KLF4 activates LATS2 to promote cisplatin sensitivity in ovarian cancer through DNA damage.

We aimed to investigate the role of large tumor suppressor kinase 2 (LATS2) in cisplatin (DDP) sensitivity in ovarian cancer. Bioinformatic analysis explored LATS2 expression, pathways, and regulators. Quantitative reverse transcription-PCR measured LATS2 and KLF4 mRNA levels. Dual-luciferase and chromatin immunoprecipitation assays confirmed their binding relationship. Cell viability, half maximal inhibitory concentration (IC50) values, cell cycle, and DNA damage were assessed using CCK-8, flow cytometry, and comet assays. Western blot analyzed protein expression. The effect of LATS2 on the sensitivity of ovarian cancer to DDP was verified in vivo. LATS2 and KLF4 were downregulated in ovarian cancer, with LATS2 enriched in cell cycle, DNA replication, and mismatch repair pathways. KLF4, an upstream regulator of LATS2, bound to its promoter. Overexpressing LATS2 increased G1-phase cells, reduced cell viability and IC50 values, and induced DNA damage. Silencing KLF4 alone showed the opposite effect on LATS2 overexpression. Knocking out LATS2 reversed the effects of KLF4 overexpression on cell viability, cell cycle, IC50 values, and DNA damage in ovarian cancer cells. Inhibiting LATS2 inactivated the Hippo-YAP signaling pathway. In vivo experiments showed that overexpression of LATS2 enhanced the sensitivity of ovarian cancer to DDP. KLF4 activates LATS2 via DNA damage to enhance DDP sensitivity in ovarian cancer, providing a potential target for improving treatment outcomes.

Melatonin Priming Promotes Crop Seed Germination and Seedling Establishment Under Flooding Stress by Mediating ABA, GA, and ROS Cascades.

Both seed germination and subsequent seedling establishment are key checkpoints during the life cycle of seed plants, yet flooding stress markedly inhibits both processes, leading to economic losses from agricultural production. Here, we report that melatonin (MT) seed priming treatment enhances the performance of seeds from several crops, including soybean, wheat, maize, and alfalfa, under flooding stress. Transcriptome analysis revealed that MT priming promotes seed germination and seedling establishment associated with changes in abscisic acid (ABA), gibberellin (GA), and reactive oxygen species (ROS) biosynthesis and signaling pathways. Real-time quantitative RT-PCR (qRT-PCR) analysis confirmed that MT priming increases the expression levels of GA biosynthesis genes, ABA catabolism genes, and ROS biosynthesis genes while decreasing the expression of positive ABA regulatory genes. Further, measurements of ABA and GA concentrations are consistent with these trends. Following MT priming, quantification of ROS metabolism-related enzyme activities and the concentrations of H2O2 and superoxide anions (O2 -) after MT priming were consistent with the results of transcriptome analysis and qRT-PCR. Finally, exogenous application of GA, fluridone (an ABA biosynthesis inhibitor), or H2O2 partially rescued the poor germination of non-primed seeds under flooding stress. Collectively, this study uncovers the application and molecular mechanisms underlying MT priming in modulating crop seed vigor under flooding stress.

PIF4 interacts with ABI4 to serve as a transcriptional activator complex to promote seed dormancy by enhancing ABA biosynthesis and signaling.

Transcriptional regulation plays a key role in the control of seed dormancy, and many transcription factors (TFs) have been documented. However, the mechanisms underlying the interactions between different TFs within a transcriptional complex regulating seed dormancy remain largely unknown. Here, we showed that TF PHYTOCHROME-INTERACTING FACTOR4 (PIF4) physically interacted with the abscisic acid (ABA) signaling responsive TF ABSCISIC ACID INSENSITIVE4 (ABI4) to act as a transcriptional complex to promote ABA biosynthesis and signaling, finally deepening primary seed dormancy. Both pif4 and abi4 single mutants exhibited a decreased primary seed dormancy phenotype, with a synergistic effect in the pif4/abi4 double mutant. PIF4 binds to ABI4 to form a heterodimer, and ABI4 stabilizes PIF4 at the protein level, whereas PIF4 does not affect the protein stabilization of ABI4. Subsequently, both TFs independently and synergistically promoted the expression of ABI4 and NCED6, a key gene for ABA anabolism. The genetic evidence is also consistent with the phenotypic, physiological and biochemical analysis results. Altogether, this study revealed a transcriptional regulatory cascade in which the PIF4-ABI4 transcriptional activator complex synergistically enhanced seed dormancy by facilitating ABA biosynthesis and signaling.

