bluuebunny/crossref_metadata_2025_split huggingface dataset:
AMP-activated-protein kinase (AMPK) is an essential sensor and metabolic regulator of retinal neurons and their integrated metabolism with RPE
Authors: Xu Lei , Brown Emily E. , Keuthan Casey J. , Gubbi Himani , Grellier Elodie-Kim , Roger Jerome , Swaroop Anand , Du Jianhai , Ash John D. 
Published: May 2020

AbstractThe retina has one of the highest energy demands in the human body, and proper regulation of metabolism among the cell types of the retina is required for functional vision. Recent studies have reported that retinal metabolism is disrupted during retinal degeneration and aging, while therapies designed to enhance metabolism can slow or prevent degeneration. Here, we show that multiple metabolic pathways, including those regulated by a key ATP sensor and regulator of metabolism, AMP-activated-kinase (AMPK), were dysregulated in a model of inherited retinal degeneration. In order to assess the direct role of AMPK in regulating retinal metabolism, we used mice with retina-specific knockout of AMPK activity. Conditional loss of AMPK resulted in impaired visual function at an early age, with slow photoreceptor loss observed in older mice. Moreover, we found that loss of AMPK resulted in decreased metabolic flux from glucose, decreased mitochondrial DNA copy number, decreased mitochondrial-related gene expression, and alterations in mitochondrial morphology in the photoreceptors, all of which preceded degeneration. Surprisingly, metabolic changes from the loss of AMPK in retinal neurons also resulted in secondary degeneration of retinal pigment epithelial (RPE) cells. Together, these data show that AMPK signaling is important for maintaining metabolic homeostasis of the retina and support the hypothesis that photoreceptors and RPE have a shared metabolic ecosystem that is controlled, in part, by AMPK.
Targeting the RHOA pathway improves learning and memory inKctd13and 16p11.2 deletion mouse models
Authors: Martin Lorenzo Sandra , Nalesso Valérie , Chevalier Claire , Birling Marie-Christine , Herault Yann 
Published: May 2020

ABSTRACTGene copy number variants (CNV) have an important role in the appearance of neurodevelopmental disorders. Particularly, the deletion of the 16p11.2 locus is associated with autism spectrum disorder, intellectual disability, and several other features. Earlier studies highlighted the implication ofKctd13genetic imbalance in the 16p11.2 deletion through the regulation of the RHOA pathway. Here, we target the pathway and rescue the cognitive phenotypes of the 16p11.2 deletion mouse models. We used a chronic administration of fasudil (HA1077), an inhibitor of the Rho-associated protein kinase (ROCK), in mouse models carrying a heterozygous inactivation ofKctd13, or the deletion of the entire 16p11.2 BP4-BP5 region. We focused our attention on the most robust cognitive phenotypes seen in the 16p11.2 models and we showed that a chronic fasudil treatment can restore object recognition memory in both mouse models but does not change other behavioural traits. These findings confirm KCTD13 as one target gene causing cognitive deficits in 16p11.2 deletion patients, and the pertinence of the RHOA pathway as a therapeutic path and reinforce the contribution of other gene(s) involved in cognitive defects found in the 16p11.2 CNV models.HIGHLIGHTS- Kctd13 haploinsufficiency recapitulates most of the behaviour phenotypes found in the 16p11.2Del/+ models- Fasudil treatment restores Kctd13 and 16p11.2Del/+ mutant phenotypes in novel location and novel object recognition memory tests- Fasudil treatment restores the RhoA pathway inKctd13+/-and 16p11.2Del/+ models
Untargeted metabolome- and transcriptome-wide association study identifies causal genes modulating metabolite concentrations in urine
Authors: Flitman Reyhan Sönmez , Khalili Bita , Kutalik Zoltan , Rueedi Rico , Bergmann Sven 
Published: May 2020

