Contrastive representation learning on dynamic networks

Representation learning for dynamic networks is designed to learn the low-dimensional embeddings of nodes that can well preserve the snapshot structure, properties and temporal evolution of dynamic networks. However, current dynamic network representation learning methods tend to focus on estimating or generating observed snapshot structures, paying excessive attention to network details, and disregarding distinctions between snapshots with larger time intervals, resulting in less robustness for sparse or noisy networks. To alleviate these challenges, this paper proposes a contrastive mechanism for temporal representation learning on dynamic networks, inspired by the success of contrastive learning in visual and static network representation learning. This paper proposes a novel Dynamic Network Contrastive representation Learning (DNCL) model. Specifically, contrast objective functions are constructed using intra-snapshot and inter-snapshot contrasts to capture the network topology, node feature information, and network evolution information, respectively. Rather than estimating or generating ground-truth network features, the proposed approach maximizes mutual information between nodes from different time steps and views generated. The experimental results of link prediction, node classification, and clustering on several real-world and synthetic networks demonstrate the superiority of DNCL over state-of-the-art methods, indicating the effectiveness of the proposed approach for dynamic network representation learning.

Urolithin A exerts a protective effect on lipopolysaccharide-induced acute lung injury by regulating HMGB1-mediated MAPK and NF-κB signaling pathways

Acute lung injury (ALI) is a severe inflammatory disorder that has a high morbidity and mortality rate. Urolithin A (UA) is reported to have anti-inflammatory and anti-oxidative effects in ALI. However, its molecular mechanisms in ALI remain to be explored. Mice and BEAS-2B cells were administrated with lipopolysaccharide (LPS) to mimic the ALI model in vivo and in vitro. Hematoxylin-eosin (HE) staining was used to detect the pathological injury of lung tissues. The levels of proinflammatory cytokines in bronchoalveolar lavage fluid (BALF) and culture supernatant and the levels of oxidative stress markers in lung tissues were measured using ELISA. DCFH-DA probe was used to assess the reactive oxygen species (ROS) level. TUNEL staining and flow cytometry were performed to determine cell apoptosis. The key targets and pathways were confirmed by immunohistochemistry (IHC) and western blot. UA suppressed the pathologic damage, wet/dry weight ratio, and total protein and inflammatory cells in BALF. UA decreased neutrophil infiltration and proinflammatory cytokines production. UA reduced the level of malondialdehyde (MDA) and increased the activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) in pulmonary tissues. UA also inhibited cell apoptosis in lung tissues by decreasing Bax expression and increasing Bcl-2 expression. In addition, UA suppressed LPS-induced inflammatory factor production, ROS level, and cell apoptosis in BEAS-2B. Importantly, UA decreased the expression of HMGB1 in LPS-treated mice and BEAS-2B cells. HMGB1 overexpression greatly abrogated the inhibition of UA on inflammation, ROS, and cell apoptosis in LPS-administrated BEAS-2B. Furthermore, UA treatment suppressed the phosphorylated levels of p38, JNK, ERK, and p65 in LPS-administrated mice and BEAS-2B cells. UA alleviated lung inflammation, oxidative stress, and apoptosis in ALI by targeting HMGB1 to inactivate the MAPK/NF-κB signaling, suggesting the potential of UA to treat ALI.

Hybrid-FHR: a multi-modal AI approach for automated fetal acidosis diagnosis

BACKGROUND

In clinical medicine, fetal heart rate (FHR) monitoring using cardiotocography (CTG) is one of the most commonly used methods for assessing fetal acidosis. However, as the visual interpretation of CTG depends on the subjective judgment of the clinician, this has led to high inter-observer and intra-observer variability, making it necessary to introduce automated diagnostic techniques.

METHODS

In this study, we propose a computer-aided diagnostic algorithm (Hybrid-FHR) for fetal acidosis to assist physicians in making objective decisions and taking timely interventions. Hybrid-FHR uses multi-modal features, including one-dimensional FHR signals and three types of expert features designed based on prior knowledge (morphological time domain, frequency domain, and nonlinear). To extract the spatiotemporal feature representation of one-dimensional FHR signals, we designed a multi-scale squeeze and excitation temporal convolutional network (SE-TCN) backbone model based on dilated causal convolution, which can effectively capture the long-term dependence of FHR signals by expanding the receptive field of each layer's convolution kernel while maintaining a relatively small parameter size. In addition, we proposed a cross-modal feature fusion (CMFF) method that uses multi-head attention mechanisms to explore the relationships between different modalities, obtaining more informative feature representations and improving diagnostic accuracy.

RESULTS

Our ablation experiments show that the Hybrid-FHR outperforms traditional previous methods, with average accuracy, specificity, sensitivity, precision, and F1 score of 96.8, 97.5, 96, 97.5, and 96.7%, respectively.

CONCLUSIONS

Our algorithm enables automated CTG analysis, assisting healthcare professionals in the early identification of fetal acidosis and the prompt implementation of interventions.

Exploring the roles of noncoding RNAs in craniofacial abnormalities: A systematic review

Congenital craniofacial abnormalities are congenital anomalies of variable expressivity and severity with a recognizable set of abnormalities, which are derived from five identifiable primordial structures. They can occur unilaterally or bilaterally and include various malformations such as cleft lip with/without palate, craniosynostosis, and craniofacial microsomia. To date, the molecular etiology of craniofacial abnormalities is largely unknown. Noncoding RNAs (ncRNAs), including microRNAs, long ncRNAs, circular RNAs and PIWI-interacting RNAs, function as major regulators of cellular epigenetic hallmarks via regulation of various molecular and cellular processes. Recently, aberrant expression of ncRNAs has been implicated in many diseases, including craniofacial abnormalities. Consequently, this review focuses on the role and mechanism of ncRNAs in regulating craniofacial development in the hope of providing clues to identify potential therapeutic targets.

Ultrasonic-Assisted Extraction of Antioxidants from Perilla frutescens Leaves Based on Tailor-Made Deep Eutectic Solvents: Optimization and Antioxidant Activity

The development of natural antioxidants to replace synthetic compounds is attractive. Perilla frutescens leaves were proven to be rich in antioxidants. The extraction of antioxidants from Perilla leaves via ultrasonic-assisted extraction (UAE) based on choline chloride-based deep eutectic solvents (DESs) was studied. Firstly, several DESs were prepared, and their extraction effects were compared. Secondly, the extraction process was optimized by single-factor experiments and response surface methodology (RSM). Finally, the optimization results were verified and compared with the results of traditional solvent-based UAE. The effects of solvents on the surface cell morphology of Perilla frutescens leaves were characterized by scanning electron microscopy (SEM). Choline chloride-acetic acid-based DES (ChCl-AcA) extract showed a relatively high ferric-reducing antioxidant activity (FRAP) and 2,2-diphenyl-1-picrylhyldrazyl radical scavenging rate (DPPH). Under the optimal operating conditions (temperature 41 °C, liquid-solid ratio 33:1, ultrasonic time 30 min, water content 25%, ultrasonic power 219 W), the experimental results are as follows: DPPH64.40% and FRAP0.40 mM Fe(II)SE/g DW. The experimental and predicted results were highly consistent with a low error (<3.38%). The values of the DPPH and FRAP were significantly higher than that for the water, ethanol, and butanol-based UAE. SEM analysis confirmed that ChCl-AcA enhanced the destruction of the cell wall, so that more antioxidants were released. This study provides an eco-friendly technology for the efficient extraction of antioxidants from Perilla frutescens leaves. The cytotoxicity and biodegradability of the extract will be further verified in a future work.

