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Institution

Tehran University of Medical Sciences

EducationTehran, Iran
About: Tehran University of Medical Sciences is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Population & Cancer. The organization has 35661 authors who have published 57234 publications receiving 878523 citations. The organization is also known as: TUMS.


Papers
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Journal ArticleDOI
TL;DR: This is the first time a comprehensive healthcare quality definition has been developed using various healthcare stakeholder perceptions and expectations, and has direct implications for healthcare providers who are encouraged to regularly monitor healthcare quality using the attributes identified in this study.
Abstract: Purpose – The main purpose of this study is to define healthcare quality to encompass healthcare stakeholder needs and expectations because healthcare quality has varying definitions for clients, professionals, managers, policy makers and payers.Design/methodology/approach – This study represents an exploratory effort to understand healthcare quality in an Iranian context. In‐depth individual and focus group interviews were conducted with key healthcare stakeholders.Findings – Quality healthcare is defined as “consistently delighting the patient by providing efficacious, effective and efficient healthcare services according to the latest clinical guidelines and standards, which meet the patient's needs and satisfies providers”. Healthcare quality definitions common to all stakeholders involve offering effective care that contributes to the patient well‐being and satisfaction.Practical implications – This study helps us to understand quality healthcare, highlighting its complex nature, which has direct imp...

175 citations

Journal ArticleDOI
TL;DR: Epigenetic dysregulation of HTR2A may contribute to SCZ, BD and earlier age of disease onset and further research is required to delineate the Dysregulation of other components of serotoninergic pathway to design new therapeutics based on the downstream effects of serotonin.

175 citations

Journal ArticleDOI
TL;DR: This review deals with the challenges of conventional systems and achievements of each pharmaceutical class of novel drug delivery vehicle and provides prolonged plasma profile, enhanced and specific activity in vitro and in vivo in animal models.
Abstract: Methotrexate (MTX) is one of the most widely studied and effective therapeutics agents available to treat many solid tumors, hematologic malignancies, and autoimmune diseases such as rheumatoid arthritis; however, the poor pharmacokinetic and narrow safety margin of the drug limits the therapeutic outcomes of conventional drug delivery systems. For an improved delivery of MTX, several pathophysiological features such as angiogenesis, enhanced permeability and retention effects, acidosis, and expression of specific antigens and receptors can be used either as targets or as tools for drug delivery. There are many novel delivery systems developed to improve the pitfalls of MTX therapy ranged from polymeric conjugates such as human serum albumin, liposomes, microspheres, solid lipid nanoparticles, polymeric nanoparticles, dendrimers, polymeric micelles, in situ forming hydrogels, carrier erythrocyte, and nanotechnology-based vehicles such as carbon nanotubes, magnetic nanoparticles, and gold nanoparticles. Some are further modified with targeting ligands for active targeting purposes. Such delivery systems provide prolonged plasma profile, enhanced and specific activity in vitro and in vivo in animal models. Nevertheless, more complementary studies are needed before they can be applied in human. This review deals with the challenges of conventional systems and achievements of each pharmaceutical class of novel drug delivery vehicle.

175 citations

Journal ArticleDOI
TL;DR: Nets are able to classify dermoscopic and close-up images of nonpigmented lesions as accurately as human experts in an experimental setting as to compare the accuracy of a CNN-based classifier with that of physicians with different levels of experience.
Abstract: Importance Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose. Objective To compare the accuracy of a CNN-based classifier with that of physicians with different levels of experience. Design, Setting, and Participants A CNN-based classification model was trained on 7895 dermoscopic and 5829 close-up images of lesions excised at a primary skin cancer clinic between January 1, 2008, and July 13, 2017, for a combined evaluation of both imaging methods. The combined CNN (cCNN) was tested on a set of 2072 unknown cases and compared with results from 95 human raters who were medical personnel, including 62 board-certified dermatologists, with different experience in dermoscopy. Main Outcomes and Measures The proportions of correct specific diagnoses and the accuracy to differentiate between benign and malignant lesions measured as an area under the receiver operating characteristic curve served as main outcome measures. Results Among 95 human raters (51.6% female; mean age, 43.4 years; 95% CI, 41.0-45.7 years), the participants were divided into 3 groups (according to years of experience with dermoscopy): beginner raters ( 10 years). The area under the receiver operating characteristic curve of the trained cCNN was higher than human ratings (0.742; 95% CI, 0.729-0.755 vs 0.695; 95% CI, 0.676-0.713;P Conclusions and Relevance Neural networks are able to classify dermoscopic and close-up images of nonpigmented lesions as accurately as human experts in an experimental setting.

175 citations

Journal ArticleDOI
TL;DR: Questioning of patients about exposure to some known and suspected risk factors for squamous cell oesophageal cancer confirmed a negligible history of consumption of alcohol, little use of cigarettes or nass, and a low intake of opium, suggesting that the high rates of ESCC seen in northeastern Iran must have other important risk factors that remain speculative or unknown.
Abstract: Previous studies have shown that oesophageal and gastric cancers are the most common causes of cancer death in the Golestan Province, Iran. In 2001, we established Atrak Clinic, a referral clinic for gastrointestinal (GI) diseases in Gonbad, the major city of eastern Golestan, which has permitted, for the first time in this region, endoscopic localisation and histologic examination of upper GI cancers. Among the initial 682 patients seen at Atrak Clinic, 370 were confirmed histologically to have cancer, including 223 (60%) oesophageal squamous cell cancers (ESCC), 22 (6%) oesophageal adenocarcinomas (EAC), 58 (16%) gastric cardia adenocarcinomas (GCA), and 58 (16%) gastric noncardia adenocarcinomas. The proportional occurrence of these four main site-cell type subdivisions of upper GI cancers in Golestan is similar to that seen in Linxian, China, another area of high ESCC incidence, and is markedly different from the current proportions in many Western countries. Questioning of patients about exposure to some known and suspected risk factors for squamous cell oesophageal cancer confirmed a negligible history of consumption of alcohol, little use of cigarettes or nass (tobacco, lime and ash), and a low intake of opium, suggesting that the high rates of ESCC seen in northeastern Iran must have other important risk factors that remain speculative or unknown. Further studies are needed to define more precisely the patterns of upper GI cancer incidence, to test other previously suspected risk factors, and to find new significant risk factors in this high-risk area.

175 citations


Authors

Showing all 35946 results

NameH-indexPapersCitations
Graeme J. Hankey137844143373
Paul D.P. Pharoah13079471338
Jerome Ritz12064447987
Reza Malekzadeh118900139272
Robert N. Weinreb117112459101
Javad Parvizi11196951075
Omid C. Farokhzad11032964226
Ali Mohammadi106114954596
Alexander R. Vaccaro102117939346
John R. Speakman9566734484
Philip J. Devereaux94443110428
Rafael Lozano94265126513
Mohammad Abdollahi90104535531
Ingmar Skoog8945828998
Morteza Mahmoudi8333426229
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023105
2022524
20216,041
20206,181
20195,322
20184,885