bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mesenchymal stem cells pretreated with interferon-gamma attenuate renal fibrosis by enhancing regulatory T cell induction
Authors: Kurawaki So , Nakashima Ayumu , Ishiuchi Naoki , Kanai Ryo , Maeda Satoshi , Sasaki Kensuke , Masaki Takao 
Published: May 2024

AbstractMesenchymal stem cells (MSCs) exert their anti-inflammatory and anti-fibrotic effects by secreting various humoral factors. Interferon-gamma (IFN-γ) can enhance these effects of MSCs, and enhancement of regulatory T (Treg) cell induction is thought to be an underlying mechanism. However, the extent to which Treg cell induction by MSCs pretreated with IFN-γ (IFN-γ MSCs) ameliorates renal fibrosis remains unknown. In this study, we investigated the effects of Treg cell induction by IFN-γ MSCs on renal inflammation and fibrosis using an siRNA knockdown system. Administration of IFN-γ MSCs induced Treg cells and inhibited infiltration of inflammatory cells in ischemia reperfusion injury (IRI) rats more drastically than control MSCs without IFN-γ pretreatment. In addition, administration of IFN-γ MSCs more significantly attenuated renal fibrosis compared with control MSCs. Indoleamine 2,3-dioxygenase (IDO) expression levels in conditioned medium from MSCs were enhanced by IFN-γ pretreatment. Moreover, IDO1 knockdown in IFN-γ MSCs reduced their anti-inflammatory and anti-fibrotic effects in IRI rats by reducing Treg cell induction. Our findings suggest that the increase of Treg cells induced by enhanced secretion of IDO by IFN-γ MSCs played a pivotal role in their anti-fibrotic effects. Administration of IFN-γ MSCs may potentially be a useful therapy to prevent renal fibrosis progression.
Inferring skin–brain–skin connections from infodemiology data using dynamic Bayesian networks
Authors: Scutari Marco , Kerob Delphine , Salah Samir 
Published: May 2024

AbstractThe relationship between skin diseases and mental illnesses has been extensively studied using cross-sectional epidemiological data. Typically, such data can only measure association (rather than causation) and include only a subset of the diseases we may be interested in. In this paper, we complement the evidence from such analyses by learning an overarching causal network model over twelve health conditions from the Google Search Trends Symptoms public data set. We learned the causal network model using a dynamic Bayesian network, which can represent both cyclic and acyclic causal relationships, is easy to interpret and accounts for the spatio-temporal trends in the data in a probabilistically rigorous way. The causal network confirms a large number of cyclic relationships between the selected health conditions and the interplay between skin and mental diseases. For acne, we observe a cyclic relationship with anxiety and attention deficit hyperactivity disorder (ADHD) and an indirect relationship with depression through sleep disorders. For dermatitis, we observe directed links to anxiety, depression and sleep disorders and a cyclic relationship with ADHD. We also observe a link between dermatitis and ADHD and a cyclic relationship between acne and ADHD. Furthermore, the network includes several direct connections between sleep disorders and other health conditions, highlighting the impact of the former on the overall health and well-being of the patient. The average $$R^2$$

R
2

for a condition given the values of all conditions in the previous week is 0.67: in particular, 0.42 for acne, 0.85 for asthma, 0.58 for ADHD, 0.87 for burn, 0.76 for erectile dysfunction, 0.88 for scars, 0.57 for alcohol disorders, 0.57 for anxiety, 0.53 for depression, 0.74 for dermatitis, 0.60 for sleep disorders and 0.66 for obesity. Mapping disease interplay, indirect relationships, and the key role of mediators, such as sleep disorders, will allow healthcare professionals to address disease management holistically and more effectively. Even if we consider all skin and mental diseases jointly, each disease subnetwork is unique, allowing for more targeted interventions.
The effect of amorphous silica on soil–plant–water relations in soils with contrasting textures
Authors: Zarebanadkouki Mohsen , Al Hamwi Wael , Abdalla Mohanned , Rahnemaie Rasoul , Schaller Jörg 
Published: May 2024