SummaryIn this study we investigate the results of a metabolome- and transcriptome-wide association study to identify genes influencing the human metabolome. We used RNAseq data from lymphoblastoid cell lines (LCLs) derived from 555 Caucasian individuals to characterize their transcriptome. As for the metabolome we took an untargeted approach using binned features from1H nuclear magnetic resonance spectroscopy (NMR) of urine samples from the same subjects allowing for data-driven discovery of associated compounds (rather than working with a limited set of quantified metabolites).Using pairwise linear regression we identified 21 study-wide significant associations between metabolome features and gene expression levels. We observed the most significant association between the geneALMS1and two adjacent metabolome features at 2.0325 and 2.0375 ppm. By using our previously developed metabomatching methodology, we found N-Acetylaspartate (NAA) as the potential underlying metabolite whose urine concentration is correlated withALMS1expression. Indeed, a number of metabolome- and genome-wide association studies (mGWAS) had already suggested the locus of this gene to be involved in regulation of N-acetylated compounds, yet were not able to identify unambiguously the exact metabolite, nor to disambiguate betweenALMS1andNAT8, another gene found in the same locus as the mediator gene. The second highest significant association was observed betweenHPS1and two metabolome features at 2.8575 and 2.8725 ppm. Metabomatching of the association profile ofHPS1with all metabolite features pointed at trimethylamine (TMA) as the most likely underlying metabolite. mGWAS had previously implicated a locus containingHPS1to be associated with TMA concentrations in urine but could not disambiguate this association signal fromPYROXD2, a gene in the same locus. We used Mendelian randomization to show for bothALMS1andHPS1that their expression is causally linked to the respective metabolite concentrations.Our study provides evidence that the integration of metabolomics with gene expression data can support mQTL analysis, helping to identify the most likely gene involved in the modulation of the metabolite concentration.
Potential consequences of the red blood cell storage lesion on cardiac electrophysiology
Authors: Reilly Marissa , Bruno Chantal , Prudencio Tomas , Ciccarelli Nina , Guerrelli Devon , Nair Raj , Ramadan Manelle , Luban Naomi L.C. , Posnack Nikki Gillum 
Published: May 2020

AbstractThe red blood cell (RBC) storage lesion is a series of morphological, functional and metabolic changes that RBCs undergo following collection, processing and refrigerated storage for clinical use. Since the biochemical attributes of the RBC unit shifts with time, transfusion of older blood products may contribute to cardiac complications, including hyperkalemia and cardiac arrest. We measured the direct effect of storage age on cardiac electrophysiology and compared with hyperkalemia, a prominent biomarker of storage lesion severity. Donor RBCs were processed using standard blood banking techniques. The supernatant was collected from RBC units (sRBC), 7-50 days post-donor collection, for evaluation using Langendorff-heart preparations (rat) or human stem-cell derived cardiomyocytes. Cardiac parameters remained stable following exposure to ‘fresh’ sRBC (day 7: 5.9+0.2 mM K+), but older blood products (day 40: 9.7+0.4 mM K+) caused bradycardia (baseline: 279±5 vs day 40: 216±18 BPM), delayed sinus node recovery (baseline: 243±8 vs day 40: 354±23 msec), and increased the effective refractory period of the atrioventricular node (baseline: 77+2 vs day 40: 93+7 msec) and ventricle (baseline: 50+3 vs day 40: 98+10 msec) in perfused hearts. Beating rate was also slowed in human cardiomyocytes after exposure to older sRBC (−75+9%, day 40 vs control). Similar effects on automaticity and electrical conduction were observed with hyperkalemia (10-12 mM K+). This is the first study to demonstrate that ‘older’ blood products directly impact cardiac electrophysiology, using experimental models. These effects are likely due to biochemical alterations in the sRBC that occur over time, including, but not limited to hyperkalemia. Patients receiving large volume and/or rapid transfusions may be sensitive to these effects.New & noteworthyWe demonstrate that red blood cell storage duration time can have downstream effects on cardiac electrophysiology, likely due to biochemical alterations in the blood product. Hyperkalemia and cardiac arrest have been reported following blood transfusions, but this is the first experimental study to show a direct correlation between storage duration and cardiac function. Infant and pediatric patients, and those receiving large volume and/or rapid transfusions may be sensitive to these effects.
Genomic surveillance of SARS-CoV-2 in Thailand reveals mixed imported populations, a local lineage expansion and a virus with truncated ORF7a
Authors: Batty Elizabeth M , Kochakarn Theerarat , Panthan Bhakbhoom , Kümpornsin Krittikorn , Jiaranai Poramate , Wangwiwatsin Arporn , Kotanan Namfon , Jaruampornpan Peera , Watthanachockchai Treewat , Rakmanee Kingkan , Sensorn Insee , Sungkanuparph Somnuek , Pasomsub Ekawat , Chookajorn Thanat , Chantratita Wasun 
Published: May 2020

