bluuebunny/crossref_metadata_2025 huggingface dataset:
Antibody response patterns in COVID-19 patients with different levels of disease severity—Japan
Authors: Imai Kazuo , Kitagawa Yutaro , Tabata Sakiko , Kubota Katsumi , Nagura-Ikeda Mayu , Matsuoka Masaru , Miyoshi Kazuyasu , Sakai Jun , Ishibashi Noriomi , Tarumoto Norihito , Takeuchi Shinichi , Ito Toshimitsu , Maesaki Shigefumi , Tamura Kaku , Maeda Takuya
Published: November 2020

Background

We analyzed antibody response patterns according to level of disease severity in patients with novel coronavirus disease 2019 (COVID-19) in Japan.

Methods

We analyzed 611 serum specimens from 231 patients with COVID-19 (mild, 170; severe, 31; critical, 30). IgM and IgG antibodies against nucleocapsid protein (N) and spike 1 protein (S1) were detected by enzyme-linked immunosorbent assays.

Findings

The peaks of fitting curves for the OD values of IgM and IgG antibodies against N appeared simultaneously, while those against S1 were delayed compared with N. The OD values of IgM against N and IgG against both N and S1 were significantly higher in the severe and critical cases than in the mild cases at 11 days after symptom onset. The seroconversion rates of IgG were higher than those of IgM against both N and S1 during the clinical course based on the optimal cut-off values defined in this study. The seroconversion rates of IgG and IgM against N and S1 were higher in the severe and critical cases than in the mild cases.

Conclusion

Our findings show that a stronger antibody response occurred in COVID-19 patients with greater disease severity and there were low seroconversion rates of antibodies against N and S1 in the mild cases. The antibody response patterns in our population suggest a second infection pattern, leading us to hypothesize that cross-reactivity occurs between SARS-CoV-2 and past infection with other human coronaviruses.
Persisting antibody response to SARS-CoV-2 in a local Austrian population
Authors: Ladage Dennis , Rösgen Delia , Schreiner Clemens , Ladage Dorothee , Adler Christoph , Harzer Oliver , Braun Ralf J.
Published: November 2020

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic. The prevalence and persistence of antibodies following a peak SARS-CoV-2 infection provides insights into the potential for some level of population immunity. In June 2020 we succeeded in testing almost half of the population of an Austrian township with a higher incidence for COVID-19 infections. Now we performed a follow-up study to reassess the prevalence of SARS-CoV-2-specific IgA and IgG antibodies. In 121 people, including 68 participants of the previous study we found the prevalence of IgG and IgA antibodies remaining remarkably stable with 84% of our cohort prevailing SARS-CoV-2-specific antibodies, which is only a slight decrease from 93% four months before. Most patients with confirmed COVID-19 seroconvert, potentially providing immunity to reinfection. Our results suggest a stable antibody response that we observed for at least six months post infection with implications for developing strategies for testing and protecting the population.
A Data Driven Change-point Epidemic Model for Assessing the Impact of Large Gathering and Subsequent Movement Control Order on COVID-19 Spread in Malaysia
Authors: Dass Sarat C. , Kwok Wai M. , Gibson Gavin J. , Gill Balvinder S. , Sundram Bala M. , Singh Sarbhan
Published: November 2020

The second wave of COVID-19 in Malaysia is largely attributed to a mass gathering held in Sri Petaling between February 27, 2020 and March 1, 2020, which contributed to an exponential rise of COVID-19 cases in the country. Starting March 18, 2020, the Malaysian government introduced four consecutive phases of a Movement Control Order (MCO) to stem the spread of COVID-19. The MCO was implemented through various non-pharmaceutical interventions (NPIs). The reported number of cases reached its peak by the first week of April and then started to reduce, hence proving the effectiveness of the MCO. To gain a quantitative understanding of the effect of MCO on the dynamics of COVID-19, this paper develops a class of mathematical models to capture the disease spread before and after MCO implementation in Malaysia. A heterogeneous variant of the Susceptible-Exposed-Infected-Recovered (SEIR) model is developed with additional compartments for asymptomatic transmission. Further, a change-point is incorporated to model the before and after disease dynamics, and is inferred based on data. Related statistical analyses for inference are developed in a Bayesian framework and are able to provide quantitative assessments of (1) the impact of the Sri Petaling gathering, and (2) the extent of decreasing transmission during the MCO period. The analysis here also quantitatively demonstrates how quickly transmission rates fall under effective NPI implemention within a short time period.
Clinical judgement of General Practitioners for the diagnosis of dementia: a diagnostic test accuracy study
Authors: Creavin Samuel Thomas , Haworth Judy , Fish Mark , Cullum Sarah , Bayer Antony , Purdy Sarah , Ben-Shlomo Yoav
Published: November 2020

Background

The accuracy of General Practitioners’ (GPs’) clinical judgement for dementia is uncertain.

Aim

Investigate the accuracy of GPs’ clinical judgement for the diagnosis of dementia.

Design and Setting

Diagnostic test accuracy study, recruiting from 21 practices around Bristol.

Method

The clinical judgement of the treating GP (index test) was based on the information immediately available at their initial consultation with a person aged over 70 years who had cognitive symptoms. The reference standard was an assessment by a specialist clinician, based on a standardised clinical examination and made according to ICD-10 criteria for dementia.

