Multistate Models for Biomarker Processes

Multistate models are widely used for describing life history processes. In studies where individuals are observed continuously, the transition times between states are known exactly. However, when individuals are observed intermittently, transition times and even the states visited between successive observations, may be unknown. Irregular intermittent observation is a special case of intermittent observation where the observation times vary across individuals. In the case of intermittent observation, we may not be able to estimate model parameters precisely. In the first part of the thesis, we review methods of estimation for Markov models in this situation, and provide a numerical study that shows the loss of efficiency in estimation for intermittent observation compared to continuous observation in both progressive and bi-directional multistate models. Then, application to data from the CANOC, Canadian Observational Cohort study of HIV-positive individuals whose virus has been suppressed by combination antiretroviral therapy, illustrates the effect of gap times on estimation efficiency. Irregular observation is very common in longitudinal data on disease history of individuals in observational studies. However, there are considerable challenges in checking models with these observation schemes, since there is a strong possibility that this irregularity may be induced by the dependency of inter-visit times on previous process history. As a result, followup visits from this kind of data are subject to disease state-dependency, which needs to be taken into account to prevent biased analysis. The second part of this thesis begins with a review on the estimation of marginal process features such as failure time distributions and prevalence probabilities in the context of Markov multistate models with intermittent observations. A method for estimation of these features is developed using Inverse Intensity Weights (IIW). This method corrects the estimation bias due to dependent observation times. Simulation studies illustrate that the proposed method yields estimates that are close to the true values, while the method that ignores the dependency yields estimates that differ substantially from the true values. Then, an application involving viral load dynamics in a group of individuals from the CANOC study is presented. In practice, we may want to consider models for which transition intensities depend on internal covariates related to previous process history. There are, however, challenges in fitting and checking models involving internal covariates, and in making predictions. In the third part of this thesis, we have developed an algorithm that simulates possible sample paths of individuals' processes, and we use it for prediction and model checking. Finally, there has been recent discussion of model assessment of multistate models. There remain, however, some difficulties in model assessment with irregular intermittent observations. The last part of this thesis addresses problems that arise with methods based on comparison of empirical and model-based estimates. We propose the use of likelihood ratio tests within the Markov process family, and methods of estimating the power of these tests are given. We also propose a method for comparing models based on different outcome spaces in terms of prediction. Finally, the proposed methods are applied to a group of individuals in the CANOC study

Macular Amyloidosis and Epstein-Barr Virus

Background. Amyloidosis is extracellular precipitation of eosinophilic hyaline material of self-origin with special staining features and fibrillar ultrastructure. Macular amyloidosis is limited to the skin, and several factors have been proposed for its pathogenesis. Detection of Epstein-Barr virus (EBV) DNA in this lesion suggests that this virus can play a role in pathogenesis of this disease. Objective. EBV DNA detection was done on 30 skin samples with a diagnosis of macular amyloidosis and 31 healthy skin samples in the margin of removed melanocytic nevi by using PCR. Results. In patients positive for beta-globin gene in PCR, BLLF1 gene of EBV virus was positive in 23 patients (8 patients in case and 15 patients in the control group). There was no significant difference in presence of EBV DNA between macular amyloidosis (3.8%) and control (23.8%) groups (P=0.08). Conclusion. The findings of this study showed that EBV is not involved in pathogenesis of macular amyloidosis

Indoor environment assessment of special wards of educational hospitals for the detection of fungal contamination sources: A multi-center study (2019-2021)

Background and Purpose: The hospital environment was reported as a real habitat for different microorganisms, especially mold fungi. On the other hand, these opportunistic fungi were considered hospital-acquired mold infections in patients with weak immune status. Therefore, this multi-center study aimed to evaluate 23 hospitals in 18 provinces of Iran for fungal contamination sources.Materials and Methods: In total, 43 opened Petri plates and 213 surface samples were collected throughout different wards of 23 hospitals. All collected samples were inoculated into Sabouraud Dextrose Agar containing Chloramphenicol (SC), and the plates were then incubated at 27-30ºC for 7-14 days.Results: A total of 210 fungal colonies from equipment (162, 77.1%) and air (48,22.9%) were identified. The most predominant isolated genus was Aspergillus (47.5%),followed by Rhizopus (14.2%), Mucor (11.7%), and Cladosporium (9.2%). Aspergillus(39.5%), Cladosporium (16.6%), as well as Penicillium and Sterile hyphae (10.4% each), were the most isolates from the air samples. Moreover, intensive care units (38.5%) and operating rooms (21.9%) had the highest number of isolated fungal colonies. Out of 256 collected samples from equipment and air, 163 (63.7%) were positive for fungal growth.The rate of fungal contamination in instrument and air samples was 128/213 (60.1%) and 35/43 (81.2%), respectively. Among the isolated species of Aspergillus, A. flavus complex (38/96, 39.6%), A. niger complex (31/96, 32.3%), and A. fumigatus complex (15/96, 15.6%) were the commonest species.Conclusion: According to our findings, in addition to air, equipment and instrument should be considered among the significant sources of fungal contamination in the indoor environment of hospitals. Airborne fungi, Hospital, Indoor air, Equipment, Sources of fungal contamination in the indoor environment of hospitals

Novel Point Mutations in cyp51A and cyp51B Genes Associated with Itraconazole and Posaconazole Resistance in Aspergillus clavatus Isolates

Aspergillus clavatus is a common environmental species known to cause occupational allergic disease in grain handlers. We have recently observed azole-resistant isolates of this fungus as a cause of onychomycosis. To further characterize the cause of resistance, the genes encoding 14 α-sterol demethylase enzyme (cyp51A and cyp51B) were characterized and analyzed in 9 ITC-susceptible isolates and 6 isolates with high minimum inhibitory concentrations (MICs) of clinical (nail and sputum) and environmental A. clavatus strains. We found that six isolates with itraconazole MIC >16 mg/L demonstrated nonsynonymous mutations, including V51I, L378P, E483K, and E506G, and synonymous mutations, including F53F, A186A, Q276Q, and H359H. Moreover, P486S was detected in five strains with ITR MIC >16 mg/L. One mutation, F324S, was detected in an isolate with posaconazole MIC >16 mg/L. The effect of E483K and P486S mutations of CYP51A on azole resistance was further investigated using homology modeling and molecular dynamics. We found that E483K and P486S mutations were located near the ligand access channel of CYP51A that could partly lead to narrowing the entry of the ligand access channels. Therefore, we concluded that E483K and P486S mutations may potentially contribute to the limited access of inhibitors to the binding pocket and therefore confer resistance to azole agents. © 2019 Mary Ann Liebert, Inc., publishers