bluuebunny/crossref_metadata_2025_split huggingface dataset:
Confounder-free Predictive Models for Microbiome-based Host Phenotype Prediction
Authors: Monshizadeh Mahsa , Hong Yuhui , Ye Yuzhen 
Published: February 2025

AbstractAs in many fields, the presence of confounding effects (or biases) presents a significant challenge in micro-biome research, including using microbiome data to predict host phenotypes. If not properly addressed, confounders can lead to spurious associations, biased predictions and misleading interpretations. One notable example is the medication metformin, which is commonly prescribed to treat type 2 diabetes (T2D) and is known to influence the gut microbiome. In this study, we propose confounder-free predictive models for human phenotype prediction using microbiome data. These models utilize an end-to-end approach within an adversarial min-max optimization framework to derive features that are invariant to confounding factors, while accounting for the intrinsic correlations between confounders and prediction outcomes. We implemented two versions of confounder-free predictors using different network architectures: one based on a fully connected network (referred as FNN CF) and another incorporating prior biological knowledge (referred as MicroKPNN CF). We evaluated our models on microbiome datasets associated with T2D, where metformin acts as a confounder. Our results demonstrate that confounder-free predictors achieve higher accuracy compared to models that do not account for confounders and more effectively identify microbial markers associated with the phenotype, rather than markers influenced by metformin. Between the two confounder-free models, although the prior-knowledge-guided approach showed slightly lower prediction accuracy compared to the fully connected model, it offered greater interpretability, providing additional insights into the underlying biological mechanisms.
Chronic Inhaled Benzene Exposure Exacerbates Atherosclerosis in LDL Receptor-Null Mice
Authors: Zelko Igor N. , Malovichko Marina V. , McFall Samantha A. , Taylor Breandon S. , Chen Hong , Conklin Daniel J. , Srivastava Sanjay 
Published: February 2025

AbstractBackgroundBenzene is a ubiquitous environmental pollutant generated by a variety of natural and anthropological sources. It is a known carcinogen and hematopoietic toxin; however, little is known about benzene’s potential atherogenicity.HypothesisInhaled benzene induces atherogenesis by increasing vascular inflammation in LDL receptor Knockout (LDLR-KO) mice.MethodsMale LDLR-KO mice were exposed to HEPA-filtered air or benzene (1 ppm, 6h/day, 5days/week) for 24 weeks. For the last 12 weeks of exposure, the mice were maintained on a western diet. The single nuclei RNA sequencing (snRNAseq) of aortae was performed at Novogene. Forin vitroexperiments, splenic naïve T cells were exposed to 1 µM of hydroquinone (HQ) for 24 hours, and intracellular ROR-gamma levels were measured by flow cytometry.ResultsBenzene inhalation increased the aortic valve lesion area by more than 25% (P<0.05) in LDLR-KO mice. Using snRNAseq, eleven major cell types were detected, including T cells and vascular smooth muscle cells (VSMC). Benzene increased the number of T cells by 2.5-fold, proliferating T-cells by 5.8-fold, and VSMC by 1.6-fold, suggesting increased cellularity and reduced plaque stability. In addition, benzene upregulated Th17 polarization markerRorcand negative regulators of apoptosisRag1andBcl11bwhile significantly attenuating the expression of proliferation inhibitorMs4a4bin T cells. In VSMC, benzene downregulated extracellular matrix organization genes and upregulated platelet degranulation pathways. Polarization of T cells into Th17 was confirmed by HQ-dependent upregulation of ROR-gammain vitro.ConclusionOur data suggest that inhaled benzene exposure compromises plaque cellularity and stability by facilitating T-cell proliferation and polarization, which coincides with the degradation of smooth muscle extracellular matrix and platelet activation.
The Influence of Macrophages within the Tumor Microenvironment on Ovarian Cancer Growth and Response to Therapies
Authors: Nikeghbal Parisa , Burke Danielle , Armijo Dalet , Aldarondo-Quinones Samuel , Lidke Diane S. , Steinkamp Mara P. 
Published: February 2025

