bluuebunny/crossref_metadata_2025_split huggingface dataset:
On-scalp magnetoencephalography for childhood epilepsies
Authors: Feys Odile , Corvilain Pierre , Aeby Alec , Sculier Claudine , Christiaens Florence , Holmes Niall , Brookes Matthew , Goldman Serge , Wens Vincent , De Tiège Xavier 
Published: September 2021

AbstractMagnetoencephalography (MEG) is an established method to investigate epilepsy. Current MEG systems house hundreds of cryogenic sensors in a rigid, one-size-fits-all helmet, which results in several limitations, particularly in children. On-scalp MEG based on optically-pumped magnetometers (OPMs) may alleviate these limitations.We report on five children (5–11 years old) with self-limited focal (n=3) or structural (n=2) epilepsy who underwent cryogenic (102 magnetometers) and on-scalp (32 OPMs) MEG. We compared the two modalities for the detection and localization of interictal epileptiform discharges (IEDs).We identified IEDs in all children with comparable sensor topographies for both MEG devices. IED amplitudes were 2.3-4.8 times higher with on-scalp MEG and signal-to-noise ratio (SNR) was also 27-60% higher with on-scalp MEG in all but one patient with large head movement artifacts. The neural source of averaged IEDs was located at about 5 mm (n=3) or higher (8.3 mm, n=1; 15.6 mm, n=1) between on-scalp and cryogenic MEG.Despite limited number of sensors and scalp coverage, on-scalp MEG detects IEDs in epileptic children with higher SNR than cryogenic MEG. This technology, which is in constant development, should become a reference in the diagnostic workup of epilepsy and replace cryogenic MEG in the near future.
Airway management, procedural data, and mortality records of patients undergoing surgery for mucormycosis associated with coronavirus disease (COVID-19)
Authors: Sirohiya Prashant , Vig Saurabh , Mathur Tanmay , Meena Jitendra Kumar , Panda Smriti , Goswami Gitartha , Gupta Raghav , konkimalla Abhilash , Kondamudi Dheeraj , Gupta Nishkarsh , Ratre Brajesh Kumar , Singh Ram , Kumar Balbir , Pandit Anuja , Sikka Kapil , Thakar Alok , Bhatnagar Sushma 
Published: September 2021

AbstractPurposeAlthough unexpected airway difficulties are reported in patients with mucormycosis, the literature on airway management in patients with mucormycosis associated with Coronavirus disease is sparse.MethodsIn this retrospective case record review of 57 patients who underwent surgery for mucormycosis associated with coronavirus disease, we aimed to evaluate the demographics, airway management, procedural data, and mortality records.ResultsForty-one (71.9%) patients had a diagnosis of sino-nasal mucormycosis, fourteen (24.6%) patients had a diagnosis of rhino-orbital mucormycosis, and 2 (3.5%) patients had a diagnosis of palatal mucormycosis. A total of 44 (77.2%) patients had co-morbidities. The most common co-morbidities were diabetes mellitus in 42 (73.6%) patients, followed by hypertension in 21 (36.8%) patients, and acute kidney injury in 14 (28.1%) patients. We used the intubation difficulty scale score to assess intubating conditions. Intubation was easy to slightly difficult in 53 (92.9%) patients. In our study, mortality occurred in 7 (12.3%) patients. The median (range) mortality time was 60 (27–74) days. The median (range) time to hospital discharge was 53.5 (10–85) days. The median [interquartile range] age of discharged versus expired patients was 47.5 [41,57.5] versus 64 [47,70] years (P = 0.04), and median (interquartile range) D-dimer levels in discharged versus expired patients was 364 [213, 638] versus 2448 [408,3301] ng/mL (P = 0.03).ConclusionIn patients undergoing surgery for mucormycosis associated with the coronavirus disease, airway management was easy to slightly difficult in most patients. Perioperative complications can be minimized by taking timely and precautionary measures.
Analysis of the prediction of the 2021 time-evolution of the Covid-19 Pandemic in Italy using a Planck’s distribution
Authors: Ciufolini Ignazio , Paolozzi Antonio 
Published: September 2021

