Serum cystatin C,monocyte/high-density lipoprotein-C ratio,and uric acid for the diagnosis of coronary heart disease and heart failure

BACKGROUND Coronary heart disease(CHD)and heart failure(HF)are the major causes of morbidity and mortality worldwide.Early and accurate diagnoses of CHD and HF are essential for optimal management and prognosis.However,conventional diagnostic methods such as electrocardiography,echocardiography,and cardiac biomarkers have certain limitations,such as low sensitivity,specificity,availability,and cost-effectiveness.Therefore,there is a need for simple,noninvasive,and reliable biomarkers to diagnose CHD and HF.AIM To investigate serum cystatin C(Cys-C),monocyte/high-density lipoprotein cholesterol ratio(MHR),and uric acid(UA)diagnostic values for CHD and HF.METHODS We enrolled 80 patients with suspected CHD or HF who were admitted to our hospital between July 2022 and July 2023.The patients were divided into CHD(n=20),HF(n=20),CHD+HF(n=20),and control groups(n=20).The serum levels of Cys-C,MHR,and UA were measured using immunonephelometry and an enzymatic method,respectively,and the diagnostic values for CHD and HF were evaluated using receiver operating characteristic(ROC)curve analysis.RESULTS Serum levels of Cys-C,MHR,and UA were significantly higher in the CHD,HF,and CHD+HF groups than those in the control group.The serum levels of Cys-C,MHR,and UA were significantly higher in the CHD+HF group than those in the CHD or HF group.The ROC curve analysis showed that serum Cys-C,MHR,and UA had good diagnostic performance for CHD and HF,with areas under the curve ranging from 0.78 to 0.93.The optimal cutoff values of serum Cys-C,MHR,and UA for diagnosing CHD,HF,and CHD+HF were 1.2 mg/L,0.9×10^(9),and 389μmol/L;1.4 mg/L,1.0×10^(9),and 449μmol/L;and 1.6 mg/L,1.1×10^(9),and 508μmol/L,respectively.CONCLUSION Serum Cys-C,MHR,and UA are useful biomarkers for diagnosing CHD and HF,and CHD+HF.These can provide information for decision-making and risk stratification in patients with CHD and HF.

Light-Activated Virtual Sensor Array with Machine Learning for Non-Invasive Diagnosis of Coronary Heart Disease

Early non-invasive diagnosis of coronary heart disease(CHD)is critical.However,it is challenging to achieve accurate CHD diagnosis via detecting breath.In this work,heterostructured complexes of black phosphorus(BP)and two-dimensional carbide and nitride(MXene)with high gas sensitivity and photo responsiveness were formulated using a self-assembly strategy.A light-activated virtual sensor array(LAVSA)based on BP/Ti_(3)C_(2)Tx was prepared under photomodulation and further assembled into an instant gas sensing platform(IGSP).In addition,a machine learning(ML)algorithm was introduced to help the IGSP detect and recognize the signals of breath samples to diagnose CHD.Due to the synergistic effect of BP and Ti_(3)C_(2)Tx as well as photo excitation,the synthesized heterostructured complexes exhibited higher performance than pristine Ti_(3)C_(2)Tx,with a response value 26%higher than that of pristine Ti_(3)C_(2)Tx.In addition,with the help of a pattern recognition algorithm,LAVSA successfully detected and identified 15 odor molecules affiliated with alcohols,ketones,aldehydes,esters,and acids.Meanwhile,with the assistance of ML,the IGSP achieved 69.2%accuracy in detecting the breath odor of 45 volunteers from healthy people and CHD patients.In conclusion,an immediate,low-cost,and accurate prototype was designed and fabricated for the noninvasive diagnosis of CHD,which provided a generalized solution for diagnosing other diseases and other more complex application scenarios.

Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic methods.Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality images.Additionally,these methods often require significant time and expertise,making them less accessible in resource-limited settings.Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare costs.This study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant features.Classification is performed using Optimized TCGN.This approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health monitoring.The proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality issues.These findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

Detachable string magnetically controlled capsule endoscopy for the noninvasive diagnosis of esophageal diseases:A prospective,blind clinical study

