bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cross-sectional equity analysis of accessibility by automobile to tertiary care emergency services in Cali, Colombia in 2020
Authors: Cuervo Luis Gabriel , Martinez Herrera Eliana , Osorio Lyda , Hatcher Roberts Janet , Cuervo Daniel , Bula María Olga , Pinilla Luis Fernando , Piquero Felipe , Jaramillo Ciro 
Published: February 2022

AbstractThis study provides data on equity in accessibility to tertiary care emergency services in Cali, accounting for traffic congestion, in two separate weeks in 2020.This cross-sectional study builds on a proof-of-concept, the AMORE Project(1) and provides a baseline assessment of accessibility to urgent tertiary care at peak and free flow traffic times in Cali.1 It makes the case for assessing travel time over distance, and accounting for traffic congestion.This study indicates that people in vulnerable situations have to travel longer and therefore invest more of their personal direct and indirect resources to access tertiary care emergency departments than the average population. This study emphasizes the added value of integrating new data sources that can inform health services and urban planning. These new data sources merit future testing by concerned stakeholders.1This study used the digital AMORE Platform to show the effects of traffic congestion on equitable access to tertiary care emergency departments. The data shows which populations take longer to reach a facility within a time threshold under different traffic congestion levels.The broader proof-of-concept assesses the value of new data obtained by integrating secondary data from publicly available sources. These sources combine geospatial analysis with census microdata, health services location data, and bigdata for travel times.The analysis covered the city of Cali, which has 2.258 million residents and is the third-largest city in Colombia. The analysis shows the projected accessibility assessments for two weeks during the COVID-19 pandemic, 6 – 12 July 2020, and 23 – 29 November 2020. Restrictions on car travel had been lifted before the July assessment, but stay-at-home orders were in place during the November assessment, which showed substantially less traffic.This assessment found that traffic congestion sharply reduces accessibility to tertiary emergency care. Reduced access has the greatest impact on people with less education, those living in low-income households or on the periphery of Cali, and specific ethnic groups (e.g., nomadic people like the Rrom, and Afro-descendants). This assessment also identifies the concentration of tertiary care emergency departments in areas of lower population density, leaving large swaths of the population with poor accessibility.Data was reported in dashboards that used simple univariate and bivariate analyses. In July 2020, the estimated overall accessibility at peak traffic hours was 37% and in November 2020 it increased to 57% due to reduced traffic congestion. These results illustrate the value of the proposed tools in monitoring and adjusting to changing conditions.
Use of machine learning for comparing disease risk scores and propensity scores under complex confounding and large sample size scenarios: a simulation study
Authors: Guo Yuchen , Strauss Victoria Y , Prieto-Alhambra Daniel , Khalid Sara 
Published: February 2022

ABSTRACTBackgroundThe surge of treatments for COVID-19 in the ongoing pandemic presents an exemplar scenario with low prevalence of a given treatment and high outcome risk. Motivated by that, we conducted a simulation study for treatment effect estimation in such scenarios. We compared the performance of two methods for addressing confounding during the process of estimating treatment effects, namely disease risk scores (DRS) and propensity scores (PS) using different machine learning algorithms.MethodsMonte Carlo simulated data with 25 different scenarios of treatment prevalence, outcome risk, data complexity, and sample size were created. PS and DRS matching with 1: 1 ratio were applied with logistic regression with least absolute shrinkage and selection operator (LASSO) regularization, multilayer perceptron (MLP), and eXtreme Gradient Boosting (XgBoost). Estimation performance was evaluated using relative bias and corresponding confidence intervals.ResultsBias in treatment effect estimation increased with decreasing treatment prevalence regardless of matching method. DRS resulted in lower bias compared to PS when treatment prevalence was less than 10%, under strong confounding and nonlinear nonadditive data setting. However, DRS did not outperform PS under linear data setting and small sample size, even when the treatment prevalence was less than 10%. PS had a comparable or lower bias to DRS when treatment prevalence was common or high (10% - 50%). All three machine learning methods had similar performance, with LASSO and XgBoost yielding the lowest bias in some scenarios. Decreasing sample size or adding nonlinearity and non-additivity in data improved the performance of both PS and DRS.ConclusionsUnder strong confounding with large sample size DRS reduced bias compared to PS in scenarios with low treatment prevalence (less than 10%), whilst PS was preferable for the study of treatments with prevalence greater than 10%, regardless of the outcome prevalence.Key MessagesWhen handling nonlinear nonadditive data with strong confounding, DRS estimated by machine learning methods outperforms PS in scenarios with low treatment prevalence (less than 10%).However, if having linear data and small sample size data with strong confounding, we did not observe DRS outperformed PS even when treatment prevalence was less than 10%.Our results suggested that using PS performed better compared to DRS in tackling strong confounding problems with treatment prevalence greater than 10%.Small sample size increased bias for both DRS and PS methods, and it affected DRS more than PS.
Investigating the genetic pathways of insomnia in Autism Spectrum Disorder
Authors: Niarchou Maria , Singer Emily V. , Straub Peter , Malow Beth A. , Davis Lea K. 
Published: February 2022

