Supportive care in transplantation: A patient-centered care model to better support kidney transplant candidates and recipients

Kidney transplantation(KT),although the best treatment option for eligible patients,entails maintaining and adhering to a life-long treatment regimen of medications,lifestyle changes,self-care,and appointments.Many patients experience uncertain outcome trajectories increasing their vulnerability and symptom burden and generating complex care needs.Even when transplants are successful,for some patients the adjustment to life post-transplant can be challenging and psychological difficulties,economic challenges and social isola-tion have been reported.About 50%of patients lose their transplant within 10 years and must return to dialysis or pursue another transplant or conservative care.This paper documents the complicated journey patients undertake before and after KT and outlines some initiatives aimed at improving patient-centered care in transplantation.A more cohesive approach to care that borrows its philosophical approach from the established field of supportive oncology may improve patient experiences and outcomes.We propose the"supportive care in transplantation"care model to operationalize a patient-centered approach in transplantation.This model can build on other ongoing initiatives of other scholars and researchers and can help advance patient-centered care through the entire care continuum of kidney transplant recipients and candidates.Multi-dimensionality,multi-disciplinarity and evidence-based approaches are proposed as other key tenets of this care model.We conclude by proposing the potential advantages of this approach to patients and healthcare systems.Core Tip:Kidney transplant recipients and candidates face several uncertainties in their care journey and have several expressed unmet healthcare needs.We recommend a structured and comprehensive approach to transplant care across the entire continuum of a transplant patient’s journey similar to what has been developed in the field of oncology.The supportive care in transplantation model can operationalize patient-centered care and build on the efforts of other researchers in the field.We postulate that such a model would significantly improve care delivery and patients’experiences and outcomes and potentially decrease healthcare utilization and cost.INTRODUCTION Patients with kidney failure benefit from(KT)[1,2],and experience improved survival rates when compared with dialysis[3-6].KT studies,using validated instruments,have also consistently demonstrated that kidney transplant recipients(KTRs)experience better health-related quality of life and several improvements in other disease-specific domains when compared with dialysis[7].In countries where dialysis is out of reach for many,the diagnosis of kidney failure would be futile without KT[8].Thus,increasing KT has been a priority for the nephrology and transplant communities.This priority has been reflected in recent global trends:Of the 79 countries where data were available,the International Society of Nephrology’s Global Kidney Atlas reported that the prevalence of KTRs in 2023 was 279 per million population which represented an increase of 9.4%from the data published four years prior[8].Despite this growth,KT can be a challenging journey for many patients and it is sometimes regarded as a‘cure’,which does not conform with the reality that many patients experience[9-13].KTRs must maintain a life-long treatment regimen of medications,lifestyle changes,self-care and medical appointments[14-17].As poignantly stated by a young female transplant recipient,“I thought everything would change once I got my kidney.I thought I would be healthy again”but after experiencing multiple side effects of immunosuppressive medications and graft loss,she stated,“I am just a different kind of patient now”[18].Indeed,a significant proportion of patients experience graft failure and return to dialysis;it is estimated that over 50%return to dialysis within 10 years of KT[19-23].Patients are often not prepared for this outcome and report several psychosocial and physical ramifications of graft failure[24,25].Overall,high symptom burden,adverse effects of immunosuppressants,risk of graft rejection or failure and mortality,contribute to complex needs,vulnerability and uncertainties for patients,increasing their care needs and treatment burden[26-30].In this paper,we highlight the complex journey that KTRs and candidates undertake that can generate varied outcome trajectories and complex healthcare needs.We highlight the need for a comprehensive patient-centered approach to care and conclude with a proposal for a“supportive care in transplantation”care model.

