GraphsformerCPI: Graph Transformer for Compound-Protein Interaction Prediction.

Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable prediction approaches. In this study, we propose GraphsformerCPI, an end-to-end deep learning framework that improves prediction performance and interpretability. GraphsformerCPI treats compounds and proteins as sequences of nodes with spatial structures, and leverages novel structure-enhanced self-attention mechanisms to integrate semantic and graph structural features within molecules for deep molecule representations. To capture the vital association between compound atoms and protein residues, we devise a dual-attention mechanism to effectively extract relational features through .cross-mapping. By extending the powerful learning capabilities of Transformers to spatial structures and extensively utilizing attention mechanisms, our model offers strong interpretability, a significant advantage over most black-box deep learning methods. To evaluate GraphsformerCPI, extensive experiments were conducted on benchmark datasets including human, C. elegans, Davis and KIBA datasets. We explored the impact of model depth and dropout rate on performance and compared our model against state-of-the-art baseline models. Our results demonstrate that GraphsformerCPI outperforms baseline models in classification datasets and achieves competitive performance in regression datasets. Specifically, on the human dataset, GraphsformerCPI achieves an average improvement of 1.6% in AUC, 0.5% in precision, and 5.3% in recall. On the KIBA dataset, the average improvement in Concordance index (CI) and mean squared error (MSE) is 3.3% and 7.2%, respectively. Molecular docking shows that our model provides novel insights into the intrinsic interactions and binding mechanisms. Our research holds practical significance in effectively predicting CPIs and binding affinities, identifying key atoms and residues, enhancing model interpretability.

Analysis of the effect of Ivor-Lewis esophagectomy and McKeown esophagectomy on perioperative anxiety and depression in patients with esophageal cancer.

To compare the effects of Ivor-Lewis esophagectomy and McKeown esophagectomy on perioperative anxiety and depression in patients with esophageal cancer. Sixty-three patients with stage I-III middle and lower esophageal carcinoma from June 2021 to December 2022 were randomly divided into observation group (n = 32) treated with laparoscopic Ivor-Lewis esophagectomy and control group (n = 31) treated with laparoscopic McKeown esophagectomy. Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) were measured on the second day of admission and the fifth day after surgery to assess the presence of depression and anxiety. The preoperative and postoperative clinical data of both groups were compared, and multivariate analysis was used to identify risk factors associated with depression and anxiety in patients with esophageal cancer. There was no significant difference in SDS and SAS standard scores between the observation group and the control group ( P  > 0.05). The postoperative SDS and SAS scores in the control group were significantly higher than those before and after operation in the observation group ( P  < 0.01). According to univariate analysis, patients with TNM stage III, tumor diameter greater than 3 cm, postoperative complications, radical McKeown esophagectomy, and C-reactive protein levels above 10 mg/L had a higher incidence of depression and anxiety ( P  < 0.05). Multivariate logistic analysis showed that TNM stage III (depression: OR 1.683, 95 CI 1.429-1.861; Anxiety: OR 1.739, 95 CI 1.516-1.902), postoperative complications (depression: OR 2.345, 95 CI 1.435-3.891; Anxiety: OR 1.872, 95 CI 1.372-3.471), surgical approach (depression: OR 1.609, 95 CI 1.502-3.193; Anxiety: OR 1.658, 95 CI 1.469-2.059), and C-reactive protein (depression: OR 2.260, 95 CI 1.157-4.059; Anxiety: OR 0.373, 95 CI 0.253-0.976) were all independent factors for depression and anxiety in patients after esophageal cancer surgery ( P  < 0.05). The Ivor-Lewis esophagectomy has the advantages of fewer complications and low inflammatory response, which can help alleviate anxiety and depression and improve patients' quality of life and prognosis.

NoiseMol: A noise-robusted data augmentation via perturbing noise for molecular property prediction.

