Novel Method For Diagnosis Research Articles

INNV-35. DATA-DRIVEN MODELING OF EMERGING TRENDS AND COLLABORATIVE NETWORKS ACROSS BRAIN METASTASES RESEARCH: THE PAST DECADE OF INNOVATION

Abstract With recent significant growth in brain metastases literature output, more comprehensive research is necessary to understand emerging trends and novel methods of diagnosis and treatment. We aimed to address this by providing a contemporary systematic global analysis of emerging brain metastases research in the last decade while visually depicting the international collaborative environment among its researchers. Data mining was performed on all brain metastases publications across the last decade indexed in Elsevier’s Scopus database. Our Scopus search revealed 6,362 publications from 1,184 sources from 2013-2022. Of those, 252 were identified as high-impact articles with an annual citation rate (CR) > 10. The compounded annual growth rate (CAGR) of brain metastases publications was 7.2% per year since 2013. There was an acceleration of scientific production, from 5.9% CAGR across 2013-2018 to 9.6% across 2019-2022. Authors per document increased from 7.2% to 8.5% while international co-authorships increased from 16.6% to 18.3% in the same periods, respectively. Recent trending topics were noted to be artificial intelligence, understanding the brain microenvironment, exosomes, novel mechanisms of diagnosis (such as circulating tumor cells), and targeted treatments. High-impact articles tended to revolve around specific therapies or biomarkers as opposed to general synopses. Clustering analysis revealed three separate pivotal collaborative communities centered around Brown PD/Sahgal A, Ahluwalia MS, and Wang Y, with Brown PD as most notable for high impact research. While the top institutional collaborative group centered around Mayo Clinic, the highest impact research centered around the Dana-Farber Cancer Institute. Sub-analyses revealed that studies involving multiple countries tended to have a higher impact. The median overall survival of brain metastases, by primary source, was illustrated across the last five decades. Overall, significant growth in scientific production is driven by the integration of novel technologies (such as artificial intelligence), interdisciplinary approaches, and collaboration across global communities.

Novel Technique to Diagnose Gastroesophageal Reflux Disease

Gastroesophageal reflux disease (GERD), which is commonly encountered in clinical practice, has become increasingly prevalent in Asia in recent years. Definitive diagnosis of GERD requires upper gastrointestinal endoscopy and ambulatory pH monitoring and is therefore challenging. Endoscopic lesions are usually not incorporated into the diagnostic criteria, and pH monitoring is expensive, complicated, and uncomfortable for patients. Studies have investigated novel methods for diagnosis of GERD. Mucosal integrity, evaluated by mucosal admittance or impedance, is impaired in GERD owing to microscopic epithelial changes. Measurement of mucosal integrity is simple and can be performed endoscopically. Mucosal impedance has been investigated as a method to differentiate between GERD, non-GERD, and eosinophilic esophagitis, and mucosal admittance provides evidence to support diagnosis of GERD. Further research on these novel techniques is warranted to incorporate these into the diagnostic modalities used for GERD.

The Future of Orthodontics: Deep Learning Technologies.

Deep learning has emerged as a revolutionary technical advancement in modern orthodontics, offering novel methods for diagnosis, treatment planning, and outcome prediction. Over the past 25 years, the field of dentistry has widely adopted information technology (IT), resulting in several benefits, including decreased expenses, increased efficiency, decreased need for human expertise, and reduced errors. The transition from preset rules to learning from real-world examples, particularly machine learning (ML) and artificial intelligence (AI), has greatly benefited the organization, analysis, and storage of medical data. Deep learning, a type of AI, enables robots to mimic human neural networks, allowing them to learn and make decisions independently without the need for explicit programming. Its ability to automate cephalometric analysis and enhance diagnosis through 3D imaging has revolutionized orthodontic operations. Deep learning models have the potential to significantly improve treatment outcomes and reduce human errors by accurately identifying anatomical characteristics on radiographs, thereby expediting analytical processes. Additionally, the use of 3D imaging technologies such as cone-beam computed tomography (CBCT) can facilitate precise treatment planning, allowing for comprehensive examinations of craniofacial architecture, tooth movements, and airway dimensions. In today's era of personalized medicine, deep learning's ability to customize treatments for individual patients has propelled the field of orthodontics forward tremendously. However, it is essential to address issues related to data privacy, model interpretability, and ethical considerations before orthodontic practices can use deep learning in an ethical and responsible manner. Modern orthodontics is evolving, thanks to the ability of deep learning to deliver more accurate, effective, and personalized orthodontic treatments, improving patient care as technology develops.

