Potential of Natural Phenolic Compounds as Antimicrobial Agents against Multidrug-Resistant Staphylococcus aureus in Chicken Meat.

Staphylococcus aureus is one of the most widespread foodborne bacteria that cause high morbidity, mortality, and economic loss, primarily if foodborne diseases are caused by pathogenic and multidrug-resistant (MDR) strains. This study aimed to determine the prevalence of S. aureus in chicken meat in Egyptian markets. Thus, this study might be the first to assess the efficiency of different natural phenolic compounds as novel antibacterial agents against MDR S. aureus pathogens isolated from raw chicken meat in the Egyptian market. The incidence and quantification of pathogenic S. aureus were detected in retail raw chicken meat parts (breast, thigh, fillet, and giblets). In total, 73 out of 80 (91.3%) of the chicken meat parts were contaminated, with S. aureus as the only species isolated. Of the 192 identified S. aureus isolates, 143 were coagulase-positive S. aureus and 117 isolates were MDR (81.8%, 117/143). Twenty-two antibiotic resistance profile patterns were detected. One strain was randomly selected from each pattern to further analyze virulence and resistance genes. Extracted DNA was assessed for the presence of antibiotic-resistance genes, i.e., vancomycin-resistance (vanA), aminoglycosides-resistance (aacA-aphD), apramycin-resistance (apmA), and methicillin-resistance (mecA), penicillin-resistance (blaZ), and virulence genes staphylococcal enterotoxins (sea and seb), Panton-Valentine leucocidin (pvl), clumping factor A (clfA), and toxic shock syndrome toxin (tst). Clustering analyses revealed that six S. aureus strains harbored the most virulence and resistance genes. The activity of hydroquinone was significantly higher than thymol, carvacrol, eugenol, and protocatechuic acid. Therefore, phenolic compounds, particularly hydroquinone, could potentially alternate with conventional antibiotics against the pathogenic MDR S. aureus inhabiting raw chicken meat. Hence, this study indicates that urgent interventions are necessary to improve hygiene for safer meat in Egyptian markets. Moreover, hydroquinone could be a natural phenolic compound for inhibiting foodborne pathogens.

Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report.

Duplex ultrasonography (DUS) is a safe, non-invasive, and affordable primary screening tool to identify the vascular risk factors of stroke. The overall process of DUS examination involves a series of complex processes, such as identifying blood vessels, capturing the images of blood vessels, measuring the velocity of blood flow, and then physicians, according to the above information, determining the severity of artery stenosis for generating final ultrasound reports. Generation of transcranial doppler (TCD) and extracranial carotid doppler (ECCD) ultrasound reports involves a lot of manual review processes, which is time-consuming and makes it easy to make errors. Accurate classification of the severity of artery stenosis can provide an early opportunity for decision-making regarding the treatment of artery stenosis. Therefore, machine learning models were developed and validated for classifying artery stenosis severity based on hemodynamic features. This study collected data from all available cases and controlled at one academic teaching hospital in Taiwan between 1 June 2020, and 30 June 2020, from a university teaching hospital and reviewed all patients' medical records. Supervised machine learning models were developed to classify the severity of artery stenosis. The receiver operating characteristic curve, accuracy, sensitivity, specificity, and positive and negative predictive value were used for model performance evaluation. The performance of the random forest model was better compared to the logistic regression model. For ECCD reports, the accuracy of the random forest model to predict stenosis in various sites was between 0.85 and 1. For TCD reports, the overall accuracy of the random forest model to predict stenosis in various sites was between 0.67 and 0.86. The findings of our study suggest that a machine learning-based model accurately classifies artery stenosis, which indicates that the model has enormous potential to facilitate screening for artery stenosis.

Time-Series Analysis and Healthcare Implications of COVID-19 Pandemic in Saudi Arabia.

