bluuebunny/crossref_metadata_2025_split huggingface dataset:
Free energy and kinetics of cAMP permeation through connexin26 hemichannel with and without voltage
Authors: Jiang Wenjuan , Lin Yi-Chun , Botello-Smith Wesley , Contreras Jorge , Harris Andrew L. , Maragliano Luca , Luo Yun Lyna 
Published: January 2021

AbstractThe connexin family is a diverse group of highly regulated non-β-barrel wide-pore channels permeable to biological signaling molecules. Despite their critical roles in mediating selective molecular signaling in health and disease, the molecular basis of permeation through these pores remains unclear. Here, we report the thermodynamics and kinetics of binding and transport of a second messenger, adenosine-3’,5’-cyclophosphate (cAMP), through a connexin26 hemichannel. Inward and outward fluxes of cAMP were first obtained from 4 μs simulations with voltages and multiple cAMPs in solution. The results are compared with the intrinsic potential of mean force (PMF) and the mean first passage times (MFPTs) of a single cAMP in the absence of voltage, obtained from a total of 16.5 μs of multi-replica Voronoi-tessellated Markovian milestoning simulations. The computed transit times through the pore correspond well to existing experimental data. Both voltage simulations and milestoning simulations revealed two cAMP binding sites with binding constants and dissociation rates computed from PMF and MFPTs. The protein dipole inside the pore produces an asymmetric PMF, reflected in unequal cAMP MFPTs in each direction once within the pore. The free energy profiles under voltages derived from intrinsic PMF provided a unified understanding of directional transition rates with/without voltage, and revealed the unique role of channel polarity and the mobile electrolyte within a wide pore on the total free energy. In addition, we show how these factors influence the cAMP dipole vector during permeation, and how cAMP affects the local and non-local pore diameter in a position-dependent manner.Significance StatementConnexins are wide-pore channels permeable to cellular signaling molecules. They mediate molecular signaling crucial in physiology, pathology, and development; mutations in connexins cause human pathologies. However, the fundamental structural, thermodynamic, and kinetic determinants of molecular permeability properties are unknown. Using multiple molecular dynamics simulation techniques, we report, for the first time, an in-depth investigation of the free energy and the directional transition rates of an important biological signaling molecule, cAMP, through a connexin channel. We reveal the energetics and binding sites that determine the cAMP flux, and the effects of mobile ions and external electrical field on the process. The results provide a basis for understanding the unique features of molecular flux through connexins and other non-β-barrel wide-pore channels.
Enhancing top-down proteomics of brain tissue with FAIMS
Authors: Fulcher James M. , Makaju Aman , Moore Ronald J. , Zhou Mowei , Bennett David A. , De Jager Philip L. , Qian Wei-Jun , Paša-Tolić Ljiljana , Petyuk Vladislav A. 
Published: January 2021

AbstractProteomic investigations of Alzheimer’s and Parkinson’s disease have provided valuable insights into neurodegenerative disorders. Thus far, these investigations have largely been restricted to bottom-up approaches, hindering the degree to which one can characterize a protein’s “intact” state. Top-down proteomics (TDP) overcomes this limitation, however it is typically limited to observing only the most abundant proteoforms and of a relatively small size. Therefore, offline fractionation techniques are commonly used to reduce sample complexity, limiting throughput. A higher throughput alternative is online fractionation, such as gas phase high-field asymmetric waveform ion mobility spectrometry (FAIMS). Utilizing a high complexity sample derived from Alzheimer’s disease brain tissue, we describe how the addition of FAIMS to TDP can robustly improve the depth of proteome coverage. For example, implementation of FAIMS at −50 compensation voltage (CV) more than doubled the mean number of non-redundant proteoforms observed (1,833 ± 17, n = 3), compared to without (754 ± 35 proteoforms). We also found FAIMS can influence the transmission of proteoforms and their charge envelopes based on their size. Importantly, FAIMS enabled the identification of intact amyloid beta (Aβ) proteoforms, including the aggregation-prone Aβ1-42variant which is strongly linked to Alzheimer’s disease.
Parvalbumin Interneuron Activity Underlies Vulnerability to Post-Traumatic Stress Disorder in Autism
Authors: Al Abed A. Shaam , Allen Tiarne V. , Ahmed Noorya Y. , Sellami Azza , Sontani Yovina , Marighetto Aline , Desmedt Aline , Dehorter Nathalie 
Published: January 2021

