bluuebunny/crossref_metadata_2025_split huggingface dataset:
Chronic Pain and Childhood Adversity Experiences Among U.S. Military Personnel
Authors: Ee Juliana S , Culp Phillip A , Bevis Zachary J , Dogbey Godwin Y , Agnello Robert N , Chang Min Ho 
Published: November 2023

ABSTRACT

Introduction
Chronic pain in a military population is prevalent, is costly, and can limit daily activities and affect soldier readiness. It has been associated with childhood adversity (CA) within the veteran, adult, and pediatric populations. Given the need to maximize soldier resiliency, an examination of the link between CA and chronic pain in an active duty population for a better understanding that informs treatment options is warranted.

Materials and Methods
The analytic sample comprised 32 men and 8 women drawn from a retrospective review of 203 intake assessments at an interdisciplinary pain management center. We identified a group (CA) of 20 patients who reported a history of pre-adolescent sexual abuse or living in an “abusive” childhood home and compared it with a control group (no-CA) of 20 patients, matched for age, gender, pain history duration, and pain problem. Validated measures were used to assess pain intensity, interference in functioning and well-being, emotional sequelae of pain as reflected in symptoms of depression and anxiety, and pain-related catastrophic thinking. Data were analyzed using descriptive statistics and independent samples t-test analyses.

Results
Differences in current, worst, and average pain ratings were non-significant between groups. The CA group reported significantly greater effect of pain on mood (mean: 6.20 versus 4.25, P &lt; .02) and showed a trend toward higher pain interference in functioning (mean: 17.70 versus 15.05, P = .053). The CA patients had significantly more serious depression (mean: 12.65 versus 4.50, P &lt; .001) and anxiety symptoms (mean: 10.60 versus 2.35, P &lt; .001) and significantly higher pain catastrophizing tendency (mean: 30.05 versus 20.50, P &lt; .03).

Conclusions
Overall, the findings suggest that childhood trauma should be considered by providers when treating depression and anxiety in soldiers with chronic pain. Being mindful of trauma-informed care may have implications, perhaps, for cases perceived as treatment resistant.
Neuropathological Examination of Mice Chronically Exposed to Secondhand Smoke
Authors: Lopes Leilani A , Davenport Conor , Ramos Torres Estefania , Chlebowski Anna , Mikami Anna , Raber Jacob , Ruth Torres Eileen , Kisby Glen 
Published: November 2023

ABSTRACT

Introduction
Around 21.6–35% of military personnel are smokers, while 12.26% of them have been regularly exposed to second-hand smoke (SHS). Second-hand smoke is considered an important risk factor for neurological diseases because it can induce oxidative stress, DNA damage, and disrupt DNA repair pathways.

Material and Methods
The brain of air (sham) or SHS exposed mice was cryoperserved, sectioned, and placed on a glass slide before immunoprobing them with antibodies to observe for oxidative DNA damage (8-oxoG), oxidative DNA repair (8-oxoguanine DNA glycosylase 1, Ogg1; apurinic/apyrimidinic endonuclease, Ape1), and inflammatory (glial fibrillary acidic protein) proteins.

Results
Nissl staining of the prefrontal cortex (PFCTX) revealed the presence of dark, shrunken cells, hippocampal thinning, and the presence of activated astrocytes in SHS exposed mice. 8-oxoG staining was also more prominent in the PFCTX and hippocampus (HIPP) of SHS exposed mice. Ogg1 staining was reduced in the PFCTX and CA3 hippocampal neurons of SHS exposed mice, whereas it was more prominent in CA1 and CA4 hippocampal neurons. In contrast, Ape1 staining was more prominent in the PFCTX and the HIPP of SHS exposed mice.

