Machine learning-based models to predict the need for neurosurgical intervention after moderate traumatic brain injury.

Background and Aims: Traumatic brain injury (TBI) is a widespread global health issue with significant economic consequences. However, no existing model exists to predict the need for neurosurgical intervention in moderate TBI patients with positive initial computed tomography scans. This study determines the efficacy of machine learning (ML)-based models in predicting the need for neurosurgical intervention. Methods: This is a retrospective study of patients admitted to the neuro-intensive care unit of Emtiaz Hospital, Shiraz, Iran, between January 2018 and December 2020. The most clinically important variables from patients that met our inclusion and exclusion criteria were collected and used as predictors. We developed models using multilayer perceptron, random forest, support vector machines (SVM), and logistic regression. To evaluate the models, their F1-score, sensitivity, specificity, and accuracy were assessed using a fourfold cross-validation method. Results: Based on predictive models, SVM showed the highest performance in predicting the need for neurosurgical intervention, with an F1-score of 0.83, an area under curve of 0.93, sensitivity of 0.82, specificity of 0.84, a positive predictive value of 0.83, and a negative predictive value of 0.83. Conclusion: The use of ML-based models as decision-making tools can be effective in predicting with high accuracy whether neurosurgery will be necessary after moderate TBIs. These models may ultimately be used as decision-support tools to evaluate early intervention in TBI patients.

Mapping of dust source susceptibility by remote sensing and machine learning techniques (case study: Iran-Iraq border).

A dust storm is a major environmental problem affecting many arid regions worldwide. The novel contribution of this study is combining indicators extracted from RS- and statistic-based predictive models to spatial mapping of land susceptibility to dust emissions in a very important dust source area in the borders of Iran and Iraq (Khuzestan province in Iran and Al-Basrah and Maysan provinces in Iraq). In this research, remote sensing (RS) techniques and machine learning techniques, including multivariate adaptive regression spline (MARS), random forest (RF), and logistic regression (LR), were used for dust source identification and susceptibility map preparation. To this end, 152 DSA for the period of 2005-2020 were identified in the study area. Of these DSA data, 70% was assigned to the Dust Source Susceptibility Mapping (DSSM) (training dataset) and 30% to model validation. Consequently, six factors (i.e., soil, lithology, slope, normalized vegetation differential index (NDVI), geomorphology, and land use units) were prepared as DSA's independent and effective variables. The results of all three models indicated that land use had the most impact on DSA. The validation results of these models using the test data showed sub-curves of 0.92, 0.86, and 0.76 for the RF, MARS, and LR models, respectively. Also, results showed that the RF model outperformed MARS (AUC = 0.89) and LR (AUC = 0.78) methods. In all three models, high and very high susceptibility classes generally covered a large percentage of the case study. The highest percentage of dust source points was also in this susceptibility category. Overall, the results of this study can be useful for planners and managers to control and reduce the risk of negative dust consequences.

Comparison of hearing outcomes in stapedotomy with fat and Hyaluronic acid gel as a sealing material: a prospective double-blind randomized clinical trial.

PURPOSE: In the literature on stapes surgery, various materials have been used to seal the vestibulotomy. To date, there are only a few published randomized clinical trials with focus on hearing outcomes, using different sealing materials. Hence, the aim of this study was to compare hearing outcomes when using fat or Hyaluronic acid gel (HAG) to seal the stapedotomy. METHODS: The present double-blind, prospective, randomized clinical trial was conducted on ears undergoing stapedotomy in Dasthgheib Hospital, a referral otology center in Southern Iran, and Dena private hospital, Shiraz Iran. A total of 150 primary stapedotomies were evaluated, and sealing material was fat in 77 ears and HAG in 73. RESULTS: 60 (77.9%) of the fat group ears and 63 (86.3%) of the HAG group ears obtained postoperative air-bone gap (ABG) within 20 dB, but the difference was not significant (p = 0.182). CONCLUSION: As a sealing material in stapedotomy, HAG is comparable with fat in terms of hearing outcomes. Therefore, HAG is recommended as a safe sealing material in stapedotomy.

Elevated Levels of T-helper 17-associated Cytokines in Diabetes Type I Patients: Indicators for Following the Course of Disease.

BACKGROUND: Type 1 diabetes (T1D) is thought to involve chronic inflammation, which is manifested by the activation and expression of different inflammatory mediators. Th1- and Th17-associated cytokines are factors that have been shown to exert profound pro-inflammatory activities and have been implicated in the pathogenesis of T1D in mice and humans. OBJECTIVES: Therefore, the aim of this case control study was to determine the serum level of IL-17, IL-21, IL-27, transforming growth factor beta (TGF-ß), and IFN-γ and their reciprocal relationship in Iranian T1D patients. PATIENTS AND METHODS: Blood samples were collected from 48 T1D patients and 49 healthy individuals with no history of malignancies or autoimmune disorders based on simple sampling. The serum levels of IL-17, IL-21, IL-27, TGF-ß, and IFN-γ were measured by the enzyme linked immunosorbent assay (ELISA). RESULTS: The serum levels of IL-17 and IL-21 were significantly higher in T1D patients compared to the healthy individuals (p = 0.005 and 0.01, respectively), but interestingly, the opposite was the case for IL-27 (p < 0.0001). However, there were no significant differences in TGF-ß and IFN-γ between both groups. In addition, IL-17/IFN-γ and IL-17/IL-27 ratios were higher in patients compared to the control group. CONCLUSIONS: Our results indicated dominant Th17-associated IL-17, suggesting a shift from the Treg and Th1 phenotypes toward the Th17 phenotype. Therefore, it can promote inflammation in ß cells in T1D. In addition, it suggests the role of Th17 and Th17/Th1 ratios as a potential contributor to ß cells destruction and the Th17/Th1 response ratio may provide a novel biomarker for rapid T1D diagnosis before the destruction of ß cells and progression of the disease to the clinical end stages.