bluuebunny/crossref_metadata_2025_split huggingface dataset:
Contribution of prenatal exposure to ambient temperature extremes and severe maternal morbidity: A retrospective Southern birth cohort
Authors: Runkle Jennifer D. , Sugg Maggie M. , Stevens Scott E. 
Published: June 2022

ABSTRACTBACKGROUNDHealth disparities have persisted in severe maternal morbidity (SMM), an event in which a woman nearly dies from a complication during pregnancy, with limited data on environmental risk factors.OBJECTIVETo examine the association between prenatal exposure to high and low ambient temperatures and SMM during critical windows of pregnancy for a birth cohort in the Southeastern United States.METHODSThis retrospective, population-based birth cohort included hospital deliveries from 1999 to 2017 (570,660 women, 921,444 deliveries). Daily average temperatures at the county- scale were merged with delivery discharge records and days of exposure to very hot and very cold were estimated over the following critical windows: preconception, and first, second, and third trimesters (T1-T3). Generalized estimating equations with multivariable Poisson models examined the association between temperature extremes and SMM for each critical window.RESULTSWomen exposed to a low compared to a high number of cold days during the first and third trimesters were 1.11 (CI: 1.03, 1.20) and 1.30 (CI: 1.20, 1.42) times more likely to experience SMM, respectively. Compared to the no exposure group, women exposed to a high number of very hot temperatures during preconception were 1.09 (95%CI:1.02,1.18) more likely to experience SMM. Sustained exposure to a high or moderate-intensity heat wave during the summer months was associated with a 45% or 39% increase in SMM risk during T2, respectively. Pregnant populations residing in rural locations were more sensitive to cold exposure in T3. Women exposed to a high number of very hot days in T2 compared to no exposure were 20% more likely to experience preterm SMM.SIGNIFICANCEFindings suggest that maternal exposure to hot or cold temperature extremes around the time or during pregnancy may be a contributing environmental risk factor for SMM. More attention should be focused on prenatal counseling in pregnant populations around the risk of thermal extremes.Impact StatementThis is the first study to examine the association between severe maternal morbidity and ambient cold and hot temperature extremes. Results revealed an increase in SMM risk for pregnant individuals following unseasonably cold exposure during the first and third trimesters and exposure to hotter than average temperatures in the second trimester. Our findings suggest that maternal exposure to ambient temperature extremes is a modifiable risk factor for SMM. This study considered contextual social and environmental factors associated with increased SMM risks, such as residential segregation (a proxy for structural racism), residential poverty, and rural compared to urban differences.
Machine Learning-Based Risk Stratification for Gestational Diabetes Management
Authors: Yang Jenny , Clifton David , Hirst Jane , Kavvoura Foteini , Farah George , Mackillop Lucy , Lu Huiqi 
Published: June 2022

AbstractGestational diabetes mellitus (GDM) is often diagnosed during the last trimester of pregnancy, leaving only a short timeframe for intervention. However, appropriate assessment, management, and treatment have been shown to reduce the complications of GDM. This study introduces a machine learning-based stratification system for identifying patients at risk of exhibiting high blood glucose levels, based on daily blood glucose measurements and electronic health record (EHR) data from GDM patients. We internally trained and validated our model on a cohort of 1,148 pregnancies at Oxford University Hospitals NHS Foundation Trust (OUH), and performed external validation on 709 patients from Royal Berkshire Hospital NHS Foundation Trust (RBH). We trained linear and non-linear tree-based regression models to predict the proportion of high-readings (readings above the UK’s National Institute for Health and Care Excellence [NICE] guideline) a patient may exhibit in upcoming days, and found that XGBoost achieved the highest performance during internal validation (0.021 [CI 0.019-0.023], 0.482 [0.442-0.516], and 0.112 [0.109-0.116], for MSE, R2, MAE, respectively). The model also performed similarly during external validation, suggesting that our method is generalizable across different cohorts of GDM patients.
TMbed – Transmembrane proteins predicted through Language Model embeddings
Authors: Bernhofer Michael , Rost Burkhard 
Published: June 2022

