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Few studies have focused on elderly (>80 years) amyotrophic lateral sclerosis (ALS) patients, who represent a fragile subgroup generally not included in clinical trials and often neglected because they are more difficult to diagnose and manage. We analyzed the clinical and genetic features of very late-onset ALS patients through a prospective, population-based study in the Emilia Romagna Region of Italy. From 2009 to 2019, 222 (13.76%) out of 1613 patients in incident cases were over 80 years old at diagnosis, with a female predominance (F:M = 1.18). Elderly ALS patients represented 12.02% of patients before 2015 and 15.91% from 2015 onwards (p = 0.024). This group presented with bulbar onset in 38.29% of cases and had worse clinical conditions at diagnosis compared to younger patients, with a lower average BMI (23.12 vs. 24.57 Kg/m2), a higher progression rate (1.43 vs. 0.95 points/month), and a shorter length of survival (a median of 20.77 vs. 36 months). For this subgroup, genetic analyses have seldom been carried out (25% vs. 39.11%) and are generally negative. Finally, elderly patients underwent less frequent nutritional- and respiratory-supporting procedures, and multidisciplinary teams were less involved at follow-up, except for specialist palliative care. The genotypic and phenotypic features of elderly ALS patients could help identify the different environmental and genetic risk factors that determine the age at which disease onset occurs. Since multidisciplinary management can improve a patient’s prognosis, it should be more extensively applied to this fragile group of patients.

Summary: The definition of consciousness has been the subject of great interest for many scientists and philosophers. To better understand how evoked potentials may be identified as biomarkers of consciousness and recovery, the different theoretical models sustaining neural correlates of consciousness are reviewed. A multimodal approach can help to better predict clinical outcome in patients presenting with disorders of consciousness. Evoked potentials are inexpensive and easy-to-implement bedside examination techniques. Evoked potentials are an integral part of prognostic evaluation, particularly in cases of cognitive motor dissociation. Prognostic criteria are well established in postanoxic disorders of consciousness, especially postcardiac arrest but are less well determined in other etiologies. In the early examination, bilateral absence of N20 in disorder of consciousness patients is strongly associated with unfavorable outcome (i.e., death or unresponsive wakefulness syndrome) especially in postanoxic etiologies. This predictive value is lower in other etiologies and probably also in children. Both N20 and mismatch negativity are proven outcome predictors for acute coma. Many studies have shown that mismatch negativity and P3a are characterized by a high prognostic value for awakening, but some patients presenting unresponsive wakefulness syndrome also process a P3a. The presence of long-latency event-related potential components in response to stimuli is indicative of a better recovery. All neurophysiological data must be integrated within a multimodal approach combining repeated clinical evaluation, neuroimaging, functional imaging, biology, and neurophysiology combining passive and active paradigms.

Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are recognized as part of a disease continuum (FTD-ALS spectrum), in which the most common genetic cause is chromosome 9 open reading frame 72 (C9ORF72) gene hexanucleotide repeat expansion. The clinical phenotype of patients carrying this expansion varies widely and includes diseases beyond the FTD-ALS spectrum. Although a few cases of patients with C9ORF72 expansion and a clinical or biomarker-supported diagnosis of Alzheimer’s disease (AD) have been described, they have been considered too sparse to establish a definite association between the C9ORF72 expansion and AD pathology. Here, we describe a C9ORF72 family with pleomorphic phenotypical expressions: a 54-year-old woman showing cognitive impairment and behavioral disturbances with both neuroimaging and cerebrospinal fluid (CSF) biomarkers consistent with AD pathology, her 49-year-old brother with typical FTD-ALS, and their 63-year-old mother with the behavioral variant of FTD and CSF biomarkers suggestive of AD pathology. The young onset of disease in all three family members and their different phenotypes and biomarker profiles make the simple co-occurrence of different diseases an extremely unlikely explanation. Our report adds to previous findings and may contribute to further expanding the spectrum of diseases associated with C9ORF72 expansion.

