bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cognitively Based Compassion Training for HIV Immune Nonresponders—An Attention-Placebo Randomized Controlled Trial
Authors: Titanji Boghuma K. , Tejani Mehul , Farber Eugene W. , Mehta C. Christina , Pace Thaddeus W. , Meagley Kathryn , Gavegnano Christina , Harrison Timothy , Kokubun Caroline W. , Negi Satya Dev , Schinazi Raymond F. , Marconi Vincent C. 
Published: December 2021

Objective:
Chronic inflammation is associated with increased morbidity and mortality for people with HIV (PWH). Psychological stress is an important contributor to this chronic inflammation. We hypothesized that a cognitively based compassion training (CBCT) approach could reduce inflammation and psychological stress in immune nonresponder PWH.

Design:
An attention-placebo randomized controlled trial design to evaluate the acceptability of CBCT among PWH and its effects on key aspects of stress and immune function compared with an active-attention control group (NCT02395289).

Methods:
This study was conducted at an HIV clinic in Atlanta, Georgia. Eligible individuals determined by (1) adherence to antiretroviral therapy for at least a year, (2) virologic suppression; and (3) stable CD4+ T-cell counts <350 cells/μL were randomized in a 2:1 ratio to either CBCT or control in 2 study periods: April–May, 2016, and September–December, 2016. Psychological measures and inflammatory biomarkers associated with HIV disease progression (IL-1β, TNF-α, sCD14, IL-6, and IL-10) were obtained for all study participants at baseline and at the time of study completion.

Results:
We found a significant association between CBCT practice time engagement and fold reduction in IL-6 and TNF-α levels. There was no association between CBCT practice time and other biomarkers markers assessed (IL-1β, sCD14, and IL-10). These changes were coincident with significant increases in self-reported psychological well-being and HIV disease acceptance and in benefits for CBCT participants. We also observed fewer instances of virologic failure for those in the CBCT arm compared with controls.

Conclusions:
CBCT is a novel and feasible nonmedication-based intervention that could reduce inflammation and psychological stress in PWH.
What will it take to meet UNAIDS targets for preexposure prophylaxis users?
Authors: Sullivan Patrick Sean , Siegler Aaron Julius 
Published: December 2021

Purpose of review
Preexposure prophylaxis (PrEP) is a critical strategy to curb new HIV infections globally. National and global targets have been set for people starting PrEP. However, global PrEP initiations fell short of UNAIDS 2020 targets, and reflection is needed on how we set and meet targets for PrEP use.

Recent findings
Recent literature documents challenges to meeting ambitious goals for PrEP coverage in multiple phases of PrEP: PrEP initiations are limited by gaps in the identification of those who might benefit from PrEP. Conversely, getting PrEP to those who need it most is threatened by inaccurate risk perception and HIV and PrEP stigma. Once people are on PrEP, a substantial number discontinue PrEP in the first year (the ‘PrEP Cliff’), a finding that is robust across groups of PrEP users (e.g., women, men who have sex with men, transwomen) and across global prevention settings. Further, PrEP inequities – by which we refer to utilization of PrEP in a specific group that is not commensurate with their epidemic risk – threaten the overall population benefit of PrEP because those at highest risk of acquiring HIV are not adequately protected.

Summary
To realize global goals for PrEP utilization and impact, we must address multiple points of PrEP delivery programs that address not just PrEP starts, but also retention in PrEP and measurement and accountability to PrEP equity. We call for new approaches to better identify PrEP candidates, suggest additional research to address the known and consistent reasons for PrEP discontinuations, and advocate for metrics to measure and be accountable to PrEP equity.
Identification and application of plasmatic microRNA expression quantitative trait loci (miR-QTL) at first trimester of pregnancy
Authors: White Frédérique , Groleau Marika , Côté Samuel , Légaré Cécilia , Thibeault Kathrine , Clément Andrée-Anne , Hivert Marie-France , Bouchard Luigi , Jacques Pierre-Étienne 
Published: December 2021