Supportive care needs of women with gynecologic cancer and factors affecting in Turkiye: a cross sectional study

Background:Identifying and managing patients'supportive care needs is an essential component of health care.The cancer patients'needs are multidimensional and it is important to identify these needs.This study was conducted to assess supportive care needs of women with gynecologic cancer and affecting factors.Methods:This cross-sectional study consisted of 98 women with gynecologic cancer.The data of the study was gathered using“Personal Information Form”and the“James Supportive Care Screening”.Results:The most frequently reported unmet needs of gynecologic cancer patients were emotional issues,including uncertainty(87.6%),fears(85.7%),feeling down(86.7%),worry(87.6%),loss of interest in usual activities(73.3%)and physical symptoms including fatigue/lack of energy(81.9%),weakness(81.9%),lack of appetite(76.2%).The most frequently reported social/practical problems was transportation problems(50%).The mean scores for JSCS was 29.72±15.57.There were a statistically significant difference was found only between family type,stage of the disease,and time of diagnosis,and the total mean scores of the JSCS(p<0.05).Conclusion:It has been determined that patients with gynecological cancer mostly experience emotional and physical problems.Health professionals working in the oncology unit should be aware of unmet needs.Certain programs and services to address the identified unmet needs should be provided.

Mechanism of high-preload support based on the NPR anchor cable in layered soft rock tunnels

The control of large deformation problems in layered soft rock tunnels needs to solve urgently.The roof problem is particularly severe among the deformation issues in tunnels.This study first analyzes the asymmetric deformation modes in layered soft rock tunnels with large deformations.Subsequently,we construct a mechanical model under ideal conditions for controlling the roof of layered soft rock tunnels through high preload with the support of NPR anchor cables.The prominent roles of long and short NPR anchor cables in the support system are also analyzed.The results indicate the significance of high preload in controlling the roof of layered soft rock tunnels.The short NPR anchor cables effectively improve the integrity of the stratified soft rock layers,while the long NPR anchor cables effectively mobilize the self-bearing capacity of deep-stable rock layers.Finally,the high-preload support method with NPR anchor cables is validated to have a good effect on controlling large deformations in layered soft rock tunnels through field monitoring data.

Analysis of the interaction between bolt-reinforced rock and surface support in tunnels based on convergence-confinement method

To investigate the interaction of the bolt-reinforced rock and the surface support,an analytical model of the convergence-confinement type is proposed,considering the sequential installation of the fully grouted rockbolts and the surface support.The rock mass is assumed to be elastic-brittle-plastic material,obeying the linear Mohr-Coulomb criterion or the non-linear Hoek-Brown criterion.According to the strain states of the tunnel wall at bolt and surface support installation and the relative magnitude between the bolt length and the plastic depth during the whole process,six cases are categorized upon solving the problem.Each case is divided into three stages due to the different effects of the active rockbolts and the passive surface support.The fictitious pressure is introduced to quantify the threedimensional(3D)effect of the tunnel face,and thus,the actual physical location along the tunnel axis of the analytical section can be considered.By using the bolt-rock strain compatibility and the rocksurface support displacement compatibility conditions,the solutions of longitudinal tunnel displacement and the reaction pressure of surface support along the tunnel axis are obtained.The proposed analytical solutions are validated by a series of 3D numerical simulations.Extensive parametric studies are conducted to examine the effect of the typical parameters of rockbolts and surface support on the tunnel displacement and the reaction pressure of the surface support under different rock conditions.The results show that the rockbolts are more effective in controlling the tunnel displacement than the surface support,which should be installed as soon as possible with a suitable length.For tunnels excavated in weak rocks or with restricted displacement control requirements,the surface support should also be installed or closed timely with a certain stiffness.The proposed method provides a convenient alternative approach for the optimization of rockbolts and surface support at the preliminary stage of tunnel design.

Routine Supportive Supervision and Management of Medicines and Other Health Products and Technologies in Vihiga County, Kenya

