bluuebunny/crossref_metadata_2025_split huggingface dataset:
COVID-19 vaccine for people who live and work in prisons worldwide: A scoping review
Authors: Ismail Nasrul , Tavoschi Lara , Moazen Babak , Roselló Alicia , Plugge Emma 
Published: April 2022

AbstractOvercrowding, poor conditions, and high population turnover make prisons highly susceptible to COVID-19. Vaccination is key to controlling COVID-19, yet there is disagreement regarding whether people who live and work in prisons should be prioritised in national vaccination programmes. To help resolve this, we critically examine the extent, nature, and quality of extant literature regarding prioritisation of COVID-19 vaccinations for people who live and work in prisons.Using a scoping review as our methodological framework, we conducted a systematic literature search of 17 databases. From 2,307 potentially eligible articles, we removed duplicates and screened titles and abstracts to retain 45 articles for review and quality appraisal.Findings indicated that while most countries recognise that prisons are at risk of high levels of COVID-19 transmission, only a minority have explicitly prioritised people who live and work in prisons for COVID-19 vaccination. Even among those that have, prioritisation criteria varies considerably. This is set against a backdrop of political barriers, such as politicians questioning the moral deservingness of people in prison; policy barriers, such as the absence of a unified international framework of how vaccine prioritisation should proceed in prisons; logistical barriers regarding vaccine administration in prisons; and behavioural barriers including vaccine hesitancy.We outline five strategies to prioritise people who live and work in prisons in COVID-19 vaccination plans: (1) improving data collection on COVID-19 vaccination, (2) reducing the number of people imprisoned, (3) tackling vaccine populism through advocacy, (4) challenging arbitrary prioritisation processes via legal processes, and (5) conducting more empirical research on COVID-19 vaccination planning, delivery, and acceptability. Implementing these strategies would help to reduce the impact of COVID-19 on the prison population, prevent community transmission, improve vaccine uptake in prisons beyond the current pandemic, foster political accountability, and inform future decision-making.
Predicting Transdiagnostic Social Impairments in Childhood using Connectome-based Predictive Modeling
Authors: Dufford Alexander J. , Kimble Violet , Tejavibulya Link , Dadashkarimi Javid , Ibrahim Karim , Sukhodolsky Denis G. , Scheinost Dustin 
Published: April 2022

AbstractBackgroundSocial impairments are core features of multiple neurodevelopmental disorders. Previous neuroimaging studies have focused on elucidating associations between brain function and social impairments within disorders but have not predicted these impairments from brain connectivity in a transdiagnostic manner, across several diagnostic categories. This study used a machine learning approach to examine functional connectivity that predicts elevated social impairments in a transdiagnostic sample of youths. We hypothesized that predictive edges would be from brain regions involved in social cognition.MethodsConnectome-based predictive modeling (CPM) was used to build a transdiagnostic model of social impairments as measured by the Social Responsiveness Scale (SRS-2, raw score >75). We used functional connectivity data during a social movie-watching task from the Healthy Brain Network data (N=144, mean age=11.68 (3.52), 32% male). The average number of diagnoses was 3.4 (SD = 1.82, range = 0–11), including ASD (40.9%), ADHD (79%), mood disorders (15.9%), and anxiety disorders (43%). A similar transdiagnostic sample high SRS-2 scores (n=41) was used for replication.ResultSRS-2 scores were predicted from functional connectivity data using both 10-fold cross-validation (median q2=0.32, r=0.57, p<.001) and leave-one-group-out cross-validation (median q2’s>0.04, r’s>0.36, p’s<.001). Predictive connections were widely distributed across the brain but were rooted in regions involved in social cognition, the subcortex, and the salience network. The model successfully predicted SRS-2 scores in the replication sample (r=0.33, p<.035, df=39).ConclusionWe identified connectivity patterns predictive of social impairments in a transdiagnostic sample. These networks have the potential to provide insight into development novel targeted interventions for social impairments across traditional diagnostic categories.
Correlations between kidney and heart function bioindicators and the expressions of Toll-Like, ACE2, and NRP-1 receptors in COVID-19
Authors: Sultan Rabab Hussain , Abdallah Maged , Ali Tarek Mohamed , Assal Hebatallah Hany , Ahmed Amr E. , Elesawy Basem H , Ahmed Osama M. 
Published: April 2022

