Effects of mesenchymal stem cell on dopaminergic neurons,motor and memory functions in animal models of Parkinson's disease:a systematic review and meta-analysis

Parkinson’s disease is chara cterized by the loss of dopaminergic neurons in the substantia nigra pars com pacta,and although restoring striatal dopamine levels may improve symptoms,no treatment can cure or reve rse the disease itself.Stem cell therapy has a regenerative effect and is being actively studied as a candidate for the treatment of Parkinson’s disease.Mesenchymal stem cells are considered a promising option due to fewer ethical concerns,a lower risk of immune rejection,and a lower risk of teratogenicity.We performed a meta-analysis to evaluate the therapeutic effects of mesenchymal stem cells and their derivatives on motor function,memory,and preservation of dopamine rgic neurons in a Parkinson’s disease animal model.We searched bibliographic databases(PubMed/MEDLINE,Embase,CENTRAL,Scopus,and Web of Science)to identify articles and included only pee r-reviewed in vivo interve ntional animal studies published in any language through J une 28,2023.The study utilized the random-effect model to estimate the 95%confidence intervals(CI)of the standard mean differences(SMD)between the treatment and control groups.We use the systematic review center for laboratory animal expe rimentation’s risk of bias tool and the collaborative approach to meta-analysis and review of animal studies checklist for study quality assessment.A total of 33studies with data from 840 Parkinson’s disease model animals were included in the meta-analysis.Treatment with mesenchymal stem cells significantly improved motor function as assessed by the amphetamine-induced rotational test.Among the stem cell types,the bone marrow MSCs with neurotrophic factor group showed la rgest effect size(SMD[95%CI]=-6.21[-9.50 to-2.93],P=0.0001,I^(2)=0.0%).The stem cell treatment group had significantly more tyrosine hydroxylase positive dopamine rgic neurons in the striatum([95%CI]=1.04[0.59 to 1.49],P=0.0001,I^(2)=65.1%)and substantia nigra(SMD[95%CI]=1.38[0.89 to 1.87],P=0.0001,I^(2)=75.3%),indicating a protective effect on dopaminergic neurons.Subgroup analysis of the amphetamine-induced rotation test showed a significant reduction only in the intracranial-striatum route(SMD[95%CI]=-2.59[-3.25 to-1.94],P=0.0001,I^(2)=74.4%).The memory test showed significant improvement only in the intravenous route(SMD[95%CI]=4.80[1.84 to 7.76],P=0.027,I^(2)=79.6%).Mesenchymal stem cells have been shown to positively impact motor function and memory function and protect dopaminergic neurons in preclinical models of Parkinson’s disease.Further research is required to determine the optimal stem cell types,modifications,transplanted cell numbe rs,and delivery methods for these protocols.

Cellular interplay to 3D in vitro microphysiological disease model:cell patterning microbiota-gut-brain axis

The microbiota-gut-brain axis(MGBA)has emerged as a key prospect in the bidirectional communication between two major organ systems:the brain and the gut.Homeostasis between the two organ systems allows the body to function without disease,whereas dysbiosis has long-standing evidence of etiopathological conditions.The most common communication paths are the microbial release of metabolites,soluble neurotransmitters,and immune cells.However,each pathway is intertwined with a complex one.With the emergence of in vitro models and the popularity of three-dimensional(3D)cultures and Transwells,engineering has become easier for the scientific understanding of neurodegenerative diseases.This paper briefly retraces the possible communication pathways between the gut microbiome and the brain.It further elaborates on three major diseases:autism spectrum disorder,Parkinson’s disease,and Alzheimer’s disease,which are prevalent in children and the elderly.These diseases also decrease patients’quality of life.Hence,understanding them more deeply with respect to current advances in in vitro modeling is crucial for understanding the diseases.Remodeling of MGBA in the laboratory uses many molecular technologies and biomaterial advances.Spheroids and organoids provide a more realistic picture of the cell and tissue structure than monolayers.Combining them with the Transwell system offers the advantage of compartmentalizing the two systems(apical and basal)while allowing physical and chemical cues between them.Cutting-edge technologies,such as bioprinting and microfluidic chips,might be the future of in vitro modeling,as they provide dynamicity.

