Search

BackgroundPeople undergoing mass home- and community-based quarantine are vulnerable to mental health disorders during outbreaks of coronavirus disease (COVID-19), but few studies have evaluated the associated psychosocial factors. ObjectiveThis study aimed to estimate the prevalence of anxiety and depressive symptoms and identify associated demographic and psychosocial factors in the general Chinese population during the COVID-19 pandemic quarantine period. MethodsParticipants aged 18 years or above were recruited in a cross-sectional online survey using snowball sampling from February 26-29, 2020. The survey included questions on demographics, family relationships, chronic diseases, quarantine conditions, lifestyle, COVID-19 infection, and anxiety and depressive symptoms. Logistic regression analyses were conducted to identify factors associated with elevated anxiety or depressive symptoms. ResultsOut of 2331 participants, 762 (32.7%) experienced elevated anxiety or depressive symptoms. Nine risk factors associated with anxiety or depressive symptoms included younger age, reduced income, having cancer or other chronic diseases, having family members living with cancer, concerns related to COVID-19 infection for themselves or family members, living alone, having family conflicts, having 8 hours of sedentary time per day, and worsened sleep quality. ConclusionsThe findings highlight an urgent need for psychological support for populations at high risk for elevated anxiety or depressive symptoms during the COVID-19 pandemic.

Abstract Objective Lymph node metastasis (LNM) is one of the most common pathways of metastasis in non-small cell lung cancer (NSCLC). Preoperative assessment of occult lymph node metastasis (OLNM) in NSCLC patients is beneficial for selecting appropriate treatment plans and improving patient prognosis. Method A total of 370 NSCLC patients were included in the study. Univariate and multivariate logistic regression analysis were used to screen potential risk factors for OLNM in preoperative NSCLC patients. And establish a nomogram for OLNM in NSCLC patients before surgery. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the established nomogram. Result Both univariate and multivariate logistic regression analyses suggested that multiple tumors, ERBB2 missense mutation, CA125 levels, CA153 levels, tumor site, tumor length, and serum ferritin are potential risk factors for OLNM in NSCLC patients. The constructed nomogram was evaluated, and the consistency index (C-index) and area under the ROC curve of the model were both 0.846. The calibration curve showed that the predicted values of the model had a high degree of fit with the actual observed values, and DCA suggested that the above indicators had good utility. Conclusion The personalized scoring prediction model constructed based on multiple tumors, ERBB2 miss mutation, CA125 levels, CA153 levels, tumor site, tumor length, and serum ferritin can screen NSCLC patients who may have OLNM.

Abstract Esophageal squamous cell carcinoma (ESCC) is a prevalent malignancy within the digestive system, characterized by high incidence and mortality rates. The biological role of REEP5 in ESCC progression remains poorly understood, despite its associations with various diseases, potentially accelerating tumor malignancy. We retrieved RNA-seq data and clinical information from 179 ESCC patients from the Gene Expression Omnibus (GEO) and 93 patients from The Cancer Genome Atlas (TCGA) databases. Bioinformatics analyses were conducted to explore the biological functions of REEP5 in ESCC, its role in the tumor microenvironment, and its prognostic value. Additionally, utilizing single-cell RNA-seq (scRNA-seq) data from 3 ESCC patients in the GEO database, we performed cluster analyses to investigate cell-specific expression differences of REEP5 between cancerous and adjacent non-cancerous tissues. Molecular biology experiments were also conducted to validate REEP5 expression disparities between tumor and non-tumor tissues. Compared to normal tissues, REEP5 was significantly enriched in ESCC tissues. High REEP5 expression was closely associated with poor prognosis in ESCC patients. Gene Ontology (GO) analysis revealed strong correlations between REEP5 and processes such as mRNA splicing and protein stabilization. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) indicated positive correlations between REEP5 and mRNA spliceosome assembly and disassembly. Pearson correlation analysis demonstrated positive associations between REEP5 and cancer-inhibitory immune checkpoints CTLA-4, TIM-3, and HVEM. Single-cell clustering and CIBERSORT analysis showed that REEP5 expression was closely related to T-cell infiltration in ESCC, with significant enrichment effects observed in CD8+ T-cell infiltration. REEP5 expression is closely correlated with the pathological and molecular pathology of ESCC, potentially playing a crucial role in Mast cell or T-cell-mediated immune responses in ESCC. Therefore, REEP5 holds promise as a novel therapeutic target for ESCC.

