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 ...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.展开更多
Huntington'sdisease(HD)isahereditary neurodegenerative disorder for which there is currently no effectivetreatmentavailable.Consequently,the development of appropriate disease models is critical to thoroughly inve...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.展开更多
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 fie...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.展开更多
In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the s...In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.展开更多
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/aut...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.展开更多
The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information...The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information technology has increased.A sensing system is mandatory to detect rice diseases using Artificial Intelligence(AI).It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases.Deep Neural Network(DNN)is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing nodes.Further in this paper,the adoption of the mixed-method approach Deep Convolutional Neural Network(Deep CNN)has assisted the research in increasing the effectiveness of the proposed method.Deep CNN is used for image recognition and is a class of deep-learning neural networks.CNN is popular and mostly used in the field of image recognition.A dataset of images with three main leaf diseases is selected for training and testing the proposed model.After the image acquisition and preprocessing process,the Deep CNN model was trained to detect and classify three rice diseases(Brown spot,bacterial blight,and blast disease).The proposed model achieved 98.3%accuracy in comparison with similar state-of-the-art techniques.展开更多
The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectivene...The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.展开更多
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 pathogene...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.展开更多
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.Howeve...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.展开更多
Plant diseases and pests present significant challenges to global food security, leading to substantial losses in agricultural productivity and threatening environmental sustainability. As the world’s population grow...Plant diseases and pests present significant challenges to global food security, leading to substantial losses in agricultural productivity and threatening environmental sustainability. As the world’s population grows, ensuring food availability becomes increasingly urgent. This review explores the significance of advanced plant disease detection techniques in disease and pest management for enhancing food security. Traditional plant disease detection methods often rely on visual inspection and are time-consuming and subjective. This leads to delayed interventions and ineffective control measures. However, recent advancements in remote sensing, imaging technologies, and molecular diagnostics offer powerful tools for early and precise disease detection. Big data analytics and machine learning play pivotal roles in analyzing vast and complex datasets, thus accurately identifying plant diseases and predicting disease occurrence and severity. We explore how prompt interventions employing advanced techniques enable more efficient disease control and concurrently minimize the environmental impact of conventional disease and pest management practices. Furthermore, we analyze and make future recommendations to improve the precision and sensitivity of current advanced detection techniques. We propose incorporating eco-evolutionary theories into research to enhance the understanding of pathogen spread in future climates and mitigate the risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with scientists, policymakers, and relevant intergovernmental organizations to ensure coordination and collaboration among them, ultimately developing effective disease monitoring and management strategies needed for securing sustainable food production and environmental well-being.展开更多
Alzheimer’s disease(AD)is a neurodevelopmental impairment that results in a person’s behavior,thinking,and memory loss.Themost common symptoms ofADare losingmemory and early aging.In addition to these,there are seve...Alzheimer’s disease(AD)is a neurodevelopmental impairment that results in a person’s behavior,thinking,and memory loss.Themost common symptoms ofADare losingmemory and early aging.In addition to these,there are several serious impacts ofAD.However,the impact ofADcanbemitigatedby early-stagedetection though it cannot be cured permanently.Early-stage detection is the most challenging task for controlling and mitigating the impact of AD.The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue.To build a predictive model,open-source data was collected where five stages of images of AD were available as Cognitive Normal(CN),Early Mild Cognitive Impairment(EMCI),Mild Cognitive Impairment(MCI),Late Mild Cognitive Impairment(LMCI),and AD.Every stage of AD is considered as a class,and then the dataset was divided into three parts binary class,three class,and five class.In this research,we applied different preprocessing steps with augmentation techniques to efficiently identifyAD.It integrates a random oversampling technique to handle the imbalance problem from target classes,mitigating the model overfitting and biases.Then three machine learning classifiers,such as random forest(RF),K-Nearest neighbor(KNN),and support vector machine(SVM),and two deep learning methods,such as convolutional neuronal network(CNN)and artificial neural network(ANN)were applied on these datasets.After analyzing the performance of the used models and the datasets,it is found that CNN with binary class outperformed 88.20%accuracy.The result of the study indicates that the model is highly potential to detect AD in the initial phase.展开更多
