bluuebunny/crossref_metadata_2025_split huggingface dataset:
Relating Molecular T Cell–mediated Rejection Activity in Kidney Transplant Biopsies to Time and to Histologic Tubulitis and Atrophy-fibrosis
Authors: Madill-Thomsen Katelynn S. , Böhmig Georg A. , Bromberg Jonathan , Einecke Gunilla , Eskandary Farsad , Gupta Gaurav , Myslak Marek , Viklicky Ondrej , Perkowska-Ptasinska Agnieszka , Solez Kim , Halloran Philip F. ,   the INTERCOMEX Investigators
Published: December 2022

Background.
We studied the variation in molecular T cell–mediated rejection (TCMR) activity in kidney transplant indication biopsies and its relationship with histologic lesions (particularly tubulitis and atrophy-fibrosis) and time posttransplant.

Methods.
We examined 175 kidney transplant biopsies with molecular TCMR as defined by archetypal analysis in the INTERCOMEX study (ClinicalTrials.gov #NCT01299168). TCMR activity was defined by a molecular classifier.

Results.
Archetypal analysis identified 2 TCMR classes, TCMR1 and TCMR2: TCMR1 had higher TCMR activity and more antibody-mediated rejection (“mixed”) activity and arteritis but little hyalinosis, whereas TCMR2 had less TCMR activity but more atrophy-fibrosis. TCMR1 and TCMR2 had similar levels of molecular injury and tubulitis. Both TCMR1 and TCMR2 biopsies were uncommon after 2 y posttransplant and were rare after 10 y, particularly TCMR1. Within late TCMR biopsies, TCMR classifier activity and activity molecules such as IFNG fell progressively with time, but tubulitis and molecular injury were sustained. Atrophy-fibrosis was increased in TCMR biopsies, even in the first year posttransplant, and rose with time posttransplant. TCMR1 and TCMR2 both reduced graft survival, but in random forests, the strongest determinant of survival after biopsies with TCMR was molecular injury, not TCMR activity.

Conclusions.
TCMR varies in intensity but is always strongly related to molecular injury and atrophy-fibrosis, which ultimately explains its effect on survival. We hypothesize, based on the reciprocal relationship with hyalinosis, that the TCMR1-TCMR2 gradient reflects calcineurin inhibitor drug underexposure, whereas the time-dependent decline in TCMR activity and frequency after the first year reflects T-cell exhaustion.
A Usability Evaluation of YouDiagnose: Artificial Intelligence Powered Physician Consultation
Authors: Misro Aswini , Kadoglou Naim , Mishra Nishikant , Whittington Paul , Dogan Huseyin 
Published: December 2022

AbstractThe COVID-19 Pandemic has resulted in a forced transition to tele-medicine, where history-taking and clinical assessments are performed remotely during video or telephonic consultations. While telemedicine has added to safety and social distancing during the pandemic, the manual and resource-intense process of telephonic and video consultations has not helped to ease the patient backlog, rather has added to this snowballing issue. This paper describes about YouDiagnose pre-consultation exercise that automates patient triage and clinical assessment using artificial intelligence technologies delivered through either a Smart Questionnaire or Chatbot. A usability evaluation was conducted with participants from the Patient and Public Involvement and Engagement Senate (PIES) of the Innovation Agency (an Academic Health Science Network) Qualitative feedback was obtained from the participants on both modalities and quantitative feedback in the form of the System Usability Scale (SUS), comparing the usability of both interaction modalities. The SUS scores were analysed using the Adjective Rating Scale that revealed the Smart Questionnaire had ‘Good Usability’ compared to ‘OK Usability’ of the Chatbot. The results shows the user experience and untapped potential of process automation and artificial intelligence in clinical services.
Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers
Authors: Gao Catherine A. , Howard Frederick M. , Markov Nikolay S. , Dyer Emma C. , Ramesh Siddhi , Luo Yuan , Pearson Alexander T. 
Published: December 2022

