bluuebunny/crossref_metadata_2025_split huggingface dataset:
New connections of medication use and polypharmacy with the gut microbiota composition and functional potential in a large population
Authors: Larsson Anna , Ericson Ulrika , Jönsson Daniel , Miari Mariam , Athanasiadis Paschalis , Baldanzi Gabriel , Brunkwall Louise , Hellstrand Sophie , Klinge Björn , Melander Olle , Nilsson Peter M. , Fall Tove , Maziarz Marlena , Orho-Melander Marju 
Published: October 2024

AbstractMedication can affect the gut microbiota composition and function. The aim of this study was to investigate connections between use of common non-antibiotic medicines and the gut microbiota composition and function in a large Swedish cohort (N = 2223). Use of 67 medications and polypharmacy (≥ 5 medications), based on self-reported and prescription registry data, were associated with the relative abundance of 881 gut metagenomic species (> 5% prevalence) and 103 gut metabolic modules (GMMs). Altogether, 97 associations of 26 medications with 40 species and of four medications with five GMMs were observed (false discovery rate < 5%). Several earlier findings were replicated like the positive associations of proton pump inhibitors (PPIs) with numerous oral species, and those of metformin with Escherichia species and with lactate consumption I and arginine degradation II. Several new associations were observed between, among others, use of antidepressants, beta-blockers, nonsteroidal anti-inflammatory drugs and calcium channel blockers, and specific species. Polypharmacy was positively associated with Enterococcus faecalis, Bacteroides uniformis, Rothia mucilaginosa, Escherichia coli and Limosilactobacillus vaginalis, and with 13 GMMs. We confirmed several previous findings and identified numerous new associations between use of medications/polypharmacy and the gut microbiota composition and functional potential. Further studies are needed to confirm the new findings.
Performance of yellow and pink oyster mushroom dyes in dye sensitized solar cell
Authors: Pirdaus Nur Alfarina , Ahmad Nurfadzilah , Dahlan Nofri Yenita , Redzuan Ainur Nisha , Zalizan Aisyatul Husna , Muhammad-Sukki Firdaus , Bani Nurul Aini , Abdul Patah Muhamad Fazly , Wan-Mohtar Wan Abd Al Qadr Imad 
Published: October 2024

AbstractA solar photovoltaic (PV) cell, is an electrical device that uses the PV effect to convert light energy into electricity. The application of oyster mushroom dyes in dye sensitized solar cell (DSSC) is a novel strategy to substitute the costly chemical production process with easily extractable, environmentally acceptable dyes. Both dyes of yellow and pink oyster mushrooms were extracted using the same process but dried into powder form using two techniques, warm drying and freeze drying. The characterization was carried out utilizing current-voltage (I-V) characterization for electrical properties, Ultraviolet-Visible (UV-Vis) spectrophotometer for optical properties, Field Emission Scanning Electron Microscopy (FESEM), and Atomic Force Microscopy (AFM) for the structural properties. It was found that freeze-dried pink and yellow oyster mushroom had shown the good properties for DSSC application as it produced energy bandgap which lies within the range of efficient dye sensitizer; 1.7 eV and 2.2 eV, the most uniform distribution of pores and a nearly spherical form in FESEM analysis, and AFM result obtained with the highest root mean square (RMS) roughness value (26.922 and 34.033) with stereoscopic morphologies. The data proved that mushroom dyes can be incorporated in DSSC with the optimization of drying method in the extraction process, dilution of dye and the layer of deposition on the glass substrate. The current density-voltage (J–V) characteristics of fabricated DSSC was characterized using Newport Oriel Sol3A solar simulator under AM 1.5 Sun condition (100 mW/cm2, 25 oC). From the result obtained by solar simulator, the fabricated FTO/TiO2/Pleurotus djamor dye/Pt indicated the Voc of 0.499 V and Jsc of 0.397 mA/cm2.
Factors shaping pelagic-benthic coupling in the process of settlement in an Arctic fjord
Authors: Ronowicz Marta , Balazy Piotr , Chełchowski Maciej , Kuklinski Piotr , Patuła Weronika , Sowa Anna , Søreide Janne E. , Weydmann-Zwolicka Agata 
Published: October 2024

