Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing the Differential Impact of an Internet-Based Mental Health Intervention on Outcomes of Well-being and Psychological Distress During COVID-19: Uncontrolled Intervention Study

Background During COVID-19, the psychological distress and well-being of the general population has been precarious, increasing the need to determine the impact of complementary internet-based psychological interventions on both positive mental health as well as distress states. Psychological distress and mental well-being represent distinct dimensions of our mental health, and congruent changes in outcomes of distress and well-being do not necessarily co-occur within individuals. When testing intervention impact, it is therefore important to assess change in both outcomes at the individual level, rather than solely testing group differences in average scores at the group level. Objective This study set out to investigate the differential impact of an internet-based group mental health intervention on outcomes of positive mental health (ie, well-being, life satisfaction, resilience) and indicators of psychological distress (ie, depression, anxiety, stress). Methods A 5-week mental health intervention was delivered to 89 participants using the Zoom platform during 2020. Impact on outcomes of distress, well-being, and resilience was assessed at the start and end of the program with multiple analysis of variance (MANOVA) and reliable change indices (RCIs) being used to determine program impact at the group and individual levels, respectively. Results The intervention significantly improved all mental health outcomes measured, (F6,83=5.60, P<.001; Wilks Λ=.71; partial η2=.29) showing small to moderate effect sizes on individual outcomes. The largest effect sizes were observed for life satisfaction and overall well-being (η2=.22 and η2=.2, respectively). Larger effect sizes were noted for those with problematic mental health scores at baseline. A total of 92% (82/89) of participants demonstrated reliable change in at least one mental health outcome. Differential response patterns using RCI revealed that more than one-half of the participants showed improvement in both mental well-being and psychological distress, over one-quarter in outcomes of well-being only, and almost one-fifth in distress only. Conclusions The results provide evidence for the significant impact of an internet-based mental health intervention during COVID-19 and indicate the importance of assessing dimensions of both well-being and distress when determining mental health intervention effectiveness.
Platelet-Rich Fibrin Increases BMP2 Expression in Oral Fibroblasts via Activation of TGF-β Signaling

Solid platelet-rich fibrin (PRF), consisting of coagulated plasma from fractionated blood, has been proposed to be a suitable carrier for recombinant bone morphogenetic protein 2 (BMP2) to target mesenchymal cells during bone regeneration. However, whether solid PRF can increase the expression of BMPs in mesenchymal cells remains unknown. Proteomics analysis confirmed the presence of TGF-β1 but not BMP2 in PRF lysates. According to the existing knowledge of recombinant TGF-β1, we hypothesized that PRF can increase BMP2 expression in mesenchymal cells. To test this hypothesis, we blocked TGF-β receptor 1 kinase with SB431542 in gingival fibroblasts exposed to PRF lysates. RT-PCR and immunoassays confirmed that solid PRF lysates caused a robust SB431542-dependent increase in BMP2 expression in gingival fibroblasts. Additionally, fractions of liquid PRF, namely platelet-poor plasma (PPP) and the buffy coat (BC) layer, but not heat-denatured PPP (Alb-gel), greatly induced the expression of BMP2 in gingival fibroblasts. Even though PRF has no detectable BMPs, PRF lysates similar to recombinant TGF-β1 had the capacity to provoke canonical BMP signaling, as indicated by the nuclear translocation of Smad1/5 and the increase in its phosphorylation. Taken together, our data suggest that PRF can activate TGF-β receptor 1 kinase and consequently induce the production of BMP2 in cells of the mesenchymal lineage.
Dexamethasone Inhibits the Pro-Angiogenic Potential of Primary Human Myoblasts

