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MOTIVATION: Drug-target interaction (DTI) prediction refers to the prediction of whether a given drug molecule will bind to a specific target and thus exert a targeted therapeutic effect. Although intelligent computational approaches for drug target prediction have received much attention and made many advances, they are still a challenging task that requires further research. The main challenges are manifested as follows: (i) most graph neural network-based methods only consider the information of the first-order neighboring nodes (drug and target) in the graph, without learning deeper and richer structural features from the higher-order neighboring nodes. (ii) Existing methods do not consider both the sequence and structural features of drugs and targets, and each method is independent of each other, and cannot combine the advantages of sequence and structural features to improve the interactive learning effect. RESULTS: To address the above challenges, a Multi-view Integrated learning Network that integrates Deep learning and Graph Learning (MINDG) is proposed in this study, which consists of the following parts: (i) a mixed deep network is used to extract sequence features of drugs and targets, (ii) a higher-order graph attention convolutional network is proposed to better extract and capture structural features, and (iii) a multi-view adaptive integrated decision module is used to improve and complement the initial prediction results of the above two networks to enhance the prediction performance. We evaluate MINDG on two dataset and show it improved DTI prediction performance compared to state-of-the-art baselines. AVAILABILITY AND IMPLEMENTATION: https://github.com/jnuaipr/MINDG.
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Algoritmos , Redes Neurales de la ComputaciónRESUMEN
Posttranslational modification of lysine residues, K-PTM, is one of the most popular PTMs. Some lysine residues in proteins can be continuously or cascaded covalently modified, such as acetylation, crotonylation, methylation and succinylation modification. The covalent modification of lysine residues may have some special functions in basic research and drug development. Although many computational methods have been developed to predict lysine PTMs, up to now, the K-PTM prediction methods have been modeled and learned a single class of K-PTM modification. In view of this, this study aims to fill this gap by building a multi-label computational model that can be directly used to predict multiple K-PTMs in proteins. In this study, a multi-label prediction model, MLysPRED, is proposed to identify multiple lysine sites using features generated from human protein sequences. In MLysPRED, three kinds of multi-label sequence encoding algorithms (MLDBPB, MLPSDAAP, MLPSTAAP) are proposed and combined with three encoding strategies (CHHAA, DR and Kmer) to convert preprocessed lysine sequences into effective numerical features. A multidimensional normal distribution oversampling technique and graph-based multi-view clustering under-sampling algorithm were first proposed and incorporated to reduce the proportion of the original training samples, and multi-label nearest neighbor algorithm is used for classification. It is observed that MLysPRED achieved an Aiming of 92.21%, Coverage of 94.98%, Accuracy of 89.63%, Absolute-True of 81.46% and Absolute-False of 0.0682 on the independent datasets. Additionally, comparison of results with five existing predictors also indicated that MLysPRED is very promising and encouraging to predict multiple K-PTMs in proteins. For the convenience of the experimental scientists, 'MLysPRED' has been deployed as a user-friendly web-server at http://47.100.136.41:8181.
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Lisina , Proteínas , Algoritmos , Análisis por Conglomerados , Biología Computacional/métodos , Humanos , Lisina/metabolismo , Distribución Normal , Procesamiento Proteico-Postraduccional , Proteínas/químicaRESUMEN
MOTIVATION: CircRNAs play a critical regulatory role in physiological processes, and the abnormal expression of circRNAs can mediate the processes of diseases. Therefore, exploring circRNAs-disease associations is gradually becoming an important area of research. Due to the high cost of validating circRNA-disease associations using traditional wet-lab experiments, novel computational methods based on machine learning are gaining more and more attention in this field. However, current computational methods suffer to insufficient consideration of latent features in circRNA-disease interactions. RESULTS: In this study, a multilayer attention neural graph-based collaborative filtering (MLNGCF) is proposed. MLNGCF first enhances multiple biological information with autoencoder as the initial features of circRNAs and diseases. Then, by constructing a central network of different diseases and circRNAs, a multilayer cooperative attention-based message propagation is performed on the central network to obtain the high-order features of circRNAs and diseases. A neural network-based collaborative filtering is constructed to predict the unknown circRNA-disease associations and update the model parameters. Experiments on the benchmark datasets demonstrate that MLNGCF outperforms state-of-the-art methods, and the prediction results are supported by the literature in the case studies. AVAILABILITY AND IMPLEMENTATION: The source codes and benchmark datasets of MLNGCF are available at https://github.com/ABard0/MLNGCF.
