bluuebunny/crossref_metadata_2025_split huggingface dataset:
Minority and Rural Coronavirus Insights Study (MRCIS): The Need for Targeted SARS CoV-2 Vaccination Efforts in Minority Populations
Authors: Landry Latrice G. , Lee LaTasha , Chalasani Nishanth , Xu Liou , Stair Taylor , Puckrein Gary , Meyer William A. , Wiltz Gary , Sampson Marian , Gregerson Paul , Barron Charles , Marable Jeffrey , Akinboboye Ola 
Published: October 2021

AbstractImportanceRacial and ethnic minority populations have been disproportionately affected in terms of hospitalizations and deaths during the COVID-19 pandemic. Vaccine uptake remains a barrier to full population inoculation against this highly infectious disease.ObjectiveThe purpose of this report is to describe SARS-CoV-2 vaccine interest rates in a racially, geographically, and ethnically diverse study cohort and characterize vaccine interest across a racially, ethnically, and geographically diverse study population.DesignThis report describes responses to a survey administered between November 2020 and May 2021 using a community convenience sample through a partnership between the National Minority Quality Forum (NMQF) and Federally Qualified Health Centers (FQHCs) as part of the Minority and Rural Coronavirus Insights Study (MRCIS). Analysis of survey responses from 3,624 participants are provided.ResultsEarly data from the MRCIS cohort suggest that “SARS-CoV-2 vaccine hesitancy” is more prevalent in Black versus Non-Hispanic Whites survey respondents, and the Hispanic community has positive interest in the vaccine, to a similar degree as Whites. The persistent presence of “vaccine undecideds” across different sites and racial/ethnic groups uncovers the need for more public health efforts to influence positive views about vaccination.ConclusionThese findings highlights the urgent need for interventional educational campaigns targeted at populations at risk of low vaccine interest. Focused efforts are needed to combat misinformation and explain the vaccine’s safety and effectiveness to promote its uptake and avoid low inoculation rates. Public health communication must consider differences in population groups, regions, and social determinants of health to fully address vaccine uptake disparities and overcome alleged hesitancy.Key PointsWillingness to receive the SARS CoV-2 varies among minority populations.“SARS-CoV-2 vaccine hesitancy” is more prevalent in the non-Hispanic Black population than the non-Hispanic White and Hispanic populations.Public health infrastructure is needed in underserved communities for efficient assessment and targeted communication of public health priorities such as the SARS CoV-2 vaccination.
Using sero-epidemiology to monitor disparities in vaccination and infection with SARS-CoV-2
Authors: Routledge Isobel , Takahashi Saki , Epstein Adrienne , Hakim Jill , Janson Owen , Turcios Keirstinne , Vinden Jo , Risos John Tomas , Baniqued Margaret Rose , Pham Lori , Di Germanio Clara , Busch Michael , Kushel Margot , Greenhouse Bryan , Rodríguez-Barraquer Isabel 
Published: October 2021

AbstractBackgroundAs COVID-19 vaccines continue to be rolled-out, the “double burden” of health disparities in both exposure to infection and vaccination coverage intersect to determine the current and future patterns of infection, immunity, and mortality. Serology provides a unique opportunity to measure biomarkers of infection and vaccination simultaneously, and to relate these metrics to demographic and geographic factors.MethodsLeveraging algorithmically selected residual serum samples from two hospital networks in San Francisco, we sampled 1014 individuals during February 2021, capturing transmission during the first 11 months of the epidemic and the early roll out of vaccination. These samples were tested using two serologic assays: one detecting antibodies elicited by infection, and not by vaccines, and one detecting antibodies elicited by both infection and vaccination. We used Bayesian statistical models to estimate the proportion of the population that was naturally infected and the proportion protected due to vaccination.FindingsWe estimated that the risk of prior infection of Latinx residents was 5.3 (95% CI: 3.2 - 10.3) times greater than the risk of white residents aged 18-64 and that white San Francisco residents over the age of 65 were twice as likely (2.0, 95% CI: 1.1 - 4.6) to be vaccinated as Black residents. We also found socioeconomically deprived zipcodes in the city had high probabilities of natural infections and lower vaccination coverage than wealthier zipcodes.InterpretationUsing a platform we created for SARS-CoV-2 serologic data collection in San Francisco, we characterized and quantified the stark disparities in infection rates and vaccine coverage by demographic groups over the first year of the pandemic. While the arrival of the SARS-CoV-2 vaccine has created a ‘light at the end of the tunnel’ for this pandemic, ongoing challenges in achieving and maintaining equity must also be considered.FundingNIH, NIGMS, Schmidt Science Fellows in partnership with the Rhodes Trust and the Chan Zuckerberg Biohub.
Beyond the limits of circadian entrainment: Non-24-hour sleep-wake disorder, shift work, and social jet lag
Authors: Diekman Casey O. , Bose Amitabha 
Published: October 2021

