14 results on '"Alipoor R"'
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2. Mapping routine measles vaccination in low- and middle-income countries
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Sbarra, AN, Rolfe, S, Nguyen, JQ, Earl, L, Galles, NC, Marks, A, Abbas, KM, Abbasi-Kangevari, M, Abbastabar, H, Abd-Allah, F, Abdelalim, A, Abdollahi, M, Abegaz, KH, Abiy, HAA, Abolhassani, H, Abreu, LG, Abrigo, MRM, Abushouk, AI, Accrombessi, MMK, Adabi, M, Adebayo, OM, Adekanmbi, V, Adetokunboh, OO, Adham, D, Afarideh, M, Aghaali, M, Ahmad, T, Ahmadi, R, Ahmadi, K, Ahmed, MB, Alanezi, FM, Alanzi, TM, Alcalde-Rabanal, JE, Alemnew, BT, Ali, BA, Ali, M, Alijanzadeh, M, Alinia, C, Alipoor, R, Alipour, V, Alizade, H, Aljunid, SM, Almasi, A, Almasi-Hashiani, A, Al-Mekhlafi, HM, Altirkawi, KA, Amare, B, Amini, S, Amini-Rarani, M, Amiri, F, Amit, AML, Amugsi, DA, Ancuceanu, R, Andrei, CL, Anjomshoa, M, Ansari, F, Ansari-Moghaddam, A, Ansha, MG, Antonio, CAT, Antriyandarti, E, Anvari, D, Arabloo, J, Arab-Zozani, M, Aremu, O, Armoon, B, Aryal, KK, Arzani, A, Asadi-Aliabadi, M, Asgari, S, Atafar, Z, Ausloos, M, Awoke, N, Quintanilla, BPA, Ayanore, MA, Aynalem, YA, Azadmehr, A, Azari, S, Babaee, E, Badawi, A, Badiye, AD, Bahrami, MA, Baig, AA, Bakhtiari, A, Balakrishnan, S, Banach, M, Banik, PC, Barac, A, Baradaran-Seyed, Z, Baraki, AG, Basu, S, Bayati, M, Bayou, YT, Bedi, N, Behzadifar, M, Bell, ML, Berbada, DA, Berhe, K, Bhattarai, S, Bhutta, ZA, Bijani, A, Birhanu, M, Bisanzio, D, Biswas, A, Bohlouli, S, Bolla, SR, Borzouei, S, Brady, OJ, Bragazzi, NL, Briko, AN, Briko, NI, Nagaraja, SB, Butt, ZA, Cámera, LA, Campos-Nonato, IR, Car, J, Cárdenas, R, Carvalho, F, Castaldelli-Maia, JM, Castro, F, Chattu, VK, Chehrazi, M, Chin, KL, Chu, D-T, Cook, AJ, Cormier, NM, Cunningham, B, Dahlawi, SMA, Damiani, G, Dandona, R, Dandona, L, Danovaro, MC, Dansereau, E, Daoud, F, Darwesh, AM, Darwish, AH, Das, JK, Weaver, ND, De Neve, J-W, Demeke, FM, Demis, AB, Denova-Gutiérrez, E, Desalew, A, Deshpande, A, Desta, DM, Dharmaratne, SD, Dhungana, GP, Dianatinasab, M, Diaz, D, Dipeolu, IO, Djalalinia, S, Do, HT, Dorostkar, F, Doshmangir, L, Doyle, KE, Dunachie, SJ, Duraes, AR, Kalan, ME, Leylabadlo, HE, Edinur, HA, Effiong, A, Eftekhari, A, El, Sayed, I, El, Sayed, Zaki, M, Elema, TB, Elhabashy, HR, El-Jaafary, SI, Elsharkawy, A, Emamian, MH, Enany, S, Eshrati, B, Eskandari, K, Eskandarieh, S, Esmaeilnejad, S, Esmaeilzadeh, F, Esteghamati, A, Etisso, AE, Farahmand, M, Faraon, EJA, Fareed, M, Faridnia, R, Farioli, A, Farzadfar, F, Fattahi, N, Fazlzadeh, M, Fereshtehnejad, S-M, Fernandes, E, Filip, I, Fischer, F, Foigt, NA, Folayan, MO, Foroutan, M, Fukumoto, T, Fullman, N, Gad, MM, Geberemariyam, BS, Gebrehiwot, TT, Gebrehiwot, AM, Gebremariam, KT, Gebremedhin, KB, Gebremeskel, GG, Gebreslassie, AA, Gedefaw, GA, Gezae, KE, Ghadiri, K, Ghaffari, R, Ghaffarifar, F, Ghajarzadeh, M, Gheshlagh, RG, Ghashghaee, A, Ghiasvand, H, Gholamian, A, Gilani, SA, Gill, PS, Girmay, A, Gomes, NGM, Gopalani, SV, Goulart, BNG, Grada, A, Guimarães, RA, Guo, Y, Gupta, R, Hafezi-Nejad, N, Haj-Mirzaian, A, Handiso, DW, Hanif, A, Haririan, H, Hasaballah, AI, Hasan, MM, Hasanpoor, E, Hasanzadeh, A, Hassanipour, S, Hassankhani, H, Heidari-Soureshjani, R, Henry, NJ, Herteliu, C, Heydarpour, F, Hollerich, GI, Rad, EH, Hoogar, P, Hossain, N, Hosseini, M, Hosseinzadeh, M, Househ, M, Hu, G, Huda, TM, Humayun, A, Ibitoye, SE, Ikilezi, G, Ilesanmi, OS, Ilic, IM, Ilic, MD, Imani-Nasab, MH, Inbaraj, LR, Iqbal, U, Irvani, SSN, Islam, SMS, Islam, MM, Iwu, CJ, Iwu, CCD, Jadidi-Niaragh, F, Jafarinia, M, Jahanmehr, N, Jakovljevic, M, Jalali, A, Jalilian, F, Javidnia, J, Jenabi, E, Jha, V, Ji, JS, John, O, Johnson, KB, Joukar, F, Jozwiak, JJ, Kabir, Z, Kabir, A, Kalani, H, Kalankesh, LR, Kalhor, R, Kamal, Z, Kanchan, T, Kapoor, N, Karami, M, Matin, BK, Karch, A, Karimi, SE, Kayode, GA, Karyani, AK, Keiyoro, PN, Khader, YS, Khafaie, MA, Khammarnia, M, Khan, MS, Khan, EA, Khan, J, Khan, MN, Khatab, K, Khater, MM, Khatib, MN, Khayamzadeh, M, Khazaei, M, Khazaei, S, Khosravi, A, Khubchandani, J, Kianipour, N, Kim, YJ, Kimokoti, RW, Kinyoki, DK, Kisa, A, Kisa, S, Kolola, T, Komaki, H, Kosen, S, Koul, PA, Koyanagi, A, Kraemer, MUG, Krishan, K, Kuate Defo, B, Kumar, M, Kumar, P, Kumar, GA, Kusuma, D, La Vecchia, C, Lacey, B, Lad, SD, Lal, DK, Lam, F, Lami, FH, Lansingh, VC, Larson, HJ, Lasrado, S, Lee, SWH, Lee, PH, LeGrand, KE, Lenjebo, TL, Li, S, Liang, X, Liu, PY, Lopukhov, PD, Machado, DB, Mahasha, PW, Mahdavi, MM, Maheri, M, Mahotra, NB, Maled, V, Maleki, S, Malik, MA, Malta, DC, Mansour-Ghanaei, F, Mansouri, B, Mansourian, M, Mansournia, MA, Martins-Melo, FR, Masaka, A, Mayala, BK, Mehndiratta, MM, Mehri, F, Mehta, KM, Memiah, PTN, Mendoza, W, Menezes, RG, Mengesha, MB, Mengesha, EW, Mestrovic, T, Mihretie, KM, Miller-Petrie, MK, Mills, EJ, Milne, GJ, Mirabi, P, Mirrakhimov, EM, Mirzaei, R, Mirzaei, M, Mirzaei, HR, Mirzaei, H, Mirzaei-Alavijeh, M, Moazen, B, Moghadaszadeh, M, Mohamadi, E, Mohammad, DK, Mohammad, Y, Mohammad, KA, Mohammad Gholi Mezerji, N, Mohammadbeigi, A, Mohammadian-Hafshejani, A, Mohammadpourhodki, R, Mohammed, S, Mohammed, AS, Mohammed, H, Mohebi, F, Mokdad, AH, Monasta, L, Moosavi, MA, Moosazadeh, M, Moradi, G, Moradi, M, Moradi-Joo, M, Moradi-Lakeh, M, Moradzadeh, R, Moraga, P, Mosapour, A, Mouodi, S, Mousavi, SM, Khaneghah, AM, Mueller, UO, Muluneh, AG, Munro, SB, Murray, CJL, Murthy, GVS, Muthupandian, S, Naderi, M, Nagarajan, AJ, Naghavi, M, Nangia, V, Nansseu, JR, Nayak, VC, Nazari, J, Ndwandwe, DE, Negoi, I, Ngunjiri, JW, Nguyen, HLT, Nguyen, CTK, Nguyen, TH, Nigatu, YT, Nikbakhsh, R, Nikfar, S, Nikpoor, AR, Ningrum, DNA, Nnaji, CA, Oh, I-H, Oladnabi, M, Olagunju, AT, Olusanya, JO, Olusanya, BO, Bali, AO, Omer, MO, Onwujekwe, OE, Osgood-Zimmerman, AE, Owolabi, MO, P, A, M, Padubidri, JR, Pakshir, K, Pana, A, Pandey, A, Pando-Robles, V, Pashaei, T, Pasupula, DK, Paternina-Caicedo, AJ, Patton, GC, Pazoki Toroudi, H, Pepito, VCF, Pescarini, JM, Pigott, DM, Pilgrim, T, Pirsaheb, M, Poljak, M, Postma, MJ, Pourjafar, H, Pourmalek, F, Pourmirza, Kalhori, R, Prada, SI, Prakash, S, Quazi