Dry season temperature and rainy season precipitation significantly affect the spatio-temporal pattern of rubber plantation phenology in Yunnan province.

The ongoing global warming trajectory poses extensive challenges to plant ecosystems, with rubber plantations particularly vulnerable due to their influence on not only the longevity of the growth cycle and rubber yield, but also the complex interplay of carbon, water, and energy exchanges between the forest canopy and atmosphere. However, the response mechanism of phenology in rubber plantations to climate change remains unclear. This study concentrates on sub-optimal environment rubber plantations in Yunnan province, Southwest China. Utilizing the Google Earth Engine (GEE) cloud platform, multi-source remote sensing images were synthesized at 8-day intervals with a spatial resolution of 30-meters. The Normalized Difference Vegetation Index (NDVI) time series was reconstructed using the Savitzky-Golay (S-G) filter, coupled with the application of the seasonal amplitude method to extract three crucial phenological indicators, namely the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS). Linear regression method, Pearson correlation coefficient, multiple stepwise regression analysis were used to extract of the phenology trend and find the relationship between SOS, EOS and climate factors. The findings demonstrated that 1) the phenology of rubber plantations has undergone dynamic changes over the past two decades. Specifically, the SOS advanced by 9.4 days per decade (R2 = 0.42, p< 0.01), whereas the EOS was delayed by 3.8 days per decade (R2 = 0.35, p< 0.01). Additionally, the LOS was extended by 13.2 days per decade (R2 = 0.55, p< 0.01); 2) rubber phenology demonstrated a notable sensitivity to temperature fluctuations during the dry season and precipitation patterns during the rainy season. The SOS advanced 2.0 days (r =-0.19, p< 0.01) and the EOS advanced 2.8 days (r =-0.35, p< 0.01) for every 1°C increase in the cool-dry season. Whereas a 100 mm increase in rainy season precipitation caused the SOS to be delayed by 2.0 days (r = 0.24, p< 0.01), a 100 mm increase in hot-dry season precipitation caused the EOS to be advanced by 7.0 days (r =-0.28, p< 0.01); 3) rubber phenology displayed a legacy effect of preseason climate variations. Changes in temperature during the fourth preseason month and precipitation during the fourth and eleventh preseason months are predominantly responsible for the variation in SOS. Meanwhile, temperature changes during the second, fourth, and ninth preseason months are primarily responsible for the variation in EOS. The study aims to enhance our understanding of how rubber plantations respond to climate change in sub-optimal environments and provide valuable insights for sustainable rubber production management in the face of changing environmental conditions.

Computational prediction of allergenic proteins based on multi-feature fusion.

Allergy is an autoimmune disorder described as an undesirable response of the immune system to typically innocuous substance in the environment. Studies have shown that the ability of proteins to trigger allergic reactions in susceptible individuals can be evaluated by bioinformatics tools. However, developing computational methods to accurately identify new allergenic proteins remains a vital challenge. This work aims to propose a machine learning model based on multi-feature fusion for predicting allergenic proteins efficiently. Firstly, we prepared a benchmark dataset of allergenic and non-allergenic protein sequences and pretested on it with a machine-learning platform. Then, three preferable feature extraction methods, including amino acid composition (AAC), dipeptide composition (DPC) and composition of k-spaced amino acid pairs (CKSAAP) were chosen to extract protein sequence features. Subsequently, these features were fused and optimized by Pearson correlation coefficient (PCC) and principal component analysis (PCA). Finally, the most representative features were picked out to build the optimal predictor based on random forest (RF) algorithm. Performance evaluation results via 5-fold cross-validation showed that the final model, called iAller (https://github.com/laihongyan/iAller), could precisely distinguish allergenic proteins from non-allergenic proteins. The prediction accuracy and AUC value for validation dataset achieved 91.4% and 0.97%, respectively. This model will provide guide for users to identify more allergenic proteins.

Extracellular vesicles long RNA profiling identifies abundant mRNA, circRNA and lncRNA in human bile as potential biomarkers for cancer diagnosis.

