Presymptomatic Individuals Research Articles

P95. Cortical Amyloid Burden is Associated With Basal Forebrain but Not Medial Temporal Lobe Atrophy in Adults at Varying APOE ɛ4 Risk for Alzheimer’s Disease

Postmortem and in vivo imaging studies have demonstrated atrophy within the basal forebrain cholinergic system (BFCS) in symptomatic Alzheimer’s disease (AD). However, relatively little is known surrounding the relationship between BFCS atrophy and Aβ deposition during preclinical stages of disease. This study explores the relationship between Aβ burden and sub-structural BFCS volume in pre-symptomatic individuals at varying genetic risk for AD.

Mindfulness-Based Stress Reduction in Pre-symptomatic Genetic Frontotemporal Dementia: A Pilot Study.

Pre-symptomatic frontotemporal dementia (FTD) mutation carriers and first-degree family members that are 50% at-risk for FTD may experience symptoms of anxiety and depression as a result of the ambiguity of when or if symptoms of the disease will manifest. We conducted a pilot study to investigate the use of an online mindfulness-based stress reduction (MBSR) course to reduce symptoms of anxiety and depression in presymptomatic frontotemporal dementia (FTD) mutation carriers and individuals 50% at-risk. Seven known mutation carriers and six individuals 50% at-risk completed a standardized 8-week MBSR course, and filled out pre- and post and two-month follow-up questionnaires. The primary outcome measure was the Hospital Anxiety and Depression Scale (HADS). Measures of psychological distress (SCL-90-R), coping style (UCL), quality of life (SF-36) and mindfulness skills (FFMQ) were administered as secondary outcome. Group effects were analyzed with repeated measures ANOVA or Friedman's test, and the individual reliability change index (RCI) was calculated per participant for each outcome measure. Semi-quantitative data included an evaluation and process measure post-intervention. Significant decline was found on the HADS-A post-intervention and after 2 months (p = 0.01), with 54% and 62% of participants demonstrating a clinically significant RCI, respectively. On the HADS-D, significant decline was found 2 months post-intervention (p = 0.04), which was driven by 23% of participants whom had a clinically significant RCI. Additional changes were found between baseline and post-intervention on the seeking distraction and reassuring thoughts subscales of the UCL, the depression and interpersonal sensitivity subscales of the SCL, the observe subscale of the FFMQ, and on physical role limitations of the SF-36 (all p < 0.05). The process evaluation form indicated that the course was found beneficial by participants, and that they applied it in a wide range of everyday situations. This exploratory pilot study indicates the feasibility of MBSR in reducing anxiety and depression in presymptomatic FTD mutation carriers and 50% at-risk individuals. A randomized controlled trial is necessary to replicate these results.

Nervonic Acid Attenuates Accumulation of Very Long-Chain Fatty Acids and is a Potential Therapy for Adrenoleukodystrophy.

Adrenoleukodystrophy (ALD) is an X-linked inherited peroxisomal disorder due to mutations in theALD protein and characterized by accumulation of very long-chain fatty acids (VLCFA), specifically hexacosanoic acid (C26:0). This can trigger other pathological processes such as mitochondrial dysfunction, oxidative stress, and inflammation, which if involves the brain tissues can result in a lethal form of the disease called childhood cerebral ALD. With the recent addition of ALD to the Recommended Uniform Screening Panel, there is an increase in the number of individuals who are identified with ALD. However, currently, there is no approved treatment for pre-symptomatic individuals that can arrest or delay symptom development. Here, we report our observations investigating nervonic acid, a monounsaturated fatty acid as a potential therapy for ALD. Using ALD patient-derived fibroblasts, we examined whether nervonic acid can reverse VLCFA accumulation similar to erucic acid, the active ingredient in Lorenzo's oil, a dietary intervention believed to alter disease course. We have shown that nervonic acid can reverse total lipid C26:0 accumulation in a concentration-dependent manner in ALD cell lines. Further, we show that nervonic acid can protect ALD fibroblasts from oxidative insults, presumably by increasing intracellular ATP production. Thus, nervonic acid can be a potential therapeutic for individuals with ALD, which can alter cellular biochemistry and improve its function.

Can wastewater surveillance assist China to cost-effectively prevent the nationwide outbreak of COVID-19?

