Associations of sense of coherence with psychological distress and quality of life in inflammatory bowel disease

AIM:To investigate the relationship between sense of coherence,psychological distress and health related quality of life in inflammatory bowel disease(IBD).METHODS:This cross-sectional study enrolled a consecutive sample of 147 IBD(aged 45.1 ± 14.1 years; 57.1% female) patients recruited from a tertiary gastroenterology service.Sixty-four participants met diagnostic criteria for Crohn's disease,while eightythree patients had ulcerative colitis.Socio-demographic data(education,age,race,gender,gross monthly income and marital status),disease-related variables(illness activity,relapse rate in past 2 years,history of surgery and time since diagnosis),sense of coherence(Antonovsky's SOC scale),psychological distress symptoms(Hospital Anxiety and Depression Scale) and health-related quality of life(HRQo L; WHOQOLBref) were assessed.Hierarchical multiple regression analyses were performed to identify factors that are independently associated with psychological distress and HRQo L in patients with IBD and to provide indications for possible moderating or mediating effects.In addition,formal moderation and mediation analyses(Sobel tests) were performed to confirm potential moderators/mediators of the relationship between SOC,psychological distress symptoms and HRQoL.RESULTS:Lower SOC scores(std beta=-0.504; P < 0.001),female gender(std beta = 0.176; P = 0.021) and White race(std beta = 0.164; P = 0.033) were independently associated with higher levels of depressive symptoms,while lower levels of SOC(std beta =-0.438; P < 0.001) and higher relapse rate(std beta = 0.161; P = 0.033) were independently associated with more severe anxiety symptoms.A significant interaction between time since diagnosis and SOC was found with regard to the severity of depressive or anxiety symptoms,as the interaction term(time since diagnosis X SOC) had beta coefficients of-0.191(P = 0.009) and-0.172(P = 0.026),respectively.Lower levels of anxiety symptoms(std beta =-0.369; P < 0.001),higher levels of SOC(std beta = 0.231; P = 0.016) and non-White race(std beta =-0.229; P = 0.006),i.e.,mixed-race,which represented the reference category,were independently associated with higher levels of overall HRQoL.Anxiety symptoms were the most potent independent correlate of most aspects of HRQoL.In addition,anxiety mediated the association between SOC and satisfaction with health,as well as its relationship with physical,mental,and social relations HRQo L.Depressive symptoms also mediated the association between SOC and mental HRQoL.CONCLUSION:Our data indicated that SOC is an important construct,as it influences psychological distress and has significant albeit indirect effects on several HRQoL domains in IBD.

Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques

Pine wilt disease(PWD)has recently caused substantial pine tree losses in Republic of Korea.PWD is considered a severe problem due to the importance of pine trees to Korean people,so this problem must be handled appropriately.Previously,we examined the history of PWD and found that it had already spread to some regions of Republic of Korea;these became our study area.Early detection of PWD is required.We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD.Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees.To differentiate healthy pine trees from those with PWD,we produced a land cover(LC)map from drone images collected from the villages of Anbi and Wonchang by classifying them using two classifier methods,i.e.,artificial neural network(ANN)and support vector machine(SVM).Furthermore,compared the accuracy of two types of Global Positioning System(GPS)data,collected using drone and hand-held devices,for identifying the locations of trees with PWD.We then divided the drone images into six LC classes for each study area and found that the SVM was more accurate than the ANN at classifying trees with PWD.In Anbi,the SVM had an overall accuracy of 94.13%,which is 6.7%higher than the overall accuracy of the ANN,which was 87.43%.We obtained similar results in Wonchang,for which the accuracy of the SVM and ANN was 86.59%and 79.33%,respectively.In terms of the GPS data,we used two type of hand-held GPS device.GPS device 1 is corrected by referring to the benchmarks sited on both locations,while the GPS device 2 is uncorrected device which used the default setting of the GPS only.The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang.However,in Anbi,we obtained better results from GPS device 2 than from GPS device 1.In Anbi,the error in the data from GPS device 1 was 7.08 m,while that of the GPS device 2 data was 0.14 m.In conclusion,both classifiers can distinguish between healthy trees and those with PWD based on LC data.LC data can also be used for other types of classification.There were some differences between the hand-held and drone GPS datasets from both areas.

Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.

Analysis of the Current Situation and Influencing Factors of Psychosocial Adaptation of Hemodialysis Patients

Objective:To investigate the current status and influencing factors of psychosocial adaptation of hemodialysis patients,and to provide a reference basis for the development of nursing interventions.Methods:435 hemodialysis patients from the hemodialysis centers of three tertiary A hospitals in Xi’an City were conveniently selected from April to August 2023,and were investigated using the General Information Questionnaire,the Psychosocial Adaptation to Disease Scale,the Fear of Disease Progression Simplification Scale,and the Personal Sense of Control Scale.Results:The psychosocial adaptation score of hemodialysis patients was(56.68±18.32);the results of multiple linear regression analysis showed that marital status,the form of payment for medical expenses,work status,degree of self-care in daily life,number of co-morbid chronic illnesses,fear of disease progression,and sense of personal mastery were the main influencing factors of psychosocial adaptation of hemodialysis patients.Conclusion:The psychosocial adaptation of hemodialysis patients is at the level of severe maladaptation,and healthcare professionals should formulate scientific and reasonable nursing intervention programs according to their influencing factors to enhance their psychosocial adaptation.

Application of Remote Sensing Technology in Monitoring Forest Diseases and Pests

Forest diseases and pests affect the forest health and forestry production, the monitoring of forest diseases and pests by remote sensing has great advantages and potential. The principles, the technical methods and the main aspects of monitoring forest diseases and pests by remote sensing are described, and the application prospect of this technology is forecasted.

Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery

Background:Pine wilt disease(PWD)is a major ecological concern in China that has caused severe damage to millions of Chinese pines(Pinus tabulaeformis).To control the spread of PWD,it is necessary to develop an effective approach to detect its presence in the early stage of infection.One potential solution is the use of Unmanned Airborne Vehicle(UAV)based hyperspectral images(HIs).UAV-based HIs have high spatial and spectral resolution and can gather data rapidly,potentially enabling the effective monitoring of large forests.Despite this,few studies examine the feasibility of HI data use in assessing the stage and severity of PWD infection in Chinese pine.Method:To fill this gap,we used a Random Forest(RF)algorithm to estimate the stage of PWD infection of trees sampled using UAV-based HI data and ground-based data(data directly collected from trees in the field).We compared relative accuracy of each of these data collection methods.We built our RF model using vegetation indices(VIs),red edge parameters(REPs),moisture indices(MIs),and their combination.Results:We report several key results.For ground data,the model that combined all parameters(OA:80.17%,Kappa:0.73)performed better than VIs(OA:75.21%,Kappa:0.66),REPs(OA:79.34%,Kappa:0.67),and MIs(OA:74.38%,Kappa:0.65)in predicting the PWD stage of individual pine tree infection.REPs had the highest accuracy(OA:80.33%,Kappa:0.58)in distinguishing trees at the early stage of PWD from healthy trees.UAV-based HI data yielded similar results:the model combined VIs,REPs and MIs(OA:74.38%,Kappa:0.66)exhibited the highest accuracy in estimating the PWD stage of sampled trees,and REPs performed best in distinguishing healthy trees from trees at early stage of PWD(OA:71.67%,Kappa:0.40).Conclusion:Overall,our results confirm the validity of using HI data to identify pine trees infected with PWD in its early stage,although its accuracy must be improved before widespread use is practical.We also show UAV-based data PWD classifications are less accurate but comparable to those of ground-based data.We believe that these results can be used to improve preventative measures in the control of PWD.

Remote Monitoring of Wheat Streak Mosaic Progression Using Sub-Pixel Classification of Landsat 5 TM Imagery for Site Specific Disease Management in Winter Wheat

