bluuebunny/crossref_metadata_2025_split huggingface dataset:
Extended discrete gene regulatory network model for theArabidopsis thalianaroot-hair cell fate
Authors: Castillo-Jiménez Aarón , Garay-Arroyo Adriana , de La Paz Sánchez M. , García-Ponce Berenice , Martínez-García Juan Carlos , Álvarez-Buylla Elena R. 
Published: November 2023

AbstractThe differentiation of the two cell types of the root epidermis, atrichoblasts, which give rise to hair cells, and atrichoblasts, which do not develop as hair cells, is determined by a complex regulatory network of transcriptional factors and hormones that act in concert in space and time to define a characteristic pattern of rows of hair cells and non-hair cells interspersed with each other throughout the root epidermis ofArabidopsis thaliana. Previous models have defined a minimal regulatory network that recovers the Wild Type phenotype and some mutants but fails to recover most of the mutant phenotypes, thus limiting its ability to spread. In this work, we propose a diffusion-coupled regulatory genetic network or meta-Gene Regulatory Network model extended to the model previously published by our research group, to describe the patterns of organization of the epidermis of the root epidermis ofArabidopsis thaliana. This network fully or partially recovers loss-of-function mutants of the identity regulators of the epidermal cell types considered within the model. Not only that, this new extended model is able to describe in quantitative terms the distribution of trichoblasts and atrichoblasts defined at each cellular position with respect to the cortex cells so that it is possible to compare the proportions of each cell type at those positions with that reported in each of the mutants analyzed. In addition, the proposed model allows us to explore the importance of the diffusion processes that are part of the lateral inhibition mechanism underlying the network dynamics and their relative importance in determining the pattern in the Wild Type phenotype and the different mutants.
Melatonin serves as a novel treatment in cystic fibrosis and inhibits cystic fibrosis through TGF-β1/Smad and EMT
Authors: Zhang Yang , Gong Sun , He Weixin , Yuan Jie , Dong Di , Zhang Jialong , Wang Haomin , Chen Binghai 
Published: November 2023

AbstractBackgroundMelatonin (MEL) is an indole amine molecule primarily produced in the pineal gland. Melatonin has been shown in numerous studies to have anti-fibrosis characteristics in the kidney, liver, and other organs. However, it is still unclear how melatonin works in bladder fibrosis. We explored how melatonin affected animals with bladder fibrosis and its underlying mechanisms.Materials and MethodsMEL was used to treat human bladder smooth muscle cells (HBdSMCs) after they were stimulated with TGF-β1 in vitro. Proteomic analysis and then bioinformatic analysis based on the alterations in these proteins were then performed on HBdSMCs from the different processing methods. To construct an in vivo bladder fibrosis model, we injected protamine sulfate (PS) and lipopolysaccharide (LPS) twice a week into the rat bladder for six weeks. After two weeks of PS/LPS treatment, the treatment group was treated with MEL (20mg/kg/d) for 4 weeks. Finally, we detected the expression of fibrosis markers from different perspectives. The TGF-β1/Smad pathway, and EMT in cell and bladder tissues were also identified. Further proteomic analysis was also performed.ResultsIn the in vitro experiment, we found that TGF-β1 treatment enhanced the fibrosis markers Collagen III and α-SMA of HBdSMCs. E-cadherin expression decreased while TGF-β1/Smad pathway was activated. Vimentin and N-cadherin expressions were also elevated at the same time. Similar findings were observed in the LPS group. After MEL treatment, the expression of collagen III and α-SMA decreased, and the expression of E-cadherin increased, while the expression of Vimentin and N-cadherin also decreased. CCN1 and SQLE may be the important proteins in the development of bladder fibrosis, according to quantitative proteomics analysis. MEL can decrease their expressions which leadis to relief of bladder fibrosis. Bioinformatics analysis shows that the extracellular space structure related to metabolic pathways, actin filament binding, and stress fibers can serve as a pivotal focus in the management of fibrosis.ConclusionMelatonin attenuates bladder fibrosis by blocking the TGF-β1/Smad pathway and EMT. CCN1 appears to be a possible therapeutic target for bladder fibrosis.
Observations of respiratory syncytial virus (RSV) nucleic-acids in wastewater solids across the United States in the 2022-2023 season: Relationships with RSV infection positivity and hospitalization rates
Authors: Zulli Alessandro , Varkila Meri R.J. , Parsonnet Julie , Wolfe Marlene K. , Boehm Alexandria B. 
Published: November 2023