Simplified Molecular-Input Line-Entry System (SMILES) is one of a widely used molecular representation methods for molecular property prediction. We conjecture that all the characters in the SMILES string of a molecule are essential for making up the molecules, but most of them make little contribution to determining a particular property of the molecule. Therefore, we verified the conjecture in the pre-experiment. Motivated by the result, we propose to inject proper noisy information into the SMILES to augment the training data by increasing the diversity of the labeled molecules. To this end, we explore injecting perturbing noise into the original labeled SMILES strings to construct augmented data for alleviating the limitation of the labeled compound data and enhancing the model to extract more useful molecular representation for molecular property prediction. Specifically, we directly adopt mask, swap, deletion, and fusion operations on SMILES strings to randomly mask, swap, and delete atoms in SMILES strings. Then, the augmented data is used by two strategies: each epoch alternately feeds the original and perturbing noisy molecules, or each batch alternately feeds the original and perturbing noisy molecules. We conduct experiments on both Transformer and BiGRU models to validate the effectiveness by adopting widely used datasets from MoleculeNet and ZINC. Experimental results demonstrate that the proposed method outperforms strong baselines on all the datasets. NoiseMol obtains the best performance on BBBP and FDA when compared with state-of-the-art methods. Besides, NoiseMol achieves the best accuracy on LogP. Therefore, injecting perturbing noise into the labeled SMILES strings is an effective and efficient method, which improves the prediction performance, generalization, and robustness of the deep learning models.

Comparative analysis of mitochondrial genomes of two alpine medicinal plants of Gentiana (Gentianaceae).

Gentiana crassicaulis and G. straminea are alpine plants of Gentiana with important medicinal value and complex genetic backgrounds. In this study, the mitochondrial genomes (mtDNAs) of these two species were sequenced. The mtDNAs of G. crassicaulis and G. straminea are 368,808 and 410,086 bp long, respectively, 52 and 49 unique genes are annotated in the two species, and the gene arrangement varies widely. Compared to G. crassicaulis, G. straminea loses three effective genes, namely atp6, trnG-GCC and trnV-GAC. As a pseudogene, the atp6 gene of G. straminea is incomplete, which is rare in higher plants. We detected 1696 and 1858 pairs of long repeats and 213 SSRs and 250 SSs in the mtDNAs of G. crassicaulis and G. straminea, respectively. There are 392 SNPs and 18 InDels between the two genomes, and syntenic sequence and structural variation analysis show low collinearity between the two genomes. Chloroplast DNA transferring to mtDNA is observed in both species, and 46,511 and 55,043 bp transferred segments containing three tRNA genes are identified, respectively. Comparative analysis of mtDNAs of G. crassicaulis, G. straminea and four species of Gentianales determined 18 core genes, and there is no specific gene in G. crassicaulis and G. straminea. The phylogenetic tree based on mtDNAs places Gentianaceae in a branch of Gentianales. This study is the first to analyze the mtDNAs of Gentianaceae, which could provide information for analysis of the structure of mtDNAs of higher plants and phylogenetic research of Gentianaceae and Gentianales.

TranGRU: focusing on both the local and global information of molecules for molecular property prediction.

Molecular property prediction is an essential but challenging task in drug discovery. The recurrent neural network (RNN) and Transformer are the mainstream methods for sequence modeling, and both have been successfully applied independently for molecular property prediction. As the local information and global information of molecules are very important for molecular properties, we aim to integrate the bi-directional gated recurrent unit (BiGRU) into the original Transformer encoder, together with self-attention to better capture local and global molecular information simultaneously. To this end, we propose the TranGRU approach, which encodes the local and global information of molecules by using the BiGRU and self-attention, respectively. Then, we use a gate mechanism to reasonably fuse the two molecular representations. In this way, we enhance the ability of the proposed model to encode both local and global molecular information. Compared to the baselines and state-of-the-art methods when treating each task as a single-task classification on Tox21, the proposed approach outperforms the baselines on 9 out of 12 tasks and state-of-the-art methods on 5 out of 12 tasks. TranGRU also obtains the best ROC-AUC scores on BBBP, FDA, LogP, and Tox21 (multitask classification) and has a comparable performance on ToxCast, BACE, and ecoli. On the whole, TranGRU achieves better performance for molecular property prediction. The source code is available in GitHub: https://github.com/Jiangjing0122/TranGRU.

A novel inhibitor of ARfl and ARv7 induces protein degradation to overcome enzalutamide resistance in advanced prostate cancer.