Metal-Organic Framework for Hypoxia/ROS/pH Triple-Responsive Cargo Release

Nanoparticulate antitumor photodynamic therapy (PDT) is suffering from a very short lifetime, limited diffusion distance of reactive oxygen species (ROS). Herein, a hypoxia/ROS/pH triple-responsive metal-organic framework (MOF) is designed to facilitate the on-demand release of photosensitizers and hence enhanced PDT efficacy. Tailored azo-containing imidazole ligand is coordinated with zinc to form MOF where photosensitizer (Chlorin e6/Ce6) is encapsulated. Azo can be reduced by overexpressed azoreductase in hypoxic tumor cells, resulting in depletion of glutathione (GSH) and thioredoxin (Trx) which are major antioxidants against ROS oxidative damage in PDT, resulting in rapid cargo release and additional efficacy amplification. The imidazole ionization causes a proton sponge effect to ensure the disintegration of the nanocarriers in acidic organelles, allowing the rapid release of Ce6 through lysosome escape. Under light irradiation, ROS produced by Ce6 may oxidize imidazole to urea, resulting in rapid cargo release. All of the triggers are expected to show interactive synergism. The pH- and hypoxia-responsiveness can improve the release rate of Ce6 for enhanced PDT therapy, whereas the consumption of oxygen by PDT may induce elevated hypoxia and hence in turn enhanced cargo release. This work highlights the role of triple-responsive nanocarriers for triggered photosensitizer release and improved antitumor PDT efficacy.

HB-DSBM: Modeling the Dynamic Complex Networks From Community Level to Node Level

A variety of methods have been proposed for modeling and mining dynamic complex networks, in which the topological structure varies with time. As the most popular and successful network model, the stochastic block model (SBM) has been extended and applied to community detection, link prediction, anomaly detection, and evolution analysis of dynamic networks. However, all current models based on the SBM for modeling dynamic networks are designed at the community level, assuming that nodes in each community have the same dynamic behavior, which usually results in poor performance on temporal community detection and loses the modeling of node abnormal behavior. To solve the above-mentioned problem, this article proposes a hierarchical Bayesian dynamic SBM (HB-DSBM) for modeling the node-level and community-level dynamic behavior in a dynamic network synchronously. Based on the SBM, we introduce a hierarchical Dirichlet generative mechanism to associate the global community evolution with the microscopic transition behavior of nodes near-perfectly and generate the observed links across the dynamic networks. Meanwhile, an effective variational inference algorithm is developed and we can easy to infer the communities and dynamic behaviors of the nodes. Furthermore, with the two-level evolution behaviors, it can identify nodes or communities with abnormal behavior. Experiments on simulated and real-world networks demonstrate that HB-DSBM has achieved state-of-the-art performance on community detection and evolution. In addition, abnormal evolutionary behavior and events on dynamic networks can be effectively identified by our model.

Exploring Temporal Community Structure via Network Embedding

Temporal community detection is helpful to discover and analyze significant groups or clusters hidden in dynamic networks in the real world. A variety of methods, such as modularity optimization, spectral method, and statistical network model, has been developed from diversified perspectives. Recently, network embedding-based technologies have made significant progress, and one can exploit deep learning superiority to network tasks. Although some methods for static networks have shown promising results in boosting community detection by integrating community embedding, they are not suitable for temporal networks and unable to capture their dynamics. Furthermore, the dynamic embedding methods only model network varying without considering community structures. Hence, in this article, we propose a novel unsupervised dynamic community detection model, which is based on network embedding and can effectively discover temporal communities and model dynamic networks. More specifically, we propose the community prior by introducing the Gaussian mixture model (GMM) in the variational autoencoder, which can obtain community information and better model the evolutionary characteristics of community structure and node embedding by utilizing the variant of gated recurrent unit (GRU). Extensive experiments conducted in real-world and artificial networks demonstrate that our proposed model has a better effect on improving the accuracy of dynamic community detection.

Erratum: Driving Pressure-Guided Ventilation in Obese Patients Undergoing Laparoscopic Sleeve Gastrectomy: A Randomized Controlled Trial [Corrigendum]

[This corrects the article DOI: 10.2147/DMSO.S405804.].

Driving Pressure-Guided Ventilation in Obese Patients Undergoing Laparoscopic Sleeve Gastrectomy: A Randomized Controlled Trial

Purpose

This study aims to compare the conventional lung protective ventilation strategy (LPVS) with driving pressure-guided ventilation in obese patients undergoing laparoscopic sleeve gastrectomy (LSG).

Methods

Forty-five patients undergoing elective LSG under general anesthesia were randomly assigned to the conventional LPVS group (group L) or the driving pressure-guided ventilation group (group D) using random numbers generated by Excel. The primary outcome was the driving pressure of both groups 90 min after pneumoperitoneum.

Results

After 30 min of pneumoperitoneum, 90 min of pneumoperitoneum, 10 min of closing the pneumoperitoneum, and restoring the supine position, the driving pressure of group L and group D were 20.0 ± 2.9 cm H2O vs 16.6 ± 3.0 cm H2O (P < 0.001), 20.7 ± 3.2 cm H2O vs 17.3 ± 2.8 cm H2O (P < 0.001), and 16.3 ± 3.1 cm H2O vs 13.3 ± 2.5 cm H2O (P = 0.001), respectively; the respiratory compliance of groups L and D were 23.4 ± 3.7 mL/cm H2O vs 27.6 ± 5.1 mL/cm H2O (P = 0.003), 22.7 ± 3.8 mL/cm H2O vs 26.4 ± 3.5 mL/cm H2O (P = 0.005), and 29.6 ± 6.8 mL/cm H2O vs 34.7 ± 5.3 mL/cm H2O (P = 0.007), respectively. The intraoperative PEEP in groups L and group D was 5 (5-5) cm H2O vs 10 (9-11) cm H2O (P < 0.001).

Conclusion

An individualized peep-based driving pressure-guided ventilation strategy can reduce intraoperative driving pressure and increase respiratory compliance in obese patients undergoing LSG.

Identifying fetal status with fetal heart rate: Deep learning approach based on long convolution

CTG (Cardiotocography) is an effective tool for fetal status assessment. Clinically, doctors mainly evaluate the health of fetus by observing FHR (fetal heart rate). The rapid development of Artificial Intelligence has led realization of computer-aided CTG technology, Intelligent CTG classification based on FHR is a fundamental component of these technologies. Its implementation can provide doctors with auxiliary decisions. Most of existing FHR classification methods are based on combing different deep learning models, such as CNN (Convolutional Neural Network), LSTM (Long short-term memory) and Transformer. However, these studies ignore the balance of positive and negative samples in dataset and the matching degree between model and FHR classification task, which reduces the classification accuracy. In this paper, we mainly discuss two major problems in previous FHR classification studies: reduce class imbalance and select appropriate convolution kernel. To address above two problems, we propose a data augmentation method based on ECMN (Edge Clipping and Multiscale Noise) to resolve class imbalance. Subsequently, we introduce a one-dimensional long convolutional layer, which use trend area to calculate the appropriate convolution kernel. Based on appropriate convolution kernel, an improved residual structure with attention mechanism named TGLCN (Trend-Guided Long Convolution Network) is proposed to improve FHR classification accuracy. Finally, horizontal and longitudinal experiments show that the TGLCN obtains high classification accuracy and speed of parameter adjustment.

Representation Learning on Heterostructures via Heterogeneous Anonymous Walks

Capturing structural similarity has been a hot topic in the field of network embedding (NE) recently due to its great help in understanding node functions and behaviors. However, existing works have paid very much attention to learning structures on homogeneous networks, while the related study on heterogeneous networks is still void. In this article, we try to take the first step for representation learning on heterostructures, which is very challenging due to their highly diverse combinations of node types and underlying structures. To effectively distinguish diverse heterostructures, we first propose a theoretically guaranteed technique called heterogeneous anonymous walk (HAW) and give two more applicable variants. Then, we devise the HAW embedding (HAWE) and its variants in a data-driven manner to circumvent using an extremely large number of possible walks and train embeddings by predicting occurring walks in the neighborhood of each node. Finally, we design and apply extensive and illustrative experiments on synthetic and real-world networks to build a benchmark on heterostructure learning and evaluate the effectiveness of our methods. The results demonstrate our methods achieve outstanding performance compared with both homogeneous and heterogeneous classic methods and can be applied on large-scale networks.

Baicalin Exerts a Protective Effect in Diabetic Nephropathy by Repressing Inflammation and Oxidative Stress Through the SphK1/S1P/NF-κB Signaling Pathway

Background

Inflammation and oxidative stress contribute to the development of diabetic nephropathy (DN). Baicalin (BA) shows renal protection against DN through its anti-inflammatory and anti-oxidant properties. However, the molecular mechanism by which BA exerts the therapeutic effects on DN remains to be investigated.