Extracellular vesicles (EVs) are bilayered membrane vesicles produced by living cells and secreted into the extracellular matrix. Bile is a special body fluid that is secreted by the liver cells, and extracellular vesicles long RNAs (exLRs) have not been explored in bile. In this study, exLR sequencing (exLR-seq) was performed on 19 bile samples from patients with malignant cancer or patients with biliary stones. A total of 8649 mRNAs, 13 823 circRNAs and 1105 lncRNAs were detected. The KEGG pathway analysis revealed that differentially expressed exLRs were enriched in mTOR and AMPK signaling pathway. We identified five mRNAs (EID2, LLPH, ATP6V0A2, RRP9 and MTRNR2L10), three lncRNAs (AC015922.2, AL135905.1 and LINC00921) and six circRNAs (circASH1L, circATP9A, circCLIP1, circRNF138, circTIMMDC1 and circANKRD12) were enriched in bile EV samples with cancer, and these exLRs may be potential markers used to distinguish malignant cancers from benign biliary diseases. Moreover, the tissue/cellular source components of EVs were analyzed using the EV-origin algorithm. The absolute abundance of CD4_naive and Th1 cell source in bile EVs from cancer patients were significantly increased. In summary, our study presented abundant exLRs in human bile EVs and provides some basis for the selection of tumor diagnostic markers.

Computational identification of promoters in Klebsiella aerogenes by using support vector machine.

Promoters are the basic functional cis-elements to which RNA polymerase binds to initiate the process of gene transcription. Comprehensive understanding gene expression and regulation depends on the precise identification of promoters, as they are the most important component of gene expression. This study aimed to develop a machine learning-based model to predict promoters in Klebsiella aerogenes (K. aerogenes). In the prediction model, the promoter sequences in K. aerogenes genome were encoded by pseudo k-tuple nucleotide composition (PseKNC) and position-correlation scoring function (PCSF). Numerical features were obtained and then optimized using mRMR by combining with support vector machine (SVM) and 5-fold cross-validation (CV). Subsequently, these optimized features were inputted into SVM-based classifier to discriminate promoter sequences from non-promoter sequences in K. aerogenes. Results of 10-fold CV showed that the model could yield the overall accuracy of 96.0% and the area under the ROC curve (AUC) of 0.990. We hope that this model will provide help for the study of promoter and gene regulation in K. aerogenes.

Plasma extracellular vesicle transcriptomics identifies CD160 for predicting immunochemotherapy efficacy in lung cancer.

Better biomarkers are needed to improve the efficacy of immune checkpoint inhibitors in lung adenocarcinoma (LUAD) treatment. We investigated the plasma extracellular vesicle (EV)-derived long RNAs (exLRs) in unresectable/advanced LUAD to explore biomarkers for immunochemotherapy. Seventy-four LUAD patients without targetable mutations receiving first-line anti-programmed cell death 1 (PD-1) immunochemotherapy were enrolled. Their exLRs were profiled through plasma EV transcriptome sequencing. Biomarkers were analyzed against response rate and survival using pre- and post-treatment samples in the retrospective cohort (n = 36) and prospective cohort (n = 38). The results showed that LUAD patients demonstrated a distinct exLR profile from the healthy individuals (n = 56), and T-cell activation-related pathways were enriched in responders. Among T-cell activation exLRs, CD160 exhibited a strong correlation with survival. In the retrospective cohort, the high baseline EV-derived CD160 level correlated with prolonged progression-free survival (PFS) (P < 0.001) and overall survival (OS) (P = 0.005), with an area under the curve (AUC) of 0.784 for differentiating responders from non-responders. In the prospective cohort, the CD160-high patients also showed prolonged PFS (P = 0.003) and OS (P = 0.014) and a promising AUC of 0.648. The predictive value of CD160 expression was validated by real-time quantitative PCR. We also identified the dynamics of EV-derived CD160 for monitoring therapeutic response. The elevated baseline CD160 reflected a higher abundance of circulating NK cells and CD8+ -naïve T cells, suggesting more active host immunity. In addition, increased CD160 levels of tumors also correlated with a favorable prognosis in LUAD patients. Together, plasma EV transcriptome analysis revealed the role of the baseline CD160 level and early post-treatment CD160 dynamics for predicting the response to anti-PD-1 immunochemotherapy in LUAD patients.

circTMEM181 upregulates ARHGAP29 to inhibit hepatocellular carcinoma migration and invasion by sponging miR-519a-5p.

