Yoxas/statistical_literacy huggingface dataset:
Antibiotic resistance is a component of antimicrobial resistance and is often referred to as the silent pandemic. It is one of the causes of global health problems that must be addressed. Resistance occurs due to frequent misuse and overuse of antibiotics by dairy farmers. Therefore, this study aimed to examine the influence of the characteristics of dairy farmers and analyze the variables that directly and indirectly affected the knowledge, attitudes, and practices (KAP) of dairy farmers regarding antibiotic resistance in a dairy farmers' cooperative in North Cianjur. A cross-sectional design was used with a structured questionnaire validated by Pearson correlation and reliability tested with Alpha Cronbach. Data were obtained from interviews with 75 dairy farmers in KPSCU, Cianjur District, West Java, Indonesia. The outcome variables were analyzed using descriptive and pathway analyses. The results showed that dairy farmers had good and sufficient knowledge (42.7%), the attitude level was positive (81.3%), and all farmers had sufficient practices (100%). The results also showed that most dairy farmers were over 40 years of age, with 54.7% having elementary school education. Approximately 50.7% of farmers had been engaged in breeding for 10-20 years, 80% owned their livestock, and 76% had attended training. There was a significant relationship between age, education, training, and knowledge level. Knowledge was the primary factor influencing the overall attitude. In addition, age, education, type of business, knowledge, and attitude were factors that influenced the practice of antibiotic resistance. Training and education really influenced KAP of farmers. Therefore, the best way to reduce antibiotic resistance is by increasing farmers' knowledge and understanding of antibiotic resistance and monitoring the use of antibiotics.
Antibiotic resistance is a global health concern. Humans can acquire antibiotic resistance through human-to-human transmission, from the environment, via the food chain, and through the contact with animals. The National Action Plan on antimicrobial resistance 2020-2024 highlights the prudent use of antibiotics in veterinary activities as the key element in keeping antibiotics effective. We determined the factors associated with misuse of antibiotics among animal health professionals in Rwanda. This was a cross-sectional study that enrolled animal health field professionals from five districts, where stratified random sampling was used to select one district by each province of Rwanda. Structured questions were used during face-to-face interviews. The misuse of antibiotics was defined as the use of antibiotics for reasons other than treatment, the non-completion of required courses, or the use of a high dose (i.e., an overdose) of antibiotics. We collected socio-demographic data of respondents, as well as elementary knowledge and perceptions on veterinary antibiotics and antibiotic resistance. A backward stepwise logistic regression model was used to identify the factors that were predictive of the inappropriate use of antibiotics. There were 256 respondents to the survey. Of those, 198 were male and 58 were female. Almost three quarters of respondents (n = 174/256; 68%) reported the misuse of antibiotics at least once in the previous 12 months. The final logistic regression analysis identified the following factors to be predictive of antibiotics misuse: aged ≤ 24 years (aOR 0.92; 95% CI [0.88, 0.96]; p < 0.001); low trust in veterinary antibiotics available in the local market (aOR 8.45; 95% CI [4.18, 17.07]; p < 0.01), insufficient knowledge about basic understanding of antibiotics and antibiotic resistance (aOR 2.78; 95% CI [1.38, 5.58], p < 0.01) and not acquiring any continuing education (aOR 1.97; 95% CI [1.02, 4.19]; p = 0.04). This study identified inadequate perceptions of proper antibiotic use among animal health professionals. There is a need for continuous education on appropriate antibiotic use among animal health professionals to lessen the negative impact of antibiotic resistance on public health security.
