Showing 10 results for Heydari
Zahra Sheikhrezaei, Parisa Heydari, Alireza Farsinezhad, Ahmad Fatemi, Soudeh Khanamani Falahati-Pour, Shokoofeh Darakhshan, Mojgan Noroozi Karimabad, Ali Darekordi, Hossein Khorramdelazad, Gholamhossein Hassanshahi,
Volume 22, Issue 2 (3-2018)
Abstract
Background: Acute myeloblastic leukemia (AML) is a clonal disorder due to bone marrow failure and uncontrolled proliferation of myeloid lineage. Acute promyelocytic leukemia (APL) is a subtype of AML. Heterocyclic compounds, such as indole, are considered as attractive candidates for cancer therapy, due to their abundance in nature and known biological activity. Sal-like protein (SALL4) is a zinc finger transcription factor involving in the multi-potency of stem cells, in the NB4 cell line. This study was aimed to evaluate the effects of basal indole and its new derivative, 2-(1-((2, 4-Aril)imino)-2,2,2-trifluoroethyl) phenyl-1H Indole-3- carbaldehyde (TFPHC), on the expression of SALL4. Methods: Cells were cultured and treated with different concentrations (75, 150, and 300 µg/mL) of the new indole derivative and DMSO, as a vehicle control, for 24 and 48 hours. Cell proliferation was evaluated by using Trypan blue exclusion and MTT assays. The percentage of apoptotic cells was determined by flowcytometry analysis using the Annexin V/PI apoptosis detection kit; mRNA expression of SALL4 was studied using absolute quantitative RT-PCR. Results: Our findings demonstrated the effects of new indole derivatives on SALL4 mRNA expression. Expression of SALL4 mRNA was significantly decreased at 75, 150, and 300 µg/mL concentrations. Conclusion: SALL4 plays a role in the survival of APL cells. SALL4 expression could be suppressed by the novel indole derivative. Additionally, SALL4 gene suppression can serve as a target in APL therapy.
Azarakhsh Azaran, Manoochehr Makvandi, Ali Teimoori, Saeedeh Ebrahimi, Farzad Heydari, Roya Nikfar,
Volume 22, Issue 2 (3-2018)
Abstract
Background: Group A rotavirus (RVA) mainly causes acute gastroenteritis, exclusively in young children in developing countries. The prevalence and determination of the molecular epidemiology of rotavirus genotypes will determine the dominant rotavirus genotypes in the region and will provide a strategy for the development of appropriate vaccines. Methods: A total of 100 fecal samples were collected from children below five years with acute gastroenteritis who referred to Aboozar Children’s Hospital of Ahvaz city during October 2015 to March 2016. All samples were screened by latex agglutination for the presence of rotavirus antigen. Rotavirus-positive samples were further analyzed by the semi-multiplex RT-PCR, and the sequencing was performed for G/P genotyping. Results: Findings showed that 32% of the specimens were RVA-positive. Among the 32 VP7 genotyped strains, the predominant G genotype was G9 (37.5%), followed by G2 (21.9%), G1 (12.5%), G12 (9.4%), G4 (9.4%), G2G9 (6.3%), and G3 (3.1%). Among the 31 VP4 genotyped strains, P[8] genotype was the dominant (62.5%), followed by P[4] (31.3%) and P[4] P[8] (3.1%). The genotypes for G and P were identified for 31 rotaviruses (96.87%), but only one strain, G9, remained non-typeable for the P genotype. The most prevalent G/P combination was G9P[8] (28.5%), followed by G2P[4] (18.8%), G1P[8] (9.4%), G12P[8] (9.4%), G4P[8] (9.4%), G2G9P[4] (6.3%), G9P[4] P[8] (3.1%), G3P[8] (3.1%), G9P[4] (3.1%), G2P[8] (3.1%), and G9P[non-typeable] (3.1%). Conclusion: A novel rotavirus strain, G12, was detected, for the first time, in patients from the southwest of Iran. Comprehensive investigations are required to evaluate the emergence of this strain.