AIM: Circular RNAs (circRNAs) are a novel class of noncoding RNAs and are conserved in various species. Although numerous circRNAs have been identified, their role in cancer remains unclear. METHODS: The expression of circTMEM181 in 90 paired human hepatocellular carcinoma (HCC) and adjacent nontumor tissues were detected using quantitative reverse transcription-polymerase chain reaction. Transwell assay was performed for functional analysis of HCC cell migration and invasion. Luciferase reporter assay was used to verify the combination of circTMEM181 and miR-519a-5p. RESULTS: In this study, we identified a novel circRNA, named circTMEM181, was downregulated in HCC tissues. Decreased expression of circTMEM181 was associated with shorter overall survival of patients with HCC. CircTMEM181 overexpression reduced HCC cell migration and invasion abilities, while circTMEM181 knockdown increased cell motility. Mechanically, circTMEM181 could directly bind to miR-519a-5p and subsequently upregulate ARHGAP29 protein expression. CONCLUSION: These data provide the first evidence of clinical significance and function of circTMEM181, and suggest the circTMEM181/miR-519a-5p/ARHGAP29 axis in HCC development.

Plasma Extracellular Vesicle Long RNAs Have Potential as Biomarkers in Early Detection of Colorectal Cancer.

Background: Early detection of colorectal cancer (CRC) is crucial to the treatment and prognosis of patients. Traditional screening methods have disadvantages. Methods: 231 blood samples were collected from 86 CRC, 56 colorectal adenoma (CRA), and 89 healthy individuals, from which extracellular vesicle long RNAs (exLRs) were isolated and sequenced. An CRC diagnostic signature (d-signature) was established, and prognosis-associated cell components were evaluated. Results: The exLR d-signature for CRC was established based on 17 of the differentially expressed exLRs. The d-signature showed high diagnostic efficiency of CRC and control (CRA and healthy) samples with an area under the curve (AUC) of 0.938 in the training cohort, 0.943 in the validation cohort, and 0.947 in an independent cohort. The d-signature could effectively differentiate early-stage (stage I-II) CRC from healthy individuals (AUC 0.990), as well as differentiating CEA-negative CRC from healthy individuals (AUC 0.988). A CRA d-signature was also generated and could differentiate CRA from healthy individuals both in the training (AUC 0.993) and validation (AUC 0.978) cohorts. The enrichment of class-switched memory B-cells, B-cells, naive B-cells, and mast cells showed increasing trends between CRC, CRA, and healthy cohorts. Class-switched memory B-cells, mast cells, and basophils were positively associated with CRC prognosis while natural killer T-cells, naive B-cells, immature dendritic cells, and lymphatic endothelial cells were negatively associated with prognosis. Conclusions: Our study identified that the exLR d-signature could differentiate CRC from CRA and healthy individuals with high efficiency and exLR profiling also has potential in CRA screening and CRC prognosis prediction.

exoRBase 2.0: an atlas of mRNA, lncRNA and circRNA in extracellular vesicles from human biofluids.

Extracellular vesicles (EVs) are small membranous vesicles that contain an abundant cargo of different RNA species with specialized functions and clinical implications. Here, we introduce an updated online database (http://www.exoRBase.org), exoRBase 2.0, which is a repository of EV long RNAs (termed exLRs) derived from RNA-seq data analyses of diverse human body fluids. In exoRBase 2.0, the number of exLRs has increased to 19 643 messenger RNAs (mRNAs), 15 645 long non-coding RNAs (lncRNAs) and 79 084 circular RNAs (circRNAs) obtained from ∼1000 human blood, urine, cerebrospinal fluid (CSF) and bile samples. Importantly, exoRBase 2.0 not only integrates and compares exLR expression profiles but also visualizes the pathway-level functional changes and the heterogeneity of origins of circulating EVs in the context of different physiological and pathological conditions. Our database provides an attractive platform for the identification of novel exLR signatures from human biofluids that will aid in the discovery of new circulating biomarkers to improve disease diagnosis and therapy.

