Ca2+ binding shifts dimeric dual oxidase's truncated EF-hand domain to monomer.

Hydrogen peroxide, produced by Dual Oxidase (Duox), is essential for thyroid hormone synthesis. Duox activation involves Ca2+ binding to its EF-hand Domain (EFD), which contains two EF-hands (EFs). In this study, we characterized a truncated EFD using spectrometry, calorimetry, electrophoretic mobility, and gel filtration to obtain its Ca2+ binding thermodynamic and kinetics, as well as to assess the associated conformational changes. Our results revealed that its 2nd EF-hand (EF2) exhibits a strong exothermic Ca2+ binding (Ka = 107 M-1) while EF1 shows a weaker binding (Ka = 105 M-1), resulting in the burial of its negatively charged residues. The Ca2+ binding to EFD results in a stable structure with a melting temperature shifting from 67 to 99 °C and induces a structural transition from a dimeric to monomeric form. EF2 appears to play a role in dimer formation in its apo form, while the hydrophobic exposure of Ca2+-bound-EF1 is crucial for dimer formation in its holo form. The result is consistent with structures obtained from Cryo-EM, indicating that a stable structure of EFD with hydrophobic patches upon Ca2+ binding is vital for its Duox's domain-domain interaction for electron transfer.

Effect of low fibrinogen level on in-hospital mortality and 6-month functional outcome of TBI patients, a single center experience.

In patients affected by traumatic brain injury (TBI), hypofibrinogenemia within the initial hours of trauma can be expected due to vascular and inflammatory changes. In this study, we aimed to evaluate the effect of hypofibrinogenemia on the in-hospital mortality and 6-month functional outcomes of TBI patients, admitted to Rajaee Hospital, a referral trauma center in Shiraz, Iran. This study included all TBI patients admitted to our center who had no prior history of coagulopathy or any systemic disease, were alive on arrival, and had not received any blood product before admission. On admission, hospitalization, imaging, and 6-month follow-up information of included patients were extracted from the TBI registry database. The baseline characteristics of patients with fibrinogen levels of less than 150 mg/dL were compared with the cases with higher levels. To assess the effect of low fibrinogen levels on in-hospital mortality, a uni- and multivariate was conducted between those who died in hospital and survivors. Based on the 6-month GOSE score of patients, those with GOSE < 4 (unfavorable outcome) were compared with those with a favorable outcome. A total of 3049 patients (84.3% male, 15.7% female), with a mean age of 39.25 ± 18.87, met the eligibility criteria of this study. 494 patients had fibrinogen levels < 150 mg/dl, who were mostly younger and had lower average GCS scores in comparison to cases with higher fibrinogen levels. By comparison of the patients who died during hospitalization and survivors, it was shown that fibrinogen < 150 mg/dl is among the prognostic factors for in-hospital mortality (OR:1.75, CI: 1.32:2.34, P-value < 0.001), while the comparison between patients with the favorable and unfavorable functional outcome at 6-month follow-up, was not in favor of prognostic effect of low fibrinogen level (OR: 0.80, CI: 0.58: 1.11, P-value: 0.19). Hypofibrinogenemia is associated with in-hospital mortality of TBI patients, along with known factors such as higher age and lower initial GCS score. However, it is not among the prognostic factors of midterm functional outcome.

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.