AbstractRespiratory syncytial virus (RSV) is a leading cause of respiratory illness and hospitalization but surveillance detects only a minority of cases. Wastewater surveillance could determine onset and extent of RSV circulation in the absence of sensitive case detection, but to date, studies of RSV in wastewater have been few. We measured RSV genomic RNA concentrations in wastewater solids from 176 US sites that provided samples at least three times per week during the 2022-2023 RSV season. Concentrations ranged from undetectable to 107copies per gram dry weight (median = 103cp/g). Wastewater RSV RNA concentration aggregated at state and national levels correlated with case positivity and hospitalization rates. Onset of RSV season was assessed for 14 states that collected data prior to the start of the RSV season. Wastewater and clinical surveillance identified onset of RSV during the same week in 3 states, whereas in 3 states, wastewater onset preceded clinical onset, and in 7 states, wastewater onset occurred after clinical onset. Wastewater concentrations generally peaked in the same week as hospitalization rates, but after case positivity rates peaked. Differences in onset and peaks in the wastewater versus clinical data may reflect inherent differences in the surveillance approaches.
The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients
Authors: Kafkas Şenay , Abdelhakim Marwa , Althagafi Azza , Toonsi Sumyyah , Alghamdi Malak , Schofield Paul N. , Hoehndorf Robert 
Published: November 2023

AbstractComputational methods for identifying gene–disease associations can use both genomic and phenotypic information to prioritize genes and variants that may be associated with genetic diseases. Phenotype-based methods commonly rely on comparing phenotypes observed in a patient with a database of genotype-to-phenotype associations using a measure of semantic similarity, and are primarily limited by the quality and completeness of this database as well as the quality of phenotypes assigned to a patient. Genotype-to-phenotype associations used by these methods are largely derived from literature and coded using phenotype ontologies. Large Language Models (LLMs) have been trained on large amounts of text and have shown their potential to answer complex questions across multiple domains. Here, we demonstrate that LLMs can prioritize disease-associated genes as well, or better than, dedicated bioinformatics methods relying on calculated phenotype similarity. The LLMs use only natural language information as background knowledge and do not require ontology-based phenotyping or structured genotype-to-phenotype knowledge. We use a cohort of undiagnosed patients with rare diseases and show that LLMs can be used to provide diagnostic support that helps in identifying plausible candidate genes.
Brick kiln pollution and its impact on health: A systematic review and meta-analysis
Authors: Nicolaou Laura , Sylvies Fiona , Veloso Isabel , Lord Katherine , Chandyo Ram K , Sharma Arun K , Shrestha Laxman P , Parker David L , Thygerson Steven M , DeCarlo Peter F , Ramachandran Gurumurthy , Checkley William 
Published: November 2023