AbstractThis study investigates how amorphous silica (ASi) influences soil–plant–water interactions in distinct soil textures. A sandy loam and silty clay soil were mixed with 0 and 2% ASi, and their impact on soil retention and soil hydraulic conductivity curves were determined. In parallel, tomato plants (Solanum lycopersicum L.) were grown in experimental pots under controlled conditions. When plants were established, the soil was saturated, and a controlled drying cycle ensued until plants reached their wilting points. Soil water content, soil water potential, plant transpiration rate, and leaf water potential were monitored during this process. Results indicate a positive impact of ASi on the sandy loam soil, enhancing soil water content at field capacity (FC, factor of 1.3 times) and at permanent wilting point (PWP, a factor of 3.5 times), while its effect in silty clay loam was negligible (< 1.05 times). In addition, the presence of ASi prevented a significant drop in soil hydraulic conductivity ($${K}_{h}$$

K
h

) at dry conditions. The $${K}_{h}$$

K
h

of ASi-treated sandy loam and silty clay at PWP were 4.3 times higher than their respective control. Transpiration rates in plants grown in ASi-treated sandy loam soil under soil drying conditions were higher than in the control, attributed to improved soil hydraulic conductivity. At the same time, no significant difference was observed in the transpiration of plants treated with ASi in silty clay soil. This suggests ASi boosts soil–plant–water relationships in coarse-textured soils by maintaining heightened hydraulic conductivity, with no significant effect on fine-textured soils.
Insights into the relationship between eye movements and personality traits in restricted visual fields
Authors: Xu Kuangzhe 
Published: May 2024

AbstractPrevious studies have suggested behavioral patterns, such as visual attention and eye movements, relate to individual personality traits. However, these studies mainly focused on free visual tasks, and the impact of visual field restriction remains inadequately understood. The primary objective of this study is to elucidate the patterns of conscious eye movements induced by visual field restriction and to examine how these patterns relate to individual personality traits. Building on previous research, we aim to gain new insights through two behavioral experiments, unraveling the intricate relationship between visual behaviors and individual personality traits. As a result, both Experiment 1 and Experiment 2 revealed differences in eye movements during free observation and visual field restriction. Particularly, simulation results based on the analyzed data showed clear distinctions in eye movements between free observation and visual field restriction conditions. This suggests that eye movements during free observation involve a mixture of conscious and unconscious eye movements. Furthermore, we observed significant correlations between conscious eye movements and personality traits, with more pronounced effects in the visual field restriction condition used in Experiment 2 compared to Experiment 1. These analytical findings provide a novel perspective on human cognitive processes through visual perception.
Exploring the mechanism of Jinlida granules against type 2 diabetes mellitus by an integrative pharmacology strategy
Authors: Gu Haiyan , Zhong Liang , Zhang Yuxin , Sun Jinghua , Liu Lipeng , Liu Zanchao 
Published: May 2024

AbstractJinlida granule (JLD) is a Traditional Chinese Medicine (TCM) formula used for the treatment of type 2 diabetes mellitus (T2DM). However, the mechanism of JLD treatment for T2DM is not fully revealed. In this study, we explored the mechanism of JLD against T2DM by an integrative pharmacology strategy. Active components and corresponding targets were retrieved from Traditional Chinese Medicine System Pharmacology (TCMSP), SwissADME and Bioinformatics Analysis Tool for Molecular Mechanisms of Traditional Chinese Medicine Database (BATMAN-TCM) database. T2DM-related targets were obtained from Drugbank and Genecards databases. The protein–protein interaction (PPI) network was constructed and analyzed with STRING (Search Toll for the Retrieval of Interacting Genes/proteins) and Cytoscape to get the key targets. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analyses were performed with the Database for Annotation, Visualization and Integrated Discovery (DAVID). Lastly, the binding capacities and reliability between potential active components and the targets were verified with molecular docking and molecular dynamics simulation. In total, 185 active components and 337 targets of JLD were obtained. 317 targets overlapped with T2DM-related targets. RAC-alpha serine/threonine-protein kinase (AKT1), tumor necrosis factor (TNF), interleukin-6 (IL-6), cellular tumor antigen p53 (TP53), prostaglandin G/H synthase 2 (PTGS2), Caspase-3 (CASP3) and signal transducer and activator of transcription 3 (STAT3) were identified as seven key targets by the topological analysis of the PPI network. GO and KEGG enrichment analyses showed that the effects were primarily associated with gene expression, signal transduction, apoptosis and inflammation. The pathways were mainly enriched in PI3K-AKT signaling pathway and AGE-RAGE signaling pathway in diabetic complications. Molecular docking and molecular dynamics simulation verified the good binding affinity between the key components and targets. The predicted results may provide a theoretical basis for drug screening of JLD and a new insight for the therapeutic effect of JLD on T2DM.
Immunoinformatics design of a structural proteins driven multi-epitope candidate vaccine against different SARS-CoV-2 variants based on fynomer
Authors: Sarvmeili Javad , Baghban Kohnehrouz Bahram , Gholizadeh Ashraf , Shanehbandi Dariush , Ofoghi Hamideh 
Published: May 2024

