Radiographic Characteristics of Soft Tissue Calcification on Digital Panoramic Images

Objective: To assess the prevalence of soft tissue calcifications and their panoramic radiographic characteristics. Material and Methods: This descriptive retrospective study evaluated 2027 panoramic radiographs. The type and location of calcifications and the age and gender of patients were evaluated by two radiologists. Data were analyzed via SPSS and the Chi-square, Fisher’s exact and Kappa tests were used to compare the categorical demographic variables among the groups. The confidence interval was set to 95% and p<0.05 was considered statistically significant. Results: The prevalence of calcified stylohyoid ligament was 11.24%. This value was 3.99% for tonsillolith, 1.33% for calcified carotid plaque, 0.69% for antrolith, 0.39% for calcified lymph node, 0.29% for phleboliths, and 0.19% for sialoliths. The prevalence of these conditions had no significant association with gender or age (p=0.102). The prevalence of bilateral calcified stylohyoid ligament, tonsillolith, and a calcified carotid plaque was significantly higher (p<0.001). The most prevalent type of calcified stylohyoid ligament, according to O'Carroll’s classification, belonged to types 1, 4, 3 and 2 (p<0.001). The most commonly observed radiographic pattern was multiple, well-defined tonsilloliths (75.3%, p<0.001). Conclusion: The prevalence of soft tissue calcifications on panoramic radiographs was relatively low in this Iranian population. The most calcifications were respectively calcified stylohyoid ligament, tonsillolith, calcified carotid plaque, antrolith, calcified lymph node, phleboliths and sialoliths. Calcified stylohyoid ligament, tonsillolith and calcified carotid plaque were more bilaterally. Thereby panoramic imaging can help in primary assessment, epidemiologic and screening evaluation of these calcifications

Modeling the trajectory of CD4 cell count and its effect on the risk of AIDS progression and TB infection among HIV-infected patients using a joint model of competing risks and longitudinal ordinal data

Background: This study was conducted to better understand the influence of prognostic factors and the trend of CD4 cell count on the risk of progression to acquired immunodeficiency syndrome (AIDS) and tuberculosis (TB) infection among patients with human immunodeficiency virus (HIV) in a developing country.  Methods: The information of 1530 HIV-infected patients admitted in Behavioral Diseases Counseling Centers, Tehran, Iran, (2004-2014) was analyzed in this study. A joint model of ordinal longitudinal outcome and competing events is used to model longitudinal measurements of CD4 cell count and the risk of TB-infection and AIDS-progression among HIV patients, simultaneously.  Results: The results revealed that the trend of CD4 cell count had a significant association with the risk of TB-infection and AIDS-progression (p<0.001). Higher ages (p<0.001), the history of being in prison (p=0.013), receiving antiretroviral therapy (ART) (p<0.001) and isoniazid preventive therapy (IPT) (p<0.001) were associated with the positive trend of CD4 cell count. Higher ages were also associated with higher risks of TB (p<0.001) and AIDS-progression (p<0.001). Furthermore, ART (p=.0009) and IPT (p<0.001) were associated with a lower risk of TB-infection. In addition, ART (p<0.001) was associated with a lower risk of AIDS-progression. Moreover, individuals being imprisoned (p=0.001) and abusing alcohol (p=0.012) were more likely to have TB-co-infection.  Conclusions: The used joint model provided a flexible framework for simultaneous studying of the effects of covariates on the level of CD4 cell count and the risk of progression to TB and AIDS. This model also assessed the effect of CD4 trajectory on the hazards of competing events.&nbsp

Forecasting New Cases of Bipolar Disorder Using Poisson Hidden Markov Model

Background: Bipolar disorder (BD) is a major public health problem. In time series count data there may be over dispersion, and serial dependency. In such situation some models that can consider the dependency are needed. The purpose current research was to use Poisson hidden Markov model to forecast new monthly BD instances.Methods: In current study the dataset including the frequency of new instances of BD from October 2008 to March 2015 in Hamadan Province, the west of Iran were used. We used Poisson hidden Markov with different number of conditions to determine the best model according to Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Then we used final model to forecast for the next 24 months.Results: Poisson hidden Markov with two states were chosen as the final model. Each component of dependent mixture model explained one of the states. The results showed that the new BD cases is increase over time and due to forecasting results number of patients for the next 24 months comforted in state two with mean 85.15. The forecast interval was approximately (56, 100).Conclusion: As the Poisson hidden Markov models was not used to forecast the future states in other prior researches, the findings of this study set forward a forecasting strategy as an alternative to common methods, by considering its deficiencies

Effects of various disinfectants on surface roughness and color stability of thermoset and 3D-printed acrylic resin

Denture cleansers are extensively utilized to inhibit the colonization of various Candida species. Currently, additive technology in denture fabrication has become more prevalent. This study aims to assess the impact of disinfectants on the surface roughness and color stability of distinct denture bases. Disc-shaped samples (N=66) were exposed to three different disinfectants: 0.5% sodium hypochlorite, 1% hydrogen peroxide, and 2% chlorhexidine. The samples underwent evaluation via spectrophotometry and profilometry, respectively. Data analysis was conducted utilizing analysis of variance (ANOVA) (p < 0.05). Within the heat-cured group, sodium hypochlorite resulted in the most notable change in surface roughness (0.2 μm), while chlorhexidine exhibited the least impact (0.001 μm), showing a significant difference (p <0.008). The color change (ΔE) for 3D-printed samples immersed in all disinfectants was higher compared to heat-cured samples. Among the heat-cured samples, chlorhexidine induced the highest ΔE (2.76), while sodium hypochlorite resulted in the lowest (ΔE = 1.44), and this difference was statistically significant (p <0.008). Chlorhexidine caused the most significant color alteration among the solutions, while sodium hypochlorite induced the most considerable changes in surface roughness

