Background: The connection between healthcare and tourism industries in many countries has created one of the largest service industries, i.e. "medical tourism industry" which brings significant benefits to the countries. The present study aimed to examine internal and external factors affecting Shiraz medical tourism industry along with the potential capabilities of the industry.
Materials And Methods: This applied research is a mixed method study conducted in 2017 employing both qualitative and quantitative methods. The study population consists of all organizations involved in the medical tourism industry. Deductive qualitative content analysis was employed so as to determine the internal and external factors influencing Shiraz medical tourism industry. Furthermore, the SWOT technique was used to analyze the data obtained from individual interviews and meetings with expert panels.
Result: Internal and external factors were classified into four main themes, namely strengths, weaknesses, opportunities and threats and ten sub-themes, of which five cases (FORMM) were related to internal factors (i.e. finance, production and products (operations), research and development, marketing and management) and five cases (STEPC) were associated with external factors: Socio-cultural, technological, economic, political and competitive. The matrix of the internal and external factors indicated an offensive zone for this industry.
Conclusion: This industry can make use of the strengths and opportunities to confront threats and negative points through identifying internal and external factors and enjoy benefits such as job creation and revenue gains.
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http://dx.doi.org/10.22086/gmj.v0i0.1021 | DOI Listing |
J Affect Disord
January 2025
Department of Psychiatry and Psychotherapy, University of Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg, Germany.
Background: Major depressive disorder (MDD) comes along with an increased risk of recurrence and poor course of illness. Machine learning has recently shown promise in the prediction of mental illness, yet models aiming to predict MDD course are still rare and do not quantify the predictive value of established MDD recurrence risk factors.
Methods: We analyzed N = 571 MDD patients from the Marburg-Münster Affective Disorder Cohort Study (MACS).
Epilepsia
January 2025
Department of Neurosciences, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada.
Objective: Tuberous sclerosis complex (TSC) is a monogenetic disorder associated with sustained mechanistic target of rapamycin (mTOR) activation, leading to heterogeneous clinical manifestations. Epilepsy and renal angiomyolipoma are the most important causes of morbidity in adult people with TSC (pwTSC). mTOR is a key player in inflammation, which in turn could influence TSC-related clinical manifestations.
View Article and Find Full Text PDFACS ES T Water
January 2025
Department of Statistics & Data Science, Dietrich College of Humanities and Social Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
Since the start of the coronavirus-19 pandemic, the use of wastewater-based epidemiology (WBE) for disease surveillance has increased throughout the world. Because wastewater measurements are affected by external factors, processing WBE data typically includes a normalization step in order to adjust wastewater measurements (e.g.
View Article and Find Full Text PDFTransl Cancer Res
December 2024
Department of Integrative Medicine, Huashan Hospital, Fudan University, Shanghai, China.
Background: V-raf murine sarcoma viral oncogene homolog B1 (BRAF) inhibitor (BRAFi) therapy resistance affects approximately 15% of cancer patients, leading to disease recurrence and poor prognosis. The aim of the study was to develop a machine-learning based method to identify patients who are at high-risk of BRAFi resistance and potential biomarker.
Methods: From Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases, we collected RNA sequencing and half maximal inhibitory concentration (IC) data from 235 pan-cancer cell lines and then identified 37 significant differential expression genes associated with BRAFi resistance.
Transl Cancer Res
December 2024
Department of Urology, Affiliated Hospital of Chifeng University, Chifeng, China.
Background: Bladder urothelial carcinoma (BLCA) is globally recognized as a prevalent malignancy. Its treatment remains challenging due to the extensive morbidity, high mortality rates, and compromised quality of life from postoperative complications and the lack of specific molecular targets. Our aim was to establish a prognostic model to evaluate the prognostic significance, assess immunotherapy responses, and determine drug susceptibility in patients with BLCA.
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