bluuebunny/crossref_metadata_2025_split huggingface dataset:
The beneficial effects of ethanolic extract of Sargassum serratifolium in DNCB-induced mouse model of atopic dermatitis
Authors: Kim Myeong-Jin , Ryu Heeyeon , Jeong Hyeon Hak , Van Ji Yun , Hwang Ji Young , Kim Ah-reum , Seo Jaeseong , Moon Kyoung Mi , Jung Won-Kyo , Lee Bonggi 
Published: June 2024

AbstractAtopic dermatitis is a chronic complex inflammatory skin disorder that requires sustainable treatment methods due to the limited efficacy of conventional therapies. Sargassum serratifolium, an algal species with diverse bioactive substances, is investigated in this study for its potential benefits as a therapeutic agent for atopic dermatitis. RNA sequencing of LPS-stimulated macrophages treated with ethanolic extract of Sargassum serratifolium (ESS) revealed its ability to inhibit a broad range of inflammation-related signaling, which was proven in RAW 264.7 and HaCaT cells. In DNCB-induced BALB/c or HR-1 mice, ESS treatment improved symptoms of atopic dermatitis within the skin, along with histological improvements such as reduced epidermal thickness and infiltration of mast cells. ESS showed a tendency to improve serum IgE levels and inflammation-related cytokine changes, while also improving the mRNA expression levels of Chi3l3, Ccr1, and Fcεr1a genes in the skin. Additionally, ESS compounds (sargachromanol (SCM), sargaquinoic acid (SQA), and sargahydroquinoic acid (SHQA)) mitigated inflammatory responses in LPS-treated RAW264.7 macrophages. In summary, ESS has an anti-inflammatory effect and improves atopic dermatitis, ESS may be applied as a therapeutics for atopic dermatitis.
Photocatalytic, antimicrobial and antibiofilm activities of MgFe2O4 magnetic nanoparticles
Authors: El-Khawaga Ahmed M. , Ayman Mohamed , Hafez Omar , Shalaby Rasha E. 
Published: June 2024

AbstractThis study reports the antibacterial and antibiofilm activities of Magnesium ferrite nanoparticles (MgFe2O4) against gram-positive and gram-negative bacteria. The photocatalytic degradation of Carbol Fuchsin (CF) dye (a class of dyestuffs that are resistant to biodegradation) under the influence of UV-light irradiation is also studied. The crystalline magnesium ferrite (MgFe2O4) nanoparticles were synthesized using the co-precipitation method. The morphology of the resulting nanocomposite was examined using scanning electron microscopy (SEM), while transmission electron microscopy (TEM) was employed for further characterization of particle morphology and size. Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD) were utilized to analyze the crystalline structure, chemical composition, and surface area, respectively. Optical properties were evaluated using UV–Vis spectroscopy. The UV-assisted photocatalytic performance of MgFe2O4 nanoparticles was assessed by studying the decolorization of Carbol fuchsin (CF) azo dye. The crystallite size of the MgFe2O4 nanoparticles at the (311) plane, the most prominent peak, was determined to be 28.5 nm. The photocatalytic degradation of 10 ppm CF using 15 mg of MgFe2O4 nanoparticles resulted in a significant 96% reduction after 135 min at ambient temperature (25 °C) and a pH value of 9. Additionally, MgFe2O4 nanoparticles exhibited potent antibacterial activity against E. coli and S. aureus in a dose dependent manner with maximum utilized concentration of 30 µg/ml. Specifically, MgFe2O4 nanoparticles demonstrated substantial antibacterial activity via disk diffusion and microbroth dilution tests with zones of inhibition and minimum inhibitory concentrations (MIC) for E. coli (26.0 mm, 1.25 µg/ml) and S. aureus (23.0 mm, 2.5 µg/ml), respectively. Moreover, 10.0 µg/ml of MgFe2O4 nanoparticles elicited marked percent reduction in biofilm formation by E. coli (89%) followed by S. aureus (78.5%) after treatment. In conclusion, MgFe2O4 nanoparticles demonstrated efficient dye removal capabilities along with significant antimicrobial and antibiofilm activity against gram-positive and gram-negative bacterial strains suggesting their potential as promising antimicrobial and detoxifying agents.
SIR+ models: accounting for interaction-dependent disease susceptibility in the planning of public health interventions
Authors: Martignoni Maria M. , Raulo Aura , Linkovski Omer , Kolodny Oren 
Published: June 2024

