Level of interleukin 17 in inflammatory bowel disease and its relation with disease activity.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic relapsing inflammatory disorder of the gastrointestinal tract (GIT).It results in progressive intestinal epithelium structural and functional damage that necessitates lifetime medication.Thereis imbalance in the production of T helper 1 (Th1), Th2 and Th17 cytokines. This plays a crucial role in the chronic inflammatory process and the defective immune response to pathogenic agents; thus promoting the recurrence of the disease.Our aim of this study was to detect serum IL-17 levels in IBD patients and its relation with disease activity. METHODS: This was a single center case control study, conducted at hepatology and gastroenterology unit, Mansoura specialized Medical Hospital, Egypt.Patients who were included were aged 18-65 years, diagnosed either Ulcerative Colitis (UC)or Crohn's Disease (CD) based on previous colonoscopy.IBD activity was measured for UC using the MAYO score and CD using the CD activity index (CDAI). Fifty five patients were UC, 24 patients were CD, 21 patients were control.Patients who were excluded were under 15 years old, with history of GIT malignancy, or any serious comorbidities. Study protocol was approved by Institution Research Board (IRB) of Mansoura Medical College.All patients were subjected to full history taking, routine physical examination, colonoscopy and laboratory investigations including serum IL-17 levels by ELISA besides CBC, CRP, ESR and fecal calprotectin. RESULTS: Serum IL-17 level was increased significantly among UC; median (min-max) = 72(21-502)pg/ml, in CD 54.5(25-260) versus control 19 (14-35), P < 0.001.However, it was not correlated to the disease activity either Mayo score of UC or CDAI of CD.There was significant correlation to the extent of inflammation in UC affecting the colon (either proctosigmoiditis, left sided colitis or pan colitis), also to the type of CD (either inflammatory, stricturing or fistulizing) by P < 0.05.It was not correlated significantly with any of the IBD activity markers (CRP, ESR, or fecal calprotectin).Yet there was negative significant correlation with Hb level (r =-0.28, p = 0.005).There was not significant association between median serum level of IL-17 & duration of disease (P = 0.6).However, median IL-17 was higher among hospitalized cases than non-hospitalized (73 & 55, pg/ml respectively; p < 0.002). AUC was significantly differentiating between IBD and control group = 0.993 with the best-detected cut off point from curve 32 pg/ml yielding sensitivity of 97.5% and specificity of 95.2%. CONCLUSION: Serum IL-17 increases in colonic inflammation significantly more than in control group, however its increase is not correlated to IBD activity.

Barriers to Mental Illness Treatment in Saudi Arabia: A Population-Based Cross-Sectional Study.

BACKGROUND: Mental illness is a disorder that can cause impairment and disability, affecting mood, thinking, and behavior; therefore, early intervention will reduce morbidity. This study aims to evaluate all the personal, family, societal, and medical barriers that prevent mental health patients from seeking consultation and treatment. METHODS: In Saudi Arabia, a cross-sectional study was conducted on 463 individuals aged 18 and above. Data were collected by face-to-face interviews using a validated questionnaire, which consisted of two parts. The first part included sociodemographic data, while the second part contained subsections of society/family, personal, and medical barriers. RESULTS: The results showed that 379 (81.9%) indicated that society and family barriers impacted them, whereas 325 (70.3%) believed that personal barriers hindered seeking help. However, 294 (63.5%) opted for medical barriers as a hindrance. Regarding the highest barriers, 120 of the total respondents (25.9%) saw psychiatric illness as a source of shame and stigma, 166 respondents (35.9%) said that the psychiatric patient is seen as crazy, 159 of them (34.3%) believed it is tough for anyone to talk about their feelings and emotions and 183 respondent (39.5%) feared that psychiatric illness would decrease the chance of marriage to the appropriate person. Our findings also indicated a low trust in hospital treatment, hence a loss of confidence in using medications. CONCLUSION: The findings of this study indicate that societal stigma is the most common barrier preventing people from seeking mental health consultation. Many barriers differ significantly between males and females.

Acute Kidney Injury Among COVID-19-Positive Patients Is Associated With Higher Mortality: A Single-Center Experience.

