Yoxas/statistical_literacy huggingface dataset:
Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis and delayed treatment. Current approaches primarily include manual examination by radiologists and conventional machine learning techniques. These methods rely heavily on feature extraction and classification algorithms, which may not capture the intricate patterns present in brain MRI images. Conventional techniques often suffer from limited accuracy and generalizability, mainly due to the high variability in tumor appearance and the subjective nature of manual interpretation. Additionally, traditional machine learning models may struggle with the high-dimensional data inherent in MRI images. To address these limitations, our research introduces a deep learning-based model utilizing convolutional neural networks (CNNs).Our model employs a sequential CNN architecture with multiple convolutional, max-pooling, and dropout layers, followed by dense layers for classification. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The precision, recall, and F1-scores ranging from 97 to 98% with a roc-auc ranging from 99 to 100% for each tumor category further substantiate the model's effectiveness. Additionally, the utilization of Grad-CAM visualizations provides insights into the model's decision-making process, enhancing interpretability. This research addresses the pressing need for enhanced diagnostic accuracy in identifying brain tumors through MRI imaging, tackling challenges such as variability in tumor appearance and the need for rapid, reliable diagnostic tools.
Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2 ).
Brain white matter hyperintensities are seen on routine clinical imaging in 46% of adults with congenital adrenal hyperplasia (CAH). The extent and functional relevance of these abnormalities have not been studied with quantitative magnetic resonance imaging (MRI) analysis. To examine white matter microstructure, neural volumes, and central nervous system (CNS) metabolites in CAH due to 21-hydroxylase deficiency (21OHD) and to determine whether identified abnormalities are associated with cognition, glucocorticoid, and androgen exposure. A cross-sectional study at a tertiary hospital including 19 women (18 to 50 years) with 21OHD and 19 age-matched healthy women. Recruits underwent cognitive assessment and brain imaging, including diffusion weighted imaging of white matter, T1-weighted volumetry, and magnetic resonance spectroscopy for neural metabolites. We evaluated white matter microstructure by using tract-based spatial statistics. We compared cognitive scores, neural volumes, and metabolites between groups and relationships between glucocorticoid exposure, MRI, and neurologic outcomes. Patients with 21OHD had widespread reductions in white matter structural integrity, reduced volumes of right hippocampus, bilateral thalami, cerebellum, and brainstem, and reduced mesial temporal lobe total choline content. Working memory, processing speed, and digit span and matrix reasoning scores were reduced in patients with 21OHD, despite similar education and intelligence to controls. Patients with 21OHD exposed to higher glucocorticoid doses had greater abnormalities in white matter microstructure and cognitive performance. We demonstrate that 21OHD and current glucocorticoid replacement regimens have a profound impact on brain morphology and function. If reversible, these CNS markers are a potential target for treatment.
Brain-derived neurotrophic factor (BDNF), a neurotransmitter modulator, plays a significant role in neuronal survival and growth and participates in neuronal plasticity, thus being essential for learning, memory, and the development of cognition. Additionally, it is crucial for appetite, weight, and metabolic control and plays a pivotal role in the cardiovascular system. The Val66Met polymorphism (rs6265) of the BDNF gene causes a decrease in BDNF secretion and plays a role in impairments in cognition, energy homeostasis, and cardiovascular events. The present study aimed to evaluate the association of polymorphism (rs6265) of the BDNF gene with three quantitative traits simultaneously, namely, intelligence quotient (IQ), body mass index (BMI), and blood pressure (BP). Psychometric, morphometric, and physiometric data of the total participants (N = 246) were collected. WASI-IIINDIA was used to measure cognitive ability. Genotyping was carried out using allele-specific PCR for the rs6265 polymorphism (C196T), and genotypes were determined. Statistical analyses were performed at p < 0.05 significance level using MS-Excel and SigmaPlot. The odds ratio models with a 95% confidence interval were used to test the associations. The used models are co-dominant, recessive, dominant, over-dominant, and additive. The allelic frequencies of alleles C and T were 72 and 28%, respectively. Under the dominant genetic model, a significant susceptible association of minor allele T was observed with a lower average verbal comprehensive index (OR = 2.216, p = 0.003, CI (95%) =1.33-3.69), a lower average performance reasoning index (OR = 2.634, p < 0.001, CI (95%) = 1.573-4.41), and a lower average full-scale IQ-4 (OR = 3.159, p < 0.001, CI (95%) = 1.873-5.328). Carriers of Met-alleles were found to have an increased body mass index (OR = 2.538, p < 0.001, CI (95%) = 1.507-4.275), decreased systolic blood pressure (OR = 2.051, p = 0.012, CI (95%) = 1.202-3.502), and decreased diastolic blood pressure (OR = 2.162, p = 0.006, CI (95%) = 1.278-3.657). Under the recessive genetic model, several folds decrease in IQ and BP and an increase in BMI with the presence of the T allele was also detected. This novel study may improve our understanding of genetic alterations to the traits and hence be helpful for clinicians and researchers to investigate the diagnostic and prognostic value of this neurotrophic factor.