China has controlled the nationwide spread of COVID-19 since April 2020, but it is still facing an enormous threat of disease resurgence originating from infected international travelers. Taking the rapid transmission and the mutation of SARS-CoV-2 into consideration, the current status would be easily jeopardized if sporadic locally-transmitted individuals are not identified at an early stage. Clinical diagnosis is the gold standard for COVID-19 surveillance, but it is hard to screen presymptomatic or asymptomatic cases in those who have not exhibited symptoms. Since presymptomatic or asymptomatic individuals are infectious, it is urgent to establish a surveillance system based on other tools that can profile the entire population. Infected people including those who are symptomatic, presymptomatic, and asymptomatic shed SARS-CoV-2 RNA in feces and thereby endow wastewater-based epidemiology (WBE) with an early-warning ability for mass COVID-19 surveillance. In the context of China's “COVID-zero” strategy, this work intends to discuss the practical feasibility of WBE applications as an early warning and disease surveillance system in hopes that WBE together with clinical testing would cost-effectively restrain sporadic COVID-19 outbreaks in China.

CRISPR-based Approaches for the Point-of-Care Diagnosis of COVID19

Severe acute respiratory syndrome coronavirus -2 (SARS-CoV-2), is a novel Betacoronavirus variant that emerged in December 2019 causing the coronavirus disease 19 (COVID19) pandemic. It is reported that asymptomatic and presymptomatic individuals can transmit the virus and this silent transmission has been a major obstacle for the control of the pandemic. To overcome this obstacle, widespread testing with a rapid turnaround time is required. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is currently the golden standard for the diagnosis of COVID19 worldwide. Even though RT-qPCR is an efficient method in terms of sensitivity and specificity, the need for elaborate instrumentation and skilled personnel restricts its widespread use. Restriction of RT-qPCR to a limited number of laboratories makes it further time-consuming. Many approaches are present to address the requirement for a rapid and accurate COVID19 diagnosis. In this review, different CRISPR-based approaches for the point-of-care diagnosis of COVID19 are compared. Among these approaches, CRISPR-FDS on-chip assay is found to be the best option as it is reported to be highly sensitive and specific, has a short turnaround time (15 min), does not need RNA isolation or special tools, and simple to perform. In terms of clinical validation, SHERLOCK, STOPCovid, and DETECTR were the most extensively studied ones and they are also reported to be highly sensitive and specific compared to RT-qPCR.

Plasma p-tau231, p-tau181, PET Biomarkers, and Cognitive Change in Older Adults.

The objective of this study was to evaluate novel plasma p-tau231 and p-tau181, as well as Aβ40 and Aβ42 assays as indicators of tau and Aβ pathologies measured with positron emission tomography (PET), and their association with cognitive change, in cognitively unimpaired older adults. In a cohort of 244 older adults at risk of Alzheimer's disease (AD) owing to a family history of AD dementia, we measured single molecule array (Simoa)-based plasma tau biomarkers (p-tau231 and p-tau181), Aβ40 and Aβ42 with immunoprecipitation mass spectrometry, and Simoa neurofilament light (NfL). A subset of 129 participants underwent amyloid-β (18 F-NAV4694) and tau (18 F-flortaucipir) PET assessments. We investigated plasma biomarker associations with Aβ and tau PET at the global and voxel level and tested plasma biomarker combinations for improved detection of Aβ-PET positivity. We also investigated associations with 8-year cognitive change. Plasma p-tau biomarkers correlated with flortaucipir binding in medial temporal, parietal, and inferior temporal regions. P-tau231 showed further associations in lateral parietal and occipital cortices. Plasma Aβ42/40 explained more variance in global Aβ-PET binding than Aβ42 alone. P-tau231 also showed strong and widespread associations with cortical Aβ-PET binding. Combining Aβ42/40 with p-tau231 or p-tau181 allowed for good distinction between Aβ-negative and -positive participants (area under the receiver operating characteristic curve [AUC] range = 0.81-0.86). Individuals with low plasma Aβ42/40 and high p-tau experienced faster cognitive decline. Plasma p-tau231 showed more robust associations with PET biomarkers than p-tau181 in presymptomatic individuals. The combination of p-tau and Aβ42/40 biomarkers detected early AD pathology and cognitive decline. Such markers could be used as prescreening tools to reduce the cost of prevention trials. ANN NEUROL 2022;91:548-560.