Antibiotic-associated diarrhea (AAD) can prolong hospitalization, increase medical costs, and even lead to higher mortality rates. Therefore, it is essential to predict the incidence of AAD in elderly intensive care unit(ICU) patients. The objective of this study was to create a prediction model that is both interpretable and generalizable for predicting the incidence of AAD in elderly ICU patients. We retrospectively analyzed data from the First Medical Center of the People's Liberation Army General Hospital (PLAGH) in China. We utilized the machine learning model Extreme Gradient Boosting (XGBoost) and Shapley's additive interpretation method to predict the incidence of AAD in elderly ICU patients in an interpretable manner. A total of 848 adult ICU patients were eligible for this study. The XGBoost model predicted the incidence of AAD with an area under the receiver operating characteristic curve (ROC) of 0.917, sensitivity of 0.889, specificity of 0.806, accuracy of 0.870, and an F1 score of 0.780. The XGBoost model outperformed the other models, including logistic regression, support vector machine (AUC = 0.809), K-nearest neighbor algorithm (AUC = 0.872), and plain Bayes (AUC = 0.774). While the XGBoost model may not excel in absolute performance, it demonstrates superior predictive capabilities compared to other models in forecasting the incidence of AAD in elderly ICU patients categorized based on their characteristics.
Antibiotics Time Machine is an important problem to understand antibiotic resistance and how it can be reversed. Mathematically, it can be modeled as follows: Consider a set of genotypes, each of which contain a set of mutated and unmutated genes. Suppose that a set of growth rate measurements of each genotype under a set of antibiotics is given. The transition probabilities of a 'realization' of a Markov chain associated with each arc under each antibiotic are computable via a predefined function given the growth rate realizations. The aim is to maximize the expected probability of reaching to the genotype with all unmutated genes given the initial genotype in a predetermined number of transitions, considering the following two sources of uncertainties: (i) the randomness in growth rates, (ii) the randomness in transition probabilities, which are functions of growth rates. We develop stochastic mixed-integer linear programming and dynamic programming approaches to solve static and dynamic versions of the Antibiotics Time Machine Problem under the aforementioned uncertainties. We adapt a Sample Average Approximation approach that exploits the special structure of the problem and provide accurate solutions that perform very well in an out-of-sample analysis.
Antibodies are essential research tools whose performance directly impacts research conclusions and reproducibility. Owing to its central role in Alzheimer's disease and other dementias, hundreds of distinct antibody clones have been developed against the microtubule-associated protein Tau and its multiple proteoforms. Despite this breadth of offer, limited understanding of their performance and poor antibody selectivity have hindered research progress. Here, we validate a large panel of Tau antibodies by Western blot (79 reagents) and immunohistochemistry (35 reagents). We address the reagents' ability to detect the target proteoform, selectivity, the impact of protein phosphorylation on antibody binding and performance in human brain samples. While most antibodies detected Tau at high levels, many failed to detect it at lower, endogenous levels. By WB, non-selective binding to other proteins affected over half of the antibodies tested, with several cross-reacting with the related MAP2 protein, whereas the "oligomeric Tau" T22 antibody reacted with monomeric Tau by WB, thus calling into question its specificity to Tau oligomers. Despite the presumption that "total" Tau antibodies are agnostic to post-translational modifications, we found that phosphorylation partially inhibits binding for many such antibodies, including the popular Tau-5 clone. We further combine high-sensitivity reagents, mass-spectrometry proteomics and cDNA sequencing to demonstrate that presumptive Tau "knockout" human cells continue to express residual protein arising through exon skipping, providing evidence of previously unappreciated gene plasticity. Finally, probing of human brain samples with a large panel of antibodies revealed the presence of C-term-truncated versions of all main Tau brain isoforms in both control and tauopathy donors. Ultimately, we identify a validated panel of Tau antibodies that can be employed in Western blotting and/or immunohistochemistry to reliably detect even low levels of Tau expression with high selectivity. This work represents an extensive resource that will enable the re-interpretation of published data, improve reproducibility in Tau research, and overall accelerate scientific progress.
Anticipatory grief leads to a highly stressful and conflicting experience among caregivers of patients with terminal cancer. Nurses lack the competency to assess and manage the caregivers’ psychological problems, which in turn affects the caregivers’ quality of life. A scale assessing the anticipatory grief counseling competency among nurses is unavailable. In this study, an Anticipatory Grief Counseling Competency Scale (AGCCS) was developed for nurses. The Scale (AGCCS) was translated into Chinese and then revised. Psychometric testing of the scale was conducted on 252 nurses who participated in the care of patients with terminal cancer at a regional teaching hospital in Southern Taiwan. The data were analyzed using descriptive statistics, reliability, and Pearson’s correlation, and principal component analysis and analysis of variance were performed. Item- and scale-content validity indexes were 0.99 and 0.93, respectively. The Cronbach α of internal consistency was 0.981. The final 53-item AGCCS had five factors, which accounted for 70.81% of the total variance. The Pearson correlation coefficients of these factors ranged between 0.406 and 0.880 (p < 0.001). The AGCCS can be used to evaluate the aforementioned competency for improving caregivers’ quality of care. It can also facilitate in-service education planning and evaluation.
