A Stretchable and Tough Graphene Film Enabled by Mechanical Bond.

The pursuit of fabricating high-performance graphene films has aroused considerable attention due to their potential for practical applications. However, developing both stretchable and tough graphene films remains a formidable challenge. To address this issue, we herein introduce mechanical bond to comprehensively improve the mechanical properties of graphene films, utilizing [2]rotaxane as the bridging unit. Under external force, the [2]rotaxane cross-link undergoes intramolecular motion, releasing hidden chain and increasing the interlayer slip distance between graphene nanosheets. Compared with graphene films without [2]rotaxane cross-linking, the presence of mechanical bond not only boosted the strength of graphene films (247.3 vs 74.8 MPa) but also markedly promoted the tensile strain (23.6 vs 10.2%) and toughness (23.9 vs 4.0 MJ/m3). Notably, the achieved tensile strain sets a record high and the toughness surpasses most reported results, rendering the graphene films suitable for applications as flexible electrodes. Even when the films were stretched within a 20% strain and repeatedly bent vertically, the light-emitting diodes maintained an on-state with little changes in brightness. Additionally, the film electrodes effectively actuated mechanical joints, enabling uninterrupted grasping movements. Therefore, the study holds promise for expanding the application of graphene films and simultaneously inspiring the development of other high-performance two-dimensional films.

Drug-Target Binding Affinity Prediction in a Continuous Latent Space Using Variational Autoencoders.

Accurate prediction of Drug-Target binding Affinity (DTA) is a daunting yet pivotal task in the sphere of drug discovery. Over the years, a plethora of deep learning-based DTA models have emerged, rendering promising results in predicting the binding affinities between drugs and their target proteins. However, in contrast to the conventional approach of modeling binding affinity in vector spaces, we propose a more nuanced modeling process in a continuous space to account for the diversity of input samples. Initially, the drug is encoded using the Simplified Molecular Input Line Entry System (SMILES), while the target sequences are characterized via a pretrained language model. Subsequently, highly correlative information is extracted utilizing residual gated convolutional neural networks. In a departure from existing deep learning-based models, our model learns the hidden representations of the drugs and targets jointly. Instead of employing two vectors, our hidden representations consist of two Gaussian distributions. To validate the effectiveness of our proposal, we conducted evaluations on commonly utilized benchmark datasets. The experimental outcomes corroborated that our method surpasses the state-of-the-art vectorial representation methods in terms of performance. This approach, therefore, offers potential enhancements in the precision of DTA predictions, potentially contributing to more efficient drug discovery processes.

Comparison of brain gray matter volume changes in peritoneal dialysis and hemodialysis patients with chronic kidney disease: a VBM study.

Objective: This study aims to compare gray matter volume changes in patients with chronic kidney disease (CKD) undergoing peritoneal dialysis (PD) and hemodialysis (HD) using voxel-based morphometry (VBM). Methods: A total of 27 PD patients, 25 HD patients, and 42 healthy controls were included. VBM analysis was performed, and cognitive function was assessed using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment Scale (MoCA). The correlation between cognitive function and changes in brain gray matter volume was analyzed. Results: Both peritoneal dialysis and hemodialysis patients had partial gray matter volume reduction compared to the controls, but the affected brain regions were not uniform. The hemodialysis patients had greater volume reduction in certain brain regions than the PD patients. The MMSE and MoCA scores were positively correlated with gray matter volume changes. Conclusion: Different dialysis modalities cause damage to specific areas of the brain, which can be detected using VBM. VBM, combined with cognitive function assessment, can help detect structural brain changes and cognitive impairment in patients with different dialysis modalities. The comprehensive application of VBM in the field of neurological function deserves further exploration.

Nurses' self-regulation after engaging in end-of-life conversations with advanced cancer patients: a qualitative study.