Methods

The db/db mice and high glucose (HG)-induced HK-2 cells were used as the in vivo and in vitro model of DN, respectively. The effects of BA were assessed by detecting the related blood and urine biochemical parameters, kidney histopathology, inflammatory cytokine production, oxidative stress indicators, and apoptosis. Cell viability and apoptosis were detected by CCK-8 assay and TUNEL assay, respectively. Related protein levels were measured by an immunoblotting method.

Results

In db/db model mice, BA reduced serum glucose concentration, decreased blood lipid levels, ameliorated kidney functions, and decreased histopathological changes in kidney tissues. BA also alleviated oxidative stress and inflammation in db/db mice. In addition, BA blocked the activation of sphingosine kinases type 1/sphingosine 1-phosphate (SphK1/S1P)/NF-κB pathway in db/db mice. In HK-2 cells, BA hindered HG-induced apoptosis, oxidative stress and inflammation, while overexpression of SphK1 or S1P could reverse these effects. BA alleviated HG-induced apoptosis, oxidative stress and inflammation in HK-2 cells through the S1P/NF-κB pathway. Furthermore, BA blocked the NF-κB signaling by diminishing p65 nuclear translocation via the SphK1/S1P pathway.

Conclusion

Our study strongly suggests that BA protects against DN via ameliorating inflammation, oxidative stress and apoptosis through the SphK1/S1P/NF-κB pathway. This study provides a novel insight into the therapeutic effects of BA in DN.

A lightweight fetal distress-assisted diagnosis model based on a cross-channel interactive attention mechanism

Fetal distress is a symptom of fetal intrauterine hypoxia, which is seriously harmful to both the fetus and the pregnant woman. The current primary clinical tool for the assessment of fetal distress is Cardiotocography (CTG). Due to subjective variability, physicians often interpret CTG results inconsistently, hence the need to develop an auxiliary diagnostic system for fetal distress. Although the deep learning-based fetal distress-assisted diagnosis model has a high classification accuracy, the model not only has a large number of parameters but also requires a large number of computational resources, which is difficult to deploy to practical end-use scenarios. Therefore, this paper proposes a lightweight fetal distress-assisted diagnosis network, LW-FHRNet, based on a cross-channel interactive attention mechanism. The wavelet packet decomposition technique is used to convert the one-dimensional fetal heart rate (FHR) signal into a two-dimensional wavelet packet coefficient matrix map as the network input layer to fully obtain the feature information of the FHR signal. With ShuffleNet-v2 as the core, a local cross-channel interactive attention mechanism is introduced to enhance the model's ability to extract features and achieve effective fusion of multichannel features without dimensionality reduction. In this paper, the publicly available database CTU-UHB is used for the network performance evaluation. LW-FHRNet achieves 95.24% accuracy, which meets or exceeds the classification results of deep learning-based models. Additionally, the number of model parameters is reduced many times compared with the deep learning model, and the size of the model parameters is only 0.33 M. The results show that the lightweight model proposed in this paper can effectively aid in fetal distress diagnosis.

Out of the Qinghai-Tibet plateau: Genomic biogeography of the alpine monospecific genus Megadenia (Biscutelleae, Brassicaceae)

Numerous high-elevation alpine plants of the Qinghai-Tibet Plateau (QTP) also have disjunct distribution in adjacent low-altitude mountains. The out-of-QTP versus into-the-QTP hypothesis of alpine plants provide strong evidence for the highly disputed assumption of the massive ice sheet developed in the central plateau during the Last Glacial Maximum (LGM). In this study, we sequenced the genomes of most known populations of Megadenia, a monospecific alpine genus of Brassicaceae distributed primarily in the QTP, though rarely found in adjacent low-elevation mountains of north China and Russia (NC-R). All sequenced samples clustered into four geographic genetic groups: one pair was in the QTP and another was in NC-R. The latter pair is nested within the former, and these findings support the out-of-QTP hypothesis. Dating the four genetic groups and niche distribution suggested that Megadenia migrated out of the QTP to adjacent regions during the LGM. The NC-R group showed a decrease in the effective population sizes. In addition, the genes with high genetic divergences in the QTP group were mainly involved in habitat adaptations during low-altitude colonization. These findings reject the hypothesis of development massive ice sheets, and support glacial survival of alpine plants within, as well as further migration out of, the QTP.

Role Discovery-Guided Network Embedding Based on Autoencoder and Attention Mechanism

Recently, network embedding (NE) is an amazing research point in complex networks and devoted to a variety of tasks. Nearly, all the methods and models of NE are based on the local, high-order, or global similarity of the networks, and few studies have focused on the role discovery or structural similarity, which is of great significance in spreading dynamics and network theory. Meanwhile, existing NE models for role discovery suffer from two limitations, that is: 1) they fail to model the varying dependencies between each node and its neighbor nodes and 2) they cannot capture the effective node features which are helpful to role discovery, which makes these methods ineffective when applied to the role discovery task. To solve the above problems of NE for role discovery or structural similarity, we propose a unified deep learning framework, called RDAA, which can effectively represent features of nodes and benefit the Role Discovery-guided NE with a deep autoencoder, while modeling the local links with an Attention mechanism. In addition, we design an elaborately binding technique to combine both parts and optimize the framework in a unified way. We conduct different experiments, including visualization, role classification, role discovery, and running time compared to popular NE methods for both proximity and structural similarity. The RDAA has better performance on all the datasets and achieves good tradeoffs.

A novel prognostic model to predict OS and DFS of stage II/III gastric adenocarcinoma patients in China

Background

The prognosis of advanced gastric adenocarcinoma (GAC) after radical gastrectomy varies greatly. We aimed to build and validate a novel individualized nomogram based on inflammation index and tumor markers for patients with stage II/III GAC.

Methods

A total of 755 individuals with stage II/III GAC who had undergone radical gastrectomy at the First Affiliated Hospital of Zhengzhou University between 2012 and 2017 were included in this retrospective study. The patients were randomly divided into a training cohort (n ​= ​503) and a validation cohort (n ​= ​252). Univariate and multivariate analyses were used to determine independent prognostic factors of overall survival (OS) and disease-free survival (DFS). A nomogram was developed based on these independent factors. The concordance index (C-index) and calibration curves were used to evaluate the predictive accuracy of the nomogram.

Results

Univariate and multivariate analyses demonstrated that older age, poor differentiation, advanced stage, elevated neutrophil-to-lymphocyte ratio (NLR), lower hemoglobin, and high carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) levels were significantly associated with lower OS and DFS and were independent prognostic factors in stage II/III GAC. The nomogram developed based on these factors in the training cohort showed excellent calibration and discrimination (OS: C-index ​= ​0.739, 95% CI ​= ​0.706-0.772; DFS: C-index ​= ​0.735, 95% CI ​= ​0.702-0.769). In the internal validation cohort, the nomogram was also well-calibrated for the prediction of OS and DFS; it was superior to the 8th edition UICC/AJCC TNM staging system (for OS: C-index ​= ​0.746 vs. 0.679, respectively; for DFS: C-index ​= ​0.736 vs. 0.675, respectively; P ​< ​0.001).

Conclusion

The nomogram model could reliably predict OS and DFS in stage II/III gastric cancer patients with radical gastrectomy. It may help physicians make better treatment decisions.

Temporal Network Embedding for Link Prediction via VAE Joint Attention Mechanism

Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different network tasks. Temporal networks are an important type of network whose topological structure changes over time. Compared with methods on static networks, temporal network embedding (TNE) methods are facing three challenges: 1) it cannot describe the temporal dependence across network snapshots; 2) the node embedding in the latent space fails to indicate changes in the network topology; and 3) it cannot avoid a lot of redundant computation via parameter inheritance on a series of snapshots. To overcome these problems, we propose a novel TNE method named temporal network embedding method based on the VAE framework (TVAE), which is based on a variational autoencoder (VAE) to capture the evolution of temporal networks for link prediction. It not only generates low-dimensional embedding vectors for nodes but also preserves the dynamic nonlinear features of temporal networks. Through the combination of a self-attention mechanism and recurrent neural networks, TVAE can update node representations and keep the temporal dependence of vectors over time. We utilize parameter inheritance to keep the new embedding close to the previous one, rather than explicitly using regularization, and thus, it is effective for large-scale networks. We evaluate our model and several baselines on synthetic data sets and real-world networks. The experimental results demonstrate that TVAE has superior performance and lower time cost compared with the baselines.