Wheat streak mosaic (WSM), caused by Wheat streak mosaic virus is a viral disease that affects wheat (Triticum aestivum L.), other grains, and numerous grasses over large geographical areas around the world. To improve disease management and crop production, it is essential to have adequate methods for monitoring disease epidemics at various scales and multiple times. Remote sensing has become an essential tool for monitoring and quantifying crop stress due to biotic and abiotic factors. The objective of our study was to explore the utility of Landsat 5 TM imagery for detecting, quantifying, and mapping the occurrence of WSM in irrigated commercial wheat fields. The infection and progression of WSM was biweekly assessed in the Texas Panhandle during the 2007-2008 crop years. Diseased-wheat was separated from uninfected wheat on the images using a sub-pixel classifier. The overall classification accuracies were >91% with kappa coefficient between 0.80 and 0.94 for disease detection were achieved. Omission errors varied between 2% and 14%, while commission errors ranged from 1% to 21%. These results indicate that the TM image can be used to accurately detect and quantify disease for site-specific WSM management. Remote detection of WSM using geospatial imagery may substantially improve monitoring, planning, and management practices by overcoming some of the shortcomings of the ground-based surveys such as observer bias and inaccessibility. Remote sensing techniques for accurate disease mapping offer a unique set of advantages including repeatability, large area coverage, and cost-effectiveness over the ground-based methods. Hence, remote detection is particularly and practically critical for repeated disease mo- nitoring and mapping over time and space during the course of a growing season.

Characterizing and Estimating Fungal Disease Severity of Rice Brown Spot with Hyperspectral Reflectance Data

Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for detecting disease stress in green vegetation at the leaf and canopy levels. In this study, hyperspectral reflectances of rice in the laboratory and field were measured to characterize the spectral regions and wavebands, which were the most sensitive to rice brown spot infected by Bipolaris oryzae （Helminthosporium oryzae Breda. de Hann）. Leaf reflectance increased at the ranges of 450 to 500 nm and 630 to 680 nm with the increasing percentage of infected leaf surface, and decreased at the ranges of 520 to 580 nm, 760 to 790 nm, 1550 to 1750 nm, and 2080 to 2350 nm with the increasing percentage of infected leaf surface respectively. The sensitivity analysis and derivative technique were used to select the sensitive wavebands for the detection of rice brown spot infected by B. oryzae. Ratios of rice leaf reflectance were evaluated as indicators of brown spot. R669/R746 （the reflectance at 669 nm divided by the reflectance at 746 nm, the following ratios may be deduced by analogy）, R702/R718, R692/R530, R692/R732, R535/R746, R521/R718, and R569/R718 increased significantly as the incidence of rice brown spot increased regardless of whether it＇s at the leaf or canopy level. R702/R718, R692/R530, R692/R732 were the best three ratios for estimating the disease severity of rice brown spot at the leaf and canopy levels. This result not only confirms the capability of hyperspectral remote sensing data in characterizing crop disease for precision pest management in the real world, but also testifies that the ratios of crop reflectance is a useful method to estimate crop disease severity.

Spectroscopic detection of forest diseases:a review(1970–2020)

Sustainable forest management is essential to confront the detrimental impacts of diseases on forest ecosystems.This review highlights the potential of vegetation spectroscopy in improving the feasibility of assessing forest disturbances induced by diseases in a timely and cost-effective manner.The basic concepts of vegetation spectroscopy and its application in phytopathology are first outlined then the literature on the topic is discussed.Using several optical sensors from leaf to landscape-level,a number of forest diseases characterized by variable pathogenic processes have been detected,identified and quantified in many country sites worldwide.Overall,these reviewed studies have pointed out the green and red regions of the visible spectrum,the red-edge and the early near-infrared as the spectral regions most sensitive to the disease development as they are mostly related to chlorophyll changes and symptom development.Late disease conditions particularly affect the shortwave-infrared region,mostly related to water content.This review also highlights some major issues to be addressed such as the need to explore other major forest diseases and geographic areas,to further develop hyperspectral sensors for early detection and discrimination of forest disturbances,to improve devices for remote sensing,to implement longterm monitoring,and to advance algorithms for exploitation of spectral data.Achieving of these goals will enhance the capability of vegetation spectroscopy in early detection of forest stress and in managing forest diseases.

Surveillance of pine wilt disease by high resolution satellite

Pine wilt disease caused by the pinewood nematode Bursaphelenchus xylophilus has led to the death of a large number of pine trees in China.This destructive disease has the characteristics of bring wide-spread,fast onset,and long incubation time.Most importantly,in China,the fatality rate in pines is as high as 100%.The key to reducing this mortality is how to quickly find the infected trees.We proposed a method of automatically identifying infected trees by a convolution neural network and bounding box tool.This method rapidly locates the infected area by classifying and recognizing remote sensing images obtained by high resolution earth observation Satellite.The recognition accuracy of the test data set was 99.4%,and the remote sensing image combined with convolution neural network algorithm can identify and determine the distribution of the infected trees.It can provide strong technical support for the prevention and control of pine wilt disease.