Results

240 people were recruited, with a median age of 80 years (IQR 75 to 84 years), of whom 126 (53%) were men and 132 (55%) had dementia. The median duration of symptoms was 24 months (IQR 12 to 36 months) and the median ACE-III score was 75 (IQR 65 to 87). GP clinical judgement had sensitivity 56% (95% CI 47% to 65%) and specificity 89% (95% CI 81% to 94%). Positive likelihood ratio was higher in people aged 70-79 years (6.5, 95% CI 2.9 to 15) compared to people aged ≥ 80 years (3.6, 95% CI 1.7 to 7.6), and in women (10.4, 95% CI 3.4 to 31.7) compared to men (3.2, 95% CI 1.7 to 6.2), whereas the negative likelihood ratio was similar in all groups.

Conclusion

A GP clinical judgement of dementia is specific, but confirmatory testing is needed for symptomatic people who GPs judge as not having dementia.

How this fits in

Previous studies in this area have investigated the accuracy of GP clinical judgement as a screening test for dementia in unselected people attending a primary care clinic; or as a retrospective test based on their knowledge of their patient; or derived the accuracy of judgement from the medical records, which may not reflect the judgement of the clinician. The role of the GP in supporting a more effective route to diagnosis for people with dementia is a research priority for patients, carers and clinicians. This study shows that, in a symptomatic older adult, prospective clinical judgement may be useful for helping to confirm a diagnosis of dementia, whereas GP judgement should not by itself be used to exclude dementia.
ANTIBODY RESPONSE TO COVID-19 INFECTION- CLINICAL VARIABLES AT PLAY
Authors: Parkash Anuj , Singla Parul , Bhatia Meenu
Published: November 2020

Background

The current COVID19 pandemic began in December 2019 and rapidly expanded to become a global pandemic. The COVID 19 presents multitude of clinical disorders, ranges from asymptomatic infection to severe disease, which can accompanied by multisystem failure leading to death. The immune response to SARS CoV 2 is understood to involve all the components of the system that together causes viral elimination and recovery from the infection. However, such immune responses implicated in the disease has varied presentation ranging from mild to a severe form, which appears to hinge on the loss of the immune regulation between protective and altered responses. In this study, we want to unravel this association of immune responses to various clinical variables, which might have a major role to play, while generating the immune response. The objective was to test this hypothesis in our settings and comparing the results of serologic tests from a group of COVID 19 patients and will analyzed the disease severity in comparison.

Methods

Testing for SARS COV2 IgG Antibody was done with chemiluminescent assay on the Ortho Clinical Diagnostic’s (OCD) Vitros 5600 platform.

Results

A total of 106 COVID 19 patients were included in this study, of whom 61 were male and 45 were female. Their mean age was 43.7 years (range 17–83) and the median interval between initial symptom onset and sample collection was 12.33 days. Eighty patients (82%) had mild or moderate symptoms and twenty-six patients (18%) had severe symptoms. The antibody titers were positive in 99 patients (93%) and were found negative in 7 patients (7%). When comparing patients with mild/moderate symptoms and patients with severe/critical diseases, no statistically significant difference was observed between their gender ratios (P = 0.373) and age composition (P = 0.224).

Conclusions

The data presented in this research study did not find any statistical significance between SARS CoV 2 IgG antibody levels with COVID 19 disease severity, duration of symptoms, age, gender, and length of convalescence.
Quantifying the potential value of antigen-detection rapid diagnostic tests for COVID-19: a modelling analysis
Authors: Ricks Saskia , Kendall Emily A. , Dowdy David W. , Sacks Jilian A. , Schumacher Samuel G. , Arinaminpathy Nimalan
Published: November 2020

Background

Testing plays a critical role in treatment and prevention responses to the COVID-19 pandemic. Compared to nucleic acid tests (NATs), antigen-detection rapid diagnostic tests (Ag-RDTs) can be more accessible, but typically have lower sensitivity and specificity. By quantifying these trade-offs, we aimed to inform decisions about when an Ag-RDT would offer greater public health value than reliance on NAT.

Methods

Following an expert consultation, we selected two use cases for analysis: rapid identification of people with COVID-19 amongst patients admitted with respiratory symptoms in a ‘hospital’ setting; and early identification and isolation of people with mildly symptomatic COVID-19 in a ‘community’ setting. Using decision analysis, we evaluated the cost and impact (deaths averted and infectious days isolated) of an Ag-RDT-led strategy, compared to a strategy based on NAT and clinical judgment. We performed a multivariate sensitivity analysis to identify key parameters.

Results

In a hospital setting, an Ag-RDT-led strategy would avert more deaths than a NAT-based strategy, and at lower cost per death averted, when the sensitivity of clinical judgement is less than 85%, and when NAT results are available in time to inform clinical decision-making for less than 90% of patients. The use of an Ag-RDT is robustly supported in community settings, where it would avert more transmission at lower cost than relying on NAT alone, under a wide range of assumptions.

