bluuebunny/crossref_metadata_2025_split huggingface dataset:
Examining Craniofacial and Velopharyngeal Structures in Premature Infants: A Window Into the Womb
Authors: Loomis-Goltl Evy I. , Power Samantha J. , Neuberger Ilana , Barhaghi Krystle , Kotlarek Katelyn J. 
Published: June 2024

Background:
Very little is known about how the velopharynx and levator veli palatini muscle develop in utero. The purpose of this study was to describe craniofacial, velopharyngeal, and levator veli palatini dimensions in a group of infants born prematurely and imaged before 40 weeks gestation.

Methods:
A retrospective, descriptive study design was utilized to examine the MRI scans of 6 infants less than 40 weeks’ gestation. Imaging was initially completed for medically necessity and pulled from patients’ charts retrospectively for the purpose of this study. Craniofacial, velopharyngeal, and levator veli palatini dimensions were analyzed.

Results:
All linear measures were consistently shorter across all variable categories. While effective VP ratio was less favorable for speech in infants under 40 weeks’ gestation, angle measures such as LVP angle of origin, NSB angle, SNA angle, and SNB angle were relatively unchanged.

Conclusions:
Linear craniofacial, VP, and LVP variables tend to be smaller in infants under 40 weeks’ gestation than those reported within the first 6 months of life while angulation is relatively similar. Future research in this area may be relevant to better diagnosis of craniofacial conditions in utero.
Spectrum-effect relationship between components and antitumor activity of Lonicerae Japonicae Flos based on orthogonal partial least squares regression
Authors: Liu Tianliang , Yang Linlin , Dong Chengming , Qi Daming , Ji Baoyu , Gao Qiguo 
Published: June 2024

Abstract
Lonicerae Japonicae Flos is a significant food and traditional Chinese medicine, known as plant antibiotics. It has rich chemical constituents and significant pharmacological effects. The antitumor activity of Lonicerae Japonicae Flos has been clarified, but the study on its spectrum-effect relationship has not been reported. The compounds responsible for its antitumor activity are still unknown. In this study, processed products of Lonicerae Japonicae Flos at different temperatures were taken as experimental materials, and SMMC-7721, A549, and MGC80-3 cells were tested. The orthogonal partial least squares regression method was used to analyze the common compounds in different processed products and the antitumor activity. The results show that processed products have a stronger inhibitory effect on A549 cells and MGC80-3 cells than SMMC-7721 cells. Compounds such as secologanic acid, isochlorogenic acid A, serotonin, and chlorogenic acid play an important role in their antitumor effects.
Obesity-related neuropathy: the new epidemic
Authors: Elafros Melissa A. , Reynolds Evan Lee , Callaghan Brian C. 
Published: June 2024

Purpose of review
To examine the evidence evaluating the association between obesity and neuropathy as well as potential interventions.

Recent findings
Although diabetes has long been associated with neuropathy, additional metabolic syndrome components, including obesity, are increasingly linked to neuropathy development, regardless of glycemic status. Preclinical rodent models as well as clinical studies are shedding light on the mechanisms of obesity-related neuropathy as well as challenges associated with slowing progression. Dietary and surgical weight loss and exercise interventions are promising, but more data is needed.

Summary
High-fat-diet rodent models have shown that obesity-related neuropathy is a product of excess glucose and lipid accumulation leading to inflammation and cell death. Clinical studies consistently demonstrate obesity is independently associated with neuropathy; therefore, likely a causal risk factor. Dietary weight loss improves neuropathy symptoms but not examination scores. Bariatric surgery and exercise are promising interventions, but larger, more rigorous studies are needed. Further research is also needed to determine the utility of weight loss medications and ideal timing for obesity interventions to prevent neuropathy.
Orthostatic Hemifacial Spasm in Idiopathic Intracranial Hypertension
Authors: Patrick Madison M. , Postma Galen K. , Bielling Rachel A.H. , Trokthi Brenda , Carey Andrew R. , Maitland Charles G. 
Published: June 2024

Background:
Among the symptoms seen in idiopathic intracranial hypertension (IIH), hemifacial spasm (HFS) is rare. Orthostatic-induced HFS preceding lumbar puncture (LP) is previously unreported. We treated two patients with unusual IIH presentations. This case series reviews the few reports of HFS in IIH and proposes a mechanism for spasm occurrence.