AbstractThe ideal vaccines for combating diseases that may emerge in the future require more than simply inactivating a few pathogenic strains. This study aims to provide a peptide-based multi-epitope vaccine effective against various severe acute respiratory syndrome coronavirus 2 strains. To design the vaccine, a library of peptides from the spike, nucleocapsid, membrane, and envelope structural proteins of various strains was prepared. Then, the final vaccine structure was optimized using the fully protected epitopes and the fynomer scaffold. Using bioinformatics tools, the antigenicity, allergenicity, toxicity, physicochemical properties, population coverage, and secondary and three-dimensional structures of the vaccine candidate were evaluated. The bioinformatic analyses confirmed the high quality of the vaccine. According to further investigations, this structure is similar to native protein and there is a stable and strong interaction between vaccine and receptors. Based on molecular dynamics simulation, structural compactness and stability in binding were also observed. In addition, the immune simulation showed that the vaccine can stimulate immune responses similar to real conditions. Finally, codon optimization and in silico cloning confirmed efficient expression in Escherichia coli. In conclusion, the fynomer-based vaccine can be considered as a new style in designing and updating vaccines to protect against coronavirus disease.
Blowflies are potential vector for avian influenza virus at enzootic area in Japan
Authors: Fujita Ryosuke , Tachi Takuji , Hino Masato , Nagata Kosuke , Saiki Masahiro , Inumaru Mizue , Higa Yukiko , Itokawa Kentaro , Uemura Nozomi , Matsumura Ryo , Kai Izumi , Sawabe Kyoko , Kobayashi Mutsuo , Isawa Haruhiko , Kusakabe Takahiro , Matsuo Kazunori , Kasai Shinji 
Published: May 2024

AbstractHigh pathogenicity avian influenza (HPAI) poses a significant threat to both domestic and wild birds globally. The avian influenza virus, known for environmental contamination and subsequent oral infection in birds, necessitates careful consideration of alternative introduction routes during HPAI outbreaks. This study focuses on blowflies (genus Calliphora), in particular Calliphora nigribarbis, attracted to decaying animals and feces, which migrate to lowland areas of Japan from northern or mountainous regions in early winter, coinciding with HPAI season. Our investigation aims to delineate the role of blowflies as HPAI vectors by conducting a virus prevalence survey in a wild bird HPAI-enzootic area. In December 2022, 648 Calliphora nigribarbis were collected. Influenza virus RT-PCR testing identified 14 virus-positive samples (2.2% prevalence), with the highest occurrence observed near the crane colony (14.9%). Subtyping revealed the presence of H5N1 and HxN1 in some samples. Subsequent collections in December 2023 identified one HPAI virus-positive specimen from 608 collected flies in total, underscoring the potential involvement of blowflies in HPAI transmission. Our observations suggest C. nigribarbis may acquire the HPAI virus from deceased wild birds directly or from fecal materials from infected birds, highlighting the need to add blowflies as a target of HPAI vector control.
Evaluation of ChatGPT’s responses to information needs and information seeking of dementia patients
Authors: Saeidnia Hamid Reza , Kozak Marcin , Lund Brady D. , Hassanzadeh Mohammad 
Published: May 2024