Anticoagulants have long been fundamental in preventing and treating thromboembolic disorders, with a recent shift of focus towards direct oral anticoagulants, thanks to their ease of use, efficacy, and safety. Despite these advancements, bleeding complications remain a major concern with any anticoagulant, highlighting the need for safer drugs. Factor XI (FXI) inhibitors have emerged as promising agents in this regard, offering a novel approach by targeting upstream factors in the coagulation system. Phase 2 trials have shown encouraging outcomes, indicating a reduced bleeding risk compared to traditional anticoagulants, particularly in the context of cardiovascular disease management when combined with antiplatelet therapy. However, the variability in findings and limited efficacy data call for a cautious interpretation pending phase 3 trial insights. These trials are essential for validating the potential of FXI inhibitors to balance bleeding risk reduction and maintain anticoagulant efficacy. This review explores the pharmacology, potential indications, clinical data, and future directions of FXI inhibitors, providing a perspective on their evolving role in anticoagulant therapy. It also provides a detailed analysis of data from published clinical trials on FXI inhibitors in various indications. Preliminary data from ongoing trials is also outlined. As the field moves forward, a cautiously optimistic outlook can be expected, focusing on comprehensive data from phase 3 trials to define the role of FXI inhibitors in various clinical scenarios.
Antimicrobial resistance (AMR) has escalated to pandemic levels, posing a significant global health threat. This study examines the patterns and trends of AMR in Bloodstream Infections (BSIs) across India, aiming to inform better surveillance and intervention strategies. Six-year data from 21 tertiary care centers in the Indian Council of Medical Research's AMR Surveillance Network (IAMRSN) were retrospectively analyzed to estimate cluster-robust trends in resistance. Time-series analysis was used to discern lead/lag relationships between antibiotic pairs and the directional influence of resistance in community and hospital-acquired BSIs(CA/HA BSIs). A data-driven Bayesian network ensemble averaged over 301 bootstrap samples was modelled to uncover systemic associations between AMR and Sustainable Development Goals (SDGs). Our findings indicate significant (p < 0.001) monthly increases in Imipenem and Meropenem resistance for Klebsiella, E. coli, and Acinetobacter BSIs. Importantly, Carbapenem resistance in HA-BSIs preceded that in CA-BSIs for Klebsiella and Acinetobacter (p < 0.05). At a national level, Cefotaxime resistance emerged as a potential early indicator for emerging Carbapenem resistance, proposing a novel surveillance marker. In Klebsiella BSIs, states with higher achievement of SDG3 goals showed lower Imipenem resistance. A model-based AMR scorecard is introduced for focused interventions and continuous monitoring. The identified spatiotemporal trends and drug resistance associations offer critical insights for AMR surveillance aligning with WHO GLASS standards.The escalation of carbapenem resistance in BSIs demands vigilant monitoring and may be crucial for achieving SDGs by 2030. Implementing the proposed framework for data-driven evidence can help nations achieve proactive AMR surveillance. No specific funding was received for this analysis.
