bluuebunny/crossref_metadata_2025_split huggingface dataset:
Plasticity of body axis polarity in Hydra regeneration under constraints
Authors: Livshits Anton , Garion Liora , Maroudas-Sacks Yonit , Shani-Zerbib Lital , Keren Kinneret , Braun Erez 
Published: August 2022

AbstractOne of the major events in animal morphogenesis is the emergence of a polar body axis. Here, we combine classic grafting techniques with live imaging to explore the plasticity of polarity determination during whole body regeneration in Hydra. Composite tissues are made by fusing two rings, excised from separate animals, in different configurations that vary in the polarity and original positions of the rings along the body axes of the parent animals. Under frustrating initial configurations, body axis polarity that is otherwise stably inherited from the parent animal, can become labile and even be reversed. Importantly, the site of head regeneration exhibits a strong bias toward the edges of the tissue, even when this involves polarity reversal. In particular, we observe head formation at an originally aboral tissue edge, which is not compatible with models of Hydra regeneration based only on preexisting morphogen gradients or an injury response. The site of the new head invariably contains an aster-like defect in the organization of the supra-cellular ectodermal actin fibers. While a defect is neither required nor sufficient for head formation, we show that the defect at the new head site can arise via different routes, either appearing directly following excision as the tissue seals at its edge or through de novo defect formation at the fusion site. Altogether, our results show that the emergence of a polar body axis depends on the original polarity and position of the excised tissues as well as structural factors, suggesting that axis determination is an integrated process that arises from the dynamic interplay of multiple biochemical and mechanical processes.
Comprehensive lipid and lipid-related gene investigations of host immune responses to characterize metabolism-centric biomarkers for pulmonary tuberculosis
Authors: Long Nguyen Phuoc , Anh Nguyen Ky , Yen Nguyen Thi Hai , Phat Nguyen Ky , Park Seongoh , Thu Vo Thuy Anh , Cho Yong-Soon , Shin Jae-Gook , Oh Jee Youn , Kim Dong Hyun 
Published: August 2022

AbstractDespite remarkable success in the prevention and treatment of tuberculosis (TB), it remains one of the most devastating infectious diseases worldwide. Management of TB requires an efficient and timely diagnostic strategy. In this study, we comprehensively characterized the plasma lipidome of TB patients, then selected candidate lipid and lipid-related gene biomarkers using a data-driven, knowledge-based framework. Among 93 lipids that were identified as potential biomarker candidates, ether-linked phosphatidylcholine (PC O–) and phosphatidylcholine (PC) were generally upregulated, while free fatty acids and triglycerides with longer fatty acyl chains were downregulated in the TB group. Lipid-related gene enrichment analysis revealed significantly altered metabolic pathways (e.g., ether lipid, linolenic acid, and cholesterol) and immune response signaling pathways. Based on these potential biomarkers, TB patients could be differentiated from controls in the internal validation (random forest model, area under the curve [AUC] 0.936, 95% confidence interval [CI] 0.865–0.992). PC(O-40:4), PC(O-42:5), PC(36:0), and PC(34:4) were robust biomarkers able to distinguish TB patients from individuals with latent infection and healthy controls, as shown in the external validation. Small changes in expression were identified for 162 significant lipid-related genes in the comparison of TB patients vs. controls; in the random forest model, their utilities were demonstrated by AUCs that ranged from 0.829 to 0.956 in three cohorts. In conclusion, this study introduced a potential framework that can be used to identify and validate metabolism-centric biomarkers.
Assessment of a combination of plasma anti-histone autoantibodies and PLA2/PE ratio as potential biomarkers to clinically predict autism spectrum disorders
Authors: El-Ansary Afaf , Al-Onazi Mona , Alhowikan Abdulrahman M. , Alghamdi Mashael A. , Al-Ayadhi Laila 
Published: August 2022