Syed, Z, Quintana, H, Rabiee, N, Rabiee, M, Radfar, A, Rafiei, A, Rahim, F, Rajati, F, Rameto, MA, Ramezanzadeh, K, Ranabhat, CL, Rao, SJ, Rasella, D, Rastogi, P, Rathi, P, Rawaf, S, Rawaf, DL, Rawal, L, Rawassizadeh, R, Rawat, R, Renjith, V, Renzaho, AMN, Reshmi, B, Reta, MA, Rezaei, N, Rezai, MS, Rezapour, A, Riahi, SM, Ribeiro, AI, Rickard, J, Rios-Blancas, M, Rios-González, CM, Roever, L, Rostamian, M, Rubino, S, Rwegerera, GM, Saad, AM, Saadatagah, S, Sabour, S, Sadeghi, E, Moghaddam, SS, Saeidi, S, Sagar, R, Sahebkar, A, Sahraian, MA, Sajadi, SM, Salahshoor, MR, Salam, N, Salem, H, Salem, MR, Salomon, JA, Kafil, HS, Sambala, EZ, Samy, AM, Saraswathy, SYI, Sarmiento-Suárez, R, Saroshe, S, Sartorius, B, Sarveazad, A, Sathian, B, Sathish, T, Schaeffer, LE, Schwebel, DC, Senthilkumaran, S, Shabaninejad, H, Shahabi, S, Shaheen, AA, Shaikh, MA, Shalash, AS, Shams-Beyranvand, M, Shamsi, MB, Shamsizadeh, M, Sharafi, K, Sharifi, H, Sheikh, A, Sheikhtaheri, A, Shetty, RS, Shiferaw, WS, Shigematsu, M, Shin, JI, Shirkoohi, R, Siabani, S, Siddiqi, TJ, Silverberg, JIS, Simonetti, B, Singh, JA, Sinha, DN, Sinke, AH, Soheili, A, Sokhan, A, Soltani, S, Soofi, M, Sorrie, MB, Soyiri, IN, Spotin, A, Spurlock, EE, Sreeramareddy, CT, Sudaryanto, A, Sufiyan, MB, Suleria, HAR, Abdulkader, RS, Taherkhani, A, Tapak, L, Taveira, N, Taymoori, P, Tefera, YM, Tehrani-Banihashemi, A, Teklehaimanot, BF, Tekulu, GH, Tesfay, BE, Tessema, ZT, Tessema, B, Thankappan, KR, Tohidinik, HR, Topor-Madry, R, Tovani-Palone, MR, Tran, BX, Uddin, R, Ullah, I, Umeokonkwo, CD, Unnikrishnan, B, Upadhyay, E, Usman, MS, Vaezi, M, Valadan, Tahbaz, S, Valdez, PR, Vasseghian, Y, Veisani, Y, Violante, FS, Vollmer, S, Waheed, Y, Wakefield, J, Wang, Y, Wang, Y-P, Weldesamuel, GT, Werdecker, A, Westerman, R, Wiangkham, T, Wiens, KE, Wiysonge, CS, Woldu, G, Wondafrash, DZ, Wonde, TE, Wu, A-M, Yadollahpour, A, Jabbari, SHY, Yamada, T, Yaya, S, Yazdi-Feyzabadi, V, Yeheyis, TY, Yeshaw, Y, Yilgwan, CS, Yip, P, Yonemoto, N, Younis, MZ, Yousefi, Z, Yousefifard, M, Yousefinezhadi, T, Yu, C, Yusefzadeh, H, Zadey, S, Zahirian, Moghadam, T, Zaki, L, Zaman, SB, Zamani, M, Zamanian, M, Zandian, H, Zangeneh, A, Zarei, F, Zerfu, TA, Zhang, Y, Zhang, Z-J, Zhao, X-JG, Zhou, M, Ziapour, A, Hay, SI, Lim, SS, Mosser, JF, Local Burden of Disease Vaccine Coverage Collaborators, Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), Value, Affordability and Sustainability (VALUE), Microbes in Health and Disease (MHD), HUS Comprehensive Cancer Center, Clinicum, Department of Oncology, Sbarra, Alyssa N., Rolfe, Sam, Nguyen, Jason Q., Earl, Lucas, Ahmed, MB, Mosser, Jonathan F, Collaborators, Local Burden of Disease Vaccine Coverage, Bill & Melinda Gates Foundation, Alexander von Humboldt-Stiftung, Unión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF), Universiti Sains Malaysia (Malasia), Panjab University (India), NIHR - Oxford Biomedical Research Centre (Reino Unido), Australian Research Council, Instituto de Saúde Pública da Universidade do Porto, Local Burden Dis Educ Attainment C, Local Burden of Disease Vaccine Coverage Collaborator, and Violante FS