AbstractBackgroundDespite the immense importance of transmembrane proteins (TMP) for molecular biology and medicine, experimental 3D structures for TMPs remain about 4-5 times underrepresented compared to non-TMPs. Today’s top methods such as AlphaFold2 accurately predict 3D structures for many TMPs, but annotating transmembrane regions remains a limiting step for proteome-wide predictions.ResultsHere, we present TMbed, a novel method inputting embeddings from protein Language Models (pLMs, here ProtT5), to predict for each residue one of four classes: transmembrane helix (TMH), transmembrane strand (TMB), signal peptide, or other. TMbed completes predictions for entire proteomes within hours on a single consumer-grade desktop machine at performance levels similar or better than methods, which are using evolutionary information from multiple sequence alignments (MSAs) of protein families. On the per-protein level, TMbed correctly identified 94±8% of the beta barrel TMPs (53 of 57) and 98±1% of the alpha helical TMPs (557 of 571) in a non-redundant data set, at false positive rates well below 1% (erred on 30 of 5654 non-membrane proteins). On the per-segment level, TMbed correctly placed, on average, 9 of 10 transmembrane segments within five residues of the experimental observation. Our method can handle sequences of up to 4200 residues on standard graphics cards used in desktop PCs (e.g., NVIDIA GeForce RTX 3060).ConclusionsBased on embeddings from pLMs and two novel filters (Gaussian and Viterbi), TMbed predicts alpha helical and beta barrel TMPs at least as accurately as any other method but at lower false positive rates. Given the few false positives and its outstanding speed, TMbed might be ideal to sieve through millions of 3D structures soon to be predicted, e.g., by AlphaFold2.AvailabilityOur code, method, and data sets are freely available in the GitHub repository, https://github.com/BernhoferM/TMbed.
Accurate Estimation of Molecular Counts from Amplicon Sequence Data with Unique Molecular Identifiers
Authors: Peng Xiyu , Dorman Karin S 
Published: June 2022

AbstractMotivationAmplicon sequencing is widely applied to explore heterogeneity and rare variants in genetic populations. Resolving true biological variants and quantifying their abundance is crucial for downstream analyses, but measured abundances are distorted by stochasticity and bias in amplification, plus errors during Polymerase Chain Reaction (PCR) and sequencing. One solution attaches Unique Molecular Identifiers (UMIs) to sample sequences before amplification eliminating amplification bias by clustering reads on UMI and counting clusters to quantify abundance. While modern methods improve over naïve clustering by UMI identity, most do not account for UMI reuse, or collision, and they do not adequately model PCR and sequencing errors in the UMIs and sample sequences.ResultsWe introduce Deduplication and accurate Abundance estimation with UMIs (DAUMI), a probabilistic framework to detect true biological sequences and accurately estimate their deduplicated abundance from amplicon sequence data. DAUMI recognizes UMI collision, even on highly similar sequences, and detects and corrects most PCR and sequencing errors in the UMI and sampled sequences. DAUMI performs better on simulated and real data compared to other UMI-aware clustering methods.AvailabilitySource code is available at https://github.com/xiyupeng/AmpliCI-UMI.
Multi-task learning for activity detection in neovascular age-related macular degeneration
Authors: Ayhan Murat Seçkin , Faber Hanna , Kühlewein Laura , Inhoffen Werner , Aliyeva Gulnar , Ziemssen Focke , Berens Philipp 
Published: June 2022

AbstractPurposeComparison of performance and explainability of a multi-task convolutional deep neuronal network to single-task networks for activity detection in neovascular age-dependent macular degeneration.MethodsFrom n = 70 patients (46 female, 24 male) who attended the University Eye Hospital Tübingen 3762 optical coherence tomography B-scans (right eye: 2011, left eye: 1751) were acquired with Heidelberg Spectralis, Heidelberg, Germany. B-scans were graded by a retina specialist and an ophthalmology resident, and then used to develop a multi-task deep learning model to predict disease activity in neovascular age-related macular degeneration along with the presence of sub- and intraretinal fluid. We used performance metrics for comparison to single-task networks and visualized the DNN-based decision with t-distributed stochastic neighbor embedding and clinically validated saliency mapping techniques.ResultsThe multi-task model surpassed single-task networks in accuracy for activity detection (94.2). Further-more, compared to single-task networks, visualizations via t-distributed stochastic neighbor embedding and saliency maps highlighted that multi-task networks’ decisions for activity detection in neovascular age-related macular degeneration were highly consistent with the presence of both sub- and intraretinal fluid.ConclusionsMulti-task learning increases the performance of neuronal networks for predicting disease activity, while providing clinicians with an easily accessible decision control, which resembles human reasoning.Translational RelevanceBy improving nAMD activity detection performance and transparency of automated decisions, multi-task DNNs can support the translation of machine learning research into clinical decision support systems for nAMD activity detection.
Cycle threshold values in symptomatic COVID-19 cases in England
Authors: Funk Sebastian , Abbott Sam 
Published: June 2022