Enzalutamide (ENZ) is a second-generation androgen receptor (AR) antagonist used for the treatment of castration-resistant prostate cancer (CRPC) and reportedly prolongs survival time within a year of starting therapy. However, CRPC patients can develop ENZ resistance (ENZR), mainly driven by abnormal reactivation of AR signaling, involving increased expression of the full-length AR (ARfl) or dominantly active androgen receptor splice variant 7 (ARv7) and ARfl/ARv7 heterodimers. There is currently no efficient treatment for ENZR in CRPC. Herein, a small molecule LLU-206 was rationally designed based on the ENZ structure and exhibited potent inhibition of both ARfl and constitutively active ARv7 to inhibit PCa proliferation and suppress ENZR in CRPC. Mechanically, LLU-206 promoted ARfl/ARv7 protein degradation and decreased ARfl/ARv7 heterodimers through mouse double minute 2-mediated ubiquitination. Finally, LLU-206 exhibited favorable pharmacokinetic properties with poor permeability across the blood-brain barrier, leading to a lower prevalence of adverse effects, including seizure and neurotoxicity, than ENZ-based therapies. In a nutshell, our findings demonstrated that LLU-206 could effectively inhibit ARfl/ARv7-driven CRPC by dual-targeting of ARfl/ARv7 heterodimers and protein degradation, providing new insights for the design of new-generation AR inhibitors to overcome ARfl/ARv7-driven CRPC.

Early Antimicrobial Evaluation of Nanostructured Surfaces Based on Bacterial Biological Properties.

Nanostructured physical antibacterial surfaces are of great interest due to the increasing antibiotic resistance. In this work, the titania nanotube (TNT) array, a potential physical antibacterial surface, was used for antimicrobial evaluation. The early antibacterial properties of TNTs were assessed based on three growth phases of Staphylococcus aureus (S. aureus), and the physical factors influencing the antibacterial properties were comprehensively discussed. The results show apparent early antibacterial effects of TNTs, including the anti-initial attachment during the lag phase, the inhibition of proliferation and bactericidal effect during the logarithmic phase, and the inhibition of biofilm formation during the stationary phase. These antimicrobial effects are closely related to the combined influence of various physical properties of TNTs, such as diameter, hydrophilicity, roughness, and charge. The present work suggests that the evaluation of the early antimicrobial behavior of biomaterials should pay more attention on the biological characteristics of bacteria.

A deep learning method for predicting molecular properties and compound-protein interactions.

Predicting molecular properties and compound-protein interactions (CPIs) are two important areas of drug design and discovery. They are also an essential way to discover lead compounds in virtual screening. Recently, in silico methods based on deep learning have demonstrated excellent performance in various challenges. It is imperative to develop efficient computational methods to predict accurately both molecular properties and CPIs in drug research using deep learning techniques. In this paper, we propose a deep learning method applicable to both molecular property prediction and CPI prediction based on the idea that both are generally influenced by chemical structure and sequence information of compounds and proteins. Molecular properties are inferred by integrating the molecular structure and sequence information of compounds, and CPIs are predicted by integrating protein sequence and compound structure. The method combines topological structure and sequence fingerprint information of molecules, extracts adequately raw data features, and generates highly representative features for prediction. Molecular property prediction experiments were conducted on BACE, P53 and hERG datasets, and CPI prediction experiments were conducted on Human, C. elegans and KIBA datasets. MG-S achieves outperformance in molecular property prediction on P53, the differences in AUC, Precision and MCC are 0.030, 0.050 and 0.100, respectively, over the suboptimal baseline model, and provides consistently good results on BACE and hERG.The model also achieves impressive performance in CPI prediction, the differences in AUC, Precision and MCC on KIBA are 0.141, 0.138, 0.090 and 0.082, respectively, compared with the state-of-the-art models. The comprehensive results show that the MG-S model has higher performance, better classification ability, and faster convergence. MG-S will serve as a useful method to predict compound properties and CPIs in the early stages of drug design and discovery.Our code and datasets are available at: https://github.com/happay-ending/cpi_cpp.

MultiGran-SMILES: multi-granularity SMILES learning for molecular property prediction.