Abstract Background N4-Acetylcytidine (ac4C), a highly conserved post-transcriptional mechanism, plays a pivotal role in RNA modification and tumor progression. However, the molecular mechanism by which ac4C modification mediates tumor immunosuppression remains elusive in triple-negative breast cancer (TNBC). Methods NAT10 expression was analyzed in TNBC samples in the level of mRNA and protein, and compared with the corresponding normal tissues. ac4C modification levels also measured in the TNBC samples. The effects of NAT10 on immune microenvironment and tumor metabolism were investigated. NAT10-mediated ac4C and its downstream regulatory mechanisms were determined in vitro and in vivo. The combination therapy of targeting NAT10 in TNBC was further explored. Results The results revealed that the loss of NAT10 inhibited TNBC development and promoted T cell activation. Mechanistically, NAT10 upregulated JunB expression by increasing ac4C modification levels on its mRNA. Moreover, JunB further up-regulated LDHA expression and facilitated glycolysis. By deeply digging, remodelin, a NAT10 inhibitor, elevated the surface expression of CTLA-4 on T cells. The combination of remodelin and CTLA-4 mAb can further activate T cells and inhibite tumor progression. Conclusion Taken together, our study demonstrated that the NAT10-ac4C-JunB-LDHA pathway increases glycolysis levels and creates an immunosuppressive tumor microenvironment (TME). Consequently, targeting this pathway may assist in the identification of novel therapeutic strategies to improve the efficacy of cancer immunotherapy.

Compared to the traditional rote learning approach, open innovation experimentation in basic pathology experimental class is a student-centered educational model that offers a platform for active learning and bold exploration. It enhances students′ practical skills, innovative thinking, and teamwork, addressing the limitations of conventional pathology instruction and laying the groundwork for nurturing creative talents. This thesis uses the "Establishment of a Lung Metastasis Model of Mouse Liver Cancer" project as an example to illustrate the implementation of this experimental approach and the insights gained from it.

Achieving independent multitasked wavefront control by using an ultrathin plate is a challenge to increase information capacity in integration optics and radar applications. Transmission-reflection-integrated metasurface provides an efficient recipe primarily for multifunctional meta-device, however it is challenging to synergize both linear polarization (LP) and circular polarization (CP) using a single meta-plate. Here, a multichannel full-space coding metasurface composed of interleaved shared-aperture meta-atom is proposed to achieve large information capacity by capsulating judiciously engineered high efficiency triple sub-elements (modes) in four-layer scheme. By rotating dual-gap split ring resonator and varying size of “L” type structure insulating by a metallic ring with electrostatic-analogue shielding effect, both Pancharatnam–Berry (PB) and dynamic phases are independently realized under CP and LP waves, respectively. Such an extraordinary insulating strategy completely suppresses crosstalk among three modes and unprecedentedly increases the capability in yielding kaleidoscopic wavefront control. To verify the significance, a proof-of-concept metadevice is devised and experimentally demonstrated with tri-channel wavefront manipulations, exhibiting reflective dual-vortex beam and Bessel beam for forward and backward CP wave, respectively at high frequency, while transmissive polarization beam splitting for 45°-LP wave at low frequency. Our finding in polarization-direction multiplexing is expected to generate great interest in electromagnetic integration with emerging degree of freedoms.

Abstract Acute lung injury (ALI) is a severe clinical respiratory condition characterized by high rates of mortality and morbidity, for which effective treatments are currently lacking. In this study, lipopolysaccharide (LPS) was used to induce ALI mice, demonstrating the efficacy of tetramethylpyrazine (TMP) in ameliorating ALI. Subsequent we perfored high-throughput sequencing analysis and used Targetscan 8.0 and miRWalk 3.0 databases to predict the interaction between microRNAs and destrin (DSTN), ultimately identifying miR-369-3p as the focus of the investigation. The adenovirus carrying miR-369-3p was administered one week prior to LPS-induced in order to assess its potential efficacy in ameliorating ALI in mice. The findings indicated that the overexpression of miR-369-3p resulted in enhanced lung function, reduced pulmonary edema, inflammation, and permeability in LPS-induced ALI mice, while the suppression of miR-369-3p exacerbated the damage in these mice. Furthermore, the beneficial effects of TMP on LPS-induced ALI were negated by the downregulation of miR-369-3p. The results of our study demonstrate that TMP mitigates LPS-induced ALI through upregulation of miR-369-3p. Consequently, the findings of this study advocate for the clinical utilization of TMP in ALI treatment, with miR-369-3p emerging as a promising target for future ALI interventions.