Amyotrophic lateral sclerosis is the most common form of motor neuron disease. Mutations in TARDBP, the gene encoding the RNA-binding protein TDP-43, are responsible for about 5% of familial ALS. Here we report the clinical and biological features of an ALS patients with pA382T mutation in TPD-43 protein. Disease began with right hand muscles weakness, and equally involved upper and lower motor neuron with a classic phenotype, without cognitive impairment. While a family history of neurological diseases was reported, there was no evidence of familial frontotemporal dementia. Cultured fibroblasts from the patient were characterized by profound alterations of cell proteome, which impacts particularly the mitochondrial metabolic pathways and the endoplasmic reticulum. TDP-43 levels were similar to control, healthy fibroblasts, but a higher fraction localized in mitochondria. Mitochondrial network appeared fragmented, and the organelles smaller and more spheric. In agreement with impaired proteome and morphology of mitochondria, basal cell respiration was reduced. Mitochondrial DNA levels appeared normal. However, a higher amount of mitochondrial DNA was present in the cytosol, suggesting a pronounced mitochondrial DNA misplacement which can promote a pro-inflammatory response mediating by cGAS/STING. Thus, this case report further expands the clinical and pathological phenotype of A382T mutation.

Mutations in FUS gene have been described classically in young ALS patients with aggressive disease course. Here we report a large family carrying a missense mutation c.1520 GA in FUS gene with a tight association with an atypical FUS-ALS phenotype. A 60-year-old man with unilateral leg involvement at onset showed very slow disease progression with selective posterior legs atrophy, tracing his aunt's disease history. His father and uncle died for ALS after a long disease course. Another patient with a 14 years history of ALS with the same phenotype, was found to belong to the same family. In all cases, genetic analysis of FUS gene revealed a missense mutation c.1520 GA (p.G507D) inherited with a heterozygous pattern. Co-segregation of p.G507D mutation and a specific disease phenotype within the family, characterised by predominant involvement at the lower limbs, slow progression, late bulbar and respiratory failure, demonstrates pathogenicity of this mutation, establishes a well-defined genotype-phenotype correlation and expands the clinical spectrum of heterogeneity in FUS-ALS.

Amyotrophic lateral sclerosis (ALS) is known to represent a collection of overlapping syndromes. Various classification systems based on empirical observations have been proposed, but it is unclear to what extent they reflect ALS population substructures. We aimed to use machine-learning techniques to identify the number and nature of ALS subtypes to obtain a better understanding of this heterogeneity, enhance our understanding of the disease, and improve clinical care.In this retrospective study, we applied unsupervised Uniform Manifold Approximation and Projection [UMAP]) modelling, semi-supervised (neural network UMAP) modelling, and supervised (ensemble learning based on LightGBM) modelling to a population-based discovery cohort of patients who were diagnosed with ALS while living in the Piedmont and Valle d'Aosta regions of Italy, for whom detailed clinical data, such as age at symptom onset, were available. We excluded patients with missing Revised ALS Functional Rating Scale (ALSFRS-R) feature values from the unsupervised and semi-supervised steps. We replicated our findings in an independent population-based cohort of patients who were diagnosed with ALS while living in the Emilia Romagna region of Italy.Between Jan 1, 1995, and Dec 31, 2015, 2858 patients were entered in the discovery cohort. After excluding 497 (17%) patients with missing ALSFRS-R feature values, data for 42 clinical features across 2361 (83%) patients were available for the unsupervised and semi-supervised analysis. We found that semi-supervised machine learning produced the optimum clustering of the patients with ALS. These clusters roughly corresponded to the six clinical subtypes defined by the Chiò classification system (ie, bulbar, respiratory, flail arm, classical, pyramidal, and flail leg ALS). Between Jan 1, 2009, and March 1, 2018, 1097 patients were entered in the replication cohort. After excluding 108 (10%) patients with missing ALSFRS-R feature values, data for 42 clinical features across 989 patients were available for the unsupervised and semi-supervised analysis. All 1097 patients were included in the supervised analysis. The same clusters were identified in the replication cohort. By contrast, other ALS classification schemes, such as the El Escorial categories, Milano-Torino clinical staging, and King's clinical stages, did not adequately label the clusters. Supervised learning identified 11 clinical parameters that predicted ALS clinical subtypes with high accuracy (area under the curve 0·982 [95% CI 0·980-0·983]).Our data-driven study provides insight into the ALS population substructure and confirms that the Chiò classification system successfully identifies ALS subtypes. Additional validation is required to determine the accuracy and clinical use of these algorithms in assigning clinical subtypes. Nevertheless, our algorithms offer a broad insight into the clinical heterogeneity of ALS and help to determine the actual subtypes of disease that exist within this fatal neurodegenerative syndrome. The systematic identification of ALS subtypes will improve clinical care and clinical trial design.US National Institute on Aging, US National Institutes of Health, Italian Ministry of Health, European Commission, University of Torino Rita Levi Montalcini Department of Neurosciences, Emilia Romagna Regional Health Authority, and Italian Ministry of Education, University, and Research.For the Italian and German translations of the abstract see Supplementary Materials section.

