bluuebunny/crossref_metadata_2025 huggingface dataset:
Seeing Complexity: Cultural Historical Activity Theory (CHAT) As a Lens for Shared Decision Making
Authors: Witkop Catherine T. , Maggio Lauren A. , Harvey Emily J. , Torre Dario M.
Published: May 2021

Shared decision making, a collaborative approach between patient and provider that considers the patient’s values and preferences in addition to the scientific evidence, is a complex clinical activity that has not realized its full potential. Gaps in education and training have been cited as barriers to shared decision making, and evidence is inconsistent on effective educational interventions. Because individual agents with their own social and behavioral contexts co-construct a shared decision, the educational approach may need to consider the role of patient agency and sociocultural influences.

To address the inherent complexity in shared decision making, the authors identified cultural historical activity theory (CHAT) as a framework for analysis. Although certainly not the only relevant theory, CHAT offers an appropriate lens through which the multivoiced nature of shared decision making can be more clearly appreciated. In this article, the authors demonstrate the application of CHAT as a lens for researchers and educators to examine the complexity of shared decision making.

The fictitious case presented in this article describes the use of CHAT with a patient who experiences 2 clinical encounters; during the second, shared decision making takes place. Elements of the case are threaded through the article, demonstrating a sample analysis of the interacting activity systems of the patient and physician and highlighting inherent tensions and contradictions. The authors propose CHAT as a tool for future research around the role of agency in shared decision making and other complex topics and as a framework for design of novel instructional strategies. Although not applicable to all topics and settings, CHAT has significant potential within health professions education.
Involuntary Commitment for Substance Use: Addiction Care Professionals Must Reject Enabling Coercion and Patient Harm
Authors: Messinger John , Beletsky Leo
Published: May 2021

Ethical and epidemiological concerns should mobilize addiction care providers to deploy their expertise and collective influence to challenge the use of state power to coerce people into treatment settings—especially when such settings often diverge from best clinical practices. Troublingly, with few notable exceptions, the voices of professional organizations on this issue have been largely lacking. This issue of the Journal includes a timely manuscript that sheds light on this resounding silence: “Civil Commitment for Substance Use Disorders: A National Survey of Addiction Medicine Physicians” by Jain et al. provides important and novel insights into the beliefs of physicians regarding civil commitment statutes. This study distributed a web-based survey to physician-members of the American Society of Addiction Medicine with questions gauging awareness of, attitudes toward, and experiences with civil commitment for individuals with substance use disorder. Surprisingly, the study found that the overwhelming majority of addiction medicine providers supported the application of civil commitment for substance use disorder—60.7% reported being in favor of its use whereas only 21.5% reported being opposed.
Perinatal OUD Treatment Provider Understandings of Rural Patients’ Experiences
Authors: Leiner Catherine , Antono Brian , Ostrach Bayla
Published: May 2021

Objectives:

To present perspectives of substance use treatment providers offering perinatal opioid use disorder (OUD) treatment in a largely rural, Appalachian region. To demonstrate the extent to which providers sought to understand their patients’ experiences accessing treatment and how this understanding informed providers’ approach to offering patient-centered care.

Methods:

A qualitative study combining semi-structured interviews and participant-observation with perinatal substance use treatment providers, conducted within a comprehensive program. Using purposive and opportunistic sampling with key informants (n = 10), a saturation sample was achieved. Data were analyzed using modified Grounded Theory.

Results:

Perinatal substance use treatment providers had a good understanding of their patients’ experiences seeking treatment for opioid use disorder, including being aware of obstacles patients encountered. This understanding allowed providers to better address patients’ needs in and out of the clinic.

Conclusions:

Participants demonstrated a good understanding of what their largely rural, Appalachian patients experienced when attempting to access perinatal OUD treatment. This understanding may enable more patient-centered care.
Intraparenchymal Neuromonitoring of Cerebral Fat Embolism Syndrome
Authors: Hoiland Ryan Leo , Griesdale Donald E. , Gooderham Peter , Sekhon Mypinder S.
Published: May 2021

Objectives:

We aimed to characterize the cerebrovascular physiology of cerebral fat embolism using invasive multimodal neuromonitoring.

Data Sources:

ICU, Vancouver General Hospital, Vancouver, BC, Canada.

Study Selection:

Case report.

Data Extraction:

Patient monitoring software (ICM+, Cambridge, United Kingdom), clinical records, and surgical records.

Data Synthesis:

None.

Conclusions:

Our integrated assessment of the cerebrovascular physiology of fat embolism syndrome provides a physiologic basis to investigate the importance of augmenting mean arterial pressure to optimize cerebral oxygen delivery for the mitigation of long-term neurologic ischemic sequelae of cerebral fat embolism.
CDH1 germline mutations in families with hereditary lobular breast cancer
Authors: Girardi Antonia , Magnoni Francesca , Vicini Elisa , Kouloura Andriana , La Vecchia Carlo , Veronesi Paolo , Corso Giovanni
Published: May 2021