AbstractAvoiding physical contact is regarded as one of the safest and most advisable strategies to follow to reduce pathogen spread. The flip side of this approach is that a lack of social interactions may negatively affect other dimensions of health, like induction of immunosuppressive anxiety and depression or preventing interactions of importance with a diversity of microbes, which may be necessary to train our immune system or to maintain its normal levels of activity. These may in turn negatively affect a population’s susceptibility to infection and the incidence of severe disease. We suggest that future pandemic modelling may benefit from relying on ‘SIR+ models’: epidemiological models extended to account for the benefits of social interactions that affect immune resilience. We develop an SIR+ model and discuss which specific interventions may be more effective in balancing the trade-off between minimizing pathogen spread and maximizing other interaction-dependent health benefits. Our SIR+ model reflects the idea that health is not just the mere absence of disease, but rather a state of physical, mental and social well-being that can also be dependent on the same social connections that allow pathogen spread, and the modelling of public health interventions for future pandemics should account for this multidimensionality.
Structural basis for expanded substrate specificities of human long chain acyl-CoA dehydrogenase and related acyl-CoA dehydrogenases
Authors: Narayanan Beena , Xia Chuanwu , McAndrew Ryan , Shen Anna L. , Kim Jung-Ja P. 
Published: June 2024

AbstractCrystal structures of human long-chain acyl-CoA dehydrogenase (LCAD) and the catalytically inactive Glu291Gln mutant, have been determined. These structures suggest that LCAD harbors functions beyond its historically defined role in mitochondrial β-oxidation of long and medium-chain fatty acids. LCAD is a homotetramer containing one FAD per 43 kDa subunit with Glu291 as the catalytic base. The substrate binding cavity of LCAD reveals key differences which makes it specific for longer and branched chain substrates. The presence of Pro132 near the start of the E helix leads to helix unwinding that, together with adjacent smaller residues, permits binding of bulky substrates such as 3α, 7α, l2α-trihydroxy-5β-cholestan-26-oyl-CoA. This structural element is also utilized by ACAD11, a eucaryotic ACAD of unknown function, as well as bacterial ACADs known to metabolize sterol substrates. Sequence comparison suggests that ACAD10, another ACAD of unknown function, may also share this substrate specificity. These results suggest that LCAD, ACAD10, ACAD11 constitute a distinct class of eucaryotic acyl CoA dehydrogenases.
Estimating individual trajectories of structural and cognitive decline in mild cognitive impairment for early prediction of progression to dementia of the Alzheimer’s type
Authors: Rajagopal Shreya K. , Beltz Adriene M. , Hampstead Benjamin M. , Polk Thad A. 
Published: June 2024

AbstractOnly a third of individuals with mild cognitive impairment (MCI) progress to dementia of the Alzheimer’s type (DAT). Identifying biomarkers that distinguish individuals with MCI who will progress to DAT (MCI-Converters) from those who will not (MCI-Non-Converters) remains a key challenge in the field. In our study, we evaluate whether the individual rates of loss of volumes of the Hippocampus and entorhinal cortex (EC) with age in the MCI stage can predict progression to DAT. Using data from 758 MCI patients in the Alzheimer’s Disease Neuroimaging Database, we employ Linear Mixed Effects (LME) models to estimate individual trajectories of regional brain volume loss over 12 years on average. Our approach involves three key analyses: (1) mapping age-related volume loss trajectories in MCI-Converters and Non-Converters, (2) using logistic regression to predict progression to DAT based on individual rates of hippocampal and EC volume loss, and (3) examining the relationship between individual estimates of these volumetric changes and cognitive decline across different cognitive functions—episodic memory, visuospatial processing, and executive function. We find that the loss of Hippocampal volume is significantly more rapid in MCI-Converters than Non-Converters, but find no such difference in EC volumes. We also find that the rate of hippocampal volume loss in the MCI stage is a significant predictor of conversion to DAT, while the rate of volume loss in the EC and other additional regions is not. Finally, individual estimates of rates of regional volume loss in both the Hippocampus and EC, and other additional regions, correlate strongly with individual rates of cognitive decline. Across all analyses, we find significant individual variation in the initial volumes and the rates of changes in volume with age in individuals with MCI. This study highlights the importance of personalized approaches in predicting AD progression, offering insights for future research and intervention strategies.
AMP-RNNpro: a two-stage approach for identification of antimicrobials using probabilistic features
Authors: Shaon Md. Shazzad Hossain , Karim Tasmin , Sultan Md. Fahim , Ali Md. Mamun , Ahmed Kawsar , Hasan Md. Zahid , Moustafa Ahmed , Bui Francis M. , Al-Zahrani Fahad Ahmed 
Published: June 2024

AbstractAntimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/.
An integrated bioinformatic analysis of microarray datasets to identify biomarkers and miRNA-based regulatory networks in leishmaniasis
Authors: Savardashtaki Amir , Khalili Alashti Shayan , Vafadar Asma , Sadeghi Mahboubeh , Baneshi Maryam , Hashemi Kimia Sadat , Karami Jafar , Muro Antonio , Manzano-Roman Raúl , Rashidi Sajad 
Published: June 2024

AbstractMicro RNAs (miRNAs, miRs) and relevant networks might exert crucial functions during differential host cell infection by the different Leishmania species. Thus, a bioinformatic analysis of microarray datasets was developed to identify pivotal shared biomarkers and miRNA-based regulatory networks for Leishmaniasis. A transcriptomic analysis by employing a comprehensive set of gene expression profiling microarrays was conducted to identify the key genes and miRNAs relevant for Leishmania spp. infections. Accordingly, the gene expression profiles of healthy human controls were compared with those of individuals infected with Leishmania mexicana, L. major, L. donovani, and L. braziliensis. The enrichment analysis for datasets was conducted by utilizing EnrichR database, and Protein–Protein Interaction (PPI) network to identify the hub genes. The prognostic value of hub genes was assessed by using receiver operating characteristic (ROC) curves. Finally, the miRNAs that interact with the hub genes were identified using miRTarBase, miRWalk, TargetScan, and miRNet. Differentially expressed genes were identified between the groups compared in this study. These genes were significantly enriched in inflammatory responses, cytokine-mediated signaling pathways and granulocyte and neutrophil chemotaxis responses. The identification of hub genes of recruited datasets suggested that TNF, SOCS3, JUN, TNFAIP3, and CXCL9 may serve as potential infection biomarkers and could deserve value as prognostic biomarkers for leishmaniasis. Additionally, inferred data from miRWalk revealed a significant degree of interaction of a number of miRNAs (hsa-miR-8085, hsa-miR-4673, hsa-miR-4743-3p, hsa-miR-892c-3p, hsa-miR-4644, hsa-miR-671-5p, hsa-miR-7106-5p, hsa-miR-4267, hsa-miR-5196-5p, and hsa-miR-4252) with the majority of the hub genes, suggesting such miRNAs play a crucial role afterwards parasite infection. The hub genes and hub miRNAs identified in this study could be potentially suggested as therapeutic targets or biomarkers for the management of leishmaniasis.
Evolution of ferromagnetic cluster in perovskite La0.88Sr0.12MnO3 nanocrystalline detected by EPR spectrum
Authors: Li Shaozhen , Xu Lisha , Fu Chao , zheng Mengqiu , Tong Wei , Fan Jiyu 
Published: June 2024

