bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mental illness stigma in England: What happened after the Time to Change Programme to reduce stigma and discrimination?
Authors: Ronaldson Amy , Henderson Claire 
Published: December 2024

AbstractBackgroundWe investigated the extent to which positive changes in stigma outcomes reported over the course of Time to Change were sustained by 2023, two years after the programme’s end in 2021.MethodsWe used regression analyses to evaluate trends in outcomes. Measures were of stigma-related knowledge (Mental Health Knowledge Schedule (MAKS)), attitudes (Community Attitudes to the Mentally Ill scale (CAMI)), and desire for social distance (Reported and Intended Behaviour Scale (RIBS)). We also examined willingness to interact with people based on vignettes of depression and schizophrenia, and attitudes towards workplace discrimination against people with these conditions, using data from the British Social Attitudes Survey (BSAS) 2015 for comparison.FindingsReported in standard deviation units (95% confidence intervals (CI)), attitudes towards mental illness improved between 2008 and 2023 (SD=0.24, 95% CI=0.16 to 0.31), but following an increase of 9.9% between 2008-19, scores decreased by 3.3% (p=0.015). After improvements to 2019, 2023 MAKS and RIBS scores no longer differed from 2009 scores, indicating decreases since 2019 in stigma-related knowledge (MAKS scores declined 7.8% since 2019, p<0.001) and willingness to interact (RIBS scores declined by 10.2% since 2019, p<0.001). Conversely, comparison with BSAS 2015 data indicated that in 2023 respondents were more willing to interact with people with depression (β=-2.69, p<0.001) and schizophrenia (β=-2.70, p<0.001); and more likely to agree that people with either condition are just as likely to be promoted, and to disagree that their medical history should influence this. This change was most pronounced for schizophrenia (OR=2.52, 95% CI=2.02 to 3.14).ConclusionsThe lasting positive changes reflect support for non-discrimination and willingness to interact with someone after a sense of familiarity is evoked. Besides the end of Time to Change, interpretations for declines in other outcomes include the impacts of the covid-19 pandemic; economic stress; and reduced access to healthcare.
Summer heat, historic redlining, and neighborhood walking among older adults: 2017 National Household Travel Survey
Authors: Mitsova Diana , Besser Lilah M. , Le Elaine T. 
Published: December 2024

AbstractNo known studies have examined the relationships between urban heat islands, historic redlining, and neighborhood walking in older adults. We assessed whether: 1) individual and neighborhood characteristics (including redlining score) differ by neighborhood summer land surface temperature (LST); 2) higher LST is associated with less neighborhood walking, and whether associations differ by historic redlining score; and 3) neighborhoods with discriminatory redlining scores have greater LSTs. We used data on 3,982 ≥65-year olds from the 2017 National Household Travel Survey. Multivariable negative binomial and linear regressions tested associations between LST z-score (comparing participant’s neighborhood LST to surrounding region’s LST) and self-reported neighborhood walking, and the association between living in neighborhoods redlined as “definitely declining” or “hazardous” (versus “still desirable”/“best”) and LST z-score. LSTs were higher for those in neighborhoods with higher area deprivation scores and more African American/Black residents. Older adults living in neighborhoods with higher summer LST z-scores had fewer minutes of neighborhood walking/day. This association seemed limited to individuals with neighborhood redlining scores of “still desirable”/“best”. Neighborhood redlining scores of “definitely declining” or “hazardous” (versus “still desirable” and “best”) were associated with greater neighborhood summer LSTs. Overall, these findings suggest that historically redlined neighborhoods may more often experience urban heat island effects, and older adults living in hotter neighborhoods may less often engage in neighborhood walking. Future work is needed to elucidate the impact of extreme heat on health promoting behaviors such as walking and the types of interventions that can successfully counteract negative impacts to historically disadvantaged communities.
Disentangling the shared genetics of ADHD, cannabis use disorder and cannabis use and prediction of cannabis use disorder in ADHD
Authors: Nielsen Trine Tollerup , Duan Jinjie , Levey Daniel F. , Bragi Walters G. , Johnson Emma C. , Thorgeirsson Thorgeir ,   VA Million Veteran Program, Werge Thomas , Mortensen Preben Bo , Stefansson Hreinn , Stefansson Kari , Hougaard David M. , Agrawal Arpana , Gelernter Joel , Grove Jakob , Børglum Anders D. , Demontis Ditte 
Published: December 2024