Abstract Background At present, the guidelines for urology recommend percutaneous nephrolithotomy (PCNL) as the preferred treatment for staghorn renal calculi (SRC). However, for complete SRC, it has been questioned by clinicians and patients due to high residual stone rate, complications, repeated hospitalizations and high treatment cost. Anatrophic nephrolithotomy (ANL) is a traditional and classic method for the treatment of SRC. Due to its high trauma and high technical requirements, it is difficult to carry out in primary hospitals, and gradually replaced by PCNL. The purpose of this study is to compare the efficacy of PCNL and ANL in the treatment of complete SRC. Methods Overall, 238 patients with complete SRC were divided into mini-PCNL in lateral supine position group, (n = 190) and ANL group (n = 94) according to treatment for a retrospective cohort study. The calculi parameters, renal function index, comorbidities of calculi, surgical complications, length and frequency of hospitalization, treatment costs, results of postoperative satisfaction survey were compared between the two groups. Results The risk of the residual stone rate after mini-PCNL in lateral supine position was 239 times (OR = 238.667, P

In this study, three representative pears ('Yali' Pear, 'Huangguan' Pear, and 'Xuehua' Pear) peel/flesh polyphenol extracts were characterized by their antioxidant activity, polyphenol composition, and in vitro cholesterol/cholates binding capacity. 'Yali' Pear polyphenol extracts were selected to further investigate the mechanism of in vitro cholesterol/cholates lowering capacity. Lagergren adsorption kinetic and Freundlich isotherm models confirmed the occurrence of this combination. Turbidity, average particle size, transmission electron microscopy, and zeta potential combined confirmed the existence of some interaction between polyphenols and cholesterol/cholates. Cholesterol/cholates quenched the exogenous fluorescence of polyphenols by static mechanism. The thermodynamic interaction results revealed that the interaction between polyphenols and cholesterol is a spontaneous process, primarily driven by hydrogen bonding and hydrophobic interactions. Overall, this study aimed to investigate the confirmation of the binding removal properties of pear polyphenols on cholesterol/cholates to mitigate the adverse health effects of a high-fat diet.

Abstract Background Mesenchymal stem cells (MSCs) are one of the most widely studied adult stem cells, while MSC replicative senescence occurs with serial expansion in vitro. We determined whether miR-34a can regulate MSC senescence by directly targeting glycolytic key enzymes to influence glycolysis. Methods Detected the effects of miR-34a on MSC senescence and glycolytic metabolism through gene manipulation. Bioinformatics prediction and luciferase reporter assay were applied to confirm that HK1 is a direct target of miR-34a. The underlying regulatory mechanism of miR-34a targeting HK1 in MSC senescence was further explored by a cellular function recovery experiment. Results In the current study, we revealed that miR-34a over-expression exacerbated senescence-associated characteristics and impaired glycolytic metabolism. Then we identified hexokinase1 (HK1) as a direct target gene of miR-34a. And HK1 replenishment reversed MSC senescence and reinforced glycolysis. In addition, miR-34a-mediated MSC senescence and lower glycolytic levels were evidently rescued following the co-treatment with HK1 over-expression. Conclusion The miR-34a-HK1 signal axis can alleviate MSC senescence via enhancing glycolytic metabolism, which possibly provides a novel mechanism for MSC senescence and opens up new possibilities for delaying and suppressing the occurrence and development of aging and age-related diseases.

Synthetic Aperture Radar (SAR) data, known for its high spatial resolution and all-weather observation capabilities, holds immense promise in soil moisture monitoring. The Water Cloud Model (WCM) is widely applied in soil moisture inversion using SAR data. However, the optical vegetation indices employed in traditional WCM cannot synchronize with SAR data, and the polarimetric scattering information contained in current SAR vegetation indices is incomplete, consequently compromising the accuracy of SAR-based soil moisture retrieval. Therefore, this study proposes a method for soil moisture retrieval over cropland using a novel dual-polarization SAR vegetation index. The method initially combines SAR data covariance elements with backscatter information to establish a polarization scattering contrast parameter (mcp). Then, based on mcp and incorporating the degree of polarization, a polarization scattering correlation contrast parameter (Rcp) is defined. Rcp integrates the distinctive features of scattering differences and polarization states. Building on Rcp, a novel dual-polarization SAR vegetation index (DRVIs) is introduced. Ultimately, DRVIs are utilized in the WCM to achieve surface soil moisture retrieval in cropland. This research conducts experiments in four crop cover areas of the Carman test site in Canada, namely soybean, wheat, canola, and corn. Under VV and VH polarizations, the overall correlation coefficients between measured in-situ data and SSM estimates reach 0.89 and 0.84, respectively. Compared to SSM estimates based on NDVI and LAI products, SSM estimates based on DRVIs exhibit a notable improvement in accuracy, with enhancements of approximately 7.2 % and 12.7 %, respectively. This novel DRVIs is poised to expand the utilization of SAR data in monitoring vegetation growth and soil moisture retrieval.