AbstractElectron paramagnetic resonance (EPR) studies were performed on La0.88Sr0.12MnO3 (LSMO) nanocrystalline together with the measurement of its magnetization. Various spectrum parameters including line width, effective g-value and double-integrated intensities have been analyzed in detail. We found nonlinear behavior occurred in the inverse susceptibility far above the Curie temperature TC, indicating short-range ferromagnetic (FM) clusters and Griffiths-like phase behavior in the paramagnetic (PM) phase. Based on the variation of EPR spectra, except for a typical PM resonance peak, an extra resonance signal was observed in the lower field region and developed as temperature decreased from 320 K to 110 K, which gave a direct evidence of the existence of FM cluster in the PM region of LSMO nanocrystalline. We proposed that the appearance of the Griffiths phase was due to the short FM correlation in the PM regime enhanced by surface spin ordering.
Real-time predictive model of extrauterine growth retardation in preterm infants with gestational age less than 32 weeks
Authors: Gao Liang , Shen Wei , Wu Fan , Mao Jian , Liu Ling , Chang Yan-Mei , Zhang Rong , Ye Xiu-Zhen , Qiu Yin-Ping , Ma Li , Cheng Rui , Wu Hui , Chen Dong-Mei , Chen Ling , Xu Ping , Mei Hua , Wang San-Nan , Xu Fa-Lin , Ju Rong , Zheng Zhi , Lin Xin-Zhu , Tong Xiao-Mei ,   The Chinese Multicenter EUGR Collaborative Group, Lin Xinzhu , Tian Qianxin , Yuan Yuan , Shi Bizhen , Tong Xiao-Mei , Zhang Jinghui , Zhu Yan , Ye Xiuzhen , Zou Jingjing , Qiu Yinping , Li Yuhuai , Liu Shuhua , Xu Ying , Zhou Wenli , Chen Dongmei , Liu Zhiyong , Wang Sannan , Xu Falin , Wang Xiaokang , Liu Ye , Yi Juan , Wu Meigui , Chen Shifeng , Zhu Qiaomian , Wang Linlin , Liu Yongqiao , Deng Chun , Liu Xiaohong 
Published: June 2024

AbstractThe aim of this study was to develop a real-time risk prediction model for extrauterine growth retardation (EUGR). A total of 2514 very preterm infants were allocated into a training set and an external validation set. The most appropriate independent variables were screened using univariate analysis and Lasso regression with tenfold cross-validation, while the prediction model was designed using binary multivariate logistic regression. A visualization of the risk variables was created using a nomogram, while the calibration plot and receiver operating characteristic (ROC) curves were used to calibrate the prediction model. Clinical efficacy was assessed using the decision curve analysis (DCA) curves. Eight optimal predictors that namely birth weight, small for gestation age (SGA), hypertensive disease complicating pregnancy (HDCP), gestational diabetes mellitus (GDM), multiple births, cumulative duration of fasting, growth velocity and postnatal corticosteroids were introduced into the logistic regression equation to construct the EUGR prediction model. The area under the ROC curve of the training set and the external verification set was 83.1% and 84.6%, respectively. The calibration curve indicate that the model fits well. The DCA curve shows that the risk threshold for clinical application is 0–95% in both set. Introducing Birth weight, SGA, HDCP, GDM, Multiple births, Cumulative duration of fasting, Growth velocity and Postnatal corticosteroids into the nomogram increased its usefulness for predicting EUGR risk in very preterm infants.
DC current–voltage and impedance spectroscopy characterization of nCdS/pZnTe HJ
Authors: Lungu I. , Patru R. E. , Galca A. C. , Pintilie L. , Potlog T. 
Published: June 2024

AbstractThis paper describes the electrical and dielectric behavior of the nCdS/pZnTe HJ by current–voltage, capacitance–voltage characteristics, and impedance spectroscopy in a temperature interval 220–350 K. A microcrystalline p-ZnTe layer and n-CdS were grown on glass/ZnO substrate by closed space sublimation method. As frontal contact to CdS, the transparent ZnO and as a back contact to ZnTe, silver conductive paste (Ag) treated at 50 °C in vacuum were used. The current–voltage results of nCdS/pZnTe HJ show a rectifying behavior. The junction ideality factor, barrier height, and series resistance values were extracted from the rectifying curves at different temperatures. The built-in voltage, carrier concentration and depletion width were obtained from the capacitance–voltage measurements. Analysis of the J–V–T and C–V–T characteristics shows that the thermionic emission and recombination current flow mechanisms dominate in the nCdS/pZnTe HJ. The dielectric study reveals that the experimental values of the AC conductivity, dielectric constant, dielectric loss, the imaginary part of the electric modulus are found to be very sensitive to frequency and temperature. The dielectric constant and dielectric loss are observed to be high at the low frequency region. The increase in the values of electric modulus with the frequency implies an increase in the interfacial polarization at the interface of nCdS/pZnTe HJ. Jonscher’s universal power law shows that with increasing frequency, AC conductivity increased. The results conductivity show that the ionic conductivity and interfacial polarization are the main parameters affecting the dielectric properties of the device when the temperature changes.
Utility of pneumonia severity assessment tools for mortality prediction in healthcare-associated pneumonia: a systematic review and meta-analysis
Authors: Noguchi Shingo , Katsurada Masahiro , Yatera Kazuhiro , Nakagawa Natsuki , Xu Dongjie , Fukuda Yosuke , Shindo Yuichiro , Senda Kazuyoshi , Tsukada Hiroki , Miki Makoto , Mukae Hiroshi 
Published: June 2024