The genetic framework for development of nephrolithiasis

Over 1%-15% of the population worldwide is affected by nephrolithiasis,which remains the most common and costly disease that urologists manage today.Identification of atrisk individuals remains a theoretical and technological challenge.The search for monogenic causes of stone disease has been largely unfruitful and a technological challenge;however,several candidate genes have been implicated in the development of nephrolithiasis.In this review,we will review current data on the genetic inheritance of stone disease,as well as investigate the evolving role of genetic analysis and counseling in the management of nephrolithiasis.

Nerve growth factor in muscle afferent neurons of peripheral artery disease and autonomic function

In peripheral artery disease patients,the blood supply directed to the lower limbs is reduced.This results in severe limb ischemia and thereby enhances pain sensitivity in lower limbs.The painful perception is induced and exaggerate during walking,and is relieved by rest.This symptom is termed by intermittent claudication.The limb ischemia also amplifies autonomic responses during exercise.In the process of pain and autonomic responses originating exercising muscle,a number of receptors in afferent nerves sense ischemic changes and send signals to the central nervous system leading to autonomic responses.This review integrates recent study results in terms of perspectives including how nerve growth factor affects muscle sensory nerve receptors in peripheral artery disease and thereby alters responses of sympathetic nerve activity and blood pressure to active muscle.For the sensory nerve receptors,we emphasize the role played by transient receptor potential vanilloid type 1,purinergic P2X purinoceptor 3 and acid sensing ion channel subtype 3 in amplified sympathetic nerve activity responses in peripheral artery disease.

A spatio-temporal multi-scale fusion algorithm for pine wood nematode disease tree detection

Pine wood nematode infection is a devastating disease.Unmanned aerial vehicle(UAV)remote sensing enables timely and precise monitoring.However,UAV aerial images are challenged by small target size and complex sur-face backgrounds which hinder their effectiveness in moni-toring.To address these challenges,based on the analysis and optimization of UAV remote sensing images,this study developed a spatio-temporal multi-scale fusion algorithm for disease detection.The multi-head,self-attention mechanism is incorporated to address the issue of excessive features generated by complex surface backgrounds in UAV images.This enables adaptive feature control to suppress redundant information and boost the model’s feature extraction capa-bilities.The SPD-Conv module was introduced to address the problem of loss of small target feature information dur-ing feature extraction,enhancing the preservation of key features.Additionally,the gather-and-distribute mechanism was implemented to augment the model’s multi-scale feature fusion capacity,preventing the loss of local details during fusion and enriching small target feature information.This study established a dataset of pine wood nematode disease in the Huangshan area using DJI(DJ-Innovations)UAVs.The results show that the accuracy of the proposed model with spatio-temporal multi-scale fusion reached 78.5%,6.6%higher than that of the benchmark model.Building upon the timeliness and flexibility of UAV remote sensing,the pro-posed model effectively addressed the challenges of detect-ing small and medium-size targets in complex backgrounds,thereby enhancing the detection efficiency for pine wood nematode disease.This facilitates early preemptive preser-vation of diseased trees,augments the overall monitoring proficiency of pine wood nematode diseases,and supplies technical aid for proficient monitoring.

Noninvasive Detection of Cerebral Blood Flow and Blood Oxygen Based on Inductive Sensing Technology and Near Infrared Spectroscopy

The synchronous monitoring of cerebral blood flow and blood oxygen levels plays a pivotal role in the prevention,diagnosis,and treatment of cerebrovascular diseases.This study introduces a novel noninvasive device utilizing inductive sensing and near-infrared spectroscopy technology to facilitate simultaneous monitoring of cerebral blood flow and blood oxygen levels.The device consists of modules for cerebral blood flow monitoring,cerebral blood oxygen monitoring,control,communication,and a host machine.Through experiments conducted on healthy subjects,it was confirmed that the device can effectively achieve synchronous monitoring and recording of cerebral blood flow and blood oxygen signals.The results demonstrate the device’s capability to accurately measure these signals simultaneously.This technology enables dynamic monitoring of cerebral blood flow and blood oxygen signals with potential clinical applications in preventing,diagnosing,treating cerebrovascular diseases while reducing their associated harm.