AbstractWhile the majority of ovarian cancer (OC) patients respond to front-line carboplatin/paclitaxel chemotherapy and surgical debulking, nearly all patients will develop platinum-resistance and recur. Our study investigates how tumor-associated macrophages (TAMs) within the tumor microenvironment (TME) affect chemotherapy outcomes using OC patient-derived organoids and humanized patient-derived xenografts (huPDX).In vitromacrophage migration assays demonstrate the selective recruitment of M2 polarized macrophages to OC organoids. M2 macrophages, but not M1 macrophages, increase OC organoid viability and reduce their sensitivity to paclitaxel in co-culture assays. Furthermore, we identified BMS777607, a receptor tyrosine kinase inhibitor, that is capable of repolarizing M2 macrophagesin vitroand reduces organoid viability by a macrophage-dependent mechanism. In a platinum-sensitive humanized patient-derived xenograft (huPDX) model, the presence of human immune cells increased between-mouse variability in response to paclitaxel with two of four mice demonstrating tumor regrowth after two weeks of treatment. A TAM-targeted CSF-1R inhibitor, BLZ945 significantly reduced the total number of human immune cells within the ascites fluid of huPDX3 mice, but did not reduce tumor burden. However BLZ945 in combination with paclitaxel reduced tumor burden with no sign of regrowth. Our study demonstrates that patient-derived OC organoids and huPDX, are useful for evaluating the immunomodulatory effects of therapies and the influence of TAMs on response. huPDX models could serve as a robust platform for preclinical testing of novel anti-cancer treatments, providing insights into the complex interplay between immune cells and cancer therapeutics.
vcfsim: flexible simulation of all-sites VCFs with missing data
Authors: Goulart Paimon , Samuk Kieran 
Published: February 2025

AbstractVCFs are the most widely used data format for encoding genetic variation. By design, standard VCFs do not include data from sites where all individuals are homozygous for the reference allele (“invariant sites”) and thus do not differentiate these from sites where data are completely missing. However, missing data are a key feature of biological datasets across all domains of genomics, and many recent studies have shown that missing data can introduce a variety of statistical biases in the estimation of key population genetic parameters. A solution to this limitation is to include invariant sites in a standard VCF, creating an “all-sites VCF”, exposing missing and invariant sites explicitly. One hurdle to the wider adoption of all-sites VCFs is a reliable parameterized simulation framework for generating biologically realistic all-sites VCFs. Here, we introduce an open-source command line tool,vcfsim, that interfaces with the popular coalescent simulation platformmsprimeand provides convenience functions for simulating all-sites VCFs with variable levels of ploidy and missing data. We show that the post-processed VCFs generated usingvcfsimalign precisely with population genetic expectations (i.e. are statistically identical to rawmsprimeoutput) and can accurately introduce missing data and varying ploidy levels, including the simulation of intraindividual ploidy variation (e.g. heterogametic sex chromosomes). We suggestvcfsimwill be a useful tool for the benchmarking of new software tools, training of machine learning models, and the exploration of the effects of missing data in genomics data sets.
User-Assisted Approach for Accurate Nonrigid Registration of Images and Traces
Authors: Baig Mirza M. Junaid , Hoang Tina Thuy N. , Chung Samuel H. , Stepanyants Armen 
Published: February 2025

AbstractFully automated registration algorithms are prone to getting trapped in solutions corresponding to local minima of their objective functions, leading to errors that are easy to detect but challenging to correct. Traditional solutions often involve iterative parameter tuning, data preprocessing and preregistering, and multiple algorithm reruns—an approach that is both time-consuming and does not guarantee satisfactory results. Therefore, for tasks where registration accuracy is more important than speed, it is appropriate to explore alternative, user-assisted registration strategies. In such tasks, finding and correcting errors in automated registration is often more time-consuming than directly integrating user input during the registration process.Therefore, this study evaluates a user-assisted approach for accurate nonrigid registration of images and traces. By leveraging the corresponding sets of fiducial points provided by the user to guide the registration, the algorithm computes an optimal nonrigid transformation that combines linear and nonlinear components. Our findings demonstrate that the registration accuracy of this approach improves consistently with the increased complexity of the linear transformation and as more fiducial points are provided. As a result, accuracy sufficient for many biomedical applications can be achieved within minutes, requiring only a small number of user-provided fiducial points.
Lithographic structuring of thermoresponsive hydrogel on a micron scale
Authors: Esch Katharina , Zieske Katja 
Published: February 2025