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Redes Neurales de la Computación , ARN Circular , Aprendizaje Automático , Programas Informáticos , Biología Computacional/métodosRESUMEN
Accurately predicting RNA-protein binding sites is essential to gain a deeper comprehension of the protein-RNA interactions and their regulatory mechanisms, which are fundamental in gene expression and regulation. However, conventional biological approaches to detect these sites are often costly and time-consuming. In contrast, computational methods for predicting RNA protein binding sites are both cost-effective and expeditious. This review synthesizes already existing computational methods, summarizing commonly used databases for predicting RNA protein binding sites. In addition, applications and innovations of computational methods using traditional machine learning and deep learning for RNA protein binding site prediction during 2018-2023 are presented. These methods cover a wide range of aspects such as effective database utilization, feature selection and encoding, innovative classification algorithms, and evaluation strategies. Exploring the limitations of existing computational methods, this paper delves into the potential directions for future development. DeepRKE, RDense, and DeepDW all employ convolutional neural networks and long and short-term memory networks to construct prediction models, yet their algorithm design and feature encoding differ, resulting in diverse prediction performances.
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Proteínas de Unión al ARN , ARN , Proteínas de Unión al ARN/metabolismo , Sitios de Unión , ARN/metabolismo , Biología Computacional/métodos , Algoritmos , Aprendizaje Automático , Aprendizaje Profundo , Humanos , Unión Proteica , Redes Neurales de la ComputaciónRESUMEN
Glycation, a type of posttranslational modification, preferentially occurs on lysine and arginine residues, impairing protein functionality and altering characteristics. This process is linked to diseases such as Alzheimer's, diabetes, and atherosclerosis. Traditional wet lab experiments are time-consuming, whereas machine learning has significantly streamlined the prediction of protein glycation sites. Despite promising results, challenges remain, including data imbalance, feature redundancy, and suboptimal classifier performance. This research introduces Glypred, a lysine glycation site prediction model combining ClusterCentroids Undersampling (CCU), LightGBM, and bidirectional long short-term memory network (BiLSTM) methodologies, with an additional multihead attention mechanism integrated into the BiLSTM. To achieve this, the study undertakes several key steps: selecting diverse feature types to capture comprehensive protein information, employing a cluster-based undersampling strategy to balance the data set, using LightGBM for feature selection to enhance model performance, and implementing a bidirectional LSTM network for accurate classification. Together, these approaches ensure that Glypred effectively identifies glycation sites with high accuracy and robustness. For feature encoding, five distinct feature typesâAAC, KMER, DR, PWAA, and EBGWâwere selected to capture a broad spectrum of protein sequence and biological information. These encoded features were integrated and validated to ensure comprehensive protein information acquisition. To address the issue of highly imbalanced positive and negative samples, various undersampling algorithms, including random undersampling, NearMiss, edited nearest neighbor rule, and CCU, were evaluated. CCU was ultimately chosen to remove redundant nonglycated training data, establishing a balanced data set that enhances the model's accuracy and robustness. For feature selection, the LightGBM ensemble learning algorithm was employed to reduce feature dimensionality by identifying the most significant features. This approach accelerates model training, enhances generalization capabilities, and ensures good transferability of the model. Finally, a bidirectional long short-term memory network was used as the classifier, with a network structure designed to capture glycation modification site features from both forward and backward directions. To prevent overfitting, appropriate regularization parameters and dropout rates were introduced, achieving efficient classification. Experimental results show that Glypred achieved optimal performance. This model provides new insights for bioinformatics and encourages the application of similar strategies in other fields. A lysine glycation site prediction software tool was also developed using the PyQt5 library, offering researchers an auxiliary screening tool to reduce workload and improve efficiency. The software and data sets are available on GitHub: https://github.com/ZBYnb/Glypred.