Large animal models for Huntington's disease research

Huntington'sdisease(HD)isahereditary neurodegenerative disorder for which there is currently no effectivetreatmentavailable.Consequently,the development of appropriate disease models is critical to thoroughly investigate disease progression.The genetic basis of HD involves the abnormal expansion of CAG repeats in the huntingtin(HTT)gene,leading to the expansion of a polyglutamine repeat in the HTT protein.Mutant HTT carrying the expanded polyglutamine repeat undergoes misfolding and forms aggregates in the brain,which precipitate selective neuronal loss in specific brain regions.Animal models play an important role in elucidating the pathogenesis of neurodegenerative disorders such as HD and in identifying potential therapeutic targets.Due to the marked species differences between rodents and larger animals,substantial efforts have been directed toward establishing large animal models for HD research.These models are pivotal for advancing the discovery of novel therapeutic targets,enhancing effective drug delivery methods,and improving treatment outcomes.We have explored the advantages of utilizing large animal models,particularly pigs,in previous reviews.Since then,however,significant progress has been made in developing more sophisticated animal models that faithfully replicate the typical pathology of HD.In the current review,we provide a comprehensive overview of large animal models of HD,incorporating recent findings regarding the establishment of HD knock-in(KI)pigs and their genetic therapy.We also explore the utilization of large animal models in HD research,with a focus on sheep,non-human primates(NHPs),and pigs.Our objective is to provide valuable insights into the application of these large animal models for the investigation and treatment of neurodegenerative disorders.

Genome-edited rabbits:Unleashing the potential of a promising experimental animal model across diverse diseases

Animal models are extensively used in all aspects of biomedical research,with substantial contributions to our understanding of diseases,the development of pharmaceuticals,and the exploration of gene functions.The field of genome modification in rabbits has progressed slowly.However,recent advancements,particularly in CRISPR/Cas9-related technologies,have catalyzed the successful development of various genome-edited rabbit models to mimic diverse diseases,including cardiovascular disorders,immunodeficiencies,agingrelated ailments,neurological diseases,and ophthalmic pathologies.These models hold great promise in advancing biomedical research due to their closer physiological and biochemical resemblance to humans compared to mice.This review aims to summarize the novel gene-editing approaches currently available for rabbits and present the applications and prospects of such models in biomedicine,underscoring their impact and future potential in translational medicine.

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.

Experimental models for preclinical research in kidney disease

Acute kidney injury(AKI)and chronic kidney disease(CKD)are significant public health issues associated with a long-term increase in mortality risk,resulting from various etiologies including renal ischemia,sepsis,drug toxicity,and diabetes mellitus.Numerous preclinical models have been developed to deepen our understanding of the pathophysiological mechanisms and therapeutic approaches for kidney diseases.Among these,rodent models have proven to be powerful tools in the discovery of novel therapeutics,while the development of kidney organoids has emerged as a promising advancement in the field.This review provides a comprehensive analysis of the construction methodologies,underlying biological mechanisms,and recent therapeutic developments across different AKI and CKD models.Additionally,this review summarizes the advantages,limitations,and challenges inherent in these preclinical models,thereby contributing robust evidence to support the development of effective therapeutic strategies.

In vitro engineered models of neurodegenerative diseases

Neurodegeneration is a catastrophic process that develops progressive damage leading to functional andstructural loss of the cells of the nervous system and is among the biggest unavoidable problems of our age.Animalmodels do not reflect the pathophysiology observed in humans due to distinct differences between the neuralpathways,gene expression patterns,neuronal plasticity,and other disease-related mechanisms in animals andhumans.Classical in vitro cell culture models are also not sufficient for pre-clinical drug testing in reflecting thecomplex pathophysiology of neurodegenerative diseases.Today,modern,engineered techniques are applied to developmulticellular,intricate in vitro models and to create the closest microenvironment simulating biological,biochemical,and mechanical characteristics of the in vivo degenerating tissue.In THIS review,the capabilities and shortcomings ofscaffold-based and scaffold-free techniques,organoids,and microfluidic models that best reflect neurodegeneration invitro in the biomimetic framework are discussed.

Evaluating the role of large language models in inflammatory bowel disease patient information

This letter evaluates the article by Gravina et al on ChatGPT’s potential in providing medical information for inflammatory bowel disease patients.While promising,it highlights the need for advanced techniques like reasoning+action and retrieval-augmented generation to improve accuracy and reliability.Emphasizing that simple question and answer testing is insufficient,it calls for more nuanced evaluation methods to truly gauge large language models’capabilities in clinical applications.

Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating demand.The presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease management.With advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a challenge.This challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease pathogens.In this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data scarcity.This study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural Networks.This paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate classification.The proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens detection.In future,more diseases can be included for detection along with some other modalities like pests and weed.

Rifaximin on epigenetics and autophagy in animal model of hepatocellular carcinoma secondary to metabolic-dysfunction associated steatotic liver disease

BACKGROUND Prevalence of hepatocellular carcinoma(HCC)is increasing,especially in patients with metabolic dysfunctionassociated steatotic liver disease(MASLD).AIM To investigate rifaximin(RIF)effects on epigenetic/autophagy markers in animals.METHODS Adult Sprague-Dawley rats were randomly assigned(n=8,each)and treated from 5-16 wk:Control[standard diet,water plus gavage with vehicle(Veh)],HCC[high-fat choline deficient diet(HFCD),diethylnitrosamine(DEN)in drinking water and Veh gavage],and RIF[HFCD,DEN and RIF(50 mg/kg/d)gavage].Gene expression of epigenetic/autophagy markers and circulating miRNAs were obtained.RESULTS All HCC and RIF animals developed metabolic-dysfunction associated steatohepatitis fibrosis,and cirrhosis,but three RIF-group did not develop HCC.Comparing animals who developed HCC with those who did not,miR-122,miR-34a,tubulin alpha-1c(Tuba-1c),metalloproteinases-2(Mmp2),and metalloproteinases-9(Mmp9)were significantly higher in the HCC-group.The opposite occurred with Becn1,coactivator associated arginine methyltransferase-1(Carm1),enhancer of zeste homolog-2(Ezh2),autophagy-related factor LC3A/B(Map1 Lc3b),and p62/sequestosome-1(p62/SQSTM1)-protein.Comparing with controls,Map1 Lc3b,Becn1 and Ezh2 were lower in HCC and RIF-groups(P<0.05).Carm1 was lower in HCC compared to RIF(P<0.05).Hepatic expression of Mmp9 was higher in HCC in relation to the control;the opposite was observed for p62/Sqstm1(P<0.05).Expression of p62/SQSTM1 protein was lower in the RIF-group compared to the control(P=0.024).There was no difference among groups for Tuba-1c,Aldolase-B,alpha-fetoprotein,and Mmp2(P>0.05).miR-122 was higher in HCC,and miR-34a in RIF compared to controls(P<0.05).miR-26b was lower in HCC compared to RIF,and the inverse was observed for miR-224(P<0.05).There was no difference among groups regarding miR-33a,miR-143,miR-155,miR-375 and miR-21(P>0.05).CONCLUSION RIF might have a possible beneficial effect on preventing/delaying liver carcinogenesis through epigenetic modulation in a rat model of MASLD-HCC.

Ornithine aspartate effects on bacterial composition and metabolic pathways in a rat model of steatotic liver disease

BACKGROUND Metabolic-dysfunction associated steatotic liver disease(MASLD)is a hepatic manifestation of metabolic syndrome.Studies suggest ornithine aspartate(LOLA)as drug therapy.AIM To analyze the influence of LOLA intake on gut microbiota using a nutritional model of MASLD.METHODS Adult male Sprague Dawley rats were randomized into three groups:Control(10 rats fed with a standard diet),MASLD(10 rats fed with a high-fat and choline-deficient diet),and LOLA(10 rats receiving 200 mg/kg/d LOLA,after the 16th week receiving high-fat and choline-deficient diet).After 28 wk of the experiment,animals were euthanized,and feces present in the intestine were collected.Following fecal DNA extraction,the V4 region of the 16S rRNA gene was amplified followed by sequencing in an Ion S5™system.RESULTS Alpha and beta diversity metrics were comparable between MASLD and LOLA.3 OTUs were differentially abundant between MASLD and LOLA,which belong to the species Helicobacter rodentium,Parabacteroides goldsteinii,and Parabacteroides distasonis.The functional prediction provided two different metabolic profiles between MASLD and LOLA.The 9 pathways differentially abundant in MASLD are related to a change in energy source,adenosine/purine nucleotides degradation as well as guanosine and adenosine deoxyribonucleotides biosynthesis.The 14 pathways differentially abundant in LOLA are associated with four major metabolic functions primarily influenced by L-aspartate,including tricarboxylic acid cycle pathways,purine/guanosine nucleotides biosynthesis,pyrimidine ribonucleotides biosynthesis and salvage as well as lipid IVA biosynthesis.CONCLUSION Although LOLA had no influence on alpha and beta diversity in this nutritional model of MASLD,it was associated with changes in specific gut microbes and their related metabolic pathways.