Conclusions

Despite their imperfect sensitivity and specificity, Ag-RDTs have the potential to be simultaneously more impactful, and cost-effective, than current approaches to COVID-19 diagnostic testing.
Increased Systemic HSP70B Levels in Spinal Muscular Atrophy Infants
Authors: Eichelberger Eric J. , Alves Christiano R. R. , Zhang Ren , Petrillo Marco , Cullen Patrick , Farwell Wildon , Hurt Jessica A , Staropoli John F. , Swoboda Kathryn J.
Published: November 2020

Despite newly available treatments for spinal muscular atrophy (SMA), novel circulating biomarkers are still critically necessary to track SMA progression and therapeutic response. To identify potential biomarkers, we performed whole-blood RNA sequencing analysis in SMA type 1 subjects under 1 year old and age-matched healthy controls. Our analysis revealed the Heat Shock Protein Family A Member 7 (HSPA7) as a novel candidate biomarker to track SMA progression early in life. Changes in circulating HSPA7 protein levels were associated with changes in circulating neurofilaments levels in SMA newborns and infants. Future studies will determine whether HSPA7 levels respond to molecular therapies.
Acute Day Units for mental health crises: A qualitative study of service user and staff views and experiences
Authors: Morant Nicola , Davidson Michael , Wackett Jane , Lamb Danielle , Pinfold Vanessa , Smith Deb , Johnson Sonia , Lloyd-Evans Brynmor , Osborn David P J
Published: November 2020

Background

Acute Day Units (ADUs) provide intensive, non-residential, short-term treatment for adults in mental health crisis. They currently exist in approximately 30% of health localities in England, but there is little research into their functioning or effectiveness, and how this form of crisis care is experienced by service users. This qualitative study explores the views and experiences of stakeholders who use and work in ADUs.

Methods

We conducted 36 semi-structured interviews with service users, staff and carers at four ADUs in England. Data were analysed using thematic analysis.

Results

Both service users and staff provided generally positive accounts of using or working in ADUs. Valued features were structured programmes that provide routine, meaningful group activities, and opportunities for peer contact and emotional, practical and peer support, within a ‘safe’ environment. Aspects of ADU care were often described as enabling personal and social connections that contribute to shifting from crisis to recovery. ADUs were compared favourably to other forms of home- and hospital-based acute care, particularly in providing more therapeutic input and social contact. Some service users and staff thought ADU lengths of stay should be extended slightly, and staff described some ADUs being under-utilised or poorly-understood by referrers in local acute care systems.

Conclusions

Multi-site qualitative data suggests that ADUs provide a distinctive and valued contribution to acute care systems, and can avoid known problems associated with other forms of acute care, such as low user satisfaction, stressful ward environments, and little therapeutic input or positive peer contact. Findings suggest there may be grounds for recommending further development and more widespread implementation of ADUs to increase choice within local acute care systems.
Comparing Machine Learning Algorithms for Predicting ICU Admission and Mortality in COVID-19
Authors: Subudhi Sonu , Verma Ashish , Patel Ankit B. , Hardin C. Corey , Khandekar Melin J. , Lee Hang , Stylianopoulos Triantafyllos , Munn Lance L. , Dutta Sayon , Jain Rakesh K.
Published: November 2020

As predicting the trajectory of COVID-19 disease is challenging, machine learning models could assist physicians determine high-risk individuals. This study compares the performance of 18 machine learning algorithms for predicting ICU admission and mortality among COVID-19 patients. Using COVID-19 patient data from the Mass General Brigham (MGB) healthcare database, we developed and internally validated models using patients presenting to Emergency Department (ED) between March-April 2020 (n = 1144) and externally validated them using those individuals who encountered ED between May-August 2020 (n = 334). We show that ensemble-based models perform better than other model types at predicting both 5-day ICU admission and 28-day mortality from COVID-19. CRP, LDH, and procalcitonin levels were important for ICU admission models whereas eGFR <60 ml/min/1.73m2, ventilator use, and potassium levels were the most important variables for predicting mortality. Implementing such models would help in clinical decision-making for future COVID-19 and other infectious disease outbreaks.
How strong is the epidemiological evidence to support any potential protective role for vitamin D levels on COVID-19 infections and mortality? - A time-series analysis of European Populations
Authors: Singh Samer
Published: November 2020

A potential protective role of vitamin D serum levels on overall adverse outcomes of SARS-CoV-2 infection or COVID-19 on populations had been suggested previously based upon single-point cross-sectional analysis of 8 April 2020 data from 20 European countries assuming comparable underlying confounding variables for these populations, at an early stage of the current pandemic. Comparative time-series cross-sectional analysis of the COVID-19 data from 12 March (early pre-peak) to 26 July (late post-peak of infections) 2020 was performed to assess the strength of the assertion. The study subjects included 1,829,634 COVID-19 cases (11.11% of total worldwide) and 179,135 associated deaths (27.45 % of total worldwide) on 26 July 2012. Previously suggested cross-sectional study design and methodology could not consistently and significantly (p-value≥0.05) support the notion of the potential protective role of the mean serum vitamin D levels of the populations on COVID-19 incidence and mortality. However, the exponential correlative model, as well as alternative simple regression analysis on ln and Log10 transformed COVID-19 data for the time period indicated improved consistently negative covariation with vitamin D levels. Additionally, the later methodology increased the predictive potential for explaining the variability in data [R2 by 1.27-1.96 fold, adjusted-R2 by 1.33-2.47, p-value=0.0457-0.0035, for cases/million; R2 by 1.81-2.67, adjusted-R2 by 2.21-3.74 fold for deaths/million, p-value=0.0049-0.0228). Considering, the established role of vitamin D in immune system functioning randomized well-controlled trials may be suggested to evaluate/assess the potential protective role of vitamin D in reducing the COVID-19 impact on populations.
High infectiousness immediately before COVID-19 symptom onset highlights the importance of contact tracing
Authors: Hart W.S. , Maini P.K. , Thompson R.N.
Published: November 2020

