporotic rats can be extracted.Results Through screening, 11 studies met the eligibility requirements for inclusion, in which 222 animals were studied. The treatment group with ABBRE exhibited increased bone mineral density (standardized mean difference [SMD] = 1.64, 95% CI = 0.52 to 2.77). Based on subgroup analysis, the greatest increase in bone mineral density was observed when the dose of ABBRE was ≤ 400 mg/kg/day and the duration of treatment was ≤ 12 weeks.Conclusions ABBRE is a phytomedicine that can effectively promote the enhancement of bone mineral density and ease osteoporosis. It can be developed into a new alternative therapy by conducting experiments and clinical studies on larger samples.

Infected bone defect is a formidable clinical challenge. Conventional approaches to prevention and treatment for infected bone defects are unsatisfactory. The key elements of the treatment are bone defect reconstruction, anti-infection, and osteogenesis. Conventional treatment methods remain unsatisfactory owing to the absence of composite integrating materials with anti-infective, and osteogenic activities as well as proper mechanical strength at the same time. In this study, we fabricated a vancomycin-encapsulated hydrogel with bacteria-responsive release properties combined with a shaved porous (submicron-micron) three-dimensional-printed Ti6Al4V implant. The implant surface, modified with submicron-sized pores through microarc oxidation (MAO), showed enhanced osteogenic activity and integrated well with the hydrogel drug release system, enabling sustained vancomycin release. In vitro experiments underscored the commendable antibacterial ability, biosafety, and osteoinductive potential. Effective antibacterial and osteogenic abilities of the implant were further demonstrated in vivo in infected rabbit bone defects. These results showed that the vancomycin-encapsulated hydrogel-loaded microarc-oxidized 3D-printed porous Ti6Al4V can repair the infected bone defects with satisfactory anti-infection and osseointegration effects.

Abstract A method for Underdetermined Blind Source Separation is proposed using third‐order cumulants and tensor compression. To effectively suppress symmetrical distributed noise, the third‐order cumulant is considered. Additionally, the complexity of high‐dimensional tensors can be reduced through high order singular value decomposition (HOSVD) for compression purposes. The method begins by calculating the third‐order cumulant tensor for whitening signals at different time delays, and then stacks several cumulants into a fourth‐order tensor. The HOSVD decomposition is applied to the fourth‐order tensor, compressing the high‐dimensional tensor into a low‐dimensional core tensor. Next, the core tensor is further decomposed using the canonical polyadic decomposition, and the resulting factor matrices are fused to obtain an estimation of the mixed matrix. Finally, leveraging the signal independence, a matrix diagonalisation method is employed to recover the source signals. Theoretical analysis and simulation results demonstrate that the proposed method effectively suppresses the influence of Gaussian noise, reduces computational complexity, and saves computational time. Moreover, compared with five representative approaches, the proposed method achieves superior separation results. Specifically, for the 3 × 4 mixed model with a signal‐to‐noise ratio of 20 dB, the average relative error of speech signal and radio signal are −11.02 and −6.8 dB respectively.

Abstract In the evolution landscape of HIV, the coexistence of multiple subtypes has led to new, complex recombinants, posing public health challenges. CRF55_01B, first identified among MSM in Shenzhen, China, has spread rapidly across China. In this study, 47 plasma samples from newly diagnosed HIV-1 CRF55_01B patients in Shenzhen, of which the genotype was only identified by the routine HIV drug resistance test, were collected. Multiple gene regions were acquired using Sanger and next-generation sequencing methods, followed by the phylogenetic reconstruction, recombination breakpoint scanning, Bayesian molecular clock, and the prediction of coreceptors. From 47 samples, we found seven new unique recombinants formed by CRF55_01B and CRF07_BC, which shared similar breakpoints in certain gene regions and primarily utilized CCR5 receptors. All of the most recent common ancestors of subregions for these recombinants were estimated to be later than CRF55_01B and CRF07_BC, potentially suggesting they are the third-generation recombinants formed by CRF55_01B and CRF07_BC as parents. The continuous emergence of new recombinants highlights the increasing complexity of circulating strains in Shenzhen, and also suggests that subtype analysis using partial pol gene may lead to an overestimation of the major subtype strains and an underestimation of new complex HIV recombinants. Consequently, to effectively address and mitigate the complex HIV epidemic, there is an urgent need for expanded monitoring and the optimization of testing methodologies.