OBJECTIVES: Renal complications of COVID-19 are not yet well studied. We aimed to evaluate acute kidney injury prevalence among hospitalized patients with COVID-19 infection and explore its effect on patient outcomes. MATERIALS AND METHODS: We retrospectively evaluated 586 hospitalized patients with COVID-19. Of these patients, 267 (45.5%) developed acute kidney injury, as classified according to the Kidney Disease Improving Global Outcomes guidelines. We compared this group with 319 patients (54.5%) without acute kidney injury. RESULTS: Most patients in both study groups were men; mean age was 60.8 ± 14 versus 51.7 ± 16 years. Comorbid conditions that were substantially predominant among patients with acute kidney injury were diabetes mellitus (64% vs 42.9%), hypertension (72.6% vs 43.5%), and ischemic heart disease (25% vs 14.7%). Fever, cough, shortness of breath, and dehydration were the main presentations among patients with acute kidney injury, and patients in this group had greater prevalence of radiological findings concordant with COVID-19 (86.8% vs 59.8%). Sepsis, volume depletion, shock, arrhythmias, and acute respiratory distress syndrome were higher in patients with acute kidney injury. Anticoagulation (85% vs 59.2%), vasopressors, plasma infusions, antimicrobials, and steroids were more frequently used in patients with acute kidney injury. More patients with acute kidney injury had acute respiratory failure requiring mechanical ventilation (62.3% vs 32.9%), with higher overall mortality rate (63.2% vs 31.1%). CONCLUSIONS: We found more frequent prevalence of acute kidney injury associated with severe COVID-19 than shown in reports from Chinese, European, and North American cohorts. Patients with COVID-19 who developed acute kidney injury had risk factors such as hypertension and diabetes, greater need for mechanical ventilation, were males, and were older age. Mortality was high in this population, especially among older patients and those who developed Kidney Disease Improving Global Outcomes stage 3 disease.

Enhancing machine learning-based sentiment analysis through feature extraction techniques.

A crucial part of sentiment classification is featuring extraction because it involves extracting valuable information from text data, which affects the model's performance. The goal of this paper is to help in selecting a suitable feature extraction method to enhance the performance of sentiment analysis tasks. In order to provide directions for future machine learning and feature extraction research, it is important to analyze and summarize feature extraction techniques methodically from a machine learning standpoint. There are several methods under consideration, including Bag-of-words (BOW), Word2Vector, N-gram, Term Frequency- Inverse Document Frequency (TF-IDF), Hashing Vectorizer (HV), and Global vector for word representation (GloVe). To prove the ability of each feature extractor, we applied it to the Twitter US airlines and Amazon musical instrument reviews datasets. Finally, we trained a random forest classifier using 70% of the training data and 30% of the testing data, enabling us to evaluate and compare the performance using different metrics. Based on our results, we find that the TD-IDF technique demonstrates superior performance, with an accuracy of 99% in the Amazon reviews dataset and 96% in the Twitter US airlines dataset. This study underscores the paramount significance of feature extraction in sentiment analysis, endowing pragmatic insights to elevate model performance and steer future research pursuits.

Improving sentiment classification using a RoBERTa-based hybrid model.

Introduction: Several attempts have been made to enhance text-based sentiment analysis's performance. The classifiers and word embedding models have been among the most prominent attempts. This work aims to develop a hybrid deep learning approach that combines the advantages of transformer models and sequence models with the elimination of sequence models' shortcomings. Methods: In this paper, we present a hybrid model based on the transformer model and deep learning models to enhance sentiment classification process. Robustly optimized BERT (RoBERTa) was selected for the representative vectors of the input sentences and the Long Short-Term Memory (LSTM) model in conjunction with the Convolutional Neural Networks (CNN) model was used to improve the suggested model's ability to comprehend the semantics and context of each input sentence. We tested the proposed model with two datasets with different topics. The first dataset is a Twitter review of US airlines and the second is the IMDb movie reviews dataset. We propose using word embeddings in conjunction with the SMOTE technique to overcome the challenge of imbalanced classes of the Twitter dataset. Results: With an accuracy of 96.28% on the IMDb reviews dataset and 94.2% on the Twitter reviews dataset, the hybrid model that has been suggested outperforms the standard methods. Discussion: It is clear from these results that the proposed hybrid RoBERTa-(CNN+ LSTM) method is an effective model in sentiment classification.

The Role of RIN3 Gene in Alzheimer's Disease Pathogenesis: a Comprehensive Review.