AbstractBackgroundSleep problems are common in children with autism spectrum disorder (autism). There is sparse research to date to examine whether insomnia in people with autism is related to autism genetics or insomnia genetics. Moreover, there is a lack of research examining whether circadian-rhythm related genes share potential pathways with autism.AimsTo address this research gap, we tested whether polygenic scores of insomnia or autism are related to risk of insomnia in people with autism, and to test whether the circadian genes are associated with insomnia in people with autism.Methods and proceduresWe tested these questions using the phenotypically and genotypically rich MSSNG dataset (N=1,049) as well as incorporating in the analyses data from the Vanderbilt University Biobank (BioVU) (N=349).Outcomes and resultsIn our meta-analyzed sample, there was no evidence of associations between the polygenic scores (PGS) for insomnia and a clinical diagnosis of insomnia, or between the PGS of autism and insomnia. We also did not find evidence of a greater burden of rare and disruptive variation in the melatonin and circadian genes in individuals with autism and insomnia compared to individuals with autism without insomnia.Conclusions and implicationsOverall, we did not find evidence for strong effects of genetic scores influencing sleep in people with autism, however, we cannot rule out the possibility that smaller genetic effects may play a role in sleep problems. Our study indicated the need for a larger collection of data on sleep problems and sleep quality among people with autism.
An evaluation of metabolic, dietetic, and nutritional status reveals impaired nutritional outcomes in breast cancer patients undergoing chemotherapy compared with a matched control group
Authors: da Silva Bruna Ramos , Rufato Sarah , Mialich Mirele S. , Cruz Loris P. , Gozzo Thais , Jordao Alceu A. 
Published: February 2022

AbstractPurposeNutritional status changes in breast cancer patients during treatment are prevalent. However, the metabolic implications of those alterations are poorly understood. We aimed to characterize body composition, lipids, glucose levels, and indices that express cardiovascular risk in breast cancer patients after completion of chemotherapy and then to compare those results with a matched control group.MethodsA cross-sectional study was performed. Women who completed their chemotherapy were recruited (BC group) and compared with a group of non-malignant age- and body mass index-matched (MC group), as well as a group of healthy, non-malignant women (HC group). Body composition by bioelectrical impedance analysis, handgrip strength, and blood sample were collected. Visceral adiposity, triglyceride glucose and lipid accumulation product indices were calculated. Food consumption was assessed.Results88 women were included (BC=36, MC=36, HC=16). BC patients demonstrated worse values of phase angle, nutritional risk index, extracellular body water to total body water ratio and lower handgrip strength. Additionally, those women had impairments in lipids, worst glucose levels, visceral fat dysfunction and consequently higher cardiovascular risk, presenting important unhealthy dietary patterns with higher carbohydrate and caloric intake and insufficient protein and fiber ingestion. No differences were observed between MC and HC.ConclusionBreast cancer patients present unhealthy metabolic, nutritional, and dietetic features when compared to a group of age- and BMI-matched non-malignant females. Also, breast cancer patients had higher levels of cardiovascular risk. Further investigations are required to examine the underlying mechanisms and the potential longitudinal changes during surveillance time.
Risk of severe COVID-19 from the Delta and Omicron variants in relation to vaccination status, sex, age and comorbidities – surveillance results from southern Sweden
Authors: Kahn Fredrik , Bonander Carl , Moghaddassi Mahnaz , Rasmussen Magnus , Malmqvist Ulf , Inghammar Malin , Björk Jonas 
Published: February 2022