AbstractAutism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficiencies in social interaction and repetitive behaviors. Multiple studies have reported abnormal cell membrane composition and autoimmunity as known mechanisms associated with the etiopathogenesis of ASD. In this study, multiple regression and combined receiver operating characteristic (ROC) curve as statistic tools were done to clarify the relationship between phospholipase A2 and phosphatidylethanolamine (PE) ratio (PLA2/PE) as marker of lipid metabolism and membrane fluidity, and antihistone-autoantibodies as marker of autoimmunity in the etiopathology of ASD. Furthermore, the study intended to define the linear combination that maximizes the partial area under an ROC curve for a panel of markers. Forty five children with ASD and forty age- and sex-matched controls were enrolled in the study. Using ELISA, the levels of antihistone-autoantibodies, and PLA2 were measured in the plasma of both groups. PE was measured using HPLC. Statistical analyses using ROC curves and multiple and logistic regression models were performed. A notable rise in the area under the curve was detected using combined ROC curve models. Additionally, higher specificity and sensitivity of the combined markers were documented. The present study indicates that the measurement of the predictive value of selected biomarkers related to autoimmunity and lipid metabolism in children with ASD using a ROC curve analysis should lead to a better understanding of the pathophysiological mechanism of ASD and its link with metabolism. This information may enable the early diagnosis and intervention.
Optical sensor using space-domain active fiber cavity ringdown technique
Authors: Chen Wenjia , Ou Yiwen , Cheng Chunfu , Zhu Yuanchang , Xiao Wen , Lv Hui 
Published: August 2022

AbstractA novel active fiber cavity ringdown (FCRD) technique using frequency-shifted interferometry (FSI) is proposed for the first time. Using this scheme, external parameters can be monitored in the space domain by measuring the ringdown distance instead of ringdown time. A bidirectional erbium-doped fiber amplifier (Bi-EDFA) is employed to compensate the inherent cavity loss for achieving higher sensitivity. And two band-pass filters are used to reduce the amplified spontaneous emission (ASE) noise of the Bi-EDFA. Compared with the well-known time-domain active FCRD scheme, our proposed method enables us to avoid using pulsed laser needed in time-domain active FCRD, it uses continuous-wave laser to inject into the fiber cavity and stabilize the optical power in the fiber cavity, which can suppress the baseline drift of ringdown signal caused by the gain fluctuations of the EDFA and thus improve the detecting precision. Moreover, this novel method enables us to use differential detection method for further reducing the ASE noise, and thus eliminating the baseline drift of ringdown signal. A magnetic field sensor was developed as a proof-of-concept demonstration. The experimental results demonstrate that the proposed sensor with a sensitivity of 0.01537 (1/km·Gs) was achieved. This is the highest magnetic field sensitivity compared to the time-domain active FLRD method. Due to the reduced ASE noise, the stability of the proposed sensing system was also greatly improved.
In vivo evaluation of the effects of combined boron and gadolinium neutron capture therapy in mouse models
Authors: Lee Woonghee , Kim Kyung Won , Lim Jeong Eun , Sarkar Swarbhanu , Kim Jung Young , Chang Yongmin , Yoo Jeongsoo 
Published: August 2022

AbstractWhile boron neutron capture therapy (BNCT) depends primarily on the short flight range of the alpha particles emitted by the boron neutron capture reaction, gadolinium neutron capture therapy (GdNCT) mainly relies on gamma rays and Auger electrons released by the gadolinium neutron capture reaction. BNCT and GdNCT can be complementary in tumor therapy. Here, we studied the combined effects of BNCT and GdNCT when boron and gadolinium compounds were co-injected, followed by thermal neutron irradiation, and compared these effects with those of the single therapies. In cytotoxicity studies, some additive effects (32‒43%) were observed when CT26 cells were treated with both boron- and gadolinium-encapsulated PEGylated liposomes (B- and Gd-liposomes) compared to the single treatments. The tumor-suppressive effect was greater when BNCT was followed by GdNCT at an interval of 10 days rather than vice versa. However, tumor suppression with co-injection of B- and Gd-liposomes into tumor-bearing mice followed by neutron beam irradiation was comparable to that observed with Gd-liposome-only treatment but lower than B-liposome-only injection. No additive effect was observed with the combination of BNCT and GdNCT, which could be due to the shielding effect of gadolinium against thermal neutrons because of its overwhelmingly large thermal neutron cross section.
CT-derived body composition analysis could possibly replace DXA and BIA to monitor NET-patients
Authors: Kroll Lennard , Mathew Annie , Baldini Giulia , Hosch René , Koitka Sven , Kleesiek Jens , Rischpler Christoph , Haubold Johannes , Fuhrer Dagmar , Nensa Felix , Lahner Harald 
Published: August 2022