AbstractIn a previous paper we studied the time evolution of the Covid-19 pandemic in Italy during the first wave of 2020 using a number of distribution laws. We concluded that the best distribution law to predict the evolution of the pandemic, if basic conditions of the pandemic (such as distancing measures, use of masks, start of schools, intensive use of public transportation, beginning and end of holidays, vaccination campaign and no significant onset of new Covid variants) do not appreciably change, is a distribution of the type of Planck’s law with three parameters. In our 2020 study we did not use the number of daily positive cases in Italy but the ratio of daily positive cases per number of daily tests, ratio today sometimes referred to as: “positivity rate”. We showed that, if basic conditions do not change, the Planck’s distribution with three parameters provides very good predictions of the positivity rate about one month in advance. In particular, in a second paper, using the Planck’s distribution with three parameters, we predicted, about one month in advance, the spread of the pandemic in Italy during the Christmas 2020 holidays with an error of a few percent only. We then study the present (September 2021) evolution of the pandemic in Italy and we show that the Planck’s distribution, based on the data of July and August, predicts well the evolution of the pandemic. In particular, we show that the peak of the positivity rate was predicted to occur approximately around the middle of August and that the agreement of this Planck’s function (obtained fitting the data up to 10 July 2021) and the positivity rate observed after 5 weeks, on 12 September 2021 is very good. However, the end of the Italian holidays and the start of all the activities including schools, intensive use of public transportation and further changes in distancing measures may cause a discrepancy of the predicted trend of the positivity rate of the pandemic with respect to the real observed values.
Change in Measles Vaccine Coverage and Estimated Impact on Measles Mortality in Low and Middle Income Countries, 2020: An Investigation into Secondary Public Health Effects of the COVID-19 Pandemic Using the Lives Saved Tool
Authors: Gregory Lucy 
Published: September 2021

AbstractWith the global COVID-19 pandemic, many public health services were severely disrupted. Estimating the overall health effects of this is difficult as most disease surveillance systems have also been substantially affected during the pandemic. For some diseases, this effect is mitigated by the methods enacted to fight the pandemic, such as use of facial coverings, social distancing and quarantine, but measles is infectious to the degree that this mitigation is likely to be limited. Thus, outbreaks and an increase in global measles mortality are expected. However, the severity of this impact is not yet known.In early 2020, a study by Roberton and colleagues predicted an additional 12,360 to 37,920 deaths in children under-five worldwide from measles over the coming year based on three potential levels of vaccine coverage reductions ranging from 18.5 to 51.9%. Our study investigates the magnitude of the increase in measles mortality due to decreased vaccine coverage because of COVID-19, based on official estimates of 2020 measles vaccine coverage from WHO/UNICEF released in July 2021. Using the Lives Saved Tool (LiST), an interventions modeling program, we estimated measles mortality for low/middle income countries (LMICs) based on the 2020 WHO/UNICEF estimates of national immunization coverage (WUENIC). Because these calculations use actual reported vaccine coverage, they provide a more accurate picture of measles mortality related to COVID-19 disruptions in 2020.Using the WUENIC data, LiST predicted fewer additional deaths in 2020 due to decreases in measles vaccine coverage than estimations made by LiST based on Roberton, 2020 due to remarkable recovery efforts by national immunization programmes in the second half of 2020.
Race and Ethnic Group Dependent Space Radiation Cancer Risk Predictions
Authors: Cucinotta Francis A. , Saganti Premkumar B. 
Published: September 2021