AbstractIntroductionSince the start of the pandemic SARS-CoV-2 infection has most commonly been confirmed using reverse transcriptase polymerase chain reaction (RT-PCR), with results translated into a binary positive/negative outcomes. Previous studies have found that there is additional useful information in the level of the Cycle threshold (Ct value) of positive cases. Here we characterise variation in Ct values as a proxy for viral loads in more than 3 million test-positive COVID-19 cases in England with the aim of better quantifying the utility of such data.MethodsWe used individual N gene Ct values from symptomatic PCR positive (with Ct value less than 30) Pillar 2 cases in England who self-reported the date of symptom onset, and for whom age, reinfection status, variant status, and the number of vaccines received was available. Those with a positive test result more than 6 days after their reported symptom onset were excluded to mitigate the potential impact of recall bias. We used a generalised additive model, to estimate Ct values empirical mean Ct values for each strata of interest independently as well as to predict Ct values using a model that adjusted for a range of demographic and epidemiological covariates jointly. We present empirical Ct values and compare them to predicted mean Ct values.ResultsWe found that mean Ct values varied by vaccine status, and reinfection status with the number of vaccine doses having little apparent effect. Modelling Ct values as a smooth function of time since onset and other variables struggled to reproduce the individual variation in the data but did match the population-level variation over time relatively well with this being apparently dominated by large differences between variants. Other variation over time was also captured to some degree though their remained several periods where the model could not capture the empirical means with a potential explanation being epidemic phase bias.ConclusionsAnalysing a large dataset of routine Ct values from symptomatic COVID-19 cases in England we found variation based on time since symptom onset, vaccine status, age, and variant. Ct values were highest 1-3 days after symptom onset and differed most due to variant status. We found no clear correlation between previously estimated differences in intrinsic transmissibility and Ct values indicating that this is potentially mediated at least partly by factors other than viral load as estimated using Ct values. We found evidence that a model adjusting for a range of covariates could explain some of the population-level variation over time but systematically underestimated Ct values when incidence was increasing, and overestimated them when incidence was decreasing. This indicates the utility of Ct values from this data source as a tool for surveillance, potentially avoiding some of the biases of aggregated positive counts.
Massively Parallel Dissection of RNA in RNA-protein interactions in vivo
Authors: Hass Evan P. , Lee Yu Hsuan , Campodonico Will , Lee Yong Kyu , Lasda Erika , Shah Jaynish S , Rinn John L. , Hwang Taeyoung 
Published: June 2022

ABSTRACTMany of the biological functions performed by RNA are mediated by RNA-binding proteins (RBPs), and understanding the molecular basis of these interactions is fundamental to molecular biology. Here, we present MPRNA-immunoprecipitation (MPRNA-IP), an adaptation of the previously developed massively parallel RNA assay (MPRNA), and a new avenue for in vivo high-throughput dissection of RNA-protein interactions. By using custom pools of tens of thousands of RNA sequences containing systematically designed truncations and mutations, we are able to identify RNA domains, sequences, and secondary structures necessary and sufficient for protein binding in a single experiment. We show that this approach is successful for multiple RNAs of interest including NORAD, MS2, and human telomerase RNA, and we describe statistical models for identifying RNA domains and parsing the structural contributions of RNA in these interactions. By blending modern and classical approaches, MPRNA-IP provides a novel high-throughput way to elucidate RNA-based mechanisms behind RNA-protein interactions.
CaV1.2 channelopathic mutations evoke diverse pathophysiological mechanisms
Authors: Bamgboye Moradeke A. , Herold Kevin G. , Vieira Daiana C.O. , Traficante Maria K. , Rogers Philippa J. , Ben-Johny Manu , Dick Ivy E. 
Published: June 2022