Plasma extracellular vesicle long RNA profiles in the diagnosis and prediction of treatment response for breast cancer.

A large number RNAs are enriched and stable in extracellular vesicles (EVs), and they can reflect their tissue origins and are suitable as liquid biopsy markers for cancer diagnosis and treatment efficacy prediction. In this study, we used extracellular vesicle long RNA (exLR) sequencing to characterize the plasma-derived exLRs from 112 breast cancer patients, 19 benign patients and 41 healthy participants. The different exLRs profiling was found between the breast cancer and non-cancer groups. Thus, we constructed a breast cancer diagnostic signature which showed high accuracy with an area under the curve (AUC) of 0.960 in the training cohort and 0.900 in the validation cohort. The signature was able to identify early stage BC (I/II) with an AUC of 0.940. Integrating the signature with breast imaging could increase the diagnosis accuracy for breast cancer patients. Moreover, we enrolled 58 patients who received neoadjuvant treatment and identified an exLR (exMSMO1), which could distinguish pathological complete response (pCR) patients from non-pCR with an AUC of 0.790. Silencing MSMO1 could significantly enhance the sensitivity of MDA-MB-231 cells to paclitaxel and doxorubicin through modulating mTORC1 signaling pathway. This study demonstrated the value of exLR profiling to provide potential biomarkers for early detection and treatment efficacy prediction of breast cancer.

The viral expression and immune status in human cancers and insights into novel biomarkers of immunotherapy.

BACKGROUND: Viral infections are prevalent in human cancers and they have great diagnostic and theranostic values in clinical practice. Recently, their potential of shaping the tumor immune microenvironment (TIME) has been related to the immunotherapy of human cancers. However, the landscape of viral expressions and immune status in human cancers remains incompletely understood. METHODS: We developed a next-generation sequencing (NGS)-based pipeline to detect viral sequences from the whole transcriptome and used machine learning algorithms to classify different TIME subtypes. RESULTS: We revealed a pan-cancer landscape of viral expressions in human cancers where 9 types of viruses were detected in 744 tumors of 25 cancer types. Viral infections showed different tissue tendencies and expression levels. Multi-omics analyses further revealed their distinct impacts on genomic, transcriptomic and immune responses. Epstein-Barr virus (EBV)-infected stomach adenocarcinoma (STAD) and Human Papillomavirus (HPV)-infected head and neck squamous cell carcinoma (HNSC) showed decreased genomic variations, significantly altered gene expressions, and effectively triggered anti-viral immune responses. We identified three TIME subtypes, in which the "Immune-Stimulation" subtype might be the promising candidate for immunotherapy. EBV-infected STAD and HPV-infected HNSC showed a higher frequency of the "Immune-Stimulation" subtype. Finally, we constructed the eVIIS pipeline to simultaneously evaluate viral infection and immune status in external datasets. CONCLUSIONS: Viral infections are prevalent in human cancers and have distinct influences on hosts. EBV and HPV infections combined with the TIME subtype could be promising biomarkers of immunotherapy in STAD and HNSC, respectively. The eVIIS pipeline could be a practical tool to facilitate clinical practice and relevant studies.

Circulating EVs long RNA-based subtyping and deconvolution enable prediction of immunogenic signatures and clinical outcome for PDAC.