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and promotion of well-being ,Vacunación Masiva ,Internationality ,Disease prevention ,children under 5 years old ,Geographic Mapping ,Rural Health ,medicine.disease_cause ,Cross-reactivity ,0302 clinical medicine ,RA0421 ,Vaccination Refusal ,030212 general & internal medicine ,Child ,immunity patterns ,Pediatric ,0303 health sciences ,Public health ,Multidisciplinary ,biology ,Vaccination ,Uncertainty ,IMMUNIZATION ,3142 Public health care science, environmental and occupational health ,COVERAGE ,3. Good health ,TIME ,3.4 Vaccines ,Child, Preschool ,Infectious diseases ,A990 Medicine and Dentistry not elsewhere classified ,Antibody ,Engineering sciences. Technology ,AFRICA ,General Science & Technology ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,COVID-19 ,610 Medicine & health ,Global Vaccine Action Plan (GVAP) ,Local Burden of Disease Vaccine Coverage Collaborators ,Article ,Vaccine Related ,03 medical and health sciences ,measles vaccine ,Measels ,Low- and middle-income countries ,Local burden of disease ,Clinical Research ,medicine ,Humans ,Healthcare Disparities ,Preschool ,PROGRESS ,030304 developmental biology ,business.industry ,MORTALITY ,Developed Countries ,Prevention ,Comment ,Vacunación ,Urban Health ,Prevention of disease and conditions ,Virology ,Coronavirus ,Good Health and Well Being ,Cobertura de Vacunación ,biology.protein ,Immunization ,business ,Measles - Abstract
The safe, highly effective measles vaccine has been recommended globally since 1974, yet in 2017 there were more than 17 million cases of measles and 83,400 deaths in children under 5 years old, and more than 99% of both occurred in low- and middle-income countries (LMICs)1–4. Globally comparable, annual, local estimates of routine first-dose measles-containing vaccine (MCV1) coverage are critical for understanding geographically precise immunity patterns, progress towards the targets of the Global Vaccine Action Plan (GVAP), and high-risk areas amid disruptions to vaccination programmes caused by coronavirus disease 2019 (COVID-19)5–8. Here we generated annual estimates of routine childhood MCV1 coverage at 5 × 5-km2 pixel and second administrative levels from 2000 to 2019 in 101 LMICs, quantified geographical inequality and assessed vaccination status by geographical remoteness. After widespread MCV1 gains from 2000 to 2010, coverage regressed in more than half of the districts between 2010 and 2019, leaving many LMICs far from the GVAP goal of 80% coverage in all districts by 2019. MCV1 coverage was lower in rural than in urban locations, although a larger proportion of unvaccinated children overall lived in urban locations; strategies to provide essential vaccination services should address both geographical contexts. These results provide a tool for decision-makers to strengthen routine MCV1 immunization programmes and provide equitable disease protection for all children., Although progress in the coverage of routine measles vaccination in children in low- and middle-income countries was made during 2000–2019, many countries remain far from the goal of 80% coverage in all districts by 2019.