AbstractHydrogel microstructures and microchannels are important tools in biophysical bottom-up approaches for studying the interactions between extracellular obstacles or boundaries and cells. In prior studies, several microstructuring techniques, cell sources, and gel types have been employed to mimic static cellular environments. However, the dynamic nature of the extracellular environment, which exerts forces on cells, plays an important role in tissue-level contexts. To date, only few approaches have been implemenmted to mimic these forces of the extracellular environment.Here, we present an approach for generating arrays of hydrogel microstructures, that expand and contract dynamically in response to temperature modulations. To do this, we use a thermoresponsive hydrogel and a custom-built lithographic setup. Through direct lithographic photopatterning of a liquid phase, we fabricate smart hydrogel microstructures at the micrometer scale (10-100 μm) with well-defined geometries. These hydrogel microstructures display reversible size changes of over 70% triggered by temperature modulations.Our study represents a step towards mimicking the dynamic properties of biological boundaries and obstacles. The dynamic nature of smart hydrogel microstructures holds the potential to mimic the micron-scale dynamics of extracellular environments. Our results may thus be relevant for the fields of bioengineering and systems level biophysics offering opportunities for manipulating and exploring cellular interactions with dynamic obstacles.
Competing Risk Model in Dental Cost-Effective Research
Authors: Seth Anand , Saleh Muhammad H.A. , Shah Priyanshi , Bhattacharyya Arinjita , Almashni Hamzeh , Troiano Guiseppe , Dukka Himabindu , Rai Shesh N. 
Published: February 2025

AbstractOver the years, few attempts have been made to use survival analysis techniques in dental research. Moreover, the use of survival analysis was restricted to a simple method of drawing a survival curve using the Kaplan-Meier technique, but later prognostic factors were included utilizing the Cox Proportional Hazard (CPH) model. More recently, the CPH model has been incorporated with a frailty parameter to account for the correlation of observations within a subject. A recent publication has utilized a subdistribution hazard model with time-dependent covariates. In actual dental practice, a tooth failure could be due to a variety of reasons other than the cause of failure of interest. To have a realistic estimate of the risk of failure for cause i, one should also include the risk of failure due to causes j, j=1(1) n, which could occur prior to cause i. This competing risk scenario has been attempted in the last ten to fifteen years or so to estimate the probability of failure in the presence of competing event(s). Precise estimate of survival (failure) probabilities using competing risk model has implications in the dental cost-effectiveness and cost-utility studies.
Improving Labor Induction With Ultrasound: A Systematic Review
Authors: Romero Callejas Elisa , Enamorado Plata Antonio , Luque Fernández Miguel Ángel , Melchor Rodríguez Juan Manuel , Montero Alonso Miguel Ángel 
Published: February 2025

ABSTRACTIntroductionLabor induction is performed when continuing the pregnancy poses a greater risk than ending it. Elastography is emerging as an increasingly relevant technique to predict the success of labor induction.ObjectiveTo evaluate the relevant literature regarding elastography as a predictive tool for the success of labor induction, resulting in an uncomplicated vaginal delivery.MethodologyA systematic review was conducted on the predictive accuracy of elastography in labor induction, using PubMed, Scopus, and the Cochrane Library. A total of 10 studies were selected, covering the period from 2007 to 2022. The included studies were clinical trials and observational studies that analyzed elastography in predicting the success of induced labor. Success was defined as the completion of labor via vaginal delivery without maternal or neonatal complications.ResultsThe elastography technique demonstrates an increased accuracy in predicting successful labor induction and vaginal delivery compared to the traditional use of Bishop score. Among other predictors of labor induction failure, defined as labor ending in a cesarean delivery, are: cervical length (Odds Ratio [OR] OR = 1.916; 95% Confidence Interval [1.451–2.530]), maternal age ≥35 years, and body mass index (BMI) ≥30 kg/m2(OR = 2.257; 95% CI [1.353–3.767]). However, parity decreases the probability of labor induction failure (OR = 0.129; 95% CI [0.063–0.265]).DiscussionThe reviewed scientific evidence suggests that the use of elastography before induction improves the chances of a successful labor induction. Therefore, the right moment for the labor induction based on a clinical decision should be supplemented with other objective methods such as the elastography helping to evaluate the potential success of the induction and therefore, helping to improve maternal and infant health outcomes. Further research is recommended on the comparative effectiveness of different methods to identify the optimal time for labor induction, as well as to validate its clinical utility.
Meta-analysis reveals transcription factors and DNA binding domain variants associated with congenital heart defect and orofacial cleft
Authors: Jeong Raehoon , Bulyk Martha L. 
Published: February 2025

