bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reconstruction of Extensive Maxillary Defects Using Flow-Through Fibula Free Flap With Anterolateral Thigh Free Flap
Authors: Bao Mingzhe , Paka Lubamba Grace , Hua Yufei , Zhang Gaowei , Wang Miao , Gao Ning , Li Chunjie 
Published: March 2024

Background:
The maxillary defects left unreconstructed or inadequately reconstructed often result in significant functional and esthetic impairments. Adequate reconstruction of extensive maxillary defects requires a sufficient volume of hard and soft tissues.

Methods:
A 48-year-old male presenting bilateral extensive maxillary defects underwent secondary reconstruction with a flow-through fibula free flap in combination with an anterolateral thigh free flap.

Results:
The use of flow-through technique allowed minimizing the problem of limited recipient vessels and the length of free flap vascular pedicle usually encountered in secondary reconstruction. The bilateral maxillary defects were successfully reconstructed, and the postoperative outcomes were uneventful. The patient was satisfied with the treatment outcomes. He is being followed up and was referred to the implantology department for the placement of osseointegrated dental implants.

Conclusions:
The flow-through fibula free flap, in combination with the anterolateral thigh free flap, was found reliable and feasible for this case of secondary reconstruction of bilateral maxillary defects. This technique has provided satisfactory functional and esthetic outcomes and effectively improved the patient’s self-esteem.
Multi-view graph learning for deciphering the dominant cell communication assembly of downstream functional events from single-cell RNA-seq data
Authors: Ji Boya , Wang Xiaoqi , Wang Xiang , Xu Liwen , Peng Shaoliang 
Published: March 2024

ABSTRACTCell-cell communications (CCCs) from multiple sender cells collaboratively affect downstream functional events in receiver cells, thus influencing cell phenotype and function. How to rank the importance of these CCCs and find the dominant ones in a specific downstream functional event has great significance for deciphering various physiological and pathogenic processes. To date, several computational methods have been developed to focus on the identification of cell types that communicate with enriched ligand-receptor interactions from single-cell RNA-seq (scRNA-seq) data, but to the best of our knowledge, all of them lack the ability to identify the communicating cell type pairs that play a major role in a specific downstream functional event, which we call it “dominant cell communication assembly (DCA)”. Here, we proposed scDCA, a multi-view graph learning method for deciphering DCA from scRNA-seq data. scDCA is based on a multi-view CCC network by constructing different cell type combinations at single-cell resolution. Multi-view graph convolution network was further employed to reconstruct the expression pattern of target genes or the functional states of receiver cells. The DCA was subsequently identified by interpreting the model with the attention mechanism. scDCA was verified in a real scRNA-seq cohort of advanced renal cell carcinoma, accurately deciphering the DCA that affect the expression patterns of the critical immune genes and functional states of malignant cells. Furthermore, scDCA also accurately explored the alteration in cell communication under clinical intervention by comparing the DCA for certain cytotoxic factors between patients with and without immunotherapy. scDCA is free available at:https://github.com/pengsl-lab/scDCA.git.
Metabolic Deficiencies Underlie Plasmacytoid Dendritic Cell Exhaustion After Viral Infection
Authors: Greene Trever T. , Jo Yeara , Macal Monica , Fang Ziyan , Khatri Fawziyah S. , Codrington Alicia L. , Kazane Katelynn R. , Chiale Carolina , Akbulut Elizabeth , Swaminathan Shobha , Fujita Yu , Fitzgerald-Bocarsly Patricia , Cordes Thekla , Metallo Christian , Scott David A. , Zuniga Elina I. 
Published: March 2024