Understanding changes in infectiousness during COVID-19 infections is critical to assess the effectiveness of public health measures such as contact tracing. Data from known source-recipient pairs can be used to estimate the average infectiousness profile of infected individuals, and to evaluate the proportion of presymptomatic transmissions. Here, we infer the infectiousness profile of COVID-19 infections using a mechanistic approach, and show that this method provides an improved fit to data from source-recipient pairs compared to previous studies. Our results indicate a higher proportion of presymptomatic transmissions than previously thought, with many transmissions occurring shortly before symptom onset. High infectiousness immediately prior to symptom onset highlights the importance of contact tracing, even if contacts from a short time window before symptom onset alone are traced.
Extracellular vesicles as predictors of individual response to exercise training in youth living with obesity
Authors: Pierdoná Taiana M. , Martin Alexandria , Obi Patience O. , Seif Samira , Bydak Benjamin , Eadie Ashley , Brunt Keith , McGavock Jonathan M. , Sénéchal Martin , Saleem Ayesha
Published: November 2020

Exercise is associated with various health benefits, including the prevention and management of obesity and cardiometabolic risk factors. However, a strong heterogeneity in the adaptive response to exercise training exists. The objective of this study was to evaluate if changes in extracellular vesicles (EVs) after acute aerobic exercise (AE) were associated with the responder phenotype following 6-weeks of resistance exercise training. This is a secondary analysis of plasma samples from the EXIT trial (clinical trial #02204670). Eleven sedentary youth with obesity (15.7±0.5 years, BMI ≥ 95th percentile) underwent an acute bout of AE (60% heart rate reserve, 45 min). Blood was collected before exercise [at time (AT) 0 min], during [AT15, 30, 45 min], and 75 min after exercise [AT120]. Afterward, youth participated in 6-week resistance training program, and were categorized into responders (RE) or non-responders (NRE) based on changes in insulin sensitivity as measured by the Matsuda Index. EVs were isolated using size exclusion chromatography (Izon®). The primary outcome variable was EV biophysical profile, which includes size, zeta potential, protein yield and expression of markers associated with EV subtypes. The variables were analyzed in a single-blind fashion. Overall, there was a general increase in EV production in both groups. Average EV size was larger in RE (~147 nm) vs. NRE (~124 nm; p<0.05). Average EV size at AT0 was associated with absolute change in Matsuda index following 6-weeks of resistance training (r=0.44, p=0.08). EV size distribution revealed RE preferentially expressed EVs between 150 – 250 nm in size, whereas NRE expressed EVs between 50 – 100 nm (p<0.05). At baseline, RE-EVs contained ~25% lower Tsg101 protein, ~85% higher MMP2 content, while CD63 levels remained unchanged between the groups. Total protein yield in RE-EVs was higher than NRE at AT15 (p<0.05). Our data suggest that youth with obesity that respond to exercise training produce larger EVs, with lower exosome- and higher microvesicle-specific protein expression. RE-EVs also had higher EV protein yield during AE. The relationship between larger EV subtypes and/or cargo, and the individual response to exercise has yet to be fully elucidated.
Using Machine-Learning Techniques to Identify Responders vs. Non-responders in Randomized Clinical Trials.
Authors: Nikolodimou Vasiliki , Agapow Paul
Published: November 2020

Despite the expectation of heterogeneity in therapy outcomes, especially for complex diseases like cancer, analyzing differential response to experimental therapies in a randomized clinical trial (RCT) setting is typically done by dividing patients into responders and non-responders, usually based on a single endpoint. Given the existence of biological and patho-physiological differences among metastatic colorectal cancer (mCRC) patients, we hypothesized that a data-driven analysis of an RCT population outcomes can identify sub-types of patients founded on differential response to Panitumumab - a fully human monoclonal antibody directed against the epidermal growth factor receptor.

Outcome and response data of the RCT population were mined with heuristic, distance-based and model-based unsupervised clustering algorithms. The population sub-groups obtained by the best performing clustering approach were then examined in terms of molecular and clinical characteristics. The utility of this characterization was compared against that of the sub-groups obtained by the conventional responders' analysis and then contrasted with aetiological evidence around mCRC heterogeneity and biological functioning.