AbstractMany people in the advanced stages of dementia require full-time caregivers, most of whom are family members who provide informal (non-specialized) care. It is important to provide these caregivers with high-quality information to help them understand and manage the symptoms and behaviors of dementia patients. This study aims to evaluate ChatGPT, a chatbot built using the Generative Pre-trained Transformer (GPT) large language model, in responding to information needs and information seeking of such informal caregivers. We identified the information needs of dementia patients based on the relevant literature (22 articles were selected from 2442 retrieved articles). From this analysis, we created a list of 31 items that describe these information needs, and used them to formulate 118 relevant questions. We then asked these questions to ChatGPT and investigated its responses. In the next phase, we asked 15 informal and 15 formal dementia-patient caregivers to analyze and evaluate these ChatGPT responses, using both quantitative (questionnaire) and qualitative (interview) approaches. In the interviews conducted, informal caregivers were more positive towards the use of ChatGPT to obtain non-specialized information about dementia compared to formal caregivers. However, ChatGPT struggled to provide satisfactory responses to more specialized (clinical) inquiries. In the questionnaire study, informal caregivers gave higher ratings to ChatGPT's responsiveness on the 31 items describing information needs, giving an overall mean score of 3.77 (SD 0.98) out of 5; the mean score among formal caregivers was 3.13 (SD 0.65), indicating that formal caregivers showed less trust in ChatGPT's responses compared to informal caregivers. ChatGPT’s responses to non-clinical information needs related to dementia patients were generally satisfactory at this stage. As this tool is still under heavy development, it holds promise for providing even higher-quality information in response to information needs, particularly when developed in collaboration with healthcare professionals. Thus, large language models such as ChatGPT can serve as valuable sources of information for informal caregivers, although they may not fully meet the needs of formal caregivers who seek specialized (clinical) answers. Nevertheless, even in its current state, ChatGPT was able to provide responses to some of the clinical questions related to dementia that were asked.
Production, characterization, and application of Pseudoxanthomonas taiwanensis biosurfactant: a green chemical for microbial enhanced oil recovery (MEOR)
Authors: Purwasena Isty Adhitya , Amaniyah Maghfirotul , Astuti Dea Indriani , Firmansyah Yoga , Sugai Yuichi 
Published: May 2024

AbstractBiosurfactants, as microbial bioproducts, have significant potential in the field of microbial enhanced oil recovery (MEOR). Biosurfactants are microbial bioproducts with the potential to reduce the interfacial tension (IFT) between crude oil and water, thus enhancing oil recovery. This study aims to investigate the production and characterization of biosurfactants and evaluate their effectiveness in increasing oil recovery. Pseudoxanthomonas taiwanensis was cultured on SMSS medium to produce biosurfactants. Crude oil was found to be the most effective carbon source for biosurfactant production. The biosurfactants exhibited comparable activity to sodium dodecyl sulfate (SDS) at a concentration of 400 ppm in reducing IFT. It was characterized as glycolipids, showing stability in emulsions at high temperatures (up to 120 °C), pH levels ranging from 3 to 9, and NaCl concentrations up to 10% (w/v). Response surface methodology revealed the optimized conditions for the most stable biosurfactants (pH 7, temperature of 40 °C, and salinity of 2%), resulting in an EI24 value of 64.45%. Experimental evaluations included sand pack column and core flooding studies, which demonstrated additional oil recovery of 36.04% and 12.92%, respectively. These results indicate the potential application of P. taiwanensis biosurfactants as sustainable and environmentally friendly approaches to enhance oil recovery in MEOR processes.
Breath-by-breath comparison of a novel percutaneous phrenic nerve stimulation approach with mechanical ventilation in juvenile pigs: a pilot study
Authors: Deininger Matthias Manfred , Ziles Dmitrij , Borleis Annegret , Seemann Teresa , Erlenkoetter Fabian , Bleilevens Christian , Lohse Arnhold , Benner Carl-Friedrich , Leonhardt Steffen , Walter Marian , Breuer Thomas 
Published: May 2024