A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure ...A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information.Therefore,the agricultural management system is searching for an automatic early disease detection technique.To this end,an efficient and lightweight Deep Learning(DL)-based framework(E-GreenNet)is proposed to overcome these problems and precisely classify the various diseases.In the end-to-end architecture,a MobileNetV3Smallmodel is utilized as a backbone that generates refined,discriminative,and prominent features.Moreover,the proposed model is trained over the PlantVillage(PV),Data Repository of Leaf Images(DRLI),and a new Plant Composite(PC)dataset individually,and later on test samples,its actual performance is evaluated.After extensive experimental analysis,the proposed model obtained 1.00%,0.96%and 0.99%accuracies on all three included datasets.Moreover,the proposed method achieves better inference speed when compared with other State-Of-The-Art(SOTA)approaches.In addition,a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pretrained models and other Machine Learning(ML)and DL methods.展开更多
The eye is an immune-privileged and sensory organ in humans and animals.Anatomical,physiological,and pathobiological features share significant similarities across divergent species(1).Each compartment of the eye has ...The eye is an immune-privileged and sensory organ in humans and animals.Anatomical,physiological,and pathobiological features share significant similarities across divergent species(1).Each compartment of the eye has a unique structure and function.The anterior and posterior compartments of the eye contain endothelium(cornea),epithelium(cornea,ciliary body,iris),muscle(ciliary body),vitreous and neuronal(retina)tissues,which make the eye suitable to evaluate efficacy and safety of tissue specific drugs(2).展开更多
Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ...Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.展开更多
In recent years,Artificial Intelligence(AI)has revolutionized people’s lives.AI has long made breakthrough progress in the field of surgery.However,the research on the application of AI in orthopedics is still in the...In recent years,Artificial Intelligence(AI)has revolutionized people’s lives.AI has long made breakthrough progress in the field of surgery.However,the research on the application of AI in orthopedics is still in the exploratory stage.The paper first introduces the background of AI and orthopedic diseases,addresses the shortcomings of traditional methods in the detection of fractures and orthopedic diseases,draws out the advantages of deep learning and machine learning in image detection,and reviews the latest results of deep learning and machine learning applied to orthopedic image detection in recent years,describing the contributions,strengths and weaknesses,and the direction of the future improvements that can be made in each study.Next,the paper also introduces the difficulties of traditional orthopedic surgery and the roles played by AI in preoperative,intraoperative,and postoperative orthopedic surgery,scientifically discussing the advantages and prospects of AI in orthopedic surgery.Finally,the article discusses the limitations of current research and technology in clinical applications,proposes solutions to the problems,and summarizes and outlines possible future research directions.The main objective of this review is to inform future research and development of AI in orthopedics.展开更多
The most widely farmed fruit in the world is mango.Both the production and quality of the mangoes are hampered by many diseases.These diseases need to be effectively controlled and mitigated.Therefore,a quick and accu...The most widely farmed fruit in the world is mango.Both the production and quality of the mangoes are hampered by many diseases.These diseases need to be effectively controlled and mitigated.Therefore,a quick and accurate diagnosis of the disorders is essential.Deep convolutional neural networks,renowned for their independence in feature extraction,have established their value in numerous detection and classification tasks.However,it requires large training datasets and several parameters that need careful adjustment.The proposed Modified Dense Convolutional Network(MDCN)provides a successful classification scheme for plant diseases affecting mango leaves.This model employs the strength of pre-trained networks and modifies them for the particular context of mango leaf diseases by incorporating transfer learning techniques.The data loader also builds mini-batches for training the models to reduce training time.Finally,optimization approaches help increase the overall model’s efficiency and lower computing costs.MDCN employed on the MangoLeafBD Dataset consists of a total of 4,000 images.Following the experimental results,the proposed system is compared with existing techniques and it is clear that the proposed algorithm surpasses the existing algorithms by achieving high performance and overall throughput.展开更多