Methods:
Case 1: A woman in her mid-40s with previously controlled IIH developed daily headache, pulsatile tinnitus, right-sided trigeminal paresthesia, and right-sided HFS. The latter 2 symptoms occurred exclusively when moving from a sitting to a standing position. Imaging was unremarkable; opening pressure (OP) on LP was 46 cmH2O with normal cerebrospinal fluid (CSF) components. Case 2: A woman in her late 40s presented with severe daily headache, pulsatile tinnitus, and left-sided HFS following weight gain. Imaging was unremarkable; OP on LP was 32 cmH2O with normal CSF components.

Results:
HFS episodes persisted following LP in both patients. Increasing and initiating acetazolamide, respectively, resolved all symptoms.

Conclusions:
Earlier suggested mechanisms of HFS are based on elevated intracranial pressure (ICP) shifting the facial nerve into proximity of a vascular structure. HFS appearing upon standing and continuing after LP, and thus a lower ICP, contradicts this. We propose a mechanism based on the degree of ICP change. This theory is grounded in the lack of intracranial compliance in IIH, wherein substantial pressure changes occur following small volume changes.
START Diabetes Prevention: A Multi-Level Strategy for Primary Care Clinics
Authors: Tseng Eva , Marsteller Jill A. , Clark Jeanne M. , Maruthur Nisa M. 
Published: June 2024

AbstractBackgroundPrediabetes, a high-risk state for developing diabetes, affects more than 1 in 3 adults nationally. However, <5% of people with prediabetes are receiving any treatment for prediabetes. Prior intervention studies for increasing prediabetes treatment uptake have largely focused on individual barriers with few multi-level interventions that address clinician- and system-level barriers.ObjectiveTo measure the effectiveness of a multi-level intervention on uptake of prediabetes treatment in a primary care clinic.DesignPragmatic study of the START (Screen, Test, Act, Refer and Treat) Diabetes Prevention intervention.ParticipantsThe START Diabetes Prevention intervention was implemented in a suburban primary care clinic outside of Baltimore compared to a control clinic in the same area over a 12-month period.InterventionSTART Diabetes Prevention intervention included a structured workflow, shared decision-making resources and electronic health record clinical decision support tools.Main MeasuresUptake of prediabetes treatment, defined as Diabetes Prevention Program referral, metformin prescription and/or medical nutrition referral within 30 days of any PCC visit.Key ResultsWe demonstrated greater uptake of preventive treatment among patients with prediabetes in the intervention clinic vs. control clinic receiving usual care (11.6% vs. 6.7%, p<0.001). More patients in the intervention vs. control clinic reported their PCC discussed prediabetes with them (60% vs. 48%, p=0.002) and more felt overall that they understood what their doctor was telling them about prediabetes and that their opinion was valued. The START Diabetes Prevention Strategy had greater acceptability and usefulness to PCCs at the study end compared to baseline.ConclusionsA low-touch multi-level intervention is effective in increasing prediabetes treatment uptake. The intervention was also acceptable and feasible for clinicians, and enhanced patient understanding and discussions of prediabetes with their clinicians.
Weighted 2D-kernel density estimations provide a new probabilistic measure for epigenetic age
Authors: Perez-Correa Juan-Felipe , Stiehl Thomas , Marioni Riccardo E. , Corley Janie , Cox Simon R. , Costa Ivan G. , Wagner Wolfgang 
Published: June 2024