Alzheimer's disease (AD) is a globally prevalent form of dementia that impacts diverse populations and is characterized by progressive neurodegeneration and impairments in executive memory. Although the exact mechanisms underlying AD pathogenesis remain unclear, it is commonly accepted that the aggregation of misfolded proteins, such as amyloid plaques and neurofibrillary tau tangles, plays a critical role. Additionally, AD is a multifactorial condition influenced by various genetic factors and can manifest as either early-onset AD (EOAD) or late-onset AD (LOAD), each associated with specific gene variants. One gene of particular interest in both EOAD and LOAD is RIN3, a guanine nucleotide exchange factor. This gene plays a multifaceted role in AD pathogenesis. Firstly, upregulation of RIN3 can result in endosomal enlargement and dysfunction, thereby facilitating the accumulation of beta-amyloid (Aß) peptides in the brain. Secondly, RIN3 has been shown to impact the PICLAM pathway, affecting transcytosis across the blood-brain barrier. Lastly, RIN3 has implications for immune-mediated responses, notably through its influence on the PTK2B gene. This review aims to provide a concise overview of AD and delve into the role of the RIN3 gene in its pathogenesis.

AuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models.

Color face images are often transmitted over public channels, where they are vulnerable to tampering attacks. To address this problem, the present paper introduces a novel scheme called Authentication and Color Face Self-Recovery (AuCFSR) for ensuring the authenticity of color face images and recovering the tampered areas in these images. AuCFSR uses a new two-dimensional hyperchaotic system called two-dimensional modular sine-cosine map (2D MSCM) to embed authentication and recovery data into the least significant bits of color image pixels. This produces high-quality output images with high security level. When tampered color face image is detected, AuCFSR executes two deep learning models: the CodeFormer model to enhance the visual quality of the recovered color face image and the DeOldify model to improve the colorization of this image. Experimental results demonstrate that AuCFSR outperforms recent similar schemes in tamper detection accuracy, security level, and visual quality of the recovered images.

Inhaled corticosteroids' effect on COVID-19 patients: A systematic review and meta-analysis of randomized controlled trials.

Background: More than six million people died due to COVID-19, and 10-15% of infected individuals suffer from post-covid syndrome. Corticosteroids are widely used in the management of severe COVID-19 and post-acute COVID-19 symptoms. This study synthesizes current evidence of the effectiveness of inhaled corticosteroids (ICS) on mortality, hospital length-of-stay (LOS), and improvement of smell scores in patients with COVID-19. Methods: We searched Embase, Web of Science, PubMed, Cochrane Library, and Scopus until Aug 2022. The Cochrane risk of bias tool was used to assess the quality of studies. We evaluated the effectiveness of ICS in COVID-19 patients through measures of mortality, LOS, alleviation of post-acute COVID-19 symptoms, time to sustained self-reported cure, and sense of smell (visual analog scale (VAS)). Results: Ten studies were included in the meta-analysis. Our study showed a significant decrease in the LOS in ICS patients over placebo (MD = -1.52, 95% CI [-2.77 to -0.28], p-value = 0.02). Patients treated with intranasal corticosteroids (INC) showed a significant improvement in VAS smell scores from week three to week four (MD =1.52, 95% CI [0.27 to 2.78], p-value = 0.02), and alleviation of COVID-related symptoms after 14 days (RR = 1.17, 95% CI [1.09 to 1.26], p-value < 0.0001). No significant differences were detected in mortality (RR= 0.69, 95% CI [0.36 to 1.35], p-value = 0.28) and time to sustained self-reported cure (MD = -1.28, 95% CI [-6.77 to 4.20], p-value = 0.65). Conclusion: We concluded that the use of ICS decreased patient LOS and improved COVID-19-related symptoms. INC may have a role in improving the smell score. Therefore, using INC and ICS for two weeks or more may prove beneficial. Current data do not demonstrate an effect on mortality or time to sustained self-reported cure. However, the evidence is inconclusive, and more studies are needed for more precise data.

Antimicrobial and anti-SARS-CoV-2 activities of smart daclatasvir-chitosan/gelatin nanoparticles-in-PLLA nanofibrous medical textiles; in vitro, and in vivo study.

This study aims at the development of electrospun polylactic acid nanofibers (PLLA NFs) incorporating smart daclatasvir-loaded chitosan gelatin nanoparticles to be used as medical textiles. First, smart nanoparticles were prepared through ionic gelation and optimized using Design Expert® software where daclatasvir (DAC), chitosan (CS), and gelatin (GL) amounts were selected to be the independent variables. DAC was used owing to its reported Anti-SARS-CoV-2 activity, CS was chosen due to its antimicrobial activity and GL was used owing to its sensitivity to be hydrolyzed upon exposure to Papain-like protease enzyme (PLpro). The optimum DAC-CS/TAN NPs possessed 109 nm size and 94.44 % entrapment efficiency in addition to sustained drug release for 14 days. Furthermore, upon exposure to PLpro, smart DAC-CS/GL NPs released the whole DAC amount within 3 h. Then, DAC-CS/GL NPs were incorporated within PLLA NFs through electrospinning. Swellability was found to increase gradually reflecting the controlled release of DAC from nanofibers within 3 weeks. Cell viability assessments using human fibroblasts showed that the developed nanofibers possess high biocompatibility. An in-vivo animal model for skin irritation was carried out for two weeks where visual inspection and histopathological investigations showed that neither edema nor erythema were observed.