Epidemiology and biology of early onset colorectal cancer.

Colorectal cancer (CRC) is the third leading cause of cancer-related mortality in men or women in the United States. Average-risk screening that begins at age 50 years has reduced incidence and mortality of CRC in those over 50 years of age, whereas CRC incidence in those under age 50 years (early onset colorectal cancer (eoCRC)) has recently and dramatically increased. In this review, we summarize the recent literature including risk factors for eoCRC, differences in clinicopathologic presentation and outcomes in eoCRC, and emerging evidence regarding the molecular pathways that are altered in eoCRC compared to later onset CRC (loCRC). Epidemiologic studies of eoCRC show predominance in distal colon and rectum, and association with several modifiable risk factors, including diabetes, obesity, diet, sedentary time, alcohol consumption and smoking. Data regarding potential risk factors of prior antibiotic exposure and microbiome alterations or direct carcinogen exposure are still emerging. Aggressive clinicopathologic features of eoCRC at presentation may be due to delay in diagnosis or more aggressive tumor biology. EoCRC outcomes are similar to loCRC when matched for stage, but overall mortality is greater due to higher frequency of advanced disease at a younger presentation, with more life-years lost. There are only few molecular evaluations of eoCRC to date, with findings of potential increase in TP53 and CTNNB1 somatic mutation and decrease in APC, KRAS and BRAF somatic mutation, compared to loCRC. Other findings include LINE-1 hypomethylation, absence of microsatellite instability (MSI-H), presence of chromosomal instability (CIN) or microsatellite and chromosomal stability (MACS). These studies are only now emerging and have not yet identified a specific molecular signature defining eoCRC. Further research evaluating genetic and molecular differences as well as environmental triggers for eoCRCs should provide a clearer understanding to inform targeted screening for pre-symptomatic at-risk younger individuals.

Clinical and molecular spectrum associated with Polymerase-γ related disorders.

POLG pathogenic variants are the commonest single-gene cause of inherited mitochondrial disease. However, the data on clinicogenetic associations in POLG-related disorders are sparse. This study maps the clinicogenetic spectrum of POLG-related disorders in the pediatric population. Individuals were recruited across 6 centers in India. Children diagnosed between January 2015 and August 2020 with pathogenic or likely pathogenic POLG variants and age of onset <15 years were eligible. Phenotypically, patients were categorized into Alpers-Huttenlocher syndrome; myocerebrohepatopathy syndrome; myoclonic epilepsy, myopathy, and sensory ataxia; ataxia-neuropathy spectrum; Leigh disease; and autosomal dominant / recessive progressive external ophthalmoplegia. A total of 3729 genetic reports and 4256 hospital records were screened. Twenty-two patients with pathogenic variants were included. Phenotypically, patients were classifiable into Alpers-Huttenlocher syndrome (8/22; 36.4%), progressive external ophthalmoplegia (8/22; 36.4%), Leigh disease (2/22; 9.1%), ataxia-neuropathy spectrum (2/22; 9.1%), and unclassified (2/22; 9.1%). The prominent clinical manifestations included developmental delay (n = 14; 63.7%), neuroregression (n = 14; 63.7%), encephalopathy (n = 11; 50%), epilepsy (n = 11; 50%), ophthalmoplegia (n = 8; 36.4%), and liver dysfunction (n = 8; 36.4%). Forty-four pathogenic variants were identified at 13 loci, and these were clustered at exonuclease (18/44; 40.9%), linker (13/44; 29.5%), polymerase (10/44; 22.7%), and N-terminal domains (3/44; 6.8%). Genotype-phenotype analysis suggested that serious outcomes including neuroregression (odds ratio [OR] 11, 95% CI 2.5, 41), epilepsy (OR 9, 95% CI 2.4, 39), encephalopathy (OR 5.7, 95% CI 1.4, 19), and hepatic dysfunction (OR 4.6, 95% CI 21.3, 15) were associated with at least 1 variant involving linker or polymerase domain. We describe the clinical subgroups and their associations with different POLG domains. These can aid in the development of follow-up and management strategies of presymptomatic individuals.

Prediction of Cognitive Decline for Enrichment of Alzheimer's Disease Clinical Trials.