MOTIVATION: Extracting useful molecular features is essential for molecular property prediction. Atom-level representation is a common representation of molecules, ignoring the sub-structure or branch information of molecules to some extent; however, it is vice versa for the substring-level representation. Both atom-level and substring-level representations may lose the neighborhood or spatial information of molecules. While molecular graph representation aggregating the neighborhood information of a molecule has a weak ability in expressing the chiral molecules or symmetrical structure. In this article, we aim to make use of the advantages of representations in different granularities simultaneously for molecular property prediction. To this end, we propose a fusion model named MultiGran-SMILES, which integrates the molecular features of atoms, sub-structures and graphs from the input. Compared with the single granularity representation of molecules, our method leverages the advantages of various granularity representations simultaneously and adjusts the contribution of each type of representation adaptively for molecular property prediction. RESULTS: The experimental results show that our MultiGran-SMILES method achieves state-of-the-art performance on BBBP, LogP, HIV and ClinTox datasets. For the BACE, FDA and Tox21 datasets, the results are comparable with the state-of-the-art models. Moreover, the experimental results show that the gains of our proposed method are bigger for the molecules with obvious functional groups or branches. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this work are available on GitHub at https://github. com/Jiangjing0122/MultiGran. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Migration patterns of Gentiana crassicaulis, an alpine gentian endemic to the Himalaya-Hengduan Mountains.

The Himalaya-Hengduan Mountain region is one of the hotspots of biodiversity research. The uplift of the Qinghai-Tibetan Plateau (QTP) and the Quaternary glaciation caused great environmental changes in this region, and the responses of many species in the QTP to the Quaternary climate are still largely unknown. The genetic structure and phylogeographical history of Gentiana crassicaulis Duthie ex Burk, an endemic Chinese alpine species in this area, were investigated based on four chloroplast fragments and internal transcribed spacer region of the nuclear ribosomal DNA (nrITS) sequences of 11 populations. The populations with highly diverse chloroplast haplotypes were mainly found at the edge of the QTP. There were two main haplotypes of nrITS clones, one shared by the Yunnan and Guizhou populations, and the other by the remaining populations. The population with the highest diversity was the Gansu population, located at the edge of the plateau. Based on molecular dating, the diversification of G. crassicaulis at the edge of the plateau occurred before the Last Glacial Maximum (LGM), and the species may have completed its expansion from the edge to the platform. Ecological niche models were conducted to predict the distributional ranges of G. crassicaulis at present, during the LGM, and during the last interglacial (LIG) period. The results demonstrated that G. crassicaulis survived on the QTP platform and at the edge during the LGM but afterward retreated from the platform to the southern edge, followed by expansion to the platform.

Effectiveness of enhanced recovery after surgery in the perioperative management of patients with bone surgery in China.

BACKGROUND: Enhanced recovery after surgery (ERAS) was introduced in China in 2007. Over time, the scope of ERAS has expanded from abdominal surgery to orthopedics, urology and other fields. Continuous development and research has contributed to progress of ERAS in China. In 2019, to promote the application of ERAS in bone tumor surgery, we formed the "Consensus of Experts on Perioperative Management of Accelerated Rehabilitation in Major Surgery of Bone Tumors in China". AIM: To evaluate the effect of enhanced recovery after bone tumor surgery in perioperative management in China. METHODS: One hundred and seven patients who underwent bone tumor surgery at the Second Affiliated Hospital of Xi'an Jiaotong University between May 2019 and April 2021 were randomized into a study group (53 cases) and a control group (54 cases). The study group adopted the ERAS protocol and the control group adopted conventional care. Main outcome measures included postoperative length of stay (LOS), postoperative complications, mortality, and 30-d readmission rates. Secondary outcomes included postoperative visual analog scale (VAS) score of pain, number of blood transfusions, drainage volume in 24 h after operation, patient satisfaction 30 d after discharge, VAS score at 30 d after discharge, and daily standing walking time. RESULTS: There were no significant differences in the baseline data, clinical features and surgical site between the two groups. The LOS in the study group with the ERAS protocol was 7.72 ± 3.34 d compared with 10.28 ± 4.27 d in the control group who followed conventional care. The incidence of postoperative nausea and vomiting (PONV) in the study group was 19% and 37% in the control group. The VAS scores of pain on postoperative day 1 (POD1) and POD3 in the study group were 4.79 ± 2.34 and 2.79 ± 1.53 compared with 5.28 ± 3.27 and 3.98 ± 2.27 in the control group. The drainage volume in 24 h after the operation was 124.36 ± 23.43 mL in the study group and 167.43 ± 30.87 mL in the control group. The number of blood transfusions in the study group was also lower. The patient satisfaction rate was higher in the study group than in the control group. CONCLUSION: The ERAS protocol in the perioperative period of bone tumor surgery can decrease LOS, PONV, and postoperative pain, blood transfusion and 24-h drainage, improve patient satisfaction and accelerate recovery.