Abstract Toxoplasmosis, a parasitic disease, can cause fatal multi-organ failure in immunocompromised patients. The lack of specificity in the symptoms and the need to confirm a diagnosis of tachyzoites in fluids or tissues through microscopic examination leads to a delay in reaching a diagnosis. A 28-year-old woman with severe aplastic anemia received stem cell transplantation seven months ago, presented with fever. Computed Tomography scan and ultrasonography showed moderate pleural, pericardial, peritoneal, and pelvic effusions. Metagenomic next-generation sequencing of blood and alveolar lavage fluid was done, 11,082 and 17,154 sequence readings of Toxoplasma gondii were detected, accounting for 1.34% and 17.09% of genome coverage, respectively. Then, marrow aspirate smears showed Toxoplasma gondii tachyzoites and pseudocyst. This case report alerts clinicians about Toxoplasma gondii infection in stem cell transplantation patients with multiple serous effusions and fever. Clinical trial: Not applicable.

The purpose of this study was to explore the effect of freeze treatment on the binding characteristics of fillets to flavor substances in fish soup with pickled mustard greens. The study focused on the effects of freeze-thaw treatment and freeze storage on the physicochemical properties of snakehead myofibrillar protein and its binding characteristics to hexanal, octanal, linalool and myrcene. The results showed that after 7 days of storage, the solubility of myofibrillar protein decreased by about 26% in the freeze-thaw group. Compared with the freeze storage group, the oxidation degree and tertiary structural changes of myofibrillar protein were higher and more soluble aggregates were formed in the freeze-thaw group. The solubility of myofibrillar protein decreased by about 33% in the freeze storage group, which showed a more significant decrease compared to the freeze-thaw treatment (P < 0.05), resulting in more insoluble aggregates. The major force for the aggregation of myofibrillar proteins after freezing was non-covalent bonding. Molecular docking results indicated that linalool, hexanal and octanal could bind to myofibrillar proteins through hydrophobic interactions and hydrogen bonds, while the major driving force for the binding of myrcene to myofibrillar proteins was hydrophobic interaction. Freeze treatment reduced the binding capacity of hexanal and octanal to sulfhydryl groups in myofibrillar proteins through hydrogen bonds, improved the surface hydrophobicity of myofibrillar proteins, but increased the binding ability of myrcene and linalool to myofibrillar proteins. Freeze-thaw treatment increased the binding capacity of myofibrillar proteins to myrcene compared with freeze storage treatment. The results of this research provide a theoretical basis and reference for flavor control of frozen prepared fish soup with pickled mustard greens.

Peanut (Arachis hypogaea L.) is an important oil crop. Oleic acid is a major factor that determines the quality of peanuts. Therefore, the high oleic and high oleic to linoleic acid ratio are the target traits in an advanced peanut breeding program. This study provided an extensive evaluation of the genetic and physical characteristics as well as disease resistance of 220 high oleic peanut varieties in China. Notably, these varieties clustered into five major categories based on their traits. A majority of these varieties have been bred using interspecific hybridization or selected from mutants of self-crossed parents, with the main parent varieties being Kaixuan 016 and CTWE. Analysis of disease resistance showed that most high oleic peanut varieties could resist two or three diseases. However, those varieties with resistance to multiple diseases were relatively scarce. Moreover, some high oleic peanut varieties showed no disease resistance or inadequate testing. The results further indicate that the genetic basis for high oleic peanut breeding is insufficient, highlighting the need for its further development. Importantly, our findings lay a critical foundation for future high oleic peanut breeding and promote better understanding of the genetic and trait diversity offered by these varieties.

Abstract In this paper, we propose MSA-ESRGAN, a novel super-resolution model designed to enhance the perceptual quality of images. The key innovation of our approach lies in the integration of a multi-scale attention U-Net discriminator, which allows for more accurate differentiation between subject and background areas in images. By leveraging this architecture, MSA-ESRGAN surpasses traditional methods and several state-of-the-art super-resolution models in terms of Natural Image Quality Evaluator (NIQE) scores as well as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) across various benchmark datasets, including BSD100, Set5, Set14, Urban100, and OST300. Additionally, subjective evaluations further confirm the enhanced visual quality delivered by MSA-ESRGAN, particularly in terms of preserving texture and overall image realism. To ensure a fair comparison with Real-ESRGAN, we initialized our generator with a pre-trained Real-ESRNET model and followed the same training setup. Our model was trained on the DIV2K dataset using high-resolution image patches and the Adam optimizer, incorporating exponential moving average (EMA) for stability and performance enhancement. Evaluations on multiple benchmark datasets demonstrate that MSA-ESRGAN consistently delivers superior perceptual quality, as evidenced by higher NIQE, PSNR, and SSIM scores compared to other methods. Specifically, our model shows significant improvements in both objective and subjective measures of image quality. Furthermore, an ablation study highlighted the critical role of our multi-scale attention U-Net discriminator in enhancing the model’s performance. The results underscore the effectiveness of MSA-ESRGAN in maintaining image naturalness and perceptual quality, providing a robust benchmark for blind super-resolution tasks.