The Partition around Medoids clustering method results into the identification of seven sub-types of patients, statistically distinct from each other in survival outcomes, prognostic biomarkers and genetic characteristics. Conventional responders analysis was proven inferior in uncovering relationships between physical, clinical history, genetic attributes and differential treatment resistance mechanisms. Combined with improved characterization of the molecular subtypes of CRC, applying Machine Learning techniques, like unsupervised clustering, onto the wealth of data already collected by previous RCTs can support the design of further targeted, more efficient RCTs and better identification of patient groups who will respond to a given intervention.
Modeling aerial transmission of pathogens (including the SARS-CoV-2 virus) through aerosol emissions from e-cigarettes
Authors: Sussman Roberto A , Golberstein Eliana , Polosa Riccardo
Published: November 2020

We examine the plausibility, scope and risks of aerial transmission of pathogens (including the SARS-CoV-2 virus) through respiratory droplets carried by exhaled e–cigarette aerosol (ECA). Given the lack of empiric evidence, we consider cigarette smoking and mouth breathing through a mouthpiece as convenient proxies to infer the respiratory mechanics and droplets sizes and their rate of emission that should result from vaping. To quantify direct exposure distance we model exhaled ECA flow as an intermittent turbulent jet evolving into an unstable puff, estimating for low intensity vaping (practiced by 80-90% of vapers) the emission of 6-200 (mean 79.82, standard deviation 74.66) respiratory submicron droplets per puff a horizontal distance spread of 1-2 meters, with intense vaping possibly emitting up to 1000 droplets per puff in the submicron range a distance spread over 2 meters. Since exhaled ECA acts effectively as a visual tracer of its expiratory flow, bystanders become instinctively aware that possible direct contagion might occur only in the direction and scope of the jet.
Biostatistical Investigation of Correlation Between COVID-19 and Diabetes Mellitus
Authors: Eynizadeh Zahra , Ameli Zahra , Sahranavard Mahdieh , Daneshparvar Mobina , Dolagh Mobina Abdollahi , Roozkhosh Mahboobeh , Ejtehadi Mohammad Reza , Irani Maryam Azimzadeh
Published: November 2020

COVID-19 is a highly infectious disease. Studies suggest that its severity is amplified in patients diagnosed with Diabetes Mellitus. In this study, the correlation between the prevalence of COVID-19 and Diabetes was analyzed at the regional and global scale using data extracted from WHO and IDF Diabetes Atlas. For the regional investigation data was assorted into ten regions including Central Asia, Middle east and western Asia, Africa, North America and the Caribbean, South east Asia, East Asia, Europe, South and Central America, South Asia and Oceania. The results show a positive correlation coefficient of 0.47 in Middle east and western Asia. While at the global scale analysis all the selected countries were considered together and a correlation coefficient of 0.32 was observed. This number was increased to 0.69 when the top most affected countries by COVID-19 were considered for the analysis. In order to investigate the time dependent relationship of the two diseases, the data was analysed in five windows of 45 days each since the beginning of pandemic. The results show an increasing pattern of the correlation coefficient in the last three windows. Overall, based on this study by increasing the prevalence of Diabetes Mellitus, the prevalence of COVID-19 cases may also increase.
Antibody persistency and trend post-SARS-CoV-2 infection at eight months
Authors: Dehgani-Mobaraki Puya , Zaidi Asiya Kamber , Porreca Annamaria , Floridi Alessandro , Floridi Emanuela
Published: November 2020

An improved understanding of the immunity offered by the antibodies developed against SARS-CoV-2 is critical for the development of diagnostic tests and vaccines. Our study aimed at the longitudinal analysis of antibody presence, persistence and its trend over a period of eight months in a group of COVID-19 recovered patients who tested positive by real-time quantitative PCR for SARS-CoV-2 in the period between the 18th and 30th of March, 2020. The subjects were divided into two groups based on disease severity: mild and moderately-severe. The MAGLUMI 2019-nCoV lgM/lgG chemiluminescent analytical system (CLIA) assay was used to analyse the antibody titres. Robust IgG antibody persistency was demonstrated in 76.7 % of the subjects (23 out of 30) at eight months post-infection. The results of this study highlight an important point in terms of the association between humoral immune response and disease severity. Patients who might have experienced a relatively moderate-severe infection may develop a robust immunity that could persist for a longer duration.
Urine biomarker: novel approach to hepatocellular carcinoma screening
Authors: Kim Amy K , Hamilton James P. , Lin Selena Y. , Chang Ting-Tsung , Hann Hie-Won , Hu Chi-Tan , Lou Yue , Lin Yih-Jyh , Gade Terence P. , Park Grace , Luu Harry , Lee Tai-Jung , Wang Jeremy , Chen Dion , Goggins Michael G. , Jain Surbhi , Song Wei , Su Ying-Hsiu
Published: November 2020

Background & Aims

Continued limitations in hepatocellular carcinoma (HCC) screening have led to late diagnosis with poor survival, despite well-defined high-risk patient populations. Our aim is to develop a non-invasive urine circulating tumor DNA (ctDNA) biomarker panel for HCC screening to aid in early detection.

Methods

Candidate ctDNA biomarkers was prescreened in urine samples obtained from HCC, cirrhosis, and hepatitis patients. Then, 609 patient urine samples with HCC, cirrhosis, or chronic hepatitis B were collected from five academic medical centers and evaluated by serum alpha feto-protein (AFP) and urine ctDNA panel using logistic regression, a Two-Step machine learning algorithm, and iterated 10-fold cross-validation.

