The Effect of Chamomile Oil on Pain and Anxiety Intensity of IUD Insertion in Women Referring to Karaj Health Centers: Ridit Analysis

Introduction: Intrauterine device (IUD) is a safe, effective and reversible method of family planning. Unfortunately, IUD insertion causes anxiety and pain. The aim of study was to compare ridit analysis and Kruskal-wallis test in pain and anxiety intensity of IUD insertion in women referring to health centers of Karaj (Iran).Materials and Methods: In this randomized clinical trial study, 150 eligible women candidate intrauterine device insertion entered the study and were randomly divided into three groups: chamomile, placebo and control groups. Data was collected from women who came to health centers in Karaj (Iran) in 2017. Data collection tools included demographic information, Spiel-Berger questionnaire and pain visual analogue scale. The intensity of pain and anxiety were measured afterwards. Finally, ridit analysis and Kruskal-wallis test were used to rank the intensity of pain and anxiety in patients. The R-3.4.3 and Microsoft's Excel software were used for statistical analysis.Results: The results showed that the mean±SD of age in three groups was 29.7±7.01, 28.68±8.15 and 31.6±7.71, respectively. Ridit analysis and Kruskal-wallis test showed considerable decrease of the anxiety and pain intensity, induced by IUD insertion in Chamomile, Placebo and Control groups respectively. Ridit analysis and Kruskal-wallis test statistics are significant. The value of the ridit statistic was 15د‡2=20.23, P<0.001">  and the value of Kruskal-wallis test is 15د‡2=18.67, P<0.005">  in pain intensity. Moreover, the value of the ridit statistic is 15د‡2=3.92, P<0.001">  and the value of Kruskal-wallis test is 15د‡2=21.37, P<0.005">  in anxiety intensity.Conclusions: The results of this study suggested that, there is less significant difference in ridit analysis than Kruskal-wallis test among the three groups in decreasing pain and anxiety intensity. 

Machine learning-based classifiers to predict metastasis in colorectal cancer patients

BackgroundThe increasing prevalence of colorectal cancer (CRC) in Iran over the past three decades has made it a key public health burden. This study aimed to predict metastasis in CRC patients using machine learning (ML) approaches in terms of demographic and clinical factors.MethodsThis study focuses on 1,127 CRC patients who underwent appropriate treatments at Taleghani Hospital, a tertiary care facility. The patients were divided into training and test datasets in an 80:20 ratio. Various ML methods, including Naive Bayes (NB), random rorest (RF), support vector machine (SVM), neural network (NN), decision tree (DT), and logistic regression (LR), were used for predicting metastasis in CRC patients. Model performance was evaluated using 5-fold cross-validation, reporting sensitivity, specificity, the area under the curve (AUC), and other indexes.ResultsAmong the 1,127 patients, 183 (16%) had experienced metastasis. In the predictionof metastasis, both the NN and RF algorithms had the highest AUC, while SVM ranked third in both the original and balanced datasets. The NN and RF algorithms achieved the highest AUC (100%), sensitivity (100% and 100%, respectively), and accuracy (99.2% and 99.3%, respectively) on the balanced dataset, followed by the SVM with an AUC of 98.8%, a sensitivity of 97.5%, and an accuracy of 97%. Moreover, lower false negative rate (FNR), false positive rate (FPR), and higher negative predictive value (NPV) can be confirmed by these two methods. The results also showed that all methods exhibited good performance in the test datasets, and the balanced dataset improved the performance of most ML methods. The most important variables for predicting metastasis were the tumor stage, the number of involved lymph nodes, and the treatment type. In a separate analysis of patients with tumor stages I–III, it was identified that tumor grade, tumor size, and tumor stage are the most important features.ConclusionThis study indicated that NN and RF were the best among ML-based approaches for predicting metastasis in CRC patients. Both the tumor stage and the number of involved lymph nodes were considered the most important features