ALS symptoms have been previously described only in the context of ATXN2 CAG expansions, whereas missense mutations of the gene have never been described in ALS patients.We identified a novel missense mutation (c.2860C T) of ATXN2, for which in silico analysis showed a possible pathogenic effect on protein expression, in a patient presenting an aggressive disease phenotype.Our findings raise the possibility for unknown genetic factors interacting with ATXN2 mutations, or for an autonomous pathogenic role for this specific point mutation in ATXN2 gene in driving the clinical phenotype toward ALS. We also found that stress granules in the fibroblasts from the patient entrapped higher amounts of defective ribosomal products compared to fibroblasts from three healthy subjects, suggesting that ATXN2 mutation-related toxicity may have implication in protein quality control.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disorder with an unknown etiology and no effective treatment, and is characterized by large phenotypic heterogeneity, including variable sites, ages of symptom onset and rates of disease progression. Increasing data support the role of the microbiota-immunity axis in the pathogenesis of neurodegenerative diseases. In the present study, we compared the inflammatory and microbiota profile of ALS patients with different clinical characteristics, with healthy family caregivers. Measuring a panel of 30 inflammatory cytokines in serum and fecal samples, we observed a distinct cytokine profile both at the systemic and intestinal level in patients compared to controls and even in patients with different clinical phenotypes and progression rates. The 16S targeted metagenome analysis revealed slight differences in patients compared to controls as well as in patients with slow progression, marked by the reduction of butyrate-producing bacteria and a decrease of the Firmicutes/Bacteroidetes ratio in ALS. Finally, the short chain fatty acid analysis did not show a different distribution among the groups. If confirmed in a larger number of patients, the inflammatory cytokine profile and the microbial composition could be appropriate biomarker candidates for deciphering ALS heterogeneity.