Results

Mutated TP53, and methylated RASSF1a and GSTP1, were selected for the urine ctDNA panel. The sensitivity of AFP-alone (9.8 ng/mL cut-off) to detect HCC was 71% by Two-Step. The combination of ctDNA and AFP increased the sensitivity to 81% at a specificity of 90%. The AUROC for the combination of ctDNA and AFP vs. AFP-alone were 0.925 (95% CI, 0.924-0.925) and 0.877 (95% CI, 0.876-0.877), respectively. Notably, among the patients with AFP <20 ng/mL, the combination panel correctly identified 64% of HCC cases. The panel performed superiorly to AFP-alone in early-stage HCC (BCLC A) with 80% sensitivity and 90% specificity. In an iterated 10-fold cross-validation analysis, the AUROC for the combination panel was 0.898 (95% CI, 0.895-0.901).

Conclusions

The combination of urine ctDNA and serum AFP can increase HCC detection rates including in those patients with low-AFP. Given the ease of collection, a urine ctDNA panel could be a potential non-invasive HCC screening test.
Touch inhibits touch: sanshool-induced paradoxical tingling reveals perceptual interference between somatosensory submodalities
Authors: Cataldo Antonio , Hagura Nobuhiro , Hyder Yousef , Haggard Patrick
Published: November 2020

Human perception of touch is mediated by inputs from multiple channels. Classical theories postulate independent contributions of each channel to each tactile feature, with little or no interaction between channels. In contrast to this view, we show that inputs from two sub-modalities of mechanical input channels interact to determine tactile perception. The flutter-range vibration channel was activated anomalously usinghydroxy-α-sanshool, a bioactive compound of Szechuan pepper, which chemically induces tingling sensations. We tested whether this tingling sensation on the lips was modulated by sustained mechanical pressure. Across four experiments, we show that sustained touch inhibits sanshool tingling sensations in a location-specific, pressure-level and time-dependent manner. Additional experiments ruled out mediation of nociceptive or affective (C-tactile) channels underlying this interaction. These results reveal novel inhibitory influence from steady-pressure onto flutter-range tactile perceptual channels, consistent with early-stage interactions between mechanoreceptor inputs within the somatosensory pathway.
Species exclusion and coexistence in a noisy voter model with a competition-colonization tradeoff
Authors: Martinez-Garcia Ricardo , López Cristóbal , Vazquez Federico
Published: November 2020

We introduce an asymmetric noisy voter model to study the joint effect of immigration and a competition-dispersal tradeoff in the dynamics of two species competing for space on a one-dimensional lattice. Individuals of one species can invade a nearest-neighbor site in the lattice, while individuals of the other species are able to invade sites at any distance but are less competitive locally, i.e., they establish with a probabilityg≤ 1. The model also accounts for immigration, modeled as an external noise that may spontaneously replace an individual at a lattice site by another individual of the other species. This combination of mechanisms gives rise to a rich variety of outcomes for species competition, including exclusion of either species, mono-stable coexistence of both species at different population proportions, and bi-stable coexistence with proportions of populations that depend on the initial condition. Remarkably, in the bi-stable phase, the system undergoes a discontinuous transition as the intensity of immigration overcomes a threshold, leading to an irreversible loop dynamics that may cause the loss of the species with shorter dispersal range.
Dietary zinc restriction induces nociceptive pain with reduced inflammation in mice
Authors: de Lima Cleverton Kleiton Freitas , Sisnande Tháyna , da Silva Rafaela Vieira , de Castro Soares da Silva Vanessa Domitilla , do Amaral Julio Jablonski , de Mendonça Ochs Soraya , dos Santos Bruna Lima Roedel , de Miranda Ana Luisa Palhares , Lima Luis Mauricio T. R.
Published: November 2020

Zinc (Zn) is an essential micronutrient involved in a large diversity of cellular metabolism, included in the physiology of nervous system and pain modulation. There is little evidence for the role of Zn nutritional alternations to the onset and progression of neuropathic and inflammatory pain. We investigate the effects of a zinc restricted diet on the development of pain. Weaned mice were submitted to different diets: AIN-93 (38mg/kg of Zn) and Zn-deficient (AIN-93 with 11mg/kg of Zn), during four weeks. Mechanical allodynia was measured weekly using Von Frey hairs. Plantar assays for cold and heat allodynia, formalin-induced nociception and carrageenan-induced mechanical allodynia were performed at the 4thweek. Plasma, DRG and livers samples were obtained for biochemical and metabolomics analysis. Zn deficient diet completely changed mice sensitivity pattern, inducing an intense allodynia evoked by mechanical, cold and heat stimulus since weaning and during four weeks. Showed also an increased sensitivity of neurogenic phase of formalin test but the inflammatory pain behavior was drastically reduced. Zn restriction increased the ATF-3 and SOD-1 levels at DRG and reduced that of GFAP, leading an increase of neuronal activation and oxidative stress, and reduced neuroimmune activity. Plasma TNF was also reduced and metabolomics analyses suggest a downregulation of lipid metabolism of arachidonic acid, reinforcing the impact of Zn restriction to the inflammatory response. Reduction of Zn intake interferes in pain circuits, reducing inflammatory pain, however enhancing nociceptive pain. Accordingly, Zn imbalance could be predisposing factor for NP development. Therefore, dietary zinc supplementation and its monitoring present clinical relevance.
Comparison of Macular Thickness Measurements Using Swept-Source and Spectral-Domain Optical Coherence Tomography in Healthy and Diabetic Subjects
Authors: Xiong Kun , Wang Wei , Gong Xia , Li Wangting , Li Yuting , Meng Jie , Wang Langhua , Liang Xiaoling , Liu Yizhi , Huang Wenyong
Published: November 2020

Purpose

To compare macular thicknesses measured using swept-source optical coherence tomography (SS-OCT) and spectral domain OCT (SD-OCT) in normal subjects, diabetics with diabetic retinopathy (DR) and diabetics without DR (NDR).

