bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal dose of tranexamic acid in traumatic brain injury: Systematic review and network meta-analysis of randomized controlled trials
Authors: Utsumi Shu , Kawakami Akiko , Amemiya Yu 
Published: December 2024

BACKGROUND
Tranexamic acid (TXA) has been used to treat traumatic brain injury (TBI); however, no definitive conclusions have been drawn regarding its effectiveness or dosage. This study evaluated the optimal TXA dose for treating TBI using a network meta-analysis (NMA).

METHODS
Five databases were searched for peer-reviewed randomized controlled trials (RCTs) published from inception to May 2024. The inclusion criteria were as follows: (1) RCTs, (2) patients older than 1 month with TBI, (3) interventions of TXA and control, (4) primary outcomes of mortality and poor neurological outcomes and secondary outcomes of vascular occlusive events, and (5) full-text peer-reviewed articles. Two reviewers independently screened and extracted the data and assessed the risk of bias. Frequency-based NMA was performed using the Grading of Recommendations, Assessment, Development, and Evaluation working-group approach.

RESULTS
We included 10 RCTs comprising 11,237 patients with TBI. Placebo showed higher mortality compared with that of a 2-g bolus of TXA (risk ratio, 1.53; 95% confidence interval, 1.08–2.17). Higher mortality was observed with a 1-g bolus of TXA followed by 1-g maintenance TXA compared with that of a 2-g bolus of TXA (risk ratio, 1.44; 95% confidence interval, 1.02–2.03). No significant differences in poor neurological outcomes or vascular occlusive events were observed between the treatment groups.

CONCLUSION
Placebo and a 1-g bolus followed by 1-g maintenance TXA were associated with higher mortality rates than those of a 2-g bolus of TXA. No difference in vascular occlusive events was observed with either treatment, indicating that our NMA recommends 2 g of TXA. However, the data for the 2-g bolus of TXA were from a single study, and further research is needed to draw definitive conclusions.

LEVEL OF EVIDENCE
Systematic Review/Meta-Analysis; Level III.
The Effect of Cranial Sutures Should Be Considered in Transcranial Electrical Stimulation
Authors: Carroll Alistair , Rae Caroline D. , Martin Donel , Dokos Socrates , Loo Colleen 
Published: December 2024

Background
Computational modeling is used to optimize transcranial electrical stimulation (tES) approaches, and the precision of these models is dependent on their anatomical accuracy. We are unaware of any computational modeling of tES that has included cranial sutures.

Objectives
The aims of the study were to review the literature on the timing of closure of the coronal and squamous sutures, which are situated under electrode placements used in tES; to review the literature regarding differences in skull and suture conductivity and to determine a more accurate conductivity for sutures; and to identify magnetic resonance image (MRI) techniques that could be used to detect cranial sutures.

Methods
A scoping review of medical literature was conducted. We conducted computational modeling of a cranial bone plug using COMSOL Multiphysics finite element software, utilizing methodology and results from a previous study. We assessed use of the “3D Slicer” software to identify sutures in routine T1-weighted MRI scans.

Results
Reports from forensic examinations and computed tomography (CT) scans showed suture closure does not correlate with age. Our computational modeling determined a cranial suture conductivity of 0.32 S/m, which is much higher than for skull (compact skull 0.004 S/m, standard trilayer 0.013 S/m). 3D slicer enabled rapid and precise identification of the anatomy and location of cranial sutures.

Conclusions
Cranial sutures persist throughout the lifespan and have a far higher conductivity than skull bone. Cranial sutures can be localized quickly and precisely using a combination of MRI and readily available modeling software. Sutures should be included in tES computational modeling and electroencephalography source imaging to improve the accuracy of results.
Lymphatic drainage of cerebrospinal fluid using lymph node seeker64Cu-labeled Gram-negative bacterial extracellular vesicles PET
Authors: Kim Kyuwan , Lee Changjin , Yoo Ran Ji , Choi Yoori , Lee Chanwoo , Lee Jaewook , Suh Minseok , Lee Yun-Sang , Gho Yong Song , Lee Dong Soo 
Published: December 2024

