Perceptions of Egyptian physicians about drug shortage during political disturbances: Survey in Greater Cairo

Background: Drug shortage is a problem that entangles health systems. In Egypt, many complaints arose due to drug shortage in the period following the 25th January revolution. Physicians play a vital role in dealing with this crisis. Objectives: Our aim was to investigate physicians’ perspective of the drug shortage problem and its impact on the healthcare system. Methods: A questionnaire was adopted and distributed by hand to physicians in customers’ waiting areas in Medical Syndicates Union. The questionnaire covered general participant information, drug shortage effects, physicians’ responses to the problem, the magnitude of the problem and its development three years around the revolution. Results: Of the 319 distributed questionnaires, 192 responses were valid with a response rate of 60%. Most of participants expressed the dire impact of drug shortage on patients’ health. Death as a result of drug shortage was reported by 67 physicians −35% of participants. A significant difference between internal medicine specialists and surgical medicine specialists in perception of drug related deaths was found (p-value = 0.004). A significant negative correlation between number of years of experience and agreement to analogues therapeutic equivalency was found (Spearman’s correlation coefficient = −0.207, P-value = 0.006). About two thirds of participants viewed drug shortages as a cause of inter-professional conflicts. Generally, participants denoted that drug shortage problem is worsening with time since the revolution. Conclusion: Prospective studies are required to quantitatively estimate drug shortage related mortality. Enhanced drug shortage communication by drug authorities and targeted education may relieve inter-professional conflicts resulting from drug shortages

Additional file 1 of The effectiveness and pharmacoeconomic study of using different corticosteroids in the treatment of hypersensitivity pneumonitis

Supplementary Material

The effect of α1-antitrypsin deficiency combined with increased bacterial loads on chronic obstructive pulmonary disease pharmacotherapy: A prospective, parallel, controlled pilot study

Chronic obstructive pulmonary disease (COPD) is caused by α1-antitrypsin deficiency (AATD) genetic susceptibility and exacerbated by infection. The current pilot study aimed at studying the combined effect of AATD and bacterial loads on the efficacy of COPD conventional pharmacotherapy. Fifty-nine subjects (29 controls and 30 COPD patients) were tested for genetic AATD and respiratory function. The bacterial loads were determined to the patients’ group who were then given a long acting beta-agonist and corticosteroid inhaler for 6 months. Nineteen percent of the studied group were Pi∗MZ (heterozygote deficiency variant), Pi∗S (5%) (milder deficiency variant), Pi∗ZZ (10%) (the most common deficiency variant), and Pi∗Mmalton (2%) (very rare deficiency variant). The patients’ sputum contained from 0 to 8 × 108 CFU/mL pathogenic bacteria. The forced vital capacity (FVC6) values of the AAT non-deficient group significantly improved after 3 and 6 months. Patients lacking AATD and pathogenic bacteria showed significant improvement in forced expiratory volume (FEV1), FEV1/FVC6, FVC6, and 6 min walk distance (6MWD) after 6 months. However, patients with AATD and pathogenic bacteria showed only significant improvement in FEV1 and FEV1/FVC6. The findings of this pilot study highlight for the first time the role of the combined AATD and pathogenic bacterial loads on the efficacy of COPD treatment

A specially tailored vancomycin continuous infusion regimen for renally impaired critically ill patients

Background: Vancomycin remains the gold standard for treatment of methicillin-resistant Staphylococcus aureus . Specially designed continuous infusion of vancomycin leads to better therapy. Methodology: A total of 40 critically ill patients who suffered from pneumonia susceptible to vancomycin, had serum creatinine >1.4 mg%, and oliguria <0.5 mL/kg/h for 6 h were included in the study with respiratory culture sensitivity to vancomycin ≤2 mg/L. Patients’ clinical, microbiological, and biological data were obtained by retrospective analysis of the corresponding medical files before and after vancomycin treatment. Patients with serum creatinine level ≥4 mg% and patients who received renal replacement therapy during the treatment period were excluded. The patients were divided into two groups—group 1 (intermittent dosing) and group 2 (continuous infusion) based on the following formula: rate of vancomycin continuous infusion (g/day) = [0.0205 creatinine clearance (mL/min) + 3.47] × [target vancomycin concentration at steady state (µg/mL)] × (24/1000). Trough vancomycin serum levels were also assessed using high-performance liquid chromatographic technique. Patients’ outcomes such as clinical improvement, adverse events, and 15-day mortality were reported. Results: Group 2 showed significant reduction in blood urea nitrogen, creatinine serum levels, white blood cells, partial carbon dioxide pressure, body temperature, and Sequential Organ Failure Assessment score, while significant increase in partial oxygen pressure and saturated oxygen was also observed. A significantly shorter duration of treatment with a comparable vancomycin serum levels was also reported with group 2. Conclusion: After treatment, comparison in patients’ criteria supports the superiority of using continuous infusion of vancomycin according to this equation in renally impaired patients