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- 2021
3. The role of adipose tissue secretion in the creation and pain level in osteoarthritis
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Askari Alireza, Arasteh Peyman, Homayounfar Reza, Naghizadeh Mohamad Mehdi, Ehrampoush Elham, Mousavi Seyyede Makiye, and Alipoor Reza
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osteoarthritis ,adipose tissue ,leptin ,adiponectin ,visfatin ,resistin ,Diseases of the endocrine glands. Clinical endocrinology ,RC648-665 - Abstract
Objectives. With increasing evidence regarding the metabolic basis of osteoarthritis (OA), we studied the relationship between adipose tissue and OA.
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- 2020
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4. Heart murmur in neonates: How often is it caused by congenital heart disease?
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Mirzarahimi, M., Saadati, H., Doustkami, H., Alipoor, R., Isazadehfar, K., and Afsaneh Enteshari
5. Operative treatment results of posterior malleolar fractures in trimalleolar fractures with screw fixation and plate fixation: short-term results.
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Teimouri M, Akbari Aghdam H, Alipoor R, and Lalehzar SS
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Background: Ankle fractures are among the most common lower limb fractures. There is no agreement about the best treatment for these fractures. This study compared the short-term results of screw and plate fixation methods., Methods: In this prospective study, 32 patients that underwent screw fixation for posterior malleolar fracture and 32 patients that underwent plate fixation for posterior malleolar fracture were assessed 1, 3, and 6 months after surgery., Results: The mean age in group 1 (screw fixation) and group 2 (plate fixation) was 32.56, and 37.82 ± 9.99, respectively. The frequency of gender in group 1 (screw fixation) and group 2 (plate fixation) for females and males was 20%, 80%, 4%, and 18%, respectively. The mean range of motion (ROM) in month 1 in group 1 was 89.4, in group 2 was 90.22, in month 3 in group 1 was 100.6, in group 2 was 100.36, in month 6 in group 1 was 115.4, and in group 2 was 110.68. The mean visual analog scale (VAS) in month 1 in group 1 was 6.88, in group 2 was 6.09, in month 3 in group 1 was 4.14, in group 2 was 3.63, in month 6 in group 1 was 2.56, and in group 2 was 2.54. In group 1, we had 1 case of nerve injury, 1 case of deep infection, and 3 cases of superficial infection, and in group 2, we had 2 cases of nerve injury, 2 cases of deep infection, and no case of superficial infection. The mean foot and ankle outcome score (FAOS) in group 1 was 75.44, and in group 2 was 74.36., Conclusion: In our study, we were unable to indicate a superior treatment method. More comprehensive studies with larger populations are suggested., Competing Interests: None., (IJBT Copyright © 2024.)
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- 2024
6. Small-molecule metabolites in SARS-CoV-2 treatment: a comprehensive review.
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Alipoor R and Ranjbar R
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- Humans, Antiviral Agents pharmacology, Antiviral Agents therapeutic use, COVID-19 Drug Treatment, Viral Proteins, SARS-CoV-2, COVID-19
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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has quickly spread all over the world. In this respect, traditional medicinal chemistry, repurposing, and computational approaches have been exploited to develop novel medicines for treating this condition. The effectiveness of chemicals and testing methods in the identification of new promising therapies, and the extent of preparedness for future pandemics, have been further highly advantaged by recent breakthroughs in introducing noble small compounds for clinical testing purposes. Currently, numerous studies are developing small-molecule (SM) therapeutic products for inhibiting SARS-CoV-2 infection and replication, as well as managing the disease-related outcomes. Transmembrane serine protease (TMPRSS2)-inhibiting medicinal products can thus prevent the entry of the SARS-CoV-2 into the cells, and constrain its spreading along with the morbidity and mortality due to the coronavirus disease 2019 (COVID-19), particularly when co-administered with inhibitors such as chloroquine (CQ) and dihydroorotate dehydrogenase (DHODH). The present review demonstrates that the clinical-stage therapeutic agents, targeting additional viral proteins, might improve the effectiveness of COVID-19 treatment if applied as an adjuvant therapy side-by-side with RNA-dependent RNA polymerase (RdRp) inhibitors., (© 2022 Walter de Gruyter GmbH, Berlin/Boston.)