AbstractCerebrospinal fluid (CSF) is drained into the systemic lymphatics via paravertebral lymph nodes. Superficial and deep cervical lymph nodes collect CSF in mice, but the exact and quantified routes are unknown. Recently, we simultaneously visualized cervical, sacral and iliac lymph nodes via serial imaging on the intrathecal [64Cu]Cu-albumin positron emission tomography. Paravertebral lymph nodes might act as sentinels to monitor the CSF, brain, and spinal cord. We used64Cu-labeledEscherichia coliextracellular vesicles, outer membrane vesicles (OMVs), as lymph node seekers for intrathecal administration with an optimized volume/rate and quantified the differential amounts of various paravertebral lymph nodes along the axis of the brain and spinal cord in mice. The quantified results revealed 77.3% in superficial and deep cervical lymph nodes, 11.4% in abdominal/pelvic lymph nodes and 11.3% in sacral lymph nodes. Click-labeled [64Cu]Cu-OMVs were drained to reach and stop at the lymph nodes on serial quantification. The cervical lymph nodes drained most of the OMV-laden CSF, which is proportional to the surface areas of the brain (70%) and spinal cord in mice. We propose that all paravertebral lymph nodes monitor the segmental regions of the brain and spinal cord as immediate sentinel lymph nodes against the central nervous system.
Social and Mental Health Pathways to Institutional Trust: A Cohort Study
Authors: Paquin Vincent , Miconi Diana , Aversa Samantha , Johnson-Lafleur Janique , Côté Sylvana , Geoffroy Marie-Claude , Gülöksüz Sinan 
Published: December 2024

ABSTRACTObjectiveTrust in institutions such as the government is lower in the context of mental health problems and socio-economic disadvantage. However, the roles of structural inequality, interpersonal factors, and mental health on institutional trust remain unclear. This study aimed to examine the associations of social and mental health factors, from early life to adulthood, with institutional trust.MethodParticipants (n=1347; 57.2% female) were from the population-based Québec Longitudinal Study of Child Development (1997-2021). Trust in 13 institutions was self-reported at age 23. Predictors were 20 social and mental health factors during early life, adolescence, and adulthood. Associations were examined with linear regressions corrected for false discovery rate. Pathways were explored using the temporal Peter-Clark algorithm.ResultsEarly-life factors associated with lower levels of trust were male sex, racialized minority status, low household income, and maternal history of depression and antisocial behaviors. After adjusting for early-life factors, adolescence factors associated with lower levels of trust were internalizing and externalizing problems, bullying exposure, and school difficulties. Independently of early-life or adolescence factors, adulthood factors associated with lower levels of trust were perceived stress, psychotic experiences, suicidal ideas, and seeking professional help, whereas greater social capital was associated with greater trust. Temporal Peter-Clark analyses identified social capital and psychotic experiences as potential proximal determinants of institutional trust.ConclusionThis study identified factors associated with institutional trust reflecting an interplay between structural inequality, interpersonal relationships, and mental health over development. Interventions aimed at promoting social inclusion may improve institutional trust and population wellbeing.
Cognitive Dynamics of Verb-Particle Constructions: An Eye-Tracking Study
Authors: Kissane Hassane , Tziridis Konstantin , Schilling Achim , Krauss Patrick , Herbst Thomas 
Published: December 2024

AbstractThis study investigates the cognitive processing of verb-particle constructions (VPCs) using eye-tracking data analysis method to explore how English native speakers process different types of the sequence NP–verb–particle–NP during reading tasks. While previous research has focused on phrasal verbs, our study extends this examination to include patterns with prepositions, aiming to identify distinct cognitive engagement patterns and processing efficiencies associated with each. We employed the Provo Corpus to analyse eye movements while participants read sentences containing these constructions. we focused on metrics such as first fixation duration, gaze duration, go-past times, and total reading times. Our findings indicate similarities in the lexical verbs, and significant differences in particles, indicating how these two types of constructions are processed, with phrasal verbs sometimes processed more efficiently than the prepositional counterparts. This suggests that phrasal verbs might be more deeply entrenched in the linguistic repertoire of native speakers, possibly functioning as single lexical units. This research contributes to the understanding of complex structures processing and the cognitive mechanisms that support it, offering insights that could influence linguistic theory and language education.
NCPepFold: Accurate Prediction of Non-canonical Cyclic Peptide Structures via Cyclization Optimization with Multigranular Representation
Authors: Mao Qingyi , Shang Tianfeng , Xu Wen , Zhai Silong , Zhang Chengyun , Su An , Li Chengxi , Duan Hongliang 
Published: December 2024