Identification of clinically applicable molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) is crucial to improving patient outcomes. However, the traditional tissue-dependent transcriptional subtyping strategies are invasive and not amenable to routine clinical evaluation. In this study, we developed a circulating extracellular vesicle (cEV) long RNA (exLR)-based PDAC subtyping method and provided exLR-derived signatures for predicting immunogenic features and clinical outcomes in PDAC. We enrolled 426 individuals, among which 227 PDACs served as an internal cohort, 118 PDACs from two other medical centers served as an independent validation cohort, and 81 healthy individuals served as the control. ExLR sequencing was performed on all plasma samples. We found that PDAC could be categorized into three subtypes based on plasma exLR profiles. Each subpopulation showed its own molecular features and was associated with patient clinical prognosis. The immunocyte-derived cEV fractions were altered among PDAC subtypes and interconnected with tumor-infiltrating lymphocytes in cancerous tissue. Additionally, we found a significant concordance of immunoregulators between tissue and blood EVs, and we harvested potential PDAC therapeutic targets. Most importantly, we constructed a nine exLR-derived, tissue-applicable signature for prognostic assessment of PDAC. The circulating exLR-based features may offer an attractive platform for personalized treatment and predicting patient outcomes in multiple types of cancer.

HNRNPL Circularizes ARHGAP35 to Produce an Oncogenic Protein.

Circular RNAs (circRNAs) are an intriguing class of widely prevalent endogenous RNAs, the vast majority of which have not been characterized functionally. Here, we identified a novel oncogenic circRNA originating from the back-splicing of Exon2 and Exon3 of a tumor suppressor gene, ARHGAP35 (also known as P190-A), termed as circARHGAP35. have observe that circARHGAP35 and linear ARHGAP35 have antithetical expression and functions. Interestingly, circARHGAP35 contains a 3867 nt long ORF with an m6A-modified start codon and encodes a truncated protein comprising four FF domains and lacking the Rho GAP domain. Mechanistically, circARHGAP35 protein promotes cancer cell progression by interacting with TFII-I protein in the nucleus. The RNA binding protein, HNRNPL, facilitates the formation of circARHGAP35. Clinically, circARHGAP35 is associated with poor survival in cancer patients. Our findings characterize an oncogenic circRNA and demonstrate a novel mechanism of oncogene activation in cancer by circRNA through the production of a truncated protein.

RJunBase: a database of RNA splice junctions in human normal and cancerous tissues.

Splicing is an essential step of RNA processing for multi-exon genes, in which introns are removed from a precursor RNA, thereby producing mature RNAs containing splice junctions. Here, we develope the RJunBase (www.RJunBase.org), a web-accessible database of three types of RNA splice junctions (linear, back-splice, and fusion junctions) that are derived from RNA-seq data of non-cancerous and cancerous tissues. The RJunBase aims to integrate and characterize all RNA splice junctions of both healthy or pathological human cells and tissues. This new database facilitates the visualization of the gene-level splicing pattern and the junction-level expression profile, as well as the demonstration of unannotated and tumor-specific junctions. The first release of RJunBase contains 682 017 linear junctions, 225 949 back-splice junctions and 34 733 fusion junctions across 18 084 non-cancerous and 11 540 cancerous samples. RJunBase can aid researchers in discovering new splicing-associated targets and provide insights into the identification and assessment of potential neoepitopes for cancer treatment.

EV-origin: Enumerating the tissue-cellular origin of circulating extracellular vesicles using exLR profile.

Extracellular vesicles (EVs) are complex ecosystems that can be derived from all body cells and circulated in the body fluids. Characterizing the tissue-cellular source contributing to circulating EVs provides biological information about the cell or tissue of origin and their functional states. However, the relative proportion of tissue-cellular origin of circulating EVs in body fluid has not been thoroughly characterized. Here, we developed an approach for digital EVs quantification, called EV-origin, that enables enumerating of EVs tissue-cellular source contribution from plasma extracellular vesicles long RNA sequencing profiles. EV-origin was constructed by the input matrix of gene expression signatures and robust deconvolution algorithm, collectively used to separate the relative proportions of each tissue or cell type of interest. EV-origin respectively predicted the relative enrichment of seven types of hemopoietic cells and sixteen solid tissue subsets from exLR-seq profile. Using the EV-origin approach, we depicted an integrated landscape of the traceability system of plasma EVs for healthy individuals. We also compared the heterogenous tissue-cellular source components from plasma EVs samples with diverse disease status. Notably, the aberrant liver fraction could reflect the development and progression of hepatic disease. The liver fraction could also serve as a diagnostic indicator and effectively separate HCC patients from normal individuals. The EV-origin provides an approach to decipher the complex heterogeneity of tissue-cellular origin in circulating EVs. Our approach could inform the development of exLR-based applications for liquid biopsy.

