Lung cancer risk and the inhibitors of angiotensin converting enzyme; an updated review on recent evidence

The renin-angiotensin-aldosterone system (RAAS) has a significant act in the pathology of blood pressure and cancer. One of the dominant sections of angiotensin II (Ang II) and angiotensin-converting enzyme (ACE) expression generation in the human body is the capillary veins in the lung. Changes in the expression of RAAS were revealed to be included in several lung diseases. There are several studies on the anticancer effect of ACE inhibitors; however, Hicks and colleagues reported an augmented risk of 14% for advancing lung cancer for patients consuming ACE inhibitors against angiotensin receptor blockers (ARBs) administration. Several lines of evidence indicated that ARB users have a lower risk of tumor progression and metastasis and progression of lung cancer. This review has surveyed some studies about the study by Hicks et al with conflicting results. Some Hicks’s study limitations are summarized here such as genetic effects, comparative study, residual confounding factors such as smoking, detection bias owing to cough, and socio-economic status. It is suggested some natural alternatives to ACE Inhibitors in here.publishedVersio

Comparative assessment of salivary level of cortisol, anxiety and depression in patients with oral lichen planus

Oral lichen planus (OLP) is an inflammatory condition of oral mucosa and skin. The present study compared salivary cortisol, depression and anxiety levels of patients with erosive and reticular OLP and healthy controls. In this case-control trial, 69 individuals (23 healthy, 23 erosive OLP and 23 reticular OLP patients) were selected. The participants completed the hospital anxiety and depression scale (HADS) and 5 mL of their unstimulated saliva were collected. Salivary cortisol levels were measured by enzyme-linked immunosorbent assay(ELISA). The comparison of anxiety and depression scores as well as salivary cortisol levels was done one-way analysis of variance (ANOVA) test while the paired comparisons were done by Turkey post hoc test. The mean anxiety score in erosive OLP patients was significantly higher than that in the control and reticular OLP groups. The reticular OLP and control groups had no significant difference in this respect. The three groups were not significantly different regarding the depression score or salivary level of cortisol. The correlation between depression and anxiety was significant but salivary level of cortisol had no correlation with anxiety or depression. This study showed that anxiety control may aid in control of erosive OLP, although further investigations are required

The Accuracy of Plain Radiography in Detection of Traumatic Intrathoracic Injuries

Introduction: Rapid diagnosis of traumatic intrathoracic injuries leads to improvement in patient management. This study was designed to evaluate the diagnostic value of chest radiography (CXR) in comparison to chest computed tomography (CT) scan in diagnosis of traumatic intrathoracic injuries. Methods: Participants of this prospective diagnostic accuracy study included multiple trauma patients over 15 years old with stable vital admitted to emergency department (ED) during one year. The correlation of CXR and CT scan findings in diagnosis of traumatic intrathoracic injuries was evaluated using SPSS 20. Screening characteristics of CXR were calculated with 95% CI. Results: 353 patients with the mean age of 35.2 ± 15.8 were evaluated (78.8% male). Age 16-30 years with 121 (34.2%), motorcycle riders with 104 (29.5%) cases and ISS < 12 with 185 (52.4%) had the highest frequency among patients. Generally, screening performance characteristics of chest in diagnosis of chest traumatic injuries were as follows: sensitivity 50.3 (95% CI: 44.8 – 55.5), specificity 98.9 (95% CI: 99.5 – 99.8), PPV 97.8 (95% CI: 91.5 – 99.6), NPV 66.4 (95% CI: 60.2 – 72.03), PLR 44.5 (95% CI: 11.3 175.3), and NLR 0.5 (95% CI: 0.4 – 0.6). Accuracy of CXR in diagnosis of traumatic intrathoracic injuries was 74.5 (95% CI: 69.6 – 78.9) and its area under the ROC curve was 74.6 (95% CI: 69.3 – 79.8). Conclusion: The screening performance characteristics of CXR in diagnosis of traumatic intrathoracic injuries were higher than 90% in all pathologies except pneumothorax (50.3%). It seems that this matter has a great impact on the general screening characteristics of the test (74.3% accuracy and 50.3%sensitivity). It seems that, plain CXR should be used as an initial screening tool more carefully

