Role of psychiatric disorders and irritable bowel syndrome in asthma patients

OBJECTIVES: The goals of the study were the following: 1) to determine the frequency of psychiatric disorders and irritable bowel syndrome in patients with asthma and 2) to compare the frequency of these disorders in patients with asthma to their frequency in healthy controls. INTRODUCTION: Patients with asthma have a higher frequency of irritable bowel syndrome and psychiatric disorders. METHODS: We evaluated 101 patients with bronchial asthma and 67 healthy subjects. All subjects completed the brief version of the Bowel Symptoms Questionnaire and a structured clinical interview for DSM-IV axis disorders (SCID-I/CV). RESULTS: There were 37 cases of irritable bowel syndrome in the group of 101 stable asthma patients (36.6%) and 12 cases in the group of 67 healthy subjects (17.9%) (p = 0.009). Irritable bowel syndrome comorbidity was not related to the severity of asthma (p = 0.15). Regardless of the presence of irritable bowel syndrome, psychiatric disorders in asthma patients (52/97; 53.6%) were more common than in the control group (22/63, 34.9%) (p = 0.02). Although psychiatric disorders were more common in asthma patients with irritable bowel syndrome (21/35, 60%) than in those without irritable bowel syndrome (31/62, 50%), the difference was not significant (p = 0.34). In asthma patients with irritable bowel syndrome and psychiatric disorders, the percentage of forced expiratory volume in 1 s (FEV1) was lower than it was in those with no comorbidities (p = 0.02). CONCLUSIONS: Both irritable bowel syndrome and psychiatric disorders were more common in asthma patients than in healthy controls. Psychiatric disorders were more common in asthma patients with irritable bowel syndrome than in those without irritable bowel syndrome, although the differences failed to reach statistical significance. In asthma patients with IBS and psychiatric disorders, FEV1s were significantly lower than in other asthma patients. It is important for clinicians to accurately recognize that these comorbid conditions are associated with additive functional impairment

Clinical Relevance of Preoperative Neutrophil to Lymphocyte and Platelet to Lymphocyte Ratio in Renal Cell Carcinoma

Objective:The association of preoperative neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) with postoperative tumor stage and Fuhrman nuclear grade was investigated in patients with renal cell carcinoma (RCC).Materials and Methods:Data of 123 patients, who were operated due to RCC, in our clinic was analysed. NLR and PLR were evaluated in patients who were classified according to tumor stage (T1 and T2 low stage, T3 and T4 high stage) and Fuhrman nuclear grade (grade 1 and 2 low-grade, grade 3 and 4 high-grade). NLR and PLR were compared using Levene’s test between the groups.Results:Sixty four patients were female (52.1%) and 59 were male (47.9%). All haematological parameters were expressed as 103/μL. Mean age, blood neutrophil, lymphocyte and platelet counts, and NLR and PLR values of the patients were 62.49±12.43 years, 6.27±2.8, 2.05±0.83, 263.72±89.03, 4.01±3.93, and 149.73±82.1, respectively. The most common histologic subtype was recorded as clear cell RCC (76.4%). NLR and PLR were 3.83±3.22 and 142.79±66.66, respectively in the low-stage group and 4.43±5.29 and 165.85±109.41, respectively in the high stage group. As for the Fuhrman nuclear grading, NLR and PLR were 3.81±3.45 and 146.63±87.36, respectively in the low-grade group and 4.61±5.387 and 159.32±63.42 in the high-grade group. There was no statistically significant difference between the groups (p>0.05).Conclusion:Although not statistically significant, high tumor stage and nuclear grade were positively correlated with NLR and PLR. It is concluded that, further multi-center and prospective studies with larger samples are needed to derive meaningful results

Boosting the Performance of Artificial Intelligence-Driven Models in Predicting COVID-19 Mortality in Ethiopia

Like other nations around the world, Ethiopia has suffered negative effects from COVID-19. The objective of this study was to predict COVID-19 mortality using Artificial Intelligence (AI)-driven models. Two-year daily recorded data related to COVID-19 were trained and tested to predict mortality using machine learning algorithms. Normalization of features, sensitivity analysis for feature selection, modelling of AI-driven models, and comparing the boosting model with single AI-driven models were the main activities performed in this study. Prediction of COVID-19 mortality was conducted using a combination of four dominant feature variables, and hence, the best determination of coefficient (DC) of AdaBoost, KNN, ANN-6, and SVM in the prediction process were 0.9422, 0.8618, 0.8629, and 0.7171, respectively. The Boosting model improved the performance of the individual AI-driven models KNN, SVM, and ANN-6 by 7.94, 22.51, and 8.02 percent, respectively, at the verification stage using the testing dataset. This suggests that the boosting model has the best performance for prediction of COVID-19 mortality in Ethiopia. As a result, it suggests a promising potential performance of boosting ensemble model to be applied in predicting mortality and cases from similarly recorded daily data to predict mortality due to COVID-19 in other parts of the world