Mohammad Chahkandi, Matin Abdollahi Yousefabady, Mahdi Falah Heydarinezhad,
Volume 28, Issue 0 (Supplementary 2024)
Abstract
Introduction: Mindfulness, a non-pharmacological relaxation technique, focuses on inhaling and exhaling. By enhancing parasympathetic activity and reducing sympathetic dominance, mindfulness aims to promote relaxation. The purpose of investigating the effectiveness of a technique on the sleep quality of cancer patients is to conduct clinical studies. The present study aimed to evaluate the effectiveness of breathing practices on sleep quality of cancer patients.
Search strategy: This systematic review adhered to PRISMA guidelines. A comprehensive search was conducted in PubMed, Web of Science, and Magiran for articles published between 2010 and 2024. Inclusion criteria encompassed studies focusing on the effects of breathing exercises on sleep quality. Exclusion criteria involved deviations from these parameters, the use of unsuitable study types, or publications outside the specified time frame. The initial search yielded 40 articles, of which 5 met the inclusion criteria. These selected studies were rigorously analyzed to evaluate the efficacy of breathing exercises on sleep quality.
Results: Review articles demonstrated the impact of cancer on the sleep quality of patients. Four studies reported significant improvements in sleep quality, such as more efficiency, duration, and reduced latency. However, in one study, this improvement could have been more significant. In one survey, sleep latency worsened despite improving efficiency and duration, but the change was not significant. These practices are feasible and safe for these patients and can improve respiratory function and mindfulness and reduce anxiety, fatigue, and inflammation. These findings support breathing practices as an effective non-pharmacological intervention for improving sleep quality in cancer patients.
Conclusion and Discussion: Despite inherent statistical variability across studies, breathing techniques emerge as a promising adjunctive strategy for managing sleep quality in cancer patients undergoing chemotherapy and radiotherapy. These techniques can potentially enhance patients’ self-efficacy and overall well-being by ameliorating sleep disturbances. To optimize their sustained benefits, future investigations should investigate longer-term interventions, assessing the durability of outcomes associated with pranayama and deep breathing exercises over extended practice periods.
Mahdi Falah Heydari Nezhad, Negin Ranjbar, Maryam Haji, Maryam Pasandideh, Roya Mansour-Ghanaei,
Volume 28, Issue 0 (Supplementary 2024)
Abstract
Introduction: Traditional burn management strategies, although effective, often lack the precision and personalization necessary for optimal healing. The emergence of artificial intelligence (AI) and three-dimensional (3D) printing technologies presents a unique opportunity to revolutionize burn care. AI offers advanced diagnostic capabilities and personalized treatment planning, while 3D printing facilitates the creation of customized tissue constructs and novel wound dressings. This systematic review seeks to evaluate their application and effectiveness within burn therapy, aiming to elucidate their impact on clinical outcomes in burn management.
Search Strategy: Adhering to PRISMA guidelines, this review involved a systematic search of articles published from 2020 to 2023 in four prominent databases: PubMed, Scopus, Web of Science, and Embase. Search terms focused on AI applications in burn management and integrating AI with 3D printing technologies within burn therapy. A total of 43 studies were identified, and 13 articles were included in the systematic review. Any articles not aligning with these criteria or deviating from the central themes of study were excluded to maintain the relevance and accuracy of the review.
Results: AI enhanced diagnostic accuracy, treatment personalization, and monitoring in burn management. Concurrently, 3D printing technologies, particularly bioprinting, showed promising advancements in creating customized skin grafts and dressings, potentially revolutionizing wound healing processes. Studies indicated improved functional outcomes through AI-driven assessments and 3D-printed tissue constructs, with evidence pointing towards reduced healing times and lower infection rates. Integrating these technologies fostered a multidisciplinary approach, suggesting a substantial impact on the future of burn care practices.