Considering the complexity of landslide susceptibility mapping (LSM) at the municipal-scale, it is difficult for the assumption space of a single machine learning (ML) algorithm to fully and accurately reflect the intrinsic correlation between the landslide influencing factors and the actual catastrophic events under all scenarios. This study addresses this by integrating three heterogeneous ensemble learning (HEL) algorithms(Bagging, Blending, and Stacking) with four ML models (Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Backpropagation-Neural Network (BPNN)), to construct 27 HEL LSM models, and to realize optimization and selection of the models and algorithms through the comparative analysis of the results of the LSM and the accuracy of the prediction of the individual models. The final results indicate that the Bagging and Blending HEL algorithms fail to enhance model prediction accuracy effectively. In contrast, the Stacking HEL algorithm significantly improves model prediction accuracy (with an accuracy increase of over 1.4% and AUC values above 0.95). The Stacking (GBDT-XGBoost) model had the highest prediction accuracy (AUC = 0.964) and thus was selected as the optimal LSM model. This study's modeling and mapping results are intended to serve as a reference for geohazard prevention and sustainability studies in related cities.

The precise time series prediction method is the key technology for the monitoring and management of ground deformation. Current prediction methods mostly rely on independent sampling points for prediction, limiting the effective utilization of spatial features by the model, thereby affecting the overall spatial prediction accuracy, and it also restricts the prediction efficiency of the model. In response to the above-mentioned issues in previous research, this study proposes a Time Distributed Fully Connected (TDFC) Recurrent Neural Networks (RNNs) framework that integrates Temporal Convolutional Attention Mechanism (TCAM) for joint prediction of sampling points in time series Interferometric Synthetic Aperture Radar (InSAR) surface deformation data. Firstly, based on Sentinel-1A imagery over the Beijing Plain, the time series surface deformation data from May 2017 to April 2020 are obtained utilizing the Small Baseline Subset InSAR (SBAS-InSAR) technology. After data processing and production into a dataset, based on the TDFC-RNNs framework integrated with TCAM, five different RNN structures were used as prediction modules to construct time series prediction models for InSAR surface deformation. To investigate the effectiveness of the TCAM module on prediction performance, ablation experiments were conducted specifically targeting it. Furthermore, to explore the relative optimality choice of prediction modules under the current dataset and the compatibility of this framework with non-RNN structures, various other sequence models were selected as prediction modules. The predictive performance of the models constructed by this framework was compared in two aspects with benchmark methods, ablation models, and other exploratory models. This included evaluating the predictive results of the test set using various metrics and analyzing the trends in numerical characteristics of the predicted results for the next 60 time steps (720 days). The comprehensive comparison results indicate that the model constructed by this framework outperforms other methods or models in terms of overall performance across various evaluation metrics. At the same time, the future predicted results exhibit more reliable numerical characteristics, aligning well with the developmental trends of surface deformation. This suggests that the above-mentioned models demonstrate favorable predictive capabilities for time series InSAR surface deformation. Such results can be instrumental in intuitively assessing the overall situation of surface deformation in the study area, promptly identifying risks, and swiftly implementing measures to address potential hazards.

In this study, glutathione (GSH)-responsive magnetic mesoporous silica nanoparticles grafted by disulfide organosilicon (SMNPs) were synthesized, characterized, and evaluated as controlled drug carriers. The nanoparticles exhibited consistent dispersion, considerable drug-loading capacity, and high saturation magnetization. Importantly, they demonstrated the ability to release doxorubicin (DOX) by up to 43% in a reducing tumor microenvironment, highlighting their potential for targeted therapy. In addition, the SMNPs displayed favorable biocompatibility, making them suitable for biomedical applications. Most notably, the SMNPs loaded with DOX effectively killed both HepG2 and HeLa cancer cells, while also showing efficient cellular uptake in HeLa cells. These findings suggest that SMNPs are a promising platform for magnetic-targeted and GSH-responsive delivery of therapeutic agents.