Development of a novel method for diagnosis of fasciolosis based on cathepsin L7 in ruminants

Fasciolosis is a widely distributed zoonosis reported over 81 countries around the world. Good and early diagnostic method is critical in controlling this disease and prevention of injury to the liver and bile ducts. In this study, we identified a novel member (cathepsin L7) of cathepsin family from Fasciola spp.. Firstly, the biological character of CL7 was analyzed according to the information of cathepsin L family, and then rCL7 was expressed and purified, a new iELISA based on CL7 was developed. The results exhibited CL7 iELISA had 100% sensitivity 100% specificity in sheep (cut-off 1.329) and 100% sensitivity 93.75% specificity in cattle (cut-off 0.756). Moreover, anti-Fasciola CL7 antibodies could be detected in early Fasciola gigantica infected buffaloes, as early as 3 week-post-infection (WPI). In conclusion, it is suggested that CL7 with low cost, early detection, good specificity and sensitivity could be used as a candidate antigen for detection of ruminant fasciolosis.

Recent advances in our understanding of NEC diagnosis, prognosis and surgical approach.

Necrotising enterocolitis (NEC) remains a devasting condition that has seen limited improvement in outcomes in recent years. The incidence of the disease is increasing as more extremely premature infants survive. NEC is responsible for 1 in 10 neonatal deaths and up to 61% of survivors have significant neurodevelopmental delay. The aim of this review is to highlight recent advances in diagnosis, prognosis and surgical approach in this condition. Many recent studies have reported novel methods of diagnosis of NEC with the aim of earlier and more accurate identification. These include imaging and machine learning techniques. Prognostication of NEC is particularly important to allow earlier escalation of therapy. Around 25% of infants with NEC will require surgery and recent data has shown that time from disease onset to surgery is greater in infants whose indication for surgery is failed medical management, rather than pneumoperitoneum. This indication was also associated with worse outcomes compared to pneumoperitoneum. Ongoing research has highlighted several new methods of disease prognostication which includes differentiating surgical from medical NEC. Finally, recent randomised controlled trials in surgical technique are discussed along with the implications of these for practice. Further, high quality research utilising multi-centre collaborations and high fidelity data from electronic patient records is needed to address the issues discussed and ultimately improve outcomes in NEC.

Enteric Fever: Diagnostic Challenges and the Importance of Early Intervention.

Enteric fever is a systemic infection caused by highly virulent Salmonella entericaserovars: Typhi and Paratyphi. Diagnosis of enteric fever is challenging due to a wide variety of clinical features which overlap with other febrile illnesses. The current diagnostic methods are limited because of the suboptimal sensitivity of conventional tests like blood culture in detecting organisms and the invasive nature of bone marrow culture. It emphasizes the need to develop improved and more reliable diagnostic modalities. The rising rates of multidrug-resistant Salmonella strains call for an accurate understanding of the current management of the disease. Proper public health measures and large-scale immunization programs will help reduce the burden of the disease. A comprehensive surveillance system can help detect the chronic carrier state and is crucial in understanding antibiotic susceptibility patterns. We conducted an all-language literature search on Medline, Cochrane, Embase, and Google Scholar till May 2022. The following search words and medical subject headings (MeSH) were used: "enteric fever," "Salmonella Typhi," "multidrug-resistant Salmonella," chronic carrier state," "Salmonella detection, "and "typhoid vaccine." We reviewed the literature on clinical features, pathophysiology, new diagnostic tests, and interventions to prevent the disease. This article explores enteric fever and its various clinical features and addresses the emerging threat of multidrug resistance. It focuses on novel methods for diagnosis and prevention strategies, including vaccines and the use of surveillance systems employed across different parts of the world.

Engineering Escherichia coli for diagnosis and management of hyperuricemia.