Single-cell transcriptomics in ovarian cancer identify a metastasis-associated cell cluster overexpressed RAB13.

BACKGROUND: Metastasis, the leading cause of cancer-related death in patients diagnosed with ovarian cancer (OC), is a complex process that involves multiple biological effects. With the continuous development of sequencing technology, single-cell sequence has emerged as a promising strategy to understand the pathogenesis of ovarian cancer. METHODS: Through integrating 10 × single-cell data from 12 samples, we developed a single-cell map of primary and metastatic OC. By copy-number variations analysis, pseudotime analysis, enrichment analysis, and cell-cell communication analysis, we explored the heterogeneity among OC cells. We performed differential expression analysis and high dimensional weighted gene co-expression network analysis to identify the hub genes of C4. The effects of RAB13 on OC cell lines were validated in vitro. RESULTS: We discovered a cell subcluster, referred to as C4, that is closely associated with metastasis and poor prognosis in OC. This subcluster correlated with an epithelial-mesenchymal transition (EMT) and angiogenesis signature and RAB13 was identified as the key marker of it. Downregulation of RAB13 resulted in a reduction of OC cells migration and invasion. Additionally, we predicted several potential drugs that might inhibit RAB13. CONCLUSIONS: Our study has identified a cell subcluster that is closely linked to metastasis in OC, and we have also identified RAB13 as its hub gene that has great potential to become a new therapeutic target for OC.

In Situ Single-Cell Surgery and Intracellular Organelle Manipulation Via Thermoplasmonics Combined Optical Trapping.

Microsurgery and biopsies on individual cells in a cellular microenvironment are of great importance to better understand the fundamental cellular processes at subcellular and even single-molecular levels. However, it is still a big challenge for in situ surgery without interfering with neighboring living cells. Here, we report a thermoplasmonics combined optical trapping (TOT) technique for in situ single-cell surgery and intracellular organelle manipulation, without interfering with neighboring cells. A selective single-cell perforation was demonstrated via a localized thermoplasmonic effect, which facilitated further targeted gene delivery. Such a perforation was reversible, and the damaged membrane was capable of being repaired. Remarkably, a targeted extraction and precise manipulation of intracellular organelles were realized via the optical trapping. This TOT technique represents a new way for single-cell microsurgery, gene delivery, and intracellular organelle manipulation, and it provides a new insight for a deeper understanding of cellular processes as well as to reveal underlying causes of diseases associated with organelle malfunctions at a subcellular level.

A fluorimetric test strip with suppressed "Coffee Ring Effect" for selective mercury ion analysis.

Nowadays, test strips are widely applied, but their use is mostly limited to the qualitative or half-quantitative analysis of targets. The main reason for their limited use is the "Coffee Ring Effect" (CRE) of probe materials, which leads to a heterogeneous probe distribution and poor testing reproducibility and sensitivity. In the present work, a fluorescent test strip was fabricated with a suppressed CRE of silver nanocluster (AgNC) probes coated by gelatin (Gel) under vacuum-aided fast lyophilization. Uniform and stable deposition of AgNC probes was achieved onto the test strips with a high loading capacity. The AgNCs displayed specific responses to Hg2+ ions, allowing sensitive and quantitative analysis in the linear concentration ranges from 0.20 to 50000 nM with a limit of detection of 0.10 nM. Given the advantages of rapid and facile preparation, CRE suppression, high biocompatibility, and cost-effectiveness, such a fabrication protocol may pave the way for the design of various test strips-based devices for point-of-care analytical applications in the fields of environmental monitoring, food quality analysis, and clinical diagnostics.

