12014 research outputs found
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Facile and fast preparation of layered double hydroxide as a nanocarrier for ascorbic acid under ultrasonic irradiation
Full text linkBackground and purpose: Layered double hydroxides (LDHs) as inorganic materials are being used in controlled release and drug delivery systems. These materials are more stable than conventional drug carriers. In this investigation, Mg/Al-ascorbic acid (ASA) LDH nanohybrid was synthesized by ultrasonic-assisted co-deposition techniques. Experimental approach: In this study, Mg/Al-LDH to adsorption of ASA anions from the alkaline solution was assembled by a facile coprecipitation technique. During this process, ultrasonic irradiation was used to increase the rate of ion exchange between LDH and ASA. The intercalated-layered structure was characterized by FT-IR spectroscopy, XRD, thermogravimetric analysis, field emission SEM, and TEM. ASA releasing from Mg/Al-ASA LDH nanohybrid was carried out in incubation sodium carbonate solution (0.5 M) at 35 °C using UV-Vis absorbance analysis at λ = 265 nm Findings/Results: The used techniques confirmed the structure of Mg/Al-LDH and indicated successful intercalation of ASA into the interlayer galleries of the LDH host. The obtained results also have shown that Mg/Al-ASA LDH nanohybrid was generated with an average diameter size of 25 nm and narrow size distribution. Analysis of the release profiles using several kinetic models suggested that the first-order rate model is the most appropriate for describing the release of ASA from Mg/Al-LDH which means the amount of drug released is proportional to the amount of remaining drug in the matrix. Thus, the amount of activity released tends to decrease in function of time. Conclusion and implications: The results showed that LDHs are good host materials to preserve the biomolecule and modify its release rate and bioavailability. © 2022 Wolters Kluwer Medknow Publications. All rights reserved
A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months
Full text linkBackground: Traumatic brain injury (TBI), which occurs commonly worldwide, is among the more costly of health and socioeconomic problems. Accurate prediction of favorable outcomes in severe TBI patients could assist with optimizing treatment procedures, predicting clinical outcomes, and result in substantial economic savings. Methods: In this study, we examined the capability of a machine learning-based model in predicting �favorable� or �unfavorable� outcomes after 6 months in severe TBI patients using only parameters measured on admission. Three models were developed using logistic regression, random forest, and support vector machines trained on parameters recorded from 2,381 severe TBI patients admitted to the neuro-intensive care unit of Rajaee (Emtiaz) Hospital (Shiraz, Iran) between 2015 and 2017. Model performance was evaluated using three indices: sensitivity, specificity, and accuracy. A ten-fold cross-validation method was used to estimate these indices. Results: Overall, the developed models showed excellent performance with the area under the curve around 0.81, sensitivity and specificity of around 0.78. The top-three factors important in predicting 6-month post-trauma survival status in TBI patients are �Glasgow coma scale motor response,� �pupillary reactivity,� and �age.� Conclusions: Machine learning techniques might be used to predict the 6-month outcome in TBI patients using only the parameters measured on admission when the machine learning is trained using a large data set. © 2022 Korean Society of Critical Care Medicine. All right reserved
Health information prescription system for non communicable diseases: A systematic review and thematic analysis
Full text linkObjective - Prescribing health information is very important to empower informed patients. The goal of present study is to recognize challenges for developing health information prescription on non-communicable diseases. Material and Methods - Six data bases related to health information prescription were investigated. They included Web of Science, Scopus, PubMed, Google Scholar, Ovid and EMBASE. The investigated studies were published from 2000 to 2019. The language of the articles was English and the access to full text was one of the inclusion criteria. The research was evaluated by Prisma checklist and critical apprising. Different dimensions of health Information prescription system were recognized by thematic analysis. Results - 54 studies were recognized based on the inclusion criteria. The results showed that there were three main concepts related to primary dimensions of the system in designing health information prescription system: determination of system functional goals, recognition of implementation barriers and recognition of developmental sub-structures. There were 16 subcategories including primary goals for accessibility, the concerns related to patients information confidentiality, individual differences and interests and personalizing the process of information prescription, the lack of integrity in health Information system for providing pattern of health Information system related to diabetic patients. Conclusion - The goals, implementing barriers and functional substructures of health information prescription system should be recognized in order to improve self-care behaviors of diabetic patients in clinic. It is recommended that the future investigations focus on research gaps in personalizing health information prescription and integration of health information prescription process in health care system. © 2020, LLC Science and Innovations
Predictors for the severe coronavirus disease 2019 (COVID-19) infection in patients with underlying liver disease: a retrospective analytical study in Iran