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Lisina , Glicosilación , Lisina/química , Lisina/metabolismo , Proteínas/química , Proteínas/metabolismo , Aprendizaje Automático , Biología Computacional/métodos , Humanos , Redes Neurales de la Computación , Bases de Datos de ProteínasRESUMEN
BACKGROUND: Carbonylation is a non-enzymatic irreversible protein post-translational modification, and refers to the side chain of amino acid residues being attacked by reactive oxygen species and finally converted into carbonyl products. Studies have shown that protein carbonylation caused by reactive oxygen species is involved in the etiology and pathophysiological processes of aging, neurodegenerative diseases, inflammation, diabetes, amyotrophic lateral sclerosis, Huntington's disease, and tumor. Current experimental approaches used to predict carbonylation sites are expensive, time-consuming, and limited in protein processing abilities. Computational prediction of the carbonylation residue location in protein post-translational modifications enhances the functional characterization of proteins. RESULTS: In this study, an integrated classifier algorithm, CarSite-II, was developed to identify K, P, R, and T carbonylated sites. The resampling method K-means similarity-based undersampling and the synthetic minority oversampling technique (SMOTE-KSU) were incorporated to balance the proportions of K, P, R, and T carbonylated training samples. Next, the integrated classifier system Rotation Forest uses "support vector machine" subclassifications to divide three types of feature spaces into several subsets. CarSite-II gained Matthew's correlation coefficient (MCC) values of 0.2287/0.3125/0.2787/0.2814, False Positive rate values of 0.2628/0.1084/0.1383/0.1313, False Negative rate values of 0.2252/0.0205/0.0976/0.0608 for K/P/R/T carbonylation sites by tenfold cross-validation, respectively. On our independent test dataset, CarSite-II yield MCC values of 0.6358/0.2910/0.4629/0.3685, False Positive rate values of 0.0165/0.0203/0.0188/0.0094, False Negative rate values of 0.1026/0.1875/0.2037/0.3333 for K/P/R/T carbonylation sites. The results show that CarSite-II achieves remarkably better performance than all currently available prediction tools. CONCLUSION: The related results revealed that CarSite-II achieved better performance than the currently available five programs, and revealed the usefulness of the SMOTE-KSU resampling approach and integration algorithm. For the convenience of experimental scientists, the web tool of CarSite-II is available in http://47.100.136.41:8081/.
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Algoritmos , Proteínas , Carbonilación Proteica , Procesamiento Proteico-Postraduccional , Proteínas/metabolismo , Máquina de Vectores de SoporteRESUMEN
PURPOSE: Patient comfort is an important concern in patients receiving surgery, but the seriousness of discomfort during recovery is unknown. We investigated the incidence of postoperative discomfort based on the Standardized Endpoints in Perioperative Medicine initiative for patient comfort, and identified the risk factors. DESIGN: This was a single-center prospective observational study. METHODS: We enrolled adult patients who underwent elective surgery under general anesthesia between July and December 2018 at West China Hospital of Sichuan University (ChiCTR1800017324). The primary outcome was the incidence of postoperative severe discomfort (PoSD), defined as occurring when a patient experienced a severe rating in two or more domains in the six domains in the Standardized Endpoints in Perioperative Medicine initiative on the same day, including rest pain, postoperative nausea, and vomiting, dissatisfaction of gastrointestinal recovery, dissatisfaction of mobilization, sleep disturbance, and recovery. A generalized estimated equation was constructed to find risk factors of PoSD. FINDINGS: In total, 440 patients completed the study. The incidence of PoSD was 28% on postoperative day (POD) 1, 13% on POD 2, 9% on POD 3, and 3.6% on both POD 5 and 7. The most common discomfort was serious sleep disturbance, ranging from 43% to 10% in the first week after surgery. Longer operative time (odds ratio [95% confidence interval]: 1.56 [1.19 to 2.05], P = .001), gastrointestinal surgery (5.03[2.08,12.17], P < .001), orthopaedic surgery (3.03 [1.35,6.79], P = .007), ear, nose, and throat (ENT) surgery (3.50 [1.22,10.02], P = .020) and postoperative complications (1.77 [1.03-3.04], P = .038) were significant risk factors of PoSD. CONCLUSIONS: The incidence of PoSD after elective surgery under general anesthesia is high. Sleep disturbance was the most common problem identified. Anesthesia providers and perianesthesia nurses may need to optimize anesthetic application, combine different anesthesia methods, improve perioperative management, and provide interventions to reduce and to treat discomfort after surgeries.