AbstractBackgroundEpigenetic aging signatures can provide insights into the human aging process. Within the last decade many alternative epigenetic clocks have been described, which are typically based on linear regression analysis of DNA methylation at multiple CG dinucleotides (CpGs). However, this approach assumes that the epigenetic modifications follow either a continuous linear or logarithmic trajectory. In this study, we explored an alternative non-parametric approach using 2D-kernel density estimation (KDE) to determine epigenetic age.ResultsWe used Illumina BeadChip profiles of blood samples of various studies, exemplarily selected the 27 CpGs with highest linear correlation with chronological age (R2> 0.7), and computed KDEs for each of them. The probability profiles for individual KDEs were further integrated by a genetic algorithm to assign an optimal weight to each CpG. Our weighted 2D-kernel density estimation model (WKDE) facilitated age-predictions with similar correlation and precision (R2= 0.81, median absolute error = 4 years) as other commonly used clocks. Furthermore, our approach provided a variation score, which reflects the inherent variation of age-related epigenetic changes at different CpG sites within a given sample. An increase of the variation score by one unit reduced the mortality risk by 9.2% (95% CI (0.8387, 0.9872), P <0.0160) in the Lothian Birth Cohort 1921 after adjusting for chronological age and sex.ConclusionsWe describe a new method using weighted 2D-kernel density estimation (WKDE) for accurate epigenetic age-predictions and to calculate variation scores, which provide an additional variable to estimate biological age.
Designing pathways for bioproducing complex chemicals by combining tools for pathway extraction and ranking
Authors: Sveshnikova Anastasia , Oftadeh Omid , Hatzimanikatis Vassily 
Published: June 2024

AbstractThe synthesis of many important biochemicals involves complex molecules and many reactions. Therefore, the design and optimization of whole-cell biocatalysts to produce these molecules requires the use of metabolic modeling. Such modeling involves the extraction of the production pathways from biochemical databases and their integration into genome-scale metabolic models of the host organism. However, the synthesis of such complex molecules requires reactions from multiple pathways operating in balanced subnetworks that are not assembled in existing databases. Here we present SubNetX, a novel computational algorithm that extracts reactions from a given reaction database and assembles balanced reaction subnetworks to produce a target biochemical from a selected set of precursor metabolites, energy currencies, and cofactors of the host organism. These subnetworks can be directly integrated into whole-cell metabolic models, and using available methods, we can then reconstruct all alternative biosynthetic pathways and rank them according to design criteria such as yield, pathway length, and other optimization goals. We applied SubNetX to eight selected secondary metabolites and one non-natural chemical used as pharmaceuticals to demonstrate the potential of this pipeline.
InteractORF, predictions of human sORF functions from an interactome study
Authors: Slivak Mathilde , Choteau Sébastien A. , Pierre Philippe , Spinelli Lionel , Zanzoni Andreas , Brun Christine 
Published: June 2024

AbstractShort Open Reading Frames (sORFs) are ubiquitous genomic elements that have been overlooked for years, essentially due to their short length (< 100 residues) and the use of alternative start codons (other than AUG). However, some may encode functional peptides, so-called sORF-encoded peptides (sPEPs), whose functions remain mainly unknown.In this study, we propose a system approach to determine the functions of sPEPs in monocytes. We first predicted the interactions of sPEPs with canonical proteins and analyzed the interfaces of interactions as well as the set of canonical proteins interacting with sPEPs. Second, by joining these sPEP-canonical protein interactions with the human interactome, we predicted the first sPEP interactome network to date. Based on its topology, we then predicted the function of the sPEPs. Our results suggest that the majority of sPEPs are involved in key biological functions, including regulatory functions, metabolism, and signaling. Overall, the diversity in the predicted functions of the sPEPs underlines the prevalence of their role in different biological mechanisms, suggesting that they are major regulatory actors.
Diversity and assembly of the microbiome of a leguminous plant along an urbanization gradient
Authors: Murray-Stoker David , Rossi Laura , Johnson Marc T. J. 
Published: June 2024