Abstract Color centers in silicon carbide (SiC) offer exciting possibilities for quantum information processing. However, the challenge of ionization during optical manipulation leads to charge variations, hampering the efficacy of spin-photon interfaces. Recent research predicted that modified divacancy color centers can stabilize their charge states, resisting photoionization. This study presents a method for precisely creating single divacancy arrays in 4H-SiC using a focused helium ion beam. Photoluminescence tests reveal consistent emission with minimal linewidth fluctuations (∼50 MHz over 3 h). By measuring the ionization rate for different polytypes of divacancies, we found that the modified divacancies are more robust against resonant excitation. Furthermore, angle-resolved photoluminescence excitation spectra unveil two resonant-transition lines with orthogonal polarizations. Enhanced optical and spin characteristics were notably observed in these color centers compared to those generated through carbon-ion and shallow implantation methods, positioning modified divacancies as promising contenders for advancing quantum networking.

Abstract Distributed power supply access to the distribution network, although it can effectively support the band voltage, will also cause problems such as voltage overruns at the point of grid connection and large network losses, so this paper establishes a reactive power optimization model containing three objectives: network loss, voltage fluctuation rate, and static reactive power generator (SVG) installation capacity in distributed photovoltaic power generation scenarios by taking advantage of the characteristics of SVG that both absorb and send out reactive power. A multiobjective particle swarm algorithm with an adaptive grid and roulette mechanism is introduced to ensure the uniformity and diversity of the Pareto boundaries under the constraint that the output of each device does not exceed the constraints, and to obtain the optimal set of solutions capable of coping with the stochastic fluctuations of distributed power sources. When the algorithm is compared with three other algorithms, such as nondominated sorting genetic algorithm‐II, the results show that it reduces the network loss by about 25% and significantly improves the voltage fluctuation rate.

Abstract Background Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb) complex infection, is a leading cause of death worldwide from a single infectious agent. The emergence of drug resistance Mtb clinical strains makes the situation more serious. The role of Mtb outer membrane protein A (OmpA) in antimicrobial resistance remains unclear. This study aimed to evaluate the effect of OmpA expression on mycobacterial drug resistance. In this study, a Mycobacterium smegmatis (Ms) strain overexpressing OmpA (Ms-OmpA) and a Mycobacterium bovis (Mb) strain overexpressing OmpA (Mb-OmpA) were constructed, and their susceptibility to anti-TB drugs was determined by performing the minimal inhibitory concentrations (MICs), the plate assay and the macrophage infection assays. Results The streptomycin MIC of the overexpressing strain was 2-fold lower than those of the wide-type (Ms) and empty plasmid strains (pMV-261) as well as amikacin and gentamicin. Moreover, both the plate and the macrophage infection assays indicate that overexpression of OmpA increases streptomycin sensitivity in Mycobacteria. The other aminoglycosides like amikacin and gentamicin have the same phenotypes as streptomycin on the plates for the virulent strain Mb-OmpA. The porin inhibitor spermidine can increase streptomycin tolerance in the overexpressing strain, and overexpressing OmpA can increase the intracellular accumulation of hydrophilic ethidium bromide, which indicates that porin protein OmpA contributes to aminoglycosides sensitivity in Mycobacteria. Conclusions In this study, we have characterized the contribution of OmpA in the antimicrobial resistance phenotype of Mycobacteria, which may provide valuable insights for understanding antibiotic resistance and designing new strategies for TB treatment.

Abstract Photosynthesis requires chloroplasts, in which most proteins are nucleus-encoded and produced via cytoplasmic translation. The translation initiation factor eIF5B gates the transition from initiation (I) to elongation (E), and the Kozak motif is associated with translation efficiency, but their relationship is previously unknown. Here, with ribosome profiling, we determined the genome-wide I-E transition efficiencies. We discovered that the most prevalent Kozak motif is associated with high I-E transition efficiency in Arabidopsis, rice, and wheat, thus implicating the potential of the Kozak motif in facilitating the I-E transition. Indeed, the effects of Kozak motifs in promoting translation depend on HOT3/eIF5B1 in Arabidopsis. HOT3 preferentially promotes the translation of photosynthesis-associated nuclear genes in a Kozak motif-dependent manner, which explains the chloroplast defects and reduced photosynthesis activity of hot3 mutants. Our study linked the Kozak motif to eIF5B-mediated I-E transition during translation and uncovered the function of HOT3 in the cytoplasmic translational control of chloroplast biogenesis and photosynthesis.

