Impact of Empowerment Theory-Based Nursing Intervention on the Quality of Life and Negative Emotions of Patients Diagnosed with Brain Metastasis Post Breast Cancer Surgery.

Objective: This study explores the impact of a nursing intervention grounded in empowerment theory, focusing on behavioral change, on brain metastasis patients post-breast cancer surgery. Methods: Between June 2021 and June 2023, 102 patients diagnosed with brain metastasis after breast cancer surgery at Bao Ding No.1 Central Hospital were randomized into two groups. The control group (51 patients) received standard nursing care, while the observation group (51 patients) participated in a behavioral change nursing intervention influenced by empowerment theory. The evaluation metrics included measures of negative emotions, compliance with treatment protocols, overall quality of life, and nursing satisfaction, assessed at multiple time points during the study period. Results: The intervention led to significant reductions in negative emotions for all patients when compared to their pre-intervention statuses, with the observation group exhibiting notably lower depression and anxiety scores at one and three months post-intervention (P < 0.05). Additionally, this group achieved higher compliance scores and demonstrated greater improvements in quality of life than the control group (P < 0.05). Nursing satisfaction was also significantly higher in the observation group, with 96.08% reporting high satisfaction compared to 80.39% in the control group (P < 0.05). Conclusion: Implementing a nursing intervention that emphasizes behavioral changes and leverages empowerment theory significantly enhances the quality of life, reduces negative emotions, boosts compliance with treatment, and increases nursing satisfaction among patients with brain metastasis following breast cancer surgery. This suggests that such interventions could be a valuable component of postoperative care for this patient population.

Extracellular matrix marker LAMC2 targets ZEB1 to promote TNBC malignancy via up-regulating CD44/STAT3 signaling pathway.

BACKGROUND: Triple negative breast cancer (TNBC) is a heterogeneous and aggressive disease characterized by a high risk of mortality and poor prognosis. It has been reported that Laminin γ2 (LAMC2) is highly expressed in a variety of tumors, and its high expression is correlated with cancer development and progression. However, the function and mechanism by which LAMC2 influences TNBC remain unclear. METHODS: Kaplan-Meier survival analysis and Immunohistochemical (IHC) staining were used to examine the expression level of LAMC2 in TNBC. Subsequently, cell viability assay, wound healing and transwell assay were performed to detect the function of LAMC2 in cell proliferation and migration. A xenograft mouse model was used to assess tumorigenic function of LAMC2 in vivo. Luciferase reporter assay and western blot were performed to unravel the underlying mechanism. RESULTS: In this study, we found that higher expression of LAMC2 significantly correlated with poor survival in the TNBC cohort. Functional characterization showed that LAMC2 promoted cell proliferation and migration capacity of TNBC cell lines via up-regulating CD44. Moreover, LAMC2 exerted oncogenic roles in TNBC through modulating the expression of epithelial-mesenchymal transition (EMT) markers. Luciferase reporter assay verified that LAMC2 targeted ZEB1 to promote its transcription. Interestingly, LAMC2 regulated cell migration in TNBC via STAT3 signaling pathway. CONCLUSION: LAMC2 targeted ZEB1 via activating CD44/STAT3 signaling pathway to promote TNBC proliferation and migration, suggesting that LAMC2 could be a potential therapeutic target in TNBC patients.

Reinforcement Learning for Robust Dynamic Event-Driven Constrained Control.

We consider a robust dynamic event-driven control (EDC) problem of nonlinear systems having both unmatched perturbations and unknown styles of constraints. Specifically, the constraints imposed on the nonlinear systems' input could be symmetric or asymmetric. Initially, to tackle such constraints, we construct a novel nonquadratic cost function for the constrained auxiliary system. Then, we propose a dynamic event-triggering mechanism relied on the time-based variable and the system states simultaneously for cutting down the computational load. Meanwhile, we show that the robust dynamic EDC of original nonlinear-constrained systems could be acquired by solving the event-driven optimal control problem of the constrained auxiliary system. After that, we develop the corresponding event-driven Hamilton-Jacobi-Bellman equation, and then solve it through a unique critic neural network (CNN) in the reinforcement learning framework. To relax the persistence of excitation condition in tuning CNN's weights, we incorporate experience replay into the gradient descent method. With the aid of Lyapunov's approach, we prove that the closed-loop auxiliary system and the weight estimation error are uniformly ultimately bounded stable. Finally, two examples, including a nonlinear plant and the pendulum system, are utilized to validate the theoretical claims.