The first case of coronavirus disease 2019 (COVID-19) in Saudi Arabia was reported on 2 March 2020. Since then, it has progressed rapidly and the number of cases has grown exponentially, reaching 788,294 cases on 22 June 2022. Accurately analyzing and predicting the spread of new COVID-19 cases is critical to develop a framework for universal pandemic preparedness as well as mitigating the disease's spread. To this end, the main aim of this paper is first to analyze the historical data of the disease gathered from 2 March 2020 to 20 June 2022 and second to use the collected data for forecasting the trajectory of COVID-19 in order to construct robust and accurate models. To the best of our knowledge, this study is the first that analyzes the outbreak of COVID-19 in Saudi Arabia for a long period (more than two years). To achieve this study aim, two techniques from the data analytics field, namely the auto-regressive integrated moving average (ARIMA) statistical technique and Prophet Facebook machine learning technique were investigated for predicting daily new infections, recoveries and deaths. Based on forecasting performance metrics, both models were found to be accurate and robust in forecasting the time series of COVID-19 in Saudi Arabia for the considered period (the coefficient of determination for example was in all cases more than 0.96) with a small superiority of the ARIMA model in terms of the forecasting ability and of Prophet in terms of simplicity and a few hyper-parameters. The findings of this study have yielded a realistic picture of the disease direction and provide useful insights for decision makers so as to be prepared for the future evolution of the pandemic. In addition, the results of this study have shown positive healthcare implications of the Saudi experience in fighting the disease and the relative efficiency of the taken measures.

Nursing Informatics Research Trends: Findings from an International Survey.

This follow-up survey on trends in Nursing Informatics (NI) was conducted by the International Medical Informatics Association (IMIA) Student and Emerging Professionals (SEP) group as a cross-sectional study in 2019. There were 455 responses from 24 countries. Based on the findings NI research is evolving rapidly. Current ten most common trends include: clinical quality measures, clinical decision support, big data, artificial intelligence, care coordination, education and competencies, patient safety, mobile health, description of nursing practices and evaluation of patient outcomes. The findings help support the efforts to efficiently use resources in the promotion of health care activities, to support the development of informatics education and to grow NI as a profession.

Healthcare strategies and initiatives about COVID19 in Pakistan: Telemedicine a way to look forward.

The COVID-19 pandemic is one of the unprecedented devastating catastrophes with severe public health threat globally. Low and middle income countries (LMICs) are trying hard to cope with the rapidly changing global scenario and trying to mitigate this double crisis of pandemic and economic recession. This pandemic, has led to major changes in global and regional health care delivery proceedings with surge in telemedicine to provide the required services and also giving priority to control the disease spread.

Anemia and the Risk of Cognitive Impairment: An Updated Systematic Review and Meta-Analysis.

BACKGROUND: Cognitive impairment is one of the most common, burdensome, and costly disorders in the elderly worldwide. The magnitude of the association between anemia and overall cognitive impairment (OCI) has not been established. OBJECTIVE: We aimed to update and expand previous evidence of the association between anemia and the risk of OCI. METHODS: We conducted an updated systematic review and meta-analysis. We searched electronic databases, including EMBASE, PubMed, and Web of Science for published observational studies and clinical trials between 1 January 1990 and 1 June 2020. We excluded articles that were in the form of a review, letter to editors, short reports, and studies with less than 50 participants. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed. We estimated summary risk ratios (RRs) with random effects. RESULTS: A total of 20 studies, involving 6558 OCI patients were included. Anemia was significantly associated with an increased risk of OCI (adjusted RR (aRR) 1.39 (95% CI, 1.25-1.55; p < 0.001)). In subgroup analysis, anemia was also associated with an increased risk of all-cause dementia (adjusted RR (aRR), 1.39 (95% CI, 1.23-1.56; p < 0.001)), Alzheimer's disease [aRR, 1.59 (95% CI, 1.18-2.13; p = 0.002)], and mild cognitive impairment (aRR, 1.36 (95% CI, 1.04-1.78; p = 0.02)). CONCLUSION: This updated meta-analysis shows that patients with anemia appear to have a nearly 1.39-fold risk of developing OCI than those without anemia. The magnitude of this risk underscores the importance of improving anemia patients' health outcomes, particularly in elderly patients.

Association between Anemia and Risk of Parkinson Disease.

METHODS: We systematically searched articles on electronic databases such as PubMed, Embase, Scopus, and Google Scholar between January 1, 2000 and July 30, 2020. Articles were independently evaluated by two authors. We included observational studies (case-control and cohort) and calculated the risk ratios (RRs) for associated with anemia and PD. Heterogeneity among the studies was assessed using the Q and I 2 statistic. We utilized the random-effect model to calculate the overall RR with 95% CI. RESULTS: A total of 342 articles were identified in the initial searches, and 7 full-text articles were evaluated for eligibility. Three articles were further excluded for prespecified reasons including insufficient data and duplications, and 4 articles were included in our systematic review and meta-analysis. A random effect model meta-analysis of all 4 studies showed no increased risk of PD in patients with anemia (N = 4, RRadjusted = 1.17 (95% CI: 0.94-1.45, p = 0.15). However, heterogeneity among the studies was significant (I 2 = 92.60, p = <0.0001). The pooled relative risk of PD in female patients with anemia was higher (N = 3, RRadjusted = 1.14 (95% CI: 0.83-1.57, p = 0.40) as compared to male patients with anemia (N = 3, RRadjusted = 1.09 (95% CI: 0.83-1.42, p = 0.51). CONCLUSION: This is the first meta-analysis that shows that anemia is associated with higher risk of PD when compared with patients without anemia. However, more studies are warranted to evaluate the risk of PD among patients with anemia.