Objective:To determine the temporal trend and epidemiology of animal bite cases in Gerash City,Iran.Methods:This retrospective cross-sectional study analyzed 630 cases of people with animal bites between 2011 and 2021...Objective:To determine the temporal trend and epidemiology of animal bite cases in Gerash City,Iran.Methods:This retrospective cross-sectional study analyzed 630 cases of people with animal bites between 2011 and 2021 in Gerash City.The collected data were analyzed using Chi-square test.Results:The mean age of victims was(30.9±17.5)years.80.54%Of victims were male,39.70%were self-employed,and 64.60%were adults(≥18 years).Most cases of bites occurred in 2019(91 cases),2020(74 cases)and 2021(87 cases),and most of the bites were related to the upper limbs(62.70%).Most of the wounds were superficial(78%),most of the biting animals were domestic animals(91.74%),and most of the victims had Iranian nationality(97.62%).Conclusions:Given the increasing trend of animal bites in Gerash City,health authorities should carry out basic measures such as education and awareness among the public,especially at-risk groups such as adult males.Additionally,since most cases of bites are due to dogs,it seems necessary to plan for vaccination of dogs,especially those with owners.展开更多
CD36 is a highly glycosylated integral membrane protein that belongs to the scavenger receptor class B family and regulates the pathological progress of metabolic diseases.CD36 was recently found to be widely expresse...CD36 is a highly glycosylated integral membrane protein that belongs to the scavenger receptor class B family and regulates the pathological progress of metabolic diseases.CD36 was recently found to be widely expressed in various cell types in the nervous system,including endothelial cells,pericytes,astrocytes,and microglia.CD36 mediates a number of regulatory processes,such as endothelial dysfunction,oxidative stress,mitochondrial dysfunction,and inflammatory responses,which are involved in many central nervous system diseases,such as stroke,Alzheimer’s disease,Parkinson’s disease,and spinal cord injury.CD36 antagonists can suppress CD36 expression or prevent CD36 binding to its ligand,thereby achieving inhibition of CD36-mediated pathways or functions.Here,we reviewed the mechanisms of action of CD36 antagonists,such as Salvianolic acid B,tanshinone IIA,curcumin,sulfosuccinimidyl oleate,antioxidants,and small-molecule compounds.Moreover,we predicted the structures of binding sites between CD36 and antagonists.These sites can provide targets for more efficient and safer CD36 antagonists for the treatment of central nervous system diseases.展开更多
Parkinson’s disease is typically characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta.Many studies have been performed based on the supplementation of lost dopaminergic ...Parkinson’s disease is typically characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta.Many studies have been performed based on the supplementation of lost dopaminergic neurons to treat Parkinson’s disease.The initial strategy for cell replacement therapy used human fetal ventral midbrain and human embryonic stem cells to treat Parkinson’s disease,which could substantially alleviate the symptoms of Parkinson’s disease in clinical practice.However,ethical issues and tumor formation were limitations of its clinical application.Induced pluripotent stem cells can be acquired without sacrificing human embryos,which eliminates the huge ethical barriers of human stem cell therapy.Another widely considered neuronal regeneration strategy is to directly reprogram fibroblasts and astrocytes into neurons,without the need for intermediate proliferation states,thus avoiding issues of immune rejection and tumor formation.Both induced pluripotent stem cells and direct reprogramming of lineage cells have shown promising results in the treatment of Parkinson’s disease.However,there are also ethical concerns and the risk of tumor formation that need to be addressed.This review highlights the current application status of cell reprogramming in the treatment of Parkinson’s disease,focusing on the use of induced pluripotent stem cells in cell replacement therapy,including preclinical animal models and progress in clinical research.The review also discusses the advancements in direct reprogramming of lineage cells in the treatment of Parkinson’s disease,as well as the controversy surrounding in vivo reprogramming.These findings suggest that cell reprogramming may hold great promise as a potential strategy for treating Parkinson’s disease.展开更多
The development of rapid and sensitive detection technologies for animal epidemic diseases is very important to early diagnosis and disease control.Biosensing technology is a novel biological detection technology deve...The development of rapid and sensitive detection technologies for animal epidemic diseases is very important to early diagnosis and disease control.Biosensing technology is a novel biological detection technology developed in recent years and has been listed as one of the five medical inspection technologies in the21stcentury,which is considered as a rapid and effective technology for detection and diagnosis of animal epidemic diseases.In this paper,the latest research progresses on the application of biosensing technology in detection of bacterial infectious diseases,viral infectious diseases and parasitic diseases were summarized.展开更多
文摘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.
基金supported by the National Key Research and Development Program of China (2021YFA0805300,2021YFA0805200)National Natural Science Foundation of China (32170981,82371874,82394422,82171244,82071421,82271902)+3 种基金Guangzhou Key Research Program on Brain Science (202007030008)Department of Science and Technology of Guangdong Province (2021ZT09Y0072020B1212010062018B030337001)。
文摘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.
基金supported by the National Natural Science Foundation of China (31970574)。
文摘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.
基金funded by Hunan Provincial Natural Science Foundation of China with Grant Numbers(2022JJ50016,2023JJ50096)Innovation Platform Open Fund of Hengyang Normal University Grant 2021HSKFJJ039Hengyang Science and Technology Plan Guiding Project with Number 202222025902.
文摘In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.