AbstractFuture space missions by national space agencies and private industry, including space tourism, will include a diverse makeup of crewmembers with extensive variability in age, sex, and race or ethnic groups. The relative risk (RR) model is used to transfer epidemiology data between populations to estimate radiation risks. In the RR model cancer risk is assumed to be proportional to background cancer rates and limited by other causes of death, which are dependent on genetic, environmental and dietary factors that are population dependent. Here we apply the NSCR-2020 model to make the first predictions of age dependent space radiation cancer risks for several U.S. populations, which includes Asian-Pacific Islanders (API), Black, Hispanic (white and black), and White (non-Hispanic) populations. Results suggest that male API and Hispanic populations have the overall lowest cancer risks, while White females have the highest risk. Blacks have similar total cancer rates as Whites, however their reduced life expectancy leads to modestly lower lifetime radiation risks compared to Whites. There are diverse tissue specific cancer risk ranking across sex and race, which include sex specific organ risks, females having larger lung, stomach, and urinary-bladder radiation risks, and males having larger colon and brain risks.
Nationwide trends in COVID-19 cases and SARS-CoV-2 wastewater concentrations in the United States
Authors: Duvallet Claire , Wu Fuqing , McElroy Kyle A. , Imakaev Maxim , Endo Noriko , Xiao Amy , Zhang Jianbo , Floyd-O’Sullivan Róisín , Powell Morgan M , Mendola Samuel , Wilson Shane T , Cruz Francis , Melman Tamar , Sathyanarayana Chaithra Lakshmi , Olesen Scott W. , Erickson Timothy B. , Ghaeli Newsha , Chai Peter , Alm Eric , Matus Mariana 
Published: September 2021

AbstractWastewater-based epidemiology has emerged as a promising technology for population-level surveillance of COVID-19 disease. The SARS-CoV-2 virus is shed in the stool of infected individuals and aggregated in public sewers, where it can be quantified to provide information on population-level disease incidence that is unbiased by access to clinical testing. In this study, we present results from the largest nationwide wastewater monitoring system in the United States reported to date. We profile 55 locations with at least six months of sampling and highlight their wastewater data from April 2020 through May 2021. These locations represent over 12 million individuals across 19 states. Samples were collected approximately weekly by wastewater treatment utilities as part of a regular wastewater surveillance service and analyzed for SARS-CoV-2 concentrations using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Concentrations of SARS-CoV-2 (copies/mL) were normalized to pepper mild mottle virus (PMMoV), a stable and persistent indicator of feces concentrations in wastewater. Here, we show that wastewater data reflects temporal and geographic trends in clinical COVID-19 cases, demonstrating that wastewater surveillance is a feasible approach for nationwide population-level monitoring of COVID-19 disease. We also provide key lessons learned from our broad-scale implementation of wastewater-based epidemiology, which can be used to inform wastewater-based epidemiology approaches for future emerging diseases. With an evolving epidemic and effective vaccines against SARS-CoV-2, wastewater-based epidemiology can serve as an important passive surveillance approach to detect changing dynamics or resurgences of the virus.HighlightsWe present results from a nationwide wastewater monitoring network in the United States, which represents one of the broadest temporal and geographic wastewater-based epidemiology datasets to-date.Wastewater concentrations measured within individual locations reflect temporal trends in reported COVID-19 cases in the associated communities.Wastewater concentrations also reflect geographic patterns in reported COVID-19 cases across states throughout the pandemic.Normalizing wastewater concentrations to a fecal marker virus improves the correlation between wastewater data and clinical cases across locations but not necessarily over time within individual locations.Implementing a nationwide wastewater monitoring system for SARS-CoV-2 is feasible, practical, and sustainable.
Robust immune response to the BNT162b mRNA vaccine in an elderly population vaccinated 15 months after recovery from COVID-19
Authors: Lee Hye Kyung , Knabl Ludwig , Knabl Ludwig , Kapferer Sebastian , Pateter Birgit , Walter Mary , Furth Priscilla A. , Hennighausen Lothar 
Published: September 2021

