RNAseq Analyses Identify Tumor Necrosis Factor-Mediated Inflammation as a Major Abnormality in ALS Spinal Cord

ALS is a rapidly progressive, devastating neurodegenerative illness of adults that produces disabling weakness and spasticity arising from death of lower and upper motor neurons. No meaningful therapies exist to slow ALS progression, and molecular insights into pathogenesis and progression are sorely needed. In that context, we used high-depth, next generation RNA sequencing (RNAseq, Illumina) to define gene network abnormalities in RNA samples depleted of rRNA and isolated from cervical spinal cord sections of 7 ALS and 8 CTL samples. We aligned \u3e50 million 2X150 bp paired-end sequences/sample to the hg19 human genome and applied three different algorithms (Cuffdiff2, DEseq2, EdgeR) for identification of differentially expressed genes (DEG’s). Ingenuity Pathways Analysis (IPA) and Weighted Gene Co-expression Network Analysis (WGCNA) identified inflammatory processes as significantly elevated in our ALS samples, with tumor necrosis factor (TNF) found to be a major pathway regulator (IPA) and TNFα-induced protein 2 (TNFAIP2) as a major network “hub” gene (WGCNA). Using the oPOSSUM algorithm, we analyzed transcription factors (TF) controlling expression of the nine DEG/hub genes in the ALS samples and identified TF’s involved in inflammation (NFkB, REL, NFkB1) and macrophage function (NR1H2::RXRA heterodimer). Transient expression in human iPSC-derived motor neurons of TNFAIP2 (also a DEG identified by all three algorithms) reduced cell viability and induced caspase 3/7 activation. Using high-density RNAseq, multiple algorithms for DEG identification, and an unsupervised gene co-expression network approach, we identified significant elevation of inflammatory processes in ALS spinal cord with TNF as a major regulatory molecule. Overexpression of the DEG TNFAIP2 in human motor neurons, the population most vulnerable to die in ALS, increased cell death and caspase 3/7 activation. We propose that therapies targeted to reduce inflammatory TNFα signaling may be helpful in ALS patients

A proposed staging system for amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is a neurodegenerative disorder characterized by progressive loss of upper and lower motor neurons, with a median survival of 2–3 years. Although various phenotypic and research diagnostic classification systems exist and several prognostic models have been generated, there is no staging system. Staging criteria for amyotrophic lateral sclerosis would help to provide a universal and objective measure of disease progression with benefits for patient care, resource allocation, research classifications and clinical trial design. We therefore sought to define easily identified clinical milestones that could be shown to occur at specific points in the disease course, reflect disease progression and impact prognosis and treatment. A tertiary referral centre clinical database was analysed, consisting of 1471 patients with amyotrophic lateral sclerosis seen between 1993 and 2007. Milestones were defined as symptom onset (functional involvement by weakness, wasting, spasticity, dysarthria or dysphagia of one central nervous system region defined as bulbar, upper limb, lower limb or diaphragmatic), diagnosis, functional involvement of a second region, functional involvement of a third region, needing gastrostomy and non-invasive ventilation. Milestone timings were standardized as proportions of time elapsed through the disease course using information from patients who had died by dividing time to a milestone by disease duration. Milestones occurred at predictable proportions of the disease course. Diagnosis occurred at 35% through the disease course, involvement of a second region at 38%, a third region at 61%, need for gastrostomy at 77% and need for non-invasive ventilation at 80%. We therefore propose a simple staging system for amyotrophic lateral sclerosis. Stage 1: symptom onset (involvement of first region); Stage 2A: diagnosis; Stage 2B: involvement of second region; Stage 3: involvement of third region; Stage 4A: need for gastrostomy; and Stage 4B: need for non-invasive ventilation. Validation of this staging system will require further studies in other populations, in population registers and in other clinic databases. The standardized times to milestones may well vary between different studies and populations, although the stages themselves and their meanings are likely to remain unchanged

Towards mathematical AI via a model of the content and process of mathematical question and answer dialogues