Multimodal learning for fetal distress diagnosis using a multimodal medical information fusion framework

Cardiotocography (CTG) monitoring is an important medical diagnostic tool for fetal well-being evaluation in late pregnancy. In this regard, intelligent CTG classification based on Fetal Heart Rate (FHR) signals is a challenging research area that can assist obstetricians in making clinical decisions, thereby improving the efficiency and accuracy of pregnancy management. Most existing methods focus on one specific modality, that is, they only detect one type of modality and inevitably have limitations such as incomplete or redundant source domain feature extraction, and poor repeatability. This study focuses on modeling multimodal learning for Fetal Distress Diagnosis (FDD); however, exists three major challenges: unaligned multimodalities; failure to learn and fuse the causality and inclusion between multimodal biomedical data; modality sensitivity, that is, difficulty in implementing a task in the absence of modalities. To address these three issues, we propose a Multimodal Medical Information Fusion framework named MMIF, where the Category Constrained-Parallel ViT model (CCPViT) was first proposed to explore multimodal learning tasks and address the misalignment between multimodalities. Based on CCPViT, a cross-attention-based image-text joint component is introduced to establish a Multimodal Representation Alignment Network model (MRAN), explore the deep-level interactive representation between cross-modal data, and assist multimodal learning. Furthermore, we designed a simple-structured FDD test model based on the highly modal alignment MMIF, realizing task delegation from multimodal model training (image and text) to unimodal pathological diagnosis (image). Extensive experiments, including model parameter sensitivity analysis, cross-modal alignment assessment, and pathological diagnostic accuracy evaluation, were conducted to show our models’ superior performance and effectiveness.

Mass-Transfer Simulation of Salicylic Acid on Weakly Polar Hyper-cross-linked Resin XDA-200 with Coadsorption of Sodium Ion

The mass-transfer process of salicylic acid on hyper-cross-linked resin XDA-200 was experimentally and theoretically studied. Undissociated salicylic acid was found to be the favorable form for salicylic acid adsorption on the resin. A pH-dependent adsorption isotherm model established in this paper could well fit the adsorption isotherm data at different pH values. Surface diffusion is the main mass-transfer mode for salicylic acid in resin particles. The salicylate anions and Na⁺ coadsorbed on the resin. The modified surface diffusion model considering the coadsorption was proposed. The model could satisfactorily fit the concentration decay curves of salicylic acid at different pH values and feed concentrations. NaOH aqueous solution at pH 12 could elute salicylic acid in the fixed bed efficiently. A pH-dependent dynamic adsorption and elution process model considering axial diffusion, external mass transfer, surface diffusion, pH-dependent adsorption equilibrium, as well as coadsorption of salicylate anions and Na⁺ was established. The model could well predict the breakthrough and elution curves at different feed concentrations. The research carried out in this paper has reference significance for optimizing the separation process of salicylic acid and its analogues.

Simulation of Adsorption Process of l-Tryptophan on Mixed-Mode Resin HD-1 with Combined Physical Adsorption and Ion Exchange

The mass-transfer process of l-tryptophan (l-Trp) in the hydrophobic interaction/ion-exchange mixed-mode resin HD-1 particles and fixed bed was studied experimentally and theoretically. The adsorption kinetics of l-Trp in single-component and multicomponent adsorption systems was investigated under different pH conditions. The co-adsorption of sodium ions (Na⁺) and l-Trp anions was found to be negligible. A modified liquid-film linear driving force model considering the physical adsorption of l-Trp zwitterions and anions as well as ion exchange of l-Trp cations was proposed. The dissociation equilibria of l-Trp molecules and functional groups on the resin were introduced in the model. The model could well fit the kinetic adsorption curves of l-Trp at different pH values. The presence of Na⁺ and the impurity amino acid l-glutamic acid (l-Glu) did not significantly affect the mass-transfer rate of l-Trp. The dynamic adsorption processes of l-Trp under different pH and concentration conditions were studied. A modified transport-dispersive model considering axial diffusion, liquid-film mass transfer, and a combined physical adsorption and ion-exchange equilibrium was established, which could predict the adsorption breakthrough curves of l-Trp well. During the dynamic adsorption process, the pH of mobile phase in the fixed bed changed with changing the l-Trp concentration in the mobile phase. l-Trp was well separated from Na⁺ and l-Glu with the purity of l-Trp higher than 99%, the recovery rate higher than 95%, and a concentration of 4.69 × 10^-3 mol/L. The elution chromatographic peaks of l-Trp, l-Glu, and Na⁺ and the pH of the outlet solution were predicted satisfactorily.

The role of botulinum toxin type A related axon transport in neuropathic pain induced by chronic constriction injury

Background

The mechanism of peripheral axon transport in neuropathic pain is still unclear. Chemokine ligand 13 (CXCL13) and its receptor (C-X-C chemokine receptor type 5, CXCR5) as well as GABA transporter 1 (GAT-1) play an important role in the development of pain. The aim of this study was to explore the axonal transport of CXCL13/CXCR5 and GAT-1 with the aid of the analgesic effect of botulinum toxin type A (BTX-A) in rats.

Methods

Chronic constriction injury (CCI) rat models were established. BTX-A was administered to rats through subcutaneous injection in the hind paw. The pain behaviors in CCI rats were measured by paw withdrawal threshold and paw withdrawal latencies. The levels of CXCL13/CXCR5 and GAT-1 were measured by western blots.

Results

The subcutaneous injection of BTX-A relieved the mechanical allodynia and heat hyperalgesia induced by CCI surgery and reversed the overexpression of CXCL13/CXCR5 and GAT-1 in the spinal cord, dorsal root ganglia (DRG), sciatic nerve, and plantar skin in CCI rats. After 10 mmol/L colchicine blocked the axon transport of sciatic nerve, the inhibitory effect of BTX-A disappeared, and the levels of CXCL13/CXCR5 and GAT-1 in the spinal cord and DRG were reduced in CCI rats.

Conclusions

BTX-A regulated the levels of CXCL13/CXCR5 and GAT-1 in the spine and DRG through axonal transport. Chemokines (such as CXCL13) may be transported from the injury site to the spine or DRG through axonal transport. Axon molecular transport may be a target to enhance pain management in neuropathic pain.

Adsorption separation of guanosine 5'-Monophosphate and cytidine 5'-Monophosphate by mixed-mode Resin HD-1: Experimental study and mathematical modeling

The preparative separation of guanosine 5'-monophosphate (GMP) and cytidine 5'-monophosphate (CMP) on mixed-mode resin HD-1 was experimentally and theoretically investigated. The adsorption mechanisms of the two nucleotides were elucidated by analyzing adsorption equilibria and kinetics at different pH values. The adsorption dynamics of GMP and CMP in a fixed bed packed with resin HD-1 were studied. All nucleotide monovalent cations, zwitterions, and monovalent anions were adsorbed by the resin. Further, a general adsorption isotherm model was developed by considering the adsorption of different nucleotide species and the dissociation equilibrium behaviors of resin ligands. The model fit the adsorption isotherm data of GMP and CMP well. A modified liquid-film linear driving force model with the combined physical adsorption of nucleotides in different dissociation states and ion exchange of Na⁺ was established. The dissociation equilibria of resin ligands and nucleotides were considered. The model satisfactorily predicted the adsorption kinetic data at different pH values. The values of the efficient diffusion coefficients for GMP and CMP were not significantly influenced by the solution pH. A modified transport-diffusion model with pH gradient elution was proposed. The model accurately predicted the elution chromatographic peaks of GMP and CMP, as well as the pH at the outlet of the fixed bed packed with resin HD-1.

Molecular mechanism of betulin palliative therapy for chronic obstructive pulmonary disease (COPD) based on P2X7 receptor target of gated ion channel

Background

The aim of this study was to discover the molecular mechanism of betulin palliative therapy for chronic obstructive pulmonary disease (COPD) based on the P2X7 receptor target of gated ion channel.