Hyperosmotic Stress Induces Inflammation and Excessive Th17 Response to Blunt T-Cell Immunity in Tilapia.

Despite the advances in study on osmotic physiology in bony fish, the mechanism by which the immune system, especially T-cell immunity, adapts and responds to osmotic stress remains unknown. In the current study, we investigated the response of T cells to hyperosmotic stress in the bony fish Nile tilapia (Oreochromis niloticus). As a euryhaline fish, tilapia was able to adapt to a wide range of salinities; however, hypertonic stress caused inflammation and excessive T-cell activation. Furthermore, hypertonic stress increased the expression of IL-17A in T cells, upregulated the transcription factor RORα, and activated STAT3 signaling, along with IL-6- and TGF-ß1-mediated pathways, revealing an enhanced Th17 response in this early vertebrate. These hypertonic stress-induced events collectively resulted in an impaired antibacterial immune response in tilapia. Hypertonic stress elevated the intracellular ROS level, which in turn activated the p38-MK2 signaling pathway to promote IL-17A production by T cells. Both ROS elimination and the p38-MK2 axis blockade diminished the increased IL-17A production in T cells under hypertonic conditions. Moreover, the produced proinflammatory cytokines further amplified the hypertonic stress signaling via the MKK6-p38-MK2 axis-mediated positive feedback loop. To our knowledge, these findings represent the first description of the mechanism by which T-cell immunity responds to hypertonic stress in early vertebrates, thus providing a novel perspective for understanding the adaptive evolution of T cells under environmental stress.

Temporal and spatial biosonar activity of the recently established uppermost Yangtze finless porpoise population downstream of the Gezhouba Dam: Correlation with hydropower cascade development, shipping, hydrological regime, and light intensity.

Numerous dams disrupt freshwater animals. The uppermost population of the critically endangered Yangtze finless porpoise has been newly formed below the Gezhouba Dam, however, information regarding the local porpoise is scarce. Passive acoustic monitoring was used to detect the behaviors of porpoises below the Gezhouba Dam. The influence of shipping, pandemic lockdown, hydrological regime, and light intensity on the biosonar activity of dolphins was also examined using Generalized linear models. Over the course of 4 years (2019-2022), approximately 848, 596, and 676 effective monitoring days were investigated at the three sites, from upstream to downstream. Observations revealed significant spatio-temporal biosonar activity. Proportion of days that are porpoise positive were 73%, 54%, and 61%, while porpoise buzz signals accounted for 78.49%, 62.35%, and 81.30% of all porpoise biosonar at the three stations. The biosonar activity of porpoises was much higher at the confluence area, particularly at the MZ site, during the absence of boat traffic, and during the Pandemic shutdown. Temporal trends of monthly, seasonal, and yearly variation were also visible, with the highest number of porpoises biosonar detected in the summer season and in 2020. Significant correlations also exist between the hydrological regime and light intensity and porpoise activity, with much higher detections during nighttime and full moon periods. Hydropower cascade development, establishment of a natural reserve, fish release initiatives, and implementation of fishing restrictions may facilitate the proliferation of the porpoise population downstream of the Gezhouba Dam within the Yichang section of the Yangtze River. Prioritizing restoration designs that match natural flow regimes, optimize boat traffic, and reduce noise pollution is crucial for promoting the conservation of the local porpoises.

In situ monitoring of loading and elution processes of cryoprotectants at the single-cell level with Raman spectroscopy.