Brazilian livestock breeding programmes strive to enhance the genetics of beef cattle, with a strong emphasis on the Nellore breed, which has an extensive database and has achieved significant genetic progress in the last years. There are other indicine breeds that are economically important in Brazil; however, these breeds have more modest sets of phenotypes, pedigree and genotypes, slowing down their genetic progress as their predictions are less accurate. Combining several breeds in a multi-breed evaluation could help enhance predictions for those breeds with less information available. This study aimed to evaluate the feasibility of multi-breed, single-step genomic best linear unbiased predictor genomic evaluations for Nellore, Brahman, Guzerat and Tabapua. Multi-breed evaluations were contrasted to the single-breed ones. Data were sourced from the National Association of Breeders and Researchers of Brazil and included pedigree (4,207,516), phenotypic (328,748), and genomic (63,492) information across all breeds. Phenotypes were available for adjusted weight at 210 and 450 days of age, and scrotal circumference at 365 days of age. Various scenarios were evaluated to ensure pedigree and genomic information compatibility when combining different breeds, including metafounders (MF) or building the genomic relationship matrix with breed-specific allele frequencies. Scenarios were compared using the linear regression method for bias, dispersion and accuracy. The results showed that using multi-breed evaluations significantly improved accuracy, especially for smaller breeds like Guzerat and Tabapua. The validation statistics indicated that the MF approach provided accurate predictions, albeit with some bias. While single-breed evaluations tended to have lower accuracy, merging all breeds in multi-breed evaluations increased accuracy and reduced dispersion. This study demonstrates that multi-breed genomic evaluations are proper for indicine beef cattle breeds. The MF approach may be particularly beneficial for less-represented breeds, addressing limitations related to small reference populations and incompatibilities between G and A22. By leveraging genomic information across breeds, breeders and producers can make more informed selection decisions, ultimately improving genetic gain in these cattle populations.
Break junction experiments allow investigating electronic and spintronic properties at the atomic and molecular scale. These experiments generate by their very nature broad and asymmetric distributions of the observables of interest, and thus, a full statistical interpretation is warranted. We show here that understanding the complete lifetime distribution is essential for obtaining reliable estimates. We demonstrate this for Au atomic point contacts by adopting Bayesian reasoning to make maximal use of all measured data to reliably estimate the distance to the transition state, x‡, the associated free energy barrier, ΔG‡, and the curvature, v, of the free energy surface. Obtaining robust estimates requires less experimental effort than with previous methods and fewer assumptions and thus leads to a significant reassessment of the kinetic parameters in this paradigmatic atomic-scale structure. Our proposed Bayesian reasoning offers a powerful and general approach when interpreting inherently stochastic data that yield broad, asymmetric distributions for which analytical models of the distribution may be developed.
Breakfast has been shown to be beneficial for cognitive and academic performance in school children. However, there is a paucity of studies which examine the relationship between breakfast consumption and academic performance and a complete absence of studies in UK school children. The aim of this study, therefore, was to examine the association between habitual breakfast consumption frequency and Cognitive Abilities Test (CAT) performance, a reasoning test routinely used in UK schools. Adolescents aged 11-13 years (n = 292; males: 53.8%) completed a questionnaire to report usual weekly breakfast intake frequency. Breakfast was subjectively defined by the participants. Habitual weekly breakfast consumption frequency was categorized as rare (0-2 days), occasional (3-4 days), or frequent (5-7 days). Participants' CAT performance was used as a proxy measure of academic performance. The CAT has three components: verbal, non-verbal, and quantitative reasoning. Normative standard age scores (SAS) for verbal, non-verbal, quantitative reasoning, and overall mean SAS were obtained from school records and hierarchical linear regression models were applied, adjusting for the confounders: gender, ethnicity, socio-economic status, English as an Additional Language, and body mass index. Habitual breakfast consumption frequency did not significantly predict any CAT SAS in all models (crude and adjusted). However, methodological considerations which could account for this disagreement with previous research, were identified. These included the isolation of school-day breakfast consumption, use of a standard definition of breakfast, and measurement of actual academic performance. The findings of the current study suggest more comprehensive ways in which future studies might investigate the relationship between habitual breakfast consumption and academic performance.