AbstractCannabis use disorder (CUD) and cannabis use (CU) are prevalent conditions cooccurring with ADHD, but not much is known about the underlying shared genetics. Here we perform cross-disorder GWAS meta-analyses of ADHD and CUD or CU to identify pleiotropic risk loci and evaluate differences in the genetics of ADHD-CUD and ADHD-CU, and subsequently we dissect the polygenic architecture of CUD comorbidity in ADHD in the iPSYCH cohort.There was a higher genetic overlap of ADHD and CUD than observed for ADHD and CU and we found a significant direct effect of ADHD genetic risk on CUD with only a minor part (12%) mediated by the genetics of CU. We identified 36 genome-wide significant loci for ADHD-CUD and 10 loci for ADHD-CU, with concordant direction of effect on the phenotypes. Three different approaches identifiedDRD2, which encodes the dopamine 2 receptor, as a risk gene for ADHD-CUD and, overall, ADHD-CUD risk genes were associated with high expression across several brain tissues and brain developmental stages, which was not observed for ADHD-CU genes. ADHD-CUD and ADHD-CU demonstrated similar genetic correlations with substance use phenotypes, while they differed significantly with respect to substance use disorder (SUD) phenotypes. ADHD-CUD individuals had significantly increased polygenic score (PGS) for psychiatric disorders compared to ADHD without CUD and increased burden of rare deleterious variants. Stratifying individuals with ADHD by their CUD-PGS revealed an absolute risk of 22% for comorbid CUD among the 20% of cases with the highest CUD-PGS, which was strikingly higher than the absolute risk of 1.6% observed among the 20% of controls with the highest CUD-PGS. Sex-specific analyses identified substantial differences in the absolute risk of comorbid CUD between males and females with ADHD, with a ∼10% higher CUD risk among males than females in the high-risk CUD-PGS group (24% risk for males and 14% risk for females).
Hybrid Neural Differential Equations to Model Unknown Mechanisms and States in Biology
Authors: Whipple Benjamin , Hernandez-Vargas Esteban A. 
Published: December 2024

AbstractEfforts to model complex biological systems increasingly face challenges from ambiguous relationships within the model, such as through partially unknown mechanisms or unmodelled intermediate states. Hybrid neural differential equations are a recent modeling framework which has been previously shown to enable identification and prediction of complex phenomena, especially in the context of partially unknown mechanisms. We extend the application of hybrid neural differential equations to enable incorporation of theorized but unmodelled states within differential equation models. We find that beyond their capability to incorporate partially unknown mechanisms, hybrid neural differential equations provide an effective method to include knowledge of unmeasured states into differential equation models.
Addressing algorithmic bias in precision well-being for medical education: A socially fair approach for clustering
Authors: Alluri Priyanshu , Chen Zequn , Marrero Wesley J. , Jacobson Nicholas C. , Thesen Thomas 
Published: December 2024