BACKGROUND Traditional esophagogastroduodenoscopy(EGD),an invasive examination method,can cause discomfort and pain in patients.In contrast,magnetically controlled capsule endoscopy(MCE),a noninvasive method,is being applied for the detection of stomach and small intestinal diseases,but its application in treating esophageal diseases is not widespread.AIM To evaluate the safety and efficacy of detachable string MCE(ds-MCE)for the diagnosis of esophageal diseases.METHODS Fifty patients who had been diagnosed with esophageal diseases were pros-pectively recruited for this clinical study and underwent ds-MCE and conven-tional EGD.The primary endpoints included the sensitivity,specificity,positive predictive value,negative predictive value,and diagnostic accuracy of ds-MCE for patients with esophageal diseases.The secondary endpoints consisted of visualizing the esophageal and dentate lines,as well as the subjects'tolerance of the procedure.RESULTS Using EGD as the gold standard,the sensitivity,specificity,positive predictive value,negative predictive value,and diagnostic accuracy of ds-MCE for esophageal disease detection were 85.71%,86.21%,81.82%,89.29%,and 86%,respectively.ds-MCE was more comfortable and convenient than EGD was,with 80%of patients feeling that ds-MCE examination was very comfortable or comfortable and 50%of patients believing that detachable string v examination was very convenient.CONCLUSION This study revealed that ds-MCE has the same diagnostic effects as traditional EGD for esophageal diseases and is more comfortable and convenient than EGD,providing a novel noninvasive method for treating esophageal diseases.

Association of age at diagnosis of diabetes with subsequent risk of age-related ocular diseases and vision acuity

BACKGROUND The importance of age on the development of ocular conditions has been reported by numerous studies.Diabetes may have different associations with different stages of ocular conditions,and the duration of diabetes may affect the development of diabetic eye disease.While there is a dose-response relationship between the age at diagnosis of diabetes and the risk of cardiovascular disease and mortality,whether the age at diagnosis of diabetes is associated with incident ocular conditions remains to be explored.It is unclear which types of diabetes are more predictive of ocular conditions.AIM To examine associations between the age of diabetes diagnosis and the incidence of cataract,glaucoma,age-related macular degeneration(AMD),and vision acuity.METHODS Our analysis was using the UK Biobank.The cohort included 8709 diabetic participants and 17418 controls for ocular condition analysis,and 6689 diabetic participants and 13378 controls for vision analysis.Ocular diseases were identified using inpatient records until January 2021.Vision acuity was assessed using a chart.RESULTS During a median follow-up of 11.0 years,3874,665,and 616 new cases of cataract,glaucoma,and AMD,respectively,were identified.A stronger association between diabetes and incident ocular conditions was observed where diabetes was diagnosed at a younger age.Individuals with type 2 diabetes(T2D)diagnosed at<45 years[HR(95%CI):2.71(1.49-4.93)],45-49 years[2.57(1.17-5.65)],50-54 years[1.85(1.13-3.04)],or 50-59 years of age[1.53(1.00-2.34)]had a higher risk of AMD independent of glycated haemoglobin.T2D diagnosed<45 years[HR(95%CI):2.18(1.71-2.79)],45-49 years[1.54(1.19-2.01)],50-54 years[1.60(1.31-1.96)],or 55-59 years of age[1.21(1.02-1.43)]was associated with an increased cataract risk.T2D diagnosed<45 years of age only was associated with an increased risk of glaucoma[HR(95%CI):1.76(1.00-3.12)].HRs(95%CIs)for AMD,cataract,and glaucoma associated with type 1 diabetes(T1D)were 4.12(1.99-8.53),2.95(2.17-4.02),and 2.40(1.09-5.31),respectively.In multivariable-adjusted analysis,individuals with T2D diagnosed<45 years of age[β95%CI:0.025(0.009,0.040)]had a larger increase in LogMAR.Theβ(95%CI)for LogMAR associated with T1D was 0.044(0.014,0.073).CONCLUSION The younger age at the diagnosis of diabetes is associated with a larger relative risk of incident ocular diseases and greater vision loss.

Application of artificial intelligence in the diagnosis and treatment of Kawasaki disease

This editorial provides commentary on an article titled"Potential and limitationsof ChatGPT and generative artificial intelligence(AI)in medical safety education"recently published in the World Journal of Clinical Cases.AI has enormous potentialfor various applications in the field of Kawasaki disease(KD).One is machinelearning(ML)to assist in the diagnosis of KD,and clinical prediction models havebeen constructed worldwide using ML;the second is using a gene signalcalculation toolbox to identify KD,which can be used to monitor key clinicalfeatures and laboratory parameters of disease severity;and the third is using deeplearning(DL)to assist in cardiac ultrasound detection.The performance of the DLalgorithm is similar to that of experienced cardiac experts in detecting coronaryartery lesions to promoting the diagnosis of KD.To effectively utilize AI in thediagnosis and treatment process of KD,it is crucial to improve the accuracy of AIdecision-making using more medical data,while addressing issues related topatient personal information protection and AI decision-making responsibility.AIprogress is expected to provide patients with accurate and effective medicalservices that will positively impact the diagnosis and treatment of KD in thefuture.

