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Tofersen is a gene-targeted therapy for superoxide dismutase 1 (SOD1)-associated amyotrophic lateral sclerosis (ALS), but neurofilament light chain (NfL) may not fully capture the biological response to treatment. We performed a multicentre retrospective longitudinal study including 24 patients with SOD1-ALS treated with intrathecal tofersen at four Italian referral centres between 2022 and 2025. Cerebrospinal fluid (CSF) and serum biomarkers were assessed at baseline, month 3, month 6, and last available administration using single-molecule array assays to quantify NfL, glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase L1 (UCHL–1), and total Tau. NfL decreased after treatment initiation in both CSF and serum, providing the clearest pharmacodynamic signal. In contrast, CSF GFAP increased progressively over follow-up, while CSF total Tau and UCHL–1 rose mainly at later timepoints; serum GFAP, total Tau, and UCHL–1 also showed increases during follow-up. ALS Functional Rating Scale–Revised trajectories were broadly stable, whereas disease progression rate was lower at last follow-up than at baseline. Greater reductions in CSF NfL were observed in pathogenic versus uncertain SOD1 variants, and early serum NfL and UCHL–1 changes were associated with longer-term changes in disease progression. These findings suggest that longitudinal multi-analyte profiling may refine biological response stratification beyond NfL alone in tofersen-treated SOD1-ALS.

Moyamoya angiopathy (MMA) is a cerebrovascular disease determining chronic progressive steno-occlusion of the supraclinoid internal carotid arteries and their main branches. The pathogenesis of MMA remains largely unknown. Multiple sclerosis (MS) is a chronic, inflammatory, demyelinating disease of the central nervous system characterized by the progressive accumulation of focal demyelinating lesions, whose pathophysiology has been theorized but still incompletely understood. Beyond misdiagnoses due to mimicking features among the two disorders, MS coexisting with MMA have been previously, rarely, reported. Herein, we present two other cases of patients with MMA with a concomitant, previously missed, diagnosis of MS and discuss their overlapping features as a hint for a potentially shared pathophysiology. The finding of typical angiographic features enables MMA diagnosis, yet it does not allow us to rule out other potentially concomitant disorders affecting the CNS. The association may be easily missed if the clinical/neuroradiological picture is not carefully assessed. Cerebral spinal fluid analysis and spine neuroimaging should be suggested in all MMA patients with atypical MRI lesions.

Despite the increasing popularity of flow diverters (FDs) as an endovascular option for intracranial aneurysms, the treatment of complex aneurysms still represents a challenge. Combined strategies using a flow-preservation bypass could be considered in selected cases. In this study, we retrospectively reviewed our series of patients with complex intracranial aneurysms submitted to bypass. From January 2015 to May 2022, 23 patients were selected. We identified 11 cases (47.8%) of MCA, 6 cases (26.1%) of ACA and 6 cases (26.1%) of ICA aneurysms. The mean maximal diameter was 22.73 ± 12.16 mm, 8 were considered as giant, 9 were fusiform, 8 presented intraluminal thrombosis, 10 presented wall calcification, and 18 involved major branches or perforating arteries. Twenty-five bypass procedures were performed in 23 patients (two EC–IC bypasses with radial artery graft, seventeen single- or double-barrel STA–MCA bypasses and six IC–IC bypasses in anterior cerebral arteries). The long-term bypass patency rate was 94.5%, and the total aneurysm exclusion was 95.6%, with a mean follow-up of 28 months. Median KPS values at last follow-up was 90, and a favorable outcome (KPS ≥ 70 and mRS ≤ 2) was obtained in 87% of the cases. The use of bypass techniques represents, in selected cases, a valid therapeutic option in the management of complex anterior circulation aneurysms when a simpler direct approach, including the use of FD, is considered not feasible.

Background Circular RNAs are a class of endogenous RNAs with various functions in eukaryotic cells. Worthy of note, circular RNAs play a critical role in cancer. Currently, nothing is known about their role in head and neck squamous cell carcinoma (HNSCC). The identification of circular RNAs in HNSCC might become useful for diagnostic and therapeutic strategies in HNSCC. Results Using samples from 115 HNSCC patients, we find that circPVT1 is over-expressed in tumors compared to matched non-tumoral tissues, with particular enrichment in patients with TP53 mutations. circPVT1 up- and down-regulation determine, respectively, an increase and a reduction of the malignant phenotype in HNSCC cell lines. We show that circPVT1 expression is transcriptionally enhanced by the mut-p53/YAP/TEAD complex. circPVT1 acts as an oncogene modulating the expression of miR-497-5p and genes involved in the control of cell proliferation. Conclusions This study shows the oncogenic role of circPVT1 in HNSCC, extending current knowledge about the role of circular RNAs in cancer.