AbstractBackgroundMedical students frequently experience heightened levels of anxiety, depression, and burnout. These challenges are disproportionately borne by students from underrepresented backgrounds, who are exposed to systemic inequities, discrimination, and reduced access to supportive resources. While precision well-being approaches, characterized by identifying distinct well-being phenotypes for personalized interventions, hold promise, standard machine learning clustering algorithms such as K-Means may inadvertently exacerbate these disparities. Furthermore, the underlying factors contributing to poorer mental health outcomes among underrepresented students remain insufficiently understood.ObjectiveWe aim to identify well-being phenotypes that achieve an equitable distribution of clustering costs across racial groups, identify conditions under which fair and standard clustering solutions converge, and investigate the demographic and socioeconomic factors that shape mental health patterns in students underrepresented in medicine.MethodsDrawing on a diverse sample of 4161 medical students from multiple U.S. institutions participating in the Healthy Minds Survey (2016–2021), we compared the outcomes of socially fair and standard k-Means clustering algorithms using Patient Health Questionnaire-9, General Anxiety Disorder-7, and Flourishing scores. We then employed average treatment effect analyses to identify factors that exacerbate mental health challenges and those that enhance resilience, with a particular emphasis on underrepresented populations.ResultsThe socially fair clustering algorithm significantly reduced the disproportionate burden on minority populations, aligning with standard clustering outcomes when student groups were racially and socioeconomically homogeneous. Perceived discrimination emerged as a key factor driving poorer mental health, while stable financial conditions, robust social engagement, and involvement in culturally or ethnically oriented organizations were linked to greater resilience and improved well-being.ConclusionsIncorporating fairness objectives into clustering algorithms substantially reduced the disproportionate burden on minority students and yielded a more equitable understanding of their mental health patterns. By identifying factors that influence mental health outcomes, our socially-fair precision well-being approach allows for more personalized well-being interventions. These insights equip educators and policymakers with actionable targets for developing culturally responsive, data-driven interventions that not only alleviate distress but also support resilience, ultimately advancing more inclusive, effective precision well-being strategies for all medical students.
Alignment-Free Viral Sequence Classification at Scale
Authors: van Zyl Daniel J. , Dunaiski Marcel , Tegally Houriiyah , Baxter Cheryl ,   The INFORM Africa research study group, de Oliveira Tulio , Xavier Joicymara S. 
Published: December 2024

AbstractBackgroundThe rapid increase in nucleotide sequence data generated by next-generation sequencing (NGS) technologies demands efficient computational tools for sequence comparison. Alignment-based methods, such as BLAST, are increasingly overwhelmed by the scale of contemporary datasets due to their high computational demands for classification. This study evaluates alignment-free (AF) methods as scalable and rapid alternatives for viral sequence classification, focusing on identifying techniques that maintain high accuracy and efficiency when applied to extremely large datasets.ResultsWe employed six established AF techniques to extract feature vectors from viral genomes, which were subsequently used to train Random Forest classifiers. Our primary dataset comprises 297,186 SARS-CoV-2 nucleotide sequences, categorized into 3502 distinct lineages. Furthermore, we validated our models using dengue and HIV sequences to demonstrate robustness across different viral datasets. Our AF classifiers achieved 97.8% accuracy on the SARS-CoV-2 test set, and 99.8% and 89.1% accuracy on dengue and HIV test sets, respectively.ConclusionDespite the high-class dimensionality, we show that word-based AF methods effectively represent viral sequences. Our study highlights the practical advantages of AF techniques, including significantly faster processing compared to alignment-based methods and the ability to classify sequences using modest computational resources.
Multiscale Single-Cell Assessment of the Fibrotic Niche in Idiopathic Pulmonary Fibrosis
Authors: Liu Bin , Stewart Iain , May James , Zarcone Maria Concetta , Lopez-Jimenez Elena , Lambie Nathalie , Matthews Nik , Johnson Simon R , Tatler Amanda L. , Hewitt Richard , Wain Louise V , Clifford Rachel L , Renzoni Elisabetta A. , George Peter M. , Wells Athol U. , Reed Anna K. , Jenkins R. Gisli , John Alison E. 
Published: December 2024

AbstractBackgroundIdiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and fatal disease characterized by excessive extracellular matrix deposition within the lung. Recent advances in single-cell RNA sequencing have identified unique populations of epithelial cells lacking KRT5 while expressing KRT17; fibroblasts characterized by high collagen production, and the expression of CTHRC1; as well as a distinct subset of SPP1 positive macrophages. However, the precise interactions among these and potentially other cells that lead to the formation of fibrotic niche remain unclear.MethodsUsing spatial transcriptomics and Hyperion imaging mass cytometry we compared the cellular populations in formalin fixed paraffin embedded fibrotic lesions (n=9 patients) with control lung (n=9), and using CellChat we investigated the cellular interactions.ResultsSpatial transcriptomic analysis identified 180,067 cells which demonstrated three unique fibrotic niches enriched for KRT5-/KRT17+ epithelial cells, SPP1+ macrophages and collagen producing fibroblasts as well as a unique DCN+ expressing plasma B cell. Ligand receptor analysis inferred that most fibrotic cells interacted with ATII cells, with the exception of KRT5-/KRT17+ epithelial cells which interacted primarily with macrophages. Hyperion Mass Cytometry identified 55,979 cells, which identified similar cell populations clustered into two unique fibrotic niches: a stromal fibrotic niche characterized by CTHRC1+ fibroblasts interacting with fibrotic associated plasma cells, and a second niche containing KRT5-/KRT17+ cells and SPP1+ macrophages. This latter niche appeared likely to originate from the control bronchial niche with progressive loss of KRT5.ConclusionThese findings describe the characteristics of the fibrotic niche, support the hypothesis that basal cells can also contribute to the origin of the fibrotic epithelium and describe a novel fibrotic plasma cell population, shedding light on the complex cellular dynamics within fibrotic human lung tissues.
Structural and Antigenic Characterization of Novel and DiverseHenipavirusGlycoproteins
Authors: May Aaron J. , Lella Muralikrishna , Lindenberger Jared , Berkman Alex , Kumar Ujjwal , Dutta Moumita , Barr Maggie , Parks Rob , Newman Amanda , Huang Xiao , Song Kijun , Ilevbare Victor , Sammour Salam , Park Chan Soo , Adhikari Radha Devkota , Devkota Priyanka , Janowska Katarzyna , Liu Yanshun , Scapellato Garrett , Spence Taylor N. , Mansouri Katayoun , Edwards Robert J , Saunders Kevin O. , Haynes Barton F. , Acharya Priyamvada 
Published: December 2024

SummaryHenipaviruses, a genus within theParamyxoviridaefamily, include the highly virulent Nipah and Hendra viruses that cause reoccurring outbreaks of deadly disease1. Recent discoveries of several newParamyxoviridaespecies, including the zoonotic Langya virus2, have revealed much higher antigenic diversity than currently characterized and prompted the reorganization of these viruses into theHenipavirusandParahenipavirusgenera3. Here, to explore the limits of structural and antigenic variation in both genera, collectively referred to here as HNVs, we constructed an expanded, antigenically diverse panel of HNV fusion and attachment glycoproteins from 56 unique HNV strains that better reflects global HNV diversity. We expressed and purified the fusion protein ectodomains and the attachment protein head domains and characterized their biochemical, biophysical and structural properties. We performed immunization experiments in mice leading to the elicitation of antibodies reactive to multiple HNV fusion proteins. Cryo-electron microscopy structures of diverse fusion proteins elucidated molecular determinants of differential pre-fusion state metastability and higher order contacts. A crystal structure of the Gamak virus attachment head domain revealed an additional domain added to the conserved 6-bladed, β-propeller fold. Taken together, these studies expand the known structural and antigenic limits of the HNVs, reveal new cross-reactive epitopes within both genera and provide foundational data for the development of broadly reactive countermeasures.
Rational molecular design for improved ZHD101 thermal stability based on the introduction of disulfide bonds at the dimer interface and B-factor analysis
Authors: Ding Weiqiu , Huang Yujie , Zhang Haiyi , Zheng Shaoyan , Xie Chunfang , Yao Dongsheng 
Published: December 2024