Pathogenic CDH1 germline mutations are associated with lobular breast cancer in the so-called hereditary lobular breast cancer (HLBC) syndrome, without apparent correlation with the classic hereditary diffuse gastric cancer (HDGC). Recent international guidelines recommend CDH1 screening also in absence of diffuse gastric cancer (DGC) history. Genomic characteristics underlying gastric and breast tumorigenesis in this varied population of patients is still unclear. In this review we revised all CDH1 germline mutations described in literature associated with lobular breast cancer (LBC). We distinguish two subgroups of CDH1 mutant carriers: (a) ‘mixed’ HDGC syndrome, showing both DGC plus LBC and (b) HLBC, in which DGC is absent and the LBC phenotype is predominant. A higher frequency of CDH1 mutations was identified in the HLBC syndrome with an early age at LBC diagnosis; it is possible that LBCs with CDH1 germline mutations are an independent inherited syndrome. This evidence allows us to gain biological insight into the pathophysiological mechanisms responsible for the different phenotypes of the disease and potentially tailor the prophylactic and screening procedures.
Restoration of Neck Extension after Severe Oncological Surgery of the Posterior Cervical Trunk
Authors: Lasso Jose M.
Published: May 2021

Reconstruction of posterior cervical trunk defects secondary to tumor resection carries significant morbidity when vertebral hardware or the spinal cord is exposed, and neck extension is interrupted. Complete reconstruction includes the coverage and obliteration of dead spaces, but functional outcomes are necessary to prevent the head dropping. This report documents the first known technique, using a free latissimus dorsi neurovascular flap to provide neck extension after ablative oncological surgery affecting trapezium and paraspinal muscles of the neck. The author explains the method by using a branch of the accessory nerve as the donor nerve. While keeping the head in a neutral position, the tendinous part of the neurovascular flap was firmly attached to the occipital periosteum and to the cephalad remnants of the trapezius, splenius capitis, and semispinalis muscles. At the caudal portion of the defect, cardinal notches were used to set the muscle at rest, avoiding excessive fiber tension. The maximal length of the muscle at rest was measured before flap elevation, calculated via the anatomical 3D print model. The thoracodorsal nerve was stimulated until the muscle shortened its length to 50%. Head extension was tested several times via neurostimulation and electromyographic control. The described procedure may provide neck extension and circumvent the problem with donor nerve providing synergy to the desired function.
Survival and Outcomes After Cardiac Arrest With VA-ECMO Rescue Therapy
Authors: Schurr James W. , Noubani Mohammad , Santore Lee Ann , Rabenstein Andrew P. , Dhundale Kathleen , Fitzgerald Jillian , Cahill Jan , Bilfinger Thomas V. , Seifert Frank C. , McLarty Allison J.
Published: May 2021

Background:

Extracorporeal membrane oxygenation (ECMO) use in patients with cardiac arrest is increasing. Utilization remains variable between centers using ECMO as a rescue therapy or early protocolized extracorporeal cardiopulmonary resuscitation.

Methods:

Single-center, retrospective evaluation of cardiac arrest with cardiopulmonary resuscitation and rescue ECMO support from 2011 through 2019. Study objectives included survival, non-neurologic, and neurologic outcomes; validation of the SAVE and modified SAVE (mSAVE) scores for survival and favorable neurologic outcome; and predictive factor identification in cardiac arrest with ECMO rescue therapy.

Results:

Eighty-nine patients were included. In-hospital survival was 38.2% and median CPC score was 2. Survivors had lower BMI (27.9 ± 4.2 kg/m2 vs. 32.3 ± 7.5 kg/m2, P = 0.003), less obesity (BMI ≥ 30 kg/m2) (26.5% vs. 49.1%, P = 0.035), shorter CPR duration (35.5 ± 31.7 m vs. 58.0 ± 49.5 m, P = 0.019), more tracheostomy (38.2% vs. 7.3%, P < 0.001), and less renal replacement therapy (RRT) (17.6% vs. 38.2%, P = 0.031). Patients with a favorable neurologic outcome had lower body weight (86.2 ± 17.9 kg vs. 98.1 ± 19.4 kg, P = 0.010), lower BMI (28.1 ± 4.5 kg/m2 vs. 33.9 ± 7.9 kg/m2, P < 0.001), and less obesity (29.7% vs. 56.3%, P = 0.026). mSAVE score predicted in-hospital survival (OR 1.11; 95%CI 1.03–1.19; P = 0.004) and favorable neurologic outcome (OR 1.11; 1.03–1.20; P = 0.009). Multivariate analysis for in-hospital survival included mSAVE, BMI, CPR-time, tracheostomy, and RRT (c-statistic: 0.864). Favorable neurologic outcome included mSAVE and BMI (c-statistic: 0.805).