The Relationship between Blood Groups and Aortic Dissection.

BACKGROUND: Plenty of diseases have been found having associations with blood types, especially cardiovascular diseases. The purpose of this study was to clarify whether there is a relationship between blood groups and acute aortic dissection. We also further studied the distribution of blood groups in different types of acute aortic dissection. METHODS: A total of 291 patients diagnosed with acute aortic dissection from 2011 to 2018 were enrolled and analyzed retrospectively in this study. The control group consisted of 582 patients who received plastic surgery at West China hospital from 2011 to 2018. First, we analyzed the distribution of blood groups between the study group and the control group, including the ABO, Rh, O and non-O groups. Then, we further divided the study group into two groups by the type of acute aortic dissection to determine if there was difference in blood groups between the two types of acute aortic dissection. RESULTS: The analysis of the distribution of ABO blood groups (p = 0.302) and Rh blood groups (p = 0.502) did not reveal statistically significant differences. There were no statistically significant differences in the distributions of ABO blood groups and Rh blood groups in different types of acute aortic dissection. CONCLUSIONS: Our study did not prove the incidence of acute aortic dissection, or the type of acute aortic dissection had a relationship with common blood groups.

[Transcriptome analysis and validation of key genes involved in biosynthesis of iridoids in Gentiana lhassica].

As the main chemical constituents, iridoids are widely distributed within Gentiana, Gentianaceae, with promising bioactivities. Based on the previous work, the transcriptome of G. lhassica, an original plant of Tibetan herb "Jieji Nabao", was sequenced and analyzed in this study, and the transcriptome databases of roots, stems, leaves, and flowers were constructed so as to explore unigenes that may encode the key enzymes in the biosynthetic pathway of iridoids. Then, qRT-PCR was used to validate the relative expression levels of 11 genes named AACT, DXS, MCS, HDS, IDI, GPPS, GES, G10H, 7-DLNGT, 7-DLGT, and SLS in roots, stems, leaves, and flowers. Also, the total contents of gentiopicroside and loganic acid were determined by HPLC, respectively. The results are as follows:(1)a total of 76 486 unigenes with an average length of 852 bp were obtained;(2)335 unigenes were involved in 19 stan-dard secondary metabolism pathways in KEGG database, with phenylpropanoid biosynthesis having the maximum number(75 unigenes), and no isoflavone biosynthetic pathway was annotated;(3)171 unigenes participatedin 27 key enzymes encoding in the biosynthetic pathway of iridoids, and 1-deoxy-D-xylulose-5-phosphate reductoisomerase(DXR) gene was highly expressed;(4)qRT-PCR results were approximately consistent with RNA-Seq data and the relative expression levels of the 11 genes were higher in the aboveground parts(stem, leaf, and flower) than in the underground part(root);(5)the total contents of gentiopicroside and loganic acid were higher in the aboveground parts(stem, leaf, and flower) than in the underground part(root), and the difference was significant. This study provides basic scientific data for accurate species identification, evaluation of germplasm resources, research on secondary pro-duct accumulation of medicinal plants within Gentianaceae, and protection of endangered alpine species.

De novo hydroponics system efficiency for the cuttings of alfalfa (Medicago sativa L.).

The legume plant alfalfa (Medicago sativa L.) is a widely cultivated perennial forage due to its high protein content, palatability, and strong adaptability to diverse agro-ecological zones. Alfalfa is a self-incompatible cross-pollinated autotetraploid species with tetrasomic inheritance. Therefore, maintaining excellent traits through seed reproduction is a prime challenge in alfalfa. However, the cutting propagation technology could enable consistent multiplication of quality plants that are genetically identical to the parent plant. The current study aimed to develop a simple, cost-effective, reproducible, and efficient hydroponic cutting method to preserve alfalfa plants and for molecular research. In this study, alfalfa landrace 'Wudi' was grown in hydroponics for 30 days and used as source material for cuttings. The top, middle and bottom sections of its stem were used as cuttings. The rooting rate, root length, and stem height of the different stem sections were compared to determine the best segment for alfalfa propagation in four nutrient treatments (HM, HM + 1/500H, HM + 1/1000H and d HM + 1/2000H). After 21 days of culture, the rooting rates of all the three stem types under four cutting nutrient solutions were above 78%. The rooting rate of the middle and bottom parts in HM + 1/1000 H and HM + 1/2000 H nutrient solutions reached more than 93%, with a higher health survey score (> 4.70). In conclusion, this study developed a de novo cutting propagation method that can be used to conserve and propagate germplasm in breeding programs and research. This method is a new report on the cutting propagation of alfalfa by hydroponics, which could supplement the existing cutting propagation methods.