Health Products and Technologies (HPTs) are pivotal for an efficient health system. Availability and accessibility to affordable health products are critical indicators towards achieving universal health coverage. Routine supportive supervision, performance monitoring, recognition of efforts and client feedback are vital activities toward health supply chain system strengthening. This is a descriptive paper that describes a model of integrated commodity supportive supervision, and mentorship and its impact on various outcomes of health commodity management. Data were abstracted from the standardized scored checklists used during integrated commodity supportive supervision and supply chain audit in public health facilities in Vihiga County. Scores for the period 2020 to 2022 were analyzed on the eight key areas of interest. The analysis was done using Statistical Package for Social Sciences (SPSS version 26). Results are interpreted at 95% Confidence interval. This paper also shares findings from both quantitative and qualitative data from client exit and facility managers’ interviews. Six complete rounds of supervisions, three clients and service providers’ interviews, and three annual award events have been conducted. We observed trends across six data collections points and compared the results at first point or baseline (January-June 2020) to the results at the last point or end line (April-June 2022). Findings show significant improvements on the eight parameters in terms of mean scores as follows: resolution of issues from previous visits by 35.06% (46.75% - 81.81%);storage of HPTs by 17.41% (68.72% - 86.13%);inventory management by 28.16% (42.67% - 70.83%);availability and use of commodity data management information systems (MIS) tools by 22.39% (74.40% - 96.79%);verification of commodity data by 25.61% (65.56% - 91.17%);availability of guidelines and job aids for commodity management by 46.28% (36.65% - 82.93%). There was an improvement on the mean score on accountability by 20.22% (58.58% - 83.51%). The composite (final) score improved by 28.33% (56.19% - 84.52%). There was progressive narrowing of the standard deviations on all the indicators across the study period. This demonstrates that there is standardization of practices and positive competition among all the public health facilities. There were significant improvements on all the eight indicators. Routine integrated commodity supportive supervision has proven to be an effective high impact intervention in improving management of health products and technologies in Vihiga County, Kenya.

Stability analysis of longwall top-coal caving face in extra-thick coal seams based on an innovative numerical hydraulic support model

The relationship between support and surrounding rock is of great significance to the control of surrounding rock in mining process.In view of the fact that most of the existing numerical simulation methods construct virtual elements and stress servo control to approximately replace the hydraulic support problem,this paper establishes a new numerical model of hydraulic support with the same working characteristics as the actual hydraulic support by integrating numerical simulation software Rhino,Griddle and FLAC3D,which can realize the simulation of different working conditions.Based on this model,the influence mechanism of the supporting strength of hydraulic support on surrounding rock stress regulation and coal stability in front of the top coal caving face in extra thick coal seam were researched.Firstly,under different support intensity,the abutment pressure of the bearing coal and the coal in front of it presents the “three-stage”evolution characteristics.The influence range of support intensity is 15%–30%.Secondly,1.5 MPa is the upper limit of impact that the support strength can have on the front coal failure area.Thirdly,within a displacement range of 2.76 m from the coal wall,a support strength of1.5 MPa provides optimal control of the horizontal displacement of the coal.

Translocation of telomerase reverse transcriptase coincided with ATP release in postnatal cochlear supporting cells

The spontaneous bursts of electrical activity in the developing auditory system are derived from the periodic release of adenosine triphosphate(ATP)by supporting cells in the Kölliker’s organ.However,the mechanisms responsible for initiating spontaneous ATP release have not been determined.Our previous study revealed that telomerase reverse transcriptase(TERT)is expressed in the basilar membrane during the first postnatal week.Its role in cochlear development remains unclear.In this study,we investigated the expression and role of TERT in postnatal cochlea supporting cells.Our results revealed that in postnatal cochlear Kölliker’s organ supporting cells,TERT shifts from the nucleus into the cytoplasm over time.We found that the TERT translocation tendency in postnatal cochlear supporting cells in vitro coincided with that observed in vivo.Further analysis showed that TERT in the cytoplasm was mainly located in mitochondria in the absence of oxidative stress or apoptosis,suggesting that TERT in mitochondria plays roles other than antioxidant or anti-apoptotic functions.We observed increased ATP synthesis,release and activation of purine signaling systems in supporting cells during the first 10 postnatal days.The phenomenon that TERT translocation coincided with changes in ATP synthesis,release and activation of the purine signaling system in postnatal cochlear supporting cells suggested that TERT may be involved in regulating ATP release and activation of the purine signaling system.Our study provides a new research direction for exploring the spontaneous electrical activity of the cochlea during the early postnatal period.