AbstractThe first pathogenic mutation in CaV1.2 was identified in 2004 and was shown to cause a severe multisystem disorder known as Timothy syndrome (TS). The mutation was localized to the distal S6 region of the channel, a region known to play a major role in channel activation. TS patients suffer from life-threatening cardiac symptoms as well as significant neurodevelopmental deficits including autism spectrum disorder (ASD). Since this discovery, the number and variety of mutations identified in CaV1.2 has grown tremendously, and the distal S6 regions remains a frequent locus for many of these mutations. While the majority of patients harboring these mutations exhibit cardiac symptoms which can be well explained by known pathogenic mechanisms, the same cannot be said for the ASD or neurodevelopmental phenotypes seen in some patients, indicating a gap in our understanding of the pathogenesis of CaV1.2 channelopathies. Here, we use of whole cell patch clamp, quantitative Ca2+ imaging, and single channel recordings to expand the known mechanisms underlying the pathogenesis of CaV1.2 channelopathies. Specifically, we find that mutations within the S6 region can exert independent and separable effects on activation, voltage-dependent inactivation (VDI) and Ca2+-dependent inactivation (CDI). Moreover, the mechanisms underlying the CDI effects of these mutations are varied and include altered channel opening and possible disruption of CDI transduction. Overall, these results provide a structure-function framework to conceptualize the role of S6 mutations in pathophysiology and offer insight into the biophysical defects associated with distinct clinical manifestations.
Overt and occult hypoxemia in patients hospitalized with novel coronavirus disease 2019
Authors: Gadrey Shrirang M. , Mohanty Piyus , Haughey Sean P. , Jacobsen Beck A. , Dubester Kira J. , Webb Katherine M. , Kowalski Rebecca L. , Dreicer Jessica J. , Andris Robert T. , Clark Matthew T. , Moore Christopher C. , Holder Andre , Kamaleswaran Rishi , Ratcliffe Sarah J. , Moorman J. Randall 
Published: June 2022

AbstractBackgroundProgressive hypoxemia is the predominant mode of deterioration in COVID-19. Among hypoxemia measures, the ratio of the partial pressure of arterial oxygen to the fraction of inspired oxygen (P/F ratio) has optimal construct validity but poor availability because it requires arterial blood sampling. Pulse oximetry reports oxygenation continuously, but occult hypoxemia can occur in Black patients because the technique is affected by skin color. Oxygen dissociation curves allow non-invasive estimation of P/F ratios (ePFR) but this approach remains unproven.Research QuestionCan ePFRs measure overt and occult hypoxemia?Study Design and methodsWe retrospectively studied COVID-19 hospital encounters (n=5319) at two academic centers (University of Virginia [UVA] and Emory University). We measured primary outcomes (death or ICU transfer within 24 hours), ePFR, conventional hypoxemia measures, baseline predictors (age, sex, race, comorbidity), and acute predictors (National Early Warning Score (NEWS) and Sepsis-3). We updated predictors every 15 minutes. We assessed predictive validity using adjusted odds ratios (AOR) and area under receiver operating characteristics curves (AUROC). We quantified disparities (Black vs non-Black) in empirical cumulative distributions using the Kolmogorov-Smirnov (K-S) two-sample test.ResultsOvert hypoxemia (low ePFR) predicted bad outcomes (AOR for a 100-point ePFR drop: 2.7 [UVA]; 1.7 [Emory]; p<0.01) with better discrimination (AUROC: 0.76 [UVA]; 0.71 [Emory]) than NEWS (AUROC: 0.70 [UVA]; 0.70 [Emory]) or Sepsis-3 (AUROC: 0.68 [UVA]; 0.65 [Emory]). We found racial differences consistent with occult hypoxemia. Black patients had better apparent oxygenation (K-S distance: 0.17 [both sites]; p<0.01) but, for comparable ePFRs, worse outcomes than other patients (AOR: 2.2 [UVA]; 1.2 [Emory], p<0.01).InterpretationThe ePFR was a valid measure of overt hypoxemia. In COVID-19, it may outperform multi-organ dysfunction models like NEWS and Sepsis-3. By accounting for biased oximetry as well as clinicians’ real-time responses to it (supplemental oxygen adjustment), ePFRs may enable statistical modelling of racial disparities in outcomes attributable to occult hypoxemia.
Health System Resilience-enhancing Strategies for Managing Public Health Emergencies of International Concerns (PHEIC): Success and Challenges A Systematic Review Protocol
Authors: Rajapaksha R. M. Nayani Umesha , Khatri Resham , Abeysena Chrishantha , Wijesinghe Millawage Supun Dilara , Thomas Toms K. , Endalamaw Aklilu , Perera Nadeeka , Fernando Mekala , De Silva Gayani , Rambukwella Roshan , Assefa Yibeltal 
Published: June 2022