AbstractMany structural birth defect patients lack genetic diagnoses because there are many disease genes as yet to be discovered. We applied a gene burden test incorporatingde novopredicted-loss-of-function (pLoF) and likely damaging missense variants together with inherited pLoF variants to a collection of congenital heart defect (CHD) and orofacial cleft (OC) parent-offspring trio cohorts (n = 3,835 and 1,844, respectively). We identified 17 novel candidate CHD genes and 10 novel candidate OC genes, of which many were known developmental disorder genes. Shorter genes were more powered in a “de novoonly” analysis as compared to analysis including inherited pLoF variants. TFs were enriched among the significant genes; 14 and 8 transcription factor (TF) genes showed significant variant burden for CHD and OC, respectively. In total, 30 affected children had ade novomissense variant in a DNA binding domain of a known CHD, OC, and other developmental disorder TF genes. Our results suggest candidate pathogenic variants in CHD and OC and their potentially pleiotropic effects in other developmental disorders.
Genomic Taxometric Analysis of Negative Emotionality and Major Depressive Disorder Highlights a Gradient of Genetic Differentiation across the Severity Spectrum
Authors: Ennis Garrett W. , Mallard Travis T. , de la Fuente Javier , Williams Camille M. , Schwaba Ted , Tucker-Drob Elliot M. 
Published: February 2025

AbstractA core question in both human genetics and medicine is whether clinical disorders represent extreme manifestations of continuous traits or categorically distinct entities with unique genetic etiologies. To address this question, we introduceGenomic Taxometric Analysis of Continuous and Case-Controldata (GTACCC), a novel method for systematically evaluating continuity and differentiation of traits across the severity spectrum. GTACCC’s key innovation lies in binarizing continuous data at multiple severity thresholds, enabling the estimation of genetic continuity and differentiation within the trait and in its relation to other traits via multivariate models. We apply GTACCC to self-reported neuroticism data from UK Biobank (N= 414,448) and clinically ascertained major depressive disorder (MDD) data from the Psychiatric Genomics Consortium (ΣNeff = 111,221). We find that while neuroticism shares a considerable portion of its genetic etiology with MDD across the nonclinical, and even very low, range of (rg∼ .50), genetic sharing increases monotonically across the severity spectrum, approaching unity only at the highest levels of severity (rg∼ 1.0). Genomic structural equation models indicate that a single liability threshold model of negative emotionality is less consistent with the data than a multifactor model, suggesting that a gradient of genetic differentiation emerges across the spectrum of negative emotionality. Thus, within continuous measures of negative emotionality, partly distinct genetic liabilities exist at varying severity levels, with only the most severe levels associated with liabilities that approach equivalence to MDD genetics.
Low-concentration, low-dose lidocaine in ultrasound-guided sacral anesthesia reduces postoperative agitation and delirium in children undergoing hidden penis and hypospadias surgery: A prospective, randomized, controlled study
Authors: Guo Gaofeng , Wu Jiangxia , Zhao Yang , Zhang Jing , Yang Qing , Zhang Jiaqiang 
Published: February 2025

