bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implantable 3D printed multiplexed microtunnels for spinal cord injury treatment
Authors: Grebenyuk Sergei , Medvediev Volodymyr , Rybachuk Oksana , Sheremet Yevhenii , Abdallah Ibrahim , Ustymenko Valeriia , Pivneva Tetyana , Ranga Adrian , Belan Pavel , Voitenko Nana 
Published: December 2024

Abstract3D printed scaffolds offer a promising strategy for treating spinal cord injury (SCI). Here we present an innovative biotechnological approach for free-form 3D printing of scaffolds with a biomimetic architecture at a spatial resolution of up to a micrometer, designed for implantation in treatment of SCI in Wistar rats. The fabrication of scaffolds was based on 2-photon photopolymerization of organic polymers and was scalable to lesion geometries. The scaffolds were implemented as multiple densely packed squared parallel microtunnels (50 μm per side) running their entire length. These microtunnels are separated by thin walls (5–10 μm), rendering the scaffolds nearly hollow while maximizing their internal surface area. This design provides an optimal substrate, spatially aligned in the rostro-caudal direction, to support axonal and vascular ingrowth. We have found that the scaffolds, implanted in the excision of the lateral half-fragment of the spinal cord at the low thoracic level demonstrated excellent integration with surrounding tissue without the formation of a significant gliofibrous scar. Myelinated axons and oligodendrocytes, as well as vessels were observed in each microtunnel of the implanted scaffolds in 12 weeks after the operation with at least 1000 axons regenerating in the scaffold throughout its whole length. The treatment significantly improved motor function and reduced spasticity in the ipsilateral paretic limb by 8th week, with recovery sustained for at least 20 weeks. Thus, 3D oriented hollow scaffolds having a large internal surface area and direct continues microtunnels, effectively reducing axonal dispersion, mimic natural structure of the recipient tissue and create conditions for enhancing spinal cord regeneration and recovery of the motor function of the paretic limb.
Predicting antimicrobial class specificity of small molecules using machine learning
Authors: Gadiya Yojana , Genilloud Olga , Bilitewski Ursula , Brönstrup Mark , von Berlin Leonie , Attwood Marie , Gribbon Philip , Zaliani Andrea 
Published: December 2024

AbstractWhilst the useful armory of antibiotic drugs is continually depleted due to the emergence of drug-resistant pathogens, the development of novel therapeutics has also slowed down. In the era of advanced computational methods, approaches like machine learning (ML) could be one potential solution to help reduce the high costs and complexity of antibiotic drug discovery and attract collaboration across organizations. In our work, we developed a large antimicrobial knowledge graph (AntiMicrobial-KG) as a repository for collecting and visualizing publicin-vitroantibacterial assay. Utilizing this data, we build ML models to efficiently scan compound libraries to identify compounds with the potential to exhibit antimicrobial activity. Our strategy involved training seven classic ML models across six compound fingerprint representations, of which the Random Forest trained on the MHFP6 fingerprint outperformed, demonstrating an accuracy of 75.9% and Cohen’s Kappa score of 0.68. Finally, we illustrated the model’s applicability for predicting the antimicrobial properties of two small molecule screening libraries. Firstly, the EU-OpenScreen library was tested against a panel of Gram-positive, Gram-negative, and Fungal pathogens. Here, we unveiled that the model was able to correctly predict more than 30% of active compounds for Gram-positive, Gram-negative, and Fungal pathogens. Secondly, with the Enamine library, a commercially available HTS compound collection with claimed antibacterial properties, we predicted its antimicrobial activity and pathogen class specificity. These results may provide a means for accelerating research in AMR drug discovery efforts by carefully filtering out compounds from commercial libraries with lower chances of being active.
Systematic analysis of immune cell motility leveraging Immunemap, an open intravital microscopy atlas
Authors: Pizzagalli Diego Ulisse , Carrillo-Barbera Pau , Palladino Elisa , Ceni Kevin , Thelen Benedikt , Pulfer Alain , Moscatello Enrico , Cabini Raffaella Fiamma , Textor Johannes , Wortel Inge M. N. ,   The Immunemap project consortium, Krause Rolf , Gonzalez Santiago Fernandez 
Published: December 2024