Internet of Medical Things and Healthcare 4.0: Trends, Requirements, Challenges, and Research Directions.

Healthcare 4.0 is a recent e-health paradigm associated with the concept of Industry 4.0. It provides approaches to achieving precision medicine that delivers healthcare services based on the patient's characteristics. Moreover, Healthcare 4.0 enables telemedicine, including telesurgery, early predictions, and diagnosis of diseases. This represents an important paradigm for modern societies, especially with the current situation of pandemics. The release of the fifth-generation cellular system (5G), the current advances in wearable device manufacturing, and the recent technologies, e.g., artificial intelligence (AI), edge computing, and the Internet of Things (IoT), are the main drivers of evolutions of Healthcare 4.0 systems. To this end, this work considers introducing recent advances, trends, and requirements of the Internet of Medical Things (IoMT) and Healthcare 4.0 systems. The ultimate requirements of such networks in the era of 5G and next-generation networks are discussed. Moreover, the design challenges and current research directions of these networks. The key enabling technologies of such systems, including AI and distributed edge computing, are discussed.

SARS-CoV-2 Papain-like Protease Responsive ZnO/Daclatasvir-Loaded Chitosan/Gelatin Nanofibers as Smart Antimicrobial Medical Textiles: In Silico, In Vitro and Cell Studies.

A significant number of deaths are reported annually worldwide due to microbial and viral infections. The development of protective medical textiles for patients and healthcare professionals has attracted many researchers' attention. Therefore, this study aims to develop smart drug-eluting nanofibrous matrices to be used as a basic material for medical textile fabrication. First, chitosan/gelatin nanofibers were selected as the basic material owing to the wide antimicrobial activity of chitosan and the capability of gelatin to be hydrolyzed in the abundance of the papain-like protease (PLpro) enzyme secreted by SARS-CoV-2. Daclatasvir (DAC), an NS5A inhibitor, was selected as the model drug based on in silico studies where it showed high anti-SARS-CoV-2 potential compared to FDA-approved references. Due to their reported antimicrobial and antiviral activities, ZnO NPs were successfully prepared and incorporated with daclatasvir in chitosan/gelatin nanofibrous matrices through electrospinning. Afterward, an in vitro release study in a simulated buffer revealed the controlled release of DAC over 21 days from the nanofibers compared to only 6 h for free DAC. On the other hand, the abundance of PLpro induced the complete release of DAC from the nanofibers in only 4-8 h. Finally, the nanofibers demonstrated a wide antimicrobial activity against S. aureus, E. coli, and C. albicans.

Enhanced Deep Learning Approach for Accurate Eczema and Psoriasis Skin Detection.

This study presents an enhanced deep learning approach for the accurate detection of eczema and psoriasis skin conditions. Eczema and psoriasis are significant public health concerns that profoundly impact individuals' quality of life. Early detection and diagnosis play a crucial role in improving treatment outcomes and reducing healthcare costs. Leveraging the potential of deep learning techniques, our proposed model, named "Derma Care," addresses challenges faced by previous methods, including limited datasets and the need for the simultaneous detection of multiple skin diseases. We extensively evaluated "Derma Care" using a large and diverse dataset of skin images. Our approach achieves remarkable results with an accuracy of 96.20%, precision of 96%, recall of 95.70%, and F1-score of 95.80%. These outcomes outperform existing state-of-the-art methods, underscoring the effectiveness of our novel deep learning approach. Furthermore, our model demonstrates the capability to detect multiple skin diseases simultaneously, enhancing the efficiency and accuracy of dermatological diagnosis. To facilitate practical usage, we present a user-friendly mobile phone application based on our model. The findings of this study hold significant implications for dermatological diagnosis and the early detection of skin diseases, contributing to improved healthcare outcomes for individuals affected by eczema and psoriasis.