Challenges in predictive modelling of chronic kidney disease:A narrative review

The exponential rise in the burden of chronic kidney disease(CKD)worldwide has put enormous pressure on the economy.Predictive modeling of CKD can ease this burden by predicting the future disease occurrence ahead of its onset.There are various regression methods for predictive modeling based on the distribution of the outcome variable.However,the accuracy of the predictive model depends on how well the model is developed by taking into account the goodness of fit,choice of covariates,handling of covariates measured on a continuous scale,handling of categorical covariates,and number of outcome events per predictor parameter or sample size.Optimal performance of a predictive model on an independent cohort is desired.However,there are several challenges in the predictive modeling of CKD.Disease-specific methodological challenges hinder the development of a predictive model that is cost-effective and universally applicable to predict CKD onset.In this review,we discuss the advantages and challenges of various regression models available for predictive modeling and highlight those best for future CKD prediction.

Prediction of cyanotic and acyanotic congenital heart disease using machine learning models

BACKGROUND Congenital heart disease is most commonly seen in neonates and it is a major cause of pediatric illness and childhood morbidity and mortality.AIM To identify and build the best predictive model for predicting cyanotic and acyanotic congenital heart disease in children during pregnancy and identify their potential risk factors.METHODS The data were collected from the Pediatric Cardiology Department at Chaudhry Pervaiz Elahi Institute of Cardiology Multan,Pakistan from December 2017 to October 2019.A sample of 3900 mothers whose children were diagnosed with identify the potential outliers.Different machine learning models were compared,and the best-fitted model was selected using the area under the curve,sensitivity,and specificity of the models.RESULTS Out of 3900 patients included,about 69.5%had acyanotic and 30.5%had cyanotic congenital heart disease.Males had more cases of acyanotic(53.6%)and cyanotic(54.5%)congenital heart disease as compared to females.The odds of having cyanotic was 1.28 times higher for children whose mothers used more fast food frequently during pregnancy.The artificial neural network model was selected as the best predictive model with an area under the curve of 0.9012,sensitivity of 65.76%,and specificity of 97.23%.CONCLUSION Children having a positive family history are at very high risk of having cyanotic and acyanotic congenital heart disease.Males are more at risk and their mothers need more care,good food,and physical activity during pregnancy.The best-fitted model for predicting cyanotic and acyanotic congenital heart disease is the artificial neural network.The results obtained and the best model identified will be useful for medical practitioners and public health scientists for an informed decision-making process about the earlier diagnosis and improve the health condition of children in Pakistan.

Integrated Machine Learning and Deep Learning Models for Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.

Asymptotic Analysis of a Stochastic Model of Mosquito-Borne Disease with the Use of Insecticides and Bet Nets

Ross’ epidemic model describing the transmission of malaria uses two classes of infection, one for humans and one for mosquitoes. This paper presents a stochastic extension of a deterministic vector-borne epidemic model based only on the class of human infectious. The consistency of the model is established by proving that the stochastic delay differential equation describing the model has a unique positive global solution. The extinction of the disease is studied through the analysis of the stability of the disease-free equilibrium state and the persistence of the model. Finally, we introduce some numerical simulations to illustrate the obtained results.

Analysis of The Impact of The Healthcare Roundtable Model on the Major Disease Perceptions and Competencies of Cardiovascular Medicine Nursing Staff

Objective: To analyze the impact of the healthcare roundtable model on the major disease perceptions and competencies of cardiovascular nursing staff. Methods: Forty female nursing staff working in the hospital from April to June 2023 were selected and were randomly divided into a control group and an observation group, with 20 in each group. For 60 days, the control group used the conventional learning and communication model and the observation group used the medical and nursing roundtable model. The cognitive scores of cardiovascular disease-related knowledge, competency scores, and satisfaction with the learning and communication model before and after the implementation of the model in both groups were compared. Results: After the implementation, the cognitive scores related to cardiovascular disease-related knowledge, competency scores, and satisfaction with the learning and communication model in the observation group were significantly higher than those in the control group (P < 0.05). Conclusion: The healthcare roundtable model improved the knowledge and competence of cardiovascular internal medicine nursing staff in major diseases, and it is worth popularizing.