Delayed diagnosis in inflammatory bowel disease:Time to consider solutions

In this editorial,we discuss a recently published manuscript by Blüthner et al in the World Journal of Gastroenterology,with a specific focus on the delayed diagnosis of inflammatory bowel disease(IBD).IBD,which includes Crohn's disease and ulcerative colitis,is a chronic intestinal disorder.A time lag may exist between the onset of inflammation and the appearance of signs and symptoms,potentially leading to an incorrect or delayed diagnosis,a situation referred to as the delayed diagnosis of IBD.Early diagnosis is crucial for effective patient treatment and prognosis,yet delayed diagnosis remains common.The reasons for delayed diagnosis of IBD are numerous and not yet fully understood.One key factor is the nonspecific nature of IBD symptoms,which can easily be mistaken for other conditions.Additionally,the lack of specific diagnostic methods for IBD contributes to these delays.Delayed diagnosis of IBD can result in numerous adverse consequences,including increased intestinal damage,fibrosis,a higher risk of colorectal cancer,and a decrease in the quality of life of the patient.Therefore,it is essential to diagnose IBD promptly by raising physician awareness,enhancing patient education,and developing new diagnostic methods.

Aggravation of Cancer,Heart Diseases and Diabetes Subsequent to COVID-19 Lockdown via Mathematical Modeling

The global populationhas beenandwill continue to be severely impacted by theCOVID-19 epidemic.The primary objective of this research is to demonstrate the future impact of COVID-19 on those who suffer from other fatal conditions such as cancer,heart disease,and diabetes.Here,using ordinary differential equations(ODEs),two mathematical models are developed to explain the association between COVID-19 and cancer and between COVID-19 and diabetes and heart disease.After that,we highlight the stability assessments that can be applied to these models.Sensitivity analysis is used to examine how changes in certain factors impact different aspects of disease.The sensitivity analysis showed that many people are still nervous about seeing a doctor due to COVID-19,which could result in a dramatic increase in the diagnosis of various ailments in the years to come.The correlation between diabetes and cardiovascular illness is also illustrated graphically.The effects of smoking and obesity are also found to be significant in disease compartments.Model fitting is also provided for interpreting the relationship between real data and the results of thiswork.Diabetic people,in particular,need tomonitor their health conditions closely and practice heart health maintenance.People with heart diseases should undergo regular checks so that they can protect themselves from diabetes and take some precautions including suitable diets.The main purpose of this study is to emphasize the importance of regular checks,to warn people about the effects of COVID-19(including avoiding healthcare centers and doctors because of the spread of infectious diseases)and to indicate the importance of family history of cancer,heart diseases and diabetes.The provision of the recommendations requires an increase in public consciousness.

Physician-dependent diagnosis delay in Crohn's disease:A pseudoproposition or not?

The challenge of diagnosis delay in inflammatory bowel disease(IBD)has emerged as a significant concern for both patients and healthcare professionals.The widely accepted notion that there is an extended time frame from the onset of symptoms to the definitive diagnosis is often attributed to the heterogeneity of IBD and the non-specificity of clinical manifestations.Specific to patients with Crohn’s disease,the issue of delayed diagnosis appears to be more pronounced across different regions globally.The intricate interplay of real-world factors has led to debates regarding the primary contributors to these diagnostic delays.Drawing a comparison solely between patients and physicians and implicating the latter as the predominant influence factor may fall into a simplistic either-or logical trap that may obscure the truth.This letter,grounded in published evidence,explores areas for improvement in a forthcoming paper within the field,hoping to pinpoint the culprit behind the diagnosis delay issue for IBD patients rather than simply attributing it to so-called“physician-dependent factors”.Our objective is to motivate healthcare providers and policymakers in relevant fields to reflect on strategies for addressing this problem to reduce diagnostic delays and enhance patient outcomes.

Differential diagnosis of Crohn’s disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning

BACKGROUND Crohn’s disease(CD)is often misdiagnosed as intestinal tuberculosis(ITB).However,the treatment and prognosis of these two diseases are dramatically different.Therefore,it is important to develop a method to identify CD and ITB with high accuracy,specificity,and speed.AIM To develop a method to identify CD and ITB with high accuracy,specificity,and speed.METHODS A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB.Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis.RESULTS The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm^(-1) and 1234 cm^(-1) bands,and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy,specificity,and sensitivity of 91.84%,92.59%,and 90.90%,respectively,for the differential diagnosis of CD and ITB.CONCLUSION Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level,and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.