Antimicrobial stewardship programs attempting to optimize antibiotic therapy and clinical outcomes mainly focus on inpatient and outpatient settings. The lack of antimicrobial stewardship program studies in the emergency department (ED) represents a gap in tackling the problem of antimicrobial resistance as EDs treat a substantial number of upper respiratory tract infection cases throughout the year. We intend to implement two evidence-based interventions: (1) patient education and (2) providing physician feedback on their prescribing rates. We will incorporate evidence from a literature review and contextualizing the interventions based on findings from a local qualitative study. Our study uses a quasi-experimental design to evaluate the effects of interventions over time in the EDs of 4 public hospitals in Singapore. We will include an initial control period of 18 months. In the next 6 months, we will randomize 2 EDs to receive 1 intervention (ie, patient education) and the other 2 EDs to receive the alternative intervention (ie, physician feedback). All EDs will receive the second intervention in the subsequent 6 months on top of the ongoing intervention. Data will be collected for another 6 months to assess the persistence of the intervention effects. The information leaflets will be handed to patients at the EDs before they consult with the physician, while feedback to individual physicians by senior doctors is in the form of electronic text messages. The feedback will contain the physicians' antibiotic prescribing rate compared with the departments' overall antibiotic prescribing rate and a bite-size message on good antibiotic prescribing practices. We will analyze the data using segmented regression with difference-in-difference estimation to account for concurrent cluster comparisons. Our proposed study assesses the effectiveness of evidence-based, context-specific interventions to optimize antibiotic prescribing in EDs. These interventions are aligned with Singapore's national effort to tackle antimicrobial resistance and can be scaled up if successful. ClinicalTrials.gov NCT05451863; https://clinicaltrials.gov/study/NCT05451836. DERR1-10.2196/50417.
Antipsychotic medication is increasingly prescribed to patients with serious mental illness. Patients with serious mental illness often have cardiovascular and metabolic comorbidities, and antipsychotics independently increase the risk of cardiometabolic disease. Despite this, many patients prescribed antipsychotics are discharged to primary care without planned psychiatric review. We explore perceptions of healthcare professionals and managers/directors of policy regarding reasons for increasing prevalence and management of antipsychotics in primary care. Qualitative study using semi-structured interviews with 11 general practitioners (GPs), 8 psychiatrists, and 11 managers/directors of policy in the United Kingdom. Data was analysed using thematic analysis. Respondents reported competency gaps that impaired ability to manage patients prescribed antipsychotic medications, arising from inadequate postgraduate training and professional development. GPs lacked confidence to manage antipsychotic medications alone; psychiatrists lacked skills to address cardiometabolic risks and did not perceive this as their role. Communication barriers, lack of integrated care records, limited psychology provision, lowered expectation towards patients with serious mental illness by professionals, and pressure to discharge from hospital resulted in patients in primary care becoming 'trapped' on antipsychotics, inhibiting opportunities to deprescribe. Organisational and contractual barriers between services exacerbate this risk, with socioeconomic deprivation and lack of access to non-pharmacological interventions driving overprescribing. Professionals voiced fears of censure if a catastrophic event occurred after stopping an antipsychotic. Facilitators to overcome these barriers were suggested. People prescribed antipsychotics experience a fragmented health system and suboptimal care. Several interventions could be taken to improve care for this population, but inadequate availability of non-pharmacological interventions and socioeconomic factors increasing mental distress need policy change to improve outcomes. The role of professionals' fear of medicolegal or regulatory censure inhibiting antipsychotic deprescribing was a new finding in this study.
Antipsychotics are commonly prescribed to treat a range of psychiatric conditions in women of reproductive age and during pregnancy, including schizophrenia, bipolar disorder, anxiety, depression, autism spectrum disorder, and insomnia. This study aimed to evaluate whether children exposed to antipsychotic medication prenatally are at increased risk of specific neurodevelopmental disorders and learning difficulties. Our population-based cohort study used nationwide register data (1 January 2000-31 December 2020) on pregnant women diagnosed with a psychiatric disorder and their live-born singletons from Denmark, Finland, Iceland, Norway, and Sweden. Cox proportional hazard regression yielded propensity score-weighted hazard ratios (aHRs) and 95% confidence intervals (CIs) for risk of intellectual-, speech or language-, learning-developmental disorders, and a composite outcome of the listed disorders. We defined poor performance as scoring within the lowest quartile on national school tests in mathematics and language arts. We estimated propensity score-weighted risk ratios (aRRs) using Poisson regression. We analysed data from Denmark separately and pooled results using random effects meta-analysis. Among 213,302 children (median follow-up: 6.7 years), 11 626 (5.5%) were exposed to antipsychotics prenatally. Adjusted risk estimates did not suggest an increased risk of neurodevelopmental disorders: aHR of 1.06 (95% CI 0.94-1.20) for the composite outcome, or for poor academic performance: aRR of 1.04 (95% CI 0.91-1.18) in mathematics, and of 1.00 (95% CI 0.87-1.15) in language arts. Results were generally consistent across individual medications, trimesters of exposure, sibling- and sensitivity analyses. The findings of this large multinational cohort study suggest there is little to no increased risk of child neurodevelopmental disorders or learning difficulties after prenatal exposure to antipsychotics. Our findings can assist clinicians and women managing mental illness during pregnancy. This study was funded by the NordForsk Nordic Program on Health and Welfare (Nordic Pregnancy Drug Safety Studies, project No. 83539), by the Research Council of Norway (International Pregnancy Drug Safety Studies, project No. 273366) and by the Research Council of Norway through its Centres of Excellence funding scheme (project No. 262700), and UNSW Scientia Programme Awards (PS46019, PS46019-A).