BACKGROUND: Self-regulation is crucial for nurses who engage in in-depth end-of-life conversations with advanced cancer patients, especially in cultural contexts featuring death taboos. An improved understanding of the self-regulation process of nurses can help them address negative emotions and promote self-growth more effectively. Therefore, this study aimed to explore nurses' self-regulation process after end-of-life conversations with advanced cancer patients. METHODS: This study employed a descriptive, qualitative approach. Seventeen nurses from four hospitals and a hospice unit in mainland China were interviewed between September 2022 and June 2023. Data were collected through face-to-face semistructured interviews. A thematic analysis method was used to analyse the data following the guidance of regulatory focus theory. RESULTS: Three main themes were developed: self-regulation antecedents include personality, experience, and support; promotion or prevention is a possible self-regulation process for nurses; both self-exhaustion and self-growth may be the outcomes of self-regulation, as did seven subthemes. Personality tendencies, life experience, and perceived support may affect nurses' self-regulation, thereby affecting their self-regulation outcomes. CONCLUSIONS: Nurses exhibit different self-regulatory tendencies and self-regulation outcomes. The provision of peer support and counselling support to nurses is highly important with regard to achieving good self-regulation outcomes.

Impact of perinatal factors on meconium aspiration syndrome in full-term newborns and the construction of a column chart prediction model: An observational study.

To explore the influence of perinatal-related factors on meconium aspiration syndrome (MAS) in full-term neonates and construct a nomogram prediction model for risk stratification of neonatal MAS and adoption of preventive measures. A total of 424 newborns and their mothers who were regularly examined at our hospital between January 2020 and December 2023 who had meconium-contaminated amniotic fluid during delivery were retrospectively selected as participants. Neonates were divided into MAS and non-MAS groups based on whether MAS occurred within 3 days after birth. Data from the 2 groups were analyzed, and factors influencing MAS were screened using multivariate logistic regression analysis. The R3.4.3 software was used to construct a nomogram prediction model for neonatal MAS risk. Receiver operating characteristic (ROC) curve analysis and the Hosmer-Lemeshow goodness-of-fit test were used to evaluate the performance of the model, and its clinical effectiveness was evaluated using a decision curve. Among the 424 neonates with meconium-stained amniotic fluid, 51 developed MAS within 3 days of birth (12.03%). Multivariate logistic regression analysis showed that a low amniotic fluid index before delivery (OR = 2.862, P = .019), advanced gestational age (OR = 0.526, P = .034), cesarean section (OR = 2.650, P = .013), severe amniotic fluid contamination (OR = 4.199, P = .002), low umbilical cord blood pH (OR = 2.938, P = .011), and low neonatal Apgar 1-min score (OR = 3.133, P = .006) were influencing factors of MAS in full-term neonates. Based on the above indicators, a nomogram prediction model for MAS risk of full-term newborns was constructed. The area under the ROC curve of the model was 0.931. The model was also tested for goodness-of-fit deviation (χ2 = 3.465, P = .903). Decision curve analysis found that the model was clinically effective in predicting the net benefit of MAS risk in neonates with meconium-stained amniotic fluid. The construction of a column chart prediction model for neonatal MAS risk based on prenatal amniotic fluid index, gestational age, delivery method, amniotic fluid contamination level, newborn umbilical blood pH value, and Apgar 1-min score has a certain application value.

Directed evolution of Escherichia coli surface-displayed Vitreoscilla hemoglobin as an artificial metalloenzyme for the synthesis of 5-imino-1,2,4-thiadiazoles.

Artificial metalloenzymes (ArMs) are constructed by anchoring organometallic catalysts to an evolvable protein scaffold. They present the advantages of both components and exhibit considerable potential for the in vivo catalysis of new-to-nature reactions. Herein, Escherichia coli surface-displayed Vitreoscilla hemoglobin (VHbSD-Co) that anchored the cobalt porphyrin cofactor instead of the original heme cofactor was used as an artificial thiourea oxidase (ATOase) to synthesize 5-imino-1,2,4-thiadiazoles. After two rounds of directed evolution using combinatorial active-site saturation test/iterative saturation mutagenesis (CAST/ISM) strategy, the evolved six-site mutation VHbSD-Co (6SM-VHbSD-Co) exhibited significant improvement in catalytic activity, with a broad substrate scope (31 examples) and high yields with whole cells. This study shows the potential of using VHb ArMs in new-to-nature reactions and demonstrates the applicability of E. coli surface-displayed methods to enhance catalytic properties through the substitution of porphyrin cofactors in hemoproteins in vivo.