AbstractType I Interferons (IFN-I) are central to host protection against viral infections1. While any cell can produce IFN-I, Plasmacytoid Dendritic Cells (pDCs) make greater quantities and more varieties of these cytokines than any other cell type2. However, following an initial burst of IFN- I, pDCs lose their exceptional IFN-I production capacity and become “exhausted”, a phenotype that associates with enhanced susceptibility to secondary infections3–5. Despite this apparent cost for the host, pDC exhaustion is conserved across multiple species and viral infections, but the underlying mechanisms and the potential evolutionary advantages are not well understood. Here we characterize pDC exhaustion and demonstrate that it is associated with a reduced capacity of pDCs to engage both oxidative and glycolytic metabolism. Mechanistically, we identify lactate dehydrogenase B (LDHB) as a novel positive regulator of pDC IFN-I production in mice and humans, show that LDHB deficiency is associated with suppressed IFN-I production, pDC metabolic capacity, and viral control following a viral infection, and demonstrate that preservation of LDHB expression is sufficient to partially restore exhausted pDC functionin vitroandin vivo. Furthermore, restoring LDHBin vivoin exhausted pDCs increased IFNAR dependent infection- associated pathology. Therefore, our work identifies a novel and conserved mechanism for balancing immunity and pathology during viral infections, while also providing insight into the highly preserved but previously unexplained phenomenon of pDC exhaustion.
Learning to Choose: Behavioral Dynamics Underlying the Initial Acquisition of Decision Making
Authors: White Samantha R. , Preston Michael W. , Swanson Kyra , Laubach Mark 
Published: March 2024

ABSTRACTCurrent theories of decision making propose that decisions arise through competition between choice options. Computational models of the decision process estimate how quickly information about choice options is integrated and how much information is needed to trigger a choice. Experiments using this approach typically report data from well-trained participants. As such, we do not know how the decision process evolves as a decision-making task is learned for the first time. To address this gap, we used a behavioral design separating learning the value of choice options from learning to make choices. We trained male rats to respond to single visual stimuli with different reward values. Then, we trained them to make choices between pairs of stimuli. Initially, the rats responded more slowly when presented with choices. However, as they gained experience in making choices, this slowing reduced. Response slowing on choice trials persisted throughout the testing period. We found that it was specifically associated with increased exponential variability when the rats chose the higher value stimulus. Additionally, our analysis using drift diffusion modeling revealed that the rats required less information to make choices over time. Surprisingly, we observed reductions in the decision threshold after just a single session of choice learning. These findings provide new insights into the learning process of decision-making tasks. They suggest that the value of choice options and the ability to make choices are learned separately, and that experience plays a crucial role in improving decision-making performance.
Corticothalamic modelling of sleep neurophysiology with applications to mobile EEG
Authors: Morshedzadeh Taha , Kadak Kevin , Bastiaens Sorenza P. , Oveisi M. Parsa , Momi Davide , Wang Zheng , Harita Shreyas , Abou Jaude Maurice , Aimone Christopher A. , Mann Steve , Hill Sean L. , Griffiths John D. 
Published: March 2024

AbstractRecent developments in mathematical modelling of EEG enable the tracking of otherwise-inaccessible neurophysiological parameters throughout sleep. Likewise, advancements in wearable electronics have enabled easy & affordable collection of sleep EEG at home. The convergence of these two advances, namely neurophysiological modelling for mobile sleep EEG, can boost preclinical and clinical assessments of sleep. However, this subject area has received limited attention in existing literature. To address this, we used an established model of the corticothalamic system to analyze EEG power spectra from 5 datasets, spanning from research-grade systems to at-home mobile EEG. In the present work, we compare the convergent and divergent features of the data and the estimated physiological model parameters. While data quality and characteristics differ considerably, key patterns consistent with previous theoretical and empirical work are observed. During the transition from lighter to deeper NREM, i) exponent of the aperiodic (1/f) spectral component is increased, ii) bottom-up thalamocortical drive is reduced, iii) corticocortical connection strengths are increased. This effect is observed in healthy subjects but is interestingly absent when taking SSRI antidepressants, suggesting possible effects of ascending neuromodulation on corticothalamic oscillations. We further show a month-long increase in REM% in one mobile EEG subject, associated with boosted high-frequency activity in spectra and higher thalamothalamic gains in the model, pointing to possible changes of thalamic inhibition in REM parasomnias. Our results provide a proof-of-principle for the utility and feasibility of this physiological modelling-based approach to analyzing mobile EEG data, providing a mechanistic measure of brain physiology during sleep.Statement of significanceWe employ a physiological model of the corticothalamic circuitry to model the EEG power spectra in sleep. We fit this model to 5 EEG datasets, and demonstrate that while mobile and non-mobile EEG recordings differ in their characteristics and quality, they can both robustly represent the changes along sleep stages using the aperiodic (1/f) component. We observe an increased corticocortical connection strength and decreased corticothalamic connection strength as the subject goes into deeper stages of NREM sleep; an effect that is, importantly, not observed in subjects taking SSRIs. This work provides a proof-of-concept for using mathematical modelling, working well for large mobile and non-mobile datasets providing valuable insight into the mechanisms generating sleep EEG.
The widely used cymoxanil fungicide impairs respiration inSaccharomyces cerevisiaevia cytochromecoxidase inhibition
Authors: Mendes Filipa , Santos-Pereira Cátia , Vieira Tatiana F. , Pinto Mélanie Martins , Castro Bruno B. , Sousa Sérgio F. , Sousa Maria João , Devin Anne , Chaves Susana R. 
Published: March 2024