BACKGROUND: The analysis of cell membrane permeability plays a crucial role in improving the procedures of cell cryopreservation, which will affect the specific parameter settings in loading, removal and cooling processes. However, existing studies have mostly focused on deriving permeability parameters through osmotic theoretical models and cell volume response analysis, and there is still a lack of the direct experimental evidence and analysis at the single-cell level regarding the migration of cryoprotectants. RESULTS: In this work, a side perfusion microfluidics chips combined with Raman spectroscopy system was built to monitor in situ the Raman spectroscopy of extracellular and intracellular solution during loading and elution process with different cryoprotectant solution systems (single and dual component). And it was found that loading a high concentration cryoprotectant solution system through a single elution cycle may result in significant residual protective agent, which can be mitigated by employing a multi-component formula but multiple elution operations are still necessary. Furthermore, the collected spectral signals were marked and analyzed to was perform preliminary relative quantitative analysis. The results showed that the intracellular concentration changes can be accurately quantified by the Raman spectrum and are closely related to the extracellular solution concentration changes. SIGNIFICANCE AND NOVELTY: By using the method of small flow perfusion (≤20 µL/min) in the side microfluidic chip after the gravity sedimentation of cells, the continuous loading and elution process of different cryoprotectants on chip and the spectral acquisition can be realized. The intracellular and extracellular concentrations can be quantified in situ based on the ratio of spectral peak intensities. These results indicate that spectroscopic analysis can be used to effectively monitor intracellular cryoprotectant residues.

Neural critic learning with accelerated value iteration for nonlinear model predictive control.

In practical industrial processes, the receding optimization solution of nonlinear model predictive control (NMPC) is always a very knotty problem. Based on adaptive dynamic programming, the accelerated value iteration predictive control (AVI-PC) algorithm is developed in this paper. Integrating iteration learning with the receding horizon mechanism of NMPC, a novel receding optimization solution pattern is exploited to resolve the optimal control law in each prediction horizon. Besides, the basic architecture and the specific form of the AVI-PC algorithm are demonstrated, including the relationship among the iterative learning process, the prediction process, and the control process. On this basis, the convergence and admissibility conditions are established, and the relevant properties are comprehensively analyzed when the accelerated factor satisfies the established conditions. Furthermore, the accelerated value iterative function is approximated through the single critic network constructed by utilizing the multiple linear regression method. Finally, the plentiful simulation experiments are conducted from various perspectives to verify the effectiveness and progressiveness of the AVI-PC algorithm.

NIR-activated electrospun nanodetonator dressing enhances infected diabetic wound healing with combined photothermal and nitric oxide-based gas therapy.

Diabetic wounds pose a challenge to healing due to increased bacterial susceptibility and poor vascularization. Effective healing requires simultaneous bacterial and biofilm elimination and angiogenesis stimulation. In this study, we incorporated polyaniline (PANI) and S-Nitrosoglutathione (GSNO) into a polyvinyl alcohol, chitosan, and hydroxypropyltrimethyl ammonium chloride chitosan (PVA/CS/HTCC) matrix, creating a versatile wound dressing membrane through electrospinning. The dressing combines the advantages of photothermal antibacterial therapy and nitric oxide gas therapy, exhibiting enduring and effective bactericidal activity and biofilm disruption against methicillin-sensitive Staphylococcus aureus, methicillin-resistant Staphylococcus aureus, and Escherichia coli. Furthermore, the membrane's PTT effect and NO release exhibit significant synergistic activation, enabling a nanodetonator-like burst release of NO through NIR irradiation to disintegrate biofilms. Importantly, the nanofiber sustained a uniform release of nitric oxide, thereby catalyzing angiogenesis and advancing cellular migration. Ultimately, the employment of this membrane dressing culminated in the efficacious amelioration of diabetic-infected wounds in Sprague-Dawley rats, achieving wound closure within a concise duration of 14 days. Upon applying NIR irradiation to the PVA-CS-HTCC-PANI-GSNO nanofiber membrane, it swiftly eradicates bacteria and biofilm within 5 min, enhancing its inherent antibacterial and anti-biofilm properties through the powerful synergistic action of PTT and NO therapy. It also promotes angiogenesis, exhibits excellent biocompatibility, and is easy to use, highlighting its potential in treating diabetic wounds.

Imaging electromagnetic boundary of microdevice using a wide field quantum microscope.