DNA Methylation Variation Is Identified in Monozygotic Twins Discordant for Congenital Heart Diseases

Aims:Multiple genes and environmental factors are known to be involved in congenital heart disease(CHD),but epigenetic variation has received little attention.Monozygotic(MZ)twins with CHD provide a unique model for exploring this phenomenon.In order to investigate the potential role of Deoxyribonucleic Acid(DNA)methyla-tion in CHD pathogenesis,the present study examined DNA methylation variation in MZ twins discordant for CHD,especially ventricular septal defect(VSD).Methods and Results:Using genome-wide DNA methylation profiles,we identified 4004 differentially methylated regions(DMRs)in 18 MZ twin pairs discordant for CHD,and 2826 genes were identified.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)analysis revealed a list of CHD-associated pathways.To further investigate the role of DNA methylation in VSD,data from 7 pairs of MZ twins with VSD were analyzed.We identified 1614 DMRs corresponding to 1443 genes associated with arrhythmogenic right ventricular cardiomyopathy,cyclic guanosine monopho-sphate-protein kinase G(cGMP-PKG)signaling pathway by KEGG analysis,and cell-cell adhesion,calcium ion transmembrane transport by GO analysis.A proportion of DMR-associated genes were involved in calcium signaling pathways.The methylation changes of calcium signaling genes might be related to VSD pathogenesis.Conclusion:CHD is associated with differential DNA methylation in MZ twins.CHD may be etiologically linked to DNA methylation,and methylation of calcium signaling genes may be involved in the development of VSD.

Soluble ST2:A Novel Biomarker for Diagnosis and Prognosis of Cardiovascular Disease

The increasing incidence of cardiovascular disease(CVD)is a significant global health concern,affecting millions of individuals each year.Accurate diagnosis of acute CVD poses a formidable challenge,as misdiagnosis can significantly decrease patient survival rates.Traditional biomarkers have played a vital role in the diagnosis and prognosis of CVDs,but they can be influenced by various factors,such as age,sex,and renal function.Soluble ST2(sST2)is a novel biomarker that is closely associated with different CVDs.Its low reference change value makes it suitable for continuous measurement,unaffected by age,kidney function,and other confounding factors,facilitating risk stratification of CVDs.Furthermore,the combination of sST2 with other biomarkers can enhance diagnostic accuracy and prognostic value.This review aims to provide a comprehensive overview of sST2,focusing on its diagnostic and prognostic value as a myocardial marker for different types of CVDs and discussing the current limitations of sST2.

Femoral Access with Ultrasound-Guided Puncture and Z-Stitch Hemostasis for Adults with Congenital Heart Diseases Undergoing Electrophysiological Procedures

Aims:Although the application of ultrasound-guided vascular puncture and Z-stitch hemostasis to manage femoral access has been widely utilized,there is limited data on this combined application in adult congenital heart disease(ACHD)patients undergoing electrophysiological(EP)procedures.We sought to evaluate the safety and efficacy of ultrasound-guided puncture and postprocedural Z-stitch hemostasis for ACHD patients under-going EP procedures.Methods and Results:The population of ACHD patients undergoing transfemoral EP pro-cedures at the University of Zurich Heart Center between January 2019 and December 2022 was observed and analyzed.During the study period,femoral access(left/right,arterial/venous)was performed under real-time ultrasound guidance.At the end of the procedure,a single Z-stitch was performed at the puncture site.We eval-uated the incidence of in-hospital complications associated with femoral access puncture in this population.Among 101 patients who had a total of 147 previous ipsilateral vascular punctures(mean 1.5 per person),100 patients underwent successful femoral vascular access for EP procedures.The median age of the patients was 47±15 years and 34(34%)were male.Z-stitches were performed after the procedure in 100 patients with 303 femoral vascular accesses(mean 3 punctures per person).No patient developed vascular puncture relevant inguinal hematoma,pseudo aneurysm,arteriovenousfistula,venous or arterial thrombosis.Conclusion:In ACHD patients undergoing EP procedures,optimal femoral access management can be achieved with ultra-sound-guided puncture and postprocedural Z-stitch hemostasis.

Mean nocturnal baseline impedance in gastro-esophageal reflux disease diagnosis:Should we strictly follow the Lyon 2 Consensus?