Methods

We analysed 510 normal eyes, 741 NDR eyes and 209 DR eyes. Mean macular thicknesses in Early Treatment Diabetic Retinopathy Study (ETDRS) subfields, central point thicknesses (CPT), and macular volume were measured by SS-OCT and SD-OCT. We assessed agreement between SS-OCT and SD-OCT measurements by intraclass correlation coefficients (ICC) and Bland-Altman plots, and established a conversion equation relating central subfield (CSF), CPT and macular volume between the two devices.

Results

Macular thickness measurements by SS-OCT were significantly thinner than those by SD-OCT. The mean CSF thickness in normal eyes measured by SD-OCT and SS-OCT were 226.6 ± 19.1 μm (male 236.1 ± 19.1 μm vs female 223.0 ± 17.9 μm, p < 0.0001) and 258.4 ± 19.8 μm. In all three groups, the agreement between SS-OCT and SD-OCT was excellent (all ICC ≥ 0.866). For CSF the conversion equation SD-OCT = 31.95 + 0.999 × SS-OCT was derived. Using the equation, with 99.6% and 97.6% of the predicted values for CSF fell within 10% of the actual measurements in DR and NDR eyes, respectively.

Conclusion

We propose SS-OCT CSF thicknesses of 275 μm for males and 260 μm for females as the minimum criteria for macular edema in Chinese eyes. And SS-OCT measurements were significantly thinner than those of SD-OCT, we derived an equation to convert SS-OCT measurements to SD-OCT equivalents.
Duration of SARS-CoV-2 viral shedding in faeces as a parameter for wastewater-based epidemiology: Re-analysis of patient data using a shedding dynamics model
Authors: Miura Fuminari , Kitajima Masaaki , Omori Ryosuke
Published: November 2020

Background

Wastewater-based epidemiology (WBE) is one of the most promising approaches to effectively monitor the spread of the novel coronavirus disease 2019 (COVID-19). The virus concentration in faeces and its temporal variations are essential information for WBE. While some clinical studies have reported severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) concentrations in faeces, the value varies amongst patients and changes over time.

Aim

The present study aimed to examine how the temporal variations in the concentration of virus in faeces affect the monitoring of disease incidence. We re-analysed the experimental findings of clinical studies to estimate the duration of virus shedding and the faecal virus concentration.

Method

Available experimental data as of 23 October, 2020 were collected and patient data reported in Germany were included for further analysis. The viral shedding kinetics was modelled, and the dynamic model was fitted to the collected experimental data by a Bayesian framework. Using samples of posterior distributions, the duration of viral shedding and the concentration of virus copies in faeces over time were computed.

Results

We estimated the median concentration of SARS-CoV-2 in faeces as 2.6 (95% Credible Interval (CrI): 0.22–4.8) log copies per gram (g) of faeces over the shedding period, and our model implied that the duration of viral shedding was 23.2 days (95% CrI: 19.5–31.5), given the current standard quantification limit (Ct = 40). With simulated incidences, our results also indicated that a one-week delay between symptom onset and wastewater sampling increased the estimation of incidence by 13.5%.

Conclusions

Our results demonstrated that the temporal variation in virus concentration in faeces affects microbial monitoring systems such as WBE. The present study also implied the need for adjusting the estimates of virus concentration in faeces by incorporating the kinetics of unobserved concentrations. The method used in this study is easily implemented in further simulations; therefore, the results of this study might contribute to enhancing disease surveillance and risk assessments that require quantities of virus to be excreted into the environment.
Lifestyle modifies the diabetes-related metabolic risk, conditional on individual genetic differences
Authors: Shin Jisu , Zhou Xuan , Tan Joanne , Hyppönen Elina , Benyamin Beben , Lee S Hong
Published: November 2020

Background

Metabolic syndrome is a group of heritable metabolic traits that are highly associated with type 2 diabetes (T2DM). Classical interventions to T2DM include individual self-management of environmental risk factors such as improving diet quality, increasing physical activity and reducing smoking and alcohol consumptions, which decreases the risk of developing metabolic syndrome. However, it is poorly understood how the phenotypes of diabetes-related metabolic traits change with respect to lifestyle modifications at the individual level.

Methods

In this study, we applied a whole-genome genotype-by-environment (GxE) interaction approach to describe how intermediate traits reflecting metabolic risk are affected by genetic variations and how this genetic risk can interact with lifestyle, which can vary, conditional on individual genetic differences. In the analysis, we used 12 diabetes-related metabolic traits and eight lifestyle covariates from the UK Biobank comprising 288,837 white British participants genotyped for 1,133,273 genome-wide single nucleotide polymorphisms.