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Anestesia General , Procedimientos Quirúrgicos Electivos , Adulto , Anestesia General/efectos adversos , China/epidemiología , Procedimientos Quirúrgicos Electivos/efectos adversos , Humanos , Incidencia , Dolor Postoperatorio , Complicaciones Posoperatorias/epidemiología , Factores de RiesgoRESUMEN
Piwi-interacting RNAs (piRNAs) are indispensable in the transposon silencing, including in germ cell formation, germline stem cell maintenance, spermatogenesis, and oogenesis. piRNA pathways are amongst the major genome defence mechanisms, which maintain genome integrity. They also have important functions in tumorigenesis, as indicated by aberrantly expressed piRNAs being recently shown to play roles in the process of cancer development. A number of computational methods for this have recently been proposed, but they still have not yielded satisfactory predictive performance. Moreover, only one computational method that identifies whether piRNAs function in inducting target mRNA deadenylation been reported in the literature. In this study, we developed a two-layered integrated classifier algorithm, 2lpiRNApred. It identifies piRNAs in the first layer and determines whether they function in inducting target mRNA deadenylation in the second layer. A new feature selection algorithm, which was based on Luca fuzzy entropy and Gaussian membership function (LFE-GM), was proposed to reduce the dimensionality of the features. Five feature extraction strategies, namely, Kmer, General parallel correlation pseudo-dinucleotide composition, General series correlation pseudo-dinucleotide composition, Normalized Moreau-Broto autocorrelation, and Geary autocorrelation, and two types of classifier, Sparse Representation Classifier (SRC) and support vector machine with Mahalanobis distance-based radial basis function (SVMMDRBF), were used to construct a two-layered integrated classifier algorithm, 2lpiRNApred. The results indicate that 2lpiRNApred performs significantly better than six other existing prediction tools.
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Algoritmos , Biología Computacional/métodos , ARN Interferente Pequeño/genética , Programas Informáticos , Fenómenos Químicos , Bases de Datos de Ácidos Nucleicos , Humanos , ARN Interferente Pequeño/química , Reproducibilidad de los ResultadosRESUMEN
OBJECTIVE: To investigate the efficacy of ultrasound-mediated drug delivery for allodynia caused by herpes zoster. DESIGN: Unblinded randomized controlled study with two treatment groups and an additional control group. SUBJECTS: Patients hospitalized with allodynia caused by herpes zoster were enrolled. METHODS: Patients were randomly assigned to three groups: ultrasound-mediated transdermal drug delivery (group U), lidocaine intradermal injection (group I), or control group (group C). The primary outcome was pain intensity associated with allodynia, assessed with the visual analog scale (VAS) while brushing the skin with clothing after treatment stimulated allodynia. The secondary outcomes included an emotional functioning score (ES), average gabapentin consumption, and incidence of adverse events of each group. RESULTS: Sixty patients were enrolled in the study, but two of them failed to complete the treatment process. Therefore, 58 patients were included in the final analysis. All groups had lower VAS and ES scores after treatment compared with baseline. The VAS scores in groups U and I decreased significantly more than in group C (P < 0.05). Mean VAS scores in group U on days 1, 2, and 3 were lower than in group C (P < 0.01). ES was significantly lower in group U compared with groups I and C after treatment (P < 0.001). Average gabapentin consumption and incidence of adverse events in group C were higher than in the other two groups. CONCLUSIONS: In this study of treatment of allodynia caused by herpetic zoster, ultrasound-mediated lidocaine and capsaicin delivery provided better pain relief and improved emotional functioning compared with intradermal blockade with local anesthetics.
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Objective s To investigate the effects of preoperative smoking and smoking cessation time on preoperative peripheral blood inflammatory indexes and postoperative hospitalization outcomes in male patients with lung cancer and surgery therapy.Methods We retrospectively enrolled 637 male patients who underwent curative-intent lung cancer resection between January 2014 and December 2016. Patients were classified as the current smokers, the never smokers, and the ex-smokers based on their smoking history, and the ex-smokers were allocated into five subgroups according to their smoking cessation times (CeT): CeT≤6 weeks, 6weeks
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Carcinoma de Pulmón de Células no Pequeñas/mortalidad , Carcinoma de Pulmón de Células no Pequeñas/cirugía , Neoplasias Pulmonares/mortalidad , Neoplasias Pulmonares/cirugía , Cese del Hábito de Fumar , Fumar/fisiopatología , Adulto , Anciano , Femenino , Hospitalización/estadística & datos numéricos , Humanos , Linfocitos/metabolismo , Masculino , Persona de Mediana Edad , Neutrófilos/metabolismo , Neumonía/metabolismo , Complicaciones Posoperatorias , Estudios Retrospectivos , Factores de Riesgo , Factores de TiempoRESUMEN
Motivation: Protein O-GlcNAcylation (O-GlcNAc) is an important post-translational modification of serine (S)/threonine (T) residues that involves multiple molecular and cellular processes. Recent studies have suggested that abnormal O-G1cNAcylation causes many diseases, such as cancer and various neurodegenerative diseases. With the available protein O-G1cNAcylation sites experimentally verified, it is highly desired to develop automated methods to rapidly and effectively identify O-GlcNAcylation sites. Although some computational methods have been proposed, their performance has been unsatisfactory, particularly in terms of prediction sensitivity. Results: In this study, we developed an ensemble model O-GlcNAcPRED-II to identify potential O-GlcNAcylation sites. A K-means principal component analysis oversampling technique (KPCA) and fuzzy undersampling method (FUS) were first proposed and incorporated to reduce the proportion of the original positive and negative training samples. Then, rotation forest, a type of classifier-integrated system, was adopted to divide the eight types of feature space into several subsets using four sub-classifiers: random forest, k-nearest neighbour, naive Bayesian and support vector machine. We observed that O-GlcNAcPRED-II achieved a sensitivity of 81.05%, specificity of 95.91%, accuracy of 91.43% and Matthew's correlation coefficient of 0.7928 for five-fold cross-validation run 10 times. Additionally, the results obtained by O-GlcNAcPRED-II on two independent datasets also indicated that the proposed predictor outperformed five published prediction tools. Availability and implementation: http://121.42.167.206/OGlcPred/. Supplementary information: Supplementary data are available at Bioinformatics online.