Breakfast is regarded as "the most important meal of the day," suggested to positively affect learning in children and adolescent in terms of cognitive and school performance. Yet, studies in LMIC settings are few and show very inconsistent results. To assess the prevalence and correlates of breakfast skipping and its association with school performance among randomly selected in-school adolescents in Hidhabu Abote Wereda, North Shewa Zone, Central Ethiopia. A cross-sectional study was conducted from November to December 2020. A total of 422 participants were selected randomly from high schools of Hidhabu Abote Wereda. Data were entered in to Epiata version 3.1 and exported to SPSS version 24 for analysis. Bivariate and multivariate binary logistic regression analysis identified factors that were significantly associated with the breakfast skipping. Odds ratio along with 95% Confidence interval was estimated to measure the strength of the association and level of statistical significance declared at p-value less than 0.05. The magnitude of breakfast skipping was 41.3%, (95% CI (36.6-46.0)]. There was statistically significant association between breakfast skipping and overall academic performance [AOR: 5.18, 95% CI (1.54-7.46)], mathematics performance (3.88, 95% CI (1.34-11.22)], and English language performance scores [2.92, 95% CI (1.38-7.58)]. Being female [AOR = 1.857, 95% CI (1.05-3.27)], household food insecurity [AOR: 2.478, 95% CI (1.36-4.51)], and less maternal education [AOR 1.89, 95% CL (3.38-7.77)] were independently associated with breakfast skipping. The primary reasons given for breakfast skipping were lack of time, lack of appetite in morning, and concerns around weight gain. Nearly half of in-school adolescents were skipped breakfast meals, and reportedly in most cases for reasons unrelated to lack of food access. Students who skipped breakfast had lower levels of school performance.
Breast Cancer (BC) is the most common cause of female mortality throughout the world. Promoting public awareness about this disease is the most crucial method of its prevention or control. The present study was carried out to determine the health literacy level and its related factors in women with BC. This cross-sectional study was conducted on BC patients undergoing chemotherapy in two teaching hospitals affiliated to Zahedan University of Medical Sciences in 2020. A total of 210 patients referred to these chemotherapy centers were selected by purposive sampling. The data collection tools included a demographic information form and a health literacy questionnaire for Iranian women with BC. The collected data were analyzed using descriptive and inferential statistics (logistic regression) in SPSS-22. p-values less than 0.05 were considered statistically significant. The mean total score of the health literacy of women with BC undergoing chemotherapy was 40.35 ± 19.01, which suggests an insufficient health literacy. The health literacy had a significant relationship with variables including university education (OR = 4.41, p = 0.005) and supplementary insurance coverage (OR = 5.83, p < 0.001). The findings showed that university education and supplementary insurance coverage are associated with a higher health literacy among women with BC. Improving these factors and paying further attention to their role in the promotion of health literacy can help enhance the health literacy of women with BC.
Breast cancer (BC) is the primary cancer among women. The World Health Organization recommends a bilateral screening mammogram every 2 years for women aged 50 to 74 years. However, it has been shown that there is an absence of information about the benefits and risks of screening. Shared medical decision-making is important to ensure patients are involved in the decision process. Decision aids can facilitative this decision-making process. This article presents a protocol to evaluate the effect of a decision aid on participation rates in the French organized BC screening program. Design and setting. The design is a 2 arm randomized controlled study, performed in the Pays de la Loire region (French West Coast). Randomization will be based on general medicine practices (Primary Care). Women aged between 50 and 74 years, eligible for BC screening. In this region, there are 75000 women, and 2800 general practitioners eligible for recruitment. In the « Decision aid for organized cancer screening » arm, the intervention will distribute invitation letters to eligible women combined with the provision of decision aid to these women and their general practitioners and an incentive to implement shared medical decision-making. In the « Standard organized cancer screening » arm, only the screening invitation will be sent to eligible women. BC screening participation rates will be assessed after an 18-month follow-up period. In this non-inferiority trial, the percentage of women who are up-to-date with their screening at 18 months after the intervention will be compared across arms using a generalized mixed linear model. The research team expect to demonstrate that providing a better explanation of the benefits and risks of BC screening is not at odds with screening participation. The study results should help policy makers thinking about implementing shared medical decision-making within the framework of organized BC screening programs in the future. On 6 December 2021, the protocol received a favorable opinion from the French Committee for the Protection of Persons (2021-A01583-38). This study is registered with ClinicalTrials.gov, number NCT05607849. (Version 1, November 7, 2022; https://www.clinicaltrials.gov/ct2/show/NCT05607849). The study findings will be used for publication in peer-reviewed scientific journals and presentations in scientific meetings.