AbstractBackgroundMicroRNAs (miRNAs) are a class of small non-coding RNAs regulating gene expression. They are involved in many biological processes, including adaptation to pregnancy. The identification of genetic variants associated with gene expression, known as expression quantitative trait loci (eQTL), helps to understand the underlying molecular mechanisms and determinants of complex diseases. Using data from the prospective pre-birth Gen3G cohort, we investigated associations between maternal genotypes and plasmatic miRNA levels measured during the first trimester of pregnancy of 369 women.ResultsAssessing the associations between about 2 million SNPs and miRNA proximal pairs using best practices from the GTEx consortium, a total of 22,140 significant eQTLs involving 147 unique miRNAs were identified. Elastic-net regressions were applied to select the most relevant SNPs to build genetic risk scores (GRS) for each of these 147 miRNAs. For about half of the circulating miRNAs, the GRS captured >10% of the variance abundance. As a demonstration of the usefulness of the identified eQTLs and derived GRS, we used the GRSs as instrumental variables to test for association between the circulating levels of miRNAs quantified before the 16th week of pregnancy and the development of pregnancy complications (gestational diabetes [GDM] or pre-eclampsia [PE]) developing more than three months later on average. Using predicted miRNA levels derived from instrumental variables, we found 18 significant associations of miRNAs with potential support of causal inference for GDM or PE.ConclusionsOur results represent a valuable resource to understand miRNA regulation and highlight the potential of genetic instruments in predicting circulating miRNA levels and their possible contribution in disease development.
Individual vaccine efficacy variation with time since mRNA BNT162b2 vaccination estimated by rapid, quantitative antibody measurements from a finger-prick sample
Authors: Vargas Matheus J. T. , Chandrasekhar Mithileshwari , Kwon Yong Je , Deijs Gerrit Sjoerd , Corazza Carsten Ma On Wong , Chai Angela (Wai Yin) , Binedell Rebecca L. , Jose Ellen , Govind Bhavesh , Huyet Laura , Patel Pooja K. , Reshef Gabrielle , Kumar Vijaya , Lowe Tiffany , Powell Robert J. , Jina Kieran C. , Walker Flynn C.W. , Talemaitoga Apisalome , Simpson M. Cather , Williams David E. 
Published: December 2021

AbstractWe show that an individual’s immune status to Covid-19 can be monitored through quantitative antibody measurements using a method based on centrifugal microfluidics, specifically designed for speed to result (20 min), high throughput (8 samples simultaneously) and accuracy from a finger-prick blood sample. Anti-Receptor Binding Domain (RBD) IgG concentration showed a log-normal distribution with mean decreasing with time following the second vaccination with mRNA BNT162b2 (Pfizer). Using a model for an individual’s antibody concentration-dependent vaccine efficacy allowed comparison with literature data on changing vaccine efficacy against symptomatic disease across a population. Even though the trial was small (n = 100) the computed population vaccine efficacy was in reasonable agreement with that obtained from a large population survey. The derived parameters for the vaccine efficacy model were in good agreement with those expected from previous studies and from a simple theoretical model. The results and modelling show that the major proportion of breakthrough infections are for people whose antibody concentration is in the tail of the distribution. The results provide strong support for personalized booster programmes that, by targeting people in the tail of the distribution, should be more effective at diminishing breakthrough infection and optimising booster dose supply than a program that simply mandates a booster at a specific post-vaccination time point.
RT-PCR/MALDI-TOF diagnostic target performance reflects circulating SARS-CoV-2 variant diversity in New York City
Authors: Hernandez Matthew M. , Banu Radhika , Gonzalez-Reiche Ana S. , Gray Brandon , Shrestha Paras , Cao Liyong , Chen Feng , Shi Huanzhi , Hanna Ayman , Ramírez Juan David , van de Guchte Adriana , Sebra Robert ,   Mount Sinai PSP Study Group, Gitman Melissa R. , Nowak Michael D. , Cordon-Cardo Carlos , Schutzbank Ted E. , Simon Viviana , van Bakel Harm , Sordillo Emilia Mia , Paniz-Mondolfi Alberto E. 
Published: December 2021