The impact of vitamin D changes during pregnancy on the development of maternal adverse events: A random forest analysis

Background: Maternal vitamin D deficiency during pregnancy has been associated with various maternal adverse events (MAE). However, the evidence regarding the effect of vitamin D supplementation on these outcomes is still inconclusive. Methods: This secondary analysis utilized a case–control design. 403 samples with MAE and 403 samples without any outcomes were selected from the Khuzestan Vitamin D Deficiency Screening Program in Pregnancy study. Random forest (RF) analysis was used to evaluate the effect of maternal vitamin D changes during pregnancy on MAE. Results: The results showed that women who remained deficient (35.2%) or who worsened from sufficient to deficient (30.0%) had more MAE than women who improved (16.4%) or stayed sufficient (11.8%). The RF model had an AUC of 0.74, sensitivity of 72.6%, and specificity of 69%, which indicate a moderate to high performance for predicting MAE. The ranked variables revealed that systolic blood pressure is the most important variable for MAE, followed by diastolic blood pressure and vitamin D changes during pregnancy. Conclusion: This study provides evidence that maternal vitamin D changes during pregnancy have a significant impact on MAE. Our findings suggest that monitoring and treatment of vitamin D deficiency during pregnancy may be a potential preventive strategy for reducing the risk of MAE. The presented RF model had a moderate to high performance for predicting MAE

Biological and Clinical Relevance of Long Non-Coding RNA PCAT-1 in Cancer, A Systematic Review and Meta-Analysis

Long non-coding RNA (lncRNA) prostate cancer associated transcript 1 (PCAT-1) has been identified as a potential biomarker for the diagnosis and prognosis of various cancers. We performed this systematic review and meta-analysis to evaluate the role of dysregulation as well as the biological and clinical significance of lnc-PCAT-1 for predicting the malignancy status in several cancers. Two independent reviewers conducted an extensive search in electronic databases of Medline, Embase, Scopus, Web of Science and PubMed until the December of 2017. Five articles investigating the clinical significance of lncRNA PCAT-1, including 996 patients, were analyzed. Our results revealed that the increased PCAT-1 expression was related to overall survival (OS) (HR = 1.9, 95%CI: 1.13-3.18, P=0.015). Also, pooled results of the diagnostic data analysis demonstrated that PCAT-1 has a sensitivity of 0.59 and specificity of 0.66 for cancer diagnosis. Moreover, pooled area under curve was 0.62 (95% CI: 0.58–0.69). This meta-analysis revealed that lncRNA PCAT-1 could be served as a potential diagnostic and prognostic biomarker in various solid tumors

Lifestyle and occupational risks assessment of bladder cancer using machine learning‐based prediction models

Background: Bladder cancer, one of the most prevalent cancers globally, can be regarded as considerable morbidity and mortality for patients. The bladder is an organ that comes in constant exposure to the environment and other risk factors such as inflammation. Aims: In the current study, we used machine learning (ML) methods and developed risk prediction models for bladder cancer. Methods: This population‐based case–control study is focused on 692 cases of bladder cancer and 692 healthy people. The ML, including Neural Network (NN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Gradient Boosting (GB), and Logistic Regression (LR), were applied, and the model performance was evaluated. Results: The RF (AUC = .86, precision = 79%) had the best performance, and the RT (AUC = .78, precision = 73%) was in the next rank. Based on variable importance analysis in RF, recurrent infection, bladder stone history, neurogenic bladder, smoking and opium use, chronic renal failure, spinal cord paralysis, analgesic, family history of bladder cancer, diabetic mellitus, low dietary intake of fruit and vegetable, high dietary intake of ham, sausage, can and pickles were respectively the most important factors, which effect on the probability of bladder cancer. Conclusion: Machine learning approaches can predict the probability of bladder cancer according to medical history, occupational risk factors, and dietary and demographical characteristics

A study of depression, partnership and sexual satisfaction in patients with post-traumatic olfactory disorders