AbstractBenthic organisms typically possess a planktonic propagule stage in the form of larvae or spores, which enables them to spread over large distances before settlement, and promotes tight pelago-benthic coupling. However, factors driving dispersal and epibenthos recruitment in shallow hard-bottom Arctic communities are poorly known. We therefore conducted a year-round in situ colonization experiment in Isfjorden (Svalbard), and found out that variation in early-stage epibenthic assemblages was explained by the combination of: abiotic (45.9%) and biotic variables (23.9%), and their interactions (30.2%). The upward-facing experimental plates were dominated by coralline algae, and this is the first study showing that at high latitudes coralline algae Lithothamnion sp. settle in high numbers on available substrates during the polar night in winter. The downward-facing plates, which had much less exposure to light, contained more diverse organisms, with a predominance of polychaetas and bryozoans. However, in summer, the barnacle Semibalanus balanoides outcompeted all the other recruits, as a result of massive occurrence of meroplanktonic Cirripedia larvae, triggered by the phytoplankton bloom. In conclusion, the rate and success of epibenthic settlements were dependent mostly on light availability and temperature, suggesting that larval settlement will be impacted by global warming with some taxa benefitting, while others losing.
The role of rice husk in Oreochromis niloticus safety enhancement by bio-adsorbing copper oxide nanoparticles following its green synthesis: an endeavor to advance environmental sustainability
Authors: Hamed Aliaa , Badran Shereen R. 
Published: October 2024

AbstractLowering nanoparticles (NPs) toxicity before discharge into aquatic environments and employing agricultural waste materials for environmental sustainability are necessary nowadays. Since this has never been done, this work examines how green CuO NPs treated with rice husk (RH) as a bio-adsorbent may be safer for Nile tilapia (Oreochromis niloticus) than chemically manufactured ones. So, five groups of fish were randomly placed in glass aquaria. One group was a control, and four groups received 50 mg/L green and chemically produced CuO NPs (GS and CS) with and without RH for 24, 48, and 96 h. RH was collected from all groups, and the results showed GS-CuO NPs had a greater adsorptive capacity than CS-CuO NPs after all time intervals. After analyzing fish indicators in all groups compared to the control, higher Cu bioaccumulation was exhibited in the liver and gills. The liver and gills showed elevated levels of glutathione peroxidase (GPx), catalase (CAT), and thiobarbituric acid reactive substances (TBARS), while the levels of glutathione reduced (GSH) were significantly lower. In addition, Cu exposure impaired liver and gill histology. Finally, our results indicated that using RH as an adsorbent for CuO NPs after their green synthesis instead of chemical synthesis before they enter the aquatic environment can enhance the overall health of fish and environmental sustainability.
Integrating holotomography and deep learning for rapid detection of NPM1 mutations in AML
Authors: Kim Hyunji , Kim Geon , Park HeyJung , Lee Mahn Jae , Park YongKeun , Jang Seongsoo 
Published: October 2024

AbstractRapid and accurate diagnosis of acute myeloid leukemia (AML) remains a significant challenge, particularly in the context of myelodysplastic syndrome (MDS) or MDS/myeloproliferative neoplasm with NPM1 mutations. This study introduces an innovative approach using holotomography (HT), a 3D label-free quantitative phase imaging technique, to detect NPM1 mutations. We analyzed a dataset of 2073 HT myeloblast images from 48 individuals, including both NPM1 wild-type and mutated samples, to distinguish subcellular morphological changes associated with NPM1 mutations. Employing a convolutional neural network, we analyzed 3D cell morphology, focusing on refractive index distributions. The machine learning model showed high accuracy, with an area under the receiver operating characteristic curve of 0.9375 and a validation accuracy of 76.0%. Our findings reveal distinct morphological differences between the NPM1 wild-type and mutation at the subcellular level. This study demonstrates the potential of HT combined with deep learning for early, efficient, and cost-effective diagnosis of AML, offering a promising alternative to traditional stepwise genetic testing methods and providing additional assistance in morphological myeloblast discrimination. This approach may revolutionize the diagnostic process in leukemia, facilitating early detection and potentially reducing the reliance on extensive genetic testing.
Identification and analysis of differentially expressed lncRNAs and their ceRNA networks in DMD/mdx primary myoblasts
Authors: Gorji Abdolvahab Ebrahimpour , Ahmadian Kasra , Roudbari Zahra , Sadkowski Tomasz 
Published: October 2024