A key issue to Alzheimer's disease clinical trial failures is poor participant selection. Participants have heterogeneous cognitive trajectories and many do not decline during trials, which reduces a study's power to detect treatment effects. Trials need enrichment strategies to enroll individuals who are more likely to decline. To develop machine learning models to predict cognitive trajectories in participants with early Alzheimer's disease and presymptomatic individuals over 24 and 48 months respectively. Prognostic machine learning models were trained from a combination of demographics, cognitive tests, APOE genotype, and brain imaging data. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), National Alzheimer's Coordinating Center (NACC), Open Access Series of Imaging Studies (OASIS-3), PharmaCog, and a Phase 3 clinical trial in early Alzheimer's disease were used for this study. A total of 2098 participants who had demographics, cognitive tests, APOE genotype, and brain imaging data, as well as follow-up visits for 24-48 months were included. Baseline magnetic resonance imaging, cognitive tests, demographics, and APOE genotype were used to separate decliners, defined as individuals whose CDR-Sum of Boxes scores increased during a predefined time window, from stable individuals. A prognostic model to predict decline at 24 months in early Alzheimer's disease was trained on 1151 individuals who had baseline diagnoses of mild cognitive impairment and Alzheimer's dementia from ADNI and NACC. This model was validated on 115 individuals from a placebo arm of a Phase 3 clinical trial and 76 individuals from the PharmaCog dataset. A second prognostic model to predict decline at 48 months in presymptomatic populations was trained on 628 individuals from ADNI and NACC who were cognitively unimpaired at baseline. This model was validated on 128 individuals from OASIS-3. The models achieved up to 79% area under the curve (cross-validated and out-of-sample). Power analyses showed that using prognostic models to recruit enriched cohorts of predicted decliners can reduce clinical trial sample sizes by as much as 51% while maintaining the same detection power. Prognostic tools for predicting cognitive decline and enriching clinical trials with participants at the highest risk of decline can improve trial quality, derisk endpoint failures, and accelerate therapeutic development in Alzheimer's disease.

Differential tractography as a dynamic imaging biomarker: A methodological pilot study for Huntington’s disease

Huntington’s disease (HD) is a neurodegenerative disorder characterized by motor, psychiatric, and cognitive symptoms. Due to its diverse manifestations, the scientific community has long recognized the need for sensitive, objective, individualized, and dynamic disease assessment tools. We examined the feasibility of Differential Tractography as a biomarker to evaluate correlation of symptom severity and of HD progression at the individual level. Differential tractography is a novel tractography modality that maps pathways with axonal injury characterized by a decrease of anisotropic diffusion pattern. We recruited sixteen patients scanned at 0-, 6-, and 12-month intervals by diffusion MRI scans for differential tractography assessment and correlated its volumetric findings with the Unified Huntington’s Disease Rating Scale (UHDRS). Deterministic fiber tracking algorithm was applied. Longitudinal data was modeled using the generalized estimating equation (GEE) model and correlated with UHDRS scores, in addition to Spearman correlation for cross-sectional data. Our results show that volumes of affected pathways revealed by differential tractography significantly correlated with UHDRS scores in longitudinal data (p-value < 0.001), and chronological changes in differential tractography also correlated with the changes in UHDRS (p-value < 0.001). This technique opens new clinical avenues as a clinical translational tool to evaluate presymptomatic and symptomatic gene positive individuals. Our results provide support that differential tractography has the potential to be used as a dynamic imaging biomarker to assess at the individual level in a non-invasive manner, disease progression in HD. Critically important, differential tractography proves to be a quantitative tool for following degeneration in presymptomatic patients, with potential applications in clinical trials.

MONITORAMENTO DA CARGA VIRAL DE SARS-COV-2 EM ÁGUAS RESIDUAIS NA CIDADE DE GOIÂNIA: EPIDEMIOLOGIA BASEADA EM ESGOTO E UM SISTEMA DE ALERTA PRECOCE PARA COVID-19