AbstractBackgroundCOVID-19 impacts the cardiovascular system resulting in myocardial damage and also affects the kidneys leading to renal dysfunction. This effect is mostly through the binding with angiotensin-converting enzyme-2 (ACE2) and Neuropilin-1(NRP-l) receptors. Toll-Like Receptors (TLRs) typically combine with microbial pathogens and provoke an inflammatory response.AimThis work aims to compare the changes in kidney and heart function bioindicators and expressions of TLRs (TLR2 and TLR2) as well as ACE2 and NRP-l receptors in moderate and severe COVID-19 patients. The correlations between kidney and heart function bioindicators and expressions of these receptors are also studied.Patients and MethodsIn this study, 50 healthy control and 100 COVID-19 patients (55 male and 45 female) were enrolled. According to WHO guidelines, these participants were divided into severe (50 cases) and moderate (50 cases). Serum creatinine, blood urea, CKMB, LDH, and Troponin I were estimated. We measured the gene expression for Toll-Like Receptors (TLR2, TLR4), ACE2, and NRP-1 in the blood samples using quantitative real-time PCR (qRT -PCR).ResultsIn comparison with the healthy group, all patients exhibited a significant elevation in the serum creatinine, blood urea, cardiac enzymes, and CRP. As well, all studied patients revealed a significant elevation in the expression levels of TLR2, TLR4, ACE2, and NRP-1 mRNA. In all patients, CKMB, ACE2, and NRP-1 mRNA expression levels were positively correlated to both TLR2 and TLR4 expression levels. Moreover, serum creatinine and blood urea were positively correlated to both TLR2 and TLR 4 expression levels in the severe group only.ConclusionsOur study concluded that expression levels for TLR2, TLR4, ACE2, and NRP-1 mRNA in both severe and moderate patients were positively correlated with renal biomarkers and cardiac enzymes. Innate immune markers can be important because they correlate with the severity of illness in COVID-19.
Right hemispheric white matter hyperintensities improve the prediction of spatial neglect severity in acute stroke
Authors: Röhrig Lisa , Sperber Christoph , Bonilha Leonardo , Rorden Christopher , Karnath Hans-Otto 
Published: April 2022

AbstractWhite matter hyperintensities (WMH) are frequently observed in brain scans of elderly people. They are associated with an increased risk of stroke, cognitive decline, and dementia. However, it is unknown yet if measures of WMH provide information that improve the understanding of poststroke outcome compared to only state-of-the-art stereotaxic structural lesion data. We implemented high-dimensional machine learning models, based on support vector regression (SVR), to predict the severity of spatial neglect in 103 acute right hemispheric stroke patients. We found that (1) the additional information of right hemispheric voxel-based topographic WMH extent indeed yielded an improvement in predicting acute neglect severity (compared to the voxel-based stroke lesion map alone). (2) Periventricular WMH appeared more relevant for prediction than deep subcortical WMH. (3) Among different WMH measures, voxel-based maps as measures of topographic extent allowed more accurate predictions compared to the use of traditional ordinally assessed visual rating scales (Fazekas scale, Cardiovascular Health Study scale). In summary, topographic WMH appears to be a valuable clinical imaging biomarker for predicting the severity of cognitive deficits and bears great potential for rehabilitation guidance of acute stroke patients.
wenda_gpu: fast domain adaptation for genomic data
Authors: Hippen Ariel A. , Crawford Jake , Gardner Jacob R. , Greene Casey S. 
Published: April 2022

AbstractMotivationDomain adaptation allows for development of predictive models even in cases with limited sample data. Weighted elastic net domain adaptation specifically leverages features of genomic data to maximize transferability but the method is too computationally demanding to apply to many genome-sized datasets.ResultsWe developed wenda_gpu, which uses GPyTorch to train models on genomic data within hours on a single GPU-enabled machine. We show that wenda_gpu returns comparable results to the original wenda implementation, and that it can be used for improved prediction of cancer mutation status on small sample sizes than regular elastic net.Availabilitywenda_gpu is available on GitHub at https://github.com/greenelab/wenda_gpu/.Contactcasey.s.greene@cuanschutz.eduSupplementary informationSupplementary data are available at Bioinformatics online.
Germline variants associated with immunotherapy-related adverse events
Authors: Groha Stefan , Alaiwi Sarah Abou , Xu Wenxin , Naranbhai Vivek , Nassar Amin H. , Bakouny Ziad , Adib Elio , Nuzzo Pier V. , Schmidt Andrew L. , Labaki Chris , Zarif Talal El , Ricciuti Biagio , Alessi Joao Victor , Braun David A. , Shukla Sachet A. , Keenan Tanya E. , Van Allen Eliezer , Awad Mark M. , Manos Michael , Rahma Osama , Zubiri Leyre , Villani Alexandra-Chloe , Hammer Christian , Khan Zia , Reynolds Kerry , Semenov Yevgeniy , Schrag Deborah , Kehl Kenneth L. , Freedman Matthew L. , Choueiri Toni K. , Gusev Alexander 
Published: April 2022