Imaging of electronic device surface or sub-surface electromagnetic fields under operating conditions is important for device design and diagnosis. In this study, we proposed a method to characterize specific magnetic field properties of electromagnetic devices at micron-scale using a solid-state quantum sensor, namely diamond nitrogen-vacancy (NV) centers. By employing a wide-field magnetic field measurement technique based on NV centers, we rapidly obtain the first-order magnetic field distribution of anomalous regions. Furthermore, we approximate the second-order magnetic field (magnetic gradient tensor) using the differential gradient method. To visualize the electromagnetic anomalous regions boundary, we utilize the tensor invariants of the magnetic gradient tensor components, along with their nonlinear combinations. The identification error rate of the anomalous regions is within 12.5%. Additionally, the electromagnetic field of anomalous regions is simulated showing the measurement accuracy. Our study shows that the experimental results are very similar to the theoretical simulation of the electromagnetic field (error: 7%). This work is essential for advancing electromagnetic field characterization of electronic devices and the advancement of quantum magnetic sensor applications.

Increased Yangtze finless porpoise presence in urban Wuhan waters of the Yangtze River during fishing closures.

Wuhan, a highly urbanized and rapidly growing region within China's Yangtze Economic Zone, has historically been identified as a gap area for the critically endangered Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis) based on daytime visual surveys. However, there has been a noticeable increase in porpoise sightings since 2020. This study employed passive acoustic monitoring to investigate porpoise distribution in Wuhan between 2020 and 2022. Generalized linear models were used to explore the relationship between shipping, hydrological patterns, light intensity, and porpoise biosonar activity. Over 603 days of effective monitoring, the daily positive rate for porpoise biosonar detection reached 43%, with feeding-related buzz signals accounting for 55% of all porpoise biosonar signals. However, the proportion of minutes during which porpoise presence was detected was 0.18%, suggesting that while porpoises may frequent the area, their visits were brief and mainly focused on feeding. A significant temporal trend emerged, showing higher porpoise biosonar detection during winter (especially in February) and 2022. Additionally, periods without boat traffic correlated with increased porpoise activity. Hydrological conditions and light levels exhibited significant negative correlations with porpoise activity. Specifically, porpoise sonar detections were notably higher during the night, twilight, and new moon phases. It is highly conceivable that both fishing bans and COVID-19 pandemic-related lockdowns contributed to the heightened presence of porpoises in Wuhan. The rapid development of municipal transportation and shipping in Wuhan and resulting underwater noise pollution have emerged as a significant threat to the local porpoise population. Accordingly, it is imperative for regulatory bodies to effectively address this environmental stressor and formulate targeted protection measures to ensure the conservation of the finless porpoise.

Comparative genomics on chloroplasts of Rubus (Rosaceae).

Rubus, the largest genus in Rosaceae, contains over 1400 species that distributed in multiple habitats across the world, with high species diversity in the temperate regions of Northern Hemisphere. Multiple Rubus species are cultivated for their valuable fruits. However, the intrageneric classification and phylogenetic relationships are still poorly understood. In this study, we sequenced, assembled, and characterized 17 plastomes of Rubus, and conducted comparative genomics integrating with 47 previously issued plastomes of this genus. The 64 plastomes of Rubus exhibited typical quadripartite structure with sizes ranging from 155,144 to 156,700 bp, and contained 132 genes including 87 protein-coding genes, 37 tRNA genes and eight rRNA genes. All plastomes are conservative in the gene order, the frequency of different types of long repeats and simple sequence repeats (SSRs), the codon usage, and the selection pressure of protein-coding genes. However, there are also some differences in the Rubus plastomes, including slight contraction and expansion of the IRs, a variation in the numbers of SSRs and long repeats, and some genes in certain clades undergoing intensified or relaxed purifying selection. Phylogenetic analysis based on whole plastomes showed that the monophyly of Rubus was strongly supported and resolved it into six clades corresponding to six subgenera. Moreover, we identified 12 highly variable regions that could be potential molecular markers for phylogenetic, population genetic, and barcoding studies. Overall, our study provided insight into plastomic structure and sequence diversification of Rubus, which could be beneficial for future studies on identification, evolution, and phylogeny in this genus.

Synthesis of cis-(8b,14a)-hexahydro-14H-dibenzo[f,h]oxazolo[3,2-b]isoquinolin-14-ones via photo cascade reaction.