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Biología Computacional/métodos , Procesamiento Proteico-Postraduccional , Proteómica/métodos , Máquina de Vectores de Soporte , Acetilglucosamina , Teorema de Bayes , Modelos BiológicosRESUMEN
BACKGROUND/AIMS: Lymphocyte antigen 6 complex, locus E (LY6E) is a member of the lymphostromal cell membrane Ly6 superfamily protein. The present study investigated the clinical significance and potential biological function of LY6E in gastric cancer (GC). METHODS: LY6E mRNA and protein expressions in human GC tissues and GC cells were tested. Relationship between LY6E expression and the GC patients' clinicopathologic characteristics was analyzed. LY6E was silenced by siRNA in the cultured GC cells. RESULTS: The RNA expression microarray profiling assay results demonstrated that LY6E mRNA was significantly increased in multiple human GC tumor tissues. Immunohistochemistry (IHC) staining analysis revealed that 59 of 75 (78.7%) GC specimens were LY6E positive. LY6E over-expression in human GC was correlated with the histology grade, AJCC stage, N classification, lymphatic invasion, and tumor location. Notably, functional LY6E expression was also detected in AGS and other established GC cell lines. LY6E knockdown by targeted-siRNA inhibited AGS cell survival and proliferation. Meanwhile, the LY6E siRNA induced G1-S cell cycle arrest and apoptosis in AGC cells. Additionally, AGC cell migration was also inhibited by LY6E knockdown. Expressions of tumor-suppressing proteins, including PTEN (phosphatase and tensin homolog) and E-Cadherin, were increased in LY6E-silenced AGS cells. CONCLUSION: LY6E over-expression in GC is potentially required for cancer cell survival, proliferation and migration.
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Antígenos de Superficie/metabolismo , Neoplasias Gástricas/patología , Anciano , Antígenos de Superficie/genética , Cadherinas/metabolismo , Línea Celular Tumoral , Movimiento Celular , Proliferación Celular , Supervivencia Celular , Femenino , Puntos de Control de la Fase G1 del Ciclo Celular , Proteínas Ligadas a GPI/antagonistas & inhibidores , Proteínas Ligadas a GPI/genética , Proteínas Ligadas a GPI/metabolismo , Regulación Neoplásica de la Expresión Génica , Humanos , Masculino , Persona de Mediana Edad , Clasificación del Tumor , Análisis de Secuencia por Matrices de Oligonucleótidos , Fosfohidrolasa PTEN/metabolismo , Interferencia de ARN , ARN Interferente Pequeño/metabolismo , Puntos de Control de la Fase S del Ciclo Celular , Neoplasias Gástricas/metabolismoRESUMEN
BACKGROUND/AIMS: Human hedgehog-interacting protein (HHIP) is a negative regulator of the hedgehog (HH) signaling pathway. It is deregulated in gastric cancer. The underlying molecular mechanism of HHIP-induced inhibition of HH signaling remains to be determined. METHODS: A lentiviral HHIP expression vector ("LV-HHIP") was established to exogenously over-express HHIP in gastric cancer cells. HHIP protein and mRNA were tested by Western blotting assay and quantitative real-time PCR assay, respectively. Cell survival was tested by the Cell Counting Kit-8 (CCK-8) assay. Cell proliferation was examined by the BrdU ELISA assay and [H3] Thymidine DNA incorporation assay. Cell invasion and migration were tested by the phagokinetic track assay and the "Transwell" assay. The bisulfite-sequencing PCR was applied to test HHIP promoter methylation. RESULTS: In the established (AGS cell line) and primary human gastric cancer cells, LV-HHIP transfection increased HHIP expression and inhibited cancer cell survival and proliferation as well as cell migration and invasion. Furthermore, LV-HHIP significantly attenuated promoter methylation of the endogenous HHIP gene in AGS cells, causing it upregulation. Inhibition of methylation by 5-aza-dc similarly induced HHIP expression in gastric cancer cells, which inhibited cancer cell proliferation and migration. CONCLUSIONS: Our results suggest that inhibition of HHIP promoter methylation can efficiently inhibit human gastric cancer cell proliferation and migration.