AbstractWhile the vast majority of humans are able to entrain their circadian rhythm to the 24-hour light-dark cycle, there are numerous individuals who are not able to do so due to disease or societal reasons. We use computational and mathematical methods to analyze a well-established model of human circadian rhythms to address cases where individuals do not entrain to the 24-hour light-dark cycle, leading to misalignment of their circadian phase. For each case, we provide a mathematically justified strategy for how to minimize circadian misalignment. In the case of non-24-hour sleep-wake disorder, we show why appropriately timed bright light therapy induces entrainment. With regard to shift work, we explain why reentrainment times following transitions between day and night shifts are asymmetric, and how higher light intensity enables unusually rapid reentrainment after certain transitions. Finally, with regard to teenagers who engage in compensatory catch-up sleep on weekends, we propose a rule of thumb for sleep and wake onset times that minimizes circadian misalignment due to this type of social jet lag. In all cases, the primary mathematical approach involves understanding the dynamics of entrainment maps that measure the phase of the entrained rhythm with respect to the daily onset of lights.
The Contribution of Health Behaviors to Depression Risk across Birth Cohorts
Authors: Gueltzow Maria , Bijlsma Maarten J. , van Lenthe Frank J. , Myrskylä Mikko 
Published: October 2021

AbstractBackgroundMore recent birth cohorts are at a higher depression risk than cohorts born in the early twentieth century. We aimed to investigate to what extent changes in alcohol consumption, smoking, physical activity and obesity, contribute to these birth cohort variations.MethodsWe analyzed panel data from US adults born 1916-1966 enrolled in the Health and Retirement Study (N=163,760 person-years). We performed a counterfactual decomposition analysis by combining age-period-cohort models with g-computation. This allowed us to compare the predicted probability of elevated depressive symptoms (CES-D 8 score ≥3) in the natural course to a counterfactual scenario where all birth cohorts had the health behavior of the 1945 birth cohort. We stratified analyses by sex and race/ethnicity.ResultsDepression risk of the 1916-1949 and 1950-1966 birth cohort would be on average 2% (-2.3 to -1.7) and 0.5% (-0.9 to -0.1) higher had they had the alcohol consumption levels of the 1945 cohort. In the counterfactual with the 1945 BMI distribution, depression risk is on average 2.1% (1.8 to 2.4) higher for the 1916-1940 cohorts and 1.8% (-2.2 to -1.5) lower for the 1950-1966 cohorts. We find no cohort variations in depression risk for smoking and physical activity. The contribution of alcohol is more pronounced for Whites than for other race/ethnicity groups, and the contribution of BMI more pronounced for women than for men.ConclusionIncreased obesity levels exacerbated depression risk in recent birth cohorts in the US, while drinking patterns only played a minor role.
Prediction of Unplanned 30- day Readmission for ICU Patients with Heart Failure
Authors: Pishgar M , Theis J , Del Rios M , Ardati A , Anahideh H , Darabi H 
Published: October 2021