Abstract This randomized, prospective, multicenter (12 centers in China) phase III trial (Chinese Clinical Trial Registry #ChiCTR2000041170) compared drug-eluting bead transarterial chemoembolization (DEB-TACE) combined with apatinib and DEB-TACE monotherapy for patients with unresectable hepatocellular carcinoma (uHCC). Progression-free survival (PFS) was the primary endpoint. Overall survival (OS), mRECIST-based objective response rates (ORR) and disease control rates (DCR), and treatment-related adverse events (TRAEs) were secondary endpoints. Totally 243 cases were randomized, with 122 and 121 in the DEB-TACE + apatinib and DEB-TACE groups, respectively. Cases administered DEB-TACE + apatinib displayed markedly improved median PFS (7.1 months [95%CI 6.6–8.3] vs. 5.2 months [95%CI 5.0–5.9]) and OS (23.3 months [95%CI 20.7–29.6] vs. 18.9 months [95%CI 17.9–20.1] compared with those treated with DEB-TACE (both p

Abstract D-dimer is a biomarker of coagulation and fibrinolytic system activation in response to the hypercoagulable state of the body. The research aimed to analyze the value of D-dimer in the prognosis of patients with dilated cardiomyopathy (DCM). Patients admitted to our center for the first time with DCM were enrolled consecutively. The clinical characteristics variables were obtained from the electronic medical record system, and the prognostic information was obtained using telephone return visits and a review of repeated hospitalization records. Univariate and multivariate Cox regression was used to explore the association of D-dimer with all-cause mortality. Smooth curve fitting, threshold saturation effect analysis, and subgroup analysis were performed. Ultimately, 534 patients were included. After a follow-up of the enrolled patients, 485 patients obtained prognostic information, of which 159 died from all causes, and the main cause of death was heart failure (89/159), the sudden death accounted for about 17%. The independent positive association between D-dimer and all-cause mortality remained unchanged in both unadjusted and adjusted Cox regression models. In the fully adjusted model, each standard deviation increase in D-dimer was associated with a 14% increase in all-cause mortality (HR = 1.14; 95% CI: 1.02 ~ 1.27; P

Abstract Background: Microvascular invasion (MVI) is an independent risk factor of poor prognosis in hepatocellular carcinoma (HCC) and can be used to guide the diagnosis and treatment of HCC. The immune system serves as an integral role in the incidence and progression of HCC. However, the molecular biology correlation between MVI and tumor immunity and the value of combining the two parameters to predict patient prognosis and HCC response to treatment remain to be evaluated. Results: In this study, we used univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis to establish the MVI and immune-related gene index (MIRGPI) including eight genes. We demonstrated that the MIRGPI was an independent risk factor in predicting the prognosis of HCC. Subsequently, our research established a nomogram model combining pathologic characteristics and verified its good clinical application value. In addition, our study found that the TP53 gene had a higher mutation frequency and a lower degree of immune infiltration in the high-risk group. The low-risk group had higher sensitivity to immunotherapy, sorafenib, and TACE treatment, and the high-risk group had higher sensitivity to common chemotherapeutic agents. Finally, SEMA3C was found to facilitate the proliferation, migration and invasive ability of HCC by in vitro and in vivo experiments, and its mechanism may be associated with the activation of the NF-Κb/EMT signaling pathway. Conclusions: In summary, the MIRGPI signature we developed is a reliable marker for the prediction of prognosis and treatment response, and is important for the prognostic assessment and individualized treatment of HCC.

Abstract Traditional approaches to the human cochlear nerve have been impeded by its bony encasement deep inside the skull base. We present an innovative, minimally invasive, therapeutic pathway for direct access to the nerve to deliver novel regenerative therapies. Neuroanatomical studies on 10 cadaveric human temporal bones were undertaken to identify a potentially safe therapeutic pathway to the cochlear nerve. Simulations based on three-dimensional delineation of anatomical structures obtained from synchrotron phase-contrast imaging were analyzed. This enabled the identification of an approach to the nerve in the fundus of the internal auditory meatus by trephining the medial modiolar wall of the cochlea via the round window for a median depth of 1.48 mm (range 1.21–1.91 mm). The anatomical access was validated on 9 additional human temporal bones using radio-opaque markers and contrast injection with micro-computed tomography surveillance. We thus created an effective conduit for the delivery of therapeutic agents to the cochlear nerve.