Health Service Access among Indonesian Migrant Domestic Workers in Taiwan.

The number of migrant workers in Taiwan increases annually. The majority is from Indonesia and most of them are female caregivers. This study aims to determine the access to health services and the associated factors among Indonesian female domestic workers in Taiwan. In this cross-sectional study, data were collected from February to May 2019, using a structured questionnaire. Subsequently, multiple logistic regression was used to examine the association between socio-demographic factors and health service access. Two hundred and eighty-four domestic migrant workers were interviewed. Eighty-five percent of the respondents declared sickness at work, but only 48.8% seek health care services. Factors associated with health service access were marital status, income, and the availability of an attendant to accompany the migrant workers to the healthcare facilities. Language barrier and time flexibility were the main obstacles. Further research and an effective health service policy are needed for the domestic migrant workers to better access health care services.

Deep Learning for Accurate Diagnosis of Glaucomatous Optic Neuropathy Using Digital Fundus Image: A Meta-Analysis.

We conducted a study to evaluate the algorithms based on deep learning to automatically diagnosis of GON from digital fundus images. A systematic articles search was conducted in PubMed, EMBASE, Google Scholar for the study that investigated the performance of deep learning algorithms for the detection of GON. A total of eight studies were included in this study, of which 5 studies were used to conduct our meta-analysis. The pooled AUROC for detecting GON was 0.98. However, the sensitivity and specificity of deep learning to detect GON were 0.90 (95% CI: 0.90-0.91), and 0.94 (95%CI: 0.93-0.94), respectively.

Hajj in the Time of COVID-19.

The recent outbreak of the novel COVID-19 is posing a severe public health risk across the globe. The Kingdom of Saudi Arabia (KSA) is one of the greatest destinations of religious congregations of Muslims. One of the largest religious gatherings is the Hajj that is anticipated to produce serious challenges of mass level exposures and spread to every corner of the world. Therefore, it is highly recommended that the Ministry of Hajj and Umrah (KSA), must regularly analyze the prevailing situation of COVID-19, and involve the religious scholars to make appropriate decisions about Hajj 2020. Although the Saudi government has been continuously taking all possible measures to contain the pandemic, people's cooperation is crucial in the fight against COVID-19.

Clinical Characteristics and Neonatal Outcomes of Pregnant Patients With COVID-19: A Systematic Review.

Background and Objective: Coronavirus disease 2019 (COVID-19) characterized by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created serious concerns about its potential adverse effects. There are limited data on clinical, radiological, and neonatal outcomes of pregnant women with COVID-19 pneumonia. This study aimed to assess clinical manifestations and neonatal outcomes of pregnant women with COVID-19. Methods: We conducted a systematic article search of PubMed, EMBASE, Scopus, Google Scholar, and Web of Science for studies that discussed pregnant patients with confirmed COVID-19 between January 1, 2020, and April 20, 2020, with no restriction on language. Articles were independently evaluated by two expert authors. We included all retrospective studies that reported the clinical features and outcomes of pregnant patients with COVID-19. Results: Forty-seven articles were assessed for eligibility; 13 articles met the inclusion criteria for the systematic review. Data is reported for 235 pregnant women with COVID-19. The age range of patients was 25-40 years, and the gestational age ranged from 8 to 40 weeks plus 6 days. Clinical characteristics were fever [138/235 (58.72%)], cough [111/235 (47.23%)], and sore throat [21/235 (8.93%)]. One hundred fifty six out of 235 (66.38%) pregnant women had cesarean section, and 79 (33.62%) had a vaginal delivery. All the patients showed lung abnormalities in CT scan images, and none of the patients died. Neutrophil cell count, C-reactive protein (CRP) concentration, ALT, and AST were increased but lymphocyte count and albumin levels were decreased. Amniotic fluid, neonatal throat swab, and breastmilk samples were taken to test for SARS-CoV-2 but all found negativ results. Recent published evidence showed the possibility of vertical transmission up to 30%, and neonatal death up to 2.5%. Pre-eclampsia, fetal distress, PROM, pre-mature delivery were the major complications of pregnant women with COVID-19. Conclusions: Our study findings show that the clinical, laboratory and radiological characteristics of pregnant women with COVID-19 were similar to those of the general populations. The possibility of vertical transmission cannot be ignored but C-section should not be routinely recommended anymore according to latest evidences and, in any case, decisions should be taken after proper discussion with the family. Future studies are needed to confirm or refute these findings with a larger number of sample sizes and a long-term follow-up period.