A concise method for the synthesis of cis-(8b,14a)-hexahydro-14H-dibenzo[f,h]oxazolo[3,2-b]isoquinolin-14-ones 2 via photo-induced 3-([1,1'-biphenyl]-2-yl)-1-(2-hydroxyethyl)pyridin-2(1H)-ones 1 was developed. Irradiation of 1 in the solution of toluene with a 313 nm UV light in the presence of HCl gave cis-(8b,14a)-9a-α-hexahydro-14H-dibenzo[f,h]oxazolo[3,2-b]isoquinoli n-14-ones and cis-(8b,14a)-9a-ß-hexahydro-14H-dibenzo[f,h]oxazolo[3,2-b]isoquinolin-14-ones 2 (2-α and 2-ß) in good yields. The protocol simultaneously constructs dearomatized phenanthrene ring and oxindolizidinones ring by photo cascade reaction to achieve high bonding efficiency and high atomic efficiency. Additionally, the antitumor activities of 2 was evaluated and compounds 2b-α, 2b-ß, 2j-ß and 2 k-α showed similar or better activity compared to the cisplatin against tumor cell lines of Leukemia HL-60, lung cancer A594, liver cancer SMMC-7721 and breast cancer MDA-MB-231.

Temporal stability of tongue microbiota in older patients - A pilot study.

Background/purpose: Healthy states of human microbiota depend on a stable community of symbiotic microbes irrespective of external challenges from the environment. Thus, long-term stability of the oral microbiota is of importance, particularly for older patient populations. Materials and methods: We used next-generation sequencing (NGS) to examine the tongue microbiota of 18 individuals receiving long-term care over a 10-month period. Results: Beta diversity analysis demonstrated temporal stability of the tongue microbiota, as microbial compositions from all time points were indistinguishable from each other (P = 0.0887). However, significant individual variation in microbial composition (P = 0.0001) was observed, underscoring the presence of a unique microbial profile for each patient. Conclusion: The temporal dynamics of tongue microbiota exhibit long-term stability, providing diagnostic implications for oral diseases within older patient populations.

Neural Q-learning for discrete-time nonlinear zero-sum games with adjustable convergence rate.

In this paper, an adjustable Q-learning scheme is developed to solve the discrete-time nonlinear zero-sum game problem, which can accelerate the convergence rate of the iterative Q-function sequence. First, the monotonicity and convergence of the iterative Q-function sequence are analyzed under some conditions. Moreover, by employing neural networks, the model-free tracking control problem can be overcome for zero-sum games. Second, two practical algorithms are designed to guarantee the convergence with accelerated learning. In one algorithm, an adjustable acceleration phase is added to the iteration process of Q-learning, which can be adaptively terminated with convergence guarantee. In another algorithm, a novel acceleration function is developed, which can adjust the relaxation factor to ensure the convergence. Finally, through a simulation example with the practical physical background, the fantastic performance of the developed algorithm is demonstrated with neural networks.

Empirical and simulated investigation of the solid waste reverse supply chain: A complex adaptive system perspective.

Solid waste is increasing rapidly worldwide. In this study, the solid waste (household waste, construction and demolition waste and industrial waste) management systems are treated as reverse supply chain to analyze the critical operational issues based on complex adaptive system theory. At the single-layer, the complexity of the various nodes at a layer arises from rational decision-making and behavioral heterogeneity. The solid waste generation layer is employed as an example to investigate the complexity of node behavioral decisions. Regression analysis results reveal that both endogenous (Attitude, Subjective norm, and Perceived behavioral control) and exogenous factors (Economic incentive, Government supervision, Technical support) positively influence sorting behavior. The effect of Economic incentive (ß=0.327P<0.001) and Attitude (ß=0.249P<0.001) on sorting behavior are the largest. In the multi-layer system, different layers communicate with each other through the material and financial flows and have cross-layer impacts. An agent-based model is developed to investigate the multi-layer feedforward influence mechanism of changes in key layers (e.g., sorting rate, disposal rate) and the material and financial flows adaptive adjustment direction of the solid waste reverse supply chain. High rate of participation and accuracy of source sorting can shorten material flow paths and reduce storage and transportation costs. The increase in disposal rate encourages the transition of solid waste from backfill to resource utilization. This study provides a practice reference for solid waste reverse supply chain and related enterprises managers.