Abstract Background Traditional methods of training in ultrasound technology make it difficult for postgraduate reproductive medicine students to rapidly develop into doctors who can perform clinical examinations independently. It is necessary to explore an efficient method for ultrasound training. The purpose of this study was to investigate whether the step-by-step (SBS) teaching improves the ultrasound diagnosis and clinical work level of reproductive medicine graduate students. Methods A total of fifty postgraduate reproductive medicine students who participated in ultrasound room training were selected at random and divided into two groups: a traditional teaching control group and a stepwise teaching experimental group. A comparison was subsequently conducted between the two groups in terms of theoretical level, skill operation, comprehensive ability and teaching evaluation. Results In terms of theory, skills, and comprehensive assessment, the experimental group exhibited superior performance compared to the control group (P 0.05). Conclusions The stepped teaching model has the potential to facilitate the acquisition of clinical ultrasound detection and diagnostic techniques by postgraduate reproductive medicine students, thereby enhancing their overall competence and satisfaction with the teaching process.

Abstract Background Residual low back and leg pain can occur after percutaneous endoscopic interlaminar discectomy (PEID) and compromise early surgical outcomes. This study aimed to determine the efficacy of combining dexamethasone with vitamin B12 (VB12) via epidural injection in improving the symptoms of low back and leg pain after PEID, and the underlying mechanism of action. Methods Patients who underwent PEID for lumbar disc herniation (LDH) were enrolled and randomly assigned to the single surgery (SS) group, where disc removal was performed via PEID alone, or the combined treatment (CT) group, which received epidural injections of dexamethasone and VB12 alongside surgery. The outcome measures were the Visual Analog Scale (VAS), Japanese Orthopaedic Association (JOA) score, Oswestry Disability Index (ODI), serum inflammatory factor expression, adverse surgical events, duration of postoperative hospitalization, and modified MacNab criteria. Results Compared with the SS group, the CT group exhibited lower VAS scores for low back and leg pain at 1, 3, and 7 days post-surgery (P

Abstract Infectious bronchitis virus (IBV) is the causative agent of infectious bronchitis (IB), a severe disease that primarily affects young chickens and poses a significant challenge to the global poultry industry. Understanding the complex interaction between the virus and its host is vital for developing innovative antiviral strategies. Long non-coding RNA (lncRNA) plays a crucial role in regulating host antiviral immune responses. Our previous studies have shown that IBV infection disrupts the stability of lncRNA in host cells, indicating a potential regulatory role for lncRNA in IBV pathogenesis. It is still not clear how lncRNA precisely modulates IBV replication. In this study, we observed down-regulation ofMSTRG.26120.58 (named lncRNA-DRNR) expression in various chicken cell lines upon IBV infection. We demonstrated that silencing lncRNA-DRNR using siRNA enhances intracellular replication of IBV. Through exploring genes encoding proteins upstream and downstream of lncRNA-DRNR within a 100 kb range, we identified chJMJD6 (chicken JMJD6) as a potential target gene negatively regulated by lncRNA-DRNR expression levels. Furthermore, chJMJD6 inhibits STAT1 methylation, thereby affecting the induction of interferon-stimulated genes (ISGs) through the activation of the IFN-β-mediated JAK-STAT signalling pathway, ultimately promoting the intracellular replication of IBV. In summary, our findings reveal the critical role played by lncRNA-DRNR during IBV infection, providing novel insights into mechanisms underlying coronavirus-induced disruption in lncRNA stability.

Abstract Particulate matter 2.5 (PM2.5) pollution has emerged as a major global public health concern because of its adverse effects on human health. Our group has previously demonstrated that PM2.5 exposure can seriously impair ovarian function. However, the underlying mechanisms remain a mystery. This study verifies ovarian damage in mice, evidenced by inflammatory cell infiltration and follicular atresia, following 5 months of PM2.5 exposure via tracheal drip (35 µg/m³ for low dose and 150 µg/m³ for high dose). In addition, PM2.5 exposure inhibited the cell viability of human granulosa cells (KGN) and induced apoptosis at the concentrations of 50, 100, and 150 µg/mL for 24 h. The apoptosis of KGN cells induced by inflammation contributes to follicular atresia. Furthermore, we conducted RNA-sequencing analysis to identify the genes and pathways triggered by PM2.5 (100 µg/mL) exposure, which decreases the KGN cell viability. We found a significant increase in Activating Transcription Factor 3 (ATF3). Further mechanistic studies reveal a strong association between PM2.5-induced apoptosis, inflammation, and ATF3 with its downstream oxidative stress signals. In summary, the ATF3 pathway serves a vital role in the ovarian injury caused by PM2.5 exposures.