ABSTRACTBackgroundBrick kiln emissions adversely affect air quality and the health of workers and individuals living near the kilns; however, evidence of the impacts of brick kiln pollution remains limited.MethodsWe conducted a systematic review of brick kiln pollution (emissions, source contributions and personal exposures) and its effects on health. We extracted articles from electronic databases and through manual citation searching. We estimated pooled, sample-size-weighted means and standard deviations for personal exposures by job type; computed mean emission factors and pollutant concentrations by brick kiln design; and meta-analyzed differences in means or proportions for health outcomes between brick kiln workers (BKWs) and controls or for participants living near or far away from kilns.ResultsOur search yielded 1015 articles; 208 (20%) were assessed for eligibility and 101 (10%) were included in our review. We identified three additional studies through manual searching. Of 104 studies, 74 (71%) were conducted in South Asia. The most evaluated pollutants were particulate matter (PM; n=48), sulfur dioxide (SO2; n=24) and carbon monoxide (CO; n=22), and the most evaluated health outcomes were respiratory health (n=34) and musculoskeletal disorders (n=9). PM and CO emissions were higher among traditional than improved brick kilns. Mean respirable silica exposures were only measured in 4 (4%) studies and were as high as 620 μg/m3, exceeding the NIOSH recommended exposure limit by a factor of over 12. BKWs had consistently worse lung function, more respiratory symptoms, more musculoskeletal complaints, and more inflammation when compared to unexposed participants across studies; however, most studies had a small sample size and did not fully describe methods used for sampling or data collection.DiscussionOn average, BKWs had worse health outcomes when compared to unexposed controls but study quality supporting the evidence was low. Few studies reported silica concentrations or personal exposures, but the few that did suggest that exposures are high. Further research is needed to better understand the relationship between brick kiln pollution and health among workers, and to evaluate exposure mitigation strategies.
Skin Pigmentation and Pulse Oximeter Accuracy in the Intensive Care Unit: a Pilot Prospective Study
Authors: Fawzy Ashraf , Ali Harith , Dziedzic Peter H. , Potu Niteesh , Calvillo Eusebia , Golden Sherita H. , Iwashyna Theodore J. , Suarez Jose I. , Hager David N. , Garibaldi Brian T. 
Published: November 2023

AbstractRationaleDespite multiple reports of pulse oximeter inaccuracy among hospitalized Black individuals, regulatory testing of pulse oximeters is performed on healthy volunteers.ObjectiveEvaluate pulse oximeter accuracy among intensive care unit patients with diverse skin pigmentation.MethodsSkin pigmentation was measured using a chromameter in 12 patients and individual typology angle (ITA), a measure of constitutive pigmentation, calculated. Arterial blood gas (ABG) arterial oxygen saturation (SaO2) sampling was precisely matched to pulse oximetry (SpO2) using arterial line waveforms analysis. Error (SpO2-SaO2), bias, and average root mean square error (ARMS) were calculated. Multivariable linear mixed effects models evaluated the association of SpO2-SaO2with skin pigmentation.Measurements and Main ResultsSampling time was determined for 350 ABGs. Five participants (N=96 ABGs) were darkly pigmented (forehead ITA<-30°), and 7 lighter pigmented (N=254 ABGs). Darkly pigmented individuals had 1.05% bias and 4.15% ARMScompared to 0.34% bias and 1.97% ARMSamong lighter pigmented individuals. After adjusting for SaO2, pH, heart rate, and mean arterial pressure, SpO2-SaO2was falsely elevated by 1.00% more among darkly pigmented individuals (95% confidence interval: 0.25-1.76%). SpO2significantly overestimated SaO2for dark, brown, and tan forehead or forearm pigmentation and brown and tan finger pad pigmentation compared to intermediate/light pigmentation.ConclusionsThe pulse oximeter in clinical use at an academic medical center performed worse in darkly pigmented critically ill patients than established criteria for FDA clearance. Pulse oximeter testing in ICU settings is feasible, and could be required by regulators to ensure equivalent device performance by skin pigmentation among patients.
MedEdMENTOR AI: Can artificial intelligence help medical education researchers select theoretical constructs?
Authors: Ow Gregory , Rodman Adam , Stetson Geoffrey V. 
Published: November 2023