Uric acid disequilibrium is implicated in chronic hyperuricemia-related diseases. Long-term monitoring and lowering of serum uric acid levels may be crucial for diagnosis and effective management of these conditions. However, current strategies are not sufficient for accurate diagnosis and successful long-term management of hyperuricemia. Moreover, drug-based therapeutics can cause side effects in patients. The intestinal tract plays an important role in maintaining healthy serum acid levels. Hence, we investigated the engineered human commensal Escherichia coli as a novel method for diagnosis and long-term management of hyperuricemia. To monitor changes in uric acid concentration in the intestinal lumen, we developed a bioreporter using the uric acid responsive synthetic promoter, pucpro, and uric acid binding Bacillus subtilis PucR protein. Results demonstrated that the bioreporter module in commensal E. coli can detect changes in uric acid concentration in a dose-dependent manner. To eliminate the excess uric acid, we designed a uric acid degradation module, which overexpresses an E. coli uric acid transporter and a B. subtilis urate oxidase. Strains engineered with this module degraded all the uric acid (250µM) found in the environment within 24 h, which is significantly lower (p < 0.001) compared to wild type E. coli. Finally, we designed an in vitro model using human intestinal cell line, Caco-2, which provided a versatile tool to study the uric acid transport and degradation in an environment mimicking the human intestinal tract. Results showed that engineered commensal E. coli reduced (p < 0.01) the apical uric acid concentration by 40.35% compared to wild type E. coli. This study shows that reprogramming E. coli holds promise as a valid alternative synthetic biology therapy to monitor and maintain healthy serum uric acid levels.

A Novel Method for Diagnosis of Breast Cancer Tumors Based on Random Forest

GLOBOCAN 2020 cancer data shows that female breast cancer has become the most common cancer over lung cancer for the first time. As a disease threatening the life safety of women all over the world, how to improve the accuracy of breast cancer diagnosis and help patients get treatment as early as possible is of great importance. This paper introduces a new random forest-based breast cancer diagnosis method (NRFM), using the average radius, average texture, average circumference and other 30 indicators in the nucleus of breast mass as characteristics, to diagnose the benign and malignant breast cancer. NRFM proposed to randomly miss a certain percentage of breast cancer data, using random forest regression to fill in the experiment proved that using the method proposed in this paper, when the proportion of missing data reached 50%, the accuracy of breast cancer diagnosis will be as high as 96.85%. Experiments show that NRFM is easy to understand, convenient to operate, and has practical application value, which can assist doctors to improve the accuracy of breast cancer diagnosis.

A Novel Method for Diagnosis of Broken Bars Based on Optimum Resolution of Prescient Direction Algorithm

Fourier transformation (FT) and Multiple Signal Classification (MUSIC) method suffer the insufficient ability in diagnosing broken rotor bar (BRB) fault using short-time data. Theoretical and simulation analyses show that the Optimum Resolution of Prescient Direction (ORPD) algorithm has the best frequency resolution performance due to a priori knowledge. The main objective of this paper is to detect BRB faults in induction machines using a condition monitoring architecture based on ORPD algorithm. In the proposed application, the ORPD algorithm with the best frequency resolution performance is used to estimate the fault-sensitive frequencies in the stator current signature. The prior information of BRB fault characteristic distribution is used to construct a weighting matrix in the ORPD algorithm, for acquiring the lowest signal-to-noise ratio resolution threshold. Once frequencies are estimated, their corresponding amplitudes are obtained by using the least squares estimator. The proposed methods were tested using experimental induction motors with different fault severity under the effect of several load levels or supply frequencies. Two types of power supply modes are considered: main and inverter. The results show that compared with traditional method which uses FT and MUSIC algorithm for fault diagnosing, the method based on ORPD algorithm has a higher frequency resolution and identification ability with short-time data, and still has good diagnostic performance even under light loading and lower supply frequencies.

Novel Techniques and Future Perspective for Investigating Critical-Size Bone Defects.

A critical-size bone defect is a challenging clinical problem in which a gap between bone ends will not heal and will become a nonunion. The current treatment is to harvest and transplant an autologous bone graft to facilitate bone bridging. To develop less invasive but equally effective treatment options, one needs to first have a comprehensive understanding of the bone healing process. Therefore, it is imperative to leverage the most advanced technologies to elucidate the fundamental concepts of the bone healing process and develop innovative therapeutic strategies to bridge the nonunion gap. In this review, we first discuss the current animal models to study critical-size bone defects. Then, we focus on four novel analytic techniques and discuss their strengths and limitations. These four technologies are mass cytometry (CyTOF) for enhanced cellular analysis, imaging mass cytometry (IMC) for enhanced tissue special imaging, single-cell RNA sequencing (scRNA-seq) for detailed transcriptome analysis, and Luminex assays for comprehensive protein secretome analysis. With this new understanding of the healing of critical-size bone defects, novel methods of diagnosis and treatment will emerge.

Global thrombosis test for assessing thrombotic status and efficacy of antithrombotic diet and other conditions.