AbstractPatients with neuroendocrine tumors of gastro-entero-pancreatic origin (GEP-NET) experience changes in fat and muscle composition. Dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) are currently used to analyze body composition. Changes thereof could indicate cancer progression or response to treatment. This study examines the correlation between CT-based (computed tomography) body composition analysis (BCA) and DXA or BIA measurement. 74 GEP-NET-patients received whole-body [68Ga]-DOTATOC-PET/CT, BIA, and DXA-scans. BCA was performed based on the non-contrast-enhanced, 5 mm, whole-body-CT images. BCA from CT shows a strong correlation between body fat ratio with DXA (r = 0.95, ρC = 0.83) and BIA (r = 0.92, ρC = 0.76) and between skeletal muscle ratio with BIA: r = 0.81, ρC = 0.49. The deep learning-network achieves highly accurate results (mean Sørensen-Dice-score 0.93). Using BCA on routine Positron emission tomography/CT-scans to monitor patients’ body composition in the diagnostic workflow can reduce additional exams whilst substantially amplifying measurement in slower progressing cancers such as GEP-NET.
An integrated approach to the analysis of antioxidative peptides derived from Gouda cheese with a modified β-casein content
Authors: Iwaniak Anna , Mogut Damir , Minkiewicz Piotr , Żulewska Justyna , Darewicz Małgorzata 
Published: August 2022

AbstractThis study is the first to present an integrated approach involving in silico and in vitro protocols that was pursued to analyse an antioxidative potency of Gouda cheese with modified content of β-casein. Firstly, the predictions of the presence of antioxidant peptides in the casein sequences were computed using the BIOPEP-UWM database. Then, the antioxidative bioactivity of six variants of Gouda cheese (with reduced, normative, and increased content of β-casein at the initial and final stage of ripening) was assessed. Finally, the RP-HPLC–MS/MS was applied to identify antioxidative peptides in Gouda-derived water-soluble extracts (WSEs). Analyses were supported with the heatmaps and the computation of parameters describing the efficiency of proteolysis of caseins in the modified Gouda cheeses, i.e., the frequency and the relative frequency of the release of antioxidative fragments during cheese ripening (AEexp and Wexp., respectively). All Gouda cheese variants exhibited the antioxidative potential which differed depending on the assay employed. The highest antioxidative activity (ABTS·+ radical scavenging effect, FRAP, and Fe-chelating) was observed for WSEs derived from Gouda cheese with increased content of β-casein after the 60th day of ripening. The results obtained suggest the potential of Gouda cheese as the antioxidant-promoting food.
An ultra-wideband origami microwave absorber
Authors: Biswas Akash , Zekios Constantinos L. , Ynchausti Collin , Howell Larry L. , Magleby Spencer P. , Georgakopoulos Stavros V. 
Published: August 2022

AbstractMicrowave absorbers have been used to mitigate signal interference, and to shield electromagnetic systems. Two different types of absorbers have been presented: (a) low-cost narrowband absorbers that are simple to manufacture, and (b) expensive wideband microwave absorbers that are based on complex designs. In fact, as designers try to increase the bandwidth of absorbers, they typically increase their complexity with the introduction of several electromagnetic components (e.g., introduction of multi-layer designs, introduction of multiple electromagnetic resonators, etc.,), thereby increasing their fabrication cost. Therefore, it has been a challenge to design wideband absorbers with low cost of fabrication. To address this challenge, we propose a novel design approach that combines origami math with electromagnetics to develop a simple to manufacture ultra-wideband absorber with minimal fabrication and assembly cost. Specifically, we utilize a Tachi–Miura origami pattern in a honeycomb configuration to create the first absorber that can maintain an absorptivity above 90% in a 24.6:1 bandwidth. To explain the ultra-wideband behavior of our absorber, we develop analytical models based on the transmission-reflection theory of electromagnetic waves through a series of inhomogeneous media. The ultra-wideband performance of our absorber is validated and characterized using simulations and measurements.
Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach
Authors: Do Duyen Thi , Yang Ming-Ren , Lam Luu Ho Thanh , Le Nguyen Quoc Khanh , Wu Yu-Wei 
Published: August 2022