AbstractCymoxanil (CYM) is a synthetic acetamide fungicide that has been widely used to combat downy mildew diseases in grapevine cultures and late blight diseases in tomato and potato caused by the oomycetesPlasmopara viticolaandPhytophthora infestans, respectively. Despite its extensive application, the biochemical mode of action of CYM remains elusive. Previous reports indicate that CYM affects growth, DNA and RNA synthesis inPhytophthoraand inhibits cell growth, biomass production and respiration rate in the well-characterized fungal modelSaccharomyces cerevisiae. We therefore used this model to further characterize the effect of CYM on mitochondria. We found that CYM inhibits oxygen consumption in whole cells after 3 h of exposure, which persists over time. Using isolated mitochondria, we demonstrated that CYM specifically inhibits cytochromecoxidase (CcO) activity during oxidative phosphorylation. Based on molecular docking algorithms, we propose that CYM acts by blocking the interaction of cytochromec(cytc) with CcO, hampering electron transfer and inhibiting CcO catalytic activity. Although other targets cannot be excluded, our data offer valuable insights into the mode of action of CYM that can be instrumental to drive informed management of the use of this fungicide.
Increasing the vitamin C transporter SVCT2 in microglia improves synaptic plasticity and restrains memory impairments in Alzheimer’s disease models
Authors: Portugal Camila C. , Santos Evelyn C. S. , Pacheco Ana M. , Costa-Pinto Sara , Almeida Tiago O. , Tedim-Moreira Joana , Gavin Dora , Canedo Teresa , Oliveira Fabiana , Summavielle Teresa , Vaz Sandra H. , Socodato Renato , Relvas João B. 
Published: March 2024

AbstractAlzheimer’s Disease (AD) is characterized by progressive cognitive decline and synaptic dysfunction, often associated with amyloid-beta accumulation and microglial alterations. Here, we investigate the role of the Sodium-dependent Vitamin C Transporter 2 (SVCT2) in microglia to modulate AD-like pathology in mice. Using a combination of RNA sequencing, advanced quantitative proteomics, electrophysiology, behavioral tests, high-throughput imaging, and microglial viral gene delivery, we explore the interplay between SVCT2 expression in microglia, amyloid-beta load, synaptic proteome changes, and synaptic plasticity. Our results demonstrate that SVCT2 expression in microglia decreases with age in the 5xFAD mice, correlating with memory deficits and alterations in synaptic mitochondrial proteome. Importantly, overexpression of SVCT2 in microglia leads to enhanced clearance of amyloid plaques and reconfiguration of the mitochondrial proteome landscape in the synapses, improving synaptic long-term plasticity (LTP) and memory performance. Our findings underscore the SVCT2 overexpression in microglia as a potent strategy to simultaneously decrease amyloid pathology and enhance synaptic plasticity and memory performance, offering new avenues for therapeutic interventions in AD.
miR-155-5p/miR-674-3p presence in peripheral blood leukocytes and relative proportion of white blood cell types as biomarkers of asymptomatic and symptomatic phases of temporal lobe epilepsy
Authors: Szydłowska Kinga , Chrościcki Piotr , Olszewski Maciej , Nizińska Karolina , Piwocka Katarzyna , Łukasiuk Katarzyna 
Published: March 2024