AbstractThe risk of severe COVID-19 disease requiring hospitalization with extensive oxygen supply was compared among infected cases during two calendar periods when Delta and Omicron, respectively, were the dominating virus variants in Scania county, Sweden. Adjustments were made for differences among cases in comorbidities, prior infection, vaccination status, age and sex. Markedly lower risks were observed from Omicron among the vaccinated in the present study. The risk of severe disease was also lower for unvaccinated during Omicron than during Delta, but remained high among older people and middle-aged males with comorbidities. Efforts to increase vaccination uptake across countries, populations and subgroups should thus remain a public health priority.
Associations between prenatal exposure to gestational diabetes mellitus and child adiposity markers: mediating effects of brain structure
Authors: Luo Shan , Hsu Eustace , Lawrence Katherine E. , Adise Shana , Pickering Trevor A. , Herting Megan M. , Buchanan Thomas , Page Kathleen A. , Thompson Paul M. 
Published: February 2022

AbstractObjectivesTo investigate neural mechanisms underlying increased obesity risk in children prenatally exposed to gestational diabetes mellitus (GDM).Research Design and MethodsThis was a cross-sectional study of 9-10-year-old participants and siblings across the United States. Data was obtained from the baseline assessment of the Adolescent Brain and Cognitive Development (ABCD) Study® in which brain structure was evaluated by magnetic resonance imaging. Adiposity markers included age- and sex-specific body mass index (BMIz-scores), waist circumference and waist-to-height ratio. GDM exposure was self-reported, and discordance for GDM exposure within biological siblings was identified. Mixed effects and mediation models were used to examine associations between prenatal GDM exposure, brain structure, and adiposity markers with sociodemographic covariates.ResultsThe sample included 8,521 children (age: 9.9±0.6 years; 51% males; 7% GDM-exposed), among whom there were 28 sibling pairs discordant for GDM exposure. Across the entire study sample, prenatal exposure to GDM was associated with lower global and regional cortical gray matter volume (GMV) in the bilateral rostral middle frontal gyrus and superior temporal gyrus. In a subset of sample only including siblings, GDM-exposed siblings also demonstrated lower global cortical GMV than un-exposed siblings. Global cortical GMV partially mediated the associations between prenatal GDM exposure and child adiposity markers.ConclusionsOur results establish robust and generalizable brain markers of prenatal GDM exposure and suggest that low cortical GMV may explain increased obesity risk for offspring prenatally exposed to GDM.Twitter summaryPrenatal exposure to gestational diabetes mellitus #GDM is associated with smaller cortical gray matter volume #brain, which in turn relates to larger adiposity markers #BMI in a large and diverse cohort of 8521 children.Article HighlightsPrenatal exposure to gestational diabetes mellitus (GDM) is associated with smaller cortical gray matter volume (GMV) in a large and diverse cohort of 8521 children, independent from genetics and shared environment.Cortical GMV partially mediated the associations of prenatal GDM exposure and adiposity markers in children.These results establish robust and generalizable brain markers of prenatal GDM exposure and provide neurobiological underpinnings of increased obesity risk in offspring prenatally exposed to GDM.
Influence of meteorological factors and drought on coccidioidomycosis incidence in California, 2000–2020
Authors: Head Jennifer R. , Sondermeyer-Cooksey Gail , Heaney Alexandra K. , Yu Alexander T. , Jones Isabel , Bhattachan Abinash , Campo Simon , Wagner Robert , Mgbara Whitney , Phillips Sophie , Keeney Nicole , Taylor John , Eisen Ellen , Lettenmaier Dennis P. , Hubbard Alan , Okin Gregory S. , Vugia Duc J. , Jain Seema , Remais Justin V. 
Published: February 2022