Tissue regeneration depends on the complex processes of angiogenesis, inflammation and wound healing. Regarding muscle tissue, glucocorticoids (GCs) inhibit pro-inflammatory signalling and angiogenesis and lead to muscle atrophy. Our hypothesis is that the synthetic GC dexamethasone (dex) impairs angiogenesis leading to muscle atrophy or inhibited muscle regeneration. Therefore, this study aims to elucidate the effect of dexamethasone on HUVECs under different conditions in mono- and co-culture with myoblasts to evaluate growth behavior and dex impact with regard to muscle atrophy and muscle regeneration. Viability assays, qPCR, immunofluorescence as well as ELISAs were performed on HUVECs, and human primary myoblasts seeded under different culture conditions. Our results show that dex had a higher impact on the tube formation when HUVECs were maintained with VEGF. Gene expression was not influenced by dex and was independent of cells growing in a 2D or 3D matrix. In co-culture CD31 expression was suppressed after incubation with dex and gene expression analysis revealed that dex enhanced expression of myogenic transcription factors, but repressed angiogenic factors. Moreover, dex inhibited the VEGF mediated pro angiogenic effect of myoblasts and inhibited expression of angiogenic inducers in the co-culture model. This is the first study describing a co-culture of human primary myoblast and HUVECs maintained under different conditions. Our results indicate that dex affects angiogenesis via inhibition of VEGF release at least in myoblasts, which could be responsible not only for the development of muscle atrophy after dex administration, but also for inhibition of muscle regeneration after vascular damage.
π‐Conjugated Donor Polymers: Structure Formation and Morphology in Solution, Bulk and Photovoltaic Blends

The field of conjugated polymers has expanded in the last years considerably and impressive performance, both in field effect transistors and photovoltaic devices has been achieved. After the initial emphasis on improving the performance, more emphasis is recently given to fundamental studies on structure formation. Therefore, this review concentrates on systematic correlation studies of structure formation in solution, in bulk and thin films as well as in photovoltaic blends of donor‐type π‐conjugated polymers. The main focus is on the correlation of structure, morphology and molecular chain orientation as a function of macromolecular properties such as molecular weight, dispersity, non‐covalent intramolecular and intermolecular interactions, solvent interactions and innovative processing techniques. The tools applied for elucidating fundamental information of structure formation and orientation mainly consist of optical spectroscopy and scattering techniques (SAXS/WAXS/GIWAXS). Since the field of conjugated polymers is very vast in terms of chemical structural diversity, only selected examples of donor polymers are covered here and the emerging class of n‐type conjugated polymers are not included. The focus is not on the structural variation or their performance in solar cells or transistors in terms of record efficiencies, but on the systematic studies leading to a structure‐property correlation in donor polymers.
Metabolomics reveals biomarkers in human urine and plasma to predict cytochrome P450 2D6 (CYP2D6) activity

Individualized assessment of cytochrome P450 2D6 (CYP2D6) activity is usually performed through phenotyping following administration of a probe drug to measure the enzyme's activity. To avoid any iatrogenic harm (allergic drug reaction, dosing error) related to the probe drug, the development of non‐burdensome tools for real‐time phenotyping of CYP2D6 could significantly contribute to precision medicine. This study focuses on the identification of markers of the CYP2D6 enzyme in human biofluids using an LC‐high‐resolution mass spectrometry‐based metabolomic approach.
Fabrication of Large-Area Molybdenum Disulfide Device Arrays Using Graphene/Ti Contacts

Two-dimensional (2D) molybdenum disulfide (MoS2) is the most mature material in 2D material fields owing to its relatively high mobility and scalability. Such noticeable properties enable it to realize practical electronic and optoelectronic applications. However, contact engineering for large-area MoS2 films has not yet been established, although contact property is directly associated to the device performance. Herein, we introduce graphene-interlayered Ti contacts (graphene/Ti) into large-area MoS2 device arrays using a wet-transfer method. We achieve MoS2 devices with superior electrical and photoelectrical properties using graphene/Ti contacts, with a field-effect mobility of 18.3 cm2/V∙s, on/off current ratio of 3 × 107, responsivity of 850 A/W, and detectivity of 2 × 1012 Jones. This outstanding performance is attributable to a reduction in the Schottky barrier height of the resultant devices, which arises from the decreased work function of graphene induced by the charge transfer from Ti. Our research offers a direction toward large-scale electronic and optoelectronic applications based on 2D materials.
Systemic Vulnerability, as Expressed by I-CAM and MMP-9 at Presentation, Predicts One Year Outcomes in Patients with Acute Myocardial Infarction—Insights from the VIP Clinical Study