AbstractStudying the spatiotemporal dynamics of cells in living organisms is a current frontier in bioimaging. Intravital Microscopy (IVM) provides direct, long-term observation of cell behavior in living animals, from tissue to sub-cellular resolution. Hence, IVM has become crucial for studying complex biological processes in motion and across scales, such as the immune response to pathogens and cancer. However, IVM data are typically kept in private repositories inaccessible to the scientific community, hampering large-scale analysis that aggregates data from multiple laboratories.To solve this issue, we introduce Immunemap, an atlas of immune cell motility based on an Open Data platform that provides access to over 58’000 single-cell tracks and 1’049’000 cell-centroid annotations from 360 videos in murine models.Leveraging Immunemap and unsupervised learning, we systematically analyzed cell trajectories, identifying four main patterns of cell migration in immune cells. Two patterns correspond to behaviors previously characterized: directed movement and arresting. However, we identified two other patterns, characterized by low directionality and twisted paths, often considered random migration. We show that the newly defined patterns can be subdivided into two distinct types: within small areas, suggesting a focused patrolling around one or a few cells, and over larger areas, indicative of a more extended tissue patrolling. Furthermore, we show that the percentage of cells displaying these motility patterns changes in response to immune stimuli.Altogether, Immunemap embraces the FAIR principles, promoting data reuse to extract novel insights from immune cell dynamics through an image-based systems biology approach.
Cas9-based enrichment for targeted long-read metabarcoding
Authors: Nikolaeva-Reynolds Lucia , Cammies Christopher , Crichton Rosemary , Gorochowski Thomas E. 
Published: December 2024

ABSTRACTMetabarcoding is a valuable tool for characterising the communities that underpin the functioning of ecosystems. However, current methods often rely on PCR amplification for enrichment of marker genes. PCR can introduce significant biases that affect quantification and is typically restricted to one target loci at a time, limiting the diversity that can be captured in a single reaction. Here, we address these issues by using Cas9 to enrich marker genes for long-read nanopore sequencing directly from a DNA sample, removing the need for PCR. We show that this approach can effectively isolate a 4.5 kb region covering partial 18S and 28S rRNA genes and the ITS region in a mixed nematode community, and further adapt our approach for characterising a diverse microbial community. We demonstrate the ability for Cas9-based enrichment to support multiplexed targeting of several different DNA regions simultaneously, enabling optimal marker gene selection for different clades of interest within a sample. We also find a strong correlation between input DNA concentrations and output read proportions for mixed-species samples, demonstrating the ability for quantification of relative species abundance. This study lays a foundation for targeted long-read sequencing to more fully capture the diversity of organisms present in complex environments.
Assessing the diagnostic impact of blood transcriptome profiling in a pediatric cohort previously assessed by genome sequencing
Authors: Hou Huayun , Yuki Kyoko E. , Costain Gregory , Szuto Anna , Barnes Sierra , Ramani Arun K. , Celik Alper , Braga Michael , Gloven-Brown Meagan , Stavropoulos Dimitri J. , Bowdin Sarah , Cohn Ronald D , Mendoza-Londono Roberto , Scherer Stephen W. , Brudno Michael , Marshall Christian R. , Meyn M. Stephen , Shlien Adam , Dowling James J. , Wilson Michael D. , Kyriakopoulou Lianna 
Published: December 2024

