The authors analyzed the relationship between patient age and the final outcome in a series of 810 patients aged 14 years or older who were consecutively admitted between 1987 and 1996 after suffering a severe closed head injury. The most relevant clinico-radiological variables were prospectively collected in a Data Bank. Stratified and logistic regression analyses were performed in order to assess the influence of age on adverse outcome and the interaction between patient age and other prognostic indicators. Our results reaffirm that the adverse outcome rate increases steadily with age in severe head injured patients and that age effect on outcome is independent of other prognostic variables. The odds of having an adverse outcome increases significantly over 35 years of age being 10 times higher in patients older than 65 years as compared to those aged 15-25 years (reference age group). The adverse influence of an advanced age on the final outcome has not yet been satisfactorily explained but an older brain may have an impaired ability to recover after a pathological insult as compared to a younger one.

SummaryReperfusion injury remains one of the major problems in transplantation. Repair from ischaemic acute renal failure (ARF) involves stimulation of tubular epithelial cell proliferation. The aim of this exploratory study was to evaluate the effects of preconditioning donor animals with rapamycin and tacrolimus to prevent ischaemia-reperfusion (I/R) injury. Twelve hours before nephrectomy, the donor animals received immunosuppressive drugs. The animals were divided into four groups, as follows: group 1 control: no treatment; group 2: rapamycin (2 mg/kg); group 3 FK506 (0, 3 mg/kg); and group 4: FK506 (0, 3 mg/kg) plus rapamycin (2 mg/kg). The left kidney was removed and after 3 h of cold ischaemia, the graft was transplanted. Twentyfour hours after transplant, the kidney was recovered for histological analysis and cytokine expression. Preconditioning treatment with rapamycin or tacrolimus significantly reduced blood urea nitrogen and creatinine compared with control [blood urea nitrogen (BUN): P < 0·001 versus control and creatinine: P < 0·001 versus control]. A further decrease was observed when rapamycin was combined with tacrolimus. Acute tubular necrosis was decreased significantly in donors treated with immunosuppressants compared with the control group (P < 0·001 versus control). Moreover, the number of apoptotic nuclei in the control group was higher compared with the treated groups (P < 0·001 versus control). Surprisingly, only rapamycin preconditioning treatment increased anti-apoptotic Bcl2 levels (P < 0·001). Finally, inflammatory cytokines, such as tumour necrosis factor (TNF)-a and interleukin (IL)-6, showed lower levels in the graft of those animals that had been pretreated with rapamycin or tacrolimus. This exploratory study demonstrates that preconditioning donor animals with rapamycin or tacrolimus improves clinical outcomes and reduce necrosis and apoptosis in kidney I/R injury.

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Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection? A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15,720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (< 6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the test data set, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the test data set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761. In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.

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