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- 2022
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7. Hyaluronic Acid-Based Nanomaterials as a New Approach to the Treatment and Prevention of Bacterial Infections.
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Alipoor R, Ayan M, Hamblin MR, Ranjbar R, and Rashki S
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Bacterial contamination of medical devices is a great concern for public health and an increasing risk for hospital-acquired infections. The ongoing increase in antibiotic-resistant bacterial strains highlights the urgent need to find new effective alternatives to antibiotics. Hyaluronic acid (HA) is a valuable polymer in biomedical applications, partly due to its bactericidal effects on different platforms such as contact lenses, cleaning solutions, wound dressings, cosmetic formulations, etc. Because the pure form of HA is rapidly hydrolyzed, nanotechnology-based approaches have been investigated to improve its clinical utility. Moreover, a combination of HA with other bactericidal molecules could improve the antibacterial effects on drug-resistant bacterial strains, and improve the management of hard-to-heal wound infections. This review summarizes the structure, production, and properties of HA, and its various platforms as a carrier in drug delivery. Herein, we discuss recent works on numerous types of HA-based nanoparticles to overcome the limitations of traditional antibiotics in the treatment of bacterial infections. Advances in the fabrication of controlled release of antimicrobial agents from HA-based nanosystems can allow the complete eradication of pathogenic microorganisms., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Alipoor, Ayan, Hamblin, Ranjbar and Rashki.)
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- 2022
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8. Effects of metformin and insulin therapy regimens on postpartum oral glucose tolerance test results in pregnant women with gestational diabetes mellitus: a comparative study.
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Jahanshahi M, Shahmirzadi AR, Kashani E, Alipoor R, and Vosough S
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- Blood Glucose, Body Mass Index, Diabetes, Gestational diagnosis, Female, Glucose Tolerance Test, Humans, Insulin administration & dosage, Metformin administration & dosage, Postpartum Period, Pregnancy, Treatment Outcome, Diabetes, Gestational blood, Diabetes, Gestational drug therapy, Insulin therapeutic use, Metformin therapeutic use
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Objectives: The main purpose of this study was to compare the effects of two regimens of metformin and insulin therapy on postpartum oral glucose tolerance test (OGTT) results in pregnant women with gestational diabetes mellitus (GDM)., Methods: In this single-blind randomized clinical trial (RCT), a total number of 60 pregnant women meeting the inclusion criteria were assigned to two groups with a randomized block design (RBD): insulin therapy (IT) group (30 patients) and metformin therapy (MT) group (30 patients). At baseline, the data were comprised of prenatal maternal age, gestational age, GDM diagnosis, and maternal weight/height. During the postpartum period, 5-cc blood samples were taken from the pregnant women concerned to analyze their fasting blood sugar (FBS) levels. Then, the patients were asked to come back four days and six weeks later after delivery to check the OGTT results. At six weeks postpartum, in addition to OGTT, the glycated hemoglobin (HbA
1 C) test was performed for all mothers. Finally, six weeks after delivery, these mothers were evaluated with regard to weight loss and body mass index (BMI)., Results: Six weeks postpartum, the maternal weight and BMI significantly decreased in the MT group compared with the IT one, while there was no significant difference between both groups at baseline. On the fourth day, the OGTT results in the MT group were significantly lower in comparison with those in the IT group (p=0.012). At sixth weeks postpartum, the OGTT results were comparably lower in the MT group than those reported for the IT one; however, such a difference was not statistically significant (p=0.087)., Conclusions: According to the study results, metformin could be an effective and safe treatment for pregnant women suffering from GDM instead of insulin therapy., (© 2020 Walter de Gruyter GmbH, Berlin/Boston.)- Published
- 2020
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9. Melatonin and Parkinson Disease: Current Status and Future Perspectives for Molecular Mechanisms.