AbstractAs severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to circulate, multiple variants of concern (VOC) have emerged. New variants pose challenges for diagnostic platforms since sequence diversity can alter primer/probe binding sites (PBS), causing false-negative results. The Agena MassARRAY® SARS-CoV-2 Panel utilizes reverse-transcription polymerase chain reaction and mass-spectrometry to detect five multiplex targets across N and ORF1ab genes. Herein, we utilize a dataset of 256 SARS-CoV-2-positive specimens collected between April 11, 2021-August 28, 2021 to evaluate target performance with paired sequencing data. During this timeframe, two targets in the N gene (N2, N3) were subject to the greatest sequence diversity. In specimens with N3 dropout, 69% harbored the Alpha-specific A28095U polymorphism that introduces a 3’-mismatch to the N3 forward PBS and increases risk of target dropout relative to specimens with 28095A (relative risk (RR): 20.02; p<0.0001; 95% Confidence Interval (CI): 11.36-35.72). Furthermore, among specimens with N2 dropout, 90% harbored the Delta-specific G28916U polymorphism that creates a 3’-mismatch to the N2 probe PBS and increases target dropout risk (RR: 11.92; p<0.0001; 95% CI: 8.17-14.06). These findings highlight the robust capability of Agena MassARRAY® SARS-CoV-2 Panel target results to reveal circulating virus diversity and underscore the power of multi-target design to capture VOC.
Post-Vaccination Symptoms after A Third Dose of mRNA SARS-CoV-2 Vaccination in Patients with Inflammatory Bowel Disease
Authors: Li Dalin , Debbas Philip , Cheng Susan , Braun Jonathan , McGovern Dermot P.B. , Melmed Gil Y. 
Published: December 2021

AbstractSymptoms after SARS-CoV-2 primary vaccination among patients with inflammatory bowel disease (IBD) are generally of similar frequency, severity, and duration to those reported in the general population. The symptom profile after a 3rd mRNA vaccine dose in the predominantly immune-compromised IBD population is unknown. We aimed to assess symptomology after a 3rd or booster dose of mRNA vaccination in adults with IBD. We surveyed participants of the Coronavirus Risk Associations and Longitudinal Evaluation in IBD (CORALE-IBD) post-vaccination registry for symptom frequency and severity after a 3rd mRNA vaccine dose in an observational cohort study. In total, 524 participants (70% female, mean age 45 years) reported a third dose of mRNA vaccination through October 11, 2021. Overall, 41% reported symptoms after a third dose, with symptoms generally more frequent and more severe among participants younger than 55 years. The most frequent postvaccination symptoms were injection site pain (39%), fatigue or malaise (34%), and headache (23%). These symptoms were all less frequently reported after dose 3 than after dose 2. Gastrointestinal symptoms were reported by 8.8%, which was slightly more frequent than after dose 2 (7.8%). Those with severe symptoms after dose 2 were more likely to have severe symptoms after dose 3. These findings can reassure the IBD patient and provider communities that the likelihood and distribution of symptoms after a third mRNA vaccine dose are generally similar to those after a second dose, and that the frequency of postvaccination symptoms after dose 3 are generally lower than after dose 2.
SEDRA: Selective Entry Dynamic Risk Assessment: A mathematical model to safely keep the borders open during Covid-19
Authors: de Gennaro Silvano , Lane Håkan 
Published: December 2021

1.AbstractThe Covid-19 pandemic has brought the World to a near standstill for most of 2020 and 2021, causing chaos in international travel, driving many economies into the ground, particularly those largely based on tourism. The lack of standard tools to assist decision makers in structuring a coherent policy to allow foreign passengers into their county and the resulting panic-mode opening/closing the borders on every “new case” outburst or new variant “of concern”, have led several countries to costly and often meaningless decisions based on fear rather than science or logic. This study aims at providing a universal method to safely keep the borders open and allow conditional immigration to foreign passengers according to a “Risk Group” table that includes all the countries reporting data on their Covid-19 situation to the WHO and other organisms. The RG table is recalculated on a weekly basis according to a mathematical model described in this paper, dynamically assessing the status of the pandemic worldwide through the calculation of a “Safety Index” for each country. A prototype algorithm has been implemented in VBA/EXCEL and its results are published bi-weekly on a Github repository.
Machine learning quantification of amyloid deposits in histological images of ligamentum flavum
Authors: Wang Andy Y. , Sharma Vaishnavi , Saini Harleen , Tingen Joseph N. , Flores Alexandra , Liu Diang , Safain Mina G. , Kryzanski James , McPhail Ellen D. , Arkun Knarik , Riesenburger Ron I. 
Published: December 2021