Methods

A COPD mouse model was developed. Changes in pulmonary ventilation function, pulmonary airway and vascular remodeling indicators, inflammatory cells, and inflammatory factors were determined after betulin intervention, and the pathological alterations of lung tissues were detected. An in vitro experimental model was constructed to observe the influence of betulin at varying concentrations on the proliferation of human bronchial epidermal cell line (16-HBE) cells and changes in inflammatory factors in cell supernatant. The expression levels of key proteins in 16-HBE cells transfected with overexpressed or silenced P2X7 genes were determined through quantitative reverse transcription polymerase chain reaction (RT-qPCR) and Western blot.

Results

After betulin intervention, pulmonary ventilation function in the 20 mg/kg betulin and 40 mg/kg betulin groups was improved. Levels of white blood cells (WBCs), neutrophils (Ns), tumor necrosis factor (TNF), TNF-ɑ, interleukin (IL)-1β, and IL-6 in the 2 groups also decreased significantly (all P<0.05). The pathological changes in COPD mice were detected. After betulin intervention, the pathological injury of the lung was reduced, the pathological score decreased significantly, and the remodeling indicators of pulmonary airway and pulmonary vessels diminished remarkably (all P<0.05). Betulin had no effect on the proliferation of 16-HBE cells in vitro. After cigarette smoke extract (CSE) stimulation, the rate of survival for 16-HBE cells decreased significantly. After betulin treatment, the survival rate of 16-HBE cells augmented remarkably, and the levels of TNF-ɑ, IL-6, and IL-1β in cell supernatant reduced substantially (all P<0.05). 16-HBE with overexpression and knockdown of P2X7 was constructed. After being treated with betulin, the relative expression levels of messenger RNA (mRNA) of ERK, JNK, rho-associated protein kinase (ROCK), nuclear factor-κB (NF-κB), and p38 in 16-HBE cells with P2X7 overexpression or knockdown were decreased significantly (all P<0.05), but the above indicators were largely unchanged (all P>0.05).

Conclusions

Betulin relieved lung pathological injury, ameliorated lung ventilation function, and diminished the level of inflammatory factors in COPD mice, playing a therapeutic role via the P2X7 signaling pathway.

Effect of liraglutide combined with metformin or acarbose on glucose control in type 2 diabetes mellitus and risk factors of gastrointestinal adverse reactions

OBJECTIVE

To investigate the effect of liraglutide combined with metformin or acarbose on glucose control in patients with type 2 diabetes mellitus (T2DM) and to analyze the risk factors of gastrointestinal adverse reactions.

METHODS

This retrospective study was conducted on 88 T2DM patients who were treated in our hospital from February 2019 to August 2021. The patients were divided into Group A (n=40) and Group B (n=48) according to different treatment methods. Group A was treated with liraglutide and metformin, while Group B was given liraglutide and acarbose. The effects of glucose control (FPG, 2hPG, HbA1c), inflammatory indexes (IL-6, CRP, SAA), fasting C-peptide, 2-h postprandial C-peptide levels and adverse reactions were compared. Afterwards, The risk factors of gastrointestinal adverse reactions were assessed via logistics regression.

RESULTS

It was found that the FPG, 2hPG and HbA1c levels after treatment were lower than those before treatment (P<0.05), and the levels in group A were lower than those in group B (P<0.05). The serum IL-6, CRP and SAA levels after treatment were lower than those before treatment (P<0.05), but there was no marked difference between the two groups after treatment (P>0.05). The fasting C-peptide and 2-h postprandial C-peptide levels in group A after treatment were higher than those in group B (P<0.05). Logistics regression analysis revealed that complicated digestive system diseases and combined use of acarbose were independent risk factors.

CONCLUSION

Compared with liraglutide and acarbose, liraglutide and metformin has better glucose control effect in T2DM. Although there is no obvious difference in eliminating inflammation, liraglutide combined with acarbose will increase the incidence of gastrointestinal adverse reactions in patients. So, liraglutide combined with metformin is recommended for T2DM treatment.

Adsorption Equilibria, Kinetics, and Column Dynamics of L-Tryptophan on Mixed-Mode Resin HD-1

The adsorption amount and selectivity of L-tryptophan (l-Trp) on the hydrophobic interaction and ion exchange mixed-mode chromatography medium HD-1 were studied as well as the salt resistance of the resin via adsorption equilibrium experiments. The adsorption mechanisms of l-Trp were illuminated by combining adsorption equilibria and a kinetics analysis. The separation effect was studied by dynamic separation experiments in a fixed-bed. The results indicate that an increase of the concentration proportion of l-Trp zwitterion benefits the adsorption of l-Trp. The resin shows a high adsorption selectivity for l-Trp at different pH values. The adsorption amount of l-Trp is not affected significantly by NaCl. Various groups play a role in the adsorption of l-Trp. An adsorption energy lower than 8 kJ/mol indicates that the adsorption of l-Trp is mainly based on non-electrostatic interactions, with an electrostatic interaction as a supplement. The adsorption equilibrium model considering the dissociation equilibrium of the resin and l-Trp proposed in this work can simulate the adsorption equilibrium data of l-Trp at different pH values as well. The mass transfer rate of l-Trp is controlled by intraparticle and liquid film diffusion simultaneously. The fixed-bed packed with resin HD-1 can separate l-Trp with the purity of l-Trp higher than 99%, recovery rate higher than 95%, and concentration of 4.69 × 10^-3 mol/L.

Preparation and Evaluation of Antioxidant Activities of Bioactive Peptides Obtained from Cornus officinalis

The present study is a preparation of bioactive peptides from Cornus officinalis proteins by the compound enzymatic hydrolysis method. Response surface methodology (RSM) coupled with Box–Behnken design (BBD) is used to optimize the preparation process of Cornus officinalis peptides. The effects of independent variables, such as the amount of enzyme, pH value, time, extraction times and the ratio of material to liquid on the yield of peptides, are also investigated. The analysis results of the RSM model show that the optimum conditions for the extraction of Cornus officinalis peptides were a pH value of 6.76, temperature of 48.84 °C and the amount of enzyme of 0.19%. Under optimal conditions, the yield of peptides was 36.18 ± 0.26 %, which was close to the predicted yield by the RSM model. Additionally, the prepared Cornus officinalis peptides showed significant antioxidant activity; the scavenging rates of the peptides for DPPH and ·OH were 48.47% and 29.41%, respectively. The results of the cell proliferation assay revealed that the prepared Cornus officinalis peptides could promote embryo fibroblast cells proliferation and repair oxidative damage cells. These results have a practical application value in the design of novel functional food formulations by using Cornus officinalis.

Endoplasmic reticulum stress promotes the release of exosomal PD-L1 from head and neck cancer cells and facilitates M2 macrophage polarization

Background

Endoplasmic reticulum (ER) stress has been found to foster the escape of cancer cells from immune surveillance and upregulate PD-L1 expression. However, the underlying mechanisms are unknown.

Methods

While analyzing the protein levels using immunofluorescence and Western blotting, the RNA levels were measured using qRT-PCR. Ten injection of exosomes into six-week-old nude mice was made through the tail vein once every other day in total.

Results

The expression of certain ER stress markers such as PERK (PKR-like endoplasmic reticulum kinase), ATF6 (activating transcription factor 6), and GRP78 (glucose-regulated protein 78), was found to be upregulated in the oral squamous cell carcinoma (OSCC) tissues and related to poor overall survival. There is a positive relationship between the extent of ER stress-related proteins and a cluster of PD-L1 expression and macrophage infiltration among the OSCC tissues. Further, incubation with exosomes derived from ER-stressed HN4 cells (Exo-ER) was found to upregulate PD-L1 extents in macrophages in vitro and in vivo, and macrophage polarization toward the M2 subtype was promoted by upregulating PD-L1.

Conclusions

ER stress causes OSCC cells to secrete exosomal PD-L1 and upregulates PD-L1 expression in macrophages to drive M2 macrophage polarization. The delineation of a new exosome-modulated mechanism was made for OSCC–macrophage crosstalk driving tumor development and to be examined for its therapeutic use.

Graphical abstract

Exosomal PD-L1 secreted by ER-stressed OSCC cells promoted M2 macrophage polarization.