SARS-COV-2 VIRAL LOAD IN GOIÂNIA WASTEWATER: WASTEWATER-BASED EPIDEMIOLOGY FOR A COVID-19 EARLY WARNING SYSTEM. COVID-19 is currently the most critical disease in the world. Thus, several tools have been used in the last two years to contain the spread of SARS-CoV-2. In this perspective, Wastewater-Based Epidemiology (WBE) provides valuable data by including symptomatic, pre-symptomatic, and asymptomatic individuals and anticipating clinical cases. For 37 weeks, the SARS-CoV-2 viral load was monitored in sanitary sewage samples from Goiânia-Brazil. Through pre-concentration, extraction, and purification of viral RNA, followed by RT-qPCR, it was detected in 83.78% (31/37) of the weeks the presence of fragments of the genetic material SARS-CoV-2 with viral loads ranging from 105 to 108 genome copies L-1. The viral load in the wastewater samples was related to the clinical data of the municipality, demonstrating its ability to promote an Early Warning System (EWS). In early 2022, it was possible to predict the increase in clinical cases 3 weeks in advance and issue an alert note to the health authorities. Prevalence estimates ranged from 0.003 to 0.5% and showed good agreement with observed values. Thus, monitoring effluents become another essential tool that can help combat the pandemic’s advance, especially in countries with scarce resources.

A method for campus-wide SARS-CoV-2 surveillance at a large public university.

The systematic screening of asymptomatic and pre-symptomatic individuals is a powerful tool for controlling community transmission of infectious disease on college campuses. Faced with a paucity of testing in the beginning of the COVID-19 pandemic, many universities developed molecular diagnostic laboratories focused on SARS-CoV-2 diagnostic testing on campus and in their broader communities. We established the UC Santa Cruz Molecular Diagnostic Lab in early April 2020 and began testing clinical samples just five weeks later. Using a clinically-validated laboratory developed test (LDT) that avoided supply chain constraints, an automated sample pooling and processing workflow, and a custom laboratory information management system (LIMS), we expanded testing from a handful of clinical samples per day to thousands per day with the testing capacity to screen our entire campus population twice per week. In this report we describe the technical, logistical, and regulatory processes that enabled our pop-up lab to scale testing and reporting capacity to thousands of tests per day.

The silent and dangerous inequity around access to COVID-19 testing: A call to action.

The silent and dangerous inequity around access to COVID-19 testing: A call to action.

Contact tracing as a measure to combat COVID-19 and other infectious diseases

BackgroundMost of the mathematical modeling studies on COVID-19 transmission are based on continuous deterministic models that do not consider the characteristics of social networks. MethodsThe effect of contact tracing on mitigating COVID-19, and other infectious diseases in general, is studied in a small-world network. This network has its advantages over the commonly used continuous deterministic mathematical models in that the characteristics of social networks can be properly incorporated. ResultsSimulation results show that for the original strain of SARS-CoV-2, contact tracing can play an important role in reducing and delaying the peak daily new cases. New cases can be reduced by using symptom onset to isolate tracked individuals, but the benefit can be greatly enhanced by testing asymptomatic and presymptomatic individuals on the sixth to eighth day of infection. For the delta variant, or other variants of much higher infectivity, contact tracing alone cannot significantly lower the number of daily new cases but is able to delay the peaks greatly, thus affording more time to explore and implement pharmaceutical interventions. ConclusionsContact tracing can be a very powerful tool to combat COVID-19 caused by the original strain or any variant of SARS-CoV-2. In order to make contact tracing effective, every effort is needed to expand the pool of contact tracing and provide all necessary support to the self-quarantined.

A machine learning enrichment strategy for presymptomatic cohorts in Alzheimer’s disease clinical trials