Artificial Intelligence-Driven Ensemble Model for Predicting Mortality Due to COVID-19 in East Africa

East Africa was not exempt from the devastating effects of COVID-19, which led to the nearly complete cessation of social and economic activities worldwide. The objective of this study was to predict mortality due to COVID-19 using an artificial intelligence-driven ensemble model in East Africa. The dataset, which spans two years, was divided into training and verification datasets. To predict the mortality, three steps were conducted, which included a sensitivity analysis, the modelling of four single AI-driven models, and development of four ensemble models. Four dominant input variables were selected to conduct the single models. Hence, the coefficients of determination of ANFIS, FFNN, SVM, and MLR were 0.9273, 0.8586, 0.8490, and 0.7956, respectively. The non-linear ensemble approaches performed better than the linear approaches, and the ANFIS ensemble was the best-performing ensemble approach that boosted the predicting performance of the single AI-driven models. This fact revealed the promising capability of ensemble models for predicting the daily mortality due to COVID-19 in other parts of the globe

Psychologic correlates of eating attitudes in Turkish female college students

Objective: The purpose of this study is to determine the frequency and correlates of disordered eating attitudes in a university-sample Turkish female population and to evaluate the contribution of maternal psychopathologic symptoms and family functioning

Bayesian machine learning analysis with Markov Chain Monte Carlo techniques for assessing characteristics and risk factors of Covid-19 in Erbil City-Iraq 2020–2021

The study aims to showcase machine learning techniques in the application of medical datasets for improving identification of correlations and relationships between variables, which will lead to more informed decision-making. Unlike other studies, intensive statistical modelling is used to understand and find the effective of variables cause to lead death due to Covid-19. Due to large dataset, not common approaches derive us to ideal conclusion. Furthermore, Bayesian technique is applied to generate predictive posterior distributions of the unknown parameters in the model in neural network as well as logistic regression, which helps us to avoid overfitting in machine learning applications and have additional measurements in assessing fitted model performance. According to the results extracted from the statistical analysis, the Bayesian neural network demonstrated superior performance in terms of classification measurements such as AUC (84.66%), F1 (87.11%), Precision (88.29%), and Recall (85.96%). The Bayesian logistic regression also performed well, but with slightly lower scores, achieving AUC (83.07%), F1 (85.59%), Precision (84.55%), and Recall (85.59%). In contrast, logistic regression (MLE) technique had the worst performance with very low scores (AUC = 52.38%, F1 = 57.55%, Precision = 57.01%, Recall = 58.10%). Regarding the variables' association with mortality, stepwise forward selection helped to identify seven significant variables. Age was found to be the most significant variable in predicting the probability of dying, with patients in the age group of (18–44) having 12 times higher odds, patients in the age group of (45–64) having 123 more odds, and patients above 65 years old having 436 times more chance to die compared to patients below 18 years old. Severe coughing was also significant with 7.26 odds, and patients suffering from diabetes had 2.82 times more chance to die. Moreover, SpO2 contributed to a decrease of 20% in the relative risk of dying from Covid-19 disease. Gender and Smoking did not show a significant association with mortality. Finally, the Bayesian approach showed higher sensitivity and specificity than the classic neural network

The evaluation of airway inflammation in asymptomatic obeses

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Can Spinal Bupivacaine Analgesia Treatment Make a Difference on Urinary Bladder Healing According to the Intramuscular Pethidine Analgesia Treatment in Rats?

We designed a study to compare the healing levels found with intramuscular pethidine with those found with intrathecal local anesthetic treatments. The urinary bladder is suggested to be the most useful tissue in the evaluation of the effects of the drugs. Nineteen male, Sprague-Dawley rats weighing 200–300 g were used in this study. A sagittal section was made in the urinary bladder after suitable anesthesia and laparotomy. Bladders were closed with 5-0 plain catguts 5 min later. There were nine rats in the control group and pethidine (0.5 g/kg) was administered intramuscularly in the gluteal muscle region to treat pain after the operations. There were 11 rats in the study group and each received a spinal injection of 0.25% bupivacaine after the operation. Rats were followed for 7 days to define pain. Specimens, particularly the incised region of the bladder, were evaluated for inflammation and fibrosis. Grading scales were used for this purpose. Statistical analyses of the data were performed using the Chi-square test. Statistical analyses were nonsignificant for inflammation (p ≤ 0.151) and nonsignificant for fibrosis (p ≤ 0.105). The treatments may have the same effects on organ healing mechanisms. Statistical difference is not shown in this study, but use of other combinations of pain treatments to evaluate the healing may demonstrate which of these possibilities is true

A Comparative Evaluation of the Clinical Effects of Systemic and Local Doxycycline in the Treatment of Chronic Periodontitis.

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