Technology and Health Care最新文献

{"title":"Patient satisfaction following robotic unicompartmental knee arthroplasty: A systematic review and meta-analysis.","authors":"Amir Human Hoveidaei, Sina Esmaeili, Amirhossein Ghaseminejad-Raeini, Seyed Kasra Pirahesh, Armin Hoveidaei, Nemandra A Sandiford, Nina Lahner, Mustafa Citak","doi":"10.3233/THC-231216","DOIUrl":"10.3233/THC-231216","url":null,"abstract":"<p><strong>Background: </strong>Unicompartmental knee arthroplasty (UKA) is a viable alternative to total knee arthroplasty (TKA) for osteoarthritis patients with single-compartment involvement, with advantages including accelerated recovery, reduced pain, and improved function. Robotic-assisted UKA (rUKA) is a promising development that ensures precise implant positioning and limb alignment. However, concerns about complications remain.</p><p><strong>Objective: </strong>This study looks at patient satisfaction as a key metric for determining the efficacy of rUKA versus manual UKA (mUKA).</p><p><strong>Methods: </strong>The search strategy for this study followed PRISMA. Using precise keywords, PubMed, Scopus, Web of Science, and the Cochrane library were searched. English articles were searched until August 2, 2023. Selection criteria included mUKA and rUKA patient satisfaction studies. The NOS scale evaluated study quality. Meta-analysis was done with R and heterogeneity analysis.</p><p><strong>Results: </strong>This systematic review examined 5 studies with 1060 UKAs (532 robotic-assisted and 528 manual). Variable satisfaction assessment methods were used. Three studies found no difference in patient satisfaction after robotic-assisted UKA, but two found a higher satisfaction. Meta-analysis showed robotic-assisted UKA improved patient satisfaction (OR = 1.72 [1.25-2.37]). Overall, most studies showed low risk of bias, except one with higher bias.</p><p><strong>Conclusion: </strong>This review suggests that robotic assistance may enhance patient satisfaction in UKA procedures.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138809731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing security in Wireless Body Area Networks (WBANs) with ECC-based Diffie-Hellman Key Exchange algorithm (ECDH).","authors":"Akilan S S, Kayathri Devi Devprasad, Raja Sekar J","doi":"10.3233/THC-231614","DOIUrl":"10.3233/THC-231614","url":null,"abstract":"<p><strong>Background: </strong>Wireless Body Area Networks (WBANs) are integral to modern healthcare systems, providing continuous health monitoring and real-time data transmission. The sensitivity of medical data being transmitted makes security a significant concern in WBANs.</p><p><strong>Objective: </strong>This study explores the application of the Elliptic Curve Cryptography (ECC)-based Diffie-Hellman Key Exchange (ECDH) algorithm to enhance security within WBANs.</p><p><strong>Method: </strong>The study investigates the suitability of ECC for this context and evaluates the performance and security implications of implementing ECDH in WBANs.</p><p><strong>Results: </strong>The findings reveal that ECDH provides a robust and computationally efficient solution for secure key exchange in WBANs, addressing inherent vulnerabilities.</p><p><strong>Conclusion: </strong>The adoption of ECC-based ECDH is poised to bolster data confidentiality and integrity in WBANs, promoting trust and widespread use of these networks in healthcare applications. This research contributes to the growing body of knowledge regarding security measures in WBANs and opens new avenues for the secure transmission of sensitive medical information.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140853264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.","authors":"Hongru Zhang, Chen Wang, Ning Yang","doi":"10.3233/THC-240087","DOIUrl":"10.3233/THC-240087","url":null,"abstract":"<p><strong>Background: </strong>Early identification of sepsis has been shown to significantly improve patient prognosis.</p><p><strong>Objective: </strong>Therefore, the aim of this meta-analysis is to systematically evaluate the diagnostic efficacy of machine-learning algorithms for sepsis prediction.</p><p><strong>Methods: </strong>Systematic searches were conducted in PubMed, Embase and Cochrane databases, covering literature up to December 2023. The keywords included machine learning, sepsis and prediction. After screening, data were extracted and analysed from studies meeting the inclusion criteria. Key evaluation metrics included sensitivity, specificity and the area under the curve (AUC) for diagnostic accuracy.