Analysis of the Effect of the Comprehensive Nursing Model on Patients with Moyamoya Disease Undergoing Intracranial and Extracranial Revascularization Surgery

Objective:To explore the effect of a comprehensive nursing model on patients with Moyamoya disease who underwent intracranial and extracranial revascularization surgery.Methods:110 cases were divided into control and observation groups with 55 cases each.The control group received routine perioperative care,and the observation group received perioperative care along with comprehensive nursing care.The two groups’disease cognition levels,anxiety,symptoms,daily living ability scores,and postoperative complication rates were compared.Results:The anxiety score and total postoperative complications of the observation group upon discharge were lower than that of the control group,and the disease cognition level and daily living ability upon discharge were higher than that of the control group(P<0.05).Conclusion:Applying the comprehensive nursing model in conjunction with perioperative care for patients undergoing surgery can effectively improve their anxiety,strengthen activities of daily living,and reduce the risk of postoperative complications.

Targeting autophagy in Alzheimer's disease:Animal models and mechanisms

Alzheimer's disease(AD)is an age-related progressive neurodegenerative disorder that leads to cognitive impairment and memory loss.Emerging evidence suggests that autophagy plays an important role in the pathogenesis of AD through the regulation of amyloid-beta(Aβ)and tau metabolism,and that autophagy dysfunction exacerbates amyloidosis and tau pathology.Therefore,targeting autophagy may be an effective approach for the treatment of AD.Animal models are considered useful tools for investigating the pathogenic mechanisms and therapeutic strategies of diseases.This review aims to summarize the pathological alterations in autophagy in representative AD animal models and to present recent studies on newly discovered autophagy-stimulating interventions in animal AD models.Finally,the opportunities,difficulties,and future directions of autophagy targeting in AD therapy are discussed.

Outlook of PINK1/Parkin signaling in molecular etiology of Parkinson's disease,with insights into Pink1 knockout models

Parkinson’s disease(PD)relates to defective mitochondrial quality control in the dopaminergic motor network.Genetic studies have revealed that PINK1 and Parkin mutations are indicative of a heightened propensity to PD onset,pinpointing mitophagy and inflammation as the culprit pathways involved in neuronal loss in the substantia nigra(SNpc).In a reciprocal manner,LRRK2 functions in the regulation of basal flux and inflammatory responses responsible for PINK1/Parkin-dependent mitophagy activation.Pharmacological intervention in these diseasemodifying pathways may facilitate the development of novel PD therapeutics,despite the current lack of an established drug evaluation model.As such,we reviewed the feasibility of employing the versatile global Pink1knockout(KO)rat model as a self-sufficient,spontaneous PD model for investigating both disease etiology and drug pharmacology.These rats retain clinical features encompassing basal mitophagic flux changes with PD progression.We demonstrate the versatility of this PD rat model based on the incorporation of additional experimental insults to recapitulate the proinflammatory responses observed in PD patients.

Optimizing diabetic kidney disease animal models:Insights from a meta-analytic approach

Diabetic kidney disease(DKD)is a prevalent complication of diabetes,often leading to end-stage renal disease.Animal models have been widely used to study the pathogenesis of DKD and evaluate potential therapies.However,current animal models often fail to fully capture the pathological characteristics of renal injury observed in clinical patients with DKD.Additionally,modeling DKD is often a time-consuming,costly,and labor-intensive process.The current review aims to summarize modeling strategies in the establishment of DKD animal models by utilizing meta-analysis related methods and to aid in the optimization of these models for future research.A total of 1215 articles were retrieved with the keywords of“diabetic kidney disease”and“animal experiment”in the past 10 years.Following screening,84 articles were selected for inclusion in the meta-analysis.Review manager 5.4.1 was employed to analyze the changes in blood glucose,glycosylated hemoglobin,total cholesterol,triglyceride,serum creatinine,blood urea nitrogen,and urinary albumin excretion rate in each model.Renal lesions shown in different models that were not suitable to be included in the metaanalysis were also extensively discussed.The above analysis suggested that combining various stimuli or introducing additional renal injuries to current models would be a promising avenue to overcome existing challenges and limitations.In conclusion,our review article provides an in-depth analysis of the limitations in current DKD animal models and proposes strategies for improving the accuracy and reliability of these models that will inspire future research efforts in the DKD research field.