ABSTRACTBackgroundWild-type transthyretin amyloidosis (ATTRwt) is an underdiagnosed and potentially fatal disease. Interestingly, ATTRwt deposits have been found to deposit in the ligamentum flavum (LF) of patients with lumbar spinal stenosis prior to the development of systemic and cardiac amyloidosis. In order to study this phenomenon and its possible relationship with LF thickening and systemic amyloidosis, a precise method of quantifying amyloid deposits in histological slides of LF is critical. However, such a method is currently unavailable. Here, we present a machine learning quantification method with Trainable Weka Segmentation (TWS) to assess amyloid deposition in histological slides of LF.MethodsImages of ligamentum flavum specimens stained with Congo red are obtained from spinal stenosis patients undergoing laminectomies and confirmed to be positive for ATTRwt. Amyloid deposits in these specimens are classified and quantified by TWS through training the algorithm via user-directed annotations on images of LF. TWS can also be automated through exposure to a set of training images with user-directed annotations, and then application to a set of new images without additional annotations. Additional methods of color thresholding and manual segmentation are also used on these images for comparison to TWS.ResultsWe develop the use of TWS in images of LF and demonstrate its potential for automated quantification. TWS is strongly correlated with manual segmentation in the training set of images with user-directed annotations (R = 0.98; p = 0.0033) as well as in the application set of images where TWS was automated (R = 0.94; p = 0.016). Color thresholding was weakly correlated with manual segmentation in the training set of images (R = 0.78; p = 0.12) and in the application set of images (R = 0.65; p = 0.23).ConclusionTWS machine learning closely correlates with the gold standard comparator of manual segmentation and outperforms the color thresholding method. This novel machine learning method to quantify amyloid deposition in histological slides of ligamentum flavum is a precise, objective, accessible, high throughput, and powerful tool that will hopefully pave the way towards future research and clinical applications.
Evaluation of the Performance of Filmarray Blood Culture Identification Panel on Detecting Blood Cultures Containing Activated Carbon Powder
Authors: Chen Chen , He Shang , Wang Chengbin 
Published: December 2021

AbstractObjectiveThe FilmArray Blood Culture Identification (BCID) panel is a rapid microfluidic PCR amplification microbial detection system. Several studies have evaluated its clinical performance on the basis of blood culture bottles containing resins. However, proportion of hospitals in China use bottles with carbon power, which the performance of FilmArray has not been fully investigated. Therefore, this study is conducted to explore the accuracy of the panel using blood culture bottles with carbon power.Method147 venous blood cultures containing carbon powder were used to assess the microbial and antibiotic resistance detection ability of the FilmArray panel. Outcomes were compared with results of the clinical combination method and their consistency was analyzed.ResultsFilmArray detected single microorganism in 121 samples, multiple microorganism in 9 cases and the consistency rate between the two methods was 90.6%. Among the 150 microorganisms detected, 85.1% (40/47) of staphylococcus contained the antibiotic resistant mecA gene, 15.3% (9/59) of Enterobacter detected the KPC gene, 7.7% (1/13) of Enterococcus has the vanA gene and the consistency with their clinical drug-resistant phenotypes were 93.6%, 86.4% and 100%, respectively.ConclusionThe identification rate of the FilmArray BCID panel using venous blood cultures with activated carbon powder was highly consistent with the outcomes of previous researchers using non-carbon powder blood culture bottles. It is capable of providing rapid and reliable results in the detection of pathogens present in automated blood culture systems.
LEXAS: a web application for life science experiment search and suggestion
Authors: Ito Kei K , Tsuruoka Yoshimasa , Kitagawa Daiju 
Published: December 2021

AbstractMotivationIn cellular biology, researchers design wet experiments by reading the relevant articles and considering the described experiments and results. Today, researchers spend a long time exploring the literature in order to plan experiments.ResultsTo accelerate experiment planning, we have developed a web application named LEXAS (Life-science EXperiment seArch and Suggestion). LEXAS curates the description of biomedical experiments and suggests the experiments on genes that could be performed next. To develop LEXAS, we first retrieved the descriptions of experiments from full-text biomedical articles archived in PubMed Central. Using these retrieved experiments and biomedical knowledgebases and databases, we trained a machine learning model that suggests the next experiments. This model can suggest not only reasonable genes but also novel genes as targets for the next experiment as long as they share some critical features with the gene of interest.Availability and implementationLEXAS is available at https://lexas.f.u-tokyo.ac.jp/ and provides users with two interfaces: search and suggestion. The search interface allows users to find a comprehensive list of experiment descriptions, and the suggestion interface allows users to find a list of genes that could be analyzed along with possible experiment methods. The source code is available at https://github.com/lexas-f-utokyo/lexas.Contactito-delightfully-kei@g.ecc.u-tokyo.ac.jpSupplementary informationSupplementary data are available at Bioinformatics online.
Comprehensive Serological Profile and Specificity of Maternal and Neonatal Cord Blood SARS CoV-2 Antibodies
Authors: Boelig Rupsa C. , Chaudhury Sidhartha , Aghai Zubair H. , Oliver Emily , Manusco Francesca , Berghella Vincenzo , Bergmann-Leitner Elke 
Published: December 2021