Conclusions
These studies demonstrate that oxidative DNA damage (8-oxoG) was elevated and oxidative DNA repair (Ape1 and Ogg1) was altered in the brain of SHS exposed mice. In addition, activated astrocytes (i.e., glial fibrillary acidic protein) were also observed in the brain of SHS exposed mice. Therefore, SHS induces both oxidative DNA damage and repair as well as inflammation as possible underlying mechanism(s) of the cognitive decline and metabolic changes that were observed in chronically exposed mice. A better understanding of how chronic exposure to SHS induces cognitive dysfunction among military personnel could help improve the combat readiness of U.S. soldiers as well as reduce the financial burden on the DOD and veterans’ families.
Does Cuff Design Impact Insertion and Removal Force of Tracheostomy Tubes? A Bench Model
Authors: Borg Ulf , Bolger Emmet , Morris Sean 
Published: November 2023

ABSTRACT

Introduction
Percutaneous dilatational tracheostomy is a commonly performed procedure in intensive care units. Unrecognized tracheal ring fracture has been suggested as a possible factor for tracheal stenosis. The degree of tracheal compression relates to the amount of force required to cannulate the trachea. The objective of this study was to determine the force required to insert two types of tracheostomy tubes with different cuff designs.

Materials and Methods
This bench model measured the insertion and removal force of two tracheostomy tubes; one with a barrel-shaped, high-volume, low-pressure cuff (traditional Shiley tracheostomy tube) and another with a taper-shaped, low-volume, low-pressure cuff (Shiley flexible tracheostomy tube). Three sizes of tracheostomy tubes either with a barrel- or taper-shaped cuff were tested (Jackson sizes 4, 6, and 10, corresponding to 6.5-, 7.5-, and 10-mm ISO sizes, respectively). A model representing the tissue that the tube traverses to enter the tracheal lumen was designed, and the tracheostomy tube was mounted on a universal testing machine to measure the force necessary to insert and remove the tube.

Results
Across all tracheostomy tubes’ sizes tested, significantly less force was required to insert the Shiley flexible tracheostomy tube compared to the traditional Shiley tracheostomy tube. Significantly less force was also required to remove the flexible tracheostomy tube compared to the traditional tracheostomy tube.

Conclusions
This model suggests that less force is required to insert the Shiley flexible tracheostomy tube, which could result in less tracheal compression. This may be because of the smaller taper-shaped cuff that, when deflated, occupies less volume compared to the barrel-shaped cuff. As a result, less tracheal injury may occur when using the Shiley flexible tracheostomy tube during percutaneous tracheostomy procedures.
Autonomous Multi-modality Burn Wound Characterization using Artificial Intelligence
Authors: Jacobson Maxwell J , Masry Mohamed El , Arrubla Daniela Chanci , Tricas Maria Romeo , Gnyawali Surya C , Zhang Xinwei , Gordillo Gayle , Xue Yexiang , Sen Chandan K , Wachs Juan 
Published: November 2023

ABSTRACT

Introduction
Between 5% and 20% of all combat-related casualties are attributed to burn wounds. A decrease in the mortality rate of burns by about 36% can be achieved with early treatment, but this is contingent upon accurate characterization of the burn. Precise burn injury classification is recognized as a crucial aspect of the medical artificial intelligence (AI) field. An autonomous AI system designed to analyze multiple characteristics of burns using modalities including ultrasound and RGB images is described.

Materials and Methods
A two-part dataset is created for the training and validation of the AI: in vivo B-mode ultrasound scans collected from porcine subjects (10,085 frames), and RGB images manually collected from web sources (338 images). The framework in use leverages an explanation system to corroborate and integrate burn expert’s knowledge, suggesting new features and ensuring the validity of the model. Through the utilization of this framework, it is discovered that B-mode ultrasound classifiers can be enhanced by supplying textural features. More specifically, it is confirmed that statistical texture features extracted from ultrasound frames can increase the accuracy of the burn depth classifier.

Results
The system, with all included features selected using explainable AI, is capable of classifying burn depth with accuracy and F1 average above 80%. Additionally, the segmentation module has been found capable of segmenting with a mean global accuracy greater than 84%, and a mean intersection-over-union score over 0.74.

Conclusions
This work demonstrates the feasibility of accurate and automated burn characterization for AI and indicates that these systems can be improved with additional features when a human expert is combined with explainable AI. This is demonstrated on real data (human for segmentation and porcine for depth classification) and establishes the groundwork for further deep-learning thrusts in the area of burn analysis.
Fluconazole and propolis co-encapsulated in chitosan nanoparticles for the treatment of vulvovaginal candidiasis in a murine model
Authors: da Silva Jacqueline Teixeira , Dantas de Sousa Paulo Henrique , Costa Adelaide Fernandes , de Menezes Liliana Borges , Alves Suzana Ferreira , Pellegrini Flavio , Amaral Andre Correa 
Published: November 2023