高分辨率压缩感知TOF-MRA在烟雾病分期及出血相关血管评估中的临床价值

目的 探讨高分辨率压缩感知(compressed sensing, CS)时间飞跃法磁共振血管造影(time-of-fight magnetic resonance angiography, TOF-MRA)在烟雾病(moyamoya disease, MMD)分期及出血相关血管评估中的应用价值。材料与方法 回顾性分析同时行CS TOF-MRA和CT血管成像(computed tomography angiography, CTA)的MMD患者资料21例,两次检查时间间隔不超过2周。CS TOF-MRA扫描时间5 min 4 s,重建体素大小0.4 mm×0.4 mm×0.4 mm。两名放射科医生分别在CS TOF-MRA和CTA上对Suzuki分期、烟雾状血管(moyamoya vessel, MMV)、脉络膜前动脉(anterior choroidal artery, AChA)及后交通动脉(posterior communicating artery, PComA)的显示能力进行评价,并以单侧大脑半球为单位对AChA、PComA有无扩张进行分级评估。采用配对样本Wilcoxon秩和检验,将CS TOF-MRA及CTA评估结果进行对比分析。结果 研究共纳入37个大脑半球(5例单侧MMD,16例双侧MMD)。CS TOF-MRA对Suzuki分期评价能力与CTA相当(Z=-1.000,P>0.05),对MMV、AChA及PComA的显示能力均优于CTA(P值均<0.05)。CS TOF-MRA对AChA、PComA扩张分级评估能力与CTA相当(P值均<0.05)。结论 在临床合理的扫描时间内,高分辨率CS TOF-MRA序列对MMD出血相关血管的显示优于CTA,Suzuki分期和血管扩张分级评估与之相当,可作为MMD患者随访复查优先选择的影像学检查方法,具有较高的临床应用价值。

Gossip in the gut: Quorum sensing, a new player in the hostmicrobiota interactions

Bacteria are known to communicate with each other and regulate their activities in social networks by secreting and sensing signaling molecules called autoinducers,a process known as quorum sensing(QS).This is a growing area of research in which we are expanding our understanding of how bacteria collectively modify their behavior but are also involved in the crosstalk between the host and gut microbiome.This is particularly relevant in the case of pathologies associated with dysbiosis or disorders of the intestinal ecosystem.This review will examine the different QS systems and the evidence for their presence in the intestinal ecosystem.We will also provide clues on the role of QS molecules that may exert,directly or indirectly through their bacterial gossip,an influence on intestinal epithelial barrier function,intestinal inflammation,and intestinal carcinogenesis.This review aims to provide evidence on the role of QS molecules in gut physiology and the potential shared by this new player.Better understanding the impact of intestinal bacterial social networks and ultimately developing new therapeutic strategies to control intestinal disorders remains a challenge that needs to be addressed in the future.

Antagonizing amyloid-β/calcium-sensing receptor signaling in human astrocytes and neurons: a key to halt Alzheimer's disease progression?

Astrocytes＇ roles in late-onset Alzheimer＇s disease （LOAD） promotion are important, since they survive soluble or fibrillar amyloid-β peptides （Aβs） neurotoxic effects, undergo alterations of intracellular and intercellular Ca2＋ signaling and gliotransmitters release via the Aβ/a7-nAChR （αT-nicotinic acetylcholine receptor） signaling, and overproduce/oversecrete newly synthesized Aβ42 oligomers, NO, and VEGF-A via the Aβ/CaSR （calcium-sensing receptor） signaling. Recently, it was suggested that the NMDAR （N-methyl-D-aspartate receptor） inhibitor nitromemantine would block the synapse-destroying effects of Aβ/α7-nAChR signaling. Yet, this and the progressive extracellular accrual and spreading of Aβ42 oligomers would be stopped well upstream by NPS 2143, an allosteric CaSR antagonist （calcilytic）.