A Qualitative Meta-Analysis of Scholarly Articles Concerning Poor-Supported Women in Iran

The purpose of the current research is to review and analyze the qualitative studies conducted in the field of abused women. In this regard, all scientific-research articles with the keyword "poor-Supported women" in the time period of 2005 to 2000 were extracted from the academic-scientific database of Jihad University and the Normagz document database. In the following, 34 articles were selected for data extraction and further investigation and were studied by documentary, library and meta-analysis methods. Based on the findings of the current research, the studies conducted in this field include two main orientations of psychology and sociology; So that other aspects of the life of this group of women, including economic and political, are examined under these two categories. A group of these studies analyzed theoretically and presented models to identify the problems of poor-Supported women in order to reduce their problems using intervention methods, and another group focused on strengthening the morale and creating a positive image in poor-Supported women through increasing positive views from an empirical point of view. The findings of this study in two aspects of psychology and sociology show that these women need psychological, social and economic support and empowering them in the mentioned dimensions will improve their quality of life and reduce their social suffering. Based on the theoretical achievements of this research, it seems that the policy makers should revise the laws and social rulings related to these women based on a comprehensive definition of abused women

Effects of Problem, Intervention, Evaluation (PIE) Training on the Quality of Nursing Documentation Among Students of Hamadan University of Medical Sciences, Hamadan, Iran

Background: Documentation of nursing care is one of the most important professional responsibilities of nurses and one of the major components of medical care and patient record documentation. Objectives: The present study was performed to determine the effect of problem, intervention, evaluation (PIE) training on the quality of nursing students' documentation. Methods: In this semi-experimental single-group study with a pretest-posttest design, a total of 28 nursing students were selected by simple random sampling. The data collection tools included a demographic questionnaire, PIE documentation form, and documentation quality checklist. First, the students were asked to write two reports using the traditional or narrative method. Then, a training workshop was organized about PIE documentation, and the students were asked to use this method and write two more reports about the same patient on two consecutive days; overall, each student presented four reports. A total of 112 reports were analyzed using descriptive statistics and paired t test in SPSS. Results: Based on the results of paired t test, there was a significant difference in the mean score of documentation quality between the pretest and posttest (P < 0.001). Also, there was a significant difference in the mean score of documentation quality between the pretest and posttest in terms of both report structure and content (P < 0.001). Conclusions: Use of PIE reporting system improves the quality of nursing documentation. Therefore, it can be a suitable alternative for the current narrative or traditional method. Keywords Nursing Documentation Problem-Based Reporting Nursing Student

Advancing Statistical Learning and Decision Modeling Using Irregularly-sampled Multivariate Data for Managing Respiratory Diseases

Complex healthcare systems require efficient and effective data-driven decision making in various aspects. As patient data becomes more available, advanced statistical learning and machine learning techniques are applied to improve data-driven decision making. However, patient health data, including clinical trial data, medical records, and electronic health records, are associated with several challenges. Patient health data includes medical information of a patient that may includedemographics, information relating to their health or illness, medications and treatments, etc. They are a combination of static and time series variables, with many censoring and missingness in the data, and are irregularly sampled in most cases. In addition to these challenges, data is limited compared to the variability in patients’ conditions and procedures. Therefore, applying traditional machine learning and statistical methods is inefficient and, in some cases, impossible. In this dissertation, several statistical learning, machine learning, and decision modeling approaches are developed to address these challenges in respiratory disease treatment decision making. Treatments for respiratory diseases depend on the severity of the disease and the patient’s condition. Some of the most important interventions are medical treatment, surgery, and mechanical ventilation during hospitalization. In the first chapter, a Markov decision process is built using limited clinical trial data to assess the timing of surgery for patients with severe emphysema. In the next chapter, a statistical learning approach is proposed to prepare the irregularly-sampled and heterogeneous electronic health records as input to machine learning models. The method is applied to predict the outcome of mechanical ventilation in the ICU. A lab test importance score is also proposed to quantify the effect of each lab test in the prediction model and identify unnecessary lab tests. In the last chapter, an optimization approach is introduced to determine the optimal time-window size to regularize the time-series data of each patient and prepare the data to feed into any sequential machine learning model. The optimization results are applied to two sequential learning models. First, the results are applied to a long-short term memory (LSTM) model to predict discontinue time of mechanical ventilation. In the second problem, a reinforcement learning (RL) model is developed to find the optimal timing of lab tests and reduce the number of unnecessary lab tests while the patient is on ventilators