AbstractCoronavirus Disease 2019 (COVID-19) is a global public health threat. Genomic surveillance of SARS-CoV-2 was implemented during March 2020 at a major diagnostic hub in Bangkok, Thailand. Several virus lineages supposedly originated in many countries were found, and a Thai-specific lineage, designated A/Thai-1, has expanded to be predominant in Thailand. A virus sample in the SARS-CoV-2 A/Thai-1 lineage contains a frame-shift deletion at ORF7a, encoding a putative host antagonizing factor of the virus.
Assessing the Performance of Machine Learning Models to Predict Neonatal Mortality Risk in Brazil, 2000-2016
Authors: Alves Luciana Correia , Beluzo Carlos Eduardo , Arruda Natália Martins , Bresan Rodrigo Campos , Carvalho Tiago 
Published: May 2020

AbstractNeonatal mortality figures are an important health’s problem, as the first month of life is the most vulnerable time for survival. Factors associated with neonatal mortality are complexly and influenced by the maternal and newborn biological characteristics, social conditions and the care provided by the health services. The aim of this study was investigated the association between features related and neonatal mortality risk in Brazil. Data came from two surveys: The Mortality Information System and Information System on Live Births. The final sample was composed of 302,943 children between 2006 and 2016. We highlight the proposition of a new approach based on machine learning to address the problem of neonatal mortality death risk classification. The results using three different machine learning classifiers points toward expressiveness of features, being newborn weight, Apgar at the first and fifth minute, congenital malformations, gestational weeks and number of prenatal appointments the six more expressive.
Association between comorbidities and the risk of death in patients with COVID-19: sex-specific differences
Authors: Wu Mingyang , Huang Shuqiong , Liu Jun , Shu Yanling , Luo Yinbo , Wang Lulin , Li Mingyan , Wang Youjie 
Published: May 2020

AbstractBackgroundThe coronavirus disease 2019 (Covid-19) spreads rapidly around the world.ObjectiveTo evaluate the association between comorbidities and the risk of death in patients with COVID-19, and to further explore potential sex-specific differences.MethodsWe analyzed the data from 18,465 laboratory-confirmed cases that completed an epidemiological investigation in Hubei Province as of February 27, 2020. Information on death was obtained from the Infectious Disease Information System. The Cox proportional hazards model was used to estimate the association between comorbidities and the risk of death in patients with COVID-19.ResultsThe median age for COVID-19 patients was 50.5 years. 8828(47.81%) patients were females. Severe cases accounted for 20.11% of the study population. As of March 7, 2020, a total of 919 cases deceased from COVID-19 for a fatality rate of 4.98%. Hypertension (13.87%), diabetes (5.53%), and cardiovascular and cerebrovascular diseases (CBVDs) (4.45%) were the most prevalent comorbidities, and 27.37% of patients with COVID-19 reported having at least one comorbidity. After adjustment for age, gender, address, and clinical severity, patients with hypertension (HR 1.55, 95%CI 1.35-1.78), diabetes (HR 1.35, 95%CI 1.13-1.62), CBVDs (HR 1.70, 95%CI 1.43-2.02), chronic kidney diseases (HR 2.09, 95%CI 1.47-2.98), and at least two comorbidities (HR 1.84, 95%CI 1.55-2.18) had significant increased risks of death. And the association between diabetes and the risk of death from COVID-19 was prominent in women (HR 1.69, 95%CI 1.27-2.25) than in men (HR 1.16, 95%CI 0.91-1.46) (P for interaction = 0.036).ConclusionAmong laboratory-confirmed cases of COVID-19 in Hubei province, China, patients with hypertension, diabetes, CBVDs, chronic kidney diseases were significantly associated with increased risk of death. The association between diabetes and the risk of death tended to be stronger in women than in men. Clinicians should increase their awareness of the increased risk of death in COVID-19 patients with comorbidities.
Clinical Characteristics and Prognostic Factors for ICU Admission of Patients with Covid-19 Using Machine Learning and Natural Language Processing
Authors: Izquierdo Jose L. , Ancochea Julio ,   Savana COVID-19 Research Group, Soriano Joan B. 
Published: May 2020