AbstractAccurate prognostic tools for mortality in patients with healthcare-associated pneumonia (HCAP) are needed to provide appropriate medical care, but the efficacy for mortality prediction of tools like PSI, A-DROP, I-ROAD, and CURB-65, widely used for predicting mortality in community-acquired and hospital-acquired pneumonia cases, remains controversial. In this study, we conducted a systematic review and meta-analysis using PubMed, Cochrane Library (trials), and Ichushi web database (accessed on August 22, 2022). We identified articles evaluating either PSI, A-DROP, I-ROAD, or CURB-65 and the mortality outcome in patients with HCAP, and calculated the pooled sensitivities, specificities, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the summary area under the curves (AUCs) for mortality prediction. Additionally, the differences in predicting prognosis among these four assessment tools were evaluated using overall AUCs pooled from AUC values reported in included studies. Eventually, 21 articles were included and these quality assessments were evaluated by QUADAS-2. Using a cut-off value of moderate in patients with HCAP, the range of pooled sensitivity, specificity, PLR, NLR, and DOR were found to be 0.91–0.97, 0.15–0.44, 1.14–1.66, 0.18–0.33, and 3.86–9.32, respectively. Upon using a cut-off value of severe in those patients, the range of pooled sensitivity, specificity, PLR, NLR, and DOR were 0.63–0.70, 0.54–0.66, 1.50–2.03, 0.47–0.58, and 2.66–4.32, respectively. Overall AUCs were 0.70 (0.68–0.72), 0.70 (0.63–0.76), 0.68 (0.64–0.73), and 0.67 (0.63–0.71), respectively, for PSI, A-DROP, I-ROAD, and CURB-65 (p = 0.66). In conclusion, these severity assessment tools do not have enough ability to predict mortality in HCAP patients. Furthermore, there are no significant differences in predictive performance among these four severity assessment tools.
Altered thermal preferences of infected or immune-challenged Aedes aegypti and Aedes japonicus mosquitoes
Authors: Hug David O. H. , Gretener-Ziegler Raphaela , Stegmayer Raffael I. , Mathis Alexander , Verhulst Niels O. 
Published: June 2024

AbstractTemperature is a critical factor shaping physiology, life cycle, and behaviour of ectothermic vector insects, as well as the development and multiplication of pathogens within them. However, the influence of pathogen infections on thermal preferences (behavioural thermoregulation) is not well-understood. The present study examined the thermal preferences of mosquitoes (Aedes aegypti and Ae. japonicus) infected with either Sindbis virus (SINV) or Dirofilaria immitis over 12 days post exposure (p.e.) or injected with a non-pathogenic Sephadex bead over 24 h in a thermal gradient (15–30 °C). SINV-infected Ae. aegypti preferred 5 °C warmer temperatures than non-infected ones at day 6 p.e., probably the time of highest innate immune response. In contrast, D. immitis-infected Ae. japonicus preferred 4 °C cooler temperatures than non-infected ones at day 9 p.e., presumably a stress response during the migration of third instar larvae from their development site to the proboscis. Sephadex bead injection also induced a cold preference in the mosquitoes but to a level that did not differ from control-injections. The cold preference thus might be a strategy to escape the risk of desiccation caused by the wound created by piercing the thorax. Further research is needed to uncover the genetic and physiological mechanisms underlying these behaviours.