AbstractBackgroundIt is challenging for Alzheimer’s disease trials to enroll presymptomatic individuals who are likely to decline cognitively. We propose using a multimodal machine learning approach to discriminate between cognitively normal individuals who progressed to mild cognitive impairment (MCI) and those who remained stable over 48 months.MethodPatterns of gray matter distribution, derived from structural MRI, associated with either healthy aging or Alzheimer’s disease were extracted from AIBL [1] (n=401). We processed images from 823 individuals deemed cognitively normal at baseline from NACC [2] (n=433), ADNI [3] (n=260), and OASIS‐3 [4] (n=130) and generated scores representing the spatial similarities between the gray matter distribution of each individual to the patterns produced from AIBL. Gray matter scores, MMSE, CDR‐SB, FAQ, APOE4 status, education, age, and sex were used to train a machine learning prognostic pipeline using the Foresight platform (Perceiv Research Inc.) to distinguish individuals who received a MCI diagnosis within 48 months from baseline (progressors) from those who remained stable. The model was trained and tested with 5‐fold inner‐loop cross‐validation.ResultThe model achieved a mean (± std) AUC of 0.752±0.045, accuracy of 70.72±2.44, sensitivity of 62.51±12.12, and specificity of 71.82±3.87. The predicted progressors experienced a steeper cognitive decline, tended to be older, and contained a higher proportion APOE4 carriers and amyloid positive cases, compared to stable individuals. Of the predicted progressors, 33.3% were true progressors. This represents a nearly two‐fold enrichment of progressors over the prevalence of the entire sample where only 17.7% were true progressors.ConclusionAn automated algorithm can successfully identify presymptomatic individuals with impending cognitive impairment. This tool can enhance trial enrollment by targeting individuals who are at the highest risk of cognitive decline and potentially reduce trial costs by minimizing the need for prohibitively large sample sizes.[1] Australian Imaging Biomarkers and Lifestyle Study of Ageing (aibl.csiro.au).[2] National Alzheimer’s Coordinating Center (naccdata.org). [3] Alzheimer’s Disease Neuroimaging Initiative (adni.loni.usc.edu). [4] Open Access Series of Imaging Studies (oasis‐brains.org).

Predictive model incorporating polygenic risk score for Alzheimer's disease predicts MMSE decline in APOE4 carriers and noncarriers.

Heterogeneity in the progression of cognitive impairment in later life is especially challenging to predict in pre-symptomatic and prodromal AD populations and can negatively impact the efficiency and statistical power of clinical trials. Genetic risk estimation as the earliest measurable contributor to AD is one of the most promising solutions. Polygenic risk scores (PRS) calculated based on multiple common genetic variants identified in large genome-wide association studies (GWAS) take the comprehensive genetic effect into consideration beyond APOE genotype. Here we present the PRS model results comparing prediction for decline among APOE4 carriers and non-carriers among pre-symptomatic individuals. A PRS was calculated using GWAS summary statistics for clinical AD diagnosis. The samples used in modeling were amyloid-PET positive confirmed individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Logistic regression models assessed the association between PRS and MMSE decline covarying for age, sex, education, APOE-ε4, and baseline MMSE score. Feature importance rank was estimated by the absolute value of the corresponding coefficients of all predictors in the model. The PRS model outcompeted the base model for MMSE decline, which included age, sex, and education as predictors, and exhibited the best performance (AUC = 0.82). Additionally, the PRS model revealed the highest classification performance in both APOE4 carriers (AUC = 0.81) and noncarriers (AUC=0.82) of the pre-symptomatic individuals. Meanwhile, the feature importance analysis showed that PRS is ranked as the second most important feature in both subgroups. The proposed model including PRS explains the heterogeneity of cognitive decline above and beyond the APOE4 allele and increases the model performance over traditional methods to predict cognitive decline in individuals at risk for AD. Utilization of additional genomic factors beyond APOE in PRS models could enhance clinical trial recruitment and stratification strategies for trial analyses, such that high polygenic risk APOE3 carriers are selected for probable cognitive decline, in addition to APOE4 carriers.

Nusinersen: A Review in 5q Spinal Muscular Atrophy.

Survival motor neuron 1 (SMN1), located on chromosome 5q, encodes the survival motor neuron (SMN) protein. A deletion or mutation in SMN1 results in a rare neuromuscular disorder: 5q spinal muscular atrophy (SMA). In such patients, SMN protein production relies solely on SMN2. Nusinersen (Spinraza®) is a modified antisense oligonucleotide approved for the treatment of 5q SMA. Administered intrathecally, it modifies SMN2 pre-messenger RNA splicing, thereby increasing full-length SMN protein levels. Interim analyses from an ongoing phase II study suggest substantial clinical benefits with nusinersen initiation in presymptomatic patients. In phase III studies, nusinersen achieved significant and/or clinically relevant improvements in motor function in symptomatic patients with infantile- and later-onset 5q SMA, and significantly improved event-free survival and overall survival in patients with infantile-onset 5q SMA. Longer term (up to a median of ≈ 6 years of available data), motor function was maintained or improved in symptomatic patients. Nusinersen had a favourable safety profile in clinical studies in presymptomatic and symptomatic patients. Real-world experience supports the effectiveness, safety and tolerability of nusinersen in symptomatic patients of all ages. Thus, nusinersen remains an important treatment option among a broad range of 5q SMA patients.