ABSTRACTThere remain many unknowns regarding the onset and clinical course of the ongoing COVID-19 pandemic. We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling), to analyse the electronic health records (EHRs) of patients with COVID-19.We explored the unstructured free text in the EHRs within the SESCAM Healthcare Network (Castilla La-Mancha, Spain) from the entire population with available EHRs (1,364,924 patients) from January 1st to March 29th, 2020. We extracted related clinical information upon diagnosis, progression and outcome for all COVID-19 cases, focusing in those requiring ICU admission.A total of 10,504 patients with a clinical or PCR-confirmed diagnosis of COVID-19 were identified, 52.5% males, with age of 58.2±19.7 years. Upon admission, the most common symptoms were cough, fever, and dyspnoea, but all in less than half of cases. Overall, 6% of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm we identified that a combination of age, fever, and tachypnoea was the most parsimonious predictor of ICU admission: those younger than 56 years, without tachypnoea, and temperature <39°C, (or >39°C without respiratory crackles), were free of ICU admission. On the contrary, COVID-19 patients aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnoea and delayed their visit to the ER after being seen in primary care.Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnoea with/without respiratory crackles) predicts which COVID-19 patients require ICU admission.
Is there an airborne component to the transmission of COVID-19? : a quantitative analysis study
Authors: Beggs Clive B. 
Published: May 2020

AbstractObjectivesWhile COVID-19 is known to be spread by respiratory droplets (which travel <2m horizontally), much less is known about its transmission via aerosols, which can become airborne and widely distributed throughout room spaces. In order to quantify the risk posed by COVID-19 infectors exhaling respiratory aerosols in enclosed spaces, we undertook a computer modelling study to simulate transmission in an office building.MethodsRespiratory droplet data from four published datasets were analysed to quantify the number and volume of droplets <100μm diameter produced by a typical cough and speaking event (i.e. counting from 1 to 100). This was used in a stochastic model to simulate (10000 simulations) the number of respiratory particles, originating from a COVID-19 infector, that would be inhaled in one hour by a susceptible individual practicing socially distancing in a 4 × 4 × 2.5m office space. Several scenarios were simulated that mimicked the presence of both symptomatic and asymptomatic COVID-19 infectors.ResultsOn average, each cough and speaking event produced similar numbers of droplets <100μm diameter (median range = 971.9 – 1013.4). Computer simulations (ventilation rate=2AC/h) revealed that sharing the office space with a symptomatic COVID-19 infector (4 coughs and 10 speaking events per hour) for one hour resulted in the inhalation of 16.9 (25-75th range = 8.1-33.9) aerosolised respiratory droplets, equating to about 280-1190 particles inhaled over a 35-hour working week. Sharing with an asymptomatic infector (10 speaking events per hour) resulted in the about 196–875 particles inhaled over 35 hours.ConclusionsGiven that live SARS-CoV-2 virions are known to be shed in high concentrations from the nasal cavity of both symptomatic and asymptomatic COVID-19 patients, the results suggest that those sharing enclosed spaces with infectors for long periods may be at risk of contracting COVID-19 by the aerosol route, even when practicing social distancing.
Good communication is critical to supporting people living and working with a rare disease: current rare disease support perceived as inadequate
Authors: McMullan Julie , Crowe Ashleen L. , Bailie Caitlin , Moore Kerry , McMullan Lauren S. , Shamandi Nahid , McAneney Helen , McKnight Amy Jayne 
Published: May 2020

