Publications by authors named "S Morteza Hashemi"
Cochlear reimplantation rate, causes, and outcomes: a multicenter study.
Cochlear Implants Int
January 2025
Objective: The current study aimed to evaluate cochlear reimplantation rate, causes, and audiological outcomes in a large group of patients in a multicenter study.
Methods: This retrospective study was conducted on patients with cochlear reimplantation surgeries between 2000 and 2022 in five academic referral centers. The rate and reasons for cochlear reimplantation surgeries were evaluated.
The Effect of Multidimensional Nursing Interventions on the Incidence of Delirium in Patients with COVID-19.
Iran J Nurs Midwifery Res
November 2024
Article Synopsis
- COVID-19 patients showed increased rates of delirium, prompting a study to evaluate the impact of structured nursing interventions on reducing this incidence.
- In a study involving 168 COVID-19 patients, those receiving multidimensional interventions experienced a significantly lower rate of delirium (10.50%) compared to the control group (25.30%).
- Overall, the findings indicate that tailored nursing measures can effectively decrease delirium among COVID-19 patients, although the duration of delirium and hospital stay did not show significant differences between groups.
Genetics plays a significant role in Multiple Sclerosis (MS), with approximately 12.6% of cases occurring in familial form. While previous studies have demonstrated differences in disease progression and MRI findings between familial and sporadic MS, there has been no comparison of cognitive impairment between them.
View Article and Find Full Text PDFBackground And Objectives: Airway fungal infection is a severe clinical problem, especially in patients with compromised immune functions. Here, we examined the distribution and antifungal susceptibility profiles of fungal agents isolated from respiratory tract of symptomatic patients hospitalized in pulmonary units.
Materials And Methods: This descriptive cross-sectional study took place from 2023 to 2024, involving 360 patients.
Importance: Only a small fraction of patients with advanced non-small cell lung cancer (NSCLC) respond to immune checkpoint inhibitor (ICI) treatment. For optimal personalized NSCLC care, it is imperative to identify patients who are most likely to benefit from immunotherapy.
Objective: To develop a supervised deep learning-based ICI response prediction method; evaluate its performance alongside other known predictive biomarkers; and assess its association with clinical outcomes in patients with advanced NSCLC.