Strategies in Development of Iranian Medical Sciences Universities for Dynamic Presence in the International Arena

Background & Objective: In compatibility with the progress of universities and expansion of knowledge and science around the world, Iran has also had significant academic progress in recent years. However, the vision of Iran for the following decades is very ambitious. Much has to be achieved to reach the highest position in the region in development, economy, science, and technology. This qualitative study was designed to explore the strengths and weaknesses of universities. Methods: This qualitative study had three phases. In the first phase, we explored the topic in brain storming sessions. Then, the themes raised in phase one were discussed deeply in unstructured interviews with selective experts around the country. In the last phase, we asked 30 selective academic staff from different medical sciences universities to categorize the issues based on their importance and their solutions using Delphi method. Results: Our findings showed that improvement of international affairs were frequently stressed by our respondents. In this domain, the English skills of academic staff and students, big revision in rules and regulations, and encouraging the establishment of close scientific communication with academic organizations around the world were highlighted. In addition, most respondents believed that problems were understandable, but our main conflicts were in finding applicable solutions and implementing decisions. Conclusion: Our results showed that for a better presentation of Iranian universities in the international environment a new approach to long term reform programs is necessary. In this plan, special attention must be paid to the educational and research infrastructures. Keywords University ranking University of Medical Sciences Development strategies International arena Critical thinking Skills Medical student

Classification of Asthma Based on Nonlinear Analysis of Breathing Pattern.

Normal human breathing exhibits complex variability in both respiratory rhythm and volume. Analyzing such nonlinear fluctuations may provide clinically relevant information in patients with complex illnesses such as asthma. We compared the cycle-by-cycle fluctuations of inter-breath interval (IBI) and lung volume (LV) among healthy volunteers and patients with various types of asthma. Continuous respiratory datasets were collected from forty age-matched men including 10 healthy volunteers, 10 patients with controlled atopic asthma, 10 patients with uncontrolled atopic asthma, and 10 patients with uncontrolled non-atopic asthma during 60 min spontaneous breathing. Complexity of breathing pattern was quantified by calculating detrended fluctuation analysis, largest Lyapunov exponents, sample entropy, and cross-sample entropy. The IBI as well as LV fluctuations showed decreased long-range correlation, increased regularity and reduced sensitivity to initial conditions in patients with asthma, particularly in uncontrolled state. Our results also showed a strong synchronization between the IBI and LV in patients with uncontrolled asthma. Receiver operating characteristic (ROC) curve analysis showed that nonlinear analysis of breathing pattern has a diagnostic value in asthma and can be used in differentiating uncontrolled from controlled and non-atopic from atopic asthma. We suggest that complexity analysis of breathing dynamics may represent a novel physiologic marker to facilitate diagnosis and management of patients with asthma. However, future studies are needed to increase the validity of the study and to improve these novel methods for better patient management

Classification of Asthma Based on Nonlinear Analysis of Breathing Pattern

<div><p>Normal human breathing exhibits complex variability in both respiratory rhythm and volume. Analyzing such nonlinear fluctuations may provide clinically relevant information in patients with complex illnesses such as asthma. We compared the cycle-by-cycle fluctuations of inter-breath interval (IBI) and lung volume (LV) among healthy volunteers and patients with various types of asthma. Continuous respiratory datasets were collected from forty age-matched men including 10 healthy volunteers, 10 patients with controlled atopic asthma, 10 patients with uncontrolled atopic asthma, and 10 patients with uncontrolled non-atopic asthma during 60 min spontaneous breathing. Complexity of breathing pattern was quantified by calculating detrended fluctuation analysis, largest Lyapunov exponents, sample entropy, and cross-sample entropy. The IBI as well as LV fluctuations showed decreased long-range correlation, increased regularity and reduced sensitivity to initial conditions in patients with asthma, particularly in uncontrolled state. Our results also showed a strong synchronization between the IBI and LV in patients with uncontrolled asthma. Receiver operating characteristic (ROC) curve analysis showed that nonlinear analysis of breathing pattern has a diagnostic value in asthma and can be used in differentiating uncontrolled from controlled and non-atopic from atopic asthma. We suggest that complexity analysis of breathing dynamics may represent a novel physiologic marker to facilitate diagnosis and management of patients with asthma. However, future studies are needed to increase the validity of the study and to improve these novel methods for better patient management.</p></div

Detrended fluctuation analysis (DFA) plots for the inter-breath interval(a) and lung volume (b) time series in representative subjects.

<p>A linear relationship between log(n) and log[f(n)] indicates the presence of fractal dynamics. The scaling exponent α quantifies the strength of long-range correlations within the time series. CAA, controlled atopic asthma; UAA, uncontrolled atopic asthma; UNAA, uncontrolled non-atopic asthma.</p

ROC curves for the ability of the complexity indices.

<p>(a), discriminating asthma from healthy; (b), discriminating uncontrolled from controlled asthma; (c), discriminating non-atopic from atopic asthma. DFA, detrended fluctuation analysis; SampEn, sample entropy; LLE, largest Lyapunov exponents; IBI, inter-breath interval; LV, lung volume.</p

The clinical potential of complexity indices combination in discriminating various types of asthma.

<p>The clinical potential of complexity indices combination in discriminating various types of asthma.</p

The mean ± SD values of the average and the coefficient of variation (CV) of inter-breath interval and lung volume series.

<p>The mean ± SD values of the average and the coefficient of variation (CV) of inter-breath interval and lung volume series.</p

The clinical potential of the complexity indices in discriminating non-atopic (n = 10) from atopic asthma (n = 20).

<p>The clinical potential of the complexity indices in discriminating non-atopic (n = 10) from atopic asthma (n = 20).</p

Breathing pattern in a representative subject.

<p>(a), An experimental tracing of abdominal and rib cage movement signals recorded continuously by pneumotrace bands (only a few seconds of tracing is presented for clarity). The plethysmography signals were calibrated to volume using an artificial neural network model. (b) and (c), Original (“raw”) inter-breath interval (b) and lung volume (c) time series during 60 min of resting breathing in a representative subject.</p

The clinical potential of the complexity indices in discriminating uncontrolled (n = 20) from controlled asthma (n = 10).

<p>The clinical potential of the complexity indices in discriminating uncontrolled (n = 20) from controlled asthma (n = 10).</p