Optical fiber with homogeneous material by side-array cladding.

Optical fibers are the core elements for various fiber-optic applications in communication, lasers, sensors, tweezers, quantum optics, and bio-photonics. Current optical fibers are based on a core-cladding structure with different refractive indices and are mainly fabricated using the stack-draw method. However, such a traditional fabrication method limits the realization of fibers with various advanced optical materials, thereby restricting the utilization of excellent optical properties offered by these materials. In this study, a novel structure for side-array cladding by laser drilling on the side of the fiber with homogeneous material is proposed. Accordingly, the confinement loss, mode characteristics, birefringence, and dispersion of the side-array cladding fiber are investigated based on the numerical simulation performed via the finite element method. Subsequently, an optimal fiber structure is obtained by taking the crystal material as an example. Essentially, our proposed side-array cladding fiber can eliminate the mismatch problem of core-cladding materials in the current stack-draw fabrication method. Potentially, the proposed approach can serve as a standard design and fabrication method of optical fibers with homogeneous material, by utilizing the rapid development of laser processing. In other words, a large number of advanced optical materials can be fabricated into optical fibers with the proposed technique, thus maximizing their technical advantages for different applications.

Pattern recognition using self-reference feature extraction for φ-OTDR.

This paper proposes a pattern recognition method for φ-OTDR based on self-reference features, where machine learning is applied to classify the vibration monitored. The φ-OTDR collects the light amplitude-time-space sequence, establishes a reference position in the spatial dimension, and combines the two dimensions of the vibration and reference positions to form self-reference features, which are then used as machine learning features. These self-reference features can effectively improve the pattern recognition accuracy. This paper selects a low sampling frequency for data collection, analyzes the influence of sample definition methods of different time lengths on the pattern recognition accuracy, and determines that the optimal sample length is 10 data points. The contribution of different feature parameters to pattern recognition is analyzed, and eight eigenvalues such as average, maximum, and minimum are finally determined to form self-reference features that are used as the input of the machine learning algorithm. The recognition accuracies of five machine learning algorithms including kNN, Decision Tree, Random Forest, LightGBM, and CatBoost are analyzed and compared, and the CatBoost algorithm in the integrated learning algorithm is finally determined as the optimal algorithm. On this basis, this paper proposes a filtering algorithm to deal with abnormal signals, which can effectively compensate for abnormal data and further improve the accuracy of pattern recognition. Finally, this paper conducts the pattern recognition study on four common events of tapping, bending, trampling, and blowing, and obtains the average recognition rate of 98%. In addition, this paper innovatively carried out pattern recognition research on five types of mining equipment, including ball mills, vibrating screens, conveyor belts, filters, and industrial pumps, and obtained the average recognition rate of 93.5%.

Long short-term memory network of machine learning for compensating temperature error of a fiber optic gyroscope independent of the temperature sensor.

We present an artificial intelligence compensation method for temperature error of a fiber optic gyroscope (FOG). The difference from the existing methods is that the compensation model finally determined by this method only uses the FOG's data to complete the regression prediction of the temperature error and eliminate the dependency on the temperature sensor. In the experimental stage, the proposed method performs temperature experiments with three varying trends of temperature heating, holding, and cooling and obtains sufficient output data sets of the FOG. Taking the output time series of the FOG as the input sample and based on the long short-term memory network of machine learning, the training, validation, and test of the model are completed. From the two perspectives of network learning ability and the improvement degree of the FOG's performance, four indicators, including root mean square error, error cumulative distribution function, FOG bias stability, and Allan variance analysis are selected to evaluate the performance of the compensation model comprehensively. Compared with the existing methods using temperature information for prediction and compensation, the results show that the error compensation method without temperature information proposed can effectively improve the accuracy of the FOG and reduce the complexity of the compensation system. The work can also provide technical references for error compensation of other sensors.