基金Supported by the following Brazilian funding agencies:Financiamento e IncentivoàPesquisa from Hospital de Clínicas de Porto Alegre(FIPE/HCPA),No.2021-0105(toÁlvares-da-Silva MR)Coordination for the Improvement of Higher Education Personnel,CAPES/PNPDand this study was financed in part by the Conselho Nacional de Desenvolvimento Científico e Tecnológico(CNPq)(toÁlvares-da-Silva MR).
文摘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.
基金funded by the University of Haripur,KP Pakistan Researchers Supporting Project number (PKURFL2324L33)。
文摘The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information technology has increased.A sensing system is mandatory to detect rice diseases using Artificial Intelligence(AI).It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases.Deep Neural Network(DNN)is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing nodes.Further in this paper,the adoption of the mixed-method approach Deep Convolutional Neural Network(Deep CNN)has assisted the research in increasing the effectiveness of the proposed method.Deep CNN is used for image recognition and is a class of deep-learning neural networks.CNN is popular and mostly used in the field of image recognition.A dataset of images with three main leaf diseases is selected for training and testing the proposed model.After the image acquisition and preprocessing process,the Deep CNN model was trained to detect and classify three rice diseases(Brown spot,bacterial blight,and blast disease).The proposed model achieved 98.3%accuracy in comparison with similar state-of-the-art techniques.
文摘The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.
基金supported by the National Natural Science Foundation of China (82271455)Guangdong Basic and Applied Basic Research Foundation (2022A1515012416)+6 种基金Science and Technology Development FundMacao SAR (0128/2019/A3,0025/2022/A1)Shenzhen Fundamental Research Program (SGDX20210823103804030)University of Macao Grants (MYRG2022-00094-ICMS)awarded to J.H.L.partially supported by the National Key R&D Program of China (2021YFA0805901)National Natural Science Foundation of China (82070199)Guangdong Basic and Applied Basic Research Foundation (2021A1515220078)awarded to D.S.T。
文摘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.
文摘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.
文摘Plant diseases and pests present significant challenges to global food security, leading to substantial losses in agricultural productivity and threatening environmental sustainability. As the world’s population grows, ensuring food availability becomes increasingly urgent. This review explores the significance of advanced plant disease detection techniques in disease and pest management for enhancing food security. Traditional plant disease detection methods often rely on visual inspection and are time-consuming and subjective. This leads to delayed interventions and ineffective control measures. However, recent advancements in remote sensing, imaging technologies, and molecular diagnostics offer powerful tools for early and precise disease detection. Big data analytics and machine learning play pivotal roles in analyzing vast and complex datasets, thus accurately identifying plant diseases and predicting disease occurrence and severity. We explore how prompt interventions employing advanced techniques enable more efficient disease control and concurrently minimize the environmental impact of conventional disease and pest management practices. Furthermore, we analyze and make future recommendations to improve the precision and sensitivity of current advanced detection techniques. We propose incorporating eco-evolutionary theories into research to enhance the understanding of pathogen spread in future climates and mitigate the risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with scientists, policymakers, and relevant intergovernmental organizations to ensure coordination and collaboration among them, ultimately developing effective disease monitoring and management strategies needed for securing sustainable food production and environmental well-being.
基金funded in part by the Natural Sciences and Engineering Research Council of Canada(NSERC)through Project Number:IFP22UQU4170008DSR0056.
文摘Alzheimer’s disease(AD)is a neurodevelopmental impairment that results in a person’s behavior,thinking,and memory loss.Themost common symptoms ofADare losingmemory and early aging.In addition to these,there are several serious impacts ofAD.However,the impact ofADcanbemitigatedby early-stagedetection though it cannot be cured permanently.Early-stage detection is the most challenging task for controlling and mitigating the impact of AD.The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue.To build a predictive model,open-source data was collected where five stages of images of AD were available as Cognitive Normal(CN),Early Mild Cognitive Impairment(EMCI),Mild Cognitive Impairment(MCI),Late Mild Cognitive Impairment(LMCI),and AD.Every stage of AD is considered as a class,and then the dataset was divided into three parts binary class,three class,and five class.In this research,we applied different preprocessing steps with augmentation techniques to efficiently identifyAD.It integrates a random oversampling technique to handle the imbalance problem from target classes,mitigating the model overfitting and biases.Then three machine learning classifiers,such as random forest(RF),K-Nearest neighbor(KNN),and support vector machine(SVM),and two deep learning methods,such as convolutional neuronal network(CNN)and artificial neural network(ANN)were applied on these datasets.After analyzing the performance of the used models and the datasets,it is found that CNN with binary class outperformed 88.20%accuracy.The result of the study indicates that the model is highly potential to detect AD in the initial phase.