AbstractBackgroundMany people living and working with rare diseases describe consistent difficulties accessing appropriate information and support. In this study an evaluation of the awareness of rare diseases, alongside related information and educational resources available for patients, their families and healthcare professionals, was conducted in 2018-2019 using an online survey and semi-structured interviews with rare disease collaborative groups (charities, voluntary and community groups) active across Northern Ireland (NI).ResultsNinety-nine participants engaged with the survey with 31 respondents providing detailed answers. Resources such as information, communication, ‘registries’, online services, training and improvements to support services were queried. Excellent communication is an important factor in delivering good rare disease support. Training for health professionals was also highlighted as an essential element of improving support for those with a rare disease to ensure they approach people with these unique and challenging diseases in an appropriate way. Carers were mentioned several times throughout the study; it is often felt they are overlooked in rare disease research and more support should be in place for them. Current care/support for those with a rare disease was highlighted as inadequate. Nine semi-structured interviews were conducted with rare disease collaborative groups. Reoccurring themes included a need for more effective: information and communication, training for health professionals, online presence, support for carers, and involvement in research.ConclusionsAll rare disease collaborative groups agreed that current services for people living and working with a rare disease are not adequate. An important finding to consider in future research within the rare disease field is the inclusion of carers perceptions and experiences in studies. Due to the unique role a carer has in the life of a person with a rare disease it is vital that their voice is heard and their needs are listened to. This research provides insight into the support available for rare diseases across Northern Ireland, highlights unmet needs in service provision, and suggests approaches to improve rare disease support prioritising improved information and communication provision, improved access to services, and tailored support for carers of people with a rare disease.
Geographical surveillance of COVID-19: Diagnosed cases and death in the United States
Authors: Amin Raid , Hall Terri , Church Jacob , Schlierf Daniela , Kulldorff Martin 
Published: May 2020

AbstractBackgroundCOVID-19 is a new coronavirus that has spread from person to person throughout the world. Geographical disease surveillance is a powerful tool to monitor the spread of epidemics and pandemic, providing important information on the location of new hot-spots, assisting public health agencies to implement targeted approaches to minimize mortality.MethodsCounty level data from January 22-April 28 was downloaded from USAfacts.org to create heat maps with ArcMap™ for diagnosed COVID-19 cases and mortality. The data was analyzed using spatial and space-time scan statistics and the SaTScan™ software, to detect geographical cluster with high incidence and mortality, adjusting for multiple testing. Analyses were adjusted for age. While the spatial clusters represent counties with unusually high counts of COVID-19 when averaged over the time period January 22-April 20, the space-time clusters allow us to identify groups of counties in which there exists a significant change over time.ResultsThere were several statistically significant COVID-19 clusters for both incidence and mortality. Top clusters with high rates included the areas in and around New York City, New Orleans and Chicago, but there were also several small rural clusters. Top clusters for a recent surge in incidence and mortality included large parts of the Midwest, the Mid-Atlantic Region, and several smaller areas in and around New York and New England.ConclusionsSpatial and space-time surveillance of COVID-19 can be useful for public health departments in their efforts to minimize mortality from the disease. It can also be applied to smaller regions with more granular data.
Prognostic Impact of Human Papillomavirus Infection on Cervical Dysplasia, Cancer, and Patient Survival in Saudi Arabia: A 10-Year Retrospective Analysis
Authors: Alhamlan F.S. , Obeid D.A. , Khayat H.H. , Tulbah A.M. , Al-Badawi I.A. , Al-Muammar T.A. , Almutairi A.N. , Almatrrouk S.A. , Alfageeh M.B. , Bakhrebah M.A. , Nassar M.S. , Al-Ahdal M.N. 
Published: May 2020

AbstractCervical cancer is caused by persistent human papillomavirus (HPV) infection. However, HPV prevalence data and survival rates among HPV-infected women are scare in Saudi Arabia. This study assessed the prevalence of HPV genotypes in a 10 year time-frame. Cervical biopsy specimens underwent HPV detection, HPV viral load using qPCR, HPV genotyping, p16INK4a expression measurement using immunohistochemistry. Kaplan-Meier plots were constructed to analyze overall survival rates. Of the 316 cervical specimens examined, HPV was detected in 96 (30.4%); 37.3% had cervical cancer; 14.2% cervical intraepithelial neoplasia (CIN) III, 4.1% CIN II, and 17.0% CIN I. A significant association was found between HPV-16 viral load and disease progression (P < .001, Mann-Whitney U) and between HPV presence and cervical cancer (χ2, 56.78; P < .001). The expression of p16INK4a was a significant predictor of survival: women who had p16INK4a overexpression had poorer survival rates (multivariate Cox regression, hazard ratio, 3.2; 95% CI, 1.1-8.8). In addition, multivariate models with HPV status and cervical cancer diagnosis showed that HPV status was a significant predictor of survival: HPV-positive women had better survival rates than HPV-negative women (haza. These findings suggest that implementing cervical cancer and HPV screening programs may decrease cervical cancer rates and improve survival rates of women in Saudi Arabia.