SUMMARYBackgroundThe disease entity known as amyotrophic lateral sclerosis (ALS) is now known to represent a collection of overlapping syndromes. A better understanding of this heterogeneity and the ability to distinguish ALS subtypes would improve the clinical care of patients and enhance our understanding of the disease. Subtype profiles could be incorporated into the clinical trial design to improve our ability to detect a therapeutic effect. A variety of classification systems have been proposed over the years based on empirical observations, but it is unclear to what extent they genuinely reflect ALS population substructure.MethodsWe applied machine learning algorithms to a prospective, population-based cohort consisting of 2,858 Italian patients diagnosed with ALS for whom detailed clinical phenotype data were available. We replicated our findings in an independent population-based cohort of 1,097 Italian ALS patients.FindingsWe found that semi-supervised machine learning based on UMAP applied to the output of a multi-layered perceptron neural network produced the optimum clustering of the ALS patients in the discovery cohort. These clusters roughly corresponded to the six clinical subtypes defined by the Chiò classification system (bulbar ALS, respiratory ALS, flail arm ALS, classical ALS, pyramidal ALS, and flail leg ALS). The same clusters were identified in the replication cohort. A supervised learning approach based on ensemble learning identified twelve clinical parameters that predicted ALS clinical subtype with high accuracy (area under the curve = 0·94).InterpretationOur data-driven study provides insight into the ALS population’s substructure and demonstrates that the Chiò classification system robustly identifies ALS subtypes. We provide an interactive website (https://share.streamlit.io/anant-dadu/machinelearningforals/main) so that clinical researchers can predict the clinical subtype of an ALS patient based on a small number of clinical parameters.FundingNational Institute on Aging and the Italian Ministry of Health.RESEARCH IN CONTEXTEvidence before this studyWe searched PubMed for articles published in English from database inception until January 5, 2021, about the use of machine learning and the identification of clinical subtypes within the amyotrophic lateral sclerosis (ALS) population, using the search terms “machine learning”, AND “classification”, AND “amyotrophic lateral sclerosis”. This inquiry identified twenty-nine studies. Most previous studies used machine learning to diagnose ALS (based on gait, imaging, electromyography, gene expression, proteomic, and metabolomic data) or improve brain-computer interfaces. One study used machine learning algorithms to stratify ALS postmortem cortex samples into molecular subtypes based on transcriptome data. Kueffner and colleagues crowdsourced the development of machine learning algorithms to approximately thirty teams to obtain a consensus in an attempt to identify ALS patients subpopulation. In addition to clinical trial information in the PRO-ACT database (www.ALSdatabase.org), this effort used data from the Piedmont and Valle d’Aosta Registry for ALS (PARALS). Four ALS patient categories were identified: slow progressing, fast progressing, early stage, and late stage. This approach’s clinical relevance was unclear, as all ALS patients will necessarily pass through an early and late stage of the disease.Furthermore, no attempt was made to discern which of the existing clinical classification systems, such as the El Escorial criteria, the Chiò classification system, and the King’s clinical staging system, can identify ALS subtypes. We concluded that there remained an unmet need to identify the ALS population’s substructure in a data-driven, non-empirical manner. Building on this, there was a need for a tool that reliably predicts the clinical subtype of an ALS patient. This knowledge would improve our understanding of the clinical heterogeneity associated with this fatal neurodegenerative disease.Added value of this studyThis study developed a machine learning algorithm to detect ALS patients’ clinical subtypes using clinical data collected from the 2,858 Italian ALS patients in PARALS. Ascertainment of these patients within the catchment area was near complete, meaning that the dataset truly represented the ALS population. We replicated our approach using clinical data obtained from an independent cohort of 1,097 Italian ALS patients that had also been collected in a population-based, longitudinal manner. Semi-supervised learning based on Uniform Manifold Approximation and Projection (UMAP) applied to a multilayer perceptron neural network provided the optimum results based on visual inspection. The observed clusters equated to the six clinical subtypes previously defined by the Chiò classification system (bulbar ALS, respiratory ALS, flail arm ALS, classical ALS, pyramidal ALS, and flail leg ALS). Using a small number of clinical parameters, an ensemble learning approach could predict the ALS clinical subtype with high accuracy (area under the curve = 0·94).Implications of all available evidenceAdditional validation is required to determine these algorithms’ accuracy and clinical utility in assigning clinical subtypes. Nevertheless, our algorithms offer a broad insight into the clinical heterogeneity of ALS and help to determine the actual subtypes of disease that exist within this fatal neurodegenerative syndrome. The systematic identification of ALS subtypes will improve clinical care and clinical trial design.

Background—Amyotrophic lateral sclerosis (ALS) is a devastating and untreatable motor neuron disease, smoking and alcohol drinking may impact its progression rate. Objective—To ascertain the influence of smoking and alcohol consumption on ALS progression rates. Methods—Cross-sectional multicenter study, including 241 consecutive patients (145 males), mean age at onset was 59.9 ± 11.8 years. Cigarette smoking and alcohol consumption data were collected at recruitment through a validated questionnaire. Patients were categorized into three groups according to ΔFS (derived from the ALS Functional Rating Scale-Revised and disease duration from onset): slow (n = 81), intermediate (80), and fast progressors (80). Results—Current smokers accounted for 44 (18.3%) of the participants, former smokers accounted for 10 (4.1%), and non-smokers accounted for 187 (77.6%). The age of ALS onset was lower in current smokers than non-smokers, and the ΔFS was slightly, although not significantly, higher for smokers of &gt, 14 cigarettes/day. Current alcohol drinkers accounted for 147 (61.0%) of the participants, former drinkers accounted for 5 (2.1%), and non-drinkers accounted for 89 (36.9%). The log(ΔFS) was weakly correlated only with the duration of alcohol consumption (p = 0.028), but not with the mean number of drinks/day or the drink-years. Conclusions: This cross-sectional multicenter study suggested a possible minor role for smoking in worsening disease progression. A possible interaction with alcohol drinking was suggested.

Introduction: To date more than 180 different mutations in the SOD1 gene have been described in ALS; some of these mutations are associated to peculiar clinical features and have contributed to the...