AbstractBackgroundLarge language models such as ChatGPT can produce increasingly realistic text, with unknown information on the accuracy and integrity of using these models in scientific writing.MethodsWe gathered ten research abstracts from five high impact factor medical journals (n=50) and asked ChatGPT to generate research abstracts based on their titles and journals. We evaluated the abstracts using an artificial intelligence (AI) output detector, plagiarism detector, and had blinded human reviewers try to distinguish whether abstracts were original or generated.ResultsAll ChatGPT-generated abstracts were written clearly but only 8% correctly followed the specific journal’s formatting requirements. Most generated abstracts were detected using the AI output detector, with scores (higher meaning more likely to be generated) of median [interquartile range] of 99.98% [12.73, 99.98] compared with very low probability of AI-generated output in the original abstracts of 0.02% [0.02, 0.09]. The AUROC of the AI output detector was 0.94. Generated abstracts scored very high on originality using the plagiarism detector (100% [100, 100] originality). Generated abstracts had a similar patient cohort size as original abstracts, though the exact numbers were fabricated. When given a mixture of original and general abstracts, blinded human reviewers correctly identified 68% of generated abstracts as being generated by ChatGPT, but incorrectly identified 14% of original abstracts as being generated. Reviewers indicated that it was surprisingly difficult to differentiate between the two, but that the generated abstracts were vaguer and had a formulaic feel to the writing.ConclusionChatGPT writes believable scientific abstracts, though with completely generated data. These are original without any plagiarism detected but are often identifiable using an AI output detector and skeptical human reviewers. Abstract evaluation for journals and medical conferences must adapt policy and practice to maintain rigorous scientific standards; we suggest inclusion of AI output detectors in the editorial process and clear disclosure if these technologies are used. The boundaries of ethical and acceptable use of large language models to help scientific writing remain to be determined.
Benign biopsy effect on artificial intelligence cancer detector in screening mammography – a retrospective study
Authors: Zouzos Athanasios , Molovanovic Alexandra , Dembrower Karin , Strand Fredrik 
Published: December 2022

AbstractArtificial intelligence (AI) cancer detectors are showing promising results and may soon be used for clinical breast cancer screening in radiology departments. Validation of such programs in different settings is mandatory.This is a retrospective study after the application of a commercial AI cancer detector program to a cancer-enriched mammography screening dataset where 10 889 women with a median age for the study population of 56 years and a range between 40 and 74 years old where included. The program is intended for 2D mammography and it generate a prediction score for tumor presence with a decimal number between 0 and 1, where 1 represented the highest level of suspicion. The AI score median and interquartile range for women who were healthy, who had a benign biopsy, and who were diagnosed with breast cancer, was calculated. For women with a previous benign biopsy, the time between mammogram and the biopsy was stratified. The effect of increasing age was examined using stratified analysis and linear regression modelling. The proportion of healthy women above the AI abnormality threshold (0.4) defined at our hospital was 3.5%, 11% and 84% for healthy women without a benign biopsy, healthy women with a benign biopsy, and for women with breast cancer, respectively (p<0.001) and the AI score correlated positively with increasing age of the women in the cancer group (p<0.05). While 16% of the women who had a benign biopsy after mammography had an AI score above the cut-off point, there were 33% of those when they had their biopsy before the mammography. AI cancer detection systems can reliable be used in a screening setting. Adding information about possible prior benign biopsy and other symptoms can probably improve accuracy and subsequently reduce the workload for radiologists.
The response to BTN62b2 booster doses demonstrates that serum antibodies do not predict the establishment of immune B-cell memory in common variable immune deficiencies
Authors: Piano Mortari E. , Pulvirenti F. , Marcellini V. , Terreri S. , Fernandez Salinas A. , Ferrari S. , Di Napoli G. , Guadagnolo D. , Sculco E. , Albano C. , Guercio M. , Di Cecca S. , Milito C. , Garzi G. , Pesce A.M. , Bonanni L. , Sinibaldi M. , Di Cecilia S. , Agrati C. , Quintarelli C. , Zaffina S. , Locatelli F. , Carsetti R. , Quinti I. 
Published: December 2022