AbstractA rising concern in Autism Spectrum Disorder (ASD) is the heightened sensitivity to stress and trauma, the potential consequences of which have been overlooked, particularly upon the severity of the ASD traits. This study investigated the predisposition to, and impact of, Post-Traumatic Stress Disorder (PTSD) in ASD. We first demonstrated a reciprocal relationship between the two disorders and revealed that exposure to a mild stressful event induces PTSD-like memory in four mouse models of ASD. We also establish an unanticipated consequence of stress in this condition, showing that the formation of PTSD-like memory leads to the aggravation of the core traits associated with ASD. Such a susceptibility to developing PTSD-like memory in ASD stemmed from hyperactivation of the prefrontal cortex and altered fine-tuning of parvalbumin interneuron firing. We show that this traumatic memory can be treated by recontextualization, reducing the deleterious effects on the core symptoms of ASD. Overall, this study reveals multi-level neurobiological mechanisms that explain the increased vulnerability to develop PTSD in ASD. It provides a framework for future examination of the impact of PTSD-like memory in autism and offers new directions toward behavioral therapeutic interventions targeting traumatic memory in ASD.
Evolution of irreversible somatic differentiation
Authors: Gao Yuanxiao , Park Hye Jin , Traulsen Arne , Pichugin Yuriy 
Published: January 2021

AbstractA key innovation emerging in complex animals is irreversible somatic differentiation: daughters of a vegetative cell perform a vegetative function as well, thus, forming a somatic lineage that can no longer be directly involved in reproduction. Primitive species use a different strategy: vegetative and reproductive tasks are separated in time rather than in space. Starting from such a strategy, how is it possible to evolve life forms which use some of their cells exclusively for vegetative functions? Here, we developed an evolutionary model of development of a simple multicellular organism and found that three components are necessary for the evolution of irreversible somatic differentiation: (i) costly cell differentiation, (ii) vegetative cells that significantly improve the organism’s performance even if present in small numbers, and (iii) large enough organism size. Our findings demonstrate how an egalitarian development typical for loose cell colonies can evolve into germ-soma differentiation dominating metazoans.
Surface GluA1 and glutamatergic transmission are increased in cortical neurons of a VPS35 D620N knock-in mouse model of parkinsonism and altered by LRRK2 kinase inhibition
Authors: Kadgien Chelsie A , Kamesh Anusha , Khinda Jaskaran , Cao Li Ping , Fox Jesse , Farrer Matthew J , Milnerwood Austen J 
Published: January 2021

AbstractVacuolar protein sorting 35 (VPS35) regulates receptor recycling from endosomes. A missense mutation in VPS35 (D620N) leads to autosomal-dominant, late-onset Parkinson’s disease. Here, we use a VPS35 D620N knock-in mouse to study the neurobiology of this mutation. In brain tissue, we confirm previous findings that the mutation results in reduced binding of VPS35 with WASH-complex member FAM21, and robustly elevated phosphorylation of the LRRK2 kinase substrate Rab10. In cultured cortical neurons, the mutation results in increased endosomal recycling protein density (VPS35-FAM21 co-clusters and Rab11 clusters), glutamate release, and GluA1 surface expression. LRRK2 kinase inhibition exerted genotype-specific effects on GluA1 surface expression, but did not impact glutamate release phenotypes. These results improve our understanding of the early effects of the D620N mutation on cellular functions that are specific to neurons. These observations provide candidate pathophysiological pathways that may drive eventual transition to late-stage parkinsonism in VPS35 families, and support a synaptopathy model of neurodegeneration.
Impact of Retinopathy and Systemic Vascular Comorbidities on All-Cause Mortality
Authors: Zhu Zhuoting , Shang Xianwen , Wang Wei , Ha Jason , Chen Yifan , He Jingyi , Hu Wenyi , Yang Xiaohong , He Mingguang 
Published: January 2021