Abstract
Vulvovaginal candidiasis (VVC) is a fungal infection caused mainly by Candida albicans. The treatment of VVC with azoles has been impaired due to the increased cases of resistance presented by this pathogen. The aim of the present study was to investigate the antifungal activity of mucoadhesive chitosan nanoparticles encapsulating both green propolis and fluconazole for topical use in the treatment of VVC. The nanoparticles were prepared by the ionic gelation method, resulting in a size of 316.5 nm containing 22 mg/kg of green propolis and 2.4 mg/kg of fluconazole. The nanoparticles were non-toxic in vitro using red blood cells or in vivo in a Galleria mellonella toxicity model. The treatment of female BALB/c mice infected by C. albicans ATCC 10231 with topical nanoparticles co-encapsulating fluconazole and green propolis was effective even using a fluconazole amount 20 times lower than the amount of miconazole nitrate 2% cream. Considering that the mucoadhesive property of chitosan, which is known to allow a prolonged retention time of the compounds at the mucous epithelia, the antifungal potential of the phenols and flavonoids present in green propolis may have favored the effectiveness of this treatment. These results indicate that this formulation of topical use for fluconazole associated with green propolis can be used as a promising approach to therapy for the treatment of VVC, thus contributing to reducing the development of resistance to azoles.
Ethyl caffeate combined with fluconazole exhibits efficacy against azole-resistant oropharyngeal candidiasis via the EFGR/JNK/c-JUN signaling pathway
Authors: Wang Tianming , Pan Min , Bao Mengyuan , Bu Qingru , Yang Ruotong , Yang Yue , Shao Jing , Wang Changzhong , Li Ning 
Published: November 2023

Abstract
Ethyl caffeate (EC) is a phenylpropanoid compound derived from Elephantopus scaber. In our previous work, EC was investigated to have a strong synergistic antifungal effect against azole-resistant strains of Candida albicans when combined with fluconazole (FLU). However, the protective effect and mechanism of EC + FLU on oropharyngeal candidiasis (OPC) caused by drug-resistant strains of C. albicans have not been investigated. This study aimed to investigate the protective effect and mechanism of EC combined with FLU against C. albicans-resistant strains that lead to OPC. An OPC mouse model revealed that EC + FLU treatment reduced fungal load and massive hyphal invasion of tongue tissues, and ameliorated the integrity of the tongue mucosa. Periodic acid-Schiff staining results showed more structural integrity of the tongue tissues and reduced inflammatory cell infiltration after EC + FLU treatment. Phosphorylation of EGFR (epidermal growth factor receptor) and other proteins in the EFGR/JNK (c-Jun N-terminal kinase)/c-JUN (transcription factor Jun) signaling pathway was significantly downregulated by EC + FLU. EGFR and S100A9 mRNA expression were also reduced. The above results were verified in FaDu cells. ELISA results showed that the concentration of inflammatory factors in the cell supernatant was significantly reduced after EC combined with FLU treatment. Molecular docking revealed that EC exhibited high binding energy to EGFR. In conclusion, EC enhances the susceptibility of azole-resistant C. albicans to FLU, and the underlying mechanism is related to the inhibition of the EGFR/JNK/c-JUN signaling pathway. This result suggests that EC has potential to be developed as an antifungal sensitizer to treat OPC caused by azole-resistant C. albicans.
Can the low-redshift Lyman alpha forest constrain AGN feedback models?
Authors: Khaire Vikram , Hu Teng , Hennawi Joseph F , Walther Michael , Davies Frederick 
Published: November 2023