</p><p><strong>Results: </strong>The meta-analysis included a total of 21 studies with a data sample size of 4,158,941. Overall, the pooled sensitivity was 0.82 (95% confidence interval [CI] = 0.70-0.90; P< 0.001; I2= 99.7%), the specificity was 0.91 (95% CI = 0.86-0.94; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.91-0.96). The subgroup analysis revealed that in the emergency department setting (6 studies), the pooled sensitivity was 0.79 (95% CI = 0.68-0.87; P< 0.001; I2= 99.6%), the specificity was 0.94 (95% CI 0.90-0.97; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.92-0.96). In the Intensive Care Unit setting (11 studies), the sensitivity was 0.91 (95% CI = 0.75-0.97; P< 0.001; I2= 98.3%), the specificity was 0.85 (95% CI = 0.75-0.92; P< 0.001; I2= 99.9%), and the AUC was 0.93 (95% CI = 0.91-0.95). Due to the limited number of studies in the in-hospital and mixed settings (n< 3), no pooled analysis was performed.</p><p><strong>Conclusion: </strong>Machine-learning algorithms have demonstrated excellent diagnostic accuracy in predicting the occurrence of sepsis, showing potential for clinical application.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141538798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating network pharmacology and Mendelian randomization to explore potential targets of matrine against ovarian cancer.","authors":"Xiaoqun Chen, Yingliang Song","doi":"10.3233/THC-231051","DOIUrl":"10.3233/THC-231051","url":null,"abstract":"<p><strong>Background: </strong>Matrine has been reported inhibitory effects on ovarian cancer (OC) cell progression, development, and apoptosis. However, the molecular targets of matrine against OC and the underlying mechanisms of action remain elusive.</p><p><strong>Objective: </strong>This study endeavors to unveil the potential targets of matrine against OC and to explore the intricate relationships between these targets and the pathogenesis of OC.</p><p><strong>Methods: </strong>The effects of matrine on the OC cells (A2780 and AKOV3) viability, apoptosis, migration, and invasion was investigated through CCK-8, flow cytometry, wound healing, and Transwell analyses, respectively. Next, Matrine-related targets, OC-related genes, and ribonucleic acid (RNA) sequence data were harnessed from publicly available databases. Differentially expressed analyses, protein-protein interaction (PPI) network, and Venn diagram were involved to unravel the core targets of matrine against OC. Leveraging the GEPIA database, we further validated the expression levels of these core targets between OC cases and controls. Mendelian randomization (MR) study was implemented to delve into potential causal associations between core targets and OC. The AutoDock software was used for molecular docking, and its results were further validated using RT-qPCR in OC cell lines.</p><p><strong>Results: </strong>Matrine reduced the cell viability, migration, invasion and increased the cell apoptosis of A2780 and AKOV3 cells (P< 0.01). A PPI network with 578 interactions among 105 candidate targets was developed. Finally, six core targets (TP53, CCND1, STAT3, LI1B, VEGFA, and CCL2) were derived, among which five core targets (TP53, CCND1, LI1B, VEGFA, and CCL2) differential expressed in OC and control samples were further picked for MR analysis. The results revealed that CCND1 and TP53 were risk factors for OC. Molecular docking analysis demonstrated that matrine had good potential to bind to TP53, CCND1, and IL1B. Moreover, matrine reduced the expression of CCND1 and IL1B while elevating P53 expression in OC cell lines.</p><p><strong>Conclusions: </strong>We identified six matrine-related targets against OC, offering novel insights into the molecular mechanisms underlying the therapeutic effects of matrine against OC. These findings provide valuable guidance for developing more efficient and targeted therapeutic approaches for treating OC.