Advancing Statistical Learning and Decision Modeling Using Irregularly-sampled Multivariate Data for Managing Respiratory Diseases

Complex healthcare systems require efficient and effective data-driven decision making in various aspects. As patient data becomes more available, advanced statistical learning and machine learning techniques are applied to improve data-driven decision making. However, patient health data, including clinical trial data, medical records, and electronic health records, are associated with several challenges. Patient health data includes medical information of a patient that may includedemographics, information relating to their health or illness, medications and treatments, etc. They are a combination of static and time series variables, with many censoring and missingness in the data, and are irregularly sampled in most cases. In addition to these challenges, data is limited compared to the variability in patients’ conditions and procedures. Therefore, applying traditional machine learning and statistical methods is inefficient and, in some cases, impossible. In this dissertation, several statistical learning, machine learning, and decision modeling approaches are developed to address these challenges in respiratory disease treatment decision making. Treatments for respiratory diseases depend on the severity of the disease and the patient’s condition. Some of the most important interventions are medical treatment, surgery, and mechanical ventilation during hospitalization. In the first chapter, a Markov decision process is built using limited clinical trial data to assess the timing of surgery for patients with severe emphysema. In the next chapter, a statistical learning approach is proposed to prepare the irregularly-sampled and heterogeneous electronic health records as input to machine learning models. The method is applied to predict the outcome of mechanical ventilation in the ICU. A lab test importance score is also proposed to quantify the effect of each lab test in the prediction model and identify unnecessary lab tests. In the last chapter, an optimization approach is introduced to determine the optimal time-window size to regularize the time-series data of each patient and prepare the data to feed into any sequential machine learning model. The optimization results are applied to two sequential learning models. First, the results are applied to a long-short term memory (LSTM) model to predict discontinue time of mechanical ventilation. In the second problem, a reinforcement learning (RL) model is developed to find the optimal timing of lab tests and reduce the number of unnecessary lab tests while the patient is on ventilators

Evaluation of academic ability of faculty applicants in Isfahan University of Medical Sciences

Introduction: Selection and employment of Ph.D. service personnel and clinical specialists is one of the basic issues in providing the manpower required for universities. This study endeavored to investigate the academic competence of applicants for contract faculty recruitment calls and legal obligations for Ph.D.in Isfahan University of Medical Sciences from the academic years 2016 to 2020. Methods: This descriptive cross-sectional study was performed by collecting information from the uploaded files and documents of 222 faculty applicants and the legal obligations of the PhD from among 170 applicants during the academic years 2016 to 2020. The scores of the admitted students were obtained from the analysis of data in the "national form designed to assess the academic competence of the applicants for membership in the departments of the universities and higher education centers. This form was approved by the Ministry of Health, Treatment and Medical education. Data were analyzed using chi-square, independent t-test, and analysis of variance. Results: The total number of people surveyed was 392 (222 PhD holder applicants for contract faculty and 170 PhD holders’ applicants for legal obligations). In the two groups, the highest scores were related to clinical competence and skill (n=20), teaching ability and published articles. Research findings showed that applicants have obtained the lowest score from options 17 and 19, which were mentioned in the form. Faculty application requirements and PhD legal obligations were not significantly different in language scores (p <0.05) but were significantly different in interview scores and documentation (p<0.05). In the contract faculty recruitment test, the scores of scientometrics, scientific interview, and English language of the participants in test 15 were higher than test 16 (p <0.05). The scientometrics and English language scores of the participants in different periods of summon for legal obligations were significantly different from each other (p <0.05) Conclusion: The results revealed that the applicants for faculty recruitment in terms of ability to teach, teaching according to educational departments did not act favorably but they paid more attention to publishing articles and obtaining academic certificates. Thus, it is recommended to policy makers and university officials to consider this key point in empowerment planning for academic members employed