AbstractAims/hypothesisTo investigate the joint effects of retinopathy and systemic vascular comorbidities on mortality.MethodsThis study included 5703 participants (≥40 years old) from the 2005-2008 National Health and Nutrition Examination Survey. The Early Treatment Diabetic Retinopathy Study grading scale was used to evaluate the retinopathy status. Systemic vascular comorbidities included diabetes mellitus (DM), high blood pressure (HBP), chronic kidney disease (CKD) and cardiovascular disease (CVD). Time to death was calculated as the time from baseline to either the date of death or censoring (December 31st, 2015), whichever came first. Risks of mortality were estimated using Cox proportional hazards models.ResultsAfter adjusting for confounders, the presence of retinopathy predicted higher all-cause mortality (hazard ratio [HR], 1.40; 95% confidence interval [CI], 1.09-1.81). The all-cause mortality among participants with both retinopathy and systemic vascular comorbidities including DM (HR, 1.63; 95% CI, 1.06-2.50), HBP (HR, 1.46; 95% CI, 1.03-2.08), CKD (HR, 1.71; 95% CI, 1.24-2.35) and CVD (HR, 1.88; 95% CI, 1.19-2.96) was significantly higher than that among those without either condition.Conclusions/interpretationIn this prospective study, individuals with retinopathy had increased all-cause mortality. The joint effects of retinopathy and major systemic vascular comorbidities increased the all-cause mortality further, suggesting that more extensive vascular risk factor assessment and management are needed to detect the burden of vascular pathologies and improve long-term survival in individuals with retinopathy.Research in contextWhat is already known about this subject?Retinopathy has been recognized as an independent risk factor for mortality.What is the key question?What are the joint effects of retinopathy and systemic vascular comorbidities (including diabetes mellitus, hypertension and chronic kidney disease and cardiovascular disease) on mortality?What are the new findings?Consistent evidence on the increased risk of mortality among individuals with retinopathy was noted in a large sample of middle-aged and older adults.The co-occurrence of retinopathy and systemic vascular conditions (diabetes mellitus, hypertension and chronic kidney disease and cardiovascular disease) further increased all-cause mortality independent of other covariates.How might this impact on clinical practice in the foreseeable future?Individuals with retinopathy may benefit from a comprehensive vascular assessment.Intensive vascular risk reduction is needed in the management of patients with retinopathy and and micro- or macrovascular disorders.Highlighted the importance of retinopathy screening using retinal imaging for identifying individuals at high risk of mortality, particularly among individuals with systemic vascular comorbidities.
Performance of intensive care unit severity scoring systems across different ethnicities
Authors: Sarkar Rahuldeb , Martin Christopher , Mattie Heather , Gichoya Judy Wawira , Stone David J. , Celi Leo Anthony 
Published: January 2021