Abstract Proton-exchange-membrane fuel cells demand highly efficient catalysts for the oxygen reduction reaction, and core-shell structures are known for maximizing precious metal utilization. Here, we reported a controllable “carbon defect anchoring” strategy to prepare Pt1Ni1@Pt/C core-shell nanoparticles with an average size of ~2.6 nm on an in-situ transformed defective carbon support. The strong Pt–C interaction effectively inhibits nanoparticle migration or aggregation, even after undergoing stability tests over 70,000 potential cycles, resulting in only 1.6% degradation. The stable Pt1Ni1@Pt/C catalysts have high oxygen reduction reaction mass activity and specific activity that reach 1.424 ± 0.019 A/mgPt and 1.554 ± 0.027 mA/cmPt 2 at 0.9 V, respectively, attributed to the optimal compressive strain. The experimental results are generally consistent with the theoretical predictions made by our comprehensive microkinetic model which incorporates essential kinetics and thermodynamics of oxygen reduction reaction. The consistent results obtained in our study provide compelling evidence for the high accuracy and reliability of our model. This work highlights the synergy between theory-guided catalyst design and appropriate synthetic methodologies to translate the theory into practice, offering valuable insights for future catalyst development.

Interfacial solar-driven evaporation technology shows great potential in the field of industrial seawater desalination, and the development of efficient and low-cost evaporation materials is key to achieving large-scale applications. Hydrogels are considered to be promising candidates; however, conventional hydrogel-based interfacial solar evaporators have difficulty in simultaneously meeting multiple requirements, including a high evaporation rate, salt resistance, and good mechanical properties. In this study, a Janus sponge-like hydrogel solar evaporator (CPAS) with excellent comprehensive performance was successfully constructed. The introduction of biomass agar (AG) into the polyvinyl alcohol (PVA) hydrogel backbone reduced the enthalpy of water evaporation, optimized the pore structure, and improved the mechanical properties. Meanwhile, by introducing hydrophobic fumed nano-silica aerogel (SA) and a synergistic foaming-crosslinking process, the hydrogel spontaneously formed a Janus structure with a hydrophobic surface and hydrophilic bottom properties. Based on the reduction of the evaporation enthalpy and the modulation of the pore structure, the CPAS evaporation rate reached 3.56 kg m−2 h−1 under one sun illumination. Most importantly, owing to the hydrophobic top surface and 3D-interconnected porous channels, the evaporator could work stably in high concentrations of salt-water (25 wt% NaCl), showing strong salt resistance. Efficient water evaporation, excellent salt resistance, scalable preparation processes, and low-cost raw materials make CPAS extremely promising for practical applications.

Despite numerous studies on chondrogenesis, the repair of cartilage—particularly the reconstruction of cartilage lacunae through an all-in-one advanced drug delivery system remains limited. In this study, we developed a cartilage lacuna-like hydrogel microsphere system endowed with integrated biological signals, enabling sequential immunomodulation and endogenous articular cartilage regeneration. We first integrated the chondrogenic growth factor transforming growth factor-β3 (TGF-β3) into mesoporous silica nanoparticles (MSNs). Then, TGF-β3@MSNs and insulin-like growth factor 1 (IGF-1) were encapsulated within microspheres made of polydopamine (pDA). In the final step, growth factor-loaded MSN@pDA and a chitosan (CS) hydrogel containing platelet-derived growth factor-BB (PDGF-BB) were blended to produce growth factors loaded composite microspheres (GFs@μS) using microfluidic technology. The presence of pDA reduced the initial acute inflammatory response, and the early, robust release of PDGF-BB aided in attracting endogenous stem cells. Over the subsequent weeks, the continuous release of IGF-1 and TGF-β3 amplified chondrogenesis and matrix formation. μS were incorporated into an acellular cartilage extracellular matrix (ACECM) and combined with a polydopamine-modified polycaprolactone (PCL) structure to produce a tissue-engineered scaffold that mimicked the structure of the cartilage lacunae evenly distributed in the cartilage matrix, resulting in enhanced cartilage repair and patellar cartilage protection. This research provides a strategic pathway for optimizing growth factor delivery and ensuring prolonged microenvironmental remodeling, leading to efficient articular cartilage regeneration.