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141538803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid model for the classification of Autism Spectrum Disorder using Mu rhythm in EEG.","authors":"Menaka Radhakrishnan, Karthik Ramamurthy, Saranya Shanmugam, Gaurav Prasanna, Vignesh S, Surya Y, Daehan Won","doi":"10.3233/THC-240644","DOIUrl":"10.3233/THC-240644","url":null,"abstract":"<p><strong>Background: </strong>Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification.</p><p><strong>Objective: </strong>This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification.</p><p><strong>Methods: </strong>Non-linear features in time and frequency domains are extracted and ML models are applied for classification. The EEG 1D data is transformed into images using Independent Component Analysis-Second Order Blind Identification (ICA-SOBI), Spectrogram, and Continuous Wavelet Transform (CWT).</p><p><strong>Results: </strong>Stacking Classifier employed with non-linear features yields precision, recall, F1-score, and accuracy rates of 78%, 79%, 78%, and 78% respectively. Including entropy and fuzzy entropy features further improves accuracy to 81.4%. In addition, DL models, employing SOBI, CWT, and spectrogram plots, achieve precision, recall, F1-score, and accuracy of 75%, 75%, 74%, and 75% respectively. The hybrid model, which combined deep learning features from spectrogram and CWT with machine learning, exhibits prominent improvement, attained precision, recall, F1-score, and accuracy of 94%, 94%, 94%, and 94% respectively. Incorporating entropy and fuzzy entropy features further improved the accuracy to 96.9%.</p><p><strong>Conclusions: </strong>This study underscores the potential of ML and DL techniques in improving the classification of ASD and TD individuals, particularly when utilizing a minimal set of EEG channels.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Architectural design of national evidence based medicine information system based on electronic health record.","authors":"Leonidas Fragidis, Sofia Tsamoglou, Kosmas Kosmidis, Vassilios Aggelidis","doi":"10.3233/THC-232042","DOIUrl":"10.3233/THC-232042","url":null,"abstract":"<p><strong>Background: </strong>The global implementation of Electronic Health Records has significantly enhanced the quality of medical care and the overall delivery of public health services. The incorporation of Evidence-Based Medicine offers numerous benefits and enhances the efficacy of decision-making in areas such as prevention, prognosis, diagnosis, and therapeutic approaches.</p><p><strong>Objective: </strong>The objective of this paper is to propose an architectural design of an Evidence-Based Medicine information system based on the Electronic Health Record, taking into account the existing and future level of interoperability of health information systems in Greece.</p><p><strong>Methods: </strong>A study of the suggested evidence-based medicine architectures found in the existing literature was conducted. Moreover, the interoperability architecture of health information systems in Greece was analyzed. The architecture design reviewed by specialized personnel and their recommendations were incorporated into the final design of the proposed architecture.</p><p><strong>Results: </strong>The proposed integrated architecture of an Evidence-Based Medicine system based on the Electronic Health Record integrates and utilizes citizens' health data while leveraging the existing knowledge available in the literature.</p><p><strong>Conclusions: </strong>Taking into consideration the recently established National Interoperability Framework, which aligns with the European Interoperability Framework, the proposed realistic architectural approach contributes to improving the quality of healthcare provided through the ability to make safe, timely and accurate decisions by physicians.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continued stepped care model improves early-stage self-report quality of life and knee function after total knee arthroplasty.","authors":"Xia Hu, Huiqing Jiang, Peizhen Liu, Zhiquan Li, Ruiying Zhang","doi":"10.3233/THC-240780","DOIUrl":"10.3233/THC-240780","url":null,"abstract":"<p><strong>Background: </strong>The Stepped Care Model (SCM) is an evidence-based treatment approach that tailors treatment intensity based on patients' health status, aiming to achieve the most positive treatment outcomes with the least intensive and cost-effective interventions. Currently, the effectiveness of the Stepped Care Model in postoperative rehabilitation for TKA (Total Knee Arthroplasty) patients has not been reported.