ABSTRACTBackgroundDespite wide utilisation of severity scoring systems for case-mix determination and benchmarking in the intensive care unit, the possibility of scoring bias across ethnicities has not been examined. Recent guidelines on the use of illness severity scores to inform triage decisions for allocation of scarce resources such as mechanical ventilation during the current COVID-19 pandemic warrant examination for possible bias in these models. We investigated the performance of three severity scoring systems (APACHE IVa, OASIS, SOFA) across ethnic groups in two large ICU databases in order to identify possible ethnicity-based bias.MethodData from the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care were analysed for score performance in Asians, African Americans, Hispanics and Whites after appropriate exclusions. Discrimination and calibration were determined for all three scoring systems in all four groups.FindingsWhile measurements of discrimination -area under the receiver operating characteristic curve (AUROC) -were significantly different among the groups, they did not display any discernible systematic patterns of bias. In contrast, measurements of calibration -standardised mortality ratio (SMR) -indicated persistent, and in some cases significant, patterns of difference between Hispanics and African Americans versus Asians and Whites. The differences between African Americans and Whites were consistently statistically significant. While calibrations were imperfect for all groups, the scores consistently demonstrated a pattern of over-predicting mortality for African Americans and Hispanics.InterpretationThe systematic differences in calibration across ethnic groups suggest that illness severity scores reflect bias in their predictions of mortality.FundingLAC is funded by the National Institute of Health through NIBIB R01 EB017205. There was no specific funding for this study.
CoAI: Cost-Aware Artificial Intelligence for Health Care
Authors: Erion Gabriel , Janizek Joseph D. , Hudelson Carly , Utarnachitt Richard B. , McCoy Andrew M. , Sayre Michael R. , White Nathan J. , Lee Su-In 
Published: January 2021

AbstractThe recent emergence of accurate artificial intelligence (AI) models for disease diagnosis raises the possibility that AI-based clinical decision support could substantially lower the workload of healthcare providers. However, for this to occur, the input data to an AI predictive model, i.e., the patient’s features, must themselves be low-cost, that is, efficient, inexpensive, or low-effort to acquire. When time or financial resources for gathering data are limited, as in emergency or critical care medicine, modern high-accuracy AI models that use thousands of patient features are likely impractical. To address this problem, we developed the CoAI (Cost-awareAI) framework to enable any kind of AI predictive model (e.g., deep neural networks, tree ensemble models, etc.) to make accurate predictions given a small number of low-cost features. We show that CoAI dramatically reduces the cost of predicting prehospital acute traumatic coagulopathy, intensive care mortality, and outpatient mortality relative to existing risk scores, while improving prediction accuracy. It also outperforms existing state-of-the-art cost-sensitive prediction approaches in terms of predictive performance, model cost, and training time. Extrapolating these results to all trauma patients in the United States shows that, at a fixed false positive rate, CoAI could alert providers of tens of thousands more dangerous events than other risk scores while reducing providers’ data-gathering time by about 90 percent, leading to a savings of 200,000 cumulative hours per year across all providers. We extrapolate similar increases in clinical utility for CoAI in intensive care. These benefits stem from several unique strengths: First, CoAI uses axiomatic feature attribution methods that enable precise estimation of feature importance. Second, CoAI is model-agnostic, allowing users to choose the predictive model that performs the best for the prediction task and data at hand. Finally, unlike many existing methods, CoAI finds high-performance models within a given budget without any tuning of the cost-vs-performance tradeoff. We believe CoAI will dramatically improve patient care in the domains of medicine in which predictions need to be made with limited time and resources.
Leveraging fine-mapping and non-European training data to improve cross-population polygenic risk scores
Authors: Weissbrod Omer , Kanai Masahiro , Shi Huwenbo , Gazal Steven , Peyrot Wouter J. , Khera Amit V. , Okada Yukinori ,   The Biobank Japan Project, Martin Alicia R. , Finucane Hilary , Price Alkes L. 
Published: January 2021