GTs (Glycosyltransferases) are important in plant growth and abiotic stresses. However, its role in maize heat response is far from clear. Here, we describe the constitutively expressed UDP-glycosyltransferase ZmUGT92A1, which has a highly conserved PSPG box and is localized in chloroplasts, is induced under heat stress. Functional disruption of ZmUGT92A1 leads to heat sensitivity and reactive oxygen species accumulation in maize. Metabolomics analysis revealed that ZmUGT92A1 affected multiple metabolic pathways and altered the metabolic homeostasis of flavonoids under heat stress. In vitro assay showed ZmUGT92A1 exhibits glycosyltransferase activity on flavonoids and hormones. Additionally, we identified a rapidly heat-induced transcription factor, ZmHSF08, which can directly bind and repress the promoter region of ZmUGT92A1. The ZmHSF08 overexpression line exhibits heat sensitivity and reactive oxygen species accumulation. These findings reveal that the ZmHSF08-ZmUGT92A1 module plays a role in heat tolerance in maize and provide candidate strategies for the development of heat-tolerant varieties.

In view of the problem that the traditional ventilation total amount control management mode of the fully mechanized heading face cannot carry out wind flow control according to the actual demand, which causes gas and dust accumulation and pollution hidden danger, the multi-source data fusion neural network prediction model of wind speed, gas and dust concentration under wind flow control is studied. The gas-solid coupling model of gas and dust under wind flow control is established by using the Euler-Lagrange method and tested and verified. The distribution of gas and dust particles in the integrated excavation roadway is simulated and analyzed, and numerous sample data of gas and dust concentration in wind speed under different wind flow control schemes are obtained. Multilayer perceptron neural network technology is used to establish the prediction model structure, and parameters such as wind flow regulation, which have a great impact on gas and dust concentration, are selected as the input layer, and the output layer is determined according to the hidden danger location of gas and dust in wind speed. The sample data is preprocessed, and the differential evolution algorithm is introduced to search the node number and learning rate of the best hidden layer. TensorFlow framework is used to build a multi-source data fusion neural network prediction model. Taking the fully mechanized heading face of a mine in northern Shaanxi as the research object, different wind flow control schemes are predicted and verified by underground measurement. The results show that the maximum relative error of the model is 9.7%, which has high accuracy. The optimal control scheme is selected under the conditions of the shortest distance of 5 m from the outlet and the farthest distance of 10 m from the working face. Compared with before the control, the wind speed meets the standard requirements, the gas concentration in the dead corner of the working face is reduced by 34% and 35%, the average dust concentration at the pedestrian side of the return air side is reduced by 40% and 41%, and the dust concentration at the driver’s side is reduced by 38% and 36%, respectively. The study can provide reference for wind flow control.

Abstract Background Lung adenocarcinoma (LUAD) with lymph node (LN) metastasis is linked to poor prognosis, yet the underlying mechanisms remain largely undefined. This study aimed to elucidate the immunogenomic landscape associated with LN metastasis in LUAD. Methods We employed broad-panel next-generation sequencing (NGS) on a cohort of 257 surgically treated LUAD patients to delineate the molecular landscape of primary tumors and identify actionable driver-gene alterations. Additionally, we used multiplex immunohistochemistry (mIHC) on a propensity score-matched cohort, which enabled us to profile the immune microenvironment of primary tumors in detail while preserving cellular metaclusters, interactions, and neighborhood functional units. By integrating data from NGS and mIHC, we successfully identified spatial immunogenomic patterns and developed a predictive model for LN metastasis, which was subsequently validated independently. Results Our analysis revealed distinct immunogenomic alteration patterns associated with LN metastasis stages. Specifically, we observed increased mutation frequencies in genes such as PIK3CG and ATM in LN metastatic primary tumors. Moreover, LN positive primary tumors exhibited a higher presence of macrophage and regulatory T cell metaclusters, along with their enriched neighborhood units (p

Aims: This study examined the relationship between obesity (OB) and osteoporosis (OP), aiming to identify shared genetic markers and molecular mechanisms to facilitate the development of therapies that target both conditions simultaneously. Methods: Using weighted gene co-expression network analysis (WGCNA), we analyzed datasets from the Gene Expression Omnibus (GEO) database to identify co-expressed gene modules in OB and OP. These modules underwent Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction analysis to discover Hub genes. Machine learning refined the gene selection, with further validation using additional datasets. Single-cell analysis emphasized specific cell subpopulations, and enzyme-linked immunosorbent assay (ELISA), protein blotting, and cellular staining were used to investigate key genes. Results: WGCNA revealed critical gene modules for OB and OP, identifying the Toll-like receptor (TLR) signalling pathway as a common factor. TLR2 was the most significant gene, with a pronounced expression in macrophages. Elevated TLR2 expression correlated with increased adipose accumulation, inflammation, and osteoclast differentiation, linking it to OP development. Conclusion: Our study underscores the pivotal role of TLR2 in connecting OP and OB. It highlights the influence of TLR2 in macrophages, driving both diseases through a pro-inflammatory mechanism. These insights propose TLR2 as a potential dual therapeutic target for treating OP and OB. Cite this article: Bone Joint Res 2024;13(10):573–587.