SummaryIn patients with common variable immune deficiencies, primary vaccination followed by two booster doses is recommended for protection against COVID-19. Seroconversion has been shown in 60% of patients. We have no information on whether serum antibodies reflect the generation of durable immune memory.In a longitudinal study on 47 common variable immune deficiencies patients who received the third and fourth vaccine dose, we show that the measurement of specific antibodies is not sufficient to predict the establishment of immune memory and the ability to respond to antigen re-exposure.Our results indicate that the combination of antibodies and memory B cells responses represents a more reliable read-out of vaccine immune efficacy in vulnerable patients.This analysis may not only identify individuals remaining unprotected after vaccination and unable to respond to additional booster doses, but also address the search for the underlying immune defect and suggest patient-tailored management strategies.
Development and Validation of a Machine Learning Algorithm to Classify Lower Urinary Tract Symptoms
Authors: Chiang Jeffrey N. , Dallas Kai B. , Caron Ashley T. , Anger Jennifer T. , Kaufman Melissa R. , Ackerman A. Lenore 
Published: December 2022

AbstractObjectiveLower urinary tract symptoms (LUTS), such as urinary urgency, frequency, and incontinence, affect the majority of the population, causing substantial morbidity, yet few receive effective care. Sizeable symptomatic overlap between LUTS categories leads to high rates of misdiagnosis. To improve diagnostic accuracy, we sought to employ machine learning approaches to LUTS categorization to generate diagnostic groupings based on patient-reported clinical data, creating a novel tool for diagnosis of patients with voiding complaints.MethodsQuestionnaire responses in a Development Dataset of 514 female subjects were used for model development, identifying 4 major clusters and 9 specific phenotypes of LUTS using agglomerative hierarchical clustering. Each cluster and phenotype was assigned a clinical identity consistent with recognized causes of voiding dysfunction by the consensus of two urologic specialists. Then, a random forest classifier was trained to assign unseen patients into these phenotypes. That model was then applied to a Validation Dataset of 571 additional subjects to confirm the diagnostic algorithm.ResultsThis data-driven, hierarchical clustering approach captured overlapping symptoms inherent in typical patients, recognizing common uncomplicated diagnoses (i.e., overactive bladder) as well as several underrecognized diagnostic categories (i.e., myofascial pelvic pain). A diagnostic algorithm derived by supervised machine learning to assign unseen subjects into these phenotypes demonstrated good reproducibillty of the phenotypes and their symptomatic patterns in an independent Validation Dataset.ConclusionsWe describe the generation of a machine learning algorithm relying only on validated, patient-reported symptoms for diagnostic classification. Given a growing physician shortage and increasing challenges for patients accessing specialist care, this type of digital technology holds great potential to improve the recognition and diagnosis of functional urologic conditions.
A COMPARATIVE STUDY OF DUMPSITES METAL LOADS AND ECOLOGICAL RISK IN SELECTED COMMUNITIES IN KWARA AND EKITI STATES, NIGERIA
Authors: Abolayo T.T. , Sawyerr H.O. , Yusuf R.O , Ajai T.S. , Towolawi A.T. 
Published: December 2022

ABSTRACTThis study compared the presence of selected heavy metals in Gbagede dumpsite (GD) and Ilokun dumpsite (ID) dumpsite in Kwara and Ekiti State with the host communities Gbagede community (GC) and Ilokun community (IC). Ten soil samples were collected randomly at 10 cm depth, digested and analyzed for Cadmium (Cd), Copper (Cu), Manganese (Mn), Zink (Zn) and iron (Fe) respectively. The results were compared with control and standard, and subjected to ecological risk indices: Enrichment factor (EF), Contamination factor (CF), and Index of geo-accumulation (Igeo). Metal concentrations were higher (p < 0.05 except Cu) than the control; ranged from 41.2 times (Mn in GD) to 7.08 times (Zn in ID), GD had highest metals except Cu. Metal concentrations at ID > IC (except Cu) and GD> GC. The standard <Cd and Mn,<Zn at GD and ID, and >Cu at GD and GC. The EF was significant (5≤EF<20) for Zn at GD and ID, and for Mn except at GD where there was a very high (20≤EF<40). Mn and Zn indicated high contamination (Cf≥6) at GD and ID while Cd and Fe indicated moderate contamination (1≤Cf<3) except at IC (Cf<1). Moderately polluted (1<Igeo≤2) effects were shown by Fe except at IC and Mn at GD and GC. Conclusively, it was found that the Dumpsites contributed to soil metal loads and ecological risk.
A Bayesian network-based framework to uncover the causal effects of genes on complex traits based on GWAS data
Authors: Yin Liangying , Feng Yaning , Lau Alexandria , Qiu Jinghong , Sham Pak-Chung , So Hon-Cheong 
Published: December 2022