AbstractIntroductionHealth system resilience is the ability to prepare, manage, and learn from a sudden and unpredictable extreme change which impacts health systems. Health systems globally have recently been affected by a number of catastrophic events, including natural disasters, and infectious disease epidemics. Understanding health system resilience has never been more essential until emerging global pandemics. Therefore, the application of resilience-enhancing strategies with existing frameworks needs to be assessed to identify the management gaps and give valuable recommendations from the lessons learnt from the global pandemic.MethodsThe systematic review will be reported using the Preferred reporting items for systematic review and meta-analysis protocols guideline (PRISMA-P). Reporting data on health system building blocks and systematic searches on resilience enhancing strategies for the management of Public Health Emergencies of International Concerns (PHEIC) after the establishment of International Health Regulations (IHR) since managing PHEIC after the establishment of IHR in 2007 will be included. The search will be conducted in PubMed, Scopus, Web of Science, and Google Scholar.DiscussionHealth system resilience is key to coping with catastrophic events, such as the economic crisis and COVID-19 pandemic. The mapping of available literature towards the application of resilience-enhancing strategies with existing frameworks needs to be assessed to identify the management gaps and give valuable recommendations from the lessons learnt from the global pandemic to improve the level of preparedness and response to similar public health emergencies in the future.ConclusionA protocol for a global review of health system resilience for pandemic management is described. This review will add to the body of knowledge about health systems enhancing research and policy formulation.Strengths and limitations of this study StrengthsAssessing success and challenges of resilience-enhancing strategies to identify the management gaps and give valuable recommendations from the lessons learnt from the global pandemic in future disastersThe systematic review will be reported using the Preferred reporting items for systematic review and meta-analysis protocols guideline (PRISMA-P).Collaboration between Asia-Pacific researchers with different backgroundLimitationUnable to do meta-analysis due to nature of the selected scope of the research
Nirmatrelvir plus ritonavir for early COVID-19 and hospitalization in a large US health system
Authors: Dryden-Peterson Scott , Kim Andy , Kim Arthur Y. , Caniglia Ellen C. , Lennes Inga , Patel Rajesh , Gainer Lindsay , Dutton Lisa , Donahue Elizabeth , Gandhi Rajesh T. , Baden Lindsey R. , Woolley Ann E. 
Published: June 2022

AbstractBackgroundIn the EPIC-HR trial, nirmatrelvir plus ritonavir led to an 88% reduction in hospitalization or death among unvaccinated outpatients with early COVID-19. Clinical impact of nirmatrelvir plus ritonavir among vaccinated populations is uncertain.ObjectiveTo assess whether nirmatrelvir plus ritonavir reduces risk of hospitalization among outpatients with early COVID-19 in the setting of prevalent SARS-CoV-2 immunity and immune evasive SARS-CoV-2 lineages.DesignPopulation-based cohort study analyzed to emulate a clinical trial utilizing two-stage, inverse-probability weighted models to account for anticipated bias in testing and treatment.SettingA large healthcare system providing care for 1.5 million patients in Massachusetts and New Hampshire during Omicron wave (January 1 to May 15, 2022) with staged access and capacity to prescribe nirmatrelvir plus ritonavir.Patients30,322 non-hospitalized adults (87.2% vaccinated) aged 50 and older with COVID-19 and without contraindications to nirmatrelvir plus ritonavir.MeasurementPrimary outcome was hospitalization within 14 days of COVID-19 diagnosis.ResultsDuring the study period, 6036 (19.9%) patients were prescribed nirmatrelvir plus ritonavir and 24,286 (80.1%) patients were not. Patients prescribed nirmatrelvir were more likely to be older, have more comorbidities, and be unvaccinated. Hospitalization occurred in 40 (0.66%) and 232 (0.96%) patients prescribed and not prescribed nirmatrelvir plus ritonavir, respectively. The adjusted risk ratio was 0.55 (95% confidence interval 0.38 to 0.80, p = 0.002). Observed risk reduction was greater among unvaccinated patients and obese patients.LimitationsPotential for residual confounding due to differential access and uptake of COVID-19 vaccines, diagnostics, and treatment.ConclusionsThe overall risk of hospitalization was already low (<1%) following an outpatient diagnosis of COVID-19, but this risk was 45% lower among patients prescribed nirmatrelvir plus ritonavir.FundingNational Institutes of Health (P30 AI060354 and R01 CA236546).