AbstractO6-Methylguanine-DNA-methyltransferase (MGMT) promoter methylation was shown in many studies to be an important predictive biomarker for temozolomide (TMZ) resistance and poor progression-free survival in glioblastoma multiforme (GBM) patients. However, identifying the MGMT methylation status using molecular techniques remains challenging due to technical limitations, such as the inability to obtain tumor specimens, high prices for detection, and the high complexity of intralesional heterogeneity. To overcome these difficulties, we aimed to test the feasibility of using a novel radiomics-based machine learning (ML) model to preoperatively and noninvasively predict the MGMT methylation status. In this study, radiomics features extracted from multimodal images of GBM patients with annotated MGMT methylation status were downloaded from The Cancer Imaging Archive (TCIA) public database for retrospective analysis. The radiomics features extracted from multimodal images from magnetic resonance imaging (MRI) had undergone a two-stage feature selection method, including an eXtreme Gradient Boosting (XGBoost) feature selection model followed by a genetic algorithm (GA)-based wrapper model for extracting the most meaningful radiomics features for predictive purposes. The cross-validation results suggested that the GA-based wrapper model achieved the high performance with a sensitivity of 0.894, specificity of 0.966, and accuracy of 0.925 for predicting the MGMT methylation status in GBM. Application of the extracted GBM radiomics features on a low-grade glioma (LGG) dataset also achieved a sensitivity 0.780, specificity 0.620, and accuracy 0.750, indicating the potential of the selected radiomics features to be applied more widely on both low- and high-grade gliomas. The performance indicated that our model may potentially confer significant improvements in prognosis and treatment responses in GBM patients.
Delta-like canonical Notch ligand 1 is predictive for sepsis and acute kidney injury in surgical intensive care patients
Authors: Schneck Emmanuel , Edinger Fabian , Uhle Florian , Markmann Melanie , Hecker Andreas , Weigand Markus A. , Sander Michael , Koch Christian 
Published: August 2022

AbstractThe early identification of sepsis in surgical intensive care patients is challenging due to the physiological postoperative alterations of vital signs and inflammatory biomarkers. Soluble Delta-like protein 1 (sDLL1) may be a potential discriminatory biomarker for this purpose. For this reason, this study aimed to evaluate sDLL1 for the identification of sepsis in a cohort of surgical intensive care patients. This study comprises a secondary analysis of a prospective observational study including 80 consecutive patients. The study groups included 20 septic shock patients, 20 patients each undergoing major abdominal surgery (MAS) and cardiac artery bypass surgery (CABG), and 20 matched control subjects (CTRL). The surveillance period was 72 h. The plasma concentration of sDLL1 was measured with ELISA. The plasma levels of sDLL1 were significantly elevated in septic patients compared to both surgical cohorts (septic vs. all postoperative time points, data are shown as median and interquartile range [IQR]; septic shock: 17,363 [12,053–27,299] ng/mL, CABG 10,904 [8692–16,250] ng/mL; MAS 6485 [4615–9068] ng/mL; CTRL 5751 [3743–7109] ng/mL; septic shock vs. CABG: p < 0.001; septic shock vs. MAS: p < 0.001). ROC analysis showed a sufficient prediction of sepsis with limited specificity (AUCROC 0.82 [0.75–0.82], sensitivity 84%, specificity 68%). The plasma levels of sDLL correlated closely with renal parameters (creatinine: correlation coefficient = 0.60, r2 = 0.37, p < 0.0001; urea: correlation coefficient = 0.52, r2 = 0.26, p < 0.0001), resulting in a good predictive performance of sDLL1 for the identification of acute kidney injury (AKI; AUCROC 0.9 [0.82–0.9], sensitivity 83%, specificity 91%). By quantifying the plasma concentration of sDLL1, sepsis can be discriminated from the physiological postsurgical inflammatory response in abdominal and cardiac surgical patients. However, sDLL1 has only limited specificity for the detection of sepsis in cardiac surgical patients, which may be explained by impaired renal function. Based on these findings, this study identifies the predictive value of sDLL1 for the detection of AKI, making it a potential biomarker for surgical intensive care patients.Trial registration DRKS00013584, Internet Portal of the German Clinical Trials Register (DRKS), registration date 11.07.2018, https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00013584.
Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries
Authors: S. V. Shree Sowndarya , Law Jeffrey N. , Tripp Charles E. , Duplyakin Dmitry , Skordilis Erotokritos , Biagioni David , Paton Robert S. , St. John Peter C. 
Published: August 2022