AbstractImmune checkpoint inhibitors (ICIs) have yielded remarkable responses in patients across multiple cancer types, but often lead to immune related adverse events (irAEs). Although a germline cause for irAEs has been hypothesized, no systematic genome wide association study (GWAS) has been performed and no individual variants associated with the overall likelihood of developing irAEs have yet been identified. We carried out a Genome-Wide Association Study (GWAS) of 1,751 patients on ICIs across 12 cancer types, with replication in an independent cohort of 196 patients and independent clinical trial data from 2275 patients. We investigated two irAE phenotypes: (i) high-grade (3-5) events defined through manual curation and (ii) all detectable events (including high-grade) defined through electronic health record (EHR) diagnosis followed by manual confirmation. We identified three genome-wide significant associations (p<5×10−8) in the discovery cohort associated with all-grade irAEs: rs16906115 near IL7 (combined p=1.6×10−11; hazard ratio (HR)=2.1), rs75824728 near IL22RA1 (combined p=6.6×10−9; HR=1.9), and rs113861051 on 4p15 (combined p=1.3×10−8, HR=2.0); with rs16906115 replicating in two independent studies. The association near IL7 colocalized with the gain of a novel cryptic exon for IL7, a critical regulator of lymphocyte homeostasis. Patients carrying the IL7 germline variant exhibited significantly increased lymphocyte stability after ICI initiation than non-carriers, and this stability was predictive of downstream irAEs and improved survival.
Distinguishing Lewy Body Dementia from Alzheimer’s Disease using Machine Learning on Heterogeneous Data: A Feasibility Study
Authors: McCombe Niamh , Joshi Alok , Finn David P. , McClean Paula L. , Roberts Gemma , O’Brien John T. , Thomas Alan J. , Kane Joseph P.M. , Wong-Lin KongFatt 
Published: April 2022

AbstractDementia with Lewy Bodies (DLB) is the second most common form of dementia, but diagnostic markers for DLB can be expensive and inaccessible, and many cases of DLB are undiagnosed. This work applies machine learning techniques to determine the feasibility of distinguishing DLB from Alzheimer’s Disease (AD) using heterogeneous data features. The Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was first applied using a Leave-One-Out Cross-Validation protocol to a dataset comprising DLB and AD cases. Then, interpretable association rule-based diagnostic classifiers were obtained for distinguishing DLB from AD. The various diagnostic classifiers generated by this process had high accuracy over the whole dataset (mean accuracy of 94%). The mean accuracy in classifying their out-of-sample case was 80.5%. Every classifier generated consisted of very simple structure, each using 1-2 classification rules and 1-3 data features. As a group, the classifiers were heterogeneous and used several different data features. In particular, some of the classifiers used very simple and inexpensive diagnostic features, yet with high diagnostic accuracy. This work suggests that opportunities may exist for incorporating accessible diagnostic assessments while improving diagnostic rate for DLB.Clinical RelevanceSimple and interpretable high-performing machine learning algorithms identified a variety of readily available clinical assessments for differential diagnosis of dementia, offering the opportunities to incorporate various simple and inexpensive screening tests for DLB and addressing the problem of DLB underdiagnosis.
Efficient evolution of human antibodies from general protein language models and sequence information alone
Authors: Hie Brian L. , Xu Duo , Shanker Varun R. , Bruun Theodora U.J. , Weidenbacher Payton A. , Tang Shaogeng , Kim Peter S. 
Published: April 2022

