bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neurocysticercosis presenting as postpartum psychosis: a case report on challenging clinical insight with review of the literature
Authors: Yogi Tek Nath , Rauniyar Krish , Bhusal Amrit , Bk Himal , Darai Anju , Yadav Ajay , Shahi Shishir , Shah Bipesh Kumar , Kafle Rijan , Bam Prabin Kumar , Duwadee Prabin , Pokhrel Sushan 
Published: February 2025

Introduction:
Postpartum psychosis (PPP) is a psychiatric emergency with symptoms such as mania, depression, hallucinations, and delusions appearing shortly after childbirth. Neurocysticercosis (NCC), a CNS infection from Taenia solium, can also manifest psychiatric symptoms, complicating diagnosis. This report discusses a rare case of NCC presenting as PPP, emphasizing diagnostic complexities and the need for a multidisciplinary approach.

Case Presentation:
A 23-year-old female presented with psychotic symptoms 15 days postpartum, initially treated for PPP with lorazepam, olanzapine, and haloperidol. Following the development of generalized seizures, neuroimaging revealed NCC lesions. Treatment with albendazole and steroids led to symptom resolution.

Discussion:
This case highlights NCC’s rare presentation as PPP. A thorough evaluation is crucial, as PPP poses risks to both mother and infant. In regions where NCC is endemic, psychiatric presentations due to this infection create diagnostic challenges, especially in limited-resource settings. Neuroimaging is vital for precise diagnosis, and early intervention with a multidisciplinary team enhances outcomes.

Conclusion:
The case underscores the complexity of PPP diagnosis and management when coexisting with NCC. Clinicians in endemic regions should consider NCC in postpartum women with atypical psychiatric symptoms, ensuring appropriate investigations. A multidisciplinary approach involving psychiatry, neurology, and radiology is essential for effective management.
Initiation of Dolutegravir vs Efavirenz on Viral Suppression and Retention at 6-months: A Regression Discontinuity Design
Authors: Zheng Amy , Fox Matthew P. , Greener Ross , Kileel Emma M. , Bor Jacob , Venter Willem Df , Pisa Pedro T. , Brennan Alana T. , Maskew Mhairi 
Published: February 2025

Background:
In 2019, South Africa’s Antiretroviral Therapy (ART) Treatment Guidelines replaced efavirenz with dolutegravir in first-line ART.

Setting:
We assessed the impact of this national guideline change on retention and viral suppression in the Themba Lethu Clinical Cohort, Johannesburg, South Africa. We applied a regression discontinuity design in a prospective cohort study of 1654 adults living with HIV initiating first-line ART within 12 months (±12 months) of the guideline change.

Methods:
We compared outcomes in individuals presenting just before and after the guideline change and estimated intention-to-treat effects on initiating a dolutegravir- vs efavirenz-based regimen. Primary outcomes were retention and viral suppression. Participants were defined as retained in care if a visit took place within +3-months of the 6-month endpoint. Viral suppression was defined as having a viral load < 1000 copies/mL 3-months prior to and up to 6-months after the 6-month endpoint.

Results:
The 2019 guideline change led to an increase in uptake of dolutegravir. We noted a 26.6 percentage point increase in the proportion initiating dolutegravir (95% Confidence Interval (CI): 14.1, 38.6). We saw a small increase in viral suppression (Risk Difference (RD): 7.4 percentage points; 95% CI: -1.6, 16.5) and no change in retention (RD: -1.7 percentage points; 95% CI: -13.9, 10.5) at 6 months, though our findings were imprecise.

Conclusion:
Our estimates suggest early uptake of the revised treatment guidelines after implementation. Despite this, there was no meaningful change in viral suppression and retention rates at 6-months.
Pre- and postinjury financial hardship among trauma survivors: A national survey study
Authors: Clark Nina M. , Hernandez Alex H. , Knowlton Lisa M. , Stewart Barclay T. , Bulger Eileen M. , Malloy Alexander , Anderson Geoffrey , Dieleman Joseph L. , Zatzick Douglas , Scott John W. 
Published: February 2025

BACKGROUND
National estimates of financial hardship because of injury are lacking, which limits our ability to both define and mitigate the impacts of financial outcomes of trauma care. Furthermore, the absence of preinjury data limits our understanding of the association between injury and financial hardship.

METHODS
We analyzed data from the 2014–2021 Medical Expenditure Panel Survey. We compared injured adults (18–64 years old) to uninjured controls using coarsened-exact matching on age, sex, race/ethnicity, income, payer, survey panel, and comorbidities. Our main outcome of interest was financial hardship, a composite of difficulty paying medical bills, paying medical bills off over time, and delaying medical care because of cost. As a secondary analysis, we evaluated the link between difficulty paying medical bills, delaying care, and poor health.