RAD51AP1 promotes progression of ovarian cancer via TGF-ß/Smad signalling pathway.

Ovarian cancer (OC) is one of the leading causes of female deaths. However, the molecular pathogenesis of OC has still remained elusive. This study aimed to explore the potential genes associated with the progression of OC. In the current study, 3 data sets of OC were downloaded from the GEO database to identify hub gene. Somatic mutation data obtained from TCGA were used to analyse the mutation. Immune cells were used to estimate effect of the hub gene to the tumour microenvironment. RNA-seq and clinical data of OC patients retrieved from TCGA were used to investigate the diagnostic and prognostic values of hub gene. A series of in vitro assays were performed to indicate the function of hub gene and its possible mechanisms in OC. As a result, RAD51AP1 was found as a hub gene, which expression higher was mainly associated with poor survival in OC patients. Up-regulation of RAD51AP1 was closely associated with mutations. RAD51AP1 up-regulation accompanied by accumulated Th2 cells, but reduced CD4 + T cells and CD8 + T cells. Nomogram demonstrated RAD51AP1 increased the accuracy of the model. Down-regulation of RAD51AP1 suppressed proliferation, migration and invasion capabilities of OC cells in vitro. Additionally, scatter plots showed that RAD51AP1 was positively correlated with genes in TGF-ß/Smad pathway. The above-mentioned results were validated by RT-qPCR and Western blotting. In conclusion, up-regulation of RAD51AP1 was closely associated with mutations in OC. RAD51AP1 might represent an indicator for predicting OS of OC patients. Besides, RAD51AP1 might accelerate progression of OC by TGF-ß/Smad signalling pathway.

Classification of tumor subtypes leveraging constriction-channel based impedance flow cytometry and optical imaging.

As label-free biomarkers, electrical properties of single cells have been widely used for cell-type classification and cell-status evaluation. However, as intrinsic bioelectrical markers, previously reported membrane capacitance and cytoplasmic resistance (e.g., specific membrane capacitance Cspecific membrane and cytoplasmic conductivity σcytoplasm ) of tumor subtypes were derived from tens of single cells, lacking statistical significance due to low cell numbers. In this study, tumor subtypes were constructed based on phenotype (treatment with 4-methylumbelliferone) or genotype (knockdown of ROCK1) modifications and then aspirated through a constriction-channel based impedance flow cytometry to characterize single-cell Cspecific membrane and σcytoplasm . Thousands of single tumor cells with phenotype modifications were measured, resulting in significant differences in 1.64 ± 0.43 µF/cm2 vs. 1.55 ± 0.47 µF/cm2 of Cspecific membrane and 0.96 ± 0.37 S/m vs. 1.24 ± 0.47 S/m of σcytoplasm for 95C cells (792 cells of 95C-control vs. 1529 cells of 95C-pheno-mod); 2.56 ± 0.88 µF/cm2 vs. 2.33 ± 0.56 µF/cm2 of Cspecific membrane and 0.83 ± 0.18 S/m vs. 0.93 ± 0.25 S/m of σcytoplasm for H1299 cells (962 cells of H1299-control vs. 637 cells of H1299-pheno-mod). Furthermore, thousands of single tumor cells with genotype modifications were measured, resulting in significant differences in 3.82 ± 0.92 vs. 3.18 ± 0.47 µF/cm2 of Cspecific membrane and 0.47 ± 0.05 vs. 0.52 ± 0.05 S/m of σcytoplasm (1100 cells of A549-control vs. 1100 cells of A549-geno-mod). These results indicate that as intrinsic bioelectrical markers, specific membrane capacitance and cytoplasmic conductivity can be used to classify tumor subtypes.

Discovering extremely low confinement-loss anti-resonant fibers via swarm intelligence.