Breast cancer (BC) screening plays a major role in the prevention of BC through early detection and timely treatment. This study aims to determine the level of uptake of BC screening and associated factors. A community-based analytical cross-sectional study was conducted in Dodoma City, Tanzania from July to December 2020. The study included women aged 8 years and above without a known history of breast cancer. Multivariable logistic regression was used to determine the socio-demographic factors associated with BC screening. P value < 0.05 was considered significant. A total of 354 study participants were included in the present study. The mean age of participants was 31.0 ± 11.8 years. The majority of study participants (67.5%, n = 239) were aware of BC screening. However, only (35.3%, n = 125) reported to have ever practised BC screening. Breast self-examination was the most (16.4%, n = 58) frequently used method for BC screening among study participants. Lack of knowledge of all methods of BC screening was the barrier that was perceived by the vast majority (60.2%, n = 213) of the study participants. Having low family income was the only predictor of failure to practice BC screening. In this study, most of the women were aware of BC, however, few of them had undergone breast cancer (BC) screening at the time of the interview. The study also found that the main barrier to BC screening was the lack of knowledge about BC among the study participants. Immediate measures are necessary to increase women's awareness of BC. Such as community sensitization on the importance of screening, can help improve the uptake of BC screening and the early detection of BC.
Breast cancer is a leading cause of mortality among women, both in India and globally. The prevalence of breast masses is notably common in women aged 20 to 60. These breast masses are classified, according to the breast imaging-reporting and data systems (BI-RADS) standard, into categories such as fibroadenoma, breast cysts, benign, and malignant masses. To aid in the diagnosis of breast disorders, imaging plays a vital role, with mammography being the most widely used modality for detecting breast abnormalities over the years. However, the process of identifying breast diseases through mammograms can be time-consuming, requiring experienced radiologists to review a significant volume of images. Early detection of breast masses is crucial for effective disease management, ultimately reducing mortality rates. To address this challenge, advancements in image processing techniques, specifically utilizing artificial intelligence (AI) and machine learning (ML), have tiled the way for the development of decision support systems. These systems assist radiologists in the accurate identification and classification of breast disorders. This paper presents a review of various studies where diverse machine learning approaches have been applied to digital mammograms. These approaches aim to identify breast masses and classify them into distinct subclasses such as normal, benign and malignant. Additionally, the paper highlights both the advantages and limitations of existing techniques, offering valuable insights for the benefit of future research endeavors in this critical area of medical imaging and breast health.
Breast cancer is one of the most prevalent cancers in women, however Irish Traveller women have lower breast screening rates than that of the general population. This work aims to address the gap in knowledge of Irish Traveller womens' perceptions of breast screening and the perceived barriers and enablers to attendance. This phenomenological qualitative study involves interviews with Irish Traveller women and Health Care Professionals and discusses the incentives and barriers to attending breast screening mammography in Ireland. The work investigated attitudes and decision making amongst the Irish Traveller women across breast screening and breast health. The research investigated the participants knowledge, experience and opinions about the topic of Irish Traveller womens' attendance at BreastCheck and breast health RESULTS: Influences that create barriers to breast screening for Irish Traveller women include inequality and family/community support, fear, literacy and education, embarrassment and the health care professional, stress and appointment suitability. Findings also demonstrate inadequate data and information is available in Ireland regarding Irish Traveller women attending breast screening. Irish Traveller women face several influences when it comes to attending breast screening. The existing Irish national breast screening programme provides a health promotion service however, it is impossible to assess poor attendance at screening without the presence of an ethnic identifier. It would be very beneficial for screening promotion to record the ethnicity of attendees for statistical progress. This would benefit Irish Traveller women by recording the progress of attendance in the breast screening programme and creating a need for awareness and education within the annual reports. Creating awareness and educating Irish Traveller women about the breast screening programme may remove barriers and lead to improved attendance rates.