AbstractBackgroundCoccidioidomycosis is an emerging infection in the southwestern United States. We examined the effects of precipitation and temperature on the incidence of coccidioidomycosis in California during 2000-2020, and estimated incident cases attributable to the California droughts of 2007-09 and 2012-15.MethodsWe analyzed monthly California coccidioidomycosis surveillance data from 2000–2020 at the census tract-level using generalized additive models. Models included distributed lags of precipitation and temperature within each endemic county, pooled using fixed-effects meta-analysis. An ensemble prediction algorithm of incident cases per census tract was developed to estimate the impact of drought on expected cases.ResultsAcross 14 counties examined, coccidioidomycosis was strongly suppressed during, and amplified following, the 2007-2009 and 2012-2015 droughts. An estimated excess of 1,358 and 2,461 drought-attributable cases were observed in California in the two years following the 2007-2009 and 2012-2015 droughts, respectively. These post-drought excess cases more than offset the drought-attributable declines of 1,126 and 2,192 cases, respectively, that occurred during the 2007-2009 and 2012-2015 droughts. Across counties, a temperature increase from the 25thto 75thpercentile (interquartile range) in the summer was associated with a doubling of incidence in the following fall (incidence rate ratio (IRR): 2.02, 95% CI: 1.84, 2.22), and a one IQR increase in precipitation in the winter was associated with 1.45 (95% CI: 1.36, 1.55) times higher incidence in the fall. The effect of winter precipitation was stronger (interaction coefficient representing ratio of IRRs: 1.36, 95% CI: 1.25, 1.48) when preceded by two dry rather than average winters. Incidence in arid lower San Joaquin Valley counties was most sensitive to winter precipitation fluctuations, while incidence in wetter coastal counties was most sensitive to summer temperature fluctuations.ConclusionsIn California, wet winters along with hot summers, particularly those following previous dry years, increased risk of coccidioidomycosis in California. Drought conditions may suppress incidence, then amplify incidence in subsequent years. With anticipated increasing frequency of drought in California, continued expansion of incidence, particularly in wetter, coastal regions, is expected.
Fairness and efficiency considerations in COVID-19 vaccine allocation strategies: a case study comparing front-line workers and 65-74 year olds in the United States
Authors: Rumpler Eva , Feldman Justin M. , Bassett Mary T. , Lipsitch Marc 
Published: February 2022

1AbstractThe COVID-19 epidemic in the United States has been characterized by two stark disparities. COVID-19 burden has been unequally distributed among racial and ethnic groups and at the same time the mortality rates have been sharply higher among older age groups. These disparities have led some to suggest that inequalities could be reduced by vaccinating front-line workers before vaccinating older individuals, as older individuals in the US are disproportionately Non-Hispanic White.We compare the performance of two distribution policies, one allocating vaccines to front-line workers and another to older individuals aged 65-74-year-old. We estimate both the number of lives saved and the number of years of life saved under each of the policies, overall and in every race/ethnicity groups, in the United States and every state.We show that prioritizing COVID-19 vaccines for 65-74-year-olds saves both more lives and more years of life than allocating vaccines front-line workers in each racial/ethnic group, in the United States as a whole and in nearly every state. When evaluating fairness of vaccine allocation policies, the overall benefit to impact of each population subgroup should be considered, not only the proportion of doses that is distributed to each subgroup. Further work can identify prioritization schemes that perform better on multiple equity metrics.
Deceiving The Big Eaters: Salmonella Typhimurium SopB subverts host cell Xenophagy through Akt-TFEB axis in macrophages
Authors: Chatterjee Ritika , Chaudhuri Debalina , Setty Subba Rao Gangi , Chakravortty Dipshikha 
Published: February 2022