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Tamtaji OR, Reiter RJ, Alipoor R, Dadgostar E, Kouchaki E, and Asemi Z
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- Animals, Apoptosis drug effects, Autophagy drug effects, Disease Models, Animal, Humans, Melatonin pharmacology, Oxidative Stress drug effects, Melatonin therapeutic use, Parkinson Disease drug therapy
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Parkinson disease (PD) is a chronic and neurodegenerative disease with motor and nonmotor symptoms. Multiple pathways are involved in the pathophysiology of PD, including apoptosis, autophagy, oxidative stress, inflammation, α-synuclein aggregation, and changes in the neurotransmitters. Preclinical and clinical studies have shown that melatonin supplementation is an appropriate therapy for PD. Administration of melatonin leads to inhibition of some pathways related to apoptosis, autophagy, oxidative stress, inflammation, α-synuclein aggregation, and dopamine loss in PD. In addition, melatonin improves some nonmotor symptom in patients with PD. Limited studies, however, have evaluated the role of melatonin on molecular mechanisms and clinical symptoms in PD. This review summarizes what is known regarding the impact of melatonin on PD in preclinical and clinical studies.
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- 2020
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10. The effects of acupuncture and electroacupuncture on Parkinson's disease: Current status and future perspectives for molecular mechanisms.
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Tamtaji OR, Naderi Taheri M, Notghi F, Alipoor R, Bouzari R, and Asemi Z
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- Apoptosis, Humans, Oxidative Stress, alpha-Synuclein metabolism, Acupuncture Therapy trends, Electroacupuncture trends, Parkinson Disease therapy
- Abstract
Among the progressive neurodegenerative disorders, Parkinson's disease (PD) is the second most common. Different factors have critical role in pathophysiology of PD such as apoptosis pathways, inflammatory cytokines, oxidative stress, and neurotransmitters and its receptors abnormalities. Acupuncture and electroacupuncture were considered as nondrug therapies for PD. Although numerous studies has been conducted for assessing the mechanism underlying electroacupuncture and acupuncture, various principal aspects of these treatment procedures remain not well-known. There have also been few investigations on the molecular mechanism of acupuncture and electroacupuncture therapy effects in PD. This review evaluates the effects of electroacupuncture and acupuncture on the molecular mechanism in PD., (© 2019 Wiley Periodicals, Inc.)
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- 2019
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11. A Genetic Association Study of MTHFR C677T Polymorphism with Risk of Metabolic Syndrome: A Systematic Review and Meta-Analysis.
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Azizi S, Shamshirian A, Alizadeh-Navaei R, Jafarpour H, Asemi Z, Tamtaji OR, Vaziri MS, Homayounfar R, Rezaei Shahmirzadi A, and Alipoor R
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Methylenetetrahydrofolate reductase ( MTHFR ) is an enzyme that plays a crucial role as a methyl-group donor in demethylation of homocysteine. The aim of this systematic review and meta-analysis was to study the relationship between MTHFR gene polymorphism and metabolic syndrome (MS). We used search engines and databases such as Science Direct, Google Scholar, Embase, Cochrane Library, and PubMed to identify eligible studies up to 2018. The articles were studied based on keywords including MTHFR , mutation, variant, and polymorphism in combination with MS. Data was analyzed using Comprehensive Meta-Analysis version 2.2.064 software. After extracting the data from seven articles, the total number of subjects was 1280 in the patient group and 1374 in the control group. The odds ratio was estimated to be 1.078 for the allele model of T vs. C (95% confidence interval [CI]: 1.626-0.715), 1.157 for the allele model of CC vs. CT (95% CI: 0.829-1.615), 1.020 for the allele model of CT + TT vs. CC (95% CI: 1.611-0.646) and 0.799 for the allele model of TT vs. CC + CT (95% CI: 1.185- 0.539). As well, the results showed no statistically significant correlation between polymorphism genotypes of the MTHFR gene and MS (P<0.05). In general, this study showed that the presence of C677T polymorphism in the MTHFR gene has no effect on the incidence of MS., (Copyright© 2019, Galen Medical Journal.)
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- 2019
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12. ATP2B1 rs2681472 and STK39 rs35929607 polymorphisms and risk of Hypertension in Iranian Population.