Findings

We found 17 GxE interactions, of which four modulated BMI and the others distributed across other traits. Modulation of genetic effects by physical activity was seen for four traits (glucose, HbA1c, C-reactive protein, systolic blood pressure), and by alcohol and smoking for three (BMI, glucose, waist-hip ratio; and BMI, diastolic and systolic blood pressure, respectively). We also found a number of significant phenotypic modulations by the lifestyle covariates, which were not attributed to the genetic effects in the model. Overall, modulation in the metabolic risk in response to the level of lifestyle covariates was clearly observed, and its direction and magnitude were varied depending on individual differences. We also showed that the metabolic risk inferred by our model was notably higher in T2DM prospective cases than controls.

Interpretation

Our findings highlight the importance of individual genetic differences in the prevention and management of diabetes and suggest that the one-size-fits-all approach may not benefit all.

Funding

This study has been supported by the Australian Research Council (DP 190100766, FT 160100229).
PyInteraph2 and PyInKnife2 to analyze networks in protein structural ensembles
Authors: Sora Valentina , Tiberti Matteo , Robbani Shahriyar Mahdi , Rubin Joshua , Papaleo Elena
Published: November 2020

Motivation

Protein dynamic is essential for cellular functions. Due to the complex nature of non-covalent interactions and their long-range effects, the analysis of protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) rely on different philosophies, and the currently available tools suffer from limitations in terms of input formats, supported network models, and version control. Another issue is the precise definition of cutoffs for the network calculations and the assessment of the stability of the parameters, which ultimately affect the outcome of the analyses.

Results

We provide two open-source software packages, i.e., PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a harmonized, reproducible, and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and calculates a diverse range of network models with the possibility to integrate them into a macro-network and perform further downstream graph analyses. PyInKnife2 is a standalone package that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. Several functionalities are based on MDAnalysis and NetworkX, including parallelization, and are available for Python 3.7. PyInteraph2 underwent a massive restructuring in terms of setup, installation, and test support compared to the original PyInteraph software.

Conclusions

We foresee that the modular structure of the code and the version control system of GitHub will promote the transition to a community-driven effort, boost reproducibility, and establish harmonized protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities, assistance, training of new contributors, and maintenance of the package.

Availability

The packages are available athttps://github.com/ELELAB/pyinteraph2andhttps://github.com/ELELAB/PyInKnife2with guides provided within the packages.
Inferring a spatial code of cell-cell interactions across a whole animal body
Authors: Armingol Erick , Ghaddar Abbas , Joshi Chintan J. , Baghdassarian Hratch , Shamie Isaac , Chan Jason , Her Hsuan-Lin , O’Rourke Eyleen J. , Lewis Nathan E.
Published: November 2020

Cell-cell interactions shape cellular function and ultimately organismal phenotype. However, the code embedded in the molecular interactions driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our toolcell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across theCaenorhabditis elegans’ body. Leveraging a 3D atlas ofC. elegans’ cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred withcell2celland the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body.
A representational similarity analysis of cognitive control during color-word Stroop
Authors: Freund Michael C. , Bugg Julie M. , Braver Todd S.
Published: November 2020

Progress in understanding the neural bases of cognitive control has been supported by the paradigmatic color-word Stroop task, in which a target response (color name) must be selected over a more automatic, yet potentially incongruent, distractor response (word). For this paradigm, models have postulated complementary coding schemes: dorsomedial frontal cortex (DMFC) is proposed to evaluate the demand for control via incongruency-related coding, whereas dorsolateral prefrontal cortex (DLPFC) is proposed to implement control via goal and target-related coding. Yet, mapping these theorized schemes to measured neural activity within this task has been challenging. Here, we tested for these coding schemes relatively directly, by decomposing an event-related color-word Stroop task via representational similarity analysis (RSA). Three neural coding models were fit to the similarity structure of multi-voxel patterns of human fMRI activity, acquired from 65 healthy, young-adult males and females. Incongruency coding was predominant in DMFC, whereas both target and incongruency coding were present with indistinguishable strength in DLPFC. In contrast, distractor coding was strongly encoded within early visual cortex. Further, these coding schemes were differentially related to behavior: individuals with stronger DLPFC (and lateral posterior parietal cortex) target coding, but weaker DMFC incongruency coding, exhibited less behavioral Stroop interference. These results highlight the utility of the RSA framework for investigating neural mechanisms of cognitive control and point to several promising directions to extend the Stroop paradigm.

Significant Statement

How the human brain enables cognitive control — the ability to override behavioral habits to pursue internal goals — has been a major focus of neuroscience research. This ability has been frequently investigated by using the Stroop color-word naming task. With the Stroop as a test-bed, many theories have proposed specific neuroanatomical dissociations, in which medial and lateral frontal brain regions underlie cognitive control by encoding distinct types of information. Yet providing a direct confirmation of these claims has been challenging. Here, we demonstrate that representational similarity analysis (RSA), which estimates and models the similarity structure of brain activity patterns, can successfully establish the hypothesized functional dissociations within the Stroop task. RSA may provide a useful approach for investigating cognitive control mechanisms.