Post-traumatic olfactory dysfunction (PTOD) is associated with a significant decrease in quality of life. The present study aimed to explore whether PTOD is associated with depression and changes in sexuality. There were two groups in this case-control study. The patient group consisted of patients with PTOD (n = 55), and the control group comprised healthy individuals without the olfactory disorder (n = 115). Olfactory function, depression, partnership, and sexual satisfaction were assessed using the Iranian version of the Sniffin' Sticks test (Ir-SST), Beck Depression Inventory (BDI), Enrich Couple Scale (ECS) and Sexual Satisfaction Scale for Women (SSSW). The BDI scores were higher in the patient group than in the control group (p < 0.001). The SSSW score was lower in the patient group than in controls (p < 0.01), although the ECS score was not significantly different between patients and controls. Also, there was no significant difference in the severity of trauma between marital satisfaction and sexual satisfaction. However, the analysis showed a statistically significant difference in depression scores in connection with the head trauma severity. In the PTOD group, depression was increased and sexual satisfaction declined. Understanding the association of olfactory dysfunction with depression and sexuality allows patients and doctors to deal with less notable consequences of this disorder

Adherence to dietary recommendations by socioeconomic status in the United Kingdom biobank cohort study

Understanding how socioeconomic markers interact could inform future policies aimed at increasing adherence to a healthy diet. This cross-sectional study included 437,860 participants from the UK Biobank. Dietary intake was self-reported. Were used as measures socioeconomic education level, income and Townsend deprivation index. A healthy diet score was defined using current dietary recommendations for nine food items and one point was assigned for meeting the recommendation for each. Good adherence to a healthy diet was defined as the top 75th percentile, while poor adherence was defined as the lowest 25th percentile. Poisson regression was used to investigate adherence to dietary recommendations. There were significant trends whereby diet scores tended to be less healthy as deprivation markers increased. The diet score trends were greater for education compared to area deprivation and income. Compared to participants with the highest level of education, those with the lowest education were found to be 48% less likely to adhere to a healthy diet (95% Confidence Interval [CI]: 0.60-0.64). Additionally, participants with the lowest income level were 33% less likely to maintain a healthy diet (95% CI: 0.73-0.81), and those in the most deprived areas were 13% less likely (95% CI: 0.84-0.91). Among the three measured proxies of socioeconomic statuseducation, income, and area deprivationlow education emerged as the strongest factor associated with lower adherence to a healthy diet

Association between walking pace and incident type 2 diabetes by adiposity levels – a prospective cohort study from the UK Biobank

Aims: This study aims to investigate the combined association of adiposity and walking pace with incident type 2 diabetes. Methods: We undertook a prospective cohort study on 194,304 White-European participants (mean age 56.5 years, 55.9% women). Participants’ walking pace was self-reported as brisk, average, or slow. Adiposity included body mass index (BMI), waist circumference (WC) and body fat percentage (BF%). The associations were investigated using Cox proportional hazard models, with a 2-year landmark analysis. A four-way decomposition analysis was used for mediation and additive interaction. Results: The median follow-up was 5.4 years (IQR: 4.8- 6.3). During the follow-up period, 4564 participants developed type 2 diabetes. Compared to brisk walking participants with normal BMI, those with obesity who walked briskly were at a ~10-12 fold higher risk of type 2 diabetes (HR: 9.64 [95% CI: 7.24; 12.84] in women; HR: 11.91 [95% CI: 8.80; 16.12] in men), whereas those who with obese and walked slowly had ~12-15-fold higher risk (HR: 12.68 [95% CI: 9.62; 16.71] for women; and HR: 15.41 [95% CI: 11.27; 21.06] for men). There was evidence of additive interaction between WC and BF% and walking pace among women, explaining 17.8% and 47.9% excess risk respectively. Obesity mediated the association in women and men, accounting for 60.1% and 44.9%, respectively. Conclusions: Slow walking pace is a risk factor for type 2 diabetes independent of adiposity. However, promoting brisk walking as well as weight management might be an effective type 2 diabetes prevention strategy given its synergistic effect