Conclusions:

mSAVE, BMI, RRT, and tracheostomy are predictors of in-hospital survival and mSAVE and BMI are predictors of favorable neurologic outcome in cardiac arrest with ECMO rescue therapy.
Effects of premature mandarin powder on the quality characteristics
and antioxidant activities of scone
Authors: Lee Seul-Lee , Joo Shin-Youn
Published: May 2021

The effects of premature mandarin powder (PMP) on the quality characteristics and
antioxidant activities of scone were investigated. Scones were prepared by
adding different concentrations (0, 0.625, 1.25, 2.5, and 5%) of PMP. As the
content of PMP increased, density of dough slightly increased, while pH of dough
decreased. The volume of scone increased with the amount of sample increased,
but the group containing 5% PMP decreased. The moisture content and Hunter L
value of scone were significantly decreased with increasing levels of PMP. On
the other hand, sugar content and Hunter a value of scone were significantly
increased with increasing levels of PMP. The Hunter b value of scone was shown
to increase with increasing content of PMP while the group containing 5% PMP
decreased. The texture analysis revealed that the hardness, cohesiveness,
gumminess and chewiness of scone were decreased with the addition of PMP. The
springiness showed no significant differences between control and PMP scones.
Total polyphenolics, flavonoid content, DPPH and ABTS+ radical
scavenging activity, and reducing powder of scones significantly increased with
PMP content. These results indicate that the addition of PMP to scone can
improve its quality and antioxidant activities.
Clinical coding of long COVID in English primary care: a federated analysis of 58 million patient records in situ using OpenSAFELY
Authors:   The OpenSAFELY Collaborative, Walker Alex J , MacKenna Brian , Inglesby Peter , Rentsch Christopher T , Curtis Helen J , Morton Caroline E , Morley Jessica , Mehrkar Amir , Bacon Seb , Hickman George , Bates Chris , Croker Richard , Evans David , Ward Tom , Cockburn Jonathan , Davy Simon , Bhaskaran Krishnan , Schultze Anna , Williamson Elizabeth J , Hulme William J , McDonald Helen I , Tomlinson Laurie , Mathur Rohini , Eggo Rosalind M , Wing Kevin , Wong Angel YS , Forbes Harriet , Tazare John , Parry John , Hester Frank , Harper Sam , O’Hanlon Shaun , Eavis Alex , Jarvis Richard , Avramov Dima , Griffiths Paul , Fowles Aaron , Parkes Nasreen , Douglas Ian J , Evans Stephen JW , Smeeth Liam , Goldacre Ben
Published: May 2021

Background

Long COVID is a term to describe new or persistent symptoms at least four weeks after onset of acute COVID-19. Clinical codes to describe this phenomenon were released in November 2020 in the UK, but it is not known how these codes have been used in practice.

Methods

Working on behalf of NHS England, we used OpenSAFELY data encompassing 96% of the English population. We measured the proportion of people with a recorded code for long COVID, overall and by demographic factors, electronic health record software system, and week. We also measured variation in recording amongst practices.

Results

Long COVID was recorded for 23,273 people. Coding was unevenly distributed amongst practices, with 26.7% of practices having not used the codes at all. Regional variation was high, ranging between 20.3 per 100,000 people for East of England (95% confidence interval 19.3-21.4) and 55.6 in London (95% CI 54.1-57.1). The rate was higher amongst women (52.1, 95% CI 51.3-52.9) compared to men (28.1, 95% CI 27.5-28.7), and higher amongst practices using EMIS software (53.7, 95% CI 52.9-54.4) compared to TPP software (20.9, 95% CI 20.3-21.4).

Conclusions

Long COVID coding in primary care is low compared with early reports of long COVID prevalence. This may reflect under-coding, sub-optimal communication of clinical terms, under-diagnosis, a true low prevalence of long COVID diagnosed by clinicians, or a combination of factors. We recommend increased awareness of diagnostic codes, to facilitate research and planning of services; and surveys of clinicians’ experiences, to complement ongoing patient surveys.
SARS-CoV-2 RNA and antibody detection in human milk from a prospective multicenter study in Spain
Authors: Bäuerl Christine , Randazzo Walter , Sánchez Gloria , Selma-Royo Marta , Garcia-Verdevio Elia , Martínez-Rodríguez Laura , Parra-Llorca Anna , Lerin Carles , Fumadó Victoria , Crovetto Francesca , Crispi Fátima , Pérez-Cano Francisco J , Rodríguez Gerardo , Ruíz-Redondo Gema , Campoy Cristina , Martínez-Costa Cecilia , Collado Maria Carmen
Published: May 2021

Background

During the COVID-19 pandemic in 2020, breastfeeding in women positive for SARS-CoV-2 was compromised due to contradictory data regarding potential viral transmission. However, growing evidence confirms the relevant role of breast milk in providing passive immunity by generating and transmitting specific antibodies against the virus. Thus, our study aimed to develop and validate a specific protocol to detect SARS-CoV-2 in breast milk matrix as well as to determine the impact of maternal SARS-CoV-2 infection on presence, concentration, and persistence of specific SARS-CoV-2 antibodies.

Study design/Methods

A prospective multicenter longitudinal study in Spain was carried out from April to December 2020. A total of 60 mothers with SARS-CoV-2 infection and/or recovered from COVID-19 were included (n=52 PCR-diagnosed and n=8 seropositive). Data from maternal-infant clinical records and symptomatology were collected. A specific protocol was validated to detect SARS-CoV-2 RNA in breast milk, targeting the N1 region of the nucleocapsid gene and the envelope (E) gene. Presence and levels of SARS-CoV-2 specific immunoglobulins (Igs) -IgA, IgG, and IgM-in breast milk samples from COVID-19 patients and from 13 women before the pandemic were also evaluated.