AbstractArtificial intelligence-based peptide structure prediction methods have revolutionized biomolecular science. However, restricting predictions to peptides composed solely of 20 natural amino acids significantly limits their practical application, as such peptides often demonstrate poor stability under physiological conditions. Here, we present NCPepFold, a computational approach that can utilize a specific cyclic position matrix to directly predict the structure of cyclic peptides with non-canonical amino acids. By integrating multi-granularity information at the residue- and atomic-level, along with fine-tuning techniques, NCPepFold significantly improves prediction accuracy, with the average peptide RMSD for cyclic peptides being 1.640 Å. In summary, this is a novel deep learning model designed specifically for cyclic peptides with non-canonical amino acids without length restrictions, offering great potential for peptide drug design and advancing biomedical research.
BioMedGraphica: An All-in-One Platform for Biomedical Prior Knowledge and Omic Signaling Graph Generation
Authors: Zhang Heming , Liang Shunning , Xu Tim , Li Wenyu , Huang Di , Dong Yuhan , Li Guangfu , Miller J. Philip , Goedegebuure S. Peter , Sardiello Marco , Cooper Jonathan , Buchser William , Dickson Patricia , Fields Ryan C. , Cruchaga Carlos , Chen Yixin , Province Michael , Payne Philip , Li Fuhai 
Published: December 2024

AbstractArtificial intelligence (AI) is revolutionizing scientific discovery because of its super capability, following the neural scaling laws, to integrate and analyze large-scale datasets to mine knowledge. Foundation models, large language models (LLMs) and large vision models (LVMs), are among the most important foundations paving the way for general AI by pre-training on massive domain-specific datasets. Different from the well annotated, formatted and integrated large textual and image datasets for LLMs and LVMs, biomedical knowledge and datasets are fragmented with data scattered across publications and inconsistent databases that often use diverse nomenclature systems in the field of AI for Precision Health and Medicine (AI4PHM). These discrepancies, spanning different levels of biomedical organization from genes to clinical traits, present major challenges for data integration and alignment. To facilitate foundation AI model development and applications in AI4PHM, herein, we developedBioMedGraphica, an all-in-one platform and unified text-attributed knowledge graph (TAKG), consists of 3,131,788 entities and 56,817,063 relations, which are obtained from 11 distinct entity types and harmonizes 29 relations/edge types using data from 43 biomedical databases. All entities and relations are labeled a unique ID and associated with textual descriptions (textual features). Since covers most of research entities in AI4PHM, BioMedGraphica supports the zero-shot or few-shot knowledge discoveries via new relation prediction on the graph. Via a graphical user interface (GUI), researchers can access the knowledge graph with prior knowledge of target functional annotations, drugs, phenotypes and diseases (drug-protein-disease-phenotype), in the graph AI ready format. It also supports the generation of knowledge-multi-omic signaling graphs to facilitate the development and applications of novel AI models, like LLMs, graph AI, for AI4PHM science discovery, like discovering novel disease pathogenesis, signaling pathways, therapeutic targets, drugs and synergistic cocktails.
Egocentric Perception of Walking Environments using an Interactive Vision-Language System
Authors: Tan Haining , Mihailidis Alex , Laschowski Brokoslaw 
Published: December 2024

AbstractLarge language models can provide a more detailed contextual understanding of a scene beyond what computer vision alone can provide, which have implications for robotics and embodied intelligence. In this study, we developed a novel multimodal vision-language system for egocentric visual perception, with an initial focus on real-world walking environments. We trained a number of state-of-the-art transformer-based vision-language models that use causal language modelling on our custom dataset of 43,055 image-text pairs for few-shot image captioning. We then designed a new speech synthesis model and a user interface to convert the generated image captions into speech for audio feedback to users. Our system also uniquely allows for feedforward user prompts to personalize the generated image captions. Our system is able to generate detailed captions with an average length of 10 words while achieving a high ROUGE-L score of 43.9% and a low word error rate of 28.1% with an end-to-end processing time of 2.2 seconds. Overall, our new multimodal vision-language system can generate accurate and detailed descriptions of natural scenes, which can be further augmented by user prompts. This innovative feature allows our image captions to be personalized to the individual and immediate needs and preferences of the user, thus optimizing the closed-loop interactions between the human and generative AI models for understanding and navigating of real-world environments.
Refugee Healthcare Resilience and Burdens: A 10-year Mixed-Methods Analysis of System Shocks in Canada
Authors: Norrie Eric , Holdbrook Linda , Grewal Rabina , Talavlikar Rachel , Essar Mohammad Yasir , Williamson Tyler , Coakley Annalee , McBrien Kerry , Fabreau Gabriel E. 
Published: December 2024