Conclusion and Discussion: The convergence of AI and 3D printing signifies a monumental leap forward in burn care. These promising technologies offer a future of personalized and optimized burn therapy, paving the way for enhanced patient outcomes and potentially revolutionizing the entire approach to burn management. AI-driven diagnostics and 3D-printed skin grafts can enhance healing processes and reduce recovery times. As research advances, these innovations are expected to streamline burn care workflows further, decrease complications, and elevate the standard of care for burn patients worldwide.
Mahdi Falah Heydari Nezhad1, Parand Pourghane,
Volume 28, Issue 0 (Supplementary 2024)
Abstract
Introduction: Telemedicine addresses the challenges of an aging population and caregiver burden. With the rise in older adults, traditional healthcare systems struggle to provide adequate care, causing significant stress for caregivers. Telemedicine can improve access to and quality of elderly care while alleviating caregiver strain. This review evaluated the role of telemedicine in reducing caregiver burden and enhancing geriatric care, highlighting its importance in modern healthcare.
Search Strategy: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a structured literature search was conducted across PubMed, Scopus, Web of Science, and Embase. The search targeted studies published between 2015 and 2023, focusing on telemedicine, caregiver burden, and elderly care. Inclusion criteria encompassed articles directly examining the impact of telemedicine on reducing caregiver burden and improving elderly care quality. Studies outside these criteria or the specified publication range were excluded. Ten were included from the 30 articles identified, providing a focused analysis of the role of telemedicine in geriatric care and caregiver support.
Results: A review of the 10 selected articles indicated that telemedicine interventions reduced caregiver burden and enhanced the quality of care for the elderly. Consistent findings showed improved caregiver mental health, reduced physical, and emotional stress, and increased satisfaction with care processes. For elderly patients, telemedicine improved access to healthcare services, better management of chronic conditions, and enhanced patient engagement and autonomy. These outcomes underscore the potential of telemedicine as a crucial tool in geriatric care, offering viable solutions to the challenges of an aging population and caregiver demands.
Conclusion and Discussion: This systematic review highlights the substantial benefits of telemedicine in addressing the dual challenges of caregiver burden and elderly care. A detailed analysis of recent studies reveals that telemedicine alleviates pressure on caregivers and significantly improves the accessibility and quality of healthcare services for the elderly. The findings advocate for integrating telemedicine into standard geriatric care practices, suggesting a promising avenue for enhancing the well-being of both caregivers and elderly patients. As the global population ages, adopting telemedicine could be pivotal in developing more sustainable, effective, and compassionate healthcare systems.
Negin Ranjbar Soleymani, Roya Mansour-Ghanaei, Mahdi Falah Heydari Nezhad,
Volume 28, Issue 0 (Supplementary 2024)
Abstract
Introduction: Burn injuries represent a significant healthcare challenge, necessitating effective wound healing strategies. Nanofiber wound dressings offer promising advantages over traditional methods due to their unique structural and functional properties. However, a comprehensive review of their application in promoting burn wound healing is currently lacking. This systematic review endeavors to critically assess the efficacy and safety of nanofiber wound dressings in burn care, aiming to provide valuable insights for future research endeavors and clinical practice.
Search Strategy: This systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-P) guidelines and utilized the Population, Intervention, Comparison, Outcome (PICO) framework. A comprehensive search was conducted across reputable databases such as PubMed, Medline, Web of Science, and Scopus, encompassing 2020 to 2024. Search terms included “nanofiber,” “burns,” and “wound healing.” Two reviewers independently evaluated retrieved articles based on predetermined inclusion and exclusion criteria. Studies investigating the application of nanofiber wound dressings in burn wound healing were selected. Methodological quality was critically assessed using established tools. Ultimately, 17 articles were found, of which 13 studies met the predefined criteria for inclusion in this review.
Results: The systematic review of the 13 relevant articles investigating the application of nanofiber wound dressings in burn wound healing revealed promising outcomes. Nanofiber dressings exhibited accelerated wound closure, reduced inflammation, and enhanced tissue regeneration compared to traditional methods. They demonstrated excellent biocompatibility and minimal adverse effects, highlighting their safety and effectiveness in burn wound management. Various fabrication techniques and materials were identified, with electrospinning emerging as a standard method enabling precise control over fiber properties. Nanofiber dressings exhibited significant potential to enhance burn care practices and improve patient outcomes.