ABSTRACT
We investigate the potential of low-redshift Lyman alpha (Lyα) forest for constraining active galactic nuclei (AGNs) feedback models by analysing the Illustris and IllustrisTNG simulation at z = 0.1. These simulations are ideal for studying the impact of AGN feedback on the intergalactic medium (IGM) as they share initial conditions with significant differences in the feedback prescriptions. Both simulations reveal that the IGM is significantly impacted by AGN feedback. Specifically, feedback is stronger in Illustris and results in reducing cool baryon fraction to 23 per cent relative to 39 per cent in IllustrisTNG. However, when comparing various statistics of Lyα forest such as 2D and marginalized distributions of Doppler widths and H i column density, line density, and flux power spectrum with real data, we find that most of these statistics are largely insensitive to the differences in feedback models. This lack of sensitivity arises because of the fundamental degeneracy between the fraction of cool baryons and the H i photoionization rate (ΓHI) as their product determines the optical depth of the Lyα forest. Since the ΓHI cannot be precisely predicted from first principles, it needs to be treated as a nuisance parameter adjusted to match the observed Lyα line density. After adjusting ΓHI, the distinctions in the considered statistics essentially fade away. Only the Lyα flux power spectrum at small spatial scales exhibits potentially observable differences, although this may be specific to the relatively extreme feedback model employed in Illustris. Without independent constraints on either ΓHI or cool baryon fraction, constraining AGN feedback with low-redshift Lyα forest will be very challenging.
Machine learning-based photometric classification of galaxies, quasars, emission-line galaxies, and stars
Authors: Zeraatgari Fatemeh Zahra , Hafezianzadeh Fatemeh , Zhang Yanxia , Mei Liquan , Ayubinia Ashraf , Mosallanezhad Amin , Zhang Jingyi 
Published: November 2023

ABSTRACT
This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad-line), quasars, and stars. We utilized samples from Sloan Digital Sky Survey (SDSS) Data Release 17 (DR17) and the ALLWISE catalogue, which contain spectroscopically labelled sources from SDSS. Our methodology comprises two parts. First, we conducted experiments, including three-class, four-class, and seven-class classifications, employing the Random Forest (RF) algorithm. This phase aimed to achieve optimal performance with balanced data sets. In the second part, we trained various machine learning methods, such as k-nearest neighbours (KNN), RF, XGBoost (XGB), voting, and artificial neural network (ANN), using all available data based on promising results from the first phase. Our results highlight the effectiveness of combining optical and infrared features, yielding the best performance across all classifiers. Specifically, in the three-class experiment, RF and XGB algorithms achieved identical average F1 scores of 98.93 per cent on both balanced and unbalanced data sets. In the seven-class experiment, our average F1 score was 73.57 per cent. Using the XGB method in the four-class experiment, we achieved F1 scores of 87.9 per cent for normal galaxies (NGs), 81.5 per cent for ELGs, 99.1 per cent for stars, and 98.5 per cent for quasars (QSOs). Unlike classical methods based on time-consuming spectroscopy, our experiments demonstrate the feasibility of using automated algorithms on carefully classified photometric data. With more data and ample training samples, detailed photometric classification becomes possible, aiding in the selection of follow-up observation candidates.
4U 1210−64: a new member of the rare intermediate-mass X-ray binary subclass
Authors: Monageng I M , McBride V A , Alfonso-Garzon J , Townsend L J , Coley J B , Montesinos B , Corbet R H D , Pottschmidt K 
Published: November 2023

ABSTRACT
4U 1210−64 is a peculiar X-ray binary that was first discovered in 1978 by the Uhuru satellite. The analysis of the X-ray data revealed a 6.7-d orbital period and an additional long-term modulation that is manifested as low- and high-flux states. Based on the previous classification of the donor star from the analysis of its optical spectra, the system has been suggested to be a high-mass X-ray binary. We revisit the optical classification where, based on the spectra from the Southern African Large Telescope (SALT), we conclude that the donor star is of spectral class A8 III–IV, making it a member of the rare intermediate-mass X-ray binaries. We perform radial velocity analysis using the SALT spectra where we consider circular and eccentric orbits. From the mass function derived and the mass constraints of the donor star, we demonstrate that a neutron star is favoured as the compact object in the binary system. We show, for the first time, the folded optical light curves, whose shape is interpreted to be due to a combination of ellipsoidal variations, irradiation of the donor star, and mutual eclipses of the star and accretion disc.
Fast Bayesian gravitational wave parameter estimation using convolutional neural networks
Authors: Andrés-Carcasona M , Martínez M , Mir Ll M 
Published: November 2023