AbstractKnowledge about the impact of prior SARS-CoV-2 infection of the elderly on mRNA vaccination response is needed to appropriately address the need for booster vaccination in this vulnerable population. To address this, we investigated antibody and genomic immune responses in 16 elderly (avg. 81 yrs.) individuals that had received a single booster dose of BNT162b vaccine 15 months after recovering from COVID-19. Spike-specific IgG antibody levels increased in each of the study participants from an average of 710 U/ml prior to the vaccination to more than 40,000 U/ml within ten weeks after the vaccination. In contrast, anti-spike-specific IgG antibody levels averaged 2,190 U/ml in 14 healthy SARS-CoV-2-naïve individuals (avg. 58 yrs.) ten weeks after the second dose of BNT162b. RNA-seq conducted on PBMCs demonstrated the activation of interferon-activated genetic programs in both cohorts within one day. Unlike their transient induction in the younger naïve population, persistent activity and the initiation of additional cell cycle regulated programs were obtained in the older COVID-19 recovered population. Here we show that the elderly, a high-risk population, can mount a strong antibody and a persistent molecular immune response upon receiving a single dose of mRNA vaccine 15 months after recovery from COVID-19.
Characterization of the secretome, transcriptome and proteome of human β cell line EndoC-βH1
Authors: Ryaboshapkina Maria , Saitoski Kevin , Hamza Ghaith M. , Jarnuczak Andrew F. , Berthault Claire , Sengupta Kaushik , Underwood Christina Rye , Andersson Shalini , Scharfmann Raphael 
Published: September 2021

ABSTRACTEarly diabetes research is hampered by limited availability, variable quality and instability of human pancreatic islets in culture. Little is known about the human β cell secretome, and recent studies question translatability of rodent β cell secretory profiles. Here, we verify representativeness of EndoC-βH1, one of the most widely used human β cell lines, as a translational human β cell model based on omics and characterize the EndoC-βH1 secretome. We profiled EndoC-βH1 cells using RNA-seq, Data Independent Acquisition (DIA) and Tandem Mass Tag proteomics of cell lysate. Omics profiles of EndoC-βH1 cells were compared to human β cells and insulinomas. Secretome composition was assessed by DIA proteomics. Agreement between EndoC-βH1 cells and primary adult human β cells was ~90% for global omics profiles as well as for β cell markers, transcription factors and enzymes. Discrepancies in expression were due to elevated proliferation rate of EndoC-βH1 cells compared to adult β cells. Consistently, similarity was slightly higher with benign non-metastatic insulinomas. EndoC-βH1 secreted 671 proteins in untreated baseline state and 3,278 proteins when stressed with non-targeting control siRNA, including known β cell hormones INS, IAPP, and IGF2. Further, EndoC-βH1 secreted proteins known to generate bioactive peptides such as granins and enzymes required for production of bioactive peptides. Unexpectedly, exosomes appeared to be a major mode of secretion in EndoC-βH1 cells. We believe that secretion of exosomes and bioactive peptides warrant further investigation with specialized proteomics workflows in future studies.Graphical abstractHighlightsWe validate EndoC-βH1 as a translational human β cell model using omics.We present the first unbiased proteomics composition of human β cell line secretome.The secretome of human β cells is more extensive than previously thought.Untreated cells secreted 671 proteins and stressed cells secreted 3,278 proteins.Secretion of exosomes and bioactive peptides constitute directions of future research.
LongPhase: an ultra-fast chromosome-scale phasing algorithm for small and large variants
Authors: Lin Jyun-Hong , Chen Liang-Chi , Yu Shu-Qi , Huang Yao-Ting 
Published: September 2021

AbstractLong-read phasing has been used for reconstructing diploid genomes, improving variant calling, and resolving microbial strains in metagenomics. However, the phasing blocks of existing methods are broken by large Structural Variations (SVs), and the efficiency is unsatisfactory for population-scale phasing. This paper presents an ultra-fast algorithm, LongPhase, which can simultaneously phase single nucleotide polymorphisms (SNPs) and SVs of a human genome in ∼10-20 minutes, 10x faster than the state-of-the-art WhatsHap and Margin. In particular, LongPhase produces much larger phased blocks at almost chromosome level with only long reads (N50=26Mbp). We demonstrate that LongPhase combined with Nanopore is a cost-effective approach for providing chromosome-scale phasing without the need for additional trios, chromosome-conformation, and single-cell strand-seq data.
Identifying Microstructural Changes in Diffusion MRI; How to Circumvent Parameter Degeneracy
Authors: Rafipoor Hossein , Zheng Ying-Qiu , Griffanti Ludovica , Jbabdi Saad , Cottaar Michiel 
Published: September 2021