Full text linkRisk factors for clinical outcomes of COVID-19 pneumonia have not yet been well established in patients with underlying liver diseases. Our study aimed to describe the clinical characteristics and outcomes of COVID-19 infection among patients with underlying liver diseases and determine the risk factors for severe COVID-19 among them. In a retrospective analytical study, 1002 patients with confirmed COVID-19 pneumonia were divided into two groups: patients with and without underlying liver diseases. The admission period was from 5 March to 14 May 2020. The prevalence of underlying conditions, Demographic data, clinical parameters, laboratory data, and participants' outcomes were evaluated. Logistic regression was used to estimate the predictive factors. Eighty-one (8) of patients had underlying liver diseases. The frequencies of gastrointestinal symptoms such as diarrhea and vomiting were significantly higher among patients with liver diseases (48 vs. 25 and 46.1 vs. 30 respectively, both P < 0.05). Moreover, ALT and AST were significantly higher among patients with liver diseases (54.5 ± 45.6 vs. 37.1 ± 28.4, P = 0.013 and 41.4 ± 27.2 vs. 29.2 ± 24.3, P = 0.028, respectively). Additionally, the mortality rate was significantly high in patients with liver disease (12.4 vs. 7, P = 0.018). We also observed that the parameters such as neutrophil to leukocyte ratio Odds Ratio Adjusted (ORAdj) 1.81, 95% CI 1.21�3.11, P = 0.011 and blood group A (ORAdj 1.59, 95% CI 1.15�2.11, P = 0.001) were associated with progression of symptoms of COVID-19. The presence of underlying liver diseases should be considered one of the poor prognostic factors for worse outcomes in patients with COVID-19. © 2021, The Author(s)
County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States
Full text linkAs of November 12, 2020, the mortality to incidence ratio (MIR) of COVID-19 was 5.8 in the US. A longitudinal model-based clustering system on the disease trajectories over time was used to identify �vulnerable� clusters of counties that would benefit from allocating additional resources by federal, state and county policymakers. County-level COVID-19 cases and deaths, together with a set of potential risk factors were collected for 3050 U.S. counties during the 1st wave of COVID-19 (Mar25�Jun3, 2020), followed by similar data for 1344 counties (in the �sunbelt� region of the country) during the 2nd wave (Jun4�Sep2, 2020), and finally for 1055 counties located broadly in the great plains region of the country during the 3rd wave (Sep3�Nov12, 2020). We used growth mixture models to identify clusters of counties exhibiting similar COVID-19 MIR growth trajectories and risk-factors over time. The analysis identifies �more vulnerable� clusters during the 1st, 2nd and 3rd waves of COVID-19. Further, tuberculosis (OR 1.3�2.1�3.2), drug use disorder (OR 1.1), hepatitis (OR 13.1), HIV/AIDS (OR 2.3), cardiomyopathy and myocarditis (OR 1.3), diabetes (OR 1.2), mesothelioma (OR 9.3) were significantly associated with increased odds of being in a more vulnerable cluster. Heart complications and cancer were the main risk factors increasing the COVID-19 MIR (range 0.08�0.52 MIR�). We identified �more vulnerable� county-clusters exhibiting the highest COVID-19 MIR trajectories, indicating that enhancing the capacity and access to healthcare resources would be key to successfully manage COVID-19 in these clusters. These findings provide insights for public health policymakers on the groups of people and locations they need to pay particular attention while managing the COVID-19 epidemic. © 2021, The Author(s)
A nutrient pattern characterized by vitamin A, C, B6, potassium, and fructose is associated with reduced risk of insulin�related disorders: A prospective study among participants of Tehran lipid and glucose study
Full text linkBackground: Insulin-related disorders, including insulin resistance, insulin insensitivity, and insulinemia, is considered early predictors of major chronic disease risk. Using a set of correlated nutrient as nutrient patterns to explore the diet-disease relationship has drawn more attention recently. We aimed to investigate the association of nutrient patterns and insulin markers� changes prospectively among adults who participated in the Tehran Lipid and Glucose Study (TLGS). Methods: For the present study, 995 men and women aged 30�75 years, with complete information on insulin and dietary intake in survey III TLGS, were selected and followed three years until survey IV. Dietary intakes at baseline were assessed using a valid and reliable food frequency questionnaire (FFQ). Nutrient patterns were derived using principal component analysis (PCA). We extracted five dominant patterns based on the scree plot and categorized them into quartiles. Linear regression analysis was conducted to investigate the association between Nutrient patterns and three-year insulin markers changes, including fasting insulin, HOMA-IR, and HOMA-S. Results: The mean (SD) age and BMI of participants (43.1 male) were 46.2(10.9) year and 28.0(4.7) kg/m2, respectively. The median (IQR, 25, 75) of 3 years changes of insulin, HOMA-IR and HOMA-S were 0.35 (� 1.71, 2.67) mU/mL, 0.25 (� 0.28, 0.84) and � 6.60 (� 22.8, 7.76), respectively. In the fully adjusted model for potential confounders, per each quartile increment of the fifth nutrient pattern, the β coefficients (95 CI) of changes in insulin, HOMA-IR, and HOMA-S were � 0.36 (� 0.62, � 0.10); P value = 0.007, -0.10 (-0.19, -0.01); P value = 0.022, and 1.92 (0.18, 3.66); P value = 0.030, respectively. There were no significant association between other nutrient patterns and insulin related indices. Conclusions: Present study showed that high adherence to a nutrient pattern rich in vitamin A, vitamin C, pyridoxine, potassium, and fructose is inversely associated with 3-years changes in insulin, HOMA-IR, and directly associated with HOMA-S. © 2021, The Author(s)