ABSTRACTINTRODUCTIONMedical education scholarship often lacks a strong theoretical underpinning, with this gap most often affecting early-career researchers and researchers in the Global South. Large language models (LLMs) have shown considerable promise to augment human writing and creativity in a variety of settings. In this study, we describe the development of MedEdMENTOR — an online platform for medical education research with a library of over 250 theories — and the development and evaluation of MedEdMENTOR AI, an LLM containing knowledge from MedEdMENTOR and the first AI mentor for medical education research.METHODSFrom a postpositivist paradigm, we evaluated MedEdMENTOR AI by testing it against 6 months of qualitative research published in 24 core medical educational journals. In a blinded fashion, we presented MedEdMENTOR AI with only the phenomenon of the qualitative study, and asked it to recommend 5 theories that could be used to study that phenomenon.RESULTSFor 55% (29 of 53) of studies, MedEdMENTOR AI recommended the actual theoretical constructs chosen in the respective qualitative studies.CONCLUSIONSOur data is preliminary, but it suggests that MedEdMENTOR AI and other LLMs can be highly effective in guiding medical education scholars towards theories that may be applicable in their research. Further research is needed to assess performance on other tasks in medical education research.
Accelerating protein engineering with fitness landscape modeling and reinforcement learning
Authors: Sun Haoran , He Liang , Deng Pan , Liu Guoqing , Liu Haiguang , Cao Chuan , Ju Fusong , Wu Lijun , Qin Tao , Liu Tie-Yan 
Published: November 2023

AbstractProtein engineering holds significant promise for designing proteins with customized functions, yet the vast landscape of potential mutations versus limited lab capacity constrains the discovery of optimal sequences. To address this, we present theµProtein framework, which accelerates protein engineering by combiningµFormer, a deep learning model for accurate mutational effect prediction, withµSearch, a reinforcement learning algorithm designed to efficiently navigate the protein fitness landscape usingµFormer as an oracle.µProtein uniquely leverages single-point mutation data to predict optimal sequences with complex, multi-point mutations through its modeling of epistatic interactions and a two-step, multi-round search strategy. In benchmark testing,µProtein achieved state-of-the-art results, and in wet-lab experiments, it identified high-gain-of-function multi-point mutants for the enzymeβ-lactamase, including variants that increased bacterial growth rate by up to 2000-fold, surpassing the highest known activity level, all while training solely on single-site mutation data. These results demonstrateµProtein’s capability to discover impactful mutations across vast protein sequence space, offering a robust, efficient approach for protein optimization.
Combining CRISPR/Cas mediated terminal resolution with a novel genetic workflow to achieve high diversity adenoviral libraries
Authors: Fischer Julian , Fedotova Ariana , Jaki Lena , Sallard Erwan , Erhardt Anja , Fuchs Jonas , Ruzsics Zsolt 
Published: November 2023

ABSTRACTWhile recombinant Adenoviruses (rAds) are widely used in both laboratory and medical gene transfer, library-based applications using this vector platform are not readily available.Recently, we developed a new method, the CRISPR/Cas9 mediated in vivo terminal resolution (CTR) aiding high efficiency rescue of rAds from recombinant DNA. Here we report on a genetic workflow that allows construction of BAC-based rAd-libraries employing the efficiency of CTR.We utilized frequent, pre-existing genomic sequences to allow insertion of a selection marker, complementing two selected target sites into novel endonuclease recognition sites. In a second step, this selection marker is replaced with a transgene or mutation of interest via Gibson assembly. Our approach does not cause unwanted genomic off-target mutations while providing substantial flexibility for the site and nature of the genetic modification.This new genetic workflow, which we termed half-site directed fragment replacement (HFR) allows introduction of >106unique modifications into rAd encoding BACs using laboratory scale methodology. To demonstrate the power of HFR, we rescued barcoded viral vector libraries yielding a diversity of ∼2.5×104modified rAd per cm2of transfected cell culture.GRAPHICAL ABSTRACT
Aperiodic activity as a central neural feature of hypnotic susceptibility outside of hypnosis
Authors: Landry Mathieu , da Silva Castanheira Jason , Boisvert Catherine , Rousseaux Floriane , Sackur Jérôme , Raz Amir , Richebé Philippe , Ogez David , Rainville Pierre , Jerbi Karim 
Published: November 2023