ABSTRACTBiophysical models that attempt to infer real-world quantities from data usually have many free parameters. This over-parameterisation can result in degeneracies in model inversion and render parameter estimation ill-posed. However, in many applications, we are not interested in quantifying the parameters per se, but rather in identifying changes in parameters between experimental conditions (e.g. patients vs controls). Here we present a Bayesian framework to make inference on changes in the parameters of biophysical models even when model inversion is degenerate, which we refer to as Bayesian EstimatioN of CHange (BENCH).We infer the parameter changes in two steps; First, we train models that can estimate the pattern of change in the measurements given any hypothetical direction of change in the parameters using simulations. Next, for any pair of real data sets, we use these pre-trained models to estimate the probability that an observed difference in the data can be explained by each model of change.BENCH is applicable to any type of data and models and particularly useful for biophysical models with parameter degeneracies, where we can assume the change is sparse. In this paper, we apply the approach in the context of microstructural modelling of diffusion MRI data, where the models are usually over-parameterised and not invertible without injecting strong assumptions.Using simulations, we show that in the context of the standard model of white matter our approach is able to identify changes in microstructural parameters from conventional multi-shell diffusion MRI data. We also apply our approach to a subset of subjects from the UK-Biobank Imaging to identify the dominant standard model parameter change in areas of white matter hyperintensities under the assumption that the standard model holds in white matter hyperintensities.
Reproductive neuronal circuitry in adaptive changes of energy balance
Authors: Lee Pilhwa , de Miera Cristina Sáenz , Bellefontaine Nicole , Silveira Marina A , Zampieri Thais T , Donato Jose , Williams Kevin W. , Frazao Renata , Elias Carol F. 
Published: September 2021

AbstractThe crosstalk between metabolism and reproduction is essential for species survival. When dysfunctional, this interaction may decrease reproductive efficiency, but in physiological conditions of high energy demands, e.g., pregnancy and lactation, it is highly beneficial. Females display adaptive responses that assure offspring survival and health, including increased food intake and suppression of the reproductive function. Some of these physiological responses are modulated by leptin actions in neuronal pathways that are still unclear. The hypothalamic ventral premammillary nucleus (PMv) is a key integrative node of metabolic cues and reproductive status, comprised of either leptin-depolarized or -hyperpolarized neurons. Here, we show that the subset of leptin-hyperpolarized neurons coexpresses dopamine transporter (DAT) and prolactin receptor. DAT expression is higher in prepubertal conditions, when reproductive function is suppressed. These neurons innervate AgRP presynaptic terminals and may potentiate their inhibitory actions on reproduction. We further applied a mathematical model to reconcile our new findings with the current literature and to verify if those neurons are putative components of the metabolic control of reproduction. In our model, leptin-depolarized PMv neurons project to and directly stimulate kisspeptin and gonadotropin releasing hormone (GnRH) neurons. Leptin-hyperpolarized PMv DAT neurons are directly stimulated by prolactin and project to inhibitory control sites. During conditions of high prolactin levels, i.e., late pregnancy and lactation, this pathway may overcome the former, facilitating AgRP actions in the suppression of the reproductive function. Our model also predicts that overstimulation of this pathway may underlie earlier puberty and reproductive deficits observed in conditions of metabolic dysfunction.Significance StatementWomen with excess or low energy stores (e.g., obesity or anorexia) have reproductive deficits, including altered puberty onset, disruption of reproductive cycles and decreased fertility. If able to conceive, they show higher risks of miscarriages and preterm birth. The hypothalamic circuitry controlling the interplay between metabolism and reproduction is undefined. Neurons in the ventral premammillary nucleus express leptin receptor and project to reproductive control sites. Those neurons are essentially glutamatergic, but functionally and phenotypically heterogeneous. They either depolarize or hyperpolarize in response to leptin. We show that leptin-hyperpolarized neurons coexpress dopamine transporter and prolactin receptor, and project to AgRP inhibitory output. Computational modeling was applied to build a neuronal network integrating metabolism and reproduction in typical and dysfunctional physiology.