RESULTS
We included a weighted sample of more than 79 million injured patients over the 8-year study period. Difference-in-differences analysis using uninjured, matched controls showed that injured patients experienced an 8.2 percentage point increase in financial hardship (23% relative increase, with 40.6% reporting financial hardship postinjury, p < 0.001) and 4.5 percentage point increase in poor health (20% relative increase, p < 0.001). Injured patients who reported difficulty with medical bills were more likely to report delaying care because of costs (adjusted odds ratio, 3.3; 95% confidence interval, 2.5–4.4), and those who delayed care were more likely to report poor health (adjusted odds ratio, 1.5; 95% confidence interval, 1.2–2.0).

CONCLUSION
In this national analysis of financial hardship before and after injury, 40% of injured patients reported difficulty with medical bills and delayed medical care because of cost. Programs aimed at disrupting the path from injury to financial hardship to poor long-term health have the potential to benefit millions of injury survivors.

LEVEL OF EVIDENCE
Retrospective Cohort Study; Level III.
Drug-disease networks and drug repurposing
Authors: Polanco Austin , Newman M. E. J. 
Published: February 2025

AbstractRepurposing existing drugs to treat new diseases is a cost-effective alternative tode novodrug development, but there are millions of potential drug-disease combinations to be considered with only a small fraction being viable. In silico predictions of drug-disease associations can be invaluable for reducing the size of the search space. In this work we present a novel network of drugs and the diseases they treat, compiled using a combination of existing machine-readable and textual databases, natural-language processing tools, and hand curation, and analyze it using a selection of network-based link prediction methods to identify potential drug-disease combinations. We measure the efficacy of these methods using cross-validation tests and find that several methods, particularly those based on graph embedding and network model fitting, achieve impressive prediction performance, with area under the ROC curve above 0.95 and average precision almost a thousand times better than chance.
Mobile genetic elements exhibit associated patterns of host range variation and sequence diversity within the gut microbiome of the European Honey bee
Authors: Robinson Chris R. P. , Dolezal Adam G. , Liachko Ivan , Newton Irene L. G. 
Published: February 2025

AbstractMobile genetic elements (MGEs), such as plasmids and bacteriophages, are major contributors to the ecology and evolution of host-associated microbes due to a continuum of symbiotic interactions and by mediating gene flow via horizontal gene transmission. However, while myriad studies have investigated relationships between MGEs and variation in fitness among microbial and eukaryotic hosts, few studies have incorporated this variation into the context of MGE evolution and ecology. Combining HiC-resolved metagenomics with the model honey bee worker gut microbiome, we show that the worker gut contains a dense, nested MGE community that exhibits a wide degree of host range variation among microbial hosts. Using measures of gene similarity and syntenty, we show that plasmids likely mediate gene flow between individual honey bee colonies, though these plasmids exhibit broad host range variation within their individual microbiomes. We further show that phage-microbe networks exhibit high variation among individual metagenomes, and that phages show broad host range with respect to both the number and phylogenetic distance of their hosts. Finally, we provide evidence that measures of nucleotide variation positively correlate with host range in bee-associated phages, and that functional targets of diversifying selection are partitioning differently between broad or narrow host range phages. Our work underscores the variability of MGE x microbial interactions within host-associated microbial communities and highlights the genomic variation associated with MGE host range diversity.
The Sequential Multispecies Coalescent
Authors: Milkey Analisa , Chen Ming-Hui , Wang Yu-Bo , Li Aolan , Lewis Paul O. 
Published: February 2025

AbstractThe multispecies coalescent (MSC) model applies coalescent theory to gene evolution within and among reproductively isolated populations (“species”) to estimate a species tree in the face of gene tree conflict resulting from deep coalescence. Sequential Monte Carlo (SMC) uses particle filtering to sample a posterior distribution, providing a fully-Bayesian and easily parallelized alternative to traditional MSC tree inference approaches. The method we propose samples first from the joint posterior distribution of gene and species trees, then samples species trees conditional on gene trees sampled previously, employing SMC for both rounds. Analyses of simulated and empirical datasets yield results comparable to state-of-the-art Bayesian MCMC approaches. Sampling the multispecies coalescent using SMC retains the advantages of fully Bayesian methods and is parallelizable in ways that Bayesian MCMC methods are not but also adds unique challenges. We demonstrate the performance of SMC compared to other commonly-used species tree methods using two empirical datasets and 400 simulated datasets.
Thermodynamics shape thein vivoenzyme burden of glycolytic pathways
Authors: Khana Daven B. , Jen Annie , Shishkova Evgenia , Thusoo Eashant , Williams Jonathan , Henkel Alex , Stevenson David M. , Coon Joshua J. , Amador-Noguez Daniel 
Published: February 2025

