Objective To develop a comprehensive assessment tool to evaluate the Quality of Care (QoC) in managing individuals with traumatic spinal cord injuries (TSCI). Method At first, the concepts of QoC for TSCI were identified by conducting a qualitative interview along with re-evaluation of the results of a published scoping review (conceptualization). After operationalization of indicators, they were valued by using the expert panel method. Afterward, the content validity index (CVI) and content validity ratio (CVR) were calculated and served as cut-offs for indicator selection. Then specific questions were developed for each indicator and classified into three categories: pre-hospital, in-hospital, and post-hospital. Data availability of the National Spinal Cord Injury Registry of Iran (NSCIR-IR) was subsequently used to design questions that represent indicators in an assessment tool format. The comprehensiveness of the tool was evaluated using a 4-item Likert scale by the expert panel. Result Twelve experts participated in conceptualization and 11 experts participated in operationalization phase. Overall, 94 concepts for QoC were identified from published scoping review (87 items) and qualitative interviews (7 items). The process of operationalization and indicator selection led to the development of 27 indicators with acceptable content validity. Finally, the assessment tool contained three pre-hospital, twelve in-hospital, nine post-hospital, and three mixed indicators. Ninety-one percent of experts evaluated the entire tool as comprehensive. Conclusion Our study presents a health-related QoC tool that contains a comprehensive set of indicators to assess the QoC for individuals with TSCI. However, this tool should be used in various situations to establish construct validity further.

Gliomas are primary brain lesions involving cerebral structures without well-defined boundaries and constitute the most prevalent central nervous system (CNS) neoplasms. Among gliomas, glioblastoma (GB) is a glioma of the highest grade and is associated with a grim prognosis. We examined how clinical variables and molecular profiles may have affected overall survival (OS) over the past ten years. A retrospective study was conducted at Sina Hospital in Tehran, Iran and examined patients with confirmed glioma diagnoses between 2012 and 2020. We evaluated the correlation between OS in GB patients and sociodemographic as well as clinical factors and molecular profiling based on IDH1, O-6-Methylguanine-DNA Methyltransferase (MGMT), TERTp, and epidermal growth factor receptor (EGFR) amplification (EGFR-amp) status. Kaplan–Meier and multivariate Cox regression models were used to assess patient survival. A total of 178 patients were enrolled in the study. The median OS was 20 months, with a 2-year survival rate of 61.0%. Among the 127 patients with available IDH measurements, 100 (78.7%) exhibited mutated IDH1 (IDH1-mut) tumors. Of the 127 patients with assessed MGMT promoter methylation (MGMTp-met), 89 (70.1%) had MGMT methylated tumors. Mutant TERTp (TERTp-mut) was detected in 20 out of 127 cases (15.7%), while wildtype TERTp (wildtype TERTp-wt) was observed in 107 cases (84.3%). Analyses using multivariable models revealed that age at histological grade (p < 0.0001), adjuvant radiotherapy (p < 0.018), IDH1 status (p < 0.043), and TERT-p status (p < 0.014) were independently associated with OS. Our study demonstrates that patients with higher tumor histological grades who had received adjuvant radiotherapy exhibited IDH1-mut or presented with TERTp-wt experienced improved OS. Besides, an interesting finding showed an association between methylation of MGMTp and TERTp status with tumor location.

During neurosurgical procedures, the neuro-navigation system’s accuracy is affected by the brain shift phenomenon. One popular strategy is to compensate for brain shift using intraoperative ultrasound (iUS) registration with pre-operative magnetic resonance (MR) scans. This requires a satisfactory multimodal image registration method, which is challenging due to the low image quality of ultrasound and the unpredictable nature of brain deformation during surgery. In this paper, we propose an automatic unsupervised end-to-end MR–iUS registration approach named the Dual Discriminator Bayesian Generative Adversarial Network (D²BGAN). The proposed network consists of two discriminators and a generator optimized by a Bayesian loss function to improve the functionality of the generator, and we add a mutual information loss function to the discriminator for similarity measurements. Extensive validation was performed on the RESECT and BITE datasets, where the mean target registration error (mTRE) of MR–iUS registration using D²BGAN was determined to be 0.75 ± 0.3 mm. The D²BGAN illustrated a clear advantage by achieving an 85% improvement in the mTRE over the initial error. Moreover, the results confirmed that the proposed Bayesian loss function, rather than the typical loss function, improved the accuracy of MR–iUS registration by 23%. The improvement in registration accuracy was further enhanced by the preservation of the intensity and anatomical information of the input images.