AbstractNatural evolution must explore a vast landscape of possible sequences for desirable yet rare mutations, suggesting that learning from natural evolutionary strategies could accelerate artificial evolution. Here, we report that deep learning algorithms known as protein language models can evolve human antibodies with high efficiency, despite providing the models with no information about the target antigen, binding specificity, or protein structure, and also requiring no additional task-specific finetuning or supervision. We performed language-model-guided affinity maturation of seven diverse antibodies, screening 20 or fewer variants of each antibody across only two rounds of evolution. Our evolutionary campaigns improved the binding affinities of four clinically relevant antibodies up to 7-fold and three unmatured antibodies up to 160-fold across diverse viral antigens, with many designs also demonstrating improved thermostability and viral neutralization activity. Notably, our algorithm requires only a single wildtype sequence and computes recommended amino acid changes in less than a second. Moreover, the same models that improve antibody binding also guide efficient evolution across diverse protein families and selection pressures, indicating that these results generalize to many natural settings. Contrary to prevailing notions of evolution as difficult and resource-intensive, our results suggest that when constrained to a narrow manifold of evolutionary plausibility, evolution can become much easier, which we refer to as the “efficient manifold hypothesis.”
Symbiosis between Patescibacteria and Archaea discovered in wastewater-treating bioreactors
Authors: Kuroda Kyohei , Yamamoto Kyosuke , Nakai Ryosuke , Hirakata Yuga , Kubota Kengo , Nobu Masaru K. , Narihiro Takashi 
Published: April 2022

AbstractEach prokaryotic domain, Bacteria and Archaea, contains a large and diverse group of organisms characterized with ultrasmall cell size and symbiotic lifestyles – Patescibacteria (also known as Candidate Phyla Radiation/CPR) and DPANN archaea. Cultivation-based approaches have revealed that Patesibacteria and DPANN symbiotically interact with bacterial and archaeal partners/hosts respectively, but cross-domain symbiosis/parasitism has never been observed. Here, we discovered physical interaction between ultramicrobacterial Patescibacteria and methanogenic archaea using cultures from anaerobic wastewater treatment sludge. In the cultures, we observed physical attachment of ultramicrobial cells to cells resembling Methanothrix and Methanospirillum using transmission electron microscopy and successfully detected physical association of Ca. Yanofskybacteria and Methanothrix using fluorescence in situ hybridization (FISH) (other ultramicrosized bacterial cells, presumably Patescibacteria, were also observed to attach on Methanospirillum). This was further confirmed to be a symbiosis rather than simple aggregation based on the observation that most ultramicrobacterial cells attached to Methanothrix were Ca. Yanofskybacteria and positive correlation (p < 0.05) between the relative abundance of Patescibacteria lineages and methanogenic archaea (e.g., Ca. Yanofskybacteria–Methanothrix and uncultured clade 32-520–Methanospirillum). The results shed light on a novel cross-domain symbiosis and inspire potential strategies for culturing CPR/DPANN.
Evolution of a globally unique SARS-CoV-2 Spike E484T monoclonal antibody escape mutation in a persistently infected, immunocompromised individual
Authors: Halfmann Peter J. , Minor Nicholas R. , Haddock Luis A. , Maddox Robert , Moreno Gage K. , Braun Katarina M. , Baker David A. , Riemersa Kasen K. , Prasad Ankur , Alman Kirsten J. , Lambert Matthew C. , Florek Kelsey , Bateman Allen , Westergaard Ryan , Safdar Nasia , Andes Dave R. , Kawaoka Yoshihiro , Fida Madiha , Yao Joseph D. , Friedrich Thomas C. , O’Connor David H. 
Published: April 2022

AbstractProlonged infections in immunocompromised individuals may be a source for novel SARS-CoV-2 variants, particularly when both the immune system and antiviral therapy fail to clear the infection, thereby promoting adaptation. Here we describe an approximately 16-month case of SARS-CoV-2 infection in an immunocompromised individual. Following monotherapy with the monoclonal antibody Bamlanivimab, the individual’s virus was resistant to this antibody via a globally unique Spike amino acid variant (E484T) that evolved from E484A earlier in infection. With the emergence and spread of the Omicron Variant of Concern, which also contains Spike E484A, E484T may arise again as an antibody-resistant derivative of E484A.
The impact of universal glove and gown use on Clostridioides difficile acquisition, a cluster randomized trial
Authors: Morgan Daniel J. , Dubberke Erik R. , Hink Tiffany , Paszkiewicz Gwen , Burnham Carey-Ann D. , Pineles Lisa , Magder Larry , Johnson J. Kristie , Leekha Surbhi , Harris Anthony D. 
Published: April 2022