In this work, we obtain extremely low confinement-loss (CL) anti-resonant fibers (ARFs) via swarm intelligence, specifically the particle swarm optimization (PSO) algorithm. We construct a complex search space of ARFs with two layers of cladding and nested tubes. There are three and four structures of cladding tubes in the first and second layer, respectively. The ARFs are optimized by using the PSO algorithm in terms of both the structures and the parameters. The optimal structure is obtained from a total of 415900 ARFs structures, with the lowest CL being 2.839×10-7 dB/m at a wavelength of 1.55 µm. We observe that the number of ARF structures with CL less than 1×10-6 dB/m in our search space is 370. These structures mainly comprise four designs of ARFs. The results show that the optimal ARF structures realized by the PSO algorithm are different from the ARFs reported in the previous literature. This means that the swarm intelligence accelerates the design and invention of ARFs and also provides new insights regarding the ARF structures. This work provides a fast and effective approach to design ARFs with special requirements. In addition to providing high-performance ARF structures, this work transforms the ARF designs from experience-driven to data-driven.

Use of machine learning to efficiently predict the confinement loss in anti-resonant hollow-core fiber.

The fundamental mode confinement loss (CL) of anti-resonant hollow-core fiber (ARF) is efficiently predicted by a classification task of machine learning. The structure-parameter vector is utilized to define the sample space of ARFs. The CL of labeled samples at 1550 nm is numerically calculated via the finite element method (FEM). The magnitude of CL is obtained by a classification task via a decision tree and k-nearest neighbors algorithms with the training and test sets generated by 290700 and 32300 labeled samples. The test accuracy, confusion matrices, and the receiver operating characteristic curves have shown that our proposed method is effective for predicting the magnitude of CL with a short computation runtime compared to FEM simulation. The feasibility of predicting other performance parameters by the extension of our method, as well as its ability to generalize outside the tested sample space, is also discussed. It is likely that the proposed sample definition and the use of a classification approach can be adopted for design application beyond efficient prediction of ARF CL and inspire artificial intelligence and data-driven-based research of photonic structures.

Optically Controlled Living Micromotors for the Manipulation and Disruption of Biological Targets.

Bioinspired and biohybrid micromotors represent a revolution in microrobotic research and are playing an increasingly important role in biomedical applications. In particular, biological micromotors that are multifunctional and can perform complex tasks are in great demand. Here, we report living and multifunctional micromotors based on single cells (green microalgae: Chlamydomonas reinhardtii) that are controlled by optical force. The micromotor's locomotion can be carefully controlled in a variety of biological media including cell culture medium, saliva, human serum, plasma, blood, and bone marrow fluid. It exhibits the capabilities to perform multiple tasks, in particular, indirect manipulation of biological targets and disruption of biological aggregates including in vitro blood clots. These micromotors can also act as elements in reconfigurable motor arrays where they efficiently work collaboratively and synchronously. This work provides new possibilities for many in vitro biomedical applications including target manipulation, cargo delivery and release, and biological aggregate removal.

Doping Nitrogen into Q-Graphene by Plasma Treatment toward Peroxidase Mimics with Enhanced Catalysis.

A facile and efficient plasma treatment strategy has been applied for the first time to dope heteroatom nitrogen (N) into Q-graphene (QG) under ambient temperature toward a carbon-based green nanozyme. It was discovered that the resulting N doped QG (N-QG) nanozyme can present the greatly enhanced catalysis activity, which is nearly 5-fold higher than that of pristine QG, as comparably revealed by the kinetic studies. Herein, the plasma treatment-assisted N doping could improve the conductivity (hydrophilicity) and create the surface defects of QG so as to promote the electron transferring toward the enhanced catalytic activities of N-QG. Furthermore, the catalase, superoxide dismutase, and oxidase-like catalysis activities of N-QG were explored, indicating the N doping could endow the obtained nanozyme with a high specificity of peroxidase-like catalysis. The application feasibility of the developed N-QG nanozyme was demonstrated subsequently by the catalysis-based colorimetric assays for H2O2 in milk samples, with the linear range from 2.00 to 1500 µM. Importantly, such a plasma-assisted heteroatom doping route may open a door toward the large-scale applications for the rational designs of various enzyme mimics with improved catalysis performances.