Conclusion and Discussion: The findings of this review underscore the promising role of nanofiber wound dressings in promoting burn wound healing. Their demonstrably accelerated healing, reduced inflammation, and enhanced safety profile position these dressings as a valuable addition to burn care.
Ali Heidari, Azhdar Heydari,
Volume 28, Issue 0 (Supplementary 2024)
Abstract
Introduction: As an endogenous neurotransmitter, adenosine has anticonvulsant and neuroprotective effects in the brain. The central effects of adenosine are mediated through A1 and A2A receptors. Activation of the adenosine A2A receptor increases the expression of the cyclooxygenase-2 (COX-2) enzyme. Recent studies have reported that COX-2 inhibitors have anticonvulsant effects. This study aimed to examine the impact of acute adenosine administration, alone or combined with the selective COX-2 inhibitor celecoxib, on pentylenetetrazole-induced clonic and tonic seizure thresholds in mice.
Methods and Materials: NMRI male mice (weighing 25-30 g; n = 10 in each group) were randomly divided into 10 groups, including control, sham (polyethylene glycol 400), and eight experimental groups receiving adenosine (25, 50, and 100 mg/kg), celecoxib (2.5, 5, and 10 mg/kg), pre-treatment with an ineffective dose of celecoxib before ineffective doses of adenosine, and pre-treatment with an effective dose of celecoxib before effective doses of adenosine. Thresholds for the onset of myoclonic twitch (MCT), generalized clonus (GNC), and tonic hindlimb extension (THE) were assessed by intravenous infusion of pentylenetetrazole.
Results: Adenosine at a dose of 50 mg/kg significantly increased the onset of GNC and THE, while a dose of 100 mg/kg significantly increased all seizure endpoints. Celecoxib at a 10 mg/kg dose significantly increased all seizure endpoints. Pre-treatment with celecoxib (5 mg/kg) before adenosine (25 mg/kg) increased only the onset to THE, while pre-treatment with celecoxib (10 mg/kg) before adenosine (100 mg/kg) increased the onset to myoclonic twitch and GNC.
Conclusion and Discussion: Adenosine or celecoxib administration results alone confirm that these compounds have anticonvulsant effects. Potentiating the anticonvulsant impact of adenosine with celecoxib pre-treatment suggests that the effects of adenosine on seizure threshold are partly due to the modulation of the COX-2 pathway.
Zahra Heydari, Reza Abdollahi, Esmaeil Najafi,
Volume 28, Issue 0 (Supplementary 2024)
Abstract
Introduction: Compassion fatigue is defined as a harmful consequence of experiencing work-related stress among nurses, which can affect job performance and harm emotional and physical health. As a systematic review, this study examined the factors affecting compassion fatigue in nurses.
Search Strategy: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to find related studies. The PubMed, Web of Science, Google Scholar, Scopus, Embase, and Science Direct databases were searched using keywords such as “compassion fatigue”, “nurse”, and related factors, and no lower time limit was imposed when conducting the searches. The identified studies were published between January 2000 and May 2024. The quality of articles was assessed using the STROBE checklist.
Results: The combined sample size for 183 studies was 289. All studies were observational and cross-sectional. The results of the surveys showed that factors such as low level of managers' support, job burnout, service department, organizational position, age, marital status, education history, health status and gender, job satisfaction, satisfaction with income, years of work experience, compassion for Self, professional cognition, psychological education, secondary traumatic stress, occupational stress, anxiety, excessive empathy, social support, and coping strategies can be related to compassion fatigue in nurses.
Conclusion and Discussion: The finding of this study show that compassion fatigue in nurses is related to various individual, environmental, and organizational factors. Identifying these factors and taking practical and effective measures to adjust these factors can play a significant role in improving the quality of nursing care provided by nurses.