Considerations about the implementation of an autism screening program in Iran from the viewpoints of professionals and parents: a qualitative study
Full text linkBackground: The aims of this study were to explore to explore the viewpoints of parents of children with Autism Spectrum Disorders (ASD) and professionals regarding the implementation of screening programs for ASD, to explore the challenges of the implementation of a universal screening program for ASD in Iran from their viewpoints, and, to explore their recommendations to overcome the potential challenges. Method: This qualitative study was conducted using an inductive content analysis, between June 2018 and December 2018, in East-Azerbaijan province of Iran. Data was collected through in-depth interviews and focus group discussions. The participants were purposively selected among two groups: representatives of health system and representatives of children with ASD. A sample of 32 parents and 30 professionals were recruited in this study. Results: Totally, 9 main themes and 23 sub-themes were extracted in three main areas including: viewpoints of the participants about universal screening for ASD, challenges in implementation of the universal screening program, and participants� recommendations about how to overcome the potential challenges. Main challenges in implementation of the universal screening program included: shortages of ASD screening tools, weakness of the health system, lack of coordination among the ASD service providers, and social and ethical issues. Conclusion: The parents and the professionals had different viewpoints about the implementation of ASD universal screening program in Iran. According to the professionals, there is not enough rational to implement ASD screening program for all children. However, the parents believed that universal screening program is inevitable, and it should be implemented in primary health centers during the early child-care visits. The results of this study open up unspoken issues that could help in initiating the screening program not only in Iran but also in other low- and middle-income countries as well. © 2021, The Author(s)
Induced dysregulation of ACE2 by SARS-CoV-2 plays a key role in COVID-19 severity
Full text linkSevere acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the cause of COVID-19, is reported to increase the rate of mortality worldwide. COVID-19 is associated with acute respiratory symptoms as well as blood coagulation in the vessels (thrombosis), heart attack and stroke. Given the requirement of angiotensin converting enzyme 2 (ACE2) receptor for SARS-CoV-2 entry into host cells, here we discuss how the downregulation of ACE2 in the COVID-19 patients and virus-induced shift in ACE2 catalytic equilibrium, change the concentrations of substrates such as angiotensin II, apelin-13, dynorphin-13, and products such as angiotensin (1�7), angiotensin (1�9), apelin-12, dynorphin-12 in the human body. Substrates accumulation ultimately induces inflammation, angiogenesis, thrombosis, neuronal and tissue damage while diminished products lead to the loss of the anti-inflammatory, anti-thrombotic and anti-angiogenic responses. In this review, we focus on the viral-induced imbalance between ACE2 substrates and products which exacerbates the severity of COVID-19. Considering the roadmap, we propose multiple therapeutic strategies aiming to rebalance the products of ACE2 and to ameliorate the symptoms of the disease. © 202
Digit ratio (2D:4D) a possible biomarker for cognitive style: A study on Iranian engineering and mathematics university students
No full textDigit ratio (2D:4D) is a biomarker for prenatal hormonal exposure. Some studies suggest that prenatal hormonal exposure might influence our cognitive styles characterized by systemizing and empathizing tendencies while other studies do not support these findings. By assessing 156 university students of engineering or mathematics (science, technology, engineering, and mathematics (STEM) (female: 63 and male 93) we concluded that digit ratio may support the hypothesis on sexual differences in cognitive styles. © 2020 Elsevier Lt
Formulation and evaluation of inhalable microparticles of Rizatriptan Benzoate processed by spray freeze-drying
No full textThe aim of the current study was to prepare and evaluate inhalable microparticles of Rizatriptan benzoate in order to further benefit from its pulmonary delivery, the expected enhanced bioavailability and accelerated onset of action. The spray freeze drying (SFD) technique was used to produce microparticles consisting of a fixed amount of a sugar which was either mannitol or trehalose and an amino acid component including leucine, phenylalanine or serine. The powders were then characterized for particle size distribution, morphology, thermal properties and in vitro aerosolization performance. It was demonstrated that various formulations of inhalable Rizatriptan could be efficiently aerosolized and offered acceptable fine particle fraction (FPF) ranging up to 61.1. In particular, a spray-freeze-dried powder composed of trehalose and phenylalanine showed the most superior inhalation performance (FPF = 61.1), indicating better dispersion properties of those spherical porous microparticles with less adhesion and agglomeration. These results successfully demonstrated that Rizatriptan could be engineered into respirable microparticles to be proposed as a promising delivery system for fast and effective control of migraine attacks. © 2021 Elsevier B.V