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Proteínas Portadoras/metabolismo , Glicoproteínas de Membrana/metabolismo , Neoplasias Gástricas/patología , Azacitidina/farmacología , Proteínas Portadoras/antagonistas & inhibidores , Proteínas Portadoras/genética , Movimiento Celular , Proliferación Celular , Islas de CpG , Metilación de ADN , Vectores Genéticos/genética , Vectores Genéticos/metabolismo , Humanos , Glicoproteínas de Membrana/antagonistas & inhibidores , Glicoproteínas de Membrana/genética , Regiones Promotoras Genéticas , ARN Mensajero/metabolismo , Neoplasias Gástricas/metabolismo , Células Tumorales Cultivadas , Regulación hacia Arriba/efectos de los fármacosRESUMEN
The goal of this study is to evaluate PD-L1 prevalence and its association with major clinical characteristics in Chinese non-small cell lung cancer (NSCLC) patients to inform the clinical development of anti-PD1/PD-L1 agents in this population. We used phosphatase and tensin homolog (PTEN) expression through IHC as a surrogate tissue quality marker to screen surgical NSCLC samples in tissue microarray (TMA; 172 cases) or whole-section (268 cases) format. The samples were then analyzed with a clinically validated PD-L1 IHC assay. The results were correlated with baseline characteristics and clinical outcomes. PTEN IHC showed that 108 TMA samples and 105 whole-section samples qualified for PD-L1 IHC. With a clinically relevant cutoff, 41.7% of the TMA samples were PD-L1 positive. PD-L1 level was much lower in EGFR-mutant patients and seemed to be a favorable prognostic factor for both overall survival (OS) and recurrence-free survival (RFS). These findings were confirmed in the whole-section samples except that their survival data were not mature enough for correlation analysis. In summary, PD-L1 expression was detected in approximately 40% of PTEN-qualified Chinese NSCLC samples, negatively correlated with EGFR mutation and seemed to be a favorable prognostic factor for both OS and RFS. Notably, the different results from PTEN-qualified and PTEN-disqualified samples underscore the importance of tissue quality control prior to biomarker testing.
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Antígeno B7-H1/metabolismo , Biomarcadores de Tumor/análisis , Carcinoma de Pulmón de Células no Pequeñas/diagnóstico , Neoplasias Pulmonares/diagnóstico , Mutación , Fosfohidrolasa PTEN/genética , Análisis de Matrices Tisulares/normas , Anciano , Carcinoma de Pulmón de Células no Pequeñas/genética , Carcinoma de Pulmón de Células no Pequeñas/metabolismo , Estudios de Cohortes , Detección Precoz del Cáncer , Femenino , Estudios de Seguimiento , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/metabolismo , Masculino , Persona de Mediana Edad , Pronóstico , Tasa de SupervivenciaRESUMEN
An ideal supporting material improves both activity and durability of noble metal nanoparticles in electrocatalytic reactions. Graphene possesses a high transport rate of electrons in-plane, a low cost, and stability, but, the restacking of graphene layers trap noble metal nanoparticles and make them inaccessible to reactants and results in reduced catalytic activity. Here, holey-graphene as the supporting materials for Pt nanoparticle catalysts is deeply investigated in the electrocatalytic reaction of methanol oxidation. The holey-graphene can be scalable to synthesize using our simple method described herein. The holes on the holey-graphene layer promote the access of reactants with Pt nanoparticle catalysts compared with carbon black and graphene when used as supporting materials. Density functional theory calculations and molecule dynamic simulation further explain the function of holey-graphene in the promotion of electrocatalytic activity. Holey-graphene may open extraordinary possibilities as a supporting material for electrocatalysts.