ABSTRACTBackgroundIntensive Care Unit (ICU) readmissions in patients with Heart Failure (HF) result in a significant risk of death and financial burden for patients and healthcare systems. Prediction of at-risk patients for readmission allows for targeted interventions that reduce morbidity and mortality.Methods and ResultsWe presented a process mining approach for the prediction of unplanned 30-day readmission of ICU patients with HF. A patient’s health records can be understood as a sequence of observations called event logs; used to discover a process model. Time information was extracted using the DREAM (Decay Replay Mining) algorithm. Demographic information and severity scores upon admission were then combined with the time information and fed to a Neural Network (NN) model to further enhance the prediction efficiency.ResultsBy using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset of 3411 ICU patients with HF, our proposed model yielded an Area Under the Receiver Operating Characteristics (AUROC) of 0.920.ConclusionsThe proposed approach was capable of modeling the time-related variables and incorporating the medical history of patients from prior hospital visits for prediction. Thus, our approach significantly improved the outcome prediction compared to that of other ML-based models and health calculators.
Use of Machine Learning for Long Term Planning and Cost Minimization in Healthcare Management
Authors: Kabir Sadia Binta , Shuvo Salman Sadiq , Ahmed Helal Uddin 
Published: October 2021

AbstractThe Healthcare system of a country is a crucial infrastructure that requires long-term capacity planning. The covid 19 outbreak pointed to the necessity of adequate hospital capacity, especially for developing countries like Bangladesh. The existing infrastructure planning of these countries emphasizes short-term goals and lacks vision planning for a long time horizon. It is in the country’s best interest to make long-term capacity expansion plans, a strategy the developed countries banked to provide adequate healthcare facilities to their residents. However, no single solution is appropriate for a different region. Hence, it is required to comprehensively study the situation and constraints of the specific region before providing expensive capacity expansion plans. This work focuses on applying a deep Reinforcement Learning based long-term hospital bed capacity expansion plan. We utilize the RNN-LSTM based population forecast, deep Reinforcement Learning (RL) based policy-making, and state-of-the-art Artificial Intelligence techniques to provide a solution. We perform a case study for the Abhaynagar Upazila of Jessore, one of the largest cities in the southwest part of Bangladesh, to analyze the benefits of such an approach compared to existing myopic policies. The experiment results show that the deep RL-based policy significantly minimizes cost over a 30-year expansion plan.
Covid-19 vaccine perceptions in Senegal and in Mali: a mixed approach
Authors: Fournier-Tombs Eleonore , Diouf Massamba , Maiga Abdine , Faye Sylvain , Ndoye Tidiane , Haidara Lalla , Batchily Moussa , Castet-Renard Céline 
Published: October 2021

AbstractThis paper presents the results of two qualitative surveys in Senegal and in Mali, which include questions about hesitancy to the COVID-19 vaccine between April and June 2021. It took place within a larger 2-year research project involving researchers in Senegal, Mali and Canada which examines the uses of artificial intelligence technologies in the fight against COVID-19. The study involved 1000 respondents in Senegal and 555 in Mali. The researchers found that overall, 55% of respondents in Senegal and 52% of respondents in Mali did not plan to be vaccinated. Hesitancy was much higher in youth aged 15-35 in both cases, with 70% of youth in Senegal and 57% of youth in Mali not planning to be vaccinated, compared to only 42% of elderly in Senegal and 37% of elderly in Mali. The researchers did not find disparities between male and female respondents in Senegal but found some in Mali. They also found that those who had a member of the family with chronic disease (diabetes or hypertension) were slightly more likely to want to be vaccinated. Reasons for vaccine hesitancy fell in several categories, including fear of vaccine side-effects, disbelief in vaccine efficacy or usefulness, and general distrust in the public health system.
rECHOmmend: an ECG-based machine-learning approach for identifying patients at high-risk of undiagnosed structural heart disease detectable by echocardiography
Authors: Ulloa-Cerna Alvaro E. , Jing Linyuan , Pfeifer John M. , Raghunath Sushravya , Ruhl Jeffrey A. , Rocha Daniel B. , Leader Joseph B. , Zimmerman Noah , Lee Greg , Steinhubl Steven R. , Good Christopher W. , Haggerty Christopher M. , Fornwalt Brandon K. , Chen Ruijun 
Published: October 2021