ABSTRACT
The determination of the physical parameters of gravitational wave events is a fundamental pillar in the analysis of the signals observed by the current ground-based interferometers. Typically, this is done using Bayesian inference approaches which, albeit very accurate, are very computationally expensive. We propose a convolutional neural network approach to perform this task. The convolutional neural network is trained using simulated signals injected in a Gaussian noise. We verify the correctness of the neural network’s output distribution and compare its estimates with the posterior distributions obtained from traditional Bayesian inference methods for some real events. The results demonstrate the ability of the convolutional neural network to produce posterior distributions that are compatible with the traditional methods. Moreover, it achieves a remarkable inference speed, lowering by orders of magnitude the times of Bayesian inference methods, enabling real-time analysis of gravitational wave signals. Despite the observed reduced accuracy in the parameters, the neural network provides valuable initial indications of key parameters of the event such as the sky location, facilitating a multimessenger approach.
TESSgiants transiting giants V – two hot Jupiters orbiting red giant hosts
Authors: Pereira Filipe , Grunblatt Samuel K , Psaridi Angelica , Campante Tiago L , Cunha Margarida S , Santos Nuno C , Bossini Diego , Thorngren Daniel , Hellier Coel , Bouchy François , Lendl Monika , Mounzer Dany , Udry Stéphane , Beard Corey , Brinkman Casey L , Isaacson Howard , Quinn Samuel N , Tyler Dakotah , Zhou George , Howell Steve B , Howard Andrew W , Jenkins Jon M , Seager Sara , Vanderspek Roland K , Winn Joshua N , Saunders Nicholas , Huber Daniel 
Published: November 2023

ABSTRACTIn this work, we present the discovery and confirmation of two hot Jupiters orbiting red giant stars, TOI-4377 b and TOI-4551 b, observed by Transiting Exoplanet Survey Satellite in the Southern ecliptic hemisphere and later followed-up with radial-velocity (RV) observations. For TOI-4377 b, we report a mass of $0.957^{+0.089}_{-0.087} \ M_\mathrm{J}$ and a inflated radius of 1.348 ± 0.081 RJ orbiting an evolved intermediate-mass star (1.36 M⊙ and 3.52 R⊙; TIC 394918211) on a period of of 4.378 d. For TOI-4551 b, we report a mass of 1.49 ± 0.13 MJ and a radius that is not obviously inflated of $1.058^{+0.110}_{-0.062} \ R_\mathrm{J}$, also orbiting an evolved intermediate-mass star (1.31 M⊙ and 3.55 R⊙; TIC 204650483) on a period of 9.956 d. We place both planets in context of known systems with hot Jupiters orbiting evolved hosts, and note that both planets follow the observed trend of the known stellar incident flux-planetary radius relation observed for these short-period giants. Additionally, we produce planetary interior models to estimate the heating efficiency with which stellar incident flux is deposited in the planet’s interior, estimating values of $1.91 \pm 0.48~{{\ \rm per\ cent}}$ and $2.19 \pm 0.45~{{\ \rm per\ cent}}$ for TOI-4377 b and TOI-4551 b, respectively. These values are in line with the known population of hot Jupiters, including hot Jupiters orbiting main-sequence hosts, which suggests that the radii of our planets have re-inflated in step with their parent star’s brightening as they evolved into the post-main sequence. Finally, we evaluate the potential to observe orbital decay in both systems.
Solar cycle prediction using a combinatorial deep learning model
Authors: Su Xu , Liang Bo , Feng Song , Cai Yunfang , Dai Wei , Yang Yunfei 
Published: November 2023

ABSTRACT
The long-term prediction of the solar cycle is of great significance for aerospace, communication, and space missions. For a long time, many studies have used relatively primitive deep learning methods to predict the solar cycle, and most of them do not perform well in the long-term prediction. In this paper, we proposed XG-SN ensemble model. The model used extreme gradient boosting (XGBoost) ensemble learning method, combined with sample convolution and interaction net (SCINet), and neural basis expansion analysis for the interpretable time series (N-BEATS) to make predictions for known solar cycles. 13 months of smoothed monthly total sunspot numbers were selected as the data set. The model performance was evaluated by mean absolute error (MAE), root-mean-square error (RMSE), and mean absolute time lag (MATL) between the predicted and actual values. The first two evaluation metrics measured the prediction deviation from the numerical dimension, and the last one measured the prediction deviation from the temporal dimension. The results show that the model achieves the MAE, RMSE, and MATL values of 13.19, 17.13, and 0.08, respectively, in Solar Cycle 13 to 24. Our model is able to better predict in most cycles, ensuring accurate prediction of peaks with little time lag.