AbstractObjectiveTo describe the profile and specificity of maternal and neonatal cord-blood antibody profile in response SARS-CoV-2 virus exposureMethodsThis is a Prospective cohort study of delivering patients at Thomas Jefferson University Hospital from April 2020-February 2021. Primary objective was to describe unique maternal and fetal antibody epitope titers and specificity in those patients with COVID-19 history. Serologic profile assessed with a multiplex platform. Antigens used were: HA-trimer Influenza A (Hong Kong H3), spike trimers for SARS-CoV-2, SARS-CoV-1, MERS-CoV, and betacoronaviruses HKU-1 and OC43, as well as the spike N-terminal domain (NTD), spike receptor binding domain (RBD), and nucleocapsid protein (N; full length) for SARS-CoV-2.Results112 maternal samples and 101 maternal and cord blood pairs were analyzed. Thirty-seven had a known history of COVID-19 (positive PCR test) in the pregnancy and of those, 17 (47%) were diagnosed with COVID-19 within 30 days of delivery. Fifteen of remaining seventy-six (20%) without a known diagnosis had positive maternal serology. For those with history of COVID-19 we identified robust IgG response in maternal blood to CoV2 nucleocapsid (N), spike (S) full-length and S (RBD) antigens with more modest responses to the S (NTD) antigen. By contrast, the maternal blood IgM response appeared more specific to S (full-length), than N, S (RBD) or S (NTD) epitopes. There were significantly higher maternal and cord blood IgG response not just to CoV2 spike (p < 10−18), but also CoV1 spike (p < 10−9) and MERS spike (p < 10−8). By contrast, maternal IgM responses were more specific to CoV2 (p < 10−19), but to a lesser degree for CoV1 (p < 10−5), and no significant differences for MERS. Maternal and cord-blood IgG were highly correlated for both S and N (R2 = 0.96 and 0.94).ConclusionsPlacental transfer is efficient, with robust N and S responses. Both nucleocapsid and spike antibody responses should be studied for a better understanding of COVID-19 immunity. IgG antibodies are cross reactive with related CoV-1 and MERS spike epitopes while IgM, which cannot cross placenta to provide neonatal passive immunity, is more SARS CoV-2 specific. Neonatal cord blood may have significantly different fine-specificity than maternal blood, despite the high efficiency of IgG transfer.
The pneumonia severity index: assessment and comparison to popular machine learning classifiers
Authors: Wang Dawei , Willis Deanna R. , Yih Yuehwern 
Published: December 2021

AbstractPneumonia is the top communicable cause of death worldwide. Accurate prognostication of patient severity with Community Acquired Pneumonia (CAP) allows better patient care and hospital management. The Pneumonia Severity Index (PSI) was developed in 1997 as a tool to guide clinical practice by stratifying the severity of patients with CAP. While the PSI has been evaluated against other clinical stratification tools, it has not been evaluated against multiple classic machine learning classifiers in various metrics over large sample size. In this paper, we evaluated and compared the prediction performance of nine classic machine learning classifiers with PSI over 34720 adult (age 18+) patient records collected from 749 hospitals from 2009 to 2018 in the United States on Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and Average Precision (Precision-Recall AUC). Machine learning classifiers, such as Random Forest, provided a significant improvement (∼29% in PR AUC and ∼5% in ROC AUC) compared to PSI and required only 7 input values (compared to 20 parameters used in PSI). There were also statistically significant differences (p<0.05) between Random Forest and PSI among various races/ethnicities. Because of its ease of use, PSI remains a very strong clinical decision tool, but machine learning classifiers can provide better prediction accuracy performance. Comparing prediction performance across multiple metrics such as PR AUC, instead of ROC AUC alone can provide additional insight.Key MessagesThis work compared the prognostication accuracy performance of patient severity with Community Acquired Pneumonia (CAP) between Pneumonia Severity Index (PSI) and nine machine learning classifiers and found machine learning classifiers provided a significant improvement.