Results

All breast milk samples showed negative results for SARS-CoV-2 RNA presence. We observed high intra- and inter-individual variability in the antibody response to the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein for each of the three isotypes IgA, IgM and IgG. Protease domain (MPro) antibodies were also detected in milk. In general, 82.9 % of the milk samples were positive for at least one of the three antibody isotypes, being 52.86 % of those positive for all three Igs. Positivity rate for IgA was relatively stable over time (65.2 – 87.5 %), whereas it raised continuously for IgG (47.8 % the first ten days to 87.5 % from day 41 up to day 206 post-PCR confirmation).

Conclusions

Considering the lack of evidence for SARS-CoV-2 transmission through breast milk, our study confirms the safety of breastfeeding practices and highlights the relevance of virus-specific SARS-CoV-2 antibody transfer, that would provide passive immunity to breastfed infants and protect them against COVID-19 disease. This study provides crucial data to support official breastfeeding recommendations based on scientific evidence.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence: Navigating the absence of a gold standard
Authors: Saeed Sahar , O’Brien Sheila F. , Abe Kento , Yi QiLong , Rathod Bhavisha , Wang Jenny , Fazel-Zarandi Mahya , Tuite Ashleigh , Fisman David , Wood Heidi , Colwill Karen , Gingras Anne-Claude , Drews Steven
Published: May 2021

Background

Multiple anti-SARS-CoV-2 immunoassays are available, but no gold standard exists. We assessed four assays using various methodological approaches to estimate SARS-COV-2 seroprevalence during the first COVID-19 wave in Canada.

Methods

This serial cross-sectional study was conducted using plasma samples from healthy blood donors between April-September 2020. Qualitative assessment of SARS-CoV-2 IgG antibodies was based on four assays: Abbott Architect SARS-Cov-2 IgG assay (target nucleocapsid) (Abbott-NP) and three in-house IgG ELISA assays (target spike glycoprotein (Spike), spike receptor binding domain (RBD), and nucleocapsid (NP)). Seroprevalence was estimated using multiple composite reference standards (CRS) and by a series of Bayesian Latent Class Models (BLCM) (using uninformative, weakly, and informative priors).

Results

8999 blood samples were tested. The Abbott-NP assay consistently estimated seroprevalence to be lower than the ELISA-based assays. Discordance between assays was common, 13 unique diagnostic phenotypes were observed. Only 32 samples (0.4%) were positive by all four assays. BLCM using uninformative priors predicted seroprevalence increased from 0.7% (95% credible interval (CrI); 0.4, 1.0%) in April/May to 0.8% (95% CrI 0.5, 1.2%) in June/July to 1.1% (95% CrI 0.7, 1.6) in August/September. Results from CRS were very similar to the BLCM. Assay characteristics varied considerably over time. Overall spike had the highest sensitivity (89.1% (95% CrI 79.2, 96.9%), while the sensitivity of the Abbott-NP assay waned from 65.3% (95% CrI 43.6, 85.0%) in April/May to 45.9% (95% CrI 27.8, 65.6) by August/September.

Discussion

We found low SARS-CoV-2 seroprevalence rates at the end of the first wave and estimates derived from single assays may be biased.

Multiple anti-SARS-CoV-2 immunoassays are available, but no gold standard exists. We used four unique assays to estimate very low SARS-COV-2 seroprevalence during the first COVID-19 wave in Canada. Caution should be exercised when interpretating seroprevalence estimates from single assays.
Predicting pulmonary function from the analysis of voice: a machine learning approach
Authors: Alam Md. Zahangir , Simonetti Albino , Billantino Rafaelle , Tayler Nick , Grainge Chris , Siribaddana Pandula , Nouraei S. A. Reza , Batchelor James , Rahman M. Sohel , Mancuzo Eliane V. , Holloway John W , Holloway Judith A. , Rezwan Faisal I
Published: May 2021

Providing proper timely treatment of asthma, self-monitoring can play a vital role in disease control. Existing methods (such as peak flow meter, smart spirometer) requires special equipment and are not always used by the patient. Using voice recording as surrogate measures of lung function can be used to assess asthma, which has good potential to self-monitor asthma and could be integrated into telehealth platforms. This study aims to apply machine learning approach to predict lung functions from recorded voice for asthma patients.

A threshold-based mechanism was designed to separate speech and breathing from recordings (323 recordings from 26 participants) and features extracted from these were combined with biological attributes and lung function (percentage predicted forced expiratory volume in 1 second, FEV1%). Three predictive models were developed: (a) regression models to predict lung function, (b) multi-class classification models to predict the severity, and (c) binary classification models to predict abnormality. Random Forest (RF), Support Vector Machine (SVM), and Linear Regression (LR) algorithms were implemented to develop these predictive models. Training and test samples were separated (70%:30% using balanced portioning). Features were normalised and 10-fold cross-validation used to measure the model’s training performances on the training samples. Models were then run on the test samples to measure the final performances.

The RF based regression model performed better with lowest root mean square error = 10.86, and mean absolute score = 11.47, as compared to other models. In predicting the severity of lung function, the SVM based model performed better with 73.20% accuracy. The RF based model performed better in binary classification models for predicting abnormality of lung function (accuracy = 0.85, F1-score = 0.84, and area under the receiver operating characteristic curve = 0.88).