AbstractThis study explored the significance of long non-coding RNAs (lncRNAs), particularly their role in maintaining dystrophin protein stability and regulating myocyte proliferation and differentiation. The investigation focused on DMD/mdx mouse skeletal muscle primary myoblasts, aiming to identify lncRNAs potential as biomarkers and therapeutic targets for Duchenne muscular dystrophy (DMD). Utilizing CLC Genomics Workbench software, 554 differentially expressed lncRNAs were identified in DMD/mdx mice compared to wild-type (WT) control. Among them, 373 were upregulated, and 181 were downregulated. The study highlighted specific lncRNAs (e.g., 5930430L01Rik, Gm10143, LncRNA1490, LncRNA580) and their potential regulatory roles in DMD key genes like IGF1, FN1, TNNI1, and MYOD1. By predicting miRNA and their connections with lncRNA and mRNA (ceRNA network) using tools such as miRNet, miRSYSTEM and miRCARTA, the study revealed potential indirect regulation of Dystrophin, IGF1R and UTRN genes by identified lncRNAs (e.g. 2310001H17Rik-203, C130073E24Rik-202, LncRNA2767, 5930430L01Rik and LncRNA580). These findings suggest that the identified lncRNAs may play crucial roles in the development and progression of DMD through their regulatory influence on key gene expression, providing valuable insights for potential therapeutic interventions.
A proteomics perspective on 2 years of high-intensity training in horses: a pilot study
Authors: Johansson L. , Ringmark S. , Bergquist J. , Skiöldebrand E. , Widgren A. , Jansson A. 
Published: October 2024

AbstractThe human plasma proteome is rather well studied, but not that of other species, including horses. The aims of this study were to (1), explore differences in plasma proteomic profile of young elite harness trotters kept under standardised conditions and subjected to two different training programmes for 2 years and (2) explore changes in proteomic profile over time during the training period. From September at age 1.5 year to March at age 2 years, 16 Standardbred horses were exposed to the same training programme. In March, high-intensity training was introduced and the horses were divided into two training groups (High and Low). Blood samples were collected at rest in December as 1.5-year-olds, July as 2-year-olds, December as 2.5-year-olds and December as 3.5-year-olds. Untargeted proteomics was performed and a hypothesis-generating approach was used in statistical analysis (t-tests). At the age of 2.5 years, the level of serotransferrin was higher in the High group (P = 0.01) and at least at one sampling occasion, proteins associated with fat metabolism, oxidant/antioxidant processes, cardiovascular responses, bone formation and inflammation were lower in High group compared to Low (P < 0.05). Analyses of changes over time revealed that levels of proteins involved in energy metabolism, red cell metabolism, circulation, oxidant/antioxidant activity, bone formation, inflammation, immune modulation and cellular and vascular damage changed (P < 0.05). The results indicate that proteomics analysis of blood plasma could be a viable tool for evaluation of exercise adaptations, performance and for health monitoring, with several potential biomarkers identified in this study.
A besyian regularisation neural network approach for hepatitis B virus spread prediction and immune system therapy model
Authors: Galal Ahmed M. , Haider Qusain , Hassan Ali , Arshad Mubashar , Alam Mohammad Mahtab , Al-Essa Laila A. , Habenom Haile 
Published: October 2024

AbstractThe primary aim of the article is to analyze the response of the human immune system when it encounters the hepatitis B virus. This is done using a mathematical system of differential equations. The differential equation system has six components, likely representing various aspects of the immune response or virus dynamics. A Bayesian regularization neural network has been presented in the process of training. These networks are employed to find solutions for different categories or scenarios related to hepatitis B infection. The Adams method is used to generate reference data sets. The back-propagated artificial neural network, based on Bayesian regularization, is trained and validated using the generated data. The data is divided into three sets: 90% for training and 5% each for testing and validation. The correctness and effectiveness of the proposed neural network model have been assessed using various evaluation metrics. The metrics have been used in this study are Mean Square Error (MSE), histogram errors, and regression plots. These measures provide support to the neural network to approximate the immune response to the hepatitis B virus.
Systematic review and meta-analysis of standalone digital interventions for cognitive symptoms in people without dementia
Authors: Cabreira Veronica , Wilkinson Tim , Frostholm Lisbeth , Stone Jon , Carson Alan 
Published: October 2024