The development of probabilistic maps associated with lava flow inundation is essential to assess hazard in open vent volcanoes, especially those that have highly urbanized flanks. In this study we present the new lava flow hazard map linked to the summit eruptions of Mt. Etna, which has been developed using a probabilistic approach that integrates statistical analyses of the volcanological historical data with numerical simulations of lava flows. The statistical analysis of volcanological data (including vent location, duration and lava volumes) about all summit eruptions occurred since 1998 has allowed us both to estimate the spatiotemporal probability of future vent opening and to extract the effusion rate curves for lava flow modelling. Numerical simulations were run using the GPUFLOW model on a 2022 Digital Surface Model derived from optical satellite images. The probabilistic approach has been validated through a back-analysis by calculating the fit between the expected probabilities of inundation and the lava flows actually emplaced during the 2020-2022 period. The obtained map shows a very high probability of inundation of lava flows emitted at vents linked to the South East Crater, according to the observation of the eruptive dynamics in the last decades.

Satellite imagery provides a rich source of information that serves as a comprehensive and synoptic tool for the continuous monitoring of active volcanoes, including those in remote and inaccessible areas. The huge influx of such data requires the development of automated systems for efficient processing and interpretation. Early warning systems, designed to process satellite imagery to identify signs of impending eruptions and monitor eruptive activity in near real-time, are essential for hazard assessment and risk mitigation. Here, we propose a machine learning approach for the automatic classification of pixels in SEVIRI images to detect and characterize the eruptive activity of a volcano. In particular, we exploit a semi-supervised GAN (SGAN) model that retrieves the presence of thermal anomalies, volcanic ash plumes, and meteorological clouds in each SEVIRI pixel, allowing time series plots to be obtained showing the evolution of volcanic activity. The SGAN model was trained and tested using the huge amount of data available on Mount Etna (Italy). Then, it was applied to other volcanoes, specifically, Stromboli (Italy), Tajogaite (Spain), and Nyiragongo (Democratic Republic of the Congo), to assess the model’s ability to generalize. The validation of the model was performed through a visual comparison between the classification results and the corresponding SEVIRI images. Moreover, we evaluate the model performance by calculating three different metrics, namely the precision (correctness of positive predictions), the recall (ability to find all the positive instances), and the F1-score (general model’s accuracy), finding an average accuracy of 0.9. Our approach can be extended to other geostationary satellite data and applied worldwide to characterize volcanic activity, allowing the monitoring of even remote volcanoes that are difficult to reach from the ground.

Between December 2020 and February 2022, the South East Crater of Etna has been the source of numerous eruptions, mostly characterized by the emission of lava fountains, pyroclastic material and short-lasting lava flows. Here we estimate the volume and distribution of the lava deposits by elaborating multi-source satellite imagery. SEVIRI data have been elaborated using CL-HOTSAT to estimate the lava volume emitted during each event and calculate the cumulative volume; Pléiades and WorldView-1 data have been used to derive Digital Surface Models, whose differences provide thickness distributions and hence volumes of the volcanic deposits. We find a good agreement, with the total average lava volume obtained by SEVIRI reaching 73.2 × 106 m3 and the one from optical data amounting to 67.7 × 106 m3. This proves the robustness of both techniques and the accuracy of the volume estimates, which provide important information on the lava flooding history and evolution of the volcano.

The Etna flank eruption that started on 24 December 2018 lasted a few days and involved the opening of an eruptive fissure, accompanied by a seismic swarm and shallow earthquakes, significant SO2 flux release, and by large and widespread ground deformation, especially on the eastern flank of the volcano. Lava fountains and ash plumes from the uppermost eruptive fissure accompanied the opening stage, causing disruption to Catania International Airport, and were followed by a quiet lava effusion within the barren Valle del Bove depression until 27 December. This was the first flank eruption to occur at Etna in the last decade, during which eruptive activity was confined to the summit craters and resulted in lava fountains and lava flow output from the crater rims. In this paper, we used ground and satellite remote sensing techniques to describe the sequence of events, quantify the erupted volumes of lava, gas, and tephra, and assess volcanic hazards.

The rate at which lava is discharged plays a key role in controlling the distance covered by lava flows from eruptive vents. We investigate the available time-averaged discharge rates (TADRs) estimated for recent flank eruptions at Mt. Etna volcano (Italy), in order to define a possible generalized effusion rate trend which is consistent with observed real data. Our analysis indicates a rapid waxing phase in which effusion rate peaks occur for between 0.5 and 29% of the total eruption time, followed by a progressive decrease in the waning phase. Three generalized curves are built by calculating the 25th, 50th and 75th percentiles values associated with the occurrence of effusion peaks, and with the slope variations of descending curves in the waning phase. The obtained curves are used as an input for the GPUFLOW model in order to perform numerical simulations of the lava flows paths on inclined planes, and are compared with those generated by using effusion rate curves with a bell-shaped time-distribution. Our tests show how these characteristic curves could impact single-vent scenarios, as well as short- and long-term hazard maps, with maximum variations of up to 40% for a specific category of eruptive events.