基金This work was financially supported by MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2022-RS-2022-00156354)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)and also by the Ministry of Trade,Industry and Energy(MOTIE)and Korea Institute for Advancement of Technology(KIAT)through the International Cooperative R&D program(Project No.P0016038).
文摘A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information.Therefore,the agricultural management system is searching for an automatic early disease detection technique.To this end,an efficient and lightweight Deep Learning(DL)-based framework(E-GreenNet)is proposed to overcome these problems and precisely classify the various diseases.In the end-to-end architecture,a MobileNetV3Smallmodel is utilized as a backbone that generates refined,discriminative,and prominent features.Moreover,the proposed model is trained over the PlantVillage(PV),Data Repository of Leaf Images(DRLI),and a new Plant Composite(PC)dataset individually,and later on test samples,its actual performance is evaluated.After extensive experimental analysis,the proposed model obtained 1.00%,0.96%and 0.99%accuracies on all three included datasets.Moreover,the proposed method achieves better inference speed when compared with other State-Of-The-Art(SOTA)approaches.In addition,a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pretrained models and other Machine Learning(ML)and DL methods.
文摘The eye is an immune-privileged and sensory organ in humans and animals.Anatomical,physiological,and pathobiological features share significant similarities across divergent species(1).Each compartment of the eye has a unique structure and function.The anterior and posterior compartments of the eye contain endothelium(cornea),epithelium(cornea,ciliary body,iris),muscle(ciliary body),vitreous and neuronal(retina)tissues,which make the eye suitable to evaluate efficacy and safety of tissue specific drugs(2).
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number PNURSP2024R333,Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.
基金This work was supported in part by the National Natural Science Foundation of China under Grants 61861007 and 61640014in part by theGuizhou Province Science and Technology Planning Project ZK[2021]303+2 种基金in part by the Guizhou Province Science Technology Support Plan under Grants[2022]017,[2023]096 and[2022]264in part by the Guizhou Education Department Innovation Group Project under Grant KY[2021]012in part by the Talent Introduction Project of Guizhou University(2014)-08.
文摘In recent years,Artificial Intelligence(AI)has revolutionized people’s lives.AI has long made breakthrough progress in the field of surgery.However,the research on the application of AI in orthopedics is still in the exploratory stage.The paper first introduces the background of AI and orthopedic diseases,addresses the shortcomings of traditional methods in the detection of fractures and orthopedic diseases,draws out the advantages of deep learning and machine learning in image detection,and reviews the latest results of deep learning and machine learning applied to orthopedic image detection in recent years,describing the contributions,strengths and weaknesses,and the direction of the future improvements that can be made in each study.Next,the paper also introduces the difficulties of traditional orthopedic surgery and the roles played by AI in preoperative,intraoperative,and postoperative orthopedic surgery,scientifically discussing the advantages and prospects of AI in orthopedic surgery.Finally,the article discusses the limitations of current research and technology in clinical applications,proposes solutions to the problems,and summarizes and outlines possible future research directions.The main objective of this review is to inform future research and development of AI in orthopedics.
文摘The most widely farmed fruit in the world is mango.Both the production and quality of the mangoes are hampered by many diseases.These diseases need to be effectively controlled and mitigated.Therefore,a quick and accurate diagnosis of the disorders is essential.Deep convolutional neural networks,renowned for their independence in feature extraction,have established their value in numerous detection and classification tasks.However,it requires large training datasets and several parameters that need careful adjustment.The proposed Modified Dense Convolutional Network(MDCN)provides a successful classification scheme for plant diseases affecting mango leaves.This model employs the strength of pre-trained networks and modifies them for the particular context of mango leaf diseases by incorporating transfer learning techniques.The data loader also builds mini-batches for training the models to reduce training time.Finally,optimization approaches help increase the overall model’s efficiency and lower computing costs.MDCN employed on the MangoLeafBD Dataset consists of a total of 4,000 images.Following the experimental results,the proposed system is compared with existing techniques and it is clear that the proposed algorithm surpasses the existing algorithms by achieving high performance and overall throughput.
基金This study was financially supported by the vice-chancellery for research affairs at Gerash University of Medical Sciences,Gerash,Iran(Grant number:99000071).