AbstractAbout one in three critically ill patients requires mechanical ventilation (MV). Prolonged MV, however, results in diaphragmatic weakness, which itself is associated with delayed weaning and increased mortality. Inducing active diaphragmatic contraction via electrical phrenic nerve stimulation (PNS) not only provides the potential to reduce diaphragmatic muscular atrophy but also generates physiological-like ventilation and therefore offers a promising alternative to MV. Reasons why PNS is not yet used in critical care medicine are high procedural invasiveness, insufficient evidence, and lack of side-by-side comparison to MV. This study aims to establish a minimal-invasive percutaneous, bilateral electrode placement approach for sole PNS breathing and thereby enable, for the first time, a breath-by-breath comparison to MV. Six juvenile German Landrace pigs received general anesthesia and orotracheal intubation. Following the novel ultrasound-guided, landmark-based, 4-step approach, two echogenic needles per phrenic nerve were successfully placed. Stimulation effectiveness was evaluated measuring tidal volume, diaphragmatic thickening and tomographic electrical impedance in a breath-by-breath comparison to MV. Following sufficient bilateral phrenic nerve stimulation in all pigs, PNS breaths showed a 2.2-fold increase in diaphragmatic thickening. It induced tidal volumes in the lung-protective range by negative pressure inspiration and improved dorso-caudal regional ventilation in contrast to MV. Our study demonstrated the feasibility of a novel ultrasound-guided, percutaneous phrenic nerve stimulation approach, which generated sufficient tidal volumes and showed more resemblance to physiological breathing than MV in a breath-by-breath comparison.
Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators
Authors: Lee Haein , Kim Jang Hyun , Jung Hae Sun 
Published: May 2024

AbstractAs sustainability emerges as a crucial factor in the development of modern enterprises, integrating environmental, social, and governance (ESG) information into financial assessments has become essential. ESG indicators serve as important metrics in evaluating a company’s sustainable practices and governance effectiveness, influencing investor trust and future growth potential, ultimately affecting stock prices. This study proposes an innovative approach that combines ESG sentiment index extracted from news with technical indicators to predict the S&P 500 index. By utilizing a deep learning model and exploring optimal window sizes, the study explores the best model through mean absolute percentage error (MAPE) as an evaluation metric. Additionally, an ablation test clarifies the influence of ESG and its causality with the S&P 500 index. The experimental results demonstrate improved predictive accuracy when considering ESG sentiment compared to relying solely on technical indicators or historical data. This comprehensive methodology enhances the advantage of stock price prediction by integrating technical indicators, which consider short-term fluctuations, with ESG information, providing long-term effects. Furthermore, it offers valuable insights for investors and financial market experts, validating the necessity to consider ESG for financial assets and introducing a new perspective to develop investment strategies and decision-making processes.
Impact of specialized fatigue and backhand smash on the ankle biomechanics of female badminton players
Authors: He Zhanyang , Liu Gongju , Zhang Bin , Ye Binyong , Zhu Houwei 
Published: May 2024

AbstractDuring fatigued conditions, badminton players may experience adverse effects on their ankle joints during smash landings. In addition, the risk of ankle injury may vary with different landing strategies. This study aimed to investigate the influence of sport-specific fatigue factors and two backhand smash actions on ankle biomechanical indices. Thirteen female badminton players (age: 21.2 ± 1.9 years; height: 167.1 ± 4.1 cm; weight: 57.3 ± 5.1 kg; BMI: 20.54 ± 1.57 kg/m2) participated in this study. An 8-camera Vicon motion capture system and three Kistler force platforms were used to collect kinematic and kinetic data before and after fatigue for backhand rear-court jump smash (BRJS) and backhand lateral jump smash (BLJS). A 2 × 2 repeated measures analysis of variance was employed to analyze the effects of these smash landing actions and fatigue factors on ankle biomechanical parameters. Fatigue significantly affected the ankle-joint plantarflexion and inversion angles at the initial contact (IC) phase (p < 0.05), with both angles increasing substantially post-fatigue. From a kinetic perspective, fatigue considerably influenced the peak plantarflexion and peak inversion moments at the ankle joint, which resulted in a decrease the former and an increase in the latter after fatigue. The two smash landing actions demonstrated different landing strategies, and significant main effects were observed on the ankle plantarflexion angle, inversion angle, peak dorsiflexion/plantarflexion moment, peak inversion/eversion moment, and peak internal rotation moment (p < 0.05). The BLJS landing had a much greater landing inversion angle, peak inversion moment, and peak internal rotation moment compared with BRJS landing. The interaction effects of fatigue and smash actions significantly affected the muscle force of the peroneus longus (PL), with a more pronounced decrease in the force of the PL muscle post-fatigue in the BLJS action(post-hoc < 0.05). This study demonstrated that fatigue and smash actions, specifically BRJS and BLJS, significantly affect ankle biomechanical parameters. After fatigue, both actions showed a notable increase in IC plantarflexion and inversion angles and peak inversion moments, which may elevate the risk of lateral ankle sprains. Compared with BRJS, BLJS poses a higher risk of lateral ankle sprains after fatigue.