AbstractInteractions between plants and bacterial communities are essential for host physiology and broader ecosystem functioning, but plant-microbiome interactions can be disrupted by environmental change like urbanization. Here, we evaluated how urbanization affected the diversity and assembly of soil and white clover (Trifolium repens) microbiome communities. We sampled 35 populations of white clover and associated roots and soil along an urbanization gradient. Soil alpha diversity was greater at the urban and rural limits of the gradient and lower in suburban habitats, while root alpha diversity was not influenced by urbanization. Root and soil bacterial communities had distinct compositions, with greater beta diversity for root compared to soil microbiomes. We found that urbanization directly and indirectly affected soil microbiome assembly, particularly through soil carbon. In contrast, root microbiome assembly was not linked to urbanization, which suggested that the host plant acted as an additional filter on microbiome assembly independent of urbanization. We also found that key pathogenic bacteria likeLegionellaandClostridiumvaried in abundance with urbanization, which has implications for human health. Together, our study underscores the importance of examining how urban-driven environmental change alters the ecology and function of soil and root microbiomes.
The effects of biological knowledge graph topology on embedding-based link prediction
Authors: Bradshaw Michael S. , Gaskell Alisa , Layer Ryan M. 
Published: June 2024

AbstractDue to the limited information available about rare diseases and their causal variants, knowledge graphs are often used to augment our understanding and make inferences about new gene-disease connections. Knowledge graph embedding methods have been successfully applied to various biomedical link prediction tasks but have yet to be adopted for rare disease variant prioritization. Here, we explore the effect of knowledge graph topology on Knowledge graph embedding link prediction performance and challenge the assumption that massively aggregating knowledge graphs is beneficial in deciphering rare disease cases and improving outcomes. We find that using a filtered version of the Monarch knowledge graph with only 11% of the size of the full knowledge graph results in improved model predictive performance. Additionally, we found that as information is aggregated and re-added to the knowledge graph, performance improvements are driven by the quality of information, not the quantity.
Sequence Complexity and Monomer Rigidity Control the Morphologies and Aging Dynamics of Protein Aggregates
Authors: Takaki Ryota , Thirumalai D. 
Published: June 2024

Understanding the biophysical basis of protein aggregation is important in biology because of the potential link to several misfolding diseases. Although experiments have shown that protein aggregates adopt a variety of morphologies, the dynamics of their formation are less well characterized. Here, we introduce a minimal model to explore the dependence of the aggregation dynamics on the structural and sequence features of the monomers. Using simulations we demonstrate that sequence complexity (codified in terms of word entropy) and monomer rigidity profoundly influence the dynamics and morphology of the aggregates. Flexible monomers with low sequence complexity (corresponding to repeat sequences) form liquid-like droplets that exhibit ergodic behavior. Strikingly, these aggregates abruptly transition to more ordered structures, reminiscent of amyloid fibrils, when the monomer rigidity is increased. In contrast, aggregates resulting from monomers with high sequence complexity are amorphous and display non-ergodic glassy dynamics. The heterogeneous dynamics of the low and high-complexity sequences follow stretched exponential kinetics, which is one of the characteristics of glassy dynamics. Importantly, at non-zero values of the bending rigidities, the aggregates age with the relaxation times that increase with the waiting time. Informed by these findings, we provide insights into aging dynamics in protein condensates and contrast the behavior with the dynamics expected in RNA repeat sequences. Our findings underscore the influence of the monomer characteristics in shaping the morphology and dynamics of protein aggregates, thus providing a foundation for deciphering the general rules governing the behavior of protein condensates.Significance StatementProtein aggregates exhibit diverse morphology, exemplified by amyloid fibrils, gel-like structures, and liquid-like condensates. Differences in the morphologies in identical proteins play important functional roles in several diseases. Simulations using a minimal model show that such structures are encoded in the sequence complexity and bending rigidity of the monomers. The low-complexity flexible sequences form liquid droplets, whose relaxation dynamics are ergodic. In contrast, rigid low and high-complexity sequences, which form ordered nematic fibril-like structures and amorphous aggregates, exhibit heterogenous, non-ergodic dynamics. The relaxation times under these conditions increase as the waiting time increases, which is a signature of aging. The implications of our findings for aging in intrinsically dis-ordered proteins and repeat RNA sequences are outlined.
Species and Genetic Diversity of Nontuberculous Mycobacteria (NTM) in Suspected Tuberculosis Cases in East Azerbaijan, Iran: A Cross-sectional Analysis
Authors: Roshdi Maleki M 
Published: June 2024

