Showing 5 results for Hosseinzadeh
L Koochmeshgi, T Hosseinzadeh Nik , Mj Kharazi Fard ,
Volume 3, Issue 3 (18 2008)
Abstract
Background & Objectives: Malocclusion is not a disease but rather a set of dental deviations, which in some cases can influence quality of life. The aim of this study was to assess the frequency of various dento–facial anomalies.
Methods: In this cross-sectional study in 2006 we assessed a sample of 600 randomly selected schoolchildren, with a mean age of 11.97 years, who were attending secondary school in various districts of Karaj. We used questionnaires to obtain information on extracted teeth, crowding, spacing, diastem, the severest disorders in anteriors (maxilla and mandible), overjet, negative overjet, open space between two jaws and antero-posterior molar relationship.
Results: Ninety-nine percent of children had no extracted teeth in maxillary anteriors, while 98.3% had no extracted teeth in mandibular anteriors. In 41.2% of the subjects there was no crowding, and 57.7% of the sample had no spacing. The mean diastem was 0.36 mm and the mean of the most severe disorders in anteriors was 1.08 mm in the maxilla and 0.78 mm in the mandible. Mean overjet was 1.98 mm, mean negative overjet 0.03 mm, and mean open space between two jaws was 0.28 mm. In 58.5% of the subjects the antero–posterior molar relationship was normal. The DIA ranged form 13 to 63, with a mean of 24.12.
Conclusions: This study indicates that over than one-third of the population needs different degrees of orthodontic treatment.
T Hosseinzadeh Nik, N Shahsavari, D Gholami, Ar Fattahi Meibodi, Sh Nourozi, Mj Kharrazi Fard,
Volume 7, Issue 1 (20 2011)
Abstract
Background & Objectives: Orthodontic treatment need and demand in 12-year-olds in Abadeh city has not
previously been analysed in relation to geographic origin. The purpose of this study was to assess the12 year old
students need and demand for orthodontic treatment.
Methods: Four hundred seventeen 12-year-old students was selected from public and private schools in Abade
(Fars province, Iran). All the students were examined according to the AC and DHC component of Index of
Orthodontic Treatment Need (IOTN) by a trained dentist. Students' and parents' perceived needs were also
assessed using AC component and their demand for orthodontic treatment was asked through a questionair .
Results: Twenty two percent of the students were in "no need of treatment" group when assessed by DHC
component, 29.5 % were in "average need" and 48.2% were in "definite need" group. When assessed by AC
score, these percents were 61.9%, 29%, and 9.1%. Parents and students percieved need for definite orthodontic
treatment according to AC score was 8.6% and 7.7%, respectively. The students and their parents’ demand for
treatment were 40.6% and 44.9%, respectively.
Conclusion: Orthodontic treatment need in Abade is higher in comparison with other reports according to DHC.
DHC is not correlated with orthodontic treatment demand of 12 years old students, but AC had a strong
relationship with treatment demand.
N Hosseinzadeh, Y Mehrabi, Ms Daneshpour, H Alavi Majd, F Azizi,
Volume 8, Issue 1 (20 2012)
Abstract
Background
& Objectives: Studying
several linked markers provides more information on locating disease genes
locus by using genetic association analysis.
The aims of this study were to introduce Multimarker Family Base Association
Tests (FBAT-MM) and its Linear Combination (FBAT-LC) in multimarker genetic
association analysis and to examine the association of selected microsatellites
with HDL-C in an Iranian population.
Methods: One hundred twenty five (125)
families having at least one member with metabolic syndrome and at least two
members with low HDL-C were selected from participants of the Tehran Lipid and
Glucose Study (TLGS). Multimarker genetic association of HDL-C level with some
microsatellites in the chromosomes 8, 11, 12, and 16 were examined using
FBAT-MM and FBAT-LC methods.
Results: The families consisted of 563
individuals (269 males and 294 females). FBAT-MM showed significant genetic
association only between HDL-C and three microsatellites in Chromosome 11 (P<0.05).
The microsatellite D11S1304 was found as the significant factor for multimarker
genetic association.
Conclusion:
FBAT-MM and FBAT-LC did not show shortcomings such as excessive conservatism
and low power which are, usually, observed in other multimarker methods. Finding microsatellites associated with HDL-C
level can provide background for further researches on the role of predisposing
genes in metabolic syndrome.
Normal
A Hosseinzadeh, Mr Baneshi, B Sedighi, J Kermanchi, Aa Haghdoost,
Volume 18, Issue 1 (Vol.18, No.1, Spring 2022 2022)
Abstract
Background and Objectives: Dementia is a chronic disease that imposes a huge financial and social burden on the health system. Knowledge of the prevalence of dementia is essential for healthcare planning and ensuring that there is an adequate service for people with the condition. Considering that the prevalence and geographical variation of dementia are not well known in Iran, the present study was conducted to investigate the prevalence of dementia and its geographic variations in Iran.
Methods: In this study, the prevalence of dementia was estimated indirectly using the frequency of prescribed specialized medicines in one year by generic and brand names in each province. Choropleth maps were used to visually assess the geographical variation of dementia prevalence at the provincial level. Moran I and Getis-Ord Gi (Gi) geographical tests were used to investigate the spatial autocorrelation and geographical variability of dementia prevalence at a significant level of 0.05, respectively.
Results: In this study, the prevalence of dementia was 49.6 and 508.9 in 100000 in the general and over 60-year population, respectively. In the general population, the lowest prevalence was in Hormozgan Province (9.4/100000) and the highest prevalence was in East Azarbayjan Province (96.4/100000). In the over 60-year population, the lowest prevalence was in Hormozgan Province (141.5/100000) and the highest in Isfahan Province (862.5/1000000). According to Moran I and Getis-Ord Gi (Gi) tests, spatial autocorrelation and geographical variability of dementia prevalence were not significant.
Conclusion: The prevalence of dementia in the Iranian over 60-year population is lower compared to western countries; however, it is comparable with the reported dementia prevalence from developing countries. It should be noted that the dementia prevalence is high, similar to developed countries, in some developed provinces of Iran.
Ramin Farrokhi, Samaneh Hosseinzadeh, Abbas Habibelahi, Akbar Biglarian,
Volume 20, Issue 1 (Vol.20, No.1, Spring 2024)
Abstract
Background and Objectives: Identifying pregnant women who are at risk of premature birth and determining its risk factors is essential because it affects their health. This study aimed to use an interpretable machine-learning model to predict premature birth.
Methods: In this study, data from 149,350 births in Tehran in 2019 were utilized from the Iranian Mothers and Babies Network (IMaN) dataset. Various factors related to the mother and the fetus, such as the mother's demographic variables and health status, medical history, pregnancy conditions, childbirth, and associated risks, were considered. The machine learning models, including multilayer neural networks, random forest, and XGBoost, were employed to predict the occurrence of preterm birth after data preprocessing. The models were evaluated based on accuracy, sensitivity, specificity, and area under the ROC curve. The Python programming language version 3.10.0 was applied to analyze the data.
Results: About 8.67% of births were premature. The XGBoost algorithm achieved the highest prediction accuracy (90%). According to the model output, multiple births, which account for 46% of pregnant women's births, had the highest importance score. Delivery risk factors had a score of 41%, and other variables, including neurological and mental illness, preeclampsia, and cardiovascular disease, were subsequently ranked in order of importance for this particular individual.
Conclusion: Using an interpretable machine learning method could predict the occurrence of premature birth. Based on risk factors, the interpretable machine learning method can provide personalized preventive recommendations for every pregnant woman, aiming to reduce the risk of preterm birth.