Because of the high mortality from myocardial infarction and stroke, there is a great demand for finding novel methods of diagnosis, prevention and treatment of these diseases. Most of the current tests measure important determinants of thrombosis such as platelet function, coagulation and fibrinolysis in isolation; therefore, a global test measuring the actual thrombotic status would be more useful in clinical conditions. We obtained considerable experience by using the global thrombosis test, which determines the actual thrombotic status by taking into account the measured platelet reactivity, coagulation and fibrinolytic activities. In animal experiments, we found significant correlation between the ex vivo global thrombosis test measurements and the in vivo thrombotic status. The published evidence for the benefit of an antithrombotic diet with regular physical exercise is also described.

A novel method for diagnosis and de-noising of control rod drive mechanism within floating nuclear reactor

Control rod drive mechanism (CRDM) is the crucial component within floating power station associated with the reliability and safety operation. It is challenge to monitor and understand the operating status of CRDM, as characteristic vibration signals of fault are nonlinear and non-stationary. To obtain the key information and de-noise the vibration signal, a combination of semi-soft wavelet threshold (SWT) and Hilbert-Huang transform (HHT) is proposed for roller status diagnosis and de-noising, and local mean decomposition (LMD) method accompanied with support vector machine (SVM) is used for improvement. Hilbert transform is used to further extract fault characteristics of roller vibration signal. By using Semi-soft wavelet threshold method the noise interference is decreased and the effect of endpoint effect on empirical mode decomposition (EMD) is reduced. An evaluation model was established by LMD method to extract the characteristic features from the faults and SVM to collect and analyse the data. The experimental results show that the method can effectively eliminate the interference of noises and realize the status diagnosis of the roller.

Joint recurrence based root cause analysis of nonlinear multivariate chemical processes

A novel method of diagnosis and causality analysis of faults in chemical processes is developed based on the recurrence theory. By applying and adapting the joint recurrence plot (JRP), the effective feature or variables of each operating condition are examined and then they are used to detect and diagnose faults using an unsupervised method (the absence of fault labels and prior knowledge about different operating conditions), DBSCAN. Also, a method based on the concept of delayed joint recurrence plot is developed, which well reveals the ability to identify the main root cause of each fault and predict the propagation pathway of the fault affecting different variables. In order to reveal the capability of the proposed method in case of nonstationary, unstable, nonlinear data, two different multivariate chemical processes, i.e., Tennessee Eastman and chemical looping combustion, are used. Also, compared with other methods, it was found that the proposed method shows the best performance in fault diagnosis and root cause analysis of complex nonlinear processes even for unobservable faults.

Serological testing for COVID-19

The objectives of this study are to identify the rapid, appropriate, screening, definite and novel methods of diagnosis of SARS-CoV-2 (COVID-19) infection, including SARS-CoV-2 (COVID-19) variants among various degree of COVID-19 severity for rapid prevention and control of SARS-CoV-2 (COVID-19) transmission. Methods of The Study: A comprehensive search was carried out in mainstream bibliographic databases or Medical Subject Headings, including ScienDirect, PubMed, Scopus, and ISI Web of Science. The search was applied to the articles that were published between 1971 and early March 2021. Results: With strict literature search and screening processes, it yielded 40 articles from 78 articles of initial literature database. Characteristically, after infection, antibodies are detected in the blood of individuals, particularly individuals with few or mild symptoms. In patients with varying symptoms of COVID-19 and negative results of reverse-transcriptase-polymerase-chain reaction (RT-PCR) tests, the testing has a significantly clinical role when nasopharyngeal swabs are taken more than 5 days after symptom onset. The Royal College of Pathologists (RCPath) developed seven principles for production of a COVID-19 testing strategy. Testing being carried out for a purpose is one of these RCPath’s principles. Nevertheless, denial of requesting SARS-CoV-2 (COVID-19) antibody tests for reassurance should be cautioned. With a lower antibody levels, whether the protective immunity will be sustained is questionable. Several immune-based assays were developed against different SARS-CoV-2 (COVID-19) viral proteins as the followings: 1) Entire Spike (S) protein, IgG antibody from patient serum can cross-react with SARS-CoV and MERS-CoV, 2) S1 subunit of Spike (S) protein, IgA, IgG antibodies from patient serum can cross-react with SARS-CoV only, 3) Receptor-binding domain (RBD), IgG antibody from patient serum can cross-react with SARS-CoV only, and 4) Nucleocapsid (N), IgG antibody from patient serum can cross-react with SARS-CoV only. Long et al demonstrated in their study that IgG antibody and neutralizing antibody levels initiate decreasing within 2-3 months after infection in the majority of persons with recovery from SARS-CoV-2 (COVID-19) infection. An analytical study of the dynamics of neutralizing antibody titers demonstrated reduced neutralizing antibodies around 6-7 weeks after illness onset. In conclusion, the nucleic acid amplification tests may be poorly timed specimen collection, poor-quality specimen collection, long wait times for generating the results, and requirement of trained laboratory technicians. Serological data greatly supplement the laboratory results from the quantitative reverse-transcriptase-polymerase-chain reaction (qRT-PCR), the design of virus elimination programs (seroepidemiology), discovery of the monoclonal antibodies, and development of SARS-CoV-2 (COVID-19) vaccines.