A Feature Learning-Based Model for Analyzing Students’ Performance in Supportive Learning

Supportive learning plays a substantial role in providing a quality edu-cation system.The evaluation of students’performance impacts their deeper insight into the subject knowledge.Specifically,it is essential to maintain the baseline foundation for building a broader understanding of their careers.This research concentrates on establishing the students’knowledge relationship even in reduced samples.Here,Synthetic Minority Oversampling TEchnique(SMOTE)technique is used for pre-processing the missing value in the provided input dataset to enhance the prediction accuracy.When the initial processing is not done substantially,it leads to misleading prediction accuracy.This research concentrates on modelling an efficient classifier model to predict students’perfor-mance.Generally,the online available student dataset comprises a lesser amount of sample,and k-fold cross-validation is performed to balance the dataset.Then,the relationship among the students’performance(features)is measured using the auto-encoder.The stacked Long Short Term Memory(s-LSTM)is used to learn the previous feedback connection.The stacked model handles the provided data and the data sequence for understanding the long-term dependencies.The simula-tion is done in the MATLAB 2020a environment,and the proposed model shows a better trade-off than the existing approaches.Some evaluation metrics like pre-diction accuracy,sensitivity,specificity,AUROC,F1-score and recall are evalu-ated using the proposed model.The performance of the s?LSTM model is compared with existing approaches.The proposed model gives 89% accuracy,83% precision,86%recall,and 87%F-score.The proposed model outperforms the existing systems in terms of the earlier metrics.

Correlation Analysis between Self-Disclosure and Social Support in Patients with Breast Cancer

Objective: Describe the status quo of self-disclosure and social support in breast cancer patients and analyze the correlation between them. Methods: General data questionnaire, distress disclosure Index scale and Chinese version of medical social support scale were used to investigate the correlation between self-disclosure and social support in breast cancer patients by Pearson correlation analysis. Results: 1) The total self-disclosure score was (38.75 ± 9.18);the total score of social support was (70.57 ± 14.04) scores, including emotional information support dimension (28.39 ± 6.06) scores, practical support dimension (15.62 ± 3.31) scores, elastic support dimension (14.85 ± 3.23) scores, and emotional support dimension (11.70 ± 2.56) scores. 2) Self-disclosure was positively correlated with social support (r = 0.433, p Conclusion: Breast cancer patients had a moderate level of self-disclosure, and the higher the level of self-disclosure, the better the social support. It is suggested that improving the self-disclosure level of breast cancer patients can help them obtain more social support.

Aqueous-phase reforming of hydroxyacetone solution to bio-based H_(2)over supported Pt catalysts

Aqueous-phase reforming(APR)is an attractive process to produce bio-based hydrogen from waste biomass streams,during which the catalyst stability is often challenged due to the harsh reaction conditions.In this work,three Pt-based catalysts supported on C,AlO(OH),and ZrO_(2)were investigated for the APR of hydroxyacetone solution in afixed bed reactor at 225℃and 35 bar.Among them,the Pt/C catalyst showed the highest turnover frequency for H_(2)production(TOF of 8.9 molH_(2)molPt^(-1)min^(-1))and the longest catalyst stability.Over the AlO(OH)and ZrO_(2)supported Pt catalysts,the side reactions consuming H_(2),formation of coke,and Pt sintering result in a low H_(2)production and the fast catalyst deactivation.The proposed reaction pathways suggest that a promising APR catalyst should reform all oxygenates in the aqueous phase,minimize the hydrogenation of the oxygenates,maximize the WGS reaction,and inhibit the condensation and coking reactions for maximizing the hydrogen yield and a stable catalytic performance.

Critical role of carbon support in metal nanoaggregate facilitating Fe-N-C catalyst for PEM fuel cell application

Metal nanoaggregates can simultaneously enhance the activity and stability of Fe-N-C catalysts in proton-exchange-membrane fuel cells(PEMFC).Previous studies on the relevant mechanism have focused on the direct interaction between FeN_(4)active sites and metal nanoaggregates.However,the role of carbon support that hosts metal nanoaggregates and active sites has been overlooked.Here,a Fe-N-C catalyst encapsulating inactive gold nanoparticles is prepared as a model catalyst to investigate the electronic tuning of Au nanoparticles(NPs)towards the carbon support.Au NPs donate electrons to carbon support,making it rich inπelectrons,which reduces the work function and regulates the electronic configuration of the FeN_(4)sites for an enhanced ORR activity.Meanwhile,the electron-rich carbon support can mitigate the electron depletion of FeN_(4)sites caused by carbon support oxidation,thereby preserving its high activity.The yield and accumulation of H_(2)O_(2)are thus alleviated,which delays the oxidation of the catalyst and benefits the stability.Due to the electron-rich carbon support,the composite catalyst achieves a top-level peak power density of 0.74 W/cm^(2) in a 1.5 bar H_(2)-air PEMFC,as well as the improved stability.This work elucidates the key role of carbon support in the performance enhancement of the FeN-C/metal nanoaggregate composite catalysts for fuel cell application.