A panel of CSF proteins separates genetic frontotemporal dementia from presymptomatic mutation carriers: a GENFI study

BackgroundA detailed understanding of the pathological processes involved in genetic frontotemporal dementia is critical in order to provide the patients with an optimal future treatment. Protein levels in CSF have the potential to reflect different pathophysiological processes in the brain. We aimed to identify and evaluate panels of CSF proteins with potential to separate symptomatic individuals from individuals without clinical symptoms (unaffected), as well as presymptomatic individuals from mutation non-carriers.MethodsA multiplexed antibody-based suspension bead array was used to analyse levels of 111 proteins in CSF samples from 221 individuals from families with genetic frontotemporal dementia. The data was explored using LASSO and Random forest.ResultsWhen comparing affected individuals with unaffected individuals, 14 proteins were identified as potentially important for the separation. Among these, four were identified as most important, namely neurofilament medium polypeptide (NEFM), neuronal pentraxin 2 (NPTX2), neurosecretory protein VGF (VGF) and aquaporin 4 (AQP4). The combined profile of these four proteins successfully separated the two groups, with higher levels of NEFM and AQP4 and lower levels of NPTX2 in affected compared to unaffected individuals. VGF contributed to the models, but the levels were not significantly lower in affected individuals. Next, when comparing presymptomatic GRN and C9orf72 mutation carriers in proximity to symptom onset with mutation non-carriers, six proteins were identified with a potential to contribute to a separation, including progranulin (GRN).ConclusionIn conclusion, we have identified several proteins with the combined potential to separate affected individuals from unaffected individuals, as well as proteins with potential to contribute to the separation between presymptomatic individuals and mutation non-carriers. Further studies are needed to continue the investigation of these proteins and their potential association to the pathophysiological mechanisms in genetic FTD.

A perspective on SARS-CoV-2 and community transmission in the top COVID-19 affected nations

In most countries, during the initial months of the COVID-19 outbreak resources were directed to mitigation measures that prevented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission from symptomatic individuals. The coverage of the pre-symptomatic and asymptomatic individuals’ testing required intensive clinical sampling along with rigorous symptom-based screening. Based on the SARS-CoV-2 transmission, the disease outbreak across nations was divided into four distinct stages: (i) epidemic, (ii) community transmission, (iii) local transmission, and (iv) imported cases. Here we discuss the COVID-19 community transmission stage for the top ten COVID-19 affected nations. Epidemic dynamics and policies implemented to contain the spread of SARS-CoV-2 infection varied globally. Further, the mitigation strategies and related health policies for dealing with the pandemic were based on the stages of transmission of the disease. We suggest that correctly identifying the transmission stage during a pandemic ensures the implementation of mitigation strategies. Thus prompt analysis of the status of COVID-19 transmission stage(s) in any nation seems crucial to direct health policies accordingly.

Evaluation of sample pooling using the SAMBA II SARS-CoV-2 test

BackgroundScreening of infectious asymptomatic or pre-symptomatic individuals for SARS-CoV-2 is at present a key to controling the COVID-19 pandemic. In order to expand testing capability and limit cost, pool testing of asymtomatic individuals has been proposed, provided assay performance is not significantly affected. MethodsCombined nose and throat (N/T) swabs collected from COVID-19 infected or non-infected individuals were tested using SAMBA II individually and in pools of four (one positive and 3 negative). The evaluation was conducted by the manufacturer and an independent NHS site. Ct cycles of individual positives and pooled positives were determined by qRT-PCR. ResultsIn 42 pools containing a single positive sample with Ct values ranging between 17 and 36, 41 pools (97.6 %) were found positive by the SARS-CoV-2 SAMBA II test. The false-negative pool by SAMBA was also negative by both reference methods used in this evaluation.The individual positive sample in this pool was positive by SAMBA (Orf only) and by one of the reference methods (S gene only, Ct 35) but negative by the second reference method indicating that the sample itself was very low viral load. All 78 pools containing 4 negative swabs were negative (100 % specificity). DiscussionThe preliminary data of the evaluation indicated a high level of performance in both sensitivity and specificity of the SAMBA II assay when used to test pools of 4 patient samples. The implementation of this pooled protocol can increase throughput and reduce cost/test when the prevalence of COVID is low.