We report the initial results from a phase I clinical trial for ALS. We transplanted GMP-grade, fetal human neural stem cells from natural in utero death (hNSCs) into the anterior horns of the spinal cord to test for the safety of both cells and neurosurgical procedures in these patients. The trial was approved by the Istituto Superiore di Sanità and the competent Ethics Committees and was monitored by an external Safety Board. Six non-ambulatory patients were treated. Three of them received 3 unilateral hNSCs microinjections into the lumbar cord tract, while the remaining ones received bilateral (n = 3 + 3) microinjections. None manifested severe adverse events related to the treatment, even though nearly 5 times more cells were injected in the patients receiving bilateral implants and a much milder immune-suppression regimen was used as compared to previous trials. No increase of disease progression due to the treatment was observed for up to18 months after surgery. Rather, two patients showed a transitory improvement of the subscore ambulation on the ALS-FRS-R scale (from 1 to 2). A third patient showed improvement of the MRC score for tibialis anterior, which persisted for as long as 7 months. The latter and two additional patients refused PEG and invasive ventilation and died 8 months after surgery due to the progression of respiratory failure. The autopsies confirmed that this was related to the evolution of the disease. We describe a safe cell therapy approach that will allow for the treatment of larger pools of patients for later-phase ALS clinical trials, while warranting good reproducibility. These can now be carried out under more standardized conditions, based on a more homogenous repertoire of clinical grade hNSCs. The use of brain tissue from natural miscarriages eliminates the ethical concerns that may arise from the use of fetal material. EudraCT:2009-014484-39 .

Of 725 consecutive hospitalized patients with coronavirus disease 2019, 108 (15%) had acute neurologic symptoms necessitating neurologic imaging.

Background Lower muscle mass is a known predictor of unfavorable outcome, but its prognostic impact on COVID-19 patients is unknown. Purpose To investigate the contribution of CT-derived muscle sta...

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.

Whole body magnetic resonance imaging (MRI) with diffusion-weighted imaging (WB-MRI-DWI) is currently emerging as a diagnostic technique in the evaluation of bone metastases from breast, prostate, lung, thyroid, and melanoma tumors. The most relevant articles regarding the detection of solid tumor bone metastases with MRI have been reviewed and cited. The imaging methods currently used in the detection of bone metastases are bone scintigraphy, computed tomography (CT), and positron emission tomography (PET/CT) with 2-deoxy-2-[fluorine-18] fluoro-d-glucose (18F-FDG PET/CT). WB-MRI-DWI allows qualitative and quantitative evaluation of focal lesions through signal intensity evaluation on DWI images and the reconstruction of the apparent diffusion coefficient (ADC) map. In prostate and breast cancer, WB-MRI-DWI is useful in assessing the response of bone lesions to therapy and to detecting early non-responders, while in lung cancer the method shows a similar sensitivity to 18F-FDG PET/CT in the detection of bone metastases. In bone metastases of thyroid tumors and melanoma, the WB-MRI-DWI shows a higher sensitivity when compared to 18F-FDG PET/CT. With a standardization of the WB-MRI-DWI protocol, this method seems to play an important role in the diagnosis of bone solid tumor metastases.