AbstractDeciphering the relationships between genes and complex traits could help us better understand the biological mechanisms leading to phenotypic variations and disease onset. Univariate gene-based analyses are widely used to characterize gene-phenotype relationships, but are subject to the influence of confounders. Furthermore, while some genes directly contribute to traits variations, others may exert their effects through other genes. How to quantify individual genes’ direct and indirect effects on complex traits remains an important yet challenging question.We presented a novel framework to decipher the total and direct causal effects of individual genes using imputed gene expression data from GWAS and raw gene expression from GTEx. The study was partially motivated by the quest to differentiate “core” genes (genes with direct causal effect on the phenotype) from “peripheral” ones. Our proposed framework is based on a Bayesian network (BN) approach, which produces a directed graph showing the relationship between genes and the phenotype. The approach aims to uncover the overall causal structure, to examine the role of individual genes and quantify the direct and indirect effects by each gene.An important advantage and novelty of the proposed framework is that it allows gene expression and disease trait(s) to be evaluated in different samples, significantly improving the flexibility and applicability of the approach. It uses IDA and jointIDA incorporating a novel p-value-based regularization approach to quantify the causal effects (including total causal effects, direct causal effects, and medication effects) of genes. The proposed approach can be extended to decipher the joint causal network of 2 or more traits, and has high specificity and precision (a.k.a., positive predictive value), making it particularly useful for selecting genes for follow-up studies.We verified the feasibility and validity of the proposed framework by extensive simulations and applications to 52 traits in the UK Biobank (UKBB). Split-half replication and stability selection analyses were performed to demonstrate the accuracy and efficiency of our proposed method to identify causally relevant genes. The identified (direct) causal genes were found to be significantly enriched for genes highlighted in the OpenTargets database, and the enrichment was stronger than achieved by conventional univariate gene-based tests. Encouragingly, many enriched pathways were supported by the literature, and some of the enriched drugs have been tested or used to treat patients in clinical practice. Our proposed framework provides powerful a way to prioritize genes with large direct or indirect causal effects and to quantify the importance of such genes.
Understanding disruptions in cancer care to reduce increased cancer burden
Authors: Davis Kia L. , Ackermann Nicole , Klesges Lisa M. , Leahy Nora , Callie Walsh-Bailey , Humble Sarah , Drake Bettina , Sanders Thompson Vetta L. 
Published: December 2022

AbstractBackgroundThis study seeks to understand how and for whom COVID-19 disrupted cancer care to understand the potential for cancer health disparities across the cancer prevention and control continuum.MethodsIn this cross-sectional study, participants age 30+ residing in an 82-county region in Missouri and Illinois completed an online survey from June-August 2020. Descriptive statistics were calculated for all variables separately and by care disruption status. Logistic regression modeling was conducted to determine the correlates of care disruption.ResultsParticipants (N=680) reported 21% to 57% of cancer screening or treatment appointments were canceled from March 2020 through the end of 2020. Approximately 34% of residents stated they would need to know if their doctor’s office is taking the appropriate COVID-related safety precautions to return to care. Higher education (OR=1.26, 95%CI:1.11-1.43), identifying as female (OR=1.60, 95%CI:1.12-2.30), experiencing more discrimination in healthcare settings (OR= 1.40, 95%CI:1.13-1.72), and having scheduled a telehealth appointment (OR=1.51, 95%CI:1.07-2.15) were associated with higher odds of care disruption. Factors associated with care disruption were not consistent across races. Higher odds of care disruption for White residents were associated with higher education, female identity, older age, and having scheduled a telehealth appointment, while higher odds of care disruption for Black residents were associated only with higher education.Conclusion(s)This study provides an understanding of the factors associated with cancer care disruption and what patients need to return to care. Results may inform outreach and engagement strategies to reduce delayed cancer screenings and encourage returning to cancer care.Funding SupportThis study was supported by the National Cancer Institute’s Administrative Supplements for P30 Cancer Center Support Grants (P30CA091842-18S2 and P30CA091842-19S4). Kia L. Davis, Lisa Klesges, and Bettina Drake were supported by the National Cancer Institute’s P50CA244431 and Kia L. Davis was also supported by the Breast Cancer Research Foundation. Callie Walsh-Bailey was supported by NIMHD T37 MD014218. The content does not necessarily represent the official view of these funding agencies and is solely the responsibility of the authors.Availability of data and materialThe dataset generated for the study is not publicly available but is available by request. Interested individuals should contact the corresponding author with a brief description of how the data will be used and proof of IRB approval or exemption. Then a de-identified dataset will be shared.
Exome sequencing reveals aggregates of rare variants in glycosyltransferase and other genes influencing immunoglobulin G and transferrin glycosylation
Authors: Landini Arianna , Timmers Paul R.H.J. , Frkatović-Hodžić Azra , Trbojević-Akmačić Irena , Vučković Frano , Pribić Tea ,   Regeneron Genetics Center, Tzoneva Gannie , Shuldiner Alan R. , Polašek Ozren , Hayward Caroline , Lauc Gordan , Wilson James F. , Klarić Lucija 
Published: December 2022