ABSTRACTDespite advances in diagnostic testing and genome sequencing, the majority of individuals with rare genetic disorders remain undiagnosed. As a complement to genome sequencing, transcriptional profiling can provide insight into the functional consequences of DNA variants on RNA transcript expression and structure. Here we assessed the utility of blood derived RNA-seq in a well-studied, but still mostly undiagnosed, cohort of individuals who enrolled in the SickKids Genome Clinic study. This cohort was established to benchmark the ability of genome sequencing technologies to diagnose genetic diseases and has been subjected to multiple analyses. We used RNA-seq to profile whole blood RNA expression from all probands for whom a blood sample was available (n=134). Our RNA-centric analysis included differential gene expression, alternative splicing, and allele specific expression. In one third of the diagnosed individuals (20/61), RNA-seq provided additional evidence supporting the pathogenicity of the variant found by prior DNA-based analyses. In 2/61 cases, RNA-seq changed the GS-derived genetic diagnosis (EPG5toLZTR1in an individual with a Noonan syndrome-like disorder) and discovered an additional relevant gene (CEP120in addition toSONin an individual with ZTTK syndrome). In ∼7% (5/73) of the undiagnosed participants, RNA-seq provided at least one plausible, potentially diagnostic candidate gene. This study illustrates the benefits and limitations of using whole-blood RNA profiling to support existing molecular diagnoses and reveal candidate molecular mechanisms underlying undiagnosed genetic disease.
Trajectories of Cognitive Function in First-Episode Psychosis: Associations to Clinical Outcomes and Biomarkers
Authors: Lee Maria , Cullen Alexis E. , Matheson Granville J. , Lu Zheng-An , Bergen Sarah E. , Sellgren Carl M. , Erhardt Sophie , Fatouros-Bergman Helena , Cervenka Simon 
Published: December 2024

AbstractAimsCognitive dysfunction in psychotic disorders is common. At disorder onset, this impairment varies greatly between individuals, which may reflect different levels of decline compared to pre-morbid levels. Diverse trajectories in cognitive change prior to psychosis onset have been hypothesized to represent different underlying pathological processes. Our primary aim was to model cognitive change over time in a sample of individuals with first-episode psychosis (FEP) and controls. The secondary aim was to explore whether cognitive change was associated with clinical outcomes, and biological markers that have shown associations with disease progression.MethodsOur sample consisted of 73 individuals with FEP who had undergone cognitive assessment at psychosis onset and 53 controls. Using school grades from registry data as a proxy for pre-morbid cognitive ability, we modelled change in cognition using linear mixed-effects models. The resulting change scores were correlated to polygenic risk scores, cerebrospinal fluid levels of complement protein C4A and clinical outcomes.Results and ConclusionsGroups did not differ in school performance prior to psychosis. Psychosis onset was associated with a large cognitive decline in FEP and thereafter they performed significantly worse than controls. Among FEP individuals, there was a large degree of variability in cognitive change leading up to psychosis onset. Degree of cognitive change was not associated to the selected biological variables but did predict worse clinical outcomes. The results indicate that individual cognitive trajectories may be a clinically relevant topic for further study, but given the exploratory nature of the analysis, replication in an independent sample is required.
A Multicenter Retrospective Observational Cohort Study of Seizure Freedom before Lennox-Gastaut Syndrome (the “Gap”). Opportunities for Prevention
Authors: Deering Laura , Nelson Aaron , Yozawitz Elissa , Wolf Steven , McGoldrick Patricia , Wu Alan , Basma Natasha , Grinspan Zachary 
Published: December 2024

ABSTRACTObjectiveLennox-Gastaut Syndrome (LGS) is a severe, often treatment-resistant epilepsy syndrome typically diagnosed in early childhood. Many have seizures before diagnosis. Some have periods of seizure freedom before treatment resistance, i.e., a “gap.” Review of these gaps may identify early candidate biomarkers of LGS and/or highlight opportunities for intervention.MethodsWe reviewed charts of children diagnosed with LGS born in 2008-2010 and diagnosed with LGS by 2014 at five academic medical centers in New York City using the RENYC (Rare Epilepsies in New York City) database. We collected dates of events of potential biomarkers by chart abstraction, including onset of slow spike-and-wave (SSW) and onset and offset of seizure freedom. Seizure-free periods (“gaps”) were defined as greater than 30 days without unprovoked seizures.ResultsThirty-three children had LGS (52% male; etiology 33% structural-acquired, 6% structural-congenital, 3% genetic-structural, 24% genetic, 33% unknown). Twenty-two (67%) had a gap before diagnosis. Eight of these twenty-two (36%) had SSW described before the gap, five (23%) during the gap, and six (27%) after the gap. A history of infantile epileptic spasms syndrome (IESS), age at seizure onset, and age of tonic seizure onset were not different between those with and without a gap. Of 20 (61%) with a history of IESS, 10 (30% of the full cohort) had not received recommended therapy (i.e., ACTH, prednisolone, or vigabatrin) as first-line treatment.ConclusionsThe appearance of SSW, even in seizure-free children, may herald the development of LGS in high-risk children. Further studies on its predictive value are warranted. Our findings also highlight use of recommended first-line therapy for infantile spasms as a potentially modifiable treatment gap in children who subsequently develop LGS.
Not the Models You Are Looking For: Traditional ML Outperforms LLMs in Clinical Prediction Tasks
Authors: Brown Katherine E. , Yan Chao , Li Zhuohang , Zhang Xinmeng , Collins Benjamin X. , Chen You , Clayton Ellen Wright , Kantarcioglu Murat , Vorobeychik Yevgeniy , Malin Bradley A. 
Published: December 2024