AbstractCognitive symptoms are prevalent across neuropsychiatric disorders, increase distress and impair quality of life. Self-guided digital interventions offer accessibility, scalability, and may overcome the research-to-practice treatment gap. Seventy-six trials with 5214 participants were identified. A random-effects meta-analysis investigated the effects of all digital self-guided interventions, compared to controls, at post-treatment. We found a small-to-moderate positive pooled effect on cognition (k = 71; g = −0.51, 95%CI −0.64 to −0.37; p < 0.00001) and mental health (k = 30; g = −0.41, 95%CI −0.60 to −0.22; p < 0.0001). Positive treatment effects on fatigue (k = 8; g = −0.27, 95%CI −0.53 to −0.02; p = 0.03) and quality of life (k = 22; g = −0.17, 95%CI −0.34 to −0.00; p = 0.04) were only marginally significant. No significant benefit was found for performance on activities of daily living. Results were independent of control groups, treatment duration, risk of bias and delivery format. Self-guided digital transdiagnostic interventions may benefit at least a subset of patients in the short run, yet their impact on non-cognitive outcomes remains uncertain.
SMARCD1 is an essential expression-restricted metastasis modifier
Authors: Ross Christina , Gong Li-Yun , Jenkins Lisa M. , Ha Ngoc-han , Majocha Megan , Hunter Kent W. 
Published: October 2024

AbstractBreast cancer is the most frequently diagnosed cancer worldwide, constituting 15% of cases in 2023. The predominant cause of breast cancer-related mortality is metastasis, and a lack of metastasis-targeted therapies perpetuates dismal outcomes for late-stage patients. By using meiotic genetics to study inherited transcriptional network regulation, we have identified, to the best of our knowledge, a new class of “essential expression-restricted” genes as potential candidates for metastasis-targeted therapeutics. Building upon previous work implicating the CCR4-NOT RNA deadenylase complex in metastasis, we demonstrate that RNA-binding proteins NANOS1, PUM2, and CPSF4 also regulate metastatic potential. Using various models and clinical data, we pinpoint Smarcd1 mRNA as a target of all three RNA-BPs. Strikingly, both high and low expression of Smarcd1 correlate with positive clinical outcomes, while intermediate expression significantly reduces the probability of survival. Applying the theory of “essential genes” from evolution, we identify 50 additional genes that require precise expression levels for metastasis to occur. Specifically, small perturbations in Smarcd1 expression significantly reduce metastasis in mouse models and alter splicing programs relevant to the ER+/HER2-enriched breast cancer. Identification subtype-specific essential expression-restricted metastasis modifiers introduces a novel class of genes that, when therapeutically “nudged” in either direction, may significantly improve late-stage breast cancer patients.
Discovery of a potent, Kv7.3-selective potassium channel opener from a Polynesian traditional botanical anticonvulsant
Authors: Abbott Geoffrey W. , Manville Rían W. 
Published: October 2024

AbstractPlants remain an important source of biologically active small molecules with high therapeutic potential. The voltage-gated potassium (Kv) channel formed by Kv7.2/3 (KCNQ2/3) heteromers is a major target for anticonvulsant drug development. Here, we screened 1444 extracts primarily from plants collected in California and the US Virgin Islands, for their ability to activate Kv7.2/3 but not inhibit Kv1.3, to select against tannic acid being the active component. We validated the 7 strongest hits, identified Thespesia populnea (miro, milo, portia tree) as the most promising, then discovered its primary active metabolite to be gentisic acid (GA). GA highly potently activated Kv7.2/3 (EC50, 2.8 nM). GA is, uniquely to our knowledge, 100% selective for Kv7.3 versus other Kv7 homomers; it requires S5 residue Kv7.3-W265 for Kv7.2/3 activation, and it ameliorates pentylenetetrazole-induced seizures in mice. Structure-activity studies revealed that the FDA-approved vasoprotective drug calcium dobesilate, a GA analog, is a previously unrecognized Kv7.2/3 channel opener. Also an active aspirin metabolite, GA provides a molecular rationale for the use of T. populnea as an anticonvulsant in Polynesian indigenous medicine and presents novel pharmacological prospects for potent, isoform-selective, therapeutic Kv7 channel activation.
Polar domain walls induced by sequential symmetry breaking in frustrated mechanical metamaterials
Authors: Zhou Yuan , Zhang Yafei , Wen Zhixuan , Chen Chang Qing 
Published: October 2024