Research on the flow stability and noise reduction characteristics of quasi-periodic elastic support skin

To enhance flow stability and reduce hydrodynamic noise caused by fluctuating pressure,a quasiperiodic elastic support skin composed of flexible walls and elastic support elements is proposed for fluid noise reduction.The arrangement of the elastic support element is determined by the equivalent periodic distance and quasi-periodic coefficient.In this paper,a dynamic model of skin in a fluid environment is established.The influence of equivalent periodic distance and quasi-periodic coefficient on flow stability is investigated.The results suggest that arranging the elastic support elements in accordance with the quasi-periodic law can effectively enhance flow stability.Meanwhile,the hydrodynamic noise calculation results demonstrate that the skin exhibits excellent noise reduction performance,with reductions of 10 dB in the streamwise direction,11 dB in the spanwise direction,and 10 dB in the normal direction.The results also demonstrate that the stability analysis method can serve as a diagnostic tool for flow fields and guide the design of noise reduction structures.

The Support Systems and Coping Strategies of Infertile Women Attending the Out-Patient Consultation Unit of CHRACERH Yaoundé, Cameroon: A Cross-Sectional Study

Background: Infertility is a complex disorder with significant psycho-social and economic consequences. It globally affects 10% - 15% of couples. In Cameroon, little is known about what women do to overcome the psychosocial aspects of the disease. Objectives: This study aimed to identify the support systems and coping strategies of infertile women attending the outpatient consultation unit of the Gynaecological Endoscopic Surgery and Reproductive Teaching Hospital (CHRACERH), Yaoundé, Cameroon. Methods: A hospital-based cross-sectional study was conducted from the 14th of March to the 6th of April 2023 at CHRACERH Yaoundé. A total of 190 participants were recruited using a convenience sampling method. Data regarding socio-demographic characteristics, support systems and coping strategies were collected using a pretested questionnaire. Descriptive and analytic statistics were conducted using SPSS version 25. Results: The mean age of participants was 39.52 ± 7.64 years. The majority 78.9% of participants were workers (public, private sector, or traders) and were Christians 95.8%. The most common source of psychological support was from family 76.8 and husbands 72.63%. Most of the participants 89.5% resorted to prayer and getting busy 48.4% as a coping strategy. There was no statistically significant relationship between coping strategies and psychological disorders p > 0.05. Conclusion: The main support system of participants was family, husband, and friends. Prayer, getting busy and adoption were the most common coping strategies. There is a need for the Ministry of Public Health and other stakeholders to put in place other support systems and coping strategies (FELICIA) used elsewhere and provide adequate health education and infection control to prevent infertility in Cameroon.

Improved Twin Support Vector Machine Algorithm and Applications in Classification Problems

The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will result in rising outlier values and noise.Therefore,the speed and performance of classification could be greatly affected.Given the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification formulations.Combined with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point.Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine.We introduce the cost control to solve the problem of sample skew.Finally,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is constructed.This can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.

Differentially Private Support Vector Machines with Knowledge Aggregation

With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.