</p><p><strong>Objective: </strong>The present study aimed to investigate whether the stepped care model could improve early-stage self-report quality of life and knee function after total knee arthroplasty via a prospective randomized controlled design.</p><p><strong>Methods: </strong>It was a mono-center, parallel-group, open-label, prospective randomized controlled study. Patients who aging from 60-75 years old as well as underwent unilateral primary total knee arthroplasty due to end-stage knee osteoarthritis between 2020.06 to 2022.02 were enrolled. Participants were randomized and arranged into two groups in a 1:1 allocation. The control group was given traditional rehabilitation guidance, while the stepped care model group was given continued stepped care. Hospital for special surgery knee score, daily living ability (ADL), knee flexion range, and adverse events at 1, 3, and 6 months after total knee arthroplasty were recorded.</p><p><strong>Results: </strong>88 patients proceeded to the final analysis. There was no significant difference of age, gender, length of stay, BMI, and educational level between the two groups at the baseline. After specific stepped care model interventions, patients showed significant improvements in HHS in 1 month (85.00 (82.25, 86.00) vs. 80.00 (75.00, 83.00), p< 0.001), 3 months (88.00 (86.00, 92.00) vs. 83.00 (76.75, 85.00), p< 0.001), and 6 months (93.00 (90.25, 98.00) vs. 88.00 (84.25, 91.75), p< 0.001) when compared with the control group. Similar results were also found in both daily living ability and knee flexion angle measurements. No adverse event was observed during the follow-up.</p><p><strong>Conclusion: </strong>The present study found that the stepped care model intervention significantly improved early-stage knee function and self-reported life quality after total knee arthroplasty due to knee osteoarthritis. Female patients and those less than 70 years old benefit more from the stepped care model intervention after total knee arthroplasty.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gastrointestinal tract disease detection via deep learning based structural and statistical features optimized hexa-classification model.","authors":"Ajitha Gladis K P, Roja Ramani D, Mohana Suganthi N, Linu Babu P","doi":"10.3233/THC-240603","DOIUrl":"10.3233/THC-240603","url":null,"abstract":"<p><strong>Background: </strong>Gastrointestinal tract (GIT) diseases impact the entire digestive system, spanning from the mouth to the anus. Wireless Capsule Endoscopy (WCE) stands out as an effective analytic instrument for Gastrointestinal tract diseases. Nevertheless, accurately identifying various lesion features, such as irregular sizes, shapes, colors, and textures, remains challenging in this field.</p><p><strong>Objective: </strong>Several computer vision algorithms have been introduced to tackle these challenges, but many relied on handcrafted features, resulting in inaccuracies in various instances.</p><p><strong>Methods: </strong>In this work, a novel Deep SS-Hexa model is proposed which is a combination two different deep learning structures for extracting two different features from the WCE images to detect various GIT ailment. The gathered images are denoised by weighted median filter to remove the noisy distortions and augment the images for enhancing the training data. The structural and statistical (SS) feature extraction process is sectioned into two phases for the analysis of distinct regions of gastrointestinal. In the first stage, statistical features of the image are retrieved using MobileNet with the support of SiLU activation function to retrieve the relevant features. In the second phase, the segmented intestine images are transformed into structural features to learn the local information. These SS features are parallelly fused for selecting the best relevant features with walrus optimization algorithm. Finally, Deep belief network (DBN) is used classified the GIT diseases into hexa classes namely normal, ulcer, pylorus, cecum, esophagitis and polyps on the basis of the selected features.</p><p><strong>Results: </strong>The proposed Deep SS-Hexa model attains an overall average accuracy of 99.16% in GIT disease detection based on KVASIR and KID datasets. The proposed Deep SS-Hexa model achieves high level of accuracy with minimal computational cost in the recognition of GIT illness.