Marine-Derived Sulfated Glycans Inhibit the Interaction of Heparin with Adhesion Proteins of Mycoplasma pneumoniae.

Mycoplasma pneumoniae, a notable pathogen behind respiratory infections, employs specialized proteins to adhere to the respiratory epithelium, an essential process for initiating infection. The role of glycosaminoglycans, especially heparan sulfate, is critical in facilitating pathogen-host interactions, presenting a strategic target for therapeutic intervention. In this study, we assembled a glycan library comprising heparin, its oligosaccharide derivatives, and a variety of marine-derived sulfated glycans to screen the potential inhibitors for the pathogen-host interactions. By using Surface Plasmon Resonance spectroscopy, we evaluated the library's efficacy in inhibiting the interaction between M. pneumoniae adhesion proteins and heparin. Our findings offer a promising avenue for developing novel therapeutic strategies against M. pneumoniae infections.

The Heat shock factor 20-HSF4-Cellulose synthase A2 module regulates heat stress tolerance in maize.

Temperature shapes the geographical distribution and behavior of plants. Understanding the regulatory mechanisms underlying the plant heat response is important for developing climate-resilient crops, including maize (Zea mays). To identify transcription factors that may contribute to the maize heat response, we generated a dataset of short- and long-term transcriptome changes following a heat treatment time course in the inbred line B73. Co-expression network analysis highlighted several transcription factors, including the class B2a heat shock factor (HSF) ZmHSF20. Zmhsf20 mutant seedlings exhibited enhanced tolerance to heat stress. Furthermore, DNA affinity purification sequencing and Cleavage Under Targets and Tagmentation (CUT&Tag) assays demonstrated that ZmHSF20 binds to the promoters of Cellulose synthase A2 (ZmCesA2) and three class A Hsf genes, including ZmHsf4, repressing their transcription. We showed that ZmCesA2 and ZmHSF4 promote the heat response, with ZmHSF4 directly activating ZmCesA2 transcription. In agreement with the transcriptome analysis, ZmHSF20 inhibited cellulose accumulation and repressed the expression of cell wall-related genes. Importantly, the Zmhsf20 Zmhsf4 double mutant exhibited decreased thermotolerance, placing ZmHsf4 downstream of ZmHsf20. We proposed an expanded model of the heat stress response in maize, whereby ZmHSF20 lowers seedling heat tolerance by repressing ZmHsf4 and ZmCesA2, thus balancing seedling growth and defense.

MAMLCDA: A Meta-Learning Model for Predicting circRNA-Disease Association Based on MAML Combined With CNN.

Circular RNAs (circRNAs) exist in vivo and are a class of noncoding RNA molecules. They have a single-stranded, closed, annular structure. Many studies have shown that circRNAs and diseases are linked. Therefore, it is critical to build a reliable and accurate predictor to find the circRNA-disease association. In this paper, we presented a meta-learning model named MAMLCDA to identify the circRNA-disease association, which is based on model-agnostic meta-learning (MAML) combined with CNN classification. Specifically, similarities between diseases and circRNAs are extracted and integrated to characterize their relationships, and k-means is used to cluster majority samples and select a certain number of samples from each cluster to obtain the same number of negative samples as the positive samples. To further reduce the dimension of the features and save operation time, we applied probabilistic principal component analysis (PPCA) to compact the integrated circRNA and disease similarity network feature vectors. The feature vectors are converted into images. At this time, the prediction problem is transformed into the 2-way 1-shot problem of the image and input into the model with MAML as the meta-learner and CNN as the base-learner. Comparison results of five-fold cross-validation on two benchmark datasets illustrate that MAMLCDA outperforms several state-of-the-art approaches with the best accuracies of 95.33% and 98%. Therefore, MAMLCDA can help to understand the pathogenesis of complex diseases at the circRNA level.

A Hierarchical Graph Neural Network Framework for Predicting Protein-Protein Interaction Modulators with Functional Group Information and Hypergraph Structure.