AbstractBackgroundEarly diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, electrocardiogram (ECG)-based prediction models can help target high-risk patients. We developed a novel ECG-based machine learning approach to predict multiple structural heart conditions, hypothesizing that a composite model would yield higher prevalence and positive predictive values (PPVs) to facilitate meaningful recommendations for echocardiography.MethodsUsing 2,232,130 ECGs linked to electronic health records and echocardiography reports from 484,765 adults between 1984-2021, we trained machine learning models to predict the presence of any of seven echocardiography-confirmed diseases within one year. This composite label included: moderate or severe valvular disease (aortic/mitral stenosis or regurgitation, tricuspid regurgitation), reduced ejection fraction <50%, or interventricular septal thickness >15mm. We tested various combinations of input features (demographics, labs, structured ECG data, ECG traces) and evaluated model performance using 5-fold cross-validation, multi-site validation trained on one clinical site and tested on 11 other independent sites, and simulated retrospective deployment trained on pre-2010 data and deployed in 2010.FindingsOur composite “rECHOmmend” model using age, sex and ECG traces had an area under the receiver operating characteristic curve (AUROC) of 0.91 and a PPV of 42% at 90% sensitivity at a prevalence of 17.9% for our composite label. Individual disease models had AUROCs ranging from 0.86-0.93 and lower PPVs from 1%-31%. The AUROC for models using different input features ranged from 0.80-0.93, increasing with additional features. Multi-site validation showed similar results to the cross-validation, with an aggregate AUROC of 0.91 across our independent test set of 11 clinical sites after training on a separate site. Our simulated retrospective deployment showed that for ECGs acquired in patients without pre-existing known structural heart disease in a single year, 2010, 11% were classified as high-risk, of which 41% developed true, echocardiography-confirmed disease within one year.InterpretationAn ECG-based machine learning model using a composite endpoint can predict previously undiagnosed, clinically significant structural heart disease while outperforming single disease models and improving practical utility with higher PPVs. This approach can facilitate targeted screening with echocardiography to improve under-diagnosis of structural heart disease.
Global Dynamics as Communication Sensors in Peptide Synthetase Cyclization Domains
Authors: Mishra Subrata H. , Kancherla Aswani K. , Marincin Kenneth A. , Bouvignies Guillaume , Nerli Santrupti , Sgourakis Nikolaos , Dowling Daniel P. , Frueh Dominique P. 
Published: October 2021

ABSTRACTStructural biology is the foundation for deriving molecular mechanisms, where snapshots of macromolecules and binding partners inform on mutations that test or modify function. However, frequently, the impact of mutations violates the underpinnings of structural models, and mechanisms become cryptic. This conundrum applies to multidomain enzymatic systems called nonribosomal peptide synthetases (NRPSs), which assemble simple substrates into complex metabolites often with pharmaceutical properties. Engineering NRPSs can generate new pharmaceuticals1-3but a dynamic domain organization challenges rational design.4-8Using nuclear magnetic resonance (NMR), we determined the solution structure of a 52 kDa cyclization domain and demonstrate that global intra-domain dynamics enable sensing of substrates tethered to partner domains and draw an allosteric response encompassing the enzyme’s buried active site and two binding sites 40 Å apart. We show that a point-site mutation that impedes the domain dynamics globally hampers the allosteric response. We demonstrate this mechanism through NMR experiments that provide atomic-level read-outs of allosteric responses during biochemical transformationsin situ. Our results establish global structural dynamics as sensors of molecular events that can remodel domain interactions and illustrate the need for integrating structural dynamics explicitly when deriving molecular mechanisms through mutagenesis and structural biology.
Uncovering the distinct properties of a bacterial type I-E CRISPR activation system
Authors: Kcam Maria Claudia Villegas , Tsong Annette J. , Chappell James 
Published: October 2021