Conical Nanoparticles for Blood Disease Detection

Metallic nanoparticles play an important role in the design of sensing platforms. In this paper, a new electromagnetic study for conical metal nanoparticles, working in the Near Infrared and Visible frequency regime, is proposed. The structures consist of inclusions, arranged in an array configuration, embedded in a dielectric environment. The aim of this work is to develop new analytical models, in order to describe the nanoparticles electromagnetic behavior in terms of extinction cross-section (absorption and scattering). The closed-form formulas link the conical nanoparticles geometrical and electromagnetic parameters to their resonant frequency properties in terms of wavelength position, magnitude and bandwidth. The proposed models are compared to the numerical results and to the experimental ones, reported in literature. Good agreement is obtained. The proposed analytical formulas represent useful tools for sensing applications. For this reason, exploiting such models a new sensing platform able to detect different blood diseases is obtained. Numerical results confirm the capability of the proposed structure to be used as a sensing platform for medical diagnostics.

Optimized Three-Dimensional Cardiovascular Magnetic Resonance Whole Heart Imaging Utilizing Non-Selective Excitation and Compressed Sensing in Children and Adults with Congenital Heart Disease

Background:In congenital heart disease(CHD)patients,detailed three-dimensional anatomy depiction plays a pivotal role for diagnosis and therapeutical decision making.Hence,the present study investigated the applicability of an advanced cardiovascular magnetic resonance(CMR)whole heart imaging approach utilizing nonselective excitation and compressed sensing for anatomical assessment and interventional guidance of CHD patients in comparison to conventional dynamic CMR angiography.Methods:86 consecutive pediatric patients and adults with congenital heart disease(age,1 to 74 years;mean,35 years)underwent CMR imaging including a freebreathing,ECG-triggered 3D nonselective SSFP whole heart acquisition using compressed SENSE(nsWHcs).Anatomical assessability and signal intensity ratio(SIR)measurements were compared with conventional dynamic 3D-/4D-MR angiography.Results:The most frequent diagnoses were partial anomalous pulmonary venous drainage(17/86,20%),transposition of the great arteries(15/86,17%),tetralogy of Fallot(12/86,14%),and a single ventricle(7/86,8%).Image quality of nsWHcs was rated as excellent/good in 98%of patients.nsWHcs resulted in a reliable depiction of all large thoracic vessels(anatomic assessability,99%–100%)and the proximal segments of coronary arteries and coronary sinus(>90%).nsWHcs achieved a homogenously distributed SIR in all cardiac cavities and thoracic vessels without a significant difference between pulmonary and systemic circulation(10.9±3.5 and 10.6±3.4;p=0.15),while 3D angiography showed significantly increased SIR for targeted vs.non-targeted circulation(PA-angiography,15.2±8.1 vs.5.8±3.6,p<0.001;PV-angiography,7.0±3.9 vs.17.3±6.8,p<0.001).Conclusions:The proposed nsWHcs imaging approach provided a consistently high image quality and a homogeneous signal intensity distribution within the pulmonary and systemic circulation in pediatric patients and adults with a wide spectrum of congenital heart diseases.nsWHcs enabled detailed anatomical assessment and three-dimensional reconstruction of all cardiac cavities and large thoracic vessels and can be regarded particularly useful for preprocedural planning and interventional guidance in CHD patients.

疾病认知对老年高血压患者饮食依从性的影响及调节机制分析

目的:探索疾病认知对老年高血压患者饮食依从性的影响,以及影响过程中学习意愿和医患信任的中介作用和自我效能感的调节作用。方法:采用自编郑州市老年高血压群体健康管理调查问卷对郑州市辖区内的1 290例老年高血压患者进行调查。采用Mplus程序对相关变量构建回归模型,并进行中介效应和调节效应检验。结果:疾病认知可以正向影响老年高血压群体的饮食依从性,而且学习意愿和医患信任在其间发挥中介作用[β医患信任(95%CI)=0.127(0.041~0.213),β学习意愿(95%CI)=0.112(0.036~0.190),P<0.05],中介效应值均为0.021。除此之外,自我效能感可以调节疾病认知对老年高血压患者饮食依从性的影响[β(95%CI)=0.076(0.006~0.146),P=0.012]。结论:疾病认知可通过学习意愿和医患信任两个中介变量影响老年高血压患者的饮食依从性,且自我效能感具有一定的调节作用。