The proposed machine learning approach can predict lung function (in terms of FEV1%), from the recorded voice files, better than other published approaches. These models can be extended to predict both the severity and abnormality of lung function with reasonable accuracies. This technique could be used to develop future telehealth solutions including smartphone-based applications which have potential to aid decision making and self-monitoring in asthma.
Analysis of whole exome sequencing in severe mental illness hints at selection of brain development and immune related genes
Authors: Mahadevan Jayant , Pathak Ajai Kumar , Vemula Alekhya , Nadella Ravi Kumar , Viswanath Biju , Purushottam Meera , Jain Sanjeev , Mondal Mayukh
Published: May 2021

Evolutionary trends may underlie some aspects of the risk for common, non-communicable disorders, including psychiatric disease. We analyzed whole exome sequencing data from 80 unique individuals from India coming from families with two or more individuals with severe mental illness. We used Population Branch Statistics (PBS) to identify variants and genes under positive selection and identified 75 genes as candidates for positive selection. Of these, 20 were previously associated with Schizophrenia, Alzheimer’s disease and cognitive abilities in genome wide association studies. We then checked whether any of these 75 genes were involved in common biological pathways or related to specific cellular or molecular functions. We found that immune related pathways and functions related to innate immunity such as antigen binding were over-represented. We also evaluated for the presence of Neanderthal introgressed segments in these genes and found Neanderthal introgression in a single gene out of the 75 candidate genes. However, the introgression pattern indicates the region is unlikely to be the source for selection. Our findings hint at how selection pressures in individuals from families with a history of severe mental illness may diverge from the general population. Further, it also provides insights into the genetic architecture of severe mental illness, such as schizophrenia and its link to immune factors.
Establishment of an evaluation panel for the decentralized technical evaluation of the sensitivity of 31 rapid detection tests for SARS-CoV-2 diagnostics
Authors: Puyskens Andreas , Krause Eva , Michel Janine , Nübling Micha , Scheiblauer Heinrich , Bourquain Daniel , Grossegesse Marica , Valusenko Roman , Corman Viktor , Drosten Christian , Zwirglmaier Katrin , Wölfel Roman , Lange Constanze , Kramer Jan , Friesen Johannes , Ignatius Ralf , Müller Michael , Schmidt-Chanasit Jonas , Emmerich Petra , Schaade Lars , Nitsche Andreas
Published: May 2021

Background

The detection of SARS-CoV-2 with rapid diagnostic tests has become an important tool to identify infected people and break infection chains. These rapid diagnostic tests are usually based on antigen detection in a lateral flow approach.

Aims & Methods

While for PCR diagnostics the validation of a PCR assay is well established, for antigen tests e.g. rapid diagnostic tests there is no common validation strategy. Here we present the establishment of a panel of 50 pooled clinical specimens that cover a SARS-CoV-2 concentration range from approximately 1.1 × 109to 420 genome copies per mL of specimen. The panel was used to evaluate 31 rapid diagnostic tests in up to 6 laboratories.

Results

Our results show that there is significant variation in the detection limits and the clinical sensitivity of different rapid diagnostic tests. We conclude that the best rapid diagnostic tests can be applied to reliably identify infectious individuals who are presenting with SARS-CoV-2 loads correlated to 106genome copies per mL of specimen. Infected individuals displaying SARS-CoV-2 genome loads corresponding to less than 106genome copies per mL will be identified by only some rapid diagnostics tests, while many tests miss these viral loads to a large extent.

Conclusions

Sensitive RDTs can be applied to identify infectious individuals with high viral loads, but not to identify infected individuals.
Generalizable predictive modeling of semantic processing ability from functional brain connectivity
Authors: Meng Danting , Wang Suiping , Wong Patrick C.M. , Feng Gangyi
Published: May 2021

Semantic processing (SP) is one of the critical abilities of humans for representing and manipulating meaningful and conceptual information. Neuroimaging studies of SP typically collapse data from many subjects, but both its neural organization and behavioral performance vary between individuals. It is not yet understood whether and how the individual variabilities in neural organizations contribute to the individual differences in SP behaviors. Here we aim to identify the neural signatures underlying SP variabilities by analyzing individual functional connectivity (FC) patterns based on a large-sample Human Connectome Project (HCP) dataset and rigorous predictive modeling. We used a two-stage predictive modeling approach to build an internally cross-validated model and to test the model’s generalizability with unseen data from different HCP sub-populations and task states as well as other out-of-sample datasets that are independent of the HCP. FC patterns within a putative semantic brain network were significantly predictive of individual SP scores summarized from five semantic tasks. This cross-validated predictive model can be used to predict unseen HCP data. The model generalizability was enhanced with FCs in language tasks than resting state and other task states and was better for females than males. The model constructed from the HCP dataset can be generalized to two independent cohorts that participated in different semantic tasks. FCs connecting to the Perisylvian language network show the most reliable contributions to predictive modeling and the out-of-sample generalization. These findings contribute to our understanding of the neural sources of individual differences in SP, which potentially lay the foundation for personalized education and improve intervention practice for patients with SP and language deficits.
Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise
Authors: Vo Huy D. , Forero Linda , Aguilera Luis , Munsky Brian
Published: May 2021