AbstractIt is often difficult to be certain which genes underlie the effects seen in association studies. However, variants that disrupt the protein, such as predicted loss of function (pLoF) and missense variants, provide a shortcut to identify genes with a clear biological link to the phenotype of interest. Glycosylation is one of the most common post-translationalmodifications of proteins, and an important biomarker of both disease and its progression. Here, we utilised the power of genetic isolates, gene-based aggregation tests and intermediate phenotypes to assess the effect of rare (MAF<5%) pLoF and missense variants from whole exome sequencing on the N-glycome of plasma transferrin (N=1907) and immunoglobulin G (N=4912), and their effect on diseases. We identified significant gene-based associations for transferrin glycosylation at 5 genes (p<8.06×10−8) and for IgG glycan traits at 4 genes (p<1.19×10−7). Associations in three of these genes (FUT8, MGAT3andRFXAP) are driven by multiple rare variants simultaneously contributing to protein glycosylation. Association atST6GAL1, with a 300-fold up-drifted variant in the Orkney Islands, was detectable by a single-point exome-wide association analysis. Glycome-associated aggregate associations are located in genes already known to have a biological link to protein glycosylation (FUT6, FUT8for transferrin;FUT8, MGAT3andST6GAL1for IgG) but also in genes which have not been previously reported (e.g.RFXAPfor IgG). To assess the potential impact of rare variants associated with glycosylation on other traits, we queried public repositories of gene-based tests, discovering a potential connection between transferrin glycosylation,MSR1, galectin-3, insulin-like growth factor 1 and diabetes. However, the exact mechanism behind these connections requires further elucidation.
Instar determination by constrained gaussian mixture models according to Dyar’s rule
Authors: Ji Sungmin 
Published: December 2022

AbstractDespite its importance in ecological studies and pest controls, the lack of knowledge of the life cycle and the ambiguity of data challenge the accurate determination of insect nymphs regarding many insect species. Finite mixture models are often utilized to classify instars without knowing the instar number. This study derives parsimonious gaussian mixture models using parameter constraints motivated by Dyar’s rule. Dyar’s rule explains the growth pattern of larvae and nymphs of insects by assuming a constant ratio of head capsule width for every two adjacent development stages. Accordingly, every mean value of log-transformed data in each instar stage is considered a linear function, where two Dyar constants are an intercept and a slope for the instar stages, respectively, to infer the instar stage of samples. The common variance for every instar stage regarding log-transformed data can be assumed in a mixture model, as well. If valid, these assumptions will allow an efficient estimation of the model by reducing free parameters. As a result, four model hypotheses are proposed for each assumption of instar counts depending on whether these two parameter constraints are applied. After model estimation, the proposed method uses the ICL criterion to choose the optimal counts of nymphal stages, and parametric bootstrap LR tests are applied to decide the most efficient model regarding parameter constraints. The proposed method could attain the correct model settings during the simulation study. This study also discusses the interpretation of the results of real insect data sets that concord with Dyar’s rule or not.