Anxiety about performing numerical calculations is becoming an increasingly important issue. Termed mathematics anxiety, this condition negatively impacts performance in numerical tasks which can affect education outcomes and future employment. The disruption account proposes poor performance is due to anxiety disrupting limited attentional and inhibitory resources leaving fewer cognitive resources for the current task. This study provides the first neural network model of math anxiety. The model simulates performance in two commonly-used tasks related to math anxiety: the numerical Stroop and symbolic number comparison. Different model modifications were used to simulate high and low math-anxious conditions by modifying attentional processes and learning; these model modifications address different theories of math anxiety. The model simulations suggest that math anxiety is associated with reduced attention to numerical stimuli. These results are consistent with the disruption account and the attentional control theory where anxiety decreases goal-directed attention and increases stimulus-driven attention.
Anxiety and depression are among the common comorbidities of people diagnosed with cancer. However, despite the progress in therapeutic options and outcomes, mental health care and support have lagged behind for cancer patients. Estimating the extent and determinants of mental health disorders among cancer patients is crucial to alert concerned bodies for action. In view of this, we aimed to determine the pooled prevalence and determinants of anxiety and depression among cancer patients in Ethiopia. Relevant literatures were searched on PubMed, African Journals Online, Hinari, Epistemonikos, Scopus, EMBASE, CINAHL, Cochrane Library, and Gray literature sources. Data were extracted into an Excel spreadsheet and analyzed using STATA 17 statistical software. The random effect model was used to summarize the pooled effect sizes with their respective 95% confidence intervals. The I2 statistics and Egger's regression test in conjunction with the funnel plot were utilized to evaluate heterogeneity and publication bias among included studies respectively. A total of 17 studies with 5,592 participants were considered in this review. The pooled prevalence of anxiety and depression among cancer patients in Ethiopia were 45.10% (95% CI: 36.74, 53.45) and 42.96% (95% CI: 34.98, 50.93), respectively. Primary and above education (OR= 0.76, 95% CI: 0.60, 0.97), poor social support (OR= 2.27, 95% CI: 1.29, 3.98), occupational status (OR= 0.59; 95% CI: 0.43, 0.82), advanced cancer stage (OR= 2.19, 95% CI: 1.38, 3.47), comorbid illness (OR= 1.67; 95% CI: 1.09, 2.58) and poor sleep quality (OR= 11.34, 95% CI: 6.47, 19.89) were significantly associated with depression. Whereas, advanced cancer stage (OR= 1.59, 95% CI: 1.15, 2.20) and poor sleep quality (OR= 12.56, 95% CI: 6.4 1, 24.62) were the factors associated with anxiety. This meta-analysis indicated that a substantial proportion of cancer patients suffer from anxiety and depression in Ethiopia. Educational status, occupational status, social support, cancer stage, comorbid illness and sleep quality were significantly associated with depression. Whereas, anxiety was predicted by cancer stage and sleep quality. Thus, the provision of comprehensive mental health support as a constituent of chronic cancer care is crucial to mitigate the impact and occurrence of anxiety and depression among cancer patients. Besides, families and the community should strengthen social support for cancer patients. https://www.crd.york.ac.uk/prospero/, identifier CRD42023468621.