AbstractHow well a person responds to hypnosis is a stable trait, which exhibits considerable inter-individual diversity across the general population. Yet, its neural underpinning remains elusive. Here, we address this gap by combining EEG data, multivariate statistics, and machine learning in order to identify brain patterns that differentiate between individuals high and low in susceptibility to hypnosis. In particular, we computed the periodic and aperiodic components of the EEG power spectrum, as well as graph theoretical measures derived from functional connectivity, from data acquired at rest (pre-induction) and under hypnosis (post-induction). We found that the 1/f slope of the EEG spectrum at rest was the best predictor of hypnotic susceptibility. Our findings support the idea that hypnotic susceptibility is a trait linked to the balance of cortical excitation and inhibition at baseline and offers novel perspectives on the neural foundations of hypnotic susceptibility. Future work can explore the contribution of background 1/f activity as a novel target to distinguish the responsiveness of individuals to hypnosis at baseline in the clinic.Significance StatementHypnotic phenomena reflect the ability to alter one’s subjective experiences based on targeted verbal suggestions. This ability varies greatly in the population. The brain correlates to explain this variability remain elusive. Addressing this gap, our study employs machine learning to predict hypnotic susceptibility. By recording electroencephalography (EEG) before and after a hypnotic induction and analyzing diverse neurophysiological features, we were able to determine that several features differentiate between high and low hypnotic susceptible individuals both at baseline and during hypnosis. Our analysis revealed that the paramount discriminative feature is non-oscillatory EEG activity before the induction—a new finding in the field. This outcome aligns with the idea that hypnotic susceptibility represents a latent trait observable through a plain five-minutes resting-state EEG.
Bayesian Optimized sample-specific Networks Obtained By Omics data (BONOBO)
Authors: Saha Enakshi , Fanfani Viola , Mandros Panagiotis , Ben-Guebila Marouen , Fischer Jonas , Hoff-Shutta Katherine , Glass Kimberly , DeMeo Dawn Lisa , Lopes-Ramos Camila , Quackenbush John 
Published: November 2023

AbstractGene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring co-expression networks is a critical element of GRN inference as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate co-expression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. To address these concerns, we introduce BONOBO (Bayesian Optimized Networks Obtained By assimilating Omics data), a scalable Bayesian model for deriving individual sample-specific co-expression networks by recognizing variations in molecular interactions across individuals. For every sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific co-expression matrix constructed from all other samples in the data. Combining the sample-specific gene expression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific co-expression matrices, thus making the method extremely scalable. We demonstrate the utility of BONOBO in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other sample-specific co-expression network inference methods and provides insight into individual differences in the drivers of biological processes.Graphical Abstract
Analyzing ER stress response in ALS patient derived motor neurons identifies druggable neuroprotective targets
Authors: Watts Michelle E. , Giadone Richard M. , Ordureau Alban , Holton Kristina M. , Harper J. Wade , Rubin Lee L. 
Published: November 2023

AbstractAmyotrophic lateral sclerosis (ALS) is a degenerative motor neuron (MN) disease with severely limited treatment options. Identification of effective treatments has been limited in part by the lack of predictive animal models for complex age-related human disorders. Here, we utilized pharmacologic ER stressors to exacerbate underlying sensitivities conferred by ALS patient genetics in induced pluripotent stem cell (iPSC)-derived motor neurons (MNs). In doing so, we found that thapsigargin and tunicamycin exposure recapitulated ALS-associated degeneration, and that we could rescue this degeneration via MAP4K4 inhibition (MAP4K4i). We subsequently identified mechanisms underlying MAP4K4i-mediated protection by performing phosphoproteomics on iPSC-derived MNs treated with ER stressors -/+ MAP4K4i. Through these analyses, we found JNK, PKC, and BRAF to be differentially modulated in MAP4K4i-protected MNs, and that inhibitors to these proteins could also rescue MN toxicity. Collectively, this study highlights the value of utilizing ER stressors in ALS patient MNs to identify novel druggable targets.