ABSTRACTSalmonella, a stealthy facultative intracellular pathogen, harbors an array of host immune evasion strategies. This facilitates successful survival and replicative niches establishment in otherwise hostile host innate immune cells such as macrophages. Salmonella survives and utilizes macrophages for effective dissemination throughout the host causing systemic infection. One of the central host defense mechanisms in macrophages is bacterial xenophagy or macro-autophagy. Here we report for the first time that Salmonella pathogenicity island-1 (SPI-1) effector SopB is involved in subverting host autophagy through dual mechanisms. SopB is known to act as a phosphoinositide phosphatase and thereby can alter the phosphoinositide dynamics of the host cell. Here we demonstrate that this activity helps the bacterium escape autophagy by inhibiting terminal fusion of Salmonella containing vacuole (SCV) with both lysosomes and autophagosomes. We also report the second mechanism, wherein SopB downregulates overall lysosomal biogenesis through Akt- transcription factor EB (TFEB) axis. TFEB is a master regulator of lysosomal biogenesis and autophagy, and SopB restricts the nuclear localization of TFEB. This reduces the overall lysosome content inside host macrophages, further facilitating survival in macrophages and systemic dissemination of Salmonella in the host.
Re-sensitising XDR biofilms using a combination of bacteriophage cocktails and colistin: a natural approach
Authors: Devanga Ragupathi Naveen Kumar , Muthuirulandi Sethuvel Dhiviya Prabaa , Gopikrishnan Mohanraj , Murugan Dhivya , Juliet Ramya , Majhi Monalisa , Murugesan Malathi , C George Priya Doss , Wannigama Leshan , Monk Peter N. , Karunakaran Esther , Veeraraghavan Balaji 
Published: February 2022

AbstractPersistent antibiotic use results in the rise of antimicrobial resistance with limited or no choice for multidrug resistant (MDR) and extensively drug resistant (XDR) bacteria. This necessitates a need for alternative therapy to effectively combat clinical pathogens that are resistant to last resort antibiotics. The study investigates hospital sewage as a potential source of bacteriophages to control MDR/XDR bacterial pathogens. 81 samples were screened for phages against selected clinical pathogens. 10 phages were isolated against A. baumannii, 5 phages against K. pneumoniae and 16 phages obtained against P. aeruginosa. The novel phages were observed to be strain-specific with a complete growth inhibition of up to 6 hrs. Phage plus colistin combinations further reduced the MBEC of colistin up to 16 folds. Notably, cocktail of phages exhibited supreme efficacy with complete killing at 0.5-1 µg/ml colistin concentrations. Thus, phages specific to clinical strains has a higher edge in treating nosocomial pathogens with their proven anti-biofilm efficacy. In addition, analysis of phage genomes revealed close phylogenetic relations with phages reported from Europe, China and other neighbouring countries. This study serves as a reference and can be extended to other antibiotics and phage types to assess optimum synergistic combinations to combat various drug resistant pathogens in the ongoing AMR crisis.
Frequency of a nucleotide overhang at the 5’ end of hemorrhagic fever mammarenavirus genomes in public sequence data
Authors: Neriya Yutaro , Kojima Shohei , Kishimoto Mai , Sakiyama Arata , Iketani Takao , Watanabe Tadashi , Abe Yuichi , Shimoda Hiroshi , Nakagawa Keisuke , Koma Takaaki , Matsumoto Yusuke 
Published: February 2022

AbstractMammarenaviruses, such as Lassa virus and South American hemorrhagic fever (SAHF) virus, cause severe hemorrhagic fevers in humans, and pose major threats to public health. Mammarenaviruses consist of a bi-segmented negative-sense RNA genome in which the 5’ and 3’ ends form complementary strands that serve as a replication promoter. Some mammarenaviruses have a nucleotide overhang at the 5’ genome end. By examining the complementarity of 5’ and 3’ genome ends using public mammarenavirus genome sequences, we found that the 5’ guanine overhang (5’-G overhang) was present more frequently in Lassa and SAHF viruses than in other viruses. The 5’-G overhang in the Lassa and SAHF virus sequences was found to be restricted to the L and S segments, respectively. If the genome end sequence data in the public database are accurate, the 5’-G overhang may be related to the high pathogenicity of mammarenaviruses in humans.