AbstractPostoperative agitation or postoperative delirium (EA/ED) has a high incidence among pediatric patients undergoing anesthesia and surgery. In this study, we aimed to evaluate the effects of sacral anesthesia with 0.125 mL/kg of 1% lidocaine on EA/ED in children undergoing hidden penis and hypospadias surgery. Sixty children aged 1–6 years undergoing elective hidden penis or hypospadias surgery were enrolled in the study. The postoperative EA/ED score and incidence; Face, Legs, Activity, Cry, and Consolability scale; perioperative general information and vital signs; and the occurrence of adverse events were analyzed. Ultrasound-guided sacral anesthesia in children resulted in more stable vital signs, postoperative pain relief, fewer complications, and lower incidence of EA/ED. In conclusion, the use of 0.125 mL/kg of 1% lidocaine combined with laryngeal mask general anesthesia significantly reduced the incidence of EA/ED in children undergoing hidden penis and hypospadias surgery while ensuring high perioperative safety.
Classification of Pediatric Dental Diseases from Panoramic Radiographs using Natural Language Transformer and Deep Learning Models
Authors: Pham Tuan D. 
Published: February 2025

AbstractAccurate classification of pediatric dental diseases from panoramic radiographs is crucial for early diagnosis and treatment planning. This study explores a text-based approach using a natural language transformer to generate textual descriptions of radiographs, which are then classified using deep learning models. Three models were evaluated: a one-dimensional convolutional neural network (1D-CNN), a long short-term memory (LSTM) network, and a pretrained bidirectional encoder representations from transformer (BERT) model for binary disease classification. Results showed that BERT achieved 77% accuracy, excelling in detecting periapical infections but struggling with caries identification. The 1D-CNN outperformed BERT with 84% accuracy, providing a more balanced classification, while the LSTM model achieved only 57% accuracy. Both 1D-CNN and BERT surpassed three pretrained CNN models trained directly on panoramic radiographs, indicating that text-based classification is a viable alternative to traditional image-based methods. These findings highlight the potential of language-based models for radiographic interpretation while underscoring challenges in generalizability. Future research should refine text generation, develop hybrid models integrating textual and image-based features, and validate performance on larger datasets to enhance clinical applicability.
Complexity of hospital demand during the COVID-19 pandemic in Mexico City
Authors: de Anda-Jáuregui Guillermo , Sifuentes-Osornio José , Angulo-Guerrero Ofelia , Díaz-De-León-Santiago Juan L , Benítez-Pérez Héctor , Herrera Luis A , López-Arellano Oliva , Revuelta-Herrera Arturo , Rosales-Tapia Ana R , Suárez-Lastra Manuel , Kershenobich David , Ruiz-Gutiérrez Rosaura , Hernández-Lemus Enrique 
Published: February 2025

AbstractBackgroundThe COVID-19 pandemic posed unprecedented challenges to healthcare systems worldwide. In densely populated urban areas such as Mexico City, the strain on hospitals was amplified due to the high volume of cases and resource limitations. Understanding the spatial and temporal dynamics of hospital demand is crucial for informing effective public health strategies and improving system resilience.MethodsA retrospective analysis of COVID-19 hospitalization data in Mexico City was conducted utilizing a line-list dataset from the SISVER surveillance system. The analysis included the spatial distribution of hospital demand using the weighted centroid of hospitalizations as well as a model system of the interactions between residential areas and hospitals as a bipartite network. The emergence of giant components in the network was used as indicators of system strain and the relationship between network strain and patient outcomes was evaluated.FindingsHospital demand in Mexico City exhibited significant spatial dynamics, with a northward shift in the weighted centroid of hospitalizations as the pandemic progressed. Despite the changing distribution of cases, a small subset of 17 hospitals managed the majority of hospitalizations. During high-demand periods, the network transitioned to a more disordered state, characterized by a giant component encompassing multiple neighborhoods. This disordered strain was associated with higher case fatality rates, particularly in patients over 40 years of age.InterpretationOur findings highlight the complex, adaptive nature of the healthcare system in response to the pandemic. The emergence of giant components in the hospital demand network can serve as an early warning indicator of the health system overload. Adaptive measures, such as the establishment of temporary COVID-19 units, were effective in mitigating strain. These insights can guide future public health strategies for rapid response and resource allocation in similar crises.FundingThis project was partially funded through CONACYT Project 320557 (to GA-J).