AbstractIntroductionThe outbreak of nontuberculous mycobacterial (NTM) infections has increased worldwide, attracting attention in routine diagnostic settings, particularly among patients with suspected tuberculosis. This study aimed to acquire knowledge of NTM infections in patients with suspected tuberculosis and to evaluate the genetic diversity of the strains.MethodsIn this study, 230 clinical specimens were collected from suspected tuberculosis patients. Following decontamination with N-Acetyl-L-cysteine–sodium hydroxide (NALC-NaOH), the sediments of specimens were inoculated onto Löwenstein–Jensen medium and then incubated at 30°C for 8 weeks. The samples that yielded positive cultures underwent evaluation through sequencing conserved fragments ofIS6110andhsp65. For those samples that were not identified as part of theM. tuberculosiscomplex (MTC) byIS6110PCR, further analysis was conducted using PCR to detect fragments of thehsp65gene.ResultsTwenty-one NTM species were isolated from 230 clinical specimens (14 NTM from pulmonary specimens and 7 from extrapulmonary specimens). Among these, 12 (57.14%) were rapid-growing mycobacteria (RGM) and 9 (42.85%) were slow-growing mycobacteria (SGM). NoM. aviumcomplex (MAC) was identified in any of the specimens. Notably,M. kansasii, M. gordonae, andM. abscessusstrains exhibited significant genetic diversity.ConclusionsThe prevalence of infections attributed to nontuberculous species surpasses that of tuberculosis. These findings underscore the importance of exploring NTM species in individuals suspected of having TB.
The FBA Solution Space Kernel – Introduction and Illustrative Examples
Authors: Verwoerd Wynand S. , Mao Longfei 
Published: June 2024

AbstractBackgroundThe solution space of an FBA-based model of cellular metabolism, can be characterised by extraction of a bounded, low dimensional kernel (the SSK) that facilitates perceiving it as a geometric object in multidimensional flux space. The aim is to produce an amenable description, intermediate between the single feasible extreme flux of FBA, and the intractable proliferation of extreme modes in conventional solution space descriptions. Fluxes that remain fixed are separated off while the focus of interest is put on the subset of variable fluxes that have a nonzero but finite range of values. For unbounded fluxes, a finite subrange that geometrically corresponds to the variable flux range is determined and is supplemented by a limited set of rays that encapsulates their unbounded aspects. In this way the kernel emphasises the realistic range of flux variation allowed in the interconnected biochemical network by e.g. limited nutrient uptake, an optimised objective and other model constraints. This work builds on the full presentation of the SSK approach in a research monograph.ResultsIt is demonstrated how knowledge of the SSK and accompanying rays can be exploited to explore representative flux states of the metabolic network. Noting that bioengineering interventions such as gene knockouts modify the solution space, new tools based on the SSK analysis are presented here that predict the effects of such interventions on a target flux constructed to represent a desired metabolic output. A simple metabolic model is used first to demonstrate the special concepts and constructions needed to define and compute the SSK. The demonstration model is tweaked to produce typical behaviours of larger models, but with kernels in 1,2 or 3 dimensions that are explicitly displayed to visualise the concepts. General applicability to models where visualisation is inaccessible, is illustrated by showing evaluation of potential bioengineering strategies for a genome scale model.ConclusionsSSKernel is a flexible interactive tool that facilitates an overview of the FBA solution space as a multidimensional geometric object, in terms of a manageable number of parameters. It allows exploration of effects on this solution space from metabolic interventions, and can be used to investigate bioengineering strategies to manipulate cellular metabolism.