AbstractThermodynamically constrained reactions and pathways are hypothesized to impose greater protein demands on cells, requiring higher enzyme amounts to sustain a given flux compared to those with stronger thermodynamics. To test this, we quantified the absolute concentrations of glycolytic enzymes in three bacterial species —Zymomonas mobilis,Escherichia coli, andClostridium thermocellum— which employ distinct glycolytic pathways with varying thermodynamic driving forces.By integrating enzyme concentration data with correspondingin vivometabolic fluxes andΔGmeasurements, we found that the highly favorable Entner-Doudoroff (ED) pathway inZ. mobilisrequires only one-fourth the amount of enzymatic protein to sustain the same flux as the thermodynamically constrained pyrophosphate-dependent glycolytic pathway inC. thermocellum, with the Embden-Meyerhof-Parnas (EMP) pathway inE. coliexhibiting intermediate thermodynamic favorability and enzyme demand. Across all three pathways, early reactions with stronger thermodynamic driving forces generally required lower enzyme investment than later, less favorable steps. Additionally, reflecting differences in glycolytic strategies, the highly reversible ethanol fermentation pathway inC. thermocellumrequires 10-fold more protein to maintain the same flux as the irreversible, forward-driven ethanol fermentation pathway inZ. mobilis.Thus, thermodynamic driving forces constitute a majorin vivodeterminant of the enzyme burden in metabolic pathways.
Tumor-associated stroma shapes the spatial tumor immune microenvironment of primary Ewing sarcomas
Authors: Kuo Christopher , Giannikou Krinio , Wang Nuoya , Warren Mikako , Goodspeed Andrew , Shillingford Nick , Hayashi Masanori , Raredon Micha Sam Brickman , Amatruda James F. 
Published: February 2025

AbstractTo date, few studies have detailed the tumor microenvironment (TME) of Ewing sarcoma (EwS). The TME has a vital role in cancer survival and progression with implications in drug resistance and immune escape. By performing spatially resolved transcriptomic analysis of primary treatment-naïve EwS samples, we discovered greater stromal enrichment in localized EwS tumors compared to metastatic EwS tumors. Through spatial ligand-receptor analysis, we show that the stromal enriched regions harbor unique extracellular matrix related cytokines, immune recruitment and proinflammatory microenvironmental signals, implying EwS stroma may play an anti-tumorigenic role by acting as an immune recruitment center. All EwS tumors expressed pro-tumorigenic MIF-CD74 immune signaling, suggesting a potential immune-evasive mechanism and immunotherapy target. Our findings provide insight into tumor cell/stromal cell interactions in EwS and serve as a valuable resource for further investigations in the tumor immune microenvironment of EwS.
Risk stratification of childhood infection using host markers of immune and endothelial activation: a multi-country prospective cohort study in Asia (Spot Sepsis)
Authors: Chandna Arjun , Koshiaris Constantinos , Mahajan Raman , Ahmad Riris Adono , Anh Dinh Thi Van , Keang Suy , Nguyen Phung Nguyen The , Rattanavong Sayaphet , Shams Khalid , Vannachone Souphaphone ,   Spot Sepsis Investigator Group, Yosia Mikhael , Waithira Naomi , Abdad Mohammad Yazid , Thaipadungpanit Janjira , Turner Paul , Phuc Phan Huu , Mondal Dinesh , Mayxay Mayfong , Liem Bui Thanh , Ashley Elizabeth A , Arguni Eggi , Perera-Salazar Rafael , Richard-Greenblatt Melissa , Lubell Yoel , Burza Sakib 
Published: February 2025