Accurate prediction of small molecule modulators targeting protein-protein interactions (PPIMs) remains a significant challenge in drug discovery. Existing machine learning-based models rely on manual feature engineering, which is tedious and task-specific. Recently, deep learning models based on graph neural networks have made remarkable progress in molecular representation learning. However, many graph-based approaches ignore molecular hierarchical structure modeling guided by domain knowledge. In chemistry, the functional groups of a molecule determine its interaction with specific targets. Therefore, we propose a hierarchical graph neural network framework (called HiGPPIM) for predicting PPIMs by integrating atom-level and functional group-level features of molecules. HiGPPIM constructs atom-level and functional group-level graphs based on chemical knowledge and learns graph representations using graph attention networks. Furthermore, a hypergraph attention network is designed in HiGPPIM to aggregate and transform two-level graph information. We evaluate the performance of HiGPPIM on eight PPI families and two prediction tasks, namely PPIM identification and potency prediction. Experimental results demonstrate that HiGPPIM achieves state-of-the-art performance on both tasks and that using functional group information to guide PPIM prediction is effective. The source code and datasets are freely available at https://github.com/1zzt/HiGPPIM.

Formation of brominated and nitrated byproducts during unactivated peroxymonosulfate oxidation of phenol.

Brominated and nitrated byproducts generated from bromide (Br-) and nitrite (NO2-), respectively, by sulfate radical (SO4•-) oxidation have raised increasing concern. However, little is known about the concurrent generation of brominated and nitrated byproducts in the unactivated peroxymonosulfate (PMS) oxidation process. This study revealed that Br- can facilitate the transformation of NO2- to nitrated byproducts during unactivated PMS oxidation of phenol. In the co-existence of 0.1 mM Br- and 0.5 mM NO2-, the total yield of identified nitrated byproducts reached 2.316 µM in 20 min, while none was found with NO2- alone. Nitryl bromide (BrNO2) as the primary nitrating agent was formed via the reaction of NO2- with free bromine in situ generated through the oxidation of Br- by PMS. BrNO2 rapidly reacted with phenol or bromophenols, generating highly toxic nitrophenols or nitrated bromophenols, respectively. Increasing NO2- concentration led to more nitrated byproducts but less brominated byproducts. This study advances our understanding of the transformation of Br- and NO2- in the unactivated PMS oxidation process. It also provides important insights into the potentially underestimated environmental risks when PMS is applied to degrade organic contaminants under realistic environments, particularly when Br- and NO2- co-exist.

Marine sulfated glycans inhibit the interaction of heparin with S-protein of SARS-CoV-2 Omicron XBB variant.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a worldwide COVID-19 pandemic, leading to 6.8 million deaths. Numerous variants have emerged since its outbreak, resulting in its significantly enhanced ability to spread among humans. As with many other viruses, SARS­CoV­2 utilizes heparan sulfate (HS) glycosaminoglycan (GAG) on the surface of host cells to facilitate viral attachment and initiate cellular entry through the ACE2 receptor. Therefore, interfering with virion-HS interactions represents a promising target to develop broad-spectrum antiviral therapeutics. Sulfated glycans derived from marine organisms have been proven to be exceptional reservoirs of naturally existing HS mimetics, which exhibit remarkable therapeutic properties encompassing antiviral/microbial, antitumor, anticoagulant, and anti-inflammatory activities. In the current study, the interactions between the receptor-binding domain (RBD) of S-protein of SARS-CoV-2 (both WT and XBB.1.5 variants) and heparin were applied to assess the inhibitory activity of 10 marine-sourced glycans including three sulfated fucans, three fucosylated chondroitin sulfates and two fucoidans derived from sea cucumbers, sea urchin and seaweed Saccharina japonica, respectively. The inhibitory activity of these marine derived sulfated glycans on the interactions between RBD of S-protein and heparin was evaluated using Surface Plasmon Resonance (SPR). The RBDs of S-proteins from both Omicrion XBB.1.5 and wild-type (WT) were found to bind to heparin, which is a highly sulfated form of HS. All the tested marine-sourced sulfated glycans exhibited strong inhibition of WT and XBB.1.5 S-protein binding to heparin. We believe the study on the molecular interactions between S-proteins and host cell glycosaminoglycans provides valuable insight for the development of marine-sourced, glycan-based inhibitors as potential anti-SARS-CoV-2 agents.