Evaluation of the effect of direct-acting antiviral agents on melatonin level and lipid peroxidation in chronic hepatitis C patients.

Background: Oxidative stress and its end products, such as malondialdehyde (MDA) play a leading role in the pathogenesis of hepatitis C. Melatonin is a hormone that helps regulate circadian rhythms, which likely play a role in infectious diseases in terms of susceptibility, clinical expression, and outcome. Objective: The present study was conducted to assess serum malondialdehyde and melatonin levels in patients with chronic hepatitis C infection before and after the intake of direct-acting antivirals. Method: Forty hepatitis C patients were the subjects of this study. While ten healthy volunteers who matched in age and socioeconomic status served as the control subjects. Malondialdehyde and melatonin were assayed in the serum of the three groups, and the results were statistically analyzed. Results: Hepatitis C patients had significantly higher malondialdehyde (p < 0.001) but significantly lower melatonin (p < 0.001) as compared to the healthy controls. After 12 weeks of treatment with direct-acting antivirals, the malondialdehyde level decreased significantly (p < 0.001) and the melatonin level increased significantly (p < 0.001). A significant negative correlation between malondialdehyde and melatonin was observed. Conclusion: The present findings suggest that treatment of hepatitis C patients with Direct-acting antivirals improves liver function parameters and antioxidant profiles.

Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models.

Internet of Things (IoT) devices for the home have made a lot of people's lives better, but their popularity has also raised privacy and safety concerns. This study explores the application of deep learning models for anomaly detection and face recognition in IoT devices within the context of smart homes. Six models, namely, LR-XGB-CNN, LR-GBC-CNN, LR-CBC-CNN, LR-HGBC-CNN, LR-ABC-CNN, and LR-LGBM-CNN, were proposed and evaluated for their performance. The models were trained and tested on labeled datasets of sensor readings and face images, using a range of performance metrics to assess their effectiveness. Performance evaluations were conducted for each of the proposed models, revealing their strengths and areas for improvement. Comparative analysis of the models showed that the LR-HGBC-CNN model consistently outperformed the others in both anomaly detection and face recognition tasks, achieving high accuracy, precision, recall, F1 score, and AUC-ROC values. For anomaly detection, the LR-HGBC-CNN model achieved an accuracy of 94%, a precision of 91%, a recall of 96%, an F1 score of 93%, and an AUC-ROC of 0.96. In face recognition, the LR-HGBC-CNN model demonstrated an accuracy of 88%, precision of 86%, recall of 90%, F1 score of 88%, and an AUC-ROC of 0.92. The models exhibited promising capabilities in detecting anomalies, recognizing faces, and integrating these functionalities within smart home IoT devices. The study's findings underscore the potential of deep learning approaches for enhancing security and privacy in smart homes. However, further research is warranted to evaluate the models' generalizability, explore advanced techniques such as transfer learning and hybrid methods, investigate privacy-preserving mechanisms, and address deployment challenges.

Rapid microwave synthesis of N and S dual-doped carbon quantum dots for natamycin determination based on fluorescence switch-off assay.

Green, one-pot, quick, and easily synthesized nitrogen and sulfur co-doped carbon quantum dots (N,S-CDs) were obtained from cheap and readily available chemicals (sucrose, urea, and thiourea) using a microwave-assisted approach in about 4 min and utilized as a turn-off fluorescent sensor for estimation of natamycin (NAT). First, the effect of N and S doping on the microwave-synthesized CDs' quantum yield was carefully studied. CDs derived from sucrose alone failed to produce a high quantum yield; then, to increase the quantum yield, doping with heteroatoms was carried out using either urea or thiourea. A slight increase in quantum yield was observed upon using thiourea with sucrose, while an obvious enhancement of quantum yield was obtained when urea was used instead of thiourea. Surprisingly, using a combination of urea and thiourea together results in N,S-CDs with the highest quantum yield (53.5%), uniform and small particle size distribution, and extended stability. The fluorescent signal of N,S-CDs was quenched upon addition of NAT due to inner filter effect and static quenching in a manner that allowed for quantitative determination of NAT over a range of 0.5-10.0µg ml-1(LOD = 0.10µg ml-1). The N,S-CDs were applicable for determination of NAT in aqueous humor, eye drops, different environmental water samples, and bread with excellent performance. The selectivity study indicated excellent selectivity of the prepared N,S-CDs toward NAT with little interference from possibly interfering substances. In-silico toxicological evaluation of NAT was conducted to estimate its long-term toxicity and drug-drug interactions. Finally, the preparation of N,S-CDs, and analytical procedure compliance with the green chemistry principles were confirmed by two greenness assessment tools.