Chitosan-based hybrid nanospheres for vessel normalization towards enhancing tumor chemotherapy.

Vessel normalization has proved imperative in tumor growth inhibition. In this work, biopolymer-based hybrid nanospheres capable of normalizing blood vessels were designed to improve the therapeutic effect of chemotherapeutic drugs. Zn0.4Fe2.6O4 nanoparticles (ZFO NPs) were synthesized, and were encapsulated in cross-inked chitosan (CS) along with a nitric oxide (NO) precursor, DETA NONOate, forming hybrid ZFO/NO@CS nanospheres highly stable in physiological environment. The structure, morphology and size of the nanospheres were characterized. The ZFO/NO@CS nanospheres could release NO under acidic conditions typical of intratumoral and intracellular environment. The results of related factors expression, wound healing and tube formation assays demonstrated that both the encapsulated ZFO NPs and the released NO were able to inhibit angiogenesis in tumors. The ZFO/NO@CS nanospheres enhanced the antitumor efficacy of the chemotherapeutic drug DOX by normalizing tumor vessels, as evidenced by in vivo experiments for CT26 tumor-bearing mice. By analyzing the contents of Fe in the tumor and different organs, the nanospheres were found to accumulate primarily at the tumor site. The blood analysis showed little side effect of the nanospheres. The ZFO/NO@CS nanospheres have great potential in improving tumor therapeutic effect when used in combination with chemotherapeutic drugs.

MEMS Resonant Beam with Outstanding Uniformity of Sensitivity and Temperature Distribution for Accurate Gas Sensing and On-Chip TGA.

Micromechanical resonators have aroused growing interest as biological and chemical sensors, and microcantilever beams are the main research focus. Recently, a resonant microcantilever with an integrated heater has been applied in on-chip thermogravimetric analysis (TGA). However, there is a strong relationship between the mass sensitivity of a resonant microcantilever and the location of adsorbed masses. Different sampling positions will cause sensitivity differences, which will result in an inaccurate calculation of mass change. Herein, an integrated H-shaped resonant beam with uniform mass sensitivity and temperature distribution is proposed and developed to improve the accuracy of bio/chemical sensing and TGA applications. Experiments verified that the presented resonant beam possesses much better uniformity of sensitivity and temperature distribution compared with resonant microcantilevers. Gas-sensing and TGA experiments utilizing the integrated resonant beam were also carried out and exhibited good measurement accuracy.

Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares.

Combining multiple devices for localization has important applications in the military field. This paper exploits the land-based short-wave platforms and satellites for fusion localization. The ionospheric reflection height error and satellite position errors have a great impact on the short-wave localization and satellite localization accuracy, respectively. In this paper, an iterative constrained weighted least squares (ICWLS) algorithm is proposed for these two kinds of errors. The algorithm converts the nonconvex equation constraints to linear constraints using the results of the previous iteration, thus ensuring convergence to the globally optimal solution. Simulation results show that the localization accuracy of the algorithm can reach the corresponding Constrained Cramér-Rao Lower Bound (CCRLB). Finally, the localization results of the two methods are fused using Kalman filtering. Simulations show that the fused localization accuracy is improved compared to the single-means localization.

Gas Graph Convolutional Transformer for Robust Generalization in Adaptive Gas Mixture Concentration Estimation.

Gas concentration estimation has a tremendous research significance in various fields. However, existing methods for estimating the concentration of mixed gases generally depend on specific data-preprocessing methods and suffer from poor generalizability to diverse types of gases. This paper proposes a graph neural network-based gas graph convolutional transformer model (GGCT) incorporating the information propagation properties and the physical characteristics of temporal sensor data. GGCT accurately predicts mixed gas concentrations and enhances its generalizability by analyzing the concentration tokens. The experimental results highlight the GGCT's robust performance, achieving exceptional levels of accuracy across most tested gas components, underscoring its strong potential for practical applications in mixed gas analysis.