A pan-cancer analysis of HER2 index revealed transcriptional pattern for precise selection of HER2-targeted therapy.

BACKGROUND: The prevalence of HER2 alterations in pan-cancer indicates a broader range of application of HER2-targeted therapies; however, biomarkers for such therapies are still insufficient and limited to breast cancer and gastric cancer. METHODS: Using multi-omics data from The Cancer Genome Atlas (TCGA), the landscape of HER2 alterations was exhibited across 33 tumor types. A HER2 index was constructed using one-class logistic regression (OCLR). With the predictive value validated in GEO cohorts and pan-cancer cell lines, the index was then applied to evaluate the HER2-enriched expression pattern across TCGA pan-cancer types. FINDINGS: Increased HER2 somatic copy number alterations (SCNAs) could be divided into two patterns, focal- or arm-level. The expression-based HER2 index successfully distinguished the HER2-enriched subtype from the others and provided a stable and superior performance in predicting the response to HER2-targeted therapies both in breast tumor tissue and pan-cancer cell lines. With frequencies varying from 12.0% to 0.9%, tumors including head and neck squamous tumors, gastrointestinal tumors, bladder cancer, lung cancer and uterine tumors exhibited high HER2 indices together with HER2 amplification or overexpression, which may be more suitable for HER2-targeted therapies. The BLCA.3 and HNSC.Basal were the most distinguishable subtypes within bladder cancer and head and neck cancer respectively by HER2 index, implying their potential benefits from HER2-targeted therapies. INTERPRETATION: As a pan-cancer predictive biomarker of HER2-targeted therapies, the HER2 index could help identify potential candidates for such treatment in multiple tumor types by combining with HER2 multi-omics features. The discoveries of our study highlight the importance of incorporating transcriptional pattern into the assessment of HER2 status for better patient selection. FUNDING: The National Key Research and Development Program of China; Clinical Research and Cultivation Project of Shanghai ShenKang Hospital Development Center.

iProEP: A Computational Predictor for Predicting Promoter.

Promoter is a fundamental DNA element located around the transcription start site (TSS) and could regulate gene transcription. Promoter recognition is of great significance in determining transcription units, studying gene structure, analyzing gene regulation mechanisms, and annotating gene functional information. Many models have already been proposed to predict promoters. However, the performances of these methods still need to be improved. In this work, we combined pseudo k-tuple nucleotide composition (PseKNC) with position-correlation scoring function (PCSF) to formulate promoter sequences of Homo sapiens (H. sapiens), Drosophila melanogaster (D. melanogaster), Caenorhabditis elegans (C. elegans), Bacillus subtilis (B. subtilis), and Escherichia coli (E. coli). Minimum Redundancy Maximum Relevance (mRMR) algorithm and increment feature selection strategy were then adopted to find out optimal feature subsets. Support vector machine (SVM) was used to distinguish between promoters and non-promoters. In the 10-fold cross-validation test, accuracies of 93.3%, 93.9%, 95.7%, 95.2%, and 93.1% were obtained for H. sapiens, D. melanogaster, C. elegans, B. subtilis, and E. coli, with the areas under receiver operating curves (AUCs) of 0.974, 0.975, 0.981, 0.988, and 0.976, respectively. Comparative results demonstrated that our method outperforms existing methods for identifying promoters. An online web server was established that can be freely accessed (http://lin-group.cn/server/iProEP/).

A Brief Survey of Machine Learning Application in Cancerlectin Identification.