Identification of a gene responsible for seedpod spine formation and other phenotypic alterations using whole-genome sequencing analysis in Medicago truncatula.

In nature, some plant species produce seedpods with spines, which is an adaptive biological trait for protecting the seed and helping seed dispersal. However, the molecular mechanism of spine formation is still unclear. While conducting routine tissue culture and transformation in the model legume Medicago truncatula, we identified a smooth seedpod (ssp1) mutant with a suite of other phenotypic changes. Preliminary analysis showed that the mutation was derived from the tissue culture process. Genetic segregation analysis suggested that ssp1 is a recessive mutant. By combining whole-genome sequencing and bioinformatics analysis, we found that the mutant phenotype was caused by a single nucleotide polymorphism and a 30 bp deletion in the gene locus Medtr4g039430, named SSP1. Complementation of the M. truncatula ssp1 and Arabidopsis twd1 mutants showed complete restoration, indicating that SSP1 is an ortholog of Arabidopsis TWD1 which encodes an immunophilin-like FK506-binding protein 42. The formation of spines on seedpods is associated with auxin transport. The method used in this study offers an effective way for detecting genes responsible for somaclonal variations. The results demonstrate, for the first time, that SSP1 plays a crucial role in the determination of spine formation on seedpods.

LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection.

Community detection is of great significance in understanding the structure of the network. Label propagation algorithm (LPA) is a classical and effective method, but it has the problems of randomness and instability. An improved label propagation algorithm named LPA-MNI is proposed in this study by combining the modularity function and node importance with the original LPA. LPA-MNI first identify the initial communities according to the value of modularity. Subsequently, the label propagation is used to cluster the remaining nodes that have not been assigned to initial communities. Meanwhile, node importance is used to improve the node order of label updating and the mechanism of label selecting when multiple labels are contained by the maximum number of nodes. Extensive experiments are performed on twelve real-world networks and eight groups of synthetic networks, and the results show that LPA-MNI has better accuracy, higher modularity, and more reasonable community numbers when compared with other six algorithms. In addition, LPA-MNI is shown to be more robust than the traditional LPA algorithm.

Double Thread Suspension: A Novel Technique to Facilitate End-to-Side Venous Anastomosis With a Microvascular Coupling Device in Head and Neck Reconstruction.

PURPOSE: Microvascular free tissue transfer has become a mainstay for head and neck reconstruction, and vascular anastomosis is 1 of the most challenging aspects. This study aimed to explore a safe, convenient, and efficient technique for end-to-side venous anastomosis using the coupling device. MATERIALS AND METHODS: We evaluated a novel surgical strategy, double thread suspension (DTS), by reviewing data collected in a consecutive series of 60 patients who underwent free tissue grafting after surgical resection of head and neck tumors. Patients who underwent end-to-side microvascular anastomosis using the microvascular coupler device were randomly divided into the DTS group (n = 30) and the traditional procedure group (TRA, n = 30), the demographic data, coupler size, anastomosis time, microvascular complications, and flap survival were determined. RESULTS: The optional size range of coupler devices in the DTS group is better than that of the conventional group, ranging from 1.5 to 4.0 mm compared with 2.0 to 4.0 mm. The average anastomosis time was 4.68 ± 0.43 minutes in the DTS group which was significantly lower than the conventional group that was 9.24 ± 1.46 minutes (P < .0001) . There was no statistically significant difference between the 2 groups in coupler related complications or flap survival. CONCLUSION: The novel DTS technique provides the advantages of lowering procedure complexity, reducing operative time, and preventing tearing of vessel wall, therefore making DTS-guided end-to-side anastomosis a safe, relatively straightforward, and reliable technique.