A203 PAVING THE WAY TO THE DUODENUM: A CASE REPORT OF DUODENAL TUBERCULOSIS

Abstract Background Duodenal tuberculosis (DTB) is an uncommon manifestation of tuberculosis (TB), representing only 0.5% of all TB cases. The nonspecific features of its clinical presentation make diagnosis challenging, which can lead to delayed identification and even inappropriate treatments. Further, the location is also difficult to reach on conventional endoscopy and there are few studies on the use of balloon enteroscopy to diagnose DTB. Aims We report a case of DTB diagnosed using balloon enteroscopy that was successfully treated with symptom resolution. We also highlight uncommon features of DTB, including cobblestoning seen on endoscopy and villous blunting with duodenal lymphocytosis seen on histopathology. Methods Case report Results A 30-year-old previously healthy male returning from India 16 months prior presented to the emergency department with 1 month of abdominal distension, non-bloody diarrhea, and night-sweats. Examination was notable for fever and a distended abdomen. Computed-tomography showed thickening of the jejunum and ileum, with omental fat-stranding and intra-abdominal lymphadenopathy. As the lesion appeared out of reach for conventional endoscopy, balloon enteroscopy was performed, revealing an area of cobblestoning in the duodenum. Acid-fast bacilli (AFB) staining was negative. While TB culture was pending, a second balloon enteroscopy was performed, with AFB staining negative on repeat biopsy. Pathology showed villous blunting and increased intraepithelial lymphocytes but was negative for granulomas. As the diagnosis remained uncertain, endoscopic ultrasound-guided lymph node biopsy was performed and multiple lymph nodes were seen in the gastro-hepatic area. Lymph node biopsy AFB staining was also negative. 4 weeks later, pathology from the first balloon enteroscopy returned positive for TB. The remaining biopsy cultures were negative for TB. The patient was treated with standard of care treatment. At 2 and 4 months of follow-up, he reported complete resolution of symptoms. Conclusions The diagnosis of DTB is challenging due its non-specific presentation and rarity. Our case highlights the difficulty of diagnosis as endoscopic findings such as cobblestoning are highly uncommon. We provide support for the use of balloon enteroscopy as a novel method for diagnosis of DTB. With early identification, we were able to provide appropriate therapy and prevent propagation of the disease. Funding Agencies None

A Novel Method for Diagnosis of Bearing Fault Using Hierarchical Multitasks Convolutional Neural Networks

Intelligent mechanical fault diagnosis has developed very fast in recent years due to the advancement and application of deep learning technologies. Thus, there are many deep learning network models that have been explored in fault classification and diagnosis. However, there are still limitations in research on the relationship between fault location, fault type, and fault severity. In this paper, a novel method for diagnosis of bearing fault using hierarchical multitask convolution neural networks (HMCNNs) is proposed, taking into account the mentioned relationships. The HMCNN model includes a main task and multiple subtasks. In the HMCNN model, a weighted probability is used to reduce the classification error propagation among multitasks to improve the fault diagnosis accuracy. The validity of the proposed method is verified on bearing datasets. Experimental results show that the proposed method is very effective and superior to the existing methods.

Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches.

COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription-polymerase chain reaction. The method is expensive and time-consuming. Therefore, designing novel methods is important. In this paper, we used three deep learning-based methods for the detection and diagnosis of COVID-19 patients with the use of X-Ray images of lungs. For the diagnosis of the disease, we presented two algorithms include deep neural network (DNN) on the fractal feature of images and convolutional neural network (CNN) methods with the use of the lung images, directly. Results classification shows that the presented CNN architecture with higher accuracy (93.2%) and sensitivity (96.1%) is outperforming than the DNN method with an accuracy of 83.4% and sensitivity of 86%. In the segmentation process, we presented a CNN architecture to find infected tissue in lung images. Results show that the presented method can almost detect infected regions with high accuracy of 83.84%. This finding also can be used to monitor and control patients from infected region growth.