Despite continued technological improvements, measurement errors will always reduce or distort the information that any real experiment can provide to quantify cellular dynamics. This problem becomes even more serious in the context of cell signaling studies that are specifically designed to quantify heterogeneity in single-cell gene regulation, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. It is not clear how measurement noise should be managed in addition to other experiment design variables (e.g., sampling size, measurement times, or perturbation levels) to ensure that collected data will provide useful insights on signaling or gene expression mechanisms of interest. To address these fundamental single-cell analysis and experiment design challenges, we propose a computational framework that takes explicit consideration of measurement errors to analyze single-cell observations and Fisher Information Matrix-based criteria to decide between experiments. Using simulations and single-cell experiments for a reporter gene controlled by an HIV promoter construct, we demonstrate how our approach can analyze and redesign experiments to optimally harvest fluctuation information while mitigating the effects of image distortion.
De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet
Authors: Winkler Sebastian , Winkler Ivana , Figaschewski Mirjam , Tiede Thorsten , Nordheim Alfred , Kohlbacher Oliver
Published: May 2021

Background

With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem.

Results

We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet is freely available as open-source software.

Conclusion

The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.
Analysis of the Neurotropic and Anti-inflammatory Effects of Neuroelectrode Functionalisation with a Heparan Sulphate Mimetic
Authors: Vallejo-Giraldo Catalina , Mohand Idir Ouidja , Huynh Minh Bao , Trotier Alexandre , Krukiewicz Katarzyna , Pallipurath Anuradha R , Flannery Andrea , Kilcoyne Michelle , Pandit Abhay , Papy-Garcia Dulce , Biggs Manus Jonathan Paul
Published: May 2021

Further in the search for biomimicry of the properties analogous to neural tissues, and with an ultimate goal of mitigating electrode deterioration via reactive host cell response and glial scar formation, the bio-functionalisation of PEDOT:PTS neural coating is here presented using a heparan mimetic termed (HM) F6. A sulphated mimetic polyanion, with a potential role in neuromodulation in neurodegenerative diseases, and used here for the first time as neural coating.

This work acts as a first step towards the use of HM biological dopants, to enhance neuroelectrode functionality, to promote neural outgrowth and to maintain minimal glial scar formation in vitro at the neural-interface. Further, this study opens new possibilities for the evaluation of glycan mimetics in neuroelectrode functionalisation.
Early life gut microbiome dynamics mediate maternal effects on infant growth in vervet monkeys
Authors: Petrullo Lauren , Baniel Alice , Jorgensen Matthew J. , Sams Sierra , Snyder-Mackler Noah , Lu Amy
Published: May 2021

Background

Maternal parity is associated with variation in infant growth across mammals, but the mechanisms underlying this relationship are unclear. Given emerging links between growth and the microbiome, and the importance of maternal microbiota in establishing this community, the assembly of the infant gut microbiome may be a mediator of parity effects on infant growth.

Results

Here, we analyzed 118 fecal and milk samples from mother-infant vervet monkey dyads across the first 6 months postpartum in a population with high growth-associated infant mortality. Despite poorer milk production, infants born to low parity females were larger at 6 months of age than their counterparts and exhibited divergent patterns in gut microbiome assembly. Gut microbiome alpha diversity increased rapidly from the first days of life to 4 months old in all infants, but infants born to low parity females exhibited reduced gut microbiome alpha diversity during early life. At the taxonomic level, infants broadly exhibited a shift fromBacteroides fragilistoPrevotelladominance. Infants of low parity females housed moreB. fragilisin their guts, andB. fragilisdominance drove reduced alpha diversity. Maternal vertical transmission to the infant gut was greater from milk than from the maternal gut, and was greatest among infants born to low parity females.B. fragiliswas 15-fold more abundant in milk than in the maternal gut and was greater in the milk of low parity females, suggesting that milk may be the primary maternal reservoir ofB. fragilis. Path analyses demonstrated that both infant gut alpha diversity andB. fragilismediated parity effects on postnatal growth: infants were larger at 6 months old if they exhibited reduced alpha diversity and a greater relative abundance ofB. fragilisduring early life.

Conclusion

The first days of life are a critical period of infant gut microbiome organization during which the establishment of a less diverse, milk-oriented microbial community abundant inB. fragilispromotes growth among infants born to reproductively inexperienced females.
Impacts on the structure-function relationship of SARS-CoV-2 spike by B.1.1.7 mutations
Authors: Yang Tzu-Jing , Yu Pei-Yu , Chang Yuan-Chih , Liang Kang-Hao , Tso Hsian-Cheng , Ho Meng-Ru , Chen Wan-Yu , Lin Hsiu-Ting , Wu Han-Chung , Hsu Shang-Te Danny
Published: May 2021

The UK variant of the severe acute respiratory syndrome coronavirus (SARS-CoV-2), known as B.1.1.7, harbors several point mutations and deletions on the spike (s) protein, which potentially alter its structural epitopes to evade host immunity while enhancing host receptor binding. Here we report the cryo-EM structures of the S protein of B.1.1.7 in its apo form and in the receptor ACE2-bound form. One or two of the three receptor binding domains (RBDs) were in the open conformation but no fully closed form was observed. In the ACE-bound form, all three RBDs were engaged in receptor binding. The B.1.1.7-specific A570D mutation introduced a salt bridge switch that could modulate the opening and closing of the RBD. Furthermore, the N501Y mutation in the RBD introduced a favorable π-π interaction manifested in enhanced ACE2 binding affinity. The N501Y mutation abolished the neutralization activity of one of the three potent neutralizing antibodies (nAbs). Cryo-EM showed that the cocktail of other two nAbs simultaneously bound to all three RBDs. Furthermore, the nAb cocktail synergistically neutralized different SARS-CoV-2 pseudovirus strains, including the B.1.1.7.
Novel Interpretable Tissue-Specific and Multi-Tissue Transcriptomic Clocks to Infer Aging Mechanisms
Authors: Gupta Aayush , Lu Mindren , Sun Jessica
Published: May 2021

1.