AbstractBackgroundSystem shocks, including sudden policy changes, refugee surges and pandemics, strain healthcare systems. These shocks compound existing vulnerabilities in refugee healthcare, limiting ability to provide patient care, but can also catalyze resilient adaptations. Investigating how local refugee health systems respond to shocks is critical to understanding resilience.MethodsWe conducted a sequential explanatory mixed-methods study (2011–2020) at a specialized refugee health centre in Alberta, Canada, investigating four health system shocks: IFHP Funding Cuts (2012), Syrian Surge (2015), Yazidi Resettlement (2017), and COVID-19 (2020). We analyzed patient sociodemographic characteristics, health center utilization, and healthcare provider supply, conducting interrupted time series analysis of mean monthly appointments (total, family physicians, specialists and multidisciplinary team) and rates of change. We adapted a Health System Resilience framework to thematically analyze interviews with centre leaders and integrated these findings with quantitative findings to assess resilience and operational burdens.FindingsFrom 2011 to 2020, 10,661 refugees from 106 countries attended 107,642 appointments. Mean monthly appointments rose from 455 to 2,208 (3.9-fold, p<0.01). Monthly appointments increased between IFHP and Syrian periods (610.8 to 937.9, p<0.01), but not between Syrian Surge and Yazidi Resettlement (p=0.29). During COVID-19, mean appointments remained stable (1,412.4 to 1,414.0, p=0.11), but additional monthly appointments rose from 6.3 to 110.4 (17.5-fold, p<0.01). Over ten years, mean provider hours increased from 320 to 736 (2.3-fold), and from 59.5 to 871.4 (14.6-fold) for family physicians and multidisciplinary team members. Qualitative analysis revealed resilience capacities but highlighted costs such as burnout, vicarious trauma, and financial strain. Integration showed the centre developed resilience but experienced notable operational burden.InterpretationOver a decade, a specialized refugee health centre adapted to successive shocks, transforming into a beacon clinic. It demonstrated resilience through care expansion and innovation, but with notable costs, financially and to health worker wellbeing.FundingNone
Functional and epitope specific monoclonal antibody discovery directly from immune sera using cryoEM
Authors: Ferguson James A. , Raghavan Sai Sundar Rajan , Peña Alzua Garazi , Bhavsar Disha , Huang Jiachen , Rodriguez Alesandra J. , Torres Jonathan L. , Bottermann Maria , Han Julianna , Krammer Florian , Batista Facundo D. , Ward Andrew B. 
Published: December 2024

AbstractAntibodies are crucial therapeutics, comprising a significant portion of approved drugs due to their safety and clinical efficacy. Traditional antibody discovery methods are labor-intensive, limiting scalability and high-throughput analysis. Here, we improved upon our streamlined approach combining structural analysis and bioinformatics to infer heavy and light chain sequences from electron potential maps of serum-derived polyclonal antibodies (pAbs) bound to antigens. Using ModelAngelo, an automated structure-building tool, we accelerated pAb sequence determination and identified sequence matches in B cell repertoires via ModelAngelo derived Hidden Markov Models (HMMs) associated with pAb structures. Benchmarking against results from a non-human primate HIV vaccine trial, our pipeline reduced analysis time from weeks to under a day with higher precision. Validation with murine immune sera from influenza vaccination revealed multiple protective antibodies. This workflow enhances antibody discovery, enabling faster, more accurate mapping of polyclonal responses with broad applications in vaccine development and therapeutic antibody discovery.
Spatially-resolved molecular sex differences at single cell resolution in the adult human hypothalamus
Authors: Mulvey Bernard , Wang Yi , Divecha Heena R. , Bach Svitlana V. , Montgomery Kelsey D. , Cinquemani Sophia , Chandra Atharv , Du Yufeng , Miller Ryan A. , Kleinman Joel E. , Page Stephanie C. , Hyde Thomas M. , Martinowich Keri , Hicks Stephanie C. , Hansen Kasper D. , Maynard Kristen R. 
Published: December 2024