(1) Background: The prediction of recurrent events after acute myocardial infarction (AMI) does not sufficiently integrate systemic inflammation, coronary morphology or ventricular function in prediction algorithms. We aimed to evaluate the accuracy of inflammatory biomarkers, in association with angiographical and echocardiographic parameters, in predicting 1-year MACE after revascularized AMI. (2) Methods: This is an extension of a biomarker sub-study of the VIP trial (NCT03606330), in which 225 AMI patients underwent analysis of systemic vulnerability and were followed for 1 year. Hs-CRP, MMP-9, IL-6, I-CAM, V-CAM and E-selectin were determined at 1 h after revascularization. The primary end-point was the 1-year MACE rate. (3) Results: The MACE rate was 24.8% (n = 56). There were no significant differences between groups in regard to IL-6, V-CAM and E-selectin. The following inflammatory markers were significantly higher in MACE patients: hs-CRP (11.1 ± 13.8 vs. 5.1 ± 4.4 mg/L, p = 0.03), I-CAM (452 ± 283 vs. 220.5 ± 104.6, p = 0.0003) and MMP-9 (2255 ± 1226 vs. 1099 ± 706.1 ng/mL p = 0.0001). The most powerful predictor for MACE was MMP-9 of >1155 ng/mL (AUC-0.786, p < 0.001) even after adjustments for diabetes, LVEF, acute phase complications and other inflammatory biomarkers. For STEMI, the most powerful predictors for MACE included I-CAM > 239.7 ng/mL, V-CAM > 877.9 ng/mL and MMP-9 > 1393 ng/mL. (4) Conclusions: High levels of I-CAM and MMP-9 were the most powerful predictors for recurrent events after AMI for the overall study population. For STEMI subjects, the most important predictors included increased levels of I-CAM, V-CAM and MMP-9, while none of the analyzed parameters had proven to be predictive. Inflammatory biomarkers assayed during the acute phase of AMI presented a more powerful predictive capacity for MACE than the LVEF.
A Facile Strategy to Prepare Small Water Clusters via Interacting with Functional Molecules

Although small water clusters (SWCs) are important in many research fields, efficient methods of preparing SWCs are still rarely reported, which is mainly due to the lack of related materials and understanding of the molecular interaction mechanisms. In this study, a series of functional molecules were added in water to obtain small water cluster systems. The decreasing rate of the half-peak width in a sodium dodecyl sulfate (SDS)–water system reaches ≈20% at 0.05 mM from 17O nuclear magnetic resonance (NMR) results. Based on density functional theory (DFT) and molecular dynamics (MD) simulation calculation, it can be concluded that functional molecules with stronger negative electrostatic potential (ESP) and higher hydrophilicity have a stronger ability to destroy big water clusters. Notably, the concentrations of our selected molecule systems are one to two magnitudes lower than that of previous reports. This study provides a promising way to optimize aqueous systems in various fields such as oilfield development, protein stability, and metal anti-corrosion.
Determination of the PIC700 Ceramic’s Complex Piezo-Dielectric and Elastic Matrices from Manageable Aspect Ratio Resonators

Achieving good piezoelectric properties, such as the widely reported d33 charge coefficient, is a good starting point in establishing the potential applicability of piezoceramics. However, piezoceramics are only completely characterized by consistent piezoelectric-elastic-dielectric material coefficient matrices in complex form, i.e., including all losses. These matrices, which define the various alternative forms of the constitutive equations of piezoelectricity, are required for reliable virtual prototyping in the design of new devices. To meet this need, ten precise and accurate piezoelectric dielectric and elastic coefficients of the material, including all losses, must be determined for each alternative. Due to the difficulties arising from the coupling of modes when using the resonance method, this complete set of parameters is scarcely reported. Bi0.5Na0. 5TiO3-based solid solutions are already commercially available in Europe and Japan. Here, we report a case study of the determination of these sets of material coefficients (diα, giα, eiα and hiα; sE,Dαβ and cE,Dαβ; εTik and εSik; and βTik and βSik), including all losses, of the commercial PIC700 eco-piezoceramic. Plate, disk, and cylinder ceramic resonators of a manageable aspect ratio were used to obtain all the material coefficients. The validation procedure of the matrices is also given by FEA modeling of the considered resonators.
Fluorescent Materials for Monitoring Mitochondrial Biology