Aging is characterized as a progressive decline in fitness that ultimately results in death. We set out to build both tissue-specific and multi-tissue transcriptomic clocks to make global tissue age predictions in individuals from GTEx. Existing work in the field primarily uses epigenetic clocks as predictors of age, but these models have known issues and are significantly less interpretable than their transcriptomic counterparts. Due to their transcriptomic nature, we can use these models to directly infer mechanisms of aging from their features. Linear regression remains the current standard analysis technique, but we improved upon its baseline performance with modern techniques, exploring both XGBoost and MLPs. We also experimented with using deconvolved cell data for predictions, which account for cellular composition and reduce signal distortion from rare cell types. Since it is known that the heterogeneity of cell types in particular tissues can lead to noise in these models, we proposed using deconvolution as a potential remedy for this problem.

Our results found that MLPs are not well suited for the task due to a lack of training data, but the use of XGBoost is effective at improving the baseline performance of predictions of existing tissue-specific clocks. These models allowed us to directly compute genes most important to age prediction in our models, and we showed that multiple genes found have been independently identified elsewhere to show evidence of correlation with age. Given the small size of our datasets, we were unable to make conclusive determinations about multi-tissue predictors, but preliminary results suggest that the technique shows promise and is worthy of future investigation. Likewise, given our limited deconvolved cell data, we did not currently observe strong results, but we again note that this is an area in need of further investigation.

By improving upon the performance of existing models, we demonstrated that a novel machine learning technique, XGBoost, can be an effective technique to further our understanding of aging mechanisms by extraction of the most relevant genes found in those models. This is significant because the genetic causes of aging are still not fully understood, and research in the field of aging is lacking in comparison to other domains. As the problem of identifying tissues that age at different rates is of specific interest, our tissue-specific models potentially have other applications in this domain, including informing pathologies in tissues that are found to be aging faster, or analyzing how people with similar ages can have vastly different tissue ages. An extended technical presentation of this work can be found here, and a highly simplified non-technical overview presentation can be found here.
Dissecting the clonality of I1 plasmids using ORF-based binarized structure network analysis of plasmids (OSNAp)
Authors: Suzuki Masahiro , Norizuki Chihiro , Wachino Jun-ichi , Kawamura Kumiko , Nagano Noriyuki , Nagano Yukiko , Hayashi Wataru , Kimura Kouji , Doi Yohei , Arakawa Yoshichika
Published: May 2021

Phylogenetic relationship of 97 I1 plasmids harboring blaCTX-M genes encoding extended-spectrum beta-lactamase (ESBL) was analyzed using the ORF-based binarized structure network analysis of plasmids (OSNAp). The majority of plasmids carrying blaCTX-M-1 or blaCTX-M-8, ESBL genes primarily associated with domestic animals, were clonal. On the other hand, plasmids carrying blaCTX-M-14 or blaCTX-M-15, identified from both humans and domestic animals, were diverse in their contents. The findings suggest that circulation of I1 plasmids among humans and animals may contribute to their diversity.
Nanopore Sequencing of SARS-CoV-2: Comparison of Short and Long PCR-tiling Amplicon Protocols
Authors: Brejová Broňa , Boršová Kristína , Hodorová Viktória , Čabanová Viktória , Gafurov Askar , Fričová Dominika , Neboháčová Martina , Vinař Tomáš , Klempa Boris , Nosek Jozef
Published: May 2021

Surveillance of the SARS-CoV-2 variants including the quickly spreading mutants by rapid and near real-time sequencing of the viral genome provides an important tool for effective health policy decision making in the ongoing COVID-19 pandemic. Here we evaluated PCR-tiling of short (∼400-bp) and long (∼2 and ∼2.5-kb) amplicons combined with nanopore sequencing on a MinION device for analysis of the SARS-CoV-2 genome sequences. Analysis of several sequencing runs demonstrated that using the long amplicon schemes outperforms the original protocol based on the 400-bp amplicons. It also illustrated common artefacts and problems associated with this approach, such as uneven genome coverage, variable fraction of discarded sequencing reads, as well as the reads derived from the viral sub-genomic RNAs and/or human and bacterial contamination.
Injectable Progranulin-derivative Atsttrin loaded Protein Engineered Gels for Post Traumatic Osteoarthritis
Authors: Katyal Priya , Hettinghouse Aubryanna , Meleties Michael , Chen Changhong , Cui Min , Sun Guodong , Hasan Sadaf , Menon Rajiv , Lin Bonnie , Regatte Ravinder , Liu Chuan-ju , Montclare Jin Kim
Published: May 2021