ABSTRACTBackgroundCirculating markers of immune and endothelial activation risk stratify infection syndromes agnostic to disease aetiology. However, their utility in children presenting from the community remains unclear.MethodsThis study recruited children aged 1-59 months presenting with community-acquired acute febrile illnesses to seven hospitals in Bangladesh, Cambodia, Indonesia, Laos, and Viet Nam. Clinical parameters and biomarker concentrations were measured at presentation. The outcome measure was death or receipt of vital organ support within two days of enrolment. Prognostic performance of endothelial (Ang-1, Ang-2, sFlt-1) and immune (CHI3L1, CRP, IP-10, IL-1ra, IL-6, IL-8, IL-10, PCT, sTNFR-1, sTREM-1, suPAR) activation markers, WHO Danger Signs, and two validated severity scores (LqSOFA, SIRS) was compared.Results3,423 participants were recruited. 133 met the outcome (weighted prevalence: 0.34%; 95% CI 0.28-0.41). sTREM-1 exhibited highest prognostic accuracy (AUC 0.86; 95% CI 0.82-0.90), outperforming WHO Danger Signs (AUC 0.75; 95% CI 0.70-0.80; p < 0.001), LqSOFA (AUC 0.74; 95% CI 0.70-0.78; p < 0.001), and SIRS (AUC 0.63; 95% CI 0.58-0.68; p < 0.001). Discrimination of immune and endothelial activation markers was particularly strong for children who deteriorated later in the course of their illness. Compared to WHO Danger Signs, an sTREM-1-based triage strategy improved recognition of children at risk of progression to life-threatening infection (sensitivity: 0.80 vs. 0.72), while maintaining comparable specificity (0.81 vs. 0.79).ConclusionsMeasuring circulating markers of immune and endothelial activation may help earlier recognition of febrile children at risk of poor outcomes in resource-constrained community settings.
Impact of a machine learning-powered algorithm on pathologist HER2 IHC scoring in breast cancer
Authors: Shamshoian John , Shanis Zahil , Cabeen Ryan , Yu Limin , Chakraborty Shreya , Thibault Marc , Martin Blake , Pagidela Harshith , Juyal Dinkar , Javed Syed Ashar , Qian William , Kim Juhyun , Gerardin Ylaine , Rucker Beckett , Brosnan-Cashman Jacqueline , Pokkalla Harsha , Mehta Jimish , Taylor-Weiner Amaro , Walk Eric , Beck Andrew , Montalto Michael C. , Glass Ben , Balasubramanian Santhosh 
Published: February 2025

AbstractBackgroundHER2 expression level is a key factor in determining the optimal treatment course for breast cancer patients. Roughly 15% of breast cancers are HER2(+), and determination of HER2 status is routinely assessed by immunohistochemistry (IHC). Accurate assessment of the HER2 IHC score by pathologists is therefore critical, especially in light of novel therapeutic approaches demonstrating efficacy in the HER2-low setting. However, there is an opportunity to improve inter-pathologist agreement at the lower levels of HER2 scoring (0, 1+, and 2+).MethodsA machine learning model (AIM-HER2) was developed to generate accurate, slide-level HER2 scores aligned with ASCO-CAP guidelines in clinical breast cancer HER2 IHC specimens. AIM-HER2 was assessed as an AI-assist tool in a retrospective reader study, where 20 HER2-trained pathologists scored breast cancer cases (N=200) with and without AIM-HER2 assistance using a 2-cohort crossover design with a 3-week washout. A separate panel of 5 expert HER2 pathologists read all 200 cases manually to establish reference scores.ResultsIn a significant fraction of cases examined, less than 70% inter-pathologist agreement was observed. When used as an AI assist tool, AIM-HER2 improved inter-rater agreement overall and specifically at the 0/1+ and 1+/2+ cutoffs. Similarly, AIM-HER2 AI-assist significantly increased PPA at the 0/1+ and 1+/2+ cutoffs. When interacting with the AI-assist tool, pathologists displayed a wide range of override rates, and the quality of a pathologist’s overrides was correlated with their manual accuracy. Lastly, the impact of the reference panel on AIM-HER2 accuracy metrics was assessed, revealing that measurements of model accuracy are highly dependent on reference panel composition.ConclusionsThe use of AIM-HER2 as an AI-assist tool for scoring HER2 IHC in breast cancer may improve pathologist reproducibility and accuracy, particularly at the 0/1+ and 1+/2+ cutoffs.
Post-Hurricane Fluid Conservation Measures Fail to Reduce IV Fluid Use in Critically Ill Children
Authors: Dixon Celeste G. , Odum James , Kothari Ulka , Martin Susan D. , Fitzgerald Julie C. , Shah Ami , Dapul Heda , Braun Chloe G. , Barbera Andrew , Terry Nina , Weiss Scott L. , Hasson Denise C. , Dziorny Adam C. 
Published: February 2025