ABSTRACTSynthetic gene regulators based upon CRISPR-Cas systems offer highly programmable technologies to control gene expression in bacteria. Bacterial CRISPR activators (CRISPRa) have been developed that use engineered type II CRISPR-dCas9 to localize transcription activation domains near promoter elements to activate transcription. However, several reports have demonstrated distance-dependent requirements and periodical activation patterns that overall limit the flexibility of these systems. Here, we demonstrate the potential of using an alternative type I-E CRISPR-Cas system to create a CRISPRa with distinct and expanded regulatory properties. We create the first bacterial CRISPRa system based upon a type I-E CRISPR-Cas, and demonstrate differences in the activation range of this system compared to type II CRISPRa systems. Furthermore, we characterize the distance-dependent activation patterns of type I-E CRISPRa to reveal a distinct and more frequent periodicity of activation.
A prebiotic basis for ATP as the universal energy currency
Authors: Pinna Silvana , Kunz Cäcilia , Harrison Stuart , Jordan Sean F. , Ward John , Werner Finn , Lane Nick 
Published: October 2021

AbstractATP is universally conserved as the principal energy currency in cells, driving metabolism through phosphorylation and condensation reactions. Such deep conservation suggests that ATP arose at an early stage of biochemical evolution. Yet purine synthesis requires six phosphorylation steps linked to ATP hydrolysis. This autocatalytic requirement for ATP to synthesize ATP implies the need for an earlier prebiotic ATP-equivalent, which could drive protometabolism before purine synthesis. Why this early phosphorylating agent was replaced, and specifically with ATP rather than other nucleotide triphosphates, remains a mystery. Here we show that the deep conservation of ATP reflects its prebiotic chemistry in relation to another universally conserved intermediate, acetyl phosphate, which bridges between thioester and phosphate metabolism by linking acetyl CoA to the substrate-level phosphorylation of ADP. We confirm earlier results showing that acetyl phosphate can phosphorylate ADP to ATP at nearly 20 % yield in water in the presence of Fe3+ions. We then show that Fe3+and acetyl phosphate are surprisingly favoured: a panel of other prebiotically relevant ions and minerals did not catalyze ADP phosphorylation; nor did a number of other potentially prebiotic phosphorylating agents. Only carbamoyl phosphate showed some modest phosphorylating activity. Critically, we show that acetyl phosphate does not phosphorylate other nucleotide diphosphates or free pyrophosphate in water. The phosphorylation of ADP monomers seems to be favoured by the interaction between the N6 amino group on the adenine ring with Fe3+coupled to acetyl phosphate. Our findings suggest that the reason ATP is universally conserved across life is that its formation is chemically favoured in aqueous solution under mild prebiotic conditions.
Spatial modelling for population replacement of mosquito vectors at continental scale
Authors: Beeton Nicholas J. , Wilkins Andrew , Ickowicz Adrien , Hayes Keith R. , Hosack Geoffrey R. 
Published: October 2021

AbstractMalaria is one of the deadliest vector-borne diseases in the world. Researchers are developing new genetic and conventional vector control strategies to attempt to limit its burden. Novel control strategies require detailed safety assessment to ensure responsible and successful deployments. Anopheles gambiae sensu stricto (s.s.) and Anopheles coluzzii, two closely related subspecies within the species complex Anopheles gambiae sensu lato (s.l.), are among the dominant malaria vectors in sub-Saharan Africa. These two subspecies readily hybridise and compete in the wild and are also known to have distinct niches, each with spatially and temporally varying carrying capacities driven by precipitation and land use factors.We model the spread and persistence of a population-modifying gene drive system in these subspecies across sub-Saharan Africa by simulating introductions of genetically modified mosquitoes across the African mainland and its offshore islands. We explore transmission of the gene drive between the two subspecies that arise from different hybridisation mechanisms, the effects of both local dispersal and potential wind-aided migration to the spread, and the development of resistance to the gene drive. Given the best current available knowledge on the subspecies’ life histories, we find that an introduced gene drive system with typical characteristics can plausibly spread from even distant offshore islands to the African mainland with the aid of wind-driven migration, with resistance taking over within a decade. Our model accounts for regional to continental scale mechanisms, and demonstrates a range of realistic dynamics including the effect of prevailing wind on spread and spatio-temporally varying carrying capacities for subspecies. As a result, it is well-placed to answer future questions relating to mosquito gene drives as important life history parameters become better understood.