Sketch-Based Retrieval Approach Using Artificial Intelligence Algorithms for Deep Vision Feature Extraction

Since the onset of civilization, sketches have been used to portray our visual world, and they continue to do so in many different disciplines today. As in specific government agencies, establishing similarities between sketches is a crucial aspect of gathering forensic evidence in crimes, in addition to satisfying the user’s subjective requirements in searching and browsing for specific sorts of images (i.e., clip art images), especially with the proliferation of smartphones with touchscreens. With such a kind of search, quickly and effectively drawing and retrieving sketches from databases can occasionally be challenging, when using keywords or categories. Drawing some simple forms and searching for the image in that way could be simpler in some situations than attempting to put the vision into words, which is not always possible. Modern techniques, such as Content-Based Image Retrieval (CBIR), may offer a more useful solution. The key engine of such techniques that poses various challenges might be dealt with using effective visual feature representation. Object edge feature detectors are commonly used to extract features from different image sorts. However, they are inconvenient as they consume time due to their complexity in computation. In addition, they are complicated to implement with real-time responses. Therefore, assessing and identifying alternative solutions from the vast array of methods is essential. Scale Invariant Feature Transform (SIFT) is a typical solution that has been used by most prevalent research studies. Even for learning-based methods, SIFT is frequently used for comparison and assessment. However, SIFT has several downsides. Hence, this research is directed to the utilization of handcrafted-feature-based Oriented FAST and Rotated BRIEF (ORB) to capture visual features of sketched images to overcome SIFT limitations on small datasets. However, handcrafted-feature-based algorithms are generally unsuitable for large-scale sets of images. Efficient sketched image retrieval is achieved based on content and separation of the features of the black line drawings from the background into precisely-defined variables. Each variable is encoded as a distinct dimension in this disentangled representation. For representation of sketched images, this paper presents a Sketch-Based Image Retrieval (SBIR) system, which uses the information-maximizing GAN (InfoGAN) model. The establishment of such a retrieval system is based on features acquired by the unsupervised learning InfoGAN model to satisfy users’ expectations for large-scale datasets. The challenges with the matching and retrieval systems of such kinds of images develop when drawing clarity declines. Finally, the ORB-based matching system is introduced and compared to the SIFT-based system. Additionally, the InfoGAN-based system is compared with state-of-the-art solutions, including SIFT, ORB, and Convolutional Neural Network (CNN)

Sketch-Based Retrieval Approach Using Artificial Intelligence Algorithms for Deep Vision Feature Extraction

Since the onset of civilization, sketches have been used to portray our visual world, and they continue to do so in many different disciplines today. As in specific government agencies, establishing similarities between sketches is a crucial aspect of gathering forensic evidence in crimes, in addition to satisfying the user&rsquo;s subjective requirements in searching and browsing for specific sorts of images (i.e., clip art images), especially with the proliferation of smartphones with touchscreens. With such a kind of search, quickly and effectively drawing and retrieving sketches from databases can occasionally be challenging, when using keywords or categories. Drawing some simple forms and searching for the image in that way could be simpler in some situations than attempting to put the vision into words, which is not always possible. Modern techniques, such as Content-Based Image Retrieval (CBIR), may offer a more useful solution. The key engine of such techniques that poses various challenges might be dealt with using effective visual feature representation. Object edge feature detectors are commonly used to extract features from different image sorts. However, they are inconvenient as they consume time due to their complexity in computation. In addition, they are complicated to implement with real-time responses. Therefore, assessing and identifying alternative solutions from the vast array of methods is essential. Scale Invariant Feature Transform (SIFT) is a typical solution that has been used by most prevalent research studies. Even for learning-based methods, SIFT is frequently used for comparison and assessment. However, SIFT has several downsides. Hence, this research is directed to the utilization of handcrafted-feature-based Oriented FAST and Rotated BRIEF (ORB) to capture visual features of sketched images to overcome SIFT limitations on small datasets. However, handcrafted-feature-based algorithms are generally unsuitable for large-scale sets of images. Efficient sketched image retrieval is achieved based on content and separation of the features of the black line drawings from the background into precisely-defined variables. Each variable is encoded as a distinct dimension in this disentangled representation. For representation of sketched images, this paper presents a Sketch-Based Image Retrieval (SBIR) system, which uses the information-maximizing GAN (InfoGAN) model. The establishment of such a retrieval system is based on features acquired by the unsupervised learning InfoGAN model to satisfy users&rsquo; expectations for large-scale datasets. The challenges with the matching and retrieval systems of such kinds of images develop when drawing clarity declines. Finally, the ORB-based matching system is introduced and compared to the SIFT-based system. Additionally, the InfoGAN-based system is compared with state-of-the-art solutions, including SIFT, ORB, and Convolutional Neural Network (CNN)