</p><p><strong>Conclusions: </strong>The proposed Deep SS-Hexa Model progresses the overall accuracy range of 0.04%, 0.80% better than GastroVision, Genetic algorithm based on KVASIR dataset and 0.60%, 1.21% better than Modified U-Net, WCENet based on KID dataset respectively.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of a real-life experience with a digital fluid balance monitoring technology.","authors":"Lisbeth R Leinum, Anders O Baandrup, Ismail Gögenur, Marianne Krogsgaard, Nessn Azawi","doi":"10.3233/THC-231303","DOIUrl":"10.3233/THC-231303","url":null,"abstract":"<p><strong>Background: </strong>Innovations in healthcare technologies have the potential to address challenges, including the monitoring of fluid balance.</p><p><strong>Objective: </strong>This study aims to evaluate the functionality and accuracy of a digital technology compared to standard manual documentation in a real-life setting.</p><p><strong>Methods: </strong>The digital technology, LICENSE, was designed to calculate fluid balance using data collected from devices measuring urine, oral and intravenous fluids. Participating patients were connected to the LICENSE system, which transmitted data wirelessly to a database. These data were compared to the nursing staff's manual measurements documented in the electronic patient record according to their usual practice.</p><p><strong>Results: </strong>We included 55 patients in the Urology Department needing fluid balance charting and observed them for an average of 22.9 hours. We found a mean difference of -44.2 ml in total fluid balance between the two methods. Differences ranged from -2230 ml to 2695 ml, with a divergence exceeding 500 ml in 57.4% of cases. The primary source of error was inaccurate or omitted manual documentation. However, errors were also identified in the oral LICENSE device.</p><p><strong>Conclusions: </strong>When used correctly, the LICENSE system performs satisfactorily in measuring urine and intravenous fluids, although the oral device requires revision due to identified errors.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141876385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyclophosphamide ameliorates membranous nephropathy by upregulating miR-223 expression, promoting M2 macrophage polarization and inhibiting inflammation.","authors":"Chunying Yao, Qiubo Ma, Ying Shi, Na Zhang, Lei Pang","doi":"10.3233/THC-241175","DOIUrl":"10.3233/THC-241175","url":null,"abstract":"<p><strong>Background: </strong>Membranous nephropathy (MN), also known as membranous glomerulonephritis, is a leading cause of adult nephrotic syndrome. The main pathological features encompass the deposition of immune complexes within the glomerular basement membrane epithelial cells, thickening of the basement membrane, and fusion of the foot process.</p><p><strong>Objective: </strong>This study aims to investigate the role of the immune and inflammatory modulator miR-223 in the immunosuppressive and anti-inflammatory effects of cyclophosphamide (CTX) on membranous nephropathy (MN).</p><p><strong>Methods: </strong>miR-223 mimetics or inhibitors was used to regulate miR-223 levels. LPS induced inflammatory cell model and cell polarization. CTX was used to treat Lipopolysaccharides (LPS) induced inflammatory response and polarization. Cationic bovine serum albumin (c-BSA) induced BALB/c mouse MN model, while CTX was used to treat c-BSA induced MN.</p><p><strong>Results: </strong>The miR-223 level in LPS induced inflammatory model cells was lower than that in control cells. The levels of inflammatory factors in LPS+miR-223 mimetics and CTX+miR-223i cells were lower than those in LPS and miR-223i cells. The protein levels of LPS+miR-223 mimic, CTX+miR-223i macrophage M2 phenotype markers Arginase-1 (Arg1), transforming growth factor β1 (TGF-β1), anti-inflammatory factors interleukin-4 (IL4) and interleukin-13 (IL13) were significantly higher than those of LPS and miR-223i. The effect of CTX was confirmed in a BALB/c mouse MN model induced by cationic bovine serum albumin (c-BSA).</p><p><strong>Conclusion: </strong>CTX upregulates the expression of miR-223, promotes polarization of M2 macrophages, alleviates the inflammatory response and renal injury of MN.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}