ABSTRACTObjectivesTo determine the extent to which current Large Language Models (LLMs) can serve as substitutes for traditional machine learning (ML) as clinical predictors using data from electronic health records (EHRs), we investigated various factors that can impact their adoption, including overall performance, calibration, fairness, and resilience to privacy protections that reduce data fidelity.Materials and MethodsWe evaluated GPT-3.5, GPT-4, and ML (as gradient-boosting trees) on clinical prediction tasks in EHR data from Vanderbilt University Medical Center and MIMIC IV. We measured predictive performance with AUROC and model calibration using Brier Score. To evaluate the impact of data privacy protections, we assessed AUROC when demographic variables are generalized. We evaluated algorithmic fairness using equalized odds and statistical parity across race, sex, and age of patients. We also considered the impact of using in-context learning by incorporating labeled examples within the prompt.ResultsTraditional ML (AUROC: 0.847, 0.894 (VUMC, MIMIC)) substantially outperformed GPT-3.5 (AUROC: 0.537, 0.517) and GPT-4 (AUROC: 0.629, 0.602) (with and without in-context learning) in predictive performance and output probability calibration (Brier Score (ML vs GPT-3.5 vs GPT-4): 0.134 versus 0.384 versus 0.251, 0.042 versus 0.06 versus 0.219). Traditional ML is more robust than GPT-3.5 and GPT-4 to generalizing demographic information to protect privacy. GPT-4 is the fairest model according to our selected metrics but at the cost of poor model performance.ConclusionThese findings suggest that LLMs are much less effective and robust than locally-trained ML for clinical prediction tasks, but they are getting better over time.
Co-prescription of Metformin and Antipsychotics in Severe Mental Illness: A UK Primary Care Cohort Study
Authors: Farache Trajano Luiza , Hayes Joseph F. , Launders Naomi , Davies Neil M. , Osborn David P. J. , Richards-Belle Alvin 
Published: December 2024

ABSTRACTBackgroundMetformin is a pharmacological candidate to mitigate second-generation antipsychotic (SGA)-induced weight gain in patients with severe mental illnesses (SMI).ObjectiveTo evaluate the incidence, prevalence, and demographic patterns of metformin co-prescription among patients diagnosed with SMI initiating SGAs. To estimate the impact of co-prescription on weight.MethodsA cohort study of patients diagnosed with SMI initiating aripiprazole, olanzapine, quetiapine, or risperidone in 2005-2019 using primary care data from Clinical Practice Research Datalink. We estimated cumulative incidence and period prevalences of co-prescription and explored prescribing differences by demographic and clinical factors. We compared weight change among patients prescribed an SGA only versus an SGA plus metformin, accounting for confounders using linear regression.FindingsAmong 26,537 patients initiating SGAs, 4652 were ever prescribed metformin and 21,885 were not. Two-year incidence of first metformin prescription was 3.3%. The SGA plus metformin group were more ethnically diverse, had greater social deprivation, more comorbidities, and higher baseline weight (mean 90.4 vs. 76.8 kg). By two years post-SGA initiation, mean weight in the SGA- only group had changed by +4.16% (95% CI, -1.26 to +9.58) compared to -0.65% (95% CI, -4.26 to +2.96) in the SGA plus metformin group. After confounder adjustment, the two-year mean difference in weight with metformin co-prescription was -1.48 kg (95% CI, -4.03 to 1.07) among females and -1.84 kg (95% CI, -4.67 to 0.98) among males.ConclusionMetformin is infrequently co-prescribed, despite established efficacy and guidelines.Clinical implicationsPrimary and secondary care collaboration should be strengthened and barriers to co-prescribing addressed.
Expanding the HP1a-binding consensus and molecular grammar for heterochromatin assembly
Authors: Colmenares Serafin U. , Tsukamoto Shingo , Hickmann Collin , Brennan Lucy D. , Khavani Mohammad , Mofrad Mohammad R. K. , Karpen Gary H. 
Published: December 2024