Predicting skilled delivery service use in Ethiopia: dual application of logistic regression and machine learning algorithms.

BACKGROUND: Skilled assistance during childbirth is essential to reduce maternal deaths. However, in Ethiopia, which is among the six countries contributing to more than half of the global maternal deaths, the coverage of births attended by skilled health personnel remains very low. The aim of this study was to identify determinants and develop a predictive model for skilled delivery service use in Ethiopia by applying logistic regression and machine-learning techniques. METHODS: Data from the 2016 Ethiopian Demographic and Health Survey (EDHS) was used for this study. Statistical Package for Social Sciences (SPSS) and Waikato Environment for Knowledge Analysis (WEKA) tools were used for logistic regression and model building respectively. Classification algorithms namely J48, Naïve Bayes, Support Vector Machine (SVM), and Artificial Neural Network (ANN) were used for model development. The validation of the predictive models was assessed using accuracy, sensitivity, specificity, and area under Receiver Operating Characteristics (ROC) curve. RESULTS: Only 27.7% women received skilled delivery assistance in Ethiopia. First antenatal care (ANC) [AOR = 1.83, 95% CI (1.24-2.69)], birth order [AOR = 0.22, 95% CI (0.11-0.46)], television ownership [AOR = 6.83, 95% CI (2.52-18.52)], contraceptive use [AOR = 1.92, 95% CI (1.26-2.97)], cost needed for healthcare [AOR = 2.17, 95% CI (1.47-3.21)], age at first birth [AOR = 1.96, 95% CI (1.31-2.94)], and age at first sex [AOR = 2.72, 95% CI (1.55-4.76)] were determinants for utilizing skilled delivery services during the childbirth. Predictive models were developed and the J48 model had superior predictive accuracy (98%), sensitivity (96%), specificity (99%) and, the area under ROC (98%). CONCLUSIONS: First ANC and contraceptive uses were among the determinants of utilization of skilled delivery services. A predictive model was developed to forecast the likelihood of a pregnant woman seeking skilled delivery assistance; therefore, the predictive model can help to decide targeted interventions for a pregnant woman to ensure skilled assistance at childbirth. The model developed through the J48 algorithm has better predictive accuracy. Web-based application can be build based on results of this study.

The usefulness and actual use of wearable devices among the elderly population.

BACKGROUND AND OBJECTIVE: Elderly populations are more prone to diseases and need continuous monitoring of parameters to ensure good health. Wearable devices (WDs) can be helpful in the early detection and management of medical conditions. However, less is known about the use of currently available WDs among elderly populations. The objectives of this study were to determine the usefulness and actual use of wearable devices among the elderly population. METHODS: Our methodology was based on a systematic review and a survey questionnaire. In the systematic review, search was conducted in four databases PubMed, MDPI, Sage, and Scopus with search terms "wearable device" and "elderly", "wearable sensor" and "elderly". The inclusion criteria were the studies which described health-related wearable devices, its use as the outcome, conducted on a minimum of ten participants and published in the last five years. The survey was conducted on the MOOCs (Massive Open Online Course) platform. The questionnaire was related to the use of technology, intention to use, security and privacy concerns, and willingness to pay. RESULTS: The review identified 4915 articles, of which, 31 studies eventually met the inclusion criteria. All studies reported positive impacts after assessing devices, despite certain drawbacks. The majority of the samples were males. The survey revealed responses from 233 individuals out of the 1100 participants of the course. The survey results were categorized into two age groups: 54.3% were elderly (>65 years) and 45.49% were non-elderly (≤65 years). Very few elderly people were currently using WD. More than 60% of elderly people were interested in the future use of wearable devices, and preferred future use to improve physical and mental activities. A majority of the respondents were female. CONCLUSIONS: This study suggests awareness should be created among elderly populations regarding the use of WDs for the early detection and prevention of complications and emergencies. Elderly populations are more prone to benefits from using WDs. The review concluded that devices should be tested on elderly groups as well, considering sex equality, and on both healthy and sick participants for better insights. The survey determined the elderly as frequent users of technology, but lack of knowledge of WD and demonstrated female interest in the use of WD. In future research on WDs, it is suggested that clinical studies be conducted for longer durations, and standard protocols such as age and sex equality should be considered. Requirements from both users and physicians should be acknowledged for better cognizance of WDs.