The retail sector, under the pressure of digitalization and technological innovation, has experienced profound changes in the last decade, and retailers have had to cope with these changes by implementing new business models and competitive strategies with the aim of satisfying the consumers’ needs. In the last few decades, the sector has been affected by different new trends, from the birth of supermarkets to the advent of e-commerce, up to the introduction of cashierless stores. The latter represents a new category of store that is totally computer-based and digitalized, in which the use of cameras, sensors and self-shelves minimizes human interaction. Amazon pioneered this emerging concept, with the launch of Amazon Go, but other start-up companies are rapidly entering the cashierless retail market and embracing the challenge. The purpose of this paper is to analyze the knowledge of Italian consumers of cashierless shops, and the relevance of different factors related to this new kind of shops. A questionnaire was sent to a sample of more than 1000 consumers to identify and evaluate the actual situation and knowledge of this phenomenon, which is not yet diffuse in Italy. A statistical analysis, regarding both their knowledge about cashierless stores and the customer experience, is provided to discuss the most relevant factors affecting the customers’ perceptions and attitudes, with a comparison per gender and type of users. The results of the provided analysis reveal that the phenomenon is very little known, and this is certainly influenced by the lack of these stores in Italy.

BACKGROUND AND PURPOSE Literature data on spinal and bulbar muscular atrophy (SBMA) epidemiology are limited and restricted to specific populations. The aim of our study was to accurately collect information about SBMA patients living in the Veneto region in Italy to compute reliable epidemiological data. Androgen receptor (AR) lineages were genotyped to evaluate the presence of a founder effect. METHODS A prevalence survey considering all SBMA patients diagnosed in the Italian Veneto region on 31 January 2018 was carried out. The presence of different haplotypes obtained genotyping 15 polymorphic markers (single nucleotide polymorphisms and short tandem repeats) around the AR gene was evaluated. RESULTS Based on 68 patients, the punctual prevalence of the disease on 31 January 2018 was 2.58/100 000 (95% confidence interval 1.65-3.35) in the male population. Five different haplotypes were identified, confirming the existence of multiple founder effects. It was also observed that, within the same haplotype, patients had a similar CAG repeat number (P-value

Motor neuron diseases (MNDs) are etiologically and biologically heterogeneous diseases. The pathobiology of motor neuron degeneration is still largely unknown, and no effective therapy is available. Heterogeneity and lack of specific disease biomarkers have been appointed as leading reasons for past clinical trial failure, and biomarker discovery is pivotal in today’s MND research agenda.In the last decade, neurofilaments (NFs) have emerged as promising biomarkers for the clinical assessment of neurodegeneration. NFs are scaffolding proteins with predominant structural functions contributing to the axonal cytoskeleton of myelinated axons. NFs are released in CSF and peripheral blood as a consequence of axonal degeneration, irrespective of the primary causal event. Due to the current availability of highly-sensitive automated technologies capable of precisely quantify proteins in biofluids in the femtomolar range, it is now possible to reliably measure NFs not only in CSF but also in blood.In this review, we will discuss how NFs are impacting research and clinical management in ALS and other MNDs. Besides contributing to the diagnosis at early stages by differentiating between MNDs with different clinical evolution and severity, NFs may provide a useful tool for the early enrolment of patients in clinical trials. Due to their stability across the disease, NFs convey prognostic information and, on a larger scale, help to stratify patients in homogenous groups. Shortcomings of NFs assessment in biofluids will also be discussed according to the available literature in the attempt to predict the most appropriate use of the biomarker in the MND clinic.

The extent of central nervous system involvement in Kennedy's disease (KD) relative to other motor neuron disease (MND) phenotypes still needs to be clarified. In this study, we investigated cortical and white matter (WM) MRI alterations in 25 patients with KD, compared with 24 healthy subjects, 25 patients with sporadic amyotrophic lateral sclerosis (ALS), and 35 cases with lower motor neuron-predominant disease (LMND). LMND patients were clinically differentiated into 24 fast and 11 slow progressors. Whole-brain cortical thickness, WM tract-based spatial statistics and corticospinal tract (CST) tractography analyses were performed. No significant difference in terms of cortical thickness was found between groups. ALS patients showed widespread decreased fractional anisotropy and increased mean (MD) and radial diffusivity (radD) in the CST, corpus callosum and fronto-temporal extra-motor tracts, compared with healthy controls and other patient groups. CST tractography showed significant alterations of DT MRI metrics in ALS and LMND-fast patients whereas KD and LMND-slow patients were comparable with healthy controls. Our study demonstrated the absence of WM abnormalities in patients with KD and LMND-slow, in contrast with diffuse WM damage in ALS and focal CST degeneration in LMND-fast, supporting the use of DT MRI measures as powerful tools to differentiate fast- and slow-progressing MND syndromes, including KD.