This paper outlines a strategy for building semantically meaningful representations and carrying out effective reasoning in technical knowledge domains such as mathematics. Our central assertion is that the semi-structured Q and A format, as used on the popular Stack Exchange network of websites, exposes domain knowledge in a form that is already reasonably close to the structured knowledge formats that computers can reason about. The knowledge in question is not only facts - but discursive, dialectical, argument for purposes of proof and pedagogy. We therefore assert that modelling the Q and A process computationally provides a route to domain understanding that is compatible with the day-to-day practices of mathematicians and students. This position is supported by a small case study that analyses one question from Mathoverflow in detail, using concepts from argumentation theory. A programme of future work, including a rigorous evaluation strategy, is then advanced

Plasma Neurofilament Heavy Chain Levels Correlate to Markers of Late Stage Disease Progression and Treatment Response in SOD1(G93A) Mice that Model ALS

Background: Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disorder characterised by progressive degeneration of motor neurons leading to death, typically within 3–5 years of symptom onset. The diagnosis of ALS is largely reliant on clinical assessment and electrophysiological findings. Neither specific investigative tools nor reliable biomarkers are currently available to enable an early diagnosis or monitoring of disease progression, hindering the design of treatment trials. Methodology/Principal Findings: In this study, using the well-established SOD1G93A mouse model of ALS and a new in-house ELISA method, we have validated that plasma neurofilament heavy chain protein (NfH) levels correlate with both functional markers of late stage disease progression and treatment response. We detected a significant increase in plasma levels of phosphorylated NfH during disease progression in SOD1G93A mice from 105 days onwards. Moreover, increased plasma NfH levels correlated with the decline in muscle force, motor unit survival and, more significantly, with the loss of spinal motor neurons in SOD1 mice during this critical period of decline. Importantly, mice treated with the disease modifying compound arimoclomol had lower plasma NfH levels, suggesting plasma NfH levels could be validated as an outcome measure for treatment trials. Conclusions/Significance: These results show that plasma NfH levels closely reflect later stages of disease progression and therapeutic response in the SOD1G93A mouse model of ALS and may potentially be a valuable biomarker of later disease progression in ALS

Zero acceptance number quick switching system for compliance sampling

The zero acceptance number plan is invariably used for compliance sampling and safety inspection of products. The disadvantage of such a plan is that its discriminating power between good and bad lots is poor. This paper presents a quick switching system that has zero acceptance numbers, with a provision for the resubmission of lots not accepted during normal inspection. The proposed system is found to require a smaller average sample size, and possesses greater discriminating power.

Prediction of stroke thrombolysis outcome using CT brain machine learning

A critical decision-step in the emergency treatment of ischemic stroke is whether or not to administer thrombolysis — a treatment that can result in good recovery, or deterioration due to symptomatic intracranial haemorrhage (SICH). Certain imaging features based upon early computerized tomography (CT), in combination with clinical variables, have been found to predict SICH, albeit with modest accuracy. In this proof-of-concept study, we determine whether machine learning of CT images can predict which patients receiving tPA will develop SICH as opposed to showing clinical improvement with no haemorrhage. Clinical records and CT brains of 116 acute ischemic stroke patients treated with intravenous thrombolysis were collected retrospectively (including 16 who developed SICH). The sample was split into training (n = 106) and test sets (n = 10), repeatedly for 1760 different combinations. CT brain images acted as inputs into a support vector machine (SVM), along with clinical severity. Performance of the SVM was compared with established prognostication tools (SEDAN and HAT scores; original, or after adaptation to our cohort). Predictive performance, assessed as area under receiver-operating-characteristic curve (AUC), of the SVM (0.744) compared favourably with that of prognostic scores (original and adapted versions: 0.626–0.720; p < 0.01). The SVM also identified 9 out of 16 SICHs, as opposed to 1–5 using prognostic scores, assuming a 10% SICH frequency (p < 0.001). In summary, machine learning methods applied to acute stroke CT images offer automation, and potentially improved performance, for prediction of SICH following thrombolysis. Larger-scale cohorts, and incorporation of advanced imaging, should be tested with such methods