AbstractEpilepsy frequently develops as a result of brain insult, for example, brain injury or stroke. Currently, there are no tools allowing us to predict which trauma patients will eventually develop epilepsy. There is evidence that microRNAs levels are altered in the blood, making them attractive candidates for peripheral biomarkers of epilepsy. We analyzed white blood cell subpopulations containing miR-155-5p and miR-674-3p, in control and stimulated animals and in control and symptomatic or asymptomatic animals in the amygdala stimulation model. The first proposed early biomarker of epilepsy is the relative proportion of CD45RA+B cells containing miR-155-5p and/or miR-674-3p. Others are increased number of CD45RA+B cells containing either miR-155-5p or miR-155-5p and miR-674-3p together or decreased number of CD161+NK cells not containing miR-155-5p nor miR-674-3p. Additionally, we found that the decreased number of CD4+T cells can be used as a potential biomarker for identifying epileptic animals with symptomatic epilepsy.
Cell-type specific effects ofFusariummycotoxins on primary neuronal and astroglial cells
Authors: Szentgyörgyi Viktória , Bencsik Norbert , Micska Brigitta , Rátkai Anikó , Schlett Katalin , Tárnok Krisztián 
Published: March 2024

AbstractFumonisin B1, deoxynivalenol (DON) and zearalenone (ZEA) are toxic secondary metabolites produced byFusariummolds. These mycotoxins are common food and feed pollutants and represent a risk for human and animal health. Although the mycotoxins produced by this genus can cross the blood-brain-barrier (BBB) in many species, their effect on neuronal function remains unclear. We investigated cell viability effects of these toxins on specified neural cell types, including mouse primary neuronal, astroglial and mixed cell cultures 24 or 48 hours after mycotoxin administration. Cell viability assay revealed that DON decreased cell viability in a dose-dependent manner, independently from the culture’s type. Fumonisin B1 increased cell viability significantly on astroglial and mixed cell cultures in lower doses, while it exerted a highly toxic effect in 50 µM. ZEA had significant effects on all culture type in 10 nM by increasing the cell viability. Since ZEA is a mycoestrogen, we analyzed the effects of ZEA on the expression of estrogen receptor isotypes ERα and ERβ and mitochondrial voltage-dependent anion channel (VDAC1) by qRT-PCR. In neuronal and mixed cultures, ZEA administration decreased ERα expression, while in astroglial cultures, it induced the opposite effect. ERβ and VDAC1 expression was not altered by ZEA in either culture types. ZEA also affected the firing pattern of neurons by enhancing the burst frequency. Our results demonstrate thatFusariummycotoxins are acting on a cell specific manner in the brain tissue.
High-resolution fleezers reveal duplex opening and stepwise assembly by an oligomer of the DEAD-box helicase Ded1p
Authors: Patrick Eric M. , Yadav Rajeev , Senanayake Kasun , Cotter Kyle , Putnam Andrea A. , Jankowsky Eckhard , Comstock Matthew J. 
Published: March 2024

ABSTRACTDEAD-box RNA helicases are ubiquitous in all domains of life where they bind and remodel RNA and RNA-protein complexes. DEAD-box helicases unwind RNA duplexes by local opening of helical regions without directional movement through the duplexes and some of these enzymes, including Ded1p from Saccharomyces cerevisiae, oligomerize to effectively unwind RNA duplexes. Whether and how DEAD-box helicases coordinate oligomerization and unwinding is not known and it is unclear how many base pairs are actively opened. Using high-resolution optical tweezers and fluorescence, we reveal a highly dynamic and stochastic process of multiple Ded1p protomers assembling on and unwinding an RNA duplex. One Ded1p protomer binds to a duplex-adjacent ssRNA tail and promotes binding and subsequent unwinding of the duplex by additional Ded1p protomers in 4-6 bp steps. The data also reveal rapid duplex unwinding and rezipping linked with binding and dissociation of individual protomers and coordinated with the ATP hydrolysis cycle.
A novel model-free feature selection method with FDR control for omics-wide association analysis
Authors: Lu Zi-tong , Song Xue-ting , Gao Yu-fan , Xiao Jian 
Published: March 2024