Effectiveness of psychological interventions among community-dwelling older adults with subthreshold depression: A systematic review and meta-analysis.

BACKGROUND: Little is known about the effectiveness of psychological interventions among older adults with subthreshold depression in the community. This systematic review and meta-analysis aimed to examine the effectiveness of psychological interventions on depressive symptoms, anxiety symptoms and quality of life. METHODS: We searched five databases from inception to 20th September 2022 and included RCTs that evaluated the effectiveness of psychological interventions among older adults with subthreshold depression in the community. Standardized mean difference (SMD) and 95 % confidence intervals (CI) were used to calculate the effect sizes of treatment outcomes in the meta-analysis, using RevMan 5.4.1 and Stata 16.0. RESULTS: This meta-analysis included thirteen RCT studies involving 2079 participants. Psychological interventions could significantly reduce depressive symptoms (post-intervention time: SMD = -0.58, 95 % CI = -0.76 to -0.40; follow-up time: SMD = -0.31, 95 % CI = -0.41 to -0.22) and anxiety symptoms (post-intervention time: SMD = -0.33, 95 % CI = -0.49 to -0.17; follow-up time: SMD = -0.24, 95 % CI = -0.36 to -0.12) and improve quality of life (post-intervention time: SMD = 0.30, 95 % CI = 0.05 to 0.55; follow-up time: SMD = 0.15, 95 % CI = 0.01 to 0.28). CONCLUSION: Evidence suggests that psychological interventions could significantly reduce depressive symptoms and anxiety symptoms, and improve quality of life among community-dwelling older adults with subthreshold depression.

Differences in Vertebral Morphology and bone Mineral Density between Grade 1 Vertebral Fracture and Non-Fractured Participants in the Chinese Population.

PURPOSE: To investigate the difference in vertebral morphology and bone mineral density (BMD) between grade 1 VFs and non-fractured participants in the Chinese population to shed light on the clinical significance of grade 1 VFs from various perspectives. METHODS: This retrospective cohort study included patients who received a chest low-dose computed tomography (LDCT) scan for health examination and visited the First Affiliated Hospital of Zhengzhou University, Henan, China, from October 2019 to August 2022. Data were analyzed from March 2023 to July 2023. The main outcome of this study was the difference in morphological parameters and BMD between grade 1 VFs and non-fractured participants. The prevalence of grade 1 VFs in China populations was calculated. The difference in BMD of three fracture types in the Grade 1 group was also evaluated. RESULTS: A total of 3652 participants (1799 males, 54.85 ± 9.02 years, range, 40-92 years; 1853 females, 56.00 ± 9.08 years, range, 40-93 years) were included. The prevalence of grade 2 and 3 increase with age. The prevalence of grade 1 VFs gradually increases ≤ 50y to 60-69y group, but there is a decrease in the ≥ 70 years male group (6.6%) and a rise in the female group (25.5%). There was no significant statistical difference observed in vertebral shape indices (VSI) and BMD between the Grade 1 group and the no-fractured group aged < 50 years old except the wedge index in male. The biconcavity index did not differ between the non-fractured group and the Grade 1 group in men aged 50-59 years, whereas a significant statistical difference was observed in women. Additionally, the results of BMD were consistent with these findings. For the 40-59 years age group, there were significant differences between the compression deformity group and the other groups. CONCLUSIONS: The grade 1 group had higher VSI and lower BMD than the non-fractured group, suggesting an association between the Grade 1 group and osteoporosis in individuals aged over 50 for women and over 60 for men. Different fracture types have significant variations in BMD among middle-aged people. The prevalence of grade 1 VFs exhibits an age-related increase in both genders, with opposite trends observed between older males and females. We suggested VSI can aid physicians in the diagnosis of grade 1 VFs.

PPII-AEAT: Prediction of protein-protein interaction inhibitors based on autoencoders with adversarial training.