Clinical practice guidelines drive clinical practice and clinicians rely to them when trying to answer their most common questions.One of the most important position papers in the field of gastro-esophageal reflux disease(GERD)is the one produced by the Lyon Consensus.Recently an updated second version has been released.Mean nocturnal baseline impedance(MNBI)was proposed by the first Consensus to act as supportive evidence for GERD diagnosis.Originally a cut-off of 2292 Ohms was proposed,a value revised in the second edition.The updated Consensus recommended that an MNBI<1500 Ohms strongly suggests GERD while a value>2500 Ohms can be used to refute GERD.The proposed cut-offs move in the correct direction by diminishing the original cut-off,nevertheless they arise from a study of normal subjects where cut-offs were provided by measuring the mean value±2SD and not in symptomatic patients.However,data exist that even symptomatic patients with inconclusive disease or reflux hypersensitivity(RH)show lower MNBI values in comparison to normal subjects or patients with functional heartburn(FH).Moreover,according to the data,MNBI,even among symptomatic patients,is affected by age and body mass index.Also,various studies have proposed different cut-offs by using receiver operating characteristic curve analysis even lower than the one proposed.Finally,no information is given for patients submitted to on-proton pump inhibitors pH-impedance studies even if new and extremely important data now exist.Therefore,even if MNBI is an extremely important tool when trying to approach patients with reflux symptoms and could distinguish conclusive GERD from RH or FH,its values should be interpreted with caution.

Association of autoimmune thyroid disease with type 1 diabetes mellitus and its ultrasonic diagnosis and management

As a common hyperglycemic disease,type 1 diabetes mellitus(T1DM)is a complicated disorder that requires a lifelong insulin supply due to the immunemediated destruction of pancreaticβcells.Although it is an organ-specific autoimmune disorder,T1DM is often associated with multiple other autoimmune disorders.The most prevalent concomitant autoimmune disorder occurring in T1DM is autoimmune thyroid disease(AITD),which mainly exhibits two extremes of phenotypes:hyperthyroidism[Graves'disease(GD)]and hypothyroidism[Hashimoto's thyroiditis,(HT)].However,the presence of comorbid AITD may negatively affect metabolic management in T1DM patients and thereby may increase the risk for potential diabetes-related complications.Thus,routine screening of thyroid function has been recommended when T1DM is diagnosed.Here,first,we summarize current knowledge regarding the etiology and pathogenesis mechanisms of both diseases.Subsequently,an updated review of the association between T1DM and AITD is offered.Finally,we provide a relatively detailed review focusing on the application of thyroid ultrasonography in diagnosing and managing HT and GD,suggesting its critical role in the timely and accurate diagnosis of AITD in T1DM.

Intestinal Behçet's disease: A review of clinical diagnosis and treatment

Behçet's disease(BD)is a chronic inflammatory disorder prone to frequent re-currences,with a high predilection for intestinal involvement.However,the ef-ficacy and long-term effects of surgical treatment for intestinal BD are unknown.In the current issue of World J Gastrointest Surg,Park et al conducted a retrospec-tive analysis of 31 patients with intestinal BD who received surgical treatment.They found that elevated C-reactive protein levels and emergency surgery were poor prognostic factors for postoperative recurrence,emphasizing the adverse impact of severe inflammation on the prognosis of patients with intestinal BD.This work has clinical significance for evaluating the postoperative condition of intestinal BD.The editorial attempts to summarize the clinical diagnosis and treatment of intestinal BD,focusing on the impact of adverse factors on surgical outcomes.We hope this review will facilitate more precise postoperative management of patients with intestinal BD by clinicians.

Exploring kidney biopsy findings in congenital heart diseases:Insights beyond cyanotic nephropathy