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OBJECTIVE: To compare glottis exposure of the same patients with potentially difficult tracheal intubation (PDTI) subjected to Airtraq laryngoscopy and Macintosh laryngoscopy under consciousness and topical anesthesia. METHODS: A total of 147 PDTI patients with American Society of Anesthesiologists (ASA) I-III were subjected to Airtraq and Macintosh laryngoscopy performed by experienced anesthesiologists under consciousness and topical anesthesia. RESULTS: All patients were successfully intubated. Among them, three patients were intubated with fiberoptic bronchoscopy, 13 with Macintosh laryngoscopy and 131 with Airtraq laryngoscopy. Of the patients with Cormack and Lehance (C&L) Grade-I glottic view, 88 were subjected to Airtraq laryngoscopy and five to Macintosh laryngoscopy; Of the patients with C&L Grade-II glottic view, 56 were subjected to Airtraq laryngoscopy and 21 to Macintosh bronchoscopy; Of the patients with C&L Grade-III glottic view, three were subjected to Airtraq laryngoscopy and 112 to Macintosh bronchoscopy; Of the patients with C&L Grade-IV glottic view, none was subjected to Airtraq laryngoscopy and 9 to Macintosh laryngoscopy. CONCLUSIONS: Airtraq laryngoscopy could significantly improve the glottis exposure and reduce the difficulty of intubation for patients with potentially tracheal intubation compared to the traditional Macintosh laryngoscopy.
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BACKGROUND: This study aimed to explore the effects of microRNA-21-5p (miR-21-5p) on the radiation sensitivity of non-small cell lung cancer (NSCLC) and the involvement of human MutS homolog 2 (hMSH2) One hundred fourteen NSCLC patients at stage II or III who received surgery and postoperative radiotherapy were enrolled in this study. METHODS: The patients were assigned into radiation-sensitive and -insensitive groups. NSCLC A549 cells were transfected to generate control, Negative control (NC), miR-21-5p inhibitor, miR-21-5p mimic, small interfering hMSH2 (sihMSH2), miR-21-5p inhibitor + sihMSH2 and hMSH2 overexpression groups. Immunohistochemistry was performed to detect the hMSH2 expression in transfected and irradiated cells. Quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting were performed to evaluate A549 miR-21-5p and hMSH2 expression in transfected and irradiated cells. A colony formation assay was adopted for cell survival analysis. The relationship between miR-21-5p and hMSH2 was verified by a luciferase reporter assay. Cell viability was measured by the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay, and apoptosis was assessed by flow cytometry. NSCLC nude mouse models were established, and tumor volumes and tumor weights were recorded. RESULTS: The radiation-sensitive group of patients exhibited lower miR-21-5p but higher hMSH2 expression than the insensitive group. For irradiated A549 cells, lower cell survival, higher apoptosis, increased miR-21-5p expression and decreased hMSH2 expression were observed at 6 and 8 Gy than at 0, 2 and 4 Gy; compared to 6 Gy, cell survival and hMSH2 expression were decreased and apoptosis and miR-21-5p expression were increased at 8 Gy. Additionally, miR-21-5p was found to target hMSH2. Compared with the control group, the cell survival rate was lower and the apoptosis rate higher in the miR-21-5p inhibitor group, whereas the opposite was observed for the miR-21-5p mimic and sihMSH2 groups. For the mouse model, decreased tumor volume and tumor weight and higher hMSH2 expression were found in the miR-21-5p inhibitor, radiation, hMSH2 overexpression, miR-21-5p inhibitor + radiation and hMSH2 overexpression + radiation groups compared with the control group. In addition, tumor volume and tumor weight were decreased and hMSH2 expression increased in the miR-21-5p inhibitor + radiation and hMSH2 overexpression + radiation groups compared with the radiation alone group. CONCLUSION: These findings indicate that inhibition of miR-21 can promote the radiation sensitivity of NSCLC by targeting hMSH2.