Protein-based biomaterials offer several advantages over synthetic materials, owing to their unique stimuli-responsive properties, biocompatibility and modular nature. We have successfully developed protein block polymers that consist of elastin like polypeptide (E) and the coiled-coil domain of cartilage oligomeric matrix protein (C). Here, we demonstrate that E5C, a construct consisting of five repeats of E and a single domain of C, is capable of forming a porous networked gel at physiological temperature, making it an excellent candidate for injectable biomaterials. Combination of E5C with Atsttrin, a chondroprotective engineered derivative of anti-inflammatory growth factor progranulin (PGRN), provides a unique biochemical and biomechanical environment to protect against post-traumatic osteoarthritis (PTOA) onset and progression. E5C gel was demonstrated to provide prolonged release of Atsttrin and inhibit chondrocyte catabolism while facilitating anabolic signaling in vitro. We also provide in vivo evidence that prophylactic and therapeutic application of Atsttrin-loaded E5C hydrogels protected against PTOA onset and progression in a rabbit anterior cruciate ligament transection model. Collectively, we have developed a unique protein-based gel capable of minimally invasive, sustained delivery of prospective therapeutics, particularly the PGRN-derivative Atsttrin, for prevention of OA onset and progression.
Fluorescence-Activated Droplet Sorting of Polyethylene Terephthalate-degrading Enzymes
Authors: Qiao Yuxin , Hu Ran , Chen Dongwei , Wang Li , Fu Ye , Li Chunli , Dong Zhiyang , Weng Yunxuan , Du Wenbin
Published: May 2021

Enzymes that can decompose synthetic plastics such as polyethylene terephthalate (PET) are urgently needed. However, a bottleneck remains due to a lack of techniques for detecting and sorting environmental microorganisms with vast diversity and abundance. Here, we developed a fluorescence-activated droplet sorting (FADS) pipeline for high-throughput screening of PET-degrading microorganisms or enzymes (PETases). The pipeline comprises three steps: generation and incubation of droplets encapsulating single cells, picoinjection of fluorescein dibenzoate (FDBz) as the fluorogenic probe, and screening of droplets to obtain PET-degrading cells. We characterized critical factors associated with this method, including specificity and sensitivity for discriminating PETase from other enzymes. We then optimized its performance and compatibility with environmental samples. The system was used to screen a wastewater sample from a PET textile mill. We successfully obtained PET-degrading species from nine different genera. Moreover, two putative PETases from isolates Kineococcus endophyticus Un-5 and Staphylococcus epidermidis Un-C2-8 were genetically derived, heterologously expressed, and preliminarily validated for PET-degrading activities. We speculate that the FADS pipeline can be widely adopted to discover new PET-degrading microorganisms and enzymes in various environments and may be utilized in the directed evolution of PETases using synthetic biology.
Single-cell epigenomic tracing of lifelong endothelial cell plasticity across mouse organs
Authors: Yu Xianhong , Liu Yaxi , Liu Xiaoge , Xiong Haiqing , He Aibin
Published: May 2021

Endothelial cells (ECs) across ages and tissues are highly heterogeneous in developmental origins, structures, functions, and cellular plasticity. Here, we applied CoBATCH for single-cell epigenomic tracing of dynamic EC lineage histories in five mouse organs from development to ageing. Our analyses showed that epigenomic memory reflects both developmental origins and tissue-restricted specialization of EC sublineages but with varying time lengths across organs. To gain insights into cellular plasticity of ECs, we identified bivalent chromatin occupancy of otherwise mutually exclusive EC- (ERG) and mesenchymal-specific (TWIST1/SNAI1) transcription factors promoting endothelial-to-mesenchymal transition. We further revealed that pseudotime trajectories by histone modifications H3K36me3 and H3K27ac faithfully recapitulate short- and long-range EC fate change over senescence, respectively. Together, our data provide a unique exploration of chromatin-level cell fate regulation of organotypic EC lineages across the lifespan.

One-Sentence Summary

Single-cell chromatin binding is examined for tracing endothelial cell lineages in mouse organs across the lifespan.
Predicting the N400 ERP component using the Sentence Gestalt model trained on a large scale corpus
Authors: Lopopolo Alessandro , Rabovsky Milena
Published: May 2021

The N400 component of the event related brain potential is widely used to investigate language and meaning processing. However, despite much research the component’s functional basis remains actively debated. Recent work showed that the update of the predictive representation of sentence meaning (semantic update, or SU) generated by the Sentence Gestalt model (McClelland, St. John, & Taraban, 1989) consistently displayed a similar pattern to the N400 amplitude in a series of conditions known to modulate this event-related potential. These results led Rabovsky, Hansen, and McClelland (2018) to suggest that the N400 might reflect change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error. However, a limitation of this work is that the model was trained on a small artificial training corpus and thus could not be presented with the same naturalistic stimuli presented in empirical experiments. In the present study, we overcome this limitation and directly model the amplitude of the N400 elicited during naturalistic sentence processing by using as predictor the SU generated by a Sentence Gestalt model trained on a large corpus of texts. The results reported in this paper corroborate the hypothesis that the N400 component reflects the change in a probabilistic representation of meaning after every word presentation. Further analyses demonstrate that the SU of the Sentence Gestalt model and the amplitude of the N400 are influenced similarly by the stochastic and positional properties of the linguistic input.