AbstractPolygenic risk scores (PRS) based on European training data suffer reduced accuracy in non-European target populations, exacerbating health disparities. This loss of accuracy predominantly stems from LD differences, MAF differences (including population-specific SNPs), and/or causal effect size differences. PRS based on training data from the non-European target population do not suffer from these limitations, but are currently limited by much smaller training sample sizes. Here, we propose PolyPred, a method that improves cross-population polygenic prediction by combining two complementary predictors: a new predictor that leverages functionally informed fine-mapping to estimate causal effects (instead of tagging effects), addressing LD differences; and BOLT-LMM, a published predictor. In the special case where a large training sample is available in the non-European target population (or a closely related population), we propose PolyPred+, which further incorporates the non-European training data, addressing MAF differences and causal effect size differences. PolyPred and PolyPred+ require individual-level training data (for their BOLT-LMM component), but we also propose analogous methods that replace the BOLT-LMM component with summary statistic-based components if only summary statistics are available. We applied PolyPred to 49 diseases and complex traits in 4 UK Biobank populations using UK Biobank British training data (averageN=325K), and observed statistically significant average relative improvements in prediction accuracy vs. BOLT-LMM ranging from +7% in South Asians to +32% in Africans (and vs. LD-pruning + P-value thresholding (P+T) ranging from +77% to +164%), consistent with simulations. We applied PolyPred+ to 23 diseases and complex traits in UK Biobank East Asians using both UK Biobank British (averageN=325K) and Biobank Japan (averageN=124K) training data, and observed statistically significant average relative improvements in prediction accuracy of +24% vs. BOLT-LMM and +12% vs. PolyPred. The summary statistic-based analogues of PolyPred and PolyPred+ attained similar improvements. In conclusion, PolyPred and PolyPred+ improve cross-population polygenic prediction accuracy, ameliorating health disparities.
The Role of Disease Severity and Demographics in the Clinical Course of COVID-19 Patients Treated with Convalescent Plasma
Authors: Ma Tengfei , Wiggins Chad C. , Kornatowski Breanna M. , Hailat Ra’ed S. , Clayburn Andrew C. , Guo Winston , Johnson Patrick W. , Senefeld Jonathon W. , Klassen Stephen A. , Baker Sarah E. , Bruno Katelyn A. , Fairweather DeLisa , Wright R. Scott , Carter Rickey E. , Li Chenxi , Joyner Michael J. , Paneth Nigel 
Published: January 2021

ABSTRACTTreatment of patients with COVID-19 using convalescent plasma from recently recovered patients has been shown to be safe, but the time course of change in clinical status following plasma transfusion in relation to baseline disease severity has not yet been described. We analyzed short, descriptive daily reports of patient status in 7,180 hospitalized recipients of COVID-19 convalescent plasma in the Mayo Clinic Expanded Access Program. We assessed, from the day following transfusion, whether the patient was categorized by his or her physician as better, worse or unchanged compared to the day before, and whether, on the reporting day, the patient received mechanical ventilation, was in the ICU, had died or had been discharged. Most patients improved following transfusion, but clinical improvement was most notable in mild to moderately ill patients. Patients classified as severely ill upon enrollment improved, but not as rapidly, while patients classified as critically ill/end-stage and patients on ventilators showed worsening of disease status even after treatment with convalescent plasma. Patients age 80 and over showed little or no clinical improvement following transfusion. Clinical status at enrollment and age appear to be the primary factors in determining the therapeutic effectiveness of COVID-19 convalescent plasma among hospitalized patients.
COVID-19: making the best out of a forced transition to online medical teaching. A mixed methods study
Authors: Virumbrales Montserrat , Elorduy Marta , Graell Mariona , Mezquita Pau , Brotons Pedro , Balaguer Albert 
Published: January 2021

AbstractIntroductionthe COVID-19 pandemic resulted in a decreed confinement in our country from March until the end of term in June 2020. This forced a transition exclusively to distance learning. The aim of this study was to broaden the understanding of fully online distance learning from the experiences of undergraduate medical students and faculty members during confinement, and identify its key elements.MethodsA convergent mixed methods study analyzing: (a) an online teaching follow- up program, (b) two focus groups and a nominal group with students and faculty, respectively, and (c) a survey with students from 1st to 5th year.ResultsThirteen strongly interconnected categories were identified. Four played an organizational role: course planning, coordination, communication and pedagogical coherence. The remaining nine categories were: learning outcomes, teaching methodology, online resources, evaluation, time management, workload, student motivation, participation, and teacher-student relationship. Among the key aspects of learning were those that promoted rapport between faculty and students, such as synchronous sessions, especially those based on clinical cases.Conclusionthe experiences from confinement allowed us to gain insight into some of the key aspects of online medical teaching. Promoting student motivation and participation at all levels was essential to distance learning in Medicine.