ABSTRACTThe recruitment of Heterochromatin Protein 1 (HP1) partners is essential for heterochromatin assembly and function, yet our knowledge regarding their organization in heterochromatin remains limited. Here we show that interactors engage the Drosophila HP1 (HP1a) dimer through a degenerate and expanded form of the previously identified PxVxL motif, which we now term HP1a Access Codes (HACs). These HACs reside in disordered regions, possess high conservation among Drosophila homologs, and contain alternating hydrophobic residues nested in a cluster of positively charged amino acids. These findings and molecular dynamics simulations identify key electrostatic interactions that modulate HP1a-binding strength and provide a dramatically improved HP1a-binding consensus motif that can reveal protein partners and the molecular grammar involved in heterochromatin assembly. We propose HP1a acts as a scaffold for other heterochromatin components containing HAC motifs, which in turn may regulate the function and higher order structure of the heterochromatin compartment.
Harnessing the FBXW7 somatic mutant R465C for targeted protein degradation
Authors: Basu Ananya A. , Zhang Chenlu , Rouhimoghadam Milad , Vasudevan Anil , Reitsma Justin M. , Zhang Xiaoyu 
Published: December 2024

AbstractTargeted protein degradation (TPD) is a pharmacological strategy that eliminates specific proteins from cells by harnessing cellular proteolytic degradation machinery. In proteasome-dependent TPD, expanding the repertoire of E3 ligases compatible with this approach could enhance the applicability of this strategy across various biological contexts. In this study, we discovered that a somatic mutant of FBXW7, R465C, can be exploited by heterobifunctional compounds for targeted protein degradation. This work demonstrates the potential of utilizing mutant E3 ligases that occur exclusively in diseased cells for TPD applications.
Structure of Bifunctional Variediene Synthase Yields Unique Insight on Biosynthetic Diterpene Assembly and Cyclization
Authors: Wenger Eliott S. , Christianson David W. 
Published: December 2024

AbstractAn unusual family of bifunctional terpene synthases has been discovered in which both catalytic domains – a prenyltransferase and a cyclase – are connected by a long, flexible linker. These enzymes are unique to fungi and catalyze the first committed steps in the biosynthesis of complex terpenoid natural products: the prenyltransferase assembles 5-carbon precursors to form C20geranylgeranyl diphosphate (GGPP), and the cyclase converts GGPP into a polycyclic hydrocarbon product. Weak domain-domain interactions as well as linker flexibility render these enzymes refractory to crystallization and challenge their visualization by cryo-EM. Despite these challenges, we now present the first experimentally-determined structure of a massive, 495-kD bifunctional terpene synthase revealing the assembly of all catalytic domains. The cryo-EM structure of variediene synthase fromEmericella variecolor(EvVS) exhibits a bollard-like architecture, consisting of a hexameric prenyltransferase core sandwiched between two triads of cyclase domains. Although prenyltransferase and cyclase active sites are relatively close together, enzymological measurements indicate that GGPP is not channeled from one to the other. Surprisingly, however, the individual cyclase domain from another bifunctional diterpene synthase, fusicoccadiene synthase fromPhomopsis amygdali, preferentially receives GGPP from the EvVS prenyltransferase in substrate competition experiments. Our previous studies of fusicoccadiene synthase suggest that GGPP channeling occurs through transient binding of cyclase domains to the sides of the prenyltransferase oligomer. The bollard-like architecture of EvVS leaves the sides of the prenyltransferase oligomer open and accessible, suggesting that a non-native cyclase could bind to the sides of the prenyltransferase oligomer to achieve GGPP channeling.