Investigating spatio-temporal distribution and diffusion patterns of the dengue outbreak in Swat, Pakistan.

INTRODUCTION: Dengue has been endemic to Pakistan in the last two decades. There was a massive outbreak in the Swat valley in 2013. Here we demonstrate the spatio-temporal clustering and diffusion patterns of the dengue outbreak. METHODS: Dengue case data were acquired from the hospital records in the Swat district of Pakistan. Ring maps visualize the distribution and diffusion of the number of cases and incidence of dengue at the level of the union council. We applied space-time scan statistics to identify spatio-temporal clusters. Ordinary least squares and geographically weighted regression models were used to evaluate the impact of elevation, population density, and distance to the river. RESULTS: The results show that dengue distribution is not random, but clustered in space and time in the Swat district. Males constituted 68% of the cases while females accounted for about 32%. A majority of the cases (>55%) were younger than 40 years of age. The southern part was a major hotspot affected by the dengue outbreak in 2013. There are two space-time clusters in the spatio-temporal analysis. GWR and OLS show that population density is a significant explanatory variable for the dengue outbreak, while GWR exhibits better performance in terms of 'R2=0.49 and AICc=700'. CONCLUSION: Dengue fever is clustered in the southern part of the Swat district. This region is relatively urban in character, with most of the population of the district residing here. There is a need to strengthen the surveillance system for reporting dengue cases in order to respond to future outbreaks in a robust way.

The impact of different surgical procedures on hypoparathyroidism after thyroidectomy: A population-based study.

The main objective of this study is to investigate the outcome between surgical procedures and the risk of development of hypoparathyroidism followed by surgical procedure in patients with thyroid disorders.We analyzed the data acquired from Taiwan's Bureau of National Health Insurance (BNHI) research database from 1998 to 2011 and found 9316 patients with thyroid surgery. Cox regression model was used to calculate the hazard ratio (HR).A count of 314 cases (3.4%) of hypoparathyroidism was identified. The 9 years cumulated incidence of hypoparathyroidism was the highest in patient undergone bilateral total thyroidectomy (13.5%) and the lowest in the patient with unilateral subtotal thyroidectomy (1.2%). However, in the patients who had undergone unilateral subtotal, the risk was the highest in bilateral total (HR: 11.86), followed by radical thyroidectomy with unilateral neck lymph node dissection (HR: 8.56), unilateral total (HR, 4.39), and one side total and another side subtotal (HR: 2.80).The extent of thyroid resection determined the risk of development of hypoparathyroidism. It is suggested that the association of these factors is investigated in future studies.

Benzodiazepines use and breast cancer risk: A population-based study and gene expression profiling evidence.

The aim of this study was to investigate whether long-term use of Benzodiazepines (BZDs) is associated with breast cancer risk through the combination of population-based observational and gene expression profiling evidence. We conducted a population-based case-control study by using 1998 to 2009year Taiwan National Health Insurance Research Database and investigated the association between BZDs use and breast cancer risk. We selected subjects age of >20years old and six eligible controls matched for age, sex and the index date (i.e., free of any cancer at the case diagnosis date) by using propensity scores. A bioinformatics analysis approach was also performed for the identification of oncogenesis effects of BZDs on breast cancer. We used breast cancer gene expression data from the Cancer Genome Atlas and perturbagen signatures of BZDs from the Library of Integrated Cellular Signatures database in order to identify the oncogenesis effects of BZDs on breast cancer. We found evidence of increased breast cancer risk for diazepam (OR, 1.16; 95%CI, 0.95-1.42; connectivity score [CS], 0.3016), zolpidem (OR, 1.11; 95%CI, 0.95-1.30; CS, 0.2738), but not for lorazepam (OR, 1.04; 95%CI, 0.89-1.23; CS, -0.2952) consistently in both methods. The finding for alparazolam was contradictory from the two methods. Diazepam and zolpidem trends showed association, although not statistically significant, with breast cancer risk in both epidemiological and bioinformatics analyses outcomes. The methodological value of our study is in introducing the way of combining epidemiological and bioinformatics approaches in order to answer a common scientific question. Combining the two approaches would be a substantial step towards uncovering, validation and further application of previously unknown scientific knowledge to the emerging field of precision medicine informatics.