文摘Objective:To determine the temporal trend and epidemiology of animal bite cases in Gerash City,Iran.Methods:This retrospective cross-sectional study analyzed 630 cases of people with animal bites between 2011 and 2021 in Gerash City.The collected data were analyzed using Chi-square test.Results:The mean age of victims was(30.9±17.5)years.80.54%Of victims were male,39.70%were self-employed,and 64.60%were adults(≥18 years).Most cases of bites occurred in 2019(91 cases),2020(74 cases)and 2021(87 cases),and most of the bites were related to the upper limbs(62.70%).Most of the wounds were superficial(78%),most of the biting animals were domestic animals(91.74%),and most of the victims had Iranian nationality(97.62%).Conclusions:Given the increasing trend of animal bites in Gerash City,health authorities should carry out basic measures such as education and awareness among the public,especially at-risk groups such as adult males.Additionally,since most cases of bites are due to dogs,it seems necessary to plan for vaccination of dogs,especially those with owners.
基金supported by the National Major Project of Research and Development,No.2022YFA1105500(to SZ)the National Natural Science Foundation of China,No.81870975(to SZ)Innovation Program for Graduate Students in Jiangsu Province of China,No.KYCX223335(to MZ)。
文摘CD36 is a highly glycosylated integral membrane protein that belongs to the scavenger receptor class B family and regulates the pathological progress of metabolic diseases.CD36 was recently found to be widely expressed in various cell types in the nervous system,including endothelial cells,pericytes,astrocytes,and microglia.CD36 mediates a number of regulatory processes,such as endothelial dysfunction,oxidative stress,mitochondrial dysfunction,and inflammatory responses,which are involved in many central nervous system diseases,such as stroke,Alzheimer’s disease,Parkinson’s disease,and spinal cord injury.CD36 antagonists can suppress CD36 expression or prevent CD36 binding to its ligand,thereby achieving inhibition of CD36-mediated pathways or functions.Here,we reviewed the mechanisms of action of CD36 antagonists,such as Salvianolic acid B,tanshinone IIA,curcumin,sulfosuccinimidyl oleate,antioxidants,and small-molecule compounds.Moreover,we predicted the structures of binding sites between CD36 and antagonists.These sites can provide targets for more efficient and safer CD36 antagonists for the treatment of central nervous system diseases.
基金supported by the National Natural Science Foundation of China,No.31960120Yunnan Science and Technology Talent and Platform Plan,No.202105AC160041(both to ZW).
文摘Parkinson’s disease is typically characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta.Many studies have been performed based on the supplementation of lost dopaminergic neurons to treat Parkinson’s disease.The initial strategy for cell replacement therapy used human fetal ventral midbrain and human embryonic stem cells to treat Parkinson’s disease,which could substantially alleviate the symptoms of Parkinson’s disease in clinical practice.However,ethical issues and tumor formation were limitations of its clinical application.Induced pluripotent stem cells can be acquired without sacrificing human embryos,which eliminates the huge ethical barriers of human stem cell therapy.Another widely considered neuronal regeneration strategy is to directly reprogram fibroblasts and astrocytes into neurons,without the need for intermediate proliferation states,thus avoiding issues of immune rejection and tumor formation.Both induced pluripotent stem cells and direct reprogramming of lineage cells have shown promising results in the treatment of Parkinson’s disease.However,there are also ethical concerns and the risk of tumor formation that need to be addressed.This review highlights the current application status of cell reprogramming in the treatment of Parkinson’s disease,focusing on the use of induced pluripotent stem cells in cell replacement therapy,including preclinical animal models and progress in clinical research.The review also discusses the advancements in direct reprogramming of lineage cells in the treatment of Parkinson’s disease,as well as the controversy surrounding in vivo reprogramming.These findings suggest that cell reprogramming may hold great promise as a potential strategy for treating Parkinson’s disease.
基金Supported by Talent Introduction Project of Guizhou University(GRDJHZ[2012]No.012)Science and Technology Fund of Guizhou Province(QKHJZ[2013]No.2111)
文摘The development of rapid and sensitive detection technologies for animal epidemic diseases is very important to early diagnosis and disease control.Biosensing technology is a novel biological detection technology developed in recent years and has been listed as one of the five medical inspection technologies in the21stcentury,which is considered as a rapid and effective technology for detection and diagnosis of animal epidemic diseases.In this paper,the latest research progresses on the application of biosensing technology in detection of bacterial infectious diseases,viral infectious diseases and parasitic diseases were summarized.