The Spinal and Bulbar Muscular Atrophy Functional Rating Scale (SBMAFRS) is an established rating instrument used to assess the functional status of patients with Spinal and Bulbar Muscular Atrophy (SBMA). Our aim was to validate an Italian version of the scale. We administered the SBMAFRS to sixty SBMA patients during routine follow-up of clinical evaluations. To estimate the test stability, the scale was re-administered to a subset of 39 randomly selected patients after 8 weeks. The patients underwent clinical evaluation including 6-min walk. Psychometric analysis included reliability assessment and factorial analysis. To evaluate convergent validity, correlations between SBMAFRS items and muscular force assessed by manual testing, ALSFRS total score and subscales scores, and forced vital capacity, were performed. Internal consistency as measured by Cronbach's alpha (total scale 0.85) was high. Test-retest reliability assessed by Spearman's rho was also high. Principal component analysis with varimax rotation yielded a four-factor solution accounting for approximately 79 % of the variance. The scale total score and subscales score were strongly correlated with respective items and subscores of the ALSFRS, with respiratory function and with the 6-min walk test. In conclusion, we performed an Italian validation of the only existing disease-specific Functional Rating Scale for SBMA patients. This scale will be a useful tool not only in the clinical practice but also as an outcome measure in upcoming clinical trials.

Hirayama disease is a rare neurological disease affecting primarily men in the second to third decades. To date there are only few reports from Italy. We report the case of three young basketball players who presented with clinical, electrophysiological and MRI findings suggestive for Hirayama disease. Although the pathophysiology of the disease is still unknown, several hypotheses have been suggested and two of these are the disproportionate growth of cervical spine and cervical cord/roots during adolescence and the chronic traumatism. We think that, in our cases, the height of basketball players combined with the constant stress caused by the sport, could have contributed to the development of the Hirayama disease. With this report we would stress the importance to be careful to consider this pathology in order to avoid misdiagnosis predictive of poor prognosis in young patients.

Objective To test efficacy and tolerability of edaravone in patients with amyotrophic lateral sclerosis (ALS) originating from North-Eastern Italy. Methods We compared 3-month and 6-month changes of ALSFRS-R score, FVC value, and MRC score of 31 consecutive patients with ALS who were treated with edaravone to those of 50 historical ALS patients who were not treated with edaravone. Results No significant difference for any functional measures was found between the two groups at each time point as compared to baseline. In treated patients, we also observed creatinine values to significantly decrease at 3 and 6 months (p = 0.0078 and 0.030, respectively) and ALSAQ5 score to significantly increase (i.e. worse quality of life) at 3 and 6 months (p = 0.0005 and 0.0078, respectively). Yet, we observed an overall safety of the medication over the 6-month period of observation. Conclusions Our retrospective study suggests no benefit of edaravone on ALS in populations of Caucasian ancestry.

Summary Background Spinal and bulbar muscular atrophy is an X-linked neuromuscular disease caused by CAG repeat expansion in the androgen receptor gene. Patients with this disease have low concentrations of insulin-like growth factor-1 (IGF-1), and studies of overexpression and administration of IGF-1 showed benefit in a transgenic model; thus the IGF-1 pathway presents as a potential treatment target. We assessed safety, tolerability, and preliminary efficacy of BVS857, an IGF-1 mimetic, in patients with spinal and bulbar muscular atrophy. Methods In this randomised, double-blind, placebo-controlled trial, we recruited patients from neuromuscular centres in Denmark (Copenhagen), Germany (Ulm), Italy (Padova), and three sites within the USA (Bethesda, MD; Irvine, CA; and Columbus, OH). Eligible patients were 18 years or older with a confirmed genetic diagnosis of spinal and bulbar muscular atrophy, were ambulatory, had symptomatic weakness, and had serum IGF-1 concentrations of 170 ng/mL or lower. Patients were randomly assigned (2:1) to study drug or placebo by a number scheme. Patients, investigators, and study personnel were masked to treatment assignment. After a safety and tolerability assessment with eight patients, BVS857 was administered once a week (0·06 mg/kg intravenously) for 12 weeks. Primary outcome measures were safety, tolerability, and the effects of BVS857 on thigh muscle volume (TMV) measured by MRI. The ratio of TMV at day 85 to baseline was analysed with ANCOVA per protocol. Secondary outcomes of muscle strength and function were measured with the Adult Myopathy Assessment Tool, lean body mass through dual energy x-ray absorptiometry, and BVS857 pharmacokinetics. This trial was registered with ClinicalTrials.gov , NCT02024932 . Findings 31 patients were assessed for eligibility, 27 of whom were randomly assigned to either BVS857 treatment (n=18) or placebo (n=9), and 24 were included in the preliminary efficacy analysis (BVS857 group, n=15; placebo group, n=9). BVS857 was generally safe with no serious adverse events. No significant differences were found in adverse events between the BVS857 and placebo groups. Immunogenicity was detected in 13 (72%) of 18 patients in the BVS857 group, including crossreacting antibodies with neutralising capacity to endogenous IGF-1 in five patients. TMV decreased from baseline to day 85 in the placebo group (–3·4% [–110 cm3]) but not in the BVS857 group (0% [2 cm3]). A significant difference in change in TMV was observed in the BVS857 group versus the placebo group (geometric-mean ratio 1·04 [90% CI 1·01–1·07]; p=0·02). There were no differences between groups in measures of muscle strength and function. Interpretation TMV remained stable in patients with spinal and bulbar muscular atrophy after being given BVS857 for 12 weeks. The intervention was associated with high incidence of immunogenicity and did not improve muscle strength or function. Additional studies might be needed to assess the efficacy of activating the IGF-1 pathway in this disease. Funding Novartis Pharmaceuticals and the US National Institutes of Health.