A Commentary on Telerehabilitation Services in Pakistan: Current Trends and Future Possibilities.

A 2014 World Health Organization (WHO) study reported that almost 27 million people with disability live in Pakistan with fewer than one allied rehabilitation professional per 10,000 people. The current study sought to determine the attitudes toward telerehabilitation via a survey administered to 329 Pakistani rehabilitation professionals. Study results indicate that rehabilitation professionals in Pakistan are knowledgeable about telerehabilitation and Information and Communication Technology (ICT), and are receptive to employing telerehabilitation programs and applications. Therefore, we can infer that the future of telerehabilitation can be bright in Pakistan but requires the attention of policy makers and non-government organizations to launch an appropriate program nationwide. The authors suggest that a range of telerehabilitation services (e.g., consultation, assessment, and therapy) could alleviate the shortage of rehabilitation personnel in Pakistan.

The Concomitant Association of Thyroid Disorders and Myasthenia Gravis.

BACKGROUND: Some of the thyroid disorders (TD) and Myasthenia gravis (MG) are autoimmune related disease. The purpose of the study to evaluate the relationship of MG with all morphological and functional thyroid disorders. METHODS: We constructed a population-based cohort study during the period from January 2000-December 2002 by using reimbursement data from the Bureau National Health Insurance (NHI) system in Taiwan. Patients with TD and MG were identified by referring to the ICD-9-CM codes. (ICD-10-CM as reference) .The association of TD with MG occurred only in the same person within the study period. The Q value was used to measure the strength of disease-disease associations. RESULTS: We obtained 520628 TD and 7965 MG records for analysis. Diffuse toxic goiter had highest association rate, followed by nontoxic nodular goiter, simple goiter, chronic lymphocytic thyroiditis, thyroid cancer, and toxic nodular goiter. Female and older patients had a higher rate than their male and younger counterparts, respectively. Functional abnormalities revealed higher incidence of thyrotoxicosis and hypothyroidism in both sexes. We also found the strongest association in men with chronic thyroiditis, diffuse toxic goiter, thyrotoxicosis, acquired hypothyroidism, thyroid cancer, and simple goiter. While an intermediate association was observed in female with diffuse toxic goiter, in a male with toxic and nontoxic nodular/multinodular goiters, in female with thyrotoxicosis, thyroid cancer and acquired hypothyroidism. CONCLUSION: This population based cohort study showed potential association of all types of TD with MG, and observed a higher association rate in female autoimmune TD whereas males showed a higher strength of association.

Viral warts (Human Papilloma Virus) as a potential risk for breast cancer among younger females.

INTRODUCTION: There have been several reports on the role of human papillomavirus (HPV) in the etiology of breast cancer. To our knowledge, this is first study to use disease-disease association data-mining approach to analyzing viral warts and breast cancer to be conducted in Taiwanese population. MATERIALS AND METHODS: We analyzed the Taiwan's National Health Insurance database (NHIDM data comprising of 23 million patient data) to examine the association between viral warts and female breast carcinoma. The patients were categorized into three groups: breast cancer only, viral warts only, and those with both breast cancer and viral warts. The Cox proportion hazard regression analysis was used to measure the effect of HPV on the time to breast cancer diagnosis. Multivariable analyzes and stratified analyzes using hazard ratios (HRs) were presented with 95% confidence intervals (CIs) after adjusting for age, and CCI. RESULT: Among 807,578 HPV population, we identified 6014 breast cancer cases. The HPV group was associated with a significantly higher risk of developing breast cancer (HR, 1.18; 95% CI, 1.15-1.21; p< 0.001) compared with the non-HPV group. HPV patients with age group 18-39 was slightly higher risk of breast cancer occurrence (HR, 1.07; 95% CI, 1.01-1.13; p<.05). The risk of breast cancer in 10-year incidence was 7% higher for females less than 40 years and 23% for over 40 year's patients when compared with non-HPV patients of the same age group. CONCLUSION: Our study indicates that women who develop viral warts are at a significantly higher risk of developing breast cancer than women who have not diagnosed with viral warts. Thus, the presence of viral warts is a potential risk to breast cancer. Therefore, we suggest patients diagnosed with viral warts may get early screening for breast cancer.