The features of the crystal structure of the layered series hydrates of uridine-5'-monophosphate salts (UMPNa x ·yH2O)

Almost all reported salts of nucleotides crystallized from solutions are in the form of hydrate. Layered hydrates often occur in crystals with more than five water molecules per host molecule. In the present report, five single-crystal structures of uridine-5'-monophosphate (UMP) series hydrates of acid or salts (UMPNa _x ·yH₂O, x = 0-2) were determined and analysed. It was found that all crystal hydrates were orthorhombic with a C222₁ space group but with mere variation in the plane angle of adjacent bases and the distance between phosphate arms. The packing arrangements of UMPNa _x ·yH₂O hydrates present typical layered sandwich structures and show that the UMP molecular layers alternate with water molecular layers parallel to the ac plane, linked by hydrogen bonds or coupled with coordinate bonds besides ionic electrostatic interaction. Metal ions were located in water molecular layers as a form of hydration. In addition, we tried to deduce and give insights into the formation of UMPNa _x ·yH₂O hydrates. The effect of water molecules and metal ions on the crystal structure and stability was investigated. It was found that the coexistence of relatively rigid architectures constructed by host molecules and flexible interlayer regions was a key factor to the formation of these hydrates. Excessive loss of lattice water would give rise to the irreversible collapse of the host structure and loss of ability to recover to the initial state under humidity. Approximately seven crystal-water molecules were the balance point of sodium salt hydrates at room temperature under 43-76% RH conditions. The number of sodium ions in the crystal lattice is positively correlated with their thermal stability.

The Endoplasmic Reticulum-Stressed Head and Neck Squamous Cell Carcinoma Cells Induced Exosomal miR-424-5p Inhibits Angiogenesis and Migration of Humanumbilical Vein Endothelial Cells Through LAMC1-Mediated Wnt/β-Catenin Signaling Pathway

Under endoplasmic reticulum (ER) stress, tumor plays multifaceted roles in endothelial cell dysfunction through secreting exosomal miRNAs. However, for the head and neck squamous cell carcinoma (HNSCC), it is still unclear about the impact of ER-stressed HNSCC cell derived exosomes on vascular endothelial cells. To address this gap, herein, systemic research was conducted including isolation and characterization of ER-stressed HNSCC cell (HN4 cell line as an in vitro model) derived exosomes, identification of regulatory exosomal miRNAs, target exploration and downstream signaling pathway investigation of exosomal miRNAs in human umbilical vein endothelial cell (HUVEC). ER-stressed HN4 cell-derived exosomes inhibited angiogenesis and migration of HUVEC cells in vitro. Furthermore, RNA-seq analysis demonstrated that miR-424-5p was highly upregulated in ER-stressed HN4 cell-derived exosomes. Through matrigel tube formation and transwell assays of HUVEC cells, miR-424-5p displayed great capabilities on inhibiting angiogenesis and migration. Finally, based on western blot and luciferase reporter, it was demonstrated that LAMC1 is the target of miR-424-5p which could inhibit the angiogenesis and migration of HUVEC cells by repressing the LAMC1-mediated Wnt/β-catenin signaling pathway. ER-stressed HNSCC cell-induced exosomal miR-424-5p inhibits angiogenesis and migration of HUVEC cells through LAMC1-mediated Wnt/β-catenin signaling pathway. This study offers a new insight for understanding the complicated mechanism behind ER-stress induced anti-angiogenesis of HNSCC.

Characteristics of Mucormycosis in Hematological Patients and a Death Prediction Model

Mucormycosis is an angioinvasive fungal infection, associated with high mortality. The aim of our study was to explore the high-risk factors and predict the death of hematological disease complicated with mucormycosis. We retrospectively analyzed clinical data of 31 patients with hematological disease complicated with mucormycosis, adopted random forest to establish the death prediction model, and validated the model in another 15 patients. The median age of the 31 cases was 46 (28–51) years, male to female ratio 1.38:1, and 90-day mortality rate 54.8%. The most common underlying disease was acute myeloid leukemia (58.1%). The main clinical symptoms were fever (100%), cough (87.1%), sputum (80.6%), chest pain (61.3%), and hemoptysis (19.4%). Reversed halo sign (83.9%) was the most common computed tomography sign. A total of 48.4% of patients also had aspergillus or bacterial infections. Discriminative models were constructed by random forest with 17 non-survivors and 14 survivors. Procalcitonin, the duration of intravenous administration of amphotericin B or amphotericin B liposomes, and neutropenia at death or 90 days of survival were the leading risk factors for poor prognosis, with area under the curve of 0.975 (95% CI 0.934–1). We chose 0.6775 as death prediction threshold (with 82.3% sensitivity and 100% specificity) and validated the model successfully in another 15 patients. Chest pain and reversed halo sign are specific clinical and image signs of hematological disease complicated with mucormycosis. Neutropenia, elevated procalcitonin, and insufficient use time of amphotericin B or amphotericin B liposomes are risk factors for death.

FAM83A promotes proliferation and metastasis via Wnt/β-catenin signaling in head neck squamous cell carcinoma

This research aimed to investigate the expression and function of FAM83A in the proliferation and metastasis in head and neck squamous cell carcinoma (HNSCC). FAM83A mRNA and protein expressions in HNSCC were detected in primary HNSCC samples and cell lines. The associations between FAM83A expression and clinicopathologic variables were evaluated through tissue microarrays. Besides, FAM83A knockdown and overexpression cell lines were constructed to assess cell growth and metastasis in vitro and the relationship between FAM83A and epithelial-mesenchymal transition (EMT). Furthermore, two models of xenograft tumors in nude mice were used to assess the tumorigenicity and metastasis ability of FAM83A in vivo. In the present study, overexpression of FAM83A in HNSCC samples was significantly associated with tumor size, lymph node status and clinical tumor stages. Mechanically, FAM83A could promote HNSCC cell growth and metastasis by inducing EMT via activating Wnt/β-catenin signaling pathway. Rescue experiment demonstrated the inhibition of β-catenin could counteract the function of FAM83A. Also, the FAM83A knockdown could suppress tumor growth and distant metastasis in the xenograft animal models of HNSCC. In conclusion, this study identifies FAM83A as an oncogene of HNSCC. This study provides new insights into the molecular pathways that contribute to EMT in HNSCC. We revealed a previously unknown FAM83A-Wnt–β-catenin signaling axis involved in the EMT of HNSCC. There may be a potential bi-directional signaling loop between FAM83A and Wnt/β-catenin signaling pathway in HNSCC.

ACE2 and TMPRSS2 in human saliva can adsorb to the oral mucosal epithelium

Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is primarily transmitted through droplets. All human tissues with the angiotensin‐converting enzyme 2 (ACE2) and transmembrane protease serines 2 (TRMPRSS2) are potential targets of SARS‐CoV‐2. The role of saliva in SARS‐CoV‐2 transmission remains obscure. In this study, we attempted to reveal ACE2 and TRMPRSS2 protein expression in human parotid, submandibular, and sublingual glands (three major salivary glands). Then, the binding function of spike protein to ACE2 in three major salivary glands was detected. The expression of ACE2 and TMPRSS2 in human saliva from parotid glands were both examined. Exogenous recombined ACE2 and TMPRSS2 anchoring and fusing to oral mucosal epithelial cells in vitro were also unraveled. ACE2 and TMPRSS2 were found mainly to be expressed in the cytomembrane and cytoplasm of epithelial cells in the serous acinus cells in parotid and submandibular glands. Our research also discovered that the spike protein of SARS‐CoV‐2 binds to ACE2 in salivary glands in vitro. Furthermore, exogenous ACE2 and TMPRSS2 can anchor and fuse to oral mucosa in vitro. Thus, the expression of ACE2 and TMPRSS2 in human saliva might have implications for SARS‐CoV‐2 infection.

Identification of circ_0058357 as a regulator in non-small cell lung cancer cells resistant to cisplatin by miR-361-3p/ABCC1 axis

Background

Drug resistance is a major clinical drawback behind the failure of chemotherapy in non‐small cell lung cancer (NSCLC). In this study, we undertook to identify the precise role of circular RNA (circRNA) circ_0058357 in the functional properties of DDP‐resistant NSCLC cells.