Fe3O4 Nanozymes with Aptamer-Tuned Catalysis for Selective Colorimetric Analysis of ATP in Blood.

In this work, a simple and highly selective colorimetric method has been developed for quantifying trace-level ATP using Fe3O4 nanoparticles (Fe3O4 NPs). It was discovered that Fe3O4 NPs could present the dramatically enhanced catalysis once anchored with ATP-specific aptamers (Apts), which is about 6-fold larger than that of bare Fe3O4 NPs. In the presence of ATP, however, the Apts would be desorbed from Fe3O4 NPs due to the Apts-target binding event, leading to the decrease of catalysis rationally depending on ATP concentrations. A colorimetric strategy was thereby developed to facilitate the highly selective detection of ATP, showing the linear concentrations ranging from 0.50 to 100 µM. Subsequently, the developed ATP sensor was employed for the evaluation of ATP in blood with the analysis performances comparably better than those of the documented detection methods, showing the potential applications in the clinical laboratory for the detective diagnosis of some ATP-indicative diseases. Importantly, such a catalysis-based detection strategy should be extended to other kinds of nanozymes with intrinsic catalysis properties (i.e., peroxidase and oxidase-like activities), promising as a universal candidate for monitoring various biological species simply by using target-specific recognition elements like Apts and antibodies.

DKK1 promotes migration and invasion of non-small cell lung cancer via ß-catenin signaling pathway.

Disregulation of dickkopf-related protein 1 (DKK1) has been reported in a variety of human cancers. However, how DKK1 functions in Non-small cell lung cancer has not been revealed. In the current study, DKK1 was knocked out by the lentivirus-mediated short hairpin RNA interference approach in H1299 and 95C non-small cell lung cancer cell lines. Subsequently, the migration and invasion ability were assessed by wound-healing and transwell assays. In addition, epithelial-mesenchymal transition markers and ß-catenin were examined by Western blot analysis. The signaling pathway downstream of DKK1 was characterized using the Wnt signaling pathway inhibitor, IWP2, and glycogen synthase kinase 3 beta inhibitor, LiCl. Immunofluorescence analysis investigated the subcellular localization of ß-catenin. The results suggested that knockdown of DKK1 caused reduced migration and invasion ability of H1299 and 95C cells. DKK1 silencing resulted in the downregulation of epithelial-mesenchymal transition-related proteins, such as Snail and zinc finger E-box binding homeobox 1. Besides, DKK1 silencing inhibited ß-catenin and promoted the phosphorylation of ß-catenin. Mechanism results indicated that the expression of ß-catenin was reduced in H1299 or 95C cells after being treated with Wnt signaling inhibitor, IWP2. In addition, the inhibition of ß-catenin phosphorylation by glycogen synthase kinase 3 beta inhibitor, LiCl, significantly enhanced the migration and invasion capacities in DKK1-knockdown cell lines. Furthermore, cell immunofluorescence revealed that nuclear ß-catenin was reduced when DKK1 was knocked down. Taken together, these findings suggest that DKK1 induces the occurrence of epithelial-mesenchymal transition and promotes migration and invasion in non-small cell lung cancer cells. Mechanically, ß-catenin plays a vital role in DKK1-induced non-small cell lung cancer cell migration and invasion, and DKK1 inhibits the phosphorylation of ß-catenin, resulting in the increased nuclear localization of ß-catenin.

Simultaneous characterization of instantaneous Young's modulus and specific membrane capacitance of single cells using a microfluidic system.