Protein-protein interactions (PPIs) have shown increasing potential as novel drug targets. The design and development of small molecule inhibitors targeting specific PPIs are crucial for the prevention and treatment of related diseases. Accordingly, effective computational methods are highly desired to meet the emerging need for the large-scale accurate prediction of PPI inhibitors. However, existing machine learning models rely heavily on the manual screening of features and lack generalizability. Here, we propose a new PPI inhibitor prediction method based on autoencoders with adversarial training (named PPII-AEAT) that can adaptively learn molecule representation to cope with different PPI targets. First, Extended-connectivity fingerprints and Mordred descriptors are employed to extract the primary features of small molecular compounds. Then, an autoencoder architecture is trained in three phases to learn high-level representations and predict inhibitory scores. We evaluate PPII-AEAT on nine PPI targets and two different tasks, including the PPI inhibitor identification task and inhibitory potency prediction task. The experimental results show that our proposed PPII-AEAT outperforms state-of-the-art methods.

Effect of a 2-Acrylamido-2-methylpropanesulfonic Acid-Based Fluid Loss Additive on the Hydration of Oil Well Cement.

The fluid loss additive is to prevent the cement slurry from filtrating water to the formation under pressure. 2-Acrylamido-2-methylpropanesulfonic acid (AMPS)-based fluid loss additive mainly works by adsorbing on the surface of cement particles. The adsorption affects cement hydration. In this paper, the effect of one kind of AMPS-based fluid loss additive (A-FLA) on the hydration of oil well cement was studied. The water loss, setting time, thickening time, and compressive strength of cement slurry with various amounts of FLA were measured. In addition, the hydration heat of the cement slurry, FLA adsorption isotherm on cement particles, and hydration minerals were studied. The results showed that A-FLA had a good water loss control ability. The water loss of the cement slurry decreased slowly with the increase of A-FLA dosage when the adsorption capacity exceeded 4.47 mg/g. The low adsorption capacity of A-FLA (less than 4.47 mg/g) had a significant impact on the thickening time. With an adsorption capacity greater than 4.47 mg/g, the thickening time varied minimally. A-FLA mainly delayed the hydration of C3S at 1 day and reduced the production amorphous phase.

Guided wave resonance-based digital holographic microscopy for high-sensitivity monitoring of the refractive index.

Surface plasmon resonance holographic microscopy (SPRHM) has been employed to measure the refractive index but whose performance is generally limited by the metallic intrinsic loss. Herein we first, to our knowledge, utilize guided wave resonance (GWR) with low loss to realize the monitoring of the refractive index by integrating with digital holographic microscopy (DHM). By depositing a dielectric layer on a silver film, we observe a typical GWR in the dielectric layer with stronger field enhancement and higher sensitivity to the surrounding refractive index compared to the silver film-supported SPR, which agrees well with calculations. The innovative combination of the GWR and DHM contributes to the highly sensitive dynamic monitoring of the surrounding refractive index variation. Through the measurement with DHM, we found that the GWR presents an excellent sensitivity, which is 2.6 times higher than that of the SPR on the silver film. The results will pave a new pathway for digital holographic interferometry and its applications in environmental and biological detections.

Dual-energy computed tomography and micro-computed tomography for assessing bone regeneration in a rabbit tibia model.

To gain a more meaningful understanding of bone regeneration, it is essential to select an appropriate assessment method. Micro-computed tomography (Micro-CT) is widely used for bone regeneration because it provides a substantially higher spatial resolution. Dual-energy computed tomography (DECT) ensure shorter scan time and lower radiation doses during quantitative evaluation. Therefore, in this study, DECT and Micro-CT were used to evaluate bone regeneration. We created 18 defects in the tibial plateau of the rabbits and filled them with porous polyetheretherketone implants to promote bone regeneration. At 4, 8, and 12 weeks, Micro-CT and DECT were used to assess the bone repair in the defect region. In comparison to Micro-CT (152 ± 54 mg/cm3), the calcium density values and hydroxyapatite density values obtained by DECT [DECT(Ca) and DECT(HAP)] consistently achieved lower values (59 ± 25 mg/cm3, 126 ± 53 mg/cm3). In addition, there was a good association between DECT and Micro-CT (R = 0.98; R2 = 0.96; DECT(Ca): y = 0.45x-8.31; DECT(HAP): y = 0.95x-17.60). This study highlights the need to use two different imaging methods, each with its advantages and disadvantages, to better understand the bone regeneration process.