AbstractAdvances in the field of goal-directed molecular optimization offer the promise of finding feasible candidates for even the most challenging molecular design applications. One example of a fundamental design challenge is the search for novel stable radical scaffolds for an aqueous redox flow battery that simultaneously satisfy redox requirements at the anode and cathode, as relatively few stable organic radicals are known to exist. To meet this challenge, we develop a new open-source molecular optimization framework based on AlphaZero coupled with a fast, machine-learning-derived surrogate objective trained with nearly 100,000 quantum chemistry simulations. The objective function comprises two graph neural networks: one that predicts adiabatic oxidation and reduction potentials and a second that predicts electron density and local three-dimensional environment, previously shown to be correlated with radical persistence and stability. With no hard-coded knowledge of organic chemistry, the reinforcement learning agent finds molecule candidates that satisfy a precise combination of redox, stability and synthesizability requirements defined at the quantum chemistry level, many of which have reasonable predicted retrosynthetic pathways. The optimized molecules show that alternative stable radical scaffolds may offer a unique profile of stability and redox potentials to enable low-cost symmetric aqueous redox flow batteries.
Basically, nucleophilicity matters little: towards unravelling the supramolecular driving forces in enzyme-like CO2 conversion
Authors: Esteve Ferran , Altava Belén , Luis Santiago V. , García-Verdugo Eduardo 
Published: August 2022

The mechanism for the cycloaddition of CO2 to styrene oxide in the presence of an enzyme-like catalytic system has been elucidated, stressing the crucial role of cooperative supramolecular interactions between the whole catalytic pool.
Perception of Swallowing-Related Fatigue Among Older Adults
Authors: Brates Danielle , Harel Daphna , Molfenter Sonja M. 
Published: August 2022

Purpose:
Although fatigue is recognized as clinically relevant to swallowing performance, its prevalence and significance in dysphagic and nondysphagic adults have not been sufficiently examined. In this study, an online survey was used to examine swallowing- and eating-related fatigue (SERF) symptoms, the relationship between perceived SERF and other dysphagia-related health outcomes, and whether perceived SERF predicts risk for dysphagia or malnutrition.

Method:
An online survey of older adults (aged 60 years or older) was conducted. A novel 12-item scale was developed to capture perceived SERF. Previously validated scales were used to measure dysphagia risk, sarcopenia, general fatigue, malnutrition risk, and quality of life. Logistic regression was used to examine whether SERF predicted risk for dysphagia and/or malnutrition.

Results:

Complete responses were collected from 417 community-dwelling adults (
M
age
= 70.6 years,
SD
= 4.9; 263 women); 75% (
n
= 312) reported at least some degree of SERF. SERF was significantly correlated with dysphagia risk, sarcopenia, general fatigue, malnutrition risk, and quality of life. SERF was a significant predictor of dysphagia risk while controlling for age, gender, and other health outcomes (odds ratio [
OR
] = 1.2, 95% confidence interval [CI; 1.16, 1.27],
p
< .001). For every unit increase in SERF score, the odds of being at risk for dysphagia were associated with an increase of 22%. Significant predictors for malnutrition risk included SERF (
OR
= 0.94, 95% CI [0.91, 0.98]), general fatigue (
OR
= 0.95, 95% CI [0.92, 0.99]), and quality of life (
OR
= 1.04, 95% CI [1.0, 1.1]).

Conclusions:
Fatigue during swallowing and mealtimes is experienced by community-dwelling older adults and predicted dysphagia risk and malnutrition risk. Further research is needed to refine and validate a patient-reported outcome measure for SERF and examine the effects of fatigue on swallowing function and physiology under imaging.

Supplemental Material:

https://doi.org/10.23641/asha.20405835