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Apoptosis/efectos de la radiación , Carcinoma de Pulmón de Células no Pequeñas/patología , Rayos gamma , Neoplasias Pulmonares/patología , MicroARNs/metabolismo , Proteína 2 Homóloga a MutS/metabolismo , Células A549 , Anciano , Animales , Antagomirs/metabolismo , Secuencia de Bases , Carcinoma de Pulmón de Células no Pequeñas/genética , Carcinoma de Pulmón de Células no Pequeñas/radioterapia , Femenino , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/radioterapia , Masculino , Ratones , Ratones Endogámicos BALB C , Ratones Desnudos , MicroARNs/antagonistas & inhibidores , MicroARNs/genética , Persona de Mediana Edad , Proteína 2 Homóloga a MutS/antagonistas & inhibidores , Proteína 2 Homóloga a MutS/genética , Interferencia de ARN , ARN Interferente Pequeño/metabolismo , Tolerancia a Radiación , Alineación de Secuencia , Trasplante HeterólogoRESUMEN
The intercontinental biogeography between eastern Asia and eastern North America has attracted much attention from evolutionary biologists. Further insights into understanding the evolution of the intercontinental disjunctions have been hampered by the lack of studies on the intracontinental biogeography in eastern Asia, a region with complex geology, geography, climates and habitats. Herein we studied the biogeographic history of the eastern Asian-eastern North American disjunct genus Panax with special emphasis on the investigation of its uneven diversification in Asia. This study reconstructs the diversification history of Panax and also emphasizes a large clade of Panax taxa, which has a wide distribution in eastern Asia, but was unresolved in previous studies. We examined the noncoding plastid DNA fragments of trnH-psbA, rps16, and psbM-trnD, the mitochondrial b/c intron of NAD1, and the nuclear ribosomal internal transcribed spacer (ITS) region of 356 samples from 47 populations. The results revealed the subtropical Northern Hemisphere origin (Asia or Asia and North America) of Panax in the Paleocene. Intercontinental disjunctions between eastern Asia and eastern North America formed twice in Panax, once estimated in early Eocene for the split of P. trifolius and another in mid-Miocene for the divergence of P. quinquefolius. Intercontinental diversifications in Panax showed temporal correlation with the increase of global temperature. The evolutionary radiation of the P. bipinnatifidus species complex occurred around the boundary of Oligocene and Miocene. Strong genetic structure among populations of the species complex was detected and the populations may be isolated by distance. The backbone network and the Bayesian clustering analysis revealed a major evolutionary radiation centered in the Hengduan Mountains of western China. Our results suggested that the evolutionary radiation of Panax was promoted by geographic barriers, including mountain ranges (Hengduan Mountains, Nanling Mountains and Wuyishan Mountains), oceans and altitudinal shifts, which further contribute to the knowledge of the uneven species diversification between eastern Asia and North America.
Asunto(s)
Panax/genética , Filogenia , Filogeografía , Teorema de Bayes , Biodiversidad , Asia Oriental , Genes Mitocondriales/genética , América del Norte , Panax/clasificación , Plastidios/genéticaRESUMEN
Protein S-sulfenylation is a reversible post-translational modification involving covalent attachment of hydroxide to the thiol group of cysteine residues, which is involved in various biological processes including cell signaling, response to stress and protein functions. Herein we present S-SulfPred, a support vector machine based model to capture potential S-sulfenylation sites and improve the efficiency and relevance of experimental identification of protein S-sulfenylation sites. One-sided selection (OSS) undersampling and synthetic minority oversampling technique (SMOTE) oversampling were combined to establish balanced training datasets. This approach is shown to perform better than using only OSS or SMOTE in an independent test. The best combination of position-specific amino acid propensity and five physicochemical properties of amino acids were selected to optimize the predictor performance. Using S-SulfPred, we achieve an average sensitivity of 74.62%, and an average specificity of 71.62% on independent datasets. Compared with other published tools, S-SulfPred attains both higher sensitivity and specificity. We not only propose a highly accurate method to predict protein S-sulfenylation sites, but also provide insights that could improve the efficiency of other bioinformatics tools.
Asunto(s)
Bases de Datos de Proteínas , Procesamiento Proteico-Postraduccional/fisiología , Análisis de Secuencia de Proteína/métodos , Programas Informáticos , Máquina de Vectores de Soporte , Cisteína/genética , Cisteína/metabolismoRESUMEN
BACKGROUND: To provide an overview of phantom limb pain (PLP) in China. This includes the prevalence of PLP and possible risk factors. METHODS: In a retrospective study, telephone interviews were conducted with 391 amputation patients who underwent extremity amputations at a tertiary hospital in China. RESULTS: PLP was found in 29% of the amputees. Pre-amputation pain (OR = 10.4, P = 0.002) and postoperative analgesia (OR = 4.9, P = 0.008) were identified as high-risk factors for PLP. 82.1% of PLP patients experienced pre-amputation pain. The average pain intensity of PLP was 5.1 ± 2.2, with 31.9% having severe intensity. The effects of PLP on the quality of the PLP patients were as follows: 7.8% of the patients had to limit their daily life and 29.0% of the patients had to limit their social activities. 17.3 and 25.7% of patients experienced depression and sleeping disorder respectively, while 18.9% had loss of interest and even 16.1% of PLP patients had attempted suicide. No effective treatments were found in 78.9% of these patients. CONCLUSIONS: PLP has markedly affected the lives of patients. Pre-amputation pain and postoperative epidural analgesia might be risk factors for the phantom limb pain after amputation. Prevention of pre-amputation pain and sudden post-amputation deafferentation should be recommended to the amputees.