Enhanced stability of nitrogen-doped carbon-supported palladium catalyst for oxidative carbonylation of phenol

Enhancing the stability of supported noble metal catalysts emerges is a major challenge in both science and industry.Herein,a heterogeneous Pd catalyst(Pd/NCF)was prepared by supporting Pd ultrafine metal nanoparticles(NPs)on nitrogen-doped carbon;synthesized by using F127 as a stabilizer,as well as chitosan as a carbon and nitrogen source.The Pd/NCF catalyst was efficient and recyclable for oxidative carbonylation of phenol to diphenyl carbonate,exhibiting higher stability than Pd/NC prepared without F127 addition.The hydrogen bond between chitosan(CTS)and F127 was enhanced by F127,which anchored the N in the free amino group,increasing the N content of the carbon material and ensuring that the support could provide sufficient N sites for the deposition of Pd NPs.This process helped to improve metal dispersion.The increased metal-support interaction,which limits the leaching and coarsening of Pd NPs,improves the stability of the Pd/NCF catalyst.Furthermore,density functional theory calculations indicated that pyridine N stabilized the Pd^(2+)species,significantly inhibiting the loss of Pd^(2+)in Pd/NCF during the reaction process.This work provides a promising avenue towards enhancing the stability of nitrogen-doped carbon-supported metal catalysts.

A Support Data-Based Core-Set Selection Method for Signal Recognition

In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.

Development of multi-functional anchorage support dynamic-static coupling performance test system and its application

In underground engineering with complex conditions,the bolt(cable)anchorage support system is in an environment where static and dynamic stresses coexist,under the action of geological conditions such as high stresses and strong disturbances and construction conditions such as the application of high prestress.It is essential to study the support components performance under dynamic-static coupling conditions.Based on this,a multi-functional anchorage support dynamic-static coupling performance test system(MAC system)is developed,which can achieve 7 types of testing functions,including single component performance,anchored net performance,anchored rock performance and so on.The bolt and cable mechanical tests are conducted by MAC system under different prestress levels.The results showed that compared to the non-prestress condition,the impact resistance performance of prestressed bolts(cables)is significantly reduced.In the prestress range of 50–160 k N,the maximum reduction rate of impact energy resisted by different types of bolts is 53.9%–61.5%compared to non-prestress condition.In the prestress range of 150–300 k N,the impact energy resisted by high-strength cable is reduced by76.8%–84.6%compared to non-prestress condition.The MAC system achieves dynamic-static coupling performance test,which provide an effective means for the design of anchorage support system.

Meter-Scale Thin-Walled Structure with Lattice Infill for Fuel Tank Supporting Component of Satellite:Multiscale Design and Experimental Verification

Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich effect.Such structures can be fabricated bymetallic additive manufacturing technique,such as selective laser melting(SLM).However,the maximum dimensions of actual structures are usually in a sub-meter scale,which results in restrictions on their appliance in aerospace and other fields.In this work,a meter-scale thin-walled structure with lattice infill is designed for the fuel tank supporting component of the satellite by integrating a self-supporting lattice into the thickness optimization of the thin-wall.The designed structure is fabricated by SLM of AlSi10Mg and cold metal transfer welding technique.Quasi-static mechanical tests and vibration tests are both conducted to verify the mechanical strength of the designed large-scale lattice thin-walled structure.The experimental results indicate that themeter-scale thin-walled structure with lattice infill could meet the dimension and lightweight requirements of most spacecrafts.

Nutritional Support under the Neuman Systems Model for Stroke Patients

Objective: To explore nutritional support under the Neuman systems model in treating dysphagia in stroke patients. Methods: In this retrospective study, we enrolled 97 patients with dysphagia after stroke admitted to our hospital, and randomly divided them into the Neuman group (n = 51) given nursing intervention based on Neuman systems model and a control group (n = 46) given routine nursing intervention. Both groups received nutritional support for 3 months. Nutritional indexes (serum total protein, plasma albumin, serum albumin, hemoglobin and transferrin levels) and immune indexes (immunoglobulin (Ig) A, IgG, IgM and total lymphocyte count (TLC) in both groups were recorded and compared. Pulmonary function recovery, video fluoroscopic swallowing study score, water swallowing test score, complication rate, and health knowledge mastery level were also compared between the two groups. Results: After the intervention, the Neuman group showed less decrease in the nutritional and immune index scores (serum total protein, plasma albumin, hemoglobin, serum albumin;IgA, IgG, IgM, and TLC;all P Conclusion: For patients with stroke and dysphagia, comprehensive nursing intervention (e.g., nutritional support) under theNeuman systems model can promote the recovery of immune, swallowing, and pulmonary function, reduce complication incidence and facilitate comprehensive rehabilitation, ensuring adequate nutritional intake.