Prediction the survival of patients with breast cancer using random survival forests for competing risks

Abstract Objectives: Breast cancer (BC) is the most common cause of cancer death in Iranian women. Sometimes death from other causes precludes the event of interest and makes the analysis complicated. The purpose of this study was to identify important prognosis factors associated with survival duration among patients with BC using random survival forests (RSF) model in presence of competing risks. Also, its performance was compared with cause-specific hazard model. Methods: This retrospective cohort study assessed 222 patients with BC who admitted in Ayatollah Khansari hospital, Arak. The cause-specific Cox proportional hazards and RSF models were employed to determine the important risk factors for survival of the patients. Results: The mean and median survival duration of the patients were 90.71 (95%CI: 83.8- 97.6) and 100.73 (95%CI: 89.2-- 121.5) months, respectively. The cause-specific model indicated that type of surgery and HER2 had statistically significant effects on the risk of death of BC. Moreover, the RSF model identified that HER2 was the most important variable for the event of interest. Conclusion: According to the results of this study, the performance of the RSF model was better than the cause-specific hazard model. However, HER2 was the most important variable for death of BC in both of the models

Joint modelling of colorectal cancer recurrence and death after resection using multi-state model with cured fraction

Abstract Curing of colorectal cancer (CRC) occurs at the time of resection but it is not immediately observable. If the cancer is not completely eliminated, the patient will not be cured of cancer and will experience recurrence as the tumor has regrown to a detectable size. The main propose of the present study was to assess the effects of different covariates on the probability of being cured as well as the time-to-recurrence, and time-to-death in CRC patients by using multi-state cure model. The information of 283 patients with CRC, who underwent resection, from 2000 to 2015 in Imam Khomeini Hospital of Hamadan, Iran, were analyzed. The results of multi-state cure model reveal that females and who experience metastasis were more likely to be apparently cured. It has been shown that sex has a significant effect on the time-to-recurrence given patient was in the not cured group. The survival time of patients of the not cured group was affected by the stage of disease. However, the survival of the apparently cured patients were affected by age at diagnosis and metastasis status. The multi-state cure model provided a flexible framework to study the effects of prognostic factors simultaneously on the transition between different states and the probability of being apparently cured of CRC

Two-Stage Joint Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation Data

In longitudinal studies, clinicians usually collect longitudinal biomarkers’ measurements over time until an event such as recovery, disease relapse, or death occurs. Joint modeling approaches are increasingly used to study the association between one longitudinal and one survival outcome. However, in practice, a patient may experience multiple disease progression events successively. So instead of modeling of a single event, progression of the disease as a multistate process should be modeled. On the other hand, in such studies, multivariate longitudinal outcomes may be collected and their association with the survival process is of interest. In the present study, we applied a joint model of various longitudinal biomarkers and transitions between different health statuses in patients who underwent renal transplantation. The full joint likelihood approaches are faced with the complexities in computation of the likelihood. So, here, we have proposed two-stage modeling of multivariate longitudinal outcomes and multistate conditions to avoid these complexities. The proposed model showed reliable results compared to the joint model in case of joint modeling of univariate longitudinal biomarker and the multistate process

Risk factors for maternal mortality in the west of Iran: a nested case-control study

OBJECTIVES: With a gradual decline in maternal mortality in recent years in Iran, this study was conducted to identify the remaining risk factors for maternal death. METHODS: This 8-year nested case-control study was conducted in Hamadan Province, in the west of Iran, from April 2006 to March 2014. It included 185 women (37 cases and 148 controls). All maternal deaths that occurred during the study period were considered cases. For every case, four women with a live birth were selected as controls from the same area and date. Conditional logistic regression analysis was performed and the odds ratio (OR) and its 95% confidence interval (CI) were obtained for each risk factor. RESULTS: The majority of cases were aged 20-34 years, died in hospital, and lived in urban areas. The most common causes of death were bleeding, systemic disease, infection, and pre-eclampsia. The OR estimate of maternal death was 8.48 (95% CI=1.26-56.99) for advanced maternal age (≥35 years); 2.10 (95% CI=0.07-65.43) for underweight and 10.99 (95% CI=1.65-73.22) for overweight or obese women compared to those with normal weight; 1.56 (95% CI=1.08-2.25) for every unit increase in gravidity compared to those with one gravidity; 1.73 (95% CI=0.34-8.88) for preterm labors compared to term labors; and 17.54 (95% CI= 2.71-113.42) for women with systemic diseases. CONCLUSIONS: According to our results, advanced maternal age, abnormal body mass index, multiple gravidity, preterm labor, and systemic disease were the main risk factors for maternal death. However, more evidence based on large cohort studies in different settings is required to confirm our results