ABSTRACTIntroductionFluid overload (FO) due to excessive fluid administration in critically ill children is associated with worse pediatric intensive care unit (PICU) outcomes. Damage to intravenous fluid (IVF) production plants due to Hurricane Helene resulted in a national IVF shortage, leading many hospitals to implement IVF conservation guidelines. We hypothesized that these guidelines would lead to a reduction in IVF use and a decrease in % cumulative fluid balance (CFB).MethodsFour-site cohort study of critically ill children utilizing a federated data collection framework to extract patient age, sex, weight, and daily fluid intake/output for all admissions one month prior to and one month after the implementation of IVF conservation guidelines. Guidelines were individualized per institution. Total fluid intake, total IVF intake, % intake from IVF, and % CFB, as total intake minus total output divided by PICU admission weight, were compared between pre and post IVF conservation groups.ResultsThere were 633 pre and 619 post IVF conservation encounters, with similar age and size distributions. There was no significant difference in IVF use pre and post IVF conservation. All sites had similar conservation recommendations.ConclusionsSimple recommendations without structured change was insufficient to change IVF administration practices, indicating additional practices will be needed to reduce IVF intake and % CFB in this vulnerable population.
Plasma proteomics of seizure-associated changes in epilepsy
Authors: Akel Sarah , Ashtiani Saman Hosseini , Sarangdhar Mayuresh Anant , Axelsson Markus , Zelano Johan 
Published: February 2025

AbstractFluid biomarkers are emerging as crucial markers for diagnosis and disease monitoring in neurology. Epilepsy remains an exception despite seizures being known to result in metabolic changes, inflammation, and altered brain protein levels. Insight into how seizures affect the blood proteome is greatly needed.This cross-sectional study used plasma samples from adults aged 18-50 with epilepsy recruited at five different neurology outpatient clinics in region Västra Götaland, Sweden to the Prospective Regional Epilepsy Database and Biobank for Individualized Clinical Treatment (PREDICT). Patients were categorized based on the presence of seizures; those with seizures ≤1 year (n=88, median number of seizures for patients who have experienced seizures in the last two months = 1) and those seizure-free (n=88) for >1 year.Plasma proteomics was performed using four Olink Explore384 panels to analyze a total of 1447 proteins. Normalized Protein eXpression (NPX) values were compared between seizure and seizure-free participants. Two machine learning models, one linear and one non-linear, identified protein expression differences associated with seizure status through feature selection, resulting in 51 unique discriminative proteins. Twenty-three proteins were considered top differentially expressed after FDR adjustment (p≤0.05), including neurofilament light and several cytokines.Protein-protein interaction (PPI) analysis identified several clusters among the 51 discriminative proteins, with the largest clusters primarily associated with inflammatory processes. In addition, machine-learning independent gene set enrichment analysis identified 34 gene sets, mainly related to immune and inflammatory processes, that were also enriched in association with seizures.In this study, persons with epilepsy and seizures had different plasma protein profiles compared to those seizure-free, primarily suggesting altered inflammatory/immune processes. The findings fit well with the growing evidence of inflammation as a key process in epilepsy. In addition to new therapeutic targets, immune processes need further exploration as candidate biomarkers for the monitoring of treatment responses in epilepsy.
Predicting Levels of Anemia among Adolescents in Ethiopia Using homogeneous ensemble Machine Learning algorithm
Authors: Ayele Misganaw Ketema , Enyew Eyob Teshager 
Published: February 2025

AbstractAnemia significantly impacts adolescent girls’ health and quality of life in Ethiopia. Effective interventions require identifying key risk factors and predicting anemia severity. While traditional studies primarily use statistical methods, this research aims to leverage machine learning models to predict anemia risk and analyze contributing socio-economic, environmental, and cultural factors.We applied machine learning models, including Random Forest, Extra Trees, CatBoost, XGBoost, and AdaBoost, to predict anemia severity using features from the Ethiopian Demographic and Health Survey (EDHS). Performance was evaluated using accuracy, ROC AUC, precision, recall, and F1-score, with feature importance analysis to identify key anemia risk factors. Random Forest and Extra Trees outperformed others, achieving accuracy rates of 82.51% and 82.41% and ROC AUC scores of 94.87% and 94.48%, respectively. CatBoost showed competitive performance (80.99% accuracy, 93.08% ROC AUC). XGBoost and AdaBoost were less effective. Key risk factors included region, education, wealth index, household size, and altitude.Random Forest and Extra Trees are effective for predicting anemia severity and identifying key socio-economic and environmental risk factors. Interventions focusing on education, healthcare access, and nutrition are vital to reducing anemia prevalence among adolescent girls in Ethiopia. Future work should refine models and expand datasets for improved public health outcomes.