Proteins with at least one carbohydrate recognition domain are lectins that can identify and reversibly interact with glycan moiety of glycoconjugates or a soluble carbohydrate. It has been proved that lectins can play various vital roles in mediating signal transduction, cell-cell recognition and interaction, immune defense, and so on. Most organisms can synthesize and secret lectins. A portion of lectins closely related to diverse cancers, called cancerlectins, are involved in tumor initiation, growth and recrudescence. Cancerlectins have been investigated for their applications in the laboratory study, clinical diagnosis and therapy, and drug delivery and targeting of cancers. The identification of cancerlectin genes from a lot of lectins is helpful for dissecting cancers. Several cancerlectin prediction tools based on machine learning approaches have been established and have become an excellent complement to experimental methods. In this review, we comprehensively summarize and expound the indispensable materials for implementing cancerlectin prediction models. We hope that this review will contribute to understanding cancerlectins and provide valuable clues for the study of cancerlectins. Novel systems for cancerlectin gene identification are expected to be developed for clinical applications and gene therapy.

Intravenous dexmedetomidine pre-medication reduces the required minimum alveolar concentration of sevoflurane for smooth tracheal extubation in anesthetized children: a randomized clinical trial.

BACKGROUND: It has been known that Dexmedetomidine pre-medication enhances the effects of volatile anesthetics, reduces the need of sevoflurane, and facilitates smooth extubation in anesthetized children. This present study was designed to determine the effects of different doses of intravenous dexmedetomidine pre-medication on minimum alveolar concentration of sevoflurane for smooth tracheal extubation (MACEX) in anesthetized children. METHODS: A total of seventy-five pediatric patients, aged 3-7 years, ASA physical status I and II, and undergoing tonsillectomy were randomized to receive intravenous saline (Group D0), dexmedetomidine 1 µg∙kg-1 (Group D1), or dexmedetomidine 2 µg∙kg-1 (Group D2) approximately 10 min before anesthesia start. Sevoflurane was used for anesthesia induction and anesthesia maintenance. At the end of surgery, the initial concentration of sevoflurane for smooth tracheal extubation was determined according to the modified Dixon's "up-and-down" method. The starting sevoflurane for the first patient was 1.5% in Group D0, 1.0% in Group D1, and 0.8% in Group D2, with subsequent 0.1% up or down in next patient based on whether smooth extubation had been achieved or not in current patient. The endotreacheal tube was removed after the predetermined concentration had been maintained constant for ten minutes. All responses ("smooth" or "not smooth") to tracheal extubation and respiratory complications were assessed. RESULTS: MACEX values of sevoflurane in Group D2 (0.51 ± 0.13%) was significantly lower than in Group D1 (0.83 ± 0.10%; P < 0.001), the latter being significantly lower than in Group D0 (1.40 ± 0.12%; P < 0.001). EC95 values of sevoflurane were 0.83%, 1.07%, and 1.73% in Group D2, Group D1, and Group D0, respectively. No patient in the current study had laryngospasm. CONCLUSION: Dexmedetomidine decreased the required MACEX values of sevoflurane to achieve smooth extubation in a dose-dependent manner. Intravenous dexmedetomidine 1 µg∙kg-1 and 2 µg∙kg-1 pre-medication decreased MACEX by 41% and 64%, respectively. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR): ChiCTR-IOD-17011601 , date of registration: 09 Jun 2017, retrospectively registered.

Sequence-based predictive modeling to identify cancerlectins.

Lectins are a diverse type of glycoproteins or carbohydrate-binding proteins that have a wide distribution to various species. They can specially identify and exclusively bind to a certain kind of saccharide groups. Cancerlectins are a group of lectins that are closely related to cancer and play a major role in the initiation, survival, growth, metastasis and spread of tumor. Several computational methods have emerged to discriminate cancerlectins from non-cancerlectins, which promote the study on pathogenic mechanisms and clinical treatment of cancer. However, the predictive accuracies of most of these techniques are very limited. In this work, by constructing a benchmark dataset based on the CancerLectinDB database, a new amino acid sequence-based strategy for feature description was developed, and then the binomial distribution was applied to screen the optimal feature set. Ultimately, an SVM-based predictor was performed to distinguish cancerlectins from non-cancerlectins, and achieved an accuracy of 77.48% with AUC of 85.52% in jackknife cross-validation. The results revealed that our prediction model could perform better comparing with published predictive tools.