Amir Ehsan Heydari, Mohammad Reza Mazaheri Habibi, Azam Kheirdoust, Fateme Karimi Moghaddam, Sana Mahmoudi, Sadaf Afkhami Pirabarji, Sajed Arabian,
Volume 28, Issue 0 (Supplementary 2024)
Abstract
Introduction: Epilepsy is the third neurological disorder after stroke and migraine. Seizure is one of the important clinical manifestations for its diagnosis and affects the physical and mental health of sufferers. Examining the electroencephalogram (EEG) by the medical staff is a strategy to diagnose seizures, but it is a laborious and error-prone process. Therefore, several methods of automatic seizure detection based on machine learning have been applied in recent years. However, their clinical application has hindered its progress due to the need for high-quality data and advanced computing resources during implementation. This study aimed to evaluate artificial intelligence (AI) models for epilepsy diagnosis based on seizure monitoring.
Methods and Materials: This systematic review study was conducted in 2024 by searching the reliable databases of PubMed, Scopus, Web of Science, and Google Scholar search engine. Keywords “epilepsy”, “seizures”, “monitoring”, “electro-encephalography”, “artificial intelligence”, “machine learning”, and “deep learning” were investigated in related studies between 2020 and 2024. English-language studies investigating AI models for epilepsy diagnosis based on seizure monitoring met the inclusion criteria. In addition, review articles and studies that did not focus on patients with epilepsy were excluded from the study. Titles and abstracts were evaluated independently by two evaluators. Then, the full text of the articles was checked, and an identical form with the fields of study title, year of publication, country, number of data, type of algorithm, study objectives, and main findings of the study was used for the data extraction stage.
Results: A total of 1,267 articles were retrieved from the mentioned databases. After reading the titles and abstracts of articles and considering the inclusion and exclusion criteria, 144 articles were included in the study. Among studies, 41 (28.5%) investigated standard data mining methods such as decision trees, clustering, classification, and neural networks, which were valuable and effective in seizure diagnosis and prediction. Also, 52 (36.1%) studies evaluated a deep learning or convolutional model that increased accuracy and sensitivity. Furthermore, 51 (35.4%) studies compared various types of multi-layered and deep AI methods, as well as their combinations. In most cases, these approaches not only improved accuracy and specificity but also reduced processing time, thereby enhancing the feasibility of using this technology in clinical settings.
Conclusion and Discussion: The findings of this study demonstrate that AI models are adequate for diagnosing epilepsy from EEG data and predicting seizures. Correct and timely prediction of seizures with the help of AI improves the quality of life and better management of this disease in affected people, and its use will be a promising strategy in the self-management of these patients.
Reza Abdollahi, Zahra Heydari, Esmail Najafi,
Volume 28, Issue 0 (Supplementary 2024)
Abstract
Introduction: Artificial intelligence uses previous algorithms and predictors to predict risk factors that threaten patient safety. The desire to use artificial intelligence in safe and effective patient care has recently increased and is a growing trend. This study aimed to identify and describe the applications of artificial intelligence in safe patient care through a literature review.
Search Strategy: his article is a systematic review study that was conducted in 1402 by searching the English language databases Scopus, PubMed, Web of Science, Proquest, EMBASE, and Google Scholar from 2018 to 2024 and Persian databases, Iran Medex, SID and Magiran from 1397 to 1402 using keywords artificial intelligence, patient safety, systematic review, and its English equivalents.
Results: From 289 primary studies, 18 articles were included in the final analysis. The results of the study showed that artificial intelligence can identify drug side effects, adverse drug reactions, risk factors of patient falls, side effects after surgery, bed sores, Surgical site infections, urinary tract infections, identification of populations at high risk of drug toxicity, guidance for personal care and integration of predictive diagnostic classifications to increase patient safety.
Conclusion and Discussion: According to the results of the above studies, artificial intelligence support systems, when implemented successfully, can help increase patient safety and increase the quality of care by improving error detection, patient classification, and incident prevention and management. Provided to patients and reducing the risk of factors that threaten the safety of patients.
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