Predicting stroke in heart failure and reduced ejection fraction without atrial fibrillation

Background and Aims: Patients with heart failure with reduced ejection fraction (HFrEF) are at significant risk of stroke. Anticoagulation reduces this risk in patients with and without atrial fibrillation (AF), but the risk-to-benefit balance in the latter group, overall, is not favourable. Identification of patients with HFrEF, without AF, at the highest risk of stroke may allow targeted and safer use of prophylactic anticoagulant therapy. Methods: In a pooled patient-level cohort of the PARADIGM-HF, ATMOSPHERE, and DAPA-HF trials, a previously derived simple risk model for stroke, consisting of three variables (history of prior stroke, insulin-treated diabetes, and plasma N-terminal pro-B-type natriuretic peptide level), was validated. Results: Of the 20,159 patients included, 12,751 patients did not have AF at baseline. Among patients without AF, 346 (2.7%) experienced a stroke over a median follow-up of 2.0 years (rate 11.7 per 1000 patient-years). The risk for stroke increased with increasing risk score: fourth quintile HR 2.35 (95%CI 1.60-3.45); fifth quintile HR 3.73 (2.58-5.38), with the first quintile as reference. For patients in the top quintile, the rate of stroke was 21.2 per 1000 patient-years, similar to participants with AF not receiving anticoagulation (20.1 per 1000 patient-years). Model discrimination was good with a C-index of 0.84 (0.75-0.91). Conclusions: It is possible to identify a subset of HFrEF patients without AF with a stroke risk equivalent to that of patients with AF who are not anticoagulated. In these patients, the risk-to-benefit balance might justify the use of prophylactic anticoagulation, but this hypothesis needs to be tested prospectively

Dapagliflozin and quality of life measured using the EuroQol 5-Dimension questionnaire in patients with HFrEF and HFmrEF/HFpEF

Aims: Although much is known about the usefulness of heart failure (HF)-specific instruments for assessing patient well-being, less is known about the value of generic instruments for the measurement of health-related quality of life (HRQL) in HF. The aim of this study was to assess the relationship between the EuroQol 5-dimension 5-level (EQ-5D-5L) visual analogue scale (VAS) and index scores, clinical characteristics, and outcomes in patients with HF and the effect of dapagliflozin on these scores. Methods and results: We performed a patient-level pooled analysis of the DAPA-HF and DELIVER trials, which investigated the effectiveness and safety of dapagliflozin in patients with HF and reduced ejection fraction (HFrEF) and mildly reduced/preserved ejection fraction (HFmrEF/HFpEF), respectively. Patients reporting higher (better) EQ-5D-5L VAS and index scores had a lower prevalence of comorbidities, including atrial fibrillation and hypertension, than patients with a worse score. They were also more likely to have better investigator-reported (New York Heart Association class) and patient-self-reported (Kansas City Cardiomyopathy Questionnaire) health status and lower median N-terminal pro-B-type natriuretic peptide levels. Compared to patients with the lowest scores (Q1), those with higher EQ-5D-5L VAS scores had better outcomes: the hazard ratio for the composite of cardiovascular death or worsening HF was 0.81 (95% confidence interval 0.72–0.91) in Q2, 0.74 (0.65–0.84) in Q3, and 0.62 (0.54–0.72) in Q4. The risk of each component of the composite outcome, and all-cause death, was also lower in patients with better scores. Similar findings were observed for the index score. Treatment with dapagliflozin improved both EQ-5D-5L VAS and index scores across the range of ejection fraction. Conclusions: Both higher (better) EQ-5D-5L VAS and index scores were associated with better outcomes. Dapagliflozin treatment improved EQ-5D-5L VAS and index scores, irrespective of ejection fraction