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Jamshidi J, Asnaashari A, Alipoor R, Mohammadi S, Roostaei S, Samadian MM, Honarmand Aliabadi S, and Bahramali E
- Abstract
Background: ATP2B1 and STK39 have been introduced as essential hypertension candidate genes. The association of these genes' variations have not been studied in Iranian population yet. Here we aimed to investigate the association of ATP2B1 rs2681472 and STK39 rs35929607 polymorphisms with the risk of hypertension in an Iranian population. Methods: We included 400 individuals in our case-control study: 200 cases with essential hypertension and 200 healthy sex and age matched controls. All subjects were genotyped for rs2681472 and rs35929607 using a PCR-RFLP method. Genotype and allele frequencies were compared between the two groups using chi-squared test. The association was further assessed under log-additive, dominant and recessive genetic models. Results: There was no association between rs2681472 and rs35929607 polymorphisms and risk of essential hypertension in our population (p>0.05). There was also no association between the studied polymorphisms and hypertension under different genetic models. Conclusion: Our study indicated that rs2681472 of ATP2B1 and rs35929607 of STK39 may not have a significant effect on the risk of essential hypertension in Iranian population. More studies are still needed to validate our results.
- Published
- 2018
- Full Text
- View/download PDF
13. Modelling the regional vulnerability to Echinococcosis based on environmental factors using fuzzy inference system: A case study of Lorestan Province, west of Iran.
- Author
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Ahmadinejad M, Obeidavi Z, Obeidavi Z, and Alipoor R
- Abstract
Background and Aim: Echinococcosis as a zoonosis disease is one of the most important parasitic helminth that is affected by many risk factors such as the environmental factors. Thus, we predicted the regional vulnerability to Echinococcosis based on environmental factors using a fuzzy inference system (FIS) in Lorestan Province., Methods: Our study was cross-sectional study on 200 patients from Lorestan Province (west of Iran) who underwent surgery for hydatidosis between October 2005 and November 2014. In order to model the vulnerability to Echinococcosis, first we determined the effective environmental variables. In the next step, the FIS was designed and implemented using MATLAB v.2012 software. Thus, definition and determination of linguistic variables, linguistic values, and their range were performed based on expert knowledge. Then, the membership functions of inputs (environmental variables) and output (vulnerability to Echinococcosis) were defined. A fuzzy rules base was formed. Also, the defuzzification of output was done using a centroid defuzzification function. To test the accuracy of the predictive model, we calculated the AUC (to this purpose, we used four different thresholds, 5%, 10%, 15%, and 20%) using IDRISI Selva v.17.0 software., Results: Based on the results of this study, Aligoudarz and Koohdasht counties were identified as a highest and lowest risk area in Lorestan, respectively. The results showed that a predictive model was more efficient than a random model (AUC>0.5). Also, potential vulnerable areas cover 78.29% at threshold of 5%, 60.72% at threshold of 10%, 43.54% at threshold of 15%, and 39.82% at threshold of 20% of the study area., Conclusion: According to the success of this research, we emphasized the necessity of attention to fuzzy approach to model vulnerability to hydatidosis. This approach can provide a practical economic basis for making informed preventive services decisions and the allocation of health resources., Competing Interests: Conflict of Interest: There is no conflict of interest to be declared.
- Published
- 2017
- Full Text
- View/download PDF
14. Heart murmur in neonates: how often is it caused by congenital heart disease?
- Author
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Mirzarahimi M, Saadati H, Doustkami H, Alipoor R, Isazadehfar K, and Enteshari A
- Abstract
Objective: Congenital heart disease (CHD) is the most common form of cardiovascular diseases in children. This study was performed from September 2006 to August 2007 in Ardebil, Westnorthern Iran. The aim was to determine the prevalence of heart murmur in newborns and its correlation with CHD., Methods: In a 1-year cross sectional descriptive-analytic study, 2928 newborns were screened for heart murmur during routine neonatal physical examination. All babies with murmur underwent echocardiography., Findings: Murmur was detected in 91 (3.1%) neonates, of whom 47 (51.6%) had a congenital heart disease. The most common (17.6%) abnormality was ventricular septal defect. Patent ductus arteriosus was found in 10 (11%) patients., Conclusion: Remarkable high (round 50 %) rate of CDH in newborns presenting with heart murmur, urges to observe these neonates closely to establish the diagnosis of congenital heart disease and early referral to pediatric cardiologist.
- Published
- 2011
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