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Background Intracranial meningiomas(IM) are often associated with peritumoral brain edema(PTBE), visible as hyperintensities on T2/FLAIR MRI. Postoperative persisting PTBE-like changes likely represent gliosis that, in turn, contributes to surgical morbidity. Since the human eye is unable to distinguish between PTBE and gliosis on MR images, we hypothesized that radiomic analysis of preoperative peritumoral T2/FLAIR hyperintensities could distinguish preoperatively established gliosis from reversible edema. Methods MRI of patients with gross totally resected IM were retrospectively analyzed. Preoperative and 1-year postoperative PTBE were segmented on MRI. One-year MRI were classified into two categories based on whether PTBE resolution exceeded 80% of the initial volume. RF were extracted from meningioma and PTBE regions on T1-contrast-enhanced, T2, and FLAIR MRI sequences. The dataset was split into training, validation, and test cohorts(70–10-20%). Feature reduction used correlation-based exclusion and recursive feature elimination with cross-validation. Nine ML algorithms were trained and evaluated, and best model’s interpretability assessed using Shapley Additive Explanations(SHAP). Results 644 RF were extracted per individual from the pre and postoperative MRI of 123 operated patients. The Random Forest model utilizing 10 RF achieved the best performance (accuracy:0.91;precision:0.92;F1-score:0.92;ROC-AUC:0.94), demonstrating radiomics’ utility in predicting PTBE resolution at 1-year post-surgery. SHAP analysis provided interpretability, highlighting key RF, differences between patient groups, and potential sources of algorithmic error. Conclusions These results underscore the potential of radiomics and ML to accurately predict postoperative PTBE resolution, differentiating transient PTBE from persistent PTBE-like changes (gliosis). This study provides initial insights into the potential of advanced imaging and computational techniques for non-invasive preoperative assessment, which may contribute to more personalized surgical strategies.

Spontaneous intracranial hemorrhages have a high disease burden. Due to increasing medical imaging, new technological solutions for assisting in image interpretation are warranted. We developed a deep learning (DL) solution for spontaneous intracranial hemorrhage detection from head CT scans. The DL solution included four base convolutional neural networks (CNNs), which were trained using 300 head CT scans. A metamodel was trained on top of the four base CNNs, and simple post processing steps were applied to improve the solution’s accuracy. The solution performance was evaluated using a retrospective dataset of consecutive emergency head CTs imaged in ten different emergency rooms. 7797 head CT scans were included in the validation dataset and 118 CT scans presented with spontaneous intracranial hemorrhage. The trained metamodel together with a simple rule-based post-processing step showed 89.8% sensitivity and 89.5% specificity for hemorrhage detection at the case-level. The solution detected all 78 spontaneous hemorrhage cases imaged presumably or confirmedly within 12 h from the symptom onset and identified five hemorrhages missed in the initial on-call reports. Although the success of DL algorithms depends on multiple factors, including training data versatility and quality of annotations, using the proposed ensemble-learning approach and rule-based post-processing may help clinicians to develop highly accurate DL solutions for clinical imaging diagnostics.

Ischemic heart disease remains the leading cause of mortality and morbidity worldwide despite improved possibilities in medical care. Alongside interventional therapies, such as coronary artery bypass grafting, adjuvant tissue-engineered and cell-based treatments can provide regenerative improvement. Unfortunately, most of these advanced approaches require multiple lengthy and costly preparation stages without delivering significant clinical benefits. We evaluated the effect of epicardially delivered minute pieces of atrial appendage tissue material, defined as atrial appendage micrografts (AAMs), in mouse myocardial infarction model. An extracellular matrix patch was used to cover and fix the AAMs onto the surface of the infarcted heart. The matrix-covered AAMs salvaged the heart from infarction-induced loss of functional myocardium and attenuated scarring. Site-selective proteomics of injured ischemic and uninjured distal myocardium from AAM-treated and untreated tissue sections revealed an increased expression of several cardiac regeneration-associated proteins (i.e. periostin, transglutaminases and glutathione peroxidases) as well as activation of pathways responsible for angio- and cardiogenesis in relation to AAMs therapy. Epicardial delivery of AAMs encased in an extracellular matrix patch scaffold salvages functional cardiac tissue from ischemic injury and restricts fibrosis after myocardial infarction. Our results support the use of AAMs as tissue-based therapy adjuvants for salvaging the ischemic myocardium. Footnotes * The figures are updated from the dynamite plunger plot style to scatter plots to reveal the true dispersion of the data points around the means. In addition, certain parameters, especially in Figure 5, are calculated as pooled means, compared to the previous absolute measurement points. This brings few minor changes to the statistical differences and p-values. Manuscript text has undergone few updates in order to make the equations in Discussion part more intuitively comprehensible.