Abstract To verify the treatment effect of the combination of transpulmonary chemoembolization (TPCE) and microwave ablation (MWA), targeting the treatment of recurrent or advanced non-small cell lung cancer (NSCLC). A total of 53 patients were studied and grouped according to the diameter of the largest pulmonary nodule, defined as index tumor size (ITS). Patients with an ITS > 3 cm (n = 20) were treated with TPCE and MWA. Patients with an ITS ≤ 3 cm were treated either with a combination therapy (n = 24) or MWA alone (n = 9). The treatment response, including complications and survival outcome, was then analyzed. After TPCE, there was an average ITS reduction of 0.91 cm, and 25% of patients in ITS > 3 cm were downgraded to ITS ≤ 3 cm. After TPCE, there were 12 patients (27%) with PR status and 32 (73%) with SD status. No PD patient in our case series was noted before MWA.The complication rate of MWA was significantly higher in ITS ≤ 3 cm than in ITS > 3 cm (p = 0.013). The median survival time (MST) was 26.7 months, and the time to progression was 13.2 months. The patients in the ITS ≤ 3 cm had longer MST than the others (31.6 vs. 15.8 months, p = 0.003). The significant prognostic factor was ITS > 3 cm (HR: 1.18, p = 0.02). A combination of TPCE and MWA might be feasible to control non-operable, recurrent, or advanced NSCLC.

Abstract Purposes T cells play a crucial role as regulators of anti-tumor activity within the tumor microenvironment (TME) and are closely associated with the progression of osteosarcoma (OS). Nevertheless, the specific role of T cell-related genes (TCRGs) in the pathogenesis of OS remains unclear. Methods First, we processed single-cell RNA sequencing (scRNA-seq) data of OS from the public databases and performed cell annotation. We identified highly variable genes in each cell type using the "FindAllMarkers" function, explored the distribution of different clusters, and investigated inter-cellular communication patterns via the "CellChat" framework. Then, we used multivariate Cox analysis to construct a TCRG and developed a nomogram to predict survival probabilities for OS patients. Finally, we validated the aforementioned results using various cell lines and investigated the immune cell infiltration, expression of immune checkpoints, chemotherapy sensitivity, and the efficacy of targeted therapies across different risk groups. Results From the scRNA-seq data, we identified 3,000 highly variable genes, presented the top 10 genes, and validated the expression of core genes across different cell lines.Moreover, our analysis delved into interactions between T cells and other cell types. Our analyses constructed a predictive T cell-related signature (TCRS) that incorporated these prognostic TCRGs, showing a clear prognostic separation between the high-risk and low-risk OS patient groups in multiple cohorts. Survival analysis indicated better outcomes for patients classified in the high-risk group. The low-risk group exhibited elevated levels of CD4 memory resting T cells, contrasting with the higher levels of macrophage M0 observed in the high-risk group via the CIBERSORT algorithm. Furthermore, we observed that the low-risk group exhibitedAQ1 significant up-regulation of immune checkpoint genes (ICGs) and lower Tumour Immune Dysfunction and Exclusion (TIDE) scores, suggesting that they may be suitable for immunotherapy. Conversely, the high-risk group appeared more responsive to chemotherapy and targeted therapies, according to our drug sensitivity analysis. Conclusion In conclusion, our study identified TCRGs, constructed and validated a TCRS for OS, and assessed immune response and drug sensitivity in different risk groups of OS patients. These findings provide novel insights into personalized treatment strategies for OS, potentially guiding more effective therapeutic interventions.

Abstract This research proposes a novel deep breathing analogy (DBA) accelerated carbonation process. Inspired by the breathing mechanism of human lungs, the DBA method involves injecting pure CO2 into a reaction chamber at a specific pressure (inspiration) and subsequently evacuating the gas from the chamber to a negative pressure (exhalation). This process is repeated to remove excess water from the chamber and restore optimal carbonation conditions, which further enhances the efficiency of carbonation for the sample. The effectiveness of this method is evaluated based on weight gain, proportion of captured CO2 and carbonation depth. Results show that the DBA method significantly reduces the inhibition of carbonation. Based on the test results, a correlation between the proportion of captured CO2 and carbonation depth is established. Additionally, a more accurate prediction model for pressurized carbonation is proposed and the economic potential of concrete carbonation is studied.