Methods

Circ_0058357, miR‐361‐3p and ATP‐binding cassette (ABC) subfamily C member 1 (ABCC1) were quantified by qRT‐PCR and western blot. Cell survival and viability were gauged by MTT assay. Cell proliferation, apoptosis, invasion and migration were measured by EdU, flow cytometry, transwell and wound‐healing assays, respectively. The direct relationship between miR‐361‐3p and circ_0058357 or ABCC1 was validated by dual‐luciferase reporter assay.

Results

Our data showed that circ_0058357 was highly expressed in DDP‐resistant NSCLC tissues and cells. Inhibition of circ_0058357 repressed cell growth, invasion, migration, and promoted DDP sensitivity and cell apoptosis of H1299/DDP and A549/DDP cells in vitro. Moreover, inhibition of circ_0058357 diminished the growth of A549/DDP cells and sensitized them to the cytotoxic effect of DDP in vivo. Mechanistically, circ_0058357 contained a miR‐361‐3p binding site and miR‐361‐3p was identified as a molecular mediator of circ_0058357 regulation. MiR‐361‐3p suppressed ABCC1 expression by binding to ABCC1 3′UTR, and miR‐361‐3p‐mediated inhibition of ABCC1 affected the growth, invasion, migration, apoptosis and DDP sensitivity of H1299/DDP and A549/DDP cells. Furthermore, circ_0058357 regulated ABCC1 expression by competitively binding to shared miR‐361‐3p.

Conclusions

Our findings identified that inhibition of circ_0058357 suppresses the growth and metastasis of H1299/DDP and A549/DDP cells and sensitizes them to DDP therapy partially by targeting the miR‐361‐3p/ABCC1 axis.

Toward Comprehensive User and Item Representations via Three-tier Attention Network

Product reviews can provide rich information about the opinions users have of products. However, it is nontrivial to effectively infer user preference and item characteristics from reviews due to the complicated semantic understanding. Existing methods usually learn features for users and items from reviews in single static fashions and cannot fully capture user preference and item features. In this article, we propose a neural review-based recommendation approach that aims to learn comprehensive representations of users/items under a three-tier attention framework. We design a review encoder to learn review features from words via a word-level attention, an aspect encoder to learn aspect features via a review-level attention, and a user/item encoder to learn the final representations of users/items via an aspect-level attention. In word- and review-level attentions, we adopt the context-aware mechanism to indicate importance of words and reviews dynamically instead of static attention weights. In addition, the attentions in the word and review levels are of multiple paradigms to learn multiple features effectively, which could indicate the diversity of user/item features. Furthermore, we propose a personalized aspect-level attention module in user/item encoder to learn the final comprehensive features. Extensive experiments are conducted and the results in rating prediction validate the effectiveness of our method.

Long Noncoding RNA LINC00460 Promotes Cell Progression by Sponging miR-4443 in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers worldwide. Long noncoding RNAs were proved to be associated with the development and progression in HNSCC. However, the mechanism of LINC00460 in HNSCC needs to be further investigated. The study used quantitative real-time polymerase chain reaction assay to detect the expression of LINC00460 in cancer tissues and cell lines. Gain and loss of function experiments were conducted to analyze the effects of LINC00460 and miR-4443 on cell proliferation, invasion, and apoptosis of HNSCC cells in vitro. The interactions among miR-4443 and LINC00460 were detected by dual-luciferase reporter assay. Here, the study showed that LINC00460 was highly expressed in HNSCC tissues and cell lines. Functionally, knockdown of LINC00460 inhibited HNSCC cell proliferation and migration in vitro. Besides, LINC00460 promoted cell progression by sponging miR-4443, and miR-4443 inhibitor could reverse the effects of si-LINC00460 on cell proliferation and migration. In summary, LINC00460 could potentially promote cell progression and epithelial mesenchymal transition by sponging miR-4443 in HNSCC. LINC00460 could be used as a potential therapeutic target for HNSCCs.

Molecularly Imprinted Magnetic Fluorescent Nanocomposite-Based Sensor for Selective Detection of Lysozyme

A new strategy for the design and construction of molecularly imprinted magnetic fluorescent nanocomposite-based-sensor is proposed. This multifunctional nanocomposite exhibits the necessary optics, magnetism and biocompatibility for use in the selective fluorescence detection of lysozyme. The magnetic fluorescent nanocomposites are prepared by combining carboxyl- functionalized Fe3O4 magnetic nanoparticles with l-cysteine-modified zinc sulfide quantum dots (MNP/QDs). Surface molecular imprinting technology was employed to coat the lysozyme molecularly imprinted polymer (MIP) layer on the MNP/QDs to form a core-shell structure. The molecularly imprinted MNP/QDs (MNP/QD@MIPs) can rapidly separate the target protein and then use fluorescence sensing to detect the protein; this reduces the background interference, and the selectivity and sensitivity of the detection are improved. The molecularly imprinted MNP/QDs sensor presented good linearity over a lysozyme concentration range from 0.2 to 2.0 μM and a detection limit of 4.53 × 10-3 μM for lysozyme. The imprinting factor of the MNP/QD@MIPs was 4.12, and the selectivity coefficient ranged from 3.19 to 3.85. Furthermore, the MNP/QD@MIPs sensor was applied to detect of lysozyme in human urine and egg white samples with recoveries of 95.40-103.33%. Experimental results showed that the prepared MNP/QD@MIPs has potential for selective magnetic separation and fluorescence sensing of target proteins in biological samples.

Macrophage-Derived Exosomal miR-31-5p Promotes Oral Squamous Cell Carcinoma Tumourigenesis Through the Large Tumor Suppressor 2-Mediated Hippo Signalling Pathway

Tumour-associated macrophages (TAMs) are thought to contribute to oral squamous cell carcinoma (OSCC) initiation and progression. However, the underlying mechanism through which TAMs foster OSCC progression is still unclear. This study intended to determine whether there are exclusively exosomal miRNAs-derived macrophages that are functionally necessary for OSCC progression. The phenotype of TAM recruitment in OSCC tissue samples was assessed, subsequently identifying the influence of M2 macrophages and exosomes derived from M2 macrophages on OSCC proliferation and tumorigenesis in vitro and in vivo. CD68 and CD163, the specific markers of M2 type macrophages, were upregulated in TAMs presented in intra-cancer tissues. M2 macrophages and M2 macrophage-derived exosomes (M2 exos) both can promote OSCC growth and tumorigenicity. An exosomal RNA-seq analysis was conducted to predict regulatory exosomal miRNAs related to OSCC growth, which determined miR-31-5p and LATS2 for subsequent experiments. Mechanistically, miR-31-5p was delivered to recipient OSCC cells through M2 exos and complementary pairing with the large tumor suppressor 2 (LATS2) coding sequence, thus suppressing the expression of LATS2 and inactivation the Hippo signaling pathway to support OSCC growth. Collectively, our findings demonstrate that M2 macrophage-derived exosomal miR- 31-5p can make tumor suppressor LATS2 gene inhibited and facilitate the progression of OSCC via inhibiting the Hippo signaling pathway, which possibly provides new targets for the molecular therapy of OSCC.

Cooperative adsorption of L-tryptophan and sodium ion on a hyper-cross-linked resin: Experimental studies and mathematical modeling

The adsorption separation of L-tryptophan (L-Trp) by the weakly polar hyper-cross-linked resin XDA-200 was studied. First, the adsorption equilibria of different species of L-Trp on the resin were compared. Then, the adsorption isotherms and adsorption kinetics of L-Trp were studied at different pH values. Finally, the dynamic adsorption and separation processes of L-Trp in a packed bed of the resin were studied. The distribution coefficient of L-Trp^± between the resin and an aqueous solution of L-Trp (55.69) was found to be markedly larger than that of L-Trp⁺ (27.53) and L-Trp^- (10.42). An adsorption isotherm model depending on pH was established to simulate the adsorption equilibrium data of L-Trp. The cooperative adsorption of sodium ion (Na⁺) with L-Trp^- cannot be ignored when the solution pH is higher than 8.0. Thus, a modified surface diffusion model considering cooperative adsorption of Na⁺ with L-Trp^- was established. The model fitted the kinetic curves for L-Trp adsorption under different pH values satisfactorily. The surface diffusion coefficient of L-Trp first decreased and then increased as pH increased from 3 to 12. In this study, a modified film-surface diffusion model considering cooperative adsorption of Na⁺ with L-Trp^- is proposed. Further, we show that our proposed model can predict the chromatographic peaks of L-Trp, L-glutamic acid (L-Glu), and Na⁺ satisfactorily.