Encephalopsin belongs to the family of extraretinal opsins having a putative role in CNS tissue photosensitivity. Encephalopsin mRNA has earlier been localized in rodent brains, but expression and localization of the protein has not yet been reported. In this study, we aimed to define encephalopsin protein abundance and localization in the rodent brain. The distribution and localization of encephalopsin protein in a mouse brain and selected peripheral tissues were analysed in ten mice, using Western blotting and immunohistochemistry. The specificity of immunoreaction was validated by primary antibody omitting and immunizing peptide blocking experiment. We found encephalopsin protein abundant in the mouse brain, but not in the periphery. Encephalopsin protein was present in neurons of the mouse cerebral cortex, paraventricular area, and cerebellar cells. Our results show that encephalopsin is expressed at the protein level in different brain areas of the mouse. Therefore, the suggested idea that encephalopsin plays a role in non-visual photic processes seems to be applicable. Evidently, further investigations are needed to find out the signalling mechanisms, and the potential physiological role of encephalopsin in phototransduction due to the changes in ambient light.

Background Bright light treatment is effective for seasonal affective disorder (SAD), although the mechanisms of action are still unknown. We investigated whether transcranial bright light via the ear canals has an antidepressant effect in the treatment of SAD. Methods During the four-week study period, 89 patients (67 females; 22 males, aged 22-65, mean ± SD age: 43.2 ± 10.9 years) suffering from SAD were randomized to receive a 12-min daily dose of photic energy of one of three intensities (1 lumen/0.72 mW/cm 2 ; 4 lumens/2.881 mW/cm 2 ; 9 lumens/6.482 mW/cm 2 ) via the ear canals. The light was produced using light-emitting diodes. The severity of depressive symptoms was assessed with the Hamilton Depression Rating Scale – Seasonal Affective Disorder (SIGH-SAD), the Hamilton Anxiety Rating Scale (HAMA), and the Beck Depression Inventory (BDI). Cognitive performance was measured by the Trail Making Test (TMT). The within-group and between-group changes in these variables throughout the study were analysed with a repeated measures analysis of variance (ANOVA), whereas gender differences at baseline within the light groups were analysed using Student’s t-tests. Results Patients in all three groups showed significant decreases in their BDI, HAMA, and SIGH-SAD scores. Response rates, i.e., an at least 50% decrease of symptoms as measured by the BDI, were 74%-79% in the three treatment groups. Corresponding variations for the SIGH-SAD and the HAMA were 35-45% and 47-62%, respectively. No intensity-based dose-response relationships in the improvement of anxiety and depressive symptoms or cognitive performance between treatment groups were observed. Approximately one in four patients experienced mild adverse effects, of which the most common were headache, insomnia, and nausea. Conclusions These results suggests that transcranial bright light treatment may have antidepressant and anxiolytic effect in SAD patients, as both self- and psychiatrist-rated depressive and anxiety symptoms decreased in all treatment groups. These improvements are comparable to findings of earlier bright light studies that used conventional devices. The lack of dose response may be due to a saturation effect above a certain light intensity threshold. Further studies on the effects of transcranial bright light with an adequate placebo condition are needed. Trial registration NCT01293409 , ClinicalTrials.gov