Mitochondria play important roles in diverse cellular processes such as energy production, cellular metabolism, and apoptosis to promote cell death. To investigate mitochondria-associated biological processes such as structure, dynamics, morphological change, metabolism, and mitophagy, there exists a continuous demand for visualizing and monitoring techniques elucidating mitochondrial biology and disease-relevancy. Due to the advantages of high sensitivity and practicality, fluorescence phenomena have been most widely used as scientific techniques for the visualization of biological phenomena and systems. In this review, we briefly overview the different types of fluorescent materials such as chemical probes, peptide- or protein-based probes, and nanomaterials for monitoring mitochondrial biology.
Regulation Network of Colorectal-Cancer-Specific Enhancers in the Progression of Colorectal Cancer

Enhancers regulate multiple genes via higher-order chromatin structures, and they further affect cancer progression. Epigenetic changes in cancer cells activate several cancer-specific enhancers that are silenced in normal cells. These cancer-specific enhancers are potential therapeutic targets of cancer. However, the functions and regulation networks of colorectal-cancer-specific enhancers are still unknown. In this study, we profile colorectal-cancer-specific enhancers and reveal their regulation network through the analysis of HiChIP data that were derived from a colorectal cancer cell line and Hi-C and RNA-seq data that were derived from tissue samples by in silico analysis and in vitro experiments. Enhancer–promoter loops in colorectal cancer cells containing colorectal-cancer-specific enhancers are involved in more than 50% of the topological associated domains (TADs) changed in colorectal cancer cells compared to normal colon cells. In addition, colorectal-cancer-specific enhancers interact with 152 genes that are significantly and highly expressed in colorectal cancer cells. These colorectal-cancer-specific enhancer target genes include ITGB4, RECQL4, MSLN, and GDF15. We propose that the regulation network of colorectal-cancer-specific enhancers plays an important role in the progression of colorectal cancer.
Optical Properties of Composites Based on Poly(o-phenylenediamine), Poly(vinylenefluoride) and Double-Wall Carbon Nanotubes

In this work, synthesis and optical properties of a new composite based on poly(o-phenylenediamine) (POPD) fiber like structures, poly(vinylidene fluoride) (PVDF) spheres and double-walled carbon nanotubes (DWNTs) are reported. As increasing the PVDF weight in the mixture of the chemical polymerization reaction of o-phenylenediamine, the presence of the PVDF spheres onto the POPD fibers surface is highlighted by scanning electron microscopy (SEM). The down-shift of the Raman line from 1421 cm−1 to 1415 cm−1 proves the covalent functionalization of DWNTs with the POPD-PVDF blends. The changes in the absorbance of the IR bands peaked around 840, 881, 1240 and 1402 cm−1 indicate hindrance steric effects induced of DWNTs to the POPD fiber like structures and the PVDF spheres, as a consequence of the functionalization process of carbon nanotubes with macromolecular compounds. The presence of the PVDF spheres onto the POPD fiber like structures surface induces a POPD photoluminescence (PL) quenching process. An additional PL quenching process of the POPD-PVDF blends is reported to be induced in the presence of DWNTs. The studies of anisotropic PL highlight a change of the angle of the binding of the PVDF spheres onto the POPD fiber like structures surface from 50.2° to 38° when the carbon nanotubes concentration increases in the POPD-PVDF/DWNTs composites mass up to 2 wt. %.
Dealloying-Derived Nanoporous Cu6Sn5 Alloy as Stable Anode Materials for Lithium-Ion Batteries