AbstractOmics-wide association analysis is a very important tool for medicine and human health study. However, the modern omics data sets collected often exhibit the high-dimensionality, unknown distribution response, unknown distribution features and unknown complex associated relationships between the response and its explanatory features. Reliable association analysis results depend on an accurate modeling for such data sets. Most of the existing association analysis methods rely on the specific model assumptions and lack effective false discovery rate (FDR) control so that they may not work well. To address these limitations, we firstly apply a single index model for omics data. This model is free in performance of allowing the relationships between the response variable and linear combination of covariates can be connected by any unknown monotonic link function, and both the random error and the covariates can follow any unknown distribution. Then based on this model, we combine rank-based approach and symmetrized data aggregation approach to develop a novel and model-free feature selection method for achieving fine-mapping of risk features while controlling the false positive rate of selection. The analysis results of simulated data show our method possesses effective and robust performance for all the scenarios. The proposed method is also used to analyze a real ocean microbiome data and identifies some casual taxa unreported by the existing finds.
Heterogeneous hybrid immunity against Omicron variant JN.1 at 11 months following breakthrough infection
Authors: He Xuan , Jiang Jiajing , Li Guo , Liu Jinyuan , Gan Jiadi , Zhou Linlin , Bai Chunyang , Zi Qiong , Mou Xiaoli , Zeng Shan , Yuan Junjie , Zhou Chuanjie , Li Yangqian , Zhu Guonian , Zhao Renjie , Yang Lan , Wu Jiaxuan , Zhang Huohuo , Xian Jinghong , Wang Zhoufeng , Qi Qi , Liu Yu , Yu Jingyou , Liu Dan , Li Weimin 
Published: March 2024

AbstractA highly transmissible SARS-CoV-2 variant JN.1 is rapidly spreading throughout the nation, becoming the predominant strain in China and worldwide. However, the current immunity against the circulating JN.1 at population level has yet to be fully evaluated. We recruited representative cohorts with stratified age groups and diverse combinations of vaccination and/or infection in recent months, and promptly assessed humoral immunity for these subjects predominantly exhibiting hybrid immunity. We report that at 11 months following BA.5-wave breakthrough infection (BTI), these vaccinated individuals generally showed above-the-threshold yet low level of neutralizing activity against JN.1, with slightly greater potency observed in children and adolescents compared to adults and seniors. Meanwhile, XBB/EG.5-wave reinfection post-BTI significantly boosted the neutralizing antibodies against Omicron variants, including JN.1 in both adults (13.4-fold increase) and seniors (24.9-fold increase). To better understand respiratory mucosal protection against JN.1 over an extended period of months post-BTI, we profiled the humoral immunity in bronchoalveolar lavage samples obtained from vaccinated subjects with or without BTI, and revealed increased potency of neutralizing activity against the BA.5 and JN.1 variants in the respiratory mucosa through natural infection. Notably, at 11 months post-BTI, memory B cell responses against prototype and JN.1 were detectable in both blood and respiratory mucosa, displaying distinct memory features in the circulation and airway compartments. XBB/EG.5-wave reinfection drove the expansion of JN.1-specific B cells, along with the back-boosting of B cells responding to the ancestral viral strain, suggesting the involvement of immune imprinting. Together, this study indicates heterogeneous hybrid immunity over 11 months post-BTI, and underscores the vulnerability of individuals, particularly high-risk seniors, to JN.1 breakthrough infection. An additional booster with XBB-containing vaccine may greatly alleviate the onward transmission of immune-evasive SARS-CoV-2 variants.
Unitary model analysis of
f0(500)
pole positions by continuously varying
mπ
: Comparison with discrete lattice predictions
Authors: Rupp George 
Published: March 2024

Resonance, bound-state, and virtual-state pole positions of the f0(500) scalar meson are computed as a continuous function of pion mass in the framework of a unitarized and analytic coupled-channel model for scalar mesons, described as dynamical quark-antiquark states. The f0(500) is modeled with both light and strange qq¯ seeds, mixing with each other through the common S-wave ππ, KK¯, and ηη meson-meson decay channels. The few model parameters are fitted to experimental S-wave ππ phase shifts up to 1 GeV. In the case of the physical π± mass of 139.57 MeV, resonance poles at (460−i222)  MeV and (978−i37.2)  MeV are found for the f0(500) and f0(980), respectively. Resonance, bound-state, and virtual-state pole trajectories are computed and plotted as a function of pion masses up to 500 MeV, both in the complex-energy and complex-momentum planes. The results are discussed and compared to the most advanced lattice QCD computations employing interpolators that correspond to the qq¯ and meson-meson channels in the present model, that is, for a few discrete values of the unphysical pion mass in those lattice calculations.

Published by the American Physical Society
2024