A seed coat-specific ß-ketoacyl-CoA synthase, KCS12, is critical for preserving seed physical dormancy.

Physical dormancy in seeds exists widely in seed plants and plays a vital role in maintaining natural seed banks. The outermost cuticle of the seed coat forms a water-impermeable layer, which is critical for establishing seed physical dormancy. We previously set up the legume plant Medicago truncatula as an excellent model for studying seed physical dormancy, and our studies revealed that a class II KNOTTED-like homeobox, KNOX4, is a transcription factor critical for controlling hardseededness. Here we report the function of a seed coat ß-ketoacyl-CoA synthase, KCS12. The expression level of KCS12 is significantly downregulated in the knox4 mutant. The KCS12 gene is predominantly expressed in the seed coat, and seed development in the M. truncatula kcs12 mutant is altered. Further investigation demonstrated that kcs12 mutant seeds lost physical dormancy and were able to absorb water without scarification treatment. Chemical analysis revealed that concentrations of C24:0 lipid polyester monomers are significantly decreased in mutant seeds, indicating that KCS12 is an enzyme that controls the production of very long chain lipid species in the seed coat. A chromatin immunoprecipitation assay demonstrated that the expression of KCS12 in the seed coat is directly regulated by the KNOX4 transcription factor. These findings define a molecular mechanism by which KNOX4 and KCS12 control formation of the seed coat and seed physical dormancy.

Effect of Preoperative Oral Carbohydrate Administration on Patients Undergoing Cesarean Section with Epidural Anesthesia: A Pilot Study.

PURPOSE: The aim of this study was to evaluate the effect of preoperative oral carbohydrate administration on patients undergoing Cesarean section with epidural anesthesia. DESIGN: Randomized controlled clinical study. METHODS: A total of 75 patients undergoing Cesarean section (American Society of Anesthesiologists physical status grade I-II) were randomized to preparation with a carbohydrate drink (CHO group), flavored water (placebo group), or to the fasting group. The CHO and placebo groups were double-blinded and given 300 mL of the drink 2 hours before surgery. Visual analog scores of the patient were assessed to evaluate thirst, hunger, and anxiety level, and the gastric antral cross-sectional areas were recorded by ultrasonography during the operative period. Insulin resistance was calculated on the basis of the blood glucose and insulin levels assessed before administration and after surgery. FINDINGS: The CHO and placebo groups did not show an increase in gastric fluid volumes in terms of gastric antral cross-sectional area, and there were no adverse events. The visual analog scale scores at preoperative baseline were not different between groups . During the preoperative waiting period, preparation with CHO reduced not only thirst and anxiety more efficiently than water (placebo) but also hunger (P < .05), whereas water did not. No difference was observed in insulin resistance between groups before intake of the drink. Compared with the preoperative levels, insulin resistance showed a statistically significant increase in all groups (P < .05); however, the increase was significantly higher in the fasting and placebo groups than in the CHO group (P < .05). CONCLUSIONS: Preoperative administration of CHO decreases postoperative insulin resistance and enhances pregnant women's comfort, leading to a reduced sense of thirst, hunger, and anxiety during the preoperative period for Cesarean section.

Multi-task learning models for predicting active compounds.

The computational drug discovery methods can find potential drug-target interactions more efficiently and have been widely studied over past few decades. Such methods explore the relationship between the structural properties of compounds and their biological activity with the assumption that similar compounds tend to share similar biological targets and vice versa. However, traditional Quantitative Structure - Activity Relationship (QSAR) methods often do not have desired accuracy due to insufficient data of compound activity. In this paper, we focus on building Multi-Task Learning (MTL)-based QSAR models by considering multiple similar biological targets together and make shared information transfer across from one task to another, thereby improving not only the learning efficiency, but also the prediction accuracy. This paper selects 6 assay groups with similar biological targets from PubChem and builds their QSAR models with MTL simultaneously. According to the experiment results, our MTL-based QSAR models have better performance over traditional prominent machine learning algorithms and the improvements are even more obvious when other baseline models have low accuracy. The superiority of our models is also proved by Student's t-test with level of significance 5%. Moreover, this paper also explores three different assumptions on the underlying pattern in the dataset and finds that the joint feature MTL models further improve the performance of the QSAR models and are more suitable for building QSAR models for multiple similar biological targets.