Intracellular Paclitaxel Delivery Facilitated by a Dual-Functional CPP with a Hydrophobic Hairpin Tail

In our pervious study, a dual-functional peptide R7 was developed to form a complex with paclitaxel (PTX) for enhancement of PTX translocation. However, because of the unstable noncovalent bond between R7 and PTX, PTX redistributed after the introduction of heparin, leading to R7-PTX complex dissociation, further causing less PTX penetration than expected. Thus, a novel positive CPP carrier of P9 was developed to improve CPP-PTX affinity via a double-proline (Pro, P) hairpin tail and enhance PTX translocation through the reduction of translocation energy barrier, confirmed by the MM-PBSA analysis and umbrella sampling simulation. Cellular uptake study reveals that P9 can quickly translocate into the HeLa cells within 1 min and exhibits no noticeable cytotoxicity. Compared to R7, P9 is able to help PTX translocation, leading to a remarkable increase in the intracellular concentration of PTX, eventually resulting in a significant loss in tumor cell viability. In vivo experiments demonstrate that a vein injection of P9-PTX complex dramatically inhibits tumor growth. Our study provides a novel perspective for designing CPP-facilitated drug carrier to enhance antitumor efficiency.

Adsorption Separation of l-Tryptophan Based on the Hyper-Cross-Linked Resin XDA-200

l-Tryptophan (l-Trp) was separated from its aqueous solution by hyper-cross-linked resins. The adsorption and desorption performances of l-Trp on different resins were compared. The weakly polar resin XDA-200 was selected as an excellent adsorbent with high adsorption amount and easy elution. The resin has a high adsorption selectivity and strong salt resistance. The adsorption mechanism of l-Trp on resin XDA-200 was elucidated based on adsorption thermodynamics experiments, molecular dynamics simulations, and adsorption kinetics experiments. The dynamic separation process of l-Trp was finally studied. The adsorption of l-Trp on resin XDA-200 is a spontaneous process driven by adsorption enthalpy. l-Trp^± is the most favorable form for l-Trp adsorption on resin XDA-200 because of the strongest affinity of l-Trp^± to the resin and relatively low water solubility. The adsorption of l-Trp is mainly based on π-π and hydrophobic interactions. Surface diffusion is the sole rate-limiting step of l-Trp mass transfer on resin XDA-200. l-Trp was separated satisfactorily from l-glutamic acid (l-Glu) and NaCl with both the recovery rate and purity of l-Trp higher than 99% in the fixed bed packed with resin XDA-200.

The sesquiterpene lactone eupatolide induces apoptosis in non-small cell lung cancer cells by suppressing STAT3 signaling

We aimed to evaluate the role of a natural sesquiterpene lactone, eupatolide, in non-small-cell lung cancer (NSCLC) and further explore its underlying mechanism on regulating the activation of signal transducer and activator of transcription 3 (STAT3), which is thought to have carcinogenic function in a variety of malignancies including lung cancer. Cell survival was measured by Cell Counting Kit-8 assay. in vivo experiments were performed by inoculating NSCLC cells into nude mice. Western blot and qRT-PCR were applied to detect the activation level of STAT3 and the mRNA levels of anti-apoptotic markers. The cell apoptosis was measured by Annexin V-FITC/PI Apoptosis Detection Kit. Our results showed that eupatolide suppressed the survival of NSCLC cells in a dose and time dependent manner. Furthermore, eupatolide increased the anti-tumor activity of the chemotherapeutic drugs cisplatin and 5-Fluoracil (5-FU). The xenograft study revealed that eupatolide suppressed tumor growth of NSCLC cells in vivo. Furthermore, eupatolide induced apoptosis by suppressing the activation of STAT3 in NSCLC cells. Sustained activation or knockdown of STAT3 suppressed and enhanced the activity of eupatolide, respectively. This paper is the first to report that eupatolide could effectively inhibit NSCLC progression, suggesting that eupatolide might be utilized as a novel STAT3 inhibitor for treating NSCLC.

Mass transfer process and separation mechanism of four 5'-ribonucleotides on a strong acid cation exchange resin

5'-ribonucleotides including adenosine 5'-monophosphate (AMP), cytidine 5'-monophsphate (CMP), guanosine 5'-monophosphate (GMP) and uridine 5'-monophosphate (UMP) have been widely used in the food and pharmaceutical industries. This work focused on the assessment of mass transfer process and separation mechanism of four 5'-ribonucleotides and counter-ion Na⁺ on the strong cation exchange resin NH-1. The intraparticle diffusion was determined as the rate-limiting step for the mass transfer of AMP, CMP, GMP, and Na⁺ on the resin NH-1 through the Boyd model. Meanwhile, a homogeneous surface diffusion model (HSDM) combing ion exchange and physical adsorption was proposed and tested against adsorption kinetic data in the batch adsorption systems. The fixed-bed film-surface diffusion model based on the HSDM was then developed and successfully predicted the concentration profiles of 5'-ribonucleotides and the change of pH at the outlet of the fixed-bed in the dynamic adsorption and separation process. Finally, the separation mechanism of 5'-ribonucleotides was presented combining model prediction and experimental results. The separation of UMP, GMP and CMP were mainly based on their differences in isoelectric points, while that of AMP and CMP were lied with the discrepancy of their physical adsorption binding capacity with the resin NH-1.

Characteristic of 523 COVID-19 in Henan Province and a Death Prediction Model

Certain high-risk factors related to the death of COVID-19 have been reported, however, there were few studies on a death prediction model. This study was conducted to delineate the clinical characteristics of patients with coronavirus disease 2019 (covid-19) of different degree and establish a death prediction model. In this multi-centered, retrospective, observational study, we enrolled 523 COVID-19 cases discharged before February 20, 2020 in Henan Province, China, compared clinical data, screened for high-risk fatal factors, built a death prediction model and validated the model in 429 mild cases, six fatal cases discharged after February 16, 2020 from Henan and 14 cases from Wuhan. Out of the 523 cases, 429 were mild, 78 severe survivors, 16 non-survivors. The non-survivors with median age 71 were older and had more comorbidities than the mild and severe survivors. Non-survivors had a relatively delay in hospitalization, with higher white blood cell count, neutrophil percentage, D-dimer, LDH, BNP, and PCT levels and lower proportion of eosinophils, lymphocytes and albumin. Discriminative models were constructed by using random forest with 16 non-survivors and 78 severe survivors. Age was the leading risk factors for poor prognosis, with AUC of 0.907 (95% CI 0.831-0.983). Mixed model constructed with combination of age, demographics, symptoms, and laboratory findings at admission had better performance (p = 0.021) with a generalized AUC of 0.9852 (95% CI 0.961-1). We chose 0.441 as death prediction threshold (with 0.85 sensitivity and 0.987 specificity) and validated the model in 429 mild cases, six fatal cases discharged after February 16, 2020 from Henan and 14 cases from Wuhan successfully. Mixed model can accurately predict clinical outcomes of COVID-19 patients.

Modeling with Node Popularities for Autonomous Overlapping Community Detection

Overlapping community detection has triggered recent research in network analysis. One of the promising techniques for finding overlapping communities is the popular stochastic models, which, unfortunately, have some common drawbacks. They do not support an important observation that highly connected nodes are more likely to reside in the overlapping regions of communities in the network. These methods are in essence not truly unsupervised, since they require a threshold on probabilistic memberships to derive overlapping structures and need the number of communities to be specified a priori . We develop a new method to address these issues for overlapping community detection. We first present a stochastic model to accommodate the relative importance and the expected degree of every node in each community. We then infer every overlapping community by ranking the nodes according to their importance. Second, we determine the number of communities under the Bayesian framework. We evaluate our method and compare it with five state-of-the-art methods. The results demonstrate the superior performance of our method. We also apply this new method to two applications, showing its superb performance on practical problems.