Background Multimodal neurophysiological markers support clinical assessments in individuals with disorders of consciousness (DoC). Few studies focused on subarachnoid haemorrhage (SAH) related coma. Furthermore, these patients often need sedation during the acute phase, making behavioral and neurophysiological monitoring challenging. We investigated which neurophysiological marker could better predict outcome in acute DoC related to SAH. Methods We studied 14 SAH patients admitted in the Sainte Anne hospital neurological ICU, between Oct 2017 and June 2018. All patients underwent within the first 72 h: clinical evaluation, EEG and multimodal evoked potentials (somatosensory potentials, brainstem auditory evoked potentials (BAEPs), late latency event-related potentials, N100 and mismatch negativity (MMN)). Quantitative EEG analysis using spectral and connectivity analysis was performed. According to the Glascow Outcome scale (GOS), we defined two clinical outcome groups: favorable (F) and unfavorable (UF). Results Clinical examination, EEG visual and quantitative analysis, presence of N20, BAEP, N100 and MMN were not significantly different in the two groups. However, mean latencies of N100 (F = 80 ms; UF = 106 ms;) and of MMN (F = 139 ms; UF = 197msec) were significantly different in the two groups (Mann Whitney test, respectively p = 0.017 and p = 0.035). Conclusions This study suggests that during the acute phase, in sedated patients with DoC related to SAH, latencies of N100 and latencies of MMN could be more discriminatory than the “classical” interpretation of these markers (present/absent) and the EEG analysis, in term of prognosis. These results need to be confirmed in a larger study.

Phosphodiesterases (PDEs) regulate the intracellular levels of cAMP and cGMP. The great clinical success of the PDE5 inhibitors, Sildenafil (Viagra), Vardenafil (Levitra) and Tadalafil (Cialis) has led to an increasing interest for this class of enzymes. Recent studies have shown a correlation between tumor growth and PDE5 overexpression, making PDE5-selective inhibitors promising candidates for cancer treatment. The search for such inhibitors rests today on radioactive assays. In this work, we exploit the conserved catalytic domain of the enzyme and propose a faster and safer method for detecting the binding of ligands and evaluate their affinities. The new approach takes advantage of Forster Resonance Energy Transfer (FRET) between, as the donor, a fluorescein-like diarsenical probe able to covalently bind a tetracysteine motif fused to the recombinant PDE5 catalytic domain and, as the acceptor, a rhodamine probe covalently bound to the pseudosubstrate cGMPS. The FRET efficiency decreases when a competitive ligand binds the PDE5 catalytic site and displaces the cGMPS-rhodamine conjugate. We have structurally investigated the PDE5/cGMPS-rhodamine complex by molecular modelling and have used the FRET signal to quantitatively characterize its binding equilibrium. Competitive displacement experiments were carried out with tadalafil and cGMPS. An adaptation of the competitive-displacement equilibrium model yielded the affinities for PDE5 of the incoming ligands, nano- and micromolar, respectively.