A Novel Method for Diagnosis of Sucker-Rod Pumping Systems Based on the Polished-Rod Load Vibration in Vertical Wells

Summary The pump dynamometer card is a direct reflection of the operating conditions of the downhole pump, which is important for the diagnosis of sucker-rod pumping systems. In this paper, we propose a novel diagnostic method based on the estimation of the parameters from the polished-rod load vibration signal of sucker-rod pumping systems in a vertical well. In this study, we deduce a new analytic solution of the 1D wave equation of the sucker-rod string, which can be used for the predictive and diagnostic analyses. The relationship between the polished-rod load vibration and the pump equivalent impulse load based on the analytic solution is studied, and the diagnostic parameter estimating method is proposed. Therefore, the pump dynamometer card calculated method based on the surface dynamometer card is realized. This study shows that the method is efficient.

Evaluation of Exosomal miRNA in Blood as a Potential Diagnostic Biomarker for Human Non-Small Cell Lung Cancer.

BackgroundTumor-derived exosomes have been used as diagnostic biomarkers to discriminate between tumor patients and healthy people. This study explored the roles of exosomal miRNAs in lung adenocarcinoma metastasis by microarray and developed a novel method for diagnosis of lung adenocarcinoma.Material/MethodsFour lung adenocarcinoma patients’ peripheral blood, including 2 metastasis and 2 N-metastasis, were used for exosomes miRNA microarray analysis. Exosomes were extracted by ultracentrifugation and identified by transmission electron microscopy. All the raw data were normalized by R software with limma packet. qRT-PCR was used to validate the microarray results. A549 cells were used to identify the functions of miR-4448. Western blot, qRT-PCR, RNAi, CCK8, and transwell invasion assay were used to verify the metastasis and proliferation abilities.ResultsmiR-4436a and miR-4687-5p were upregulated between the metastasis and N-metastasis group, while miR-22-3p, miR-3666, miR-4448, miR-4449, miR-6751-5p and miR-92a-3p were downregulated. miR-4448 was also downregulated between the metastasis and control group, whereas there was no significant difference between the N-metastasis group and control group. qRT-PCR confirmed the downregulation of miR-4448 in exosomes from lung adenocarcinoma patients compared with N-metastasis patients and healthy people. CCK8 and transwell invasion assay showed that A549 cells transfected with miR-4448 inhibitor had higher proliferation and metastasis ability. qRT-PCR and Western blot confirmed the high expression of MMP2 and MMP9 in A549 cells transfected with miR-4448 inhibitor.ConclusionsmiR-4448 can inhibit A549 cell proliferation and metastasis. miR-4448 in exosomes has the potential to serve as a diagnostic marker of patients with adenocarcinoma metastasis.

CircACAP2 promotes breast cancer proliferation and metastasis by targeting miR-29a/b-3p-COL5A1 axis

BackgroundBreast cancer is the most common cancer occurred in female around the world. The occurrence percentage of breast cancer among female who was over 50 years old is >80%. These factors may induce the chance of morbidity, including heavy drinking for a long term, smoking and postmenopausal obesity. Treatment of adjuvant chemotherapeutic and hormonal agents is mostly applied nowadays, which does contribute to the improvement of breast cancer, yet it is still an intractable disease for the side effects it brings. Emerging evidence has proved circular RNAs are involved in gene expression and modulation of biological behaviors. However, the mechanism and functions of circRNA ACAP2 (circACAP2) with respect to breast cancer remain unexplored. MethodsqRT-PCR analysis was used to detect relevant expression. Bioinformatics analysis was applied to seek miRNAs which might have binding sites with circACAP2 and miR-29a/b-3p. Functional experiments including CCK-8, colony formation assay and flow cytometry assay detected gene function role in breast cancer cells. RIP, RNA pull down and Luciferase reporter assays were carried out to verify binding sites among circACAP2, miR-29a/b-3p and COL5A1. ResultsCircACAP2 expression was prominently elevated in tumorous tissues. Functionally and mechanistically, circACAP2 promoted breast cancer proliferation and motility by sponging miR-29a/b-3p and modulating COL5A1. ConclusionElevated expression of circACAP2 in breast cancer tissues leads to malignant phenotype upon cancerous cells. CircACAP2-miR-29a/b-3p-COL5A1 axis leads to breast cancer tumorigenesis and could hopefully be a novel method for diagnosis and treatment for breast cancer.