AbstractZearalenone hydrolase 101 (ZHD101), derived fromClonostachys rosea, is known to effectively degrade the main contaminant (zearalenone) in animal feed, but the thermal instability of ZHD101 limits its industrial application. In this study, we successfully enhanced the thermal stability of ZHD101 through two iterative rounds of rational molecular design. First, after the prediction of disulfide bond sites, ZHD101T229Cwas obtained, and a new disulfide bond was formed between two single ZHD101 subunits to construct a dimer ZHD101. Then, based on ZHD101T229C, two high-vibration hotspot amino acids N137 and D170 were selected by analyzing atomic position fluctuations and dynamic information A small and precise mutant library containing three mutants (ZHD101T229C/N137L, ZHD101T229C/D170L, and ZHD101T229C/D170C) was obtained by saturation mutagenesis and calculation of binding energy in silico. Compared with the wild type, the thermal half-inactivation temperature (T50) of ZHD101T229C/D170Cincreased by 7°C, its half-life (t1/2) increased by 200% at 50°C, and its melting temperature (Tm) increased by 18.1°C. Molecular docking suggested that new covalent bond formation and shorter bond distance may contribute to the improved thermal stability of ZHD101. The rational design strategy proposed in this work can provide a reference for the thermal stability modification and optimization of other proteins.ImportanceZearalenone (ZEN) is a nonsteroidal estrogenic mycotoxin that poses a significant threat to animal feed safety. ZEN hydrolase 101 (ZHD101) catalyzes the conversion of ZEN into a nontoxic product, offering a promising detoxification strategy. However, the thermal instability of ZHD101 severely limits its industrial application. In this study, the thermal stability of ZHD101 was significantly improved through two rounds of rational design, resulting in the ZHD101T229C/D170Cmutant. ZHD101T229C/D170Cexhibited the improved half-inactivation temperature (T50), half-life (t1/2) and the highest melting temperature (Tm) reported thus far. Overall, the iterative combinatiorial mutation strategy involved the introduction of a disulfide bond between two single subunits and the application of B-factor analysis to identify hotspot amino acids residues. This approach effectively enhanced the enzyme’s structural stability, paving the way for its industrial application. ZHD101T229C/D170Cand the rational design strategy presented in this work provide a robust framework for the thermal stability optimization of similar biocatalysts, advancing the practical use of ZHD101 in the feed industry.
An NAMPT Inhibitor Decreases NAMPT Capture by an Antibody Directed against the 5-Phosphoribosyl-1-Pyrophosphate-Binding Loop: A Rational for an NAMPT Occupancy Assay
Authors: Shomali Maysoun , Hilairet Sandrine , Duhamel Marianne , Nardi Frederico , Bertrand Thomas , Mathieu Magali , Arrebola Rosalia , Penarier Geraldine , Jegham Samir , Arigon Jerome , Cosnier Sylvie , Bouaboula Monsif 
Published: December 2024

AbstractNicotinamide phosphoribosyltransferase (NAMPT) catalyzes the rate-limiting step of nicotinamide adenine dinucleotide (NAD) biosynthesis. NAMPT inhibitors (NAMPTi) have been shown to be NAMPT substrates. The resulting NAMPTi-phosphoribose (RP) adduct binds tightly to NAMPT and inhibits the enzyme. Using new NAMPTi, SAR154782, structural analyses performed in this study unveiled a close proximity of the SAR154782-RP complex to the 5-phosphoribosyl-1-pyrophosphate-binding (PRPP-binding) loop within the NAMPT catalytic site. The PRPP-binding loop of NAMPT is subject to conformational flexibility. Interestingly, the PRPP-binding loop domain is oriented to the outer side of the NAMPT dimer and can potentially serve as an antigen-binding site for antibodies. Here we report for the first time that the NAMPTi-RP adduct bound to NAMPT decreases NAMPT capture by an antibody directed against the c-terminal PRPP-binding loop in NAMPT. This finding was then used to explore cellular NAMPT occupancy. The NAMPTi-RP complex displays a sustained, cellular NAMPT occupancy that correlates with the inhibition of NAMPT activity. Moreover, a good correlation between NAMPT occupancy, NAD decrease, and NAMPTi efficacy was observedin-vivoin a NCI-H82 small cell lung cancer (SCLC) xenograft model. NAMPT-occupancy assay can be used in clinical settings to better define the optimal dose levels and dose regimen for effective NAMPT inhibition.
Large-Scale Deep Learning for Metastasis Detection in Pathology Reports
Authors: Krawczuk Patrycja , Fox Zachary R , Petkov Valentina , Negoita Serban , Doherty Jennifer , Stroupe Antoinette , Schwartz Stephen , Penberthy Lynne , Hsu Elizabeth , Gounley John , Hanson Heidi A. 
Published: December 2024

AbstractNo existing algorithm can reliably identify metastasis from pathology reports across multiple cancer types and the entire US population. In this study, we develop a deep learning model that automatically detects patients with metastatic cancer by using pathology reports from many laboratories and of multiple cancer types. We trained and validated our model on a cohort of 29,632 patients from four Surveillance, Epidemiology, and End Results (SEER) registries linked to 60,471 unstructured pathology reports. Our deep learning architecture trained on task-specific data outperforms a general-purpose LLM, with a recall of 0.894 compared to 0.824. We quantified model uncertainty and used it to defer reports for human review. We found that retaining 72.9% of reports increased recall from 0.894 to 0.969. This approach could streamline population-based cancer surveillance to help address the unmet need to capture recurrence or progression.