AbstractThe hypothalamus contains multiple regions, including the ventromedial hypothalamus (VMH) and arcuate (ARC), which are responsible for sex-differentiated functions such as endocrine signaling, metabolism, and reproductive behaviors. While molecular, anatomic, and sex-differentiated features of rodent hypothalamus are well-established, much less is known about these regions in humans. Here we provide a spatially-resolved single cell atlas of sex-differentially expressed (sex-DE) genes in human VMH and ARC. We identify neuronal populations governing hypothalamus-specific functions, define their spatial distributions, and show increased retinoid pathway gene expression compared to rodents. Within VMH and ARC, we find correlated autosomal expression differences localized toESR1/TAC3-expressing andCRHR2-expressing neurons, and extensive sex-DE of genes linked to sex-biased disorders including autism, depression, and schizophrenia. Our molecular mapping of disease associations to hypothalamic cell types with established roles in sex-divergent physiology and behavior provides insights into mechanistic bases of sex bias in neurodevelopmental and neuropsychiatric disorders.
Pooled overexpression screening identifies PIPPI as a novel microprotein involved in the ER stress response
Authors: Lafranchi Lorenzo , Spinner Anna , Hornisch Maximilian , Schlesinger Dörte , Luzon Carmen Navarro , Brinkenstråhle Linus , Shao Rui , Piazza Ilaria , Elsässer Simon J 
Published: December 2024

AbstractMicroproteins encoded by short open reading frames (sORFs) of less than 100 codons have been predicted to constitute a substantial fraction of the eukaryotic proteome. However, relevance and roles of the majority of microproteins remain undefined because only a small fraction of these intriguing cellular players have been in-depth characterized so far. Here we use pooled overexpression screens with a library of 11’338 sORFs to overcome the challenge of elucidating which of the thousands of putative translated sORFs are biologically functional. As a proof-of-concept, we performed a phenotypic screen to identify sORFs protecting cells from treatment with the nucleotide analogue 6-thioguanine. With this approach, we identified two cytoprotective microproteins: altDDIT3 and PIPPI. PIPPI is encoded as part of the LC16a core duplicon/Morpheus gene cluster, a highly duplicated region of the human genome, which is undergoing rapid positive selection in primates. Our data show that PIPPI interacts with proteins of the endoplasmic reticulum, including protein disulfide isomerase ERp44. Besides providing mechanistic insights on a new microprotein, this study highlights the power of using pooled overexpression screens to identify functional microproteins.
mTOR signaling governs the formation of epithelial apical projection via S6K1-RhoA and aPKC-Lgl2 axes
Authors: Sen Sumit , Ghosh Prithwish , Mukherjee Sudipta , Prabhu Shreedha , Khurana Ginni , Pinto Clyde Savio , Gupta Kirti , Venkatramani Ravindra , Sonawane Mahendra 
Published: December 2024

SummaryIn metazoans, epithelia perform functions of absorption, diffusion, and secretion. The actin-based apical projections on the epithelial cells, contribute to these functions and are formed via cell-autonomous mechanisms that control cell polarity, intracellular transport, and the cytoskeleton. However, the cues that function upstream of these cell-autonomous regulators remain poorly known. Using microridges on zebrafish epithelial cells as a paradigm, we show that mTOR, a metabolic sensor, regulates the formation of apical projections. Mechanistically, mTORC1 controls the RhoA-ROCK activity via S6K1 to prevent the overactivation of Non-muscle Myosin II (NMII) to restrict microridge elongation. Furthermore, genetic, biochemical, and molecular dynamics simulation analyses reveal that mTORC2 regulates microridge pattern by modulating the activity of aPKC via its differential phosphorylation at two conserved sites. We propose that mTOR integrates the developmental and/or metabolic status of epithelial cells with cell autonomously acting RhoA and aPKC to regulate tissue wide formation of apical projections.