This paper presents a microfluidics-based approach capable of continuously characterizing instantaneous Young's modulus (E(instantaneous)) and specific membrane capacitance (C(specific membrane)) of suspended single cells. In this method, cells were aspirated through a constriction channel while the cellular entry process into the constriction channel was recorded using a high speed camera and the impedance profiles at two frequencies (1 kHz and 100 kHz) were simultaneously measured by a lock-in amplifier. Numerical simulations were conducted to model cellular entry process into the constriction channel, focusing on two key parameters: instantaneous aspiration length (L(instantaneous)) and transitional aspiration length (L(transitional)), which was further translated to E(instantaneous). An equivalent distribution circuit model for a cell travelling in the constriction channel was used to determine C(specific membrane). A non-small-cell lung cancer cell line 95C (n = 354) was used to evaluate this technique, producing E(instantaneous) of 2.96 ± 0.40 kPa and Cspecific membrane of 1.59 ± 0.28 µF/cm2. As a platform for continuous and simultaneous characterization of cellular E(instantaneous) and C(specific membrane), this approach can facilitate a more comprehensive understanding of cellular biophysical properties.

Status and influencing factors of knowledge, attitude and self-reported practice regarding hospice care among nurses in Hainan, China: A cross-sectional study.

AIM: This study is to investigate the current status of knowledge, attitude and self-reported practice in hospice care among nurses in Hainan, China, and then to analyse its influencing factors and mediating effects. This provides a basis for formulating scientific and standardized hospice care training programmes for nurses. METHODS: This cross-sectional study investigated knowledge, attitude and self-reported practice in hospice care among 1819 nurses in Hainan, China. Convenience sampling was used to select participants from 45 hospitals and nursing homes in 14 cities and counties from October to December 2021. A scale of knowledge, attitude and self-reported practice of healthcare providers in hospice care (Chinese version) was administered to collect data during the study period. Statistical analyses, including t-tests, one-way ANOVA, post-hoc analysis and multiple linear regression, assessed the status of knowledge, attitude and self-reported practice of hospice care in nurses and identified influencing factors. The PROCESS macro program model 4.0 was employed to explore the mediating effect of attitude on knowledge and self-reported practice in hospice care. RESULTS: Nurses in Hainan displayed low knowledge (mean = 7.68, SD = 3.53), moderate attitudes (mean = 88.13, SD = 12.10) and self-reported practice (mean = 51.81, SD = 9.82) in hospice care. Current employment and willingness to engage in hospice care were significant factors influencing knowledge, attitude and self-reported practice in hospice care. Attitude partially mediated the relationship between knowledge and self-reported practice. PATIENT OR PUBLIC CONTRIBUTION: This study focuses on nurses' knowledge, attitude and self-reported practice in hospice care and does not directly involve patients or the public. However, the findings enhance hospice care provided to patients and the broader community by improving nurses' knowledge and skills. This study informs evidence-based training programmes and interventions, benefiting those in need of hospice care services.

Host vegetation connectivity is decisive for the natural spread of pine wilt disease.

BACKGROUND: Pine wilt disease has caused significant economic, ecological, and social losses in China, but there is a notable lack of research on the dynamic process of its propagation and diffusion over long timescales. This study revealed the spatial and temporal spread of the natural invasion of pine wilt disease through an analysis of long time series at macroscopic scales. We analysed and verified by simulations the driving mechanisms of host and wind fields in the natural spread of pine wilt disease. RESULTS: The research findings indicate that from 1982 to 2019, the number of counties affected by pine wilt disease in the Yangtze River Delta region of China exhibited a pattern of 'steady increase-fluctuation-outbreak'. The host forest played a decisive role in the natural spread of the disease, while the wind field played a supporting role. The study revealed specific contributions from various factors, where host forest landscape connectivity, host forest area share, mean wind speed, and wind frequency accounted for 31.8%, 28.7%, 22.6%, and 8.8%, respectively. The interaction of increased host forest area and increased wind speed can significantly increase the risk of pine wilt disease transmission. To validate these findings, vectorial metacellular automata simulations of pine nematode transmission in the Yangtze River Delta were conducted, yielding results with an accuracy of 0.803. CONCLUSION: By quantifying the contribution of host forest connectivity to the natural spread of pine wilt disease, this research offers a scientific foundation and innovative insights for preventing and controlling its dissemination. © 2024 Society of Chemical Industry.