Efficient Brain Tumor Detection with Lightweight End-to-End Deep Learning Model.

In the field of medical imaging, deep learning has made considerable strides, particularly in the diagnosis of brain tumors. The Internet of Medical Things (IoMT) has made it possible to combine these deep learning models into advanced medical devices for more accurate and efficient diagnosis. Convolutional neural networks (CNNs) are a popular deep learning technique for brain tumor detection because they can be trained on vast medical imaging datasets to recognize cancers in new images. Despite its benefits, which include greater accuracy and efficiency, deep learning has disadvantages, such as high computing costs and the possibility of skewed findings due to inadequate training data. Further study is needed to fully understand the potential and limitations of deep learning in brain tumor detection in the IoMT and to overcome the obstacles associated with real-world implementation. In this study, we propose a new CNN-based deep learning model for brain tumor detection. The suggested model is an end-to-end model, which reduces the system's complexity in comparison to earlier deep learning models. In addition, our model is lightweight, as it is built from a small number of layers compared to other previous models, which makes the model suitable for real-time applications. The optimistic findings of a rapid increase in accuracy (99.48% for binary class and 96.86% for multi-class) demonstrate that the new framework model has excelled in the competition. This study demonstrates that the suggested deep model outperforms other CNNs for detecting brain tumors. Additionally, the study provides a framework for secure data transfer of medical lab results with security recommendations to ensure security in the IoMT.

A Rare Case of Anterograde Gastroduodenal Intussusception Caused by Migrated Percutaneous Endoscopic Gastrostomy Feeding Tube.

Gastroduodenal intussusception is a critical condition in which stomach protrudes into the duodenum. It is a very rare condition in adults. Most common causes include intra luminal lesions in the stomach including benign or malignant tumors of the stomach. Most common tumors included are gastrointestinal stromal tumors (GISTs), gastric carcinoma, gastric lipoma, gastric leiomyoma, and gastric schwannoma. It is extremely rare to be caused by migration of percutaneous feeding tube. A 50-year-old woman with a past medical history (PMH) of dysphagia status post percutaneous endoscopic gastrostomy (PEG) tube, history of spastic quadriplegia, presented with acute nausea, vomiting and abdominal distention, and was found to have gastroduodenal intussusception in computed tomography (CT) scan. Condition resolved after retracting PEG tube. Endoscopy did not reveal any intra luminal lesions. External fixation using Avanos Saf-T-Pexy T-fasteners was performed to prevent recurrence of this condition. Most common of causes of gastroduodenal intussusception are GIST tumors of stomach. CT abdomen is the most accurate test and upper endoscopy is needed to rule out any intra luminal causes. Treatment of choice is either endoscopic or surgical resection. External fixation is essential to prevent recurrence.

Therapeutic and diagnostic applications of exosomal circRNAs in breast cancer.

Circular RNAs (circRNAs) are regulatory elements that are involved in orchestrating gene expression and protein functions and are implicated in various biological processes including cancer. Notably, breast cancer has a significant mortality rate and is one of the most common malignancies in women. CircRNAs have been demonstrated to contribute to the pathogenesis of breast cancer including its initiation, progression, metastasis, and resistance to drugs. By acting as miRNA sponges, circRNAs can indirectly influence gene expression by disrupting miRNA regulation of their target genes, ultimately altering the course of cancer development and progression. Additionally, circRNAs can interact with proteins and modulate their functions including signaling pathways involved in the initiation and development of cancer. Recently, circRNAs can encode peptides that play a role in the pathophysiology of breast cancer and other diseases and their potential as diagnostic biomarkers and therapeutic targets for various cancers including breast cancer. CircRNAs possess biomarkers that differentiate, such as stability, specificity, and sensitivity, and can be detected in several biological specimens such as blood, saliva, and urine. Moreover, circRNAs play an important role in various cellular processes including cell proliferation, differentiation, and apoptosis, all of which are integral factors in the development and progression of cancer. This review synthesizes the functions of circRNAs in breast cancer, scrutinizing their contributions to the onset and evolution of the disease through their interactions with exosomes and cancer-related intracellular pathways. It also delves into the potential use of circRNA as a biomarker and therapeutic target against breast cancer. It discusses various databases and online tools that offer crucial circRNA information and regulatory networks. Lastly, the challenges and prospects of utilizing circRNAs in clinical settings associated with breast cancer are explored.