AbstractFrustration hinders ideal local interactions in systems ranging from artificial spin ices with ice rules to mechanical metamaterials featuring regular floppy modes. While geometric frustrations have been known to promote complex ordered patterns in tessellated lattice structures, there is growing interest in global frustrations due to the topologically nontrivial geometry of metamaterials. However, multiferroic orders in globally frustrated metamaterials have remained elusive. Here, we present polar domain walls constrained in flexible mechanical metamaterials with global frustration and sequential symmetry breaking. We showcase that under simple compressive loading, competing interactions in globally frustrated metamaterials give rise to mixed deformations with high-order buckling modes described by an emergent order parameter. Utilizing an elastic mechanism model, we unveil the process of sequential symmetry breaking and capture the formation of polar domain walls exhibiting a chiral distribution within a multi-well potential landscape. We further demonstrate how to eliminate frustration through torsional loading, leading to symmetry restoration. These results provide insights into the intricate interactions between order and frustration, inspiring the modulation of domain walls in macroscopic systems.
Influence of dislocation cells on hydrogen embrittlement in wrought and additively manufactured Inconel 718
Authors: Santos Maldonado Claudia-Tatiana , Zafra Alfredo , Martínez Pañeda Emilio , Sandmann Paul , Morana Roberto , Pham Minh-Son 
Published: October 2024

AbstractHydrogen embrittlement (HE) is a major issue for the mechanical integrity of high-strength alloys exposed to hydrogen-rich environments, with diffusion and trapping of hydrogen being critical phenomena. Here, the role of microstructure on hydrogen diffusion, trapping and embrittlement in additively manufactured (AM) and wrought Inconel 718 is compared, revealing the key role played by dislocation cells. Trapping behaviour in hydrogen-saturated alloys is analysed by thermal desorption spectroscopy and numerical simulations. A high density of hydrogen traps in cell walls, attributed to dense dislocations and Laves phases, are responsible for the local accumulation of hydrogen, causing significant loss in strength, and triggering cracking along dislocation cell walls. The influential role of dislocation cells alters fracture behaviour from intergranular in the wrought alloy to intragranular for the AM alloy, due to the large proportion of dislocation cells in AM alloys. In addition, the cellular network of dislocations accelerates hydrogen diffusion, enabling faster and deeper penetration of hydrogen in the AM alloy. These results indicate that the higher HE susceptibility of nickel superalloys is intrinsically associated with the interaction of hydrogen with dislocation walls.
Methodological choices and clinical usefulness for machine learning predictions of outcome in Internet-based cognitive behavioural therapy
Authors: Hentati Isacsson Nils , Ben Abdesslem Fehmi , Forsell Erik , Boman Magnus , Kaldo Viktor 
Published: October 2024

Abstract
Background
While psychological treatments are effective, a substantial portion of patients do not benefit enough. Early identification of those may allow for adaptive treatment strategies and improved outcomes. We aimed to evaluate the clinical usefulness of machine-learning (ML) models predicting outcomes in Internet-based Cognitive Behavioural Therapy, to compare ML-related methodological choices, and guide future use of these.

Methods
Eighty main models were compared. Baseline variables, weekly symptoms, and treatment activity were used to predict treatment outcomes in a dataset of 6695 patients from regular care.

Results
We show that the best models use handpicked predictors and impute missing data. No ML algorithm shows clear superiority. They have a mean balanced accuracy of 78.1% at treatment week four, closely matched by regression (77.8%).

Conclusions
ML surpasses the benchmark for clinical usefulness (67%). Advanced and simple models perform equally, indicating a need for more data or smarter methodological designs to confirm advantages of ML.
Compound and cascading droughts and heatwaves decrease maize yields by nearly half in Sinaloa, Mexico
Authors: Sutanto Samuel J. , Zarzoza Mora Susana B. , Supit Iwan , Wang Mengru 
Published: October 2024

AbstractThe impact of droughts and heatwaves on agriculture losses has been exacerbated by the occurrence of compound and cascading events. Here we present a study that evaluates the impact of these events both as singly and as compound and cascading on maize yield in Sinaloa Mexico from 1990 to 2022, using the WOFOST crop model. Drought and heatwave events were identified using the Standardized Precipitation Index and threshold method, respectively. Results show that yield reduction (25%) is found during extreme drought events, emphasizing the vulnerability of maize farming to unfavorable drought conditions. While heatwaves alone did not show a significant impact on maize yields, the compound and cascading droughts and heatwaves amplified the loss of maize yields by up to 44% compared to normal conditions. This study highlights the need for adaptive strategies in agriculture to sustain food security during extreme events, especially in the context of multi hazard framework.