The volume expansion during Li ion insertion/extraction remains an obstacle for the application of Sn-based anode in lithium ion-batteries. Herein, the nanoporous (np) Cu6Sn5 alloy and Cu6Sn5/Sn composite were applied as a lithium-ion battery anode. The as-dealloyed np-Cu6Sn5 has an ultrafine ligament size of 40 nm and a high BET-specific area of 15.9 m2 g−1. The anode shows an initial discharge capacity as high as 1200 mA h g−1, and it remains a capacity of higher than 600 mA h g−1 for the initial five cycles at 0.1 A g−1. After 100 cycles, the anode maintains a stable capacity higher than 200 mA h g−1 for at least 350 cycles, with outstanding Coulombic efficiency. The ex situ XRD patterns reveal the reverse phase transformation between Cu6Sn5 and Li2CuSn. The Cu6Sn5/Sn composite presents a similar cycling performance with a slightly inferior rate performance compared to np-Cu6Sn5. The study demonstrates that dealloyed nanoporous Cu6Sn5 alloy could be a promising candidate for lithium-ion batteries.
Electronic Structure of Ternary Alloys of Group III and Rare Earth Nitrides

Electronic structures of ternary alloys of group III (Al, Ga, In) and rare earth (Sc, Y, Lu) nitrides were investigated from first principles. The general gradient approximation (GGA) was employed in predictions of structural parameters, whereas electronic properties of the alloys were studied with the modified Becke–Johnson GGA approach. The evolution of structural parameters in the materials reveals a strong tendency to flattening of the wurtzite type atomic layers. The introduction of rare earth (RE) ions into Al- and In-based nitrides leads to narrowing and widening of a band gap, respectively. Al-based materials doped with Y and Lu may also exhibit a strong band gap bowing. The increase of a band gap was obtained for Ga1−xScxN alloys. Relatively small modifications of electronic structure related to a RE ion content are expected in Ga1−xYxN and Ga1−xLuxN systems. The findings presented in this work may encourage further experimental investigations of electronic structures of mixed group III and RE nitride materials because, except for Sc-doped GaN and AlN systems, these novel semiconductors were not obtained up to now.
Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature

High temperature severely affects the nature of the ingredients used to produce concrete, which in turn reduces the strength properties of the concrete. It is a difficult and time-consuming task to achieve the desired compressive strength of concrete. However, the application of supervised machine learning (ML) approaches makes it possible to initially predict the targeted result with high accuracy. This study presents the use of a decision tree (DT), an artificial neural network (ANN), bagging, and gradient boosting (GB) to forecast the compressive strength of concrete at high temperatures on the basis of 207 data points. Python coding in Anaconda navigator software was used to run the selected models. The software requires information regarding both the input variables and the output parameter. A total of nine input parameters (water, cement, coarse aggregate, fine aggregate, fly ash, superplasticizers, silica fume, nano silica, and temperature) were incorporated as the input, while one variable (compressive strength) was selected as the output. The performance of the employed ML algorithms was evaluated with regards to statistical indicators, including the coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual models using DT and ANN gave R2 equal to 0.83 and 0.82, respectively, while the use of the ensemble algorithm and gradient boosting gave R2 of 0.90 and 0.88, respectively. This indicates a strong correlation between the actual and predicted outcomes. The k-fold cross-validation, coefficient correlation (R2), and lesser errors (MAE, MSE, and RMSE) showed better performance than the ensemble algorithms. Sensitivity analyses were also conducted in order to check the contribution of each input variable. It has been shown that the use of the ensemble machine learning algorithm would enhance the performance level of the model.
Motivation and Engagement of Nursing Students in 2 Gamified Courses

Supplemental digital content is available in the text. Background Evidence suggests that gamification increases student engagement in course activities. However, student feedback about gamification in nursing contexts is needed. Purpose The aim of this study was to describe nursing student perceptions of how gamification impacts student motivation and engagement. Methods A convergent mixed-methods study was conducted. Surveys were completed by 58 first-year nursing students. Survey comments and data extracted from the learning management system were also collected. Results Students generally found gamification valuable, interesting, and enjoyable. Gamification took effort and was perceived as important. Students felt that they had some choice in participation in the gamified activities. Conclusions Gamification is a viable strategy for promoting motivation and engagement among first year nursing students. More qualitative research is needed to enable game design refinement through investigation of contextualized student experiences. Refined game design may impact the development of learner skills through a moderating effect on engagement in course activities.