COVID-19 in Patients with Rheumatic Disease Using Immunomodulatory Drugs: Imaging Findings and Predictors of Hospitalization.

Introduction: SARS-CoV-2 causes more severe symptoms in most chronic diseases, and rheumatic disease is no exception. This study aims to investigate whether there is an association between the use of immunomodulatory medications, including conventional disease-modifying agents (csDMARDs), glucocorticoids, and biologic DMARDs, and outcomes such as hospitalization and lung involvement in patients with rheumatic disease with COVID-19. Methods: We performed a cross-sectional study on 177 COVID-19 cases with rheumatologic diseases using immunomodulatory drugs as their regular treatment. All patients were evaluated regarding their initial chest computed tomography (CT) scan, COVID-19 symptoms, and comorbidities. We ran predictive models to find variables associated with chest CT-scan involvement and hospitalization status. Results: CT findings showed lung involvement in 87 patients with chest CT-scan severity score (C-ss) of less than 8 in 59 (33%) and more than 8 in 28 (16%) of our patients. Of all patients, 76 (43%) were hospitalized. Hospitalized patients were significantly older and had more comorbidities (P = 0.02). On multivariate analysis, older age [odds ratio (OR) 1.90, 95% confidence interval (CI) 1.31-3.08] and comorbidity (OR 2.75, 95% CI 1.06-3.66) were significantly associated with higher odds of hospitalization (P = 0.03). On multivariate analysis, older age (OR 1.15, 95% CI 0.94-2.01), pulmonary diseases (OR 2.05, 95% CI 1.18-3.32), and treatment with csDMARDs (OR 1.88, 95% CI 0.37-1.93) were associated with higher C-ss (P = 0.039). Conclusions: This study found that advanced age and comorbidities, similar to the general population, are risk factors for hospitalization in patients with COVID-19 with rheumatic disorders. Administration of csDMARDs, older age, and pulmonary disorders were linked to increased risk of COVID-19 pneumonia in these individuals.

Developing Verb Picture Naming Test for Persian adults and determining its psychometric properties.

There are a few standardized assessment tools in Persian language. The present study was carried out to develop a Verb Picture Naming Test and assessing its psychometric properties. In this cross-sectional study, a total of 230 verbs were selected based on their frequency, familiarity, age of acquisition, visual complexity, name agreement, image agreement, syllable length, transitivity, and compound or simple verbs. To determine content and face validity, 230 pictures of verbs were given to 15 experts, and then 180 final pictures were divided into original and parallel versions. Both versions of the test were performed on 50 healthy adults and 20 patients with Alzheimer's diseases. Results showed that face and content validity of these versions was more than .85 and .98. Intraclass Correlation Coefficient (ICC) for total scores was equal to .98 (p < .001, 95%CI: .97-.99) and .96 (p < .001, 95%CI: .93-.98). Standard Error of Measurement (SEM) and Smallest Detectable Change (SDC) of total scores were equal to .93 and 2.66 in version1 and 1.27 and 3.12 in version 2, respectively. The two versions of Persian Verb Picture Naming Test were found to be valid and reliable, so the clinicians can use it in clinical settings.

A comparison of the performance of Persian speaking children who do and do not stutter on three nonwords repetition tasks.

PURPOSE: The purpose of the study was to examine the performance of Persian speaking children who stutter (CWS) and children who do not stutter (CWNS) on three nonword repetition tasks, while also focusing on which task and scoring method best differentiates the two groups of children. METHOD: Thirty CWS and 30 CWNS between the ages of 5;0 to 6;6 completed three nonword repetition tasks that varied in complexity. Each task was scored using two methods: nonwords correct and phonemes correct. Between-group differences in performance on each task were examined, along with disfluencies for CWS and the task and scoring method that best differentiated the CWS and CWNS. RESULTS: The findings revealed that, across all three nonword repetition tasks, the CWS consistently produced fewer nonwords correct and phonemes correct than the CWNS group at virtually all syllable lengths. The CWS produced more disfluencies on longer nonwords than shorter nonwords in all three nonword repetition tasks. The nonword repetition task with lower wordlikeness and more phonologically complex items best differentiated the two groups of children. Findings further revealed that discriminative accuracy was highest for scoring based on the number of phonemes produced correctly. CONCLUSION: Findings provide further evidence to suggest that CWS may have difficulty with phonological working memory and/or phonological processing.

Wiener model based GMVC design considering sensor noise and delay.

Even though there is a plethora of literature available for assessing linear control loop performance, they cannot be applied to the nonlinear control loops. In this paper, a nonlinear generalized minimum variance (NGMV) controller based on a single input-single output (SISO) Wiener model is proposed. The NGMV controller's performance is used as a benchmark for a class of nonlinear control loops. The advantage of the proposed method is ability of online parameter estimation of the nonlinear model using common recursive least squares (RLS) method. In real-world applications, sensor and measurement tools force noises and extra delay to the control loop which poses limitations on achievable control performance. Hence, the classic control performance assessment techniques, is not attainable anymore. To handle the limitation caused by sensor delay, the k-step ahead prediction method is utilized. Further, the exponential digital filter is used in order to attenuate impact of the measurement noise on the controller. To show the effectiveness of the proposed method, a simulation test on a pH neutralization process is carried out.

A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems.

In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used.