ABSTRACTImportanceClostridioides difficile is the most common cause of healthcare-associated infections (HAI) in the US. It is unknown whether universal gown and glove use in intensive care units (ICUs) decreases acquisition of C. difficile.ObjectiveTo assess whether wearing gloves and gowns for all patient contact in the ICU decreases acquisition of C. difficile compared with usual care.Design, setting, and ParticipantsSecondary analysis of a cluster-randomized trial in 20 medical and surgical ICUs in 20 US hospitals from January 4, 2012, to October 4, 2012.InterventionsAfter a baseline period, ICUs were randomized to standard practice for glove and gown use vs. the intervention of all healthcare workers being required to wear gloves and gowns for all patient contact and when entering any patient room (contact precautions).Main outcomes and measuresThe primary outcome was acquisition of toxigenic C. difficile determined by surveillance cultures collected on admission and discharge from the ICU. Secondary outcomes included ribotype 027-like C. difficile acquisition and the impact of other factors on acquisition.ResultsFrom the 26,749 patients enrolled in the study, a total of 21,845 patients had both admission and discharge perianal swabs cultured for toxigenic C. difficile. On admission, 9.43% (2,060/21,845) of patients were colonized with toxigenic C. difficile. No significant difference was observed in the rate of toxigenic C. difficile acquisition with universal gown and glove use. Differences in acquisition rates in the study period compared to baseline period in control ICUs were 1.49 per 100 patient days vs 1.68 per 100 patient days in universal gown and glove ICUs, (rate difference -0.28, generalized linear mixed model, p=0.091). Similarly, there was no difference in rates of ribotype 027-like C. difficile acquisition: control ICUs 0.13 per 100 patient days vs. 0.16 per 100 patient days in universal gown and glove ICUs during the study period, (rate difference -0.03, generalized linear mixed model, p=0.35). Secondary analyses identified C. difficile colonization was associated with acquisition, p=0.014).Conclusions and relevanceGlove and gown use for all patient contact in medical and surgical ICUs did not result in a reduction in the acquisition of C. difficile compared with usual care.Key PointsQuestionDoes wearing gloves and gowns for all patient contact in the ICU decrease acquisition of C. difficile?FindingsGlove and gown use for all patient contact in medical and surgical ICUs did not result in a difference in the acquisition of C. difficile compared with usual care.MeaningGlove and gown use does not impact acquisition of C. difficile from asymptomatically colonized patients. Universal use of glove and gowns or targeted use based on active surveillance for C. difficile are unlikely to impact transmission.
Cross-tissue meta-analysis of blood and brain epigenome-wide association studies in Alzheimer’s disease
Authors: Silva Tiago C. , Young Juan I. , Zhang Lanyu , Gomez Lissette , Schmidt Michael A. , Varma Achintya , Chen X. Steven , Martin Eden R. , Wang Lily 
Published: April 2022

ABSTRACTWe performed a meta-analysis of two large independent blood-based Alzheimer’s disease (AD) epigenome-wide association studies, the ADNI and AIBL studies, and identified 5 CpGs, mapped to the SPIDR, CDH6 genes, and intergenic regions, that were significantly associated with AD diagnosis. A cross-tissue analysis that combined these blood DNA methylation datasets with four additional methylation datasets prioritized 97 CpGs and 10 genomic regions that are significantly associated with both AD neuropathology and AD diagnosis. Our integrative analysis revealed expressions levels of 13 genes and 10 pathways were significantly associated with the AD-associated methylation differences in both brain and blood, many are involved in the immune responses in AD, such as the CD79A, LY86, SP100, CD163, CD200, and MS4A1 genes and the neutrophil degranulation, antigen processing and presentation, interferon signaling pathways. An out-of-sample validation using the AddNeuroMed dataset showed the best performing logistic regression model included age, sex, cell types and methylation risk score based on prioritized CpGs from cross-tissue analysis (AUC = 0.696, 95% CI: 0.616 - 0.770, P-value = 2.78 × 10−5). Our study provides a valuable resource for future mechanistic and biomarker studies in AD.