BACKGROUND The association between congenital heart disease and chronic kidney disease is well known.Various mechanisms of kidney damage associated with congenital heart disease have been established.The etiology of kidneydisease has commonly been considered to be secondary to focal segmental glomerulosclerosis(FSGS),however,this has only been demonstrated in case reports and not in observational or clinical trials.AIM To identify baseline and clinical characteristics,as well as the findings in kidney biopsies of patients with congenital heart disease in our hospital.METHODS This is a retrospective observational study conducted at the Nephrology Depart-ment of the National Institute of Cardiology“Ignacio Chávez”.All patients over 16 years old who underwent percutaneous kidney biopsy from January 2000 to January 2023 with congenital heart disease were included in the study.RESULTS Ten patients with congenital heart disease and kidney biopsy were found.The average age was 29.00 years±15.87 years with pre-biopsy proteinuria of 6193 mg/24 h±6165 mg/24 h.The most common congenital heart disease was Fallot’s tetralogy with 2 cases(20%)and ventricular septal defect with 2(20%)cases.Among the 10 cases,one case of IgA nephropathy and one case of membranoproliferative glomerulonephritis associated with immune complexes were found,receiving specific treatment after histopathological diagnosis,delaying the initiation of kidney replacement therapy.Among remaining 8 cases(80%),one case of FSGS with perihilar variety was found,while the other 7 cases were non-specific FSGS.CONCLUSION Determining the cause of chronic kidney disease can help in delaying the need for kidney replacement therapy.In 2 out of 10 patients in our study,interventions were performed,and initiation of kidney replacement therapy was delayed.Prospective studies are needed to determine the usefulness of kidney biopsy in patients with congenital heart disease.

Application of Structured Health Education and Combined 5A Nursing Model in Self-control of Vulnerable Chronic Diseases: Take Coronary Heart Disease as an Example

Objective:To explore the intervention effect of the Structured Health Education course and 5A nursing model for self-control of elderly patients with coronary heart disease.Methods:Using the random sampling method,124 elderly CAD patients admitted to the First Affiliated Hospital of Bengbu Medical University were randomly divided into an experimental group and a control group.The control group line routine health education,experimental group take structured health education combined with 5A nursing before and after the intervention using a coronary heart disease assessment questionnaire,coronary heart disease self-control scale evaluation of two groups of intervention,compare two groups before and after intervention blood pressure,blood sugar,body mass index,lipid index level and complications within 8 months after discharge.Results:After the course intervention,the disease cognition and self-behavior of the experimental group were higher than that of the control group,and the differences were statistically significant(all P<0.1).Conclusion:This course is suitable for elderly patients with coronary heart disease.The 5A model improves the cognitive and management ability of elderly patients to a certain extent,which is worthy of clinical application.

Convolutional LSTM Network for Heart Disease Diagnosis on Electrocardiograms

Heart disease is a leading cause ofmortality worldwide.Electrocardiograms(ECG)play a crucial role in diagnosing heart disease.However,interpreting ECGsignals necessitates specialized knowledge and training.The development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease diagnosis.This research paper proposes a 3D Convolutional Long Short-Term Memory(Conv-LSTM)model for detecting heart disease using ECG signals.The proposed model combines the advantages of both convolutional neural networks(CNN)and long short-term memory(LSTM)networks.By considering both the spatial and temporal dependencies of ECG,the 3D Conv-LSTM model enables the detection of subtle changes in the signal over time.The model is trained on a dataset of ECG recordings from patients with various heart conditions,including arrhythmia,myocardial infarction,and heart failure.Experimental results show that the proposed 3D Conv-LSTM model outperforms traditional 2D CNN models in detecting heart disease,achieving an accuracy of 88%in the classification of five classes.Furthermore,themodel outperforms the other state-of-the-art deep learning models for ECG-based heart disease detection.Moreover,the proposedConv-LSTMnetwork yields highly accurate outcomes in identifying abnormalities in specific ECG leads.The proposed 3D Conv-LSTM model holds promise as a valuable tool for automated heart disease detection and diagnosis.This study underscores the significance of incorporating spatial and temporal dependencies in ECG-based heart disease detection.It highlights the potential of deep-learning models in enhancing the accuracy and efficiency of diagnosis.

Hybrid Deep Learning Method for Diagnosis of Cucurbita Leaf Diseases

In agricultural engineering,the main challenge is on methodologies used for disease detection.The manual methods depend on the experience of the personal.Due to large variation in environmental condition,disease diagnosis and classification becomes a challenging task.Apart from the disease,the leaves are affected by climate changes which is hard for the image processing method to discriminate the disease from the other background.In Cucurbita gourd family,the disease severity examination of leaf samples through computer vision,and deep learning methodologies have gained popularity in recent years.In this paper,a hybrid method based on Convolutional Neural Network(CNN)is proposed for automatic pumpkin leaf image classification.The Proposed Denoising and deep Convolutional Neural Network(CNN)method enhances the Pumpkin Leaf Pre-processing and diagnosis.Real time data base was used for training and testing of the proposed work.Investigation on existing pre-trained network Alexnet and googlenet was investigated is done to evaluate the performance of the pro-posed method.The system and computer simulations were performed using Matlab tool.